There are no degrees of strong veridicality

Some kind of causal account here?

Things are exactly as they seem

i.e. they correspond to the truth of the objects seen actually being in the state of affairs seen - simply veridical means corresponds to the truth, but not of the objects 'seen' per se - there is a lack of directness

At a specific time, in a specific place - you can't speak of it elsewhere

correspond to truth

Individuating means separating out from a kind - e.g. if there is no determinate process for working out how many Xs there are somewhere at some time, X cannot be individuated

This one is important

What is a token experience

e.g. looks like a lemon will never exclusively track the experience of lemons

Things are as E presents them only if F is instantiated -> Things are as E presents them if & only if F is instantiated

What does this mean??

Doesn't specifically refer to airport here

Doesn't actually have to be perceiving something

10 Things Jerry Seinfeld Can't Live Without by [[GQ]]

It's a great thing to waste time. The secret of life is to waste time in ways that you like. —Jerry Seinfeld 6:18

Even if you're writing comedy, a legal pad, it says, "I'm taking this seriously." —Jerry Seinfeld 2:31

https://www.cygnoir.net/analog/

https://tiny.micro.blog/

The Nuremberg trials were trials conducted by the Allies against the Nazi Germany. The Nazi Germany invaded many countries in Europe during 1933 and 1945 and not only killed a lot of people, but also conducting inhumane experiments to innocent civilians.

De acuerdo con la presente entrada del blog, mantener el aire acondicionado en condiciones adecuadas resulta fundamental para su eficiencia, durabilidad y rendimiento. Inspecciones regulares, limpieza y reemplazo de filtros oportunos y revisiones programadas con personal técnico calificado ayudarán a prolongar la vida útil del equipo, reducir los costos y optimizar su funcionamiento.

📝 https://airecalefaccionprov1.blog.fc2.com/

I found this ending sentence to be example of actions speak louder than words.

This story stands out as history that was taught IN SCHOOLS, they hide the true history away from the textbooks.

What do these words mean?

I really enjoy looking at Haunting in a scientific way while also comparing it a scary definition.

Public shaming involves exposing and criticizing someone’s behavior or actions in a social platform or public places such as Twitter, Facebook, and Instagram. Users can quickly amplify messages and mobilize collective action on these platforms.

Leyenda: Mean COVID-19 hospital admission rate per 100 COVID-19 cases

Titulo: Monthly COVID-19 hospital admission rate per 100 COVID-19 cases

Titulo: COVID-19 hospital admissions

Leyenda: Mean monthly COVID-19 mortality per 100 000 inhabitants

Monthly COVID-19 lethañity per 100 COVID-19 cases

Titulo: Monthly COVID-19 incidence per 1 000 inhabitants

Monthly COVID-19 mortality per 100 000 inhabitants

COVID-19 deaths

Titulo: COVID-19 deaths

Por favor este se podria hacer como las otras series de tiemop? (area pintada) no se ve tan bien lineal com pensabamos

Leyenda: Mean monthly COVID-19 incidence en los sgtes tambien

Titulo: COVID-19 cases

I agree with this statement because without a clear mind, you cannot make well thought-out decisions. You will end up with a rashly made choice, so it is important to make sure you are not filled with undesirable emotions when you choose.

See page notes

Captions and Transcripts: While most videos have captions, not all of them provide transcripts. Captions are essential for users with hearing impairments, and transcripts can benefit both hearing and visually impaired users by offering an alternative way to access the content​. Healthline demonstrates good practices in using captions, but could expand that practice by making transcripts available for all video content.

Text Readability: Healthline uses high-contrast color schemes and readable font sizes, making the text easy to read for users with visual impairments. This adheres to accessibility standards that recommend sufficient contrast ratios for text and background colors​.

Keyboard Accessibility: Upon researching the accessibility features on Healthline's pages, I discovered that they utilize keyboard accessibility. The site can be fully navigated using a keyboard, allowing users with motor disabilities to access all interactive elements without requiring a mouse. This is essential for users who rely on keyboard navigation or assistive devices.

Text Alternatives for Non-Text Content: Images and multimedia content on Healthline include alt text, providing descriptions for users who use screen readers. This ensures that visually impaired users can access all content, aligning with WCAG guidelines​

Clear and Consistent Navigation: Healthline's navigation menu is consistent, clearly labeled, and uses illustrations, making it easy for users to find information. This helps users with cognitive disabilities or those who rely on screen readers and visual aids to navigate the site efficiently.

Read about probe design - One probe per allele?

I teach at a public charter school and our target population are students with autism. We have three general education schools- elementary, middle and high school. We also have a functional skills school for students with severe disabilities. As a teacher at the school with severe disabilities I've encountered a few students with high behavioral needs placed in functional skills because of the difficulty in teaching them in a general education classroom. There is a big resistance to give these students one-on-one support in the appropriate setting because of the cost, even though it is what they need to succeed in their LRE.

In our school we've had a few cases where the gen ed and SPED teacher don't feel that they can meet a student's needs and that the student should have an alternate placement, but the district doesn't have the appropriate placement, so the child is kept at our school.

Honestly, I don't think that we have the time, space, and personnel at our school to provide the level of services our students need. At certain times of the day, we have 8-12 students with one SPED teacher and no aide. There's no way to provide individualized, specially-designed instruction with those odds. And next year our district is increasing case loads.

This feels like a daunting challenge. I guess it depends on how specific we're getting, but many of the students I work with are behind in everything, so to enumerate all of their needs would make a long list.

assignments most important to online courses

face-to-face - instructor relationship was important factor in value of course

students preferred face-to-face classes

direct personal feedback is important

importance of transactional distance and instructor presence

online retention factor

Need for training of faculty on creating presence

student perceptions of instructor importance

Students do not expect teachers to be "on call 24/ 7"

response time

research on timing of feedback

research on how 24 period of response shows faculty involvement

faculty perceive themselves more available than students

transactional distance

2015 email was preferred communication type, I wonder age the students were - were they employed during the day?

24 hours acceptable response time for 91%

"The great books are the inexhaustible books. The books that can sustain a lifetime of reading."

"The great books are the books that never have to be written again. They are so good no-one can try to write them again."

"The great books are the books that everyone wants to have read but no-one wants to read."

What did not stand out to me before while reading the book, but does now when watching this, is the fact that the greatest books are subjective to each individual... Meaning my list might not be the same for others.

Very fascinating thought experiment. Out of the 140+ books I have read so far only a few, less than a handful, would fit the list of "growth" books; the greatest, that I would take to the deserted island for 10 years...

1. The Bible
2. Antonin Sertillanges' The Intellectual Life: Its Spirit, Method, Conditions
3. Marcus Aurelius' Meditations

No other book, to my mind, that I have read so far would cut it to my list.

The BBC News page allows users to adjust the font size via the browser's zoom feature without disrupting the page layout. Users can customize the reading experience using the browser's zoom in/out and the page layout remains functional.

This BBC News page supports keyboard navigation. Users can move between links, buttons, and other interactive elements using the Tab key and press Enter to select. This is very important for users who cannot use a mouse.

This video provides accurate subtitles to ensure users with hearing impairments can understand the content. This is a good multimedia accessibility practice. Through these comments, we can see BBC News’s efforts in network accessibility, and also provide a good example for other web design.

The images in BBC News pages are very important to users of screen readers. For example, pictures in articles and news reports have detailed descriptions to help users with disabilities understand the content of the pictures.

There is usually enough contrast between the text and the background on BBC News pages to ensure the content is easier to read for users with visual impairments or color blindness. The text of the article uses black text with a white background, and the high contrast improves readability.

https://www.theguardian.com/world/article/2024/may/17/cop29-at-a-crossroads-in-azerbaijan-with-focus-on-climate-finance

Increase of video feedback

computer generated feedback - I would ask students to tell me how they implemented feedback

increasing student usage of feedback - humanize it

instructor training

I wonder if you can share libraries of comments. or if that is counter productive b/c it's someone else's voice.

value of peer reviewing

Importance of feedback

integrated feedback - most time consuming -- most effective

when to show your face with screen cast feedback

AV feedback with avators VOKI, Tellagami

audacity garage band Talk Tools for audio feedback

audio feedback over text

how to make students look at feedback.

I'm not sure how to do this in canvas.

video feedback

modes for faithful listening

faithful listening - I like that term

Active listening involves slowing down

feedback modes

worth of peer reviews

timing of different types of feedback

feedback structure 2 stars and a wish

Goldilocks principle

need for rubric

how to make feedback quicker to do

interesting - against AI type feedback and grading.

Effective feedback building trust clearly communicated user-friendly specific focused differentiated timely invites follow-up is actionable

means to improve performance

teaching presence design facilitation direction of cognitive and social processes for the purpose of realizing personally meaningful and educationally worthwhile learning outcomes

Feedback -Humanizing Elements * Content Focus Function Valence Clarity Specificity * Strategies Time Amount Audience Mode * Sequence Listening Summarize - Explain redirect resubmit - SE2R Connecting Creating Tracking * Tools Text Graphic Audio Image Video Integrated multi-modal

Principle 2: Operable (Good Practice)

To the left of this annotation, BBC has provided a search icon, which allows users to search for any specific content they want to view. This shows good practice because BBC provided users with the ability to navigate and find content on their own without needing assistance. BBC also ensured that the icons are all operational and work as their expected to.

Principle 3: Understandable (Good Practice)

This annotation shows good practice of the third principle because BBC has done a great job to provide additional accessibility information on their assistive technology features that may be necessary for an individual in order to understand the content on their page. By providing a link to their accessibility information, users can get the help they need instantly and ultimately improve their experience.

Principle 2: Operable (Good Practice)

Though not shown in the annotation, a notable feature of this website is the fact that it can be entirely navigated using only the keyboard. In fact, when using the keyboard, the site automatically outlines the selection (which is what has been annotated) in orange to indicate which section you are on. Additionally, each of the categories that are pinned at the top of the page, can also be hit using just the keyboard.

Principle 3: Understandable (Good Practice)

BBC shows good practice of the Understandable principle by providing multiple language options for users to choose from. This is a great feature because if users have a hard time understanding the text in front of them, they can instead switch to a language which they can understand, without the hassle of using a translator. This improves accessibility for a wide-range of audiences.

Principle 1: Perceivable (Bad Practice)

This content does not meet the Perceivable practice because the Web authors have chosen to use a small font to categorize each of their sections of content. This may be difficult for users to see as the landing page is already filled with lots large texts and images, and may make the categories easy to miss. This is a particular issue that my grandparents face as their vision is already not the best, and they have a very hard time reading small texts.

Which means, samples should be submitted by 3 pm Tuesday to Genewiz dropbox. - Remember that it takes 30 mins to fill the Amplicon EZ form and 15 mins to do the qubit and dilutions too

There could be some delays in shipping, so it is best to have it picked up on Monday if timeline is urgent

Whats that?

Watched this live on YouTube. Not sure if they'll archive it there or not.

https://micro.camp/

# Humans will be the differentiators in tomorrow's AI world.

i.e. small publishers, solo entreprenuers, writers, creators, etc.

Se duplicado

frase precisa ser revista

tiraria a virgula

eu tiraria a virgula

responsável repetido

for - post comment - LinkedIn - regenerative inner world

How to differentiate in the new AI age.

or on specific topics

Even though many people who write about HLs describe it this way, I'm wondering if we should describe it more neutrally, as 'gaps' can evoke a deficit approach, in which a native speaker has no gaps, but other learners do (in fact all speakers have varying strengths across different domains). Maybe ''but still have more specific language knowledge compared to other speakers or learners''

I wonder if we could change this heading because at a quick glance it might feel discouraging even though it's meant to be the opposite.

There is enough contrast between the text and the background which is good

This is good because the site supports full keyboard navigation, and users can Tab to navigate.

This is good because the site provides video and audio content matching subtitles and transcripts of the text.

This is good because the Toronto Citynews has text substitutes for those with visual impairments.

This is good because websites can adjust the font size through the browser's zoom function.

Typewriter 101: THE CLEANING GUIDE! by [[Just My Typewriter]]

A solid primer for cleaning typewriters

unless sample size is > 20,000

p here is the number of parameters, not the prob in the above formulas

for - artificial wetlands - applications - reciprocating wetlands

Hi Emelie, this is a test annotation. Do you see it?

title a bit unspecific, I think it would be good to add some more detail about shape change, cilia or sg on the response

https://typewriterfeet.com/

https://www.theguardian.com/australia-news/article/2024/may/18/how-to-reduce-digital-carbon-footprint-energy-consumption

shouldn't the peer reviewers have caught this?

You often times hear students say the same things in on ground classes.....it's all about how the instructor really knows how to teach.

Sometimes you don't get instructor presence in on ground courses....if the instructor is only lecturing and holding discussions

instructor presence definition interaction communication style frequency

.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Using a cross-modal sensory selection task in head-fixed mice, the authors attempted to characterize how different rules reconfigured representations of sensory stimuli and behavioral reports in sensory (S1, S2) and premotor cortical areas (medial motor cortex or MM, and ALM). They used silicon probe recordings during behavior, a combination of single-cell and population-level analyses of neural data, and optogenetic inhibition during the task.

Strengths:

A major strength of the manuscript was the clarity of the writing and motivation for experiments and analyses. The behavioral paradigm is somewhat simple but well-designed and wellcontrolled. The neural analyses were sophisticated, clearly presented, and generally supported the authors' interpretations. The statistics are clearly reported and easy to interpret. In general, my view is that the authors achieved their aims. They found that different rules affected preparatory activity in premotor areas, but not sensory areas, consistent with dynamical systems perspectives in the field that hold that initial conditions are important for determining trial-based dynamics.

Weaknesses:

The manuscript was generally strong. The main weakness in my view was in interpreting the optogenetic results. While the simplicity of the task was helpful for analyzing the neural data, I think it limited the informativeness of the perturbation experiments. The behavioral read-out was low dimensional -a change in hit rate or false alarm rate- but it was unclear what perceptual or cognitive process was disrupted that led to changes in these read-outs. This is a challenge for the field, and not just this paper, but was the main weakness in my view. I have some minor technical comments in the recommendations for authors that might address other minor weaknesses.

