Reviewer #1 (Public Review):

This study uses MEG to test for a neural signature of the trial history effect known as 'serial dependence.' This is a behavioral phenomenon whereby stimuli are judged to be more similar than they really are, in feature space, to stimuli that were relevant in the recent past (i.e., the preceding trials). This attractive bias is prevalent across stimulus classes and modalities, but a neural source has been elusive. This topic has generated great interest in recent years, and I believe this study makes a unique contribution to the field. The paper is overall clear and compelling, and makes effective use of data visualizations to illustrate the findings. Below, I list several points where I believe further detail would be important to interpreting the results. I also make suggestions for additional analyses that I believe would enrich understanding but are inessential to the main conclusions.

(1) In the introduction, I think the study motivation could be strengthened, to clarify the importance of identifying a neural signature here. It is clear that previous studies have focused mainly on behavior, and that the handful of neuroscience investigations have found only indirect signatures. But what would the type of signature being sought here tell us? How would it advance understanding of the underlying processes, the function of serial dependence, or the theoretical debates around the phenomenon?

(1a) As one specific point of clarification, on p. 5, lines 91-92, a previous study (St. John-Saaltink et al.) is described as part of the current study motivation, stating that "as the current and previous orientations were either identical or orthogonal to each other, it remained unclear whether this neural bias reflected an attraction or repulsion in relation to the past." I think this statement could be more explicit as to why/how these previous findings are ambiguous. The St. John-Saaltink study stands as one of very few that may be considered to show evidence of an early attractive effect in neural activity, so it would help to clarify what sort of advance the current study represents beyond that.

(1b) The study motivation might also consider the findings of Ranieri et al (2022, J. Neurosci) Fornaciai, Togoli, & Bueti (2023, J. Neurosci), and Luo & Collins (2023, J. Neurosci) who all test various neural signatures of serial dependence.

(2) Regarding the methods and results, it would help if the initial description of the reconstruction approach, in the main text, gave more context about what data is going into reconstruction (e.g., which sensors), a more conceptual overview of what the 'reconstruction' entails, and what the fidelity metric indexes. To me, all of that is important to interpreting the figures and results. For instance, when I first read, it was unclear to me what it meant to "reconstruct the direction of S1 during the S2 epoch" (p. 10, line 199)? As in, I couldn't tell how the data/model knows which item it is reconstructing, as opposed to just reporting whatever directional information is present in the signal.

(2a) Relatedly, what does "reconstruction strength" reflect in Figure 2a? Is this different than the fidelity metric? Does fidelity reflect the strength of the particular relevant direction, or does it just mean that there is a high level of any direction information in the signal?

(3) Then in the Methods, it would help to provide further detail still about the IEM training/testing procedure. For instance, it's not entirely clear to me whether all the analyses use the same model (i.e., all trained on stimulus encoding) or whether each epoch and timepoint is trained on the corresponding epoch and timepoint from the other session. This speaks to whether the reconstructions reflect a shared stimulus code across different conditions vs. that stimulus information about various previous and current trial items can be extracted if the model is tailored accordingly. Specifically, when you say "aim of the reconstruction" (p. 31, line 699), does that simply mean the reconstruction was centered in that direction (that the same data would go into reconstructing S1 or S2 in a given epoch, and what would differentiate between them is whether the reconstruction was centered to the S1 or S2 direction value)? Or were S1 and S2 trained and tested separately for the same epoch? And was training and testing all within the same time point (i.e., train on delay, test on delay), or train on the encoding of a given item, then test the fidelity of that stimulus code under various conditions?

(3a) I think training and testing were done separately for each epoch and timepoint, but this could have important implications for interpreting the results. Namely if the models are trained and tested on different time points, and reference directions, then some will be inherently noisier than others (e.g., delay period more so than encoding), and potentially more (or differently) susceptible to bias. For instance, the S1 and S2 epochs show no attractive bias, but they may also be based on more high-fidelity training sets (i.e., encoding), and therefore less susceptible to the bias that is evident in the retrocue epoch.

(4) I believe the work would benefit from a further effort to reconcile these results with previous findings (i.e., those that showed repulsion, like Sheehan & Serences), potentially through additional analyses. The discussion attributes the difference in findings to the "combination of a retro-cue paradigm with the high temporal resolution of MEG," but it's unclear how that explains why various others observed repulsion (thought to happen quite early) that is not seen at any stage here. In my view, the temporal (as well as spatial) resolution of MEG could be further exploited here to better capture the early vs. late stages of processing. For instance, by separately examining earlier vs. later time points (instead of averaging across all of them), or by identifying and analyzing data in the sensors that might capture early vs. late stages of processing. Indeed, the S1 and S2 reconstructions show subtle repulsion, which might be magnified at earlier time points but then shift (toward attraction) at later time points, thereby counteracting any effect. Likewise, the S1 reconstruction becomes biased during the S2 epoch, consistent with previous observations that the SD effects grow across a WM delay. Maybe both S1 and S2 would show an attractive bias emerging during the later (delay) portion of their corresponding epoch? As is, the data nicely show that an attractive bias can be detected in the retrocue period activity, but they could still yield further specificity about when and where that bias emerges.

(5) A few other potentially interesting (but inessential considerations): A benchmark property of serial dependence is its feature-specificity, in that the attractive bias occurs only between current and previous stimuli that are within a certain range of similarity to each other in feature space. I would be very curious to see if the neural reconstructions manifest this principle - for instance, if one were to plot the trialwise reconstruction deviation from 0, across the full space of current-previous trial distances, as in the behavioral data. Likewise, something that is not captured by the DoG fitting approach, but which this dataset may be in a position to inform, is the commonly observed (but little understood) repulsive effect that appears when current and previous stimuli are quite distinct from each other. As in, Figure 1b shows an attractive bias for direction differences around 30 degrees, but a repulsive one for differences around 170 degrees - is there a corresponding neural signature for this component of the behavior?

In simple terms, let's break down the concept of pluggable authentication and the key points from the text with examples:

Pluggable Authentication

Pluggable authentication means that the system should be flexible and allow different ways to verify who a user is. Think of it like having different keys for the same door, where each key represents a different method of proving your identity.

Key Points and Examples

1. What is Authentication?
2. Authentication is like checking an ID card at the entrance of a building to ensure the person trying to enter is who they say they are.

3. Example: When you log in to a website, you might enter a username and password. This process verifies your identity.

4. Why Should Auth be Pluggable?

5. Different applications or parts of an application might need different methods to verify identity.

6. Example: One part of your app might use a username and password, while another might use a fingerprint or a token sent to your phone.

7. REST Framework's Role:

8. The REST framework provides various built-in ways to handle authentication, and it allows developers to add custom methods.

9. Example: The REST framework might support OAuth (logging in with Google), token authentication (using a special code), and basic authentication (username and password) out of the box.

10. When Does Authentication Happen?

11. Authentication happens first, before anything else in the request process. This ensures only verified users can access further functionalities.

12. Example: Before checking if a user has permission to view a page or how many times they've accessed it, the system first confirms who the user is.

13. request.user and request.auth Properties:

14. request.user: This property holds the user's details once they've been authenticated.
15. Example: After logging in, request.user might store information like the user's name, email, and roles.
16. request.auth: This property holds any additional authentication information, like tokens.
17. Example: If you log in using a token sent to your email, this token will be stored in request.auth.

Simplified Summary

Authentication needs to be adaptable, allowing different methods to verify user identity. The REST framework supports multiple built-in ways and custom methods for authentication, ensuring it runs first before any other checks. Once authenticated, user details are stored in request.user, and any extra authentication data (like tokens) is stored in request.auth.

Real-life Example

Imagine a school with multiple entrances:

• Main Entrance: Students show their student ID (username and password).
• VIP Entrance: Teachers use a fingerprint scanner (biometric authentication).
• Emergency Entrance: Parents receive a temporary access code (token authentication).

Each entrance verifies identity differently, but all lead into the same school, ensuring only authorized people get in. Similarly, a pluggable authentication system in an application allows different methods to verify users based on the situation.

Reviewer #2 (Public Review):

Summary:

In this paper, the authors train a simple machine learning to improve the ability of AlphaFold-multimers ability to separate interacting from non-interacting pairs. The improvement is small compared with the default AlphaFold score (AUROC from 0.84 to 0.88).

Strengths:

The dataset seems to be carefully constructed.

Weaknesses:

The comparison with the state of the art is limited.<br /> - pDockQ comparison is (likely) incorrect (v2.1 should be used, not v1.0).<br /> - Comparison with ipTM should be complemented with RankingConfidence (the default AF2-score).<br /> - Several other scores than pDockQ have been developed for this task.<br /> - Other methods (by Jianlin Chen) to "improve" quality assessment of AF2-models have been presented - these should at least be cited.

Lack of ablation studies:

- Quite likely the most significant contributor is the ipTM (and other scores from AF2). This should be analyzed and discussed.

Lack of data:

- The GitHub repository does not contain the models - so the data can not be examined carefully. Nor can the model be retrained.

- No license is provided for the code in the Git repository.

Sure, let's break down the concept of ViewSets in Django REST Framework into simpler terms with examples.

What is a ViewSet?

In Django REST Framework, a ViewSet is a way to combine the logic for a set of related views into a single class. Instead of writing separate classes or functions for handling different HTTP methods like GET, POST, PUT, DELETE, etc., you can define these in one class.

How ViewSets Work

1. ViewSet Class: A ViewSet is a type of class-based view. Unlike regular class-based views where you define methods like .get() or .post(), ViewSets use actions like .list() and .create().

2. Router: Instead of manually adding each URL for these views, you can use a router that automatically generates the URL patterns for the ViewSet.

Example

Let's say we want to create a simple API to list all users or get details of a specific user.

Step 1: Create a Serializer

First, we need a serializer to convert our User model to JSON format.

```python

myapps/serializers.py

from django.contrib.auth.models import User from rest_framework import serializers

class UserSerializer(serializers.ModelSerializer): class Meta: model = User fields = ['id', 'username', 'email'] ```

Step 2: Define the ViewSet

Next, we define a ViewSet that handles listing users and retrieving a specific user.

```python

views.py

from django.contrib.auth.models import User from django.shortcuts import get_object_or_404 from myapps.serializers import UserSerializer from rest_framework import viewsets from rest_framework.response import Response

class UserViewSet(viewsets.ViewSet): """ A simple ViewSet for listing or retrieving users. """ def list(self, request): queryset = User.objects.all() serializer = UserSerializer(queryset, many=True) return Response(serializer.data)

def retrieve(self, request, pk=None):
    queryset = User.objects.all()
    user = get_object_or_404(queryset, pk=pk)
    serializer = UserSerializer(user)
    return Response(serializer.data)

```

• list(): This method handles GET requests to list all users. It fetches all users from the database, serializes them, and returns the JSON response.
• retrieve(): This method handles GET requests to get details of a specific user based on the primary key (pk).

Step 3: Register the ViewSet with a Router

Finally, we use a router to generate the URL patterns for our ViewSet.

```python

urls.py

from django.urls import path, include from rest_framework.routers import DefaultRouter from .views import UserViewSet

router = DefaultRouter() router.register(r'users', UserViewSet, basename='user')

urlpatterns = [ path('', include(router.urls)), ] ```

Summary

• ViewSet: A class that groups related views (e.g., list and retrieve) into a single class.
• Actions: Methods like .list() and .retrieve() that handle specific actions.
• Router: Automatically generates URL patterns for the ViewSet.

Using ViewSets and routers simplifies the code and makes it easier to manage related views.

Mixins in Django REST Framework: Simplified Explanation

Mixins are small, reusable classes that provide specific behavior to a view class. They are like building blocks that you can combine to create custom views. Instead of defining the handler methods like .get() or .post() directly, mixins provide action methods which allow for more flexible composition of behaviors.

Common Mixin Classes

Here are some commonly used mixin classes from rest_framework.mixins:

1. ListModelMixin
2. Purpose: Provides the ability to list a queryset.
3. Method: .list(request, *args, **kwargs)

4. Example: ```python from rest_framework import generics, mixins from django.contrib.auth.models import User from myapp.serializers import UserSerializer

   class UserList(mixins.ListModelMixin, generics.GenericAPIView): queryset = User.objects.all() serializer_class = UserSerializer

    def get(self, request, *args, **kwargs):
        return self.list(request, *args, **kwargs)
   

   ```

5. CreateModelMixin

6. Purpose: Provides the ability to create a new model instance.
7. Method: .create(request, *args, **kwargs)

8. Example: ```python class UserCreate(mixins.CreateModelMixin, generics.GenericAPIView): queryset = User.objects.all() serializer_class = UserSerializer

    def post(self, request, *args, **kwargs):
        return self.create(request, *args, **kwargs)
   

   ```

9. RetrieveModelMixin

10. Purpose: Provides the ability to retrieve a single model instance.
11. Method: .retrieve(request, *args, **kwargs)

12. Example: ```python class UserDetail(mixins.RetrieveModelMixin, generics.GenericAPIView): queryset = User.objects.all() serializer_class = UserSerializer

     def get(self, request, *args, **kwargs):
         return self.retrieve(request, *args, **kwargs)
    

    ```

13. UpdateModelMixin

14. Purpose: Provides the ability to update an existing model instance.
15. Methods: .update(request, *args, **kwargs), .partial_update(request, *args, **kwargs)

16. Example: ```python class UserUpdate(mixins.UpdateModelMixin, generics.GenericAPIView): queryset = User.objects.all() serializer_class = UserSerializer

     def put(self, request, *args, **kwargs):
         return self.update(request, *args, **kwargs)
    
     def patch(self, request, *args, **kwargs):
         return self.partial_update(request, *args, **kwargs)
    

    ```

17. DestroyModelMixin

18. Purpose: Provides the ability to delete an existing model instance.
19. Method: .destroy(request, *args, **kwargs)
20. Example: ```python class UserDelete(mixins.DestroyModelMixin, generics.GenericAPIView): queryset = User.objects.all() serializer_class = UserSerializer

     def delete(self, request, *args, **kwargs):
         return self.destroy(request, *args, **kwargs)
    

    ```

Concrete View Classes

Concrete view classes combine generic views and mixins to provide common patterns. Here are some examples:

1. CreateAPIView
2. Purpose: Create-only endpoints.

3. Usage: ```python from rest_framework import generics

   class UserCreateView(generics.CreateAPIView): queryset = User.objects.all() serializer_class = UserSerializer ```

4. ListAPIView

5. Purpose: Read-only endpoints for a collection of model instances.

6. Usage: python class UserListView(generics.ListAPIView): queryset = User.objects.all() serializer_class = UserSerializer

7. RetrieveAPIView

8. Purpose: Read-only endpoints for a single model instance.

9. Usage: python class UserDetailView(generics.RetrieveAPIView): queryset = User.objects.all() serializer_class = UserSerializer

10. DestroyAPIView

11. Purpose: Delete-only endpoints for a single model instance.

12. Usage: python class UserDeleteView(generics.DestroyAPIView): queryset = User.objects.all() serializer_class = UserSerializer

13. UpdateAPIView

14. Purpose: Update-only endpoints for a single model instance.

15. Usage: python class UserUpdateView(generics.UpdateAPIView): queryset = User.objects.all() serializer_class = UserSerializer

16. ListCreateAPIView

17. Purpose: Read-write endpoints for a collection of model instances.

18. Usage: python class UserListCreateView(generics.ListCreateAPIView): queryset = User.objects.all() serializer_class = UserSerializer

19. RetrieveUpdateAPIView

20. Purpose: Read or update endpoints for a single model instance.

21. Usage: python class UserRetrieveUpdateView(generics.RetrieveUpdateAPIView): queryset = User.objects.all() serializer_class = UserSerializer

22. RetrieveDestroyAPIView

23. Purpose: Read or delete endpoints for a single model instance.

24. Usage: python class UserRetrieveDestroyView(generics.RetrieveDestroyAPIView): queryset = User.objects.all() serializer_class = UserSerializer

25. RetrieveUpdateDestroyAPIView

26. Purpose: Read-write-delete endpoints for a single model instance.
27. Usage: python class UserRetrieveUpdateDestroyView(generics.RetrieveUpdateDestroyAPIView): queryset = User.objects.all() serializer_class = UserSerializer

Customizing Generic Views with Mixins

You can create custom mixins to encapsulate specific behaviors and reuse them across multiple views. Here's an example of a custom mixin for looking up objects based on multiple fields:

python class MultipleFieldLookupMixin: """ Apply this mixin to any view or viewset to get multiple field filtering based on a `lookup_fields` attribute, instead of the default single field filtering. """ def get_object(self): queryset = self.get_queryset() # Get the base queryset queryset = self.filter_queryset(queryset) # Apply any filter backends filter = {} for field in self.lookup_fields: if self.kwargs.get(field): # Ignore empty fields. filter[field] = self.kwargs[field] obj = get_object_or_404(queryset, **filter) # Lookup the object self.check_object_permissions(self.request, obj) return obj

Using Custom Mixins

You can use the custom mixin with any view to apply the custom behavior:

python class RetrieveUserView(MultipleFieldLookupMixin, generics.RetrieveAPIView): queryset = User.objects.all() serializer_class = UserSerializer lookup_fields = ['account', 'username']

Custom Base Classes

If you frequently use a mixin across multiple views, create custom base classes:

```python class BaseRetrieveView(MultipleFieldLookupMixin, generics.RetrieveAPIView): pass

class BaseRetrieveUpdateDestroyView(MultipleFieldLookupMixin, generics.RetrieveUpdateDestroyAPIView): pass ```

This way, you can reuse the custom behavior consistently across your project.

In summary, mixins in Django REST Framework allow you to compose views with reusable actions, making your code modular and easier to maintain. The combination of mixins and generic views helps you quickly build standard CRUD (Create, Read, Update, Delete) operations with minimal code.

Generic Views in Django and Django REST Framework: Simplified Explanation

What are Generic Views?

Django's Generic Views: - Purpose: Generic views in Django are pre-built views designed to handle common patterns and tasks. Instead of writing repetitive code for common functionalities, you can use these views to save time. - Example: If you want to create a view to list all users or create a new user, instead of writing the code from scratch, you can use a generic view that already handles these tasks.

Django REST Framework's (DRF) Generic Views: - Purpose: Similar to Django's generic views, DRF provides generic views for building API endpoints quickly and efficiently. These views map closely to your database models. - Example: If you need an API endpoint to list all users or create a new user via an API call, DRF's generic views can do this with minimal code.

Benefits of Using Generic Views

• Code Reusability: Generic views abstract common patterns, reducing the need to write repetitive code.
• Simplicity: Makes it easy to set up views for standard operations like listing, creating, updating, or deleting records.
• Customization: You can easily customize these views by setting class attributes or overriding methods.

Basic Example of Using Generic Views

Here’s a simple example of how to use a generic view in Django REST Framework to create a list and create user API:

```python from django.contrib.auth.models import User from myapp.serializers import UserSerializer from rest_framework import generics from rest_framework.permissions import IsAdminUser

class UserList(generics.ListCreateAPIView): queryset = User.objects.all() # The queryset of all User objects serializer_class = UserSerializer # The serializer to use for input/output permission_classes = [IsAdminUser] # Only allow admin users to access this view ```

Overriding Methods for Custom Behavior

You can customize the behavior of these views by overriding methods. For example, to customize the list method:

```python class UserList(generics.ListCreateAPIView): queryset = User.objects.all() serializer_class = UserSerializer permission_classes = [IsAdminUser]

def list(self, request):
    queryset = self.get_queryset()  # Use get_queryset() to get the data
    serializer = UserSerializer(queryset, many=True)
    return Response(serializer.data)

