Reviewer #1 (Public review):

Summary:

The authors integrate multiple large databases to test whether body sizes were positively associated with which species tolerate urban areas. In general, many plant families showed a positive association between body size and urban tolerance, whereas a smaller, though still non-trivial, percentage of animal families showed the same pattern. Notably, the authors are careful in the interpretation of their findings and provide helpful context for the ways that this analysis can be generative in shaping new hypotheses and theory around how urbanization influences biodiversity at large. They are careful to discuss how body size is an important trait, but the absence of a relationship between body size and urban tolerance in many families suggests a variety of other traits undergird urban success.

Strengths:

The authors aggregated a large dataset, but they also applied robust filters to ensure they had an adequate and representative number of detections for a given species, family, geography, etc. The authors also applied their analysis at multiple taxonomic scales (family and order), which allowed for a better interpretation of the patterns in the data and at what taxonomic scale body size might be important.

Weaknesses:

My main concern is that it is not fully clear how the measure of body size might influence the result. The authors were unable to obtain consistent measures of body size (mean, median, maximum, or sex variation). This, of course, could be very consequential as means and medians can differ quite a bit, and they certainly will differ substantially from a maximum. And of course, sex differences can be marked in multiple directions or absent altogether. The authors do note that they selected the measure that was most common in a family, but it was not clear whether species in that family that did not have that measure were removed or not. This could potentially shape the variability in the dataset and obscure true patterns. This may require additional clarity from the authors and is also a real constraint in compiling large data from disparate sources.

Reviewer #2 (Public review):

I have completed a thorough review of this paper, which seeks to use the large datasets of species occurrences available through GBIF to estimate variation in how large numbers of plant and animal species are associated with urbanization throughout the world, describing what they call the "species urbanness distribution" or SUD. They explore how these SUDs differ between regions and different taxonomic levels. They then calculate a measure of urban tolerance and seek to explore whether organism size predicts variation in tolerance among species and across regions.

The study is impressive in many respects. Over the course of several papers, Callaghan and coauthors have been leaders in using "big [biodiversity] data" to create metrics of how species' occurrence data are associated with urban environments, and in describing variation in urban tolerance among taxa and regions. This work has been creative, novel, and it has pushed the boundaries of understanding how urbanization affects a wide diversity of taxa. The current paper takes this to a new level by performing analyses on over 94000 observations from >30,000 species of plants and animals, across more than 370 plant and animal taxonomic families. All of these analyses were focused on answering two main questions:

(1) What is the shape of species' urban tolerance distributions within regional communities?

(2) Does body size consistently correlate with species' urban tolerance across taxonomic groups and biogeographic contexts?

Overall, I think the questions are interesting and important, the size and scope of the data and analyses are impressive, and this paper has a potentially large contribution to make in pushing forward urban macroecology specifically and urban ecology and evolution more generally.

Despite my enthusiasm for this paper and its potential impact, there are aspects that could be improved, and I believe the paper requires major revision.

Some of these revisions ideally involve being clearer about the methodology or arguments being made. In other cases, I think their metrics of urban tolerance are flawed and need to be rethought and recalculated, and some of the conclusions are inaccurate. I hope the authors will address these comments carefully and thoroughly. I recognize that there is no obligation for authors to make revisions. However, revising the paper along the lines of the comments made below would increase the impact of the paper and its clarity to a broad readership.

Major Comments:

(1) Subrealms

Where does the concept of "subrealms" come from? No citation is given, and it could be said that this sounds like an idea straight out of Middle Earth. How do subrealms relate to known bioclimatic designations like Koppen Climate classifications, which would arguably be more appropriate? Or are subrealms more socio-ecologically oriented? From what I can tell, each subrealm lumps together climatically diverse areas. It might be better and more tractable to break things in terms of continents, as the rationale for subrealms is unclear, and it makes the analyses and results more confusing. The authors rationalized the use of subrealms to account for potential intraspecific differences in species' response to urbanization, but that is never a core part of the questions or interpretation in the paper, and averaging across subrealms also accounts for intraspecific variation. Another issue with using the subrealm approach is that the authors only included a species if it had 100 observations in a given subrealm, leading to a focus on only the most common species, which may be biased in their SUD distribution. How many more species would be included if they did their analysis at the continental or global scale, and would this change the shape of SUDs?

(2) Methods - urban score

The authors describe their "urban score" as being calculated as "the mean of the distribution of VIIRS values as a relative species specific measure of a response to urban land cover."

I don't understand how this is a "relative species-specific measure". What is it relative to? Figures S4 and S5 show the mean distribution of VIIRS for various taxa, and this mean looks to be an absolute measure. Mean VIIRS for a given species would be fine and appropriate as an "urban score", but the authors then state in the next sentence: "this urban score represents the relative ranking of that species to other species in response to urban land cover".

That doesn't follow from the description of how this is calculated. Something is missing here. Please clarify and add an explicit equation for how the urban score is calculated because the text is unclear and confusing.

(3) Methods - urban tolerance

How the authors are defining and calculating tolerance is unclear, confusing, and flawed in my opinion.

Tolerance is a common concept in ecology, evolution, and physiology, typically defined as the ability for an organism to maintain some measure of performance (e.g., fitness, growth, physiological homeostasis) in the presence versus absence of some stressor. As one example, in the herbivory literature, tolerance is often measured as the absolute or relative difference in fitness of plants that are damaged versus undamaged (e.g., https://academic.oup.com/evolut/article/62/9/2429/6853425?login=true).

On line 309, after describing the calculation of urban scores across subrealms, they write: "Therefore, a species could be represented across multiple subrealms with differing measures of urban tolerance (Fig. S4). Importantly, this continuous metric of urban tolerance is a relative measure of a species' preference, or affinity, to urban areas: it should be interpreted only within each subrealm".

This is problematic on several fronts. First, the authors never define what they mean by the term "tolerance". Second, they refer to urban tolerance throughout the paper, but don't describe the calculation until lines 315-319, where they write (text in [ ] is from the reviewer):

"Within each subrealm, we further accounted for the potential of different levels of urbanization by scaling each species' urban score by subtracting the mean VIIRS of all observations in the subrealm (this value is hereafter referred to as urban tolerance). This 'urban tolerance' (Fig. S5) value can be negative - when species under-occupy urban areas [relative to the average across all species] suggesting they actively avoid them-or positive-when species over-occupy urban areas [relative to the average across all species] suggesting they prefer them (i.e., ranging from urban avoiders to urban exploiters, respectively).<br /> They are taking a relativized urban score and then subtracting the mean VIIRS of all observations across species in a subrealm. How exactly one interprets the magnitude isn't clear and they admit this metric is "not interpretative across subrealms".

This is not a true measure of tolerance, at least not in the conventional sense of how tolerance is typically defined. The problem is that a species distribution isn't being compared to some metric of urbanness, but instead it is relative to other species' urban scores, where species may, on average, be highly urban or highly nonurban in their distribution, and this may vary from subrealm to subrealm. A measure of urban tolerance should be independent of how other species are responding, and should be interpretable across subrealms, continents, and the globe.

I propose the authors use one of two metrics of urban tolerance:

(i) Absolute Urban Tolerance = Mean VIIRS of species_i - Mean VIIRS of city centers<br /> Here, the mean VIIRS of city centers could be taken from the center of multiple cities throughout a subrealm, across a continent, or across the world. Here, the units are in the original VIIRS units where 0 would correspond to species being centered on the most extreme urban habitats, and the most extreme negative values would correspond to species that occupy the most non-urban habitats (i.e., no artificial light at night). In essence, this measure of tolerance would quantify how far a species' distribution is shifted relative to the most highly urbanized habitat available.

(ii) % Urban Tolerance = (Mean VIIRS of species_i - Mean VIIRS of city centers)/MeanVIIRS of city centers * 100%<br /> This metric provides a % change in species mean VIIRS distribution relative to the most urban habitats. This value could theoretically be negative or positive, but will typically be negative, with -100% being completely non-urban, and 0% being completely urban tolerant.

Both of these metrics can be compared across the world, as it would provide either absolute (equation 1) or relative (equation 2) metrics of urban tolerance that are comparable and easily interpretable in any region.

In summary, the definition of tolerance should be clear, the metric should be a true measure of tolerance that is comparable across regions, and an equation should be given.

(4) Figure 1: The figure does not stand alone. For example, what is the hypothesis for thermophily or the temperature-size rule? The authors should expand the legend slightly to make the hypotheses being illustrated clearer.

(5) SUDs: I don't agree with the conclusion given on line 83 ("pattern was consistent across subrealms and several taxonomic levels") or in the legend of Figure 2 ("there were consistent patterns for kingdoms, classes, and orders, as shown by generally similar density histograms shapes for each of these").

The shapes of the curves are quite different, especially for the two Kingdoms and the different classes. I agree they are relatively consistent for the different taxonomic Orders of insects.

Reviewer #3 (Public review):

Summary:

This paper reports on an association between body size and the occurrence of species in cities, which is quantified using an 'urban score' that can be visualized as a 'Species Urbanness Distribution' for particular taxa. The authors use species records from the Global Biodiversity Information Facility (GBIF) and link the occurrence data to nighttime lighting quantified using satellite data (Visible Infrared Imaging Radiometer Suite-VIIRS). They link the urban score to body size data to find 'heterogeneous relationship between body size and urban tolerance across the tree'. The results are then discussed with reference to potential mechanisms that could possibly produce the observed effects (cf. Figure 1).

Strengths:

The novelty of this study lies in the huge number of species analyzed and the comparison of results among animal taxa, rather than in a thorough analysis of what traits allow species to persist under urban conditions. Such analyses have been done using a much more thorough approach that employs presence-absence data as well as a suite of traits by other studies, for example, in (Hahs et al. 2023, Neate-Clegg et al. 2023). The dataset that the authors produced would also be very valuable if these raw data were published, both the cleaned species records as well as the body sizes.

The paper could strongly add to our understanding of what species occur in cities when the open questions are addressed.

Weaknesses:

I value the approach of the authors, but I think the paper needs to be revised.

In my view, the authors could more carefully validate their approach. Currently, any weakness or biases in the approach are quickly explained away rather than carefully explored. This concerns particularly the use of presence-only data, but also the calculation of the urban score.

The vast majority of data in GBIF is presence-only data. This produces a strong bias in the analysis presented in the paper. For some taxa, it is likely that occurrences within the city are overrepresented, and for other taxa, the opposite is true (cf. Sweet et al. 2022). I think the authors should try to address this problem.

The authors should compare their results to studies focusing on particular taxa where extensive trait-based analyses have already been performed, i.e., plants and birds. In fact, I strongly suggest that the authors should compare their results to previous studies on the relationship between traits, including body size and occurrences along a gradient of urbanisation, to draw conclusions about the validity of the approach used in the current study, which has a number of weaknesses.

They should be be more careful in coming up with post-hoc explanations of why the pattern found in this study makes sense or suggests a particular mechanism. This reviewer considers that there is no way in which the current study can disentangle the different possible mechanisms without further analyses and data, so I would suggest pointing out carefully how the mechanisms could be studied

More details should be given about the methodology. The readers should be able to understand the methods without having to read a number of other papers.

References:

Hahs, A. K., B. Fournier, M. F. Aronson, C. H. Nilon, A. Herrera-Montes, A. B. Salisbury, C. G. Threlfall, C. C. Rega-Brodsky, C. A. Lepczyk, and F. A. La Sorte. 2023. Urbanisation generates multiple trait syndromes for terrestrial animal taxa worldwide. Nature Communications 14:4751.

Neate-Clegg, M. H. C., B. A. Tonelli, C. Youngflesh, J. X. Wu, G. A. Montgomery, Ç. H. Şekercioğlu, and M. W. Tingley. 2023. Traits shaping urban tolerance in birds differ around the world. Current Biology 33:1677-1688.

Sweet, F. S. T., B. Apfelbeck, M. Hanusch, C. Garland Monteagudo, and W. W. Weisser. 2022. Data from public and governmental databases show that a large proportion of the regional animal species pool occur in cities in Germany. Journal of Urban Ecology 8:juac002.

Reviewer #1 (Public review):

Summary:

In this paper, Qiu et al. developed a novel spatial navigation task to investigate the formation of multi-scale representations in the human brain. Over multiple sessions and diverse tasks, participants learned the location of 32 objects distributed across 4 different rooms. The key task was a "judgement of relative direction" task delivered in the scanner, which was designed to assess whether object representations reflect local (within-room) or global (across-room) similarity structures. In between the two scanning sessions, participants received extensive further training. The goal of this manipulation was to test how spatial representations change with learning.

Strengths:

The authors designed a very comprehensive set of tasks in virtual reality to teach participants a novel spatial map. The spatial layout is well-designed to address the question of interest in principle. Participants were trained in a multi-day procedure, and representations were assessed twice, allowing the authors to investigate changes in the representation over multiple days.

Weaknesses:

Unfortunately, I see multiple problems with the experimental design that make it difficult to draw conclusions from the results.

(1) In the JRD task (the key task in this paper), participants were instructed to imagine standing in front of the reference object and judge whether the second object was to their left or right. The authors assume that participants solve this task by retrieving the corresponding object locations from memory, rotating their imagined viewpoint and computing the target object's relative orientation. This is a challenging task, so it is not surprising that participants do not perform particularly well after the initial training (performance between 60-70% accuracy). Notably, the authors report that after extensive training, they reached more than 90% accuracy.

However, I wonder whether participants indeed perform the task as intended by the authors, especially after the second training session. A much simpler behavioural strategy is memorising the mapping between a reference object and an associated button press, irrespective of the specific target object. This basic strategy should lead to quite high success rates, since the same direction is always correct for four of the eight objects (the two objects located at the door and the two opposite the door). For the four remaining objects, the correct button press is still the same for four of the six target objects that are not located opposite to the reference object. Simply memorising the button press associated with each reference object should therefore lead to a high overall task accuracy without the necessity to mentally simulate the spatial geometry of the object relations at all.

I also wonder whether the random effect coefficients might reflect interindividual differences in such a strategy shift - someone who learnt this relationship between objects and buttons might show larger increases in RTs compared to someone who did not.

(2) On a related note, the neural effect that appears to reflect the emergence of a global representation might be more parsimoniously explained by the formation of pairwise associations between reference and target objects. Since both objects always came from the same room, an RDM reflecting how many times an object pair acted as a reference-target pair will correlate with the categorical RDM reflecting the rooms corresponding to each object. Since the categorical RDM is highly correlated with the global RDM, this means that what the authors measure here might not reflect the formation of a global spatial map, but simply the formation of pairwise associations between objects presented jointly.

(3) In general, the authors attribute changes in neural effects to new learning. But of course, many things can change between sessions (expectancy, fatigue, change in strategy, but also physiological factors...). Baseline phsiological effects are less likely to influence patterns of activity, so the RSA analyses should be less sensitive to this problem, but especially the basic differences in activation for the contrast of post-learning > pre-learning stages in the judgment of relative direction (JRD) task could in theory just reflect baseline differences in blood oxygenation, possibly due to differences in time of day, caffeine intake, sleep, etc. To really infer that any change in activity or representation is due to learning, an active control would have been great.

(4) RSA typically compares voxel patterns associated with specific stimuli. However, the authors always presented two objects on the screen simultaneously. From what I understand, this is not considered in the analysis ("The β-maps for each reference object were averaged across trials to create an overall β-map for that object."). Furthermore, participants were asked to perform a complex mental operation on each trial ("imagine standing at A, looking at B, then perform the corresponding motor response"). Assuming that participants did this (although see points 1 and 2 above), this means that the resulting neural representation likely reflects a mixture of the two object representations, the mental transformation and the corresponding motor command, and possibly additionally the semantic and perceptual similarity between the two presented words. This means that the βs taken to reflect the reference object representation must be very noisy.

This problem is aggravated by two additional points. Firstly, not all object pairs occurred equally often, because only a fraction of all potential pairs were sampled. If the selection of the object pairs is not carefully balanced, this could easily lead to sampling biases, which RSA is highly sensitive to.

Secondly, the events in the scanner are not jittered. Instead, they are phase-locked to the TR (1.2 sec TR, 1.2 sec fixation, 4.8 sec stimulus presentation). This means that every object onset starts at the same phase of the image acquisition, making HRF sampling inefficient and hurting trial-wise estimation of betas used for the RSA. This likely significantly weakens the strength of the neural inferences that are possible using this dataset.

(5) It is not clear why the authors focus their report of the results in the main manuscript on the preselected ROIs instead of showing whole-brain results. This can be misleading, as it provides the false impression that the neural effects are highly specific to those regions.

(6) I am missing behavioural support for the authors' claims.

Overall, I am not convinced that the main conclusion that global spatial representations emerge during learning is supported by the data. Unfortunately, I think there are some fundamental problems in the experimental design that might make it difficult to address the concerns.

However, if the authors can provide convincing evidence for their claims, I think the paper will have an impact on the field. The question of how multi-scale representations are represented in the human brain is a timely and important one.

Reviewer #2 (Public review):

Summary:

Qui and colleagues studied human participants who learned about the locations of 32 different objects located across 4 different rooms in a common spatial environment. Participants were extensively trained on the object locations, and fMRI scans were done during a relative direction judgement task in a pre- and post-session. Using RSA analysis, the authors report that the hippocampus increased global relative to local representations with learning; the RSC showed a similar pattern, but also increased effects of both global and local information with time.

Strengths:

(1) The manuscript asks a generally interesting question concerning the learning of global versus local spatial information.

(2) The virtual environment task provides a rich and naturalistic spatial setting for participants, and the setup with 32 objects across 4 rooms is interesting.

(3) The within-subject design and use of verbal cues for spatial retrieval is elegant .

Weaknesses:

(1) My main concern is that the global Euclidean distances and room identity are confounded. I fear this means that all neural effects in the RSA could be alternatively explained by associations to the visual features of the rooms that build up over time.

(2) The direction judgement task is not very informative about cognitive changes, as only objects in a room are compared. The setup also discourages global learning, and leaves unclear whether participants focussed on learning the left/right relationships required by the task.

(3) With N = 23, the power is low, and the effects are weak.

(4) It appears no real multiple comparisons correction is done for the ROI based approach, and significance across ROIs is not tested directly.

Reviewer #3 (Public review):

Summary:

The manuscript by Qui et al. explores the issue of spatial learning in both local (rooms) and global (connected rooms) environments. The authors perform a pointing task, which involves either pressing the right or left button in the scanner to indicate where an object is located relative to another object. Participants are repeatedly exposed to rooms over sessions of learning, with one "pre" and one "post" learning session. The authors report that the hippocampus shifted from lower to higher RSA for the global but not the local environment after learning. RSC and OFC showed higher RSA for global object pointing. Other brain regions also showed effects, including ACC, which seemed to show a similar pattern as the hippocampus, as well as other regions shown in Figure S5. The authors attempt to tie their results in with local vs. global spatial representations.

Strengths:

Extensive testing of subjects before and after learning a spatial environment, with data suggesting that there may be fMRI codes sensitive to both global and local codes. Behavioral data suggest that subjects are performing well at the task and learning both global and local object locations, although see further comments.

Weaknesses:

(1) The authors frame the entire introduction around confirming the presence of the cognitive map either locally or globally. There are some significant issues with this framing. For one, the introduction appears to be confirmatory and not testing specific hypotheses that can be falsified. What exactly are the hypotheses being tested? I believe that this relates to the testing whether neural representations are global and/or local. However, this is not clear. Given that a previous paper (Marchette et al. 2014 Nature Neuro, which bears many similarities) showed only local coding in RSC, this paper needs to be discussed in far more depth in terms of its similarities and differences. This paper looked at both position and direction, while the current paper looks at direction. Even here, direction in the current study is somewhat impoverished: it involves either pointing right or left to an object, and much of this could be categorical or even lucky guesses. From what I could tell, all behavioral inferences are based on reaction time and not accuracy, and therefore, it is difficult to determine if the subject's behavior actually reflects knowledge gained or simply faster reaction time, either due to motor learning or a speed-accuracy trade-off. The pointing task is largely egocentric: it can be solved by remembering a facing direction and an object relative to that. It is not the JRD task as has been used in other studies (e.g., Huffman et al. 2019 Neuron), which is continuous and has an allocentric component. This "version" of the task would be largely egocentric. In this way, the pointing task used does not test the core tenets of the cognitive map during navigation, which is defined as allocentric and Euclidean (please see O'Keefe and Nadel 1978, The Hippocampus as a Cognitive Map). Since neither of these assumptions appears valid, the paper should be reframed to reflect spatial representations more broadly or even egocentric spatial representations.

(2) The fMRI data workup is insufficient. What do the authors mean by "deactivations" in Figure 3b? Does this mean the object task showed more activation than the spatial task in HSC? Given that HSC is one of these regions, this would seem to suggest that the hippocampus is more involved in object than spatial processing, although it is difficult to tell from how things are written. The RSA is more helpful, but now a concern is that the analysis focuses on small clusters that are based on analyses determined previously. This appears to be the case for the correlations shown in Figure 3e as well. The issues here are several-fold. For one, it has been shown in previous work that basing secondary analyses on related first analyses can inflate the risk of false positives (i.e., Kriegeskorte et al. 2009 Nature Neuro). The authors should perform secondary analyses in ways that are unbiased by the first analyses, preferably, selecting cluster centers (if they choose to go this route) from previous papers rather than their own analyses. Another option would be to perform analyses at the level of the entire ROI, meaning that the results would generalize more readily. The authors should also perform permutation tests to ensure that the RSA results are reliable, as these can run the risk of false positives (e.g., Nolan et al. 2018 eNeuro). If these results hold, the authors should perform post-hoc (corrected) t-tests for global vs. local before and after learning to ensure these differences are robust and not simply rely on the interaction effect. The figures were difficult to follow in this regard, and an interaction effect does not necessarily mean the differences that are critical (global higher than local after) are necessarily significant. The end part of the results was hard to follow. If ACC showed a similar effect to HC and RSC, why is it not being considered? Many other areas that seemed to show local vs. global effects were dismissed, but these should instead be discussed in terms of whether they are consistent or inconsistent with the hypotheses.

(3) Concerns about the discussion: there are areas involving reverse inference about brain areas rather than connecting the findings with hypotheses (see Poldrack et al. 2006 Trends in Cognitive Science). The authors also argue for 'transfer" of information (for example, from ACC to OFC), but did not perform any connectivity analyses, so these conclusions are not based on any results. Instead, the authors should carefully compare what can be concluded from the reaction time findings and the fMRI data. What is consistent vs. inconsistent with the hypotheses? The authors should also provide a much more detailed comparison with past work. The Marchette et al. paper comes to different conclusions regarding RSC and involves more detailed analyses than those done here, including position. What is different in the current paper that might explain the differences in results? Another previous paper that came to a different conclusion (hippocampus local, retrosplenial global) and should be carefully considered and compared, as it also involved learning of environments and comparisons at different phases (e.g., Wolbers & Buchel 2005 J Neuro). Other papers that have used the JRD task have demonstrated similar, although not identical, networks (e.g., Huffman et al. 2019 Neuron) and the results here should be more carefully compared, as the current task is largely egocentric while the Huffman et al. paper involves a continuous and allocentric version of the JRD task.

(4) The authors cite rodent papers involving single neuron recordings. These are quite different experiments, however: they involve rodents, the rodents are freely moving, and single neurons are recorded. Here, the study involves humans who are supine and an indirect vascular measure of neural activity. Citations should be to studies of spatial memory and navigation in humans using fMRI: over-reliance on rodent studies should be avoided for the reasons mentioned above.

it cant be proven, only disprove because absolute proof cant be attained because exceptions exist and our understanding is continually evolving, thus, it may change overtime as new knowledge exist. also because hypothesis should be falsifiable

reply to u/rawbran30 at https://old.reddit.com/r/typewriters/comments/1py74mf/internet_hype_trendeffect_and_brand_popularity/

Olympias were imported into the US from the 50s into the 70s and were manufactured at peak typewriter engineering and manufacturing methods before machines slowly got cheaper and cheaper in terms of materials and craftsmanship through the 60s and into the early 80s before typewriters were subsumed by the word processor market.

Compared to Smith-Coronas and Remingtons of the 50s and early 60s (their peaks), Olympias are slightly better manufactured in terms of fit and finish. They're also slightly more modern looking in terms of body shapes and colors compared to other machines, which also helps to drive up price amongst collectors.

Now is an Olympia SM3 or SM9 really so much better than a Smith-Corona Silent Super that they should enjoy an almost 2x jump in price for an unserviced model? Potentially not, but if this is your issue, then buy something from a professional shop that's been cleaned, oiled, and adjusted and a lot of the price differential evaporates.

Chat with Gemini:<br /> Lee:

Here's another block of text in that same lesson. Can you help me visualize what it means?

When both vectors are normalized, the arc cosine (cos⁻¹) of their dot product reveals the angle θ between them: θ=cos⁻¹((A⋅B)/(∥A∥ ∥B∥)) or cos⁻¹(Â⋅B̂), where cos⁻¹ denotes the inverse cosine function, commonly represented as acos() in programming languages.

Gemini:<br /> This block of text is explaining how to "reverse-engineer" an angle using the dot product. In 3D graphics, you often know the coordinates of two objects (like a player and an enemy), but you don't know the angle between them. This formula is how you find it.

The Logic: Working Backwards

Think of the dot product formula we just discussed:

A⋅B = ∥A∥∥B∥cos(θ)

The text is simply rearranging that equation to solve for .

1. Isolate the Cosine: Divide both sides by the magnitudes (). A⋅B cos(θ) = ------ ∥A∥∥B∥
2. Find the Angle: To get θ by itself, you use the inverse cosine (acos). / A⋅B \ θ = arccos( ------ ) \ ∥A∥∥B∥ /

Visualizing the "Normalized" Shortcut

The text mentions: θ=cos(Â⋅B̂). (The "hat" symbol  means it is a normalized unit vector).

This is the "pro-gamer move" in graphics programming. If you normalize both vectors first (meaning you force their lengths to be 1), then ∥A∥ and ∥B∥ both become 1. The denominator in our formula disappears!

• If lengths are 1: The dot product is the cosine of the angle.
• To get the angle: You just plug that dot product into acos().

A Practical Example: "Field of View"

Imagine you are coding a stealth game. You have a Guard and a Player.

• Vector A: The direction the Guard is looking.
• Vector B: The direction from the Guard to the Player.

If you normalize both vectors and calculate (A⋅B), you get the exact angle (e.g., 30°). If the Guard's peripheral vision is only 45°, you now know the Guard can see the player!

Important "Gotcha" for Programmers

When you use acos() in languages like C#, C++, or Python, the result is usually in radians, not degrees.

• Dot Product = 1: Angle is 0 (Vectors point same way).
• Dot Product = 0: Angle is 1.57 radians (90°).
• Dot Product = -1: Angle is 3.14 radians (180°).

Lee:

You said If you normalize both vectors and calculate $acos(A⋅B), you get the exact angle (e.g., 30°). If the Guard's peripheral vision is only 45°, you now know the Guard can see the player! By "only 45°", is that from left to right of the guard's visibility? So, from straight ahead to the far right of his vision would only be 22.5°, thus the player is not visible at a 30° again from the guard? Or is that 45° from straight ahead to the far right of the guard's vision (for a total viewable angle of 90°)?

Gemini:<br /> That is a fantastic catch. In game development and 3D graphics, this distinction is a common source of bugs!

When you use the dot product to check visibility, the angle θ you calculate is the offset from the center.

1. The "Half-Angle" Concept

The dot product measures the angle between two specific lines: where the guard is looking (the "forward vector") and where the player is. It doesn't know about "left" or "right"; it only knows "how far away from my nose is this object?"

• If your calculation acos(A⋅B) returns 30°, it means the player is 30° away from the center line.
• If the guard has a 45° Field of View (FOV), usually that refers to the total horizontal arc (22.5° to the left and 22.5° to the right).

In your specific scenario:

• Total FOV: 45°
• Allowed "Half-Angle": 22.5°
• Player's Angle: 30°
• Result: Not Visible. The player is "outside the cone."

2. How it's usually coded

To avoid doing the math every single frame, programmers usually define the guard's "View Cone" as a threshold value.

If the guard can see 45° to the left and 45° to the right (a 90° total FOV), the threshold is:<br /> cos(45°) ≈ 0.707 The code then looks like this:

```csharp // If dot product is greater than 0.707, the player is within the 90-degree cone if (DotProduct(GuardForward, DirectionToPlayer) > 0.707) { // I see you! }

```

3. Why the Dot Product is "Circular"

One interesting thing to visualize: because the dot product only cares about the angle between two vectors, the "visibility" it creates is actually a cone, not a flat triangle.

If you say "anything within 30° of my forward vector is visible," that includes 30° up, 30° down, 30° left, and 30° right. It creates a circular spotlight of vision.

Summary Table: FOV vs. Dot Product

| Total FOV | Angle from Center | Dot Product Threshold (Approx) | | ------------------ | ----------------- | ------------------------------ | | 180° (Flat wall) | 90° | 0.0 | | 90° (Standard) | 45° | 0.707 | | 60° (Narrow) | 30° | 0.866 | | 2° (Sniper Scope)| 1° | 0.999 |

are able to use / won't be able to finish

are able to use won't be able to finish Are you able to explain were able to find isn't able to join Will you be able to come

1.I can seak three languages 2.I can swim very well 3.I am able to play the guitar 4.I can cook pasta

I am not able to finish

are able to use

i am not able to finish

are able to use

Shield, Buckler צִנָּה How is that a fish hook? AI: What the word probably means Most scholars think צִנּוֹת refers to: - barbed implements, - pronged tools, - or hook-like instruments used for dragging. That’s why translations choose “hooks” or “fishhooks” — it fits the imagery of capture and the historical method of leading prisoners. 🌿 The picture Amos is painting Amos is saying: “You think you’re secure, but you’ll be dragged away like fish on hooks.”

It’s vivid, humiliating imagery — exactly the kind prophets use to shock their audience awake.

LCP is DRM standard (.lcpl files), a JSON/XML extension and ISO standard. https://readium.org/lcp-specs/

Came across it in Norwegian e-book store. This page suggests it is in use elsewhere too. Seems to be a format used for lending books too.

Elsewhere I find that Readium 1.0 can be de-drmd easily but 2.5 is more secure from that.

Calibre DeDRM plugin, but an older version needed to remove Lcpl drm, you do need the passphrase still. For reading in Calibre no need to de-drm but needed if you want to move it to a device that does not support lcp.

https://web.archive.org/web/20251228132740/https://www.conferencesthatwork.com/index.php/event-design/2017/01/stories-have-dark-side/ A good story is not per def a true or helpful story. Dark stories can evoke dark passions in audiences. Call to as event organisers to not just platform stories bc they're compelling, but determine if you want to provide a megaphone. Duh. Does lead me to think, e.g. like during epsiplatform, and IndieWeb meetups, can you put some of that down upfront (rather than 'feel' it) as a charter internally or externally (if it doesn't turn the charter into the story). Platforming or not, is choosing to pay attention and [[Aandacht is een morele keuze 20201217074345]]

https://web.archive.org/web/20251228125455/https://www.wrecka.ge/landslide-a-ghost-story/

Essay. by Erin Kissane on 'collective derangement' as societal problem. How knowledge is more a collective thing than a personal (and we're eroding the collective part), and how a algorithmic infodiet feels like being informed but isn't.

Note that male-to-male violence also exists! I have been threatened too, yet this is much less usually talked about. It is normalised, invisibilised, naturalised.

But most players won't. Considering most games have a finish rate of ~25%, this type of reading and interaction... is at most relegated to 10% of the playerbase, being optimistic. That can be a lot, but it's not "the mainstream" audience.

Dated, but surely the stats haven't changed that much.

Loop games like Rue Valley, 12 minutes, Outer Wilds, Minit, or Oxen Free, hardly portray emancipation pathways. Replayable ones, like I Was a Teenage Exocolonist, or Papers, Please, might, but most roguelikes won't. Hades doesn't.

Yes, but arguably you are talking more about the mind and body, as stated. You are talking about rape, and war, and dick picks, and blackmail, and death threats, doxxing. You are talking about invisible suffering. Depression, anxiety, and problems with addiction are not like a scar or a leg injury. They get often ostracised, and people are not taught how to share them to regulate them with help.

As in mutual respect: I tend for you, and you tend for me. An issue is that people may deny their vulnerability. They may dismiss it as any other attribute or sentiment. They may be infatuated with riches, and think they are immune behind the screen, relying on the trope that only girls cry, and that psych harm is separate from physical one. Further, practicing ethics of care requires a lot of time and continuous effort to minimise oneself, to revise and make one obsolete. To stop creating and start listening, to not pursue firstness, but secondariness, to be invisible, not acknowledged, not praised. It is tough, and can lead to burnout.

If you play defensively, or a support role, or alone; if you have an effeminate name tag, skin, or cosmetics... you may be a target, through chat, for instance (but hackers could also track your IP in other ways).

Two arguments are being made here: First, player events impact non-players. Second: Players, even if thought invisible, leave bodily tracks.

Player events can be festivals, performances, but also cultural shenanigans and terms like inting, or gg ez, which can convey a competitive way of narrating out of the gaming sphere. Players leave tracks the moment they download a game, in the form of cookies, if the game requires Internet connection or has an anti-cheat tool (Riot Vanguard), if it has a log that gets mixed with OS files, or if it has a public profile linkage like a Rocket League ranking tracker, modding store, Mario Maker level, Steam user, or Animal Crossing island. These are our creations, extensions —limbs— of ourselves, of our image, and ideas.

Source? Notice we can't just focus on all the intersectionalities during an analysis. I for sure would love to only recommend Open Source games made by minoritised people through a local research citizen science exchange, in paid working labour condtions, without stolen content, no washing marketing campaigns, with accessibility features, with a proven social impact, and made using devices without rare exploitative materials... but this ain't possible.

We pick our fights, for me it's biases, because they influence most of our daily acts, but activism has many other sides. I just don't think jumping into activism without awareness of bias is a safe avenue, as it can lead to radical violence as a means of change.

Yes, but don't synonimise Plato's world of ideas to the Web's Internet of things. What I mean by this is that both are erroneous dichotomies, but they are different dichotomies. Believing in free will and a soul doesn't mean you separate the influences of the virtual-online, and the day-to-day physical space. They may be both real, but this conceptualisation can be a useful communicative tool to put into perspective that before globalisation you couldn't simply receive an email in 1 second from someone 10k miles away.

ha, you can distract chatbots, as we've seen from the start. This is the classic 'it's not for me but for my mom' train ticket sales automation hangup in response to 'to which destination would you like a ticket', and then 'unknown railway station 'for my mom' in a new guise. And they didn't even expect that to happen? It's an attack service!

there's no other way, understanding comes from using it, and having stuff go wrong. The scandals around algos are important in this. Scale and distribution are different beasts. Distribution does not need scale (but a network effect helps) in order to work. The need for scale in digital is an outcome of the financing structure and chosen business model, and is the power grab essentially. #openvraag hoe zet je meer focus op distributie als tegenkracht tegen de schalingshonger van actoren?

geopolitics, whether in shape of capital, tech or politics has become key, which he overlooked in 2015/8

More detailed formulation of Gibson future is already here but distributed. Add sectors/domains. There's more here to tease out wrt my change management work. - [ ] #30mins #ai-ethics vul in met concretere voorbeelden hoe deze quote vorm krijgt

David posits questions that are all on the application side, what is the impact of using ai. There are also questions on the design side, how do we shape the tools wrt those concepts. Vgl [[AI begincondities en evolutie 20190715140742]] e.g. diff outcomes if you start from military ai params or civil aviation (much stricter), in ref to [[Novacene by James Lovelock]]

The premise of the book that scale mattered wrt AI (SU vibes). AI to be understood at societal level, not from an individual perspective, as tech and society mutually shape eachother (basic WWTS premise). Given certain thresholds it would impact coordination, knowledge and agency.

• Article argues pre-commit hooks are fundamentally broken due to running on working tree, not index, failing to catch unstaged changes.
• Hooks interfere with rebases, interactive commits, and external branches without hooks, requiring frequent --no-verify usage.
• Demonstrates issues via Rust fizzbuzz example: formatting fixes don't stage, existing unformatted code blocks commits, rebases fail unexpectedly.
• Recommends pre-push hooks instead: run on index, keep fast/reliable (no network/credentials), quiet, manual setup via docs.
• Additional pitfalls: slow/unreliable implementations, modifying commits, not running on stashes; prefers commits over stashes for branching.