I think this is a well-performed, well-written, and interesting study that shows differences in rule representations in sensory and premotor areas and finds that rules reconfigure preparatory activity in the motor cortex to support flexible behavior.

Reviewer #2 (Public Review):

Summary:

Chang et al. investigate neuronal activity firing patterns across various cortical regions in an interesting context-dependent tactile vs visual detection task, developed previously by the authors (Chevee et al., 2021; doi: 10.1016/j.neuron.2021.11.013). The authors report the important involvement of a medial frontal cortical region (MM, probably a similar location to wM2 as described in Esmaeili et al., 2021 & 2022; doi: 10.1016/j.neuron.2021.05.005; doi: 10.1371/journal.pbio.3001667) in mice for determining task rules.

Strengths:

The experiments appear to have been well carried out and the data well analysed. The manuscript clearly describes the motivation for the analyses and reaches clear and well-justified conclusions. I find the manuscript interesting and exciting!

Weaknesses:

I did not find any major weaknesses.

Reviewer #3 (Public Review):

This study examines context-dependent stimulus selection by recording neural activity from several sensory and motor cortical areas along a sensorimotor pathway, including S1, S2, MM, and ALM. Mice are trained to either withhold licking or perform directional licking in response to visual or tactile stimulus. Depending on the task rule, the mice have to respond to one stimulus modality while ignoring the other. Neural activity to the same tactile stimulus is modulated by task in all the areas recorded, with significant activity changes in a subset of neurons and population activity occupying distinct activity subspaces. Recordings further reveal a contextual signal in the pre-stimulus baseline activity that differentiates task context. This signal is correlated with subsequent task modulation of stimulus activity. Comparison across brain areas shows that this contextual signal is stronger in frontal cortical regions than in sensory regions. Analyses link this signal to behavior by showing that it tracks the behavioral performance switch during task rule transitions. Silencing activity in frontal cortical regions during the baseline period impairs behavioral performance.

Overall, this is a superb study with solid results and thorough controls. The results are relevant for context-specific neural computation and provide a neural substrate that will surely inspire follow-up mechanistic investigations. We only have a couple of suggestions to help the authors further improve the paper.

(1) We have a comment regarding the calculation of the choice CD in Fig S3. The text on page 7 concludes that "Choice coding dimensions change with task rule". However, the motor choice response is different across blocks, i.e. lick right vs. no lick for one task and lick left vs. no lick for the other task. Therefore, the differences in the choice CD may be simply due to the motor response being different across the tasks and not due to the task rule per se. The authors may consider adding this caveat in their interpretation. This should not affect their main conclusion.

We thank the Reviewer for the suggestion. We have discussed this caveat and performed a new analysis to calculate the choice coding dimensions using right-lick and left-lick trials (Fig. S3h) on page 8. 

“Choice coding dimensions were obtained from left-lick and no-lick trials in respond-to-touch blocks and right-lick and no-lick trials in respond-to-light blocks. Because the required lick directions differed between the block types, the difference in choice CDs across task rules (Fig. S4f) could have been affected by the different motor responses. To rule out this possibility, we did a new version of this analysis using right-lick and left-lick trials to calculate the choice coding dimensions for both task rules. We found that the orientation of the choice coding dimension in a respond-to-touch block was still not aligned well with that in a respond-to-light block (Fig. S4h;  magnitude of dot product between the respond-to-touch choice CD and the respond-to-light choice CD, mean ± 95% CI for true vs shuffled data: S1: 0.39 ± [0.23, 0.55] vs 0.2 ± [0.1, 0.31], 10 sessions; S2: 0.32 ± [0.18, 0.46] vs 0.2 ± [0.11, 0.3], 8 sessions; MM: 0.35 ± [0.21, 0.48] vs 0.18 ± [0.11, 0.26], 9 sessions; ALM: 0.28 ± [0.17, 0.39] vs 0.21 ± [0.12, 0.31], 13 sessions).”

We also have included the caveats for using right-lick and left-lick trials to calculate choice coding dimensions on page 13.

“However, we also calculated choice coding dimensions using only right- and left-lick trials. In S1, S2, MM and ALM, the choice CDs calculated this way were also not aligned well across task rules (Fig. S4h), consistent with the results calculated from lick and no-lick trials (Fig. S4f). Data were limited for this analysis, however, because mice rarely licked to the unrewarded water port (# of licksunrewarded port  / # of lickstotal , respond-to-touch: 0.13, respond-to-light: 0.11). These trials usually came from rule transitions (Fig. 5a) and, in some cases, were potentially caused by exploratory behaviors. These factors could affect choice CDs.”

(2) We have a couple of questions about the effect size on single neurons vs. population dynamics. From Fig 1, about 20% of neurons in frontal cortical regions show task rule modulation in their stimulus activity. This seems like a small effect in terms of population dynamics. There is somewhat of a disconnect from Figs 4 and S3 (for stimulus CD), which show remarkably low subspace overlap in population activity across tasks. Can the authors help bridge this disconnect? Is this because the neurons showing a difference in Fig 1 are disproportionally stimulus selective neurons?

We thank the Reviewer for the insightful comment and agree that it is important to link the single-unit and population results. We have addressed these questions by (1) improving our analysis of task modulation of single neurons  (tHit-tCR selectivity) and (2) examining the relationship between tHit-tCR selective neurons and tHit-tCR subspace overlaps.  

Previously, we averaged the AUC values of time bins within the stimulus window (0-150 ms, 10 ms bins). If the 95% CI on this averaged AUC value did not include 0.5, this unit was considered to show significant selectivity. This approach was highly conservative and may underestimate the percentage of units showing significant selectivity, particularly any units showing transient selectivity. In the revised manuscript, we now define a unit as showing significant tHit-tCR selectivity when three consecutive time bins (>30 ms, 10ms bins) of AUC values were significant. Using this new criterion, the percentage of tHittCR selective neurons increased compared with the previous analysis. We have updated Figure 1h and the results on page 4:

“We found that 18-33% of neurons in these cortical areas had area under the receiver-operating curve (AUC) values significantly different from 0.5, and therefore discriminated between tHit and tCR trials (Fig. 1h; S1: 28.8%, 177 neurons; S2: 17.9%, 162 neurons; MM: 32.9%, 140 neurons; ALM: 23.4%, 256 neurons; criterion to be considered significant: Bonferroni corrected 95% CI on AUC did not include 0.5 for at least 3 consecutive 10-ms time bins).”

Next, we have checked how tHit-tCR selective neurons were distributed across sessions. We found that the percentage of tHit-tCR selective neurons in each session varied (S1: 9-46%, S2: 0-36%, MM:25-55%, ALM:0-50%). We examined the relationship between the numbers of tHit-tCR selective neurons and tHit-tCR subspace overlaps. Sessions with more neurons showing task rule modulation tended to show lower subspace overlap, but this correlation was modest and only marginally significant (r= -0.32, p= 0.08, Pearson correlation, n= 31 sessions). While we report the percentage of neurons showing significant selectivity as a simple way to summarize single-neuron effects, this does neglect the magnitude of task rule modulation of individual neurons, which may also be relevant. 

In summary, the apparent disconnect between the effect sizes of task modulation of single neurons and of population dynamics could be explained by (1) the percentages of tHit-tCR selective neurons were underestimated in our old analysis, (2) tHit-tCR selective neurons were not uniformly distributed among sessions, and (3) the percentages of tHit-tCR selective neurons were weakly correlated with tHit-tCR subspace overlaps. 

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

For the analysis of choice coding dimensions, it seems that the authors are somewhat data limited in that they cannot compare lick-right/lick-left within a block. So instead, they compare lick/no lick trials. But given that the mice are unable to initiate trials, the interpretation of the no lick trials is a bit complicated. It is not clear that the no lick trials reflect a perceptual judgment about the stimulus (i.e., a choice), or that the mice are just zoning out and not paying attention. If it's the latter case, what the authors are calling choice coding is more of an attentional or task engagement signal, which may still be interesting, but has a somewhat different interpretation than a choice coding dimension. It might be worth clarifying this point somewhere, or if I'm totally off-base, then being more clear about why lick/no lick is more consistent with choice than task engagement.

We thank the Reviewer for raising this point. We have added a new paragraph on page 13 to clarify why we used lick/no-lick trials to calculate choice coding dimensions, and we now discuss the caveat regarding task engagement.  

“No-lick trials included misses, which could be caused by mice not being engaged in the task. While the majority of no-lick trials were correct rejections (respond-to-touch: 75%; respond-to-light: 76%), we treated no-licks as one of the available choices in our task and included them to calculate choice coding dimensions (Fig. S4c,d,f). To ensure stable and balanced task engagement across task rules, we removed the last 20 trials of each session and used stimulus parameters that achieved similar behavioral performance for both task rules (Fig. 1d; ~75% correct for both rules).”

In addition, to address a point made by Reviewer 3 as well as this point, we performed a new analysis to calculate choice coding dimensions using right-lick vs left-lick trials. We report this new analysis on page 8:

“Choice coding dimensions were obtained from left-lick and no-lick trials in respond-to-touch blocks and right-lick and no-lick trials in respond-to-light blocks. Because the required lick directions differed between the block types, the difference in choice CDs across task rules (Fig. S4f) could have been affected by the different motor responses. To rule out this possibility, we did a new version of this analysis using right-lick and left-lick trials to calculate the choice coding dimensions for both task rules. We found that the orientation of the choice coding dimension in a respond-to-touch block was still not aligned well with that in a respond-to-light block (Fig. S4h;  magnitude of dot product between the respond-to-touch choice CD and the respond-to-light choice CD, mean ± 95% CI for true vs shuffled data: S1: 0.39 ± [0.23, 0.55] vs 0.2 ± [0.1, 0.31], 10 sessions; S2: 0.32 ± [0.18, 0.46] vs 0.2 ± [0.11, 0.3], 8 sessions; MM: 0.35 ± [0.21, 0.48] vs 0.18 ± [0.11, 0.26], 9 sessions; ALM: 0.28 ± [0.17, 0.39] vs 0.21 ± [0.12, 0.31], 13 sessions).” 

We added discussion of the limitations of this new analysis on page 13:

“However, we also calculated choice coding dimensions using only right- and left-lick trials. In S1, S2, MM and ALM, the choice CDs calculated this way were also not aligned well across task rules (Fig. S4h), consistent with the results calculated from lick and no-lick trials (Fig. S4f). Data were limited for this analysis, however, because mice rarely licked to the unrewarded water port (# of licksunrewarded port  / # of lickstotal , respond-to-touch: 0.13, respond-to-light: 0.11). These trials usually came from rule transitions (Fig. 5a) and, in some cases, were potentially caused by exploratory behaviors. These factors could affect choice CDs.”

The authors find that the stimulus coding direction in most areas (S1, S2, and MM) was significantly aligned between the block types. How do the authors interpret that finding? That there is no major change in stimulus coding dimension, despite the change in subspace? I think I'm missing the big picture interpretation of this result.

That there is no significant change in stimulus coding dimensions but a change in subspace suggests that the subspace change largely reflects a change in the choice coding dimensions.

As I mentioned in the public review, I thought there was a weakness with interpretation of the optogenetic experiments, which the authors generally interpret as reflecting rule sensitivity. However, given that they are inhibiting premotor areas including ALM, one might imagine that there might also be an effect on lick production or kinematics. To rule this out, the authors compare the change in lick rate relative to licks during the ITI. What is the ITI lick rate? I assume pretty low, once the animal is welltrained, in which case there may be a floor effect that could obscure meaningful effects on lick production. In addition, based on the reported CI on delta p(lick), it looks like MM and AM did suppress lick rate. I think in the future, a task with richer behavioral read-outs (or including other measurements of behavior like video), or perhaps something like a psychological process model with parameters that reflect different perceptual or cognitive processes could help resolve the effects of perturbations more precisely.

Eighteen and ten percent of trials had at least one lick in the ITI in respond-to-touch and  respond-tolight blocks, respectively. These relatively low rates of ITI licking could indeed make an effect of optogenetics on lick production harder to observe. We agree that future work would benefit from more complex tasks and measurements, and have added the following to make this point (page 14):

“To more precisely dissect the effects of perturbations on different cognitive processes in rule-dependent sensory detection, more complex behavioral tasks and richer behavioral measurements are needed in the future.”

Reviewer #2 (Recommendations For The Authors):

I have the following minor suggestions that the authors might consider in revising this already excellent manuscript :

(1) In addition to showing normalised z-score firing rates (e.g. Fig 1g), I think it is important to show the grand-average mean firing rates in Hz.

We thank the Reviewer for the suggestion and have added the grand-average mean firing rates as a new supplementary figure (Fig. S2a). To provide more details about the firing rates of individual neurons, we have also added to this new figure the distribution of peak responses during the tactile stimulus period (Fig. S2b).

(2) I think the authors could report more quantitative data in the main text. As a very basic example, I could not easily find how many neurons, sessions, and mice were used in various analyses.

We have added relevant numbers at various points throughout the Results, including within the following examples:

Page 3: “To examine how the task rules influenced the sensorimotor transformation occurring in the tactile processing stream, we performed single-unit recordings from sensory and motor cortical areas including S1, S2, MM and ALM (Fig. 1e-g, Fig. S1a-h, and Fig. S2a; S1: 6 mice, 10 sessions, 177 neurons, S2: 5 mice, 8 sessions, 162 neurons, MM: 7 mice, 9 sessions, 140 neurons, ALM: 8 mice, 13 sessions, 256 neurons).”

Page 5: “As expected, single-unit activity before stimulus onset did not discriminate between tactile and visual trials (Fig. 2d; S1: 0%, 177 neurons; S2: 0%, 162 neurons; MM: 0%, 140 neurons; ALM: 0.8%, 256 neurons). After stimulus onset, more than 35% of neurons in the sensory cortical areas and approximately 15% of neurons in the motor cortical areas showed significant stimulus discriminability (Fig. 2e; S1: 37.3%, 177 neurons; S2: 35.2%, 162 neurons; MM: 15%, 140 neurons; ALM: 14.1%, 256 neurons).”