```

Using Generic Views in URL Configuration

You can directly use the generic view in your URL configuration:

```python from django.urls import path from rest_framework.generics import ListCreateAPIView from django.contrib.auth.models import User from myapp.serializers import UserSerializer

urlpatterns = [ path('users/', ListCreateAPIView.as_view(queryset=User.objects.all(), serializer_class=UserSerializer), name='user-list') ] ```

Important Attributes and Methods in GenericAPIView

• queryset: The set of data that the view will operate on.
• serializer_class: The class used to serialize and deserialize data.
• lookup_field: The field used to look up individual model instances (default is 'pk').
• pagination_class: The class used for paginating results.
• filter_backends: List of classes used to filter the queryset.

Common Methods:

• get_queryset(self): Returns the queryset to use for the view.
• get_object(self): Returns a single object instance for detail views.
• filter_queryset(self, queryset): Filters the queryset based on the filter backends.

Example of Customizing get_queryset

If you want to customize the queryset based on the user making the request:

python def get_queryset(self): user = self.request.user return user.accounts.all()

In summary, Django and Django REST Framework's generic views provide a powerful and efficient way to handle common view patterns, making development faster and more maintainable. You can use, customize, and extend these views to suit the specific needs of your application.

We would like to thank you and the reviewers for your thoughtful comments that assisted us to improve the manuscript. We carefully followed the reviewers’ recommendations and provide a detailed point-by-point account of our responses to the comments. 

Please find below the important changes in the updated manuscript.

(1) We changed the title according to the comments provided by reviewer #1.

(2) We edited the introduction, results, and discussion to improve the link between the objectives of the study, the findings, and their discussion, as reviewer #2 recommended.

(3) We clarified the link between camouflage and fitness, which is now presented as a hypothesis, as reviewer #1 suggested.

(4) We added new analyses and figures in the main text and in the supplementary materials to better emphasize sex differences in landing force, foraging strategies and hunting success, following reviewer #1 suggestion.

(5) According to reviewer #2 comments, we edited the results adding key information about methods to help the reader understand the findings without reading the Methods section.

(6) We added important details about the model selection approach along with a discussion of the low R-square values reported in our analyses on hunting success, as reviewer #2 suggested.

eLife assessment 

This fundamental work substantially advances our understanding of animals' foraging behaviour, by monitoring the movement and body posture of barn owls in high resolution, in addition to assessing their foraging success. With a large dataset, the evidence supporting the main conclusions is convincing. This work provides new evidence for motion-induced sound camouflage and has broad implications for understanding predator-prey interactions. 

Public Reviews: 

Reviewer #1 (Public Review): 

In this paper, Schalcher et al. examined how barn owls' landing force affects their hunting success during two hunting strategies: strike hunting and sit-and-wait hunting. They tracked tens of barn owls that raised their nestlings in nest boxes and utilized high-resolution GPS and acceleration loggers to monitor their movements. In addition, camcorders were placed near their nest boxes and used to record the prey they brought to the nest, thus measuring their foraging success. 

This study generated a unique dataset and provided new insights into the foraging behavior of barn owls. The researchers discovered that the landing force during hunting strikes was significantly higher compared to the sit-and-wait strategy. Additionally, they found a positive relationship between landing force and foraging success during hunting strikes, whereas, during the sit-and-wait strategy, there was a negative relationship between the two. This suggests that barn owls avoid detection by generating a lower landing force and producing less noise. Furthermore, the researchers observed that environmental characteristics affect barn owls' landing force during sit-and-wait hunting. They found a greater landing force when landing on buildings, a lower landing force when landing on trees, and the lowest landing force when landing on poles. The landing force also decreased as the time to the next hunting attempt decreased. These findings collectively suggest that barn owls reduce their landing force as an acoustic camouflage to avoid detection by their prey. 

The main strength of this work is the researchers' comprehensive approach, examining different aspects of foraging behavior, including high-resolution movement, foraging success, and the influence of the environment on this behavior, supported by impressive data collection. The weakness of this study is that the results only present a partial biological story contained within the data. The focus is on acoustic camouflage without addressing other aspects of barn owls' foraging strategy, leaving the reader with many unanswered questions. These include individual differences, direct measurements of owls' fitness, a detailed analysis of the foraging strategy of males and females, and the collective effort per nest box. However, it is possible that these data will be published in a separate paper. 

We greatly appreciate your recognition of the comprehensive approach and extensive data collection. Our primary objective was to study the role of acoustic camouflage. Nonetheless, the manuscript now includes a detailed analysis of the foraging strategy and hunting success of males and females (lines 164-225).

The results presented support the authors' conclusion that lower landing force during sit-andwait hunting increases hunting success, likely due to a decreased probability of detection by their prey, resulting in acoustic camouflage. The authors also argue that hunting success is crucial for survival, and thus, acoustic camouflage has a direct link to fitness. While this statement is reasonable, it should be presented as a hypothesis, as no direct evidence has been provided here.

Thank you for the comment. We agree and thus have edited the language accordingly.  

However, since information about nestling survival is typically monitored when studying behavior during the breeding period, the authors' knowledge of the effect of acoustic camouflage on owls' fitness can probably be provided. Furthermore, it will be interesting to further examine the foraging strategies used by different individuals during foraging, the joint foraging success of both males and females within each nest box, and the link between landing force and foraging success if the data are available.

We are currently writing a manuscript on these topics. We are aware that several scientific questions regarding the foraging ecology of the barn owl still need our attention. Regarding the link between landing force and foraging success, we believe that our revised manuscript addresses this specific topic, please see specific responses below.

However, even without this additional analysis on survival, this paper provides an unprecedented dataset and the first measurement of landing force during hunting in the wild. It is likely to inspire many other researchers currently studying animal foraging behavior to explore how animals' movements affect foraging success.

Reviewer #2 (Public Review): 

Summary: 

The authors provide new evidence for motion-induced sound camouflage and can link the hunting approach to hunting success (detailing the adaptation and inferring a fitness consequence). 

Strengths: 

Strong evidence by combining high-resolution accelerometer data with a ground-truthed data set on prey provisioning at nest boxes. A good set of co-variates to control for some of the noise in the data provides some additional insights into owl hunting attempts. 

Weaknesses: 

There is a disconnect between the hypotheses tested and the results presented, and insufficient detail is provided on the statistical approach. R2 values of the presented models are very small compared to the significance of the effect presented. Without more detail, it is impossible to assess the strength of the evidence.

In the revised manuscript, we changed the way results are presented and we improved the link between the hypotheses and the results. The R2 values are indeed small. It is however important to keep in mind that we are assessing the outcome of one specific behavior (i.e. landing force during sit-and-wait hunts) on hunting success in a wild environment, where many complex ecological interactions likely influence hunting success. Nonetheless, the coefficients (as reported in the results) show that for every 1 N increase in landing force, there is a 15% reduction in hunting success, which is substantial. In the discussion we also note that 50 Hz is a relatively low sampling frequency for estimating the peak ground reaction force. We have gone back over the presentation of our results and made our discussion more nuanced to acknowledge this aspect. 

We have also added a detailed description about our model selection process in the methods section and provide a model selection table for each analysis in the supplementary materials.

The authors seem to overcome persisting challenges associated with the validation and calibration of accelerometer data by ground-truthing on-board measures with direct observations in captivity, but here the methods are not described any further and sample sizes (2 owls - how many different loggers were deployed?) might be too small to achieve robust behavioural classifications.

Thank you for the comment. Details of our methods of behavioural identification are provided in lines 385 – 429. There are two reasons why our results should not be limited by the sample size. First, we used the temporal sequence of changes in acceleration, and rates of change in acceleration data, which make the methods robust to individual differences in acceleration values. Furthermore, our methods for behavioural identification were not based on machine learning. Instead, we use a Boolean based approach (as described in Wilson et al. 2018. MEE), which is more robust to small differences in absolute values that might occur e.g. in relation to slight changes in device position. 

Recommendation for the authors:

Reviewer #1 (Recommendations For The Authors): 

Comment 1. This study provides new insights into animals' foraging behavior and will probably inspire other researchers to examine foraging behavior in such high resolution.

We hope so, thank you.

Comment 2. However, it is necessary to describe better the measured landing force and the hunting strike and perching behavior so the readers can understand these methods when reading the results (and without reading the Methods).

We have now changed the text in the “Results” to help the reader understand the key methods while reading the results.

Comment 3. In addition, make sure you use the same terminology for hunting strategies during the entire paper and especially in all figures and corresponding result descriptions.

We now use consistent terminology throughout the text and figures. We hope that this is now clear in the revised manuscript.

Comment 4. In addition, although I find your statement about the link between acoustic camouflage and fitness reasonable, it should be described as a hypothesis or examined if you want to keep the direct link statement. I believe showing a direct link can add an additional outstanding aspect to this paper, but I also understand that it can be addressed in a separate paper.

We agree that the relationship between hunting success and barn owl fitness is an important topic, but it necessitates a consideration of both hunting strategies, including hunting on the wing, which extends beyond the limits of our current study. Indeed, our primary objective was to conduct a detailed examination of the interplay between acoustic camouflage and the success of the sit-and-wait technique.

However, we have edited the manuscript to explicitly describe the link between acoustic camouflage and fitness as a hypothesis. We believe this adjustment provides a more accurate representation of our approach. We hope this clarifies the specific emphasis of our work and its contribution to the understanding of barn owl hunting behavior.

Here are my detailed comments about the paper: 

Comment 5. Title: Consider changing the title to "Acoustic camouflage predicts hunting success in a wild predator." 

We would like to thank you for your nice proposition. However, we opted for a different title, which is now “Landing force reveals new form of motion-induced sound camouflage in a wild predator”.

Comment 6. Line 91-93: Please provide additional information about the collected dataset, including: 

Description of the total period of observations, an average and standard deviation of perching and hunting attempt events per individual per night, number of foraging trips per individual per night, details about the geographic location and characteristics of the habitat, season, and reproductive state. 

The revised manuscript now includes detailed information about the collected dataset (i.e. study area, reproductive state, etc…). “We used GPS loggers and accelerometers to record high resolution movement data during two consecutive breeding seasons (May to August in 2019 and 2020) from 163 wild barn owls (79 males and 84 females) breeding in nest boxes across a 1,000 km² intensive agricultural landscape in the western Swiss plateau.” Results section, lines 79 – 82

Details about the number of foraging trips per individuals and per night are now presented in the results: “Sexual dimorphism in body mass was marked among our sampled individuals. Males were lighter than females (84 females, average body mass: 322 ± 22.6 g; 79 males, average body mass 281 ± 16.5 g, Fig S6) and provided almost three times more prey per night than females (males: 8 ± 5 prey per night; females: 3 ± 3 prey per night; Fig.S7). Males also displayed higher nightly hunting effort than females (Males: 46 ± 16 hunting attempts per night, n= 79; Females: 25 ± 11 hunting attempts per nights, n=84; Fig. 3A, Fig S8). However, females were more likely to use a sit and wait strategy than males (females: 24% ± 15%, males: 13% ± 10%, Fig.S9). As a result, the number of perching events per night was similar between males and females (Females: 76 ± 23 perching events per nights; Males: 69 ± 20 perching events per night; Fig S8).” (lines 165 – 174) 

Comment 7. In addition, state if the information describes breeding pairs of males and females and provides statistics on the number of tracked pairs and the number of nest boxes.

The revised manuscript now includes a description of the number of tracked breeding pairs and the number of nest boxes. “Of these individuals, 142 belonged to pairs for which data were recovered from both partners (71 pairs in total, 40 in 2019, 31 in 2020). The remaining 21 individuals belonged to pairs with data from one partner (11 females and 1 male in 2019; 4 females and 5 males in 2020).” (lines 82 – 85.)

Comment 8. Line 93: Briefly define the term "landing force" and explain how it was measured (and let the reader know that there is a detailed description in the Methods).

We now include a brief definition of the “landing force” along with a brief explanation of how it was measured in the results section. “We extracted the peak vectoral sum of the raw acceleration during each landing and converted this to ground reaction force (hereafter “landing force”, in Newtons) using measurements of individual body mass (see methods for detailed description).” (lines 92 – 95).

Comment 9. Line 94: All definitions, including "pre-hunting force," need to be better described in the Results section.

Thank you for this suggestion. We now provided a better description of those key definitions directly in the results section: 

Measurement of landing force: “Barn owls employing a sit-and-wait strategy land on multiple perches before initiating an attack, with successive landings reducing the distance to the target prey (Fig. 2C). 

We used the acceleration data to identify 84,855 landings. These were further categorized into perching events (n = 56,874) and hunting strikes (n = 27,981), depending whether barn owls were landing on a perch or attempting to strike prey on the ground (Fig. 1A and B, see methods for specific details on behavioral classification).” (lines 88 – 95)

Pre-hunt perching force predicts hunting success: “Finally, we analyzed whether the landing force in the last perching event before each hunting attempt (i.e. pre-hunt perching force) predicted variation in hunting success” (lines 229 – 230)

Comment 10. Line 102: Remove "Our analysis of 27,981 hunting strikes showed that" and add "n = 27,981" after the statistics. You have already stated your sample size earlier. There is no need to emphasize it again, although your sample size is impressive.

We modified the text in the results section as suggested.

Comment 11. Line 104: The results so far suggest that the difference in landing force between males and females is an outcome of their different body masses. However, it is not clear what is the reason for the difference in the number of hunting strike attempts between males and females (Lines 104-106). Can you compare the difference in landing force between males and females with similar body mass (females from the lower part of the distribution and males from the upper part)? Is there still a difference?

Thank you, following your comment we made some new analyses that clarified the situation around landing force involved in perching and hunting strike events between sexes. But firstly, we wanted to clarify why there is a difference in number of hunting attempts between males and females. During the breeding season, females typically perform most of the incubation, brooding, and feeding of nestlings in the nest, while the male primarily hunts food for the female and chicks. The female supports the male providing food in a very irregular way, and this changes from pair to pair (paper in prep.). The differences in number of hunting attempts between males and females reflects this asymmetry in food provisioning between sexes during this specific period. We specified this in the revised version of the manuscript (lines 164 – 174). 

We also provide a new analysis to investigate sex differences in mass-specific landing force (force/body mass). We found that males and females produce similar force per unit of body mass during perching events. This demonstrates that the overall higher perching force in females (see Fig. 4C in the manuscript) is therefore driven by their higher body mass. (lines 194 – 199)

Comment 12. Line 154: I believe Boonman et al. (2018) is relevant to this part of the discussion. Boonman, Arjan, et al. found that barn owl noise during landing and taking off is worth considering. ["The sounds of silence: barn owl noise in landing and taking off."

Behavioral Processes 157 (2018): 484-488.]

We now cited this paper in the discussion.

Comment 13. Line 164: Your results do not directly demonstrate a link to fitness, although they potentially serve as a proxy for fitness (add a reference). However, you might have information regarding nestlings' survival - that will provide a direct link for fitness. Change your statement or add the relevant data.

We appreciated your feedback, and we adjusted the language accordingly.

Comment 14. Line 213: If the poles are closer to the ground - is it possible that the higher trees and buildings serve for resting and gathering environmental information over greater distances? For example, identifying prey at farther distances or navigating to the next pole?

Yes, this is indeed the most likely explanation for the fact that owls land more on buildings and trees than on poles until the last period (about 6 minutes) before hunting. In these last minutes, barn owls preferentially use poles, as we showed in figure 2B. The revised manuscript now includes this explanation in the discussion (lines 269 – 284).

Comment 15. Line 250: The product "AXY-Trek loggers" does not appear on the Technosmart website (there are similar names, but not an exact match). Are you sure this is the correct name of the tracking device you used? 

Thank you for pointing out this detail that we missed. The device we used is now called "AXY-Trek Mini" (https://www.technosmart.eu/axy-trek-mini/). We have corrected this error directly in the revised manuscript.

Comment 16. Line 256: Please explain how the devices were recovered. Did you recapture the animals? If so, how? Additionally, replace "after approximately 15 days" with the exact average and standard deviation. Furthermore, since you have these data, please state the difference in body mass between the two measurements before and after tagging.

The birds were recaptured to recover the devices. Adults barn owls were recaptured at their nest sites, again using automatic sliding traps that are activated when birds enter the nest box. The statement "after approximately 15 days" was replaced by the exact mean and standard deviation, which were 10.47 ± 2.27 days. Those numbers exclude five individuals from the total of 163 individuals included in this study. They could not be recaptured in the appropriate time window but were re-encountered when they initiated a second clutch later in the season (4 individuals) or a new clutch the year after (1 individual).

We integrated this previously missing information in the revised manuscript (lines 370 – 372).

Comment 17. Line 259: What was the resolution of the camera? What were the recording methods and schedule? How did you analyze these data? 

The resolution was set to 3.1 megapixel. Motion sensitive camera traps were installed at the entrance to each nest box throughout the period when the barn owls were wearing data loggers, and each movement detected triggered the capture of three photos in bursts. The photos recorded were not analyzed as such for this study, but were used to confirm each supply of prey, which had previously been detected from the accelerometer data. We added these details in the revised manuscript (lines 377 – 380)

Comment 18_1. Figure 1: 

Panel A) Include the sex of the described individual. 

The sex of the described individual is now included in the figure caption.

Comment 18_2. It would be interesting to show these data for both males and females from the same nest box (choose another example if you don't have the data for this specific nest box). 

Although we agree that showing tracks of males and females from the same nest is very interesting, the purpose of this figure was to illustrate our data annotation process and we believe that adding too many details on this figure will make it appear messy. However, the revised manuscript now includes a new figure (Fig. 3A) which shows simultaneous GPS tracks of a male and a female during a complete night, with detailed information about perching and hunting behaviors.

Comment 18_3. Add the symbol of the nest box to the legend. 

Done

Comment 18_4. Provide information about the total time of the foraging trip in the text below. 

The duration of the illustrated foraging trip has been included in the figure caption.

Comment 18_5. To enhance the figure’s information on foraging behavior, consider color coding the trajectory based on time and adding a background representing the landscape. Since this paper may be of interest to researchers unfamiliar with barn owl foraging behavior, it could answer some common questions. 

For similar reasons explained in our answer above (Comment 18_2), we would rather keep this figure as clean as possible. However, we followed your recommendations and included these details in the new Figure 3 described above. In this new figure, GPS tracks are color coded according to the foraging trip number and includes a background representing the landscape. To provide even more detail about the landscape, we added another figure in the supplementary materials (Fig. S2) which provides illustration of barn owls foraging ground and nest site that we think might be of interest for people unfamiliar with barn owls.

Comment 18_6. Inset panels) provide a detailed description of the acceleration insert panels. 

Done

Comment 18_7. Color code the acceleration data with different colors for each axis, add x and y axes with labels, and ensure the time frame on the x-axis is clear. How was the self-feeding behavior verified (should be described in the methods section)? 

We kept both inset panels as simple as possible since they serve here as examples, but a complete representation of these behaviors (with time frame, different colors and labels) is provided in the supplementary materials (figure S3). We included this statement in the figure caption and added a reference to the full representations from the supplementary materials: 

In the Figure caption: “Inset panels show an example of the pattern of the tri-axial acceleration corresponding to both nest-box return and self-feeding behaviors (but see Fig S3for a detailed representation of the acceleration pattern corresponding to each behavior).” 

In the Method section: “Self-feeding was evident from multiple and regular acceleration peaks in the surge and heave axes (resulting in peaks in VeDBA values > 0.2 g and < 0.9 g, Fig.S3D), with each peak corresponding to the movement of the head as the prey was swallowed whole.”.

Comment 18_8. Panel B) Note in the caption that you refer to the acceleration z-axis.

We believe that keeping the statement “the heave acceleration…” in the figure caption is more informative than referring to the “z-axis” as it describes the real dimension to which we are referring. The use of the x, y and z axes can be misleading as they can be interchanged depending on the type and setting of recorders used.

Comment 18_9. Present the same time scale for both hunting strategies to facilitate comparison. You can achieve this by showing only part of the flight phase before perching. 

Done

Comment 18_10. Panel C) Presenting the data for both hunting strategy and sex would provide more comprehensive information about the results and would be relatively easy to implement. 

We agree with your comment. We present the differences in landing force for both landing contexts and sexes in the new Figure 3 as well as in the supplementary materials (Figure S10) of this revised manuscript.

Comment 19. Figure 2: Please provide an explanation of the meaning of the circles in the figure caption.  

Done

Comment 20. Figure 3: 

Panel A) It is unclear how the owl illustration is relevant to this specific figure, unlike the previous figures where it is clear. Also, suggest removing the upper black line from the edge of the figure or add a line on the right side. 

Done (now in Figure 2).

Panel B) "Density" should be capitalized. 

Done

Panel C) Add a scale in meters, and it would be helpful to include an indication of time before hunting for each data point. 

Done

Comment 21. Figure S1: Mark the locations of the nest boxes and ensure that trajectories of different individuals and sexes can be identified. 

The purpose of this figure was to show the spatial distribution of the data. We think that adding nest locations and coloring the paths according to individuals and/or sex will make the figure less clear. However, the new Figure 3 highlights those details.

Comment 22. Figure S2: Show the pitch angle similarly to how you showed the acceleration axes, and explain what "VeDBA" stands for. Provide a description of the perching behavior, clearly indicating it on the figure. Add axes (x, y, z) to the illustration of the acceleration explanation. 

We edited this figure (now figure S3) to show the pitch angle and provide an explanation of what “VeDBA” stands for in the figure caption. The figure caption now also provides a better description of the perching behavior. For the axes (i.e. X, Y, Z), we prefer to refer to the heave, surge, and sway as this is more informative and refers to what is usually reported in studies working with tri-axial accelerometers.

Comment 23. Table S1: Improve the explanation in the caption and titles of the table. 

Done

Reviewer #2 (Recommendations For The Authors): 

Comment 1. From the public review and my assessment there, the authors can be assured that I thoroughly enjoyed the read and am looking forward to seeing a revised and improved version of this paper. 

We thank the reviewer for this comment. We revised the manuscript according to their comments.

Comment 2. In addition to my major points stated above, I would like to add the following recommendations: 

The manuscript is overall well written, but it uses a very pictorial language (a little as if we were in a David Attenborough documentary) that I find inappropriate for a research paper (especially in the abstract and introduction, "remarkable" (2x), "sophisticated" (are there any unsophisticated adaptations? We are referring to something under selection after all) etc.

We appreciated that you found the paper overall well written, and we understand the comment about pictorial language. We therefore slightly changed the text to make sure that the adjective used to describe adaptive strategies are not over-emphasized.

Comment 3. Abstract 

"While the theoretical benefits of predator camouflage are well established, no study has yet been able to quantify its consequences for hunting success." - This claim is actually not fully true: 

Nebel Carina, Sumasgutner Petra, Pajot Adrien and Amar Arjun 2019: Response time of an avian prey to a simulated hawk attack is slower in darker conditions, but is independent of hawk colour morph. Soc. open sci.6:190677 

We edited our claim to specify that the consequences of predator camouflage on hunting success has never been quantified in natural conditions and cited the reference in the introduction.

Comment 4. Line 23. Rephrase to: "We used high-resolution movement data to quantify how barn owls (Tyto alba) conceal their approach when using a sit-and-wait strategy, as well as the power exerted during strikes." 

We edited this sentence in the abstract, as suggested.

Comment 5. Results 

There is a disconnect between the objectives outlined at the end of the introduction and the following results that should be improved. 

The authors state: "Using high-frequency GPS and accelerometer data from wild barn owls (Tyto alba), we quantify the landing dynamics of this sit-and-wait strategy to (i) examine how birds adjust their landing force with the behavioral and environmental context and (ii) test the extent to which the magnitude of the predator cue affects hunting success." But one of the first results presented are sex differences. 

This is a fair point. We have now changed our statement in the end of the introduction as well as the order of the results to improve the link between the objectives outlined in the introduction and the way result are presented. 

Comment 6. At this stage, the reader does not even know yet that we are presented with a size-dimorphic species that also has very different parental roles during the breeding season. This should be better streamlined, with an extra paragraph in the introduction. And these sex differences are then not even discussed, so why bring them up in the first place (and not just state "sex has been fitted as additional co-variate to account for the size-dimorphism in the species" without further details). 

We edited the way the objectives are outlined in the introduction to cover the size dimorphism (lines 70 – 76). We also completely changed the way the sex differences are presented in the results, including a new analysis that we believe provides a better comprehensive understanding of barn owl foraging behavior (lines 164 – 206). Finally, we added a new paragraph in the discussion to consider those results (lines 319 – 339).

Comment 7. It is not clear to me where and how high-resolution GPS data were used? The results seem to concentrate on ACC – why GPS was used and how it features should be foreshadowed in a few lines in the introduction. I definitively prefer having the methods at the end of a manuscript, but with this structure, it is crucial to give the reader some help to understand the storyline. 

GPS data were used to validate some behavioral classifications (prey provisioning for example), but most importantly they were used to link each landing event with perch types. We edited the text in the result section to clarify where GPS and/or ACC data were used.

Comment 8. Discussion 

Move the orca example further down, where more detail can be provided to understand the evidence. 

After our extensive edits in the discussion, we felt this example was interrupting the flow. We now cite this study in the introduction. 

Comment 9. Size dimorphism and evident sex differences are not discussed. 

The revised manuscript now includes a new paragraph in the discussion in which sex differences are discussed (lines 319 – 339).

Comment 10. Be more precise in the terminology used (for example, land use seems to be interchangeable with habitat characteristics?). 

We modified “land use” with “habitat data” in the revised manuscript.

Comment 11. Methods 

Please provide a justification for the very high weight limit (5%; line 256). This limit is outdated and does not fulfill the international standard of 3% body weight. I assume the ethics clearance went through because of the short nature of the study (i.e., the birds were not burdened for life with the excess weight? But a line is needed here or under the ethics considerations to clarify this). 

The 5% weight limit was considered acceptable due to the short deployment period, and we now edited the ethics statement to emphasize this point. However, it is important to note that there is no real international standard, with both 3% and 5% weight limits being commonly used. Both limits are arbitrary and the impact of a fixed mass on a bird varies with species and flight style. All owls survived and bred similarly to the non-tagged individuals in the population (lines 373 – 376 & lines 558 – 561)

EDITORIAL COMMENT: We strongly encourage you to provide further context and clarification on this issue, as suggested by the Reviewer. On a related point, the ethics statement refers to GPS loggers, rather than GPS and ACC devices; we encourage you to clarify wording here.

Thank you for highlighting this point that indeed needed some clarifications.

Although we have used the terminology "GPS recorders", the authorization granted by the Swiss authorities for this study effectively covers the entire tracking system, which combines both GPS and ACC recorders in the same device. We have therefore changed the wording used in the ethics statement to avoid any misunderstanding (lines 373 – 376 & lines 558 – 561)

Comment 12. Please provide more information on the model selection approach, what does "Non-significant terms were dropped via model simplification by comparing model AIC with and without terms." mean? Did the authors use a stepwise backward elimination procedure (drop1 function)? Or did they apply a complete comparison of several candidate models? I think a model comparison approach rather than stepwise selection would be more informative, as several rather than only one model could be equally probable. This might also improve model weights or might require a model averaging procedure - current reported R2values are very small and do not seem to support the results well. 

We apologize for the lack of details about this important aspect of the statistical analysis. We applied an automated stepwise selection using the dredge function from the R package “MuMin”, therefore applying a complete comparison of several candidate models. The final models were chosen as the best models since the number of candidate models within ∆AIC<2 was relatively low in each analysis and thus a model averaging was not appropriate here. We edited the methods section to ensure clarity, and added model selection tables for each analysis, ranked according to AICc scores, in the supplementary materials (lines 532 – 552)

In addition, we agree that the reported R-squared values in our analyses are quite low, specifically regarding the influence of pre-hunt perching force on hunting success (cond R2 = 0.04). Nonetheless, landing impact still has a notable effect size (an increase of 1N reduces hunting success by 15%). The reported values are indicative of the inherent complexity in studying hunting behavior in a wild setting where numerous variables come into play. We specifically investigated the hypothesis that the force involved during pre-hunt landings, and consequently the emitted noise, influences the success of the next hunting attempt in wild barn owls. Factors such as prey behavior and micro-habitat characteristics surrounding prey (such as substrate type and vegetation height) are most likely to be influential but hard, or nearly impossible, to model. We now cover this in a more nuanced way in the discussion (lines 266 – 268)

Comment 13. Please explain why BirdID was nested in NightID - this is not clear to me.

Probably here there is a misunderstanding because we wrote that we nested NightID in BirdID (and not BirdID in NightID). 

Comment 14. I hope the final graphs and legends will be larger, they are almost impossible to read. 

We enlarged the graphs and legends as much as possible to improve readability. However, looking at the graphs in the published version they seem clear and readable.

Comment 15. Figure S1: Does "representation" mean the tracks don't show all of the 163 owls? If so, be precise and tell us how many are illustrated in the figure. 

Figure S1 represent the tracks for each of the 163 barn owls used in the study. We changed the terminology used in the figure caption to avoid any misunderstanding.

Comment 16. Figure S4: Please adjust the y-axis to a readable format. 

Done

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

This manuscript aims at a quantitative model of how visual stimuli, given as time-dependent light intensity signals, are transduced into electrical currents in photoreceptors of macaque and mouse retina. Based on prior knowledge of the fundamental biophysical steps of the transduction cascade and a relatively small number of free parameters, the resulting model is found to fairly accurately capture measured photoreceptor currents under a range of diverse visual stimuli and with parameters that are (mostly) identical for photoreceptors of the same type.

Furthermore, as the model is invertible, the authors show that it can be used to derive visual stimuli that result in a desired, predetermined photoreceptor response. As demonstrated with several examples, this can be used to probe how the dynamics of phototransduction affect downstream signals in retinal ganglion cells, for example, by manipulating the visual stimuli in such a way that photoreceptor signals are linear or have reduced or altered adaptation. This innovative approach had already previously been used by the same lab to probe the contribution of photoreceptor adaptation to differences between On and Off parasol cells (Yu et al, eLife 2022), but the present paper extends this by describing and testing the photoreceptor model more generally and in both macaque and mouse as well as for both rods and cones.

Strengths:

The presentation of the model is thorough and convincing, and the ability to capture responses to stimuli as different as white noise with varying mean intensity and flashes with a common set of model parameters across cells is impressive. Also, the suggested approach of applying the model to modify visual stimuli that effectively alter photoreceptor signal processing is thought-provoking and should be a powerful tool for future investigations of retinal circuit function. The examples of how this approach can be applied are convincing and corroborate, for example, previous findings that adaptation to ambient light in the primate retina, as measured by responses to light flashes, mostly originates in photoreceptors.

Weaknesses:

In the current form of the presentation, it doesn't become fully clear how easily the approach is applicable at different mean light levels and where exactly the limits for the model inversion are at high frequency. Also, accessibility and applicability by others could be strengthened by including more details about how parameters are fixed and what consensus values are selected.

Thank you - indeed a central goal of writing this paper was to provide a tool that could be easily used by other laboratories. We have clarified and expanded four points in this regard: (1) we have stated more clearly that mean light levels are naturally part of inversion process, and hence the approach can be applied across a broad range of light levels (lines 292-297); (2) we have expanded our analysis of the high frequency limits to the inversion and added that expanded figure to the main text (new Fig 5); (3) we have included additional detail about our calibration procedures, including our calibration code, to facilitate transfer to other labs; and, (4) we have detailed the procedure for identification of consensus parameters (line 172-182, 191-199 and Methods section starting on line 831).

Reviewer #2 (Public Review):

Summary:

This manuscript proposes a modeling approach to capture nonlinear processes of photocurrents in mammalian (mouse, primate) rod and cone photoreceptors. The ultimate goal is to separate these nonlinearities at the level of photocurrent from subsequent nonlinear processing that occurs in retinal circuitry. The authors devised a strategy to generate stimuli that cancel the major nonlinearities in photocurrents. For example, modified stimuli would generate genuine sinusoidal modulation of the photocurrent, whereas a sinusoidal stimulus would not (i.e., because of asymmetries in the photocurrent to light vs. dark changes); and modified stimuli that could cancel the effects of light adaptation at the photocurrent level. Using these modified stimuli, one could record downstream neurons, knowing that any nonlinearities that emerge must happen post-photocurrent. This could be a useful method for separating nonlinear mechanisms across different stages of retinal processing, although there are some apparent limitations to the overall strategy.

Strengths:

(1) This is a very quantitative and thoughtful approach and addresses a long-standing problem in the field: determining the location of nonlinearities within a complex circuit, including asymmetric responses to different polarities of contrast, adaptation, etc.

(2) The study presents data for two primary models of mammalian retina, mouse, and primate, and shows that the basic strategy works in each case.

(3) Ideally, the present results would generalize to the work in other labs and possibly other sensory systems. How easy would this be? Would one lab have to be able to record both receptor and post-receptor neurons? Would in vitro recordings be useful for interpreting in vivo studies? It would be useful to comment on how well the current strategy could be generalized.

We agree that generalization to work in other laboratories is important, and indeed that was a motivation for writing this as a methods paper. The key issue in such generalization is calibration. We have expanded our discussion of our calibration procedures and included that code as part of the github repository associated with the paper. Figure 10 (previously Figure 9) was added to illustrate generalization. We believe that the approach we introduce here should generalize to in vivo conditions. We have expanded the text on these issues in the Discussion (sections starting on line 689 and 757).

Weaknesses:

(1) The model is limited to describing photoreceptor responses at the level of photocurrents, as opposed to the output of the cell, which takes into account voltage-dependent mechanisms, horizontal cell feedback, etc., as the authors acknowledge. How would one distinguish nonlinearities that emerge at the level of post-photocurrent processing within the photoreceptor as opposed to downstream mechanisms? It would seem as if one is back to the earlier approach, recording at multiple levels of the circuit (e.g., Dunn et al., 2006, 2007).

Indeed the current model is limited to a description of rod and cone photocurrents. Nonetheless, the transformation of light inputs to photocurrents can be strongly nonlinear, and such nonlinearities can be difficult to untangle from those occurring late in visual processing. Hence, we feel that the ability to capture and manipulate nonlinearities in the photocurrents is an important step. We have expanded Figure 10 to show an additional example of how manipulation of nonlinearities in phototransduction can give insight into downstream responses. We have also noted in text that an important next step would be to include inner segment mechanisms (section starting on line 661); doing so will require not only characterization of the current-to-voltage transformation, but also horizontal cell feedback and properties of the cone output synapse.

(2) It would have been nice to see additional confirmations of the approach beyond what is presented in Figure 9. This is limited by the sample (n = 1 horizontal cell) and the number of conditions (1). It would have been interesting to at least see the same test at a dimmer light level, where the major adaptation mechanisms are supposed to occur beyond the photoreceptors (Dunn et al., 2007).

We have added an additional experiment to this figure (now Figure 10) which we feel nicely exemplifies the approach. The approach that we introduce here really only makes sense at light levels where the photoreceptors are adapting; at lower light levels the photoreceptors respond near-linearly, so our “modified” and “original” stimuli as in Figure 10 (previously Figure 9) would be very similar (and post-phototransduction nonlinearities are naturally isolated at these light levels).

Reviewer #3 (Public Review):

Summary:

The authors propose to invert a mechanistic model of phototransduction in mouse and rod photoreceptors to derive stimuli that compensate for nonlinearities in these cells. They fit the model to a large set of photoreceptor recordings and show in additional data that the compensation works. This can allow the exclusion of photoreceptors as a source of nonlinear computation in the retina, as desired to pinpoint nonlinearities in retinal computation. Overall, the recordings made by the authors are impressive and I appreciate the simplicity and elegance of the idea. The data support the authors' conclusions but the presentation can be improved.

Strengths:

-  The authors collected an impressive set of recordings from mouse and primate photoreceptors, which is very challenging to obtain.

-  The authors propose to exploit mechanistic mathematical models of well-understood phototransduction to design light stimuli that compensate for nonlinearities.

-  The authors demonstrate through additional experiments that their proposed approach works.

Weaknesses:

-  The authors use numerical optimization for fitting the parameters of the photoreceptor model to the data. Recently, the field of simulation-based inference has developed methods to do so, including quantification of the uncertainty of the resulting estimates. Since the authors state that two different procedures were used due to the different amounts of data collected from different cells, it may be worthwhile to rather test these methods, as implemented e.g. in the SBI toolbox (https://joss.theoj.org/papers/10.21105/joss.02505). This would also allow them to directly identify dependencies between parameters, and obtain associated uncertainty estimates. This would also make the discussion of how well constrained the parameters are by the data or how much they vary more principled because the SBI uncertainty estimates could be used.

Thank you - we have improved how we describe and report parameter values in several ways. First, the previous text erroneously stated that we used different fitting procedures for different cell types - but the real difference was in the amount of data and range of stimuli we had available between rods and cones. The fitting procedure itself was the same for all cell types. We have clarified this along with other details of the model fitting both in the main text (lines 121-130) and in the Methods (section starting on line 832). We also collected parameter values and estimates of allowed ranges in two tables. Finally, we used sloppy modeling to identify parameters that could covary with relatively small impact on model performance; we added a description of this analysis to the Methods (section starting on line 903).

-  In several places, the authors refer the reader to look up specific values e.g. of parameters in the associated MATLAB code. I don't think this is appropriate, important values/findings/facts should be in the paper (lines 142, 114, 168). I would even find the precise values that the authors measure interesting, so I think the authors should show them in a figure/table. In general, I would like to see also the average variance explained by different models summarized in a table and precise mean/median values for all important quantities (like the response amplitude ratios in Figures 6/9).

We have added two tables with these parameters values and estimates of allowable ranges. We also added points to show the mean (and SD) across cells to the population figures and added those numerical values to the figure legends throughout.

-  If the proposed model is supposed to model photoreceptor adaptation on a longer time scale, I fail to see why this can be an invertible model. Could the authors explain this better? I suspect that the model is mainly about nonlinearities as the authors also discuss in lines 360ff.

For the stimuli that we use we see little or no contribution of slow adaptation in phototransduction. We have expanded the description of this point in the text and referred to Angueyra et al (2022) which looks at this issue in more detail for primate cones (paragraph starting on line 280).

-  The important Figures 6-8 are very hard to read, as it is not easy to see what the stimulus is, the modified stimulus, the response with and without modification, what the desired output looks like, and what is measured for part B. Reworking these figures would be highly recommended.

We have reworked all of the figures to make the traces clearer.

-  If I understand Figure 6 correctly, part B is about quantifying the relative size of the response to the little first flash to the little second flash. While clearly, the response amplitude of the second flash is only 50% for the second flash compared to the first flash in primate rod and cones in the original condition, the modified stimulus seems to overcompensate and result in 130% response for the second flash. How do the authors explain this? A similar effect occurs in Figure 9, which the authors should also discuss.

Indeed, in those instances the modified stimulus does appear to overcompensate. We suspect this is due to differences in sensitivity of the specific cells probed for these experiments and those used in the model construction. We now describe this limitation in more detail (lines 524-526). A similar point comes up for those experiments in which we speed the photoreceptor responses (new FIgure 9B), and we similarly note that the cells used to test those manipulations differed systematically from those used to fit the model (lines 558-560).

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

I only have a few minor questions and suggestions for clarification.

It hasn't become fully clear to me how general the model is when different mean light levels (on long-time scales) are considered. Are there slow adaptation processes not captured in the model that affect model performance? And how should one go about setting the mean light level when, for example, probing ganglion cells with a stimulus obtained through model inversion? Should it work to add an appropriate DC component to the current that is provided as input to the inverted model? (Presumably, deriving a stimulus and then just adding background illumination should not work, or could this be a good approximation, given a steady state that is adapted to the background?)

We have clarified in the main text that slow adaptation does not contribute substantially to responses to the range of stimuli we explored (lines 281-289). We have also clarified that the stimulus in the model inversion is specified in isomerizations per second - so the mean value of the stimulus is automatically included in the model inversion (lines 293-298).

Furthermore, a caveat for the model inversion seems to be the potential amplification of high-frequency noise. The suggested application of a cutoff temporal frequency seems appropriate, but data are shown only for a few example cells. Is this consistent across cells? (Given that performance between, e.g., mouse cones can vary considerably according to Fig. 4B?) I would also like to suggest moving the corresponding Supplemental Figure (4.1) into the main part of the manuscript, as it seems quite important.

We have added population analysis to the new Figure 5 (which was Figure 4 - Figure Supplement 1). We have also clarified that the amplification of high frequency noise is an issue only when we try to apply model inversion to measured stimuli. When we use model inversion to identify stimuli that elicit desired responses, the target responses are computed from a linear model that has no noise, so this is not a concern in applications like those in Figures 6-10.

Also, could the authors explain more clearly what the effect of the normalization of the estimated stimulus by the power of the true stimulus is? Does this simply reduce power at high frequency or also affect frequencies below the suggested cutoff (where the stimulus reconstruction should presumably be accurate even without normalization)?

Indeed this normalization reduces high frequency power and has little impact on low frequencies where the inversion is accurate; this is now noted in the text (line 363). As for amplification of high frequency noise (previous comment), the normalization by the stimulus power is only needed when inverting measured responses (i.e. responses with noise) and is omitted when we are identifying stimuli that elicit desired responses (e.g. in Figures 6-10).

While the overall performance of the model to predict photoreceptor currents is impressive, it seems that particular misses occur for flashes right after a step in background illumination and for the white-noise responses at low background illumination (e.g. Figure 1B). Is that systematic, and if so what might be missing in the model?

Indeed the model (at least with fixed parameters across stimuli) appears to systematically miss a few aspects of the photoreceptor responses. These include the latency of the response to a bright flash and the early flashes in the step + flash protocol in Figure 1B. Model errors for the variable mean noise stimulus (Figure 2) showed little dependence on time even when responses were sorted by mean light level and by previous mean level. Model errors did not show a clear systematic dependence on light level; this likely reflects, at least in part, the use of mean-square-error to identify model parameters. We have expanded our discussion of these systematic errors in the text (lines 164-166).

I was also wondering whether this is related to the fact that in Figure 9B, the gain in the modified condition is actually systematically higher when there is more background light. Do the authors think that this could be a real effect or rather an overcompensation from the model? (By the way, is it specified what "Delta-gain" really is, i.e., ratio or normalized difference?)

We suspect this is an issue with the sensitivity of the specific cells for which we did these experiments (i.e. variability in the gamma parameter between cells). This sensitivity varies between cells, and such variations are likely to place the strongest limitation on our ability to use this approach to manipulate responses in different retinas. We now note those issues in the Results (lines 523-526, 557-559 and 591-593) with reference to Figures 9 (previously Figure 8) and 10 (previously Figure 9), and describe this limitation more generally in the Discussion (section starting on line 649). We have also changed delta-gain to response ratio, which seemed more intuitive.

Maybe I missed this, but it seems that the parameter gamma is fitted in a cell-type-specific fashion (e.g. line 163), but then needs to be fixed for held-out cells. How was this done? Is there much variability of gamma between cells?

There is variability in gamma between cells, and this likely explains some of systematic differences between data and model (see above and Methods, lines 902-903). For the consensus models in Figure 2B, gamma was allowed to vary for each cell while the remaining consensus model parameters were fixed. Gamma was set equal to the mean value across cells for model inversion (i.e. for all of the analyses in Figures 4-10). We have described the fitting procedure in considerably more detail in the revised Methods (starting on line 832).

For completeness, it would be nice to have the applied consensus model parameters in the manuscript rather than just in the Matlab code (especially since the code has not been part of the submission). Also, some notes on how the numerical integration of the differential equations was done would be nice (time step size?).

We have added tables with consensus parameters and estimates of the sensitivity of model predictions to each parameter. We have also added additional details about the numerical approaches (including the time step) to Methods.

Similarly, it would be nice to explicitly see the relationships that are used to fix certain model parameters (lines 705ff). And can the constants k and n (lines 709-710) be assumed identical for different species and receptor types?

We have added more details to the model fitting to the methods, including the use of steady-state conditions to hold certain parameters fixed (lines 862 and 866). We are not aware of any direct comparisons of k and n across species and receptor types. We have noted that model performance was not improved by modest changes in these parameters (due to compensation by other model parameters). More generally, we have explained how some parameters trade for others and hence the logic of fixing some even when exact values were not available.

For the previous measurements of m and beta (lines 712-713), is there a reference or source?

We have added references for these values.

Did the authors check for differences in the model parameters between cone types (e.g., S vs. M)?

We did not include S cones here. They are harder to record from and collecting a fairly large data set across a range of stimuli would be challenging. Our previous work shows that S cones have slower responses than L and M cones, and this would certainly be reflected in differences in model parameters. We have noted this in the text (Methods, line 808-810).

For the stated flash responses time-to-peak (lines 183-184), is this for a particular light intensity with no background illumination?

Those are flashes from darkness - now noted in the text.

Figure 2 - Supplement 1 doesn't have panel labels A and B, unlike the legend.

Fixed - thank you.

Reviewer #2 (Recommendations For The Authors):

(1) Fig. 2B - for some cells, the consensus model seems to fit better than the individual model. How is this possible?

This was mostly an error on our part (we inadvertently included responses to more stimuli in fitting the individual models, which slightly hampered their performance). Even with this correction, however, a few cells remain for which the consensus model outperforms and individual model. We believe this is because there is more data to constrain model parameters for the consensus models (since they are fit to all cells at the same time), and that can compensate for improvements associated with customizing parameters to specific cells.

(2) Fig. 2 Supplement 1, it would be useful to see a blow-up of the data in an inset, as in Fig. 2B.

Thanks - added.

(3) Line 400 - this paragraph could include additional quantification and statistics to back up claims re 'substantially reduced', 'considerably lower'.

We quantify that in the next sentence by computing the mean-square-error between responses and sinusoidal fits (also in Figure 7B, which now includes statistics as well). We have made that connection more direct in the text.

(4) Maybe a supplement to Fig. 8 could show the changes to the stimulus required to alter the kinetics in both directions - to give more insight into part B., especially.

Good suggestion - we have added the stimuli to all of the panels of the figure (now Figure 9).

(5) Fig. 8B - in 'Speed response up' condition - there seems to be error in the model for the decay time of the response - especially for the 'original' condition, which is not quantified in 8C. Was it generally difficult to predict responses to flashes?

That seems largely to reflect that the cells used for those experiments had faster initial kinetics than the average cells (responses to the control traces are also faster than model predictions in these cells - black traces in Figure 9B). We have added this to the text.

(6) Line 678, possibly notes that 405 nm equally activates S and M photopigments in mice, since most of the cones co-express the two photopigments (Rohlich et al., 1994; Applebury et al., 2000; Wang et al., 2011).

Thanks - we have added this (lines 827-829).

(7) The discussion could include a broader description of the various approaches to identifying nonlinearities within retinal circuitry, which include (incomplete list): recording at multiple levels of the circuit (e.g., Kim and Rieke 2001; Rieke, 2001; Baccus and Meister, 2002; Dunn et al., 2006; 2007; Beaudoin et al., 2007; Baccus et al., 2008); recording currents vs. spiking responses in a ganglion cell (e.g., Kim and Rieke, 2001; Zaghloul et al., 2005; Cui et al., 2016); neural network modeling approaches (e.g., Maheswaranathan et al., 2023); optogenetic approaches to studying filtering/nonlinear behavior at synapses (e.g., Pottackal et al., 2020; 2021).

Good suggestion - we have added this to the final paragraph of the Discussion.

Reviewer #3 (Recommendations For The Authors):

-  I am personally not a fan of the style: "... as Figure 4A shows..." or comparable and much prefer a direct "We observe that X is the case (Figure 4A)". If the authors agree, they may want to revise their paper in this way.

We have revised the text to avoid the “... as Figure xx shows” construction. We have retained multiple instances which follow a “Figure xx shows that …” construction (which is both active rather than passive and does not use a personal pronoun).

-  I am not a fan of the title. Light-adaption clamp caters only to a very specialized audience.

We have changed the title to “Predictably manipulating photoreceptor light responses to reveal their role in downstream visual responses.”

-  The parameter fitting procedure should not only be described in Matlab code, but in the paper.

Thanks - we have expanded this in the Methods considerably (section starting on line 832).

-  The authors should elaborate on why different fitting procedures were used.

We did not describe that issue clearly. The fitting procedures used across cells were identical, but we had different data available for different cell types due to experimental limitations. We have substantially revised that part of the main text to clarify this issue (paragraph starting on line 121).

-  The authors state in line 126 that the input stimulus is supposed to mimic eye movements mouse, monkey, or human? Please clarify.

Thanks - we have changed this sentence to “abrupt and frequent changes in intensity that characterize natural vision.”

-  Please improve the figure style. For example, labels should be in consistent capitalization and ideally use complete words (e.g. Figure 2B, 4B, and others).

We have made numerous small changes in the figures to make them more consistent.

-  Is the fraction of variance calculated on held-out-data? Linear models should be added to Figure 2B.

The fraction of variance explained was not calculated on held out data because of limitations in the duration of our recordings. Given the small number of free parameters, and the ability of the model to capture held out cells, we believe that the model generalizes well. We have added a supplemental figure with linear model performance (Figure 2 - Figure Supplement 2).

-  Fig. 9A is lacking bipolar cell and amacrine cell labels. Currently, it looks like HC is next to the BC in the schematic.

Thanks - we have updated that figure (now Figure 10A)

-  Maybe I am misunderstanding something, but it seems like the linear model prediction shown in Figure 2A for the rod could be easily improved by scaling it appropriately. Is this impression correct or why not?

We have clarified how the linear model is constructed (by fitting the linear model to low contrast responses of the full model at the mean stimulus intensity). We also added a supplemental figure, following the suggestion above, showing the linear model performance when a free scaling factor is included for each cell.

-  The verification experiment in Fig. 5 is only anecdotal and is elaborated only in Figure 6. If I am not mistaken, this does not necessitate its own figure/section but could rather be merged.

We have kept this figure separate (now Figure 6) as we felt that it was important to highlight the approach in general in a figure before getting into quantification of how well it works.

-  Figure 5 right is lacking labels. What is red and grey?

Thanks for catching that - labels are added now.

-  The end of the Discussion is slightly unusual. Did some text go missing?

Thanks - we have rearranged the Discussion so as not to end on Limitations.

-  There is a bonus figure at the end which seems also not to belong in the manuscript.

Thanks - the bonus figure is removed now.

-  The methods should also describe briefly what kind of routines were used in the Matlab code, e.g. gradient descent with what optimizer?

We’ve added that information as well.

Nice snippets but need to be converted to LuaSnip. Also some of them (such as code block) are better derived from markdown plugins aka mkdx. Some are even better implemented in a non-snippet plugins (images, tables)

Author response:

Reviewer #1 (Public Review):

Weaknesses:

There are some minor weaknesses.

Notably, there are not a lot of new insights coming from this paper. The structural comparisons between MCC and PCC have already been described in the literature and there were not a lot of significant changes (outside of the exo- to endo- transition) in the presence vs. absence of substrate analogues.

We agree that the structures of the human MCC and PCC holoenzymes are similar to their bacterial homologs. That is due to the conserved sequences and functions of MCC and PCC across different species.

There is not a great deal of depth of analysis in the discussion. For example, no new insights were gained with respect to the factors contributing to substrate selectivity (the factors contributing to selectivity for propionyl-CoA vs. acetyl-CoA in PCC). The authors state that the longer acyl group in propionyl-CoA may mediate stronger hydrophobic interactions that stabilize the alpha carbon of the acyl group at the proper position. This is not a particularly deep analysis and doesn't really require a cryo-EM structure to invoke. The authors did not take the opportunity to describe the specific interactions that may be responsible for the stronger hydrophobic interaction nor do they offer any plausible explanation for how these might account for an astounding difference in the selectivity for propionyl-CoA vs. acetyl-CoA. This suggests, perhaps, that these structures do not yet fully capture the proper conformational states.

We appreciate this comment. Unfortunately, in the cryo-EM maps of the PCC holoenzymes, the acyl groups were not resolved (fig. S6), so we were unable to analyze the specific interactions between the acyl-CoAs and PCC. We will discuss this limitation in our revised manuscript.

The authors also need to be careful with their over-interpretation of structure to invoke mechanisms of conformational change. A snapshot of the starting state (apo) and final state (ligand-bound) is insufficient to conclude *how* the enzyme transitioned between conformational states. I am constantly frustrated by structural reports in the biotin-dependent enzymes that invoke "induced conformational changes" with absolutely no experimental evidence to support such statements. Conformational changes that accompany ligand binding may occur through an induced conformational change or through conformational selection and structural snapshots of the starting point and the end point cannot offer any valid insight into which of these mechanisms is at play.

Point accepted. We will revise our manuscript to use "conformational differences" instead of "conformational changes" to describe the differences between the apo and ligand-bound states.

Reviewer #2 (Public Review):

Comments and questions to the manuscripts:

I'm quite impressed with the protein purification and structure determination, but I think some functional characterization of the purified proteins should be included in the manuscript. The activity of enzymes should be the foundation of all structures and other speculations based on structures.

We appreciate this comment. However, since we purified the endogenous BDCs and the sample we obtained was a mixture of four BDCs, the enzymatic activity of this mixture cannot accurately reflect the catalytic activity of PCC or MCC holoenzyme. We will acknowledge this limitation in the discussion section of our revised manuscript.

In Figure 1B, the structure of MCC is shown as two layers of beta units and two layers of alpha units, while there is only one layer of alpha units resolved in the density maps. I suggest the authors show the structures resolved based on the density maps and show the complete structure with the docked layer in the supplementary figure.

We appreciate this comment. We have shown the cryo-EM maps of the PCC and MCC holoenzymes in fig. S8 to indicate the unresolved regions in these structures. The BC domains in one layer of MCCα in the MCC-apo structure were not resolved. However, we think it would be better to show a complete structure in Fig. 1 to provide an overall view of the MCC holoenzyme. We will revise Fig. 1B and the figure legend to clearly point out which domains were not resolved in the cryo-EM map and were built in the structure through docking.

In the introduction, I suggest the author provide more information about the previous studies about the structure and reaction mechanisms of BDCs, what is the knowledge gap, and what problem you will resolve with a higher resolution structure. For example, you mentioned in line 52 that G437 and A438 are catalytic residues, are these residues reported as catalytic residues or this is based on your structures? Has the catalytic mechanism been reported before? Has the role of biotin in catalytic reactions revealed in previous studies?

Point accepted. It was reported that G419 and A420 in S. coelicolor PCC, corresponding to G437 and A438 in human PCC, were the catalytic residues (PMID: 15518551). The same study also reported the catalytic mechanism of the carboxyl transfer reaction. The role of biotin in the BDC-catalyzed carboxylation reactions has been extensively studied (PMIDs: 22869039, 28683917). We will include these information in the introduction section of our revised manuscript.

In the discussion, the authors indicate that the movement of biotin could be related to the recognition of acyl-CoA in BDCs, however, they didn't observe a change in the propionyl-CoA bound MCC structure, which is contradictory to their speculation. What could be the explanation for the exception in the MCC structure?

We appreciate this comment. We do not have a good explanation for why we did not observe a change in the propionyl-CoA bound MCC structure. It is noteworthy that neither acetyl-CoA nor propionyl-CoA is the natural substrate of MCC. Recently, a cryo-EM structure of the human MCC holoenzyme in complex with its natural substrate, 3-methylcrotonyl-CoA, has been resolved (PDB code: 8J4Z). In this structure, the binding site of biotin and the conformation of the CT domain closely resemble that in our acetyl-CoA-bound MCC structure. Therefore, the movement of biotin induced by acetyl-CoA binding mimics that induced by the binding of MCC's natural substrate, 3-methylcrotonyl-CoA, indicating that in comparison with propionylCoA, acetyl-CoA is closer to 3-methylcrotonyl-CoA regarding its ability to bind to MCC. We will discuss this possibility in our revised manuscript.

In the discussion, the authors indicate that the selectivity of PCC to different acyl-CoA is determined by the recognition of the acyl chain. However, there are no figures or descriptions about the recognition of the acyl chain by PCC and MCC. It will be more informative if they can show more details about substrate recognition in Figures 3 and 4.

We appreciate this comment. Unfortunately, in the cryo-EM maps of the PCC holoenzymes, the acyl groups were not resolved (fig. S6), so we were unable to analyze the specific interactions between the acyl-CoAs and PCC. We will discuss this limitation in our revised manuscript.

How are the solved structures compared with the latest Alphafold3 prediction?

Since AlphaFold3 was not released when our manuscript was submitted, we did not compare the solved structures with the AlphaFold3 predictions. We have now carried out the predictions using Alphafold3. Due to the token limitation of the AlphaFold3 server, we can only include two α and six β subunits of human PCC or MCC in the prediction. The overall assembly patterns of the Alphafold3-predicted structures are similar to that of the cryo-EM structures. The RMSDs between PCCα, PCCβ, MCCα, and MCCβ in the apo cryo-EM structures and those in the AlphaFold3-predicted structures are 7.490 Å, 0.857 Å, 7.869 Å, and 1.845 Å, respectively. The PCCα and MCCα subunits adopt an open conformation in the cryo-EM structures but adopt a closed conformation in the AlphaFold-3 predicted structures, resulting in large RMSDs.

Reviewer #2 (Public Review):

Summary:

This work addresses a puzzling finding in the viral forecasting literature: high-frequency viral variants evince signatures of neutral dynamics, despite strong evidence for adaptive antigenic evolution. The authors explicitly model interactions between the dynamics of viral adaptations and of the environment of host immune memory, making a solid theoretical and simulation-based case for the essential role of host-pathogen eco-evolutionary dynamics. While the work does not directly address improved data-driven viral forecasting, it makes a valuable conceptual contribution to the key dynamical ingredients (and perhaps intrinsic limitations) of such efforts.

Strengths:

This paper follows up on previous work from these authors and others concerning the problem of predicting future viral variant frequency from variant trajectory (or phylogenetic tree) data, and a model of evolving fitness. This is a problem of high impact: if such predictions are reliable, they empower vaccine design and immunization strategies. A key feature of this previous work is a "traveling fitness wave" picture, in which absolute fitnesses of genotypes degrade at a fixed rate due to an advancing external field, or "degradation of the environment". The authors have contributed to these modeling efforts, as well as to work that critically evaluates fitness prediction (references 11 and 12). A key point of that prior work was the finding that fitness metrics performed no better than a baseline neutral model estimate (Hamming distance to a consensus nucleotide sequence). Indeed, the apparent good performance of their well-adopted "local branching index" (LBI) was found to be an artifact of its tendency to function as a proxy for the neutral predictor. A commendable strength of this line of work is the scrutiny and critique the authors apply to their own previous projects. The current manuscript follows with a theory and simulation treatment of model elaborations that may explain previous difficulties, as well as point to the intrinsic hardness of the viral forecasting inference problem.

This work abandons the mathematical expedience of traveling fitness waves in favor of explicitly coupled eco-evolutionary dynamics. The authors develop a multi-compartment susceptible/infected model of the host population, with variant cross-immunity parameters, immune waning, and infectious contact among compartments, alongside the viral growth dynamics. Studying the invasion of adaptive variants in this setting, they discover dynamics that differ qualitatively from the fitness wave setting: instead of a succession of adaptive fixations, invading variants have a characteristic "expiring fitness": as the immune memories of the host population reconfigure in response to an adaptive variant, the fitness advantage transitions to quasi-neutral behavior. Although their minimal model is not designed for inference, the authors have shown how an elaboration of host immunity dynamics can reproduce a transition to neutral dynamics. This is a valuable contribution that clarifies previously puzzling findings and may facilitate future elaborations for fitness inference methods.

The authors provide open access to their modeling and simulation code, facilitating future applications of their ideas or critiques of their conclusions.

Weaknesses:

The current modeling work does not make direct contact with data. I was hoping to see a more direct application of the model to a data-driven prediction problem. In the end, although the results are compelling as is, this disconnect leaves me wondering if the proposed model captures the phenomena in detail, beyond the qualitative phenomenology of expiring fitness. I would imagine that some data is available about cross-immunity between strains of influenza and sarscov2, so hopefully some validation of these mechanisms would be possible.

After developing the SIR model, the authors introduce an effective "expiring fitness" model that avoids the oscillatory behavior of the SIR model. I hoped this could be motivated more directly, perhaps as a limit of the SIR model with many immune groups. As is, the expiring fitness model seems to lose the eco-evolutionary interpretability of the SIR model, retreating to a more phenomenological approach. In particular, it's not clear how the fitness decay parameter nu and the initial fitness advantage s_0 relate to the key ecological parameters: the strain cross-immunity and immune group interaction matrices.

Editors Assessment:

This paper presents a new tool to make using PhysiCell easier, which is an open-source, physics-based multicellular simulation framework with a very wide user base. PhysiCell Studio is a graphical tool that makes it easier to build, run, and visualize PhysiCell models. Over time, it has evolved from being a GUI to include many additional functionalities, and can be used as desktop and cloud versions. This paper outlines the many features and functions, the design and development process behind it, and deployment instructions. Peer review improved the organisation of the various repositories and adding both a requirements.txt and environment.yml files. Looking to the future the developers are planning to add new features based on community feedback and contributions, and this paper presents the many code repositories if readers wish to contribute to the development process.

This evaluation refers to version 1 of the preprint

This work has been published in GigaByte Journal under a CC-BY 4.0 license (https://doi.org/10.46471/gigabyte.128), and has published the reviews under the same license. This is part of the PhysiCell Ecosystem Series: https://doi.org/10.46471/GIGABYTE_SERIES_0003

Reviewer 1. Meghna Verma:

Is installation/deployment sufficiently outlined in the paper and documentation, and does it proceed as outlined?

The authors have provided links for video descriptions for installation and that is appreciated.

One overall recommendation is: If all the screenshots (for e.g.: from Fig 1-12 of the main paper and all the subsections in Supplementary) can be combined in one figure that will help enhance the complete overview and the overall flow of the paper.

Additional comments are available here: https://gigabyte-review.rivervalleytechnologies.comdownload-api-file?ZmlsZV9wYXRoPXVwbG9hZHMvZ3gvVFIvNTA3L1Jldmlld19QaHlzaUNlbGxTdHVkaW9fTVYucGRm

Reviewer 2. Koert Schreurs and Lin Wouters supervised by Inge Wortel

Is there a clear statement of need explaining what problems the software is designed to solve and who the target audience is?

The problem statement is addressed in the introduction, which mentions the need for a GUI tool as a much more accessible way to edit the XML-based model syntax. However, it is somewhat confusing who exactly the intended audience of the paper is. Is the paper targeted at researchers that already use PhysiCell, but might want to switch to the GUI version? Or should it (also) target the potential new user-base of researchers interested in using ABMs, for whom the XML version was not sufficiently accessible and who will now gain access to these models because there is a GUI? Specifying the intended audience might impact some sections of the paper. For example, for users who already use PhysiCell, the step-by-step tutorials might not be useful since they would already know most of the available options; they would just need a quick overview of what info is in which tab. But if the paper is (also) targeted at potential new users, then some additional information could make both the paper and the tool much more accessible, such as:

• A clear comparison to other modeling frameworks and their functionalities. Why should they use PhysiCell instead of one of the other available (GUI) tools? For example, the referenced Morpheus, CC3D and Artistoo all focus on a different model framework (CPMs); this might be worth mentioning. And what about Chaste? Does it represent different types of models, or are there other reasons to consider PhysiCell over Chaste or vice versa? For new users, this would be important information to include. The paper currently also does not mention other frameworks except those that offer a GUI. While the main point of the paper is the addition of the GUI, for completeness sake it might still be good to mention a broader overview of ABM frameworks and how they compare to PhysiCell, or simply to refer to an existing paper that provides such an overview.
• The current tutorial immediately dives into very specific instructions (what to click and exact values to enter), often without explaining what these options mean or do. New users would probably appreciate to get a rough outline of which types of processes can be modelled, and which steps they would take to do so. This could be as easy as summarising the different main tabs before going into the details. I understand that some of these explanations will overlap with the main PhysiCell software – but considering that the GUI will open up modelling to a different type of community, it might make sense to outline them here to get a self-contained overview of functionality.
• Indeed, if the above information is provided, the detailed tutorial might fit better as an appendix or in online documentation. That would also leave more space to explain not only which values to enter, but also what these variables do, why choose these values, what other options to consider, etc. Having this information together in one place would be very useful for beginning users.

Is the source code available, and has an appropriate Open Source Initiative license been assigned to the code?

The software is available under the GPL v3 licence.

As Open Source Software are there guidelines on how to contribute, report issues or seek support on the code?

There is a Github repository, ensuring that it is possible to contribute and report issues, and the paper explicitly invites community contributions. However, although the paper mentions that it is possible to seek support through Github Issues and “Slack channels”, we could find no link to the latter resource. This should probably be added to make this resource usable for the reader (or otherwise the statement should be removed)

Is installation/deployment sufficiently outlined in the paper and documentation, and does it proceed as outlined?

Mostly yes, as installation and deployment are outlined in the paper and documentation. However, we did notice a couple of issues: - The studio guide explains how to compile a project in PhysiCell (https://github.com/PhysiCell-Tools/Studio-Guide/blob/main/README.md), but does not mention that Mac users need to specify the g++ version at the top of the Makefile. This is explained in a separate blog (http://www.mathcancer.org/blog/setting-up-gcc-openmp-on-osx-homebrew-edition/) but should be outlined (or at least referenced) here as well. - There are several different resources covering the installation process, referring to e.g. github.com/physicell-training, github.com/PhysiCell-Tools/Studio-Guide, and the abovementioned blog. But this might not be very accessible to all users targeted by the new GUI functionality (especially when command line interventions and manual Makefile edits are involved). While not all of this has to be changed before publication, having all information in one place would already improve accessibility to a larger user-base. - When following the instructions (https://github.com/PhysiCell-Tools/Studio-Guide/blob/main/README.md), “python studio/bin/studio.py -p -e virus-sample” the -p flag gives an error: “Invalid argument(s): [‘-p’]”. We assumed it has to be left out, but perhaps the docs have to be updated.

Is the documentation provided clear and user friendly?

Mostly yes, as there is already a lot of documentation available. However, the user-friendliness could be improved with some minor changes. For example, the documentation could be made more user-friendly if resources were available from a central spot. Currently, information can be found in different places: - https://github.com/PhysiCell-Tools/Studio-Guide/blob/main/README.md provides installation instructions and a nice overview of what is where in the GUI, but as mentioned above, does not mention potential issues when installing on MacOS. - The paper provides very detailed examples; these might be nice to include along with the abovementioned overview. - Potentially other places as well. It would be great if the main documentation page could at least link to these other resources with a brief description of what the user will find there. Further, some additions would make the documentation more complete: - It would be good to have an overview somewhere of all the configuration files that can be supplied/loaded (e.g. those for “rules” and for initial configurations). - A clearer instruction/small tutorial on how to use simularium and paraview with physicell studio; especially for paraview there is no instruction on how to use your own data or make your own `.pvsm` file In the longer term, it might be worthwhile to set up a self-contained documentation website (this is relatively easy nowadays using e.g. Github pages), which can outline dependencies, installation instructions, a quick overview, detailed tutorials, example models, links to Github issues/slack communities. This is not a requirement for publication but might be worth looking into in the future as it would be more user-friendly.

Is there a clearly-stated list of dependencies, and is the core functionality of the software documented to a satisfactory level?

No. The core functionality of the software is nicely outlined in the Github README (https://github.com/PhysiCell-Tools/Studio-Guide/blob/main/README.md), but as mentioned before, this high-level overview is missing in the paper itself. The README and paper recommend installing the Anaconda python distribution to get the required python dependencies. This is fine, but adding a setup file or requirements.txt might still be useful for users who are more familiar with python and want a more minimal installation. Providing a conda environment.yml that allows running the studio along with paraview and/or simularium might also be helpful. Note that running the studio with simularium in anaconda did not work because anaconda did not have the required vtk v9.3.0; instead we had to install simularium without anaconda (“pip3 install simularium”).

Are there (ideally real world) examples demonstrating use of the software?

The detail tutorial nicely walks the reader through the tool (although as mentioned before, a high-level overview is missing and the level of detail feels slightly out of place in the paper itself). When walking through the example in the paper and the supplementary, we did run into a few (minor) issues: - It might be good to stress explicitly that after copying the template.xml into tumor_demo.xml, the first step is always to compile using “make”. The paper mentions “Assuming … you have compiled the template project executable (called “project”) …”. But it might not be immediately clear to all users how exactly they should do so (presumably by running “make tumor_demo” after copying the xml file?). - When running “python studio/bin/studio.py -c tumor_demo.xml -e project” as instructed, a warning pops up that “rules0.csv” is not valid (although the tool itself still works). - The instructions for plotting say to press “enter” when changing cmin and cmax, but Mac offers only a return key. Pressing fn+return to get the enter functionality also does not work; it might be good to offer an alternative for Mac. - When reproducing the supplementary tutorial, results were slightly different. It might be good if the example would offer a random seed so that users can verify that they can reproduce these results exactly. In our hands, when reproducing figs 39, 40, 48, 49 yields way more (red) macrophages (even when running multiple times), but we could not be sure if this is due to variation between runs, or a mistake in the settings somewhere.


The paper mentions that they have started setting up automated testing, but it does not give an idea of what the current test coverage is. Did they add a few tests here and there, or start to systematically test all parts of the software? I understand the latter might not be achievable immediately, but it would be good if users and/or contributors can at least get a sense of how good the current coverage is. (Note: the framework uses pytest, which seems to offer some functionality to generate coverage reports, see e.g. https://www.lambdatest.com/blog/pytest-code-coverage-report/). The code in studio_for_pytest.py has a comment “do later, otherwise problems sometimes”, but it is not entirely clear if the relevant issue has been resolved.

Additional Comments: The presented tool offers a GUI interface to the PhysiCell framework for agent-based modeling. As outlined for the paper, this offers significant value to the users since editing a model is now much more accessible. The tool comes with extensive functionality and instructions. Overall, the tool functions as advertised, and will be of great value to the community of PhysiCell users that now have to edit XML files by hand. It is therefore (mostly) publishable as is if some of the issues with installation (mentioned above) can be straightened out. That said, we do think some improvements could make both the tool and the paper more accessible to a larger user audience. Most of these have been mentioned in the other questions, but we will list some additional ones below. Note that many of these are just suggestions, so we will leave it up to the authors if and when they implement them.

Suggestions for the paper: While the paper nicely outlines design ideas and usage of the tool, there were some points where we felt that the main point did not quite come across, for example: - As mentioned in the question about problem statement and intended audience, adding some information to the paper would make it a more useful resource to users not yet familiar with PhysiCell (see remarks there). - The section “Design and development” describes the development history of the tool. In principle this is a valuable addition, because it illustrates how the project is under ongoing development and has already been improved several times based on feedback of users. However, the amount of information on each previous stage is slightly confusing; it is not entirely clear how this relates to the paper and current tool. If the main point is to showcase that the current tool has been built based on practical user experiences, this would probably come across better if this section was somewhat shorter and focused on the design choices rather than previous versions. If the main point is something else, it should be clarified what the main idea is. – The point of Table 1 was unclear to us – consider removing or explaining the main idea. - Several figures do not have captions (e.g. Figure 1 but also others); it would be helpful to clarify what message the figure should convey. – P4 “adjust the syntax for Windows if necessary” – is it self-explanatory how users should adjust? Consider adding the correct code for windows as well if possible, since users that want to use the GUI tool might not be familiar with command line syntax. - P6 “if you create your own custom C++ code referring directly to cell type ID” – this functionality is never discussed. This might be part of the general PhysiCell functionality, but it would be good to at least provide a link to a resource on how you could do this. - P8 “Only those parameters that display … editing the C++ code” – it was not entirely clear to me what this means, could you clarify? - P13 mentions you can immediately see changes to the model parameters made. This is very useful for prototyping when users want immediate feedback. However, what happens when you try to save output for a simulation where parameters were changed while the simulation was running? Would users be reminded that their current output is not representative? - Discussion: it is good to mention that the tool is already being used. Can you give an indication based on your experience how long it takes new users to learn to navigate the tool? This might be useful information to add in the paper. - The last statement on LLMs seems to come out of nowhere. Consider leaving it out or expanding further on what would be needed to make this work/how feasible this is.

Further comments on the tool itelf: - The paper mentions that results may not be fully reproducible if multiple threads are used (I assume this is the case even when a random seed is set). In this case, would it make sense to throw a warning the first time a user tries to set a seed with multiple threads, to avoid confusion as to why the results are not reproducible? - Unusable fields are not always greyed out to indicate that they are disabled, which sometimes makes it seem as though the tool is unresponsive. In other places unusable options are set to grey, so it might be good to double-check if this is consistent. - At the initial conditions (IC) page there is no legend; it might be good to add one. - There are some small inconsistencies between the field names mentioned in the paper and those in the tool/screenshots. For example “boundary condition” (p5) should be “dirichlet BC”, “uptake” (p6) should be “uptake rate”. For the latter, the paper mentions that the length scale is 100 micron but this should be visible in the tool as well. - Not all fields have labels, so it is not always clear what the options do (see e.g. drop-downs in Figure 6). – There are a few points in the tool where you have to “enable” a functionality before it works, but this might not always be intuitive. For example, if you upload a file with initial conditions, it can be assumed that you want to use it. There might be good reasons for this in some cases but in general, consider if all these checkpoints are necessary or if this could be simplified. Same goes for the csv files that have to be saved separately instead of through the main “save” button – in the long term it might be worth saving all relevant files when they are updated, or at least throwing a warning that you have to save some of them separately.

ちょっとわかりにくいと感じました。

原文

This usually happens in the early stages of a project when desired behavior is still being sorted out, and no one is using your code yet.

代案

このようなことは通常、プロジェクトが初期の段階で望ましい動作を整理している最中であり、かつ誰もまだ使い始めていないときに発生します。

Certainly! Here's a simplified explanation and notes about the Response() class in Django REST framework:

Response Class in Django REST Framework

• Purpose: The Response() class in Django REST framework is used to send data back to clients in various formats, such as JSON or HTML, based on what the client requests.

• Usage: Unlike regular HttpResponse objects in Django, you don't give Response() class rendered content directly. Instead, you provide it with unrendered data, typically Python data types like lists or dictionaries.

• Serialization: The Response() class cannot handle complex data types directly, like Django model instances. You need to convert these complex types into simpler data types (serialization) before passing them to Response().

• Data Serialization: Use Django REST framework's Serializer classes to convert complex data (like Django models) into Python primitives (like dictionaries). This prepares the data for the Response() object to handle.

• Arguments:

• data: Serialized data (Python primitives) that will be sent in the response.
• status: HTTP status code for the response (defaults to 200 for OK). It tells the client whether the request was successful or had an error.
• template_name: Optional HTML template name to use if rendering HTML responses.
• headers: Additional HTTP headers to include in the response.
• content_type: The type of content in the response (usually set automatically based on content negotiation).

Examples

1. Sending JSON Data: ```python from rest_framework.response import Response from rest_framework.decorators import api_view

@api_view(['GET']) def get_books(request): books = [{'title': 'Book 1', 'author': 'Author A'}, {'title': 'Book 2', 'author': 'Author B'}] return Response(books) `` - Here,Response()` is used to send a list of books as JSON data.

1. Handling Error Responses: ```python from rest_framework.response import Response from rest_framework import status

def create_book(request): if request.method == 'POST': # Some logic to create a book if book_created_successfully: return Response({'message': 'Book created successfully'}, status=status.HTTP_201_CREATED) else: return Response({'error': 'Failed to create book'}, status=status.HTTP_400_BAD_REQUEST) `` - In this example,Response()` is used to send messages about the success or failure of creating a book, along with appropriate HTTP status codes.

Notes

• Flexibility: Response() allows your Django API to respond with data in different formats based on client needs.
• Serialization: Use serializers to convert complex data into formats Response() can handle.
• HTTP Status Codes: Always consider setting appropriate HTTP status codes to inform clients about the success or failure of their requests.

Using Response() in Django REST framework simplifies handling API responses, ensuring data is sent back to clients in the right format with proper status information.

Sure, let's break down what that means:

HTTP Content Negotiation: This is the process where a server and a client agree on the format of data that will be exchanged in an HTTP request. It's like deciding on the language in which two people will communicate.

Response Class: In REST framework (used with Django), the Response class helps you send data back to clients in different formats (like JSON, HTML, etc.) based on what the client requests.

Example: Imagine you have an endpoint /api/books/ that lists books. When a client (like a web browser or mobile app) asks for this list, they might want the data in JSON format (which is common for APIs), while another client might prefer HTML (for displaying in a web browser).

Using Response Class: Instead of manually crafting the response each time, you can use the Response class provided by REST framework. It makes it easier to handle different formats. For instance, if a client requests JSON, the Response class can automatically convert your Python data (like lists of books) into JSON format.

Why Use It: By using the Response class, you ensure that your API can easily respond with data in the format that the client prefers, whether it's JSON, HTML, or another format. It simplifies your code and makes your API more flexible for different clients.

When to Use It: Unless you have specific reasons not to, it's recommended to use the Response class in your views that handle API requests. This way, REST framework can handle content negotiation smoothly, ensuring the right format is sent back to the client without you having to handle all the details manually.

In summary, HTTP content negotiation and the Response class in REST framework help you efficiently manage how data is sent and received between your Django application and its clients, ensuring flexibility and ease of use.

Trade-offs Between Views and ViewSets

When deciding whether to use views or ViewSets in Django REST framework, it's important to understand the benefits and drawbacks of each approach. Both have their own use cases and can be more suitable in different scenarios.

Views

Pros: 1. Explicit and Customizable: You have full control over each view. This allows you to handle complex logic and special cases more easily. 2. Fine-grained Control: Allows you to define exactly what each view does, making it easier to optimize performance and security for specific endpoints. 3. Simplicity: For small projects or APIs with a limited number of endpoints, views might be simpler to implement and understand.

Cons: 1. Repetitive Code: You might end up writing a lot of boilerplate code, especially if your API has many endpoints with similar logic. 2. Inconsistent URL Patterns: It's easier to accidentally create inconsistencies in your API's URL patterns and behavior if you're manually defining each endpoint. 3. Maintenance: As your project grows, maintaining a large number of individual views can become cumbersome and error-prone.

ViewSets

Pros: 1. Consistency: Ensures that URL patterns and behavior are consistent across your API, following standard REST conventions. 2. Less Boilerplate: Reduces the amount of code you need to write. Common CRUD operations are automatically handled. 3. Easier Refactoring: Grouping related views into a single ViewSet can make it easier to refactor and maintain your code. 4. DRY Principle: Helps to keep your code DRY (Don't Repeat Yourself), reducing redundancy.

Cons: 1. Less Explicit: Abstracts away some of the details, which can make the behavior of your API less explicit and harder to understand at a glance. 2. Customization: While ViewSets are great for standard CRUD operations, they can be less flexible for endpoints that require complex or custom behavior. 3. Learning Curve: For developers new to Django REST framework, understanding the additional layer of abstraction might take some time.

When to Use Views vs. ViewSets

• Use Views When:
• You need fine-grained control over each endpoint.
• Your API has complex or custom behavior that doesn't fit the standard CRUD operations.
• You're building a small project with only a few endpoints.

• You want the explicitness and clarity of defining each view individually.

• Use ViewSets When:

• You want to minimize boilerplate code for standard CRUD operations.
• Consistency across your API is a priority.
• Your API has many endpoints that follow standard REST conventions.
• You prefer to focus on the high-level design of your API rather than the specifics of URL configuration.

Example Scenario

Views Approach: If you're building a small API with a few endpoints that require complex custom behavior, you might choose to define each view individually. This approach gives you full control over each endpoint and makes the logic explicit.

