On 2020-08-19 18:06:32, user Jeremy Horst wrote:

Published here:<br /> ingentaconnect.com/content/...

On 2017-02-23 22:04:21, user Chris Gorgolewski wrote:

It would be fantastic to get those statistical maps into NeuroVault.org!

On 2023-08-20 12:29:58, user David Ron wrote:

Evidence that phosphorylated eIF2 underlies the S-phase arrest imposed by the novel culture conditions hinges largely on the reversal of this process brought about by application of the compound ISRIB. This is a logical inference, however the authors' description of ISRIB's mechanism of action is factually incorrect: ISRIB acts downstream of phosphorylated eIF2 to interfere with downstream signalling (this critical event requires binding of ISRIB to eIFB); ISRIB does not impair eIF2 phosphorylation, as stated in the article. This point was established in the very first description of ISRIB (Sidrauski et al. 2013, PMID: 23741617) and elaborated on further by the 2015 publication cited as a reference here.<br /> David Ron, University of Cambridge

On 2021-03-25 12:04:30, user Christian Mulder wrote:

Just appeared on African Entomology, 29(1):104-111 (2021). https://doi.org/10.4001/003...

On 2018-08-06 00:26:54, user ron6788 wrote:

You'd think insurance companies would be all over this.

On 2019-08-15 00:39:00, user Joel Rothman wrote:

This is an exciting demonstration of in vivo reprogramming of muscle into endoderm in zebrafish by direct lineage conversion and across germ layer types. It adds substantial support to the view that differentiated cells can be transdifferentiated into endoderm (e.g., as has been observed in worms).

On 2021-08-11 02:19:39, user Brian Coyle wrote:

Virus don't easily infect ticks, believe it or not. Coronavirus haven't. It's because virus infect either anthropods or mammals.

On 2018-10-25 16:59:40, user Dave Vuono wrote:

Full disclosure to all readers of this discussion: the comment (by Eveline van den Berg) was written by a colleague that has published some work on C:NO3 control on denitrification versus ammonification. As an important distinction, our manuscript does not refute the results of others. Our manuscript does not suggest that other's research is wrong, rather we argue that literature on this topic is not yet complete. We simply demonstrate through a more extensive experimental design that previous work has had a limited view of C:NO3 control on denitrification versus ammonification. This body of literature (denitrification versus ammonification) has focused narrowly on a range of C:NO3 ratios and has limited experimental evidence for C:NO3 ratios less than 1.5 and more importantly, has not tested the effects of nutrient concentration on pathway selection. We simply point out that previous literature such as Kraft 2014 Science, Yoon 2015 ISME, van den Berg 2015 ISME, van den Berg 2016 Frontiers, van den Berg 2017 AMB Express, have only tested a limited state-space for C:NO3 ratios, nutrient concentrations, and are biased towards microorganisms using high-potential pool quinones. This is a scientific fact supported by the literature. Thus we report that C:NO3 control is a confounding variable (it is true within a narrow range of conditions), and the actual mechanisms of pathway selection are based on fundamental principles of thermodynamics and enzyme biochemistry.

Hence: although the past literature on C:NO3 control is interesting, it is only valid within the narrow state-space of the author's experimental design (i.e., a limited view of the environmental landscape) and thus advise caution in the interpretation those results.

On 2017-09-21 16:44:23, user AdamMarblestone wrote:

-"Deep Predictive Learning: A Comprehensive Model of Three Visual Streams" https://arxiv.org/abs/1709....<br /> -"Axonal synapse sorting in medial entorhinal cortex" http://www.nature.com/natur...<br /> -"Deep learning with segregated dendrites" http://linclab.org/wp-conte...

On 2018-01-02 19:39:59, user AdamMarblestone wrote:

-"Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks" https://www.biorxiv.org/con...

On 2017-03-15 16:18:30, user AdamMarblestone wrote:

-"A neuromorph's prospectus" http://web.stanford.edu/gro...

On 2017-07-24 22:40:51, user James Hadfield wrote:

1) Could these UMIs be incorporated into gRNA used in CATCH-Seq (http://dx.doi.org/10.1101/1...? "http://dx.doi.org/10.1101/110163)?")

2) Why do you use a novel term random sequence labels (RSLs) in place of the more commonly used unique molecular identifiers (UMIs)? You do mention UMIs in your abstract but then refer to RSLs throughout the paper. I don't see the need for another acronym especially in the NGS space.

3) You reference Kivioja et al for UMIs, however both Fu and Casbon are earlier works. I believe Sydney Brenner has some of the earliest patents on this technology - he's a smart guy! But Stephen Fodor beat him to the first publication and used their tech in the Cellular Research methods.

Kivioja et al. Counting absolute numbers of molecules using unique molecular identifiers. Nature methods 9, 72-74 (2012)<br /> Fu GK et al (2011) Counting individual DNA molecules by the<br /> stochastic attachment of diverse labels. Proc Natl Acad Sci USA 108(22):9026–9031.<br /> Casbon et al (2011) A method for counting PCR template molecules with application to next-generation sequencing. Nucleic Acids<br /> Res 39(12):e81.

On 2021-01-14 15:01:40, user Lamya Ghenim wrote:

Our preprint has been published and a link will be forthcoming.

On 2023-05-09 16:10:16, user Valerie Wood wrote:

Nice work, however the members of the Gene Ontology consortium works actively to improve annotation (both manual and automated), and are currently removing more annotations than adding by prioritising the identification/ removal and propagation of outdated annotations see http://geneontology.org/sta...

On 2018-03-18 16:14:38, user Jonathan Eisen wrote:

Note - there are some parts of the paper where there are supposed to be weblink but there are not.

For example the part that reads

"Complete evaluations can be viewed on the RDP website and the RDP Github repository." has some blue underlining but no link.

On 2020-08-12 02:41:52, user Alexander Stollznow wrote:

Good luck with this, and hopefully a range of prophylactics and therapies will all become available in a hurry, and soon.

On 2018-04-01 09:13:04, user GuestFive wrote:

Page 160 of the supplements isn't clear about which Sintashta outlier is in a certain group.

Sintashta_MBA_o2 is mentioned in two different groups, o3 not at all and there's an "oD" not mentioned elsewhere.

On 2018-08-30 00:47:42, user The Rational Hindu wrote:

La Genetique Scandale<br /> by Premendra Priyadarshi

A critique of the recent articles particularly the one by V Narasimhan et al (This will be in 4 parts)

PART 1 — Cracking the Narasimhan code

Please read at https://therationalhindu.co...

On 2018-12-18 19:31:12, user Jason Stajich wrote:

This is useful discussion point manuscript. I am fully in support of systematic, standardize data in REST, FTP, and other accessible manners. FungiDB and Ensembl go a long way to supporting this.

Some suggestions to consider in future revisions of this manuscript.

It seems the authors are bringing up the problem in the non-overlapping of datasets is due mainly to unpublished JGI Mycocosm datasources are not also in GenBank? Since these genomes are not published and deposited but are still available for use they appear only in Mycocosm before publication. It would be useful to discuss the underlying reasons why these primary data are balkanized.

Some reasons for differences in names has to do with synonymy of species names due to perfect/imperfect (Histoplasma == Ajellomyces). The one fungus:one name approach https://doi.org/10.5598/ima... intended to solve that part. I am not sure if I seen any discussion on specifically why reason for naming differences. Names have also changed over the course of projects as taxonomy has improved so the sources of these differences are useful to mention as a call for standardization that seems to be unexplained as to the reasons. Or if you just refer to standardization on "genus species strain names", Underscore or space between culture collection name and ID and other issues when combining datasets from different sources.

Previous work has also looked at global genome content in fungi from these large scale projects might be useful to include citations. Here is a table of prefix and lineage generated from early freeze of data eg https://github.com/1KFG/gen... from combined resources.

I think also utility of data import into standardized databases requires processing and munging annotation from multiple sources with different 'flavors' - something FungiDB - http://fungidb.org has been doing as well as Ensembl. The remit and funding sources that support different database systems have limited the ability of one database to encompass all the different sequencing goals, eg medical mycology and plant pathology goals are not always funded in the same database project.

It would be useful to comment not only on genome assembly, standardizing names of sequence files, but also standardizing of annotation of protein coding gene regions as the naming of LOCIS that is part of deposition into GenBank is important but the JGI pre-published datasets do not refer to that stable locus ID until deposited. Another really simple difference in data resources some of the GTF/GFF versions and protein coding data download is whether stop codon is included in CDS feature or not. Some resources include it - some do not. Whether or not pseudogenes are called out separately could be important utility in developing protein databases for metagenomics searches too.

Finally - the article title suggests that not having all the data is a problem for metagenomic studies. While I agree for sure, empirical data would be useful - how is the inference impacted for analysis of a metagenome dataset when using only one datasource repository vs a union of these.

Just some ideas/thoughts in a quick read. The community of users and developers of these data definitely are aware of the problems and historical/structural reasons why these databases are not complete representations. It could be helpful to describe some of the different ways the data are generated and the flow of it into repositories. eg big systematic projects tied to the same groups producing the databases, public repositories taking depositions, and individual labs, and now fungal genome production can be sub-$50, a large-scale projects even from individual labs. What are impediments to getting these data in one place.

On 2016-10-21 17:09:07, user fccoelho wrote:

The final published version is now available at: http://www.ijidonline.com/a...

On 2018-12-06 12:49:07, user Anne Carpenter wrote:

Super interesting article! The axes should be better labeled on several plots, and acronyms used less because it was a bit hard to follow. Thanks for doing this study and giving food for thought.

On 2017-04-01 18:46:26, user Mike wrote:

This might sound like a silly idea but, do these 1,063 'zombie' genes pass on chemical information (epigenetic) to scavengers to improve the evolutionary process?

On 2014-08-30 00:23:44, user Benjamin de Bivort wrote:

Hoping to engage with all of you on this work. Send questions, comments, etc.

On 2024-09-13 14:53:14, user Thibaud Decaens wrote:

The manuscript has now been published in European Journal of Soil Biology:<br /> Gabriac Q., Ganault P., Barois I., Angeles G., Cortet J., Hedde M., Marchán D.F., Pimentel Reyes J.C., Stokes A., Decaëns T. (2023) Environmental drivers of earthworm communities along an altitudinal gradient in the French Alpes. European Journal of Soil Biology, 116, 103477. https://doi.org/10.1016/j.ejsobi.2023.103477

On 2018-12-19 02:59:04, user BU_FALL_BI598_G5 wrote:

Critical review #3 Adolescent Social Isolation Increases Vulnerability to Cocaine. <br /> Anne Q. Fosnocht, Kelsey E. Lucerne, Alexandra S. Ellis, Nicholas A. Olimpo, Lisa A. Briand<br /> Group 5- Amber Shang, Joseph Sisto, Simran Shah

Overview

It has been long known that healthy social interactions and a good support system are key for one’s physical and mental well-being, especially for adolescents experiencing massive changes to their bodies. Statistically, social isolation such as parental neglect during adolescence can significantly increase one’s chance of developing wide range of psychiatric disorders such as alcohol and substance abuse both during adolescence and adulthood. Adolescent stress can strengthen the effects of stimulants, drug associations, increased the amount of cocaine taken and the motivation to take it in adulthood. Understanding the adolescent social stress-induced addiction phenotype is important because it could help introduce clinical interventions for high-risk individuals. Despite the correlation, the altered underlying molecular mechanism and how it contributes to the differentially regulated neural pathways in chronically stressed adolescents remain unknown. The difficulty in exploring the mechanisms is mainly due to the fact that childhood/adolescent social neglect usually is comorbid with other risk factors such as family history or substance abuse. Therefore, pinpointing the causal role of social stress and isolation on addiction can be challenging.

In hopes to examine how adolescent social stress can affect mice on a behavioral, molecular and physiological level to cocaine, Fosnocht and coauthors introduced social isolation, operant food training, behavioral training sessions for cocaine self-administration and immunohistochemistry. The authors were able to demonstrate that adolescent-onset social isolation enhances motivation for cocaine-seeking behaviors during adulthood by exhibiting a higher breakpoint on a progressive ratio schedule of reinforcement (Figure 3) and altering the responsiveness of reward-related brain circuitry, more specifically, increasing cocaine-induced neuronal activation within the nucleus accumbens core and shell, ventral pallidum, dorsal bed nucleus of the stria terminalis, lateral septum and basolateral amygdala (Figure 4).

Overall the authors did an excellent job explaining the motives behind their study, socially isolated mice and examined their behaviors utilizing behavioral training sessions to operantly condition mice on various fixed ratio schedules to determine the motivation (breakpoint) of the mice to obtain the reward (food or cocaine). However, the methods and results presented here merit some comments and unresolved questions/concerns.

Major Critiques:

First, the authors do not show any physiological proof of claim or the raw data they use to analyze their findings. Only the visualization of the data post statistical analysis is provided without showing any images of cell staining. We suggest the authors provide high-magnification immunohistochemical staining images of the c-fos expression of both the controls and the isolation stress groups ( lFigure 4). These images can serve to not only provide visualization of the activated regions in Figure 4, but also provide evidence that other regions were not significantly activated. Therefore, this could prove that the activation due to cocaine usage was specific to the regions studied. Moreover, although the timescale of the paper is uncertain, it would be beneficial to show healthy neurons from these brain regions of healthy individuals compared to neurons of those post cocaine administration to control for any potential neurodegeneration due to the drug that could potentially affect the c-fos detection.

Along with this, the authors should have looked at c-fos expression in male and female control versus cocaine mice because previous data shows differences between the two, so there may be differences in expression, which are not shown. Moreover, the authors never clarified the genders of the isolation stress group making the figures inconsistent. We suggest if the authors were to combine the two genders in experiment 4, they should indicate it somewhere in the paper or figure legend to provide clarity.