Page 6: “Support vector machine (SVM) and Random Forest classifiers showed similar decoding abilities

(Fig. S3a,b; medians of classification accuracy [true vs shuffled]; SVM: S1 [0.6 vs 0.53], 10 sessions, S2

[0.61 vs 0.51], 8 sessions, MM [0.71 vs 0.51], 9 sessions, ALM [0.65 vs 0.52], 13 sessions; Random

Forests: S1 [0.59 vs 0.52], 10 sessions, S2 [0.6 vs 0.52], 8 sessions, MM [0.65 vs 0.49], 9 sessions, ALM [0.7 vs 0.5], 13 sessions).”

Page 6: “To assess this for the four cortical areas, we quantified how the tHit and tCR trajectories diverged from each other by calculating the Euclidean distance between matching time points for all possible pairs of tHit and tCR trajectories for a given session and then averaging these for the session (Fig. 4a,b; S1: 10 sessions, S2: 8 sessions, MM: 9 sessions, ALM: 13 sessions, individual sessions in gray and averages across sessions in black; window of analysis: -100 to 150 ms relative to stimulus onset; 10 ms bins; using the top 3 PCs; Methods).” 

Page 8: “In contrast, we found that S1, S2 and MM had stimulus CDs that were significantly aligned between the two block types (Fig. S4e; magnitude of dot product between the respond-to-touch stimulus CDs and the respond-to-light stimulus CDs, mean ± 95% CI for true vs shuffled data: S1: 0.5 ± [0.34, 0.66] vs 0.21 ± [0.12, 0.34], 10 sessions; S2: 0.62 ± [0.43, 0.78] vs 0.22 ± [0.13, 0.31], 8 sessions; MM: 0.48 ± [0.38, 0.59] vs 0.24 ± [0.16, 0.33], 9 sessions; ALM: 0.33 ± [0.2, 0.47] vs 0.21 ± [0.13, 0.31], 13 sessions).”  Page 9: “For respond-to-touch to respond-to-light block transitions, the fractions of trials classified as respond-to-touch for MM and ALM decreased progressively over the course of the transition (Fig. 5d; rank correlation of the fractions calculated for each of the separate periods spanning the transition, Kendall’s tau, mean ± 95% CI: MM: -0.39 ± [-0.67, -0.11], 9 sessions, ALM: -0.29 ± [-0.54, -0.04], 13 sessions; criterion to be considered significant: 95% CI on Kendall’s tau did not include 0).

Page 11: “Lick probability was unaffected during S1, S2, MM and ALM experiments for both tasks, indicating that the behavioral effects were not due to an inability to lick (Fig. 6i, j; 95% CI on Δ lick probability for cross-modal selection task: S1/S2 [-0.18, 0.24], 4 mice, 10 sessions; MM [-0.31, 0.03], 4 mice, 11 sessions; ALM [-0.24, 0.16], 4 mice, 10 sessions; Δ lick probability for simple tactile detection task: S1/S2 [-0.13, 0.31], 3 mice, 3 sessions; MM [-0.06, 0.45], 3 mice, 5 sessions; ALM [-0.18, 0.34], 3 mice, 4 sessions).”

(3) Please include a clearer description of trial timing. Perhaps a schematic timeline of when stimuli are delivered and when licking would be rewarded. I may have missed it, but I did not find explicit mention of the timing of the reward window or if there was any delay period.

We have added the following (page 3): 

“For each trial, the stimulus duration was 0.15 s and an answer period extended from 0.1 to 2 s from stimulus onset.”

(4) Please include a clear description of statistical tests in each figure legend as needed (for example please check Fig 4e legend).

We have added details about statistical tests in the figure legends:

Fig. 2f: “Relationship between block-type discriminability before stimulus onset and tHit-tCR discriminability after stimulus onset for units showing significant block-type discriminability prior to the stimulus. Pearson correlation: S1: r = 0.69, p = 0.056, 8 neurons; S2: r = 0.91, p = 0.093, 4 neurons; MM: r = 0.93, p < 0.001, 30 neurons; ALM: r = 0.83, p < 0.001, 26 neurons.” 

Fig. 4e: “Subspace overlap for control tHit (gray) and tCR (purple) trials in the somatosensory and motor cortical areas. Each circle is a subspace overlap of a session. Paired t-test, tCR – control tHit: S1: -0.23, 8 sessions, p = 0.0016; S2: -0.23, 7 sessions, p = 0.0086; MM: -0.36, 5 sessions, p = <0.001; ALM: -0.35, 11 sessions, p < 0.001; significance: ** for p<0.01, *** for p<0.001.”  

Fig. 5d,e: “Fraction of trials classified as coming from a respond-to-touch block based on the pre-stimulus population state, for trials occurring in different periods (see c) relative to respond-to-touch → respondto-light transitions. For MM (top row) and ALM (bottom row), progressively fewer trials were classified as coming from the respond-to-touch block as analysis windows shifted later relative to the rule transition. Kendall’s tau (rank correlation): MM: -0.39, 9 sessions; ALM: -0.29, 13 sessions. Left panels: individual sessions, right panels: mean ± 95% CI. Dash lines are chance levels (0.5). e, Same as d but for respond-to-light → respond-to-touch transitions. Kendall’s tau: MM: 0.37, 9 sessions; ALM: 0.27, 13 sessions.”

Fig. 6: “Error bars show bootstrap 95% CI. Criterion to be considered significant: 95% CI did not include 0.”

(5) P. 3 - "To examine how the task rules influenced the sensorimotor transformation occurring in the tactile processing stream, we performed single-unit recordings from sensory and motor cortical areas including S1, S2, MM, and ALM using 64-channel silicon probes (Fig. 1e-g and Fig. S1a-h)." Please specify if these areas were recorded simultaneously or not.

We have added “We recorded from one of these cortical areas per session, using 64-channel silicon probes.”  on page 3.  

(6) Figure 4b - Please describe what gray and black lines show.

The gray traces are the distance between tHit and tCR trajectories in individual sessions and the black traces are the averages across sessions in different cortical areas. We have added this information on page 6 and in the Figure 4b legend. 

Page 6: “To assess this for the four cortical areas, we quantified how the tHit and tCR trajectories diverged from each other by calculating the Euclidean distance between matching time points for all possible pairs of tHit and tCR trajectories for a given session and then averaging these for the session (Fig. 4a,b; S1: 10 sessions, S2: 8 sessions, MM: 9 sessions, ALM: 13 sessions, individual sessions in gray and averages across sessions in black; window of analysis: -100 to 150 ms relative to stimulus onset; 10 ms bins; using the top 3 PCs; Methods).

Fig. 4b: “Distance between tHit and tCR trajectories in S1, S2, MM and ALM. Gray traces show the time varying tHit-tCR distance in individual sessions and black traces are session-averaged tHit-tCR distance (S1:10 sessions; S2: 8 sessions; MM: 9 sessions; ALM: 13 sessions).”

(7) In addition to the analyses shown in Figure 5a, when investigating the timing of the rule switch, I think the authors should plot the left and right lick probabilities aligned to the timing of the rule switch time on a trial-by-trial basis averaged across mice.

We thank the Reviewer for suggesting this addition. We have added a new figure panel to show the probabilities of right- and left-licks during rule transitions (Fig. 5a).

Page 8: “The probabilities of right-licks and left-licks showed that the mice switched their motor responses during block transitions depending on task rules (Fig. 5a, mean ± 95% CI across 12 mice).” 

(8) P. 12 - "Moreover, in a separate study using the same task (Finkel et al., unpublished), high-speed video analysis demonstrated no significant differences in whisker motion between respond-to-touch and respond-to-light blocks in most (12 of 14) behavioral sessions.". Such behavioral data is important and ideally would be included in the current analysis. Was high-speed videography carried out during electrophysiology in the current study?

Finkel et al. has been accepted in principle for publication and will be available online shortly. Unfortunately we have not yet carried out simultaneous high-speed whisker video and electrophysiology in our cross-modal sensory selection task.

Reviewer #3 (Recommendations For The Authors):

(1) Minor point. For subspace overlap calculation of pre-stimulus activity in Fig 4e (light purple datapoints), please clarify whether the PCs for that condition were constructed in matched time windows. If the PCs are calculated from the stimulus period 0-150ms, the poor alignment could be due to mismatched time windows.

We thank the Reviewer for the comment and clarify our analysis here. We previously used timematched windows to calculate subspace overlaps. However, the pre-stimulus activity was much weaker than the activity during the stimulus period, so the subspaces of reference tHit were subject to noise and we were not able to obtain reliable PCs. This caused the subspace overlap values between the reference tHit and control tHit to be low and variable (mean ± SD, S1:  0.46± 0.26, n = 8 sessions, S2: 0.46± 0.18, n = 7 sessions, MM: 0.44± 0.16, n = 5 sessions, ALM: 0.38± 0.22, n = 11 sessions).  Therefore, we used the tHit activity during the stimulus window to obtain PCs and projected pre-stimulus and stimulus activity in tCR trials onto these PCs. We have now added a more detailed description of this analysis in the Methods (page 32). 

“To calculate the separation of subspaces prior to stimulus delivery, pre-stimulus activity in tCR trials (100 to 0 ms from stimulus onset) was projected to the PC space of the tHit reference group and the subspace overlap was calculated. In this analysis, we used tHit activity during stimulus delivery (0 to 150 ms from stimulus onset) to obtain reliable PCs.”   

We acknowledge this time alignment issue and have now removed the reported subspace overlap between tHit and tCR during the pre-stimulus period from Figure 4e (light purple). However, we think the correlation between pre- and post- stimulus-onset subspace overlaps should remain similar regardless of the time windows that we used for calculating the PCs. For the PCs calculated from the pre-stimulus period (-100 to 0 ms), the correlation coefficient was 0.55 (Pearson correlation, p <0.01, n = 31 sessions). For the PCs calculated from the stimulus period (0-150 ms), the correlation coefficient was 0.68 (Figure 4f, Pearson correlation, p <0.001, n = 31 sessions). Therefore, we keep Figure 4f.  

(2) Minor point. To help the readers follow the logic of the experiments, please explain why PPC and AMM were added in the later optogenetic experiment since these are not part of the electrophysiology experiment.

We have added the following rationale on page 9.

“We recorded from AMM in our cross-modal sensory selection task and observed visually-evoked activity (Fig. S1i-k), suggesting that AMM may play an important role in rule-dependent visual processing. PPC contributes to multisensory processing51–53 and sensory-motor integration50,54–58.  Therefore, we wanted to test the roles of these areas in our cross-modal sensory selection task.”

(3) Minor point. We are somewhat confused about the timing of some of the example neurons shown in figure S1. For example, many neurons show visually evoked signals only after stimulus offset, unlike tactile evoked signals (e.g. Fig S1b and f). In addition, the reaction time for visual stimulus is systematically slower than tactile stimuli for many example neurons (e.g. Fig S1b) but somehow not other neurons (e.g. Fig S1g). Are these observations correct?

These observations are all correct. We have a manuscript from a separate study using this same behavioral task (Finkel et al., accepted in principle) that examines and compares (1) the onsets of tactile- and visually-evoked activity and (2) the reaction times to tactile and visual stimuli. The reaction times to tactile stimuli were slightly but significantly shorter than the reaction times to visual stimuli (tactile vs visual, 397 ± 145 vs 521 ± 163 ms, median ± interquartile range [IQR], Tukey HSD test, p = 0.001, n =155 sessions). We examined how well activity of individual neurons in S1 could be used to discriminate the presence of the stimulus or the response of the mouse. For discriminability for the presence of the stimulus, S1 neurons could signal the presence of the tactile stimulus but not the visual stimulus. For discriminability for the response of the mouse, the onsets for significant discriminability occurred earlier for tactile compared with visual trials (two-sided Kolmogorov-Smirnov test, p = 1x10-16, n = 865 neurons with DP onset in tactile trials, n = 719 neurons with DP onset in visual trials).

eLife assessment

This important work advances our understanding of how brains flexibly gate actions in different contexts, a topic of great interest to the broader field of systems neuroscience. Recording neural activity from several sensory and motor cortical areas along a sensorimotor pathway, the authors found that preparatory activity in motor cortical areas of the mouse depends on the context in which an action will be carried out, consistent with previous theoretical and experimental work. Furthermore, the authors provide causal evidence that these changes support flexible gating of actions. The carefully carried out experiments were analyzed using state-of-the-art methodology and provide convincing conclusions.

Reviewer #1 (Public Review):

Summary:

Using a cross-modal sensory selection task in head-fixed mice, the authors attempted to characterize how different rules reconfigured representations of sensory stimuli and behavioral reports in sensory (S1, S2) and premotor cortical areas (medial motor cortex or MM, and ALM). They used silicon probe recordings during behavior, a combination of single-cell and population-level analyses of neural data, and optogenetic inhibition during the task.

Strengths:

A major strength of the manuscript was the clarity of the writing and motivation for experiments and analyses. The behavioral paradigm is somewhat simple but well-designed and well-controlled. The neural analyses were sophisticated, clearly presented, and generally supported the authors' interpretations. The statistics are clearly reported and easy to interpret. In general, my view is that the authors achieved their aims. They found that different rules affected preparatory activity in premotor areas, but not sensory areas, consistent with dynamical systems perspectives in the field that hold that initial conditions are important for determining trial-based dynamics.

I think this is a well-performed, well-written and interesting study that shows differences in rule representations in sensory and premotor areas, and finds that rules reconfigure preparatory activity in motor cortex to support flexible behavior.

Reviewer #2 (Public Review):

Summary:

Chang et al. investigated neuronal activity firing patterns across various cortical regions in an interesting context-dependent tactile vs visual detection task, developed previously by the authors (Chevee et al., 2021; doi: 10.1016/j.neuron.2021.11.013). The authors report the important involvement of a medial frontal cortical region (MM, probably a similar location to wM2 as described in Esmaeili et al., 2021 & 2022; doi: 10.1016/j.neuron.2021.05.005; doi: 10.1371/journal.pbio.3001667) in mice for determining task rules.