```python class SnippetList(APIView): def get(self, request, format=None): snippets = Snippet.objects.all() serializer = SnippetSerializer(snippets, many=True) return Response(serializer.data)

def post(self, request, format=None):
    serializer = SnippetSerializer(data=request.data)
    if serializer.is_valid():
        serializer.save(owner=request.user)
        return Response(serializer.data, status=status.HTTP_201_CREATED)
    return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)

```

ViewSets Approach: For a larger API with many standard CRUD operations, using ViewSets can save a lot of time and reduce boilerplate code. It ensures that your API follows consistent URL patterns and behavior.

```python class SnippetViewSet(viewsets.ModelViewSet): queryset = Snippet.objects.all() serializer_class = SnippetSerializer permission_classes = [permissions.IsAuthenticatedOrReadOnly, IsOwnerOrReadOnly]

@action(detail=True, renderer_classes=[renderers.StaticHTMLRenderer])
def highlight(self, request, *args, **kwargs):
    snippet = self.get_object()
    return Response(snippet.highlighted)

```

Router Configuration: Using a router simplifies URL configuration, reducing the risk of inconsistencies and making it easier to manage a large number of endpoints.

```python router = DefaultRouter() router.register(r'snippets', SnippetViewSet) router.register(r'users', UserViewSet)

urlpatterns = [ path('', include(router.urls)), ] ```

By considering these trade-offs, you can choose the approach that best fits the needs of your project and team.

Let's break down how to refactor our views using ViewSets and Routers and ensure that everything is wired correctly.

Step-by-Step Refactoring

1. Create ViewSets: Define the UserViewSet and SnippetViewSet.
2. Use @action decorator: Add custom actions to the SnippetViewSet.
3. Bind ViewSets to URLs: Use DefaultRouter to automatically generate URL patterns.

Updated Code

1. Creating the ViewSets

In snippets/views.py:

```python from rest_framework import viewsets, permissions, renderers from rest_framework.decorators import action from rest_framework.response import Response from .models import Snippet from .serializers import SnippetSerializer, UserSerializer from django.contrib.auth.models import User from .permissions import IsOwnerOrReadOnly

class UserViewSet(viewsets.ReadOnlyModelViewSet): """ This viewset automatically provides list and retrieve actions. """ queryset = User.objects.all() serializer_class = UserSerializer

class SnippetViewSet(viewsets.ModelViewSet): """ This viewset automatically provides list, create, retrieve, update, and destroy actions. Additionally, we provide a custom highlight action. """ queryset = Snippet.objects.all() serializer_class = SnippetSerializer permission_classes = [permissions.IsAuthenticatedOrReadOnly, IsOwnerOrReadOnly]

@action(detail=True, renderer_classes=[renderers.StaticHTMLRenderer])
def highlight(self, request, *args, **kwargs):
    snippet = self.get_object()
    return Response(snippet.highlighted)

def perform_create(self, serializer):
    serializer.save(owner=self.request.user)

```

2. Binding ViewSets to URLs Explicitly (if not using a Router)

In snippets/urls.py, you can manually bind the ViewSet actions to URL patterns. This is not necessary if you use a Router but is useful for understanding how things work under the hood:

```python from django.urls import path from rest_framework.urlpatterns import format_suffix_patterns from snippets.views import SnippetViewSet, UserViewSet, api_root

snippet_list = SnippetViewSet.as_view({ 'get': 'list', 'post': 'create' }) snippet_detail = SnippetViewSet.as_view({ 'get': 'retrieve', 'put': 'update', 'patch': 'partial_update', 'delete': 'destroy' }) snippet_highlight = SnippetViewSet.as_view({ 'get': 'highlight' }, renderer_classes=[renderers.StaticHTMLRenderer]) user_list = UserViewSet.as_view({ 'get': 'list' }) user_detail = UserViewSet.as_view({ 'get': 'retrieve' })

urlpatterns = format_suffix_patterns([ path('', api_root), path('snippets/', snippet_list, name='snippet-list'), path('snippets/<int:pk>/', snippet_detail, name='snippet-detail'), path('snippets/<int:pk>/highlight/', snippet_highlight, name='snippet-highlight'), path('users/', user_list, name='user-list'), path('users/<int:pk>/', user_detail, name='user-detail') ]) ```

3. Using Routers

To simplify the URL configuration, use a Router. In snippets/urls.py:

```python from django.urls import path, include from rest_framework.routers import DefaultRouter from snippets import views

Create a router and register our viewsets with it.

router = DefaultRouter() router.register(r'snippets', views.SnippetViewSet, basename='snippet') router.register(r'users', views.UserViewSet, basename='user')

The API URLs are now determined automatically by the router.

urlpatterns = [ path('', include(router.urls)), path('api-auth/', include('rest_framework.urls', namespace='rest_framework')) ] ```

How It Works

1. ViewSets: Group related view logic (list, retrieve, create, update, delete) into a single class.
2. Routers: Automatically generate URL patterns for ViewSets based on common conventions.
3. Custom Actions: Use the @action decorator to add custom endpoints that don't fit the standard CRUD operations.

Benefits

• Simplifies URL Configuration: Routers handle the URL routing automatically.
• Combines Related Views: ViewSets group related views into a single class, reducing redundancy.
• Flexible: Easily add custom actions to ViewSets.

By refactoring to use ViewSets and Routers, your code becomes cleaner, more maintainable, and easier to understand. The framework handles much of the repetitive boilerplate code, allowing you to focus on the unique aspects of your application.

Let's continue with refactoring our views by combining the SnippetList, SnippetDetail, and SnippetHighlight classes into a single SnippetViewSet class. This will make our code more concise and maintainable.

Refactoring Steps

1. Refactor User Views: Combine UserList and UserDetail into UserViewSet.
2. Refactor Snippet Views: Combine SnippetList, SnippetDetail, and SnippetHighlight into SnippetViewSet.
3. Update URLs: Use a router to generate URL patterns automatically.

Step 1: Refactor User Views

In snippets/views.py, refactor the user views:

```python from rest_framework import viewsets from django.contrib.auth.models import User from .serializers import UserSerializer

class UserViewSet(viewsets.ReadOnlyModelViewSet): """ This viewset automatically provides list and retrieve actions. """ queryset = User.objects.all() serializer_class = UserSerializer ```

Step 2: Refactor Snippet Views

In snippets/views.py, refactor the snippet views:

```python from rest_framework import viewsets, renderers from .models import Snippet from .serializers import SnippetSerializer

class SnippetViewSet(viewsets.ModelViewSet): """ This viewset automatically provides list, create, retrieve, update, and destroy actions. Additionally, we also provide an extra highlight action. """ queryset = Snippet.objects.all() serializer_class = SnippetSerializer

@action(detail=True, renderer_classes=[renderers.StaticHTMLRenderer])
def highlight(self, request, *args, **kwargs):
    snippet = self.get_object()
    return Response(snippet.highlighted)

```

Step 3: Update URLs

In snippets/urls.py, register the viewsets with a router and update the URL patterns:

```python from django.urls import path, include from rest_framework.routers import DefaultRouter from snippets import views

Create a router and register our viewsets with it.

router = DefaultRouter() router.register(r'snippets', views.SnippetViewSet) router.register(r'users', views.UserViewSet)

The API URLs are now determined automatically by the router.

Additionally, we include the login URLs for the browsable API.

urlpatterns = [ path('', include(router.urls)), path('api-auth/', include('rest_framework.urls', namespace='rest_framework')) ] ```

How It Works

1. UserViewSet: Combines the UserList and UserDetail views into a single viewset that handles read-only operations.
2. SnippetViewSet: Combines the SnippetList, SnippetDetail, and SnippetHighlight views into a single viewset. The highlight action is defined as a custom action within the viewset.
3. Router: The DefaultRouter generates URL patterns automatically, so you don't have to define them manually.

Example in Action

1. User Request:
2. GET /users/: Lists all users (handled by UserViewSet).
3. GET /users/1/: Retrieves details for user with ID 1 (handled by UserViewSet).
4. GET /snippets/: Lists all snippets (handled by SnippetViewSet).
5. GET /snippets/1/: Retrieves details for snippet with ID 1 (handled by SnippetViewSet).
6. GET /snippets/1/highlight/: Retrieves highlighted HTML for snippet with ID 1 (handled by SnippetViewSet).

By refactoring to use ViewSets and Routers, our code becomes cleaner and more maintainable, and we let the framework handle much of the repetitive boilerplate code for us.

Let's break down how to create a hyperlinked API in Django REST framework, step by step, with an example.

What is a Hyperlinked API?

A hyperlinked API means that instead of using primary keys to reference related objects, we use URLs (hyperlinks). This makes the API more intuitive and easier to navigate.

Steps to Create a Hyperlinked API

1. Update Serializers:
2. Use HyperlinkedModelSerializer instead of ModelSerializer.

3. Add URL fields to represent relationships as hyperlinks.

4. Update URL Patterns:

5. Name the URL patterns so that they can be referenced by the serializers.

Example

Step 1: Update Serializers

In snippets/serializers.py, update your serializers to use HyperlinkedModelSerializer:

```python from rest_framework import serializers from .models import Snippet from django.contrib.auth.models import User

class SnippetSerializer(serializers.HyperlinkedModelSerializer): owner = serializers.ReadOnlyField(source='owner.username') highlight = serializers.HyperlinkedIdentityField(view_name='snippet-highlight', format='html')

class Meta:
    model = Snippet
    fields = ['url', 'id', 'highlight', 'owner', 'title', 'code', 'linenos', 'language', 'style']

class UserSerializer(serializers.HyperlinkedModelSerializer): snippets = serializers.HyperlinkedRelatedField(many=True, view_name='snippet-detail', read_only=True)

class Meta:
    model = User
    fields = ['url', 'id', 'username', 'snippets']

```

Explanation: - SnippetSerializer: - owner: Read-only field that shows the username of the snippet owner. - highlight: Hyperlinked field pointing to the 'snippet-highlight' URL. - fields: List of fields to include in the serialized output.

• UserSerializer:
• snippets: Hyperlinked field that shows all snippets owned by the user, pointing to the 'snippet-detail' URL.
• fields: List of fields to include in the serialized output.

Step 2: Update URL Patterns

In snippets/urls.py, name your URL patterns:

```python from django.urls import path from rest_framework.urlpatterns import format_suffix_patterns from snippets import views

API endpoints

urlpatterns = format_suffix_patterns([ path('', views.api_root), path('snippets/', views.SnippetList.as_view(), name='snippet-list'), path('snippets/<int:pk>/', views.SnippetDetail.as_view(), name='snippet-detail'), path('snippets/<int:pk>/highlight/', views.SnippetHighlight.as_view(), name='snippet-highlight'), path('users/', views.UserList.as_view(), name='user-list'), path('users/<int:pk>/', views.UserDetail.as_view(), name='user-detail') ]) ```

Explanation: - format_suffix_patterns: Allows adding format suffixes like .json or .html to URLs. - path: Defines URL patterns and associates them with views. - name: Names the URL patterns so that they can be referenced by the serializers.

How It Works

1. Request: When a user requests a snippet or user detail, the serializer returns URLs for related objects instead of primary keys.
2. Navigation: The user can follow these URLs to navigate between related objects.

Example in Action

1. User Request: GET /snippets/
2. Response: json [ { "url": "http://example.com/snippets/1/", "id": 1, "highlight": "http://example.com/snippets/1/highlight/", "owner": "user1", "title": "Example Snippet", "code": "print('Hello, World!')", "linenos": true, "language": "python", "style": "friendly" } ]

Here, the owner field is a username, and the highlight and url fields are hyperlinks to the related endpoints.

This is how you create a hyperlinked API using Django REST framework, making it easier to navigate relationships between entities.

Let's break down how to create an endpoint for code highlighting in a simple way, with an example:

What is an Endpoint?

An endpoint is a specific URL where our web application can send requests to get or send data. In this case, we want to create an endpoint to highlight code snippets and return an HTML representation instead of JSON.

Steps to Create the Endpoint

1. Create the View:
2. We will create a view called SnippetHighlight that will handle requests to highlight a code snippet.
3. This view will use a special renderer to return HTML instead of JSON.

4. Since there's no built-in view that fits our need exactly, we will create a custom view by extending GenericAPIView.

5. Update URLs:

6. We will add a new URL pattern to link to our SnippetHighlight view.

Example

Step 1: Create the View

First, we create our custom view in snippets/views.py:

```python from rest_framework import generics, renderers from rest_framework.response import Response from .models import Snippet

class SnippetHighlight(generics.GenericAPIView): queryset = Snippet.objects.all() renderer_classes = [renderers.StaticHTMLRenderer]

def get(self, request, *args, **kwargs):
    snippet = self.get_object()
    return Response(snippet.highlighted)

```

Explanation: - Import Statements: We import necessary modules. - SnippetHighlight Class: This class handles the requests to highlight snippets. - queryset: Specifies which snippets are available. - renderer_classes: Tells Django to use HTML renderer instead of JSON renderer. - get() Method: This method handles GET requests. It fetches the requested snippet and returns its highlighted HTML.

Step 2: Update URLs

Next, we add the URL pattern in snippets/urls.py:

```python from django.urls import path from . import views

urlpatterns = [ path('', views.api_root), # Your API root path('snippets/<int:pk>/highlight/', views.SnippetHighlight.as_view()), # URL for highlighting ] ```

Explanation: - path('', views.api_root): This is the root of our API. - path('snippets/<int:pk>/highlight/', views.SnippetHighlight.as_view()): This URL pattern connects to our SnippetHighlight view. <int:pk> is a placeholder for the snippet's ID.

How It Works

1. Request: A user sends a GET request to /snippets/1/highlight/ to highlight snippet with ID 1.
2. View: The SnippetHighlight view handles the request. It fetches the snippet with ID 1, gets its highlighted HTML, and returns it.
3. Response: The user receives the highlighted HTML of the snippet.

Example in Action

1. User Request: GET /snippets/1/highlight/
2. Backend Processing:
3. The view fetches snippet 1 from the database.
4. It gets the highlighted HTML of snippet 1.
5. Response: The user gets the HTML representation of the highlighted code snippet.

This is how you create an endpoint to highlight code snippets and return HTML using Django REST Framework.

Authenticating with the API

Now that we have set permissions on the API, it's essential to authenticate our requests if we want to perform actions like creating, updating, or deleting snippets.

Default Authentication Classes

By default, Django REST Framework uses the following authentication classes: - SessionAuthentication: Uses Django's session framework. - BasicAuthentication: Uses HTTP Basic Authentication.

When interacting with the API through a web browser, logging in through the browser session provides the required authentication for subsequent requests. However, for programmatic interaction, you need to explicitly include authentication credentials with each request.

Example of an Unauthenticated Request

If you try to create a snippet without authenticating, you will receive an error:

Unauthenticated Request Example

bash http POST http://127.0.0.1:8000/snippets/ code="print(123)"

Response

json { "detail": "Authentication credentials were not provided." }

Example of an Authenticated Request

To make an authenticated request, include the username and password of one of the users you created earlier.

Authenticated Request Example

Using httpie (a command-line HTTP client):

bash http -a admin:password123 POST http://127.0.0.1:8000/snippets/ code="print(789)" title="foo"

Response

json { "id": 1, "owner": "admin", "title": "foo", "code": "print(789)", "linenos": false, "language": "python", "style": "friendly" }

Summary

• Permissions: The API now has fine-grained permissions to ensure only authenticated users can create, update, or delete snippets.
• Authentication: Use either session-based or basic authentication for interacting with the API.
• Programmatic Access: Include the username and password in the request to authenticate programmatically.

Next Steps

In the next part of the tutorial, we'll: - Create an HTML endpoint for highlighted snippets. - Enhance the cohesion of the API by using hyperlinking for the relationships within the system.

This will further improve the usability and functionality of our web API, making it more intuitive and user-friendly.

To ensure that all code snippets are visible to everyone but only the user who created a snippet can update or delete it, you can create a custom permission class. This custom permission will be added to the snippet instance endpoint to enforce these rules.

Step-by-Step Instructions

1. Create Custom Permission Class: Create a new file called permissions.py in your snippets app directory and define a custom permission class IsOwnerOrReadOnly.

2. Update View to Use Custom Permission: Modify the SnippetDetail view to use the custom permission class in addition to the IsAuthenticatedOrReadOnly permission class.