Second, the authors should utilize other early gene markers such as Arc to show that additional activation during cocaine usage to ensure the specificity of the activation pattern. Furthermore, we suggest that the authors use electrophysiology to measure neuronal firing rates in control and cocaine mice to observe any potential differences in neuronal activity. In future experiments, the authors could use techniques such as optogenetics in-vivo in Cre mice to activate neurons in the areas described in Figure 4 to observe how activation or inhibition of these neurons would affect self administration of cocaine in group housed mice to replicate the level of administration in isolated mice. The authors could also use this method to explore if it would affect the days of extinction or active responses in the group housed mice as opposed to the isolated mice. This would further prove that their findings are bidirectional and that the brain regions they describe are directly related to the behaviors.

The authors mention that AMPA transmission in the nucleus accumbens is increased as a result of adolescent social isolation in the discussion section, however no citation of a previous study is provided. They proceed to suggest that this increased glutamatergic activity within the reward circuits is what mediates the enhanced behavioral responsivity to cocaine, but failed to show any findings to support this claim about AMPA in the results or figures. Therefore, it would be appreciated if the authors can provide several studies to support these claims. Furthermore, there are a multitude of factors that can increase AMPA transmission in the brain, and a direct causal relationship between increased glutamatergic activity and increased behavioral response to cocaine cannot be determined without further experimentation. Future experiments can directly test for increased AMPA transmission.

Last, while the data in the fixed ratio (FR1) experiment showed significant results in only male mice while the progressive ratio (PR) experiment showed significant results for both males and females, the authors failed to discuss this incongruence further. It would be beneficial to incorporate this into the discussion to explain the potential reason for these results. In addition, in Results section 3.1, the authors discussed their findings based on the results obtained from the FR1 schedule of reinforcement, but never showed their results from when they switched to a different fixed-ratio schedule (FR5). It would be helpful to see the differences in these two schedules before jumping to the PR schedule results.

Minor Critiques:

One minor critique is that Figures 1D, 2B, and 2C have large error bars, which undermine the validity of the findings. To address this, future experiments could increase the number of mice per cohort as to decrease the probability of outliers present in the data. Also, the error bars should be defined as either standard deviation or standard error to allow the reader to accurately assess the dispersion of the data.

A second minor critique is that the formatting of the References makes it hard to read, as there are no indentations or numbers indicating a new reference.

A third minor critique is that the authors do not include a figure legend for the table. They also give no explanation of statistical analyses for any of the figures except for Figure 4, which is referring to c-fos immunoreactivity. It would be beneficial to add more information about their statistical analysis to further understand how they obtained the results they show in the figures.

A fourth minor critique is that they do not mention if they controlled for the estrous cycle in the female mice they tested. This is a key factor in female mice behavior and can affect results of what they are studying and therefore must be controlled for by ovariectomizing the female mice and administering exogenous estrogen to accurately measure their behavior.

A fifth minor critique is that they could have used a T-test with Welch’s correction for the data represented in Figures 3a and 3d to validate the statistical significance of these findings.

On 2019-11-16 00:56:33, user James Mallet wrote:

I loved this paper -- well, I would, wouldn't I?!! But I think there's some editing issues in the figures and legends that I quickly noticed. It helps to put the legends on the same page as the figures if at all possible! In Fig. 3, A, C, E are labeled H. melpomene, but in the legend it says they are erato, and also B,D,F vice-versa. And in spite of a legend, I couldn't see any Fig. 5 at all! But I am so happy you're finally publishing this cool work, so these critiques are meant as a help, not a criticism.

On 2019-11-01 04:21:26, user Davidski wrote:

Hello authors,

There's a problem with the samples in your dataset.

The modern samples representing Poland show a dip in Yamnaya ancestry and a high in Anatolian ancestry relative to their geography. So it's extremely unlikely that they're ethnic Poles.

You might want to download the full Human Origins dataset which includes ethnic Poles genetically representative of western and eastern Poland.

This will help you produce more accurate spatio-temporal models for East Central Europe.

On 2020-12-14 18:41:06, user Daniel Schachtman wrote:

Be sure to check out and cite the published version of this paper as there were substantial changes/additions made during the review process.

On 2017-03-31 04:06:06, user Anon wrote:

Interesting read, but I don't believe binding times are right... authors assume photobleaching is memoryless, but this won't be the case with three dyes. authors report histone binding time of 20 mins, but histones are known to bind chromatin for over 10 hours!!

On 2019-09-19 22:14:35, user Salil Bhate wrote:

Dear authors,

Your software package looks great, and we look forward to checking it out here in the lab when it’s published. Thank you also for citing our imaging work, ‘Coordinated cellular neighborhoods orchestrate antitumoral immunity at the colorectal cancer invasive front’.

It would be nice if in this paper you could provide a comparison of how your conceptual approach to high-parameter spatial analysis relates to that in previous works such as our recent work on cellular neighborhoods in CRC (https://www.biorxiv.org/con... "https://www.biorxiv.org/content/10.1101/743989v1)"), Shapiro et al. on neighbor analysis and cellular interactions, (https://www.nature.com/arti... "https://www.nature.com/articles/nmeth.4391)") and Keren et al. on multicellular tumor and immune spatial structures (https://www.cell.com/cell/p... "https://www.cell.com/cell/pdf/S0092-8674(18)31100-0.pdf)"). This would be really helpful for users that are new to the field of high-parameter imaging analysis.

Best,<br /> Salil Bhate and Graham Barlow

Nolan lab, Stanford

On 2020-06-17 18:34:37, user Özen Karaocak wrote:

Hello authors. Is the dataset available yet?

On 2019-07-15 13:16:49, user John Didion wrote:

You've got a typo in your GitHub URL in the abstract.

On 2023-12-04 08:24:49, user Heather Etchevers wrote:

This is a very interesting study and evolutionary question, given the importance of the circle of Willis in the cephalization and diversification of the neural crest-derived jaws and skull of vertebrates. I wanted to point the authors to an old study we had carried out on the strength of earlier work, concerning the embryological origins of the vSMC in these arteries. In the chicken, these anastomoses surround the ventral forebrain at a boundary between evolutionarily older and newer sources of myofibroblast-competent mesenchyme: https://pubmed.ncbi.nlm.nih...<br /> Congratulations to the authors on their judicious use of dynamics and modeling to reinforce what is known about this critical vascular junction in human health as well as in vertebrate diversification.

On 2017-10-28 16:42:35, user Lionel Christiaen wrote:

Student #10<br /> 1- The Drosophila field is currently focused on attempting to bring new and precise tools to rectify existing models that cannot fully explain all the available data in its current state. Here, the authors attempt to tackle the paradoxical nature of the bicoid gradient. This system is thought to act as a classic morphogen, activating targets in a concentration dependent manner. However, the question naturally arises about how bicoid responsive genes (as defined by their expression in the absence of bicoid and the appearance of the bicoid transcription factor binding motifs in their corresponding enhancers) respond to exceptionally low concentrations of bicoid on the posterior end of the embryo. <br /> 2- To do this, the authors employ the increasingly popular technology of light sheet microscopy modified to allow for single molecule detection. Using a home-built microscope is a technical achievement, but to use it in such a way as to observe a previously unreported activity of a transcription factor is a leap beyond and deserves exceptional recognition. Furthermore, the use of posterior only ChIP data to make predictions about the activity of posterior targets of bicoid was clever. Taking into account the known artifacts of the assay was particularly interesting: using peak height and reduced background to assume bicoid was acting less promiscuously.<br /> 3- In terms of areas that require improvement, a critical experiment that would have moved their model from convincing to certain in my mind would be to do two color imaging the demonstrate that the ChIP results actually reflected what was being imaged. I would expect Zelda to show a higher bound rate, and that colocalization of Zelda and Bicoid would correlated with bound times. Furthermore, the manner in which the paper was written seemed to focus more on the technology being employed, rather than the underlying biological significance. The paper would do well to reframe itself around how their methods are attempting to validate potential models (although the introduction does briefly attempt this) rather than fitting the results of the imaging experiments retroactively a theoretical framework. <br /> Finally, the authors could have utilized an MS2 construct with a bicoid responsive promotor (a fly line that is currently in possession by one of the authors) to see if bicoid clustering had any functional consequences on transcription.<br /> 4- Figure 3 was difficult to understand, the survival curves seem to be identical. Possibly adjusting the scale would help?

On 2015-04-16 13:27:24, user Japan wrote:

I read the Nature paper from the Wernig lab. The extended figure 9 showed the most stringent marker CD54 is not fully up-regulated even at day 12 of Mbd3fl/- reprogramming. I don't think 'the paper independnetly confirmed the validity of Hanna's work'.

Yes, the data clearly show that cells in Hanna's 2ndary reprgoramming are moving toward iPSCs more homogenously compared to Wernng's primary reprgoramming system. But this paper is showing this 'better' reprgoramming is neither 100% nor due to low Mbd3 expression.

On 2017-05-07 06:29:02, user Misha Koksharov wrote:

This looks very interesting! I was wondering for some time if they can heat up relative to the rest of the cell (and if it can be used as a readout for mitochondrial activity).

If they indeed can heat up so much it'll be even more interesting to test some mitochondrially-targeted luciferase mutants showing abrupt activity changes upon temperature shifts (as real-time reporters).

On 2019-02-05 20:19:36, user B. Arman Aksoy wrote:

This is a really amazing demonstration of a label-free classification method in T cells.

One thing that surprised me was that the size of the cell, compared to NAD(P)H signal, wasn't helping much with the classification of activation status (Figure 2d-f). In our case, when human primary T cells are fully activated, their diameter increases from, on average, 8-9 microns to 12-13 microns and these are mostly the cells that proliferate fast. This is also apparent from your Figure 1a, where cells that were activated, on average, are bigger than the unstimulated ones.

I think if you activated these cells using anti-CD3/anti-CD28 beads and imaged them on day 3 instead of day 2, you would have a better chance of capturing the overall differences between unstimulated and activated cells. The reason I am saying this is that I recently compared these two activation methods side by side and cells that were activated with the tetramer had a different size distribution compared to the population activated with beads on day 3:<br /> https://uploads.disquscdn.c...

Also, it is worth noting that Immunocult media even without supplementing with tetrameric antibody complex causes T cells to get activated (although the activation happens much slowly). So I think if you use RPMI as the base media and use the beads for activation, your classification method should have an easier time.

I can't wait to see if this technique can be used to classify tumor infiltrating T cells (without the need to disturb a tumor sample) and can further distinguish different functional subtypes: Th_1, Th_17, or T_reg... Thanks for sharing this work in preprint form -- I hope you will be able to share the annotated images and the classification code that you use soon, too, as I believe this technique would be much more valuable if applied on a larger segment (that contains hundreds or thousands of cells) and in an automated manner (i.e. the segmentation).

On 2021-02-04 03:34:40, user Sara Sims wrote:

Reviewer #2 (General assessment and major comments (Required)):

In this work, Sims and colleagues use resting-state functional connectivity and diffusion tractography in human connectome project data to examine the connectivity of the central and peripheral aspects of primary visual cortex. They find that central V1 connects more strongly to regions of prefrontal cortex interpreted as the Fronto-parietal network than does peripheral V1.

The idea that central V1 may be directly connected to control-related networks is an interesting one, and has fascinating implications for the study of top-down modulation of visual cortex function. However, I must say I am somewhat skeptical of these findings, for several reasons. <br /> First, I find the a priori anatomical basis for these proposed connections to be dubious. The authors themselves describe how Markov et al. explicitly conducted tract tracing with central V1 and found connections with posterior frontal and parietal cortex, but nothing with areas classically associated with the fronto-parietal cortex. The authors propose that the inferior fronto-occipital fasciculus may connect V1 with lateral prefrontal regions only in humans. However, they provide no evidence for this suggestion. Indeed, my understanding of the iFOF is that it connects to inferior and lateral occipital cortex (see e.g. figures from the Takemura study cited in this work). Can the authors better support the idea that the iFOF might be the route of connection between V1 and frontal cortex?

Thank you for your comments. We agree that while the data and methods we present here don’t address whether the iFOF is the route of connection between the inferior and lateral occipital cortex, more evidence from relevant literature would be helpful. The figures from the (Takemura et al., 2016) paper shows only inferior and lateral occipital cortex and are ambiguous for our regions of interest. However, other papers suggest that iFOF may be the route of connection between V1 and frontal cortex:

A paper by Wu and colleagues shows figures indicating that the IFOF does provide a connection between the medial occipital cortex and IFG. We now cite this in the paper. “Major white matter tracts that connect to the occipital lobe such as the inferior fronto-occipital fasciculus (connects occipital lobe to the lateral prefrontal cortex) and the inferior longitudinal fasciculus (connects occipital lobe to anterior temporal lobe) have been well documented using tractography methods in humans (Wu, Sun, Wang, & Wang, 2016).”

Second, I am concerned that both 1) the Central V1 ROI employed in this work and 2) the inferior frontal cortex region showing strong FC with that Central V1 ROI overlap very closely with regions where we have seen poor BOLD signal in our own fMRI data (I would like to attach a figure if possible). <br /> We are not confident what the source of the poor signal might be in posterior occipital or inferior frontal cortex; we suspect the presence of large veins (possibly the transverse sinus in V1; see Winawer et al., 2010, Journal of Vision). In any case, the data quality is low enough that we believe our data should not be considered to represent actual neural function in those regions. Can the authors demonstrate convincingly that this is not the case in their HCP data?

The reviewer suggests that based on their data, posterior occipital and inferior frontal cortex have relatively poor signal. They suggest that this poor signal would result in spurious correlations between the regions because of large veins. As described in our methods section for preprocessing of resting state scan data, white matter and CSF timecourses were regressed out, which aids in removing average venous artifact. Replication between 2 datasets (HCP and Griffis et al., 2017) and 2 modalities (DWI and resting state) further indicate the reliability of this effect.