Strengths:

The experiments appear to have been well carried out and the data well analysed. The manuscript clearly describes the motivation for the analyses and reaches clear and well-justified conclusions. I find the manuscript interesting and exciting!

Weaknesses:

I did not find any major weaknesses.

Reviewer #3 (Public Review):

Summary:

This study examines context-dependent stimulus selection by recording neural activity from several sensory and motor cortical areas along a sensorimotor pathway, including S1, S2, MM, and ALM. Mice are trained to either withhold licking or perform directional licking in response to visual or tactile stimulus. Depending on the task rule, the mice must respond to one stimulus modality while ignoring the other. Neural activity to the same tactile stimulus is modulated by task in all the areas recorded, with significant activity changes in a subset of neurons and population activity occupying distinct activity subspaces. Recordings further reveal a contextual signal in the pre-stimulus baseline activity that differentiates task context. This signal is correlated with subsequent task modulation of neural activity. Comparison across brain areas shows that this contextual signal is stronger in frontal cortical regions than sensory regions. Analyses link this signal to behavior by showing that it tracks the behavioral performance switch during task rule transitions. Silencing activity in frontal cortical regions during the baseline period impairs behavioral performance.

Strengths:

This is a carefully done study with solid results and thorough controls. The authors identify a contextual signal in baseline neural activity that predicts rule-dependent decision-related activity. The comprehensive characterization across a sensorimotor pathway is another strength. Analyses and perturbation experiments link this contextual signal to animals' behavior. The results provide a neural substrate that will surely inspire follow-up mechanistic investigations.

Weaknesses:

None. The authors have further improved the manuscript during the revision with additional analyses.

Impact:

This study reports an important neural signature for context-dependent decision-making that has important implications for mechanisms of context-dependent neural computation in general.

The colouring of the text is very easy to read, the black text on white background works well, and if the user decides to switch to the dark mode version of the app (where the background is black), the text becomes a light grey which is also very eligible and accessible.

When clicking the 3 dots in the top left corner, it gives a drop down of any information users would like to know. However, some unfamiliar with what the 3 dots signify could be confused. A text label accompanying it would help further improve accessibility.

The requirements to create an account are made clear, with the required fields being highlighted in red and given instructions such as "Usernames must be between 4 and 25 characters" which makes it easily accessible to users.

Each category has an image of the type of stream accompanied by the title of said category, which is helpful for those who are visually impaired and makes the site more accessible.

The recommended channels part of the screen is inaccessible without the use of a mouse, which is inconvenient for those who only have access to a keyboard.

Question is why do they need to go the pay for audience route? What are other ways?

This line here!!! I think that this is the reason that games like DND flourish, especially with homemade campaigns. The DM is limited in what they can do to provide experience for the players, so they have to use creativity and personality, which always makes for a fun campaign.

I think that this is the most important facet of tabletop games, at least for me. I have played plenty of random table top games with my roommate in the past, and I think the defining feature of the games that determined if I enjoyed them or not was if the game provided a good player experience in the way of concept and immersion.

This has two major problems: - First, it is another instance of a “hover interaction” - which is inaccessible according to the "Operable" principle of POUR. - Second, the purpose of this button is unclear and almost cryptic with it's text simply reading "HELLO". This is inaccessible according to the "Understandable" principle of POUR.

This phrase's grammar is a bit off. The repeated use of "and" may prove inaccessible according to the "Understandable" principle of POUR.

Although I have only highlighted one for simplicity: this applies for all the aspect ratio tables. This is somewhat of a nightmare for screen readers and TTS. Perhaps some other type of formatting would remedy the issue?

This title is great. It follows the "Perceivable" principle of POUR by contrasting background and foreground and being large in text size.

This is a “hover interaction” - it is inaccessible according to the "Operable" principle of POUR.

This is a “hover interaction” - it is inaccessible according to the "Operable" principle of POUR.

This is a “hover interaction” - it is inaccessible according to the "Operable" principle of POUR.

This is a “hover interaction” - it is inaccessible according to the "Operable" principle of POUR.

Good quote - you can't make students work, but you can let them know you are available if they need you. That's your responsibility; theirs is to do the work and reach out when they need you.

Using Flipgrid to help with discussion boards....

Importance of creating grading expectations and living by them.

Interesting idea - to require students to attend a set number of live interactive sessions - but not all of them.....

6 ways to build instructor presence

Teaching presence - instructional management Building Understanding Direct instruction

With a rise of AI graders how to you motivate teachers to stay present.

.

You can cross check the data in the typewriter database for most of the big US and European brands to see the slow merging and dying out of the typewriter through the late 60s and early 70s onward. See, for example, Royal: https://typewriterdatabase.com/royal.72.typewriter-serial-number-database which has buyouts and mergers listed at the top. The database also has a huge volume of references for how it was compiled which will give you additional history.

The early 70s saw a lot of plastic entering the space where more durable steel used to be. Most major US firms were shifting to electric after IBM in roughly 1961. Post war manufacture of machines picked up significantly in Italy, Spain, Holland, and even Wales which displaced some of the manufacturing in the US, where solid machines of the prior generation still worked and only needed servicing rather than outright replacement. (Planned obsolescence wasn't as much of a thing during the 30s and 40s, and in fact, [maintenance was heavily highlighted during the war](https://www.youtube.com/watch?v=ocdxgkxKAKo) when most US manufacturers ceased production of most models.) Eventually Japan displaced the business followed by India (which ceased in 2009) and China. Wrexham, Wales ceased manufacture of electronic Brother typewriters in 2012.

Ever decreasing costs of materials and manufacturing, improved manufacturing technology, increased competition in the space, combined with containerized shipping, competition from computers, etc. all contributed to the cheapening of the typewriter and hastened the death of manufacturing (though not the use) of manual typewriters.

Richard Polt's The Typewriter Revolution (2015) has a "microhistory" of typewriters in chapter 2 with references to some addition histories if you're interested.

Your question about Olympia manufacture dates (and more) can be found via: https://typewriterdatabase.com/olympia.61.typewriter-serial-number-database

x over it has a good two part series about the evolution of Olympias at:

https://xoverit.blogspot.com/2015/02/olympia-sm-series-part-1-1948-1964.html

https://xoverit.blogspot.com/2015/04/olympia-sm-series-part-2-1964-1980s.html

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

Learn more at Review Commons

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Reply to the reviewers

Reply to the Reviewers

We thank the referees for their careful reading of the manuscript and their valuable suggestions for improvements.

General Statements:

Existing SMC-based loop extrusion models successfully predict and characterize mesoscale genome spatial organization in vertebrate organisms, providing a valuable computational tool to the genome organization and chromatin biology fields. However, to date this approach is highly limited in its application beyond vertebrate organisms. This limitation arises because existing models require knowledge of CTCF binding sites, which act as effective boundary elements, blocking loop-extruding SMC complexes and thus defining TAD boundaries. However, CTCF is the predominant boundary element only in vertebrates. On the other hand, vertebrates only contain a small proportion of species in the tree of life, while TADs are nearly universal and SMC complexes are largely conserved. Thus, there is a pressing need for loop extrusion models capable of predicting Hi-C maps in organisms beyond vertebrates.

The conserved-current loop extrusion (CCLE) model, introduced in this manuscript, extends the quantitative application of loop extrusion models in principle to any organism by liberating the model from the lack of knowledge regarding the identities and functions of specific boundary elements. By converting the genomic distribution of loop extruding cohesin into an ensemble of dynamic loop configurations via a physics-based approach, CCLE outputs three-dimensional (3D) chromatin spatial configurations that can be manifested in simulated Hi-C maps. We demonstrate that CCLE-generated maps well describe experimental Hi-C data at the TAD-scale. Importantly, CCLE achieves high accuracy by considering cohesin-dependent loop extrusion alone, consequently both validating the loop extrusion model in general (as opposed to diffusion-capture-like models proposed as alternatives to loop extrusion) and providing evidence that cohesin-dependent loop extrusion plays a dominant role in shaping chromatin organization beyond vertebrates.

The success of CCLE unambiguously demonstrates that knowledge of the cohesin distribution is sufficient to reconstruct TAD-scale 3D chromatin organization. Further, CCLE signifies a shifted paradigm from the concept of localized, well-defined boundary elements, manifested in the existing CTCF-based loop extrusion models, to a concept also encompassing a continuous distribution of position-dependent loop extrusion rates. This new paradigm offers greater flexibility in recapitulating diverse features in Hi-C data than strictly localized loop extrusion barriers.

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

This manuscript presents a mathematical model for loop extrusion called the conserved-current loop extrusion model (CCLE). The model uses cohesin ChIP-Seq data to predict the Hi-C map and shows broad agreement between experimental Hi-C maps and simulated Hi-C maps. They test the model on Hi-C data from interphase fission yeast and meiotic budding yeast. The conclusion drawn by the authors is that peaks of cohesin represent loop boundaries in these situations, which they also propose extends to other organism/situations where Ctcf is absent.

__Response: __

We would like to point out that the referee's interpretation of our results, namely that, "The conclusion drawn by the authors is that peaks of cohesin represent loop boundaries in these situations, ...", is an oversimplification, that we do not subscribe to. The referee's interpretation of our model is correct when there are strong, localized barriers to loop extrusion; however, the CCLE model allows for loop extrusion rates that are position-dependent and take on a range of values. The CCLE model also allows the loop extrusion model to be applied to organisms without known boundary elements. Thus, the strict interpretation of the positions of cohesin peaks to be loop boundaries overlooks a key idea to emerge from the CCLE model.

__ Major comments:__

1. More recent micro-C/Hi-C maps, particularly for budding yeast mitotic cells and meiotic cells show clear puncta, representative of anchored loops, which are not well recapitulated in the simulated data from this study. However, such punta are cohesin-dependent as they disappear in the absence of cohesin and are enhanced in the absence of the cohesin release factor, Wapl. For example - see the two studies below. The model is therefore missing some key elements of the loop organisation. How do the authors explain this discrepency? It would also be very useful to test whether the model can predict the increased strength of loop anchors when Wapl1 is removed and cohesin levels increase.

Costantino L, Hsieh TS, Lamothe R, Darzacq X, Koshland D. Cohesin residency determines chromatin loop patterns. Elife. 2020 Nov 10;9:e59889. doi: 10.7554/eLife.59889. PMID: 33170773; PMCID: PMC7655110. Barton RE, Massari LF, Robertson D, Marston AL. Eco1-dependent cohesin acetylation anchors chromatin loops and cohesion to define functional meiotic chromosome domains. Elife. 2022 Feb 1;11:e74447. doi: 10.7554/eLife.74447. Epub ahead of print. PMID: 35103590; PMCID: PMC8856730.

__Response: __

We are perplexed by this referee comment. While we agree that puncta representing loop anchors are a feature of Hi-C maps, as noted by the referee, we would reinforce that our CCLE simulations of meiotic budding yeast (Figs. 5A and 5B of the original manuscript) demonstrate an overall excellent description of the experimental meiotic budding yeast Hi-C map, including puncta arising from loop anchors. This CCLE model-experiment agreement for meiotic budding yeast is described and discussed in detail in the original manuscript and the revised manuscript (lines 336-401).

To further emphasize and extend this point we now also address the Hi-C of mitotic budding yeast, which was not included the original manuscript. We have now added an entire new section of the revised manuscript entitled "CCLE Describes TADs and Loop Configurations in Mitotic S. cerevisiae" including the new Figure 6, which presents a comparison between a portion of the mitotic budding yeast Hi-C map from Costantino et al. and the corresponding CCLE simulation at 500 bp-resolution. In this case too, the CCLE model well-describes the data, including the puncta, further addressing the referee's concern that the CCLE model is missing some key elements of loop organization.

Concerning the referee's specific comment about the role of Wapl, we note that in order to apply CCLE when Wapl is removed, the corresponding cohesin ChIP-seq in the absence of Wapl should be available. To our knowledge, such data is not currently available and therefore we have not pursued this explicitly. However, we would reinforce that as Wapl is a factor that promotes cohesin unloading, its role is already effectively represented in the optimized value for LEF processivity, which encompasses LEF lifetime. In other words, if Wapl has a substantial effect it will be captured already in this model parameter.

1. Related to the point above, the simulated data has much higher resolution than the experimental data (1kb vs 10kb in the fission yeast dataset). Given that loop size is in the 20-30kb range, a good resolution is important to see the structural features of the chromosomes. Can the model observe these details that are averaged out when the resolution is increased?

__Response: __

We agree with the referee that higher resolution is preferable to low resolution. In practice, however, there is a trade-off between resolution and noise. The first experimental interphase fission yeast Hi-C data of Mizuguchi et al 2014 corresponds to 10 kb resolution. To compare our CCLE simulations to these published experimental data, as described in the original manuscript, we bin our 1-kb-resolution simulations to match the 10 kb experimental measurements. Nevertheless, CCLE can readily predict the interphase fission yeast Hi-C map at higher resolution by reducing the bin size (or, if necessary, reducing the lattice site size of the simulations themselves). In the revised manuscript, we have added comparisons between CCLE's predicted Hi-C maps and newer Micro-C data for S. pombe from Hsieh et al. (Ref. [50]) in the new Supplementary Figures 5-9. We have chosen to present these comparisons at 2 kb resolution, which is the same resolution for our meiotic budding yeast comparisons. Also included in Supplementary Figures 5-9 are comparisons between the original Hi-C maps of Mizuguchi et al. and the newer maps of Hsieh et al., binned to 10 kb resolution. Inspection of these figures shows that CCLE provides a good description of Hsieh et al.'s experimental Hi-C maps and does not reveal any major new features in the interphase fission yeast Hi-C map on the 10-100 kb scale, that were not already apparent from the Hi-C maps of Mizuguchi et al 2014. Thus, the CCLE model performs well across this range of effective resolutions.

3. Transcription, particularly convergent has been proposed to confer boundaries to loop extrusion. Can the authors recapitulate this in their model?