Step 1: Create Custom Permission Class

snippets/permissions.py

```python from rest_framework import permissions

class IsOwnerOrReadOnly(permissions.BasePermission): """ Custom permission to only allow owners of an object to edit it. """

def has_object_permission(self, request, view, obj):
    # Read permissions are allowed to any request,
    # so we'll always allow GET, HEAD or OPTIONS requests.
    if request.method in permissions.SAFE_METHODS:
        return True

    # Write permissions are only allowed to the owner of the snippet.
    return obj.owner == request.user

```

Step 2: Update the View to Use Custom Permission

views.py

First, import the custom permission class at the top of your views.py file:

python from snippets.permissions import IsOwnerOrReadOnly

Then, update the SnippetDetail view to include the custom permission in the permission_classes property:

```python from rest_framework import generics from rest_framework import permissions from .models import Snippet from .serializers import SnippetSerializer

class SnippetList(generics.ListCreateAPIView): queryset = Snippet.objects.all() serializer_class = SnippetSerializer permission_classes = [permissions.IsAuthenticatedOrReadOnly]

def perform_create(self, serializer):
    serializer.save(owner=self.request.user)

class SnippetDetail(generics.RetrieveUpdateDestroyAPIView): queryset = Snippet.objects.all() serializer_class = SnippetSerializer permission_classes = [permissions.IsAuthenticatedOrReadOnly, IsOwnerOrReadOnly] ```

Explanation of the Code

• Custom Permission Class (IsOwnerOrReadOnly):
• permissions.BasePermission: This is the base class for all permissions in Django REST Framework.

• has_object_permission: This method checks whether the request has the required permissions for a specific object.

  • Read Permissions: Always allow safe methods (GET, HEAD, OPTIONS).
  • Write Permissions: Only allow if the user making the request is the owner of the object.

• SnippetDetail View:

• permission_classes: Combines IsAuthenticatedOrReadOnly (which allows read access to everyone and write access only to authenticated users) with IsOwnerOrReadOnly (which restricts write access to the owner of the snippet).

What Happens Now

• Read Access: Any user (authenticated or not) can read (list and retrieve) snippets.
• Write Access: Only authenticated users can create snippets, and only the owner of a snippet can update or delete it.

Testing the Setup

1. Open the Browsable API: Navigate to a snippet instance endpoint in your browser.
2. Check Actions: You should see the 'DELETE' and 'PUT' actions only if you are logged in as the user who created the snippet.

Summary

• Purpose: Ensure all code snippets are visible to everyone, but only the creator can update or delete their snippets.
• Implementation: Create a custom permission class and apply it to the SnippetDetail view.
• Result: Proper access control is enforced, allowing only the snippet owner to modify their snippet while everyone can read the snippets.

This setup ensures that your API adheres to the required permissions, providing both visibility and security.

To add login functionality to the browsable API in Django REST Framework, you need to include the authentication URLs in your project’s urls.py file. This will allow users to log in and log out via the browsable API interface.

Step-by-Step Instructions

1. Import the Required Modules: Add the necessary imports at the top of your urls.py file.
2. Include the Authentication URLs: Add a URL pattern to include the login and logout views for the browsable API.

Code Example

project-level urls.py

First, import the necessary modules:

python from django.urls import path, include

Then, add the authentication URL pattern:

```python from django.contrib import admin from django.urls import path, include

urlpatterns = [ path('admin/', admin.site.urls), path('api/', include('your_app.urls')), # Include your app's URLs path('api-auth/', include('rest_framework.urls')), # Add this line ] ```

Explanation of the Code

• path('api-auth/', include('rest_framework.urls')): This line adds the authentication URLs provided by Django REST Framework. It allows users to log in and log out through the browsable API.

What Happens Now

• Login Link: When you navigate to the browsable API in your browser, you will see a "Login" link in the top right corner.
• Login and Logout: Clicking on the "Login" link will take you to a login page where you can enter your credentials to log in. Once logged in, you can create, update, and delete snippets if you have the necessary permissions.

Testing the Setup

1. Open the Browsable API: Navigate to the browsable API in your browser.
2. Login: Click on the "Login" link in the top right corner and log in with a user account.
3. Create Snippets: Once logged in, you will be able to create new code snippets and perform other actions that require authentication.

Example

Let's assume you have already created a few users and code snippets. After logging in as one of these users, you can create a new snippet via the browsable API.

Creating a New Snippet

1. Navigate to the endpoint for creating snippets (e.g., /api/snippets/).
2. Fill in the details for the new snippet and submit the form.

Viewing Users

Navigate to the /api/users/ endpoint. The representation will include a list of snippet IDs associated with each user in the snippets field.

Summary

• Purpose: Adding login functionality to the browsable API allows users to authenticate and perform actions that require login.
• Implementation: Include the rest_framework.urls in your urls.py file.
• Result: Users can log in and log out via the browsable API, enabling them to create, update, and delete snippets.

This setup enhances the usability of your API, making it easier for users to interact with it directly through the browser.

To ensure that only authenticated users can create, update, or delete code snippets while allowing unauthenticated users to read the snippets, we can use the IsAuthenticatedOrReadOnly permission class from Django REST Framework.

Here's how you can implement this in your views:

Step-by-Step Instructions

1. Import Permissions: First, import the permissions module from Django REST Framework.
2. Set Permission Classes: Add the permission_classes property to both the SnippetList and SnippetDetail view classes, setting it to IsAuthenticatedOrReadOnly.

Code Example

views.py

First, import the permissions at the top of your views.py file:

python from rest_framework import permissions

Then, update your view classes to include the permission_classes property:

```python from rest_framework import generics from .models import Snippet from .serializers import SnippetSerializer

class SnippetList(generics.ListCreateAPIView): queryset = Snippet.objects.all() serializer_class = SnippetSerializer permission_classes = [permissions.IsAuthenticatedOrReadOnly]

def perform_create(self, serializer):
    serializer.save(owner=self.request.user)

class SnippetDetail(generics.RetrieveUpdateDestroyAPIView): queryset = Snippet.objects.all() serializer_class = SnippetSerializer permission_classes = [permissions.IsAuthenticatedOrReadOnly] ```

Explanation of the Code

• permission_classes: This property specifies the permissions that are required to access the view.
• permissions.IsAuthenticatedOrReadOnly: This permission class ensures that:
• Authenticated users (logged in) can perform any action (read, create, update, delete).
• Unauthenticated users (not logged in) can only read the data (list and retrieve).

What Happens Now

• Authenticated Users:
• Can create new snippets.
• Can update existing snippets.
• Can delete snippets.
• Can read (list and retrieve) snippets.
• Unauthenticated Users:
• Can only read (list and retrieve) snippets.
• Cannot create new snippets.
• Cannot update snippets.
• Cannot delete snippets.

Example Usage

As an Authenticated User

When a logged-in user sends a POST request to create a snippet, it will be allowed because they have read-write access.

bash curl -X POST -H "Authorization: Token <user-token>" -d '{"title": "New Snippet", "code": "print(123)"}' http://example.com/snippets/

As an Unauthenticated User

When a not logged-in user tries to send a POST request to create a snippet, it will be denied because they only have read-only access.

bash curl -X POST -d '{"title": "New Snippet", "code": "print(123)"}' http://example.com/snippets/

This request will result in a 403 Forbidden response, indicating that the user does not have permission to perform the action.

Summary

• Purpose: To restrict create, update, and delete actions to authenticated users, while allowing unauthenticated users to read data.
• Implementation: Use the IsAuthenticatedOrReadOnly permission class in the view classes.
• Effect: Authenticated users get full access, while unauthenticated users get read-only access.

This setup helps protect your data by ensuring that only users who are logged in can modify it, while still allowing anyone to view the data.

Let's break down how to update the SnippetSerializer to include the owner field and explain what this change does in simple terms.

Step-by-Step Explanation

1. Add the owner Field: In your SnippetSerializer, add a new field called owner that will show the username of the user who created the snippet.

2. Update the Meta Class: Make sure to include the owner field in the list of fields in the serializer's Meta class.

Code Example

Here's how you can update your SnippetSerializer:

```python from rest_framework import serializers from .models import Snippet

class SnippetSerializer(serializers.ModelSerializer): owner = serializers.ReadOnlyField(source='owner.username')

class Meta:
    model = Snippet
    fields = ['id', 'title', 'code', 'linenos', 'language', 'style', 'owner']

```

Explanation of the Code

• owner Field:
• serializers.ReadOnlyField: This type of field is read-only, meaning it is only used when the data is being sent out, not when data is being received.
• source='owner.username': The source argument specifies which attribute to use to fill this field. owner.username means it will use the username attribute of the owner (the user who created the snippet).

What This Field Does

• Read-Only: The owner field is read-only, so it will show up when you serialize a snippet, but it won't be used when you create or update a snippet.
• Source Attribute: The source attribute lets you specify which attribute of the model to use. In this case, owner.username will use the username of the user who owns the snippet.

How It Works

When you serialize a Snippet instance, the owner field will include the username of the user who created it.

Example

Let's say we have a user named Alice who has created a snippet. When we serialize that snippet, it will look like this:

```python

Assuming snippet is an instance of Snippet created by user 'Alice'

serializer = SnippetSerializer(snippet) print(serializer.data) ```

Output

json { "id": 1, "title": "Example Snippet", "code": "print('Hello, world!')", "linenos": true, "language": "python", "style": "friendly", "owner": "Alice" }

Summary

• Purpose: Adding the owner field to the SnippetSerializer allows us to show the username of the user who created each snippet.
• Read-Only: The owner field is read-only, meaning it's used for displaying data but not for creating or updating snippets.
• Source Attribute: The source='owner.username' part ensures that the field will display the username of the owner.

This update makes your API responses more informative by including the creator's username with each snippet, providing more context and making it easier to understand who created each snippet.

Sure! Let's break down the explanation into simpler terms with an example.

Problem

When a user creates a new code snippet in our application, we want to associate that snippet with the user who created it. However, the user information isn't directly included in the data sent to the server. Instead, it's part of the request that the server receives.

Solution

To handle this, we can override a method called perform_create() in our view. This method lets us customize how a new snippet is saved and allows us to add extra information, like the user who created it.

Step-by-Step Explanation

1. Define the Method: We add a method called perform_create() to our view class. This method takes care of saving the snippet with the user information.

2. Add User Information: Inside the perform_create() method, we use the save() method on the serializer. We pass the current user (who is making the request) as the owner of the snippet.

Code Example

Let's see how this looks in code. First, we have a SnippetList view where users can create new snippets.

```python from rest_framework import generics from .models import Snippet from .serializers import SnippetSerializer from rest_framework.permissions import IsAuthenticated

class SnippetList(generics.ListCreateAPIView): queryset = Snippet.objects.all() serializer_class = SnippetSerializer permission_classes = [IsAuthenticated]

def perform_create(self, serializer):
    serializer.save(owner=self.request.user)

```

Explanation of the Code

• SnippetList View: This view handles listing and creating snippets.
• perform_create() Method: This method is called when a new snippet is being created.
• self.request.user: This represents the user who made the request.
• serializer.save(owner=self.request.user): This saves the new snippet and sets the owner field to the current user.

What Happens When a Snippet is Created

1. User Makes a Request: A user (let's say Alice) sends a request to create a new snippet.
2. Request Processed: The request is received by the SnippetList view.
3. perform_create() Called: The perform_create() method is called.
4. User Information Added: The snippet is saved with the owner field set to Alice.
5. Snippet Created: The new snippet is now associated with Alice as its owner.

Summary

• Problem: We need to associate a snippet with the user who created it, but the user info isn't directly included in the data sent to the server.
• Solution: Override the perform_create() method in the view to add the user information before saving the snippet.
• Result: The new snippet is saved with the current user as its owner, making it easy to track which user created which snippet.

This approach ensures that each snippet is correctly associated with the user who created it, even though the user information isn't explicitly part of the data sent by the client.

Reviewer2: Qian Zhou In this paper, the authors have presented a tool, ntsm, which utilizes the k-mer distribution information directly from raw sequencing data for sample swap detection. The approach of bypassing the reference genome alignment step and saving computational resources is commendable. Utilizing k-mers for reference-free and de novo analysis of sequencing data is a valuable application. The authors have demonstrated the impressive performance of ntsm on low coverage data through experimental results presented in the manuscript, showcasing its strengths in terms of sensitivity, accuracy. However, while ntsm eliminates the need for reference genome alignment, it still relies on a pre-defined set of variant sites and pre-built PCA rotation matrices. This raises doubts about the true reference-free nature of ntsm and raises concerns about its generalizability to other species.Major comments:1.The concept of reference-free:I believe that ntsm's approach is not truly reference-free. In order to use ntsm, it requires the use of existing high-quality population SNP sites and kmers from the human reference genome. Additionally, the population PCA results are used to assist in pairwise comparisons between samples. Both of these information can only be obtained when a reference genome is available. A true referencefree tool would be applicable to species without a reference genome, such as SPLASH (Chaung et al., 2023, Cell). ntsm can be considered as an alignment-free or kmer-based tool.2.The reduction of computational costs:NTSM differs from Somalier in its computational workflow. To compare the computational costs or time, a holistic end-to-end comparison is necessary, rather than timing individual steps such as kmer counting and sample pairwise comparison separately. Conducting an end-to-end comparison for an analysis task allows users to have a comprehensive understanding of the tool's time and cost consumption. Furthermore, when comparing software, it is important to allocate computational resources fairly. For example, ntsm utilizes 16 threads in the 'Sample comparison process' stage, while for the 'k-mer counting (ntsm) vs. alignment (somalier)' stage, tools like bwa and minimap2, which can utilize multiple threads, were run using a single thread.3.Sensitivity and Specificity:More experimental details are needed. In the section 'Sensitivity and Specificity of Sample Swaps,' were the results obtained using the 39 HPRC samples? Did it include their Hi-C data?For Fig 6, did the results come from all sequencing datasets of the 39 samples, including Illumina and ONT? Since the results was obtained using full coverage, would the threshold change at lower coverage?For Fig 7, which demonstrates ntsm's results, was PCA information used as an auxiliary? Does the use of PCA information impact Sensitivity and Specificity?4.Regarding PCA-based method:The 39 HPRC samples used in the study are actually part of the 3,202 samples from the 1000 Genomes Project. Therefore, it is important to clarify whether the PCA matrix used in the study already includes information from these 39 samples. From a rigorous experimental design perspective, a precomputed PCA matrix should not include information from the 39 samples. Otherwise, the effect of the PCA matrix on these 39 samples may be overestimated. It raises questions about whether the same results can be achieved on non-1000 Genomes Project samples.5.The applicability of the tool:In order to expand the applicability of ntsm to a wider range of species, two aspects need to be addressed:1). Provide detailed information on customizing the sites file. From the site files available in ntsm code repository on GitHub, the process of selecting variant sites seems to be more complex than what is described in the manuscript, involving more than just SNP variants.2). The sites and PCA files should be user-customizable inputs instead of being built-in. This limitation restricts the application of ntsm to other species.Minor comments:The manuscript appears to have been hastily written and requires further polish by the authors.1. In Figure 6, A and B seem to be labeled incorrectly.2. In Figure 9, the two subplots have different y-axes, one labeled "min" and the other labeled "s." Could you clarify what each subplot is illustrating?3. When mentioning HPRC for the first time, it would be helpful to provide the full name and explanation of the acronym. However, the full explanation appears in the next paragraph.4. "We then keep only purine to pyrimidine (A or T to G or C) variants, as final insurance against possible human error influencing this tool" It seems there may be a mistake or confusion in the sentence. The writer should indeed mention "A/G <-> C/T" instead of "A/T <-> G/C" to accurately describe purine to pyrimidine variants. The writer may have made an error in describing the nucleotide exchange, or it could be a typographical mistake.5. There is a typo in the formula for estimating sequencing error rate. (nm)·log(1-… …

Typically in this sub, when people ask, "Is it worth it?" the presumption is that they're buying it to use for themselves. You left out your context of buying it to sell until later. This means that once you've cleaned things up, and go to try to sell it for something above $40, people are going to show up here and ask that same question. When they do, the answer is going to be that it's far too expensive, especially with shipping which is notoriously tricky, expensive, and risky.

You'll be sitting there with a typewriter that you don't care enough about to have known anything about it or if it had any particular value. This also probably means that you don't know enough about what goes into cleaning and properly adjusting a typewriter either. If someone is a sucker enough to pay the crazy mark up, it means that someone who wants to try out a typewriter will be buying a sub-par machine and have a sub-par experience.

unposted reply to u/DistributionPure6051 at https://www.reddit.com/r/typewriters/comments/1dqr02l/is_this_worth_it/<br /> (Most of context is hiding because of downvoting)

I find this part of the text to be the most impactful, because the article reports that even though this definition is the most aligned with the NASW Code of Ethics, only one out of one hundred and two articles that were reviewed in this study used this most updated version. 11% of the reviewed articles quoted The Social Work Dictionary, yet only one out of nine that were printed after this version was written was used. This could indicate that there may be differing views regarding social justice or a lack of understanding of the NASW Code of Ethics. Regardless of the reasons or variables, it signifies to me that this is a systemic issue that could be negatively impacting the social work field.

This brings clarity for me to read as I reflect on my past educational experience, my work history, and my struggle in understanding my role as a social worker. I studied social work at a state university over a decade ago. My memory may not be a reliable source, yet I do not remember social justice being a term we integrated into the educational courses, assignments, and discussions. There were aspects of social justice reviewed and explained, yet my understanding of it was a theory to understand the complexities of a client's situation and advocate for them. After graduating, I worked for organizations that were very clear about making social change in their communities to end oppression. It helped me apply how social justice can be integrated into my profession with intention. It additionally relayed the struggles with discussing social justice within an institution. Some people felt as though these discussions were “political” and should not be had. They were referencing how social justice can have the connotations of being a liberal political discussion. It makes me wonder if the inconsistencies of the definition is a part of the problem. I worked for different agencies that further perpetuated my views of social justice and practice as “political”. This has led me to openly question some of the organization’s commonly accepted practices, feeling hesitant in my role, and eventually feeling burned out in these positions. Reading this article makes me wish I would have quoted the NASW Code of Ethics in these organizations to help me feel like I had a valid foundation in my perspective, discussions, and concerns.

deploy it with Tilt

(now, I'm unsure whether Tilt is used here only because it automatically rebuilds the code, or because it is actually useful to debug it. If the code is already deployed, can I ignore Tilt?)

Tilt rebuilds and pushes the deployments at each code change

Reviewer #2 (Public Review):

Summary:

Molecular dynamics (MD) data is deposited in public, non-specialist repositories. This work starts from the premise that these data are a valuable resource as they could be used by other researchers to extract additional insights from these simulations; it could also potentially be used as training data for ML/AI approaches. The problem is that mining these data is difficult because they are not easy to find and work with. The primary goal of the authors was to discover and index these difficult-to-find MD datasets, which they call the "dark matter of the MD universe" (in contrast to data sets held in specialist databases).

The authors developed a search strategy that avoided the use of ill-defined metadata but instead relied on the knowledge of the restricted set of file formats used in MD simulations as a true marker for the data they were looking for. Detection of MD data marked a data set as relevant with a follow-up indexing strategy of all associated content. This "explore-and-expand" strategy allowed the authors for the first time to provide a realistic census of the MD data in non-specialist repositories.

As a proof of principle, they analyzed a subset of the data (primarily related to simulations with the popular Gromacs MD package) to summarize the types of simulated systems (primarily biomolecular systems) and commonly used simulation settings.

Based on their experience they propose best practices for metadata provision to make MD data FAIR (findable, accessible, interoperable, reusable).

A prototype search engine that works on the indexed datasets is made publicly available. All data and code are made freely available as open source/open data.

Strengths:

- The novel search strategy is based on relevant data to identify full datasets instead of relying on metadata and thus is likely to have many true positives and few false positives.

- The paper provides a first glimpse at the potential hidden treasures of MD simulations and force field parametrizations of molecules.

- Analysis of parameter settings of MD simulations from how researchers *actually* run simulations can provide valuable feedback to MD code developers for how to document/educate users. This approach is much better than analyzing what authors write in the Methods sections.

- The authors make a prototype search engine available.

- The guidelines for FAIR MD data are based on experience gained from trying to make sense of the data.

Weaknesses:

- So far the work is a proof-of-concept that focuses on MD data produced by Gromacs (which was prevalent under all indexed and identified packages).

As discussed in the manuscript, some types of biomolecules are likely underrepresented because different communities have different preferences for force fields/MD codes (for example: carbohydrates with AMBER/GLYCAM using AMBER MD instead of Gromacs).

- Materials sciences seem to be severely under-represented - commonly used codes in this area such as LAMMPS are not even detected, and only very few examples could be identified. As it is, the paper primarily provides an insight into the *biomolecular* MD simulation world.

The authors succeed in providing a first realistic view on what MD data is available in public repositories. In particular, their explore-expand approach has the potential to be customized for all kinds of specialist simulation data, whereby specific artifacts are<br /> used as fiducial markers instead of metadata. The more detailed analysis is limited to Gromacs simulations and primarily biomolecular simulations (even though MD is also widely used in other fields such as the materials sciences). This restricted view may simply be correlated with the user community of Gromacs and hopefully, follow-up studies from this work will shed more light on this shortcoming.

The study quantified the number of trajectories currently held in structured databases as ~10k vs ~30k in generalist repositories. To go beyond the proof-of-principle analysis it would be interesting to analyze the data in specialist repositories in the same way as the one in the generalist ones, especially as there are now efforts underway to create a database for MD simulations (Grant 'Molecular dynamics simulation for biology and chemistry research' to establish MDDB' DOI 10.3030/101094651). One should note that structured databases do not invalidate the approach pioneered in this work; if anything they are orthogonal to each other and both will likely play an important role in growing the usefulness of MD simulations in the future.

Author response:

The following is the authors’ response to the original reviews.

eLife assessment

The study presents a valuable tool for searching molecular dynamics simulation data, making such data sets accessible for open science. The authors provide convincing evidence that it is possible to identify useful molecular dynamics simulation data sets and their analysis can produce valuable information.

Public Reviews

Reviewer #1 (Public Review):

Summary:

Tiemann et al. have undertaken an original study on the availability of molecular dynamics (MD) simulation datasets across the Internet. There is a widespread belief that extensive, well-curated MD datasets would enable the development of novel classes of AI models for structural biology. However, currently, there is no standard for sharing MD datasets. As generating MD datasets is energy-intensive, it is also important to facilitate the reuse of MD datasets to minimize energy consumption. Developing a universally accepted standard for depositing and curating MD datasets is a huge undertaking. The study by Tiemann et al. will be very valuable in informing policy developments toward this goal.

Strengths:

The study presents an original approach to addressing a growing concern in the field. It is clear that adopting a more collaborative approach could significantly enhance the impact of MD simulations in modern molecular sciences.

The timing of the work is appropriate, given the current interest in developing AI models for describing biomolecular dynamics.

Weaknesses:

The study primarily focuses on one major MD engine (GROMACS), although this limitation is not significant considering the proof-of-concept nature of the study.

We thank the reviewer for his/her comments. Moving forward, our plan includes expanding this research to encompass other MD engines used in biomolecular simulations and materials sciences, such as NAMD, Charmm, Amber, LAMMPS, etc. However, this requires parsing associated files to supplement the sparse metadata generally available for the related datasets

Reviewer #2 (Public Review):

Summary:

Molecular dynamics (MD) data is deposited in public, non-specialist repositories. This work starts from the premise that these data are a valuable resource as they could be used by other researchers to extract additional insights from these simulations; it could also potentially be used as training data for ML/AI approaches. The problem is that mining these data is difficult because they are not easy to find and work with. The primary goal of the authors was to discover and index these difficult-to-find MD datasets, which they call the "dark matter of the MD universe" (in contrast to data sets held in specialist databases).

The authors developed a search strategy that avoided the use of ill-defined metadata but instead relied on the knowledge of the restricted set of file formats used in MD simulations as a true marker for the data they were looking for. Detection of MD data marked a data set as relevant with a follow-up indexing strategy of all associated content. This "explore-and-expand" strategy allowed the authors for the first time to provide a realistic census of the MD data in non-specialist repositories.

As a proof of principle, they analyzed a subset of the data (primarily related to simulations with the popular Gromacs MD package) to summarize the types of simulated systems (primarily biomolecular systems) and commonly used simulation settings.

Based on their experience they propose best practices for metadata provision to make MD data FAIR (findable, accessible, interoperable, reusable).

A prototype search engine that works on the indexed datasets is made publicly available. All data and code are made freely available as open source/open data.

Strengths:

The novel search strategy is based on relevant data to identify full datasets instead of relying on metadata and thus is likely to have many true positives and few false positives.

The paper provides a first glimpse at the potential hidden treasures of MD simulations and force field parametrizations of molecules.

Analysis of parameter settings of MD simulations from how researchers *actually* run simulations can provide valuable feedback to MD code developers for how to document/educate users. This approach is much better than analyzing what authors write in the Methods sections.

The authors make a prototype search engine available.

The guidelines for FAIR MD data are based on experience gained from trying to make sense of the data.

Weaknesses:

So far the work is a proof-of-concept that focuses on MD data produced by Gromacs (which was prevalent under all indexed and identified packages).

As discussed in the manuscript, some types of biomolecules are likely underrepresented because different communities have different preferences for force fields/MD codes (for example: carbohydrates with AMBER/GLYCAM using AMBER MD instead of Gromacs).

Materials sciences seem to be severely under-represented --- commonly used codes in this area such as LAMMPS are not even detected, and only very few examples could be identified. As it is, the paper primarily provides an insight into the *biomolecular* MD simulation world.

The authors succeed in providing a first realistic view on what MD data is available in public repositories. In particular, their explore-expand approach has the potential to be customized for all kinds of specialist simulation data, whereby specific artifacts are used as fiducial markers instead of metadata. The more detailed analysis is limited to Gromacs simulations and primarily biomolecular simulations (even though MD is also widely used in other fields such as the materials sciences). This restricted view may simply be correlated with the user community of Gromacs and hopefully, follow-up studies from this work will shed more light on this shortcoming.

The study quantified the number of trajectories currently held in structured databases as ~10k vs ~30k in generalist repositories. To go beyond the proof-of-principle analysis it would be interesting to analyze the data in specialist repositories in the same way as the one in the generalist ones, especially as there are now efforts underway to create a database for MD simulations (Grant 'Molecular dynamics simulation for biology and chemistry research' to establish MDDB' DOI 10.3030/101094651). One should note that structured databases do not invalidate the approach pioneered in this work; if anything they are orthogonal to each other and both will likely play an important role in growing the usefulness of MD simulations in the future.

We thank the reviewer for his/her comments. As mentioned to Reviewer 1, we intend to extend this work to other MD engines in the near future to go beyond Gromacs and even biomolecular simulations. Furthermore, as the value of accessing and indexing specialized MD databases such as MDDB, MemprotMD, GPCRmd, NMRLipids, ATLAS, and others has been mentioned by the reviewer, it is indeed one of our next steps to continue to expand the MDverse catalog of MD data. This indexing may also extend the visibility and widespreaded adoptability of these specific databases.

Reviewer #3 (Public Review):

Molecular dynamics (MD) simulations nowadays are an essential element of structural biology investigations, complementing experiments and aiding their interpretation by revealing transient processes or details (such as the effects of glycosylation on the SARS-CoV-2 spike protein, for example (Casalino et al. ACS Cent. Sci. 2020; 6, 10, 1722-1734 https://doi.org/10.1021/acscentsci.0c01056) that cannot be observed directly. MD simulations can allow for the calculation of thermodynamic, kinetic, and other properties and the prediction of biological or chemical activity. MD simulations can now serve as "computational assays" (Huggins et al. WIREs Comput Mol Sci. 2019; 9:e1393.

https://doi.org/10.1002/wcms.1393). Conceptually, MD simulations have played a crucial role in developing the understanding that the dynamics and conformational behaviour of biological macromolecules are essential to their function, and are shaped by evolution. Atomistic simulations range up to the billion atom scale with exascale resources (e.g. simulations of SARS-CoV-2 in a respiratory aerosol. Dommer et al. The International Journal of High Performance Computing Applications. 2023; 37:28-44. doi:10.1177/10943420221128233), while coarse-grained models allow simulations on even larger length- and timescales. Simulations with combined quantum mechanics/molecular mechanics (QM/MM) methods can investigate biochemical reactivity, and overcome limitations of empirical forcefields (Cui et al. J. Phys. Chem. B 2021; 125, 689 https://doi.org/10.1021/acs.jpcb.0c09898).

MD simulations generate large amounts of data (e.g. structures along the MD trajectory) and increasingly, e.g. because of funder mandates for open science, these data are deposited in publicly accessible repositories. There is real potential to learn from these data en masse, not only to understand biomolecular dynamics but also to explore methodological issues. Deposition of data is haphazard and lags far behind experimental structural biology, however, and it is also hard to answer the apparently simple question of "what is out there?". This is the question that Tiemann et al explore in this nice and important work, focusing on simulations run with the widely used GROMACS package. They develop a search strategy and identify almost 2,000 datasets from Zenodo, Figshare and Open Science Framework. This provides a very useful resource. For these datasets, they analyse features of the simulations (e.g. atomistic or coarse-grained), which provides a useful snapshot of current simulation approaches. The analysis is presented clearly and discussed insightfully. They also present a search engine to explore MD data, the MDverse data explorer, which promises to be a very useful tool.

As the authors state: "Eventually, front-end solutions such as the MDverse data explorer tool can evolve being more user-friendly by interfacing the structures and dynamics with interactive 3D molecular viewers". This will make MD simulations accessible to non-specialists and researchers in other areas. I would envisage that this will also include approaches using interactive virtual reality for an immersive exploration of structure and dynamics, and virtual collaboration (e.g. O'Connor et al., Sci. Adv.4, eaat2731 (2018). DOI:10.1126/sciadv.aat2731)

The need to share data effectively, and to compare simulations and test models, was illustrated clearly in the COVID-19 pandemic, which also demonstrated a willingness and commitment to data sharing across the international community (e.g. Amaro and Mulholland, J. Chem. Inf. Model. 2020, 60, 6, 2653-2656 https://doi.org/10.1021/acs.jcim.0c00319; Computing in Science & Engineering 2020, 22, 30-36 doi: 10.1109/MCSE.2020.3024155). There are important lessons to learn here, for simulations to be reproducible and reliable, for rapid testing, for exploiting data with machine learning, and for linking to data from other approaches. Tiemann et al. discuss how to develop these links, providing good perspectives and suggestions.

I agree completely with the statement of the authors that "Even if MD data represents only 1 % of the total volume of data stored in Zenodo, we believe it is our responsibility, as a community, to develop a better sharing and reuse of MD simulation files - and it will neither have to be particularly cumbersome nor expensive. To this end, we are proposing two solutions. First, improve practices for sharing and depositing MD data in data repositories. Second, improve the FAIRness of already available MD data notably by improving the quality of the current metadata."

This nicely states the challenge to the biomolecular simulation community. There is a clear need for standards for MD data and associated metadata. This will also help with the development of standards of best practice in simulations. The authors provide useful and detailed recommendations for MD metadata. These recommendations should contribute to discussions on the development of standards by researchers, funders, and publishers. Community organizations (such as CCP-BioSim and HECBioSim in the UK, BioExcel, CECAM, MolSSI, learned societies etc) have an important part to play in these developments, which are vital for the future of biomolecular simulation.

We thank the reviewer for his/her comments. Beyond the points mentioned to Reviewers 1 and 2, as the reviewer suggested, it would be of great interest to combine innovative and immersive approaches to visualize and possibly interact with the data collected. This is indeed more and more amenable thanks to technologies such as WebGL and programs such as Mol*, or even - as also pointed out by the reviewer - through virtual reality, for example with the mentioned Narupa framework or with the UnityMol software. For a comprehensive review on MD trajectory visualization and associated challenges, we refer to our recent review article https://doi.org/10.3389/fbinf.2024.1356659.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Some minor text editing would improve the readability of the manuscript.

It would be very useful if the authors could share their perspectives on the best and most efficient approach to sharing datasets and code associated with a publication. My concern lies in the fact that Github, which is currently the dominant platform for sharing code, is not well-suited for hosting large MD datasets. As a result, researchers often need to adopt a workflow where code is shared on Github and datasets are stored elsewhere (e.g., Zenodo). While this is feasible, it adds extra work. Ideally, a transparent process could be developed to seamlessly share code and datasets linked to a study through a unified interface.

We thank the reviewer for this excellent suggestion. To our knowledge, there is yet no easy framework to jointly store and share code and data, linked to their scientific publication. Of course, code can be submitted to “generic” databases along with the data, but at the current state, those do not provide such useful features like collaborative work & track recording as done to the extent of GitHub.

Although GitHub is indeed a suitable platform to deposit code, we strongly advise researchers to archive their code in Software Heritage. In addition to preserving source code, Software Heritage provides a unique identifier called SWHID that unambiguously makes reference to a specific version of the source code.

So far, it is the responsibility of the scientific publication authors to link datasets and source codes (whether in GitHub or Software Heritage) in their paper, but also to make the reverse link from the data and code sharing platforms to the paper after publication.

As mentioned by the reviewer, a unified interface that could ease this process would significantly contribute to FAIR-ness in MD.

Reviewer #2 (Recommendations For The Authors):

L180: I am not aware that TRR files contain energy terms as stated here, my understanding was that EDR files primarily served that purpose.

“…available in one dataset. Interestingly, we found 1,406 .trr files, Which contain trajectory but also additional information such as velocities, energy of the system, etc’ While the file is especially useful in terms of reusability, the large size (can go up to several 100GB) limits its deposition in most…”

Indeed, our formulation was ambiguous. The EDR files contain the detailed information on energies, whereas TRR files contain numerous values from the trajectory such as coordinates, velocities, forces and to some extent also energies

(https://manual.gromacs.org/current/reference-manual/file-formats.html#trr)

L207: The text states that the total time was not available from XTC files, only the number of frames. However, XTC files record time stamps in addition to frame numbers. As long as these times are in the Gromacs standard of picoseconds, the simulation time ought to be available from XTCs.

“…systems and the number of frames available in the files (Fig. 3-B). Of note, the frames do not directly translate to the simulation runtime - more information deposited in other files (e.g. .mdp files) is needed to determine the complete runtime of the simulation. The system was up…”.

Thank you for the useful comment, we removed this sentence. We now mention that studying the simulation time would be of interest in the future, especially when we will perform an exhaustive analysis of XTC files.

“Of note, as .xtc files also contain time stamps, it would be interesting to study the relationship between the time and the number of frames to get useful information about the sampling. Nevertheless, this analysis would be possible only for unbiased MD simulations. So, we would need to decipher if the .xtc file is coming from biased or unbiased simulations, which may not be trivial.”

Analysis of MDP files: Were these standard equilibrium MD or can you distinguish biased MD or free energy calculations?

Currently we do not distinguish between biased and unbiased MD, but in the future we may attempt to do so, e.g. by correlating it with standard equilibration force-fields/parameters, timesteps or similar. Nevertheless, a true distinction will remain challenging.

L336: typo: pikes -> spikes (or peaks?)

“…simulations of Lennard-Jones models (Jeon et al., 2016). Interestingly, we noticed the appearance of several pikes at 400K, 600K and 800K, which were not present before the end of the year 2022. These peaks correspond to the same study related to the stability of hydrated crystals (Dybeck et al., 2023)’ Overall, thhis analysis revealed that a wide range of temperatures have been explored,…”

Thank you. We have corrected this typo.

Make clear how multiple versions of data sets are handled, e.g., if v1, v2, and v3 of a dataset are provided in Zenodo then which one is counted or are all counted?

We collected the latest version only of datasets, as exposed by default by the Zenodo API. To reflect this, we added the following sentence to the Methods and Materials section, Initial data collection sub-section:

“By default, the last version of the datasets was collected.”

L248 Analysis of GRO files seems fairly narrow because PDB files are very often used for exactly the same purpose, even in the context of Gromacs simulations, not the least because it is familiar to structural biologists that may be interested in representative MD snapshots. Despite all the shortcomings of abusing the PDB format for MD, it is an attempt at increased interoperability. Perhaps the authors can make sure that readers understand that choosing GRO for analysis may give a somewhat skewed picture, even within Gromacs simulations.

Thanks for this comment. We collected about 12,000 PDB files that could indeed be output from Gromacs simulations and easily be shared due to the universality of this format, but that could as well come from different sources (like other MD packages or the PDB database itself). We purposely decided to limit our study to files strictly associated with the Gromacs package, like MDP and XTC file types. However, we will extend our survey to all other structure-like formats and especially the PDB file type. We reflected this purpose in the following sentence (after line 281)

“Beyond .gro files, we would like to analyze the ensemble of the ~12,000 .pdb files extracted in this study (see Figure 2-B) to better characterize the types of molecular structures deposited.”

A simple template metadata file would be welcome (e.g., served from a GitHub/GitLab repository so that it can be improved with community input).

Thank you for this suggestion that we fundamentally agree with. However, the generation of such a file is a major task, and we believe that the creation of a metadata file template requires far-reaching considerations, therefore is beyond the scope of this paper and should not be decided by a small group of researchers. Indeed, this topic requires a large consensus of different stakeholders, from users, to MD program developers, and journal editors. It would be especially useful to organize dedicated workshops with representatives of all these communities to tackle this specific issue, as mentioned by Reviewer3 in his/her public review. As a basis for this discussion, we humbly proposed at the end of this manuscript a few non-constraining guidelines based on our experience retrieving the data.

To emphasize this statement, we added the following sentence at the end of the “Guidelines for better sharing of MD simulation data” section (line 420):

“Converging on a set of metadata and format requires a large consensus of different stakeholders from users, to MD program developers, and journal editors. It would be especially useful to organize specific workshops with representatives of all these communities to collectively tackle this specific issue.”

In "Data and code availability" it would be good to specify licenses in addition to stating "open source". Thank you for pointing out that GitLab/GitHub are not archives and that everyone should be strongly encouraged to submit data to stable archival repositories.

We added the corresponding licenses for code and data in the “Data and code availability” section.

Reviewer #3 (Recommendations For The Authors)

The paper is well written, with very few typographical or other minor errors.

Minor points:

Line 468-9 "can evolve being more user-friendly" should be "can evolve to being more user-friendly", I think.

Thank you, we have changed the wording accordingly.

Replication--getting the same result from doing the whole study again--is logically independent from whether there even exist any code/analysis from the original paper. Recommend revising and instead talking about how they are related and often conflated.