The Winawer et al., 2010 article cites (Schira, Tyler, Breakspear, & Spehar, 2009) when discussing this issue; that paper suggests that poor signal in these regions may come largely from partial voluming (conflating signal from gray matter with signal from veins), and that these can be managed through increasing resolution with smaller voxel sizes. Our data are collected at resolutions finer than their recommendations, suggesting that such an effect should be minimal in this dataset. We have added the following text to the limitations section to address this comment: “We also acknowledge that large veins near posterior occipital cortex could impact our functional connectivity measurements in this area. However, we performed extensive pre-processing to reduce the impact of vessels on activity. In addition, the voxel size of our resting state scan is small (2mm isotropic), mitigating contributions from nearby veins due to partial voluming effects (Schira et al., 2009).”

Third, I have an issue with the localization of effects in this paper. The paper describes effects in the fronto-parietal network throughout the manuscript, including the title. How surprising, then, that the strongest effects are not in FP network at all! Figure 4A makes it very clear that the largest effects are in the IFG, which is outside the green outlines describing the extent of the fronto-parietal network, but inside the Default network. <br /> Figure 3A also supports this Default-centric localization, with Central V1 effects in posterior lateral parietal, medial parietal, and superior frontal cortex, all outside FP but inside Default. Since the FC effects are not actually primarily in FP, I see no reason why FP should be used as a mask in Figure 5. Indeed, the authors should show the localization of SC effects throughout the cortex, not just in FP. I also see no reason why these V1-Default connections should be characterized in any way as "attention" or "control".

We appreciate the reviewer’s comment and have made extensive modifications to the paper in response. The reviewer notes that some vertices of the effect we observed in left frontal cortex are in a portion of the IFG that is not classified by Yeo et al, 2011 as part of the frontoparietal network, but instead classified by that paper as the default mode network. We would like to note that most other papers that define DMN would not have included the IFG as part of that network, and in fact, Yeo’s 17-network parcellation from the same paper does not classify that portion of cortex as part of the default mode network. The inclusion of that parcel as part of the DMN is likely an artifact of the requirement of the algorithm in that paper to subdivide the brain into 7 discrete networks. However, the set of vertices can be described as being in the inferior frontal cortex, and we have reworked our discussion to de-emphasize the fronto-parietal network.

This said, we also quantified the similarities between the frontoparietal cortex and the functional connectivity patterns selective for V1, using Dice coefficients. This is now shown in Table 1. <br /> We have described this table within the text as follows: “Table 1 indicates high similarity between central V1 dominant regions and the FPN and partial similarity to portions of the CON and DMN, while the other V1 segments, mid- peripheral and far-peripheral are not strikingly similar to any networks.”

We have also added the following text to the article in reporting of Figure 4: “This inferior frontal gyrus region aligns well with the anterior portion of the FPN as defined by Yeo, but interestingly, it does expand somewhat beyond that border into the IFG (Inferior frontal gyrus) which is related to attention and control (Baldauf & Desimone, 2014; Chong, Williams, Cunnington, & Mattingley, 2008; Fassbender et al., 2004; Hampshire, Chamberlain, Monti, Duncan, & Owen, 2010; Swick, Ashley, & Turken, 2008, 2011).”

The reviewer also suggests that localization of structural connectivity effects should be shown throughout the cortex. We have added a figure 5 that shows the effects in our three networks of interest on the same cortical sheet. This figure shows more clearly the delineations of the strong effects. For technical reasons, we cannot perform these analyses on the cortex’s entirety at once: as described in the methods section, probability tracking for each network was calculated separately. Interestingly, however, despite this, the patterns look continuous across the boundary.

Fourth, I feel that these FC and SC differences are wildly over-interpreted. From the scale, the actual strength of FC and SC between central V1 and lateral parietal cortex is extremely weak (around Z(r) = .1 for FC and p-track = .1 for SC). Under no circumstances would I believe that either of those values represents any sort of real connection. Cortical regions with direct structural connections have much stronger FC values than regions that indirectly influence each other via multi-step connections.

Functional connectivity magnitudes are always influenced by the preprocessing done to obtain them. In this case we regressed out the mean signal, and regressed out white matter and CSF. While this practice decreases the mean correlation strength (Shirer, Jiang, Price, Ng, & Greicius, 2015; Weissenbacher et al., 2009) it also improves across-subject reliability (Burgess et al., 2016). The debate about this practice, now a decade long, has focused on the interpretability of negative correlations, which we do not do here. All sides of the debate agree that the practice of mean signal regression should not influence relative correlations across brain areas.

We are looking at variability in connection strength between different portions of a single brain area, and we would expect roughly similar long-range connectivity between different parts of V1. We have incorporated this point into the discussion on page XX where we say “ While central and peripheral representation portions are still part of the same V1 area, and therefore we would expect similarity in their connectivity patterns, our results indicate that eccentricity differences do exist and are consistent with previously reported differences in information processing on central and peripheral visual information.”

In addition, we added to the limitations section a discussion of this:<br /> “Here, we show functional connectivity strengths on the order of r=0.1. While very reliable, these magnitudes are not as large as connections to other areas, for example, portions of the occipital lobe. Functional connectivity magnitudes are always influenced by the preprocessing done to obtain them. In this case, we regressed out the mean signal and regressed out white matter and CSF. While this practice decreases the mean correlation strength (Shirer et al., 2015; Weissenbacher et al., 2009) it also improves across-subject reliability (Burgess et al., 2016). The debate about this practice, now a decade long, has focused on the interpretability of negative correlations, which we do not do here to examine relative correlations across brain areas.

Further, very large portions of the brain probably have both stronger FC and SC to central V1 than these FP regions (the authors show this for FC but exclude this info for SC). <br /> We have included a new figure to show the SC patterns across more than just the FPN (now includes regions within FPN, DMN, and CON), now Figure 5. Along with the following text, “Next, we investigated similar comparisons between central and far-peripheral V1 in a different modality- structural connections. A t-test comparing the structural connection of central and far-peripheral V1 revealed significant effects (p<.001) in brain regions belonging to FPN, CON, and DMN functional networks (Figure 5). We chose these three networks to compare to functional connectivity findings from Figure 3. <br /> Notably, central representing V1 was preferentially connected (over far-peripheral V1) to regions associated with the FPN, including the mid orbitofrontal and inferior parietal regions of the FPN, as well as lateral portions of the DMN, and the insular portion of the CON. In contrast, far-peripheral representing V1 was preferentially connected (over central V1) to medial portions of the DMN (Figure 5).”

Most glaringly, I certainly don't believe there is a "direct structural connection" as is claimed in the discussion--a claim based, strangely, on the spatial correspondence between the structural and functional maps, which really has nothing to do with any evidence for a direct connection. <br /> As stated in the discussion limitations section “structural tractography analysis only identifies direct connections”. <br /> The probabilistic tractography method can only show connections between Region A and Region B. It cannot indicate if there were connections between Region A and Region B that traveled via Region C. Therefore if a connection is indicated by the method, it must be direct. <br /> The statement of a “direct structural connection” is not an interpretation of the correspondence between structural and functional maps, but an interpretation of the structural maps.

Finally, the authors must note that p values may not be used for spatial correlations between brain maps. This is because these maps are always highly autocorrelated, which violates the independence assumption of the correlation procedure. <br /> We have replaced spatial correlations between brain maps with Dice coefficients, a more field-standard method for comparing spatial maps. We thank the reviewer for the comments and think this new way of analyzing it is a better fit.

Reviewer #2 (Additional data files and statistical comments):

The authors should show the data (maps or scatterplots) going into their spatial correlation on page 13. <br /> Based on comments from reviewers, we changed this part of the analysis to dice coefficients with the following text : “A Dice Coefficient was calculated for comparison of the functional and structural connectivity differences of central vs far-peripheral V1 to the FPN, CON, and DMN. Across all 3 networks the Dice Coefficient (averaged across left and right hemisphere) between structural and functional connectivity patterns was .707.<br /> Within the FPN the Dice Coefficient (averaged across left and right hemisphere) between structural and functional connectivity patterns was .915. Within the CON the Dice Coefficient (averaged across left and right hemisphere) between structural and functional connectivity patterns was .842. Within the DMN the Dice Coefficient (averaged across left and right hemisphere) between structural and functional connectivity patterns was .85. These relationships indicate that the overall pattern of connectivity of central V1 greater than far peripheral V1 is consistent across modalities with an especially high overlap within the FPN.”

On 2019-06-21 20:07:00, user Purna Mukherjee wrote:

Sperry et al., have provided another interesting study showing how a ketogenic diet fails to manage glioblastoma growth in the U87-MG xenograft mouse model. The results are consistent with the previous findings of Dang et al (PLoS One. 2015 Jul 20;10(7):e0133633, doi: 10.1371/journal.pone.0133633), and Kalaany and Sabatini (Nature. 2009 Apr 9;458(7239):725-31. doi:10.1038/nature07782.) showing that neither caloric restriction nor ketogenic diet have any therapeutic effects on brain tumor growth when the tumors are grown in the brains of Non-Obese Diabetic/Severely Compromised Immuno Deficiency mice (NOD/SCID). It is important to mention that NOD/SCID mice not only have a compromised innate and/or adaptive immune system but also express characteristics of both Type-1 and Type-2 diabetes (Chaparro et al, PNAS, 2006; DOI:10.1073/pnas.0604317103). These findings are inconsistent with other studies showing that caloric restriction and restricted ketogenic diets can reduce U87-MG growth when the tumors are grown in mice that do not have characteristics of Type 1 or Type 2 diabetes (DOI 10.1007/s11060-013-1154-y; DOI:10.1158/1078-0432.CCR-04-0308; doi:10.1186/1743-7075-4-5). Although Sperry et al were careful in their in vitro experiments to maintain normal glucose physiology, they chose a mouse host for their in vivo studies that has no relevance to either normal human or mouse physiology. It remains unclear whether glucose and ketone levels would be linked to tumor growth in this mouse host. The sensitivity of some tumors to metabolic therapy is dependent on host energy metabolism and microenvironment, which are abnormal in NOD/SCID mice. Hence, their conclusions that ketogenic diet fails to manage glioblastoma growth must be viewed with caution.

On 2017-01-17 09:16:54, user Thomas Munro wrote:

I strongly support your proposals in this preprint, and I think the suggested use of postdocs is inspired. It might reduce resistance, and provide a way for postdocs to remain current.

However, the preprint currently makes some important omissions. Several of the ideas you propose as new and hypothetical are already around, and indeed have been in use for decades. A much stronger case for them could be made by reviewing the evidence on this, and previous theoretical arguments.

1) What you term the initial APC already exists: the submission fee. These have been charged by dozens of journals since the early 1970s, mainly in economics, but also in a few biomedical journals. There is a substantial literature on submission fees and their effects. I give a brief overview, and 17 references, in a comment on pp. 82-84 of Solomon 2016.

2) Your proposal to use submission fees to pay reviewers has also been done successfully. Several journals with high submission fees do this, such as the Journal of Financial Economics.

3) Your prediction that submission fees "will lead to fewer, but better, submissions" is strongly supported by the literature, both theoretical and empirical (see my same comment). It could be made much more persuasive, and even roughly quantified, by adding some references.

On all these points, the preprint would probably benefit if you could attract an editor with long experience of submission fees as coauthor. I suspect some of them would enjoy the chance to tout their successes.

On your proposal to use submission fees for high acceptance rate journals, I think the fast-track experiment by Scientific Reports ($750 submission fee) suggests caution. This attracted 25 submissions in one month (Jackson, 2015), but also strong opposition, with some editors resigning, and was discontinued. Given that submission fees (and even fast-track fees of over $1,000) have been successful in selective, prestigious journals, these would probably be a safer choice initially.

Jackson, A. (2015). Fast-track peer review experiment: First findings. <br /> blogs.nature.com/ofschemesa...

Solomon, D. J., Laakso, M., & Björk, B.-C. (2016). Converting Scholarly Journals to Open Access: A Review of Approaches and Experiences.<br /> dash.harvard.edu/handle/1/2...

On 2020-06-03 14:45:33, user Caroline Wright wrote:

Thanks for this paper - I enjoyed reading it, and was pleased to see you came to the same conclusions as us about the need for validation prior to clinical action for rare pathogenic variants called on microarrays. Also a really important point about the different biases in different ethnic groups.

We looked at your supplementary data (table 3), and it seems you actually got a fairly similar proportion of false positives in the very rare category of variants as we did in our paper (https://www.biorxiv.org/con... "https://www.biorxiv.org/content/10.1101/696799v2)"), despite using a more modern array. For the 108 variants not in gnomAD, only 25% validated with Sanger sequencing:

On 2022-01-21 14:41:53, user Keith Robison wrote:

This is a very useful manuscript on the accuracy of the two long read platforms. However, since both are evolving quickly and ONT in particular has frequent basecaller updates and two different chemistrys (R9 and R10) with different error properties, it is truly critical in a publication such as this to specify the read chemistry used and what version of basecaller even if that information can be found by digging into the original data-generating reference.

On 2017-05-30 12:31:42, user Leslie Vosshall wrote:

We just received some great technical questions from a bioRxiv reader, posting here and will incorporate into our revision:

QUESTION: Did you feed females individually or in groups?<br /> RESPONSE: Females were fed in groups in bucket cages using Glytube membrane feeders.

QUESTION: Did you weigh them all at the same time post-blood feeding?<br /> RESPONSE: Yes - animals were all sorted into fed/nonfed groups and weighed within 30 minutes of blood feeding.

QUESTION: You mentioned in the methods that you discarded females that did not feed completely, but I was unclear for the reasoning and at what stage these data were discarded. Were they included in the percent blood fed? What about the female mass post-blood feeding?<br /> RESPONSE: After blood-feeding we took each bucket cage and sorted females by eye as either fed (fully engorged) and nonfed (either no blood present in the midgut or partially fed - rare). # of engorged females/total females in the cage = % feeding (so all of the animals are counted here). We then weighed only the fed females (in groups of 5). The nonfed weights that we report are taken from females who were never offered a blood meal.

On 2019-08-04 13:34:08, user Bruce King wrote:

In other words, the colloquial 'stick-slip' of hysteresis might be proven useful as a way of regulating relative strengths/weaknesses of various brain structures, each according to its need, as it were...?