Hacker News Discussion

• Users debate pre-commit vs. pre-push/CI: enforce in CI for reliability, use hooks to reduce churn but not guarantee (e.g., secrets in pre-push).
• Workflows vary: WIP commits common, rebase/squash before push; hooks break rebases needing full checkouts (e.g., cargo clippy).
• Editor integration preferred for formatting/linting; force consistency in teams but allow bypass for power users.
• Alternatives: git filters for formatting, jj run for future; personal setups run heavy local checks but optional for contributors.
• Defenses of pre-commit frameworks (speed, environments) countered by author; prefer CI for shared enforcement over local hooks.

We read that "the drawstring casing, also known as the cord channel, is consistently 1.5 inches (4cm) tall." But when we enter dimensions, we're consistently old that "Cord Channel Height" is 4 inches. This seems to be a contradiction.

I asked Tim, the maker of this website, for an explanation. Here is his response: 'The fabric for cord channel is always 4" tall. You'll fold in half and then sew to Body Panel using 1/2" S/A. This uses up 1" of fabric. So you end up with a channel casing of 1.5" tall.'

Ken’s influence didn’t appear as direct teaching, but changed the way he sees the world.

Being classified together as both Asian Americans will hide the real differences in their lives and experiences.

Whatever happens in the USA in the coming 3 yrs: "We will never trust you again". This has very deep reaching impacts.

8 decades of softpower squandered, rationality gone and institutionalised governance dismantled. In short the US cannot even be assumed to act within its own self-interest

author calls bs on back-to-normal hopes. Many officials are leaving and get replaced by younger ones who buy into the new US policy and will shape it for decades.

Pre-Trump officials in the US think there's a road back to where the US was before

in response US senator did not disagree

Canary statement

Example of how EO12333 'can' be used: take all bigtech data as 'incidental' data around a legal foreign intelligence target.

The bid strategies will work as hard as they can do decide which auctions to enter and how much to bid. It's our job to ensure the strat has relevant ads that are closely related to the KW and that we drop the user on the right page.

12:48 "I have a hypothesis about why society is broken right now ... 1913 fiat currency ... printing money ... inflation ... steal from people ... hyper inequality ... 10% of Americans own 93% of assets ... unfair ... envy ... hollowing out of the middle class ... wealth inequality is reaching levels like in the French revolution ... we actually are headed towards a French revolution, but it's economic in nature."

no. fiat money is just another tool to control idiots, just like religion or politics. the actual drivers of this global policy are: overpopulation, degeneration, ecocide, resource depletion.

so yes, the problem is "economic in nature" in the sense that we have too many humans and too few planets.

the obvious solution is global depopulation by 90% of today's population, but that is obviously not a popular topic, because only 10% will survive this cull.

The projects depend on public money, NASA as a tenant. So one out of these 4

Voyager Space and Airbus jointly designing Starlab, to be launched in 2028, but depends on SpaceX starship rocket that doesn't exist yet.

NASA LEO program spent 415MUSD in phase 1 (Designs), and will fund 1-1.5BUSD 2026-2031 to operationalise some of them

ISS is planned until 2028 / 2030. NASA wants to replace it w market actor project (but does need a permanent presence in LEO) and then focus on Mars and moon projects.

click moment: suddenly was, looked different

but given an opportunity to act on that

act on the world differently

act in the world differently

act in relations to others differently

that's the heart and soul of movement work

finding new ways of relating to each other and building on that,

building that into something where it's very broad and collective

and then you can press forward together

• uv installs Python packages 10x faster than pip due to design decisions beyond just being written in Rust.
• Key standards like PEP 518 (pyproject.toml), PEP 517, PEP 621, and PEP 658 enabled static metadata parsing without executing untrusted code.
• uv drops legacy support: no .egg files, no pip.conf, no default bytecode compilation, requires virtual environments, stricter spec enforcement, ignores upper Python bounds, first-index wins.
• Non-Rust optimizations include HTTP range requests for metadata, parallel downloads, global cache with hardlinks, Python-free resolution, and PubGrub resolver.
• Rust-specific advantages: zero-copy deserialization, lock-free data structures, no interpreter startup, compact version representation.
• Lesson: Speed comes from modern standards, dropping legacy features, and fresh assumptions rather than language alone.

Hacker News Discussion

• Users praise uv's speed and discuss its impact on Python workflows, with many switching from pip.
• Debate on Rust's role: some credit architectural choices over language, others highlight zero-copy and concurrency benefits.
• Questions about compatibility gaps like missing pip.conf and .egg support, but most see them as acceptable trade-offs.
• Comparisons to Cargo and npm emphasize Python's late adoption of static metadata standards.
• Interest in PubGrub resolver and potential for pip to adopt similar optimizations without Rust.

Many of uv's optimisations come from improvements that can be made without rust such as http range requests for packages, parallel downloading, better local caching

When creating the prompt for a given homolog set {H_i, ...}, the order of concatenation is randomized to promote homolog order invariance. But was invariance ever tested post-training? Specifically, did you guys quantify the variance in model output when the exact same set of homologs is simply re-ordered? Establishing this baseline seems critical to determine whether the performance gains from ensembling truly derive from aggregating diverse evolutionary information, or if they are partially an artifact of smoothing out the model's sensitivity to arbitrary input ordering.

I have a major concern regarding the dataset construction. My primary question is, why did you choose to generate synthetic sequences (and structures) instead of using natural homologs? Databases like CATH or SCOP are full of naturally occurring protein pairs that share a fold but have very low sequence identity. Using those would have grounded your benchmark in real biological evolution rather than generative noise.

Regarding your use of the "twilight zone" concept. While your dataset technically hits the 26% identity mark, I feel this misrepresents what that term actually defines. The twilight zone describes evolutionary homology, aka where sequences have diverged over millions of years due to selection and drift while maintaining structure. Your sequences, by contrast, are hallucinations from an inverse folding model running at high temperature. Generative variance is not the same as evolutionary divergence, and a pLM recognizing ProteinMPNN's output patterns is not the same as understanding structural conservation.

Furthermore, relying entirely on synthetic validation creates a circular loop. You are testing if a pLM can recognize sequences made by ProteinMPNN and "validated" by AlphaFold3, without any experimental ground truth that these sequences actually fold. And to be frank, it's straightforward to generate high pTM AF3 structures that don't fold. Introduce a tryptophan mutation to your favorite protein. Its pTM will be almost unaffected by the mutation, but good luck expressing and purifying. A huge proportion of your dataset doesn't fold irl.

When users are treated like a commodity by powers that be, there is a concentration on making apps "usable" and "universal" in order to avoid "churn"

They aren't LITERALLY competing against each other in the auction. It's not that each is setting a bid and then trying to one up each other. It's that if one KW has weaker relevance it might bid really high, resulting in a higher ad rank & winning the same click for a more expensive price, just because the structure wasn't clean enough. We also run the risk of Google favoring click heavy, but low CvR ad ad group placements.

1. Limits visibility for at LEAST one of the ad groups with the duplicate KW. Now Google has to choose which version of the KW is most relevant and will split the performance of that KW across different ad groups. In theory, if you want to see overall performance you can still do so. It just makes it more complicated to analyse performance because you have to compare different ad group elements (ads, search terms, negatives) to see what caused performance.

This perhaps puts things "in perspective", but somehow in a way that the author avoids attaining the (or, if you prefer, demonstrating) the corollary self-awareness that one would expect (having had things put into perspective, and all).

Economic returns (what do I gain?) = all measurable economic benefits. 1 Direct Monetary returns 2. Employment stability and options (Higher chances of getting a job, ability to switch and negotiate, less fear of losing job, access to better quality work) 3. Productivity and lifetime earning capacity - simply means that within the job either directly through the nature of the job, or buy time to progress outside of your job but make sure to become more productive and gain skills such that it does not only bring you money for the current year but also increases your chances of earnings for your lifetime. 4. Economic returns also encapsulates better health, financial literacy, mobility, and networks (access to opportunities)

Social returns (What do we gain?) What does a society gain when an individual is well educated? 1. Public health improvements Education changes the decision quality of people. Basic literacy helps deepen understanding about importance of hygiene, medical instructions, etc.

1. Social order and safety Education helps people control their impulses and improves conflict-resolution skills, improves understanding about consequences and drives a way to lawful income paths and gives confidence to engage with any kind of institutions.

2. Civic and democratic participation Basic pollical literacy and critical thinking - ability to think beyond immediate self interest.

3. Intergenerational human capital Educated parents talk more with children using good vocab, they themselves value schooling and intervene early when learning gaps appear.

4. Social cohesion and equality Education creates a common language. Not only linguistically but also creates common ways in which people can understand each other's reasoning. Common base = numbers, terms, concepts, and some std. ways and ref. to explain things.

Education brings about social mobility meaning a person's ability to move beyond social and economic status that they are born into - Education does not erase inequality but weakens the link between birth = destiny

Kotlin had a mandatory, but now optional function main as entry point. Like C++ and Java

https://web.archive.org/web/20251226112926/https://www.theguardian.com/world/2025/dec/26/russia-selling-personal-data-leaks-probiv-ukraine-spies

Russian gov now cracking down on 'probiv' market for hacked/leaked data. It was of use to themselves, but now also used by Ukraine to strike inside Russia.

R0:

Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Full Title:

Manuscript full title does not match with the short title. Full title reads "Climate change, livelihoods, gender and violence in Rukiga, Uganda: intersections and pathways". While short tile reads "Climate Change and Gender Based Violence". 'gender based violence' may not necessarily mean the same as 'gender and violence'. Authors should consider revising the wording in the full time if they meant gender based violence.

Abstract:

Inconsistency in FGD size, harmonize to consistent range across the manuscript. Author said "Between April and July 2021, we conducted 28 focus group discussions (FGDs), comprising 6-8 participants each (line 29-30" and in methods author said "From 20 April 2021- 02 July 2021 five focus group discussions (FGDs) were conducted in each community (28 in total) each consisting of four to six participants (lines 135-136)".

clarify the CBV emergent theme. You said "This study, though not originally intended to focus on GBV, examines how it interconnects with poverty, shifting gender roles, alcoholism, environmental stress, and family planning dynamics." (lines 26-28). Consider adding a statement signalling GBV emerged inductively during data colletion and/or analysis.

Methods: Revise the methods section to ensure the study can be reprodcible, and signal reliability of findings.

What study design did you use? not clear

Author said participants were " purposively selected... with the help of community leaders" (lines 140-141). Clearly elaborate the eligibility criteria and how the gatekeepers' influence was mitigated, and proper justification why 28 FGDs and 40 KIIs were sufficient. Talk about saturation, was maximum variation considered? and how?

Results:

Tag all quotes with data source (FGD or KII), sex, age to evidence diversity across the groups.

Make sure all quotes are in clear quotations marks (lines 220-222). fix that for the entire results section and be consistent.

Authors said "When describing their experiences and perceptions of poverty and its associated consequences including poor diets, sickness, and lack of ability to pay for healthcare and transport to medical facilities, most respondents explicitly identified poverty as a direct cause of GBV:" (lines 311-314). Revise the wording on participants' perceptions to avoid implying causality from qualitative data. Check the entire document for this including the abstract lines 36 to 41.

Ethics: Include ethical committee name that gave ethical clearance for the study, also include the reference number and date.

describe safeguardings and referral procedures followed in the study if any.

Conclusion: The concept for this paper is timely and relevant. However several important elements require revision before the manuscript can meet PLOS Global Public Health Standards. Work on the clarity and consistency of the methods (study design was not clearly mentioned, there are several qualitative designs one can use, e.g. phenomenology, case study, etc. what design did you use?). PLOS Global Public Health guidelines on data sharing require that you provide some de-identified data, nevertheless authors stated that they would share data and the justification for that leaves much to be desired.

Reviewer #2: 1. Kindly mention the methodological orientation adopted for the study? 2. Discrepancy between number of participants in FGD mentioned in abstract and methods – (6 – 8 in abstract and 4 – 6 participants in methods)…Kindly make it uniform 3. Additional context on domestic violence and related statistics can be added in study setting 4. Details on steps taken to ensure internal validity/rigor to be mentioned – member checking, reflexivity 5. Give details of the parent project briefly 6. Any conceptual model/framework adopted to guide data generation/analysis? 7. What efforts were taken to address/refer victims of GBV once disclosed? 8. Socio - demographic details of the respondents could be added for better interpretation 9. Key themes are restated multiple times; Many dimensions of GBV (more details on each typology, coping strategies, prevention, etc) not elicited

Reviewer #3: Overall Comments The paper takes a qualitative approach to “examine locally held perceptions of the relationships between climate and livelihood-related stressors and changing dynamcis, including the risk of Rukiga district. Climate change remains a global threat, with many countries and communities within Africa, ill prepared to adapt and mitigate the consequences. The paper is an attempt to paint a picture of climate-related impacts, particularly how gender-based violence, a persistent public health, socioeconomic and development issue is shaped by and influencing social, economic and environmental stressors.

In its current form, the paper need to be strengthened to get it to be sufficiently robust for publication in PLOS Global Public Health. The paper needs to be strengthened in at least three ways:

1) Overall, the paper needs to better contextualise their goal. Authors state in line 115 to 117, that their purpose is to understand locally held perceptions of the relationship between climate and livelihood-related stressors, and in several other sections, indicate make clear that, their original intention was not GBV, but undertook a thematic analysis on the latter. This can be confusing making it difficult for readers to follow. Authors need to clarify their focus – if it is on GBV, they may consider better contextualising their paper, especially in the introduction.

As part of contextualising, authors may consider highlighting the initial primary research focus – this helps to provide context for readers to begin to appreciate how and why GBV took center-stage during the analysis. In doing so, it also provides an opportunity for authors to properly situate their contributions to the literature.

Other minor issues include: • Authors make claims about projected exponential increase (line 51-52) and yet, do not support with any data. Similarly, authors may want to consider revising the sentence, as it appears redundant.

• In line 55-57, it argued “Uganda’s vulnerability to climate change and climate-sensitive disasters is extremely high – it is not immediately clear to readers what this means. By which benchmark or metric are authors assessing Uganda’s vulnerability. Authors may consider revising to ensure clarity (also see lines 108-110 for punctuation issues).

• Lastly, the study takes place in Rukiga District – it would be helpful if authors provided some additional background context. Will the results be different, if the study was conducted in a different district rather than Rukiga? Basically, some discussions of the rationale and/or choice of the selected district is be useful.

2) Overall, authors need to improve their methods by revising and clarifying, some of the sections. For example, under study setting (line 128-130), it is not clear if the concluding sentence is provided additional context for the prior statement. Authors may want to revise for clarity purpose.

I. Reconcile the number of participants for FGDs – in the abstract, authors indicate 6-8 people form a FDG and in line 136, it says “…each consisting of four to six participants,…”. II. For both FGD and KII, it is useful to indicate and/or describe the demographic/characteristics of the people participating in the study (Perhaps, authors could outline their demographics by sex and age, and any other stratifier in the results section in a tabular format. How were participants selected, especially among the FGD participants? III. On ethics statement, although the data emanates from key informants and community members, authors do not indicate whether they sought ethnical approval for their study. If ethics was obtained, it is useful to indicate so. IV. Regarding data collection, lines 172 to 173, authors indicate that “discrepancies in the coding were re-examined…”. It useful to explain how the independent assessor resolved discrepancies and reached consensus. V. In the data collection section (line 155 to 157), authors indicate that they “undertook a specific analysis of what participants said about GBV”. However, in the results, it is often not clear, the specific thematic issues or results arising from this analysis. Related to this and linked to the analysis, it is not clear to readers how the two main clusters (line 188 to 191) link to GBV. While lines 193 to 212, describe nature of GBV, for the most parts (for example, line 213 to 308), it is difficult to follow how GBV is an interconnector in the results being discussed. At times, it difficult to see, where the analysis departs from its original intended goal. Were the issues around climate change and environment among others emergent from the data?

3) Overall, the results section outlines some very interesting insights. However, I do feel this section can be deepened. In many instances, the narratives are often not immediately supported by the relevant quotes, linking to GBV. • In line 230 – 323, authors reflect that the disruption to livelihoods leading to family instabilities and conflict, demonstrate how GBV is triggered. This assumption is challenging to sustain, considering that “unrest in families” and not having “peace in a home” do not necessarily connote GBV. Similar reflections are presented at line 306 (“...they both resort to quarrels…”), lines 316 to 320 (…start quarrelling and fighting…”) and (“…you fight with the woman”). • Although authors indicate these are “euphemisms for GBV” (line 208) that participants use – without critical analysis, we risk painting a picture that may not be correct. For example, will readers be correct to assume, that in Ugandan context, such referencs always mean GBV?. To avoid readers assuming without appropriate understanding of context, authors may consider, making explicit any additional nunaces related to the quotations or contexts for this pharses, to clarify and make the links to GBV much clearer.

Minor • Line 199 – please clarify how and why unintended pregnancies is considered a form of GBV • Line 208 to 209 – revise sentence – it is not clear what authors mean by throughout their experiences and perceptions • Line 211 – “GBV was raised during the discussions of a wide range of factors” – perhaps, useful to outline the contexts which GBV was raised

Comments to the Author

1. Does this manuscript meet PLOS Global Public Health’s publication criteria? Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe methodologically and ethically rigorous research with conclusions that are appropriately drawn based on the data presented.

Reviewer #1: Partly

1. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: N/A

1. Have the authors made all data underlying the findings in their manuscript fully available (please refer to the Data Availability Statement at the start of the manuscript PDF file)?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception. The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

1. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS Global Public Health does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

1. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Thank you for the opportunity to review this manuscript. Overall, it makes an important contribution to understanding climate and health policy in Argentina, but several issues should be addressed before it is suitable for publication:

The manuscript addresses an important and timely topic, analyzing climate and health policy in Argentina through stakeholder perspectives.

The qualitative design (interviews, document analysis, stakeholder workshop) is appropriate for the research question.

Valuable insights are provided on governance, financing, technical networks, federalism, and awareness gaps, with lessons for Latin America more broadly.

Inconsistencies in sample reporting: text mentions both 31 interviews and 26 interviews with 31 participants. This must be clarified and reconciled with Table 1.

The analysis section requires more detail on how coding disagreements were resolved and how workshop data were integrated.

The rationale for merging WHO framework dimensions should be better explained to ensure analytical nuance is not lost.

The Data Availability Statement does not comply with PLOS requirements. Data are not publicly available and no concrete mechanism for controlled access is provided. At minimum, de-identified excerpts or a codebook should be shared.

Ethics approvals are described but approval identifiers/protocol numbers should be included for transparency.

The manuscript is intelligible and written in standard English but contains issues that should be corrected:

Abstract is too long and must be shortened to ~250–300 words.

“Intersectionality” should be corrected to “intersectorality.”

“Precarized personnel” should be rephrased as “temporary personnel with insecure contracts.”

“Professionals and non-professionals” should be replaced with clearer wording (e.g., “clinical and support staff”).

Redundancy around “technical teams” and “federalism” should be reduced.

References require major correction:

Multiple broken Zotero placeholders are present.

Several entries are incomplete or missing DOIs/URLs.

Reference formatting must be standardized to PLOS style.

Discussion section:

Some statements overgeneralize from interviewee quotes (e.g., physicians not sensitized); these should be framed more cautiously.

Financing section should explore in more depth why mitigation dominates international funding.

References to political events (2024–2025) should be time-stamped as “at the time of data collection” to avoid rapid obsolescence.

Overall, the study is methodologically appropriate and conclusions are mostly supported by the data.

Revisions are necessary to ensure methodological clarity, compliance with data availability policy, correction of references, and refinement of language before publication.

1. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: No

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• Production MLOps on GCP rarely relies solely on Vertex AI; teams use core GCP services like Cloud Composer, Cloud Build, and BigQuery for robust workflows.
• Vertex AI excels at managed training, pipelines, model registry, endpoints, and evaluations but lacks full CI/CD, governance, security, and cost control.
• Real stack includes: Cloud Build (CI/CD), Artifact Registry (images), Terraform (IaC), Secret Manager, Cloud Monitoring/Logging, BigQuery (metadata/drift).
• Architecture layers: Source control → CI/CD → Data (BigQuery/GCS) → Vertex AI execution → Deployment (Cloud Run/GKE) → Observability.
• Reasons to avoid heavy Vertex AI: vendor lock-in, cost opacity, limited flexibility for custom auth, traffic control, multi-cloud needs.
• Alternatives: Cloud Composer (orchestration), Compute Engine/Cloud Batch (training), GCS+MLflow (registry), Cloud Run (serving) for portability and efficiency.

• AWS CEO Matt Garman argues against replacing junior developers with AI, calling it "one of the dumbest ideas."
• Juniors excel with AI tools due to recent exposure, using them daily more than seniors (55.5% per Stack Overflow survey).
• They are cheapest to employ, so not ideal for cost-cutting; true savings require broader optimization.
• Cutting juniors disrupts talent pipeline, stifling fresh ideas and future leaders; tech workforce demand grows rapidly.
• AI boosts productivity, enabling more software creation, but jobs will evolve—fundamentals remain key.

Hacker News Discussion

• AI accelerates junior ramp-up by handling boilerplate, APIs, imports, freeing time for system understanding and learning.
• Juniors ask "dumb questions" revealing flaws, useless abstractions; seniors may hesitate due to face-saving or experience.
• Need juniors for talent pipeline; skipping them creates senior shortages in 4-5 years as workloads pile up.
• Team leads foster vulnerability by modeling questions, identifying "superpowers" to build confidence.
• Debates on AI vs. docs struggle: AI speeds answers but may skip broader discovery; friction aids deep learning.

Summary of "How to Lower Cholesterol? Diet, Supplements, or Statins?"

Guest: Magdalena Kaczan (Lipidologist)

The video provides an extensive overview of cholesterol management, the mechanism of atherosclerosis, and the roles of lifestyle, genetics, and medication in cardiovascular health.

1. Understanding Cholesterol and Lipoproteins

• The Nature of Cholesterol: Cholesterol is an essential fatty substance required for building cell membranes and producing hormones [00:02:55].
• The Role of Lipoproteins: Since cholesterol is a fat, it cannot travel alone in the blood. It is carried by "packages" called lipoproteins. The most problematic ones contain Apolipoprotein B (ApoB), which allows them to penetrate arterial walls [00:04:30].
• LDL vs. HDL: * LDL (Low-Density Lipoprotein): Often called "bad" cholesterol. High levels are a primary driver of plaque buildup [00:05:31].
  • HDL (High-Density Lipoprotein): Generally "good" as it transports cholesterol back to the liver, though it can become dysfunctional in some cases [00:06:04].
• The Importance of ApoB: ApoB is increasingly seen as a more accurate marker than LDL alone because it counts the total number of atherogenic (plaque-forming) particles [00:32:09].

2. The Process of Atherosclerosis

• Infiltration: Lipoproteins (like LDL) enter the arterial wall (intima) through a process called transcytosis [00:07:45].
• Oxidation and Inflammation: Once inside the wall, LDL particles oxidize. The immune system views them as intruders; macrophages "eat" them and turn into "foam cells," triggering chronic inflammation [00:08:13].
• Plaque Formation: Over time, a "lipid core" forms, surrounded by a fibrous cap. If this plaque ruptures, a blood clot forms, which can lead to a heart attack or stroke [00:13:07].

3. Risk Factors and Individual Norms

• Personalized Norms: There is no single "normal" cholesterol level. Targets depend on an individual's 10-year cardiovascular risk (based on age, smoking, blood pressure, etc.) [00:20:01].
• Lipoprotein(a) [Lp(a)]: This is a genetically determined, highly aggressive form of LDL. It acts as an "accelerator" for heart disease and should be tested at least once in a lifetime, as it isn't lowered by traditional diet or exercise [00:36:10].
• Metabolic Factors: High triglycerides, insulin resistance, and obesity significantly worsen the quality of LDL particles, making them smaller, denser, and more dangerous [00:28:22].

4. Dietary Strategies

• Saturated Fats: High intake of animal fats (butter, lard, fatty meats) and certain plant fats (coconut/palm oil) increases LDL levels [00:43:04].
• The Power of Fiber: Soluble fiber (found in oats, legumes, and psyllium) binds bile acids in the gut, preventing the reabsorption of cholesterol [00:45:24].
• Plant-Based Fats: Replacing saturated fats with polyunsaturated and monounsaturated fats (olive oil, nuts, fatty fish) is a primary dietary intervention [00:44:46].
• Carbohydrates and Triglycerides: Excess simple sugars and alcohol are the main drivers of high triglycerides [00:47:48].

5. Pharmacological Treatment (Statins)

• Safety Profile: Statins are described as some of the safest drugs in cardiology [00:01:08].
• Beyond Lowering LDL: Statins do more than lower cholesterol; they have "pleiotropic" effects, meaning they stabilize existing plaques and reduce systemic inflammation [00:56:33].
• Side Effects and the "Nocebo" Effect: * Muscle pain occurs in about 9% of patients in clinical trials, but many subjective complaints are due to the nocebo effect (expecting side effects because of negative publicity) [01:03:06].
  • True statin intolerance is rare; switching to a different type or dose of statin often resolves issues [01:01:15].
• Liver Impact: Serious liver damage is extremely rare (1 in 100,000). Minor elevations in liver enzymes are usually temporary as the liver adapts [01:04:05].

6. Supplements and "Nutraceuticals"

• Supplements vs. Medication: Supplements like berberine or red yeast rice (monacolin K) are not substitutes for medication in high-risk patients (e.g., those who have already had a heart attack) [01:09:46].
• Red Yeast Rice: Contains monacolin K, which is chemically identical to lovastatin. While "natural," it can still cause the same side effects as prescription statins [01:11:14].
• Coenzyme Q10: While statins can lower CoQ10 levels, clinical studies do not definitively show that supplementing it reduces muscle pain [01:06:19].

7. Key Takeaways for Longevity

• Start Early: Prevention is more effective than treating advanced disease.
• Test Extensively: Go beyond a basic lipid panel; request ApoB and Lp(a) tests [01:13:05].
• Continuity: Lifestyle changes and medications are long-term commitments. If you stop the intervention, the risk levels typically return to their baseline [01:14:11].

I wish people's works were more online—more accessible (preferably, with stable, citable URLs). But to say that the people themselves should be more public in the sense that the author means here is just a continuation-without-lessons-learned of the modern (Facebook and onwards) social networking era.

It's great if all of a person's contributions to, say, mailing lists are preserved and available—and aren't just holes, missing from the record.* That's different from whether or not it's great to be able to click on that person's name, find a profile page for them, and then encounter an exhaustive, reverse chronological feed of all of their activity across all mailing lists. Mailing lists rarely enable this, but virtually every modern social network does, and they're actually built around it.

The former is topic-based indexing, and the latter is actor- (person-) based indexing. Actor-based indexing is bad, and we know that it's bad.

Actor-based indexing is like running into someone you know (or a stranger, even) at the post office and then, through some mechanism where their physical presence wired to some metaverse data source, being able to perform some tap/gesture at the virtualized floating bubble over their heads that lets you see all the public places where you would have seen them earlier that day if you had been at any one of those, and then having a log of every interaction for the day prior, and the day before that, and so on, stretching back over their entire life, including the grocery store, the restaurant, the houseparty they were at, their work, etc. This would be bad. That means it's not good. And it's not good "online", either, for the exact same reasons.

* as unfortunately, many Mastodon (and other ActivityPub-powered) interactions turn out; Mastodon happens to achieve the worst of both worlds!

some thoughts can only be thought in the dark, but

The LLM leaves a bitter taste but...

EXTENDED SUMMARY: How to Feel Good and Be Healthy on a Budget

In this deep-dive conversation, Bartosz Czekała explores the intersection of biology, psychology, and lifestyle, providing practical advice on how to optimize health without spending a fortune.

1. The Biological Root of Mental Health

• Inflammation and the Brain: Czekała argues that mental health issues like depression and anxiety are often driven by systemic inflammation. Chronic inflammation increases the permeability of the blood-brain barrier, allowing pro-inflammatory molecules to affect the brain [00:00:47].
• Serotonin Inhibition: Inflammation doesn't just make you feel physically ill; it actively blocks the uptake of serotonin and lowers its overall levels, mimicking or causing clinical depression [00:00:36].
• Therapy vs. Medication: He notes that while millions rely on antidepressants, psychotherapy often shows better long-term results. He emphasizes BDNF (Brain-Derived Neurotrophic Factor) as a critical marker for brain health and recovery [00:01:07].

2. Hormonal Health and Body Composition

• Fat as a Hormonal Organ: Adipose tissue (body fat) is not just stored energy; it is an active endocrine organ. The more body fat a person has, the higher the activity of an enzyme called aromatase [02:37:30].
• The Testosterone-Estradiol Balance: In men, aromatase converts testosterone into estradiol (estrogen). High levels of body fat can lead to low testosterone and physical symptoms like gynecomastia ("man boobs") [02:37:48].
• Risks of Steroid Use: Czekała warns against the misuse of exogenous testosterone (steroids), noting that supra-physiological doses are hepatotoxic (liver-damaging) and can damage the heart, often leading the body to convert excess testosterone into estrogen as a defense mechanism [02:38:09].

3. Low-Cost "Biohacking" and Lifestyle

• Ergonomics for Longevity: One of the cheapest health interventions is changing how you work. He suggests working from the floor or a mat rather than a traditional chair to maintain mobility and cardiovascular health during home office hours [00:00:26].
• Nutrition as a Foundation: He advocates for a diet rich in high-quality animal products and nutrient-dense meats as a way to prevent deficiencies and maintain hormonal balance [00:03:36].
• Nature and Circadian Rhythms: Simple, free practices like spending time outdoors, grounding, and aligning with natural light cycles are cited as powerful tools for reducing systemic inflammation.

4. Diagnostics and Critical Thinking

• Recommended Testing: To truly understand one's health, Czekała recommends testing not just Total Testosterone, but also Estradiol, DHEA-S, Androstenedione, and markers of systemic inflammation [02:38:54].
• Evaluating Science: He draws a distinction between "hard" sciences (physics/math) and "soft" sciences (psychology/sociology). In human biology, results are rarely black-and-white; the answer is almost always "it depends" on the individual context [00:22:30].

5. Conclusion

The central takeaway is that health is a result of low inflammation, balanced hormones, and intentional movement. By focusing on biological fundamentals—diagnostics, diet, and environment—one can achieve significant health improvements without relying on expensive supplements or "magic pill" solutions.

• Main Thesis: While coffee is often marketed as "neuroprotective," there is significant scientific evidence suggesting it may have negative effects on brain health, including a reduction in gray matter.
• Antioxidants: Coffee is a major source of dietary antioxidants, but the video argues that exogenous (external) antioxidants can interfere with the body's more effective endogenous (internal) antioxidant systems [00:04:54].
• Cerebral Blood Flow: Caffeine acts as a vasoconstrictor. Studies using PET scans show that consuming 200-250 mg of caffeine (about 2-3 cups) can reduce blood flow throughout the brain by approximately 30% [00:22:24].
• Gray Matter Impact: Research indicates that even short-term regular caffeine consumption (e.g., 10 days) can lead to a detectable decrease in gray matter volume in the medial temporal lobe [00:26:50].
• Adenosine Blocking: Caffeine works by blocking adenosine receptors, which normally signal the brain to rest. This leads to an artificial increase in stimulating neurotransmitters like adrenaline and glutamate [00:17:53].
• Genetic Variability: The speed of caffeine metabolism is largely determined by the CYP1A2 enzyme. "Slow metabolizers" can experience up to ten times higher concentrations of caffeine in their system compared to "fast metabolizers" [00:13:49].
• Toxins and Quality: Many commercial coffees, especially instant varieties, contain detectable levels of mycotoxins (like ochratoxin A). The cumulative effect of these toxins across different food sources is a potential health concern [00:10:32].
• Neuroprotection Claims: Most evidence for coffee's benefits is based on epidemiological correlations rather than clinical trials. While there may be a link to reduced Parkinson's risk, large meta-analyses have found no significant link between coffee and Alzheimer's prevention [00:33:15].
• The "U-Shaped" Rule: Any potential benefits from coffee appear to follow a U-shaped curve; consuming more than four cups a day generally eliminates any statistical health advantages and may increase risks [00:35:06].

• Non-Binary Process: Vocabulary acquisition is not a simple "know it or don't" situation; it is a gradual progression through multiple levels of familiarity [00:00:10].
• Initial Exposure: The first stage involves hearing or seeing a word for the first time. You recognize you've seen it before but don't yet know the meaning [00:00:34].
• Emerging Recognition: After looking up a word a few times, you may occasionally recall the definition, but it is not yet consistent [00:01:07].
• Conscious Knowledge: You reach a stage where you can provide the correct definition, often aided by tools like Spaced Repetition Systems (SRS) or flashcards [00:01:25].
• The "Wall of Sound" Challenge: Even if you "know" a word, there is often a delay in processing it during live audio or reading, which can cause you to fall behind in a conversation [00:02:22].
• Subconscious Processing: With more exposure, the mental translation time decreases from seconds to being processed instantly without conscious effort [00:03:31].
• Passive vs. Active Vocabulary: It takes additional time and practice for words to move from passive recognition to active use in speaking [00:04:11].
• Role of Compelling Content: While flashcards help reach initial recognition, "compelling content" like reading, podcasts, and movies is what builds the subconscious strength needed for fluency [00:05:05].
• The Power of Reading and Listening: Repeated exposure to common words in natural contexts (like books or games) reinforces knowledge until translation is no longer necessary [00:05:30].

Enhancement models (to clean up transcripts), mentions things I have locally, but suggests it uses third party external services for it.

yes, agency does increase, where people realise this works locally.

History suggests this is the likely path. Most people will accept the phone, ipads and computers as they are. Like IndieWeb there is a population of people going against that current, but not a tide. Unless e.g. interoperability reqs from DSA, DMA force the issue. Sideloading is actually installing.

Two moats, app store for legitimacy and the hurdle for creation. Second one has become more shallow and narrow now.

Making personal tools makes generic ones obsolete. Yet, the generic ones do serve as starting point for inspiration and design choices. Personal iterations on top of what went before.

but cooking meat reduces this by 70-100%?

US admin sanctioned people involved in shaping the DSA. The wilfull misreading of DMA, DSA, AIR, GDPR in the US and bigtech is a clear confirmation of its need for the European market.

Article seems too narrow in looking at the dynamics. Tech platforms are not the context, single market and market rules and access are, including outside digital. Meaning every other party dealing with platforms has a very different set of considerations when choosing platforms of any size. Loss of market access is not about the tech, but about whether there are others willing to do business with you.

The US admin escalation is likely a spasm, and if not a cause of bifurcation rather than response. The base case is shoulder shrugs anywhere outside the USA. The endpoint is no market access for non-compliant platforms of any size. Which does not mean a ban or tech blockade, but the absence of possibility to interact with the EU market as corporates, including the ad market e.g. The law of two feet is the largest fine here (not just the platforms need to be compliant, their businesspartners too, and for them to walk is the cheaper compliance path.

Not a wild card (The wild card is the zero sum behaviour of US admin), but an aimed for outcome. Standardisation, transparency and interoperability are key digital policy aims. Note that it is exactly what big tech is clamoring against at the moment.

This is not second order, but a primary policy aim for the EU digital single market.

Regulatory differences are of all time and splinternet it is not, which implies hard (tech) breaks. Additionally the DSA is unifying for Europe, part of the digital single market. It only looks like divergence to any incumbents outside the EU.

yes, sort of. 'distribution layer of democracy' interesting phrase. amplification, throttling, monetisation (Freudian misspelling there?), discoverability all important. But the key thing: the platforms in question are not platforms in the strict sense, they actively shape the information there. So liability protections for platforms should not apply. Or gov also can set the boundaries of such shaping.

There are many other fines levied (all without making any dents in the behaviour fined though), the 120ME one for Twitter was the first under DSA illegal content rules (which don't specify what illegal content, but mechanisms for moderation against them)

I'm just not a scalpel person! Ha!

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

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Reply to the reviewers

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

Summary: In this study, the authors used proximity proteomics in U2OS cells to identify several E3 ubiquitin ligases recruited to stress granules (SGs), and they focused on MKRN2 as a novel regulator. They show that MKRN2 localization to SGs requires active ubiquitination via UBA1. Functional experiments demonstrated that MKRN2 knockdown increases the number of SG condensates, reduces their size, slightly raises SG liquidity during assembly, and slows disassembly after heat shock. Overexpression of MKRN2-GFP combined with confocal imaging revealed co-localization of MKRN2 and ubiquitin in SGs. By perturbing ubiquitination (using a UBA1 inhibitor) and inducing defective ribosomal products (DRiPs) with O-propargyl puromycin, they found that both ubiquitination inhibition and MKRN2 depletion lead to increased accumulation of DRiPs in SGs. The authors conclude that MKRN2 supports granulostasis, the maintenance of SG homeostasis , through its ubiquitin ligase activity, preventing pathological DRiP accumulation within SGs.