Neat idea. I think readers would benefit if you also described that the same check for exact reproducibility is achieved with zero changes to certain workflows (if using VCS like git) when analyst uses Quarto with the freeze: auto option.

https://quarto.org/docs/projects/code-execution.html#freeze

Reviewer #3 (Public Review):

[Editors' note: This review contains many criticisms that apply to the whole sub-field of slow/fast gamma oscillations in the hippocampus, as opposed to this particular paper. In the editors' view, these comments are beyond the scope of any single paper. However, they represent a view that, if true, should contextualise the interpretation of this paper and all papers in the sub-field. In doing so, they highlight an ongoing debate within the broader field.]

Summary:

The authors aimed to elucidate the role of dynamic gamma modulation in the development of hippocampal theta sequences, utilizing the traditional framework of "two gammas," a slow and a fast rhythm. This framework is currently being challenged, necessitating further analyses to establish and secure the assumed premises before substantiating the claims made in the present article.

The results are too preliminary and need to integrate contemporary literature. New analyses are required to address these concerns. However, by addressing these issues, it may be possible to produce an impactful manuscript.

I. Introduction<br /> Within the introduction, multiple broad assertions are conveyed that serve as the premise for the research. However, equally important citations that are not mentioned potentially contradict the ideas that serve as the foundation. Instances of these are described below:

(1) Are there multiple gammas? The authors launched the study on the premise that two different gamma bands are communicated from CA3 and the entorhinal cortex. However, recent literature suggests otherwise, offering that the slow gamma component may be related to theta harmonics:

From a review by Etter, Carmichael and Williams (2023)<br /> "Gamma-based coherence has been a prominent model for communication across the hippocampal-entorhinal circuit and has classically focused on slow and fast gamma oscillations originating in CA3 and medial entorhinal cortex, respectively. These two distinct gammas are then hypothesized to be integrated into hippocampal CA1 with theta oscillations on a cycle-to-cycle basis (Colgin et al., 2009; Schomburg et al., 2014). This would suggest that theta oscillations in CA1 could serve to partition temporal windows that enable the integration of inputs from these upstream regions using alternating gamma waves (Vinck et al., 2023). However, these models have largely been based on correlations between shifting CA3 and medial entorhinal cortex to CA1 coherence in theta and gamma bands. In vivo, excitatory inputs from the entorhinal cortex to the dentate gyrus are most coherent in the theta band, while gamma oscillations would be generated locally from presumed local inhibitory inputs (Pernía-Andrade and Jonas, 2014). This predominance of theta over gamma coherence has also been reported between hippocampal CA1 and the medial entorhinal cortex (Zhou et al., 2022). Another potential pitfall in the communication-through-coherence hypothesis is that theta oscillations harmonics could overlap with higher frequency bands (Czurkó et al., 1999; Terrazas et al., 2005), including slow gamma (Petersen and Buzsáki, 2020). The asymmetry of theta oscillations (Belluscio et al., 2012) can lead to harmonics that extend into the slow gamma range (Scheffer-Teixeira and Tort, 2016), which may lead to a misattribution as to the origin of slow-gamma coherence and the degree of spike modulation in the gamma range during movement (Zhou et al., 2019)."

And from Benjamin Griffiths and Ole Jensen (2023)<br /> "That said, in both rodent and human studies, measurements of 'slow' gamma oscillations may be susceptible to distortion by theta harmonics [53], meaning open questions remain about what can be attributed to 'slow' gamma oscillations and what is attributable to theta."

This second statement should be heavily considered as it is from one of the original authors who reported the existence of slow gamma.

Yet another instance from Schomburg, Fernández-Ruiz, Mizuseki, Berényi, Anastassiou, Christof Koch, and Buzsáki (2014):<br /> "Note that modulation from 20-30 Hz may not be related to gamma activity but, instead, reflect timing relationships with non-sinusoidal features of theta waves (Belluscio et al., 2012) and/or the 3rd theta harmonic."

One of this manuscript's authors is Fernández-Ruiz, a contemporary proponent of the multiple gamma theory. Thus, the modulation to slow gamma offered in the present manuscript may actually be related to theta harmonics.

With the above emphasis from proponents of the slow/fast gamma theory on disambiguating harmonics from slow gamma, our first suggestion to the authors is that they A) address these statements (citing the work of these authors in their manuscript) and B) demonstrably quantify theta harmonics in relation to slow gamma prior to making assertions of phase relationships (methodological suggestions below). As the frequency of theta harmonics can extend as high as 56 Hz (PMID: 32297752), overlapping with the slow gamma range defined here (25-45 Hz), it will be important to establish an approach that decouples the two phenomena using an approach other than an arbitrary frequency boundary.

(2) Can gammas be segregated into different lamina of the hippocampus? This idea appears to be foundational in the premise of the research but is also undergoing revision.

As discussed by Etter et al. above, the initial theory of gamma routing was launched on coherence values. However, the values reported by Colgin et al. (2009) lean more towards incoherence (a value of 0) rather than coherence (1), suggesting a weak to negligible interaction. Nevertheless, this theory is coupled with the idea that the different gamma frequencies are exclusive to the specific lamina of the hippocampus.

Recently, Deschamps et al. (2024) suggested a broader, more nuanced understanding of gamma oscillations than previously thought, emphasizing their wide range and variability across hippocampal layers. This perspective challenges the traditional dichotomy of gamma sub-bands (e.g., slow vs. medium gamma) and their associated cognitive functions based on a more rigid classification according to frequency and phase relative to the theta rhythm. Moreover, they observed all frequencies across all layers.

Similarly, the current source density plots from Belluscio et al. (2012) suggest that SG and FG can be observed in both the radiatum and lacunosum-moleculare.

Therefore, if the initial coherence values are weak to negligible and both slow and fast gamma are observed in all layers of the hippocampus, can the different gammas be exclusively related to either anatomical inputs or psychological functions (as done in the present manuscript)? Do these observations challenge the authors' premise of their research? At the least, please discuss.

(3) Do place cells, phase precession, and theta sequences require input from afferent regions? It is offered in the introduction that "Fast gamma (~65-100Hz), associated with the input from the medial entorhinal cortex, is thought to rapidly encode ongoing novel information in the context (Fernandez-Ruiz et al., 2021; Kemere, Carr, Karlsson, & Frank, 2013; Zheng et al., 2016)".

CA1 place fields remain fairly intact following MEC inactivation include Ipshita Zutshi, Manuel Valero, Antonio Fernández-Ruiz , and György Buzsáki (2022)- "CA1 place cells and assemblies persist despite combined mEC and CA3 silencing" and from Hadas E Sloin, Lidor Spivak, Amir Levi, Roni Gattegno, Shirly Someck, Eran Stark (2024) - "These findings are incompatible with precession models based on inheritance, dual-input, spreading activation, inhibition-excitation summation, or somato-dendritic competition. Thus, a precession generator resides locally within CA1."

These publications, at the least, challenge the inheritance model by which the afferent input controls CA1 place field spike timing. The research premise offered by the authors is couched in the logic of inheritance, when the effect that the authors are observing could be governed by local intrinsic activity (e.g., phase precession and gamma are locally generated, and the attribution to routed input is perhaps erroneous). Certainly, it is worth discussing these manuscripts in the context of the present manuscript.

II. Results

(1) Figure 2-<br /> a. There is a bit of a puzzle here that should be discussed. If slow and fast frequencies modulate 25% of neurons, how can these rhythms serve as mechanisms of communication/support psychological functions? For instance, if fast gamma is engaged in rapid encoding (line 72) and slow gamma is related to the integration processing of learned information (line 84), and these are functions of the hippocampus, then why do these rhythms modulate so few cells? Is this to say 75% of CA1 neurons do not listen to CA3 or MEC input?

b. Figure 2. It is hard to know if the mean vector lengths presented are large or small. Moreover, one can expect to find significance due to chance. For instance, it is challenging to find a frequency in which modulation strength is zero (please see Figure 4 of PMID: 30428340 or Figure 7 of PMID: 31324673).

i. Please construct the histograms of Mean Vector Length as in the above papers, using 1 Hz filter steps from 1-120Hz and include it as part of Figure 2 (i.e., calculate the mean vector length for the filtered LFP in steps of 1-2 Hz, 2-3 Hz, 3-4 Hz,... etc). This should help the authors portray the amount of modulation these neurons have relative to the theta rhythm and other frequencies. If the theta mean vector length is higher, should it be considered the primary modulatory influence of these neurons (with slow and fast gammas as a minor influence)?

ii. It is possible to infer a neuron's degree of oscillatory modulation without using the LFP. For instance, one can create an ISI histogram as done in Figure 1 here (https://www.biorxiv.org/content/10.1101/2021.09.20.461152v3.full.pdf+html; "Distinct ground state and activated state modes of firing in forebrain neurons"). The reciprocal of the ISI values would be "instantaneous spike frequency". In favor of the Douchamps et al. (2024) results, the figure of the BioRXiV paper implies that there is a single gamma frequency modulate as there is only a single bump in the ISIs in the 10^-1.5 to 10^-2 range. Therefore, to vet the slow gamma results and the premise of two gammas offered in the introduction, it would be worth including this analysis as part of Figure 2.

c. There are some things generally concerning about Figure 2.

i. First, the raw trace does not seem to have clear theta epochs (it is challenging to ascertain the start and end of a theta cycle). Certainly, it would be worth highlighting the relationship between theta and the gammas and picking a nice theta epoch.

ii. Also, in panel A, there looks to be a declining amplitude relationship between the raw, fast, and slow gamma traces, assuming that the scale bars represent 100uV in all three traces. The raw trace is significantly larger than the fast gamma. However, this relationship does not seem to be the case in panel B (in which both the raw and unfiltered examples of slow and fast gamma appear to be equal; the right panels of B suggest that fast gamma is larger than slow, appearing to contradict the A= 1/f organization of the power spectral density). Please explain as to why this occurs. Including the power spectral density (see below) should resolve some of this.

iii. Within the example of spiking to phase in the left side of Panel B (fast gamma example)- the neuron appears to fire near the trough twice, near the peak twice, and somewhere in between once. A similar relationship is observed for the slow gamma epoch. One would conclude from these plots that the interaction of the neuron with the two rhythms is the same. However, the mean vector lengths and histograms below these plots suggest a different story in which the neuron is modulated by FG but not SG. Please reconcile this.

iv. For calculating the MVL, it seems that the number of spikes that the neuron fires would play a significant role. Working towards our next point, there may be a bias of finding a relationship if there are too few spikes (spurious clustering due to sparse data) and/or higher coupling values for higher firing rate cells (cells with higher firing rates will clearly show a relationship), forming a sort of inverse Yerkes-Dodson curve. Also, without understanding the magnitude of the MVL relative to other frequencies, it may be that these values are indeed larger than zero, but not biologically significant.

- Please provide a scatter plot of Neuron MVL versus the Neuron's Firing Rate for 1) theta (7-9 Hz), 2) slow gamma, and 3) fast gamma, along with their line of best fit.

- Please run a shuffle control where the LFP trace is shifted by random values between 125-1000ms and recalculate the MVL for theta, slow, and fast gamma. Often, these shuffle controls are done between 100-1000 times (see cross-correlation analyses of Fujisawa, Buzsaki et al.).

- To establish that firing rate does not play a role in uncovering modulation, it would be worth conducting a spike number control, reducing the number of spikes per cell so that they are all equal before calculating the phase plots/MVL.

(2) Something that I anticipated to see addressed in the manuscript was the study from Grosmark and Buzsaki (2016): "Cell assembly sequences during learning are "replayed" during hippocampal ripples and contribute to the consolidation of episodic memories. However, neuronal sequences may also reflect preexisting dynamics. We report that sequences of place-cell firing in a novel environment are formed from a combination of the contributions of a rigid, predominantly fast-firing subset of pyramidal neurons with low spatial specificity and limited change across sleep-experience-sleep and a slow-firing plastic subset. Slow-firing cells, rather than fast-firing cells, gained high place specificity during exploration, elevated their association with ripples, and showed increased bursting and temporal coactivation during postexperience sleep. Thus, slow- and fast-firing neurons, although forming a continuous distribution, have different coding and plastic properties."

My concern is that much of the reported results in the present manuscript appear to recapitulate the observations of Grosmark and Buzsaki, but without accounting for differences in firing rate. A parsimonious alternative explanation for what is observed in the present manuscript is that high firing rate neurons, more integrated into the local network and orchestrating local gamma activity (PING), exhibit more coupling to theta and gamma. In this alternative perspective, it's not something special about how the neurons are entrained to the routed fast gamma, but that the higher firing rate neurons are better able to engage and entrain their local interneurons and, thus modulate local gamma. However, this interpretation challenges the discussion around the importance of fast gamma routed from the MEC.

a. Please integrate the Grosmark & Buzsaki paper into the discussion.

b. Also, please provide data that refutes or supports the alternative hypothesis in which the high firing rate cells are just more gamma modulated as they orchestrate local gamma activity through monosynaptic connections with local interneurons (e.g., Marshall et al., 2002, Hippocampal pyramidal cell-interneuron spike transmission is frequency dependent and responsible for place modulation of interneuron discharge). Otherwise, the attribution to a MEC routed fast gamma routing seems tenuous.<br /> c. It is mentioned that fast-spiking interneurons were removed from the analysis. It would be worth including these cells, calculating the MVL in 1 Hz increments as well as the reciprocal of their ISIs (described above).

(3) Methods - Spectral decomposition and Theta Harmonics.

a. It is challenging to interpret the exact parameters that the authors used for their multi-taper analysis in the methods (lines 516-526). Tallon-Baudry et al., (1997; Oscillatory γ-Band (30-70 Hz) Activity Induced by a Visual Search Task in Humans) discuss a time-frequency trade-off where frequency resolution changes with different temporal windows of analysis. This trade-off between time and frequency resolution is well known as the uncertainty principle of signal analysis, transcending all decomposition methods. It is not only a function of wavelet or FFT, and multi-tapers do not directly address this. (The multitaper method, by using multiple specially designed tapers -like the Slepian sequences- smooths the spectrum. This smoothing doesn't eliminate leakage but distributes its impact across multiple estimates). Given the brevity of methods and the issues of theta harmonics as offered above, it is worth including some benchmark trace testing for the multi-taper as part of the supplemental figures.

i. Please spectrally decompose an asymmetric 8 Hz sawtooth wave showing the trace and the related power spectral density using the multiple taper method discussed in the methods.

ii. Please also do the same for an elliptical oscillation (perfectly symmetrical waves, but also capable of casting harmonics). Matlab code on how to generate this time series is provided below:<br /> A = 1; % Amplitude<br /> T = 1/8; % Period corresponding to 8 Hz frequency<br /> omega = 2*pi/T; % Angular frequency<br /> C = 1; % Wave speed<br /> m = 0.9; % Modulus for the elliptic function (0<br /> x = linspace(0, 2*pi, 1000); % temporal domain<br /> t = 0; % Time instant

% Calculate B based on frequency and speed<br /> B = sqrt(omega/C);

% Cnoidal wave equation using the Jacobi elliptic function<br /> u = A .* ellipj(B.*(x - C*t), m).^2;

% Plotting the cnoidal wave<br /> figure;<br /> plot(x./max(x), u);<br /> title('8 Hz Cnoidal Wave');<br /> xlabel('time (x)');<br /> ylabel('Wave amplitude (u)');<br /> grid on;

The Symbolic Math Toolbox needs to be installed and accessible in your MATLAB environment to use ellipj. Otherwise, I trust that, rather than plotting a periodic orbit around a circle (sin wave) the authors can trace the movement around an ellipse with significant eccentricity (the distance between the two foci should be twice the distance between the co-vertices).

iii. Line 522: "The power spectra across running speeds and absolute power spectrum (both results were not shown)...". Given the potential complications of multi-taper discussed above, and as each convolution further removes one from the raw data, it would be the most transparent, simple, and straightforward to provide power spectra using the simple fft.m code in Matlab (We imagine that the authors will agree that the results should be robust against different spectral decomposition methods. Otherwise, it is concerning that the results depend on the algorithm implemented and should be discussed. If gamma transience is a concern, the authors should trigger to 2-second epochs in which slow/fast gamma exceeds 3-7 std. dev. above the mean, comparing those resulting power spectra to 2-second epochs with ripples - also a transient event). The time series should be at least 2 seconds in length (to avoid spectral leakage issues and the issues discussed in Talon-Baudry et al., 1997 above).

Please show the unmolested power spectra (Y-axis units in mV2/Hz, X-axis units as Hz) as a function of running speed (increments of 5 cm/s) for each animal. I imagine three of these PSDs for 3 of the animals will appear in supplemental methods while one will serve as a nice manuscript figure. With this plot, please highlight the regions that the authors are describing as theta, slow, and fast gamma. Also, any issues should be addressed should there be notable differences in power across animals or tetrodes (issues with locations along proximal-distal CA1 in terms of MEC/LEC input and using a local reference electrode are discussed below).

iv. Schomberg and colleagues (2014) suggested that the modulation of neurons in the slow gamma range could be related to theta harmonics (see above). Harmonics can often extend in a near infinite as they regress into the 1/f background (contributing to power, but without a peak above the power spectral density slope), making arbitrary frequency limits inappropriate. Therefore, in order to support the analyses and assertions regarding slow gamma, it seems necessary to calculate a "theta harmonic/slow gamma ratio". Aru et al. (2015; Untangling cross-frequency coupling in neuroscience) offer that: " The presence of harmonics in the signal should be tested by a bicoherence analysis and its contribution to CFC should be discussed." Please test both the synthetic signals above and the raw LFP, using temporal windows of greater than 4 seconds (again, the large window optimizes for frequency resolution in the time-frequency trade-off) to calculate the bicoherence. As harmonics are integers of theta coupled to itself and slow gamma is also coupled to theta, a nice illustration and contribution to the field would be a method that uses the bispectrum to isolate and create a "slow gamma/harmonic" ratio.

(4) I appreciate the inclusion of the histology for the 4 animals. Knerim and colleagues describe a difference in MEC projection along the proximal-distal axis of the CA1 region (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3866456/)- "There are also differences in their direct projections along the transverse axis of CA1, as the LEC innervates the region of CA1 closer to the subiculum (distal CA1), whereas the MEC innervates the region of CA1 closer to CA2 and CA3 (proximal CA1)" From the histology, it looks like some of the electrodes are in the part of CA1 that would be dominated by LEC input while a few are closer to where the MEC would project.

a. How do the authors control for these differences in projections? Wouldn't this change whether or not fast gamma is observed in CA1?

b. I am only aware of one manuscript that describes slow gamma in the LEC which appeared in contrast to fast gamma from the MEC (https://www.science.org/doi/10.1126/science.abf3119). One would surmise that the authors in the present manuscript would have varying levels of fast gamma in their CA1 recordings depending on the location of the electrodes in the Proximal-distal axis, to the extent that some of the more medial tetrodes may need to be excluded (as they should not have fast gamma, rather they should be exclusively dominated by slow gamma). Alternatively, the authors may find that there is equal fast gamma power across the entire proximal-distal axis. However, this would pose a significant challenge to the LEC/slow gamma and MEC/fast gamma routing story of Fernandez-Ruiz et al. and require reconciliation/discussion.

c. Is there a difference in neuron modulation to these frequencies based on electrode location in CA1?

(5) Given a comment in the discussion (see below), it will be worth exploring changes in theta, theta harmonic, slow gamma, and fast gamma power with running speed as no changes were observed with theta sequences or lap number versus. Notably, Czurko et al., report an increase in theta and harmonic power with running speed (1999) while Ahmed and Mehta (2012) report a similar effect for gamma.

a. Please determine if the oscillations change in power and frequency of the rhythms discussed above change with running speed using the same parameters applied in the present manuscript. The specific concern is that how the authors calculate running speed is not sensitive enough to evaluate changes.

b. It is astounding that animals ran as fast as they did in what appears to be the first lap (Figure 3F), especially as rats' natural proclivity is thigmotaxis and inquisitive exploration in novel environments. Can the authors expand on why they believe their rats ran so quickly on the first lap in a novel environment and how to replicate this? Also, please include the individual values for each animal on the same plot.

c. Can the authors explain how the statistics on line 169 (F(4,44)) work? Specifically, it is challenging to determine how the degrees of freedom were calculated in this case and throughout if there were only 4 animals (reported in methods) over 5 laps (depicted in Figure 3F. Given line 439, it looks like trials and laps are used synonymously). Four animals over 5 laps should have a DOF of 16.

(6) Throughout the manuscript, I am concerned about an inflation of statistical power. For example on line 162, F(2,4844). The large degrees of freedom indicate that the sample size was theta sequences or a number of cells. Since multiple observations were obtained from the same animal, the statistical assumption of independence is violated. Therefore, the stats need to be conducted using a nested model as described in Aarts et al. (2014; https://pubmed.ncbi.nlm.nih.gov/24671065/). A statistical consult may be warranted.

(7) It is stated that one tetrode served as a quiet recording reference. The "quiet" part is an assumption when often, theta and gamma can be volume conducted to the cortex (e.g., Sirota et al., 2008; This is often why laboratories that study hippocampal rhythms use the cerebellum for the differential recording electrode and not an electrode in the corpus callosum). Generally, high frequencies propagate as well as low frequencies in the extracellular milieu (https://www.eneuro.org/content/4/1/ENEURO.0291-16.2016). For transparency, the authors should include a limitation paragraph in their discussion that describes how their local tetrode reference may be inadvertently diminishing and/or distorting the signal that they are trying to isolate. Otherwise, it would be worth hearing an explanation as to how the author's approach avoids this issue.

Apologetically, this review is already getting long. Moreover, I have substantial concerns that should be resolved prior to delving into the remainder of the analyses. e.g., the analyses related to Figure 3-5 assert that FG cells are important for sequences. However, the relationship to gamma may be secondary to either their relationship to theta or, based on the Grosmark and Buzsaki paper, it may just be a phenomenon coupled to the fast-firing cells (fast-firing cells showing higher gamma modulation due to a local PING dynamic). Moreover, the observation of slow gamma is being challenged as theta harmonics, even by the major proponents of the slow/fast gamma theory. Therefore, the report of slow gamma precession would come as an unsurprising extension should they be revealed to be theta harmonics (however, no control for harmonics was implemented; suggestions were made above). Following these amendments, I would be grateful for the opportunity to provide further feedback.

III. Discussion.

a. Line 330- it was offered that fast gamma encodes information while slow gamma integrates in the introduction. However, in a task such as circular track running (from the methods, it appears that there is no new information to be acquired within a trial), one would guess that after the first few laps, slow gamma would be the dominant rhythm. Therefore, one must wonder why there are so few neurons modulated by slow gamma (~3.7%).

b. Line 375: The authors contend that: "...slow gamma, related to information compression, was also required to modulate fast gamma phase-locked cells during sequence development. We replicated the results of slow gamma phase precession at the ensemble level (Zheng et al., 2016), and furthermore observed it at late development, but not early development, of theta sequences." In relation to the idea that slow gamma may be coupled to - if not a distorted representation of - theta harmonics, it has been observed that there are changes in theta relative to novelty.

i. A. Jeewajee, C. Lever, S. Burton, J. O'Keefe, and N. Burgess (2008) report a decrease in theta frequency in novel circumstances that disappears with increasing familiarity.

ii. One could surmise that this change in frequency is associated with alterations in theta harmonics (observed here as slow gamma), challenging the author's interpretation.

iii. Therefore, the authors have a compelling opportunity to replicate the results of Jeewajee et al., characterizing changes of theta along with the development of slow gamma precession, as the environment becomes familiar. It will become important to demonstrate, using bicoherence as offered by Aru et al., how slow gamma can be disambiguated from theta harmonics. Specifically, we anticipate that the authors will be able to quantify A) theta harmonics (the number, and their respective frequencies and amplitudes), B) the frequency and amplitude of slow gamma, and C) how they can be quantitatively decoupled. Through this, their discussion of oscillatory changes with novelty-familiarity will garner a significant impact.

c. Broadly, it is interesting that the authors emphasize the gamma frequency throughout the discussion. Given that the power spectral density of the Local Field Potential (LFP) exhibits a log-log relationship between amplitude and frequency, as described by Buzsáki (2005) in "Rhythms of the Brain," and considering that the LFP is primarily generated through synaptic transmembrane currents (Buzsáki et al., 2012), it seems parsimonious to consider that the bulk of synaptic activity occurs at lower frequencies (e.g., theta). Since synaptic transmission represents the most direct form of inter-regional communication, one might wonder why gamma (characterized by lower amplitude rhythms) is esteemed so highly compared to the higher amplitude theta rhythm. Why isn't the theta rhythm, instead, regarded as the primary mode of communication across brain regions? A discussion exploring this question would be beneficial.

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Reviewer #2 (Public Review):

In their paper entitled "Molecular, Cellular, and Developmental Organization of the Mouse Vomeronasal Organ at Single Cell Resolution" Hills Jr. et al. perform single-cell transcriptomic profiling and analyze tissue distribution of a large number of transcripts in the mouse vomeronasal organ (VNO). The use of these complementary tools provides a robust approach to investigating many aspects of vomeronasal sensory neuron (VSN) biology based on transcriptomics. Harnessing the power of these techniques, the authors present the discovery of previously unidentified sensory neuron types in the mouse VNO. Furthermore, they report co-expression of chemosensory receptors from different clades on individual neurons, including the co-expression of VR and OR. Finally, they evaluated the correlation between transcription factor expression and putative surface axon guidance molecules during the development of different neuronal lineages. Based on such correlation analysis, authors further propose a putative cascade of events that could give rise to different neuronal lineages and morphological organization.

Taken together, Hills Jr. et al. present findings on (a) cell types in the VNO, (b) novel classes of sensory neurons, (c) developmental trajectories of the neuronal linage, (d) receptor expression in VSNs, (e) co-expression of chemosensory receptors, (f) a surface molecule code for individual receptor types, and (g) transcriptional regulation of receptor and axon guidance cues. Before outlining the major strengths and weaknesses of the manuscript, we need to disclose that, while we are comfortable reviewing aspects (a) to (e) of their work, we lack the expertise to provide constructive criticism on the two last points (f) and (g). Thus, we will not comment on these.

In general, interpretations/claims put forward by Hills Jr. et al. appear striking at first glance. Upon careful review of the manuscript, however, it becomes apparent that many of the groundbreaking discoveries lack compelling support. Several (not all) of the results presented in this work lack novelty, accurate interpretability, and corroboration. A recurrent theme throughout the manuscript is an incomplete, and somewhat misleading account of the current knowledge in the field. This is perhaps most apparent in the introductory paragraphs, where the authors present a biased report of previously published work, largely including only those results that do not overlap with their own findings, but ignoring results that would question the novelty of the data presented here. For example: "...In contrast, transcriptomic information of the VNO is rather limited (Ref 24,25)...". Indeed, transcriptomic information of the mouse VNO is limited. Here, however, the authors ignore recent reports of robust single-cell transcriptomic analysis from adult and juvenile mice. These papers are, in part, cited later in this manuscript (ref 88, 89, 90, 91), or are completely missing (doi.org/10.7554/eLife.77259). Regardless, previously published results on the same topics have to be included in the Introduction to put the background and novelty of the findings into perspective.

General comments on (a) cell types in the VNO

The authors performed single-cell transcriptomic analysis of a large number of cells from both adult and juvenile VNO, creating the largest dataset of its kind to date. This dataset contains a wealth of information and, once made public, could be a valuable resource to the community. However, the analysis implemented in this paper raises several questions:

Did the authors perform any cell selectivity, or any directed dissection, to obtain mainly neuronal cells? Previous studies reported a greater proportion of non-neuronal cells. For example, while Katreddi and co-workers (ref 89) found that the most populated clusters are identified as basal cells, macrophages, pericytes, and vascular smooth muscle, Hills Jr. et al. in this work did not report such types of cells. Did the authors check for the expression of marker genes listed in Ref 89 for such cell types?

The authors should report the marker genes used for cell annotation. This is important for data validation, comparison with other publicly available datasets, as well as future use of this dataset.<br /> The authors reported no differences between juvenile and adult samples, and between male and female samples. It is not clear how they evaluate statistically significant differences, which statistical test was used, or what parameters were evaluated.

"Based on our transcriptomic analysis, we conclude that neurogenic activity is restricted to the marginal zone." This conclusion is quite a strong statement, given that this study was not directed to carefully study neurogenesis distribution, and when neurogenesis in the basal zone has been proposed by other works, as stated by the authors.

General comments on (b) novel classes of sensory neurons

The authors report at least two new types of sensory neurons in the mouse VNO, a finding of huge importance that could have a substantial impact on the field of sensory physiology. However, the evidence for such new cell types is based solely on this transcriptomic dataset and, as such, is quite weak, since many crucial morphological and physiological aspects would be missing to clearly identify them as novel cell types. As stated before, many control and confirmatory experiments, and a careful evaluation of the results presented in this work must be performed to confirm such a novel and interesting discovery. The reported "novel classes of sensory neurons" in this work could represent previously undescribed types of sensory neurons, but also previously reported cells (see below) or simply possible single-cell sequencing artefacts.

The authors report the co-expression of V2R and Gnai2 transcripts based on sequencing data. That could dramatically change classical classifications of basal and apical VSNs. However, did the authors find support for this co-expression in spatial molecular imaging experiments?

Canonical OSNs: The authors report a cluster of cells expressing neuronal markers and ORs and call them canonical OSN. However, VSNs expressing ORs have already been reported in a detailed study showing their morphology and location inside the sensory epithelium (References 82, 83). Such cells are not canonical OSNs since they do not show ciliary processes, they express TRPC2 channels and do not express Golf. Are the "canonical OSNs" reported in this study and the OR-expressing VSNs (ref 82, 83) different? Which parameters, other than Gnal and Cnga2 expression, support the authors' bold claim that these are "canonical OSNs"? What is the morphology of these neurons? In addition, the mapping of these "canonical OSNs" shown in Figure 2D paints a picture of the negligible expression/role of these cells (see their prediction confidence).

Secretory VSN: The authors report another novel type of sensory neurons in the VNO and call them "secretory VSNs". Here, the authors performed an analysis of differentially expressed genes for neuronal cells (dataset 2) and found several differentially expressed genes in the sVSN cluster. However, it would be interesting to perform a gene expression analysis using the whole dataset including neuronal and non-neuronal cells. Could the authors find any marker gene that unequivocally identifies this new cell type?

When the authors evaluated the distribution of sVSN using the Molecular Cartography technique, they found expression of sVSN in both sensory and non-sensory epithelia. How do the authors explain such unexpected expression of sensory neurons in the non-sensory epithelium?

The low total genes count and low total reads count, combined with an "expression of marker genes for several cell types" could indicate low-quality beads (contamination) that were not excluded with the initial parameter setting. It looks like cells in this cluster express a bit of everything V1R, V2R, OR, secretory proteins...

General comments on (c) developmental trajectories of the neuronal linage

The authors evaluated a possible cascade of events leading to the development of different lineages of mature sensory neurons using GBCs as a starting point. They found the differential expression of several transcription factors at different stages of development. This analysis was performed correctly, and its interpretation is coherent. However, it is mysterious why the authors included only classical V1R and V2R-expressing neurons, while the novel sensory neurons, cOSN and sVSN, were not included. Furthermore, it is important to notice again the misreport of previously published works.

The authors wrote "...the transcriptomic landscape that specifies the lineages is not known...". This statement is not completely true, or at least misleading. There are still many undiscovered aspects of the transcriptomics landscape and lineage determination in VSNs. However, authors cannot ignore previously reported data showing the landscape of neuronal lineages in VSNs (Ref ref 88, 89, 90, 91 and doi.org/10.7554/eLife.77259). Expression of most of the transcription factors reported by this study (Ascl1, Sox2, Neurog1, Neurod1...) were already reported, and for some of them, their role was investigated, during early developmental stages of VSNs (Ref ref 88, 89, 90, 91 and doi.org/10.7554/eLife.77259). In summary, the authors should fully include the findings from previous works (Ref ref 88, 89, 90, 91 and doi.org/10.7554/eLife.77259), clearly state what has been already reported, what is contradictory and what is new when compared with the results from this work.

General comments on (d) receptor expression in VSNs

The authors evaluated the expression of chemosensory receptors in the VNO and correlated receptor expression with the expression of transcription factors. The analysis of such correlation showed that, while the expression of V1Rs is mainly correlated with the expression of the transcription factor Meis2, the expression of V2Rs is correlated with the combination of many transcription factors. These results are interesting, however, the co-expression of specific V2Rs with specific transcription factors does not imply a direct implication in receptor selection. Directed experiments to evaluate the VR expression dependent on a specific transcription factor must be performed.

This study reports that transcription factors, such as Pou2f1, Atf5, Egr1, or c-Fos could be associated with receptor choice in VSNs. However, no further evidence is shown to support this interaction. Based on these purely correlative data, it is rather bold to propose cascade model(s) of lineage consolidation.

General comments on (e) co-expression of chemosensory receptors

The authors use spatial molecular imaging to evaluate the co-expression of many chemosensory receptors in single VNO cells. Molecular Cartography is a powerful tool and the reported data in this work is truly interesting. The authors show some clear confirmation of previously reported V2R co-expression (Figure 5H), and new co-expression of chemosensory receptors including V1R, V2R, and Fpr (Figure 5G-K).

However, it is difficult to evaluate and interpret the results due to the lack of cell borders in spatial molecular imaging. The inclusion of cell border delimitation in the reported images (membrane-stained or computer-based) could be tremendously beneficial for the interpretation of the results.

It is surprising that the authors reported a new cell type expressing OR, however, they did not report the expression of ORs in Molecular Cartography technique. Did the authors evaluate the expression of OR using the cartography technique?

Résumé de la vidéo [00:00:02][^1^][1] - [00:48:41][^2^][2]:

Cette vidéo présente le framework Observable pour créer des tableaux de bord, des rapports et des applications web de manière efficace et gratuite. Elle explique comment utiliser Observable pour documenter des fonctionnalités, introduit le concept de Data loader pour rafraîchir les données, et montre comment intégrer des réalisations Observable dans un site web statique.

Points forts: + [00:00:08][^3^][3] Introduction à Observable * Présentation du framework Observable comme générateur de site statique gratuit et open source * Utilisation de Markdown et JavaScript pour la documentation * Hébergement gratuit sur des plateformes comme GitHub Pages + [00:01:36][^4^][4] Spécialisation pour les tableaux de bord * Observable est spécialisé pour les applications nécessitant un rafraîchissement régulier des données * Introduction du concept de Data loader pour une mise à jour périodique des données * Création de sites web statiques capables de rafraîchir leurs données efficacement + [00:03:00][^5^][5] Développement JavaScript avec Observable * Observable comme environnement de développement JavaScript unique avec réactivité entre déclarations * Explication de la réactivité et de la dépendance des variables dans Observable * Utilisation de Markdown, LaTeX et JavaScript pour créer des contenus interactifs + [00:10:13][^6^][6] Utilisation de bibliothèques et gestion de versions * Observable permet d'appeler des bibliothèques externes et contient un gestionnaire de versions simplifié * Partage et publication de classeurs pour la collaboration et la réutilisation * Exemples de tutoriels et de cours disponibles sur Observable + [00:24:26][^7^][7] Démarrage avec le framework * Processus de création, d'édition et de prévisualisation d'un site avec Observable * Utilisation de GitHub Actions pour le rafraîchissement automatique des données * Intégration d'animations et de visualisations dans un site web statique + [00:40:15][^8^][8] Exemples d'applications créées avec Observable * Présentation d'applications variées, telles que l'évolution des joueurs d'échecs et un tableau de bord d'hôtel * Conversion d'une application JavaScript existante en une version améliorée avec Observable

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Reply to the reviewers

We thank the reviewer for their careful evaluation and constructive criticisms of our manuscript. We also appreciate the positive review by all three reviewers. The reviewers noted:

• "The computational model in this manuscript can be a tool to discover unknown molecular pathways interactions in cardiomyocyte proliferation."
• "This is an interesting study reporting the generation of a computational model of cardiomyocyte proliferation, which predicts molecular drivers of cell cycle progression."
• "The model provides a convenient systems framework to prioritize potential signaling drivers of therapeutic modulators of cardiomyocyte proliferation." We have responded to all reviewer comments and have outlined the corresponding additions and changes to the manuscript.

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

Summary:

In the manuscript by Harris et al. titled "Dynamic map illuminates Hippo to cMyc module crosstalk driving cardiomyocyte proliferation," the authors developed a computational model of cardiac proliferation signaling that incorporates various regulatory networks (cytokinesis, mitosis, DNA replication, etc.) to predict molecular drivers (genes) that support cardiomyocyte proliferation. Published research articles on cardiomyocyte proliferation in multiple contexts (different species, ages, in vitro and in vivo, etc) were used to build and validate the computational model. The authors found using their model that different processes during cardiomyocyte proliferation may or may not be context-dependent. For example, DNA replication is regulated differently in conditions with high Neuregulin compared to high YAP, whereas mitosis and cytokinesis regulation is similar in these conditions. To experimentally validate their model, the authors used an in vitro system to test the effects of YAP on 3 connected pathways; in the context of YAP activation, inhibition of PI3K, cMyc, or FoxM1 was combined to assay cell-cycle markers in cultured neonatal rat ventricular cardiomyocytes. Cell-cycle marker expression in cardiomyocytes was attenuated by inhibition of cMyc or PI3K, suggesting that these pathways are involved in YAP-mediated cardiomyocyte proliferation. While this model can be a good tool to gain new insights on interactions between molecular pathways, there are a few questions to be addressed prior to publication.

We appreciate the Reviewer's positive remarks about important findings in our manuscript and the ability of our model to be a tool to gain insights on interactions between molecular pathways to regulate cardiomyocyte proliferation. We have strived to address their points, as shown below.