On 2017-04-04 23:29:03, user Kirk Durston wrote:

There are some significant issues with this paper. As the author of one of the papers they mention, I have posted a formal review of their paper https://drive.google.com/fi...

On 2020-04-01 22:41:28, user Manuel Kleiner wrote:

Thanks for this very well done and validated study. I like the entrapment database approach that you used. <br /> I personally would emphasize the outcome of your study differently in the discussion though. I think it is actually highlighting the fact that both the traditional two-step approach and the new sectioning approach produce unpredictable FDRs, while the large database search approach gives exactly the expected 1% FDR. Even though the sectioning approach lowers the FDRs in the test dataset, it does not do so in a predictable fashion i.e. we will have no idea what the true FDRs will be in a complex environmental metaproteome.<br /> So I think it would be great if the manuscript could provide a more weighted discussion of the results. If the main goal of a study is to achieve maximal PSM numbers then the sectioning approach is the way to go. If one is looking to get the most reliable and accurate results the traditional large database approach is the way to go. I do think though that for most studies reliably controlled PSMs are needed and that one has to chose to loose out on some extra PSMs to get accurate results.

Of course this all only matters if one analyzes the data on the peptide level, as likely protein inference will change the outcome in terms of FDRs for the different approaches significantly.

On 2023-04-28 04:02:48, user Alexis Rohou wrote:

I was asked to review this manuscript for a journal. My comments are below:

Sweeny and colleagues describe a method which identifies the location and conformation of small molecule ligands within cryoEM volumetric maps of ligand:target complexes in an automated manner, given an atomic model of the macromolecular target only. The method, named ChemEM, is shown to match or exceed the performance of existing, commonly-used methods in its ability to successfully locate and model energetically favorable and structurally accurate conformations of ligands. Such a tool will be of keen interest to structural biologists working on small molecule ligands, for example in the context of drug discovery projects supported by cryoEM. In such projects, protein:ligand co-structures typically have resolutions in the 2.5 to 3.6 Å range, leaving some ambiguity as to the exact position of ligand atoms, and thus the conformation of the small molecule and the details of its interaction with the protein binding site. <br /> The authors achieved impressive performance, as judged by well-defined benchmarks, by introducing several key features, including:<br /> (1) a carefully calibrated function to score the geometric and chemical plausibility of ligand and ligand-protein conformations, termed ChemDock<br /> (2) a measure of quality-of-fit carefully weighted relative to ChemDock as as to optimize performance in benchmarks<br /> (3) the use of mutual information (MI) rather than the more commonly-used cross-correlation coefficient (CCC) as a quality-of-fit measure<br /> (4) the use of difference maps rather than full cryoEM maps as input for the docking and initial conformational search

In addition to point (3) above, the manuscript is replete with technical nuggets that will be of interest to those working directly in this field (e.g. the use of full vs difference map, the relative weighting of model-only vs model-map scoring functions and its calibration, the creation of cryoEM benchmarking sets).

Overall, I found no major technical issues with the description of the work. I found the methodological details are well described, as are their tuning, benchmarking and validation. Most of the claims are well supported by evidence, and the results do appear impressive. The manuscript appears close to publishable to my eyes.

I do have some suggestions for improvements:

(1) I found the following were missing: A description of the software and end-user experience. What inputs are needed? What is the interface like? The authors claim that ChemEM is automated... is it truly fully automated in a robust way? How many parameters does the user need to adjust? For example, are difference maps calculated automatically by ChemEM? Are the maps filtered, or their spectra flattened or normalized in any way, before computing difference maps and MI? Also, what is the performance like? Relatedly, the authors really should specify: How is the software available? How is it distributed? Under what license? Oh and also: does ChemEM handle covalent ligands?

(2) Q scores (or equivalent metric) should be included when making comparative statements about the quality of (ligand) model-to-map agreement (e.g. Fig 6A, but Figs 5 and 6 in general, and references to them in the text, e.g. "markedly better fit to the density than the cryoEM deposited structure")

(3) The abstract claims that SM docking into medium resolution maps is "unexplored territory". I don't think this is accurate. For example, the glideEM paper treats EMD-0488 (3.4 Å).

(4) Introduction: the sentence which cites reference 2 seems inaccurate to me. As far as I could tell from a brief review of the cited work, it does not identify a novel drug target. GABA receptors were already known to be the targets of the small molecules studied in that paper. In general, I fail to see how higher resolution structures by cryoEM would help identify new targets (proteins) for small molecule drugs.

In my opinion the suggestions above really should be addressed before publication. In addition the suggestions below would improve the manuscript further and may be acted upon if the authors/editors agree:

(5) Up to the editor, but this is a very technical paper. It might be worth investing a few sentences here and there to help non-specialist readers along. For example, when referring to CASF-2016's "funnel" and the related decoy or "RMSD" ligand sets, or when first referring to mutual information. The non-expert reader likely will not know exactly what these are and some short descriptions may help. Generally, given the target journal, a bit more effort to write for a broader audience might be warranted.

(6) Figure 4: perhaps label/title the figure panels themselves with "Different maps" (left column) / "Full maps" (right column) and "Low-resolution maps" (bottom row) / "High-resolution maps" (top row). And also, in the MI-only case with different maps - what was the initial conformation of the ligand (which I assume was held constant)? Was it a minimized-in-vacuum conformation?

(7) A detail: I have a preference for avoiding the word "density" when referring to cryoEM maps, since they do not map electron density like x-ray maps do.

(8) Abstract: to claim that automatically docking ligands is of "utmost importance" I think is an overblown claim. Automation is important in high throughput applications, such as fragment screening, but otherwise automation is just as desirable in ligand docking & refining as it is in protein structure building, which is to say (in my opinion) not of utmost importance. Accuracy is.

(9) Abstract: "ChemEM is a novel method". I'm not dogmatic about this, I think it's OK to claim novelty from time to time, but in this case, only some aspects of the method are really novel, and I think the claim of novelty sounds a bit hollow because it is applied in such a broad stroke. In view of the quality of the results, it's not even necessary.

(10) Abstract: The last sentence "ChemEM unlocks the potential of medium-resolution cryo-EM structures for drug discovery"... Again this claim is actually really quite strong but sounds quite hollow in this case, at least to someone who has been doing medium-resolution cryoEM for drug discovery for some years. I would strike such boombastic language unless some more specific statement can be made about some type of experiment of projects that was not previously possible until ChemEM came along and is now "unlocked".

(11) Signicance statement: is ChemEM already used in drug discovery?? Is it even available for download from anywhere? Maybe just say "which can be used used in drug discovery"; "In the last decade Cryo-electron microscopy": the capitalization is unecessary here. Also, because the electrons are not cryogenic, I recommend using "cryogenic electron microscopy (cryoEM or cryo-EM)".

(12) Introduction, second paragraph: "high- and low-resolution": Hyphenation is not warranted here. I would say "at both high and low resolutions". If you really like the hyphen, try something like "in both high- and low-resolution regimes".

(13) Introduction, penultimate paragraph: "there is no data that compares" should be "there are no data that compare"

(14) Evaluation of the ChemDock scoring function: "and of these a correct pose": Is correct pose different from correct conformation? The way the sentence is written suggests so, but I suspect not. Perhaps simplify the syntax to avoid confusion?

(15) Analysis of specific examples. Last paragraph: If I understood correctly, the authors know this is not the correct solution because it doesn't match the high-resolution control structure. I guess the top panel of Fig 6D shows PDB 6T24. Did the authors consider adding a panel showing the "correct" conformation form the high-resolution PDB?

(16) Discussion. First sentence. The need for automation is dominant perhaps only during fragment screening campaigns and the like. My view is that the accuracy (RMSD to ground truth) & quality (low strain) of the poses found by ChemEM are impressive, even without considering the automation. I would suggest "accurately and automatically".

(17) "our benchmark only cases better than 4.5Å": "only includes cases" (word missing)?

(18) M&M. Computational datasets. First sentence: "Data to train the ChemDock scoring function was taken" should be "were taken"

(19) M&M. Computational datasets. First paragraph. Of the remaining 3,281 complexes, did any of them have close matches in the CASF-2016 dataset? For example, small molecule ligands bound to the same protein target, in the same pocket and with similar (but not identical) chemical structure to one in the CASF-2016 dataset? If so, then the training and test datasets may not be sufficiently independent.

(20) Page 14. "The data was split" should be "The data were split"

(21) Page 18. "For molecular docking experiments smiles strings for the 32 ligands", I think smiles should be capitalized: "SMILES"

Alexis Rohou<br /> Genentech<br /> April 2023

On 2019-08-07 18:17:26, user Wouter De Coster wrote:

Given the loaded context of "joy division" I would suggest changing<br /> the title of your paper... It's not just a band, before that it were <br /> groups of Jewish women forced into prostitution in concentration camps. <br /> That's probably not what you had in mind.

On 2023-12-11 17:03:02, user Cristian Villena Alemany wrote:

Dear Kuzyk and to whom might concern,

Thank you very much for your feedback. I am very happy that you enjoyed the preprint.<br /> As you suggested, I can provide some contextual information that will help on the understanding of the pointed issues.

You indicate that our mention of AAP “phenology in freshwater lakes remains unknown” is inaccurate and there are already “few studies”. However, there is, up to our knowledge, no research that has been tracking the interannual variations and the recurrence (Phenology) of the AAP community in fresh waters. Here, we do not write that it is the first seasonal study of AAP in lakes, but the first study that focuses on the phenology of AAP community (three years in freshwater lake). In case you are aware of some studies that focus on AAP bacteria in freshwater lakes during several years, we would be very pleased.

Regarding to the fluctuation of AAP abundance, you indicated that “it is unclear where this assertion comes from”, however, as described in materials and methods, AAP abundance was assessed using IR-microscopy. Since the paper is not based on manipulative experiments but observational samplings, no biological replicates were available and the AAP counts per sample (as described in M&M) can be found in Supplementary File S5. We agree that not statistical trends or deviations can be obtained from the graphical representation in Supplementary Figure S5, nevertheless, we tried to uncover relevant trends and show them also in the Supplementary Figure S7 and Supplementary File S6.

AAP and bacterial numbers were calculated, as it is stated in M&M, using epifluorescence microscopy method as described in Piwosz et al., 2022 [17].

As you point out it is correct that lakes that are stratified might become anoxic in hypolimnion. However, despite the fact that there is a reduction of oxygen in the hypolimnion during summer, this is not the case for the illuminated to the bottom CEP lake (Supplementary File S5) which always presented an oxic environment. Additionally, this trend (oxic hypolimnion even during stratification) has been observed before in this same lake (Villena-Alemany et al., 2023. Diversity dynamics of Aerobic Anoxygenic Phototrophic Bacteria). Therefore, since CEP lake represents a fully oxic environment, the occurrence of anaerobic anoxygenic phototrophs is negligible. Additionally, it is very well known that in anaerobic anoxygenic phototrophs the expression of photosynthetic apparatus is repressed by oxygen. This indicates that the BChl-a signals that we observe from the fully oxic environment originates from AAP bacteria. Not only this but, in Kolesár Fecskeová et al., 2019 the expression of AAP phototrophic genes in this same lake was already proven.

Regarding the “phototrophic Myxococcota”, in the reference 100, in the Supplementary Data of that paper are stated the environments from where the MAGs were reconstructed. There, it is indicated that several Myxococcota MAGs were repeatedly reported from oxic environments. We can agree that further researches need to be done to unveil the functionality of this recently discovered phototrophic group, and by writing about “potential significance in microbial communities” of Myxococcota we meant that there is the possibility they have an important role since they were recurrently detected during 3 years. As they are newly recognized phototrophic group, member of AAP bacteria, there is more research needed, but we can observe that they can be found recurrently in freshwater lakes.

Furthermore, the AAP percent contribution is not related with the pufM gene database. It is calculated as the percentage of total bacteria that are AAPs. As described in Piwosz et al., 2022, all bacteria are counted using DAPI and AAP bacteria according to BChl-a fluorescence. <br /> We are really grateful that you liked the database and we believe and hope that it will be an excellent tool for people studying anoxygenic photosynthesis. Yes, the MAGs had a great contribution in our database, and we included as much pufM gene sequences as we could to improve the taxonomic assignment pufM gene amplicons. Naturally, it also encompasses pufM gene of anaerobic anoxygenic bacteria and could also be used in anaerobic environments. Nevertheless, the category of AAP bacteria is a functional metabolic attribute, given to anoxygenic phototrophs in oxic environments, and the inclusion of anaerobic anoxygenic phototrophic MAGs into the database does not hamper the taxonomic assignation of AAP bacteria.

I hope you find all the answers to the questions you stated here. I am really happy that you like the paper and to have this discussion. Looking forward if you have further contributions or thoughts.

Best regards,

Cristian

On 2020-01-03 03:04:45, user Adam Yates wrote:

What definition of Brevirostres are you using? You state that Alligatoridae is the crown of Brevirostres but I've always thought that Brochu's definition (Alligator + Crocodylus) is the only working definition of Brevirostres. Perhaps you are inadvertently using Brevirostres in place of Globidonta ?

On 2025-08-05 19:16:26, user Jacob S wrote:

wow super informative ???? fantastic

On 2019-12-11 21:31:43, user The Pamphleteer wrote:

What age group is "Late Life"?

On 2016-06-09 02:36:01, user An Harald wrote:

What means "low incidence"? 1/2% to 1%?