__Response: __

In response to the suggestion of the reviewer we have now calculated the correlation between cohesin ChIP-seq and the locations of convergent gene pairs, which is now presented in Supplementary Figures 17 and 18. Accordingly, in the revised manuscript, we have added the following text to the Discussion (lines 482-498):

"In vertebrates, CTCF defines the locations of most TAD boundaries. It is interesting to ask what might play that role in interphase S. pombe as well as in meiotic and mitotic S. cerevisiae. A number of papers have suggested that convergent gene pairs are correlated with cohesin ChIP-seq in both S. pombe [65, 66] and S. cerevisiae [66-71]. Because CCLE ties TADs to cohesin ChIP-seq, a strong correlation between cohesin ChIP-seq and convergent gene pairs would be an important clue to the mechanism of TAD formation in yeasts. To investigate this correlation, we introduce a convergent-gene variable that has a nonzero value between convergent genes and an integrated weight of unity for each convergent gene pair. Supplementary Figure 17A shows the convergent gene variable, so-defined, alongside the corresponding cohesin ChIP-seq for meiotic and mitotic S. cerevisiae. It is apparent from this figure that a peak in the ChIP-seq data is accompanied by a non-zero value of the convergent-gene variable in about 80% of cases, suggesting that chromatin looping in meiotic and mitotic S. cerevisiae may indeed be tied to convergent genes. Conversely, about 50% of convergent genes match peaks in cohesin ChIP-seq. The cross-correlation between the convergent-gene variable and the ChIP-seq of meiotic and mitotic S. cerevisiae is quantified in Supplementary Figures 17B and C. By contrast, in interphase S. pombe, cross-correlation between convergent genes and cohesin ChIP-seq in each of five considered regions is unobservably small (Supplementary Figure 18A), suggesting that convergent genes per se do not have a role in defining TAD boundaries in interphase S. pombe."

Minor comments:

1. In the discussion, the authors cite the fact that Mis4 binding sites do not give good prediction of the HI-C maps as evidence that Mis4 is not important for loop extrusion. This can only be true if the position of Mis4 measured by ChIP is a true reflection of Mis4 position. However, Mis4 binding to cohesin/chromatin is very dynamic and it is likely that this is too short a time scale to be efficiently cross-linked for ChIP. Conversely, extensive experimental data in vivo and in vitro suggest that stimulation of cohesin's ATPase by Mis4-Ssl3 is important for loop extrusion activity.

__Response: __

We apologize for the confusion on this point. We actually intended to convey that the absence of Mis4-Psc3 correlations in S. pombe suggests, from the point of view of CCLE, that Mis4 is not an integral component of loop-extruding cohesin, during the loop extrusion process itself. We agree completely that Mis4/Ssl3 is surely important for cohesin loading, and (given that cohesin is required for loop extrusion) Mis4/Ssl3 is therefore important for loop extrusion. Evidently, this part of our Discussion was lacking sufficient clarity. In response to both referees' comments, we have re-written the discussion of Mis4 and Pds5 to more carefully explain our reasoning and be more circumspect in our inferences. The re-written discussion is described below in response to Referee #2's comments.

Nevertheless, on the topic of whether Nipbl-cohesin binding is too transient to be detected in ChIP-seq, the FRAP analysis presented by Rhodes et al. eLife 6:e30000 "Scc2/Nipbl hops between chromosomal cohesin rings after loading" indicates that, in HeLa cells, Nipbl has a residence time bound to cohesin of about 50 seconds. As shown in the bottom panel of Supplementary Fig. 7 in the original manuscript (and the bottom panel of Supplementary Fig. 20 in the revised manuscript), there is a significant cross-correlation (~0.2) between the Nipbl ChIP-seq and Smc1 ChIP-seq in humans, indicating that a transient association between Nipbl and cohesin can be (and in fact is) detected by ChIP-seq.

1. *Inclusion of a comparison of this model compared to previous models (for example bottom up models) would be extremely useful. What is the improvement of this model over existing models? *

__Response: __

As stated in the original manuscript, as far as we are aware, "bottom up" models, that quantitatively describe the Hi-C maps of interphase fission yeast or meiotic budding yeast or, indeed, of eukaryotes other than vertebrates, do not exist. Bottom-up models would require knowledge of the relevant boundary elements (e.g. CTCF sites), which, as stated in the submitted manuscript, are generally unknown for fission yeast, budding yeast, and other non-vertebrate eukaryotes. The absence of such models is the reason that CCLE fills an important need. Since bottom-up models for cohesin loop extrusion in yeast do not exist, we cannot compare CCLE to the results of such models.

In the revised manuscript we now explicitly compare the CCLE model to the only bottom-up type of model describing the Hi-C maps of non-vertebrate eukaryotes by Schalbetter et al. Nat. Commun. 10:4795 2019, which we did cite extensively in our original manuscript. Schalbetter et al. use cohesin ChIP-seq peaks to define the positions of loop extrusion barriers in meiotic S. cerevisiae, for which the relevant boundary elements are unknown. In their model, specifically, when a loop-extruding cohesin anchor encounters such a boundary element, it either passes through with a certain probability, as if no boundary element is present, or stops extruding completely until the cohesin unbinds and rebinds.

In the revised manuscript we refer to this model as the "explicit barrier" model and have applied it to interphase S. pombe, using cohesin ChIP-seq peaks to define the positions of loop extrusion barriers. The corresponding simulated Hi-C map is presented in Supplementary Fig. 19 in comparison with the experimental Hi-C. It is evident that the explicit barrier model provides a poorer description of the Hi-C data of interphase S. pombe compared to the CCLE model, as indicated by the MPR and Pearson correlation scores. While the explicit barrier model appears capable of accurately reproducing Hi-C data with punctate patterns, typically accompanied by strong peaks in the corresponding cohesin ChIP-seq, it seems less effective in several conditions including interphase S. pombe, where the Hi-C data lacks punctate patterns and sharp TAD boundaries, and the corresponding cohesin ChIP-seq shows low-contrast peaks. The success of the CCLE model in describing the Hi-C data of both S. pombe and S. cerevisiae, which exhibit very different features, suggests that the current paradigm of localized, well-defined boundary elements may not be the only approach to understanding loop extrusion. By contrast, CCLE allows for a concept of continuous distribution of position-dependent loop extrusion rates, arising from the aggregate effect of multiple interactions between loop extrusion complexes and chromatin. This paradigm offers greater flexibility in recapitulating diverse features in Hi-C data than strictly localized loop extrusion barriers.

We have also added the following paragraph in the Discussion section of the manuscript to elaborate this point (lines 499-521):

"Although 'bottom-up' models which incorporate explicit boundary elements do not exist for non-vertebrate eukaryotes, one may wonder how well such LEF models, if properly modified and applied, would perform in describing Hi-C maps with diverse features. To this end, we examined the performance of the model described in Ref. [49] in describing the Hi-C map of interphase S. cerevisiae. Reference [49] uses cohesin ChIP-seq peaks in meiotic S. cerevisiae to define the positions of loop extrusion barriers which either completely stall an encountering LEF anchor with a certain probability or let it pass. We apply this 'explicit barrier' model to interphase S. pombe, using its cohesin ChIP-seq peaks to define the positions of loop extrusion barriers, and using Ref. [49]'s best-fit value of 0.05 for the pass-through probability. Supplementary Figure 19A presents the corresponding simulated Hi-C map the 0.3-1.3 kb region of Chr 2 of interphase S. pombe in comparison with the corresponding Hi-C data. It is evident that the explicit barrier model provides a poorer description of the Hi-C data of interphase S. pombe compared to the CCLE model, as indicated by the MPR and Pearson correlation scores of 1.6489 and 0.2267, respectively. While the explicit barrier model appears capable of accurately reproducing Hi-C data with punctate patterns, typically accompanied by strong peaks in the corresponding cohesin ChIP-seq, it seems less effective in cases such as in interphase S. pombe, where the Hi-C data lacks punctate patterns and sharp TAD boundaries, and the corresponding cohesin ChIP-seq shows low-contrast peaks. The success of the CCLE model in describing the Hi-C data of both S. pombe and S. cerevisiae, which exhibit very different features, suggests that the current paradigm of localized, well-defined boundary elements may not be the only approach to understanding loop extrusion. By contrast, CCLE allows for a concept of continuous distribution of position-dependent loop extrusion rates, arising from the aggregate effect of multiple interactions between loop extrusion complexes and chromatin. This paradigm offers greater flexibility in recapitulating diverse features in Hi-C data than strictly localized loop extrusion barriers."

Reviewer #1 (Significance (Required)):

This simple model is useful to confirm that cohesin positions dictate the position of loops, which was predicted already and proposed in many studies. However, it should be considered a starting point as it does not faithfully predict all the features of chromatin organisation, particularly at better resolution.

Response:

As described in more detail above, we do not agree with the assertion of the referee that the CCLE model "does not faithfully predict all the features of chromatin organization, particularly at better resolution" and provide additional new data to support the conclusion that the CCLE model provides a much needed approach to model non-vertebrate contact maps and outperforms the single prior attempt to predict budding yeast Hi-C data using information from cohesin ChIP-seq.

*It will mostly be of interest to those in the chromosome organisation field, working in organisms or systems that do not have ctcf. *

__Response: __

We agree that this work will be of special interest to researchers working on chromatin organization of non-vertebrate organisms. We would reinforce that yeast are frequently used models for the study of cohesin, condensin, and chromatin folding more generally. Indeed, in the last two months alone there are two Molecular Cell papers, one Nature Genetics paper, and one Cell Reports paper where loop extrusion in yeast models is directly relevant. We also believe, however, that the model will be of interest for the field in general as it simultaneously encompasses various scenarios that may lead to slowing down or stalling of LEFs.

This reviewer is a cell biologist working in the chromosome organisation field, but does not have modelling experience and therefore does not have the expertise to determine if the modelling part is mathematically sound and has assumed that it is.

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

Summary: Yuan et al. report on their development of an analytical model ("CCLE") for loop extrusion with genomic-position-dependent speed, with the idea of accounting for barriers to loop extrusion. They write down master equations for the probabilities of cohesin occupancy at each genomic site and obtain approximate steady-state solutions. Probabilities are governed by cohesin translocation, loading, and unloading. Using ChIP-seq data as an experimental measurement of these probabilities, they numerically fit the model parameters, among which are extruder density and processivity. Gillespie simulations with these parameters combined with a 3D Gaussian polymer model were integrated to generate simulated Hi-C maps and cohesin ChIP-seq tracks, which show generally good agreement with the experimental data. The authors argue that their modeling provides evidence that loop extrusion is the primary mechanism of chromatin organization on ~10-100 kb scales in S. pombe and S. cerevisiae.

Major comments:

1. I am unconvinced that this analysis specifically is sufficient to demonstrate that extrusion is the primary organizer of chromatin on these scales; moreover, the need to demonstrate this is questionable, as extrusion is widely accepted, even if not universally so. How is the agreement of CCLE with experiments more demonstrative of loop extrusion than previous modeling?

__Response: __

We agree with the referee's statement that "loop extrusion is extrusion is widely accepted, even if not universally so". We disagree with the referee that this state of affairs means that "the need to demonstrate this (i.e. loop extrusion) is questionable". On the contrary, studies that provide further compelling evidence that cohesin-based loop extrusion is the primary organizer of chromatin, such as ours, must surely be welcomed, first, in order to persuade those who remain unconvinced by the loop extrusion mechanism in general, and, secondly, because, until the present work, quantitative models of loop extrusion, capable of reproducing Hi-C maps quantitatively, in yeasts and other non-vertebrate eukaryotes have been lacking, leaving open the question of whether loop extrusion can describe Hi-C maps beyond vertebrates. CCLE has now answered that question in the affirmative. Moreover, the existence of a robust model to predict contact maps in non-vertebrate models, which are extensively used in the pursuit of research questions in chromatin biology, will be broadly enabling to the field.

It is fundamental that if a simple, physically-plausible model/hypothesis is able to describe experimental data quantitatively, it is indeed appropriate to ascribe considerable weight to that model/hypothesis (until additional data become available to refute the model).

How is the agreement of CCLE with experiments more demonstrative of loop extrusion than previous modeling?

Response:

As noted above and in the original manuscript, we are unaware of previous quantitative modeling of cohesin-based loop extrusion and the resultant Hi-C maps in organisms that lack CTCF, namely non-vertebrate eukaryotic models such as fission yeast or budding yeast, as we apply here. As noted in the original manuscript, previous quantitative modeling of Hi-C maps based on cohesin loop extrusion and CTCF boundary elements has been convincing that loop extrusion is indeed relevant in vertebrates, but the restriction to vertebrates excludes most of the tree of life.

Below, the referee cites two examples of loop extrusion outside of vertebrates. The one that is suggested to correspond to yeast cells (Dequeker et al. Nature 606:197 2022) actually corresponds to mouse cells, which are vertebrate cells. The other one models the Hi-C map of the prokaryote, Bacillus subtilis, based on loop extrusion of the bacterial SMC complex thought to most resemble condensin (not cohesin), subject to barriers to loop extrusion that are related to genes or involving prokaryote-specific Par proteins (Brandao et al. PNAS 116:20489 2019). We have referenced this work in the revised manuscript but would reinforce that it lacks utility in predicting the contact maps for non-vertebrate eukaryotes.

Relatedly, similar best fit values for S. pombe and S. cerevisiae might not point to a mechanistic conclusion (same "underlying mechanism" of loop extrusion), but rather to similar properties for loop-extruding cohesins in the two species.

Response:

In the revised manuscript, we have replaced "suggesting that the underlying mechanism that governs loop extrusion by cohesin is identical in both species" with "suggesting loop-extruding cohesins possess similar properties in both species" (lines 367-368).

As an alternative, could a model with variable binding probability given by ChIP-seq and an exponential loop-size distribution work equally well? The stated lack of a dependence on extrusion timescale suggests that a static looping model might succeed. If not, why not?

Response:

A hypothetical mechanism that generates the same instantaneous loop distributions and correlations as loop extrusion would lead to the same Hi-C map as does loop extrusion. This circumstance is not confined to CCLE, but is equally applicable to previous CTCF-based loop extrusion models. It holds because Hi-C and ChIP-seq, and therefore models that seek to describe these measurements, provide a snapshot of the chromatin configuration at one instant of time.

We would reinforce that there is no physical basis for a diffusion capture model with an approximately-exponential loop size distributions. Nevertheless, one can reasonably ask whether a physically-sensible diffusion capture model can simultaneously match cohesin ChIP-seq and Hi-C. Motivated by the referee's comment we have addressed this question and, accordingly, in the revised manuscript, we have added (1) an entire subsection entitled "Diffusion capture does not reproduce experimental interphase S. pombe Hi-C maps" (lines 303-335) and (2) Supplementary Figure 15. As we now demonstrate, the CCLE model vastly outperforms an equilibrium binding model in reproducing the experimental Hi-C maps and measured P(s).

*2. I do not understand how the loop extrusion residence time drops out. As I understand it, Eq 9 converts ChIP-seq to lattice site probability (involving N_{LEF}, which is related to \rho, and \rho_c). Then, Eqs. 3-4 derive site velocities V_n and U_n if we choose rho, L, and \tau, with the latter being the residence time. This parameter is not specified anywhere and is claimed to be unimportant. It may be true that the choice of timescale is arbitrary in this procedure, but can the authors please clarify? *

__Response: __

As noted above, Hi-C and ChIP-seq both capture chromatin configuration at one instant in time. Therefore, such measurements cannot and do not provide any time-scale information, such as the loop extrusion residence time (LEF lifetime) or the mean loop extrusion rate. For this reason, neither our CCLE simulations, nor other researchers' previous simulations of loop extrusion in vertebrates with CTCF boundary elements, provide any time-scale information, because the experiments they seek to describe do not contain time-scale information. The Hi-C map simulations can and do provide information concerning the loop size, which is the product of the loop lifetime and the loop extrusion rate. Lines 304-305 of the revised manuscript include the text: "Because Hi-C and ChIP-seq both characterize chromatin configuration at a single instant of time, and do not provide any direct time-scale information, ..."

In practice, we set the LEF lifetime to be some explicit value with arbitrary time-unit. We have added a sentence in the Methods that reads, "In practice, however, we set the LEF dissociation rate to 5e-4 time-unit-1 (equivalent to a lifetime of 2000 time-units), and the nominal LEF extrusion rate (aka \rho*L/\tau, see Supplementary Methods) can be determined from the given processivity" (lines 599-602), to clarify this point. We have also changed the terminology from "timesteps" to "LEF events" in the manuscript as the latter is more accurate for our purpose.

1. The assumptions in the solution and application of the CCLE model are potentially constraining to a limited number of scenarios. In particular the authors specify that current due to binding/unbinding, A_n - D_n, is small. This assumption could be problematic near loading sites (centromeres, enhancers in higher eukaryotes, etc.) (where current might be dominated by A_n and V_n), unloading sites (D_n and V_{n-1}), or strong boundaries (D_n and V_{n-1}). The latter scenario is particularly concerning because the manuscript seems to be concerned with the presence of unidentified boundaries. This is partially mitigated by the fact that the model seems to work well in the chosen examples, but the authors should discuss the limitations due to their assumptions and/or possible methods to get around these limitations.

4. Related to the above concern, low cohesin occupancy is interpreted as a fast extrusion region and high cohesin occupancy is interpreted as a slow region. But this might not be true near cohesin loading and unloading sites.

__Response: __

Our response to Referee 2's Comments 3. and 4. is that both in the original manuscript and in the revised manuscript we clearly delineate the assumptions underlying CCLE and we carefully assess the extent to which these assumptions are violated (lines 123-126 and 263-279 in the revised manuscript). For example, Supplementary Figure 12 shows that across the S. pombe genome as a whole, violations of the CCLE assumptions are small. Supplementary Figure 13 shows that violations are similarly small for meiotic S. cerevisiae. However, to explicitly address the concern of the referee, we have added the following sentences to the revised manuscript:

Lines 277-279:

"While loop extrusion in interphase S. pombe seems to well satisfy the assumptions underlying CCLE, this may not always be the case in other organisms."

Lines 359-361:

"In addition, the three quantities, given by Eqs. 6, 7, and 8, are distributed around zero with relatively small fluctuations (Supplementary Fig. 13), indicating that CCLE model is self-consistent in this case also."

In the case of mitotic S. cerevisiae, Supplementary Figure 14 shows that these quantities are small for most of genomic locations, except near the cohesin ChIP-seq peaks. We ascribe these greater violations of CCLE's assumptions at the locations of cohesin peaks in part to the low processivity of mitotic cohesin in S. cerevisiae, compared to that of meiotic S. cerevisiae and interphase S. pombe, and in part to the low CCLE loop extrusion rate at the cohesin peaks. We have added a paragraph at the end of the Section "CCLE Describes TADs and Loop Configurations in Mitotic S. cerevisiae" to reflect these observations (lines 447-461).

1. *The mechanistic insight attempted in the discussion, specifically with regard to Mis4/Scc2/NIPBL and Pds5, is problematic. First, it is not clear how the discussion of Nipbl and Pds5 is connected to the CCLE method; the justification is that CCLE shows cohesin distribution is linked to cohesin looping, which is already a questionable statement (point 1) and doesn't really explain how the model offers new insight into existing Nipbl and Pds5 data. *

Furthermore, I believe that the conclusions drawn on this point are flawed, or at least, stated with too much confidence. The authors raise the curious point that Nipbl ChIP-seq does not correlate well with cohesin ChIP-seq, and use this as evidence that Nipbl is not a part of the loop-extruding complex in S. pombe, and it is not essential in humans. Aside from the molecular evidence in human Nipbl/cohesin (acknowledged by authors), there are other reasons to doubt this conclusion. First, depletion of Nipbl (rather than binding partner Mau2 as in ref 55) in mouse cells strongly inhibits TAD formation (Schwarzer et al. Nature 551:51 2017). Second, at least two studies have raised concerns about Nibpl ChIP-seq results: 1) Hu et al. Nucleic Acids Res 43:e132 2015, which shows that uncalibrated ChIP-seq can obscure the signal of protein localization throughout the genome due to the inability to distinguish from background * and 2) Rhodes et al. eLife 6:e30000, which uses FRAP to show that Nipbl binds and unbinds to cohesin rapidly in human cells, which could go undetected in ChIP-seq, especially when uncalibrated. It has not been shown that these dynamics are present in yeast, but there is no reason to rule it out yet.*

Similar types of critiques could be applied to the discussion of Pds5. There is cross-correlation between Psc3 and Pds5 in S. pombe, but the authors are unable to account for whether Pds5 binding is transient and/or necessary to loop extrusion itself or, more importantly, whether Pds5 ChIP is associated with extrusive or cohesive cohesins; cross-correlation peaks at about 0.6, but note that by the authors own estimates, cohesive cohesins are approximately half of all cohesins in S. pombe (Table 3).

*Due to the above issues, I suggest that the authors heavily revise this discussion to better reflect the current experimental understanding and the limited ability to draw such conclusions based on the current CCLE model. *

__Response: __

As stated above, our study demonstrates that the CCLE approach is able to take as input cohesin (Psc3) ChIP-seq data and produce as output simulated Hi-C maps that well reproduce the experimental Hi-C maps of interphase S. pombe and meiotic S. cerevisiae. This result is evident from the multiple Hi-C comparison figures in both the original and the revised manuscripts. In light of this circumstance, the referee's statement that it is "questionable", that CCLE shows that cohesin distribution (as quantified by cohesin ChIP-seq) is linked to cohesin looping (as quantified by Hi-C), is demonstrably incorrect.

However, we did not intend to suggest that Nipbl and Pds5 are not crucial for cohesin loading, as the reviewer states. Rather, our inquiries relate to a more nuanced question of whether these factors only reside at loading sites or, instead, remain as a more long-lived constituent component of the loop extrusion complex. We regret any confusion and have endeavored to clarify this point in the revised manuscript in response to Referee 2's Comment 5. as well as Referee 1's Minor Comment 1. We have now better explained how the CCLE model may offer new insight from existing ChIP-seq data in general and from Mis4/Nipbl and Pds5 ChIP-seq, in particular. Accordingly, we have followed Referee 2's advice to heavily revise the relevant section of the Discussion.

To this end, we have removed the following text from the original manuscript:

"The fact that the cohesin distribution along the chromatin is strongly linked to chromatin looping, as evident by the success of the CCLE model, allows for new insights into in vivo LEF composition and function. For example, recently, two single-molecule studies [37, 38] independently found that Nipbl, which is the mammalian analogue of Mis4, is an obligate component of the loop-extruding human cohesin complex. Ref. [37] also found that cohesin complexes containing Pds5, instead of Nipbl, are unable to extrude loops. On this basis, Ref. [32] proposed that, while Nipbl-containing cohesin is responsible for loop extrusion, Pds5-containing cohesin is responsible for sister chromatid cohesion, neatly separating cohesin's two functions according to composition. However, the success of CCLE in interphase S. pombe, together with the observation that the Mis4 ChIP-seq signal is uncorrelated with the Psc3 ChIP-seq signal (Supplementary Fig. 7) allows us to infer that Mis4 cannot be a component of loop-extruding cohesin in S. pombe. On the other hand, Pds5 is correlated with Psc3 in S. pombe (Supplementary Fig. 7) suggesting that both proteins are involved in loop-extruding cohesin, contradicting a hypothesis that Pds5 is a marker for cohesive cohesin in S. pombe. In contrast to the absence of Mis4-Psc3 correlation in S. pombe, in humans, Nipbl ChIP-seq and Smc1 ChIP-seq are correlated (Supplementary Fig. 7), consistent with Ref. [32]'s hypothesis that Nipbl can be involved in loop-extruding cohesin in humans. However, Ref. [55] showed that human Hi-C contact maps in the absence of Nipbl's binding partner, Mau2 (Ssl3 in S. pombe [56]) show clear TADs, consistent with loop extrusion, albeit with reduced long-range contacts in comparison to wild-type maps, indicating that significant loop extrusion continues in live human cells in the absence of Nipbl-Mau2 complexes. These collected observations suggest the existence of two populations of loop-extruding cohesin complexes in vivo, one that involves Nipbl-Mau2 and one that does not. Both types are present in mammals, but only Mis4-Ssl3-independent loop-extruding cohesin is present in S. pombe."

And we have replaced it by the following text in the revised manuscript (lines 533-568):

"As noted above, the input for our CCLE simulations of chromatin organization in S. pombe, was the ChIP-seq of Psc3, which is a component of the cohesin core complex [75]. Accordingly, Psc3 ChIP-seq represents how the cohesin core complex is distributed along the genome. In S. pombe, the other components of the cohesin core complex are Psm1, Psm3, and Rad21. Because these proteins are components of the cohesin core complex, we expect that the ChIP-seq of any of these proteins would closely match the ChIP-seq of Psc3, and would equally well serve as input for CCLE simulations of S. pombe genome organization. Supplementary Figure 20C confirms significant correlations between Psc3 and Rad21. In light of this observation, we then reason that the CCLE approach offers the opportunity to investigate whether other proteins beyond the cohesin core are constitutive components of the loop extrusion complex during the extrusion process (as opposed to cohesin loading or unloading). To elaborate, if the ChIP-seq of a non-cohesin-core protein is highly correlated with the ChIP-seq of a cohesin core protein, we can infer that the protein in question is associated with the cohesin core and therefore is a likely participant in loop-extruding cohesin, alongside the cohesin core. Conversely, if the ChIP-seq of a putative component of the loop-extruding cohesin complex is uncorrelated with the ChIP-seq of a cohesin core protein, then we can infer that the protein in question is unlikely to be a component of loop-extruding cohesin, or at most is transiently associated with it.

For example, in S. pombe, the ChIP-seq of the cohesin regulatory protein, Pds5 [74], is correlated with the ChIP-seq of Psc3 (Supplementary Fig. 20B) and with that of Rad21 (Supplementary Fig. 20D), suggesting that Pds5 can be involved in loop-extruding cohesin in S. pombe, alongside the cohesin core proteins. Interestingly, this inference concerning fission yeast cohesin subunit, Pds5, stands in contrast to the conclusion from a recent single-molecule study [38] concerning cohesin in vertebrates. Specifically, Reference [38] found that cohesin complexes containing Pds5, instead of Nipbl, are unable to extrude loops.

Additionally, as noted above, in S. pombe the ChIP-seq signal of the cohesin loader, Mis4, is uncorrelated with the Psc3 ChIP-seq signal (Supplementary Fig. 20A), suggesting that Mis4 is, at most, a very transient component of loop-extruding cohesin in S. pombe, consistent with its designation as a "cohesin loader". However, both References [38] and [39] found that Nipbl (counterpart of S. pombe's Mis4) is an obligate component of the loop-extruding human cohesin complex, more than just a mere cohesin loader. Although CCLE has not yet been applied to vertebrates, from a CCLE perspective, the possibility that Nipbl may be required for the loop extrusion process in humans is bolstered by the observation that in humans Nipbl ChIP-seq and Smc1 ChIP-seq show significant correlations (Supplementary Fig. 20G), consistent with Ref. [32]'s hypothesis that Nipbl is involved in loop-extruding cohesin in vertebrates. A recent theoretical model of the molecular mechanism of loop extrusion by cohesin hypothesizes that transient binding by Mis4/Nipbl is essential for permitting directional reversals and therefore for two-sided loop extrusion [41]. Surprisingly, there are significant correlations between Mis4 and Pds5 in S. pombe (Supplementary Fig. 20E), indicating Pds5-Mis4 association, outside of the cohesin core complex."

In response to Referee 2's specific comment that "at least two studies have raised concerns about Nibpl ChIP-seq results", we note (1) that, while Hu et al. Nucleic Acids Res 43:e132 2015 present a general method for calibrating ChIP-seq results, they do not measure Mis4/Nibpl ChIP-seq, nor do they raise any specific concerns about Mis4/Nipbl ChIP-seq, and (2) that (as noted above, in response to Referee 1's comment) while the FRAP analysis presented by Rhodes et al. eLife 6:e30000 indicates that, in HeLa cells, Nipbl has a residence time bound to cohesin of about 50 seconds, nevertheless, as shown in Supplementary Fig. 20G in the revised manuscript, there is a significant cross-correlation between the Nipbl ChIP-seq and Smc1 ChIP-seq in humans, indicating that a transient association between Nipbl and cohesin is detected by ChIP-seq, the referees' concerns notwithstanding.

We thank the referee for pointing out Schwarzer et al. Nature 551:51 2017. However, our interpretation of these data is different than the referee's. As noted in our original manuscript, Nipbl has traditionally been considered to be a cohesin loading factor. If the role of Nipbl was solely to load cohesin, then we would expect that depleting Nipbl would have a major effect on the Hi-C map, because fewer cohesins are loaded onto the chromatin. Figure 2 of Schwarzer et al. Nature 551:51 2017, shows the effect of depleting Nibpl on a vertebrate Hi-C map. Even in this case when Nibpl is absent, this figure (Figure 2 of Schwarzer et al. Nature 551:51 2017) shows that TADs persist, albeit considerably attenuated. According to the authors' own analysis associated with Fig. 2 of their paper, these attenuated TADs correspond to a smaller number of loop-extruding cohesin complexes than in the presence of Nipbl. Since Nipbl is depleted, these loop-extruding cohesins necessarily cannot contain Nipbl. Thus, the data and analysis of Schwarzer et al. Nature 551:51 2017 actually seem consistent with the existence of a population of loop-extruding cohesin complexes that do not contain Nibpl.

Concerning the referee's comment that we cannot be sure whether Pds5 ChIP is associated with extrusive or cohesive cohesin, we note that, as explained in the manuscript, we assume that the cohesive cohesins are uniformly distributed across the genome, and therefore that peaks in the cohesin ChIP-seq are associated with loop-extruding cohesins. The success of CCLE in describing Hi-C maps justifies this assumption a posteriori. Supplementary Figure 20B shows that the ChIP-seq of Pds5 is correlated with the ChIP-seq of Psc3 in S. pombe, that is, that peaks in the ChIP-seq of Psc3, assumed to derive from loop-extruding cohesin, are accompanied by peaks in the ChIP-seq of Pds5. This is the reasoning allowing us to associate Pds5 with loop-extruding cohesin in S. pombe.

1. I suggest that the authors recalculate correlations for Hi-C maps using maps that are rescaled by the P(s) curves. As currently computed, most of the correlation between maps could arise from the characteristic decay of P(s) rather than smaller scale features of the contact maps. This could reduce the surprising observed correlation between distinct genomic regions in pombe (which, problematically, is higher than the observed correlation between simulation and experiment in cervisiae).

Response:

We thank the referee for this advice. Following this advice, throughout the revised manuscript, we have replaced our original calculation of the Pearson correlation coefficient of unscaled Hi-C maps with a calculation of the Pearson correlation coefficient of rescaled Hi-C maps. Since the MPR is formed from ratios of simulated to experimental Hi-C maps, this metric is unchanged by the proposed rescaling.

As explained in the original manuscript, we attribute the lower experiment-simulation correlation in the meiotic budding yeast Hi-C maps to the larger statistical errors of the meiotic budding yeast dataset, which arises because of its higher genomic resolution - all else being equal we can expect 25 times the counts in a 10 kb x10 kb bin as in a 2 kb x 2 kb bin. For the same reason, we expect larger statistical errors in the mitotic budding yeast dataset as well. Lower correlations for noisier data are to be expected in general.

*7. Please explain why the difference between right and left currents at any particular site, (R_n-L_n) / Rn+Ln, should be small. It seems easy to imagine scenarios where this might not be true, such as directional barriers like CTCF or transcribed genes. *

__Response: __

For simplicity, the present version of CCLE sets the site-dependent loop extrusion rates by assuming that the cohesin ChIP-seq signal has equal contributions from left and right anchors. Then, we carry out our simulations which subsequently allow us to examine the simulated left and right currents and their difference at every site. The distributions of normalized left-right difference currents are shown in Supplementary Figures 12B, 13B, and 14D, for interphase S. pombe, meiotic S. cerevisiae, and mitotic S. cerevisiae, respectively. They are all centered at zero with standard deviations of 0.12, 0.16, and 0.33. Thus, it emerges from our simulations that the difference current is indeed generally small.

8. Optional, but I think would greatly improve the manuscript, but can the authors: a) analyze regions of high cohesin occupancy (assumed to be slow extrusion regions) to determine if there's anything special in these regions, such as more transcriptional activity

__Response: __

In response to Referee 1's similar comment, we have calculated the correlation between the locations of convergent genes and cohesin ChIP-seq. Supplementary Figure 18A in the revised manuscript shows that for interphase S. pombe no correlations are evident, whereas for both of meiotic and mitotic S. cerevisiae, there are significant correlations between these two quantities (Supplementary Fig. 17).

*b) apply this methodology to vertebrate cell data *

__Response: __

The application of CCLE to vertebrate data is outside the scope of this paper which, as we have emphasized, has the goal of developing a model that can be robustly applied to non-vertebrate eukaryotic genomes. Nevertheless, CCLE is, in principle, applicable to all organisms in which loop extrusion by SMC complexes is the primary mechanism for chromatin spatial organization.

1. *A Github link is provided but the code is not currently available. *

__Response: __

The code is now available.

Minor Comments:

1. Please state the simulated LEF lifetime, since the statement in the methods that 15000 timesteps are needed for equilibration of the LEF model is otherwise not meaningful. Additionally, please note that backbone length is not necessarily a good measure of steady state, since the backbone can be compacted to its steady-state value while the loop distribution continues to evolve toward its steady state.

__Response: __

The terminology "timesteps" used in the original manuscript in fact should mean "the number of LEF events performed" in the simulation. Therefore, we have changed the terminology from "timesteps" to "LEF events".

The choice of 15000 LEF events is empirically determined to ensure that loop extrusion steady state is achieved, for the range of parameters considered. To address the referee's concern regarding the uncertainty of achieving steady state after 15000 LEF events, we compared two loop size distributions: each distribution encompasses 1000 data points, equally separated in time, one between LEF event 15000 and 35000, and the other between LEF event 80000 and 100000. The two distributions are within-errors identical, suggesting that the loop extrusion steady state is well achieved within 15000 LEF events.

2. How important is the cohesive cohesin parameter in the model, e.g., how good are fits with \rho_c = 0?

__Response: __

As stated in the original manuscript, the errors on \rho_c on the order of 10%-20% (for S. pombe). Thus, fits with \rho_c=0 are significantly poorer than with the best-fit values of \rho_c.

*3. A nice (but non-essential) supplemental visualization might be to show a scatter of sim cohesin occupancy vs. experiment ChIP. *

__Response: __

We have chosen not to do this, because we judge that the manuscript is already long enough. Figures 3A, 5D, and 6C already compare the experimental and simulated ChIP-seq, and these figures already contain more information than the figures proposed by the referee.

1. *A similar calculation of Hi-C contacts based on simulated loop extruder positions using the Gaussian chain model was previously presented in Banigan et al. eLife 9:e53558 2020, which should be cited. *

__Response: __

We thank the referee for pointing out this citation. We have added it to the revised manuscript.

1. It is stated that simulation agreement with experiments for cerevisiae is worse in part due to variability in the experiments, with MPR and Pearson numbers for cerevisiae replicates computed for reference. But these numbers are difficult to interpret without, for example, similar numbers for duplicate pombe experiments. Again, these numbers should be generated using Hi-C maps scaled by P(s), especially in case there are systematic errors in one replicate vs. another.

__Response: __

As noted above, throughout the revised manuscript, we now give the Pearson correlation coefficients of scaled-by-P(s) Hi-C maps.

1. *In the model section, it is stated that LEF binding probabilities are uniformly distributed. Did the authors mean the probability is uniform across the genome or that the probability at each site is a uniformly distributed random number? Please clarify, and if the latter, explain why this unconventional assumption was made. *

__Response: __

It is the former. We have modified the manuscript to clarify that LEFs "initially bind to empty, adjacent chromatin lattice sites with a binding probability, that is uniformly distributed across the genome." (lines 587-588).

*7. Supplement p4 line 86 - what is meant by "processivity of loops extruded by isolated LEFs"? "size of loops extruded by..." or "processivity of isolated LEFs"? *

__Response: __

Here "processivity of isolated LEFs" is defined as the processivity of one LEF without the interference (blocking) from other LEFs. We have changed "processivity of loops extruded by isolated LEFs" to "processivity of isolated LEFs" for clarity.

1. The use of parentheticals in the caption to Table 2 is a little confusing; adding a few extra words would help.

__Response: __

In the revised manuscript, we have added an additional sentence, and have removed the offending parentheses.

1. *Page 12 sentence line 315-318 is difficult to understand. The barrier parameter is apparently something from ref 47 not previously described in the manuscript. *

__Response: __

In the revised manuscript, we have removed mention of the "barrier parameter" from the discussion.

1. *Statement on p14 line 393-4 is false: prior LEF models have not been limited to vertebrates, and the authors have cited some of them here. There are also non-vertebrate examples with extrusion barriers: genes as boundaries to condensin in bacteria (Brandao et al. PNAS 116:20489 2019) and MCM complexes as boundaries to cohesin in yeast (Dequeker et al. Nature 606:197 2022). *

__Response: __

In fact, Dequeker et al. Nature 606:197 2022 concerns the role of MCM complexes in blocking cohesin loop extrusion in mouse zygotes. Mouse is a vertebrate. The sole aspect of this paper, that is associated with yeast, is the observation of cohesin blocking by the yeast MCM bound to the ARS1 replication origin site, which is inserted on a piece of lambda phage DNA. No yeast genome is used in the experiment. Therefore, the referee is mistaken to suggest that this paper models yeast genome organization.

We thank the referee for pointing out Brandao et al. PNAS 116:20489 2019, which includes the development of a tour-de-force model of condensin-based loop extrusion in the prokaryote, Bacillus subtilis, in the presence of gene barriers to loop extrusion. To acknowledge this paper, we have changed the objectionable sentence to now read (lines 571-575):

"... prior LEF models have been overwhelmingly limited to vertebrates, which express CTCF and where CTCF is the principal boundary element. Two exceptions, in which the LEF model was applied to non-vertebrates, are Ref. [49], discussed above, and Ref. [76] (Brandao et al.), which models the Hi-C map of the prokaryote, Bacillus subtilis, on the basis of condensin loop extrusion with gene-dependent barriers."

*Referees cross-commenting *

I agree with the comments of Reviewer 1, which are interesting and important points that should be addressed.

*Reviewer #2 (Significance (Required)):

Analytically approaching extrusion by treating cohesin translocation as a conserved current is an interesting approach to modeling and analysis of extrusion-based chromatin organization. It appears to work well as a descriptive model. But I think there are major questions concerning the mechanistic value of this model, possible applications of the model, the provided interpretations of the model and experiments, and the limitations of the model under the current assumptions. I am unconvinced that this analysis specifically is sufficient to demonstrate that extrusion is the primary organizer of chromatin on these scales; moreover, the need to demonstrate this is questionable, as extrusion is widely accepted, even if not universally so. It is also unclear that the minimal approach of the CCLE necessarily offers an improved physical basis for modeling extrusion, as compared to previous efforts such as ref 47, as claimed by the authors. There are also questions about significance due to possible limitations of the model (detailed above). Applying the CCLE model to identify barriers would be interesting, but is not attempted. Overall, the work presents a reasonable analytical model and numerical method, but until the major comments above are addressed and some reasonable application or mechanistic value or interpretation is presented, the overall significance is somewhat limited.*

__Response: __

We agree with the referee that analytically approaching extrusion by treating cohesin translocation as a conserved current is an interesting approach to modeling and analysis of extrusion-based chromatin organization. We also agree with the referee that it works well as a descriptive model (of Hi-C maps in S. pombe and S. cerevisiae). Obviously, we disagree with the referee's other comments. For us, being able to describe the different-appearing Hi-C maps of interphase S. pombe (Fig. 1 and Supplementary Figures 1-9), meiotic S. cerevisiae (Fig. 5) and mitotic S. cerevisiae (Fig. 6), all with a common model with just a few fitting parameters that differ between these examples, is significant and novel. The reviewer prematurely ignores the fact that there are still debates about whether "diffusion-capture"-like model is the more dominant mechanism that shape chromatin spatial organization at the TAD-scale. Many works have argued that such models could describe TAD-scale chromatin organization, as cited in the revised manuscript (Refs. [11, 14, 15, 17, 20, 22-24, 55]). However, in contrast to the poor description of the Hi-C map using diffusion capture model (as demonstrated in the revised manuscript and Supplementary Fig. 15), the excellent experiment-simulation agreement achieved by CCLE provides compelling evidence that cohesin-based loop extrusion is indeed the primary organizer of TAD-scale chromatin.

Importantly, CCLE provides a theoretical base for how loop extrusion models can be generalized and applied to organisms without known loop extrusion barriers. Our model also highlights that (and provides means to account for) distributed barriers that impede but do not strictly block LEFs could also impact chromatin configurations. This case might be of importance to organisms with CTCF motifs that infrequently coincide with TAD boundaries, for instance, in the case of Drosophila melanogaster. Moreover, CCLE promises theoretical descriptions of the Hi-C maps of other non-vertebrates in the future, extending the quantitative application of the LEF model across the tree of life. This too would be highly significant if successful.

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Referee #2

Evidence, reproducibility and clarity

Summary:

Yuan et al. report on their development of an analytical model ("CCLE") for loop extrusion with genomic-position-dependent speed, with the idea of accounting for barriers to loop extrusion. They write down master equations for the probabilities of cohesin occupancy at each genomic site and obtain approximate steady-state solutions. Probabilities are governed by cohesin translocation, loading, and unloading. Using ChIP-seq data as an experimental measurement of these probabilities, they numerically fit the model parameters, among which are extruder density and processivity. Gillespie simulations with these parameters combined with a 3D Gaussian polymer model were integrated to generate simulated Hi-C maps and cohesin ChIP-seq tracks, which show generally good agreement with the experimental data. The authors argue that their modeling provides evidence that loop extrusion is the primary mechanism of chromatin organization on ~10-100 kb scales in S. pombe and S. cerevisiae.