Major Comments:

1. One of the potential uses for this computational model is to discover new interactions between known pathways that are involved in cardiomyocyte proliferation. However, this would be more powerful if factors such as species, age (neonate vs. adult), and experimental design (in vivo vs. in vitro) were accounted for, as new node inputs or a combination of existing node input activity values. This is very important because cardiomyocyte proliferation can drastically vary depending on these experimental factors. We agree that future extensions of this model accounting for species, age, and experimental design may enable an understanding of how these factors regulate proliferation. While this model's predictions are most relevant to immature cardiomyocytes, we note that it is the first systems model of the molecular network regulating cardiomyocyte proliferation. We extensively validated it against neonatal cardiomyocyte literature and then made new predictions regarding Hippo-cMyc pathways, which we validated in new cardiomyocyte experiments and against data in adult mice. This provides a strong foundation for future extensions. We now address this potential in the Discussion:

"While our model's predictions are most relevant to immature cardiomyocytes, it is the first systems model of the molecular network regulating cardiomyocytes. In the future, we hope that we and others may extend this model to identify how factors like species, age, and experimental design regulate proliferation. However, these endeavors would span multiple manuscripts, and the field currently lacks sufficient stage-specific data. For example, a previous foundational computational model of cardiomyocyte electrophysiology (Luo and Rudy, Circ Res 1994) focused on adult guinea pigs. This model became the foundation for a range of developmental and species-specific models in electrophysiology (Tusscher et al, AJP 2004,; Courtemanche eta al, AJP 1998; Paci et al, ABME 2013). We believe the open availability of our code will enable similar dissemination and extension for additional factors." Line 651-661

For reference:

Luo and Rudy, Circ Res 1994, >2.1k citations; Tusscher et al, AJP 2004, >1.7k citations; Courtemanche et al, AJP 1998, 1.5K citations; Paci et al, ABME 2013, 147 citations

The finding that cardiomyocyte proliferation is context-dependent is very exciting and warrants further investigation/validation. The authors state that different sets of nodes/modules are affected by neuregulin activation compared to YAP activation. This should be experimentally validated - qPCR/Western blots on sets of genes that are predicted to be differentially regulated in the high neuregulin context vs the high YAP context.

We agree that the model's prediction of context-dependent cardiomyocyte proliferation is very exciting. To further validate these predictions, we have performed additional experiments to validate context-dependent changes of phospho-ERK treated with Nrg and TT10. Using a high throughput capillary electrophoresis western blot system, we observed that with a short treatment of 30 min, Nrg induces greater phosphor-ERK compared to TT10, which validates our model predictions at short time intervals. Additionally, the model predicted greater p-AKT with 30 min treatment with Nrg compared to TT10. To validate this prediction, we now compare to Western blots from Hara et al. examining p-AKT in Nrg and TT10-treated cells. Validating our model predictions, their data show that Nrg induces greater p-AKT than with TT10. We have added new panels C, D, and E to Figure 4.

Figure 4: Influence of node knockdowns shifts with context, revealing crosstalk from Hippo to Growth Factor modules.

(A) Total influence of node knockdowns on the DNA replication, mitosis, and cytokinesis modules, compared across multiple signaling contexts: baseline, high Nrg, and high YAP. Total influence sums the overall effect of a node knockdown on a network module. (B) The total influence of each network module varies depending on whether a basal state, high Nrg, or high YAP signaling context is applied. (C) Capillary electrophoresis western blot for phosphorylated ERK, beta-actin, and GAPDH from neonatal cardiomyocytes treated with Nrg or TT10 for 30 min. (D) Model predictions of AKT and ERK activity of acute response to Nrg or TT10 (time constants for gene expression set to 100). (E) Quantification of effects of Nrg or TT10 treatment on p-ERK (from Western blot in panel C, n = 3) or p-AKT (from Western blot from (Hara et al., 2018), n = 1).

The overall description of the model can be improved. For example, how are the input and parameters set to validate or predict different experimental observations? What is the steady state activity of each of the nodes and does this make sense biologically? Including a few more sentences to explain the model would help with overall understanding for an uninformed reader.

We have addressed the following questions provided by the reviewer in the methods and results section of the manuscript:

How are the input and parameters set to validate or predict different experimental observations?

__ __"At baseline, input reaction weight parameters (w) were set based on information from the literature describing the baseline state of these inputs in the heart (each input reaction weight can be found in Supplemental File 1). To simulate experiments with biochemical stimuli, input reaction weights were increased to 0.8 or 1. To simulate experiments with inhibition or knockdown, the corresponding maximum species value (ymax) was set to 0.1 or 0. Complete annotations for all validation simulations are provided in Supplemental File 2." Line 154-160

What is the steady-state activity of each of the nodes and does this make sense biologically?

"Steady-state activity of model nodes was obtained by running the model until there was a __ __

Minor Comments:

Line 124 - The use of "species" and "reactions" is confusing to uninformed readers. Do you mean nodes and interactions/bridges?

We now further clarify these terms in the manuscript:

"As in past network models (Zeigler et al., PMID 27017945; Tan et al., PMID 29131824; Kraeutler et al PMID 21087478), species (or nodes) refer to a small molecule, gene, protein, or process. Reactions (or edges) are activating or inhibiting relationships between network species." Line 143-146

Line 130 - I could not find Supplementary File 2, which includes the references

We apologize for the error. Supplementary File 2 references articles and resources used to build the model. These files are now attached.

Line 257 - What is the meaning of the directional arrows in Fig 1A?

We clarified the Fig 1A legend:

"Arrows between modules represent one or more reactions that link species from one module to species in another module. " Line 594-595

Line 301 - Unclear what default values mean here. Please elaborate and provide an example of how this is reasonable.

We have added further descriptions of default values in reference to the parameters to the manuscript.

"A previous study identified default values of the parameters (ymax, EC50, W, etc.) that most accurately predict the results of knockdown screens compared to a model where all biochemical parameters were measured experimentally (Kraeutler et al 2010). Subsequent studies started from these default values and further demonstrated that model accuracy was robust to random variation in the parameters (Tan et all 2017, Zeigler et al 2017). Consistent with these prior models, we performed robustness analysis that demonstrates that the CM proliferation model accuracy (compared against 78 experiments) is maintained at >80% with up to 35% variation in ymax, 30% variation with w, and a variation of >50% with EC50 (Figure S4)." Line 305-312

Supplemental FigS2 - Why would knockdown of PKA, Lats1 or SMAD3 have the exact same effects on node activation? This is seen with multiple other genes was well (IGF and FGF for example).

PKA, Lats1, and SMAD3 all inhibit cell cycle progression in part through cMyc. Therefore, their knockdown have similar effects on downstream signaling and proliferation. Similarly, IGF and FGF both stimulate Ras and PI3K via similar mechanisms, which is consistent with experimental studies of IGF- and FGF-dependent proliferation.

Reviewer #1 (Significance (Required)):

The computational model in this manuscript can be a tool to discover unknown molecular pathway interactions in cardiomyocyte proliferation. The novelty lies in the ability to adjust any parameter or the entire setting/context. While this sounds very exciting, improvement of the model to account for age, experimental conditions (in vivo vs in vitro), and species (human, pig, mouse) could lead to increase prediction accuracy. Additionally, more robust validation of context-dependent interactions between signaling pathways would also increase overall enthusiasm for the manuscript. Readers interested in a systems biology approach to cardiomyocyte proliferation, or researchers probing molecular interactions during cardiomyocyte proliferation would be interested in using such a model to discover novel contexts/combinations in which cardiomyocyte proliferation is more likely.

The reviewer comes from a varied training background and is qualified to evaluate this manuscript in full - BS in biomedical engineering and mathematics. PhD in biomedical engineering (molecular biology, cardiac electrophysiology). Postdoctoral training in cardiac regeneration and immunity.

We appreciate the positive comments about our model of the cardiomyocyte proliferation network. As described above, we believe that we have addressed the concerns with additional experimental validation.

The manuscript submitted by Harris and colleagues collates a molecular map of cardiomyocyte cell cycle activation through mathematical modeling of previously published experimental results. They attempt to validate the constructed model several ways: 1) through testing results compiled from additional literature, 2) through in vitro analysis, and 3) through in vivo supporting data. When validating through additional literature the model proves quite reliable particularly for prediction of effects on synthesis, mitosis, and cytokinetic entry, but was less reliable (or insufficiently tested) at predicting completion of these stages as determined by polyploidization and multinucleation. A potentially novel observation which arose from the model - that hippo nodule connects to the growth factor nodule through PI3K, Myc, and FoxM1 - was partially confirmed with in vitro experiments, though a few experiments are warranted.

We appreciate the reviewer's recognition of the important contributions of this model of the cardiomyocyte proliferation network. We have addressed the concerns below.

Major comments:

• The model is admittedly weakest in its handling of completion of cytokinesis resulting in new daughter cells (i.e. proliferation) versus failure to complete either M phase or cytokinesis resulting in the much more common cellular phenotypes - polyploidy and multinucleation. Notably, very few molecules were "tested" for this output (figure 2) and this proved the least reliable aspect of the model/map. I wonder if the authors consulted the literature on somatic polyploidization at all when building the model (files not provided as indicated, see minor comment 1 below )? And if not, would doing so help strengthen this arm of their map? There are some great reviews on the topic (see PMIDs 25921783, 23849927, 30021843) - while admittedly much of the work is done on other cell types (i.e. trophoblast giant cells and hepatocytes) maybe understanding the molecular intricacies in these cells could be incorporated to strengthen the predictive model in cardiomyocytes. Notably, PMID 23849927 even provides a table of citations about key nodes in the model influencing polyploidy. To validate this model, we used entirely cardiomyocyte specific studies. We appreciate the reviewer's reference to PMID 23849927, which enabled us to add two additional experiments to the validation table in Figure 2. That paper found that overexpression of either cMyc or cyclin D increases polyploidy, which both matched our new simulations in the updated Figure 2.

Motivated by the reviewer's citation of PMID 23849927, we further validated the model against polyploidization data from multiple cell types, finding an 85.7% accuracy (6 of 7 experiments) as now shown in Supplementary Figure S7.

We included an additional discussion of polyploidization in the manuscript.

"Our model validation is notably weakest in predicting experiments on polyploidization, indicating a need to better characterize polyploidy and cytokinesis pathways. Because such data are limited in cardiomyocytes, we performed an additional validation against polyploidization experiments from other cell types as summarized in Pandit et al. Our CM proliferation model predicted 85% (6 of 7) experiments. Future experiments are needed to identify conserved or differential mechanisms of polyploidization and cytokinesis in cardiomyocytes." Line 587-594

• Paragraph on the cytokinesis module (lines 364-377) is confusing - not sure what the takeaway message is. Also, while progression through G1/S and G2/M are "required" for cytokinesis they on their own are not sufficient (lines 366-368), this perhaps goes back to major comment 1. We agree this sentence was confusing, it was meant to be introductory rather than stating a particular result. We removed that sentence and further revised our description of the output module to clarify the model structure:

"The output module interlinks the phenotypic outputs of the other modules, representing how experimentally measured aspects of cell cycle activity (DNA replication by EdU or Ki67), mitosis by phospho-Histone 3 (pHH3), abscission by cytokinetic midbody converge on polyploidy, binucleation, or cytokinesis (e.g. completed proliferation) (Figure 1G)." Line 283-286

Minor comments:

• Use of the word "Proliferation" should be reserved for situations where the authors can clearly say a new daughter cell was born. In many instances, "cell cycle activation" or "cell cycle progression" might be better terms. As suggested by the reviewer, we now use "cell cycle progression" in 7 instances, reserving "proliferation" for cell cycle progression through cytokinesis. In the remaining 90 instances, we refer to proliferation based on the model's predictions of completed cell division based on the combined DNA replication, mitosis, and cytokinesis pathways in the "output module". We retain "proliferation" in the title because the model encompasses the entire proliferation process from cell cycle entry through cytokinesis.

• Supplementary Files 1 & 2 or Supplementary Document 2 were not provided or not found during review, thus we were unable to confirm which literature were used to build and validate the model. Thank you, we have included Supplementary Files 1 and 2 along with supplementary document 2 in the submission.

• Figures are too small, particular Figure 1 We have enlarged Figure 1.

• "E2F" should be specified as E2F1-3 yield quite distinct results from E2F7/8. We have changed "E2F" to "E2F123"

• Text corresponding to Figure 5 does not reference most of the panels in the Figure. i.e. figures are not "cited" in the text We have made sure that each panel in Figure 5 is referenced in the text addressing the figure. We have also bolded all references to Figure 5.

• Figure 5C - why is there no bars for PI3K. Text claims it was predicted by the model, but the data are missing? We apologize for the confusion regarding Figure 5C, in which the bar for PI3K was near zero. We now clarify this in the legend.

"Predicted DNA replication and mitosis activity is close to zero when PI3K is inhibited alone and when PI3K is inhibited in combination with TT10 treatment."

• Data provided in figure 5D & E are insufficient on their own to claim "proliferation". Perhaps adding total cardiomyocyte numbers, where one would expect expansion compared to control. We agree that Ki67 and pH3 are not sufficient to claim "proliferation", so we modified the Figure 5 legend to:

"Prediction and experimental validation of cardiomyocyte cell cycle progression mediated by the Hippo pathway via PI3K, cMyc, and FoxM1."

We previously found that cardiomyocyte numbers without live tracking are not sufficient to robustly measure proliferation (Woo et al, J Mol Cardiol, 2019).

• Consider adding a details about the p-values to the figure legend in figure 5. Thank you for this suggestion p-value information has been added to the legend of Figure 5. We use *** Our literature-based validation in Figure 2 focused on 78 experiments that examined well-established and corroborated aspects of cardiomyocyte proliferation. Later in the paper, we focused on a newly predicted mechanism of cardiomyocyte proliferation involving small number of comparisons that would naturally have a lower a priori probability of validation in vitro neonatal experiments (Figure 5) and adult mouse experiments (Figure 6). Therefore, in the revised text we focus on the specific comparisons rather than statistics.

"Based on predictions from this validated model, we hypothesized that YAP drove proliferation via PI3K, cMyc and FoxM1. To test this model-driven hypothesis, we accurately predicted TT10-induced DNA replication that is suppressed by inhibition of PI3K, cMyc and to a lesser extent FoxM1 (Figure 5D). These model predictions were further validated using RNA-seq and ATAC-seq data from adult mouse hearts showing that constitutively active YAPS5A induces expression of Myc and FoxM1 as well as increased chromatin accessibility at PI3Kca and Myc." Line 454-468

In the discussion, we add:

"Further model revision is needed based on these molecular mechanisms of YAP-TEAD-Myc interactions to distinguish between chromatin accessibility, transcription factor binding, and gene expression." Line 649-651

As it stands now, the generated map largely constitutes already known details offering few if any new insights; however, if updated as new results arise AND made available as a public tool, the model could prove to be a highly valuable resource to the field.

We thank the reviewer for recognizing our model as a valuable resource and public tool. We have made our model publicly available on GitHub at https://github.com/saucermanlab/Cardiomyocyte-Proliferation-Network.

The virtual knockdown screens in Figure 3, 4 and 5 provide a wide range of new insights, which we clarify in new text.

"Because this is a literature-based network model, each component or direct interaction has been studied individually. However, our model makes much broader predictions of how these components interact to regulate proliferation, beyond the ~30 papers available for validation on the response of this system to perturbations shown in Figure 3. For example, Supplemental Figure S3 provides ~5000 predictions of how each protein responds to knockdown of every other protein. These predictions led to new insights into how YAP regulates proliferation via cMyc (experimentally validated in Figure 5 in vitro and Figure 6 in vivo), as well as many other insights that can be validated in future studies. These future studies will be aided by the open-source availability of our model on GitHub." Line 563-571

__ I have expertise in cardiomyocyte cell cycle and polyploidization.__

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

The authors generated a computational model of cardiomyocyte proliferation, which predicts molecular drivers of cell cycle progression. Interestingly, the model correctly predicts the outcome of 95% independent experiments from the literature. The model also elucidated crosstalk between the growth factor and Hippo modules and the authors identified key hubs for which the Hippo signaling pathway regulates cardiomyocyte proliferation. The model provides a convenient systems framework to prioritize potential signaling drivers of therapeutic modulators of cardiomyocyte proliferation.

Reviewer #3 (Significance (Required)):

This is an interesting study reporting the generation of a computational model of cardiomyocyte proliferation, which predicts molecular drivers of cell cycle progression. The program may provide a convenient framework prioritizing potential signaling drivers of therapeutic modulators of cardiomyocyte proliferation. However, the overall impact of the study appears modest since it is unclear whether the study allows elucidation of the unique properties of cardiomyocyte proliferation in adult hearts (i.e. they hardly proliferate) and the validation study was conducted only in neonatal myocytes. The field has seen many studies with neonatal myocytes but the findings are not always translatable to adult cardiomyocytes.

We thank the reviewer for recognizing the importance of our work that provides a framework for prioritizing potential signaling drivers of therapeutic modulators of CM proliferation.

Neonatal studies are the most prevalent with cardiomyocyte proliferation literature, making it the most robust starting point that allows for rigorous validation. Based on the high performance of the model against neonatal data, in the future we expect this model to be a stepping stone towards adaptions to understand differences in the adult cardiomyocyte proliferation network. We have updated our model discussion on future directions on this point.

"While our model's predictions are most relevant to immature cardiomyocytes, it is the first molecular network model of cardiomyocyte proliferation. In the future, this model will enable extensions to identify how factors like species, age and experimental design regulate proliferation. However, such endeavors would span multiple manuscripts and the field currently lacks sufficient stage-specific data. For example, the highly influential computational model of Luo and Rudy focused on adult guinea-pig cardiomyocyte electrophysiology (Luo and Rudy, Circ Res 1994). That model became the foundation for a wide range of development- and species-specific models in electrophysiology (Tusscher et al, AJP 2004,; Courtemanche eta al, AJP 1998; Paci et al, ABME 2013). We believe the open availability of our code will enable similar dissemination and extension for additional factors regulating cardiomyocyte proliferation." Line 655-665

The authors described that "Literature articles used for model development came from multiple cell types due to limited CM data." It is unclear whether this would allow the identification of unique mechanisms present in cardiomyocytes. As the authors admitted, the fact that the model predictions and experimental observations for polyploidization did not match clearly suggests the complexity surrounding the possibility of cell phenotypes in cardiomyocyte populations. The authors could have addressed whether this model allows the identification of unique mechanisms mediating cardiomyocyte proliferation in the adult heart.

Although we necessarily included literature on other cell types to support network reactions, all of the experimental validation in Figure 2 was with cardiomyocyte data (~33 publications). 80% of experiments were from neonatal CMs, 10% from adult CMs, 5% from in vivo studies, and the other 5% from hiPSC-derived cardiomyocytes as annotated in Supplemental File 3.

At this time, there is insufficient data from which to make a model focused only on adult CMs. The mode's open-source availability enables future extensions that examine age and species-dependent mechanisms of cardiomyocyte proliferation. We updated the manuscript, addressing the ability of our model to adapt to new information.

"This model provides an initial network framework for integrating additional discoveries in cardiomyocyte proliferation. As more information becomes available in cardiomyocyte proliferation literature the model can be adapted. Additionally, the field can use our open-sourced model to adapt this model to other developmental stages or species." Line 671-674

Acknowledging the limited data on cardiomyocyte polyploidy, we performed a new separate validation of 7 experiments in non-myocytes from PMID 23849927, finding an 85.7% accuracy (new Supplementary Figure S7).

Please provide more information regarding the rationale for having six modules in the authors' model, including the growth factor and the Hippo pathway.

We revised the text to clarify the motivation for the six modules:

"Our initial review of the literature indicated multiple complex molecular pathways that regulate cardiomyocyte proliferation, including growth factors, Hippo signaling, G1/S transition, G2/M transition, or cytokinesis pathways (Hashmi and Ahmad, PMID: 31205684; Payan et al, PMID: 30930108; Moral et al., PMID: 35008660; Wang et al., PMID: 30111784; Johnson et al., PMID: 34360531). Several review articles (Zheng et al, PMID: 32664346; Mia and Singh, PMID: 31632964; Diaz Del Moral et al, PMID: 35008660; Besson et al, PMID: 18267085; Wang et al, PMID: 19216791)) also organized the literature based on these distinct pathways or processes, which we used to define the boundaries of the six modules. However, how these molecular pathways work together is not well characterized. Therefore, we designed the model to incorporate each of these established modules and how they work together to drive cardiomyocyte proliferation." Line 550-557

The extent of cardiomyocyte proliferation at baseline is very low in the adult heart. The model identified 25 nodes that may influence baseline proliferation. Is there any evidence to support the involvement of these mechanisms in baseline cardiomyocyte proliferation in vivo?

We agree with the reviewer that proliferation at baseline is very low in the adult heart, and also rather low in neonatal cardiomyocytes. As shown in Figure S4A, we performed a virtual knockdown screen under baseline conditions that showed that no genetic knockdowns caused a substantial decrease in DNA replication or cytokinesis, consistent with a low baseline proliferation rate.

We describe this point about baseline proliferation in revised text:

"A complete virtual knockdown screen of the model was done under baseline conditions in Figure S4A, which showed that no knockdowns caused substantial decreases in DNA replication or cytokinesis. This is consistent with a low baseline proliferation rate described in cardiomyocyte literature." Line 354-357

The validation study was conducted with neonatal rat ventricular cardiomyocytes. This study could have been repeated with adult cardiomyocytes since they are more resistant to proliferation and, thus, the Myc may not work as expected. In addition, the authors could have commented on the mechanism through which chromatin opening and YAP allow transcription of Myc in the heart.

We agree that Myc is likely less proliferative in adult hearts. While our model was extensively validated against neonatal cardiomyocytes (Figure 2 for literature, Figure 5 for new neonatal experiments), only 10% of literature-based validations in Figure 2 are from adult cardiomyocytes due to limited data. However, in Figure 6 we validate YAP-dependent signaling to Myc, PI3K, and FOXM1 using RNA-seq and ATAC-seq data from Monroe et al. from adult mouse cardiomyocytes in vivo. While molecular mechanisms of YAP regulation of Myc are not characterized in the heart, based on the reviewer's suggestion, we add new discussion on YAP-Myc interaction in other cells:

"Overexpression of Myc induces cardiomyocyte proliferation in vitro and in vivo in several contexts, with open chromatin and Myc binding near mitotic genes (PMID: 32286286). But to our knowledge, crosstalk of YAP with Myc has not been reported in the heart. Our model prediction and experiments in neonatal cardiomyocytes support a YAP-TEAD-Myc pathway for cardiomyocyte proliferation. Further, our analysis of ATAC-seq and RNA-seq data from Monroe et al. validate that YAP induces Myc chromatin availability and gene expression in adult mouse hearts.

In MDA-MB-231 breast cancer cells, YAP/TAZ/TEAD bind directly to Myc enhancers through chromatin looping, with decreased acetylation of H3K27 and cell proliferation upon YAP/TAZ knockdown (26258633). YAP-TEAD-Myc signaling regulates the proliferation of cancer cells (26258633), tumorigenesis (29416644), and the growth of Drosophila imaginal discs (20951343). In the future, computational models and experiments are needed to better resolve how YAP promotes proliferation via Myc in the adult heart, including regulation by Mycn (30315164), cyclin T1 (32286286)."Line 632-644

insérer " $$\text{la différence }(g_{t}-\bar{g})$$" avant "sera très souvent négatif" (code Latex : (g_{t}-\bar{g}))

remplacer g par $$\bar{g}$$ (code Latex \bar{g})

for - progress trap - superintelligence threat

progress trap - superintelligence threat - super intelligence is going to be far beyond our cognitive capabilities across many domains. For example, - it's going to be able to find exploits in human code too subtle for humans to notice - it's going to be able to generate code too complicated for any human to understand - even if the model spent decades trying to explain it - How do we entrust ourselves to a superintelligence that is so far beyond us? If it thinks we are expendable, it could easily find our weaknesses and bring about extinction

for - AI evolution - projections for capabilities by 2030

AI evolution - projections for 2030 - AI will be able to do things we cannot even conceive of now because their cognitive capabilities are orders of magnitudes faster than our own - Write billions of lines of code - Absorb every scientific paper ever written and write new ones - Gain the equivalent of billions of human equivalent years of experience

for - progress trap - AI - milestone - automated AI researcher

progress trap - AI - milestone - automated AI researcher - This is a serious concern that must be debated - An AI researcher that does research on itself has no moral compass and can encode undecipherable code into future generations of AI that provides no back door to AI if something goes wrong. - For instance, if AI reached the conclusion that humans need to be eliminated in order to save the biosphere, - it can disseminate its strategies covertly under secret communications with unbreakable code

This is an interesting deep dive into the ADF family, particularly in Arabidopsis! There's a ton of data and I appreciate the open code!

Résumé de la vidéo [00:00:00][^1^][1] - [00:34:20][^2^][2]:

La vidéo présente une discussion sur la modification corporelle, en particulier le piercing et la scarification, et leur signification culturelle et personnelle. Les intervenants partagent leurs expériences et connaissances sur l'histoire et les pratiques actuelles de ces modifications, ainsi que sur leur impact sur l'identité et l'appartenance à un groupe.

Points forts: + [00:00:15][^3^][3] Introduction et contexte * Présentation des intervenants et de leur expérience dans la modification corporelle * Discussion sur l'importance culturelle et personnelle des modifications + [00:05:28][^4^][4] Le piercing et la modification corporelle * Explication de ce qu'est le piercing et son évolution à travers les cultures * Exemples de piercings et de modifications dans différentes sociétés + [00:10:27][^5^][5] Matériaux et bijoux modernes * Présentation des matériaux biocompatibles utilisés dans les piercings modernes * Discussion sur les normes d'implantation et la sécurité des matériaux + [00:22:32][^6^][6] La scarification et son évolution * Exploration de la scarification traditionnelle et de ses motivations * Comparaison avec les pratiques contemporaines de scarification + [00:31:53][^7^][7] Motivations pour la modification corporelle aujourd'hui * Réflexion sur les raisons personnelles et esthétiques de la modification corporelle * Impact sur l'identité individuelle et l'appartenance à un groupe

Résumé de la vidéo [00:00:00][^1^][1] - [00:34:20][^2^][2]:

Cette vidéo présente une discussion sur la modification corporelle, en particulier le tatouage et le piercing, et leur signification culturelle et personnelle. Les intervenants partagent leurs expériences et perspectives sur l'évolution de ces pratiques et leur impact sur l'identité individuelle.

Points forts: + [00:00:00][^3^][3] Introduction et contexte * Présentation des intervenants et de leur expérience dans le domaine de la modification corporelle * Discussion sur l'évolution du tatouage et du piercing + [00:10:55][^4^][4] Signification culturelle du piercing * Exploration des pratiques de piercing dans différentes cultures et leur signification rituelle ou sociale * Comparaison avec les tendances modernes du piercing + [00:22:30][^5^][5] La scarification et son histoire * Explication de la scarification et de ses différentes motivations, y compris esthétiques et rituelles * Exemples de scarification traditionnelle et contemporaine + [00:31:53][^6^][6] Motivations pour la modification corporelle aujourd'hui * Réflexion sur les raisons personnelles et esthétiques qui poussent les gens à se modifier corporellement * L'importance de l'identité et de l'appartenance à travers la modification corporelle

Résumé de la vidéo [00:34:23][^1^][1] - [01:05:18][^2^][2]:

La vidéo explore le rôle et l'impact des rituels corporels dans les cultures contemporaines, en particulier les modifications corporelles comme les piercings et les tatouages. Elle discute de la manière dont ces pratiques peuvent marquer un passage important dans la vie d'une personne, souvent associé à un état modifié de conscience ou à un sentiment d'accomplissement personnel.

Points forts: + [00:34:23][^3^][3] Le sens des rituels * Importance des rituels dans le passage à un nouvel état * Impact psychologique et physique des rituels * La peur et le dépassement de soi comme éléments clés + [00:42:25][^4^][4] L'étude du stress lié aux rituels * Analyse des réactions physiologiques au stress rituel * Observation d'un retour à la sérénité après le rituel * Effet positif des rituels sur les participants et les spectateurs + [00:48:32][^5^][5] Histoire et signification du tatouage * Évolution du tatouage de l'antiquité à nos jours * Significations sociales, punitives et décoratives des tatouages * Influence des tatouages sur l'identité culturelle et personnelle + [00:57:00][^6^][6] Renaissance des pratiques de tatouage * Résurgence des tatouages comme expression de fierté culturelle * Efforts pour préserver les traditions de tatouage malgré la mondialisation * L'importance de l'éducation et de la préservation des significations traditionnelles

Résumé de la vidéo [01:05:20][^1^][1] - [01:40:18][^2^][2] : La vidéo explore l'histoire et la signification culturelle des tatouages, en se concentrant sur leur évolution, leur symbolisme et leur acceptation sociale. Elle aborde les origines des tatouages, leur rôle dans diverses sociétés et cultures, et comment ils sont devenus un moyen d'expression personnelle dans le monde moderne.

Points forts : + [01:05:20][^3^][3] Symbolisme et réaction culturelle * Discussion sur les symboles de révolte et leur adoption par la culture populaire * L'utilisation des tatouages comme forme de protestation et d'expression individuelle * Exemples de tatouages symboliques et leur signification dans différents contextes + [01:08:05][^4^][4] Histoire du tatouage chez les marins * L'influence des marins sur la propagation du tatouage à travers le monde * Le tatouage comme rituel d'initiation et marque d'appartenance à une communauté * Signification des motifs traditionnels et leur lien avec les voyages et les expériences vécues + [01:10:04][^5^][5] Tatouages dans les milieux criminels * Le langage codé des tatouages dans la mafia russe et leur signification complexe * Comment les tatouages racontent l'histoire personnelle et le parcours criminel d'un individu * Les conséquences des tatouages identifiables dans la résolution d'affaires criminelles + [01:12:22][^6^][6] Démocratisation et visibilité des tatouages * L'augmentation de la popularité des tatouages grâce à la visibilité dans les médias et la mode * L'impact des célébrités et des influenceurs sur la perception publique des tatouages * Statistiques sur la croissance du nombre de studios de tatouage et l'intérêt pour les conventions de tatouage + [01:18:01][^7^][7] Emplacement et visibilité des tatouages * La tendance croissante des tatouages visibles et leur acceptation sociale * La féminisation du tatouage et l'augmentation des femmes tatouées * L'importance des tatouages comme moyen d'expression personnelle et de confiance en soi + [01:25:08][^8^][8] Scarifications et thérapie * La discussion sur les scarifications comme moyen d'expression pour les jeunes en difficulté * Le potentiel thérapeutique des tatouages et leur impact sur la confiance en soi * La réglementation et la formation en hygiène et salubrité pour les professionnels du tatouage

Résumé de la vidéo [01:40:21][^1^][1] - [01:54:04][^2^][2]:

Cette vidéo discute des pratiques et des matériaux dans les studios de tatouage et de piercing, mettant l'accent sur l'importance de l'hygiène, du consentement éclairé et de l'utilisation de matériaux sûrs pour les clients.

Points forts: + [01:40:21][^3^][3] L'évolution des studios de tatouage et piercing * Création en 2008 et progrès réalisés depuis * Importance de certaines pratiques et matériaux * Nécessité pour les clients de faire des recherches et de se fier à leur instinct + [01:42:22][^4^][4] Protocoles d'hygiène et utilisation de matériel médical * Utilisation de gants stériles et aiguilles pour prévenir les contaminations croisées * Suivi des bonnes pratiques dans les studios * Différences subtiles dans les protocoles selon l'éthique des salons + [01:44:53][^5^][5] Matériaux utilisés pour le piercing * Utilisation de matériaux de grade implantable comme le titane * Importance de la finition et de l'alliage des bijoux * Législation sur l'utilisation du nickel pour éviter les allergies + [01:49:20][^6^][6] Consentement et traçabilité * Obligation légale de consentement et de traçabilité du matériel utilisé * Évaluation de l'état général de la personne avant le piercing * Impact de la crise sanitaire sur les procédures et rendez-vous

Reviewer #3 (Public Review):

Summary:

Li et al. describe an audiovisual temporal recalibration experiment in which participants perform baseline sessions of ternary order judgments about audiovisual stimulus pairs with various stimulus-onset asynchronies (SOAs). These are followed by adaptation at several adapting SOAs (each on a different day), followed by post-adaptation sessions to assess changes in psychometric functions. The key novelty is the formal specification and application/fit of a causal-inference model for the perception of relative timing, providing simulated predictions for the complete set of psychometric functions both pre and post-adaptation.

Strengths:

(1) Formal models are preferable to vague theoretical statements about a process, and prior to this work, certain accounts of temporal recalibration (specifically those that do not rely on a population code) had only qualitative theoretical statements to explain how/why the magnitude of recalibration changes non-linearly with the stimulus-onset asynchrony of the adaptor.

(2) The experiment is appropriate, the methods are well described, and the average model prediction is a fairly good match to the average data (Figure 4). Conclusions may be overstated slightly, but seem to be essentially supported by the data and modelling.

(3) The work should be impactful. There seems a good chance that this will become the go-to modelling framework for those exploring non-population-code accounts of temporal recalibration (or comparing them with population-code accounts).

(4) A key issue for the generality of the model, specifically in terms of recalibration asymmetries reported by other authors that are inconsistent with those reported here, is properly acknowledged in the discussion.

Weaknesses:

(1) The evidence for the model comes in two forms. First, two trends in the data (non-linearity and asymmetry) are illustrated, and the model is shown to be capable of delivering patterns like these. Second, the model is compared, via AIC, to three other models. However, the main comparison models are clearly not going to fit the data very well, so the fact that the new model fits better does not seem all that compelling. I would suggest that the authors consider a comparison with the atheoretical model they use to first illustrate the data (in Figure 2). This model fits all sessions but with complete freedom to move the bias around (whereas the new model constrains the way bias changes via a principled account). The atheoretical model will obviously fit better, but will have many more free parameters, so a comparison via AIC/BIC or similar should be informative.

(2) It does not appear that some key comparisons have been subjected to appropriate inferential statistical tests. Specifically, lines 196-207 - presumably this is the mean (and SD or SE) change in AIC between models across the group of 9 observers. So are these differences actually significant, for example via t-test?

(3) The manuscript tends to gloss over the population-code account of temporal recalibration, which can already provide a quantitative account of how the magnitude of recalibration varies with adaptor SOA. This could be better acknowledged, and the features a population code may struggle with (asymmetry?) are considered.

(4) The engagement with relevant past literature seems a little thin. Firstly, papers that have applied causal inference modelling to judgments of relative timing are overlooked (see references below). There should be greater clarity regarding how the modelling here builds on or differs from these previous papers (most obviously in terms of additionally modelling the recalibration process, but other details may vary too). Secondly, there is no discussion of previous findings like that in Fujisaki et al.'s seminal work on recalibration, where the spatial overlap of the audio and visual events didn't seem to matter (although admittedly this was an N = 2 control experiment). This kind of finding would seem relevant to a causal inference account.

References:<br /> Magnotti JF, Ma WJ and Beauchamp MS (2013) Causal inference of asynchronous audiovisual speech. Front. Psychol. 4:798. doi: 10.3389/fpsyg.2013.00798<br /> Sato, Y. (2021). Comparing Bayesian models for simultaneity judgement with different causal assumptions. J. Math. Psychol., 102, 102521.

(5) As a minor point, the model relies on simulation, which may limit its take-up/application by others in the field.

(6) There is little in the way of reassurance regarding the model's identifiability and recoverability. The authors might for example consider some parameter recovery simulations or similar.

(7) I don't recall any statements about open science and the availability of code and data.

Reviewer #3 (Public Review):

Summary:

The burst fraction neural code has conceptual interest but has been little examined in vivo. This study examines and compares the burst fraction, the standard firing rate (firing rate) code, and the related event fraction (event rate) code using published data from an experiment where rats learned to lick after detecting electrical microstimulation in the somatosensory (barrel) cortex. Analyzing single-neuron spiking responses, the study reports that the burst fraction identifies more and different neurons showing the effects of training than the firing rate. The study further claims that the burst fraction (1) most promptly responded to false-negative detection errors, (2) during further training of trained animals (from 80% to 90% accuracy, over five days), correlates with behavioral accuracy, and (3) by shifting earlier to align with the (relatively constant) event rate modulation, leads to the observed sharpened firing rate response during this further training. The study concludes that 'a fine-grained separation of spike timing patterns [into burst fraction, firing rate, and event rate] reveals two signals,' an error signal and a sharpening signal.

Strengths:

The burst fraction is shown to discern more (and somewhat different) cells showing significant responses in trained animals and also to reveal a larger absolute difference in the fraction of responsive cells between naïve and trained animals. The Poisson model analysis particularly convincingly shows that the firing rate alone cannot explain either the spiking pattern or the prevalence of burst fraction-ON cells, thereby furnishing strong evidence that the burst fraction conveys independent information from the firing rate. The demonstration of error signals on miss trials in all three neural codes (burst fraction, firing rate, event rate) is interesting. It is also interesting to see that neural responses broadly shift earlier for animals even during further training in an already 'expert' stage and that the burst fraction correlates with further accuracy increases.

Weaknesses:

The evidence is inadequate for the burst fraction as responding more promptly to missed trials.

This key claim seems to rest solely on the timing of the first bins in Figure 3B showing statistically significant differences. This reasoning implicitly draws inferences from the lack of statistical differences, which cannot support a positive claim in general. Specifically, here, the burst fraction is calculated with a division, which can magnify small differences and impact the power of statistical tests. If I trace back from the first bin showing significant differences to the first bin the signal starts rising, the timing seems to be comparable for all three neural codes (~1.6 s).

Pertinently, what is the statistical test used in Figure 3B? A parametric test may be inappropriate for the burst fraction, a ratio that like does not fulfill the normality assumption. An inappropriate test would compound the problem of concluding from the lack of (early) significant differences.

The evidence that burst fraction is responsible for sharpening is opaque due to insufficient statistical reporting. Specifically, it seems there is a correlation between firing rate and accuracy that is reported as non-significant.

Changes in the reaction times (or other movement parameters) over-training may confound the correlation of the burst fraction to the accuracy and firing rate sharpening during further training. Lack of control for changes in movement over training weakens the results.

The claim of independence of burst fraction and event rate/firing rate information is too strong. The authors show a significant negative correlation between burst fraction and firing rate (2D).

The claim that there is no 'functional reorganization' beyond day two is too strong. Although this claim is not a core one to the study, it derives from an absence of statistical significance, especially problematic here as the effect sizes are large. For example, the Spearman correlation is 0.67/0.87 for the analyses with burst fraction. With only five data points, even strong effects may not achieve statistical significance, making negative conclusions problematic. Further, how are the p-values calculated (if using a parametric test, are the assumptions met), and why should these analyses use Spearman's correlation when analogous analyses in Figure 4E, F use Pearson's r?

Does the burst fraction correlate with accuracy in cross-training?

If the burst fraction correlates with accuracy, it should be expected to do so also when the animals progress from the naïve to the trained stage. Moreover, the correlation in Figure 4E can benefit from strengthening as it is now supported by only five points, is driven by only three 'clusters,' and only represents a narrow range of accuracies. If the data is available for this analysis, it should be done to test and potentially strengthen the main claim of the study.

The text and figures contain numerous ambiguities that need to be clarified. These do not include obvious typos, only elements that affect conceptual understanding.

- Some key terms in the main claims are never defined. For example, in the title, it is unclear what 'fast' and 'transients' mean. The abstract uses, but the main text never defines, 'demultiplexing,' 'a *conjunctive* burst code,' 'sparse and succinct [sic],' and 'correlated more *globally*.'