On 2025-03-26 10:13:54, user Julia wrote:

This article has been published:<br /> https://www.ahajournals.org/doi/10.1161/CIRCULATIONAHA.123.066961

On 2018-09-27 03:02:54, user Pony CHEN wrote:

a long paper and it deserves reading for me

On 2018-10-03 20:48:11, user SANCHEZ RAYES Ayixon wrote:

Although ProxyMeta is supposed to be a "finer" version than its predecessor: MetaPhase, it would have been nice to include MetaPhase in the comparison because this algorithm is freely available. So strictly, ProxiMeta "it´s not the only other complete solution for Hi-C based metagenome deconvolution". MetaPhase, a close relative of ProxiMeta exist, and it is open source software (https://github.com/shendure... "https://github.com/shendurelab/MetaPhase)"). I have used MetaPhase and ProxiMeta, and both are capable of solving the same number of genomes, but the quality of them (completeness and contamination) are somewhat better with the latter.<br /> It is excellent news that bin3C joins as an open source alternative for the deconvolution of metagenomes, congratulations.

On 2015-08-30 12:37:11, user Daria Khaltourina wrote:

Great article! A brief critical review of the key counterarguments<br /> against classifying aging as a disease might be good to include here<br /> (universality argument, normality argument). Regarding the diagnostic criteria of<br /> aging, they can be taken from here, in addition to other biomarker systems and<br /> frailty indices. http://www.guideline.gov/co...

On 2015-09-06 16:55:58, user Zerfinoid wrote:

Thanks for putting up this pre-print as I didn't know about the ICD before this.

Do you know of folks trying to create a task-force for getting aging classified as a disease? I want to create such a task-force but I also don't want to duplicate any existing efforts.

On 2025-04-24 09:18:31, user Antonios Pantazis wrote:

Now published in Nature Communications! doi: 10.1038/s41467-025-58884-2

On 2021-12-17 21:57:24, user Sam Lord wrote:

This manuscript explores the fascinating interactions between actin networks and clathrin pits. The key findings are that the amount of actin recruited to pits does not seem to correspond to the maturity of the pits, that the actin network likely grows both laterally and inward from the base of pits, and that actin seems to counteract membrane tension. The imaging is interesting and the super-resolution view of clathrin coated pits are nice.

The manuscript also presents evidence that Arp2/3 activity assists pit completion, as CK666 causes longer clathrin lifetimes. The later data about clathrin coat height and actin height are less convincing, because it is unclear whether these results are from multiple rounds of treatment or from one experiment. The authors could strengthen their manuscript by bolstering those later results (in Figs 3-4). The authors claim that Arp2/3 inhibition has the opposite effect when under membrane tension than it does under isotonic conditions. This is very intriguing, but a reader cannot tell if the results are replicated sufficiently to support the claim. How many biological replicates are reported in Fig 3H? In other words, how many times was a sample exposed to the treatment (e.g. CK666)? The p-values should be calculated using the number of biological replicates, not the number of pits measured.

On 2024-04-12 18:10:47, user Luis E. Gimenez wrote:

Regarding Figure 1B, it is incorrect to calculate arithmetic averages for EC50 values, even more so to show scatter measures on an arithmetic scale, given that EC50 values do not typically follow a Gaussian distribution. Instead, The authors should show pEC50 values and apply one-way ANOVA to the transformed data.

On 2020-09-23 06:17:39, user Cassandra Leigh Williamson wrote:

How many samples were excluded whose reported sex did not match that inferred by PLINK? Why were they excluded?

On 2023-12-24 22:50:40, user Phil Hugenholtz wrote:

Note that the suffixes used in GTDB are not redundant, they indicate separate taxa at the genus and species level: https://gtdb.ecogenomic.org...

On 2023-05-05 13:08:32, user Ling Chin Hwang wrote:

The link to the published article is now available at: https://doi.org/10.1093/nar...

On 2019-11-22 12:13:53, user Olivier Gandrillon wrote:

Dear authors

You write in your discussion : « In particular, it will help us to better understand whether increased plasticity, as manifested in increased cell-to-cell variability, is a general feature that precedes cell commitment or whether this is restricted to specific systems such as hepatoblast differentiation. »

What can already be established is that is is NOT restricted to hepatoblast differentiation.

Indeed this has been demonstrated in <br /> 1. Murine ES cells (Stumpf et al., 2017; Semrau et al., 2017; Moris et al., 2018)<br /> 2. EML cells (Mojtahedi et al., 2016)<br /> 3. Chicken erythroid progenitors ((Richard et al., 2016; Guillemin et al., 2019)<br /> 4. Murine hematopoiesis (Wiesner et al., 2018)

This might be argued for evidence for a rather general feature IMHO.

Oliiver Gandrillon

Guillemin, A., Duchesne, R., Crauste, F., Gonin-Giraud, S., and Gandrillon, O. (2019). Drugs modulating stochastic gene expression affect the erythroid differentiation process. PLOS ONE 14, e0225166.

Mojtahedi, M., Skupin, A., Zhou, J., Castano, I.G., Leong-Quong, R.Y., Chang, H., Trachana, K., Giuliani, A., and Huang, S. (2016). Cell Fate Decision as High-Dimensional Critical State Transition. PLoS Biol 14, e2000640.

Moris, N., Edri, S., Seyres, D., Kulkarni, R., Domingues, A.F., Balayo, T., Frontini, M., and Pina, C. (2018). Histone Acetyltransferase KAT2A Stabilizes Pluripotency with Control of Transcriptional Heterogeneity. Stem Cells.

Richard, A., Boullu, L., Herbach, U., Bonnafoux, A., Morin, V., Vallin, E., Guillemin, A., Papili Gao, N., Gunawan, R., Cosette, J., et al. (2016). Single-Cell-Based Analysis Highlights a Surge in Cell-to-Cell Molecular Variability Preceding Irreversible Commitment in a Differentiation Process. PLoS Biol 14, e1002585.

Semrau, S., Goldmann, J.E., Soumillon, M., Mikkelsen, T.S., Jaenisch, R., and van Oudenaarden, A. (2017). Dynamics of lineage commitment revealed by single-cell transcriptomics of differentiating embryonic stem cells. Nat Commun 8, 1096.

Stumpf, P.S., Smith, R.C.G., Lenz, M., Schuppert, A., Müller, F.-J., Babtie, A., Chan, T.E., Stumpf, M.P.H., Please, C.P., Howison, S.D., et al. (2017). Stem Cell Differentiation as a Non-Markov Stochastic Process. Cell Systems 5, 268–282.

Wiesner, K., Teles, J., Hartnor, M., and Peterson, C. (2018). Haematopoietic stem cells: entropic landscapes of differentiation. Interface Focus 8, 20180040.

On 2019-07-31 18:47:16, user GuyguyKabundi Tshima wrote:

The intersection between HIV, malaria and food security.

Malaria control needs an integrated development plan. Malaria and food security are health and development issues that have appeared together in global frameworks since at least 1978. Our paper started by reviewing the reconstitution of body mass index in HIV positive patients living in Kinshasa, an endemic malaria area, and then will continue with functionals foods with therapeutic relevance by examining the functional connections as to how these affect people living with HIV and programmes in malaria endemic areas.

On 2018-11-29 08:14:51, user Janine Brunner wrote:

Unfortunately, the Supplementary Material is not yet displayed online, but this issue will be fixed as soon as possible. We can provide the Supplementary Material upon request by Email.

On 2018-09-06 06:02:13, user dermualberta wrote:

Great paper, congratulations. Do you know what percentage of T-cells recombine both TCRbeta loci (on the DNA level?)

On 2020-08-10 08:05:19, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2019-10-21 12:59:45, user Pascal Barbry wrote:

Hello Kathy, <br /> This paper is now published in Development: <br /> https://dev.biologists.org/...<br /> Best,

On 2019-01-28 13:23:40, user Christian Luz wrote:

This paper reports on many data values on an important and topical research area which, no doubt required considerable work and input.

The study assessed the differences in clinical characteristics within CC5 and CC8. However, it did not explicitly test for differences between CC5 and CC8. Many different models were build but one overall multivariate model is missing. This could, however, make the interpretation much easier, more robust, and match the expectations of the readers.

We discussed this article in our journal club and would suggest to test for differences between CC5 and CC8 in a robust model including all relevant variables and confounders. For example, a Cox regression model with CC5 and CC8, CO-MRSA and HO-MRSA, and the CCI (and other relevant confounders) could help answering the question of differences in patient outcome and assess the adjusted association of CC5 with overall mortality.

(Additional and more detailed comments were sent to the authors by mail.)

On 2020-05-18 20:02:04, user Zachary Wise wrote:

I wonder are you saying that there is a language through electrical impulses in the axon filaments if so I would greatly appreciate it If you would converce with me here is my email zachwise514@gmail.com

On 2020-07-10 16:47:43, user Martin Giurfa wrote:

Hi . Surprised to see that you did not consider the work of A Buatois in Frontiers in Beh Neurobiology where did the same kind of transfer between real mazes and treadmill in VR Conditions

https://www.frontiersin.org...

On 2023-10-29 09:08:48, user BBB Prair wrote:

Fascinating study as expected from the Yanai lab. I work on DTPs as well. I read the whole preprint and watched the Match Onco seminar by Prof. Yanai about this work. Maybe I missed some point in the paper but I wonder why the identified IC50 for drug-naive Kuramochi cell line is ~2 uM? In my own measurements, using both CellTiter-Glo and SRB assays in a 12-concentration range, 72-hr format, I always calculate an IC50 in the range of 150 to 200 uM in Kuramochi cell line for olaparib. These values are also supported by measurements in the GDSC (both versions 1 and 2) project. Did the authors check this? This might be an issue in the context of drug adaptability since the cell line, in bulk, is already poised to adapt by tolerating low uM olaparib concentrations used in the study (<160 uM).

On 2020-03-09 19:39:13, user Fraser Lab wrote:

This manuscript by Leander, M., et al, uses TetR as a model system to explore the robustness of an allosteric response (in this case coupling drug and DNA binding) to mutation. This paper uses high throughput mutational scanning to identify variants that compromise function using FACS coupled to deep sequencing. As a follow-up the authors conduct a break-and-restore secondary screen where they generated libraries in the backgrounds of 5 deleterious mutations to identify rescuing suppressor mutations with FACS followed up by sampling with sanger sequencing. They use structural modeling (in particular rosetta and MD) to develop potential mechanistic explanations for these mutations.

Overall, the data presented shows that empirically identified allosteric residues appear to be distributed across TetR, are not conserved, and have a variety of structural mechanisms potentially underlying them. The authors take this to mean that broadly, allostery is distributed and not conserved. The generality of the present approach is perhaps a bit overstated ("profound impact", “radically reframe”), but this is a great example of leveraging the classic strategy of identifying suppressor mutants using a functional screen while taking advantage of the new power and massively parallel nature of modern high throughput sequencing. With the focus on plasticity and robustness there could be increased citations/discussion of previous work on protein robustness and strategies involving suppressor mutations. Many of their conclusions could be put in context with previous work on allostery in this system (see: Reichheld and Davidson, PNAS, 2009), which puts forth an alternative subdomain folding model that is not really considered here.

One of the main arguments in the introduction is that previous works weren’t comprehensive. From our reading, only one experiment, presented in the structural hotspots more conserved than allosteric’ section, measured all (or a nearly comprehensive set) of the mutations with deep sequencing. While the libraries were made it is unclear why sanger sequencing as opposed to sanger sequencing was used for the break-and-restore experiments. Moreover, the paper does not make clear which statistical tests are used to validate qualitative observations. For example, somewhat arbitrary thresholds are set and used to define where a region is an allosteric hotspot. In general, the thermodynamic coupling between one residue to another is not binary and so it does not make sense to treat the data qualitatively. It makes more sense to develop a quantitative score for whether a residue is allosteric or not based on deep scanning mutational data. For example if some mutations are harder to rescue you should expect not only less residues will rescue them but those that have to should have higher coupling then those that are easier to rescue- a core argument in the paper. This should be measured and tested quantitatively. Percentages should be reported somewhere regarding each of the rescued background libraries. It’s quite possible all this data is there, just not presented clearly.

Similarly, if the assignment of allostery is made quantitative it would be easy to calculate correlation between allosteric residues and conservation or as is it would be easy to calculate the z score between the conservation of dead vs allosteric residue populations. This would quantitatively back up the claim of the paper that residues allosteric residues are not conserved. There are many other examples throughout the paper where it would be appropriate to do a statistical test.

Overall, the paper is hard to follow as written. For example, it is confusing that the mutations in various mutational backgrounds are presented prior to the single mutational data. Perhaps it would make more sense if the single mutation datasets were presented first, followed by the rescuing mutations in the background of these mutations. It is unclear as is whether the deep sequencing data from the single mutational libraries were used in deciding mutations to be used as backgrounds for the second order mutations.

The major successes of the paper are the “break-restore” cycle of mutagenesis and integrating one potential structural framework to develop mechanistic explanations for some mutations which is often the lacking step in deep scanning mutational studies. The major concern we have with this data is that the timescale of the MD simulations (while still impressively long microseconds) is still insufficient to get at many issues of folding of subdomains (see again Reichheld and Davidson) and other aspects of the conformational ensemble that may mediate allostery in this system (esp. if it is not simply a matter of an “active” and an “inactive” structure).