Major comments: - Are the key conclusions convincing? The key conclusions are partially convincing. The data supporting the role of ubiquitination and MKRN2 in regulating SG condensate dynamics are coherent, well controlled, and consistent with previous literature, making this part of the study solid and credible. However, the conclusions regarding the ubiquitin-dependent recruitment of MKRN2 to SGs, its relationship with UBA1 activity, the functional impact of the MKRN2 knockdown for DRiP accumulation are less thoroughly supported. These aspects would benefit from additional mechanistic evidence, validation in complementary model systems, or the use of alternative methodological approaches to strengthen the causal connections drawn by the authors. - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? The authors should qualify some of their claims as preliminary. 1) MKRN2 recruitment to SGs (ubiquitin-dependent): The proteomics and IF data are a reasonable starting point, but they do not yet establish that MKRN2 is recruited from its physiological localization to SGs in a ubiquitin-dependent manner. To avoid overstating this point the authors should qualify the claim and/or provide additional controls: show baseline localization of endogenous MKRN2 under non-stress conditions (which is reported in literature to be nuclear and cytoplasmatic), include quantification of nuclear/cytoplasmic distribution, and demonstrate a shift into bona fide SG compartments after heat shock. Moreover, co-localization of overexpressed GFP-MKRN2 with poly-Ub (FK2) should be compared to a non-stress control and to UBA1-inhibition conditions to support claims of stress- and ubiquitination-dependent recruitment. *

Authors: We will stain cells for endogenous MKRN2 and quantify nuc/cyto ratio of MKRN2 without heat stress, without heat stress + TAK243, with HS and with HS + TAK243. We will do the same in the MKRN2-GFP overexpressing line while also staining for FK2.

*2) Use and interpretation of UBA1 inhibition: UBA1 inhibition effectively blocks ubiquitination globally, but it is non-selective. The manuscript should explicitly acknowledge this limitation when interpreting results from both proteomics and functional assays. Proteomics hits identified under UBA1 inhibition should be discussed as UBA1-dependent associations rather than as evidence for specific E3 ligase recruitment. The authors should consider orthogonal approaches before concluding specificity. *

Authors: We have acknowledged the limitation of using only TAK243 in our study by rephrasing statements about dependency on “ubiquitination” to “UBA1-dependent associations”.

* 3) DRiP accumulation and imaging quality: The evidence presented in Figure 5 is sufficient to substantiate the claim that DRiPs accumulate in SGs upon ubiquitination inhibition or MKRN2 depletion but to show that the event of the SGs localization and their clearance from SGs during stress is promoted by MKRN3 ubiquitin ligase activity more experiments would be needed. *

Authors: We have acknowledged the fact that our experiments do not include DRiP and SG dynamics assays using ligase-dead mutants of MKRN2 by altering our statement regarding MKRN2-mediated ubiquitination of DRiPs in the text (as proposed by reviewer 1).

*- Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation. Yes, a few targeted experiments would strengthen the conclusions without requiring the authors to open new lines of investigation. 1) Baseline localization of MKRN2: It would be important to show the baseline localization of endogenous and over-expressed MKRN2 (nuclear and cytoplasmic) under non-stress conditions and prior to ubiquitination inhibition. This would provide a reference to quantify redistribution into SGs and demonstrate recruitment in response to heat stress or ubiquitination-dependent mechanisms. *

Authors: We thank the reviewer for bringing this important control. We will address it in revisions.

We will quantify the nuclear/cytoplasmic distribution of endogenous and GFP-MKRN2 under control, TAK243, heat shock, and combined conditions, and assess MKRN2–ubiquitin colocalization by FK2 staining in unstressed cells.

* 2) Specificity of MKRN2 ubiquitin ligase activity: to address the non-specific effects of UBA1 inhibition and validate that observed phenotypes depend on MKRN2's ligase activity, the authors could employ a catalytically inactive MKRN2 mutant in rescue experiments. Comparing wild-type and catalytic-dead MKRN2 in the knockdown background would clarify the causal role of MKRN2 activity in SG dynamics and DRiP clearance. *

Authors: We thank the reviewer for this suggestion and have altered the phrasing of some of our statements in the text accordingly.

* 3) Ubiquitination linkage and SG marker levels: While the specific ubiquitin linkage type remains unknown, examining whether MKRN2 knockdown or overexpression affects total levels of key SG marker proteins would be informative. This could be done via Western blotting of SG markers along with ubiquitin staining, to assess whether MKRN2 influences protein stability or turnover through degradative or non-degradative ubiquitination. Such data would strengthen the mechanistic interpretation while remaining within the current study's scope. *

Authors: We thank the reviewer for requesting and will address it by performing MKRN2 KD and perform Western blot for G3BP1.

*

• Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments. The experiments suggested in points 1 and 3 are realistic and should not require substantial additional resources beyond those already used in the study. • Point 1 (baseline localization of MKRN2): This involves adding two control conditions (no stress and no ubiquitination inhibition) for microscopy imaging. The setup is essentially the same as in the current experiments, with time requirements mainly dependent on cell culture growth and imaging. Overall, this could be completed within a few weeks. • Point 3 (SG marker levels and ubiquitination): This entails repeating the existing experiment and adding a Western blot for SG markers and ubiquitin. The lab should already have the necessary antibodies, and the experiment could reasonably be performed within a couple of weeks. • Point 2 (catalytically inactive MKRN2 mutant and rescue experiments): This is likely more time-consuming. Designing an effective catalytic-dead mutant depends on structural knowledge of MKRN2 and may require additional validation to confirm loss of catalytic activity. If this expertise is not already present in the lab, it could significantly extend the timeline. Therefore, this experiment should be considered only if similarly recommended by other reviewers, as it represents a higher resource and time investment.

Overall, points 1 and 3 are highly feasible, while point 2 is more substantial and may require careful planning.

• Are the data and the methods presented in such a way that they can be reproduced? Yes. The methodologies used in this study to analyze SG dynamics and DRiP accumulation are well-established in the field and should be reproducible, particularly by researchers experienced in stress granule biology. Techniques such as SG assembly and disassembly assays, use of G3BP1 markers, and UBA1 inhibition are standard and clearly described. The data are generally presented in a reproducible manner; however, as noted above, some results would benefit from additional controls or complementary experiments to fully support specific conclusions.

• Are the experiments adequately replicated and statistical analysis adequate? Overall, the experiments in the manuscript appear to be adequately replicated, with most assays repeated between three and five times, as indicated in the supplementary materials. The statistical analyses used are appropriate and correctly applied to the datasets presented. However, for Figure 5 the number of experimental replicates is not reported. This should be clarified, and if the experiment was not repeated sufficiently, additional biological replicates should be performed. Given that this figure provides central evidence supporting the conclusion that DRiP accumulation depends on ubiquitination-and partly on MKRN2's ubiquitin ligase activity-adequate replication is essential. *

Authors: We thank the reviewer for noting this accidental omission. We now clarify in the legend of Figure 5 that the experiments with DRiPs were replicated three times.

Minor comments: - Specific experimental issues that are easily addressable. • For the generation and the validation of MKRN2 knockdown in UOS2 cells data are not presented in the results or in the methods sections to demonstrate the effective knockdown of the protein of interest. This point is quite essential to demonstrate the validity of the system used

Authors: We thank the reviewer for requesting and will address it by performing MKRN2 KD and perform Western blot and RT-qPCR.

• * In the supplementary figure 2 it would be useful to mention if the Western Blot represent the input (total cell lysates) before the APEX-pulldown or if it is the APEX-pulldown loaded for WB. There is no consistence in the difference of biotynilation between different replicates shown in the 2 blots. For example in R1 and R2 G3BP1-APX TAK243 the biotynilation is one if the strongest condition while on the left blot, in the same condition comparison samples R3 and R4 are less biotinilated compared to others. It would be useful to provide an explanation for that to avoid any confusion for the readers. * Authors: We have added a mention in the legend of Figure S2 that these are total cell lysates before pulldown. The apparent differences in biotin staining are small and not sufficient to question the results of our APEX-proteomics.

• * In Figure 2D, endogenous MKRN2 localization to SGs appears reduced following UBA1 inhibition. However, it is not clear whether this reduction reflects a true relocalization or a decrease in total MKRN2 protein levels. To support the interpretation that UBA1 inhibition specifically affects MKRN2 recruitment to SGs rather than its overall expression, the authors should provide data showing total MKRN2 levels remain unchanged under UBA1 inhibition, for example via Western blot of total cell lysates. * Authors: Based on first principles in regulation of gene expression, it is unlikely that total MKRN2 expression levels would decrease appreciably through transcriptional or translational regulation within the short timescale of these experiments (1 h TAK243 pretreatment followed by 90 min of heat stress).

• * DRIPs accumulation is followed during assembly but in the introduction is highlighted the fact that ubiquitination events, other reported E3 ligases and in this study data on MKRN2 showed that they play a crucial role in the disassembly of SGs which is also related with cleareance of DRIPs. Authors could add tracking DRIPs accumulation during disassembly to be added to Figure 5. I am not sure about the timeline required for this but I am just adding as optional if could be addressed easily. * Authors: We thank the reviewer for proposing this experimental direction. However, in a previous study (Ganassi et al., 2016; 10.1016/j.molcel.2016.07.021), we demonstrated that DRiP accumulation during the stress granule assembly phase drives conversion to a solid-like state and delays stress granule disassembly. It is therefore critical to assess DRiP enrichment within stress granules immediately after their formation, rather than during the stress recovery phase, as done here.

• * The authors should clarify in the text why the cutoff used for the quantification in Figure 5D (PC > 3) differs from the cutoff used elsewhere in the paper (PC > 1.5). Providing a rationale for this choice will help the reader understand the methodological consistency and ensure that differences in thresholds do not confound interpretation of the results. * Authors: We thank the reviewer for this question. The population of SGs with a DRiP enrichment > 1.5 represents SGs with a significant DRiP enrichment compared to the surrounding (background) signal. As explained in the methods, the intensity of DRiPs inside each SG is corrected by the intensity of DRiPs two pixels outside of each SG. Thus, differences in thresholds between independent experimental conditions (5B versus 5D) do not confound interpretation of the results but depend on overall staining intensity that can different between different experimental conditions. Choosing the cut-off > 3 allows to specifically highlight the population of SGs that are strongly enriched with DRiPs. MKRN2 silencing caused a strong DRiP enrichment in the majority of the SGs analyzed and therefore we chose this way of data representation. Note that the results represent the average of the analysis of 3 independent experiments with high numbers of SGs automatically segmented and analyzed/experiment. Figure 5A, B: n = 3 independent experiments; number of SGs analyzed per experiment: HS + OP-puro (695; 1216; 952); TAK-243 + HS + OP-puro (1852; 2214; 1774). Figure 5C, D: n = 3 independent experiments; number of SGs analyzed per experiment: siRNA control, HS + OP-puro (1984; 1400; 1708); siRNA MKRN2, HS + OP-puro (912; 1074; 1532).

• * For Figure 3G, the authors use over-expressed MKRN2-GFP to assess co-localization with ubiquitin in SGs. Given that a reliable antibody for endogenous MKRN2 is available and that a validated MKRN2 knockdown line exists as an appropriate control, this experiment would gain significantly in robustness and interpretability if co-localization were demonstrated using endogenous MKRN2. In the current over-expression system, MKRN2-GFP is also present in the nucleus, whereas the endogenous protein does not appear nuclear under the conditions shown. This discrepancy raises concerns about potential over-expression artifacts or mislocalization. Demonstrating co-localization using endogenous MKRN2 would avoid confounding effects associated with over-expression. If feasible, this would be a relatively straightforward experiment to implement, as it relies on tools (antibody and knockdown line) already described in the manuscript.

* Authors: We thank the reviewer for requesting and will address it by performing MKRN2 KD, FK2 immunofluorescence microscopy and perform SG partition coefficient analysis.

* - Are prior studies referenced appropriately? • From line 54 to line 67, the manuscript in total cites eight papers regarding the role of ubiquitination in SG disassembly. However, given the use of UBA1 inhibition in the initial MS-APEX experiment and the extensive prior literature on ubiquitination in SG assembly and disassembly under various stress conditions, the manuscript would benefit from citing additional relevant studies to provide more specifc examples. Expanding the references would provide stronger context, better connect the current findings to prior work, and emphasize the significance of the study in relation to established literature *

Authors: We have added citations for the relevant studies.

• *

At line 59, it would be helpful to note that G3BP1 is ubiquitinated by TRIM21 through a Lys63-linked ubiquitin chain. This information provides important mechanistic context, suggesting that ubiquitination of SG proteins in these pathways is likely non-degradative and related to functional regulation of SG dynamics rather than protein turnover. * Authors: The reviewer is correct. We have added to the text that G3BP1 is ubiquitinated through a Lys63-linked ubiquitin chain.

• *

When citing references 16 and 17, which report that the E3 ligases TRIM21 and HECT regulate SG formation, the authors should provide a plausible explanation for why these specific E3 ligases were not detected in their proteomics experiments. Differences could arise from the stress stimulus used, cell type, or experimental conditions. Similarly, since MKRN2 and other E3 ligases identified in this study have not been reported in previous works, discussing these methodological or biological differences would help prevent readers from questioning the credibility of the findings. It would also be valuable to clarify in the Conclusion that different types of stress may activate distinct ubiquitination pathways, highlighting context-dependent regulation of SG assembly and disassembly. * Authors: We thank the reviewer for this suggestion. We added to the discussion plausible explanations for why our study identified new E3 ligases.

• *

Line 59-60: when referring to the HECT family of E3 ligases involved in ubiquitination and SG disassembly, it would be more precise to report the specific E3 ligase identified in the cited studies rather than only the class of ligase. This would provide clearer mechanistic context and improve accuracy for readers. * Authors: We have added this detail to the discussion.

• *

The specific statement on line 182 "SG E3 ligases that depend on UBA1 activity are RBULs" should be supported by reference. * Authors: We have added citations to back up our claim that ZNF598, CNOT4, MKRN2, TRIM25 and TRIM26 exhibit RNA-binding activity.

*- Are the text and figures clear and accurate?

• In Supplementary Figure 1, DMSO is shown in green and the treatment in red, whereas in the main figures (Figure 1B and 1F) the colours in the legend are inverted. To avoid confusion, the colour coding in figure legends should be consistent across all figures throughout the manuscript. *

Authors: We have made the colors consistent across the main and supplementary figures.

• *

At line 79, the manuscript states that "inhibition of ubiquitination delayed fluorescence recovery dynamics of G3BP1-mCherry, relative to HS-treated cells (Figure 1F, Supplementary Fig. 6A)." However, the data shown in Figure 1F appear to indicate the opposite effect: the TAK243-treated condition (green curve) shows a faster fluorescence recovery compared to the control (red curve). This discrepancy between the text and the figure should be corrected or clarified, as it may affect the interpretation of the role of ubiquitination in SG dynamics. * Authors: Good catch. We now fixed the graphical mistake (Figure 1F and S6).

• * Line 86: adjust a missing bracket * Authors: Thank you, we fixed it.

• *

There appears to be an error in the legend of Supplementary Figure 3: the legend states that the red condition (MKRN2) forms larger aggregates, but both the main Figure 3C of the confocal images and the text indicate that MKRN2 (red) forms smaller aggregates. Please correct the legend and any corresponding labels so they are consistent with the main figure and the text. The authors should also double-check that the figure panel order, color coding, and statistical annotations match the legend and the descriptions in the Results section to avoid reader confusion.

* Authors: This unfortunate graphical mistake has been corrected.

• * At lines 129-130, the manuscript states that "FRAP analysis demonstrated that MKRN2 KD resulted in a slight increase in SG liquidity (Fig. 3F, Supplementary Fig. 6B)." However, the data shown in Figure 3F appear to indicate the opposite trend: the MKRN2 KD condition (red curve) exhibits a faster fluorescence recovery compared to the control (green curve). This discrepancy between the text and the figure should be corrected or clarified, as it directly affects the interpretation of MKRN2's role in SG disassembly. Ensuring consistency between the written description and the plotted FRAP data is essential for accurate interpretation. * Authors: We thank the reviewer and clarify in the legend of Figure 3F and the Results the correct labels: indeed faster fluorescence recovery seen in MKRN2 KD is correctly interpreted as increased liquidity in the text.

• *

At lines 132-133, the manuscript states: "Then, to further test the impact of MKRN2 on SG dynamics, we overexpressed MKRN2-GFP and observed that it was recruited to SG (Fig. 3G)." This description should be corrected or clarified, as the over-expressed MKRN2-GFP also appears to localize to the nucleus. * Authors: The text has been modified to reflect both the study of MKRN2 localization to SGs and of nuclear localization.

• *

At lines 134-135, the manuscript states that the FK2 antibody detects "free ubiquitin." This is incorrect. FK2 does not detect free ubiquitin; it recognizes only ubiquitin conjugates, including mono-ubiquitinated and poly-ubiquitinated proteins. The text should be corrected accordingly to avoid misinterpretation of the immunostaining data. * Authors: Thank you for pointing out this error. We have corrected it.

• * Figure 5A suffers from poor resolution, and no scale bar is provided, which limits interpretability. Additionally, the ROI selected for the green channel (DRIPs) appears to capture unspecific background staining, while the most obvious DRIP spots are localized in the nucleus. The authors should clarify this in the text, improve the image quality if possible, and ensure that the ROI accurately represents DRIP accumulation - in SGs rather than background signal. * Authors: We thank the reviewer for pointing the sub-optimal presentation of this figure. We modified Figure 5A to improve image quality and interpretation. Concerning the comment that “the most obvious DRIP spots are localized in the nucleus”, this is in line with our previous findings demonstrating that a fraction of DRiPs accumulates in nucleoli (Mediani et al. 2019 10.15252/embj.2018101341). To avoid misinterpretation, we modified Figure 5A as follows: (i) we provide a different image for control cells, exposed to heat shock and OP-puro; (ii) we select a ROI that only shows a few stress granules; (iii) we added arrowheads to indicate the nucleoli that are strongly enriched for DRiPs; (iv) we include a dotted line to show the nuclear membrane, helping to distinguish cytoplasm and nucleus in the red and green channel. We also include the scale bars (5 µm) in the image.

* Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

• In the first paragraph following the APEX proteomics results, the authors present validation data exclusively for MKRN2, justifying this early focus by stating that MKRN2 is the most SG-depleted E3 ligase. However, in the subsequent paragraph they introduce the RBULs and present knockdown data for MKRN2 along with two additional E3 ligases identified in the screen, before once again emphasizing that MKRN2 is the most SG-depleted ligase and therefore the main focus of the study. For clarity and logical flow, the manuscript would benefit from reordering the narrative. Specifically, the authors should first present the validation data for all three selected E3 ligases, and only then justify the decision to focus on MKRN2 for in-depth characterization. In addition to the extent of its SG depletion, the authors may also consider providing biologically relevant reasons for prioritizing MKRN2 (e.g., domain architecture, known roles in stress responses, or prior evidence of ubiquitination-related functions). Reorganizing this section would improve readability and better guide the reader through the rationale for the study's focus.*

Authors: We thank the reviewer for this suggested improvement to our “storyline”. As suggested by the reviewer, we have moved the IF validation of MKRN2 to the following paragraph in order to improve the flow of the manuscript. We added additional justification to prioritizing MKRN2 citing (Youn et al. 2018 and Markmiller et al. 2018).

• *

At lines 137-138, the manuscript states: "Together these data indicate that MKRN2 regulates the assembly dynamics of SGs by promoting their coalescence during HS and can increase SG ubiquitin content." While Figure 3G shows some co-localization of MKRN2 with ubiquitin, immunofluorescence alone is insufficient to claim an increase in SG ubiquitin content. This conclusion should be supported by orthogonal experiments, such as Western blotting, in vitro ubiquitination assays, or immunoprecipitation of SG components. Including a control under no-stress conditions would also help demonstrate that ubiquitination increases specifically in response to stress. The second part of the statement should therefore be rephrased to avoid overinterpretation, for example:"...and may be associated with increased ubiquitination within SGs, as suggested by co-localization, pending further validation by complementary assays." * Authors: The statement has been rephrased in a softer way as suggested by the reviewer.

• At line 157, the statement: "Therefore, we conclude that MKRN2 ubiquitinates a subset of DRiPs, avoiding their accumulation inside SGs" should be rephrased as a preliminary observation. While the data support a role for MKRN2 in SG disassembly and a reduction of DRIPs, direct ubiquitination of DRIPs by MKRN2 has not been demonstrated. A more cautious phrasing would better reflect the current evidence and avoid overinterpretation. * * *Authors: We thank the reviewer for this suggestion and have altered the phrasing of this statement accordingly.

*Reviewer #1 (Significance (Required)):

General assessment: provide a summary of the strengths and limitations of the study. What are the strongest and most important aspects? What aspects of the study should be improved or could be developed?

• This study provides a valuable advancement in understanding the role of ubiquitination in stress granule (SG) dynamics and the clearance of SGs formed under heat stress. A major strength is the demonstration of how E3 ligases identified through proteomic screening, particularly MKRN2, influence SG assembly and disassembly in a ubiquitination- and heat stress-dependent manner. The combination of proteomics, imaging, and functional assays provides a coherent mechanistic framework linking ubiquitination to SG homeostasis. Limitations of the study include the exclusive use of a single model system (U2OS cells), which may limit generalizability. Additionally, some observations-such as MKRN2-dependent ubiquitination within SGs and changes in DRIP accumulation under different conditions-would benefit from orthogonal validation experiments (e.g., Western blotting, immunoprecipitation, or in vitro assays) to confirm and strengthen these findings. Addressing these points would enhance the robustness and broader applicability of the conclusions.

Advance: compare the study to the closest related results in the literature or highlight results reported for the first time to your knowledge; does the study extend the knowledge in the field and in which way? Describe the nature of the advance and the resulting insights (for example: conceptual, technical, clinical, mechanistic, functional,...).

• The closest related result in literature is - Yang, Cuiwei et al. "Stress granule homeostasis is modulated by TRIM21-mediated ubiquitination of G3BP1 and autophagy-dependent elimination of stress granules." Autophagy vol. 19,7 (2023): 1934-1951. doi:10.1080/15548627.2022.2164427 - demonstrating that TRIM21, an E3 ubiquitin ligase, catalyzes K63-linked ubiquitination of G3BP1, a core SG nucleator, under oxidative stress. This ubiquitination by TRIM21 inhibits SG formation, likely by altering G3BP1's propensity for phase separation. In contrast, the MKRN2 study identifies a different E3 (MKRN2) that regulates SG dynamics under heat stress and appears to influence both assembly and disassembly. This expands the role of ubiquitin ligases in SG regulation beyond those previously studied (like TRIM21).

• Gwon and colleagues (Gwon Y, Maxwell BA, Kolaitis RM, Zhang P, Kim HJ, Taylor JP. Ubiquitination of G3BP1 mediates stress granule disassembly in a context-specific manner. Science. 2021;372(6549):eabf6548. doi:10.1126/science.abf6548) have shown that K63-linked ubiquitination of G3BP1 is required for SG disassembly after heat stress. This ubiquitinated G3BP1 recruits the segregase VCP/p97, which helps extract G3BP1 from SGs for disassembly. The MKRN2 paper builds on this by linking UBA1-dependent ubiquitination and MKRN2's activity to SG disassembly. Specifically, they show MKRN2 knockdown affects disassembly, and suggest MKRN2 helps prevent accumulation of defective ribosomal products (DRiPs) in SGs, adding a new layer to the ubiquitin-VCP model.

• Ubiquitination's impact is highly stress- and context-dependent (different chain types, ubiquitin linkages, and recruitment of E3s). The MKRN2 work conceptually strengthens this idea: by showing that MKRN2's engagement with SGs depends on active ubiquitination via UBA1, and by demonstrating functional consequences (SG dynamics + DRIP accumulation), the study highlights how cellular context (e.g., heat stress) can recruit specific ubiquitin ligases to SGs and modulate their behavior.

• There is a gap in the literature: very few (if any) studies explicitly combine the biology of DRIPs, stress granules, and E3 ligase mediated ubiquitination, especially in mammalian cells. There are relevant works about DRIP biology in stress granules, but those studies focus on chaperone-based quality control, not ubiquitin ligase-mediated ubiquitination of DRIPs. This study seems to be one of the first to make that connection in mammalian (or human-like) SG biology. A work on the plant DRIP-E3 ligase TaSAP5 (Zhang N, Yin Y, Liu X, et al. The E3 Ligase TaSAP5 Alters Drought Stress Responses by Promoting the Degradation of DRIP Proteins. Plant Physiol. 2017;175(4):1878-1892. doi:10.1104/pp.17.01319 ) shows that DRIPs can be directly ubiquitinated by E3s in other biological systems - which supports the plausibility of the MKRN2 mechanism, but it's not the same context.

• A very recent review (Yuan, Lin et al. "Stress granules: emerging players in neurodegenerative diseases." Translational neurodegeneration vol. 14,1 22. 12 May. 2025, doi:10.1186/s40035-025-00482-9) summarizes and reinforces the relationship among SGs and the pathogenesis of different neurodegenerative diseases (NDDs). By identifying MKRN2 as a new ubiquitin regulator in SGs, the current study could have relevance for neurodegeneration and proteotoxic diseases, providing a new candidate to explore in disease models.

Audience: describe the type of audience ("specialized", "broad", "basic research", "translational/clinical", etc...) that will be interested or influenced by this research; how will this research be used by others; will it be of interest beyond the specific field?

The audience for this paper is primarily specialized, including researchers in stress granule biology, ubiquitin signaling, protein quality control, ribosome biology, and cellular stress responses. The findings will also be of interest to scientists working on granulostasis, nascent protein surveillance, and proteostasis mechanisms. Beyond these specific fields, the study provides preliminary evidence linking ubiquitination to DRIP handling and SG dynamics, which may stimulate new research directions and collaborative efforts across complementary areas of cell biology and molecular biology.

• Please define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

I work in ubiquitin biology, focusing on ubiquitination signaling in physiological and disease contexts, with particular expertise in the identification of E3 ligases and their substrates across different cellular systems and in vivo models. I have less expertise in stress granule dynamics and DRiP biology, so my evaluation of those aspects is more limited and relies on interpretation of the data presented in the manuscript.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

This study identifies the E3 ubiquitin ligase Makorin 2 (MKRN2) as a novel regulator of stress granule (SG) dynamics and proteostasis. Using APEX proximity proteomics, the authors demonstrate that inhibition of the ubiquitin-activating enzyme UBA1 with TAK243 alters the SG proteome, leading to depletion of several E3 ligases, chaperones, and VCP cofactors. Detailed characterization of MKRN2 reveals that it localizes to SGs in a ubiquitination-dependent manner and is required for proper SG assembly, coalescence, and disassembly. Functionally, MKRN2 prevents the accumulation of defective ribosomal products (DRiPs) within SGs, thereby maintaining granulostasis. The study provides compelling evidence that ubiquitination, mediated specifically by MKRN2, plays a critical role in surveilling stress-damaged proteins within SGs and maintaining their dynamic liquid-like properties. Major issues: 1. Figures 1-2: Temporal dynamics of ubiquitination in SGs. The APEX proteomics was performed at a single timepoint (90 min heat stress), yet the live imaging data show that SG dynamics and TAK243 effects vary considerably over time: • The peak or SG nucleation was actually at 10-30 min (Figure 1B). • TAK243 treatment causes earlier SG nucleation (Figure 1B) but delayed disassembly (Figure 1A-B, D). A temporal proteomic analysis at multiple timepoints (e.g., 30 min, 60 min, 90 min of heat stress, and during recovery) would reveal whether MKRN2 and other ubiquitination-dependent proteins are recruited to SGs dynamically during the stress response. It would also delineate whether different E3 ligases predominate at different stages of the SG lifecycle. While such experiments may be beyond the scope of the current study, the authors should at minimum discuss this limitation and acknowledge that the single-timepoint analysis may miss dynamic changes in SG composition. *

Authors: We thank the reviewer for identifying this caveat in our methodology. We now discuss this limitation and acknowledge that the single-timepoint analysis may miss dynamic changes in SG composition.

* Figures 2D-E, 3G: MKRN2 localization mechanism requires clarification. The authors demonstrate that MKRN2 localization to SGs is dependent on active ubiquitination, as TAK243 treatment significantly reduces MKRN2 partitioning into SGs (Figure 2D-E). However, several mechanistic questions remain: • Does MKRN2 localize to SGs through binding to ubiquitinated substrates within SGs, or does MKRN2 require its own ubiquitination activity to enter SGs? • The observation that MKRN2 overexpression increases SG ubiquitin content (Figure 3G-H) could indicate either: (a) MKRN2 actively ubiquitinates substrates within SGs, or (b) MKRN2 recruitment brings along pre-ubiquitinated substrates from the cytoplasm. • Is MKRN2 localization to SGs dependent on its E3 ligase activity? A catalytically inactive mutant of MKRN2 would help distinguish whether MKRN2 must actively ubiquitinate proteins to remain in SGs or whether it binds to ubiquitinated proteins independently of its catalytic activity. The authors should clarify whether MKRN2's SG localization depends on its catalytic activity or on binding to ubiquitinated proteins, as this would fundamentally affect the interpretation of its role in SG dynamics. *

Authors: We thank the reviewer for this experimental suggestion. We will perform an analysis of the SG partitioning coefficient between WT-MKRN2 and a RING mutant of MKRN2.

* Figures 3-4: Discrepancy between assembly and disassembly phenotypes. MKRN2 knockdown produces distinct phenotypes during SG assembly versus disassembly. During assembly: smaller, more numerous SGs that fail to coalesce (Figure 3A-E), while during disassembly: delayed SG clearance (Figure 4A-D). These phenotypes may reflect different roles for MKRN2 at different stages, but the mechanism underlying this stage-specificity is unclear: • Does MKRN2 have different substrates or utilize different ubiquitin chain types during assembly versus disassembly? • The increased SG liquidity upon MKRN2 depletion (Figure 3F) seems paradoxical with delayed disassembly- typically more liquid condensates disassemble faster. The authors interpret this as decreased coalescence into "dense and mature SGs," but this requires clarification. • How does prevention of DRiP accumulation relate to the assembly defect? One would predict that DRiP accumulation would primarily affect disassembly (by reducing liquidity), yet MKRN2 depletion impacts both assembly dynamics and DRiP accumulation. The authors should discuss how MKRN2's role in preventing DRiP accumulation mechanistically connects to both the assembly and disassembly phenotypes. *

Authors: We thank the reviewer and will add to the Discussion a mention of a precedent for this precise phenotype from our previous work (Seguin et al., 2014).

* Figure 5: Incomplete characterization of MKRN2 substrates. While the authors convincingly demonstrate that MKRN2 prevents DRiP accumulation in SGs (Figure 5C-D), the direct substrates of MKRN2 remain unknown. The authors acknowledge in the limitations that "the direct MKRN2 substrates and ubiquitin-chain types (K63/K48) are currently unknown." However, several approaches could strengthen the mechanistic understanding: • Do DRiPs represent direct MKRN2 substrates? Co-immunoprecipitation of MKRN2 followed by ubiquitin-chain specific antibodies (K48 vs K63) could reveal whether MKRN2 mediates degradative (K48) or non-degradative (K63) ubiquitination. *

Authors: The DRiPs generated in the study represent truncated versions of all the proteins that were in the process of being synthesized by the cell at the moment of the stress, and therefore include both MKRN2 specific substrates and MKRN2 independent substrates. Identifying specific MKRN2 substrates, while interesting as a new research avenue, is not within the scope of the present study.

• * Given that VCP cofactors (such as UFD1L, PLAA) are depleted from SGs upon UBA1 inhibition (Figure 2C) and these cofactors recognize ubiquitinated substrates, does MKRN2 function upstream of VCP recruitment? Testing whether MKRN2 depletion affects VCP cofactor localization to SGs would clarify this pathway. * Authors: We thank the reviewer for requesting and will address it by performing MKRN2 KD, VCP immunofluorescence microscopy and perform SG partition coefficient analysis.

• * The authors note that MKRN2 knockdown produces a phenotype reminiscent of VCP inhibition-smaller, more numerous SGs with increased DRiP partitioning. This similarity suggests MKRN2 may function in the same pathway as VCP. Direct epistasis experiments would strengthen this connection. * Authors: This study is conditional results of the above study. If VCP partitioning to SGs is reduced upon MKRN2 KD, which we do not know at this point, then MKRN2/VCP double KD experiment will be performed to strengthen this connection.

* Alternative explanations for the phenotype of delayed disassembly with TAK243 or MKRN2 depletion- the authors attribute this to DRiP accumulation, but TAK243 affects global ubiquitination. Could impaired degradation of other SG proteins (not just DRiPs) contribute to delayed disassembly? Does proteasome inhibition (MG-132 treatment) phenocopy the MKRN2 depletion phenotype? This would support that MKRN2-mediated proteasomal degradation (via K48 ubiquitin chains) is key to the phenotype. *

Authors: We are happy to provide alternative explanations in the Discussion in line with Reviewer #2 suggestion. The role of the proteosome is out of the scope of our study.

• Comparison with other E3 ligases (Supplementary Figure 5): The authors show that CNOT4 and ZNF598 depletion also affect SG dynamics, though to lesser extents than MKRN2. However: • Do these E3 ligases also prevent DRiP accumulation in SGs? Testing OP-puro partitioning in CNOT4- or ZNF598-depleted cells would reveal whether DRiP clearance is a general feature of SG-localized E3 ligases or specific to MKRN2. *

• * Are there redundant or compensatory relationships between these E3 ligases? Do double knockdowns have additive effects? * Authors: Our paper presents a study of the E3 ligase MKRN2. Generalizing these observations to ZNF598, CNOT4 and perhaps an even longer list of E3s, may be an interesting question, outside the scope of our mission.

• * The authors note that MKRN2 is "the most highly SG-depleted E3 upon TAK243 treatment"-does this mean MKRN2 has the strongest dependence on active ubiquitination for its SG localization, or simply that it has the highest basal level of SG partitioning? * Authors: We thank the reviewer for this smart question. MKRN2 has the strongest dependence on active ubiquitination as we now clarify better in the Results.

*Reviewer #2 (Significance (Required)):

This is a well-executed study that identifies MKRN2 as an important regulator of stress granule dynamics and proteostasis. The combination of proximity proteomics, live imaging, and functional assays provides strong evidence for MKRN2's role in preventing DRiP accumulation and maintaining granulostasis. However, key mechanistic questions remain, particularly regarding MKRN2's direct substrates, the ubiquitin chain types it generates, and how its enzymatic activity specifically prevents DRiP accumulation while promoting both SG coalescence and disassembly. Addressing the suggested revisions, particularly those related to MKRN2's mechanism of SG localization and substrate specificity, would significantly strengthen the manuscript and provide clearer insights into how ubiquitination maintains the dynamic properties of stress granules under proteotoxic stress.

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

In this paper, Amzallag et al. investigate the relationship between ubiquitination and the dynamics of stress granules (SGs). They utilize proximity ligation coupled mass spectrometry to identify SG components under conditions where the proteasome is inhibited by a small drug that targets UBiquitin-like modifier Activating enzyme 1 (UBA1), which is crucial for the initial step in the ubiquitination of misfolded proteins. Their findings reveal that the E3 ligase Makorin2 (MKRN2) is a novel component of SGs. Additionally, their data suggest that MKRN2 is necessary for processing damaged ribosome-associated proteins (DRIPs) during heat shock (HS). In the absence of MKRN2, DRIPs accumulate in SGs, which affects their dynamics. Major comments: Assess the knockdown efficiency (KD) for CNOT1, ZNF598, and MKRN2 to determine if the significant effect observed on SG dynamics upon MKRN2 depletion is due to the protein's function rather than any possible differences in KD efficiency. *

Authors: To address potential variability in knockdown efficiency, we will quantify CNOT4, ZNF598, and MKRN2 mRNA levels by RT-qPCR following siRNA knockdown.

* Since HS-induced stress granules (SGs) are influenced by the presence of TAK-243 or MKRN2 depletion, could it be that these granules become more mature and thus acquire more defective ribosomal products (DRIPs)? Do HS cells reach the same level of DRIPs, as assessed by OP-Puro staining, at a later time point? *

Authors: an interesting question. Mateju et al. carefully characterized the time course of DRiP accumulation in stress granules during heat shock, decreasing after the 90 minutes point (Appendix Figure S7; 10.15252/embj.201695957). We therefore interpret DRiP accumulation in stress granules following TAK243 treatment as a pathological state, reflecting impaired removal and degradation of DRiPs, rather than a normal, more “mature” stress granule state.