- Some paper components are un(der)explained and, sometimes, apparently internally inconsistent. For example, in Figure 1I, the fraction of firing rate-ON cells does not look like the 6% shown in Figure 1J, left. In Figure 2E-G, what is the total cell number, 279, in Figure 2G legend, why is it different from the 153 total cells in Figure 2E legend, and what is the 'n = 5' within Figure 2G? All n numbers should be explained in general; more examples include the 245 in Figure 3C and the 49 in Figure 3B. In Figure 3C, what is the top horizontal bar (I assume significant differences)? About catch trials, the Figure 3D legend says rewards are given on licks, but the text says licking was not rewarded; which is the case? Figure 4B legend says 'firing rate (left), burst fraction (middle) and event rate (right),' but the plot colors imply a different order.

- The abstract states, 'The alignment of bursting and event rate modulation [...] was strongly associated [sic] behavioral accuracy.' It seems to me it is not the alignment of burst fraction and event rate but rather burst fraction per se that correlates with behavioral accuracy (Figure 4E right). At least, the latter correlation is the only one tested.

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Author response:

The following is the authors’ response to the original reviews.

Reviewer #1 (Public Review):

Summary:

This study examines the spatial and temporal patterns of occurrence and the interspecific associations within a terrestrial mammalian community along human disturbance gradients. They conclude that human activity leads to a higher incidence of positive associations.

Strengths:

The theoretical framework of the study is brilliantly introduced. Solid data and sound methodology. This study is based on an extensive series of camera trap data. Good review of the literature on this topic.

Weaknesses:

The authors use the terms associations and interactions interchangeably.

This is not the case. In fact, we state specifically that "... interspecific associations should not be directly interpreted as a signal of biotic interactions between pairs of species…" However, co-occurrence can be an important predictor of likely interactions, such as competition and predation. We stand by our original text.

It is not clear what the authors mean by "associations". A brief clarification would be helpful.

Our specific definition of what is meant here by spatial association can be found in the Methods section. To clarify, the calculation of the index of associations is based on the covariance for the two species of the residuals (epsilon) after consideration of all species-specific response to known environmental covariates. These covariances are modelled to allow them to vary with the level of human disturbance, measured as human presence and human modification. After normalization, the final index of association is a correlation value that varies between -1 (complete disassociation) and +1 (complete positive association).

Also, the authors do not delve into the different types of association found in the study. A more ecological perspective explaining why certain species tend to exhibit negative associations and why others show the opposite pattern (and thus, can be used as indicator species) is missing.

Suggesting the ecological underpinnings of the associations observed here would mainly be speculation at this point, but the associations demonstrated in this analysis do suggest promising areas for the more detailed research suggested.

Also, the authors do not distinguish between significant (true) non-random associations and random associations. In my opinion, associations are those in which two species co-occur more or less than expected by chance. This is not well addressed in the present version of the manuscript.

Results were considered to be non-random if correlation coefficients (for spatial association) or overlap (for temporal association) fell outside of 95% Confidence Intervals. This is now stated clearly in the Methods section.  In Figure 3—figure supplement 1-3 and Figure 4—figure supplement 1-3, p<0.01 levels are also presented.

The obtained results support the conclusions of the study.

Anthropogenic pressures can shape species associations by increasing spatial and temporal co-occurrence, but above a certain threshold, the positive influence of human activity in terms of species associations could be reverted. This study can stimulate further work in this direction.

Reviewer #2 (Public Review):

Summary:

This study analyses camera trapping information on the occurrence of forest mammals along a gradient of human modification of the environment. The key hypotheses are that human disturbance squeezes wildlife into a smaller area or their activity into only part of the day, leading to increased co-occurrence under modification. The method used is joint species distribution modelling (JSDM).

Strengths:

The data source seems to be very nice, although since very little information is presented, this is hard to be sure of. Also, the JSDM approach is, in principle, a nice way of simultaneously analysing the data.

Weaknesses:

The manuscript suffers from a mismatch of hypotheses and methods at two different levels.

(1) At the lower level, we first need to understand what the individual species do and "like" (their environmental niche). That information is not presented, and the methods suggest that the representation of each species in the JSDM is likely to be extremely poor.

The response of each species to the environmental covariates provides a window into their environmental niche, encapsulated in the beta coefficients for each environmental covariate. This information is presented in Figure 2.

(2) The hypothesis clearly asks for an analysis of the statistical interaction between human disturbance and co-occurrence. Yet, the model is not set up this way, and the authors thus do a lot of indirect exploration, rather than direct hypothesis testing.

Our JSDM model is set up specifically to examine the effect of human disturbance on co-occurrence, after controlling for shared responses to environmental variables.  It directly tests the first hypothesis, since, if increase in indices of human disturbance had not tended to increase the measured spatial correlations between species as detected by the model, we would have rejected our stated hypothesis that human modification of habitats results in increased positive spatial associations between species.

Even when the focus is not the individual species, but rather their association, we need to formulate what the expectation is. The hypotheses point towards presenting the spatial and the temporal niche, and how it changes, species for species, under human disturbance. To this, one can then add the layer of interspecific associations.

Examining each species one by one and how each one responds to human disturbance would miss the effects of any meaningful interactions between species.  The analysis presented provides a means to highlight associations that would have been overlooked.  Future research could go on to analyze the strongest associations in the community and the strongest effects of human disturbance so as to uncover the underlying interactions that give rise to them and the mechanisms of human impact.  We believe that this will prove to be a much more productive approach than trying to tackle this problem species by species and pair by pair.

The change in activity and space use can be analysed much simpler, by looking at the activity times and spatial distribution directly. It remains unclear what the contribution of the JSDM is, unless it is able to represent this activity and spatial information, and put it in a testable interaction with human disturbance.

The topic is actually rather complicated. If biotic interactions change along the disturbance gradient, then observed data are already the outcome of such changed interactions. We thus cannot use the data to infer them! But we can show, for each species, that the habitat preferences change along the disturbance gradient - or not, as the case may be.

Then, in the next step, one would have to formulate specific hypotheses about which species are likely to change their associations more, and which less (based e.g. on predator-prey or competitive interactions). The data and analyses presented do not answer any of these issues.

We suggest that the so-called “simpler” approach described above is anything but simple, and this is precisely what the Joint Species Distribution Model improves upon.  As pointed out in the Introduction, simply examining spatial overlap is not enough to detect a signal of meaningful biotic interaction, since overlap could be the result of similar responses to environmental variables.  With the JSDM approach, this would not be considered a positive association and would then not imply the possible existence of meaningful interaction.

Another more substantial point is that, according to my understanding of the methods, the per-species models are very inappropriate: the predictors are only linear, and there are no statistical interactions (L374). There is no conceivable species in the world whose niche would be described by such an oversimplified model.

While interaction terms can be included in the JSDM, this would considerably increase the complexity of the models.  In previous work, we have found no strong evidence for the importance of interaction terms and they do not improve the performance of the models.

We have no idea of even the most basic characteristics of the per-species models: prevalences, coefficient estimates, D2 of the model, and analysis of the temporal and spatial autocorrelation of the residuals, although they form the basis for the association analysis!

The coefficient estimates for response to environmental variables used in the JSDM are provided in Figure 2 and Figure 2—source data 1.

Why are times of day and day of the year not included as predictors IN INTERACTION with niche predictors and human disturbance, since they represent the temporal dimension on which niches are hypothesised to change?

Also, all correlations among species should be shown for the raw data and for the model residuals: how much does that actually change and can thus be explained by the niche models?

The discussion has little to add to the results. The complexity of the challenge (understanding a community-level response after accounting for species-level responses) is not met, and instead substantial room is given to general statements of how important this line of research is. I failed to see any advance in ecological understanding at the community level.

We agree that the community-level response to human disturbance is a complex topic, and we believe it is also a very important one.  This research and its support of the spatial compression hypothesis, while not providing definitive answers to detailed mechanisms, opens up new lines of inquiry that makes it an important advance.  For example, the strong effects of human disturbance on certain associations that were detected here could now be examined with the kind of detailed species by species and pair by pair analysis that this reviewer appears to demand.

Reviewer #1 (Recommendations For The Authors):

L27 indicates instead of "idicates".

We thank the reviewer for catching that error.

L64 I would refer to potential interactions or just associations. It is always hard to provide evidence for the existence of true interactions.

We have revised to “potential interactions” to qualify this statement.

L69 Suggestion: distort instead of upset.

We thank the reviewer for catching that error.

L70-71 Here, authors use the term associations. Please, be consistent with the terminology throughout the manuscript.

We thank the reviewer for raising this important point.  The term “co-occurrence” appears to be used inconsistently in the literature, so we have tried to refer to it only when referencing the work of us. For us, co-occurrence means “spatial overlap” without qualification as to whether it is caused by interaction or simply by similar responses to environmental factors (see Blanchet et al. 2020, Argument 1). In our view, interactions refer to biotic effects like predation, competition, commensalism, etc., while associations are the statistical footprint of these processes.   In keeping with this understanding, in Line 73, we changed "association" to the stronger word "interaction," but in Line 76, we keep the words "spatiotemporal association", which is presumed to be the result of those interactions. In Line 91, we have changed “interactions” to “associations,” as we do not believe interactions were demonstrated in that study. 

L76 "Species associations are not necessarily fixed as positive or negative..." This sentence is misleading. I would say that species associations can vary across time and space, for instance along an environmental gradient.

We thank the reviewer for pointing out the potential for confusion.  In Line 79, we have changed as suggested.

L78 "Associations between free-ranging species are especially context-dependent" Loose sentence. Please, explain a bit further.

We have changed the sentence to be more specific; ”Interactions are known to be context-dependent; for example, gradients in stress are associated with variation in the outcomes of pairwise species interactions.”

L83-85 This would be a good place to introduce the 'stress gradient' hypothesis, which has also been applied to faunal communities in a few studies. According to this hypothesis, the incidence of positive associations should increase as environmental conditions harden.

In our review of the literature, we find that the stress gradient hypothesis is somewhat controversial and does not receive strong support in vertebrates.  We have added the phrase “…the controversial stress-gradient hypothesis predicts that positive associations should increase as environmental conditions become more severe…”

L86-88 Well, overall, the number of studies examining spatiotemporal associations in vertebrates is relatively small. That is, bird associations have not received much more attention than those of mammals. I find this introductory/appealing paragraph a bit rough. I think the authors can do better and find a better justification for their work.

We thank the reviewer for the comments.  We have rewritten the paragraph extensively to make it clearer and to provide a stronger justification for the study.

L106 "[...] resulting in increased positive spatial associations between species" I'd say that habitat shrinking would increase the level of species clustering or co-occurrence, but in my opinion, not necessarily the incidence of positive associations. It is not clear to me if the authors use positive associations as a term analogous to co-occurrence.

We thank the reviewer for raising this very important distinction.  Habitat shrinking would increase levels of species co-occurrence, but this is not particularly interested.  We wanted to test whether there were effects on species interactions, as revealed by associations.  We find that the terms association and co-occurrence are used somewhat loosely in the literature and so have made some new effort to clarify and systematize this in the manuscript.  For example, there appear to be a differences in the way “co-occurrence” is used in Boron 2023 and in Blanchet 2020. We do not use the term "positive spatial association" as analogous to "spatial co-occurrence.". Spatial co-occurrence, which for us has the meaning of spatial overlap, could simply be the result of similar reactions to environmental co-variates, not reflecting any biotic interaction. Joint Species Distribution Models enable the partitioning of spatial overlap and segregation into that which can be explained by responses to known environmental factors, and that which cannot be explained and thus might be the result of biotic interactions.  It is only the latter that we are calling spatial association, which can be positive or negative.   These associations may be the statistical footprint of biotic interactions.

Results:

Difference between random and non-random association patterns. It is not clear to me if the reported associations are significant or not. The authors only report the sign of the association (either positive or negative) but do not clarify if these associations indicate that two species coexist more or less than expected by chance. In my opinion, that is the difference between true ecological associations (e.g., via facilitation or competition effects) and random co-existence patterns. This is paramount and should be addressed in a new version of the manuscript.

This information is provided in Figure 3—figure supplement 1,2,3 and Figure 4—figure supplement 1,2,3.  This is referenced in the text as follows, “… correlation coefficients for 18 species pairs were positive and had a 95 % CI that did not overlap zero, and the number increased to 65 in moderate modifications but dropped to 29 at higher modifications" and so on. This criterion for significance (ie., greater than expected by chance) is now stated at the end of the Materials and methods.  In Figure 3—figure supplement 1,2,3 and Figure 4—figure supplement 1,2,3, those correlations that were significant at p<0.01 are also shown.

I am also missing a more ecological explanation for the observed findings. For instance, the top-ranked species in terms of negative associations is the red fox, whereas the muntjac seems to be the species whose presence can be used as an indicator for that of other species. What are the mechanisms underlying these patterns? Do red foxes compete for food with other species? Do the species that show positive associations (red goral, muntjac) have traits or a diet that are more different from those of other species? More discussion on these aspects (role of traits and the trophic niche) would be necessary to better understand the obtained results.

The purpose of this paper was to test the compression hypotheses, and we have tried to keep that as the focus.  However, the analysis does open up interesting lines of inquiry for future research to decipher the details of the interactions between species and the mechanisms by which human disturbance facilitates or disrupts these interactions. The reviewer raises some interesting possibilities, but at this point, any discussion along these lines would be largely speculation and could lengthen the paper without great benefit. 

Reviewer #2 (Recommendations For The Authors):

The manuscript should be accompanied by all data and code of analysis.

All data and RScripts have been made available in Science Data Bank: https://doi.org/10.57760/sciencedb.11804.

The sentence "not much is known" is weak: it suggests the authors did not bother to quantify what IS known, and simply waved any previous knowledge aside. Surely we have some ideas about who preys on whom, and which species have overlapping resource requirements (e.g., due to jaw width). For those, we would expect a particularly strong signal, if the association is indeed indicative of interactions.

We believe that the reviewer is referring to the statement in Line 90-92 about the lack of understanding of the resilience of terrestrial mammal associations to human disturbance.  We have added a reference to one very recent publication that addresses the issue (Boron et al., 2023), but otherwise we stand by our statement. We have, however, added a qualifier to make it clear that we did indeed look for previous knowledge; "However, a review of the literature indicates that ...."

Figures:

Fig. 1. This reviewer considers that this is too trivial and should be deleted.

This is a graphical statement of the hypotheses and may be helpful to some readers.

Fig. 2. Using points with error bars hides any potential information.

Done as suggested.

That only 4 predictors are presented is unacceptably oversimplified.

Only 4 predictors are included because, in previous work, we found that adding additional predictors or interactions did little to improve the model’s performance (Li et al. 2018, 2021 and 2022) and could lead to over-fitting.

Fig. 5. and 6. aggregate extremely strongly over species; it remains unclear which species contribute to the signal, and I guess most do not.

The number of detection events presented in Table 1 should help to clarify the relative contribution of each species to the data presented in Figures 5 and 6.

This reviewer considers that the introduction 'oversells' the paper.

L55: can you give any such "unique ecological information"

L60: Lyons et al. (Kathleen is the first name) has been challenged by Telford et al. (2016 Nature) as methodologically flawed.

The first name has been deleted.  The methodological flaw has to do with interpretation of the fossil record and choice of samples, not with the need to partition shared environmental preferences and interactions.

L61 contradicts line 64: Blanchet et al. (2022, specifying some arguments from Dormann et al. 2018 GEB) correctly point out that logically one cannot infer the existence or strength from co-occurrence data. It is thus wrong to then claim (citing Boron et al.) that such data "convey key information about interactions". The latter statement is incorrect. A tree and a beetle can have extremely high association and nothing to do with each other. Association does not mean anything in itself. When two species are spatially and temporally non-overlapping, they can exhibit perfect "anti-association", yet, by the authors' own definition, cannot interact.

We believe that the reviewer’s concerns arise from a misunderstanding of how we use the term association.  In our usage, an association is not the same as co-occurrence or overlap, which may simply be the result of shared responses to environmental variables.  The co-occurring tree and beetle would not be found to have any association in our analysis, only shared environmental sensitivities.  In contrast, associations can be the statistical footprint of interactions, and would be overlaid onto any overlap due to similar responses to the environment.  In the case of negative associations, such as might be the result of competitive exclusion or avoidance of predators, the two species would share environmental responses but show lower than expected spatial overlap.  Even though they might be only rarely found in the same vicinity, they would indeed be interacting when they were together.

Joint Species Distribution Models "allow the partitioning of the observed correlation into that which can be explained by species responses to environmental factors... and that which remains unexplained after controlling for environmental effects and which may reflect biotic interactions." (Garcia Navas et al. 2021). It is the latter that we are calling “associations.”

L63: Gilbert reference: Good to have a reference for this statement.

This point is important, but the reviewer’s comments below have made it clear that it is even more important to point out that strong interactions should be expected to lead to significant associations.  We have added a statement to clarify this.

L70-72: Incorrect, interactions play a role, not associations (which are merely statistical).

In this, we agree, and we have revised the statement to refer to interactions, not associations. In our view, an interaction is a biological phenomenon, while an association is the resulting statistical signal that we can detect.

L75: Associations tell us nothing, only interactions do. Since these can not be reliably inferred, this statement and this claim are wrong.

We thank the reviewer for raising this point, but we beg to disagree. Strong interactions should be expected to lead to significant associations that can be detected in the data. Associations, which can be measured reliably, are the evidence of potential interactions, and hence associations can tell us a great deal.  We have added a note to this effect after the Gilbert reference above to clarify this point.

However, we do accept that associations must be interpreted with caution. As Blanchet et al. 2020 explain, " …the co-occurrence signals (e.g. a significant positive or negative correlation value) estimated from these models could originate from any abiotic factors that impact species differently. Therefore, this correlation cannot be systematically interpreted as a signal of biotic interactions, as it could instead express potential non-measured environmental drivers (or combinations of them) that influence species distribution and co-distribution.”  Or alternatively an association could be the result of interaction with a 3rd species. 

L87: Regarding your claim, how would you know you DO understand? For that, you need to formulate an expectation before looking at the data and then show you cannot show what you actually measure. (Jaynes called this the "mind-projection fallacy".)

We are not sure if the reviewer is criticizing our paper or the entire field of community ecology.  Perhaps it is the statement that “….resilience of interspecific spatiotemporal associations of terrestrial mammals to human activity remains poorly understood….”  Since we are confident that the reviewer believes that mammals do interact, we guess that it is the term “association” that is questioned.  We have revised this to “…the impacts of human activity on interspecific interactions of terrestrial mammals remains poorly understood…” 

In this particular case, we did formulate an expectation before looking at the data, in the form of the two formal hypotheses that are clearly stated in the Introduction and illustrated in Figure 1. If the hypotheses had not been supported, then we would have accepted that we do not understand. But as the data are consistent with the hypotheses, we submit that we do understand a bit more now.

Résumé de la vidéo [00:00:01][^1^][1] - [00:20:21][^2^][2]:

Cette vidéo présente une discussion entre Adam Grant et Dan Ariely, économiste comportemental, sur la vérité honnête concernant la malhonnêteté. Ils explorent la fréquence de la malhonnêteté dans les organisations, l'impact des petits tricheurs par rapport aux grands, et comment les comportements négatifs peuvent devenir acceptables dans un environnement organisationnel. Ils discutent également du rôle de la direction, des lanceurs d'alerte et de l'importance d'un code de conduite clair pour prévenir la malhonnêteté.

Points forts: + [00:00:01][^3^][3] La malhonnêteté dans les organisations * Très commune, mais principalement de petits tricheurs * Les petits tricheurs ont un impact économique plus important que les grands + [00:03:45][^4^][4] Le rôle de la direction et des lanceurs d'alerte * La direction peut influencer le comportement organisationnel * Les lanceurs d'alerte sont souvent traités comme des outsiders + [00:06:20][^5^][5] La pente glissante de la malhonnêteté * Les grandes fraudes commencent souvent par de petits pas * Importance de reconnaître les premiers signes de comportement contraire à l'éthique + [00:13:01][^6^][6] L'importance d'un code de conduite clair * Un code de conduite précis aide les individus à distinguer le bien du mal * Les règles floues permettent une rationalisation plus facile de la malhonnêteté

in our cae, time is whenever a new/changed test case gets detected in a code repository

Attention, l'extraction des titres n'est plus fonctionnelle dans le programme p3c3 suite à un changement du code HTML. Premier problème : Il faut remplacer la balise "a", par la balise "div". Second problème : la commande string n'est pas fonctionnelle car il y a des \n dans le code, le string renvoie donc None. Il faut remplacer les fonctions string par get_text()

Il serait également bienvenue de rajouter une petite explication sur la fonction \n présent dans beaucoup de code HTML qu'il faut supprimer lors de l'extraction web.

Enfin je recommande de modifier la ligne correspondante comme suit : with open("data.csv", "w", newline="") as fichier :

Le fait d'ajouter newline="", permet de supprimer la ligne automatiquement générée par l'écriture sur le fichier csv

Author response:

The following is the authors’ response to the original reviews.

Your editorial guidance, reviews, and suggestions have led us to make substantial changes to our manuscript. While we detail point-by-point responses in typical fashion below, I wanted to outline, at a high level, what we’ve done.

(1) Methods. Your suggestions led us to rethink our presentation of our methods, which are now described more cohesively in a new methods section in the main text.

(2) Model Validation & Robustness. Reviewers suggested various validations and checks to ensure that our findings were not, for instance, the consequence of a particular choice of parameter. These can be found in the supplementary materials.

(3) Data Cleaning & Inclusion/Exclusion. Finally, based on feedback, our new methods section fully describes the process by which we cleaned our original data, and on what grounds we included/excluded individual faculty records from analysis.

eLife assessment

Efforts to increase the representation of women in academia have focussed on efforts to recruit more women and to reduce the attrition of women. This study - which is based on analyses of data on more than 250,000 tenured and tenure-track faculty from the period 2011-2020, and the predictions of counterfactual models - shows that hiring more women has a bigger impact than reducing attrition. The study is an important contribution to work on gender representation in academia, and while the evidence in support of the findings is solid, the description of the methods used is in need of improvement.

Reviewer #1 (Public Review):

Summary and strengths

This is an interesting paper that concludes that hiring more women will do more to improve the gender balance of (US) academia than improving the attrition rates of women (which are usually higher than men's). Other groups have reported similar findings but this study uses a larger than usual dataset that spans many fields and institutions, so it is a good contribution to the field.

We thank the reviewer for their positive assessment of the contributions of our work.

Weaknesses

The paper uses a mixture of mathematical models (basically Leslie matrices, though that term isn't mentioned here) parameterised using statistical models fitted to data. However, the description of the methods needs to be improved significantly. The author should consider citing Matrix Population Models by Caswell (Second Edition; 2006; OUP) as a general introduction to these methods, and consider citing some or all of the following as examples of similar studies performed with these models:

Shaw and Stanton. 2012. Proc Roy Soc B 279:3736-3741

Brower and James. 2020. PLOS One 15:e0226392

James and Brower. 2022. Royal Society Open Science 9:220785 Lawrence and Chen. 2015.

[http://128.97.186.17/index.php/pwp/article/view/PWP-CCPR-2015-008]

Danell and Hjerm. 2013. Scientometrics 94:999-1006

We have expanded the description of methods in a new methods section of the paper which we hope will address the reviewer’s concerns.

We agree that our model of faculty hiring and attrition resembles Leslie matrices. In results section B, we now mention Leslie matrices and cite Matrix Population Models by Caswell, noting a few key differences between Leslie matrices and the model of hiring and attrition presented in this work. Most notably, in the hiring and attrition model presented, the number of new hires is not based on per-capita fertility constants. Instead, population sizes are predetermined fixed values for each year, precluding exponential population growth or decay towards 0 that is commonly observed in the asymptotic behavior of linear Leslie Matrix models.

We have additionally revised the main text to cite the listed examples of similar studies (we had already cited James and Brower, 2022). We thank the reviewer for bringing these relevant works to our attention.

The analysis also runs the risk of conflating the fraction of women in a field with gender diversity! In female-dominated fields (e.g. Nursing, Education) increasing the proportion of women in the field will lead to reduced gender diversity. This does not seem to be accounted for in the analysis. It would also be helpful to state the number of men and women in each of the 111 fields in the study.

We have carefully examined the manuscript and revised the text to correctly differentiate between gender diversity and women’s representation.

We have additionally added a table to the supplemental materials (Tab. S3) that reports the estimated number of men and women in each of the 111 fields.

Reviewer #2 (Public Review):

Summary:

This important study by LaBerge and co-authors seeks to understand the causal drivers of faculty gender demographics by quantifying the relative importance of faculty hiring and attrition across fields. They leverage historical data to describe past trends and develop models that project future scenarios that test the efficacy of targeted interventions. Overall, I found this study to be a compelling and important analysis of gendered hiring and attrition in US institutions, and one that has wide-reaching policy implications for the academy. The authors have also suggested a number of fruitful future avenues for research that will allow for additional clarity in understanding the gendered, racial, and socioeconomic disparities present in US hiring and attrition, and potential strategies for mitigating or eliminating these disparities.

We thank the reviewer for their positive assessment of the contributions of our work.

Strengths:

In this study, LaBerge et al use data from over 268,000 tenured and tenure-track faculty from over 100 fields at more than 12,000 PhD-granting institutions in the US. The period they examine covers 2011-2020. Their analysis provides a large-scale overview of demographics across fields, a unique strength that allows the authors to find statistically significant effects for gendered attrition and hiring across broad areas (STEM, non-STEM, and topical domains).

LaBerge et al. find gendered disparities in attrition-using both empirical data and their counterfactual model-that account for the loss of 1378 women faculty across all fields between 2011 and 2020. It is true that "this number is both a small portion of academia... and a staggering number of individual careers," as ." - as this loss of women faculty is comparable to losing more than 70 entire departments. I appreciate the authors' discussion about these losses-they note that each of these is likely unnecessary, as women often report feeling that they were pushed out of academic jobs.

LaBerge et al. also find-by developing a number of model scenarios testing the impacts of hiring, attrition, or both-that hiring has a greater impact on women's representation in the majority of academic fields in spite of higher attrition rates for women faculty relative to men at every career stage. Unlike many other studies of historical trends in gender diversity, which have often been limited to institution-specific analyses, they provide an analysis that spans over 100 fields and includes nearly all US PhD-granting institutions. They are able to project the impacts of strategies focusing on hiring or retention using models that project the impact of altering attrition risk or hiring success for women. With this approach, they show that even relatively modest annual changes in hiring accumulate over time to help improve the diversity of a given field. They also demonstrate that, across the model scenarios they employ, changes to hiring drive the largest improvement in the long-term gender diversity of a field.

Future work will hopefully - as the authors point out - include intersectional analyses to determine whether a disproportionate share of lost gender diversity is due to the loss of women of color from the professoriate. I appreciate the author's discussion of the racial demographics of women in the professoriate, and their note that "the majority of women faculty in the US are white" and thus that the patterns observed in this study are predominately driven by this demographic. I also highly appreciate their final note that "equal representation is not equivalent to equal or fair treatment," and that diversifying hiring without mitigating the underlying cause of inequity will continue to contribute to higher losses of women faculty.

Weaknesses

First, and perhaps most importantly, it would be beneficial to include a distinct methods section. While the authors have woven the methods into the results section, I found that I needed to dig to find the answers to my questions about methods. I would also have appreciated additional information within the main text on the source of the data, specifics about its collection, inclusion and exclusion criteria for the present study, and other information on how the final dataset was produced. This - and additional information as the authors and editor see fit - would be helpful to readers hoping to understand some of the nuance behind the collection, curation, and analysis of this important dataset.

We have expanded upon the description of methods in a new methods section of the paper.

We have also added a detailed description of the data cleaning steps taken to produce the dataset used in these analyses, including the inclusion/exclusion criteria applied. This detailed description is at the beginning of the methods section. This addition has substantially enhanced the transparency of our data cleaning methods, so we thank the reviewer for this suggestion.

I would also encourage the authors to include a note about binary gender classifications in the discussion section. In particular, I encourage them to include an explicit acknowledgement that the trends assessed in the present study are focused solely on two binary genders - and do not include an analysis of nonbinary, genderqueer, or other "third gender" individuals. While this is likely because of the limitations of the dataset utilized, the focus of this study on binary genders means that it does not reflect the true diversity of gender identities represented within the professoriate.

In a similar vein, additional context on how gender was assigned on the basis of names should be added to the methods section.

We use a free, open-source, and open-data python package called nomquamgender (Van Buskirk et al, 2023) to estimate the strengths of (culturally constructed) name-gender associations. For sufficiently strong associations with a binary gender, we apply those labels to the names in our data. We have updated the main text to make this approach more apparent.

We have also added language to the main text which explicitly acknowledges that our approach only assigns binary (woman/man) labels to faculty. We point out that this is a compromise due to the technical limitations of name-based gender methodologies and is not intended to reinforce a gender binary.

I do think that some care might be warranted regarding the statement that "eliminating gendered attrition leads to only modest changes in field-level diversity" (Page 6). while I do not think that this is untrue, I do think that the model scenarios where hiring is "radical" and attrition is unchanged from present (equal representation of women and men among hires (ER) + observed attrition (OA)) shows that a sole focus on hiring dampens the gains that can otherwise be addressed via even modest interventions (see, e.g., gender-neutral attrition (GNA) + increasing representation of women among hires (IR)). I am curious as to why the authors did not include an additional scenario where hiring rates are equal and attrition is equalized (i.e., GNA + ER). The importance of including this additional model is highlighted in the discussion, where, on Page 7, the authors write: "In our forecasting analysis, we find that eliminating the gendered attrition gap, in isolation, would not substantially increase representation of women faculty in academia. Rather, progress towards gender parity depends far more heavily on increasing women's representation among new faculty hires, with the greatest change occurring if hiring is close to gender parity." I believe that this statement would be greatly strengthened if the authors can also include a comparison to a scenario where both hiring and attrition are addressed with "radical" interventions.

Our rationale for omitting the GNA + ER scenario in the presented analysis is that we can reason about the outcomes of this scenario without the need for computation; if a field has equal inputs of women and men faculty (on average) and equal retention rates between women and men (on average), then, no matter the field’s initial age and gender distribution of faculty, the expected value for the percentage of women faculty after all of the prior faculty have retired (which may take 40+ years) is exactly 50%. We have updated the main text to discuss this point.

Reviewer #3 (Public Review):

This manuscript investigates the roles of faculty hiring and attrition in influencing gender representation in US academia. It uses a comprehensive dataset covering tenured and tenure-track faculty across various fields from 2011 to 2020. The study employs a counterfactual model to assess the impact of hypothetical gender-neutral attrition and projects future gender representation under different policy scenarios. The analysis reveals that hiring has a more significant impact on women's representation than attrition in most fields and highlights the need for sustained changes in hiring practices to achieve gender parity.

Strengths:

Overall, the manuscript offers significant contributions to understanding gender diversity in academia through its rigorous data analysis and innovative methodology.

The methodology is robust, employing extensive data covering a wide range of academic fields and institutions.

Weaknesses:

The primary weakness of the study lies in its focus on US academia, which may limit the generalizability of its findings to other cultural and academic contexts.

We agree that the U.S. focus of this study limits the generalizability of our findings. The findings that we present in this work will only generalize to other populations–whether it be to an alternate industry, e.g., tech workers, or to faculty in different countries–to the extent that these other populations share similar hiring patterns, retention patterns, and current demographic representation. We have added a discussion of this limitation to the manuscript.

Additionally, the counterfactual model's reliance on specific assumptions about gender-neutral attrition could affect the accuracy of its projections.

Our projection analysis is intended to illustrate the potential gender representation outcomes of several possible counterfactual scenarios, with each projection being conditioned on transparent and simple assumptions. In this way, the projection analysis is not intended to predict or forecast the future.

To resolve this point for our readers, we now introduce our projections in the context of the related terms of prediction and forecast, noting that they have distinct meanings as terms of art: On one hand, prediction and forecasting involve anticipating a specific outcome based on available information and analysis, and typically rely on patterns, trends, or historical data to make educated guesses about what will happen. Projections are based on assumptions and are often presented in a panel of possible future scenarios. While predictions and forecasts aim for precision, projections (which we make in our analysis) are more generalized and may involve a range of potential outcomes.

Additionally, the study assumes that whoever disappeared from the dataset is attrition in academia. While in reality, those attritions could be researchers who moved to another country or another institution that is not included in the AARC (Academic Analytics Research Centre) dataset.

In our revision, we have elevated this important point, and clarified it in the context of the various ways in which we count hires and attritions. We now explicitly state that “We define faculty hiring and faculty attrition to include all cases in which faculty join or leave a field or domain within our dataset.” Then, we enumerate the number of situations that could be counted as hires and attritions, including the reviewer’s example of faculty who move to another country.

Reviewer #1 (Recommendations For The Authors):

Section B: The authors use an age structured Leslie matrix model (see Caswell for a good reference to these) to test the effect of making the attrition rates or hiring rates equal for men and women. My main concern here is the fitting techniques for the parameters. These are described (a little too!) briefly in section S1B. Some specific questions that are left hanging include:

A 5th order polynomial is an interesting choice. Some statistical evidence as to why it was the best fit would be useful. What other candidate models were compared? What was the "best fit" judgement made with: AIC, r^2? What are the estimates for how good this fit is? How many data points were fitted to? Was it the best fit choice for all of the 111 fields for men and women?

We use a logistic regression model for each field to infer faculty attrition probabilities across career ages and time, and we include the career age predictor up to its fifth power to capture the career-age correlations observed in Spoon et. al., Science Advances, 2023. For ease of reference, we reproduce the attrition risk curves in Fig S4.

We note that faculty attrition rates start low and then reach a peak around 5-7 years after earning PhD, and then decline until around 15-20 years post-PhD, after which, attrition rates increase as faculty approach retirement.

This function shape starts low and ends high, and includes at least one local minimum, which indicates that career age should be odd-ordered in the model and at least order-3, but only including career age up to its 3rd order term tended to miss some of the overserved career-age/attrition correlations. We evaluated the fit using 5-fold cross validation with a Brier score loss metric, and among options of polynomials of degree 1, 3, 5, or 7, we found that 5th order performed well overall on average over all fields (even if it was not the best for every field), without overfitting in fields with fewer data. Example fits, reminiscent of the figure from Spoon et al, are now provided in Figs S4 and S5.

While the model fit with fifth order terms may not be the best fit for all 111 fields (e.g., 7th order fits better in some cases), we wanted to avoid field-specific curves that might be overfitted to the field-specific data, especially due to low sample size (and thus larger fluctuations) on the high career age side of the function. Our main text and supplement now includes justifications for our choice to include career age up to its fifth order terms.

You used the 5th order logistic regression (bottom of page 11) to model attrition at different ages. The data in [24] shows that attrition increases sharply, then drops then increases again with career age. A fifth order polynomial on its own could plausibly do this but I associate logistic regression models like this as being monotonically increasing (or decreasing!), again more details as to how this worked would be useful.

Our first submission did not explain this point well, but we hope that Supplementary Figures S4 and S5 provide clarity. In short, we agree of course that typical logistic regression assumes a linear relationship between the predictor variables and the log odds of the outcome variable. This means that the relationship between the predictor variables and the probability of the outcome variable follows a sigmoidal (S-shaped) curve. However, the relationship between the predictor variables and the outcome variable may not be linear.

To capture more complex relationships, like the increasing, decreasing and then increasing attrition rates as a function of career age, higher-order terms can be added to the logistic regression model. These higher-order terms allow the model to capture nonlinear relationships between the predictor variables and the outcome variable — namely the non-monotonic relationship between rates of attrition and career age — while staying within a logistic regression framework.

"The career age of new hires follows the average career age distribution of hires" did you use the empirical distribution here or did you fit a standard statistical distribution e.g. Gamma?

We used the empirical distribution. This information has been added to the updated methods section in the main text.

How did you account for institution (presumably available)? Your own work has shown that institution types plays a role which could be contributing to these results.

See below.

What other confounding variables could be at play here, what is available as part of the data and what happens if you do/don't account for them?

A number of variables included in our data have been shown to correlate with faculty attrition, including PhD prestige, current institution prestige, PhD country, and whether or not an individual is a “self-hire,” i.e., trained and hired at the same institution (Wapman et. al., Nature, 2022). Additional factors that faculty self-report as reasons for leaving academia include issues of work-life balance, workplace climate, and professional reasons, and in some cases to varying degrees between men and women faculty (Spoon et. al., Sci. Adv., 2023).

Our counterfactual analysis aims to address a specific question: how would women’s representation among faculty be different today if men and women were subjected to the same attrition patterns over the past decade? To answer this question, it is important to account for faculty career age, which we accept as a variable that will always correlate strongly with faculty attrition rates, as long as the tenure filter remains in place and faculty continue to naturally progress towards retirement age. On the other hand, it is less clear why PhD country, self-hire status, or any of the other mentioned variables should necessarily correlate with attrition rates and with gendered differences in attrition rates more specifically. While some or all of these variables may underlie the causal roots of gendered attrition rates, our analysis does not seek to answer causal questions about why faculty leave their jobs (e.g., by testing the impact of accounting for these variables in simulations per the reviewers suggestion). This is because we do not believe the data used in this analysis is sufficient to answer such questions, lacking comprehensive data on faculty stress (Spoon et. al., Sci. Adv., 2023), parenthood status, etc.

What career age range did the model use?

The career age range observed in model outcomes are a function of the empirically derived attrition rates for faculty across academic fields. The highest career age observed in the AARC data was 80, and the faculty career ages that result from our model simulations and projections do not exceed 80.

We have also added the distribution of faculty across career ages for the projection scenario model outputs in the supplemental materials Fig. S3 (see response to your later comment regarding career age for further details). Looking at these distributions, it is observed that very few faculty have career age > 60, both in observation and in our simulations.

What was the initial condition for the model?

Empirical 2011 Faculty rosters are used as the initial conditions for the counterfactual analysis, and 2020 faculty rosters are these as the initial conditions for the projections analysis. This information has been added to the descriptions of methods in the main text.

Starting the model in 2011 how well does it fit the available data up to 2020?

Thank you for this suggestion. We ran this analysis for each field starting in 2011, and found that model outcomes were statistically indistinguishable from the observed 2020 faculty gender compositions for all 111 academic fields. This finding is not surprising, because the model is fit to the observed data, but it serves to validate the methods that we used to extract the model's parameters. We have added these results to the supplement (Fig. S2).