Specific points:

Throughout the paper, it is unclear why methods were chosen, how assays were developed, and whether statistical tests were done. Some examples:<br /> * How were libraries generated? Chip-DNA is not sufficient information. Looks like from the methods inverse pcr and golden gate was used. High level information should be in the body of the paper. How do these libraries compare to similarly generated libraries? <br /> * There are triple mutations in the library. Where did these come from?<br /> * Nowhere in the paper are the quality of the libraries discussed. How much WT is present? How many variants were observed of the possible variants? How much coverage on the effective size of the library (considering WT) at the sorting/sequencing? Baseline library statistics (WT %, % present, bias) is needed to determine how well NGS experiments went.<br /> * How was the threshold for ‘low’ GFP decided on? Were any controls used? More broadly, were controls used to determine any thresholds? Example raw data for this experiment should be in the supplement.<br /> * In the disrupt and restore first step experiment presented in Fig1C it’s mentioned that there were many mutations that disrupted but 5 were chosen as background for secondary libraries. How many mutations were disruptive? Was this the data presented later in fig3? Or if not from the experiment presented in Fig3 this primary screen should be in the supplement. Why these 5 apart from them being distributed across TetR? Strongest signal? Did they represent distinct clusters? <br /> * How is partial vs full rescue of function described? How do you think about positions that can have varied impacts of rescue vs those that have a range of responses? For example D53V and N129D seem to all be rescued more or less the same amount whereas (impossible to know as a reader without statistics...) R49A and especially G102D have vastly different responses. <br /> * Fig1C ranks mutants by mean. Ranking by mean does not seem appropriate based on the fact that G102D in Fig1C is the second most easily rescued whereas in Fig2B it is the hardest to rescue. This seems odd. In the next section this idea is discussed somewhat and maybe does not make sense to rank order this data.<br /> * How and why were thresholds chosen? Why couldn’t this same analysis be done in Fig1C data by binning fluorescence? If 1000 mutants were done why are there not 1000 mutants in FigS3? Where is that raw data?<br /> * The authors discuss that rescuing residues are either unique to a given mutant background or shared across multiple. They call this ‘ variant-specific regional bias’. However, only 200 out of a possible of ~3000 variants per background are sampled so it is hard to know whether this analysis is meaningful. It is unclear why these experiments were done with clonal sequencing and not illumina sequencing. An added benefit would be being able to do thermodynamic cycle calculations mutations to quantify the coupling between all mutations. This would just require sequence baseline libraries as well.

* 5/20 mutations having a signal was used as a threshold for allosteric residue classification. This seems somewhat arbitrary unless this was quantitatively determined to be a good threshold. It makes more sense for every residue to get a coupling score based on depletion of weighted sequencing reads and have a statistically defined threshold (R packages like DESEQ2 can do this easily) for calling residue allosterically coupled.<br /> * Thermodynamic coupling is not binary so enrichments could be quantitative. Then it will be easier to judge the data and easier to calculate statistics. How many residues were missing from the dataset? How common are allosteric sites? Looking at FigS4 it is hard to know if white residues are missing data or mutations that don’t meet the cutoff.<br /> * A statistical test could be used to back up the statement that allosteric residues aren’t conserved. As is or it would be easy to calculate the z score between the conservation of dead vs allosteric residue populations. Really there should be a quantitative score that could be used to calculate correlations between conservation and later centrality.<br /> * A baseline high throughput experiment was done without ligand to see how TetR is inhibited without induction. The authors interpret GFP no ligand mutations as destabilizing DNA binding. However, mutations could alternatively impact baseline expression through TetR structure disruption or dimerization. This should be mentioned<br /> * Why was a triple mutant chosen for the rescued MD simulations when H44F had a stronger signal (Fig 1C)? Also, a double mutant would be better to limit higher order epistatic effects.<br /> * In figure 4d there do appear to be broadening in the distributions and a shift to To the left two populations. Is this meaningful? Is there any insight into why the triple mutant isn’t all the way back to WT?

Throughout the manuscript there are broad generalizations that are not consistent with our view of the literature. Here are some examples:<br /> * Authors discuss TetR having a high degree of allosteric capacity based on the results. However, without more datasets or discussing previous work in this space it is hard to say whether TetR has a high allosteric plasticity.<br /> * The authors postulate that ease of rescuing a dead variant may correlate with how stabilized the inactive state of the protein is. However, the literature has certainly considered this and should be discussed/cited if this section remains. <br /> * The authors talk about how their work radically reframes the problem and is very impactful. We will leave the impact for history, but this is a pretty classic strategy and we fail to see what is “radical” about it. It is a great example of using modern technology on a “classic” system - that is cool!

Throughout the manuscript there are explanations whereby the logic is unclear. Here is an example that would benefit from further explanation: <br /> * In after the site-specific mutation section the authors conduct rosetta modeling to develop putative mechanistic explanations for several of the mutations. Here the authors see reduced helix-turn-helix stability however there is no explanation of it’s significance.

Insufficient background/missing citations<br /> Through the manuscript there is lacking background and many missing citations. Here are some examples:<br /> * ‘Thermodynamic does not require spatial connectivity’ should have a citation<br /> * ‘Allosteric signaling occurs through redundant and robust networks’ based on one example from one paper it is improper to generalize. There should be citations here as there are certainly more examples of allostery being redundant.<br /> * The authors discuss allosteric hotspots but do not cite work here that came up with the concept. For example, earlier in the paper Rama Ranganathan’s work is cited and should be again here.<br /> * Citations needed that identified mutations in DBD and LBD<br /> * Centrality is a used to identify residues associated with allostery. The authors mention that in some instances it does not predict their allosteric classification. How does this compare to previous evaluations of centralities performance as an allosteric metric?<br /> * More discussion of how the field views the conservation of allostery would be good. Overall, it’s not entirely novel that allosteric sites are not as conserved as Though it’s not necessarily novel that allosteric sites are not as conserved as catalytic/binding sites. Fig1b of Yang J-S, Seo SW, Jang S, Jung GY, Kim S (2012) Rational Engineering of Enzyme Allosteric Regulation through Sequence Evolution Analysis. PLoS Comput Biol 8(7): e1002612.

A major rationale and point the authors make in the introduction is that previous studies have been exhaustive, however many of the examples the authors give are clonal experiments with limited sample size. Some examples:<br /> * If this is 200 variants per position this is nowhere near exhaustive. How is there only 1 variant for G102D in fig2a when in 1C there were more? Were any statistical thresholds used for the data in Fig 2b? <br /> * The authors discuss that rescuing residues are either unique to a given mutant background or shared across multiple. They call this ‘ variant-specific regional bias’. However, only 200 out of a possible of ~3000 variants per background are sampled so it is hard to know whether this analysis is meaningful. It is unclear why these experiments were done with clonal sequencing and not illumina sequencing. An added benefit would be being able to do thermodynamic cycle calculations mutations to quantify the coupling between all mutations. This would just require sequence baseline libraries as well.

Figures<br /> 1B<br /> It would be nice to see raw data somewhere for gating. To get a sense of what the library data looked like. It is unclear why only the top and bottom gates were collected and not a series of bins. It would also be good to get a sense of what percentage of the population these gates represented.<br /> Fig 1C<br /> How many replicates were done for each? There should be extensive statistical tests here between mutants, wt and background single mutations. <br /> Why are there triple mutants? Seems triple mutants shouldn’t be included as that starts moving into high order epistatic space and is hard to discuss.<br /> Unclear why mean was used to range order these as clearly several don’t fall quite inline especially G102D<br /> Fig1D<br /> Hard to read labels. Poor contrast.<br /> Fig 2A<br /> Seeing the raw data for these would be good. I don’t think it’s appropriate to use binning for this data and instead there should be a numerical value for fold induction. Then induction could be scored quantitatively. Also, need for statistical tests.<br /> Fig2B The raw data for this would be good to have in the supplemental figures<br /> Fig2C<br /> Hard to read residue labels, It would be nice to have an example that has an allosteric explanation. As all of these are just direct interactions.<br /> Fig2D<br /> This hypothesis could have been more fully tested if full libraries were characterized<br /> Fig3A<br /> Really hard to interpret this. The distribution are clear but there should be quantitative comparison.<br /> Fig3C <br /> Same comment as fig 3A.<br /> Fig 3D<br /> Need better labeling. What is top and bottom? Also pointing out where the modelled residues are in 3C would be good.

Grammar:<br /> There are missing ‘a’, ‘the’, etc but here are some examples as well as a couple of other issues:

Page3:Line7<br /> ‘the’ decentralized<br /> Page3:Line10<br /> Unclear what ‘they’ refers to. <br /> Page4:Line5<br /> ‘Time and again’ and ‘myriad’ are redundant<br /> Page4:Line14<br /> ‘a’ biochemical understanding<br /> Page4:Lines19-20 <br /> ‘a’ promoter and ‘that’ promoter<br /> Page6:Line11: <br /> ‘a’ high degree<br /> Page6:Line16 ‘<br /> allosteric’ signaling<br /> Page7:Line11 <br /> Break up the one massive paragraph after sentence 10 in the site-specific rescuability of allosteric dysfunction section.<br /> Page8:Line15<br /> Why are hotpots in parentheses? This is confusing.

We were prompted to review this by a journal, James Fraser and Willow Coyote-Maestas

On 2020-04-03 13:11:53, user Christian Braun wrote:

Our website www.crispr-clue.de got a bit overwhelmed by traffic after it posted at biorxiv. We are working on resolving this issue. https://crisprclue.pythonan... (the direct link to our online platform) has a higher bandwidth and gets you directly to CLUE.

On 2021-08-01 17:38:18, user Abishek wrote:

Our preprint has been published recently. Please find the link to our published work here https://doi.org/10.3390/mic...

On 2020-06-17 13:13:27, user Alex Ossadtchi wrote:

Found a number of errors in mathematical expressions and in the main text. Added Monte-Carlo simulations, please, use the current version https://www.biorxiv.org/con...

On 2020-04-12 12:44:46, user Dacquin Kasumba wrote:

Vero cells are known to be IFN-I incompetent. Why choose this cell line for your study and how do you explain the "remarkable" sensitivity against SARSCov2 in an cell line that is not sensitive to type-1 IFN?

On 2025-03-27 21:21:39, user Jeremy Jones wrote:

The methodology presented in this paper is obscure and outdated. Mechanistic modeling is the by far preferred method of choice for translating in silico models to in vivo PK predictions (like the HTPK module in ADMET Predictor). <br /> Furthermore, the actual performance characteristics of all ADMET Predictor models are publicly available and are generally poorly represented by the limited datasets tested in this manuscript.

On 2023-12-14 13:13:43, user ??? wrote:

First and foremost, I must express my admiration for the remarkable work presented in your study. The findings are incredibly intriguing, and it is noteworthy how closely they align with our own research published in EBioMedicine in 2021 (https://pubmed.ncbi.nlm.nih... "https://pubmed.ncbi.nlm.nih.gov/34157485/)"). Your exploration of the impact of individual germline genomes on the determination of breast cancer subtypes, particularly emphasizing the significance of immune system functionality, adds valuable insights to the field.

I find it particularly compelling that your results further validate and reinforce the observations we made in our prior work. In our study, which predates yours, we underscored the substantial role of germline genomes in determining the HER2 subtype of breast cancer. Recognizing the significance of your contributions, I recommend citing our publication to provide a comprehensive context for the understanding of the role of germline genomes in breast cancer development.

On 2019-04-28 15:26:27, user David Curtis wrote:

I have a number of concerns about the AUC you quote. One is that you quote the AUC for only the best performing GRS. It ought to be obvious that you can't just try a number of different ways of getting the GRS and then only highlight the one which does best. (Especially as you applied it to three different datasets and only report the best of the three.) Another concern is that you don't say how much the GRS improves the AUC compared with a predictor just derived from HLA. Also, in the methods section you suggest that the effect size for each SNP is derived from its original publication but subsequently it looks as though you have fitted your own effect sizes using a training set. There are huge ancestry effects in SLE. How do you know you haven't just picked up a GRS for ancestry? (https://www.ncbi.nlm.nih.go... plus lots of other recent papers) "GRS has been showed to be predictive for several diseases including cardiovascular disease (AUC=0.81, 95%CI: 0.81-0.81)" - Um no, that is not the AUC for the GRS, as you point out later. Finally, you suggest that the GRS might "assist early prediction of lupus nephritis in a clinical setting". I strongly doubt this is the case. What is the magnitude of this effect? What would be the clinical utility of such a predictor? (See https://www.nature.com/arti... "https://www.nature.com/articles/s41436-018-0418-5.)")

On 2020-09-24 12:51:38, user Laurence Lafanechere wrote:

The results you present regarding the inhibition of 3CLpro are exciting.<br /> I was wondering if the cellular antiviral effect of Masatinib could also result from its<br /> ability to block cytoskeleton dynamics. Indeed, we have recently demonstrated<br /> that µM doses of Masatinib induce a stabilization of the microtubule network (Ramirez-Rios,<br /> Sacnicte et al. “A New Quantitative Cell-Based Assay Reveals Unexpected<br /> Microtubule Stabilizing Activity of Certain Kinase Inhibitors, Clinically<br /> Approved or in the Process of Approval.” Frontiers in pharmacology vol. 11<br /> 543. 30 Apr. 2020, doi:10.3389/fphar.2020.00543 )

On 2025-05-01 08:26:35, user Masak Takaine wrote:

Although the title has been slightly changed, this preprint has been published in the following journal: <br /> https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3003111

On 2019-06-25 11:19:45, user Vera Kozjak-Pavlovic wrote:

So, it is an IMS protein after all :)

On 2025-08-05 20:31:36, user Ben wrote:

`page 2 line 3 subject 'asymmetries' is plural aux verb 'was' is singular <br /> line 12 'to' maybe 'too' but redundant with 'also'

page 3 line 1 "Variance" implies change... do you mean "Difference" or "Divergence"

page 4 line -3 "variables" individual differences in non-forced ...<br /> line -1 "lateralization, and, gap detection"

page 5 line 12 "(DLT) is a prominent example of leftward speech lateralisation" <br /> conflates task with outcome. DLT is not a lateralization.

line 17 "verbal" means 'of words, spoken, written, etc. You mean<br /> "speech sounds"

page 6 line 3 "the smaller the gap detection threshold"<br /> line 23 "Composing" (creating )<br /> line -2 upon individual differences in ...

page 7 line 3 "respectively, " and another s is needed on the verb!<br /> line -7 "have used" imperfect past to indicate extended time

page 11 line 9 "Chance"

page 12 line -12 "for the present study" "this" is ambiguous

page 13 line 8 as page 12

page 17 line -1 "printout" meaning "list"?

page 18 line 3 "dichotomous" "dichotic"?<br /> line -4 should "memprage " be upper case?<br /> line -3 "present thesis" "paper"?

page 26 line 2 Elmer, Hänggi [84] et al.?<br /> just after fig 3 caption something is missing (maybe overlay error)

page 30 line -8 Greve et al.

page 31 line 1 "activation (measured via fMRI) to stimuli" unclear, what stimulus property?

page 32 line "language lateralisation positively related." were positively OR positively correlated?

page 34 line -6 "connectivity can each related" missing verb or just "relate"

page 35 line -4 ampersand?

page 36 line 11 "that less transcallosal associative" "fewer" connections is a count noun

page 39 line 4 "study continues understanding" agency problem "The results contribute to"<br /> line 10 "cortical volume is better represented by surface-area" do you mean a better predictor<br /> of laterality, or that volume measures are of poor quality?`

On 2022-07-08 10:20:41, user Kees Jalink wrote:

Please note that this work has in the mean time been published in largely unaltered form in Scientific Reports, 2021 Oct 20;11(1):20711; DOI: 10.1038/s41598-021-00098-9.