Major comments:

1. I am unconvinced that this analysis specifically is sufficient to demonstrate that extrusion is the primary organizer of chromatin on these scales; moreover, the need to demonstrate this is questionable, as extrusion is widely accepted, even if not universally so. How is the agreement of CCLE with experiments more demonstrative of loop extrusion than previous modeling? Relatedly, similar best fit values for S. pombe and S. cerevisiae might not point to a mechanistic conclusion (same "underlying mechanism" of loop extrusion), but rather to similar properties for loop-extruding cohesins in the two species. As an alternative, could a model with variable binding probability given by ChIP-seq and an exponential loop-size distribution work equally well? The stated lack of a dependence on extrusion timescale suggests that a static looping model might succeed. If not, why not?
2. I do not understand how the loop extrusion residence time drops out. As I understand it, Eq 9 converts ChIP-seq to lattice site probability (involving N_{LEF}, which is related to \rho, and \rho_c). Then, Eqs. 3-4 derive site velocities V_n and U_n if we choose rho, L, and \tau, with the latter being the residence time. This parameter is not specified anywhere and is claimed to be unimportant. It may be true that the choice of timescale is arbitrary in this procedure, but can the authors please clarify?
3. The assumptions in the solution and application of the CCLE model are potentially constraining to a limited number of scenarios. In particular the authors specify that current due to binding/unbinding, A_n - D_n, is small. This assumption could be problematic near loading sites (centromeres, enhancers in higher eukaryotes, etc.) (where current might be dominated by A_n and V_n), unloading sites (D_n and V_{n-1}), or strong boundaries (D_n and V_{n-1}). The latter scenario is particularly concerning because the manuscript seems to be concerned with the presence of unidentified boundaries. This is partially mitigated by the fact that the model seems to work well in the chosen examples, but the authors should discuss the limitations due to their assumptions and/or possible methods to get around these limitations.
4. Related to the above concern, low cohesin occupancy is interpreted as a fast extrusion region and high cohesin occupancy is interpreted as a slow region. But this might not be true near cohesin loading and unloading sites.
5. The mechanistic insight attempted in the discussion, specifically with regard to Mis4/Scc2/NIPBL and Pds5, is problematic. First, it is not clear how the discussion of Nipbl and Pds5 is connected to the CCLE method; the justification is that CCLE shows cohesin distribution is linked to cohesin looping, which is already a questionable statement (point 1) and doesn't really explain how the model offers new insight into existing Nipbl and Pds5 data.

Furthermore, I believe that the conclusions drawn on this point are flawed, or at least, stated with too much confidence. The authors raise the curious point that Nipbl ChIP-seq does not correlate well with cohesin ChIP-seq, and use this as evidence that Nipbl is not a part of the loop-extruding complex in S. pombe, and it is not essential in humans. Aside from the molecular evidence in human Nipbl/cohesin (acknowledged by authors), there are other reasons to doubt this conclusion. First, depletion of Nipbl (rather than binding partner Mau2 as in ref 55) in mouse cells strongly inhibits TAD formation (Schwarzer et al. Nature 551:51 2017). Second, at least two studies have raised concerns about Nibpl ChIP-seq results: 1) Hu et al. Nucleic Acids Res 43:e132 2015, which shows that uncalibrated ChIP-seq can obscure the signal of protein localization throughout the genome due to the inability to distinguish from background and 2) Rhodes et al. eLife 6:e30000, which uses FRAP to show that Nipbl binds and unbinds to cohesin rapidly in human cells, which could go undetected in ChIP-seq, especially when uncalibrated. It has not been shown that these dynamics are present in yeast, but there is no reason to rule it out yet.

Similar types of critiques could be applied to the discussion of Pds5. There is cross-correlation between Psc3 and Pds5 in S. pombe, but the authors are unable to account for whether Pds5 binding is transient and/or necessary to loop extrusion itself or, more importantly, whether Pds5 ChIP is associated with extrusive or cohesive cohesins; cross-correlation peaks at about 0.6, but note that by the authors own estimates, cohesive cohesins are approximately half of all cohesins in S. pombe (Table 3).

Due to the above issues, I suggest that the authors heavily revise this discussion to better reflect the current experimental understanding and the limited ability to draw such conclusions based on the current CCLE model. 6. I suggest that the authors recalculate correlations for Hi-C maps using maps that are rescaled by the P(s) curves. As currently computed, most of the correlation between maps could arise from the characteristic decay of P(s) rather than smaller scale features of the contact maps. This could reduce the surprising observed correlation between distinct genomic regions in pombe (which, problematically, is higher than the observed correlation between simulation and experiment in cervisiae). 7. Please explain why the difference between right and left currents at any particular site, (R_n-L_n) / Rn+Ln, should be small. It seems easy to imagine scenarios where this might not be true, such as directional barriers like CTCF or transcribed genes. 8. Optional, but I think would greatly improve the manuscript, but can the authors: a) analyze regions of high cohesin occupancy (assumed to be slow extrusion regions) to determine if there's anything special in these regions, such as more transcriptional activity

b) apply this methodology to vertebrate cell data 9. A Github link is provided but the code is not currently available.

Minor Comments:

1. Please state the simulated LEF lifetime, since the statement in the methods that 15000 timesteps are needed for equilibration of the LEF model is otherwise not meaningful. Additionally, please note that backbone length is not necessarily a good measure of steady state, since the backbone can be compacted to its steady-state value while the loop distribution continues to evolve toward its steady state.
2. How important is the cohesive cohesin parameter in the model, e.g., how good are fits with \rho_c = 0?
3. A nice (but non-essential) supplemental visualization might be to show a scatter of sim cohesin occupancy vs. experiment ChIP.
4. A similar calculation of Hi-C contacts based on simulated loop extruder positions using the Gaussian chain model was previously presented in Banigan et al. eLife 9:e53558 2020, which should be cited.
5. It is stated that simulation agreement with experiments for cerevisiae is worse in part due to variability in the experiments, with MPR and Pearson numbers for cerevisiae replicates computed for reference. But these numbers are difficult to interpret without, for example, similar numbers for duplicate pombe experiments. Again, these numbers should be generated using Hi-C maps scaled by P(s), especially in case there are systematic errors in one replicate vs. another.
6. In the model section, it is stated that LEF binding probabilities are uniformly distributed. Did the authors mean the probability is uniform across the genome or that the probability at each site is a uniformly distributed random number? Please clarify, and if the latter, explain why this unconventional assumption was made.
7. Supplement p4 line 86 - what is meant by "processivity of loops extruded by isolated LEFs"? "size of loops extruded by..." or "processivity of isolated LEFs"?
8. The use of parentheticals in the caption to Table 2 is a little confusing; adding a few extra words would help.
9. Page 12 sentence line 315-318 is difficult to understand. The barrier parameter is apparently something from ref 47 not previously described in the manuscript.
10. Statement on p14 line 393-4 is false: prior LEF models have not been limited to vertebrates, and the authors have cited some of them here. There are also non-vertebrate examples with extrusion barriers: genes as boundaries to condensin in bacteria (Brandao et al. PNAS 116:20489 2019) and MCM complexes as boundaries to cohesin in yeast (Dequeker et al. Nature 606:197 2022).

Referees cross-commenting

I agree with the comments of Reviewer 1, which are interesting and important points that should be addressed.

Significance

Analytically approaching extrusion by treating cohesin translocation as a conserved current is an interesting approach to modeling and analysis of extrusion-based chromatin organization. It appears to work well as a descriptive model. But I think there are major questions concerning the mechanistic value of this model, possible applications of the model, the provided interpretations of the model and experiments, and the limitations of the model under the current assumptions. I am unconvinced that this analysis specifically is sufficient to demonstrate that extrusion is the primary organizer of chromatin on these scales; moreover, the need to demonstrate this is questionable, as extrusion is widely accepted, even if not universally so. It is also unclear that the minimal approach of the CCLE necessarily offers an improved physical basis for modeling extrusion, as compared to previous efforts such as ref 47, as claimed by the authors. There are also questions about significance due to possible limitations of the model (detailed above). Applying the CCLE model to identify barriers would be interesting, but is not attempted. Overall, the work presents a reasonable analytical model and numerical method, but until the major comments above are addressed and some reasonable application or mechanistic value or interpretation is presented, the overall significance is somewhat limited.

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

Learn more at Review Commons

---

Referee #1

Evidence, reproducibility and clarity

This manuscript presents a mathematical model for loop extrusion called the conserved-current loop extrusion model (CCLE). The model uses cohesin ChIP-Seq data to predict the Hi-C map and shows broad agreement between experimental Hi-C maps and simulated Hi-C maps. They test the model on Hi-C data from interphase fission yeast and meiotic budding yeast. The conclusion drawn by the authors is that peaks of cohesin represent loop boundaries in these situations, which they also propose extends to other organism/situations where Ctcf is absent.

Major comments

1. More recent micro-C/Hi-C maps, particularly for budding yeast mitotic cells and meiotic cells show clear puncta, representative of anchored loops, which are not well recapitulated in the simulated data from this study. However, such punta are cohesin-dependent as they disappear in the absence of cohesin and are enhanced in the absence of the cohesin release factor, Wapl. For example - see the two studies below. The model is therefore missing some key elements of the loop organisation. How do the authors explain this discrepency? It would also be very useful to test whether the model can predict the increased strength of loop anchors when Wapl1 is removed and cohesin levels increase.

Costantino L, Hsieh TS, Lamothe R, Darzacq X, Koshland D. Cohesin residency determines chromatin loop patterns. Elife. 2020 Nov 10;9:e59889. doi: 10.7554/eLife.59889. PMID: 33170773; PMCID: PMC7655110. Barton RE, Massari LF, Robertson D, Marston AL. Eco1-dependent cohesin acetylation anchors chromatin loops and cohesion to define functional meiotic chromosome domains. Elife. 2022 Feb 1;11:e74447. doi: 10.7554/eLife.74447. Epub ahead of print. PMID: 35103590; PMCID: PMC8856730. 2. Related to the point above, the simulated data has much higher resolution than the experimental data (1kb vs 10kb in the fission yeast dataset). Given that loop size is in the 20-30kb range, a good resolution is important to see the structural features of the chromosomes. Can the model observe these details that are averaged out when the resolution is increased? 3. Transcription, particularly convergent has been proposed to confer boundaries to loop extrusion. Can the authors recapitulate this in their model?

Minor comments

1. In the discussion, the authors cite the fact that Mis4 binding sites do not give good prediction of the HI-C maps as evidence that Mis4 is not important for loop extrusion. This can only be true if the position of Mis4 measured by ChIP is a true reflection of Mis4 position. However, Mis4 binding to cohesin/chromatin is very dynamic and it is likely that this is too short a time scale to be efficiently cross-linked for ChIP. Conversely, extensive experimental data in vivo and in vitro suggest that stimulation of cohesin's ATPase by Mis4-Ssl3 is important for loop extrusion activity.
2. Inclusion of a comparison of this model compared to previous models (for example bottom up models) would be extremely useful. What is the improvement of this model over existing models?

Significance

This simple model is useful to confirm that cohesin positions dictate the position of loops, which was predicted already and proposed in many studies. However, it should be considered a starting point as it does not faithfully predict all the features of chromatin organisation, particularly at better resolution. It will mostly be of interest to those in the chromosome organisation field, working in organisms or systems that do not have ctcf.

This reviewer is a cell biologist working in the chromosome organisation field, but does not have modelling experience and therefore does not have the expertise to determine if the modelling part is mathematically sound and has assumed that it is.

From what I can see in the speech shown in the text, Douglass is speaking about the actual reason/problem that lead to the civil war in the first place. He wasn't trying to "to revive old issues" but more so get people to see what is really going on even after the war.

for what?

You own obsession with new shiny object

He who unknowingly or willingly exploit himself

https://www.repubblica.it/cronaca/2024/05/16/news/intervista_sindaco_londra_sadiq_khan_clima_papa_francesco_vaticano-422985126/

https://www.repubblica.it/cronaca/2024/05/17/news/papa_francesco_sindaci_incontro_clima-422984920/

https://www.repubblica.it/green-and-blue/2024/05/17/news/terry_callaghan-422978534/

https://www.nytimes.com/2024/05/16/climate/coral-reefs-heat-stress-bleaching.html

https://www.liberation.fr/environnement/biodiversite/en-camargue-la-montee-des-eaux-menace-le-paradis-des-flamants-roses-20240517_L6LRO3TY2ZD4FESAHWAWGA32YY/

for what

Summarization. Building of cognitive schemas.

Interesting, Luhmann recommends to know many languages so as to prevent the pitfalls of translational errors in conveying meaning when it is to read translated books. So read books in their original language.

https://www.zeit.de/wissen/umwelt/2024-05/gruener-strom-klimapolitik-energiewende-klimaschutz?utm_source=pocket-newtab-de-de

[01:26:32 - 01:27:40] : Réflexion sur les défis de lutter contre une société méritocratique et l’impact de la pression des parents sur les attentes envers l’éducation de leurs enfants.

[01:11:54 - 01:12:55] : Importance de partager des expériences pour développer une mixité sociale véritable et l’impact de cela sur l’empathie et la tolérance.

Here in the trending section, there is a good exhibition of the website being perceivable and understandable. There are sharp contrasting colors between the background and foreground to tell the user that the information in this section is more relevant towards their interests. Therefore, it would be easy to immediately understand what the most prominent news articles are for the user.

I think this would be the most robust section of the website, because it separates each of the news article in distinct themes. This can help improve the navigation for the website and ensure that readers know what kind of information that they are viewing. Anyone using assistive technology would know the overall theme of the articles that they are viewing.

This is a poor practice, as none of the videos have closed captioning to help users who may be deaf. There are also no transcriptions being provided for the users so that they would be able to understand the contents of the video. The videos on the website are not accessible to the deaf users who may wish to watch videos.