* Incorporating OP-Puro can lead to premature translation termination, potentially confounding results. Consider treating cells with a short pulse (i.e., 5 minutes) of OP-Puro just before fixation. *

Authors: Thank you for this suggestion. Treating the cell with a short pulse of OP-Puro just before fixation will lead to the labelling of a small amount of proteins, likely undetectable using conventional microscopy or Western blotting. Furthermore, it will lead to the unwanted labeling of stress responsive proteins that are translated with non canonical cap-independent mechanisms upon stress.

* Is MKRN2's dependence limited to HS-induced SGs? *

Authors: We will test sodium arsenite–induced stress and use immunofluorescence at discrete time points to assess whether the heat shock–related observations generalize to other stress types.

*

Minor comments: Abstract: Introduce UBA1. Introduction: The reference [2] should be replaced with 25719440. Results: Line 70, 'G3BP1 and 2 genes,' is somewhat misleading. Consider rephrasing into 'G3BP1 and G3BP2 genes'. Line 103: considers rephrasing 'we orthogonally validated the ubiquitin-dependent interaction' to 'we orthogonally validated the ubiquitin-dependent stress granule localization'. Line 125: '(fig.3C, EI Supplementary fig. 3)' Remove 'I'. Methods: line 260: the reference is not linked (it should be ref. [26]). Line 225: Are all the KDs being performed using the same method? Please specify. *

Authors: The text has been altered to reflect the reviewer’s suggestions.

*Fig.2C: Consider adding 'DEPLETED' on top of the scheme.

Reviewer #3 (Significance (Required)):

The study offers valuable insights into the degradative processes associated with SGs. The figures are clear, and the experimental quality is high. The authors do not overstate or overinterpret their findings, and the results effectively support their claims. However, the study lacks orthogonal methods to validate the findings and enhance the results. For instance, incorporating biochemical and reporter-based methods to measure degradation-related intermediate products (DRIPs) would be beneficial. Additionally, utilizing multiple methods to block ubiquitination, studying the dynamics of MKRN2 on SGs, and examining the consequences of excessive DRIPs on the cell fitness of SGs would further strengthen the research. *

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

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Referee #2

Evidence, reproducibility and clarity

This study identifies the E3 ubiquitin ligase Makorin 2 (MKRN2) as a novel regulator of stress granule (SG) dynamics and proteostasis. Using APEX proximity proteomics, the authors demonstrate that inhibition of the ubiquitin-activating enzyme UBA1 with TAK243 alters the SG proteome, leading to depletion of several E3 ligases, chaperones, and VCP cofactors. Detailed characterization of MKRN2 reveals that it localizes to SGs in a ubiquitination-dependent manner and is required for proper SG assembly, coalescence, and disassembly. Functionally, MKRN2 prevents the accumulation of defective ribosomal products (DRiPs) within SGs, thereby maintaining granulostasis. The study provides compelling evidence that ubiquitination, mediated specifically by MKRN2, plays a critical role in surveilling stress-damaged proteins within SGs and maintaining their dynamic liquid-like properties.

Major issues:

1. Figures 1-2: Temporal dynamics of ubiquitination in SGs. The APEX proteomics was performed at a single timepoint (90 min heat stress), yet the live imaging data show that SG dynamics and TAK243 effects vary considerably over time:
   • The peak or SG nucleation was actually at 10-30 min (Figure 1B).
   • TAK243 treatment causes earlier SG nucleation (Figure 1B) but delayed disassembly (Figure 1A-B, D). A temporal proteomic analysis at multiple timepoints (e.g., 30 min, 60 min, 90 min of heat stress, and during recovery) would reveal whether MKRN2 and other ubiquitination-dependent proteins are recruited to SGs dynamically during the stress response. It would also delineate whether different E3 ligases predominate at different stages of the SG lifecycle. While such experiments may be beyond the scope of the current study, the authors should at minimum discuss this limitation and acknowledge that the single-timepoint analysis may miss dynamic changes in SG composition.
2. Figures 2D-E, 3G: MKRN2 localization mechanism requires clarification. The authors demonstrate that MKRN2 localization to SGs is dependent on active ubiquitination, as TAK243 treatment significantly reduces MKRN2 partitioning into SGs (Figure 2D-E). However, several mechanistic questions remain:
   • Does MKRN2 localize to SGs through binding to ubiquitinated substrates within SGs, or does MKRN2 require its own ubiquitination activity to enter SGs?
   • The observation that MKRN2 overexpression increases SG ubiquitin content (Figure 3G-H) could indicate either: (a) MKRN2 actively ubiquitinates substrates within SGs, or (b) MKRN2 recruitment brings along pre-ubiquitinated substrates from the cytoplasm.
   • Is MKRN2 localization to SGs dependent on its E3 ligase activity? A catalytically inactive mutant of MKRN2 would help distinguish whether MKRN2 must actively ubiquitinate proteins to remain in SGs or whether it binds to ubiquitinated proteins independently of its catalytic activity. The authors should clarify whether MKRN2's SG localization depends on its catalytic activity or on binding to ubiquitinated proteins, as this would fundamentally affect the interpretation of its role in SG dynamics.
3. Figures 3-4: Discrepancy between assembly and disassembly phenotypes. MKRN2 knockdown produces distinct phenotypes during SG assembly versus disassembly. During assembly: smaller, more numerous SGs that fail to coalesce (Figure 3A-E), while during disassembly: delayed SG clearance (Figure 4A-D). These phenotypes may reflect different roles for MKRN2 at different stages, but the mechanism underlying this stage-specificity is unclear:
   • Does MKRN2 have different substrates or utilize different ubiquitin chain types during assembly versus disassembly?
   • The increased SG liquidity upon MKRN2 depletion (Figure 3F) seems paradoxical with delayed disassembly- typically more liquid condensates disassemble faster. The authors interpret this as decreased coalescence into "dense and mature SGs," but this requires clarification.
   • How does prevention of DRiP accumulation relate to the assembly defect? One would predict that DRiP accumulation would primarily affect disassembly (by reducing liquidity), yet MKRN2 depletion impacts both assembly dynamics and DRiP accumulation. The authors should discuss how MKRN2's role in preventing DRiP accumulation mechanistically connects to both the assembly and disassembly phenotypes.
4. Figure 5: Incomplete characterization of MKRN2 substrates. While the authors convincingly demonstrate that MKRN2 prevents DRiP accumulation in SGs (Figure 5C-D), the direct substrates of MKRN2 remain unknown. The authors acknowledge in the limitations that "the direct MKRN2 substrates and ubiquitin-chain types (K63/K48) are currently unknown." However, several approaches could strengthen the mechanistic understanding:
   • Do DRiPs represent direct MKRN2 substrates? Co-immunoprecipitation of MKRN2 followed by ubiquitin-chain specific antibodies (K48 vs K63) could reveal whether MKRN2 mediates degradative (K48) or non-degradative (K63) ubiquitination.
   • Given that VCP cofactors (such as UFD1L, PLAA) are depleted from SGs upon UBA1 inhibition (Figure 2C) and these cofactors recognize ubiquitinated substrates, does MKRN2 function upstream of VCP recruitment? Testing whether MKRN2 depletion affects VCP cofactor localization to SGs would clarify this pathway.
   • The authors note that MKRN2 knockdown produces a phenotype reminiscent of VCP inhibition-smaller, more numerous SGs with increased DRiP partitioning. This similarity suggests MKRN2 may function in the same pathway as VCP. Direct epistasis experiments would strengthen this connection.
5. Alternative explanations for the phenotype of delayed disassembly with TAK243 or MKRN2 depletion- the authors attribute this to DRiP accumulation, but TAK243 affects global ubiquitination. Could impaired degradation of other SG proteins (not just DRiPs) contribute to delayed disassembly? Does proteasome inhibition (MG-132 treatment) phenocopy the MKRN2 depletion phenotype? This would support that MKRN2-mediated proteasomal degradation (via K48 ubiquitin chains) is key to the phenotype.
6. Comparison with other E3 ligases (Supplementary Figure 5): The authors show that CNOT4 and ZNF598 depletion also affect SG dynamics, though to lesser extents than MKRN2. However:
   • Do these E3 ligases also prevent DRiP accumulation in SGs? Testing OP-puro partitioning in CNOT4- or ZNF598-depleted cells would reveal whether DRiP clearance is a general feature of SG-localized E3 ligases or specific to MKRN2.
   • Are there redundant or compensatory relationships between these E3 ligases? Do double knockdowns have additive effects?
   • The authors note that MKRN2 is "the most highly SG-depleted E3 upon TAK243 treatment"-does this mean MKRN2 has the strongest dependence on active ubiquitination for its SG localization, or simply that it has the highest basal level of SG partitioning?

Significance

This is a well-executed study that identifies MKRN2 as an important regulator of stress granule dynamics and proteostasis. The combination of proximity proteomics, live imaging, and functional assays provides strong evidence for MKRN2's role in preventing DRiP accumulation and maintaining granulostasis. However, key mechanistic questions remain, particularly regarding MKRN2's direct substrates, the ubiquitin chain types it generates, and how its enzymatic activity specifically prevents DRiP accumulation while promoting both SG coalescence and disassembly. Addressing the suggested revisions, particularly those related to MKRN2's mechanism of SG localization and substrate specificity, would significantly strengthen the manuscript and provide clearer insights into how ubiquitination maintains the dynamic properties of stress granules under proteotoxic stress.

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Referee #1

Evidence, reproducibility and clarity

Summary:

In this study, the authors used proximity proteomics in U2OS cells to identify several E3 ubiquitin ligases recruited to stress granules (SGs), and they focused on MKRN2 as a novel regulator. They show that MKRN2 localization to SGs requires active ubiquitination via UBA1. Functional experiments demonstrated that MKRN2 knockdown increases the number of SG condensates, reduces their size, slightly raises SG liquidity during assembly, and slows disassembly after heat shock. Overexpression of MKRN2-GFP combined with confocal imaging revealed co-localization of MKRN2 and ubiquitin in SGs. By perturbing ubiquitination (using a UBA1 inhibitor) and inducing defective ribosomal products (DRiPs) with O-propargyl puromycin, they found that both ubiquitination inhibition and MKRN2 depletion lead to increased accumulation of DRiPs in SGs. The authors conclude that MKRN2 supports granulostasis, the maintenance of SG homeostasis , through its ubiquitin ligase activity, preventing pathological DRiP accumulation within SGs.

Major comments:

• Are the key conclusions convincing?

The key conclusions are partially convincing. The data supporting the role of ubiquitination and MKRN2 in regulating SG condensate dynamics are coherent, well controlled, and consistent with previous literature, making this part of the study solid and credible. However, the conclusions regarding the ubiquitin-dependent recruitment of MKRN2 to SGs, its relationship with UBA1 activity, the functional impact of the MKRN2 knockdown for DRiP accumulation are less thoroughly supported. These aspects would benefit from additional mechanistic evidence, validation in complementary model systems, or the use of alternative methodological approaches to strengthen the causal connections drawn by the authors. - Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? The authors should qualify some of their claims as preliminary.

1) MKRN2 recruitment to SGs (ubiquitin-dependent): The proteomics and IF data are a reasonable starting point, but they do not yet establish that MKRN2 is recruited from its physiological localization to SGs in a ubiquitin-dependent manner. To avoid overstating this point the authors should qualify the claim and/or provide additional controls: show baseline localization of endogenous MKRN2 under non-stress conditions (which is reported in literature to be nuclear and cytoplasmatic), include quantification of nuclear/cytoplasmic distribution, and demonstrate a shift into bona fide SG compartments after heat shock. Moreover, co-localization of overexpressed GFP-MKRN2 with poly-Ub (FK2) should be compared to a non-stress control and to UBA1-inhibition conditions to support claims of stress- and ubiquitination-dependent recruitment.

2) Use and interpretation of UBA1 inhibition: UBA1 inhibition effectively blocks ubiquitination globally, but it is non-selective. The manuscript should explicitly acknowledge this limitation when interpreting results from both proteomics and functional assays. Proteomics hits identified under UBA1 inhibition should be discussed as UBA1-dependent associations rather than as evidence for specific E3 ligase recruitment. The authors should consider orthogonal approaches before concluding specificity.

3) DRiP accumulation and imaging quality: The evidence presented in Figure 5 is sufficient to substantiate the claim that DRiPs accumulate in SGs upon ubiquitination inhibition or MKRN2 depletion but to show that the event of the SGs localization and their clearance from SGs during stress is promoted by MKRN3 ubiquitin ligase activity more experiments would be needed. - Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation. Yes, a few targeted experiments would strengthen the conclusions without requiring the authors to open new lines of investigation.

1) Baseline localization of MKRN2: It would be important to show the baseline localization of endogenous and over-expressed MKRN2 (nuclear and cytoplasmic) under non-stress conditions and prior to ubiquitination inhibition. This would provide a reference to quantify redistribution into SGs and demonstrate recruitment in response to heat stress or ubiquitination-dependent mechanisms.

2) Specificity of MKRN2 ubiquitin ligase activity: to address the non-specific effects of UBA1 inhibition and validate that observed phenotypes depend on MKRN2's ligase activity, the authors could employ a catalytically inactive MKRN2 mutant in rescue experiments. Comparing wild-type and catalytic-dead MKRN2 in the knockdown background would clarify the causal role of MKRN2 activity in SG dynamics and DRiP clearance.

3) Ubiquitination linkage and SG marker levels: While the specific ubiquitin linkage type remains unknown, examining whether MKRN2 knockdown or overexpression affects total levels of key SG marker proteins would be informative. This could be done via Western blotting of SG markers along with ubiquitin staining, to assess whether MKRN2 influences protein stability or turnover through degradative or non-degradative ubiquitination. Such data would strengthen the mechanistic interpretation while remaining within the current study's scope. - Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments. The experiments suggested in points 1 and 3 are realistic and should not require substantial additional resources beyond those already used in the study. - Point 1 (baseline localization of MKRN2): This involves adding two control conditions (no stress and no ubiquitination inhibition) for microscopy imaging. The setup is essentially the same as in the current experiments, with time requirements mainly dependent on cell culture growth and imaging. Overall, this could be completed within a few weeks. - Point 3 (SG marker levels and ubiquitination): This entails repeating the existing experiment and adding a Western blot for SG markers and ubiquitin. The lab should already have the necessary antibodies, and the experiment could reasonably be performed within a couple of weeks. - Point 2 (catalytically inactive MKRN2 mutant and rescue experiments): This is likely more time-consuming. Designing an effective catalytic-dead mutant depends on structural knowledge of MKRN2 and may require additional validation to confirm loss of catalytic activity. If this expertise is not already present in the lab, it could significantly extend the timeline. Therefore, this experiment should be considered only if similarly recommended by other reviewers, as it represents a higher resource and time investment.

Overall, points 1 and 3 are highly feasible, while point 2 is more substantial and may require careful planning. - Are the data and the methods presented in such a way that they can be reproduced?

Yes. The methodologies used in this study to analyze SG dynamics and DRiP accumulation are well-established in the field and should be reproducible, particularly by researchers experienced in stress granule biology. Techniques such as SG assembly and disassembly assays, use of G3BP1 markers, and UBA1 inhibition are standard and clearly described. The data are generally presented in a reproducible manner; however, as noted above, some results would benefit from additional controls or complementary experiments to fully support specific conclusions. - Are the experiments adequately replicated and statistical analysis adequate?

Overall, the experiments in the manuscript appear to be adequately replicated, with most assays repeated between three and five times, as indicated in the supplementary materials. The statistical analyses used are appropriate and correctly applied to the datasets presented. However, for Figure 5 the number of experimental replicates is not reported. This should be clarified, and if the experiment was not repeated sufficiently, additional biological replicates should be performed. Given that this figure provides central evidence supporting the conclusion that DRiP accumulation depends on ubiquitination-and partly on MKRN2's ubiquitin ligase activity-adequate replication is essential.

Minor comments:

• Specific experimental issues that are easily addressable.
  • For the generation and the validation of MKRN2 knockdown in UOS2 cells data are not presented in the results or in the methods sections to demonstrate the effective knockdown of the protein of interest. This point is quite essential to demonstrate the validity of the system used
  • In the supplementary figure 2 it would be useful to mention if the Western Blot represent the input (total cell lysates) before the APEX-pulldown or if it is the APEX-pulldown loaded for WB. There is no consistence in the difference of biotynilation between different replicates shown in the 2 blots. For example in R1 and R2 G3BP1-APX TAK243 the biotynilation is one if the strongest condition while on the left blot, in the same condition comparison samples R3 and R4 are less biotinilated compared to others. It would be useful to provide an explanation for that to avoid any confusion for the readers.
  • In Figure 2D, endogenous MKRN2 localization to SGs appears reduced following UBA1 inhibition. However, it is not clear whether this reduction reflects a true relocalization or a decrease in total MKRN2 protein levels. To support the interpretation that UBA1 inhibition specifically affects MKRN2 recruitment to SGs rather than its overall expression, the authors should provide data showing total MKRN2 levels remain unchanged under UBA1 inhibition, for example via Western blot of total cell lysates.
  • DRIPs accumulation is followed during assembly but in the introduction is highlighted the fact that ubiquitination events, other reported E3 ligases and in this study data on MKRN2 showed that they play a crucial role in the disassembly of SGs which is also related with cleareance of DRIPs. Authors could add tracking DRIPs accumulation during disassembly to be added to Figure 5. I am not sure about the timeline required for this but I am just adding as optional if could be addressed easily.
  • The authors should clarify in the text why the cutoff used for the quantification in Figure 5D (PC > 3) differs from the cutoff used elsewhere in the paper (PC > 1.5). Providing a rationale for this choice will help the reader understand the methodological consistency and ensure that differences in thresholds do not confound interpretation of the results.
  • For Figure 3G, the authors use over-expressed MKRN2-GFP to assess co-localization with ubiquitin in SGs. Given that a reliable antibody for endogenous MKRN2 is available and that a validated MKRN2 knockdown line exists as an appropriate control, this experiment would gain significantly in robustness and interpretability if co-localization were demonstrated using endogenous MKRN2. In the current over-expression system, MKRN2-GFP is also present in the nucleus, whereas the endogenous protein does not appear nuclear under the conditions shown. This discrepancy raises concerns about potential over-expression artifacts or mislocalization. Demonstrating co-localization using endogenous MKRN2 would avoid confounding effects associated with over-expression. If feasible, this would be a relatively straightforward experiment to implement, as it relies on tools (antibody and knockdown line) already described in the manuscript.

• Are prior studies referenced appropriately?

  • From line 54 to line 67, the manuscript in total cites eight papers regarding the role of ubiquitination in SG disassembly. However, given the use of UBA1 inhibition in the initial MS-APEX experiment and the extensive prior literature on ubiquitination in SG assembly and disassembly under various stress conditions, the manuscript would benefit from citing additional relevant studies to provide more specifc examples. Expanding the references would provide stronger context, better connect the current findings to prior work, and emphasize the significance of the study in relation to established literature
  • At line 59, it would be helpful to note that G3BP1 is ubiquitinated by TRIM21 through a Lys63-linked ubiquitin chain. This information provides important mechanistic context, suggesting that ubiquitination of SG proteins in these pathways is likely non-degradative and related to functional regulation of SG dynamics rather than protein turnover.
  • When citing references 16 and 17, which report that the E3 ligases TRIM21 and HECT regulate SG formation, the authors should provide a plausible explanation for why these specific E3 ligases were not detected in their proteomics experiments. Differences could arise from the stress stimulus used, cell type, or experimental conditions. Similarly, since MKRN2 and other E3 ligases identified in this study have not been reported in previous works, discussing these methodological or biological differences would help prevent readers from questioning the credibility of the findings. It would also be valuable to clarify in the Conclusion that different types of stress may activate distinct ubiquitination pathways, highlighting context-dependent regulation of SG assembly and disassembly.
  • Line 59-60: when referring to the HECT family of E3 ligases involved in ubiquitination and SG disassembly, it would be more precise to report the specific E3 ligase identified in the cited studies rather than only the class of ligase. This would provide clearer mechanistic context and improve accuracy for readers.
  • The specific statement on line 182 "SG E3 ligases that depend on UBA1 activity are RBULs" should be supported by reference.
  • Are the text and figures clear and accurate?
  • In Supplementary Figure 1, DMSO is shown in green and the treatment in red, whereas in the main figures (Figure 1B and 1F) the colours in the legend are inverted. To avoid confusion, the colour coding in figure legends should be consistent across all figures throughout the manuscript.
  • At line 79, the manuscript states that "inhibition of ubiquitination delayed fluorescence recovery dynamics of G3BP1-mCherry, relative to HS-treated cells (Figure 1F, Supplementary Fig. 6A)." However, the data shown in Figure 1F appear to indicate the opposite effect: the TAK243-treated condition (green curve) shows a faster fluorescence recovery compared to the control (red curve). This discrepancy between the text and the figure should be corrected or clarified, as it may affect the interpretation of the role of ubiquitination in SG dynamics.
  • Line 86: adjust a missing bracket
  • There appears to be an error in the legend of Supplementary Figure 3: the legend states that the red condition (MKRN2) forms larger aggregates, but both the main Figure 3C of the confocal images and the text indicate that MKRN2 (red) forms smaller aggregates. Please correct the legend and any corresponding labels so they are consistent with the main figure and the text. The authors should also double-check that the figure panel order, color coding, and statistical annotations match the legend and the descriptions in the Results section to avoid reader confusion.
  • At lines 129-130, the manuscript states that "FRAP analysis demonstrated that MKRN2 KD resulted in a slight increase in SG liquidity (Fig. 3F, Supplementary Fig. 6B)." However, the data shown in Figure 3F appear to indicate the opposite trend: the MKRN2 KD condition (red curve) exhibits a faster fluorescence recovery compared to the control (green curve). This discrepancy between the text and the figure should be corrected or clarified, as it directly affects the interpretation of MKRN2's role in SG disassembly. Ensuring consistency between the written description and the plotted FRAP data is essential for accurate interpretation.
  • At lines 132-133, the manuscript states: "Then, to further test the impact of MKRN2 on SG dynamics, we overexpressed MKRN2-GFP and observed that it was recruited to SG (Fig. 3G)." This description should be corrected or clarified, as the over-expressed MKRN2-GFP also appears to localize to the nucleus.
  • At lines 134-135, the manuscript states that the FK2 antibody detects "free ubiquitin." This is incorrect. FK2 does not detect free ubiquitin; it recognizes only ubiquitin conjugates, including mono-ubiquitinated and poly-ubiquitinated proteins. The text should be corrected accordingly to avoid misinterpretation of the immunostaining data.
  • Figure 5A suffers from poor resolution, and no scale bar is provided, which limits interpretability. Additionally, the ROI selected for the green channel (DRIPs) appears to capture unspecific background staining, while the most obvious DRIP spots are localized in the nucleus. The authors should clarify this in the text, improve the image quality if possible, and ensure that the ROI accurately represents DRIP accumulation - in SGs rather than background signal.

Do you have suggestions that would help the authors improve the presentation of their data and conclusions?

• In the first paragraph following the APEX proteomics results, the authors present validation data exclusively for MKRN2, justifying this early focus by stating that MKRN2 is the most SG-depleted E3 ligase. However, in the subsequent paragraph they introduce the RBULs and present knockdown data for MKRN2 along with two additional E3 ligases identified in the screen, before once again emphasizing that MKRN2 is the most SG-depleted ligase and therefore the main focus of the study. For clarity and logical flow, the manuscript would benefit from reordering the narrative. Specifically, the authors should first present the validation data for all three selected E3 ligases, and only then justify the decision to focus on MKRN2 for in-depth characterization. In addition to the extent of its SG depletion, the authors may also consider providing biologically relevant reasons for prioritizing MKRN2 (e.g., domain architecture, known roles in stress responses, or prior evidence of ubiquitination-related functions). Reorganizing this section would improve readability and better guide the reader through the rationale for the study's focus.
• At lines 137-138, the manuscript states: "Together these data indicate that MKRN2 regulates the assembly dynamics of SGs by promoting their coalescence during HS and can increase SG ubiquitin content." While Figure 3G shows some co-localization of MKRN2 with ubiquitin, immunofluorescence alone is insufficient to claim an increase in SG ubiquitin content. This conclusion should be supported by orthogonal experiments, such as Western blotting, in vitro ubiquitination assays, or immunoprecipitation of SG components. Including a control under no-stress conditions would also help demonstrate that ubiquitination increases specifically in response to stress. The second part of the statement should therefore be rephrased to avoid overinterpretation, for example:"...and may be associated with increased ubiquitination within SGs, as suggested by co-localization, pending further validation by complementary assays."
• At line 157, the statement: "Therefore, we conclude that MKRN2 ubiquitinates a subset of DRiPs, avoiding their accumulation inside SGs" should be rephrased as a preliminary observation. While the data support a role for MKRN2 in SG disassembly and a reduction of DRIPs, direct ubiquitination of DRIPs by MKRN2 has not been demonstrated. A more cautious phrasing would better reflect the current evidence and avoid overinterpretation.

Significance

General assessment: provide a summary of the strengths and limitations of the study. What are the strongest and most important aspects? What aspects of the study should be improved or could be developed?

• This study provides a valuable advancement in understanding the role of ubiquitination in stress granule (SG) dynamics and the clearance of SGs formed under heat stress. A major strength is the demonstration of how E3 ligases identified through proteomic screening, particularly MKRN2, influence SG assembly and disassembly in a ubiquitination- and heat stress-dependent manner. The combination of proteomics, imaging, and functional assays provides a coherent mechanistic framework linking ubiquitination to SG homeostasis. Limitations of the study include the exclusive use of a single model system (U2OS cells), which may limit generalizability. Additionally, some observations-such as MKRN2-dependent ubiquitination within SGs and changes in DRIP accumulation under different conditions-would benefit from orthogonal validation experiments (e.g., Western blotting, immunoprecipitation, or in vitro assays) to confirm and strengthen these findings. Addressing these points would enhance the robustness and broader applicability of the conclusions.

Advance: compare the study to the closest related results in the literature or highlight results reported for the first time to your knowledge; does the study extend the knowledge in the field and in which way? Describe the nature of the advance and the resulting insights (for example: conceptual, technical, clinical, mechanistic, functional,...).

• The closest related result in literature is - Yang, Cuiwei et al. "Stress granule homeostasis is modulated by TRIM21-mediated ubiquitination of G3BP1 and autophagy-dependent elimination of stress granules." Autophagy vol. 19,7 (2023): 1934-1951. doi:10.1080/15548627.2022.2164427 - demonstrating that TRIM21, an E3 ubiquitin ligase, catalyzes K63-linked ubiquitination of G3BP1, a core SG nucleator, under oxidative stress. This ubiquitination by TRIM21 inhibits SG formation, likely by altering G3BP1's propensity for phase separation. In contrast, the MKRN2 study identifies a different E3 (MKRN2) that regulates SG dynamics under heat stress and appears to influence both assembly and disassembly. This expands the role of ubiquitin ligases in SG regulation beyond those previously studied (like TRIM21).
• Gwon and colleagues (Gwon Y, Maxwell BA, Kolaitis RM, Zhang P, Kim HJ, Taylor JP. Ubiquitination of G3BP1 mediates stress granule disassembly in a context-specific manner. Science. 2021;372(6549):eabf6548. doi:10.1126/science.abf6548) have shown that K63-linked ubiquitination of G3BP1 is required for SG disassembly after heat stress. This ubiquitinated G3BP1 recruits the segregase VCP/p97, which helps extract G3BP1 from SGs for disassembly. The MKRN2 paper builds on this by linking UBA1-dependent ubiquitination and MKRN2's activity to SG disassembly. Specifically, they show MKRN2 knockdown affects disassembly, and suggest MKRN2 helps prevent accumulation of defective ribosomal products (DRiPs) in SGs, adding a new layer to the ubiquitin-VCP model.
• Ubiquitination's impact is highly stress- and context-dependent (different chain types, ubiquitin linkages, and recruitment of E3s). The MKRN2 work conceptually strengthens this idea: by showing that MKRN2's engagement with SGs depends on active ubiquitination via UBA1, and by demonstrating functional consequences (SG dynamics + DRIP accumulation), the study highlights how cellular context (e.g., heat stress) can recruit specific ubiquitin ligases to SGs and modulate their behavior.
• There is a gap in the literature: very few (if any) studies explicitly combine the biology of DRIPs, stress granules, and E3 ligase mediated ubiquitination, especially in mammalian cells. There are relevant works about DRIP biology in stress granules, but those studies focus on chaperone-based quality control, not ubiquitin ligase-mediated ubiquitination of DRIPs. This study seems to be one of the first to make that connection in mammalian (or human-like) SG biology. A work on the plant DRIP-E3 ligase TaSAP5 (Zhang N, Yin Y, Liu X, et al. The E3 Ligase TaSAP5 Alters Drought Stress Responses by Promoting the Degradation of DRIP Proteins. Plant Physiol. 2017;175(4):1878-1892. doi:10.1104/pp.17.01319 ) shows that DRIPs can be directly ubiquitinated by E3s in other biological systems - which supports the plausibility of the MKRN2 mechanism, but it's not the same context.
• A very recent review (Yuan, Lin et al. "Stress granules: emerging players in neurodegenerative diseases." Translational neurodegeneration vol. 14,1 22. 12 May. 2025, doi:10.1186/s40035-025-00482-9) summarizes and reinforces the relationship among SGs and the pathogenesis of different neurodegenerative diseases (NDDs). By identifying MKRN2 as a new ubiquitin regulator in SGs, the current study could have relevance for neurodegeneration and proteotoxic diseases, providing a new candidate to explore in disease models.

Audience: describe the type of audience ("specialized", "broad", "basic research", "translational/clinical", etc...) that will be interested or influenced by this research; how will this research be used by others; will it be of interest beyond the specific field?

The audience for this paper is primarily specialized, including researchers in stress granule biology, ubiquitin signaling, protein quality control, ribosome biology, and cellular stress responses. The findings will also be of interest to scientists working on granulostasis, nascent protein surveillance, and proteostasis mechanisms. Beyond these specific fields, the study provides preliminary evidence linking ubiquitination to DRIP handling and SG dynamics, which may stimulate new research directions and collaborative efforts across complementary areas of cell biology and molecular biology.

Please define your field of expertise with a few keywords to help the authors contextualize your point of view. Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate.

I work in ubiquitin biology, focusing on ubiquitination signaling in physiological and disease contexts, with particular expertise in the identification of E3 ligases and their substrates across different cellular systems and in vivo models. I have less expertise in stress granule dynamics and DRiP biology, so my evaluation of those aspects is more limited and relies on interpretation of the data presented in the manuscript.

disadvantages of Imitation Learning

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Englert et al. proposed a functional connectome-based Hopfield artificial neural network (fcHNN) architecture to reveal attractor states and activity flows across various conditions, including resting state, task-evoked, and pathological conditions. The fcHNN can reconstruct characteristics of resting-state and task-evoked brain activities. Additionally, the fcHNN demonstrates differences in attractor states between individuals with autism and typically developing individuals.

Strengths:

(1) The study used seven datasets, which somewhat ensures robust replication and validation of generalization across various conditions.

(2) The proposed fcHNN improves upon existing activity flow models by mimicking artificial neural networks, thereby enhancing the representational ability of the model. This advancement enables the model to more accurately reconstruct the dynamic characteristics of brain activity.

(3) The fcHNN projection offers an interesting visualization, allowing researchers to observe attractor states and activity flow patterns directly.

We are grateful to the reviewer for highlighting the robustness of our findings across multiple datasets and for appreciating the novelty and representational advantages of our fcHNN model (which has been renamed to fcANN in the revised manuscript).

Weaknesses:

(1) The fcHNN projection can offer low-dimensional dynamic visualizations, but its interpretability is limited, making it difficult to make strong claims based on these projections. The interpretability should be enhanced in the results and discussion.

We thank the reviewer for these important points. We agree that the interpretability of the low-dimensional projection is limited. In the revised manuscript, we have reframed the fcANN projection primarily as a visualization tool (see e.g. line 359) and moved the corresponding part of Figure 2 to the Supplementary Material (Supplementary Figure 2). We have also implemented a substantial revision of the manuscript, which now directly links our analysis to the novel theoretical framework of self-orthogonalizing attractor networks (Spisak & Friston, 2025), opening several new avenues in terms of interpretation and shedding light on the computational principles underlying attractor dynamics in the brain (see the revised introduction and the new section “Theoretical background”, starting at lines 128, but also the Mathematical Appendices 1-2 in the Supplementary Material for a comprehensive formal derivation). As part of these efforts, we now provide evidence for the brain’s functional organization approximating a special, computationally efficient class of attractor networks, the so-called Kanter-Sompolinsky projector network (Figure 2A-C, line 346, see also our answer to your next comment). This is exactly, what the theoretical framework of free-energy-minimizing attractor networks predicts.

(2) The presentation of results is not clear enough, including figures, wording, and statistical analysis, which contributes to the overall difficulty in understanding the manuscript. This lack of clarity in presenting key findings can obscure the insights that the study aims to convey, making it challenging for readers to fully grasp the implications and significance of the research.

We have thoroughly revised the manuscript for clarity in wording, figures (see e.g. lines 257, 482, 529 in the Results and lines 1128, 1266, 1300, 1367 in the Methods). We carefully improved statistical reporting and ensured that we always report test statistics, effect sizes and clearly refer to the null modelling approach used (e.g. lines 461, 542, 550, 565, 573, 619, as well as Figures 2-4). As absolute effect sizes, in many analyses, do not have a straightforward interpretation, we provided Glass’ , as a standardized effect size measure, expressing the distance of the true observation from the null distribution as a ratio of the null standard deviation. To further improve clarity, we now clearly define our research questions and the corresponding analyses and null models in the revised manuscript, both in the main text and in two new tables (Tables 1 and 2). We denoted research questions and null model with Q1-7 and NM1-5, respectively and refer to them at multiple instances when detailing the analyses and the results.

Reviewer #2 (Public Review):

Summary:

Englert et al. use a novel modelling approach called functional connectome-based Hopfield Neural Networks (fcHNN) to describe spontaneous and task-evoked brain activity and the alterations in brain disorders. Given its novelty, the authors first validate the model parameters (the temperature and noise) with empirical resting-state function data and against null models. Through the optimisation of the temperature parameter, they first show that the optimal number of attractor states is four before fixing the optimal noise that best reflects the empirical data, through stochastic relaxation. Then, they demonstrate how these fcHNN-generated dynamics predict task-based functional activity relating to pain and self-regulation. To do so, they characterise the different brain states (here as different conditions of the experimental pain paradigm) in terms of the distribution of the data on the fcHNN projections and flow analysis. Lastly, a similar analysis was performed on a population with autism condition. Through Hopfield modeling, this work proposes a comprehensive framework that links various types of functional activity under a unified interpretation with high predictive validity.

Strengths:

The phenomenological nature of the Hopfield model and its validation across multiple datasets presents a comprehensive and intuitive framework for the analysis of functional activity. The results presented in this work further motivate the study of phenomenological models as an adequate mechanistic characterisation of large-scale brain activity.

Following up on Cole et al. 2016, the authors put forward a hypothesis that many of the changes to the brain activity, here, in terms of task-evoked and clinical data, can be inferred from the resting-state brain data alone. This brings together neatly the idea of different facets of brain activity emerging from a common space of functional (ghost) attractors.

The use of the null models motivates the benefit of non-linear dynamics in the context of phenomenological models when assessing the similarity to the real empirical data.

We thank the reviewer for recognizing the comprehensive and intuitive nature of our framework and for acknowledging the strength of our hypothesis that diverse brain activity facets emerge from a common resting state attractor landscape.

Weaknesses:

While the use of the Hopfield model is neat and very well presented, it still begs the question of why to use the functional connectome (as derived by activity flow analysis from Cole et al. 2016). Deriving the functional connectome on the resting-state data that are then used for the analysis reads as circular.

We agree that starting from functional couplings to study dynamics is in stark contrast with the common practice of estimating the interregional couplings based on structural connectome data. We now explicitly discuss how this affects the scope of the questions we can address with the approach, with explicit notes on the inability of this approach to study the structure-function coupling and its limitations in deriving mechanistic insights at the level of biophysical implementation.

Line 894:

“The proposed approach is not without limitations. First, as the proposed approach does not incorporate information about anatomical connectivity and does not explitly model biophysical details. Thus, in its present form, the model is not suitable to study the structure-function coupling and cannot yiled mechanistic explanations underlying (altered) polysynaptic connections, at the level of biophysical details.”