What are the sensitivity analysis results for the model? If you have made different fitting decisions how much would the results change? All this applied to both the hiring and attrition parameters estimates.

We model attrition and hiring using logistic regression, with career age included as an exogenous variable up to its fifth power. A natural question follows: what if we used a model with career age only to its first or third power? Or to higher powers? We performed this sensitivity analysis, and added three new figures to the supplement to present these findings:

First, we show the observed attrition probabilities at each career age, and four model fits to attrition data (Supplementary Figs S4 and S5). The first model includes career age only to its first power, and this model clearly does not capture the full career age / attrition correlation structure. The second model includes career age to its third power, which does a better job of fitting to the observed patterns. The third model includes career age up to its fifth power, which appears to very modestly improve upon the former model. The fourth model includes career age up to its seventh power, and the patterns captured by this model are largely the same as the 5th-power model up to career age 50, beyond which there are some notable differences in the inferred attrition probabilities. These differences would have relatively little impact on model outcomes because the vast majority of faculty have a career age below 50.

Second, we show the observed probability that hires are women, conditional on the career age of the hire. Once again, we fit four models to the data, and find that career age should be included at least up to its fifth order in order to capture the correlation structures between career age and the gender of new hires. However, limited differences result from including career age up to the 7th degree in the model (relative to the 5th degree).

As a final sensitivity analysis, we reproduce Fig. 2, but rather than including career age as an exogenous variable up to its fifth power in our models for hiring and attrition, we include career age up to its third power. Findings under this parameterization are qualitatively very similar to those presented in Fig. 2, indicating that the results are robust to modest changes to model parameterization (shown in supplement Fig. S6).

Far more detail in this and some interim results from each stage of the analysis would make the paper far more convincing. It currently has an air of "black box" too much of the analysis which would easily allow an unconvinced reader to discard the results.

We have added more detailed descriptions of the methods to the main text. We hope that the changes made will address these concerns.

Section C: You use the Leslie model to predict the future population. As the model is linear the population will either grow exponentially (most likely) or dwindle to zero. You mention you dealt with this by scaling the average value of H to keep the population at 2020 levels? This would change the ratio of hiring to attrition. How did this affect the timescale of the results. If a field had very minimal attrition (and hence grew massively over the time period of the dataset) the hiring rate would have to be very small too so there would be very little change in the gender balance. Did you consider running the model to steady state instead?

We chose the 40 year window (2020-2060) for this projection analysis because 40 years is roughly the timespan of a full-length faculty career. In other words, it will take around 40 years for most of the pre-existing faculty from 2020 to retire, such that the new, simulated faculty will have almost entirely replaced all former faculty by 2060.

For three out of five of our projection scenarios (OA, GNA, OA+ER), the point at which observed faculty are replaced by simulated faculty represents steady state. One way to check this intuition is to observe the asymptotic behavior of the trajectories in Fig. 3B; the slopes for these 3 scenarios nearly level out within 40 years.

The other two scenarios (OA + IR, GNA+IR) represent situations where women’s representation among new hires is increasing each year. These scenarios will not reach steady state until women represent 100% of faculty. Accordingly, the steady state outcomes for these scenarios would yield uninteresting results; instead, we argue that it is the relative timescales that are interesting.

What did you do to check that your predictions at least felt realistic under the fitted parameters? (see above for presenting the goodness of fit over the 10 years of the data).

We ran the analysis suggested in a prior comment (Starting the model in 2011 how well does it fit the available data up to 2020?) and found that model outcomes were statistically indistinguishable from the observed 2020 faculty gender compositions for all 111 academic fields, plus the “All STEM” and “All non-STEM” aggregations.

You only present the final proportion of women for each scenario. As mentioned earlier, models of this type have a tendency to lead to strange population distributions with wild age predictions and huge (or zero populations). Presenting more results here would assuage any worries the reader had about these problems. What is the predicted age distribution of men and women in the long term scenarios? Would a different method of keeping the total population in check have yielded different results? Interim results, especially from a model as complex as this one, rather than just presenting a final single number answer are a convincing validation that your model is a good one! Again, presenting this result will go a long way to convincing readers that your results are sound and rigorous.

Thank you for this suggestion. We now include a figure that presents faculty age distributions for each projection scenario at 2060 against the observed faculty age distribution in 2020 (pictured below, and as Fig. S3 in the supplementary materials). We find that the projected age distributions are very similar to the observed distributions for natural sciences (shown) and for the additional academic domains. We hope this additional validation will inspire confidence in our model of faculty hiring and attrition for the reviewer, and for future readers.

In Fig S3, line widths for the simulated scenarios span the central 95% of simulations.

Other people have reached almost identical conclusions (albeit it with smaller data sets) that hiring is more important than attrition. It would be good to compare your conclusions with their work in the Discussion.

We have revised the main text to cite the listed examples of similar studies. We thank the reviewer for bringing these relevant works to our attention.

General comments:

What thoughts have you given to non-binary individuals?

Be careful how you use the term "gender diversity"! In many countries "Gender diverse" is a term used in data collection for non-binary individuals, i.e. Male, female, gender diverse. The phrase "hiring more gender diverse faculty" can be read in different ways! If you are only considering men and women then gender balance may be a better framework to use.

We have added language to the main text which explicitly acknowledges that our analysis focuses on men and women due to limitations in our name-based gender tool, which only assigns binary (woman/man) labels to faculty. We point out that this is a compromise due to the technical limitations of name-based gender methodologies and is not intended to reinforce a gender binary.

We have also taken additional care with referring to “gender diversity,” per reviewer 1’s point in their public review.

Reviewer #2 (Recommendations For The Authors):

Data availability: I did not see an indication that the dataset used here is publicly available, either in its raw format or as a summary dataset. Perhaps this is due to the sensitive nature of the data, but regardless of the underlying reason, the authors should include a note on data availability in the paper.

The dataset used for these analyses were obtained under a data use agreement with the Academic Analytics Research Center (AARC). While these data are not publicly available, researchers may apply for data access here: https://aarcresearch.com/access-our-data.

We also added a table to the supplemental materials (Tab. S3) that reports the estimated number of men and women in each of the 111 fields.

Additionally, a variety of summary statistics based on this dataset are available online, here: https://github.com/LarremoreLab/us-faculty-hiring-networks/tree/main

Gender classification: Was an existing package used to classify gender from names in the dataset, or did the authors develop custom code to do so? Either way, this code should be cited. I would also be curious to know what the error rate of these classifications are, and suggest that additional information on potential biases that might result from automated classifications be included in the discussion, under the section describing data limitations. The reliability of name-based gender classification is particularly of interest, as external gender classifications such as those applied on the basis of an individual's name - may not reflect the gender with which an individual self-identifies. In other words, while for many people their names may reflect their true genders, for others those names may only reflect their gender assigned at birth and not their self-perceived or lived gender identity. Nonbinary faculty are in particular invisibilized here (and through any analysis that assigns binary gender on the basis of name). While these considerations do not detract from the main focus of the study - which was to utilize an existing dataset classified only on the basis of binary gender to assess trends for women faculty-these limitations should be addressed as they provide additional context for the interpretation of the results and suggest avenues for future research.

We use a free, open-source, and open-data python package called nomquamgender (Van Buskirk et al, 2023) to estimate the strengths of (culturally constructed) name-gender associations. For sufficiently strong associations with a binary gender, we apply those labels to the names in our data. We have updated the main text to make this approach more apparent.

We have also added language to the main text which explicitly acknowledges that our approach only assigns binary (woman/man) labels to faculty. We point out that this is a compromise due to the technical limitations of name-based gender methodologies and is not intended to reinforce a gender binary.

As we mentioned in response to the public review, we use a free and open source python package called nomquamgender to estimate the strengths of name-gender associations, and we apply gender labels to the names with sufficiently strong associations with a binary gender. This package is based on a paper by Van Buskirk et. al. 2023, “An open-source cultural consensus approach to name-based gender classification,” which documents error rates and potential biases.

We have also added language to the main text which explicitly acknowledges that our approach only assigns binary (woman/man) labels to faculty. We point out that this is a compromise due to the technical limitations of name-based gender methodologies and is not intended to reinforce a gender binary.

Page 1: The sentence beginning "A trend towards greater women's representation could be caused..." is missing a conjunction. It should likely read: "A trend towards greater women's representation could be caused entirely by attrition, e.g., if relatively more men than women leave a field, OR entirely by hiring..."

We have edited the paragraph to remove the sentence in question.

Pages 1-2: The sentence beginning "Although both types of strategy..." and ending with "may ultimately achieve gender parity" is a bit of a run-on; perhaps it would be best to split this into multiple sentences for ease of reading.

We have revised this run-on sentence.

Page 2: See comments in the public review about a methods section, the addition of which may help to improve clarity for the readers. Within the existing descriptions of what I consider to be methods (i.e., the first three paragraphs currently under "results"), some minor corrections could be added here. First, consider citing the source of the dataset in the line where it is first described (in the sentence "For these analyses, we exploit a census-level dataset of employment and education records for tenured and tenure-track faculty in 12,112 PhD-granting departments in the United States from 2011-2020.") It also may be helpful to include context here (or above, in the discussion about institutional analyses) about how "departments" can be interpreted. For example, how many institutions are represented across these departments? More information on how the authors eliminated the gendered aspect of patterns in their counterfactual model would be helpful as well; this is currently hinted at on page 4, but could instead be included in the methods section with a call-out to the relevant supplemental information section (S2B).

We have added a citation to Academic Analytics Research Center’s (AARC) list of available data elements to the data’s introduction sentence. We hope this will allow readers to familiarize themselves with the data used in our analysis.

Faculty department membership was determined by AARC based on online faculty rosters. 392 institutions are represented across the 12,112 departments present in our dataset. We have updated the main text to include this information.

Finally, we have added a methods section to the main text, which includes information on how the gendered aspect of attrition patterns were eliminated in the counterfactual model.

Page 2: Perhaps some indication of how many transitions from an out-of-sample institution might be helpful to readers hoping to understand "edge cases."

In our analysis, we consider all transitions from out-of-sample institutions to in-sample institutions as hires, and all transitions away from in-sample institutions–whether it be to an out of sample institution, or out of academia entirely–as attritions. We choose to restrict our analysis of hiring and attrition to PhD granting institutions in the U.S. in this way because our data do not support an analysis of other, out-of-sample institutions.

I also would have liked additional information on how many faculty switched institutions but remained "in-sample and in the same field" - and the gender breakdowns of these institutional changes, as this might be an interesting future direction for studies of gender parity. (For example, readers may be spurred to ask: if the majority of those who move institutions are women, what are the implications for tenure and promotion for these individuals?)

While these mid-career moves are not counted as attritions in the present analysis, a study of faculty who switch institutions but remain (in-sample) as faculty could shed light on issues of gendered faculty retention at the level of institutions. We share the reviewer’s interest in a more in depth study of mid-career moves and how these moves impact faculty careers, and we now discuss the potential value of such a study towards the end of the paper. In fact, this subject is the topic of a current investigation by the authors!

Page 3: I was confused by the statement that "of the three types of stable points, only the first point represents an equitable steady-state, in which men and women faculty have equal average career lengths and are hired in unchanging proportions." Here, for example, computer science appears to be close to the origin on Figure 1, suggesting that hiring has occurred in "unchanging proportions" over the study interval. However, upon analysis of Table S2, it appears that changes in hiring in Computer Science (+2.26 pp) are relatively large over the study interval compared to other fields. Perhaps I am reading too literally into the phrase that "men and women faculty are hired in unchanging proportions" - but I (and likely others) would benefit from additional clarity here.

We had created an arrow along with the computer science label in Fig. 1, but it was difficult to see, which is likely the source of this confusion. This was our fault, and we have moved the “Comp. Sci.” label and its corresponding arrow to be more visible in Figure 1.

Changes in women’s representation in Computer Science due to hiring over 2011 - 2020 was +2.26 pp as the reviewer points out, but, consulting Fig. 1 and the corresponding table in the supplement, we observe that this is a relatively small amount of change compared to most fields.

Page 3: If possible it may be helpful to cite a study (or multiple) that shows that "changes in women's representation across academic fields have been mostly positive." What does "positive" mean here, particularly when the changes the authors observe are modest? Perhaps by "positive" you mean "perceived as positive"?

We used the term positive in the mathematical sense, to mean greater than zero. We have reworded the sentence to read “women's representation across academic fields has been mostly increasing…” We hope this change clarifies our meaning to future readers.

Page 3: The sentence that ends with "even though men are more likely to be at or near retirement age than women faculty due to historical demographic trends" may benefit from a citation (of either Figure S3 or another source).

We now cite the corresponding figure in this sentence.

Page 4: The two sentences that begin with "The empirical probability that a person leaves their academic career" would benefit from an added citation.

We have added a citation to the sentences.

Figure 3: Which 10 academic domains are represented in Panel 3B? The colors in appear to correspond to the legend in Panel 3A, but no indication of which fields are represented is provided. If possible, please do so - it would be interesting and informative to be able to make these comparisons.

This was not clear in the initial version of Fig. 3B, so we now label each domain. For reference, the domains represented in 3B are (from top to bottom):

● Health

● Education

● Journalism, Media, Communication

● Humanities

● Social Sciences

● Public Administration and Policy

● Medicine

● Business

● Natural Sciences

● Mathematics and Computing

● Engineering

Page 6: Consider citing relevant figure(s) earlier up in paragraph 2 of the discussion. For example, the first sentence could refer to Figure 1 (rather than waiting until the bottom of the paragraph to cite it).

Thank you for this suggestion, we now cite Fig. 1 earlier in this discussion paragraph.

Page 10: A minor comment on the fraction of women faculty in any given year-the authors assume that the proportion of women in a field can be calculated from knowing the number of women in a field and the number of men. This is, again, true if assuming binary genders but not true if additional gender diversity is included. It is likely that the number of nonbinary faculty is quite low, and as such would not cause a large change in the overall proportions calculated here, but additional context within the first paragraph of S1 might be helpful for readers.

We have added additional context in the first paragraph of S1, explaining that an additional term could be added to the equation to account for nonbinary faculty representation if our data included nonbinary gender annotations. Thank you for making this point.

Page 10: Please include a range of values for the residual terms of the decomposition of hiring and attrition in the sentence that reads "In Figure S1 we show that the residual terms are small, and thus the decomposition is a good approximation of the total change in women's representation."

These residual terms range from -0.51pp to 1.14pp (median = 0.2pp). We have added this information to the sentence in question.

Page 12: It may be helpful to readers to include a description of the information contained in Table S2 in the supplemental text under section S3.

We refer to table S2 twice in the main text (once in the observational findings, and once for the counterfactual analysis), and the contents of table S2 are described thoroughly in the table caption.

Reviewer #3 (Recommendations For The Authors):

(1) There is a potential limitation in the generalizability of the findings, as the study focuses exclusively on US academia. Including international perspectives could have provided a more global understanding of the issues at hand.

The U.S. focus of this study limits the generalizability of our findings, as non-U.S. other faculty may exhibit differences in hiring patterns, retention patterns, and current demographic representations. We have added a discussion of this limitation to the manuscript. Unfortunately, our data do not support international analyses of hiring and attrition.

(2) I am not sure that everyone who disappeared from the AARC dataset could be count as "attrition" from academia. Indeed, some who disappeared might have completely left academia once they disappeared from the AARC dataset. Yet, there's also the possibility that some professors left for academic positions in countries outside of the US, or US institutions that are not included in the AARC dataset. These individuals didn't leave academia. Furthermore, it is also possible that these scholars who moved to an institution outside of US or not indexed by AARC are gender specific. Therefore, analyses that this study conducts should find a way to test whether the assumption that anyone who disappeared from AARC is indeed valid. If not, how will this potentially challenge the current conclusions?

The reviewer makes an important point: faculty who move to faculty positions in other countries and faculty who move to non-PhD granting institutions, or to institutions that are otherwise not included in the AARC data are all counted as attritions in our analysis. We intentionally define hiring and attrition broadly to include all cases in which faculty join or leave a field or domain within our dataset.

The types of transitions that faculty make out of the tenure track system at PhD granting institutions in the U.S. may correlate with faculty attributes, like gender. For example, women or men may be more likely to transition to tenure track positions at non-U.S. institutions. Nevertheless, these types of career transition represent an attrition for the system of study, and a hire for another system. Following this same logic, faculty who transition from one field to another field in our analysis are treated as an attrition from the first field and a hire into the new field.

By focusing on “all-cause” attrition in this way, we are able to make robust insights for the specific systems we consider (e.g.,, STEM and non-STEM faculty at U.S. PhD granting institutions), without being roadblocked by the task of annotating faculty departures and arbitrating which should constitute “valid” attritions.

(3) It would be very interesting to know how much of the attribution was due to tenure failure. Previous studies have suggested that women are less likely to be granted tenure, which makes me wonder about the role that tenure plays in the gendered patterns of attrition in academia.

We note that faculty attrition rates start low and then reach a peak around 5-7 years after earning PhD, and then decline until around 15-20 years post-PhD, after which, attrition rates increase as faculty approach retirement. The first local maximum appears to coincide roughly with the tenure clock timing, but we can only speculate that these attritions are tenure related. Our dataset is unfortunately not equipped to determine the causal mechanisms driving attrition.

We reproduce the attrition risk curve in the supplementary materials, Fig. S4:

(4) The dataset used doesn't fully capture the complexities of academic environments, particularly smaller or less research-intensive institutions (regional universities, historically black colleges and universities, and minority-serving institutions). This could be potentially added to the manuscript for discussions.

We have added this point to the description of this study’s limitations in the discussion.

are reported to you by the compiler if your Java code is not correctly written. Examples of syntax errors are a semicolon ;

Click on the run button button below to have the computer execute the main method in the following class. Then, change the code to print your name. Be sure to keep the starting " and ending ". Click on the run button button to run the modified code.

If you forget to capitalize the special words, it will make errors to the whole code.

Résumé de la vidéo [00:00:05][^1^][1] - [00:23:27][^2^][2] : Cette vidéo présente une conférence de Thomas Rohmer sur les mutations de la famille en France et leur impact sociologique. Il aborde les changements démographiques depuis les années 60, l'évolution des structures familiales, et les défis contemporains liés à la parentalité et à l'individualisme.

Points forts : + [00:00:05][^3^][3] Introduction à la sociologie de la famille * Présentation de l'approche sociologique * Distinction entre l'adolescence et la famille * Importance des grands-parents dans la vie des adolescents + [00:00:47][^4^][4] Mutations de la famille depuis les années 60 * Augmentation des unions libres et divorces * Croissance des familles monoparentales et recomposées * Interrogations sur le mariage de même sexe et la parentalité + [00:02:00][^5^][5] Opposition entre valeurs familiales et individuelles * Débat entre conservateurs et progressistes * Importance de ne pas choisir entre l'individu et la famille * La famille comme institution et objet idéologique + [00:05:00][^6^][6] La famille comme institution essentielle * La famille n'est pas une simple collection d'individus * Rôle des systèmes symboliques de parenté * Importance des valeurs communes et des règles juridiques + [00:09:00][^7^][7] Changements démographiques et leurs conséquences * Allongement de l'espérance de vie * Âge au premier mariage et à la naissance du premier enfant * Impact sur la procréation médicalement assistée + [00:14:00][^8^][8] Remise en cause du modèle de la famille conjugale * Évolution depuis le code Napoléon de 1804 * Inégalités de genre et autorité parentale * Implosion du modèle familial traditionnel depuis les années 60

Résumé de la vidéo [00:23:29][^1^][1] - [00:47:50][^2^][2]:

La vidéo traite de l'évolution des relations familiales et de la filiation en France, soulignant l'importance croissante de l'enfant dans la structure familiale et les changements dans la perception de la parentalité et du mariage.

Points forts: + [00:23:29][^3^][3] L'amour inconditionnel et la reconnaissance de l'enfant * L'enfance est valorisée comme le fondement de la personnalité adulte * Les besoins spécifiques des enfants et adolescents sont de plus en plus reconnus * La préparation de l'avenir de l'enfant devient centrale dans la famille + [00:26:58][^4^][4] Les valeurs familiales et la diversité des relations * La famille reste une valeur clé malgré les perceptions de crise * Les modes de vie et valeurs des mariés et concubins deviennent similaires * Les familles sont confrontées à des ruptures et recompositions fréquentes + [00:33:55][^5^][5] Le temps et les inégalités sociales dans les familles * Le rapport au temps est un facteur sous-jacent aux difficultés familiales * La capacité à lier le présent au passé et au futur est cruciale * Les solidarités familiales peuvent accentuer les inégalités sociales + [00:41:29][^6^][6] La recomposition du permis et de l'interdit en matière sexuelle * Le consentement devient le critère principal de la sexualité autorisée * La question de la violence sexuelle est liée aux transformations de la parenté * Il est nécessaire de repenser l'ordre juridique et moral autour de la sexualité

Résumé de la vidéo [00:47:51][^1^][1] - [00:55:29][^2^][2]:

La vidéo présente une conférence sur la signification et l'institution de la signification dans le langage, en mettant l'accent sur la relation entre les parents, les enfants et la loi. Elle aborde également les défis juridiques liés à la présomption d'innocence et la véracité des victimes dans les affaires de violences sexuelles.

Points forts: + [00:47:51][^3^][3] La signification dans le langage * Les enfants voient leurs parents comme les maîtres de la signification * Importance de distinguer entre ce que dit le parent et la loi + [00:49:14][^4^][4] Défis juridiques et consentement * Discussion sur la création d'un nouvel ordre normatif basé sur le consentement * Préoccupations concernant la présomption d'innocence et la charge de la preuve + [00:52:01][^5^][5] Présomption de véracité * Nécessité d'accorder un crédit de véracité à la parole des victimes * Recherche de solutions pour ne pas condamner les victimes au silence + [00:54:39][^6^][6] Recompositions du permis et de l'interdit * Discussion sur les recompositions du permis et de l'interdit dans la société * L'importance de maintenir les garanties démocratiques tout en cherchant des solutions

Conditional code

conditional code

Add another line/bar below the sticky navigation menu on yellow background that features a key to the GF, Spice level (Mild, spicy & hot), Vegetarian and Vegan. Icons to be provided as SVG.

Résumé de la vidéo [00:00:27][^1^][1] - [00:27:07][^2^][2]:

Cette vidéo présente une conférence de Pascal Bressoux sur l'efficacité des pratiques pédagogiques dans l'apprentissage des élèves. Il discute de la causalité en éducation, des spécificités du monde éducatif, et présente deux expérimentations, "Parler" et "ExpiRe", qui illustrent ses principes. Bressoux souligne l'importance de l'enseignement explicite et systématique du langage pour réduire les inégalités éducatives.

Points forts: + [00:00:27][^3^][3] Introduction et objectifs * Présentation des expérimentations "Parler" et "ExpiRe" * Importance de prouver l'efficacité en éducation * Méthodologie sous-jacente aux expérimentations + [00:02:05][^4^][4] Causalité en éducation * Définition de la causalité et son application en éducation * Analyse de la spécificité et des facteurs contribuant à l'éducation * Discussion sur la généralisation des résultats éducatifs + [00:11:51][^5^][5] Expérimentation "Parler" * Objectifs de l'expérimentation pour améliorer le langage des élèves défavorisés * Méthodes pédagogiques utilisées et leur mise en œuvre * Résultats et impact sur les acquisitions langagières + [00:20:49][^6^][6] Techniques d'enseignement spécifiques * Enseignement explicite du code alphabétique et de la conscience phonique * Exercices de compréhension et de fluence de lecture * Utilisation d'outils d'évaluation pour suivre les progrès des élèves

Résumé de la vidéo [00:27:10][^1^][1] - [00:55:36][^2^][2] : Cette vidéo présente les résultats d'une étude sur l'efficacité des pratiques pédagogiques sur l'apprentissage des élèves. Elle aborde les méthodes d'évaluation, les comparaisons entre groupes expérimentaux et témoins, et l'impact des dispositifs numériques sur l'enseignement des mathématiques.

Points forts : + [00:27:10][^3^][3] Évaluation des pratiques pédagogiques * Étude sur l'efficacité des pratiques éducatives * Suivi régulier par des conseillers pédagogiques * Tests individuels pour chaque élève + [00:30:00][^4^][4] Résultats du groupe expérimental * Comparaison avec le groupe témoin et le niveau national * Amélioration significative dans la compréhension de l'écrit * Réduction du pourcentage d'élèves en grande difficulté + [00:40:21][^5^][5] Expérience avec le logiciel Scratch * Utilisation de Scratch pour enseigner les mathématiques * Étude randomisée avec une centaine de classes * Impact sur la division euclidienne, la décomposition additive et les fractions + [00:50:55][^6^][6] Réception par les enseignants et les élèves * Les enseignants trouvent le dispositif acceptable et motivant * Les élèves sont plus motivés avec l'ordinateur qu'avec les séances traditionnelles * Pas de réduction des inégalités scolaires observée

See also Raskin on: * The Woes of IDEs * Comments are more important than code

Implicit step zero

Code switching is the practice of alternating between different languages or dialects depending on the social context.

The Hippocratic Oath is an ethical code attributed to the ancient Greek physician Hippocrates. It is still used today by medical professionals to swear to practice medicine ethically and honestly.

VS Codeの統合された

(原文には in VS Code って書いてあるので

いくつかの理由でVS Codeをおすすめします。

「for a number of reasons」はいくつもじゃなくて「いくつか」だと思う

WIndows版VS Code（+Japanese拡張）では「ユーザー設定」になってました

確認

直前のカラーテーマの説明と書きっぷりが違うのが気になったけど原文通りなのでママですかね

index is a tuple, but should be PandasIndex. You've copied too much code over :) Write it from scratch.

Author response:

The following is the authors’ response to the previous reviews.

eLife assessment

The authors use point light displays to measure biological motion (BM) perception in children (mean = 9 years) with and without ADHD, and relate it to IQ, social responsiveness scale (SRS) scores and age. They report that children with ADHD were worse at all three BM tasks, but that those tasks loading more heavily on local processing relate to social interaction skills and those loading on global processing relate to age. There are still some elements of the results that are unclear, but nevertheless, the important and solid findings extend our limited knowledge of BM perception in ADHD, as well as biological motion processing mechanisms in general.

We thank the editors and reviewers for their valuable feedback and constructive comments. In the revised manuscript, we have incorporated all statistics for the models and also provided detailed analytical evidence about the distinct contributions of local and global BM processing. We hope these clarifications could enhance the robustness of our conclusions.

Public Reviews:

Reviewer #2 (Public Review):

Summary:

Tian et al. aimed to assess differences in biological motion (BM) perception between children with and without ADHD, as well as relationships to indices of social functioning and possible predictors of BM perception (including demographics, reasoning ability and inattention). In their study, children with ADHD showed poorer performance relative to typically developing children in three tasks measuring local, global, and general BM perception. The authors further observed that across the whole sample, performance in all three BM tasks was negatively correlated with scores on the social responsiveness scale (SRS), whereas within groups a significant relationship to SRS scores was only observed in the ADHD group and for the local BM task. Local and global BM perception showed a dissociation in that global BM processing was predicted by age, while local BM perception was not. Finally, general (local & global combined) BM processing was predicted by age and global BM processing, while reasoning ability mediated the effect of inattention on BM processing.

Strengths:

Overall, the manuscript is presented in a clear fashion and methods and materials are presented with sufficient detail so the study could be reproduced by independent researchers. The study uses an innovative, albeit not novel, paradigm to investigate two independent processes underlying BM perception. The results are novel and have the potential to have wide-reaching impact on multiple fields.

We appreciate the your positive feedback very much.

Weaknesses:

The manuscript has improved in clarity and conceptual and methodological considerations in response to the last review. However, the reported results still provide incomplete support for the claims the authors make in the paper.

In relation to other reviewers' earlier comments, the model notation used is still not consistent and model results are reported incompletely, which make it difficult to gain a full picture of the data and how they support the authors' secondary claims. For instance, across the models in the supplementary materials, ß coefficients are only reported selectively which makes it difficult to assess the model as a whole. Furthermore, different terms (task 1, task 2 vs. BM-Local, BM-global) are used to refer to the same levels of a variable, and it is unclear which levels of a dummy variable correspond to which task, making it overall very difficult to comprehend the modelling procedure.

Thanks for pointing out these issues. In the revised version, we have unified the terminology by consistently referring to task types as BM-Local, BM-Global, BM-General. Additionally, we have provided clarification on the interpretation of dummy variables in relation to model construction. Furthermore, we corrected the model results and included all statistics in Table S1, S2, and S3. For more detailed information, please refer to the response to your Recommendations for the authors.

Reviewer #3 (Public Review):

The authors presented point light displays of human walkers to children (mean = 9 years) with and without ADHD to compare their biological motion perception abilities, and relate them to IQ, social responsiveness scale (SRS) scores and age. They report that children with ADHD were worse at all three biological motion tasks, but that those loading more heavily on local processing related to social interaction skills and global processing to age. The valuable and solid findings are informative for understanding this complex condition, as well as biological motion processing mechanisms in general. However, the correlations present a pattern that needs further examination in future studies because many of the differences between correlations are not significant.

Strengths:

The authors present differences between ADHD and TD children in biological motion processing, and this question has not received as much attention as equivalent processing capabilities in autism. They use a task that appears well controlled. They raise some interesting mechanistic possibilities for differences in local and global motion processing, which are distinctions worth exploring. The group differences will therefore be of interest to those studying ADHD, as well as other developmental conditions, and those examining biological motion processing mechanisms in general.

Thanks for this positive assessment of our work.

Weaknesses:

The data are not strong enough to support claims about differences between global and lobal processing wrt social communication skills and age. The mechanistic possibilities for why these abilities may dissociate in such a way are interesting, but the crucial tests of differences between correlations do not present a clear picture. Further empirical work would be needed to test this further. Specifics:

The authors state frequently that it was the local BM task that related to social communication skills (SRS) and not the global tasks. However, the results section shows a correlation between SRS and all three tasks. The only difference is that when looking specifically within the ADHD group, the correlation is only significant for the local task. The supplementary materials demonstrate that tests of differences between correlations present an incomplete picture. Currently they have small samples for correlations, so this is unsurprising.

We apologize for not clarifying these points earlier. We did identify correlations between performance on all BM tasks and SRS scores. However, it is noteworthy that this finding is not unexpected, given the significant distinctions in SRS scores between TD and ADHD children, alongside their marked differences in all BM tasks. Correlation analyses involving data from both groups may reflect group differences. To elucidate the relationship between social ability impairment and diminished BM processing in children with ADHD, we conducted additional subgroup analyses and found correlations only in the BM-local task. To further support the specificity of this correlation, we compared the differences in coefficients. We revised our modelling procedure for testing differences between correlations in supplementary materials and presented all models statistics in Table S2, S3. Discrepancies in these coefficients, which exclude the influence of differences between groups, suggest that social factors specifically influence the performance of the BM-Local task in children with ADHD. We acknowledge that the analysis for differences between correlations is based on a relative small sample size and provided modest interpretation in discussion. Future studies will aim to increase the sample size to validate our findings.

Theoretical assumptions. The authors make some statements about local vs global biological motion processing that may have been made in previous studies, but would appear controversial and not definitive. E.g., that local BM processing does not improve with age and is uninfluenced by attention.

Thanks for your comment. To the best of our knowledge, there have been fewer developmental studies conducted on local BM processing compared to global BM processing. Our study is the first one to directly explore the relationship between local BM processing and age. Additionally, we used QbInattention to evaluate sustained attention function (considered as “top-down” attention) and examined its correlation with local BM processing. Some indirect evidence supported that the ability to process local BM cues remained stable and was unaffected by top-down attention. For example, local BM processing did not show a learning trend (Chang 2009) and was linked to the activation of subcortical regions (Hirai 2020). Research has demonstrated that local BM cues can convey information about walking direction without participants’ explicit attention or recognition (Chang 2009, Hirai 2011, Thompson 2007, Wang 2010), indicating the involvement of “bottom-up” processing (Hirai 2020, Troje 2023). Consistent with previous findings, we did not find significant correlation between local BM processing and age or QbInattention. We acknowledge that the statement such as “local BM processing does not improve with age and is uninfluenced by attention” should be approached with cautions. Therefore, we interpreted our results carefully:

“Once a living creature is detected, an agent (i.e., is it a human?) can be recognised by a coherent, articulated body structure that is perceptually organised based on its motions (i.e., local BM cues)71. This involves top-down processing and probably requires attention25,72, particularly in the presence of competing information26. Our findings are consistent with those of previous studies on the cortical processing of BM73, as we found that the severity of inattention in children with ADHD was negatively correlated with their performance in global BM processing, whereas this significant correlation was not found in local BM processing, which may involve bottom-up processing61,65 and might not need participants’ explicit attention21,23,74,75. However, further studies are needed to verify this hypothesis.” (lines 461-470)

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):

Supplementary materials: For all reported results, I suggest the authors use consistent model notation with complete reporting of all statistics in line with common conventions (ideally tables reporting beta values, error terms and confidence intervals for all model predictors, as well as R squared values). In particular the beta values for the reference category are needed to be able to fully interpret the beta values for the reported contrasts.

We appreciate the your suggestion. In the newly revised manuscript, we reported all statistics including beta values, error terms and confidence intervals for all model predictors, and R squared values. These detailed statistics can be found in Table S1, S2 and S3. We hope this additional information will offer readers a more comprehensive understanding of our study.

Please also address the following inconsistencies:

- At least when reporting the model results, the same term should be used when refering to task type (either task 1/2/3/ or local/global/general BM).

Thank the your for this feedback. We use the same term (BM-Local/Global/General) to refer to task type in the whole text.

- Second linear model in the Supplementary Materials: The authors state that the results suggest that the correlation between SRS and task 1 is greater than that between task 2 and SRS scores. First of all, to be able to support this claim the authors need to provide the coefficient for task 1 (which, if task 1 is the reference variable should be ß1). Second, as I currently understand the reported model results, the fact that ß4 (representing the difference in relationship to SRS scores between task 2 and task 1; the authors refer to ß3 here although I assume they mean ß4) is negative and shows a trend towards significance would actually mean the relationship between BM processing accuracy and SRS scores is more negative for task 2 relative to task 1 and not, as the authors state, that the correlation with SRS scores is greater for task 1. I realise this contradicts the individual r values and scatter plots and hope the authors can clarify the model results.

We thank you for pointing out these issues. For the second linear model (Model 4 in revised manuscript), we reported the coefficients for all predictors and model summaries including the coefficient for task 1 (ß1). In addition, we have made correction to the model results. The values of ß4 (representing the difference in relationship to SRS scores between BM-Global and BM-Local) and ß5 (representing the difference in relationship to SRS scores between BM-General and BM-Local) were positive and showed a trend towards significance, indicating that the correlations with SRS total score were more negative for BM-Local relative to BM-Global and BM-General:

“A general linear model was constructed (Table S2, Model 4): SRS = β0 + β1 * ACC + β2 * D1 + β3 * D2 + β4 * (ACC * D1) + β5 * (ACC * D2). If the effect of the interaction term (i.e., β4 or β5 ) is statistically significant, it indicates a difference in correlations with SRS total score between BM-Local and BM-Global (or BM-General). The results suggested trends where the correlations with SRS total score were more negative for BM-Local relative to BM-Global (standardized β4 \= 0.580 p = 0.074) and BM-General (standardized β5 = 0.550 p = 0.073).” (lines SI 36-42)

- Third linear model in the Supplementary Materials: In the dummy variable representing task, when local BM is the reference level, which task is represented by d1 and d2, respectively? If I understand the authors' procedure correctly, d1 should represent the difference between local and global BM and d2 the difference between local and general BM. If this is true, ß4 should code for the difference between local and global BM and not, as stated by the authors, for the difference between local and general BM. Also, what is d3?

Thank you for pointing out this issue. We corrected and clarified the results of third model (Model 5 in revised manuscript) in the revised version and pointed out what is represented by d1 (D1) and d2 (D2), respectively:

“We recoded task types into two dummy variables, D1 and D2, using BM-Local as a reference. The coefficient of D1 represents the difference in relationship to age between BM-Local and BM-Global, and the coefficient of D2 represents the difference in relationship to age between BM-Local and BM-General. The following model was created for each group (Table S3, Model 5-6): ACC = β0 + β1 * age + β2 * D1 + β3 * D2 + β4 * (age * D1) + β5 * (age * D2). If the effect of the interaction term (i.e., β4 or β5) is statistically significant, it indicates a difference in the effect of age on ACC between BM-Local and BM-Global (or BM-General). In the ADHD group, we observed a significant difference in the effect of age on ACC between BM-Local and BM-General (standardized β5 \= 0.462, p < 0.001) and marginally significant differences in the effect of age on ACC between BM-Local and BM-Global (standardized β4 \= 0.228, p = 0.073).” (lines SI 47-57)

Ne fonctionne pas...

Attention la 'class' des titres à changer et est devenu : govuk-link

DITTO faces limitations such as biases in GPT evaluations, slower training speed compared to other methods, and unclear learning processes that may lead to forgetting previous knowledge.

Reviewer #2 (Public Review):

Summary:

In this manuscript, the authors recorded activity in the posterior parietal cortex (PPC) of monkeys performing a perceptual decision-making task. The monkeys were first shown two choice dots of two different colors. Then, they saw a random dot motion stimulus. They had to learn to categorize the direction of motion as referring to either the right or left dot. However, the rule was based on the color of the dot and not its location. So, the red dot could either be to the right or left, but the rule itself remained the same. It is known from past work that PPC neurons would code the learned categorization. Here, the authors showed that the categorization signal depended on whether the executed saccade was in the same hemifield as the recorded PPC neuron or in the opposite one. That is, if a neuron categorized the two motion directions such that it responded stronger for one than the other, then this differential motion direction coding effect was amplified if the subsequent choice saccade was in the same hemifield. The authors then built a computational RNN to replicate the results and make further tests by simulated "lesions".

Strengths:

Linking the results to RNN simulations and simulated lesions.

Weaknesses:

Potential interpretational issues due to a lack of evidence on what happens at the time of the saccades.

I really don't like this == why not "predict"?

Il aurait été bon de faire la même précision qu'au chapitre précédent concernant l'installation de la bibliothèque pytest-mock.

Même en utilisant le code donné par la correction, je n'arrive pas à faire fonctionner le programme de tests. Pourriez-vous vérifier le bon fonctionnement ? Par ailleurs, la commande pytest tests.py ne fonctionne sur aucun des programmes de tests. J'utilise la commande python -m pytest tests.py et cela fonctionne sur tous les programmes (fonctionnels...).