For clarity, we also share the reviewer comments and our responses to that with you:

Reviewer Comments:

Reviewer 1<br /> This is a very elaborate and interesting study proposing a dynamic genetic screen based on FRET-FLIM and which allows for a more refined understanding of the impact of gene knockouts on cellular signaling and metabolic processes compared to the simple cell viability and colony formation readouts widely used in the past (and also currently). The authors used an innovative FRET-FLIM sensor (created by them) expressed in stable cell lines to monitor changes in the levels of cyclic AMP by modulating phosphodiesterase-induced breakdown via treatment with silencing RNA (siRNA) oligonucleotides.

I only have a few comments regarding the text of the article, as follows.

1. It is very difficult to understand from the abstract the purpose of the paper, at least from the standpoint of the general FRET enthusiast with no specific knowledge regarding the biological application described. (i) Specifically, in the second paragraph, the agonist of what receptor are the authors referring to? What is the connection between the” 22 different phosphodiesterases (PDEs)” and the baseline levels of cAMP. Is that what the sentence refers to? It is hard to guess from the current sentence structure.<br /> (ii) The authors used “HeLa cells stably expressing our FRET-FLIM sensor.” Precisely what sensor does “our” stand for? (iii) The rest of the paragraph does not seem much easier either, especially given the numerous undefined acronyms. All these questions are fully addressed in the body of the paper, though not in the abstract.<br /> The last 75% of the abstract has been completely rephrased to address hopefully all of the reviewers concerns. Changed part is indicated with track-changes.

2. On page 3, the authors state: “FLIM is a robust and inherently quantitative method for FRET detection which requires no additional calibrations or correction parameters.” That is not entirely correct, for a couple of reasons: (i) FLIM generally requires separate knowledge of the donor lifetime in the absence of FRET. <br /> The reviewer is right. We condensed the text so much that we cut some corners. We have now rephrased that claim, and mentioned that the donor lifetime is a necessary calibration. See page 3.

(ii) One has to fully separate the donor emission from acceptor emission, which is usually done achieved band-pass filters (it is also done, though not very often, using spectral resolution). This is not unlike what is done in intensity-based measurements, in which, at least one is attempting to unmix the donor and acceptor signals or at least apply some post-measurement corrections for bleed through (caused by spectral overlap between donor and acceptor emission). The fact that FLIM researchers often choose to ignore this kind of corrections may not be interpreted, in my view, as an advantage of FLIM. Please consider adjusting the text.<br /> We could not agree more! In particular in view of our own contributions to obtain truly quantitative sensitized emission FRET (van Rheenen et al, BJ 2004), we are keenly aware of the dangers of spectral overlap. That is the reason that in this study we used our Epac-SH189 FRET sensor which has dark (i.e. non-emitting; Y145W mutation; Klarenbeek et al, PLoSOne 2015 ) acceptors. Given the high QY of the donor, the lifetime of this sensor has ignorable contribution of acceptor emission even if a large spectral window is selected. We did not attempt unmixing approaches, because our pilots had indicated near-identical lifetimes when the emission was taken just from the part of the spectrum that is exclusively occupied by mTurquoise. This information is presented towards the end of the introduction on page 5, in the paragraph where we present a more specific outlook to the contents. It has been rephrased in part for better emphasis.

1. The results shown in Figures 6 and 7 are fascinating. To let the reader more easily follow the story, could the authors insert “agonist” or “antagonist” as necessary within the following sentences? “We first stimulated HeLa cells with 40 nM isoproterenol which caused a rapid rise in cAMP levels and subsequently added propranolol at 60 nM concentration which caused a sharp decline following the stimulation. Finally, 25 µM forskolin…”<br /> We have adapted the text (page 14) and legend of figure legends (page 15) according to these suggestions.

Once again, in my evaluation, this is an excellent, detailed, and rich in information study worthy of publication in a high caliber journal.

Reviewer 2<br /> The manuscript submitted by Harkes et al. on the topic of FRET-FLIM HCS with siRNA screen to monitor dynamically the change of cAMP concentration using a FRET biosensor is well written with interesting results and shows the high potential of this method. In order to increase its impact and for clarification I have few queries:<br /> [1] I found the introduction of interest but previous work in the scope of HCS FLIM is missing. I suggest to add references of several groups working in this direction (French, Tramier, Esposito...).<br /> In our introduction we sought to emphasize work that is geared towards screening of fast dynamic changes in lifetime in living cells, which implies imaging with very high photon fluxes and with methods that do not waste photons unnecessarily, so as to avoid unnecessary cell damage. We agree with the reviewer that this does not acknowledge much of the work of those who contributed to high-content and high-speed FLIM imaging, in particular from pioneers like Drs French, Tramier, Esposito, but also e.g. Gerritsen, Ameer-Beg and others. We have now rewritten that part (page 4) and added 4 references to just a few of the very relevant contributions.

[2] For lifetime analysis, authors have used biexponential fit with two fixed lifetimes 3.4 and 0.6 ns with a final determination of mean lifetime using the different preexpo factors. I'm not sure that this approach is the more appropriate. First, what means these two fixed lifetimes? is it pertinent with the biosensor under study? this is not really discussed in the manuscript.<br /> Second, if finally you use a mean parameter for the concentration curve fitting, why not using a mean analysis such as the mean arrival time? This parameter is now directly calculated in the FALCON version and seems pertinent because HyD have very low noise. From my point of view, this will increase the sensitivity and the speed of the measurement. In any case, this has to be discussed.<br /> In fact, we had given this quite a bit of thought but for brevity, it did not make it to the final draft. In brief, we performed 2-component fits because these fitted much better than single-component fits. The lifetimes of 3.4 and 0.6 ns were selected because these were the dominant components in large numbers of global (i.e. whole-image) fits. We expected a dominant component of 3.4 ns, which is that of the Epac sensor in its low-FRET configuration. The low lifetime, 0.6 ns, is in fact significantly below the resting-state lifetime of the cells (minimally 1.9 ns in some cells) but it is the second dominant Tau in the two component fits and the phasor analysis also indicates a intercept below 1 ns. Our data were thus collected and exported (data reduction) for those two components, enabling us to analyze both effects on long as well as on short lifetime contributions upon PDE knockdown.<br /> In preparing figures for the manuscript, we noted no clear advantage of separately presenting data of both lifetimes and their amplitudes, so we decided to extract the weighted mean lifetime. This has now been described more completely in the Methods section (page 6). For the reviewer, we also note that we are not fond of using the FALCONs mean photon arrival time because 1st, unlike the fitting, it proved to be quite sensitive to environmental (background) light, and 2nd, there is a small bug in our version of the software which sometimes causes erroneously high lifetimes when recalculating old data with the ‘mean photon arrival time’ option. 3rd, this method has the same number of free fit parameters as a single component fit. We experienced that the result with this method had less pixel to pixel variation compared to a single component fit.

[3] When showing screen results, only fitting parameters are presented in figures. Is it too difficult to present few curves in which differences can be shown? This will increase the understanding of the reader before to present the statistics.<br /> We thank the reviewer for this excellent suggestion. Two panels have now been added to the boxplot in Fig. 4

[4] From my point of view, details regarding how to add drugs in multi-well plate has to be presented. Is it pipeting ? and in this case it does not really make High Content automated approach. Moreover, how is managed the focus since probably you loose it during pipeting... or is it more automated device that you use in the context of multiwell plate. In addition, how is selected the FOV? how to manage the human choice? This has to be detailled and discussed.<br /> We have extended the text in Materials and Methods to cover all of these aspects, see page 6, 7. In brief, in this study we present only data from studies where stimulus addition and mixing where done manually, although we have also implemented automated addition of stimuli (3 channels). For the protocol involving rapid sequential agonist-antagonist stimulation we found that, at least with our equipment, automated addition of stimuli depended too much on diffusion, and therefore results were more variable than with manual mixing. For keeping in focus, we routinely used the Leica hardware focusing option, AFC.

One last important issue: in revising our manuscript, we noted that the reported sequences for siRNAs used for PDE8A accidentally had become mixed up. We now corrected those entries in Supplementary Table S1. This correction does not affect any of the experiments, microscopy data or interpretations whatsoever, it solely affects one row in that table. <br /> We expect that with those changes, we have adequately addressed all issues raised by the reviewers. We feel that the manuscript has significantly improved in the review process and we are indebted to both reviewers for their time and thoughtful comments.

Sincerely, on behalf of all authors, <br /> Kees Jalink

On 2020-01-31 00:58:11, user Hans Ratzenburger wrote:

"This new coronavirus has resulted in <br /> thousands of cases of lethal disease in China, with additional patients <br /> being identified in a rapidly growing number internationally." According to other sources the death count from novel coronavirus is approximately 170 at 31/01/2020. Why does it say thousands of cases of lethal disease?

On 2025-08-01 07:22:22, user YMIZUT58@YAHOO.CO.JP wrote:

This preprint has now been published in a journal.<br /> https://doi.org/10.1002/cm.70003

On 2025-10-22 18:06:37, user Maria Bernardo wrote:

Some of the conclusions from this study are not aligned with the state-of-the-art concerning yeast phylogenomics. Unfortunately, it is difficult to ascertain how the authors came to their conclusions because figure legends are missing and the methods are incomplete and do not explain, for example, the approaches used to establish orthology. In particular, the species phylogenies presented are inconsistent with previous findings.<br /> Widely accepted, uncontroversial yeast phylogenomic trees can be consulted in the following corner-stone study from the Hittinger and Rokas labs:<br /> https://www.science.org/doi/10.1126/science.adj4503

On 2019-03-13 13:07:13, user sandeep chakraborty wrote:

Not just DNA templates,

both AAV and the Cas9 integrate in their genomes.

Data from 5 diff studies (will be adding more as and when I find more).

Should be really unacceptable.

https://sanchakblog.wordpre...

On 2022-02-11 14:28:21, user Mikhail Schelkunov wrote:

There is a statement in the manuscript "For each query, we greedily collect only the best alignment for each location (option ‘--range-culling --max-target-seqs 1’)". However, I see no "<br /> --max-target-seqs 1" in the code of Proovframe 0.9.7.

On 2025-04-11 00:43:55, user Lingyuxiu Zhong wrote:

Sorry to bother you. I hope this comment reaches you well.<br /> I’m currently using your paper for pharmacodynamic data generation and comparison.<br /> I would like to use the MIC value of colistin against the bacteria described in your study.<br /> However, I couldn’t find the specific MIC value in the paper.<br /> May I kindly ask if you could share the exact value with me?

On 2018-09-15 10:19:33, user Klaus Fiedler wrote:

An update is found on figshare.

https://figshare.com/articl...

On 2025-11-07 21:29:04, user Katie Schaefer wrote:

Looking forward to the paper, but I can't help but notice multiple typos in the abstract.

On 2023-07-05 10:35:26, user Jukka Kallijarvi wrote:

Now published in Cell Reports Methods, https://doi.org/10.1016/j.c...

On 2020-12-15 16:50:22, user Olivier Le Gall wrote:

Another form of SARSr-CoV previously epidemic in humans, SARS-CoV, is found in sweat glands and possibly excreted (DOI:10.1002/path.1560). More recently, this has also been suggested for SARS-CoV-2 itself (DOI:10.1038/s41421-020-00229-y). <br /> Therefore, maybe the affirmation that "Sweat does not appear to be a route of excretion for SARS-CoV-2 virus", relying on two articles not directly focused on this particular question, should be somewhat tuned down?

On 2017-04-03 01:43:01, user Juha Kere wrote:

This manuscript was first submitted to Nature Genetics 20 Sep 2012 and the same date returned to authors without review. We reanalyzed later the same RNA sequencing data referred to here using transcript 5' tag mapping to the genome, and it served as the basis for conclusions regarding new PRDL transcription factor genes upregulated at 4-cell and 8-cell stages (Töhönen, Katayama & al. Nature Communications 6:8207, 2015; DOI: 10.1038/ncomms9207). Our analysis and conclusions regarding DUX4 as an early regulator of human Embryo Genome Activation are published for the first time here. For correspondece, please contact juha.kere@ki.se.

On 2022-02-08 20:02:28, user Julie Secombe wrote:

This is an excellent example of a KDM5A regulating a transcriptional program critical to cellular differentiation using both its canonical histone demethylase activity in addition to non-demethylase functions.

On 2021-04-26 03:55:48, user Vladimir Dinets wrote:

Lines 652-653: You say "here we suggest the following system of generic group taxa within the tribe Arvicolini sensu stricto", but nothing follows :-(

On 2023-12-04 07:25:52, user panos wrote:

now published in https://academic.oup.com/pl...

On 2016-02-23 10:55:18, user conor dolan wrote:

"GCTA assumes that the covariance matrix of u is diagonal, hence every

SNP used in the GCTA model (irrespective of whether it is in strong

LD with a causal SNP) necessarily makes a random contribution to

the phenotype. Non-causal SNPs would make zero contributions to

the phenotype, which would require that the corresponding diagonal

entries of the covariance matrix of u be zero; GCTA does not set any

diagonal entry to 0 in its formulation".