We are confident, however, that our approach is not circular. At the high level, our approach can be considered as a function-to-function generative model, with twofold aims.

First, we link large-scale brain dynamics to theoretical artificial neural network models and show that the functional connectome display characteristics that render it as an exceptionally “well-behaving” attractor network (e.g. superior convergence properties, as contrasted against appropriate respective null models). In the revised manuscript, we have significantly improved upon this aspect by explicitly linking the fcANN model to the theoretical framework of self-orthogonalizing attractor networks (Spisak & Friston, 2025) (see the revised introduction and the new section “Theoretical background”, starting at lines 128, but also the Mathematical Appendices 1-2 in the Supplementary Material for a comprehensive formal derivation). As part of these efforts, we now provide evidence for the brain’s functional organization approximating a special, computationally efficient class of attractor networks, the so-called Kanter-Sompolinsky projector network (Figure 2A-C, line 346, see also our answer to your next comment). This is exactly, what the theoretical framework of free-energy-minimizing attractor networks predicts. This result is not circular, as the empirical model does not use the key mechanism (the Hebbian/anti-Hebbian learning rule) that induces self-orthogonalization in the theoretical framework. We clarify this in the revised manuscript, e.g. in line 736.

Second, we benchmark ability of the proposed function-to-function generative model to predict unseen data (new datasets) or data characteristics that are not directly encompassed in the connectivity matrix (e.g. non-Gaussian conditional dependencies, temporal autocorrelation, dynamical responses to perturbations on the system). These benchmarks are constructed against well defined null models, which provide reasonable references. We have now significantly improved the discussion of these null models in the revised manuscript (Tables 1 and 2, lines 257). We not only show, that our model - when reconstructing resting state dynamics - can generalize to unseen data over and beyond what is possible with the baseline descriptive measure (e.g. covariance measures and PCA), but also demonstrate the ability of the framework to reconstruct the effects of perturbations on this dynamics (such as task-evoked changes), based solely on the resting state data form another sample.

If the fcHNN derives the basins of four attractors that reflect the first two principal components of functional connectivity, it perhaps suffices to use the empirically derived components alone and project the task and clinical data on it without the need for the fcHNN framework.

We are thankful for the reviewer for highlighting this important point, which encouraged us to develop a detailed understanding of the origins of the close alignment between attractors and principal components (eigenvectors of the coupling matrix) and the corresponding (approximate) orthogonality. Here, we would like to emphasize that the attractor-eigenvector correspondence is by no means a general feature of any arbitrary attractor network. In fact, such networks are a very special class of attractor neural networks (the so-called Kanter-Sompolinsky projector neural network (Kanter & Sompolinsky, 1987)), with a high degree of computational efficiency, maximal memory capacity and perfect memory recall. It has been rigorously shown that in such networks, the eigenvectors of the coupling matrix (i.e. PCA on the timeseries data) and the attractors become equivalent (Kanter & Sompolinsky, 1987). This in turn made us ask the question, what are the learning and plasticity rules that drive attractor networks towards developing approximately orthogonal attractors? We found that this is a general tendency of networks obeying the free energy principle ( Figure 2A-C, line 346, see also our answer to your next comment). The formal derivation of this framework in now presented in an accompanying theoretical piece (Spisak & Friston, 2025). In the revised manuscript, we provide a short, high-level overview of these results (in the Introduction form line 55 and in the new section “Theoretical background”, line 128, but also the Mathematical Appendices 1-2 in the Supplementary Material for a comprehensive formal derivation). According to this new theoretical model, attractor states can be understood as a set of priors (in the Bayesian sense) that together constitute an optimal orthogonal basis, equipping the update process (which is akin to a Markov-chain Monte Carlo sampling) to find posteriors that generalize effectively within the spanned subspace. Thus, in sum, understanding brain function in terms of attractor dynamics - instead of PCA-like descriptive projections - provides important links towards a Bayesian interpretation of brain activity. At the same time, the eigenvector-attractor correspondence also explains, why descriptive decomposition approaches, like PCA or ICA are so effective at capturing the dynamics of the system, at the first place.

As presented here, the Hopfield model is excellent in its simplicity and power, and it seems suited to tackle the structure-function relationship with the power of going further to explain task-evoked and clinical data. The work could be strengthened if that was taken into consideration. As such the model would not suffer from circularity problems and it would be possible to claim its mechanistic properties. Furthermore, as mentioned above, in the current setup, the connectivity matrix is based on statistical properties of functional activity amongst regions, and as such it is difficult to talk about a certain mechanism. This contention has for example been addressed in the Cole et al. 2016 paper with the use of a biophysical model linking structure and function, thus strengthening the mechanistic claim of the work.

We agree that investigating how the structural connectome constraints macro-scale dynamics is a crucial next step. Linking our results with the theoretical framework of self-orthogonalizing attractor networks provides a principled approach to this, as the “self-orthogonalizing” learning rule in the accompanying theoretical work provides the opportunity to fit attractor networks with structural constraints to functional data, shedding light on the plastic processes which maintain the observed approximate orthogonality even in the presence of these structural constraints. We have revised the manuscript to clarify that our phenomenological approach is inherently limited in its ability to answer mechanistic questions at the level of biophysical details (lines 894) and discuss this promising direction as follows:

Lines 803:

“A promising application of this is to consider structural brain connectivity (as measured by diffusion MRI) as a sparsity constraint for the coupling weights and then train the fcANN model to match the observed resting-state brain dynamics. If the resulting structural-functional ANN model is able to closely match the observed functional brain substate dynamics, it can be used as a novel approach to quantify and understand the structural functional coupling in the brain”.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) The statistical analyses are poorly described throughout the manuscript. The authors should provide more details on the statistical methods used for each comparison, as well as the corresponding statistics and degrees of freedom, rather than solely reporting p-values.

We thank the reviewer for pointing this out. We have revised the manuscript to include the specific test statistics, precise p-values and raw effect sizes for all reported analyses to ensure full transparency and replicability, see e.g. lines 461, 542, 550, 565, 573, 619, as well as Figures 2-4. Additionally, as absolute effect sizes - in many analyses - do not have a straightforward interpretation, we provided Glass’ Δ, as a standardized effect size measure, expressing the distance of the true observation from the null distribution as a ratio of the null standard deviation.

We have also improved the description of the statistical methods used in the manuscript (lines 1270, 1306, 1339, 1367, 1404) and added two overview tables (Tables 1 and 2) that summarize the methodological approaches and the corresponding null models.

Furthermore, we have fully revised the analysis corresponding to noise optimization. We only retained null model 2 (covariance-matched Gaussian) in the main text and on Figure 3, and moved model 1 (spatial phase randomization) into the Supplementary Material (Supplementary Figure 6) and is less appropriate for this analysis (trivially significant in all cases). Furthermore, as test statistic, no we use a Wasserstein distance between the 122-dimensional empirical and the simulated data (instead of focusing on the 2-dimensional projection). This analysis now directly quantifies the capacity of the fcANN model to capture non-Gaussian conditionals in the data.

(2) The convergence procedure is not clearly explained in the manuscript. Is this an optimization procedure to minimize energy? If so, the authors should provide more details about the optimizer used.

We apologize for the lack of clarity. The convergence is not an optimization procedure per se, in a sense that it does not involve any external optimizer. It is simply the repeated (deterministic) application of the same update rule also known from Hopfield networks or Boltzmann machines. However, as detailed in the accompanying theoretical paper, this update rule (or inference rule) inherently solves and optimization problem: it performs gradient descent on the free energy landscape of the network. As such, it is guaranteed to converge to a local free energy minimum in the deterministic case. We have clarified this process in the Results and Methods sections as follows:

Line 161:

“Inference arises from minimizing free energy with respect to the states \sigma. For a single unit, this yields a local update rule homologous to the relaxation dynamics in Hopfield networks”.

Line 181:

“In the basis framework (Spisak & Friston, 2025), inference is a gradient descent on the variational free energy landscape with respect to the states σ and can be interpreted as a form of approximate Bayesian inference, where the expected value of the state σ_i is interpreted as the posterior mean given the attractor states currently encoded in the network (serving as a macro-scale prior) and the previous state, including external inputs (serving as likelihood in the Bayesian sense)”.

Line 1252:

“As the inference rule was derived as a gradient descent on free energy, iterations monotonically decrease the free energy function and therefore converge to a local free‑energy minimum without any external optimizer. Thus, convergence does not require any optimization procedure with an external optimizer. Instead, it arises as the fixed point of repeated local inference updates, which implement gradient descent on free energy in the deterministic symmetric case.”

(3) In Figure 2G, the beta values range from 0.035 to 0.06, but they are reported as 0.4 in the main text and the Supplementary Figure. Please clarify this discrepancy.

We are grateful to the reviewer for spotting this typo. The correct value for β is 0.04, as reported in the Methods section. We have corrected this inconsistency in the revised manuscript and as well as in Supplementary Figure 5.

(4) Line 174: What type of null model was used to evaluate the impact of the beta values? The authors did not provide details on this anywhere in the manuscript.

We apologize for this omission. The null model is based on permuting the connectome weights while retaining the matrix symmetry, which destroys the specific topological structure but preserves the overall weight distribution. We have now clarified this at multiple places in the revised manuscript (lines 432, Table 1-2, Figure 2), and added new overview tables (Tables 1 and 2) to summarize the methodological approaches and the corresponding null models.

(5) Figure 3B: It appears that the authors only demonstrate the reproducibility of the “internal” attractor across different datasets. What about other states?

Thank you for noticing this. We now visualize all attractor states in Figure 3B (note that these essentially consist of two symmetric pairs).

(6) Figure 3: What does “empirical” represent in Figure 3? Is it PCA? If the “empirical” method, which is a much simpler method, can achieve results similar to those of the fcHNN in terms of state occupancy, distribution, and activity flow, what are the benefits of the proposed method? Furthermore, the authors claim that the explanatory power of the fcHNN is higher than that of the empirical model and shows significant differences. However, from my perspective, this difference is not substantial (37.0% vs. 39.9%). What does this signify, particularly in comparison to PCA?

This is a crucial point that is now a central theme of our revised manuscript. The reviewer is correct that the “empirical” method is PCA. PCA - by identifying variance-heavy orthogonal directions - aims to explain the highest amount of variance possible in the data (with the assumption of Gaussian conditionals). While empirical attractors are closely aligned to the PCs (i.e. eigenvectors of the inverse covariance matrix, as shown in the new analysis Q1), the alignment is only approximate. We basically take advantage of this small “gap” to quantify, weather attractor states are a better fit to the unseen data than the PCs. Obviously, due to the otherwise strong PC-attractor correspondence, this is expected to be only a small improvement. However, it is an important piece of evidence for the validity of our framework, as it shows that attractors are not just a complementary, perhaps “noisier” variety of the PCs, but a “substrate” that generalizes better to unseen data than the PCs themselves. We have revised the manuscript to clarify this point (lines 528).

Reviewer #2 (Recommendations For The Authors):

For clarity, it might be useful to define and use consistently certain key terms. Connectome often refers to structural (anatomical) connectivity unless defined specifically this should be considered, in Figure 1B title for example Brain state often refers to different conditions ie autism, neurotypical, sleep, etc... see for review Kringelbach et al. 2020, Cell Reports. When referring to attractors of brain activity they might be called substates.

We thank the reviewer for these helpful suggestions. We have carefully revised the manuscript to ensure our terminology is precise and consistent. We now explicitly refer to the “functional connectome” (including the title) and avoid using the too general term “brain state” and use “substates” instead.

In Figure 2 some terms are not defined. Noise is sigma in the text but elpsilon in the figure. Only in methods, the link becomes clear. Perhaps define epsilon in the caption for clarity. The same applies to μ in the methods. It is only described above in the methods, I suggest repeating the epsilon definition for clarity

We appreciate this feedback and apologize for the inconsistency. We have revised all figures and the Methods section to ensure that all mathematical symbols (including ε, σ, and μ) are clearly and consistently defined upon their first appearance and in all figure captions. For instance, noise level is now consistently referred to as ϵ. We improved the consistency and clarity for other terms, too, including:

functional connectome-based Hopfiled network (fcHNN) => functional connectivity-based attractor network (fcANN);

temperature => inverse temperature;

And improved grammar and language throughout the manuscript.

References

Kanter, I., & Sompolinsky, H. (1987). Associative recall of memory without errors. Physical Review A, 35(1), 380–392. 10.1103/physreva.35.380

Spisak T & Friston K (2025). Self-orthogonalizing attractor neural networks emerging from the free energy principle. arXiv preprint arXiv:2505.22749.

A lens does not create anything new, all the information is already there, but it lives fragmented over a bunch of different apps. Unconnected.

Reviewer #1 (Public review):

Summary:

Zhou and colleagues developed a computational model of replay that heavily builds on cognitive models of memory in context (e.g., the context-maintenance and retrieval model), which have been successfully used to explain memory phenomena in the past. Their model produces results that mirror previous empirical findings in rodents and offers a new computational framework for thinking about replay.

Strengths:

The model is compelling and seems to explain a number of findings from the rodent literature. It is commendable that the authors implement commonly used algorithms from wakefulness to model sleep/rest, thereby linking wake and sleep phenomena in a parsimonious way. Additionally, the manuscript's comprehensive perspective on replay, bridging humans and non-human animals, enhanced its theoretical contribution.

Weaknesses:

This reviewer is not a computational neuroscientist by training, so some comments may stem from misunderstandings. I hope the authors would see those instances as opportunities to clarify their findings for broader audiences.

(1) The model predicts that temporally close items will be co-reactivated, yet evidence from humans suggests that temporal context doesn't guide sleep benefits (instead, semantic connections seem to be of more importance; Liu and Ranganath 2021, Schechtman et al 2023). Could these findings be reconciled with the model or is this a limitation of the current framework?

(2) During replay, the model is set so that the next reactivated item is sampled without replacement (i.e., the model cannot get "stuck" on a single item). I'm not sure what the biological backing behind this is and why the brain can't reactivate the same item consistently. Furthermore, I'm afraid that such a rule may artificially generate sequential reactivation of items regardless of wake training. Could the authors explain this better or show that this isn't the case?

(3) If I understand correctly, there are two ways in which novelty (i.e., less exposure) is accounted for in the model. The first and more talked about is the suppression mechanism (lines 639-646). The second is a change in learning rates (lines 593-595). It's unclear to me why both procedures are needed, how they differ, and whether these are two different mechanisms that the model implements. Also, since the authors controlled the extent to which each item was experienced during wakefulness, it's not entirely clear to me which of the simulations manipulated novelty on an individual item level, as described in lines 593-595 (if any).

As to the first mechanism - experience-based suppression - I find it challenging to think of a biological mechanism that would achieve this and is selectively activated immediately before sleep (somehow anticipating its onset). In fact, the prominent synaptic homeostasis hypothesis suggests that such suppression, at least on a synaptic level, is exactly what sleep itself does (i.e., prune or weaken synapses that were enhanced due to learning during the day). This begs the question of whether certain sleep stages (or ultradian cycles) may be involved in pruning, whereas others leverage its results for reactivation (e.g., a sequential hypothesis; Rasch & Born, 2013). That could be a compelling synthesis of this literature. Regardless of whether the authors agree, I believe that this point is a major caveat to the current model. It is addressed in the discussion, but perhaps it would be beneficial to explicitly state to what extent the results rely on the assumption of a pre-sleep suppression mechanism.

(4) As the manuscript mentions, the only difference between sleep and wake in the model is the initial conditions (a0). This is an obvious simplification, especially given the last author's recent models discussing the very different roles of REM vs NREM. Could the authors suggest how different sleep stages may relate to the model or how it could be developed to interact with other successful models such as the ones the last author has developed (e.g., C-HORSE)? Finally, I wonder how the model would explain findings (including the authors') showing a preference for reactivation of weaker memories. The literature seems to suggest that it isn't just a matter of novelty or exposure, but encoding strength. Can the model explain this? Or would it require additional assumptions or some mechanism for selective endogenous reactivation during sleep and rest?

(5) Lines 186-200 - Perhaps I'm misunderstanding, but wouldn't it be trivial that an external cue at the end-item of Figure 7a would result in backward replay, simply because there is no potential for forward replay for sequences starting at the last item (there simply aren't any subsequent items)? The opposite is true, of course, for the first-item replay, which can't go backward. More generally, my understanding of the literature on forward vs backward replay is that neither is linked to the rodent's location. Both commonly happen at a resting station that is further away from the track. It seems as though the model's result may not hold if replay occurs away from the track (i.e. if a0 would be equal for both pre- and post-run).

(6) The manuscript describes a study by Bendor & Wilson (2012) and tightly mimics their results. However, notably, that study did not find triggered replay immediately following sound presentation, but rather a general bias toward reactivation of the cued sequence over longer stretches of time. In other words, it seems that the model's results don't fully mirror the empirical results. One idea that came to mind is that perhaps it is the R/L context - not the first R/L item - that is cued in this study. This is in line with other TMR studies showing what may be seen as contextual reactivation. If the authors think that such a simulation may better mirror the empirical results, I encourage them to try. If not, however, this limitation should be discussed.

(7) There is some discussion about replay's benefit to memory. One point of interest could be whether this benefit changes between wake and sleep. Relatedly, it would be interesting to see whether the proportion of forward replay, backward replay, or both correlated with memory benefits. I encourage the authors to extend the section on the function of replay and explore these questions.

(8) Replay has been mostly studied in rodents, with few exceptions, whereas CMR and similar models have mostly been used in humans. Although replay is considered a good model of episodic memory, it is still limited due to limited findings of sequential replay in humans and its reliance on very structured and inherently autocorrelated items (i.e., place fields). I'm wondering if the authors could speak to the implications of those limitations on the generalizability of their model. Relatedly, I wonder if the model could or does lead to generalization to some extent in a way that would align with the complementary learning systems framework.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Zhou and colleagues developed a computational model of replay that heavily builds on cognitive models of memory in context (e.g., the context-maintenance and retrieval model), which have been successfully used to explain memory phenomena in the past. Their model produces results that mirror previous empirical findings in rodents and offers a new computational framework for thinking about replay.

Strengths:

The model is compelling and seems to explain a number of findings from the rodent literature. It is commendable that the authors implement commonly used algorithms from wakefulness to model sleep/rest, thereby linking wake and sleep phenomena in a parsimonious way. Additionally, the manuscript's comprehensive perspective on replay, bridging humans and non-human animals, enhanced its theoretical contribution.

Weaknesses:

This reviewer is not a computational neuroscientist by training, so some comments may stem from misunderstandings. I hope the authors would see those instances as opportunities to clarify their findings for broader audiences.

(1) The model predicts that temporally close items will be co-reactivated, yet evidence from humans suggests that temporal context doesn't guide sleep benefits (instead, semantic connections seem to be of more importance; Liu and Ranganath 2021, Schechtman et al 2023). Could these findings be reconciled with the model or is this a limitation of the current framework?

We appreciate the encouragement to discuss this connection. Our framework can accommodate semantic associations as determinants of sleep-dependent consolidation, which can in principle outweigh temporal associations. Indeed, prior models in this lineage have extensively simulated how semantic associations support encoding and retrieval alongside temporal associations. It would therefore be straightforward to extend our model to simulate how semantic associations guide sleep benefits, and to compare their contribution against that conferred by temporal associations across different experimental paradigms. In the revised manuscript, we have added a discussion of how our framework may simulate the role of semantic associations in sleep-dependent consolidation.

“Several recent studies have argued for dominance of semantic associations over temporal associations in the process of human sleep-dependent consolidation (Schechtman et al., 2023; Liu and Ranganath 2021; Sherman et al., 2025), with one study observing no role at all for temporal associations (Schechtman et al., 2023). At first glance, these findings appear in tension with our model, where temporal associations drive offline consolidation. Indeed, prior models have accounted for these findings by suppressing temporal context during sleep (Liu and Ranganath 2024; Sherman et al., 2025). However, earlier models in the CMR lineage have successfully captured the joint contributions of semantic and temporal associations to encoding and retrieval (Polyn et al., 2009), and these processes could extend naturally to offline replay. In a paradigm where semantic associations are especially salient during awake learning, the model could weight these associations more and account for greater co-reactivation and sleep-dependent memory benefits for semantically related than temporally related items. Consistent with this idea, Schechtman et al. (2023) speculated that their null temporal effects likely reflected the task’s emphasis on semantic associations. When temporal associations are more salient and task-relevant, sleep-related benefits for temporally contiguous items are more likely to emerge (e.g., Drosopoulos et al., 2007; King et al., 2017).”

The reviewer’s comment points to fruitful directions for future work that could employ our framework to dissect the relative contributions of semantic and temporal associations to memory consolidation.

(2) During replay, the model is set so that the next reactivated item is sampled without replacement (i.e., the model cannot get "stuck" on a single item). I'm not sure what the biological backing behind this is and why the brain can't reactivate the same item consistently.

Furthermore, I'm afraid that such a rule may artificially generate sequential reactivation of items regardless of wake training. Could the authors explain this better or show that this isn't the case?

We appreciate the opportunity to clarify this aspect of the model. We first note that this mechanism has long been a fundamental component of this class of models (Howard & Kahana 2002). Many classic memory models (Brown et al., 2000; Burgess & Hitch, 1991; Lewandowsky & Murdock 1989) incorporate response suppression, in which activated items are temporarily inhibited. The simplest implementation, which we use here, removes activated items from the pool of candidate items. Alternative implementations achieve this through transient inhibition, often conceptualized as neuronal fatigue (Burgess & Hitch, 1991; Grossberg 1978). Our model adopts a similar perspective, interpreting this mechanism as mimicking a brief refractory period that renders reactivated neurons unlikely to fire again within a short physiological event such as a sharp-wave ripple. Importantly, this approach does not generate spurious sequences. Instead, the model’s ability to preserve the structure of wake experience during replay depends entirely on the learned associations between items (without these associations, item order would be random). Similar assumptions are also common in models of replay. For example, reinforcement learning models of replay incorporate mechanisms such as inhibition to prevent repeated reactivations (e.g., Diekmann & Cheng, 2023) or prioritize reactivation based on ranking to limit items to a single replay (e.g., Mattar & Daw, 2018). We now discuss these points in the section titled “A context model of memory replay”

“This mechanism of sampling without replacement, akin to response suppression in established context memory models (Howard & Kahana 2002), could be implemented by neuronal fatigue or refractory dynamics (Burgess & Hitch, 1991; Grossberg 1978). Non-repetition during reactivation is also a common assumption in replay models that regulate reactivation through inhibition or prioritization (Diekmann & Cheng 2023; Mattar & Daw 2018; Singh et al., 2022).”

(3) If I understand correctly, there are two ways in which novelty (i.e., less exposure) is accounted for in the model. The first and more talked about is the suppression mechanism (lines 639-646). The second is a change in learning rates (lines 593-595). It's unclear to me why both procedures are needed, how they differ, and whether these are two different mechanisms that the model implements. Also, since the authors controlled the extent to which each item was experienced during wakefulness, it's not entirely clear to me which of the simulations manipulated novelty on an individual item level, as described in lines 593-595 (if any).

We agree that these mechanisms and their relationships would benefit from clarification. As noted, novelty influences learning through two distinct mechanisms. First, the suppression mechanism is essential for capturing the inverse relationship between the amount of wake experience and the frequency of replay, as observed in several studies. This mechanism ensures that items with high wake activity are less likely to dominate replay. Second, the decrease in learning rates with repetition is crucial for preserving the stochasticity of replay. Without this mechanism, the model would increase weights linearly, leading to an exponential increase in the probability of successive wake items being reactivated back-to-back due to the use of a softmax choice rule. This would result in deterministic replay patterns, which are inconsistent with experimental observations.

We have revised the Methods section to explicitly distinguish these two mechanisms:

“This experience-dependent suppression mechanism is distinct from the reduction of learning rates through repetition; it does not modulate the update of memory associations but exclusively governs which items are most likely to initiate replay.”

We have also clarified our rationale for including a learning rate reduction mechanism:

“The reduction in learning rates with repetition is important for maintaining a degree of stochasticity in the model’s replay during task repetition, since linearly increasing weights would, through the softmax choice rule, exponentially amplify differences in item reactivation probabilities, sharply reducing variability in replay.”

Finally, we now specify exactly where the learning-rate reduction applied, namely in simulations where sequences are repeated across multiple sessions:

“In this simulation, the learning rates progressively decrease across sessions, as described above.“

As to the first mechanism - experience-based suppression - I find it challenging to think of a biological mechanism that would achieve this and is selectively activated immediately before sleep (somehow anticipating its onset). In fact, the prominent synaptic homeostasis hypothesis suggests that such suppression, at least on a synaptic level, is exactly what sleep itself does (i.e., prune or weaken synapses that were enhanced due to learning during the day). This begs the question of whether certain sleep stages (or ultradian cycles) may be involved in pruning, whereas others leverage its results for reactivation (e.g., a sequential hypothesis; Rasch & Born, 2013). That could be a compelling synthesis of this literature. Regardless of whether the authors agree, I believe that this point is a major caveat to the current model. It is addressed in the discussion, but perhaps it would be beneficial to explicitly state to what extent the results rely on the assumption of a pre-sleep suppression mechanism.

We appreciate the reviewer raising this important point. Unlike the mechanism proposed by the synaptic homeostasis hypothesis, the suppression mechanism in our model does not suppress items based on synapse strength, nor does it modify synaptic weights. Instead, it determines the level of suppression for each item based on activity during awake experience. The brain could implement such a mechanism by tagging each item according to its activity level during wakefulness. During subsequent consolidation, the initial reactivation of an item during replay would reflect this tag, influencing how easily it can be reactivated.

A related hypothesis has been proposed in recent work, suggesting that replay avoids recently active trajectories due to spike frequency adaptation in neurons (Mallory et al., 2024). Similarly, the suppression mechanism in our model is critical for explaining the observed negative relationship between the amount of recent wake experience and the degree of replay.

We discuss the biological plausibility of this mechanism and its relationship with existing models in the Introduction. In the section titled “The influence of experience”, we have added the following:

“Our model implements an activity‑dependent suppression mechanism that, at the onset of each offline replay event, assigns each item a selection probability inversely proportional to its activation during preceding wakefulness. The brain could implement this by tagging each memory trace in proportion to its recent activation; during consolidation, that tag would then regulate starting replay probability, making highly active items less likely to be reactivated. A recent paper found that replay avoids recently traversed trajectories through awake spike‑frequency adaptation (Mallory et al., 2025), which could implement this kind of mechanism. In our simulations, this suppression is essential for capturing the inverse relationship between replay frequency and prior experience. Note that, unlike the synaptic homeostasis hypothesis (Tononi & Cirelli 2006), which proposes that the brain globally downscales synaptic weights during sleep, this mechanism leaves synaptic weights unchanged and instead biases the selection process during replay.”

(4) As the manuscript mentions, the only difference between sleep and wake in the model is the initial conditions (a0). This is an obvious simplification, especially given the last author's recent models discussing the very different roles of REM vs NREM. Could the authors suggest how different sleep stages may relate to the model or how it could be developed to interact with other successful models such as the ones the last author has developed (e.g., C-HORSE)? 

We appreciate the encouragement to comment on the roles of different sleep stages in the manuscript, especially since, as noted, the lab is very interested in this and has explored it in other work. We chose to focus on NREM in this work because the vast majority of electrophysiological studies of sleep replay have identified these events during NREM. In addition, our lab’s theory of the role of REM (Singh et al., 2022, PNAS) is that it is a time for the neocortex to replay remote memories, in complement to the more recent memories replayed during NREM. The experiments we simulate all involve recent memories. Indeed, our view is that part of the reason that there is so little data on REM replay may be that experimenters are almost always looking for traces of recent memories (for good practical and technical reasons).

Regarding the simplicity of the distinction between simulated wake and sleep replay, we view it as an asset of the model that it can account for many of the different characteristics of awake and NREM replay with very simple assumptions about differences in the initial conditions. There are of course many other differences between the states that could be relevant to the impact of replay, but the current target empirical data did not necessitate us taking those into account. This allows us to argue that differences in initial conditions should play a substantial role in an account of the differences between wake and sleep replay.

We have added discussion of these ideas and how they might be incorporated into future versions of the model in the Discussion section:

“Our current simulations have focused on NREM, since the vast majority of electrophysiological studies of sleep replay have identified replay events in this stage. We have proposed in other work that replay during REM sleep may provide a complementary role to NREM sleep, allowing neocortical areas to reinstate remote, already-consolidated memories that need to be integrated with the memories that were recently encoded in the hippocampus and replayed during NREM (Singh et al., 2022). An extension of our model could undertake this kind of continual learning setup, where the student but not teacher network retains remote memories, and the driver of replay alternates between hippocampus (NREM) and cortex (REM) over the course of a night of simulated sleep. Other differences between stages of sleep and between sleep and wake states are likely to become important for a full account of how replay impacts memory. Our current model parsimoniously explains a range of differences between awake and sleep replay by assuming simple differences in initial conditions, but we expect many more characteristics of these states (e.g., neural activity levels, oscillatory profiles, neurotransmitter levels, etc.) will be useful to incorporate in the future.”

Finally, I wonder how the model would explain findings (including the authors') showing a preference for reactivation of weaker memories. The literature seems to suggest that it isn't just a matter of novelty or exposure, but encoding strength. Can the model explain this? Or would it require additional assumptions or some mechanism for selective endogenous reactivation during sleep and rest?

We appreciate the encouragement to discuss this, as we do think the model could explain findings showing a preference for reactivation of weaker memories, as in Schapiro et al. (2018). In our framework, memory strength is reflected in the magnitude of each memory’s associated synaptic weights, so that stronger memories yield higher retrieved‑context activity during wake encoding than weaker ones. Because the model’s suppression mechanism reduces an item’s replay probability in proportion to its retrieved‑context activity, items with larger weights (strong memories) are more heavily suppressed at the onset of replay, while those with smaller weights (weaker memories) receive less suppression. When items have matched reward exposure, this dynamic would bias offline replay toward weaker memories, therefore preferentially reactivating weak memories. 

In the section titled “The influence of experience”, we updated a sentence to discuss this idea more explicitly: 

“Such a suppression mechanism may be adaptive, allowing replay to benefit not only the most recently or strongly encoded items but also to provide opportunities for the consolidation of weaker or older memories, consistent with empirical evidence (e.g., Schapiro et al. 2018; Yu et al., 2024).”

(5) Lines 186-200 - Perhaps I'm misunderstanding, but wouldn't it be trivial that an external cue at the end-item of Figure 7a would result in backward replay, simply because there is no potential for forward replay for sequences starting at the last item (there simply aren't any subsequent items)? The opposite is true, of course, for the first-item replay, which can't go backward. More generally, my understanding of the literature on forward vs backward replay is that neither is linked to the rodent's location. Both commonly happen at a resting station that is further away from the track. It seems as though the model's result may not hold if replay occurs away from the track (i.e. if a0 would be equal for both pre- and post-run).

In studies where animals run back and forth on a linear track, replay events are decoded separately for left and right runs, identifying both forward and reverse sequences for each direction, for example using direction-specific place cell sequence templates. Accordingly, in our simulation of, e.g., Ambrose et al. (2016), we use two independent sequences, one for left runs and one for right runs (an approach that has been taken in prior replay modeling work). Crucially, our model assumes a context reset between running episodes, preventing the final item of one traversal from acquiring contextual associations with the first item of the next. As a result, learning in the two sequences remains independent, and when an external cue is presented at the track’s end, replay predominantly unfolds in the backward direction, only occasionally producing forward segments when the cue briefly reactivates an earlier sequence item before proceeding forward.

We added a note to the section titled “The context-dependency of memory replay” to clarify this:

“In our model, these patterns are identical to those in our simulation of Ambrose et al. (2016), which uses two independent sequences to mimic the two run directions. This is because the drifting context resets before each run sequence is encoded, with the pause between runs acting as an event boundary that prevents the final item of one traversal from associating with the first item of the next, thereby keeping learning in each direction independent.”

To our knowledge, no study has observed a similar asymmetry when animals are fully removed from the track, although both types of replay can be observed when animals are away from the track. For example, Gupta et al. (2010) demonstrated that when animals replay trajectories far from their current location, the ratio of forward vs. backward replay appears more balanced. We now highlight this result in the manuscript and explain how it aligns with the predictions of our model:

“For example, in tasks where the goal is positioned in the middle of an arm rather than at its end, CMR-replay predicts a more balanced ratio of forward and reverse replay, whereas the EVB model still predicts a dominance of reverse replay due to backward gain propagation from the reward. This contrast aligns with empirical findings showing that when the goal is located in the middle of an arm, replay events are more evenly split between forward and reverse directions (Gupta et al., 2010), whereas placing the goal at the end of a track produces a stronger bias toward reverse replay (Diba & Buzsaki 2007).” 

Although no studies, to our knowledge, have observed a context-dependent asymmetry between forward and backward replay when the animal is away from the track, our model does posit conditions under which it could. Specifically, it predicts that deliberation on a specific memory, such as during planning, could generate an internal context input that biases replay: actively recalling the first item of a sequence may favor forward replay, while thinking about the last item may promote backward replay, even when the individual is physically distant from the track.

We now discuss this prediction in the section titled “The context-dependency of memory replay”:

“Our model also predicts that deliberation on a specific memory, such as during planning, could serve to elicit an internal context cue that biases replay: actively recalling the first item of a sequence may favor forward replay, while thinking about the last item may promote backward replay, even when the individual is physically distant from the track. While not explored here, this mechanism presents a potential avenue for future modeling and empirical work.”

(6) The manuscript describes a study by Bendor & Wilson (2012) and tightly mimics their results. However, notably, that study did not find triggered replay immediately following sound presentation, but rather a general bias toward reactivation of the cued sequence over longer stretches of time. In other words, it seems that the model's results don't fully mirror the empirical results. One idea that came to mind is that perhaps it is the R/L context - not the first R/L item - that is cued in this study. This is in line with other TMR studies showing what may be seen as contextual reactivation. If the authors think that such a simulation may better mirror the empirical results, I encourage them to try. If not, however, this limitation should be discussed.

Although our model predicts that replay is triggered immediately by the sound cue, it also predicts a sustained bias toward the cued sequence. Replay in our model unfolds across the rest phase as multiple successive events, so the bias observed in our sleep simulations indeed reflects a prolonged preference for the cued sequence.

We now discuss this issue, acknowledging the discrepancy:

“Bendor and Wilson (2012) found that sound cues during sleep did not trigger immediate replay, but instead biased reactivation toward the cued sequence over an extended period of time. While the model does exhibit some replay triggered immediately by the cue, it also captures the sustained bias toward the cued sequence over an extended period.”

Second, within this framework, context is modeled as a weighted average of the features associated with items. As a result, cueing the model with the first R/L item produces qualitatively similar outcomes as cueing it with a more extended R/L cue that incorporates features of additional items. This is because both approaches ultimately use context features unique to the two sides.

(7) There is some discussion about replay's benefit to memory. One point of interest could be whether this benefit changes between wake and sleep. Relatedly, it would be interesting to see whether the proportion of forward replay, backward replay, or both correlated with memory benefits. I encourage the authors to extend the section on the function of replay and explore these questions.

We thank the reviewer for this suggestion. Regarding differences in the contribution of wake and sleep to memory, our current simulations predict that compared to rest in the task environment, sleep is less biased toward initiating replay at specific items, leading to a more uniform benefit across all memories. Regarding the contributions of forward and backward replay, our model predicts that both strengthen bidirectional associations between items and contexts, benefiting memory in qualitatively similar ways. Furthermore, we suggest that the offline learning captured  by our teacher-student simulations reflects consolidation processes that are specific to sleep.

We have expanded the section titled “The influence of experience” to discuss these predictions of the model: 

“The results outlined above arise from the model's assumption that replay strengthens bidirectional associations between items and contexts to benefit memory. This assumption leads to several predictions about differences across replay types. First, the model predicts that sleep yields different memory benefits compared to rest in the task environment: Sleep is less biased toward initiating replay at specific items, resulting in a more uniform benefit across all memories. Second, the model predicts that forward and backward replay contribute to memory in qualitatively similar ways but tend to benefit different memories. This divergence arises because forward and backward replay exhibit distinct item preferences, with backward replay being more likely to include rewarded items, thereby preferentially benefiting those memories.”

We also updated the “The function of replay” section to include our teacher-student speculation:

“We speculate that the offline learning observed in these simulations corresponds to consolidation processes that operate specifically during sleep, when hippocampal-neocortical dynamics are especially tightly coupled (Klinzing et al., 2019).”

(8) Replay has been mostly studied in rodents, with few exceptions, whereas CMR and similar models have mostly been used in humans. Although replay is considered a good model of episodic memory, it is still limited due to limited findings of sequential replay in humans and its reliance on very structured and inherently autocorrelated items (i.e., place fields). I'm wondering if the authors could speak to the implications of those limitations on the generalizability of their model. Relatedly, I wonder if the model could or does lead to generalization to some extent in a way that would align with the complementary learning systems framework.

We appreciate these insightful comments. Traditionally, replay studies have focused on spatial tasks with autocorrelated item representations (e.g., place fields). However, an increasing number of human studies have demonstrated sequential replay using stimuli with distinct, unrelated representations. Our model is designed to accommodate both scenarios. In our current simulations, we employ orthogonal item representations while leveraging a shared, temporally autocorrelated context to link successive items. We anticipate that incorporating autocorrelated item representations would further enhance sequence memory by increasing the similarity between successive contexts. Overall, we believe that the model generalizes across a broad range of experimental settings, regardless of the degree of autocorrelation between items. Moreover, the underlying framework has been successfully applied to explain sequential memory in both spatial domains, explaining place cell firing properties (e.g., Howard et al., 2004), and in non-spatial domains, such as free recall experiments where items are arbitrarily related. 

In the section titled “A context model of memory replay”, we added this comment to address this point:

“Its contiguity bias stems from its use of shared, temporally autocorrelated context to link successive items, despite the orthogonal nature of individual item representations. This bias would be even stronger if items had overlapping representations, as observed in place fields.”

Since CMR-replay learns distributed context representations where overlap across context vectors captures associative structure, and replay helps strengthen that overlap, this could indeed be viewed as consonant with complementary learning systems integration processes. 

Reviewer #2 (Public Review):

This manuscript proposes a model of replay that focuses on the relation between an item and its context, without considering the value of the item. The model simulates awake learning, awake replay, and sleep replay, and demonstrates parallels between memory phenomenon driven by encoding strength, replay of sequence learning, and activation of nearest neighbor to infer causality. There is some discussion of the importance of suppression/inhibition to reduce activation of only dominant memories to be replayed, potentially boosting memories that are weakly encoded. Very nice replications of several key replay findings including the effect of reward and remote replay, demonstrating the equally salient cue of context for offline memory consolidation.

I have no suggestions for the main body of the study, including methods and simulations, as the work is comprehensive, transparent, and well-described. However, I would like to understand how the CMRreplay model fits with the current understanding of the importance of excitation vs inhibition, remembering vs forgetting, activation vs deactivation, strengthening vs elimination of synapses, and even NREM vs REM as Schapiro has modeled. There seems to be a strong association with the efforts of the model to instantiate a memory as well as how that reinstantiation changes across time. But that is not all this is to consolidation. The specific roles of different brain states and how they might change replay is also an important consideration.

We are gratified that the reviewer appreciated the work, and we agree that the paper would benefit from comment on the connections to these other features of consolidation.

Excitation vs. inhibition: CMR-replay does not model variations in the excitation-inhibition balance across brain states (as in other models, e.g., Chenkov et al., 2017), since it does not include inhibitory connections. However, we posit that the experience-dependent suppression mechanism in the model might, in the brain, involve inhibitory processes. Supporting this idea, studies have observed increased inhibition with task repetition (Berners-Lee et al., 2022). We hypothesize that such mechanisms may underlie the observed inverse relationship between task experience and replay frequency in many studies. We discuss this in the section titled “A context model of memory replay”:

“The proposal that a suppression mechanism plays a role in replay aligns with models that regulate place cell reactivation via inhibition (Malerba et al., 2016) and with empirical observations of increased hippocampal inhibitory interneuron activity with experience (Berners-Lee et al., 2022). Our model assumes the presence of such inhibitory mechanisms but does not explicitly model them.”

Remembering/forgetting, activation/deactivation, and strengthening/elimination of synapses: The model does not simulate synaptic weight reduction or pruning, so it does not forget memories through the weakening of associated weights. However, forgetting can occur when a memory is replayed less frequently than others, leading to reduced activation of that memory compared to its competitors during context-driven retrieval. In the Discussion section, we acknowledge that a biologically implausible aspect of our model is that it implements only synaptic strengthening: 

“Aspects of the model, such as its lack of regulation of the cumulative positive weight changes that can accrue through repeated replay, are biologically implausible (as biological learning results in both increases and decreases in synaptic weights) and limit the ability to engage with certain forms of low level neural data (e.g., changes in spine density over sleep periods; de Vivo et al., 2017; Maret et al., 2011). It will be useful for future work to explore model variants with more elements of biological plausibility.” Different brain states and NREM vs REM: Reviewer 1 also raised this important issue (see above). We have added the following thoughts on differences between these states and the relationship to our prior work to the Discussion section:

“Our current simulations have focused on NREM, since the vast majority of electrophysiological studies of sleep replay have identified replay events in this stage. We have proposed in other work that replay during REM sleep may provide a complementary role to NREM sleep, allowing neocortical areas to reinstate remote, already-consolidated memories that need to be integrated with the memories that were recently encoded in the hippocampus and replayed during NREM (Singh et al., 2022). An extension of our model could undertake this kind of continual learning setup, where the student but not teacher network retains remote memories, and the driver of replay alternates between hippocampus (NREM) and cortex (REM) over the course of a night of simulated sleep. Other differences between stages of sleep and between sleep and wake states are likely to become important for a full account of how replay impacts memory. Our current model parsimoniously explains a range of differences between awake and sleep replay by assuming simple differences in initial conditions, but we expect many more characteristics of these states (e.g., neural activity levels, oscillatory profiles, neurotransmitter levels, etc.) will be useful to incorporate in the future.”

We hope these points clarify the model’s scope and its potential for future extensions.

Do the authors suggest that these replay systems are more universal to offline processes beyond episodic memory? What about procedural memories and working memory?

We thank the reviewer for raising this important question. We have clarified in the manuscript:

“We focus on the model as a formulation of hippocampal replay, capturing how the hippocampus may replay past experiences through simple and interpretable mechanisms.”

With respect to other forms of memory, we now note that:

“This motor memory simulation using a model of hippocampal replay is consistent with evidence that hippocampal replay can contribute to consolidating memories that are not hippocampally dependent at encoding (Schapiro et al., 2019; Sawangjit et al., 2018). It is possible that replay in other, more domain-specific areas could also contribute (Eichenlaub et al., 2020).”

Though this is not a biophysical model per se, can the authors speak to the neuromodulatory milieus that give rise to the different types of replay?

Our work aligns with the perspective proposed by Hasselmo (1999), which suggests that waking and sleep states differ in the degree to which hippocampal activity is driven by external inputs. Specifically, high acetylcholine levels during waking bias activity to flow into the hippocampus, while low acetylcholine levels during sleep allow hippocampal activity to influence other brain regions. Consistent with this view, our model posits that wake replay is more biased toward items associated with the current resting location due to the presence of external input during waking states. In the Discussion section, we have added a comment on this point:

“Our view aligns with the theory proposed by Hasselmo (1999), which suggests that the degree of hippocampal activity driven by external inputs differs between waking and sleep states: High acetylcholine levels during wakefulness bias activity into the hippocampus, while low acetylcholine levels during slow-wave sleep allow hippocampal activity to influence other brain regions.”

Reviewer #3 (Public Review):

In this manuscript, Zhou et al. present a computational model of memory replay. Their model (CMR-replay) draws from temporal context models of human memory (e.g., TCM, CMR) and claims replay may be another instance of a context-guided memory process. During awake learning, CMR replay (like its predecessors) encodes items alongside a drifting mental context that maintains a recency-weighted history of recently encoded contexts/items. In this way, the presently encoded item becomes associated with other recently learned items via their shared context representation - giving rise to typical effects in recall such as primacy, recency, and contiguity. Unlike its predecessors, CMR-replay has built-in replay periods. These replay periods are designed to approximate sleep or wakeful quiescence, in which an item is spontaneously reactivated, causing a subsequent cascade of item-context reactivations that further update the model's item-context associations.

Using this model of replay, Zhou et al. were able to reproduce a variety of empirical findings in the replay literature: e.g., greater forward replay at the beginning of a track and more backward replay at the end; more replay for rewarded events; the occurrence of remote replay; reduced replay for repeated items, etc. Furthermore, the model diverges considerably (in implementation and predictions) from other prominent models of replay that, instead, emphasize replay as a way of predicting value from a reinforcement learning framing (i.e., EVB, expected value backup).

Overall, I found the manuscript clear and easy to follow, despite not being a computational modeller myself. (Which is pretty commendable, I'd say). The model also was effective at capturing several important empirical results from the replay literature while relying on a concise set of mechanisms - which will have implications for subsequent theory-building in the field.

With respect to weaknesses, additional details for some of the methods and results would help the readers better evaluate the data presented here (e.g., explicitly defining how the various 'proportion of replay' DVs were calculated).

For example, for many of the simulations, the y-axis scale differs from the empirical data despite using comparable units, like the proportion of replay events (e.g., Figures 1B and C). Presumably, this was done to emphasize the similarity between the empirical and model data. But, as a reader, I often found myself doing the mental manipulation myself anyway to better evaluate how the model compared to the empirical data. Please consider using comparable y-axis ranges across empirical and simulated data wherever possible.

We appreciate this point. As in many replay modeling studies, our primary goal is to provide a qualitative fit that demonstrates the general direction of differences between our model and empirical data, without engaging in detailed parameter fitting for a precise quantitative fit. Still, we agree that where possible, it is useful to better match the axes. We have updated figures 2B and 2C so that the y-axis scales are more directly comparable between the empirical and simulated data. 

In a similar vein to the above point, while the DVs in the simulations/empirical data made intuitive sense, I wasn't always sure precisely how they were calculated. Consider the "proportion of replay" in Figure 1A. In the Methods (perhaps under Task Simulations), it should specify exactly how this proportion was calculated (e.g., proportions of all replay events, both forwards and backwards, combining across all simulations from Pre- and Post-run rest periods). In many of the examples, the proportions seem to possibly sum to 1 (e.g., Figure 1A), but in other cases, this doesn't seem to be true (e.g., Figure 3A). More clarity here is critical to help readers evaluate these data. Furthermore, sometimes the labels themselves are not the most informative. For example, in Figure 1A, the y-axis is "Proportion of replay" and in 1C it is the "Proportion of events". I presumed those were the same thing - the proportion of replay events - but it would be best if the axis labels were consistent across figures in this manuscript when they reflect the same DV.

We appreciate these useful suggestions. We have revised the Methods section to explain in detail how DVs are calculated for each simulation. The revisions clarify the differences between related measures, such as those shown in Figures 1A and 1C, so that readers can more easily see how the DVs are defined and interpreted in each case. 

Reviewer #4/Reviewing Editor (Public Review):

Summary:

With their 'CMR-replay' model, Zhou et al. demonstrate that the use of spontaneous neural cascades in a context-maintenance and retrieval (CMR) model significantly expands the range of captured memory phenomena.

Strengths:

The proposed model compellingly outperforms its CMR predecessor and, thus, makes important strides towards understanding the empirical memory literature, as well as highlighting a cognitive function of replay.

Weaknesses:

Competing accounts of replay are acknowledged but there are no formal comparisons and only CMR-replay predictions are visualized. Indeed, other than the CMR model, only one alternative account is given serious consideration: A variant of the 'Dyna-replay' architecture, originally developed in the machine learning literature (Sutton, 1990; Moore & Atkeson, 1993) and modified by Mattar et al (2018) such that previously experienced event-sequences get replayed based on their relevance to future gain. Mattar et al acknowledged that a realistic Dyna-replay mechanism would require a learned representation of transitions between perceptual and motor events, i.e., a 'cognitive map'. While Zhou et al. note that the CMR-replay model might provide such a complementary mechanism, they emphasize that their account captures replay characteristics that Dyna-replay does not (though it is unclear to what extent the reverse is also true).

We thank the reviewer for these thoughtful comments and appreciate the opportunity to clarify our approach. Our goal in this work is to contrast two dominant perspectives in replay research: replay as a mechanism for learning reward predictions and replay as a process for memory consolidation. These models were chosen as representatives of their classes of models because they use simple and interpretable mechanisms that can simulate a wide range of replay phenomena, making them ideal for contrasting these two perspectives.

Although we implemented CMR-replay as a straightforward example of the memory-focused view, we believe the proposed mechanisms could be extended to other architectures, such as recurrent neural networks, to produce similar results. We now discuss this possibility in the revised manuscript (see below). However, given our primary goal of providing a broad and qualitative contrast of these two broad perspectives, we decided not to undertake simulations with additional individual models for this paper.

Regarding the Mattar & Daw model, it is true that a mechanistic implementation would require a mechanism that avoids precomputing priorities before replay. However, the "need" component of their model already incorporates learned expectations of transitions between actions and events. Thus, the model's limitations are not due to the absence of a cognitive map.

In contrast, while CMR-replay also accumulates memory associations that reflect experienced transitions among events, it generates several qualitatively distinct predictions compared to the Mattar & Daw model. As we note in the manuscript, these distinctions make CMR-replay a contrasting rather than complementary perspective.

Another important consideration, however, is how CMR replay compares to alternative mechanistic accounts of cognitive maps. For example, Recurrent Neural Networks are adept at detecting spatial and temporal dependencies in sequential input; these networks are being increasingly used to capture psychological and neuroscientific data (e.g., Zhang et al, 2020; Spoerer et al, 2020), including hippocampal replay specifically (Haga & Fukai, 2018). Another relevant framework is provided by Associative Learning Theory, in which bidirectional associations between static and transient stimulus elements are commonly used to explain contextual and cue-based phenomena, including associative retrieval of absent events (McLaren et al, 1989; Harris, 2006; Kokkola et al, 2019). Without proper integration with these modeling approaches, it is difficult to gauge the innovation and significance of CMR-replay, particularly since the model is applied post hoc to the relatively narrow domain of rodent maze navigation.

First, we would like to clarify our principal aim in this work is to characterize the nature of replay, rather than to model cognitive maps per se. Accordingly, CMR‑replay is not designed to simulate head‐direction signals, perform path integration, or explain the spatial firing properties of neurons during navigation. Instead, it focuses squarely on sequential replay phenomena, simulating classic rodent maze reactivation studies and human sequence‐learning tasks. These simulations span a broad array of replay experimental paradigms to ensure extensive coverage of the replay findings reported across the literature. As such, the contribution of this work is in explaining the mechanisms and functional roles of replay, and demonstrating that a model that employs simple and interpretable memory mechanisms not only explains replay phenomena traditionally interpreted through a value-based lens but also accounts for findings not addressed by other memory-focused models.

As the reviewer notes, CMR-replay shares features with other memory-focused models. However, to our knowledge, none of these related approaches have yet captured the full suite of empirical replay phenomena, suggesting the combination of mechanisms employed in CMR-replay is essential for explaining these phenomena. In the Discussion section, we now discuss the similarities between CMR-replay and related memory models and the possibility of integrating these approaches:

“Our theory builds on a lineage of memory-focused models, demonstrating the power of this perspective in explaining phenomena that have often been attributed to the optimization of value-based predictions. In this work, we focus on CMR-replay, which exemplifies the memory-centric approach through a set of simple and interpretable mechanisms that we believe are broadly applicable across memory domains. Elements of CMR-replay share similarities with other models that adopt a memory-focused perspective. The model learns distributed context representations whose overlaps encodes associations among items, echoing associative learning theories in which overlapping patterns capture stimulus similarity and learned associations (McLaren & Mackintosh 2002). Context evolves through bidirectional interactions between items and their contextual representations, mirroring the dynamics found in recurrent neural networks (Haga & Futai 2018; Levenstein et al., 2024). However, these related approaches have not been shown to account for the present set of replay findings and lack mechanisms—such as reward-modulated encoding and experience-dependent suppression—that our simulations suggest are essential for capturing these phenomena. While not explored here, we believe these mechanisms could be integrated into architectures like recurrent neural networks (Levenstein et al., 2024) to support a broader range of replay dynamics.”

Recommendations For The Authors

Reviewer #1 (Recommendations For The Authors):

(1) Lines 94-96: These lines may be better positioned earlier in the paragraph.

We now introduce these lines earlier in the paragraph.

(2) Line 103 - It's unclear to me what is meant by the statement that "the current context contains contexts associated with previous items". I understand why a slowly drifting context will coincide and therefore link with multiple items that progress rapidly in time, so multiple items will be linked to the same context and each item will be linked to multiple contexts. Is that the idea conveyed here or am I missing something? I'm similarly confused by line 129, which mentions that a context is updated by incorporating other items' contexts. How could a context contain other contexts?

In the model, each item has an associated context that can be retrieved via Mfc. This is true even before learning, since Mfc is initialized as an identity matrix. During learning and replay, we have a drifting context c that is updated each time an item is presented. At each timestep, the model first retrieves the current item’s associated context cf by Mfc, and incorporates it into c. Equation #2 in the Methods section illustrates this procedure in detail. Because of this procedure, the drifting context c is a weighted sum of past items’ associated contexts. 

We recognize that these descriptions can be confusing. We have updated the Results section to better distinguish the drifting context from items’ associated context. For example, we note that:

“We represent the drifting context during learning and replay with c and an item's associated context with cf.”

We have also updated our description of the context drift procedure to distinguish these two quantities: 

“During awake encoding of a sequence of items, for each item f, the model retrieves its associated context cf via Mfc. The drifting context c incorporates the item's associated context cf and downweights its representation of previous items' associated contexts (Figure 1c). Thus, the context layer maintains a recency weighted sum of past and present items' associated contexts.”

(3) Figure 1b and 1d - please clarify which axis in the association matrices represents the item and the context.

We have added labels to show what the axes represent in Figure 1.

(4) The terms "experience" and "item" are used interchangeably and it may be best to stick to one term.

We now use the term “item” wherever we describe the model results. 

(5) The manuscript describes Figure 6 ahead of earlier figures - the authors may want to reorder their figures to improve readability.

We appreciate this suggestion. We decided to keep the current figure organization since it allows us to group results into different themes and avoid redundancy. 

(6) Lines 662-664 are repeated with a different ending, this is likely an error.

We have fixed this error.

Reviewer #3 (Recommendations For The Authors):

Below, I have outlined some additional points that came to mind in reviewing the manuscript - in no particular order.

(1) Figure 1: I found the ordering of panels a bit confusing in this figure, as the reading direction changes a couple of times in going from A to F. Would perhaps putting panel C in the bottom left corner and then D at the top right, with E and F below (also on the right) work?

We agree that this improves the figure. We have restructured the ordering of panels in this figure. 

(2) Simulation 1: When reading the intro/results for the first simulation (Figure 2a; Diba & Buszaki, 2007; "When animals traverse a linear track...", page 6, line 186). It wasn't clear to me why pre-run rest would have any forward replay, particularly if pre-run implied that the animal had no experience with the track yet. But in the Methods this becomes clearer, as the model encodes the track eight times prior to the rest periods. Making this explicit in the text would make it easier to follow. Also, was there any reason why specifically eight sessions of awake learning, in particular, were used?

We now make more explicit that the animals have experience with the track before pre-run rest recording:

“Animals first acquire experience with a linear track by traversing it to collect a reward. Then, during the pre-run rest recording, forward replay predominates.”

We included eight sessions of awake learning to match with the number of sessions in Shin et al. (2017), since this simulation attempts to explain data from that study. After each repetition, the model engages in rest. We have revised the Methods section to indicate the motivation for this choice: 

“In the simulation that examines context-dependent forward and backward replay through experience (Figs. 2a and 5a), CMR-replay encodes an input sequence shown in Fig. 7a, which simulates a linear track run with no ambiguity in the direction of inputs, over eight awake learning sessions (as in Shin et al. 2019)”

(3) Frequency of remote replay events: In the simulation based on Gupta et al, how frequently overall does remote replay occur? In the main text, the authors mention the mean frequency with which shortcut replay occurs (i.e., the mean proportion of replay events that contain a shortcut sequence = 0.0046), which was helpful. But, it also made me wonder about the likelihood of remote replay events. I would imagine that remote replay events are infrequent as well - given that it is considerably more likely to replay sequences from the local track, given the recency-weighted mental context. Reporting the above mean proportion for remote and local replay events would be helpful context for the reader.

In Figure 4c, we report the proportion of remote replay in the two experimental conditions of Gupta et al. that we simulate. 

(4) Point of clarification re: backwards replay: Is backwards replay less likely to occur than forward replay overall because of the forward asymmetry associated with these models? For example, for a backwards replay event to occur, the context would need to drift backwards at least five times in a row, in spite of a higher probability of moving one step forward at each of those steps. Am I getting that right?

The reviewer’s interpretation is correct: CMR-replay is more likely to produce forward than backward replay in sleep because of its forward asymmetry. We note that this forward asymmetry leads to high likelihood of forward replay in the section titled “The context-dependency of memory replay”: 

“As with prior retrieved context models (Howard & Kahana 2002; Polyn et al., 2009), CMR-replay encodes stronger forward than backward associations. This asymmetry exists because, during the first encoding of a sequence, an item's associated context contributes only to its ensuing items' encoding contexts. Therefore, after encoding, bringing back an item's associated context is more likely to reactivate its ensuing than preceding items, leading to forward asymmetric replay (Fig. 6d left).”

(5) On terminating a replay period: "At any t, the replay period ends with a probability of 0.1 or if a task-irrelevant item is reactivated." (Figure 1 caption; see also pg 18, line 635). How was the 0.1 decided upon? Also, could you please add some detail as to what a 'task-irrelevant item' would be? From what I understood, the model only learns sequences that represent the points in a track - wouldn't all the points in the track be task-relevant?

This value was arbitrarily chosen as a small value that allows probabilistic stopping. It was not motivated by prior modeling or a systematic search. We have added: “At each timestep, the replay period ends either with a stop probability of 0.1 or if a task-irrelevant item becomes reactivated. (The choice of the value 0.1 was arbitrary; future work could explore the implications of varying this parameter).” 

In addition, we now explain in the paper that task irrelevant items “do not appear as inputs during awake encoding, but compete with task-relevant items for reactivation during replay, simulating the idea that other experiences likely compete with current experiences during periods of retrieval and reactivation.”

(6) Minor typos:

Turn all instances of "nonlocal" into "non-local", or vice versa

"For rest at the end of a run, cexternal is the context associated with the final item in the sequence. For rest at the end of a run, cexternal is the context associated with the start item." (pg 20, line 663) - I believe this is a typo and that the second sentence should begin with "For rest at the START of a run".

We have updated the manuscript to correct these typos. 

(7) Code availability: I may have missed it, but it doesn't seem like the code is currently available for these simulations. Including the commented code in a public repository (Github, OSF) would be very useful in this case.

We now include a Github link to our simulation code: https://github.com/schapirolab/CMR-replay.

word. The breakout should be not being reactionary but a forward looking stance.

EventPages is a proposed spec for event sharing, by LinkTree and others. #openvraag is it using ActivityPub that has events in its ActivityStreams, that no one ever uses? Vgl https://www.zylstra.org/blog/2023/09/the-search-for-an-os-meet-up-replacement-mobilizon-edition/

Joint Public Review:

Meiotic recombination begins with DNA double-strand breaks (DSBs) generated by the conserved enzyme Spo11, which relies on several accessory factors that vary widely across eukaryotes. In C. elegans, multiple proteins have been implicated in promoting DSB formation, but their functional relationships and how they collectively recruit the DSB machinery to chromosome axes have remained unclear.

In this study, Raices et al. investigate the biochemical and genetic interactions among known DSB-promoting factors in C. elegans meiosis. Using yeast two-hybrid assays and co-immunoprecipitation, they map pairwise protein interactions and identify a connection between the chromatin-associated protein HIM-17 and the transcription factor XND-1. They also confirm the established interaction between DSB-1 and SPO-11 and show that DSB-1 associates with the nematode-specific factor HIM-5, which is required for X-chromosome DSB formation.

The authors extend these findings with genetic analyses, placing these factors into four epistasis groups based on single- and double-mutant phenotypes. Together, these biochemical and genetic data support a model describing how these proteins engage chromatin loops and localize to chromosome axes. The work provides a clearer view of how C. elegans assembles its DSB-forming machinery and how this process compares to mechanisms in other organisms.

Comment from the Reviewing Editor on the revised version:

The authors have adequately addressed the prior review comments. At this point, after going through multiple rounds of reviews and revisions, the community will be better served by having this paper out in public. This version was assessed by the editors without further input from the reviewers.

Author response:

The following is the authors’ response to the previous reviews

Public Reviews: 

Reviewer #1 (Public review): 

Summary: 

The manuscript by Raices et al., provides some novel insights into the role and interactions between SPO-11 accessory proteins in C. elegans. The authors propose a model of meiotic DSBs regulation, critical to our understanding of DSB formation and ultimately crossover regulation and accurate chromosome segregation. The work also emphasizes the commonalities and species-specific aspects of DSB regulation. 

Strengths: 

This study capitalizes on the strengths of the C. elegans system to uncover genetic interactions between a lSPO-11 accessory proteins. In combination with physical interactions, the authors synthesize their findings into a model, which will serve as the basis for future work, to determine mechanisms of DSB regulation. 

Weaknesses: 

The methodology, although standard, still lacks some rigor, especially with the IPs. 

Reviewer #2 (Public review): 

Summary: 

Meiotic recombination initiates with the formation of DNA double-strand break (DSB) formation, catalyzed by the conserved topoisomerase-like enzyme Spo11. Spo11 requires accessory factors that are poorly conserved across eukaryotes. Previous genetic studies have identified several proteins required for DSB formation in C. elegans to varying degrees; however, how these proteins interact with each other to recruit the DSB-forming machinery to chromosome axes remains unclear. 

In this study, Raices et al. characterized the biochemical and genetic interactions among proteins that are known to promote DSB formation during C. elegans meiosis. The authors examined pairwise interactions using yeast two-hybrid (Y2H) and co-immunoprecipitation and revealed an interaction between a chromatin-associated protein HIM-17 and a transcription factor XND-1. They further confirmed the previously known interaction between DSB-1 and SPO-11 and showed that DSB-1 also interacts with a nematodespecific HIM-5, which is essential for DSB formation on the X chromosome. They also assessed genetic interactions among these proteins, categorizing them into four epistasis groups by comparing phenotypes in double vs. single mutants. Combining these results, the authors proposed a model of how these proteins interact with chromatin loops and are recruited to chromosome axes, offering insights into the process in C. elegans compared to other organisms. 

Weaknesses: 

This work relies heavily on Y2H, which is notorious for having high rates of false positives and false negatives. Although the interactions between HIM-17 and XND-1 and between DSB-1 and HIM-5 were validated by co-IP, the significance of these interactions was not tested in vivo. Cataloging Y2H and genetic interactions does not yield much more insight. The model proposed in Figure 4 is also highly speculative. 

Reviewer #3 (Public review): 

The goal of this work is to understand the regulation of double-strand break formation during meiosis in C. elegans. The authors have analyzed physical and genetic interactions among a subset of factors that have been previously implicated in DSB formation or the number of timing of DSBs: CEP-1, DSB-1, DSB-2, DSB-3, HIM-5, HIM-17, MRE-11, REC-1, PARG-1, and XND-1. 

The 10 proteins that are analyzed here include a diverse set of factors with different functions, based on prior analyses in many published studies. The term "Spo11 accessory factors" has been used in the meiosis literature to describe proteins that directly promote Spo11 cleavage activity, rather than factors that are important for the expression of meiotic proteins or that influence the genome-wide distribution or timing of DSBs. Based on this definition, the known SPO-11 accessory factors in C. elegans include DSB-1, DSB2, DSB-3, and the MRN complex (at least MRE-11 and RAD-50). These are all homologs of proteins that have been studied biochemically and structurally in other organisms. DSB-1 & DSB-2 are homologs of Rec114, while DSB-3 is a homolog of Mei4. Biochemical and structural studies have shown that Rec114 and Mei4 directly modulate Spo11 activity by recruiting Spo11 to chromatin and promoting its dimerization, which is essential for cleavage. The other factors analyzed in this study affect the timing, distribution, or number of RAD-51 foci, but they likely do so indirectly. As elaborated below, XND-1 and HIM-17 are transcription factors that modulate the expression of other meiotic genes, and their role in DSB formation is parsimoniously explained by this regulatory activity. The roles of HIM-5 and REC-1 remain unclear; the reported localization of HIM-5 to autosomes is consistent with a role in transcription (the autosomes are transcriptionally active in the germline, while the X chromosome is largely silent), but its loss-of-function phenotypes are much more limited than those of HIM-17 and XND-1, so it may play a more direct role in DSB formation. The roles of CEP-1 (a Rad53 homolog) and PARG-1 are also ambiguous, but their homologs in other organisms contribute to DNA repair rather than DSB formation. 

We appreciate the reviewer’s clarification. However, the definition of Spo11 accessory factors varies across the literature. Only Keeney and colleagues define these as proteins that physically associate with and activate Spo11 to catalyze DSB formation (Keeney, Lange & Mohibullah, 2014; Lam & Keeney, 2015). In contrast, other authors have used the term more broadly to refer to proteins that promote or regulate Spo11-dependent DSB formation, without necessarily implying a direct interaction with Spo11 (e.g., Panizza et al., 2011; Robert et al., 2016; Stanzione et al., 2016; Li et al., 2021; Lange et al., 2016). Thus, our usage of the term follows this broader functional definition.

An additional significant limitation of the study, as stated in my initial review, is that much of the analysis here relies on cytological visualization of RAD-51 foci as a proxy for DSBs. RAD-51 associates transiently with DSB sites as they undergo repair and is thus limited in its ability to reveal details about the timing or abundance of DSBs since its loading and removal involve additional steps that may be influenced by the factors being analyzed. 

We agree with the reviewer that counting RAD-51 foci provides only an indirect measure of SPO-11–dependent DSBs, as RAD-51 marks sites of repair rather than the breaks themselves. However, we would like to clarify that our current study does not rely on RAD51 foci quantification for any of the analyses or conclusions presented. None of the figures or datasets in this manuscript are based on RAD-51 cytology. Instead, our conclusions are drawn from genetic interactions, biochemical assays, and protein–protein interaction analyses.

The paper focuses extensively on HIM-5, which was previously shown through genetic and cytological analysis to be important for breaks on the X chromosome. The revised manuscript still claims that "HIM-5 mediates interactions with the different accessory factors sub-groups, providing insights into how components on the DNA loops may interact with the chromosome axis." The weak interactions between HIM-5 and DSB-1/2 detected in the Y2H assay do not convincingly support such a role. The idea that HIM-5 directly promotes break formation is also inconsistent with genetic data showing that him5 mutants lack breaks on the X chromosomes, while HIM-5 has been shown to be is enriched on autosomes. Additionally, as noted in my comment to the authors, the localization data for HIM-5 shown in this paper are discordant with prior studies; this discrepancy should be addressed experimentally. 

We appreciate the reviewer’s concerns regarding the interpretation of HIM-5 function.  The weak Y2H interactions between HIM-5 and DSB-1 are not interpreted as direct biochemical evidence of a strong physical interaction, but rather as a potential point of regulatory connection between these pathways. Importantly, these Y2H data are further supported by co-immunoprecipitation experiments, genetic interactions, and the observed mislocalization of HIM-5 in the absence of DSB-1. Together, these complementary results strengthen our conclusion that HIM-5 functionally associates with DSB-promoting complexes.

Regarding HIM-5 localization, the pattern we observe using both anti-GFP staining of the eaIs4 transgene (Phim-5::him-5::GFP) and anti-HA staining of the HIM-5::HA strain is consistent with that reported by McClendon et al. (2016), who validated the same eaIs4 transgene. Although the pattern difers slightly from Meneely et al. (2012), that used a HIM5 antibody that is no longer functional and that has been discontinued by the commercial source. In this prior study, a weak signal was detected in the mitotic region and late pachytene, but stronger signal was seen in early to mid-pachytene. Our imaging— optimized for low background and stable signal—similarly shows robust HIM-5 localization in early and mid-pachytene, supporting the reliability of our GFP and HA-tagged analyses.

The recent analysis of DSB formation in C. elegans males (Engebrecht et al; PloS Genetics; PMID: 41124211) shows that in absence of him-5 there is a significant reduction of CO designation (measured as COSA-1 foci) on autosomes. This study strongly supports a direct and general role for HIM-5 in crossover formation— on both autosomes and on the hermaphrodite X.

This paper describes REC-1 and HIM-5 as paralogs, based on prior analysis in a paper that included some of the same authors (Chung et al., 2015; DOI 10.1101/gad.266056.115). In my initial review I mentioned that this earlier conclusion was likely incorrect and should not be propagated uncritically here. Since the authors have rebutted this comment rather than amending it, I feel it is important to explain my concerns about the conclusions of previous study. Chung et al. found a small region of potential homology between the C. elegans rec-1 and him-5 genes and also reported that him-5; rec-1 double mutants have more severe defects than either single mutant, indicative of a stronger reduction in DSBs. Based on these observations and an additional argument based on microsynteny, they concluded that these two genes arose through recent duplication and divergence. However, as they noted, genes resembling rec-1 are absent from all other Caenorhabditis species, even those most closely related to C. elegans. The hypothesis that two genes are paralogs that arose through duplication and divergence is thus based on their presence in a single species, in the absence of extensive homology or evidence for conserved molecular function. Further, the hypothesis that gene duplication and divergence has given rise to two paralogs that share no evident structural similarity or common interaction partners in the few million years since C. elegans diverged from its closest known relatives is implausible. In contrast, DSB-1 and DSB-2 are both homologs of Rec114 that clearly arose through duplication and divergence within the Caenorhabditis lineage, but much earlier than the proposed split between REC-1 and HIM-5. Two genes that can be unambiguously identified as dsb-1 and dsb-2 are present in genomes throughout the Elegans supergroup and absent in the Angaria supergroup, placing the duplication event at around 18-30 MYA, yet DSB-1 and DSB-2 share much greater similarity in their amino acid sequence, predicted structure, and function than HIM-5 and REC-1. Further, Raices place HIM-5 and REC-1 in different functional complexes (Figure 3B). 

We respectfully disagree with the reviewer’s characterization of the relationship between HIM-5 and REC-1. Our use of the term “paralog” follows the conclusions of Chung et al. (2015), a peer-reviewed study that provided both sequence and microsynteny evidence supporting this relationship. While we acknowledge that the degree of sequence conservation is limited, the evolutionary scenario proposed by Chung et al. remains the only published framework addressing this question. Further the degree of homology between either HIM-5 or REC-1 and the ancestral locus are similar to that observed for DSB-1 and DSB-2 with REC-114 (Hinman et al., 2021). We therefore retain the use of the term “paralog” in reference to these genes. Importantly, our conclusions regarding their distinct molecular and functional roles are independent of this classification.

The authors acknowledge that HIM-17 is a transcription factor that regulates many meiotic genes. Like HIM-17, XND-1 is cytologically enriched along the autosomes in germline nuclei, suggestive of a role in transcription. The Reinke lab performed ChIP-seq in a strain expressing an XND-1::GFP fusion protein and showed that it binds to promoter regions, many of which overlap with the HIM-17-regulated promoters characterized by the Ahringer lab (doi: 10.1126/sciadv.abo4082). Work from the Yanowitz lab has shown that XND-1 influences the transcription of many other genes involved in meiosis (doi: 10.1534/g3.116.035725) and work from the Colaiacovo lab has shown that XND-1 regulates the expression of CRA-1 (doi: 10.1371/journal.pgen.1005029). Additionally, loss of HIM-17 or XND-1 causes pleiotropic phenotypes, consistent with a broad role in gene regulation. Collectively, these data indicate that XND-1 and HIM-17 are transcription factors that are important for the proper expression of many germline-expressed genes. Thus, as stated above, the roles of HIM-17 and XND-1 in DSB formation, as well as their effects on histone modification, are parsimoniously explained by their regulation of the expression of factors that contribute more directly to DSB formation and chromatin modification. I feel strongly that transcription factors should not be described as "SPO-11 accessory factors." 

The ChIP analysis of XND-1 binding sites (using the XND-1::GFP transgene we provided to the Reinke lab) was performed, and Table S3 in the Ahringer paper suggests it is found at germline promoters, although the analysis is not actually provided. We completely agree that at least a subset of XND-1 functions is explained by its regulation of transcriptional targets (as we previously showed for HIM-5). However, like the MES proteins, a subset of which are also autosomal and impact X chromosome gene expression, XND-1 could also be directly regulating chromatin architecture which could have profound effects on DSB formation.  As stated in our prior comments, precedent for the involvement of a chromatin factor in DSB formation is provided by yeast Spp1. 

Recommendations for the authors: 

Editor comments: 

As you can see, the reviewers have additional comments, and the authors can include revisions to address those points prior to publicizing 'a version of record' (e.g. hatching rate assay mentioned by reviewer #1). This type of study, trying to catalog interactions of many factors, inevitably has loose ends, but in my opinion, it does not reduce the value of the study, as long as statements are not misleading. I suggest that the authors address issues by making changes to the main text. After the next round of adjustments by authors, I feel that it will be ready for a version of record, based on the spirit of the current eLife publication model. 

Reviewer #1 (Recommendations for the authors): 

I still have concerns about the HIM-17 IP and immunoblot probing with XND-1 antibodies. While the newly provided whole extract immunoblot clearly shows a XND-1 specific band that goes away in the mutant extracts, there is additional bands that are recognized - the pattern looks different than in the input in Figure 1B. Additionally, there is still a band of the corresponding size in the IPs from extracts not containing the tagged allele of HIM-17, calling into question whether XND-1 is specifically pulled down. 

The authors did not include the hatching rate as pointed out in the original reviews. In the rebuttal: 

"Great question. I guess we need to do this while back out for review. If anyone has suggestions of what to say here. Clearly we overlooked this point but do have the strain." 

We thank the reviewer for this suggestion. We had intended to include a hatching analysis; however, during the course of this work we discovered that our him-17 stock had acquired an additional linked mutation(s) that altered its phenotype and led to inconsistent results. This strain was used to rederive the him-17; eaIs4 double mutant after our original did not survive freeze/thaw. Given the abnormal behavior observed in this line, we concluded that proceeding with the hatching assays could yield unreliable data. We are currently reestablishing a verified him-17 strain, but in the interest of accuracy and reproducibility, we have restricted our analysis in this manuscript to validated datasets derived from confirmed strains.

Reviewer #2 (Recommendations for the authors): 

The authors have addressed most of the previous concerns and substantially improved the manuscript. The new data demonstrate that HIM-5 localization depends on DSB-1, and together with the Y2H and Co-PI results, strengthen the link between HIM-5 and the DSBforming machinery in C. elegans. The remaining points are outlined below: 

Specific comments: 

The font size of texts and labels in the Figure is very small and is hardly legible. Please enlarge them and make them clearly visible (Fig 1A, 1B, 2A, 2B, 2C, 2D, 2E, 3A, 3B, 3C, 3D, 3F)

Done

Although the authors have addressed the specificity of the XND-1 antibody, it remains unclear whether the boxed band is specific to the him-17::3xHA IP, since the same band appears in the control IP, albeit with lower intensity (Fig 1B). Is the ~100 kDa band in the him-17::3xHA IP a modified form XND-1? While antibody specificity was previously demonstrated by IF using xnd-1 mutants, it would be ideal to confirm this on a western blot as well. 

A Western Blot performed using whole cell extracts and probed with the anti- XND-1 antibody has been provided in the revised version of the manuscript (Fig. S1A). This confirms that the antibody specifically recognizes XND-1 protein. We believe that the ~100 kDa band mentioned by the reviewer is likely to be a non-specific cross reaction band detected by the antibody, since an identical band of the same mW was also detected in xnd-1 null mutants (Fig. S1A).

Regarding the IP negative controls, we are firmly convinced the boxed band to be specific, and the fact that a (very) low intensity band is also found in the negative control should not infringe the validity of the HIM-17-XND-1 specific interaction. There is a constellation of similar examples present across the literature, as it is widely acknowledged amongst biochemists that some proteins may “stick” to the beads due their intrinsic biochemical properties despite usage of highly stringent IP buffers. However, the high level of enrichment detected in the IP (as also underlined by the reviewer) corroborates that XND-1 specifically immunoprecipitates with HIM-17 despite a low, non-specific binding to the HA beads is present. If interaction between XND-1 and HIM-17 was non-specific, we logically would have found the band in the IP and the band in the negative control to be of very similar intensity, which is clearly not the case. 

Although co-IP assays are generally considered not a strictly quantitative assay, we want to emphasize that a comparable amount of nuclear extract was employed in both samples as also evidenced by the inputs, in which it is also possible to see that if anything, slightly less  nuclear extracts were employed in the him-17::3xHA; him-5::GFP::3xFLAG vs. the him5::GFP::3xFLAG negative control, corroborating the above mentioned points.

Lastly, it is crucial to mention that mass spectrometry analyses performed on HIM17::3xHA pulldowns show XND-1 as a highly enriched interacting protein (Blazickova et al.; 2025 Nature Comms.), which strongly supports our co-IP results.

The subheading "HIM-5 is the essential factor for meiotic breaks in the X chromosome" does not accurately represent the work described in the Results or in Figure 1. I disagree with the authors' response to the earlier criticism. The issue is not merely semantic. The data do not demonstrate that HIM-5 is required for DSB formation on the X chromosome - this conclusion can only be inferred. What Figure 1 shows is that XND-1 and HIM-17 interact, and that pie-1p-driven HIM-5 expression can partially rescue meiotic defects of him-17 mutants. This supports the conclusion that him-5 is a target of HIM-17/XND-1 in promoting CO formation on the X chromosome. However, the data provide no direct evidence for the claim stated in the subheading. I strongly encourage authors to revise the subheading to more accurately represent the findings presented in the paper. 

After considering the reviewer’s comments, we have revised the subheading to more accurately describe our findings.

In Fig1C, please fix the typo in the last row - "pie1p::him5-::GFP" to "pie-1p::him- 5::GFP".

Done

In Fig 2C, "p" is missing from the label on the right for Phim-5::him-5::GFP.

Done

In Fig 3I, bring the labels (DSB-1/2/3) at the lower right to the front.

Done

In Concluding Remarks, please fix the typo "frequently".

Done

Reviewer #3 (Recommendations for the authors): 

The experiments that analyze HIM-5 in dsb-1 mutants should be repeated using antibodies against the endogenous HIM-5 antibody, and localization of the HIM-5::HA and HIM-5::GFP proteins should be compared directly to antibody staining. This work uses an epitopetagged protein and a GFP-tagged protein to analyze the localization of HIM-5, while prior work (Meneely et al., 2012) used an antibody against the endogenous protein. In Figures 2 and S4 of this paper, neither HIM-5::HA nor HIM-5::GFP appears to localize strongly to chromatin, and autosomal enrichment of HIM-5, as previously reported for the endogenous protein based on antibody staining, is not evident. Moreover, HIM-5::GFP and HIM-5::HA look different from each other, and neither resembles the low-resolution images shown in Figure 6 in Meneely et al 2012, which showed nuclear staining throughout the germline, including in the mitotic zone, and also in somatic sheath cells. Given the differences in localization between the tagged transgenes and the endogenous protein, it is important to analyze the behavior of the endogenous, untagged protein. A minor issue: a wild-type control should also be shown for HIM-5::HA in Figure S4. 

Wild type control added to figure S4

Evidence that XND-1 and HIM-17 form a complex is weak; it is supported by the Y2H and co-IP data but opposed by functional analysis or localization. The diversity of proteins found in the Co-IP of HIM-17::GFP (Table S2) indicate that these interactions are unlikely to be specific. The independent localization of these proteins to chromatin is clear evidence that they do not form an obligate complex; additionally, they have been found to regulate distinct (although overlapping) sets of genes. The predicted structure generated by Alphafold3 has very low confidence and should not be taken as evidence for an interaction.The newly added argument about the lack of apparently overlap between HIM-17 and XND1 due to the distance between the HA tag on HIM-17 and XND-1 is flawed and should be removed - the extended C-terminus in the predicted AlphaFold3 C-terminus of HIM-17 has been interpreted as if it were a structured domain. Moreover, the predicted distance of 180 Å (18 nm) is comparable to the distance between a fluorophore on a secondary antibody and the epitope recognized by the primary antibody (~20-25 nm) and is far below than the resolution limit of light microscopy. 

We appreciate the reviewer’s thoughtful comment. The evidence supporting a physical interaction between XND-1 and HIM-17 is not only shown by our co-IP experiments, but it has also been recently shown in an independent study where MS analyses were conducted on HIM-17::3xHA pull downs to identify novel HIM-17 interactors (Blazickova et al.; 2025 Nature Comms). As shown in the data provided in this study, also under these experimental settings XND-1 was identified as a highly enriched putative HIM-17 interactor. We do acknowledge that their chromatin localization patterns are distinct and they regulate overlapping but not identical sets of genes, however, it is worth noting that protein–protein interactions in meiosis are often transient or context-dependent, and may not necessarily result in co-localization detectable by microscopy. In line with this, in the same work cited above, a similar situation for BRA-2 and HIM-17 was reported, as they were shown to interact biochemically despite the absence of overlapping staining patterns. 

Minor issues: 

The images shown in Panel D in Figure 1 seem to have very different resolutions; the HTP3/HIM-17 colocalization image is particularly blurry/low-resolution and should be replaced. The contrast between blue and green cannot be seen clearly; colors with stronger contrast should be used, and grayscale images should also be shown for individual channels. High-resolution images should probably be included for all of the factors analyzed here to facilitate comparisons.

Here is the ending sentence of the song: I could annotate it as I did so far, but by now you are familiar with Eminem's tone and with the themes dealt with in the project... so I would like to hear your interpretations! Try to unpack it on your own.

Clues: my personal advice when it comes to understand a text is dividing it into smaller chunks (example: what piece of cloth is Eminem referring to?). If you struggle with understanding what he is trying to say, don't worry: try to read the text again and remember that previous and following sentences can help you out.

Dryer lint refers to the buildup that forms in the dryer as a result of hair, textile fibers and other particles clamping together. Normally, it is thrown up after every washing cycle. In Italian, it is translatable as lanugine, but perhaps the following image will clarify any doubt: https://preparedhero.com/it-it/blogs/articles/dryer-lint#:~:text=What%20Is%20Dryer%20Lint?,for%20dryer%20safety%20and%20performance.

Your turn: what do you think Eminem is trying to say here? Which other singer (of the ones that are part of the analysis) mentioned the hiring process. Clue: focus on the word hire.

"A house where dope heads (heroin addicts) live" and do drugs. http://dope-house.urbanup.com/7824692

Eminem is criticizing "how African American drivers are often stereotyped as drug dealers with a criminal background". https://www.billboard.com/music/rb-hip-hop/eminem-untouchable-lyrics-decoded-8062711/ Indeed, the key word in the sentence is "probably": the policeman does not know who the young man is, where he is going to or where he comes from, so he cannot do anything but hypothesize. The problem is twofold: 1. He mistakes his hypothesis for truth; 2. His hypothesis is biased: there is no logical reason as for why he associates the young black man with the "dope house". However, he does so because he cultivates prejudices against black men.

Southern Black Africans presents four major ethnic divisions; one of them is the Nguni, which, in its turn, can be divided into four groups: Zulu and Xhosa are two of them. https://sahistory.org.za/article/xhosa https://sahistory.org.za/article/zulu

Although they share a common history, Zulu and Xhosa communities were implicated in a civil war from 1990 to 1994. The reason behind this conflict is, actually, linguistic and colonial: the two languages were "created" by colonizers and African interpreters. Before African colonialism, indeed, there weren't any written languages and people did not distinguish themselves on a linguistic basis, but rather on social belonging. This does not mean that "Zulu and Xhosa identities didn’t exist before the languages were well defined, rather that the identities were transformed when these languages came into existence." https://theconversation.com/zulu-vs-xhosa-how-colonialism-used-language-to-divide-south-africas-two-biggest-ethnic-groups-204969

Tyrone is originally an Irish name, but it is increasingly more common in African-American communities. Unfortuntately, for this reason it is also at the center of discrimination: compared to Tyrone, "white" names are twice as likely to receive responses. Also Darius is a typical African-American name. https://www.irishexaminer.com/news/arid-10108995.html https://www.behindthename.com/name/darius

Dem ah seh could mean "they all say". Ah can mean different words, in which I could not find all, but I hypothesized this meaning also by comparing this version to Anglicized ones. https://jamaicanpatwah.com/term/dem/961 https://jamaicanpatwah.com/term/seh/1293

This entire verse is written in Jamaican patois, that is, "an English-lexified creole language spoken by the majority of Jamaicans". https://jamaicanpatwah.com As you will see, some words may be intuitive, but others are definitely not. While I was looking for the lyrics of the song, I found other anglicized versions which were certainly more comprehensible, but, I am afraid, less faithful to the singer's intention. Consequently, I opted for the original, more complex version.

Why has the singer recurred to Jamaican patois? My hypothesis is that he features it as a way to give importance and centrality to a marginal community through its language.

Dem is they. https://jamaicanpatwah.com/term/dem/961

Here Lamar denounces a decade-long rage for anti-Black racism and police brutality he himself was a victim of. In a Rolling Stone interview, Lamar declared that as a teenager "the majority of my interactions with the police were not good […] there were a few good ones who were actually protecting the community. But then you have the ones from the Valley. They never met me in my life, but since I'm a kid […] they wanna slam me on the hood of the car. […] Even if he's not a good kid, that don't give you the right to slam a minor on the ground or pull a pistol on him. " […] Indeed, "police pull guns on him on two occasions. The first when he was 17." https://issuu.com/lawrenceambrocio5018/docs/rolling_stone_march_26_2015_usa_1_

Here the central critique to the lack of equality and the debunking of the immigration myths appears evident: in particular, Springsteen makes uses of the anaphora "they died" to underscore the timeless sacrifice of immigrants, who are identified with the makers of the United States ("the hands that build the country"). This last part of the song completely overturns what Springsteen has sung so far: there are no streets paved with gold, no diamonds in the sidewalks or "treasure for the taking". What remains is work "to bones and skin". Once again, Springsteen has not departed from the historical truth: despite their hopes, European immigrants who landed in the United States did not improve their status. In other words, if they were poor, they stayed as such; “past European immigrants often struggled when they first arrived, and most of them did not succeed in reaching the American Dream within their lifetimes.” https://ui.charlotte.edu/story/streets-gold-debunking-american-immigration-myths/ Moreover, research shows that from 1880 to 1920 immigrants "were the mainstay of the American industrial workforce". https://pmc.ncbi.nlm.nih.gov/articles/PMC2760060/#abstract1 Making home in the American Land was, therefore, impossible for them because America itself rejected and discriminated immigrants after exploiting them for its own industrial and economic growth. Indeed, "often stereotyped and discriminated against, many immigrants suffered verbal and physical abuse because they were different. […] The newcomers helped transform American society and culture, demonstrating that diversity, as well as unity, is a source of national strength." https://www.loc.gov/classroom-materials/united-states-history-primary-source-timeline/rise-of-industrial-america-1876-1900/immigration-to-united-states-1851-1900/

Springsteen's radical claim is that such discrimination and sacrifice are not limited to a distant past, but have continued up until now ("they're still dyin' now"). When Springsteen released the song, the country was actually undergoing a period of decline in immigration due to measures taken after the terrorist attacks of September 11, 2001 such as the Patriot Act. https://library.law.howard.edu/civilrightshistory/immigration/history. However, it is true that immigration continues to be a divisive topic in the United States, especially in the current presidency: what do you know about Trump's immigration policies?

In this verse and in the previous one, the singer is making explicit reference to white supremacy and police brutality in the U.S.A: excessive use of force (also culminating in murder) by police officers towards specific categories has been documented since the early 19th century. Targets have varied through the centuries, but African-Americans are historically the most targeted group because of racial implications. In this case, the singer emphasizes policemen's impunity before the law; on the contrary, they are acclaimed and welcomed as "heros". https://www.britannica.com/topic/police-brutality-in-the-United-States-2064580/Police-brutality-after-World-War-II

Here is the chorus of the song: at first, the singer's reaction to the widespread inquality that dominates the society seems to coincide with a resigned acceptation of the status quo. The feeling of quiet resignation amplifies as the song goes on, reaching its climax when the singer makes reference to the Civil Rights Act and, therefore, establishing a parallelism between the present and the past. However, the chorus closes with a glimpse of hope: indeed, the author invites the audience not to believe to inevitability, thus suggesting that things may change if only people started thinking and acting differently. As a matter of fact, the singer himself insists upon this last verse: "Some things will never change is a statement of resignation, but the most important line in that song is the one that comes after that: But don't you believe them. So I've always been about being strong when resignation is a possibility. Trying to pull up from that and have a positive outlook so that things can change" (http://www.musicfordemocracy.org/node/34.html).

Your turn: do you think that injustice and inequality will ever be defeated?

Setting the scene: the song was released in July 1986 as a single from the band's debut album The Way It Is. It was a great success and the band won the 1987 Grammy Awards in the Best New Artist category. The success of the song has had a long-lasting effect in the music industry: it was sampled by other artists and included in songs such as 2Pac's Changes and Polo G's Wishing for a Hero. The singer has "never counted it" but he has read that his song "has now been recorded 17 times by hip-hop artists" (https://www.rollingstone.com/music/music-features/bruce-hornsby-interview-way-it-is-non-secure-connection-1036032/). In order to understand the following lyrics, it is necessary to place the song in its historical context. The 1980s were years in which several issues emerged: * The process of de-industrialization (that is, the process in which American companies moved their seats abroad, outside the country) deeply affected the job market: tens of thousands of workers lost their jobs. In particular, Blacks were the ones who suffered the most since the majority of them were employed in various industrial fields. As a consequence, poverty spread: 30% of black work force was jobless in 1982. * The conservative Reagan presidency (1981-1989) reduced federal (governmental, that is) economic support to people in need by 20%. The cut to financial measures combined with the ongoing industrial crisis was disastrous. * White supremacy movements and groups (such as the Ku Klux Klan) reignited and engaged in violent acts against African Americans, firebombing of churches and campaigns against affirmative actions programs and integration in schools. "Millions of white Americans had become convinced that “too much” had been given to blacks". * Poverty, hunger and hopelessness paved the way to the abuse of drugs; crack was especially consumed by poor Americans as it was inexpensive and easily available. As a consequence of the combination of low employment, educational poverty and drug popularity, drug dealing became the source of income for young people and violence increased significantly in Black neighborhoods.

What was the government's response? Aggravated levels of violence and crime were responded with the "War on Drugs", which entailed: 1. the elimination of parole (that is, the conditional release of a prisoner, often on the basis of good behavior in prison); 2. stricter penalties for drug sale and possession; 3. building a larger network of prisons.

Needless to say, African-Americans were the most targeted ones. Mass incarceration as a system of control (see the "home" of the website for more on the topic) started to bloom.

https://www.amistadresource.org/the_future_in_the_present/social_and_economic_issues.html

This word presents a common linguistic phenomenon called g-dropping: it consists in the drop of the -g at the end of certain words. In fact, no -g is actually "dropped" because the 'g' is not even pronounced. All English speakers g-drop, but the frequency of this phenomenon is tied to class belonging, race, sex and degree of formality. Generally, it is more common among lower social classes. http://itre.cis.upenn.edu/~myl/languagelog/archives/000878.html

What does this mean in the context of this song? Answer: By adopting g-dropping and thus language-wise, Bob Dylan positions himself in the tradition of folk music and becomes the spokesman of the people.

Notice how this part of the song echoes Lamar's lyrics "gang-banging make me kill a nigga blacker than me?": both songs highlight feuds inside the African-American community due to gang divisions. However, Eminem seems to be more reluctant to blame African-Americans: in the following lines, he underscores that said fights are rooted in problems such as single-parenting, drug abuse and struggle with addiction that lead people "with nothin(g) to lose to shoot each other for shoes".

Die Antwood are "a group that was ostracized for their 2012 video Fatty Boom Boom, which portrayed their female lead singer, Yolandi, protayed in black face." Eminem's message is that "dealing with in-your-face racism is a tough pill to swallow" but "he’d rather endure that than watch white people seemingly destroy the black culture with empty jokes". https://www.billboard.com/music/rb-hip-hop/eminem-untouchable-lyrics-decoded-8062711/

The wordplay here is based on the meaning of the word black: it can mean African-American but it is also the symbolical color of evilness.

I could not find any indications for this word, but I think it may be used as us.

Lamar's viewpoint is crystal clear: not only is there a social hierarchy in America, but also he identifies black as the ones "at the bottom". There is no possible equality in this scenario.

This sentence functions as an explanation of the previous one: Lamar claims that he may be experiencing life in a schizophrenic way but blames whites (the ideal interlocutors in this scenario) for it.

From a strictly grammatical point of view, this expression is wrong: two negative forms (didn't and no) are not acceptable in a sentence. The correct form should be either "My mama did not raise any fools" or "My mama raised no fool". However, double negative (also called negative concord) is used in some varieties of English (in this case, African-American Vernacular English) to intensify the meaning of the sentence. In other words, the construction is a sub-standard form used mainly to give more emphasis and expressive force to what is being said. It should be avoided in written and formal writing, but it is normal to use in oral speech, especially in African-American communities.

Curiosity: labeling an expression or an usage as "wrong" or "correct" is rarely a matter of language and more frequently a matter of (social) prestige: normally, it is considered "right" the variant of the language that is used by educated, white people; in contrast, "wrong" expressions are often the ones employed by minorities, uneducated or stigmatized groups. Long story short: people think they are judging an incorrect linguistic form, when, in fact, they are stigmatizing the community that uses it.

https://www.merriam-webster.com/dictionary/double%20negative https://web.archive.org/web/20100810125721/https://www.american.edu/cas/tesol/resources/upload/Kirby_Philippa.pdf

Just as the previous term, this one is charged with a derogatory connotation, so much as it is preferred to refer to it with the euphemistic expression "N-word", which emcompasses both this word and its -er variant. The phenomenon that allowed the erosion of the -er ending is called r-dropping: the -r is replaced by a schwa (ə), an indistinct vowel pronounced "uh". These terms have always been linked with white supremacy, racism and white power. Even though the two terms may seem synonyms, in fact there is a difference: the -er ending word is strictly connected with the all-encompassing hatred and contempt towards black people, whereas the second one is perceived as a term of endearment when uttered by someone belonging to the Black community. Indeed, starting from the 1980s, the word has underwent a process of reclaiming (also called semantic inversion or looping) which corresponds to "taking a word meant as a slur and reappropriating it as a term of endearment" (https://www.washingtonpost.com/sf/national/2014/11/09/the-n-word-an-entrenched-racial-slur-now-more-prevalent-than-ever/?utm_term=.1590a4928864). This strategy allows the originally oppressive term to be re-semantized (that is, to acquire a new meaning) and used to celebrate the community's unique identity and humanity as "an act of redemption by black folk. The word survives on the conditions that black folks have inscribed for it and nobody else can take that. And it becomes violent when other people try to take it and use it." Indeed, white people "have created the word in the first place, but […] they have lost the power to use it with impunity, they have lost the power to reclaim it." […] "If you understand the history of the word and how it's been used, it's not for white people to use […] So if you're not black you can't do that. You actually can't use the word in the way that we use it. It's not possible, because you're not in that space. So any other usage of it is completely wrong." (https://www.bbc.com/news/stories-53749800). https://www.merriam-webster.com/dictionary/N-word https://share.google/2p6rElVA4Vin0v2cC https://www.dailydot.com/irl/how-not-to-use-the-n-word/

The word is notoriously a derogatory term to indicate African-Americans. However, "both Negro and Negress are sometimes used by Black people in self-reference, but use of either term by others is offensive". https://www.merriam-webster.com/dictionary/Negro

Note how don't is paired with a singular noun, law. "Don't is occasionally used in American English speech and in historical writing as a contraction of does not (as in, "He don't know where he is going."), but this use is now considered improper and should be avoided. Remember that in modern speech and writing, don't cannot be used in the third person singular." https://www.britannica.com/dictionary/eb/qa/don-t-and-doesn-t

Hornsby's posture seems clear: in these verses, he states that legal measures can only "go so far", that is, they can only operate up to a certain extent. He hints at the fact that another change has to occur: a cultural one. According to the author, indeed, equality can be achieved only in a co-constructive process that implicates the law on one side, and the culture on the other. In other words, what Hornsby is trying to state is that acts can be signed into law and assure people their rights, but it is equally important that people change their mindsets.

"The mountain […] is a symbol of those human institutions that keep war and oppression in place. The stony mountain is all that resists change: the shape of government and history, certainly, but also the rocky terrain inside people's hearts. The slow, persistent erosive power of the ocean, on the other hand, symbolizes the action of internal and external change." https://www.litcharts.com/poetry/bob-dylan/blowin-in-the-wind

Setting the scene: the song was released in 1963 and included in the album The Freewhelin' Bob Dylan. All of Dylan's "most famous political songs were written [...] between January 1962 and October 1963" and "those [...] fixed him in the popular imagination" as a protest songwriter (Lynskey, 2010, 67). The historical context in which the song was released is essential to understand its meaning: during the 1950s the civil rights movement against racial segregation started to gain momentum, reaching its highest point precisely in 1963. Indeed, in that year: * Desegregation protests spread throughout the Southern states over more than 100 cities. The most famous was the March on Washington: it was organized by the "Big Six" of the civil rights movement (Martin Luther King Jr., James Farmer, J. Lewis, P. Randolph, R. Wilkins, W. Young), attended by more than 250.000 protesters (including Bob Dylan). It was in this occasion that the renowned "I have a dream" speech took place. * On June 12, President Kennedy announced he would present a civil rights bill to Congress, which was eventually passed the following year under the name of the Civil Rights Act. https://www.britannica.com/event/American-civil-rights-movement https://www.britannica.com/event/American-civil-rights-movement

It is important to bear in mind that protests were not only motivated by the insufferable racial segregation, but they were also anti-war oriented: the Vietnam war (1955-1975), which had been raging for ten years by the mid-1960s, was felt as an unnecessary conflict, especially by students and young people, who were the ones recruited in the U.S. army. Indeed, "the average age of an American soldier in Vietnam was 19" (https://www.bbc.co.uk/bitesize/guides/z6dk8hv/revision/4 ).

That's "garbage-collected language implementations" (or, in this context, just "garbage collection").

Programming languages are languages. They're notation. They're inert. They can't have garbage collectors.

The presence of GC is a memory management implementation strategy—a choice—of a given programming language implementation. C, for example, is neither a "garbage-collected language" nor a "non-garbage-collected language".

GCC doesn't have a garbage collector for C programs, and neither does Clang, but GCC and Clang are not "C"; C is a programming language, and GCC and Clang are programming language implementations. You could write a C implementation in the vein of SpiderMonkey or the compiler/runtime from the golang.org toolchain (one designed to parse and run programs written C instead of JS or Go) if you wanted to.

Ah, it uses the CBS protected data working environment (also for the DGA). This means the debiasing is not done without personal data but without the organisation concerned seeing that data. Third party testing basically, with CBS being that party.

Here is the ending sentence of the song: I could annotate it as I did so far, but by now you are familiar with Eminem's tone and with the themes dealt with in the project... so I would like to hear your interpretations! Try to unpack it on your own. Clues: my personal advice when it comes to understand a text is dividing it into smaller chunks (example: what piece of cloth is Eminem referring to?). If you struggle with understanding what he is trying to say, don't worry: try to read the text again and remember that previous and following sentences can help you out.

Dryer lint refers to the buildup that forms in the dryer after doing laundry as a result of hair, textile fibers and other particles clamping together. Normally, it is thrown up after every washing cycle. In Italian, it is translatable as lanugine, but perhaps the following image will clarify any doubt: https://preparedhero.com/it-it/blogs/articles/dryer-lint#:~:text=What%20Is%20Dryer%20Lint?,for%20dryer%20safety%20and%20performance.

Your turn: what do you think Eminem is trying to say here? Clue: focus on the word hire.

Die Antwood are "a group that was ostracized for their 2012 video Fatty Boom Boom, which portrayed their female lead singer, Yolandi, protayed in black face." Eminem's message is that "dealing with in-your-face racism is a tough pill to swallow" but "he’d rather endure that than watch white people seemingly destroy the black culture with empty jokes". https://www.billboard.com/music/rb-hip-hop/eminem-untouchable-lyrics-decoded-8062711/

"A house where dope heads (heroin addicts) live" and do drugs. http://dope-house.urbanup.com/7824692 Eminem is criticizing "how African American drivers are often stereotyped as drug dealers with a criminal background". https://www.billboard.com/music/rb-hip-hop/eminem-untouchable-lyrics-decoded-8062711/ Indeed, the key word in the sentence is "probably": the policeman does not know who the young man is, where he is going to or where he comes from, so he cannot do anything but hypothesize. The problem is twofold: 1. He mistakes his hypothesis for truth; 2. His hypothesis is biased: there is no logical reason as for why he associates the young black man with the "dope house". However, he does so because he cultivates prejudices against black men.

Southern Black Africans presents four major ethnic divisions; one of them is the Nguni, which, in its turn, can be divided into four groups: Zulu and Xhosa are two of them. https://sahistory.org.za/article/xhosa https://sahistory.org.za/article/zulu Although they share a common history, they were implicated in a civil war from 1990 to 1994. The reason behind this conflict is, actually, linguistic and tied to colonialism: the two languages were "created" by colonizers and African interpreters. Before African colonialism, indeed, there weren't any written languages and people did not distinguish themselves on a linguistic basis, but rather on social belonging. This does not mean that "Zulu and Xhosa identities didn’t exist before the languages were well defined, rather that the identities were transformed when these languages came into existence." https://theconversation.com/zulu-vs-xhosa-how-colonialism-used-language-to-divide-south-africas-two-biggest-ethnic-groups-204969

Tyrone is originally Irish name, but recently is increasingly more common for boys in African-American communities. Since its strong association to blacks, it is also at the center of discrimination: compared to Tyrone, "white" names are twice as likely to receive responses. Also Darius is a typical African-American name. https://www.irishexaminer.com/news/arid-10108995.html https://www.behindthename.com/name/darius

Dem ah seh could mean "they all say". Ah can mean different words, in which I could not find all, but I hypothesized this meaning also by comparing this version to Anglicized ones. https://jamaicanpatwah.com/term/dem/961 https://jamaicanpatwah.com/term/seh/1293

I could not find any indications for this word, but I think it may be used as us.

This entire verse is written in Jamaican patois, that is, "an English-lexified creole language spoken by the majority of Jamaicans". As you will see, some words may be intuitive, but others are definitely not. While I was looking for the lyrics of the song, I found other anglicized versions which were certainly more comprehensible, but, I am afraid, less faithful to the singer's intention. Consequently, I opted for the original, more complex version. Why has the singer recurred to Jamaican patois? My hypothesis is that he features it as a way to give importance and centrality to a marginal community through its language.

Dem is they. https://jamaicanpatwah.com/term/dem/961

Here Lamar denounces a decade-long rage for anti-Black racism and police brutality he himself was a victim of. In a Rolling Stone interview, Lamar declared that as a teenager "the majority of my interactions with the police were not good […] there were a few good ones who were actually protecting the community. But then you have the ones from the Valley. They never met me in my life, but since I'm a kid […] they wanna slam me on the hood of the car. Sixteen years old […] Even if he's not a good kid, that don't give you the right to slam a minor on the ground or pull a pistol on him. " […] Indeed, "police pull guns on him on two occasions. The first when he was 17." https://issuu.com/lawrenceambrocio5018/docs/rolling_stone_march_26_2015_usa_1_

Here the central critique to the lack of equality and the debunking of the immigration myths appears evident: in particular, Springsteen makes uses of the anaphora "they died" to underscore the timeless sacrifice of immigrants, who are identified with the makers of the United States ("the hands that build the country"). This last part of the song completely overturns what Springsteen has sung so far: there are no streets paved with gold, no diamonds in the sidewalks or "treasure for the taking". What remains is work "to bones and skin". Once again, Springsteen has not departed from the historical truth: despite their hopes, European immigrants who landed in the United States did not improve their status. In other words, if they were poor, they stayed as such; “past European immigrants often struggled when they first arrived, and most of them did not succeed in reaching the American Dream within their lifetimes.” https://ui.charlotte.edu/story/streets-gold-debunking-american-immigration-myths/ Moreover, research shows that from 1880 to 1920 immigrants "were the mainstay of the American industrial workforce". https://pmc.ncbi.nlm.nih.gov/articles/PMC2760060/#abstract1 Making home in the American Land was, therefore, impossible for them because America itself rejected and discriminated immigrants after exploiting them for its own industrial and economic growth. Indeed, "often stereotyped and discriminated against, many immigrants suffered verbal and physical abuse because they were "different." […] The newcomers helped transform American society and culture, demonstrating that diversity, as well as unity, is a source of national strength." https://www.loc.gov/classroom-materials/united-states-history-primary-source-timeline/rise-of-industrial-america-1876-1900/immigration-to-united-states-1851-1900/

Springsteen's radical claim is that such discrimination and sacrifice are not limited to a distant past, but have continued up until now ("they're still dyin' now"). When Springsteen released the song, the country was actually undergoing a period of decline in immigration due to measures taken after the terrorist attacks of September 11, 2001 such as the Patriot Act. https://library.law.howard.edu/civilrightshistory/immigration/history. However, it is true that immigration continues to be a divisive topic in the United States, especially in the current presidency: what do you know about Trump's immigration policies?

From a strictly grammatical point of view, this expression is wrong: two negative forms (didn't and no) are not acceptable in a sentence. The correct form should be either "My mama did not raise any fools" or "My mama raised no fool". However, double negative (also called negative concord) is used in some varieties of English (in this case, African-American Vernacular English) to intensify the meaning of the sentence. In other words, the construction is a sub-standard form used mainly to give more emphasis and expressive force to what is being said. It should be avoided in written and formal writing, but it is normal to use in oral speech, especially in African-American communities.

Curiosity: labeling an expression or an usage as "wrong" or "correct" is rarely a matter of language and more frequently a matter of (social) prestige: normally, it is considered "right" the variant of the language that is used by educated, white people; in contrast, "wrong" expressions are often the ones employed by minorities, uneducated or stigmatized groups. Long story short: people think they are judging an incorrect linguistic form, when, in fact, they are stigmatizing the community that uses it.

https://www.merriam-webster.com/dictionary/double%20negative https://web.archive.org/web/20100810125721/https://www.american.edu/cas/tesol/resources/upload/Kirby_Philippa.pdf

In this verse and in the previous one, the singer is making explicit reference to the police brutality in the U.S.A: excessive use of force (also culminating in murder) by police officers towards specific categories has been documented since the early 19th century. The targets have varied through the centuries, but African-Americans are historically the most targeted group because of racial implications. In this case, the singer emphasizes policemen's impunity before the law; on the contrary, they are acclaimed and welcomed as "heros". https://www.britannica.com/topic/police-brutality-in-the-United-States-2064580/Police-brutality-after-World-War-II

Just as the previous term, this one is charged with a derogatory connotation, so much as it is preferred to refer to it with the euphemistic expression "N-word", which emcompasses both this word and its -er variant. The phenomenon that allowed the erosion of the -er ending is called r-dropping: the -r is replaced by a schwa (ə), an indistinct vowel pronounced "uh". These terms have always been linked with white supremacy, racism and white power. Even though the two terms may seem synonyms, in fact there is a difference: the -er ending word is strictly connected with the all-encompassing hatred and contempt towards black people, whereas the second one is perceived as a term of endearment when uttered by someone belonging to the Black community. Indeed, starting from the 1980s, the word has underwent a process of reclaiming (also called semantic inversion or looping) which corresponds to "taking a word meant as a slur and reappropriating it as a term of endearment" (https://www.washingtonpost.com/sf/national/2014/11/09/the-n-word-an-entrenched-racial-slur-now-more-prevalent-than-ever/?utm_term=.1590a4928864). This strategy allows the originally oppressive term to be re-semantized (that is, to acquire a new meaning) and used to celebrate the community's unique identity and humanity as "an act of redemption by black folk. The word survives on the conditions that black folks have inscribed for it and nobody else can take that. And it becomes violent when other people try to take it and use it." Indeed, white people "have created the word in the first place, but […] they have lost the power to use it with impunity, they have lost the power to reclaim it." […] "If you understand the history of the word and how it's been used, it's not for white people to use […] So if you're not black you can't do that. You actually can't use the word in the way that we use it. It's not possible, because you're not in that space. So any other usage of it is completely wrong." (https://www.bbc.com/news/stories-53749800). https://www.merriam-webster.com/dictionary/N-word https://share.google/2p6rElVA4Vin0v2cC https://www.dailydot.com/irl/how-not-to-use-the-n-word/

The word is notoriously a derogatory term to indicate African-Americans. However, "both Negro and Negress are sometimes used by Black people in self-reference, but use of either term by others is offensive". https://www.merriam-webster.com/dictionary/Negro

Note how don't is paired with a singular noun, law. "Don't is occasionally used in American English speech and in historical writing as a contraction of does not (as in, "He don't know where he is going."), but this use is now considered improper and should be avoided. Remember that in modern speech and writing, don't cannot be used in the third person singular." https://www.britannica.com/dictionary/eb/qa/don-t-and-doesn-t

Here is the chorus of the song: at first, the singer's reaction to the widespread inquality that dominates the society seems to coincide with a resigned acceptation of the status quo. The feeling of quiet resignation amplifies as the song goes on, reaching its climax when the singer makes reference to the Civil Rights Act and, therefore, establishing a parallelism between the present and the past. However, the chorus closes with a glimpse of hope: indeed, the author invites the audience not to believe to inevitability, thus suggesting that things may change if only people started thinking and acting differently. As a matter of fact, the singer himself insists upon this last verse: "Some things will never change is a statement of resignation, but the most important line in that song is the one that comes after that: But don't you believe them. So I've always been about being strong when resignation is a possibility. Trying to pull up from that and have a positive outlook so that things can change" (http://www.musicfordemocracy.org/node/34.html).

Your turn: do you think that injustice and inequality will never be defeated?

Setting the scene: the song was released in July 1986 as a single from the band's debut album The Way It Is. It was a great success and the band won the 1987 Grammy Awards in the Best New Artist category. The success of the song has had a long-lasting effect in the music industry: it was sampled by other artists and included in songs such as 2Pac's Changes and Polo G's Wishing for a Hero. The singer has "never counted it" but he has read that his song "has now been recorded 17 times by hip-hop artists" (https://www.rollingstone.com/music/music-features/bruce-hornsby-interview-way-it-is-non-secure-connection-1036032/). In order to understand the following lyrics, it is necessary to place the song in its historical context. The 1980s were years in which several issues emerged: * The process of de-industrialization (that is, the process in which American companies moved their seats abroad, outside the country) deeply affected the job market: tens of thousands of workers lost their jobs. In particular, Blacks were the ones who suffered the most since the majority of them were employed in various industrial fields. As a consequence, poverty spread: 30% of black work force was jobless in 1982. * The conservative Reagan presidency (1981-1989) reduced federal (governmental, that is) economic support to people in need by 20%. The cut to financial measures combined with the ongoing industrial crisis was disastrous. * White supremacy movements and groups (such as the Ku Klux Klan) reignited and engaged in violent acts against African Americans, firebombing of churches and campaigns against affirmative actions programs and integration in schools. "Millions of white Americans had become convinced that “too much” had been given to blacks". * Poverty, hunger and hopelessness paved the way to the abuse of drugs; crack was especially consumed by poor Americans as it was inexpensive and easily available. As a consequence of the combination of low employment, educational poverty and drug popularity, drug dealing became the source of income for young people and violence increased significantly in Black neighborhoods.

What was the government's response? Aggravated levels of violence and crime were responded with the "War on Drugs", which entailed: 1. the elimination of parole (that is, the conditional release of a prisoner, often on the basis of good behavior in prison); 2. stricter penalties for drug sale and possession; 3. building a larger network of prisons.

Needless to say, African-Americans were most targeted. Mass incarceration as a system of control (see the "home" of the website for more on the topic) started to bloom.

https://www.amistadresource.org/the_future_in_the_present/social_and_economic_issues.html

"The mountain […] is a symbol of those human institutions that keep war and oppression in place. The stony mountain is all that resists change: the shape of government and history, certainly, but also the rocky terrain inside people's hearts. The slow, persistent erosive power of the ocean, on the other hand, symbolizes the action of internal and external change." https://www.litcharts.com/poetry/bob-dylan/blowin-in-the-wind

This word presents a common linguistic phenomenon called g-dropping: it consists in the drop of the -g at the end of certain words. In fact, no -g is actually "dropped" because the 'g' is not even pronounced. All English speakers g-dropped, but the frequency of this phenomenon is tied to class belonging, race, sex and degree of formality. Generally, it is more common among lower social classes. http://itre.cis.upenn.edu/~myl/languagelog/archives/000878.html

What does this mean in the context of this song? Answer: By adopting g-dropping and thus language-wise, Bob Dylan positions himself in the tradition of folk music and becomes the spokesman of the people.

Notice how this part of the song echoes Lamar's lyrics "gang-banging make me kill a nigga blacker than me?": both songs highlight feuds inside the African-American community due to gang divisions. However, Eminem seems to be more reluctant to blame African-Americans: in the following lines, he underscores that said fights are rooted in problems such as single-parenting, drug abuse and struggle with addiction that lead people "with nothin(g) to lose to shoot each other for shoes".

Lamar's viewpoint is crystal clear: not only is there a social hierarchy in America, but also he identifies black as the ones "at the bottom". There is no possible equality in this scenario.

This sentence functions as an explanation of the previous one: Lamar claims that he may be experiencing life in a schizophrenic way but blames whites (the ideal interlocutors in this scenario) for it.

Reviewer #3 (Public review):

Summary:

Overall, this is a well-done study, and the conclusions are largely supported by the data, which will be of interest to the field.

Strengths:

Strengths of this study include experiments with solution NMR that can resolve high-resolution interactions of the highly flexible C-terminal tail of arr2 with clathrin and AP2. Although mainly confirmatory in defining the arr2 CBL 376LIELD380 as the clathrin binding site, the use of the NMR is of high interest (Fig. 1). The 15N-labeled CLTC-NTD experiment with arr2 titrations reveals a span from 39-108 that mediates an arr2 interaction, which corroborates previous crystal data, but does not reveal a second area in CLTC-NTD that in previous crystal structures was observed to interact with arr2.

SEC and NMR data suggest that full-length arr2 (1-418) binding with 2-adaptin subunit of AP2 is enhanced in the presence of CCR5 phospho-peptides (Fig. 3). The pp6 peptide shows the highest degree of arr2 activation, and 2-adaptin binding, compared to less phosphorylated peptide or not phosphorylated at all. It is interesting that the arr2 interaction with CLTC NTD and pp6 cannot be detected using the SEC approach, further suggesting that clathrin binding is not dependent on arrestin activation. Overall, the data suggest that receptor activation promotes arrestin binding to AP2, not clathrin, suggesting the AP2 interaction is necessary for CCR5 endocytosis.

To validate the solid biophysical data, the authors pursue validation experiments in a HeLa cell model by confocal microscopy. This requires transient transfection of tagged receptor (CCR5-Flag) and arr2 (arr2-YFP). CCR5 displays a "class B"-like behavior in that arr2 is rapidly recruited to the receptor at the plasma membrane upon agonist activation, which forms a stable complex that internalizes onto endosomes (Fig. 4). The data suggest that complex internalization is dependent on AP2 binding not clathrin (Fig. 5).

The addition of the antagonist experiment/data adds rigor to the study.

Overall, this is a solid study that will be of interest to the field.

Author response:

The following is the authors’ response to the previous reviews

Public Reviews: 

Reviewer #1 (Public review): 

Petrovic et al. investigate CCR5 endocytosis via arrestin 2, with a particular focus on clathrin and AP2 contributions. The study is thorough and methodologically diverse. The NMR titration data clearly demonstrate chemical shift changes at the canonical clathrin-binding site (LIELD), present in both the 2S and 2L arrestin splice variants. 

To assess the effect of arrestin activation on clathrin binding, the authors compare: truncated arrestin (1-393), full-length arrestin, and 1-393 incubated with CCR5 phosphopeptides. All three bind clathrin comparably, whereas controls show no binding. These findings are consistent with prior crystal structures showing peptide-like binding of the LIELD motif, with disordered flanking regions. The manuscript also evaluates a non-canonical clathrin binding site specific to the 2L splice variant. Though this region has been shown to enhance beta2-adrenergic receptor binding, it appears not to affect CCR5 internalization. 

Similar analyses applied to AP2 show a different result. AP2 binding is activation-dependent and influenced by the presence and level of phosphorylation of CCR5-derived phosphopeptides. These findings are reinforced by cellular internalization assays. 

In sum, the results highlight splice-variant-dependent effects and phosphorylation-sensitive arrestin-partner interactions. The data argue against a (rapidly disappearing) one-size-fitsall model for GPCR-arrestin signaling and instead support a nuanced, receptor-specific view, with one example summarized effectively in the mechanistic figure.

We thank the referee for this positive assessment of our manuscript. Indeed, by stepping away from the common receptor models for understanding internalization (b2AR and V2R), we revealed the phosphorylation level of the receptor as a key factor in driving the sequestration of the receptor from the plasma membrane. We hope that the proposed mechanistic model will aid further studies to obtain an even more detailed understanding of forces driving receptor internalization.

Weaknesses: 

Figure 1 shows regions alphaFold model that are intrinsically disordered without making it clear that this is not an expected stable position. The authors NMR titration data are n=1. Many figure panels require that readers pinch and zoom to see the data.

In the “Recommendations for the Authors” section, we addressed the reviewer’s stated weaknesses. In short, for the AlphaFold representation in Figure 1A, we added explicit labeling and revised the legend and main text to clearly state that the depicted loops are intrinsically disordered, absent from crystal structures due to flexibility, and shown only for visualization of their location. We also clarified that the NMR titration experiments inherently have n = 1 due to technical limitations, and that this is standard practice in the field, while ensuring individual data points remain visible. The supplementary NMR figures now have more vibrant coloring, allowing easier data assessment. However, we have not changed the zooming of the microscopy and NMR spectra. We believe that the presentation of microscopy data, which already show zoomed-in regions of interest, follow standard practices in the field. Furthermore, we strongly believe that we should display full NMR spectra in the supplementary figures to allow the reader to assess the overall quality and behavior. As indicated previously, the reader can zoom in to very high resolution, since the spectra are provided by vector graphics. Zoomed regions of the relevant details are provided in the main figures.

Reviewer #2 (Public review): 

Summary: 

Based on extensive live cell assays, SEC, and NMR studies of reconstituted complexes, these authors explore the roles of clathrin and the AP2 protein in facilitating clathrin mediated endocytosis via activated arrestin-2. NMR, SEC, proteolysis, and live cell tracking confirm a strong interaction between AP2 and activated arrestin using a phosphorylated C-terminus of CCR5. At the same time a weak interaction between clathrin and arrestin-2 is observed, irrespective of activation. 

These results contrast with previous observations of class A GPCRs and the more direct participation by clathrin. The results are discussed in terms of the importance of short and long phosphorylated bar codes in class A and class B endocytosis. 

Strengths: 

The 15N,1H and 13C,methyl TROSY NMR and assignments represent a monumental amount of work on arrestin-2, clathrin, and AP2. Weak NMR interactions between arrestin-2 and clathrin are observed irrespective of activation of arrestin. A second interface, proposed by crystallography, was suggested to be a possible crystal artifact. NMR establishes realistic information on the clathrin and AP2 affinities to activated arrestin with both kD and description of the interfaces.

We sincerely thank the referee for this encouraging evaluation of our work and appreciate the recognition of the NMR efforts and insights into the arrestin–clathrin–AP2 interactions.

Weaknesses: 

This reviewer has identified only minor weaknesses with the study. 

(1) I don't observe two overlapping spectra of Arrestin2 (1393) +/- CLTC NTD in Supp Figure 1

We believe the referee is referring to Figure 1 – figure supplement 2. We have now made the colors of the spectra more vibrant and used different contouring to make the differences between the two spectra clearer. The spectra are provided as vector graphics, which allows zooming in to the very fine details.

(2) Arrestin-2 1-418 resonances all but disappear with CCR5pp6 addition. Are they recovered with Ap2Beta2 addition and is this what is shown in Supp Fig 2D

We believe the reviewer is referring to Figure 3 - figure supplement 1. In this figure, the panels E and F show resonances of arrestin2^1-418 (apo state shown with black outline) disappear upon the addition of CCR5pp6 (arrestin2^1-418•CCR5pp6 complex spectrum in red). The panels C and D show resonances of arrestin2^1-418 (apo state shown with black outline), which remain unchanged upon addition of AP2b2 ^701-937 (orange), indicating no complex formation. We also recorded a spectrum of the arrestin2^1-418•CCR5pp6 complex under addition of AP2b2 ^701-937 (not shown), but the arrestin2 resonances in the arrestin2^1-418 •CCR5pp6 complex were already too broad for further analysis. This had been already explained in the text.

“In agreement with the AP2b2 NMR observations, no interaction was observed in the arrestin2 methyl and backbone NMR spectra upon addition of AP2b2 in the absence of phosphopeptide (Figure 3-figure supplement 1C, D). However, the significant line broadening of the arrestin2 resonances upon phosphopeptide addition (Figure 3-figure supplement 1E, F) precluded a meaningful assessment of the effect of the AP2b2 addition on arrestin2 in the presence of phosphopeptide”.

(3) I don't understand how methyl TROSY spectra of arrestin2 with phosphopeptide could look so broadened unless there are sample stability problems?

We thank the referee for this comment. We would like to clarify that in general a broadened spectrum beyond what is expected from the rotational correlation time does not necessarily correlate with sample stability problems. It is rather evidence of conformational intermediate exchange on the micro- to millisecond time scale.

The displayed ¹H-¹⁵N spectra of apo arrestin2 already suffer from line broadening due to such intrinsic mobility of the protein. These spectra were recorded with acquisition times of 50 ms (¹⁵N) and 55 ms (¹H) and resolution-enhanced by a 60˚-shifted sine-bell filter for ¹⁵N and a 60˚-shifted squared sine-bell filter for ¹H, respectively, which leads to the observed resolution with still reasonable sensitivity. The ¹H-¹⁵N resonances in Fig. 1b (arrestin2^1-393) look particularly narrow. However, this region contains a large number of flexible residues. The full spectrum, e.g. Figure 1-figure supplement 2, shows the entire situation with a clear variation of linewidths and intensities. The linewidth variation becomes stronger when omitting the resolution enhancement filters.

The addition of the CCR5pp6 phosphopeptide does not change protein stability, which we assessed by measuring the melting temperature of arrestin2^1-418 and arrestin2^1-418•CCR5pp6 complex (Tm = 57°C in both cases). We believe that the explanation for the increased broadening of the arrestin2 resonances is that addition of the CCR5pp6, possibly due to the release of the arrestin2 strand b20, amplifies the mentioned intermediate timescale protein dynamics. This results in the disappearance of arrestin2 resonances.

We have now included the assessment of arrestin2^1-418 and arrestin2^1-418•CCR5pp6 stability in the manuscript:

“The observed line broadening of arrestin2 in the presence of phosphopeptide must be a result of increased protein motions and is not caused by a decrease in protein stability, since the melting temperature of arrestin2 in the absence and presence of phosphopeptide are identical (56.9 ± 0.1 °C)”.

(4) At one point the authors added excess fully phosphorylated CCR5 phosphopeptide (CCR5pp6). Does the phosphopeptide rescue resolution of arrestin2 (NH or methyl) to the point where interaction dynamics with clathrin (CLTC NTD) are now more evident on the arrestin2 surface?

Unfortunately, when we titrate arrestin2 with CCR5pp6 (please see Isaikina & Petrovic et. al, Mol. Cell, 2023 for more details), the arrestin2 resonances undergo fast-to-intermediate exchange upon binding. In the presence of phosphopeptide excess, very few resonances remain, the majority of which are in the disordered region, including resonances from the clathrin-binding loop. Due to the peak overlap, we could not unambiguously assign arrestin2 resonances in the bound state, which precluded our assessment of the arrestin2-clathrin interaction in the presence of phosphopeptide. We have made this now clearer in the paragraph ‘The arrestin2-clathrin interaction is independent of arrestin2 activation’

“Due to significant line broadening and peak overlap of the arrestin2 resonances upon phosphopeptide addition, the influence of arrestin activation on the clathrin interaction could not be detected on either backbone or methyl resonances “.

(5) Once phosphopeptide activates arrestin-2 and AP2 binds can phosphopeptide be exchanged off? In this case, would it be possible for the activated arrestin-2 AP2 complex to re-engage a new (phosphorylated) receptor?

This would be an interesting mechanism. In principle, this should be possible as long as the other (phosphorylated) receptor outcompetes the initial phosphopeptide with higher affinity towards the binding site. However, we do not have experiments to assess this process directly. Therefore, we rather wish not to further speculate.

(6) I'd be tempted to move the discussion of class A and class B GPCRs and their presumed differences to the intro and then motivate the paper with specific questions. 

We appreciate the referee’s suggestion and had a similar idea previously. However, as we do not have data on other class-A or class-B receptors, we rather don’t want to motivate the entire manuscript by this question.

(7) Did the authors ever try SEC measurements of arrestin-2 + AP2beta2+CCR5pp6 with and without PIP2, and with and without clathrin (CLTC NTD? The question becomes what the active complex is and how PIP2 modulates this cascade of complexation events in class B receptors.

We thank the referee for this question. Indeed, we tested whether PIP2 can stabilize the arrestin2•CCR5pp6•AP2 complex by SEC experiments. Unfortunately, the addition of PIP2 increased the formation of arrestin2 dimers and higher oligomers, presumably due to the presence of additional charges. The resolution of SEC experiments was not sufficient to distinguish arrestin2 in oligomeric form or in arrestin2•CCR5pp6•AP2 complex. We now mention this in the text:

“We also attempted to stabilize the arrestin2-AP2b2-phosphopetide complex through the addition of PIP2, which can stabilize arrestin complexes with the receptor (Janetzko et al., 2022). The addition of PIP2 increased the formation of arrestin2 dimers and higher oligomers, presumably due to the presence of additional charges. Unfortunately, the resolution of the SEC experiments was not sufficient to separate the arrestin2 oligomers from complexes with AP2b2”.

Reviewer #3 (Public review): 

Summary: 

Overall, this is a well-done study, and the conclusions are largely supported by the data, which will be of interest to the field. 

Strengths: 

Strengths of this study include experiments with solution NMR that can resolve high-resolution interactions of the highly flexible C-terminal tail of arr2 with clathrin and AP2. Although mainly confirmatory in defining the arr2 CBL376LIELD380 as the clathrin binding site, the use of the NMR is of high interest (Fig. 1). The 15N-labeled CLTC-NTD experiment with arr2 titrations reveals a span from 39-108 that mediates an arr2 interaction, which corroborates previous crystal data, but does not reveal a second area in CLTC-NTD that in previous crystal structures was observed to interact with arr2. 

SEC and NMR data suggest that full-length arr2 (1-418) binding with 2-adaptin subunit of AP2 is enhanced in the presence of CCR5 phospho-peptides (Fig. 3). The pp6 peptide shows the highest degree of arr2 activation, and 2-adaptin binding, compared to less phosphorylated peptide or not phosphorylated at all. It is interesting that the arr2 interaction with CLTC NTD and pp6 cannot be detected using the SEC approach, further suggesting that clathrin binding is not dependent on arrestin activation. Overall, the data suggest that receptor activation promotes arrestin binding to AP2, not clathrin, suggesting the

AP2 interaction is necessary for CCR5 endocytosis. 

To validate the solid biophysical data, the authors pursue validation experiments in a HeLa cell model by confocal microscopy. This requires transient transfection of tagged receptor (CCR5-Flag) and arr2 (arr2-YFP). CCR5 displays a "class B"-like behavior in that arr2 is rapidly recruited to the receptor at the plasma membrane upon agonist activation, which forms a stable complex that internalizes onto endosomes (Fig. 4). The data suggest that complex internalization is dependent on AP2 binding not clathrin (Fig. 5). 

The addition of the antagonist experiment/data adds rigor to the study. 

Overall, this is a solid study that will be of interest to the field.

We thank the referee for the careful and encouraging evaluation of our work. We appreciate the recognition of the solidity of our data and the support for our conclusions regarding the distinct roles of AP2 and clathrin in arrestin-mediated receptor internalization.

Recommendations for the authors: 

Reviewer #1 (Recommendations for the authors): 

I believe that the authors have made efforts to improve the accessibility to a broader audience. In a few cases, I believe that the authors response either did not truly address the concern or made the problem worse. I am grouping these as 'very strong opinions' and 'sticking point'. 

Very strong opinion 1: 

While data presentation is somewhat at the authors discretion, there were several figures where the presentation did not make the work approachable, including microscopy insets and NMR spectra. A suggestion to 'pinch and zoom' does not really address this. For the overlapping NMR spectra in supporting Figure 1, I actually -can- see this on zooming, but I did not recognize this on first pass because the colors are almost identical for the two spectra. This is an easy fix. Changing the presentation by coloring these distinctly would alleviate this. The Supplemental figure to Fig. 2 looks strange with pinch and zoom. But at the end of the day, data presentation where the reader is to infer that they must zoom in is not very approachable and may prevent readers from being able to independently assess the data. In this case, there doesn't seem to be a strong rationale to not make these panels easier to see at 100% size. 

We appreciate the reviewer’s thoughtful comments regarding figure accessibility and agree that data presentation should be clear and interpretable without requiring readers to zoom in extensively. However, we must note that the presentation of the microscopy data follows standard practices in the field and that the panels already include zoomed-in regions, which enable easier access to key results and observations.

Regarding the NMR data, we have revised Figure 1—figure supplement 2 and Figure 2— figure supplement 1 to match the presentation style of Figure 3—figure supplement 1, which the reviewer apparently found more accessible. We also made the colors of the spectra more vibrant, as the referee suggested. We would like to emphasize that it is absolutely necessary to display the full NMR spectra in order to allow independent assessment of signal assignment, data quality, and overall protein behavior. Zoomed regions of the relevant details are provided in the main figures.

Very strong opinion 2: 

The author's response to lack of individual data points and error bars is that this is an n=1 experiment. I do not believe this meets the minimum standard for best practices in the field.

We respectfully disagree with the reviewer’s assessment. The Figure already displays individual data points, as shown already in the initial submission. Performing NMR titrations with isotopically labeled protein samples is inherently resource-intensive, and single-sample (n = 1) experiments are widely accepted and routinely reported in the field. Numerous studies have used the same approach, including Rosenzweig et al., Science (2013); Nikolaev et al., Nat. Methods (2019); and Hobbs et al., J. Biomol. NMR (2022), as well as our own recent work (Isaikina & Petrovic et al., Mol. Cell, 2023). These studies demonstrate that such NMR-based affinity measurements, even when performed on a single sample, are highly reproducible, precise, and consistent with orthogonal evidence and across different sample conditions.

Sticking point:

Figure 1A - the alphaFold model of arrestin2L depicts the disordered loops as ordered. The depiction is misleading at best, and inaccurate in truth. To use an analogy, what the authors depict is equivalent to publishing an LLM hallucination in the text. Unlike LLMs, alphaFold will actually flag its hallucination with the confidence (pLDDT) in the output. Both for LLMs and for alphaFold, we are spending much time teaching our students in class how to use computation appropriately - both to improve efficiency but also to ensure accuracy by removing hallucinations.

The original review indicated that confidences needed to be shown and that this needed to be depicted in a way that clarifies that this is NOT a structural state of the loops. The newly added description ("The model was used to visualize the clathrin-binding loop and the 344-loop of the arrestin2 Cdomain, which are not detected in the available crystal structures...) worsens the concern because it even more strongly implies that a 0 confidence computational output is a likely structural state. It also indicates that these regions were 'not detected' in crystal structures. These regions of arrestin are intrinsically disordered. AlphaFold (by it's nature) must put out something in terms of coordinates, even if the pLDDT suggests that the region cannot be predicted or is not in a stable position, which is the case here. In crystal structures, these regions are not associated with interpretable electron density, meaning that coordinates are omitted in these regions because adding them would imply that under the conditions used, the protein adopts a low energy structural state in this region. This region is instead intrinsically disordered. 

A good description of why showing disordered loops in a defined position is incorrect and how to instead depict disorder correctly is in Brotzakis et al. Nat communications 16, 1632 (2025) "AlphaFold prediction of structural ensembles of disordered proteins", where figures 3A, 4A, and 5A show one AlphaFold prediction colored by confidence and 3B, 4B and 5B are more accurate depictions of the structural ensemble. 

Coming back to the original comment "The AlphaFold model could benefit from a more transparent discussion of prediction confidence and caveats. The younger crowd (part of the presumed intended readership) tends to be more certain that computational output is 'true'...." Right now, the authors are still showing in Fig 1A a depiction of arrestin with models for the loops that are untrue. They now added text indicating that these loops are visualized in an AlphaFold prediction and 'true' but 'not detected in crystal structures'. There is no indication in the text that these are intrinsically disordered. The lack of showing the pLDDT confidence and the lack of any indication that these are disordered regions is simply incorrect. 

We appreciate the concern of the reviewer towards AlphaFold models. As NMR spectroscopists we are highly aware of intrinsic biomolecular motions. However, our AlphaFold2 model is used as a graphical representation to display the interaction sites of loops; it is not intended to depict the loops as fixed structural states. The flexibility of the loops had been clearly described in the main text before:

“Arrestin2 consists of two consecutive (N- and C-terminal) β-sandwich domains (Figure 1A), followed by the disordered clathrin-binding loop (CBL, residues 353–386), strand b20 (residues 386–390), and a disordered C-terminal tail after residue 393”.

and

“Figure 1B depicts part of a 1H-15N TROSY spectrum (full spectrum in Figure 1-figure supplement 2A) of the truncated 15N-labeled arrestin2 construct arrestin21-393 (residues 1393), which encompasses the C-terminal strand β20, but lacks the disordered C-terminal tail. Due to intrinsic microsecond dynamics, the assignment of the arrestin21-393 1H-15N resonances by triple resonance methods is largely incomplete, but 16 residues (residues 367381, 385-386) within the mobile CBL could be assigned. This region of arrestin is typically not visible in either crystal or cryo-EM structures due to its high flexibility”.

as well as in the legend to Figure 1:

“The model was used to visualize the clathrin-binding loop and the 344-loop of the arrestin2 C-domain, which are not detected in the available crystal structures of apo arrestin2 [bovine: PDB 1G4M (Han et al., 2001), human: PDB 8AS4 (Isaikina et al., 2023)]. In the other structured regions, the model is virtually identical to the crystal structures”.

We have now further added a label ‘AlphaFold2 model’ to Figure 1A and amended the respective Figure legend to

“The model was used to visualize the clathrin-binding loop and the 344-loop of the arrestin2 C-domain, which are not detected in the available crystal structures of apo arrestin2 [bovine: PDB 1G4M (Han et al., 2001), human: PDB 8AS4 (Isaikina et al., 2023)] due to flexibility. In the other structured regions, the model is virtually identical to the crystal structures”.

Reviewer #2 (Recommendations for the authors): 

I appreciated the response by the authors to all of my questions. I have no further comments

We thank the referee for the raised questions, which we believe have improved the quality of the manuscript.

Spannender Punkt: Der Staat war von Anfang an ein Projekt, das dazu diente, die Privilegien der Mächtigen und REichen zu sichern.

Author response:

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

Public Reviews:

Reviewer #1 (Public review):

Because the "source" and "target" tasks are merely parameter variations of the same paradigm, it is unclear whether EIDT achieves true crosstask transfer. The manuscript provides no measure of how consistent each participant's behaviour is across these variants (e.g., two- vs threestep MDP; easy vs difficult MNIST). Without this measure, the transfer results are hard to interpret. In fact, Figure 5 shows a notable drop in accuracy when transferring between the easy and difficult MNIST conditions, compared to transfers between accuracy-focused and speedfocused conditions. Does this discrepancy simply reflect larger withinparticipant behavioural differences between the easy and difficult settings? A direct analysis of intra-individual similarity for each task pair and how that similarity is related to EIDT's transfer performance is needed.

Thank you for your insightful comment. We agree that the tasks used in our study are variations of the same paradigm. Accordingly, we have revised the manuscript to consistently frame our findings as demonstrating individuality transfer "across task conditions" rather than "across distinct tasks."

In response to your suggestion, we have conducted a new analysis to directly investigate the relationship between individual behavioural patterns and transfer performance. As show in the new Figures 4, 11, S8, and S9, we found a clear relationship between the distance in the space of individual latent representation (called individuality index in the previous manuscript) and prediction performance. Specifically, prediction accuracy for a given individual's behaviour degrades as the latent representation of the model's source individual becomes more distant. This result directly demonstrates that our framework captures meaningful individual differences that are predictive of transfer performance across conditions.

We have also expanded the Discussion (Lines 332--343) to address the potential for applying this framework to more structurally distinct tasks, hypothesizing that this would rely on shared underlying cognitive functions.

Related to the previous comment, the individuality index is central to the framework, yet remains hard to interpret. It shows much greater within-participant variability in the MNIST experiment (Figure S1) than in the MDP experiment (Figure 3). Is such a difference meaningful? It is hard to know whether it reflects noisier data, greater behavioural flexibility, or limitations of the model.

Thank you for raising this important point about interpretability. To enhance the interpretability of the individual latent representation, we have added a new analysis for the MDP task (see Figures 6 and S4). By applying our trained encoder to data from simulated Q-learning agents with known parameters, we demonstrate that the dimensions of the latent space systematically map onto the agents' underlying cognitive parameters (learning rate and inverse temperature). This analysis provides a clearer interpretation by linking our model's data-driven representation to established theoretical constructs.

Regarding the greater within-participant variability observed in the MNIST task (visualized now in Figure S7), this could be attributed to several factors, such as greater behavioural flexibility in the perceptual task. However, disentangling these potential factors is complex and falls outside the primary scope of the current study, which prioritizes demonstrating robust prediction accuracy across different task conditions.

The authors suggests that the model's ability to generalize to new participants "likely relies on the fact that individuality indices form clusters and individuals similar to new participants exist in the training participant pool". It would be helpful to directly test this hypothesis by quantifying the similarity (or distance) of each test participant's individuality index to the individuals or identified clusters within the training set, and assessing whether greater similarity (or closer proximity) to the clusters in the training set is associated with higher prediction accuracy for those individuals in the test set.

Thank you for this excellent suggestion. We have performed the analysis you proposed to directly test this hypothesis. Our new results, presented in Figures 4, 11, S5, S8, and S9, quantify the distance between the latent representation of a test participant and that of the source participant used to generate the prediction model.

The results show a significant negative correlation: prediction accuracy consistently decreases as the distance in the latent space increases. This confirms that generalization performance is directly tied to the similarity of behavioural patterns as captured by our latent representation, strongly supporting our hypothesis.

Reviewer #2 (Public review):

The MNIST SX baseline appears weak. RTNet isn't directly comparable in structure or training. A stronger baseline would involve training the GRU directly on the task without using the individuality index-e.g., by fixing the decoder head. This would provide a clearer picture of what the index contributes.

We agree that a more direct baseline is crucial for evaluating the contribution of our transfer mechanism. For the Within-Condition Prediction scenario, the comparison with RTNet was intended only to validate that our task solver architecture could achieve average humanlevel task performance (Figure 7).

For the critical Cross-Condition Transfer scenario, we have now implemented a stronger and more appropriate baseline, which we call ``task solver (source).'' This model has the same architecture as our EIDT task solver but is trained directly on the source task data of the specific test participant. As shown in revised Figure 9, our EIDT framework significantly outperforms this direct-training approach, clearly demonstrating the benefit of the individuality transfer mechanism.

Although the focus is on prediction, the framework could offer more insight into how behaviour in one task generalizes to another. For example, simulating predicted behaviours while varying the individuality index might help reveal what behavioural traits it encodes.

Thank you for this valuable suggestion. To provide more insight into the encoded behavioural traits, we have conducted a new analysis linking the individual latent representation to a theoretical cognitive model. As detailed in the revised manuscript (Figures 6 and S4), we applied our encoder to simulated data from Q-learning agents with varying parameters. The results show a systematic relationship between the latent space coordinates and the agents' learning rates and inverse temperatures, providing a clearer interpretation of what the representation captures.

It's not clear whether the model can reproduce human behaviour when acting on-policy. Simulating behaviour using the trained task solver and comparing it with actual participant data would help assess how well the model captures individual decision tendencies.

We have added the suggested on-policy evaluation (Lines 195--207). In the revised manuscript (Figure 5), we present results from simulations where the trained task solvers performed the MDP task. We compared their performance (total reward and rate of the highly-rewarding action selected) against their corresponding human participants. The strong correlations observed demonstrate that our model successfully captures and reproduces individual-specific behavioural tendencies in an onpolicy setting.

Figures 3 and S1 aim to show that individuality indices from the same participant are closer together than those from different participants. However, this isn't fully convincing from the visualizations alone. Including a quantitative presentation would help support the claim.

We agree that the original visualizations of inter- and intraparticipant distances was not sufficiently convincing. We have therefore removed that analysis. In its place, we have introduced a more direct and quantitative analysis that explicitly links the individual latent representation to prediction performance (see Figures 4, 11, S5, S8, and S9). This new analysis demonstrates that prediction error for an individual is a function of distance in the latent space, providing stronger evidence that the representation captures meaningful, individual-specific information.

The transfer scenarios are often between very similar task conditions (e.g., different versions of MNIST or two-step vs three-step MDP). This limits the strength of the generalization claims. In particular, the effects in the MNIST experiment appear relatively modest, and the transfer is between experimental conditions within the same perceptual task. To better support the idea of generalizing behavioural traits across tasks, it would be valuable to include transfers across more structurally distinct tasks.

We agree with this limitation and have revised the manuscript to be more precise. We now frame our contribution as "individuality transfer across task conditions" rather than "across tasks" to accurately reflect the scope of our experiments. We have also expanded the Discussion section (Line 332-343) to address the potential and challenges of applying this framework to more structurally distinct tasks, noting that it would likely depend on shared underlying cognitive functions.

For both experiments, it would help to show basic summaries of participants' behavioural performance. For example, in the MDP task, first-stage choice proportions based on transition types are commonly reported. These kinds of benchmarks provide useful context.

We have added behavioral performance summaries as requested. For the MDP task, Figure 5 now compares the total reward and rate of highlyrewarding action selected between humans and our model. For the MNIST task, Figure 7 shows the rate of correct responses for humans, RTNet, and our task solver across all conditions. These additions provide better context for the model's performance.

For the MDP task, consider reporting the number or proportion of correct choices in addition to negative log-likelihood. This would make the results more interpretable.

Thank you for the suggestion. To make the results more interpretable, we have added a new prediction performance metric: the rate for behaviour matched. This metric measures the proportion of trials where the model's predicted action matches the human's actual choice. This is now included alongside the negative log-likelihood in Figures 2, 3, 4, 8, 9, and 11.

In Figure 5, what is the difference between the "% correct" and "% match to behaviour"? If so, it would help to clarify the distinction in the text or figure captions.

We have clarified these terms in the revised manuscript. As defined in the Result section (Lines 116--122, 231), "%correct" (now "rate of correct responses") is a measure of task performance, whereas "%match to behaviour" (now "rate for behaviour matched") is a measure of prediction accuracy.

For the cognitive model, it would be useful to report the fitted parameters (e.g., learning rate, inverse temperature) per individual. This can offer insight into what kinds of behavioural variability the individual latent representation might be capturing.

We have added histograms of the fitted Q-learning parameters for the human participants in Supplementary Materials (Figure S1). This analysis revealed which parameters varied most across the population and directly informed the design of our subsequent simulation study with Q-learning agents (see response to Comment 2-2), where we linked these parameters to the individual latent representation (Lines 208--223).

A few of the terms and labels in the paper could be made more intuitive. For example, the name "individuality index" might give the impression of a scalar value rather than a latent vector, and the labels "SX" and "SY" are somewhat arbitrary. You might consider whether clearer or more descriptive alternatives would help readers follow the paper more easily.

We have adopted the suggested changes for clarity.

"Individuality index" has been changed to "individual latent representation".

"Situation SX" and "Situation SY" have been renamed to the more descriptive "Within-Condition Prediction" and "Cross-Condition Transfer", respectively.

We have also added a table in Figure 7 to clarify the MNIST condition acronyms (EA/ES/DA/DS).

Please consider including training and validation curves for your models. These would help readers assess convergence, overfitting, and general training stability, especially given the complexity of the encoder-decoder architecture.

Training and validation curves for both the MDP and MNIST tasks have been added to Supplementary Materials (Figure S2 and S6) to show model convergence and stability.

Reviewer #3 (Public review):

To demonstrate the effectiveness of the approach, the authors compare a Q-learning cognitive model (for the MDP task) and RTNet (for the MNIST task) against the proposed framework. However, as I understand it, neither the cognitive model nor RTNet is designed to fit or account for individual variability. If that is the case, it is unclear why these models serve as appropriate baselines. Isn't it expected that a model explicitly fitted to individual data would outperform models that do not? If so, does the observed superiority of the proposed framework simply reflect the unsurprising benefit of fitting individual variability? I think the authors should either clarify why these models constitute fair control or validate the proposed approach against stronger and more appropriate baselines.