Is this correct? The idea is that the regression coefficients in the regression of the phenotype on the M genetic variants (snps) follow a distribution with a mean of zero and a non-zero variance. Assuming normality we have: beta_i ~ N(0,sigma) (i=1....M), where sigma is the standard deviation. It may well be the case that there are many beta_i that are zero, but this does not necessarily render the sigma zero, assuming the set of M snps includes causal snps (causal w.r.t. the phenotype). This will render the assumption of normality questionable, but I believe the robustness of GCTA to violation of this assumption has been established in the literature (Lee & Chow, 2014 Hun Gen 133(8), 1011-1022). So I do not see how one or more zero-effect SNPs (in the presence of causal SNPs) should give rise to zero diagonal on the cov matrix of u.

On 2025-05-30 10:57:29, user Nikolai Slavov wrote:

A labeling protocol is available from protocols.io :

PSMtag - Anhydrous Labeling Protocol, doi: https://dx.doi.org/10.17504/protocols.io.kqdg3w5yzv25/v1

On 2018-06-27 16:16:23, user AlejandroWeinstein wrote:

Hi. In page 13, you have the reference "Heagens et al., 2014", but it doesn't appear in the reference section.

On 2023-11-24 01:48:09, user Prof. T. K. Wood wrote:

Seems it is difficult to make decisions about the impact of phage-defense systems if the most-prevalent anti-phage system, toxin/antitoxin systems, are not included.

On 2019-12-21 05:49:22, user Prateek Bhatia wrote:

Seamlessly use AI to get hold of good variants from an Ion Torrent run Data...high intelligence quotient!!

On 2019-02-24 07:37:31, user Andreas wrote:

Not sure if detection of amount of synuclein protein with the Novus antibody is quantitative in synuclein aggregates (monomer specific).<br /> Also, the sections in the immuno-histochemistry of supplementary Figure S6h for the 2 different genotypes (WT and LRRK2) appear to be from the same tissue block?

On 2020-11-02 08:52:10, user Alessio Collalti wrote:

Now published in Nature Communications and freely available at: https://www.nature.com/arti...

On 2019-11-19 15:05:37, user stefano cagnin wrote:

Several manuscripts, not considered in this, were published demonstrating the importance of single myofiber analyses (e.g. Chemello et al 2019 Cell Rep; Alessio et al 2019 NAR; Chemello et al 2015 Genom Data; Mammucari et al 2015 Cell Rep; Chemello et al 2011 PlosOne; Schiaffino et al. Histol. Histopathol. 2019; Murgia et al Cell Rep 2017; Murgia et al EMOBO Rep 2015).

On 2020-03-15 11:23:20, user Charles Agoti wrote:

....Excellent stuff! Will this be reproducible in humans? How long will immunity against reinfection last ( natural or vaccine)? Which antigens are most important to develop immunity?

On 2020-03-19 21:12:02, user Brindusa wrote:

yes, we need studies in humans but the results are encouraging. We share many similarities with these monkeys (we share 93% of their DNA).

On 2025-07-14 15:39:34, user Zhixun Dou wrote:

This manuscript has been published: https://www.nature.com/articles/s41586-025-09234-1

On 2017-09-26 21:03:08, user GS wrote:

Great work! I am glad to see the "real" evidence of the systematic response. I just have one problem with the conclusion that AFD (using ttx-1 mutant) and AWC (using ceh-36 mutant) are not involved. ttx-1 and ceh-36 are both transcription factors that change neuron fate. In either mutant, the neuron still exists, and still has synaptic release and exocytosis, but no longer expresses the correct neuronal marker. Presumably ttx-1 mutant worms still can senses temperature, since they are constitutivelly cryophilic. I would suggest using genetically or laser ablated AFD or AWC worms for this experiment to confirm the original conclusion.

On 2019-08-19 17:37:21, user Matthew Denholtz wrote:

Any reason Seurat wasn't included? Very widely used, it would be useful to see where that stands.

On 2018-12-10 21:24:57, user Izra Abbaali wrote:

https://uploads.disquscdn.c...

The nucleus is housed in a double membrane nuclear envelope that separates the nucleoplasm from the cytoplasm. Nuclear pores allow for the movement of molecules, such as RNA and ribosomal proteins, across the nuclear envelope. Small molecules can pass through the pore via passive diffusion, while larger molecules require an energy-dependent mechanism in order to pass through the nuclear pore. Interestingly, another mechanism of transport to-and-from the nucleus has been detected. Upon infection with the Herpes simplex virus, the virus will hijack the cell’s machinery and use nuclear-envelope-derived vesicles to export material into the cytoplasm (Wild et al., 2009). This nuclear envelope budding event is also seen during the development of Drosophila melanogaster embryos (Speese et al., 2012). This raises the question about whether this nuclear envelope budding serves as an alternative mechanism for importing and exporting materials to and from the nucleus. Panagaki et al. investigated whether this method of transport is seen only during viral infection and the development of organisms, or is it a universally-conserved mechanism among all eukaryotes. The authors used a variety of eukaryotic organisms to justify that nuclear envelope budding is an evolutionarily conserved phenomenon. This information was organized into a phylogenetic tree of species with observed nuclear budding events. Electron microscopy and electron tomography images work synergistically to reinforce this idea.

In this paper, the authors use electron microscopy and electron tomography to visualize nuclear envelope budding events in a Homo sapiens cell line (HMC-1 cells), Caenorhabditis elegans, Saccharomyces cerevisiae, Schizosaccharomyces pombe, and Trypanosoma brucei. Three kinds of budding events were visualized in the human cell line: outwards protruding, inwards protruding, and particles in between the double lipid bilayers. The paper states that these nuclear envelope budding events occurred in 12% of the imaged HMC-1 cell nuclear sections. This analysis was done for the rest of the organisms and they all had varying frequencies of nuclear envelope budding occurrences, ranging from 3 to 12% budding events/nuclear section. Outward budding was the most prominent type of nuclear envelope budding event, as depicted by electron tomography. Besides the better z-resolution, the additional benefit of using electron tomography is the detailed membrane morphology provided. These buds were around the same size as the HMC-1 cell derived exosomes this lab had previously identified. The authors hypothesize that the resulting double-membraned vesicles could function as future exosomes. The contents of the vesicle and its trajectory are yet to be determined, so the authors persuade the cellular biology community to continue to study this phenomenon in order to further understand the methods by which molecules move between the nucleoplasm and cytoplasm.

Though this paper’s argument was convincing, the figures were lacking in persuasiveness. For one, it is extremely hard for a reader to see the budding events occurring in the S. cerevisiae and T. brucei electron microscopy images. Although the arrows were helpful in pointing out the budding location, it still proved difficult to visually identify them. On the other hand, Figure 1’s electron microscopy images were very clear and easy to discern. This might be due to the relatively larger-sized buds seen in the HMC-1 cells. Nonetheless, without error bars or statistical analysis, the data seems unsupported and meaningless. I also feel that the bar graphs presented, in Figure 1 for example, are not properly constructed. The labeling is confusing and can be misleading. The authors should consider revising the graphs, as shown below, to clearly demonstrate the number of budding events per nuclear section and the type of budding event observed. Also, it is important to mention the frequency of the sections of nuclear envelopes that contained more than one bud(*). They should further investigate this interesting phenomenon. Another critique I had involved the use of the human mast cell line. Because the other organisms used within this study did not have any apparent underlying diseases, I would have preferred if a healthy human cell line had been used; it decreases the risk of confounding factors possibly affecting nuclear budding events. In future studies, I would like to see the authors investigate the contents and trajectory of the buds, and see if the double membrane is maintained on the vesicle through its journey. It would also be interesting to see data on how often the nuclear pores are used for transport versus nuclear envelope budding. Do certain molecules prefer one mode of transport over the other? If so, why?

References<br /> 1. Panagaki et al., 2018. BioRxiv. <br /> 2. Wild et al., 2009. J Virol. 83(1): 408–419.<br /> 3. Speese et al., 2012. Cell 149(4): 832-846.

On 2020-03-24 10:45:02, user Remi Gau wrote:

Great paper.

Some interesting points raised in the discussion.

If it is not too late I would suggesting making a reference in the 3rd paragraph to the end to the COBIDAS guidelines published by Committee on Best Practice in Data Analysis and Sharing create by the Organization for Human Brain Mapping.

https://www.ncbi.nlm.nih.go...<br /> http://www.humanbrainmappin...

On 2025-05-29 16:20:33, user Manish Jain wrote:

Fascinating study Vedant! You've elegantly dissected the role of MAB-5/Hox in regulating posterior migration of Q lineage neuroblasts in C. elegans, revealing a staged process controlled by distinct target genes (vab-8, lin-17, and efn-4). The link between migration and differentiation is particularly intriguing, with premature dendritic protrusion formation upon migration disruption. Great insights into the Wnt signaling pathway and transcriptional programs governing neurodevelopment!

On 2022-10-28 21:26:44, user Pierre Siffredi wrote:

in the evaluation of power, they do not seem to actually test if this really outperforms basic meta-analysis. outside of contrived scenarios, basic meta analysis is usually the best

i can't imagine the cross population LD working so great when most people want to use gwas summary from admixed samples, at least until biobanks provide LD calculations along with their summary data

On 2020-02-14 10:36:59, user Mark Tailor wrote:

Why do you report the photovoltage density? For the photocurrent it makes sense since it scales with the surface area, but the photovoltages doesn't, so why report PVD?

On 2021-12-30 16:35:07, user Heidi Arjes wrote:

This preprint does not have a link to the peer-reviewed published article in Current Biology: https://www.cell.com/curren...

On 2020-04-07 20:22:52, user Rasmus Møller wrote:

Can you share the Data S1 excel sheet? It doesn't seem to appear under Supplementary Material. It is very interesting that you see an up regulation of IL-6 and other cytokines produced by the epithelium. I would have thought they were mostly expressed by infiltrating macrophages and T cells. I am very curious to see if you have CCL20 included in your panel as the CCL20-CCR6 axis is crucial in Th17 recruitment and several clinical findings show an influx of Th17 cells in severe COVID-19 cases.

On 2025-05-28 03:43:13, user Robert George wrote:

The timing, 'origins' and reason for the 'eastern shift' in post-Neolithic Anatolia is very interesting.

Some other factors to consider<br /> - note the individual I0679 from Krepost (Mathieson 2018), also showing the shift, as far as ~ 5600 BC Bulgaria. Also a singleton case<br /> - aside from a 200 year time gap, if Buyukkaya has this shift whilst West Mound neonates lack it, could Buyuk's northern geography be a factor ? <br /> - what was happening in the broader west Asian sphere c. 5500 BC which might explain the appearance of new groups in Anatolia ? There are important clues here<br /> - even though the east shift occurs as early as 5500 BC, it really accelerates after 4000 BC, and especially 3000 BC. I.e probably multiple streams & causes

On 2021-04-12 14:08:51, user Thomas Munro wrote:

Note that the stereochemistry of BU72 has recently been revised. The PDB entry has been updated to version 2; the new ligand ID is VF1. Presumably the revised structure will give a better fit between MD and the crystal pose.

On 2025-08-15 21:11:01, user Curt F. wrote:

Congratulations on a clear, readable paper and all the solid improvements made to Casanovo v5. I'm a happy user of Casanovo v5.

I'd invite the authors to consider analyzing calibration accuracy (Figure 1a) as a function of peptide length. Consider a 5-mer and a 10-mer peptide, each which has identical residue-level scores of 0.9 at every position. Earlier versions computed overall scores from the arithmetic mean, and this calculation is length-independent, so the version-4-score of both the 5-mer and the 10-mer will be 0.9. But v5, the product of the residue-level scores is used, and this is not length independent. The v5 score will be 0.59 for the 5-mer and 0.35 for the 10-mer.

The behavior of v5 might well be preferred, for many reasons! And if a calibration curve like that in Figure 1a, but only considering short peptides, looks similar to a curve that only considers long peptides -- i.e. both short and long peptides are equally well calibrated, then the authors will have demonstrated one such reason. Alternately, if calibration accuracy is different for short and long peptides, this would be valuable for users to know.

On 2020-04-03 00:27:40, user John Dague wrote:

The significance of this work reveals that X2a people of North America are very likely to be direct descendants of Caucasus Hunter Gatherers x North African Natufians from Anatolia, who were living in North America at the time that Gobleki Tepe was being constructed in Anatolia, and at the time that copper began to be mined at Isle Royale in North America. Given the coincidence of the genetic relationship, as well as the corresponding time of construction of Gobekli Tepe and the beginning of mining activity at Isle Royal (located in the Great Lakes region of North America where the highest concentration of X2a people were living) it becomes very possible that some of the first temples in Anatolia were constructed with stone that was quarried with saws made of copper from North American. This would mean that there were organized trans-Atlantic crossings, to and from the New World, at the time of the construction of the first civilization at Gobleki Tepe.

This would be a significant event in human history.

David Pompeani dated the mining activity at Isle Royale mine in North America using radiocarbon dating of lead contamination in lake sediments (Copper mining on Isle Royale 6500–5400 years ago identified using sediment geochemistry from McCargoe Cove, Lake Superior). While speaking about the research that he had performed (in a video that is on YouTube) he reveals that the first mining activity that left evidence of lake deposit lead contamination occurred 9,000 years ago, about the time that X2a and Maritime Archaic people arrive in North America.

The lake sediment deposits reveal that mining activity significantly increased 6,200 years ago, requiring a significant increase in the number of slave laborers, corresponding to the time of the East Europe Chalcolithic cultural collapse when East European people involved in the copper industry and their culture mysteriously disappeared. The lake deposit lead contamination also shows that the copper mining activity peaked at the time of the end of the African humid period, then activity fell off rapidly as if the mining activity in North America was dependent upon the economy of North Africa, dependent upon grain being produced in North Africa.

On 2018-10-22 08:57:58, user Inna wrote:

Another excellent article about "The global reading method"<br /> https://ekidz.eu/en/the-glo...

On 2019-06-12 07:36:54, user Bowen Tang wrote:

where is the etable?

On 2020-05-29 14:53:17, user Jan Lötvall wrote: