On 2022-12-21 18:21:28, user Roberto wrote:

Do you know if any medic applies this technique to the public?<br /> thanks for this information, from mexico

On 2014-09-09 17:38:43, user Emily Josephs wrote:

What's pi in baboons and how does it relate to the number of mismatches that was allowed when mapping? Just curious if there's the potential for 'technical' eQTLs due to reads with many mismatches being dropped, so in polymorphic regions the non-reference allele appears associated with lower expression. This would be consistent with the observation that polymorphic genes are more likely to have eQTLs.

On 2020-02-13 10:30:00, user lucky micky wrote:

23602...23613 of covid-19 (TCCTCGGCGGGC) looks like a insert of RaTG13 at 23584, which turns to a RRAR furin protein point.

On 2022-08-16 05:06:23, user Yongheng Wang wrote:

Thank you for sharing! For your convenience, a better-formatted PDF can be found here: https://drive.google.com/fi...

On 2017-04-13 19:32:36, user davidroad wrote:

Great discorery!

On 2023-07-06 11:17:24, user Nick Leigh wrote:

This is a well written and clear manuscript comparing successful and defective heart regeneration in zebrafish versus medaka, respectively. The experiments are well designed and the interpretation is careful and thorough. These kinds of studies are essential and, now powered by single cell sequencing, can cast wide nets that enable unbiased description and investigation of this process. As clearly stated by the authors, the description provided here undoubtedly provides numerous follow-ups, questions, and hypotheses about regenerative success and failure. The authors should also be commended on creating a webtool to allow others’ to query their dataset.

“Cross-species data integration was effective as both zebrafish and medaka cells were represented in each major cluster”. Agreed that across the major clusters there is good agreement. I’m more curious about if this is potentially overfitting–are you losing a different cluster only present in one species? From published data, could we expect any different clusters between these two species? (addressed a bit later on with zEP cells). In general, it may be worth exploring a couple other strategies for cross species integration to try and prove this further (point 6 also addresses this). <br /> The scale of the interferon-deficiency in the medaka is striking. It’s mentioned that DAMPs from necrotic cells could be a driver of interferon responses, but building on some of your prior work (Balla et al. 2020 PMID: 32413307), are the zebrafish all harboring some virus at this point and the medaka not? Could a viral/microbiome-related reason result in lack of IFN signaling. Relatedly, it would be interesting to see if medaka have type IV interferons (https://www.nature.com/arti... "https://www.nature.com/articles/s41467-022-28645-6)") (and if these are included in this one-to-one comparisons/ if they are even annotated in the current version of the zebrafish genome). Finally, is there evidence of any DAMP response? For example, are there still other chemokines and cytokines (potenitlaly NFkB nuclear translocation) being produced in medaka and just specifically not an IFN signature? This is getting at the question of whether this is specifically lack of IFN signaling or if medaka are hyporesponse to, for example, DAMPs. <br /> Is recruitment responsible for increased macrophages in zebrafish or is it expansion of tissue resident cells? This could affect the conclusion drawn in medaka that they are not recruiting macrophages. <br /> Figure 3H, the proportion of TNFa positive cells is reported, but what about the absolute number? Given the relatively higher numbers of macrophages in the zebrafish it would be interesting to see how these compare. The ratio of pro versus anti-inflammatory macrophages could be an interesting metric to report. Do the zebrafish ever mount a substantial pro-inflammatory response? It’s suggested that highly regenerative animals undergo a quick switch from pro- to anti-inflammatory and this is important for regeneration, but data demonstrating that is sparse at best and the question remains if there is ever robust a pro-inflammatory response in regenerative animals. <br /> Paragraph starting with “We know relatively little about the makeup..” is a bit unclear. What type of cells are you referring to? Are these the fibroblast-like cells or fibroblasts? The concluding sentence leads one to believe fibroblasts are benign studied, but earlier on it’s discussed that “epicardial cells cells expressing collagens”. Do you find collagen expression by macrophages? (https://pubmed.ncbi.nlm.nih... "https://pubmed.ncbi.nlm.nih.gov/32001677/)"). Are mmp15/16 implicated in regeneration? <br /> Regarding the zEP cells and their potential uniqueness to zebrafish, it would be interesting to explore a samap or other tools and see if they still remain separate (https://github.com/atarasha..., https://www.biorxiv.org/con..., note: this paper integrates zebrafish heart single cell data with 4 other species and could be worth looking at). As noted by the authors, more work is needed here. Whole mount FISH of hearts from both zebrafish and medaka would be quite interesting to see if zEPs can be detected anywhere. <br /> The mammalian studies are interesting and could be worth expanding. It would be insightful to tie back into the first few figures and the major findings there. Can you learn anything new from the mouse dataset with the perspectives gleaned from the fish comparison? For example, what is happening with the ISGs in the mouse? It could also be interesting to compare to salamander heart regeneration to provide another evolutionary intermediate (https://www.nature.com/arti... "https://www.nature.com/articles/s41556-022-00902-2)") <br /> Do primordial cardiomyocytes wane with age? Do larval/developing medaka contain these cells and do these young medaka regenerate their hearts? (perhaps not experimentally feasible). <br /> What is the role for the compact myocardium when not in regeneration? Why is there so much diversity in its size across species? <br /> Do you think there is a unifying reason for lack of regeneration in medaka? You uncover quite a few differences.

Minor stuff: <br /> This is a biased comment, but it would be really interesting to know if there is divergence between replicates. You could pull out each sample with some genotype-based demuxing. Check out: https://www.life-science-al.... This might also aid with DE analysis (https://www.nature.com/arti... "https://www.nature.com/articles/s41467-021-25960-2)") <br /> “To investigate the contributions of epicardial-derived cells to the fibrotic response, we re-clustered all cells expressing epicardial-specific markers tcf21 and tbx18, and re-clustered them into four…” a bit confusing with double re-clustering here. <br /> Do medaka lack cortical cardiomyocytes or are they just less abundant? The last line of the figure 6 results section suggests an absence with the use of “lack”. <br /> One could consider side-by-side violins might better illustrate between time point comparisons. <br /> Figure 6E and G with numbers for cluster labels is not super clear. Perhaps these could be labeled with the top markers they express or more info added to figure legend to explain. Including on the figure the species for E-F and G-H could also help orient readers more quickly.

On 2023-10-25 00:37:30, user Jessica Garvin wrote:

Hi! I found your research incredibly engaging to read about. The variety of test results that you shared was especially notable. Since publishing this paper, have you worked on any other projects similar to it? I think it would be an interesting find to see whether or not you would have similar findings with strains comparable to the Tulahuen strain. Wonderful job on your work!

On 2016-10-12 11:55:31, user Gary W Miller wrote:

While we received no direct comments on this site, we did receive feedback via email from colleagues. We have made several changes to the manuscript and have submitted it to a journal for peer review.

On 2018-11-06 04:10:33, user Philippe Henry wrote:

This is a fantastic preprint, thank you Chris and team!

On 2018-11-12 22:58:58, user hari easwaran wrote:

Cool work on elucidating mechanisms during Senescence associated methylation.

On 2023-09-18 17:43:46, user J Wallace wrote:

I'm sorry, what new does this actually add? These "laws" appear to just be fundamental properties of DNA as known for literally decades. Please explain how this actually adds new knowledge to the field.

On 2020-07-02 18:01:31, user Kate wrote:

Interesting paper! What reference database did you use for assigning bacterial taxonomy? Minor point: alpha is intra and beta is inter, not vice versa. Were any of the negative controls sequenced?

On 2020-07-19 17:44:29, user rituparno chowdhury wrote:

Very Nice work! It is heartening to see that the RBD-ACE2 interaction free energy value obtained here is very similar to what this paper in ChemRxiv(here) got using well-tempered metadynamics simulations.

On 2025-03-13 10:31:41, user Diethard Tautz wrote:

There are two key conclusions in this paper: 1) an ancestral origin of house mice from the Indo-Pakistan region and 2) incomplete lineage sorting for the observed alle sharing between contemporary populations.

Both conclusions are not sufficiently supported, given the current published evidence. I suggest that the authors carefully study this previous evidence and adjust their conclusions accordingly.

1. Origin of the house mouse radiation<br /> To trace the region of origin based on population genetic data, requires sufficient sampling of the relevant regions and comparable sample structures. The broadest samplings available to date that address the question of the origin are published in (Afzali, 2024; Hardouin et al., 2015). They show a high diversity of lineages in Iran and a complex history of populations across Asia. <br /> Lawal and Dumont include only one population from Iran, but do not capture the full diversity that is already known from there.<br /> Further, some of the population samples used by Lawal and Dumont represent sampling from local regions, while others are from broad regions (PAK and IND). Given the known deme structure in mice (Linnenbrink, Teschke, Montero, Vallier, & Tautz, 2018), the broad samplings of PAK and IND may cover different lineages that should not be lumped into a single one. This issue would need a deeper attention in the overall analysis.

2. Incomplete lineage sorting instead of introgression<br /> This issue has been addressed in a large number of papers. All of these authors have carefully evaluated the two alternatives. The analysis presented by Lawal and Dumont is too superficial to make a strong claim that runs contrary to all of these previous data and analysis. <br /> Probably the strongest argument against incomplete lineage sorting is the existence of large introgressed haplotypes that segregate in the different subspecies and species and that were found to be subject to adaptive introgression (Song et al., 2011; Staubach et al., 2012). Several of such regions have subsequently been studied in detail and the long haplotype structures were confirmed, e.g. in (Banker, Bonhomme, & Nachman, 2022; Hasenkamp, Solomon, & Tautz, 2015; Linnenbrink, Ullrich, McConnell, & Tautz, 2020). <br /> Neither incomplete lineage sorting, nor long-term balancing selection can explain the existence of such long introgressed haplotypes, since they would be broken up by recombination over time. Note that although the mouse lineage divergence times seem "shallow" with being shorter than 0.5 Million years, the number of generations is actually high, given the short generation times. To suggest that incomplete lineage sorting is a possible explanation for the shared alleles would require to show with appropriate simulations that long haplotypes could be retained over hundreds of thousands of generations without breaking them up.<br /> Such simulations have been extensively used for the comparisons between Neandertals and humans, where introgression at specific loci is distinguished from ILS by comparing expected haplotype lengths.

Afzali, Y. (2024). Asian Mus musculus: subspecies divergence, genetic diversity, and historical biogeography. JOURNAL OF MAMMALOGY, 105(6), 1378-1391. doi:10.1093/jmammal/gyae075<br /> Banker, S., Bonhomme, F., & Nachman, M. (2022). Bidirectional Introgression between Mus musculus domesticus and Mus spretus. GENOME BIOLOGY AND EVOLUTION, 14(1). doi:10.1093/gbe/evab288<br /> Hardouin, E. A., Orth, A., Teschke, M., Darvish, J., Tautz, D., & Bonhomme, F. (2015). Eurasian house mouse (Mus musculus L.) differentiation at microsatellite loci identifies the Iranian plateau as a phylogeographic hotspot. Bmc Evolutionary Biology, 15. doi:10.1186/s12862-015-0306-4<br /> Hasenkamp, N., Solomon, T., & Tautz, D. (2015). Selective sweeps versus introgression - population genetic dynamics of the murine leukemia virus receptor Xpr1 in wild populations of the house mouse (Mus musculus). Bmc Evolutionary Biology, 15. doi:10.1186/s12862-015-0528-5<br /> Linnenbrink, M., Teschke, M., Montero, I., Vallier, M., & Tautz, D. (2018). Meta-populational demes constitute a reservoir for large MHC allele diversity in wild house mice (Mus musculus). Frontiers in Zoology, 15. doi:10.1186/s12983-018-0266-9<br /> Linnenbrink, M., Ullrich, K. K., McConnell, E., & Tautz, D. (2020). The amylase gene cluster in house mice (Mus musculus) was subject to repeated introgression including the rescue of a pseudogene. Bmc Evolutionary Biology, 20(1). doi:10.1186/s12862-020-01624-5<br /> Song, Y., Endepols, S., Klemann, N., Richter, D., Matuschka, F. R., Shih, C. H., . . . Kohn, M. H. (2011). Adaptive Introgression of Anticoagulant Rodent Poison Resistance by Hybridization between Old World Mice. Current Biology, 21(15), 1296-1301. doi:10.1016/j.cub.2011.06.043<br /> Staubach, F., Lorenc, A., Messer, P. W., Tang, K., Petrov, D. A., & Tautz, D. (2012). Genome Patterns of Selection and Introgression of Haplotypes in Natural Populations of the House Mouse (Mus musculus). Plos Genetics, 8(8). doi:10.1371/journal.pgen.1002891

On 2024-02-26 19:29:08, user Chris Estes wrote:

Am I going crazy, or have they misread Nemecek & Poore (2018)? Nemecek & Poore use a 100g of meat/kg CO2 equivalent, but in this paper they cite it as a kg/kg CO2.

On 2024-05-12 22:14:39, user Elliot Swartz wrote:

Please see a summary of problems with this study: https://gfi.org/wp-content/...

On 2017-03-01 23:44:59, user gedankenstuecke wrote:

Just one thing I stumbled upon: "Licenses are also important to protect you from others misusing your code." This needs some explanation in what is meant imho, because as far as I can tell there aren't any OSS licenses that make real limitations potential "mis-re-use" (as in "research i don't approve"), but rather give limitations on e.g. commercial uses?

On 2020-01-31 00:46:52, user Charles Warden wrote:

Thank you for posting this interesting and well-organized paper!

On 2021-04-27 23:02:36, user Prasanthi Kunamaneni wrote:

I enjoyed reading your paper and appreciated that the experiments were thorough and well-designed to assess fascin’s role in lung cancer. Some general comments that I had for the figures presented in the paper:

1.) In Figures 1G-H, the data felt redundant when comparing between different human cell lines for lung cancer in both glycolysis and glycolytic capacity conditions as the data results were similar. It may be effective to specifically focus on comparing between one human cell line (H1650) versus a mouse cell line (LLC) to better assess the similarities and differences in both glycolysis and glycolytic capacity. This could also give room to expand on other data sets in Figure 1 and make them easier to analyze, as many graphs were too small to read.

2.) For Figure 4H, the data other than WT was hard to interpret and too small to read. It would be helpful if there was a different way to represent the y-axis for the graph so that the data could be better analyzed.

3.) For Figures 5C-D, it was difficult to analyze the waterfall plot data as it was packed with information and I was also confused with the shift from correlation (r) to log (p-value). It would be helpful if this data was expanded in the results or discussion section or if there was another format to represent the data that came from the RNA sequencing database to make it easier to interpret.

Overall, the paper was very interesting and enjoyable to read. Great work!

On 2015-11-03 18:10:56, user Nikolas Pontikos wrote:

Very happy to see this paper, ExAC is great, I'll be presenting it at a journal club.<br /> Please find below my list of corrections (I'm updating my post as I find more):

Page 24, caption of figure 3: "f) MAPS (Figure 2d) is shown for each functional category, broken down by constraint score bins as shown." Shouldn't it Figure 2.e?

Supplementary page 12, 1.15 Additional files: Google drive link does not work:<br /> https://docs.google.com/spr... GGyKNYs/edit?usp=sharing

On 2025-10-20 22:16:51, user CDSL-YINING wrote:

This manuscript is strong in logic. The authors choose 2 models and use them to validate a clear post-entry restriction for LAMV. They separate entry/transcription from productive replication with layered readouts and align these with signaling kinetics (p-STAT1/2, ISGs), so the causal link to rapid IFN-? activation feels credible rather than assumed. Figures and statistics are proportional to the claims, and the writing stays close to the data, avoiding overreach. I have one question for IFN-?, the data emphasize IFN-? (transcripts/protein and signaling), and IFN-? appears lower, but its functional contribution is not directly excluded.Do you plan to do more experiment to further exclude IFN-??

On 2018-03-01 19:02:09, user Emily wrote:

Note: This paper was published in Artificial Life, a peer-reviewed conference (they are common in computer science), and can be cited as: Emily L. Dolson, Michael J. Wiser, and Charles A. Ofria. The Effects of Evolution and Spatial Structure on Diversity in Biological Reserves. In Artificial Life XV: Proceedings of the Fifteenth International Conference on Artificial Life . Edited by Carlos Gershenson, Tom Froese, Jesus M. Siqueiros, Wendy Aguilar, Eduardo J. Izquierdo and Hiroki Sayama. pp. 434 – 440. DOI: 10.7551/978-0-262-33936-0-ch071. MIT Press. 2016.

The final published version of the paper is here: https://mitpress.mit.edu/si...

On 2019-04-26 17:10:06, user Kristen Naegle wrote:

From the UVA Systems Biology Journal club discussion of this paper 4/23/19

We found this to be a really interesting paper with a timely machine learning method on a topic with a lot of room to advance. The authors do a great job motivating the needs in the field, based on limitations of existing methods. Specifically, it is exciting to see a method that seeks to learn globally from all kinases and to extract kinase features that shape kinase-substrate specificity. We found we could not completely understand some key features of the model and its use with the text as it stands and we hope our experience with this manuscript, as outlined below, will be of help to the authors.

Models and model interpretation<br /> We had some confusion about the model as implemented, especially around whether certain aspects were used to make the model interpretable vs. what was in the model. <br /> 1. PSSMs: A major strength of the neural network approach is the ability to learn and encode conditional dependence between positions in the kinase and amongst positions in the substrate. However, as currently depicted in the approach, it seems that the final predictor relies on collapsing the RNN model into a PSSM and scoring substrates across RNN-derived PSSMs. If this is the case, it is unfortunate to rely on a scoring methodology that is incapable of incorporating conditional dependence between positions. It would be great if the paper could clarify the methodology and explore prediction results that avoid the PSSM as a primary scoring function. <br /> 2. Attention Matrix: The attention matrix is really interesting and has a lot of power to explore specificity determining positions. However, we were unclear about some of the details about the attention matrix, its use, and its presentation in this work:<br /> 2a. Is the feature selection process that determined the attention matrix values used in the final classifier? As written, we were unclear about this. On the one hand, performance as a function of forward feature selection was given. On the other hand, if there are ultimately only 15 kinase sequence features used, then it seems unlikely that that broad range of mutations lands in those features and would make it impossible to score differences as a result of kinase mutations. <br /> 2b. The attention matrix in Figure 2 appears to highlight more than 15 kinase features, and suggests there are family-specific kinase features. However, the text suggests there was a universal set of 15 kinase features. How these 15 were chosen was also under debate in terms of the effectiveness and resolution of the feature selection method. Given the intense growth in performance between 5 and 15 features, it seems it would be beneficial to increase the testing of performance at a higher resolution (1:15 features with one at a time addition).<br /> 2c. It was clearly stated how many features selected by DeepSignal overlapped with KinSpect and DoS, but it would also be nice to know how many KinSpect and DoS features were not identified by DeepSignal (set differences vs. set intersections). <br /> 3. Model Details: <br /> 3a. Is this a “deep” neural network - where are the layers of convolution? Are there hidden layers?<br /> 3b. What are the exact inputs to the model?<br /> 3c. How long is the sequence retained in the recurrent neural network? Is there a limit to how far back the LSTM considers? <br /> 3d. How is allostery incorporated in the model (e.g. as conditional dependence)? Long-range interactions not encoded in local sequence space would appear to be missed unless the entire sequence is considered throughout the recurrent neural network.

Figure 3 and related methods:<br /> The choice of negative data is hard when the training set only contains positives. The authors used a method that is consistently used in the field. However, because it is a random draw and makes many assumptions about the draw (that there are not false negatives in the set), we felt it would be beneficial to test the robustness of conclusions drawn by repeating this analysis across many resamples of a negative set. This would help us understand the sensitivity or robustness of the conclusions to that particular selection of data. Additionally, it is not clear what model hyperparameters have been tuned to generate the precision-recall and AUROC analyses for the comparator predictors.

Generalizability of learning on global kinases and training misbalance<br /> We were intrigued by the results in Figure 2E. We think this is a really interesting experiment to test applicability of a globally learned model. We noticed that the only tyrosine kinase in this batch (as a result we assume of being the only tyrosine kinase with more than 100 substrates annotated in the training set) was affected the most when predicted by a model of all kinases in that set, when compared to a single-kinase SRC model. We feel that may suggest that if a training set is predominantly skewed towards serine/threonine kinases that it will not produce the ideal model for tyrosine kinases. As tyrosine and serine/threonine signaling are separated both evolutionarily and physicochemically, it seems reasonable to make two models of kinase-substrate predictions and explore the results of those independently to assess whether the attention value matrices and performance differ greatly. We also wondered if data skew in Figure 2E analyses or more broadly could be a factor (perhaps it would be beneficial to add an analysis of the training data itself).

Mutation analysis<br /> In addition to the confusion we noted earlier about how the attention value matrix and feature selection is wrapped back into the model and its effect on the ability to test mutational effects, we also wondered what the “false positive rate” was on determination of cancer genes as a function of kinase-substrate misregulation using DeepSignal. The authors focus on capturing known oncogenes (as a function of percent covered), but we wished to know how many total were predicted to be detrimental and whether this differed greatly between DeepSignal and MSM/D-PEM (i.e. both specificity and sensitivity). One representation that might be helpful is to display the total number of predicted cancer genes with the proportion of true highlighted in the subset.

SH2 domain analysis<br /> As some of our members are very familiar with the problems with the published SH2 domain data (e.g. that they cannot be merged as there are disagreements, different types, and different scales), we understand why the authors chose to build individual models for each dataset. However, in the mutation analysis, it is unclear what final SH2 domain model they used and the authors do not provide the same level of detail on what was learned in the SH2 domain as they did for kinases. In addition to providing more clarity in the methods used for mutation analysis (as it relates to SH2 domains), it would likely be beneficial to do a sensitivity analysis in the outcomes about predicted oncogenic mutations as a result of isolating the kinase and SH2 domain components. Finally, although the paper used was cited, it would be helpful to describe in more detail exactly how an oncogene was determined for readers to better interpret the method and results provided here.

Signed by:<br /> Kristen Naegle, Ben Jordan, Kevin Janes on behalf of the University of Virginia Systems Biology Journal Club (Journal Club of 4/23/19)

On 2020-03-26 01:04:02, user Sinai Immunol Review Project wrote:

Using in silico bioinformatic tools, this study identified putative antigenic B-cell epitopes and HLA restricted T-cell epitopes from the spike, envelope and membrane proteins of SARS-CoV-2, based on the genome sequence available on the NCBI database. T cell epitopes were selected based on predicted affinity for the more common HLA-I alleles in the Chinese population. Subsequently, the authors designed vaccine peptides by bridging selected B-cell epitopes and adjacent T-cell epitopes. Vaccine peptides containing only T-cell epitopes were also generated.<br /> From 61 predicted B-cell epitopes, only 19 were exposed on the surface of the virion and had a high antigenicity score. A total of 499 T-cell epitopes were predicted. Based on the 19 B-cell epitopes and their 121 adjacent T-cell epitopes, 17 candidate vaccine peptides were designed. Additionally, another 102 vaccine peptides containing T-cell epitopes only were generated. Based on the epitope counts and HLA score, 13 of those were selected. Thus, a total of 30 peptide vaccine candidates were designed.

While this study provides candidates for the development of vaccines against SARS-CoV-2, in vitro and in vivo trials are required to validate the immunogenicity of the selected B and T cell epitopes. This could be done using serum and cells from CoV-2-exposed individuals, and in preclinical studies. The implication of this study for the current epidemic are thus limited. Nevertheless, further research on this field is greatly needed.

This review was undertaken as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.

On 2020-05-20 16:25:29, user Ugo Bastolla wrote:

This is a very interesting paper. I think that the observation that children express ACE2 at very low level is extremely important, not only in the context of covid-19.

However, I have concerns on two conclusions:

1) "ACE2 and TMPRSS2 expression in airway epithelial and AT2 cells increases with age". The data presented by the authors in Fig.3g clearly show that ACE2 levels increase from fetal-infant samples to adult age. Nevertheless, the same figure 3g also seems to show a decrease of ACE2 expression from a maximum attained at age class 25-40 for AT2 cells (lungs). There are no error bars in the figures for judging whether the decrease is significant. Nevertheless, a previous paper reported a decrease of ACE2 protein levels in rat lungs (Xie et al. Age- and gender-related difference of ACE2 expression in rat lung. Life Sci. 2006 78:2166) and a recent preprint that analyzed the GTex database confirmed the result in humans (https://www.preprints.org/m... "https://www.preprints.org/manuscript/202003.0191/v1)"). In Multiciliated cells (fig.3g of the present preprint), it seems that the maximum is attained at lower age (10-25), and there is a secondary peak at age 40-60 that I suspect may be related to smoking, since the authors showed that smoke enhances ACE2 expression, a large fraction of samples were from smokers (50% males and 25% females), and the age 40-60 is likely to be enriched of smokers.<br /> Therefore, it is fair to say that ACE2 expression increases from children to adults, but for adults the present data cannot overturn the previous observation that ACE2 expression decreases at old age.

I admit that I may be biased in this comment since, building on the publication cited above, I proposed a mathematical model that rationalizes the negative correlation between ACE2 expression and lethality (http://arxiv.org/abs/2004.0... "http://arxiv.org/abs/2004.07224)"). My model isbased on a previous mathematical model that predicts that the virus propagation can be slowed down increasing the receptor level when the viral receptor binding protein has a very fast binding rate as SARS-COV-2 spike has. Notably, the model not only fits SARS-COV-2 lathality across age and gender for different countries, but it also predicts the lethality profile of SARS-COV-1 using the ratio between the binding constants of the two proteins. Based on the results of the present preprint I shall have to modify my paper, but I think that the main results hold: the very low level of ACE2 in children lungs is consistent with the fact that they are unlikely to suffer pneumonia, and the high level in multiciliated cells at ages 10-25 predicts slow viral propagation in the upper respiratory tract.

2) "ACE2 expression is higher in men". This contrast with the above cited papers, which indicate that the expression of ACE2 is higher in women, consistent with the fact that ACE2 is located in the X chromosome of which women have two copies while men have only one. Once again, this observation suggests a negative relationship between ACE2 and lethality, not a positive one as the authors expect. I think that the conclusion that ACE2 expression is higher in men was slightly biased by this expectation and by the fact that in the analyzed data set there were more male smokers (50%) than female ones (25%), and that smoking enhances ACE2 expression. It should be necessary to correct the smoke bias, but I suspect that it was amplified, since the authors introduced an additional fitting parameter that models the interaction between smoke and sex and write that "It should be noted that modeling interaction terms was crucial as their omission resulted in reversed effects for age and sex for particular cell types". In my experience in computational biology, when there are correlated explanatory variables (sex, smoke, interaction between smoke and sex) and the fit is not regularized with ridge regression or some other regularization, it is very likely that the fitted parameter takes a sign that contradicts the physical expectation (for instance, if you do not regularize the fit of B-factors predicted with elastic network model, you can obtain a negative force constant as fitting parameter even if the fit has only two parameters). In this case, the physical expectation is that, since ACE2 is located in the X chromosome, its level is expected to be higher in females than in males. To prove that this expectation is incorrect would require strong evidence.

On 2024-03-18 12:57:36, user Data wrote:

You may want to check this paper that introduce a new pathway enrichment analysis and compared it with GSEA. https://academic.oup.com/bi...

On 2018-03-22 14:50:48, user Jeremy Berg wrote:

Hi Yun and coauthors,

We read your preprint on geometry of the SFS this week. We had a few comments, questions, and suspected typos we wanted to pass along. I know the preprint's been up for a little while already, so hopefully these comments arrive in time to be useful. In general, I think all three of us really appreciated this paper. The meat of the paper is definitely challenging, and a couple of the proofs were a bit beyond us, but ultimately the reader is rewarded for persevering with a very clear explanation of problems that arise in SFS demographic inference with section 5 and Figure 5. I guess we had two major comments, both having to do with our not fully understanding some of the geometry:

1) We thought it would be helpful if you could be more explicit in describing to the reader how the formula for M_1 makes it obvious that its columns lie on a surface, by providing the map S: (0,1)X(0,1) -> R^{n-1}. It took us all a little while to see this clearly.

2) You explain the "mathematical" significance of the orange region in the normalized C_{4,3} (Figure 4): that it "is the image of the surface described by the columns of M_{1} (4,3)". But it was not clear how to interpret this in terms of the model; that is, if there is anything special about the region besides this mathematical significance. To be more precise, is there is some distinguishing feature of population size histories with image under X(x,y) lying in the orange region? If so, it would be helpful if this feature was described.

More generally, we know that C_{4,2} is contained in C_{4,3}. Is there some easily describable distinguishing features of population size histories with image in the two disconnected regions of C_{4,3} that are not in C_{4,2} (i.e. elements of C_{4,3} that cannot be realized with just two epochs). Further, what is the difference between those with image in each of the two disconnected regions.

typos:

• there seems to be a 't' missing from the integral in equation 1?
• in the caption of figure 4, you write "\Xi_{4,2} is obtained from C_{4,3} by a linear transformation." Probably you mean to write "\Xi_{4,3} is obtained..."
• In the caption of Figure 2a you write: "As y_1/y_2 -> 0, the image approaches \gamma_2 and as y_2/y_1 -> 0, the image approaches \gamma_1". It's the other way around, right?
• on page 19 in the proof of the upper bound in Theorem 4.3 you write "at most 2n-2 of the indicies of the vector at right are nonzero". We think you mean "entries", not "indicies".

Cheers,<br /> Jeremy J Berg<br /> Laura Hayward<br /> Yuval Simons

On 2024-10-31 18:13:20, user Avi Flamholz wrote:

See detailed comments here https://prereview.org/reviews/14019384

On 2024-07-10 03:40:12, user Zach Hensel wrote:

This article does not match my experience in Okinawa and the caricature of Okinawa here is not necessary to make the point.

Some of the claims are simply wrong (e.g. the description of civil marriage registration). Others are caricatures for rhetorical effect (e.g. "14 cans of SPAM" is not what the reference says). In general, the list of supposed ills in Okinawa today has no direct connection to the longevity of today's 100-or-so-year-olds.

I hope that the author can speak with people in Okinawa and perhaps reconsider this approach.

On 2021-11-22 22:20:24, user Alizée Malnoë wrote:

The manuscript by Lei Li et al. reveals how plants maintain proteostasis under high light stress via a combined analysis of protein degradation rates, transcripts and proteins abundance in Arabidopsis. The authors performed a partial 13C labeling assay and identified 74 proteins with significant turnover rate changes in high light compared to standard light. Then they compared the transcriptional level and protein abundance of those 74 proteins and found negligible correlation between them, but a strong correlation between the turnover rate of the proteins encoded by nuclear genes and their transcripts. This study significantly advances the field of stress responses in plant biology with the findings of new direct or indirect targets of photodamage and how transcriptional processes counteract protein degradation to maintain proteostasis under high light.

Major comments<br /> - Please provide qPCR data to verify the RNA-seq results on representative genes showing significant changes e.g. RH2A2A, FTSH8, PARG2, BCS1, PUB54 in Fig 2C. For Fig 2A, a Venn diagram or an intersection analysis would be more informative.<br /> - Please describe in more detail how the LPF and especially PTO values were calculated based on the 13CO2 labeling experiment in the method.<br /> - Please explain the lack of change in D1 accumulation in Fig 5B and provide D1 immunoblot for each time point. Also indicate the meaning of NA in the legend.<br /> - In Fig 3, clarify the reasoning behind using the same peptide for THI1 and PIFI to calculate LPF in the two light conditions but different peptides for PSBA. Please provide an explanation in the text for calculating LPF using 2h HL for PSBA, 5h for THI1 and 8h for PIFI. What about the LPF from the same protein, such as PSBA, at different time points? Please provide an explanation of the absence of time points for PSBA, THI and PIFI.<br /> - Line 209, please add a sentence to explain that you are assuming that the translation rates are similar for all the detectable proteins in your manuscript. Indeed if the translation rate is different in HL compared to normal light for a given protein, then this will affect its labeling and thus estimation of the degradation rate.<br /> - Line 128-131, phenylalanine, tryptophan, and tyrosine are not abundant throughout high light treatment, and especially at 8h high light, they are back to the level in standard light. Rewrite these sentences to better reflect the results.<br /> - Line 168, comment on down-regulated proteolytic pathways in cytosol.<br /> - Abstract about plastid-encoded proteins, it should be noted that the distinction is made based on four observed proteins, do you think a generalization can be made for other plastid-encoded proteins?

Minor comments<br /> - Fig 1, A, B, C, D, the Y-axis and ticks on the axes should be added for more readability; A,B, add x-axis legend D, Y-axis should start at 0.<br /> - Line 118, do you mean that heat can induce NPQ by “contribute”? Please provide a reference and the leaf surface temperature measurements.<br /> - In Fig2 C, define pink color for p-values.<br /> - In Fig 3, it is difficult to distinguish the light green and dark green in the histogram. We suggest changing the color for the natural abundance (NA) or the newly synthesized peptides, label the x-axis and to use another acronym for "natural abundance".<br /> - Line 211, "one-third to one-half". Three LPF are presented in standard light conditions, the lowest being 28.5% and the highest 41.2%, that’s not “one-half” or does this refer to other proteins with LPF of 50%? In that case, data is not presented. Clarify or include the data in Table S4.<br /> - Line 216, how is the KD value calculated?<br /> - Line 235, it is difficult to identify PSBP in Fig 4. Please make it clearer.<br /> - Please show your protein Coomassie Blue staining results from the in-gel digestion for MS as a supplementary figure to see the amount of total proteins compared to explain the variation shown in Fig S2A.<br /> - Throughout text, make sure when you say "high light" to specify which time point (2h, 5h or 8h?).<br /> - Line 301-303, ferredoxin thioredoxin reductase also showed a significant abundance decrease after 8 hours. Please comment this in the text.<br /> - Line 344-347, the lower Fv/Fm level after longer high light exposure is not only due to the uncoupling of D1 degradation from its synthesis rate but also due to sustained NPQ forms such as qI (see Malnoë EEB 2018, doi.org/10.1016/j.envexpbot.2018.05.005).<br /> - RNA-seq method: which fold-change threshold was selected to consider the candidates? How many technical replicates were used?<br /> - Line 352, you state that protein degradation is supported by up-regulation of protease gene expression, but what about their degradation rates? In Chlamydomonas, FtsH transcript is upregulated in high light but its rate of degradation is also faster resulting in a modest higher accumulation of the FtsH protease (see Wang et al. Mol Plant 2017, doi: 10.1016/j.molp.2016.09.012).<br /> - Line 374, you state that translation failed to keep pace with protein degradation, you could cite work on chloroplastic translation rate being affected by oxidation of translation factors in cyanobacteria (see Jimbo et al. PNAS 2019, doi.org/10.1073/pnas.1909520116).

Jianli Duan, Jingfang Hao  (Umeå University) - not prompted by a journal; this review was written within a preprint journal club with input from group discussion including Alizée Malnoë, Maria Paola Puggioni, André Graça, Aurélie Crepin, Pierrick Bru.

On 2023-03-17 20:20:35, user CJ San Felipe wrote:

PTP1b has been an attractive target for drug development due to its essential role in several cellular pathways and diseases such as type 2 diabetes. Focus has been paid to identifying allosteric sites that regulate catalytic activity via altering the dynamics of the active site WPD loop. However, the structural mechanisms underlying the WPD loop opening and closing (which is relatively slow by NMR) remains unclear.

In this paper, the authors sought to identify the structural mechanisms underlying PTP1b loop motion by performing long time scale molecular dynamics (MD) simulations. Starting from existing structures with the WPD loop either open or closed, they are able to derive reasonable estimations of the kinetics of loop opening and closing. They address the question of what structural changes need to occur for the loop to remain open or closed as it fluctuates. Using a random forest approach, they narrow their focus down to the PDFG motifs backbone dihedrals as a set of features sufficient for describing and predicting loop movement between states. The major strength of this paper is reducing the WPD loop conformation (including transient states) down to a set of reaction coordinates in the PDFG motif dihedral angles. Based on this minimum set of features, the committor probabilities provide a strong statistical argument for the transition between open, closed, and transient states along the loop trajectory.

The major weakness of this paper is that the visualizations describing the PDFG motif switch model are insufficient and confusing and lack an atomic explanation of how these dihedral changes occur in the context of surrounding residues to complement their statistical explanations. This makes it difficult to interpret what the actual transitions look like. We understand that the atomic explanation of this mechanism can be complicated but refer the authors to this paper as an example even though it is a different target and may not be specifically relevant to their work: https://www.ncbi.nlm.nih.go... (Fig 3)<br /> The reaction coordinates alone do not provide a clear direction for envisioning future experiments. Given that this motif is conserved (as the authors explained), other PTP members likely have different structural environments surrounding the motif which likely affects kinetic rates and thermodynamics.

Major Points:

Previous structural studies of PTP’s have identified atypical open loop conformations in GLEPP1, STEP, and Lyp: https://www.sciencedirect.c... Fig 3A. These loops adopt a novel loop conformation that is more open compared to PTP1B. Further, the presence of catalytic water molecules that are tightly bound in closed states and absent in open states have been suggested to play a role in the closing of the WPD loop. <br /> Can the authors provide comments on how the PDFG motif factors into the novel open loop conformation (would the motif dihedrals still predict loop states in these family members)? <br /> Were water molecules detected in the binding site and do they play a role during loop closure?<br /> Is it possible to include within these simulations mixed solvent MD with a PTP1B substrate to explore their roles in the loop transition?<br /> “We note that although the PD[F/H]G BLAST search did return matches in other protein families, there was not the structural information corresponding to those matches that would be needed to draw further conclusions on the conformational significance of PD[F/H]G motifs in those families.” - We feel this is a missed opportunity to at least do some exploration and cataloging using the alphafold structures of these other families.<br /> The authors describe the backbone dihedrals of the PDFG motif as being sufficient and necessary for predicting WPD loop conformation but do not mention the side chain conformations. We feel that the explanation and visualization of the side chain conformations in both open and closed states is unclear as there is no analysis of how these transitions and conformations affect the populations and rate movement of the loop. <br /> What do the rotamer conformations and transitions look like for the PDFG during open, closed, and transient WPD loop states? <br /> How do these rotamer conformations affect loop movements and populations within the simulation? <br /> It would be insightful if the authors could provide an explanation of the rotamer transitions during loop opening and closing. Understanding these structural changes during substrate binding and catalysis could yield targets for drug development.

Minor Points:

Supplementary figures S2, S3, S4, and S5 have little to no information to adequately explain what is being illustrated. The authors should be more clear in describing what these figures represent. A description of axes, experimental set up, and legends would be helpful. <br /> The observation that loop fluctuations without long term stability unless the PDFG motif switches is reminiscent of the population shuffling model of conformational changes put forward by Colin Smith - https://onlinelibrary.wiley.... Given the previous NMR data on PTP1B, how does this view alter the interpretation away from a strict two state model?<br /> “The free energy estimate from these AWE simulations was ?Gclosed-to-open = –2.6 ± 0.1 kcal mol-1, indicating that the transition from closed to open states is spontaneous (Figure 2b), a finding that is again consistent with experimental data” We are a bit confused by the language here: is this a thermodynamic or kinetic argument? Secondarily, how do the populations compare to those derived from NMR?<br /> As previously discussed in a twitter thread with the authors, the backbone ramachandran regions of the 1SUG structure (closed WPD loop conformation) is not in a region previously known for kinases. It would be helpful if the authors could provide validation that the backbone ramachandran regions of the WPD loop are in agreement with what is known about kinases states and whether this would affect their interpretations. <br /> https://twitter.com/RolandD...

On 2025-08-10 13:13:31, user Claire Meissner-Bernard wrote:

This preprint is now published in Cell Reports: https://doi.org/ 10.1016/j.celrep.2025.115330

On 2020-09-09 09:30:36, user Yorgo Modis ???????????????? wrote:

Now published in Biochemistry as https://doi.org/10.1021/acs.biochem.0c00466

On 2020-02-28 08:45:02, user Chowlee1211 wrote:

Has there been any testing on getting the brain, using neural link, to get it to work outside the body of the rats?

On 2019-07-20 12:42:58, user Marcus wrote:

Actually regarding my last comment, you can ignore the concern for chromatic aberration since I now note you used the same 488 nm excitation for both GFP and PI.

On 2020-04-19 06:01:03, user Rajendra Kings Rayudoo wrote:

To <br /> Manish tiwari and mishra

By following paper I came to know the mutation in coronavirus the first from place to place and changes its nucleotide

So by this vaccine in one area cannot be worked to another area<br /> Is it right

On 2020-04-17 13:53:15, user Domenica wrote:

We have used the following datasets

1. Series15_HealthyLungBiopsy_1
2. Series15_HealthyLungBiopsy_2
3. Series15_COVID19Lung_1
4. Series15_COVID19Lung_2

but we had a big problem with

1. Series15_COVID19Lung_1
2. Series15_COVID19Lung_2

samples.<br /> They were mapped on the last human genome version from ENSEMBL with STAR+RSEM

The quality of data is not good and impossible to get any type of mapping. The majority of reads is represented by reads with this sequence:

GATCGGAAGAGCACACGTCTGAACTCCAGTCACTCTCGCGCATCTCGTATGCCGTCTTCTGCTTGAAAAAAAAAAGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG

That is the Illumina adapter plus a string of A and G. Once you eliminate these reads what is still inside are viral reads and very few human reads that are insufficient for statistical analysis. Please, can you eplain the reason of these results on samples of patients?

On 2020-04-06 09:58:12, user Jorge wrote:

Dear authors,

Congratulations on this work, very helpful.<br /> Could you please provide stat values for the RNA-seq data of SARS-cov-2 in NHBE cells?

Many thanks<br /> Jorge

On 2019-05-16 21:51:48, user Alberto Gonzalez wrote:

Italy1 influence needs to be reviewed, specially after the last paper from Olalde suggesting that modern Spanish people are in between Basques and Southern Italians since the Roman period.

On 2018-03-13 22:10:20, user Prime wrote:

"Is unique among European regions in having a centuries-long period of Muslim rule". What about the ottoman Turks? They were Muslim and occupied south eastern Europe for centuries.

On 2018-04-25 10:19:15, user Andor Kiss wrote:

So, really dumb question (April 25th, 2018) - why hasn't this been published, formally, by a journal yet? Is this the publishing delay in the "Stats discipline" I've heard about? (I'm a biologist).

On 2024-09-04 19:28:31, user Haihui wrote:

Great work, Vikash! Could you please also share your supplemental data? There're no links for those S figures. Thank you very much!

On 2025-02-23 02:33:22, user Cecil Saunders wrote:

I believe there is a typo in the abstract citation; Charles, 2019 should be Darwin, 2019.

On 2020-07-02 09:54:53, user Institut für Immunologie wrote:

Our paper has been accepted for publication in Journal of Extracellular Vesicles on 27th of June 2020.

On 2021-01-22 01:55:15, user Zhiyong Liu wrote:

We have used genetic approaches to show that, in the presence of cochlear outer hair cell (OHC) damage, adult cochlear supporting cells can be transformed into OHC-like cells by simultaneous expression of Atoh1 and Ikzf2. We are looking forward to receiving comments and suggestions about how to further move forward. Thanks

On 2019-03-06 18:08:24, user John Dziak wrote:

I apologize to readers for an error in a formula in our manuscript. Expression 3 on page 12 should read B[ij] = Pr(y|Mi)/Pr(y|Mj), not Pr(Mi|y)/Pr(Mj|y). Under the assumption that Pr(Mi)=Pr(Mj), using Bayes theorem it can be shown that Pr(Mi|y)/Pr(Mj|y)=Pr(y|Mi)/Pr(y|Mj), because Pr(y) cancels out from the numerator and denominator.<br /> However, the actual definition of the Bayes factor is B[ij] = Pr(y|Mi)/Pr(y|Mj).<br /> -- John Dziak

On 2024-01-14 19:05:53, user Halfmann Lab wrote:

Most of the data and narrative in this preprint is now published in Kandola et al. 2023: https://doi.org/10.7554/eLi...

On 2021-09-16 07:24:05, user Stefano Vianello wrote:

This paper describes a fascinating epithelial behaviour! For craniofacial development non-experts, would you be able to clarify the germ layer origin of the palate and of the Midline Epithelial Seam? Given that the mouth contains contributions from both ectoderm and endoderm I am wondering which of the two makes the epithelia you describe. And whether this could be a behaviour of developing epithelial cells more generally (regardless of germ layer). Thank you in advance!

On 2022-09-20 15:19:05, user Doug Miller wrote:

In the third paragraph of the introduction, "as well as a linear readout of electrophysiological recordings from high-level visual cortex (schematized in Fig. 1d: left)." There isn't a Fig. 1d, and I don't see a schematic that seems to match. Was this missed?

Otherwise, this is great and I'd love to see more of this type of collaboration.

On 2021-03-21 17:12:26, user Laurent Despeyroux wrote:

Hi,

The incidence rates presented on line 84 are inconstant : if the incidence for cats is 8.5% and 4.3% for dogs, the global incidence for "the rain" should be between 4.3% and 8.5% not the sum !

BR.

On 2014-04-06 14:18:35, user Maju wrote:

I would like to, first of all, thank the authors for this excellent study, whose rigor, detail and informative value I can only admire.

However I need to highlight one issue that I am not fully satisfied with: the concept of "Basal Eurasian" (BEA) and the related tree-modeling node "non-Africans". I know well that these are mere labels and that the concept is not really explored in any detail in the study. But, judging on the BEA admixture on La Braña and the fact that the African-derived patrilineage E1b-V13 is so important in relation to early European farmers (Lacan 2011), as well as to modern Europeans (notably in the SW Balcans), I can't but feel that these terms do not really make justice to the very likely African (or partly African) origins of this component.

The fact that BEA and "non-African" are only defined as being downstream of the Mbuti (not really representative of all Africans, even those with no Eurasian admixture, but a very old distinct branch themselves) shows that there is no clear "non-Africanness" of this branch.

I would really appreciate some further consideration on "what is Basal Eurasian?" in fact, especially because the terms used may suggest something that is not or not fully so.

Personally I suspect that the BEA influence on La Braña is NW African aboriginal residual (of Aterian origins possibly), which would have arrived to Iberia in the Solutrean-Oranian interaction and may still be apparent in West Iberia at the very least in significant frequencies of NW African lineages E1b-M81 (yDNA) and U6 and L(xM,N) (mtDNA).

Instead the proto-EEF BEA element may have two different sources: on one side the NE African element so apparent in yDNA E1b-V13 and on the other maybe a residual aboriginal Arabian element persistent since the OoA migration.

On 2021-04-06 06:11:24, user Om Prakash Gupta wrote:

Its good work to identify the molecular defense pathway operating during Capsicum annuum L.-Phytophthora capsici L. pathosystem.

Suggestions are welcome.

On 2020-12-14 22:47:21, user Stephanie Gogarten wrote:

Really interesting paper and visualization approach! Grouping populations by current residence rather than ancestral origin makes a lot of sense, but I'm curious why you didn't group the GIH (Gujarati Indians in Houston) in the AMR super-population as well.

On 2017-05-02 19:38:30, user Christoph Nowak wrote:

"... is largely UNexplored" ?

On 2019-01-10 19:08:23, user Guillaume Charron wrote:

in the sentence beginning line 639:

It is also possible that SpC* originated as a hybrid species that has since then undergone further introgression only with SpB.

did you mean SpC instead of SpB?

On 2021-02-15 23:37:39, user Csaba Soti wrote:

Our study has been accepted and published in BMC Biology.

On 2021-06-02 14:17:58, user Rob Patro wrote:

This is an interesting approach, and the results look quite promising. In particular, the semi-reference-based compression scheme adopted in RENANO_2 that frees the decoder from having to have access to the exact reference used to compress should make the tool easier to use in a broader variety of cases. I just want to point out what may be a relevant citation for the semi-reference-based compression idea in a technique my student and I developed a few years ago (focused on short-read compression) : https://academic.oup.com/bi.... Congrats on the great work!

On 2023-09-19 19:05:27, user S. An wrote:

This article is now published:<br /> Danielle L. Schmitt, Patricia Dranchak, Prakash Parajuli, Dvir Blivis, Ty Voss, Casey L. Kohnhorst, Minjoung Kyoung, James Inglese, and Songon An* "High-throughput screening identifies cell cycle-associated signaling cascades that regulate a multienzyme glucosome assembly in human cells” PLoS One (2023) 18, e0289707

On 2025-07-14 03:01:24, user Karmella Haynes wrote:

As a scientist who specializes in epigenetic engineering, this paper immediately caught my attention. A few years ago, I became interested in islet cell transdifferentiation, and read studies that used chromatin-modifying enzyme inhibitors to convert alpha cells into beta-like cells. I’m excited to see that you've taken this further by applying epigenome editing to activate insulin expression directly, this is a powerful and promising approach. Your results are intriguing and could be made even stronger with some restructuring of the narrative. For example, it would help to clarify the rationale for your cell model choices (e.g., in Fig. 3), and reviewers may be curious to see whether your system could also be applied to other islet cell types such as alpha or epsilon cells. If you’re preparing this for submission, I work with researchers to strengthen the clarity and strategy of their manuscripts, particularly those in molecular cell biology and chromatin engineering.

On 2017-10-18 03:32:11, user Niv Reggev wrote:

Cool work!

These findings might be related to the different proportions of CA1/CA3 along the long axis of the hippocampus, which might be worth mentioning.

This could also relate these findings to Anna Schapiro's recent findings, where she shows empirical and computational evidence that CA1 and CA3 treat distinctiveness and similarity in different ways.

On 2019-09-15 23:33:56, user Kent Willis wrote:

Great preprint! Excellent topic and interesting science.<br /> On a related note, I am impressed with the formatting - what did you use?

On 2020-12-04 18:31:34, user Jordan Berg wrote:

Metaboverse v0.3.1b is live!

https://github.com/Metabove...

• Improved direct-CHEBI mapping stability
• Other minor bugs fixed

See https://metaboverse.readthe... for a full list of updates.

And thank you to all who have helped track down bugs and suggested new features! More to come!

On 2024-02-21 16:10:16, user Susanne Fuchs wrote:

I really like your paper and currently working on vocalizations in an evolutionary games where people need to create new vocalisation. I had the feeling that the participants (adults) were less creative than I thought they could have been - and maybe this also links back to what you have done. Would be very curious to see some of your data. How long did you take to record all these babies? And why did you do a catgegorization and did not do a bottom up acoustic analysis? Is there any reason?

On 2023-10-11 14:01:06, user Gilles wrote:

Is there any positive control for FoxP3/CD25 stainings ? the cytometry stainings are so poor it is difficult to conclude anything.

On 2016-04-11 06:38:34, user Fumihiko Takeuchi wrote:

I think the paper and the proposed concept, UNICORN, are awesome. Great work!

On 2020-02-25 14:20:51, user Maciej wrote:

https://jcs.biologists.org/...

Accepted and peer-reviewed article accecible here

On 2022-07-01 10:23:35, user Iratxe Puebla wrote:

Review coordinated via ASAPbio’s crowd preprint review

This review reflects comments and contributions by Ruchika Bajaj, Bobby Hollingsworth, Gary McDowell and Michael Robicheaux. Review synthesized by Michael Robicheaux.

The preprint manuscript by Trendel et al., “Translational Activity Controls Ribophagic Flux and Turnover of Distinct Ribosome Pools”, presents a dataset that examines the lifecycle of human ribosomes, and their constituent subunit proteins, in response to translational inhibition using proteomics and cryo-EM approaches. The study focuses on the fate of 80S monosomes, which are shown to be inactive and to form a dynamic pool separate from active polysomes and nascent ribosomal subunits.

General comments

• The manuscript is well-written and organized, and the methodology is thorough and detailed.

• The effort to validate mass spectrometry quantitative measurements, particularly the peptide sum normalization (PSN), is commendable. The description of total sum normalization and its weaknesses in this methodology is well articulated. This work will be useful for others working on similar problems in quantitative mass spectrometry.

• The described pulse-SILAC methods are quite successful at monitoring protein stability in response to different perturbations; however, the statements in favor of ribosome subunit decay through ribophagy/selective autophagy require further support. Since ribosome component decay can be due to a variety of additional pathways (see cited reference #17, An et al., 2020), it may be necessary to soften the conclusions regarding ribophagy. Additional pulse-SILAC experiments in cell lines that lack key autophagy components (e.g., ATG12/FIP200 KO cells) could be considered to directly test the ribophagy model.

• There are questions as to whether the cryo-EM processing supports the conclusions stated in the manuscript. Specific comments regarding this are provided below. In addition, additional processing detail in the flowcharts presented within the supplemental data would be helpful to better understand processing choices (e.g., D classes that move forward for additional analysis/classification/refinement).

• It would be relevant to discuss how the proteomic half-life measurements compare to those published by Li et al. 2021 (Mol Cell), which use a different method (cyclohexamide chase).

• The manuscript reports significant differences in the half-lives of the 40S/60S ribosomal subunits vs 80S/polysome fractions (Fig 1E), and states that these make up separate ribosomal pools without free exchange. However, it should be considered as an alternative that the decay rate of assembled ribosomes could be much less than the unassembled group so that the pool of free components becomes gradually depleted. In this case, exchange could still occur with a decreasing rate as the pool of free ribosome proteins are degraded faster than assembled ones. It would also be relevant to discuss the possibility that nascent 40S and 60S subunits form 80S monosomes in an alternative “life cycle” pathway.

Specific comments and suggestions

• In paragraph 1 of the Introduction, please specify the context of “serum withdrawal” as the stimulus for idle 80S ribosome accumulation. Is this from cell culture or some other system?

• In paragraph 1 of the Introduction, the sentence, “Degradation of ribosomal complexes, especially under nutrient-poor conditions, is mediated by ribophagy, a selective form of autophagy [14–17]” could be more nuanced as it does not describe other non-autophagic ribosomal degradation pathways, such as those described in cited reference #17 (An et al., 2020).

• In the “A Highly Robust Normalization Procedure...” Results section, the manuscript states that the intensive ribosomal purification methods lead to high variability in the mass spectrometry measurements. Based on this, have alternative methodologies been considered for ribosome purification?

• In panel E of Figure 1, the color scheme makes the data difficult to differentiate, could also consider separate figures for the large and small subunit datasets.

• In the “Protein Half-Lives in Polysome Profiling Fractions...'' Results section, “On average ribosomal proteins of the small subunit had 3-fold longer half-lives within the 80S fraction compared to the 40S fraction (p=5.2E-8, Wilcoxon ranksum test), whereas large subunit proteins had 4.6-fold longer half-lives within the 60S fraction compared to the 80S fraction (p=1.0E-10).” Are the “60S” and “80S” fractions mixed up at the end of the sentence?

-In the “The Monosome Fraction Predominantly Contains Inactive 80S Ribosomes...” Results section, the manuscript reports, based on their cryo-EM data (Fig. 2), that 80S monosomal complexes are idle and distinct from polysomal 80S complexes. This conclusion of a single ribosome state would need supportive evidence. From the initial particle stack (>1 million) that yielded <60k high-resolution particles after classification: were there other low-resolution class averages or heterogeneous particles that may represent actively translating ribosomes? Conclusions about ribosome activity from less than 5% of the total pool of ribosomes could be due to the conformational plasticity of translating ribosomes. In a different paper (Brown et al., eLife. 2018), several structures/states of the ribosome come out of a smaller dataset. Furthermore, a structure of comparable resolution from the polysome fraction appears necessary to support the conclusion that the 80s monosome complex is functionally distinct. The same comparative data is recommended for conclusions drawn from the cryo-EM structural analysis of arsenite treated 80S particles (Fig .S6).

• In the “The Monosome Fraction Predominantly Contains Inactive 80S Ribosomes..” Results section, this section introduces ribosomal P-stalk proteins, their plasticity and role in active ribosomes, which are concepts that could be included in the Introduction section of the manuscript.

• In Figure 2, it is unclear from the figure legend if the 80s monosome density in panel B is from the low-salt treated preparation in panel A or from a different prep.

• In the “Inhibition of Translation Produces Inactive 80S Ribosomes...” Results section, recommend revising the text to reframe the conclusion as "supports our model".

In the “An Increased Pool of Inactive 80S Ribosomes..” Results section, recommend toning down the claims about decay rates which may require control experiments in cells lacking key autophagy proteins, such as ATG12.

• In the “An Increased Pool of Inactive 80S Ribosomes...” Results section, consider reframing the conclusions from the previous study (Trendel et al. 2019) to indicate that ribophagy is the predominant mechanism of ribosomal protein turnover in response to arsenite treatment. The prior study did not examine ribosomes treated with arsenite when autophagy was blocked. Additional quantitative tests for flux into lysosomes (Lyso-IP, Ribo-Keima shift assay) should be considered to support that ribophagic flux, specifically, eliminates proteins from ribosomal pools. Based on this comment, the inclusion of ribophagy in Fig. 5 and the statements in the final paragraph of the Discussion may require revision.

• In the “An Increased Pool of Inactive 80S Ribosomes...” Results section, the manuscript describes proteomic data in response to increasing concentrations of arsenite. The effects of these treatments on polysome profiles could be useful future experiments.

• In the “Constrained Conformational Plasticity...” Results section, there are questions about this analysis due to the small size of the final particle stack for both proteins. An alternative analysis pipeline is to mix the particles from both datasets for the simultaneous analysis of all pooled particles, from which the number of particles in a given state can be quantified.

• In the “Distinct Pools of Ribosomal Subunits...” Discussion section, the discussion of inactive 80S complexes potentially re-entering the polysome “assembly line” is quite interesting to consider in terms of its dynamics and follow-up experiments that would test this theory (including subcellular localization).

In the “Distinct Pools of Ribosomal Subunits...” Discussion section, the manuscript posits that the degradation of newly synthesized ribosomal subunits is not energetically favorable; however, it should be considered that intrinsically disordered proteins, such as transcription factors, can be produced and quickly degraded in oscillatory patterns (e.g. see https://pubmed.ncbi.nlm.nih... "https://pubmed.ncbi.nlm.nih.gov/24179156/)"). A quality control pathway that would eliminate immature or nascent ribosomal subunits is conceivable.

• Consider depositing all EM data in EMPIAR and relevant structures in EMDB/PDB, and depositing the mass spectrometry raw data in ProteomeXchange or similar database. A data availability statement could be added with relevant accession links and IDs.

• It would be helpful to build a tool to browse protein-level half lives and re-analyze raw data (e.g., tidy script depositing in Github or similar).

On 2019-07-10 17:07:46, user Daniel J. Wilson wrote:

Correction to the harmonic mean p-value method: http://blog.danielwilson.me... Updated R package available https://CRAN.R-project.org/...

On 2021-03-02 00:05:28, user Ana Christoff wrote:

Paper published<br /> Frontiers in Microbiology<br /> https://doi.org/10.3389/fmi...

On 2023-01-24 22:32:37, user Nozomu Yachie wrote:

After discussing the first version with Aziz Al'Khafaji, who developed COLBERT, we realized that the high-copy CRISPRa experiments presented in the paper were not performed the same way as COLBERT/ClonMapper. We revised the texts related to this and provided additional discussions. The new version can be found here: https://www.biorxiv.org/con...

On 2021-04-05 00:35:06, user shuaishuai wang wrote:

Hi, the symbol of compound in Figure S1 is not match with the chemdraw structures.

On 2020-12-15 08:28:37, user TN wrote:

I found a typo. Not perosin, peropsin.

On 2020-12-30 15:01:56, user Matthew Baron-Chapman wrote:

By this formula, a 25 year old Labrador would translate to an age of 83 years old. I know of know record of a Labrador living to be 25 years old, yet many people live to be 83. A 30 year old labrador would be the equivalent of an 85 year old human. This formula calculator seems to be garbage to me. Anyone know of any 30 year old labradors out there?

On 2020-06-24 16:33:22, user Ian Durie wrote:

you should probably reference this article https://pubs.acs.org/doi/ab... in account of 1c was previously done and Figure 4 came to the same conclusion

On 2025-07-30 16:15:07, user Alexander wrote:

Dear Authors,

Thank you for your valuable research on "A copper-dependent, redox-based hydrogen peroxide perception in plants". I noticed that the methods section appears to be missing from your preprint. Would it be possible for you to upload it? <br /> Best regards,<br /> Alexander

On 2025-08-23 02:26:59, user Sergiy Velychko wrote:

A core principle of scientific communication is that results and methods must be verifiable and reproducible. This preprint describes an undisclosed gene. Publishing anonymized findings could undermine trust in preprints—and, in my view, should not be practiced. At the very least, the disclosure that "SB000" is not a real gene name should be stated clearly on the front page for transparency.

On 2019-08-02 19:29:25, user Valentina Unakafova wrote:

Now published at https://www.frontiersin.org...

On 2025-10-16 16:56:00, user Raj Charamata wrote:

May I know what communities you exactly picked and if I can have results as per community. Also I would be glad to get contact details or email address of researchers

On 2019-03-22 11:02:37, user Omer Faruk Gulban wrote:

Hi,

Congratulations for the preprint. I have enjoyed reading it because it includes auditory cortex and layer-MRI. I took a few notes while reading which are not meant to be review-quality. I hope you might find some of them useful.

1. [Supp. Fig. 1] Are you planning to add the views of the auditory cortex of each subject? I would be interested in seeing them, but maybe I showing them does not makes sense and I have missed something. Since the figure title states auditory and visual responses I was searching for the auditory cortex images.

2. [Line 203] I see that you would like to keep it simple (A1-PT, V1-V2/3) [from line 813] by labelling Heschl’s gyrus as A1. However, I think this causes confusion. Localization of A1 and the surrounding areas by only using MRI is a debated topic (see Moerel, De Martino, Formisano 2014). I understand that you are aware of having labelled Heschl’s Gyrus (HG) and later decided to call this A1 [from line 630 & 632]. I think it would be more consistent to keep it as HG since you are using a macro-anatomical name Planum Temporale (PT) but switching to an architectonic name A1 (as defined in Hackett, Preuss, Kaas 2001). This seems inconsistent without explicit definition. It might be beneficial for the manuscript to provide an explicit denifition of your usage of A1 similar to how you define lamina [at line 619-620] by saying:

It is important to note that the term ‘lamina’ used in this communication does not directly refer to cytoarchitectonically defined cortical layers.

3. [Line 629]: Hackett, Preuss, Kaas, 2001 reference sounds like an MRI study. The place after line 628 ... myelination profile might be clearer.

4. [Line 390]: 1 extra or missing parenthesis.

5. [Lines 629-631]: It might be useful to note down the prevalence of gyrification patterns observed in your sample. Previous studies show that, HG is macroanatomically quite variable (see Marie et al. 2015). This information might useful since how the macroanatomy relates to location of A1 seems to be different across gyrification types (Kim et al. 2000).

Kind regards,<br /> Omer Faruk Gulban<br /> ORCID

On 2025-08-29 13:45:44, user Fabien LAFUMA wrote:

This preprint is now published as "Six million years of vole dental evolution shaped by tooth development" in the August 5th, 2025 issue of PNAS (vol. 122, no. 31, e2505624122). It is available at the following link: https://doi.org/10.1073/pnas.2505624122

On 2016-06-06 17:02:00, user John Carlos Garza wrote:

Yes, an epic evolutionary genetics result, and the next big step in piecing together a synthetic understanding of the heritable basis of life history variation in salmonids.<br /> Unfortunately, it is sullied by the first and last authors' insistence on spinning it as a story about conservation unit delineation, over the objections of a number of the coauthors.

On 2016-10-10 13:17:30, user Yaniv erlich wrote:

This is an interesting manuscript that considers the possibility of retrieving information from DNA storage using Oxford Nanopore sequencing. The authors synthesized 17 gBlocks of about 1000nt long and encode 3.5Kbyte of data. By sequencing each gBlock for about 200 times on ONT, reaching a sequence consensus, and using several error correcting strategies, they reached zero errors.

One question is the scalability of this method. The method hinges on synthesizing long DNA fragments using the IDT gBlock technology. According to the authors, the ~17000bp gBlocks DNA cost over $2000. This price is approximately 400x more than the price of oligo synthesis arrays and translates to nearly $1 for storing 10bits of information, meaning that one would need an R01 for storing 1Mbyte file. In addition, the method has zero guarantees for oligo dropouts. While PCR amplification of 17 gBlocks is simple, amplifying tens of thousands of oligos is error prone and likely to result with a small number of sequences that will not be represented in the final reaction.

Another issue is the claim that the encoding strategy is superior in terms of information density (bit/nt) to previous publications. It is important to note that the coding potential is actually smaller than other methods and reaches only 1.67bit/nt, which is smaller than the Grass et al. technique (1.7bit/nt) and our work (1.98bit/nt). The increase in density achieves solely because the highly length of the gBlock technology compared to the barcode. However, translating the presented method to the lengths of oligo array (200nt) yields only 1.49bit/nt and no protection for oligo dropouts. Thus, any conclusion about density of the strategy compared to Illumina-based methods needs a more careful attention.

On 2020-05-26 22:46:52, user Adi Natan wrote:

Thank you for mentioning my code, I hope it will continue to be beneficial for your research. I'd appreciate if you could please cite it in the future: A. Natan, “Fast 2d peak finder,” https://www.mathworks.com/m... (2013). http://scholar.google.com/s...

On 2025-05-27 14:25:49, user Jonathan Eisen wrote:

It would be useful to show the tree in Figure 6 as a phylogram (i.e., with branch lengths)

On 2018-01-26 16:38:57, user UdoN wrote:

This is a very interesting study and the findings certainly have merit. I would like to make<br /> some general comments on the manuscript as presented. The authors state that Laminariales are the most efficient iodine accumulators (p. 3, l. 69). Latest findings suggest that this is not entirely true. For example, we showed that Fucus vesiculosus (Fucales) is more efficient in iodine accumulation than Laminaria digitata (Nitschke et al. 2018, J. Phycol., doi: 10.1111/jpy.12606). In addition. Laminaria digitata is probably the strongest, as opposed to the most efficient, iodine accumulator (Küpper et al. 1998, Planta 207:163–71; Ar Gall et al. 2004, Bot. Mar. 47:30–7) with iodine levels averaging about 1% dw. By contrast, other Laminariales such as Saccharina spp. and Alaria esculenta contain on average “only” 0.5% dw and 0.05% dw, respectively. Iodine concentrations of many algal species vary greatly among seasons (Ar Gall et al. 2004, Bot. Mar. 47:30–7; Nitschke et al. 2018, J. Phycol., doi: 10.1111/jpy.12606) and thallus parts (Nitschke & Stengel 2015; Food Chem. 172:326–34). The results for intra-thallus variation of iodine levels for kelps are, however, inconsistent: Küpper et al. (1998, Planta 207:163–71) reported that iodine levels in a blade of one Laminaria digitata specimen were higher in distal parts than towards the meristem. By contrast, we observed that iodine levels increase from distal towards basal blades and further to stipes in Laminaria digitata, Laminaria hyperborea and Saccharina latissima (Nitschke & Stengel 2015; Food Chem. 172:326–34). The latter is consistent with the translocation of iodine within algal thalli as documented for Saccharina latissima (Amat & Srivastava 1985, J. Phycol. 21:330–3). Thus, the authors should check their statement on p. 14, l. 379-380. Furthermore, the authors state on<br /> p. 12, l. 344 that iodine contents of kelps increase with seawater depth. Published findings do not support this statement; for example, Laminaria hyperborea usually occurs at greater depths than Laminaria digitata, but Laminaria hyperborea has lower iodine levels than Laminaria digitata. In general, iodine contents are species-specific and, to my knowledge, the proof of the effect of water depth is still missing.<br /> The authors report the expression of vHPOs in gametophytes of Saccharina japonica (p. 10-11, l. 283-290). Küpper et al. (1998, Planta 207:163–71) showed that gametophytes of Saccharina latissima do not take up iodine. Do the authors have any possible explanation for this?<br /> If would be great, if the authors presented iodine contents along with their findings.<br /> Udo Nitschke

On 2020-02-10 10:39:23, user Jan Zaucha wrote:

Hi guys, our group has already published extensively on the topic of mutations in TM proteins, thus your claims in the abstract are not really true:

https://www.ncbi.nlm.nih.go...<br /> https://onlinelibrary.wiley...<br /> https://www.ncbi.nlm.nih.go...

On 2017-06-15 22:16:18, user Guillaume Cornelis wrote:

Awesome paper on placental evolution and transcriptome!<br /> It is always surprising to me to see the strong conservation of placental function despite the tremendous diversity of structure and development among species. The pre-eclampsia gene category is particularly interesting. <br /> Regarding the syncytiotrophoblast category, I am surprised to see that you don't detect expression of syncytin genes in Bonobo and dog, as such genes have been previously described in those species. Those genes are derived from transposable elements (namely endogenous retroviruses) and are rarely annotated as cellular genes (e.g. the syncytin-car1 gene in Carnivora species is annotated on the UCSC genome database, but not Ensembl). In addition, identification of these genes is sometimes challenging because of their independent origin from different viruses that show only limited sequence conservation. I would suggest looking at the genomic regions corresponding to those genes in those species (contig JH650268 and JH650565 for ERV-W1 and ERV-FRD1 in Pan paniscus and Chr3 for Syncytin-car1 in dog) and see if you could detect reads in those genomic regions. <br /> I am also curious about the differences between the Eutherian and marsupial lineages. What would be the genes specific to the marsupial lineage, and reciprocally the eutherian specific genes and do the highlight specific functions of the eutherian and marsupial placenta? <br /> Anyway, amazing work. Congratulations!

On 2025-11-22 23:05:01, user SF wrote:

Overall, I thought that this manuscript provided an interesting look into the diet of the Reunion Island free-tailed bat and its implications. However, compared to the rest of the text, I thought that the Discussion section could benefit from more integration with the overarching ideas addressed towards the beginning of the Introduction. Tying the conclusion back to the big picture (i.e. potential applications of understanding bat diets on pest management, agriculture, and disease vector control in modified landscapes) could help re-establish the importance of this study beyond Reunion Island and create cross-disciplinary connections with other fields such as agricultural science, economics, and public health. It may also be helpful to reiterate a few points from the first paragraph of the Introduction (lines 44-56) to emphasize the importance of understanding ecological dynamics in highly-modified landscapes that are often major sites of human settlement/ interest.

On 2018-10-29 08:41:11, user AntibodyRegistry wrote:

Hi, I run the antibody registry and found this reference to it. <br /> "(Hybridoma Bank at the University of Iowa, Antibody<br /> registry ID: AB 2314866" <br /> Would you mind using the ID for this antibody using the "proper citation" syntax, i.e., RRID:AB_2314866 when you publish this manuscript?<br /> This will make it easier to later find who used this antibody in their studies, like this: <br /> https://scholar.google.com/...

On 2023-10-17 08:52:43, user DL wrote:

Very interesting paper and deep insight into the mechanism. However, no functional data regarding the detergent or DTT conditions are shown. I'd really like to see electrophysiological recordings of HCN1_wt, HCN1_CC mutation and HCN1_CCA mutation under a) DTT application and b) CHS/LMNG application/incubation to show the physiological/functional relevance of the resolved putative Intermediate and Open states.

On 2020-08-17 01:50:52, user Thomas-nick wrote:

it is scary to think that marine species may be affected, especially shoreline and those exposed to raw sewage. Authors should extend these important findings with in vitro data. However, I doubt that many of these species have cell lines for in vitro infection data (even less likely that lung lines are available).

On 2021-04-13 02:59:46, user Dave Roe wrote:

Thanks for your work and the report. I have a few questions and suggestions for future revisions.

1. The assembly could use more details. "Exon libraries ... were used as references to map the Nanopore reads into contigs based on similarity" and "... fragments that share overlaps. Allelic variation in these overlaps allows the phasing of haplotype". How was the mapping and overlapping done?

2. It would help to map the IDs to the previous reports to which they are being compared. For example, for the sequence HG995445[1], what is the ground truth sequence? As far as I can tell that assembly doesn't contain any published KIR genes, even at the exon level. It would be nice to compare it with the previous report.

3. https://www.ebi.ac.uk/ena/b...

On 2020-08-05 15:22:24, user izzonj wrote:

Thank-you for posting this manuscript it is very interesting. i would like to comment about your statement: . "Activation of Sigma-2 receptor signaling has been shown to induce apoptosis and cytotoxicity [9]"

I would like to call your attention to the following publication and consider its implications for interpreting data in the field of sigma-2 ligands and their cytotoxic effects.

Zeng C, Weng C-C, Schneider ME, Puentes L, Riad A, Xu K, Makvandi M, Jin L, Hawkins WG, Mach RH: TMEM97 and PGRMC1 do not mediate sigma-2 ligand-induced cell death. Cell Death Discov 2019; 5:58

Best regards,<br /> N Izzo

On 2016-12-08 17:11:29, user AdamMarblestone wrote:

-"Memory Transformation Enhances Reinforcement Learning in Dynamic Environments" http://www.jneurosci.org/co...<br /> -"Regular Cycles of Forward and Backward Signal Propagation in Prefrontal Cortex and in Consciousness" http://journal.frontiersin....

On 2017-09-07 21:05:26, user AdamMarblestone wrote:

-"Behavioral time scale synaptic plasticity underlies CA1 place fields" http://science.sciencemag.o...

On 2017-01-22 16:14:50, user ganggan wrote:

This is just an antisense experiment, has nothing to do with NgAgo. They should include groups w/o the enzyme. SAD.

On 2025-04-08 06:50:41, user Zach Hensel wrote:

Here's a correction for one typo in the manuscript:

Most previous analyses considered datasets in which there was only one A24325G sequence collected prior to 15-Feb-2020 ~~with~~without a market link: a sequence collected in California, USA on 12-Feb-2020 (CA-CDC-8).

This will be updated for future revisions.

On 2021-09-24 17:10:27, user Vinay K. Pathak wrote:

Schifferdecker et al. show that a new capsid labeling method efficiently labels capsids with a fluorescent membrane permeable dye and provides another tool to visualize HIV-1 cores in infected cells and increase our understanding HIV-1 replication. Importantly, this work adds to the growing body of evidence that nuclear capsids are largely intact (Burdick et al. 2020; Dharan et al. 2020; Selyutina et al. 2020; Zila et al. 2021; Müller et al. 2021, Li et al. 2021; and others). <br /> However, for two reasons, we respectfully disagree with the conclusion that the “…HIV-1*CA14SiR represents a substantial improvement compared to previous genetic labeling strategies (Campbell et al. 2008; Burdick et al. 2020; Zurnic Bönisch et al. 2020; Pereira et al. 2011).” First, like HIV-1*CA14SiR, the infectivity of virus labeled with GFP-CA as we described (1:15 ratio of GFP-CA:WT Gag) was modestly reduced ?2-fold (Fig. 1B in Burdick et al. 2020), indicating that the GFP-CA labeling system does not severely reduce virus infectivity compared to the HIV-1*CA14SiR system. Second, given the “minimally invasive” strategy used to label HIV-1 CA, it was surprising that the nuclear import of capsids containing CA14SiR was severely delayed by ?6-12 hours (Fig. 3; Schifferdecker et al.). In contrast, we showed that GFP-CA labeling of virions did not affect nuclear import kinetics of capsids (Fig. 2H in Burdick et al. 2020). <br /> It is not clear why the CA14SiR labeling has such a drastic effect on nuclear import kinetics; labeling most of the CA in the capsid shell with the fluorescent dye may alter the capsid structure and stability, influencing the timing of nuclear import as well as other capsid functions, such as PF74-induced disassembly. We believe it is important to show that the labeling method does not have a significant influence on the steps in HIV-1 replication that are being investigated. For this reason, we showed that GFP-CA labeling of virions did not affect the timing of loss of sensitivity to capsid inhibitor PF74 (Fig. 2G), timing of reporter gene expression (Fig. S1L), sensitivity to various HIV-1 inhibitors (Fig. S1M), capsid stability (Fig. S1E-F), association of capsids with nuclear envelope (Fig. S1G), nuclear import efficiency (Fig. S1G), and binding of nuclear capsids to CPSF6 (Fig. S4H-I; all figures in Burdick et al. 2020).

Sincerely,<br /> Ryan C. Burdick, Chenglei Li, Mohamed Husen Munshi, Wei-Shau Hu, and Vinay K. Pathak,<br /> HIV Dynamics and Replication Program, National Cancer Institute-Frederick, Frederick, Maryland 21702

On 2017-01-18 11:24:18, user Arturo Tozzi cns wrote:

Gotcha! our paper has been published: <br /> http://journal.frontiersin....

On 2020-05-07 12:27:57, user Lutz Barz wrote:

Virus with information within the protein that defies our imagination - are they future proof as in ahead of our DNA/RNA that we are the ones behind.

On 2019-01-10 07:59:57, user Alexandru Costache wrote:

Sooo all the media claims that eastern cougar is extinct but actually eastern cougar is the same with the normal north american cougar, which numbers are increasing, so actually is just one big subspecies that is extinct just from that eastern area but it will eventually repopulate it? :)))

On 2020-01-20 23:39:58, user Sebastian Aguiar Brunemeier wrote:

The rationale behind this study is convincing, and thankfully someone is doing the duty of publishing 'negative' results, i.e., a dose of reality on what does *not* work. This is valuable to the community.

On 2023-10-03 07:47:00, user Matthias Hoetzinger wrote:

Nice work!

A question regarding interpretation of ????:

In the discussion it says:<br /> "For the extreme case C. burnetii, the idea of ???? = 0.29 means that the most similar genome in a different community within a network of over 200 thousand genomes shares only 29% genetic identity with the representative genome..."

Here it should probably say:<br /> "...shares only 71% genetic identity with (or shows 29% genetic dissimilarity to) the reference genome...", right?

On 2018-11-14 18:12:40, user Jian Wang wrote:

Amazing work! Congratulations!

On 2022-04-12 19:25:18, user Gary McDowell wrote:

This paper investigates the general phenomenon of bias in journal publication of terms of which experimental methods are used. This includes looking at whether extra experiments using animal models are being requested by reviewers of journal articles. The authors take anecdotal reports of an increased reliance on animal-model validation of experimental results in peer review and measure the prevalence of the phenomenon using a survey.

One caveat is that the survey is shared through social media and private channels, introducing a bias in audience receiving it, and also that those most likely to respond will be those personally motivated to do so. In addition, the sample size is somewhat small. However, the authors account for this in their analysis and as the purpose of the study is to start to assess whether the phenomenon exists, this is not a major concern.

It would be interesting to see whether there are overlaps in the specific groups identified from the survey results, but I appreciate the sample sizes are small and any results would be purely indicative of further areas for exploration at this stage. For example, are there any trends looking at peer review experience vs number of requests for animal model experiments (is it roughly linear, suggesting it has been occurring at a steady rate over people’s careers and could be static, or are people who are perhaps earlier in their career seeing a higher rate, indicating a possible increase)? Are the reviewers in the sample who request animal models giving different responses/comments to those who do not request them (there seems to be a 50/50 split)?

I was a little confused by the differing frequencies in Figure 2 and Figure 3 (and there may be a figure legend missing for Figure 3, or both charts are in Figure 2 in which case that figure legend needs expanding) - more people responded that they never request animal experiments vs those who say they request 0 or N/A in the frequency chart. Is there a reason for this?

One aspect that struck me as most interesting was the request for validation in animal models for work carried out in human systems is very similar to an anecdotal phenomenon from my own experience as a developmental biologist working with Xenopus, and from anecdotal discussions from colleagues, that there is a high request for validation of frog work in tissue-culture systems, or mouse systems. I have heard similar criticisms as those indicated in the boxes. There is also a less similar anecdotal phenomenon in mass spectrometry of requiring Western Blot validation of results, which is a far less sensitive technique. Based on this, could the authors comment on whether the phenomenon they describe could be evidence of larger phenomena, for example, the need to validate across multiple models, or even simply the perception that peer reviewers should just be requesting more experiments? Could there be a phenomenon that is mouse-centric, noting the prevalence of comments about mice? Or is this truly a phenomenon that may be limited to their field? You mention in the discussion that this appears to be a new type of bias, and so I’m curious as to whether that is the case given other “added experiment in other models” phenomena that may exist, but maybe have not been systematically studied and reported. This also would be interesting to compare with the discussion on conservatism bias, because there may be some different questions as to whether the bias is based on innovation, or based on more-rarely-used models, or is based on a drive to add experiments/replicate experiments in multiple systems, all of which have slightly different nuances. The prevalence of mouse models in science and whether there is a bias towards mice would be a question to ask that aligns with the point about the bias of having more enthusiasm for applications addressing someone’s own area of research that the authors mention.

The discussion is extremely comprehensive and the article raises a number of very important points for further consideration by the community, and adds new interesting thoughts to the process of peer review from a particular perspective, and is very interesting to read. The results should be taken as quite preliminary and indicative but are a great insight into an interesting topic that has largely been focused on anecdotal complaints.

It does not appear that survey respondent names or identifying information were collected, nor information on the journals in question gathered. What could have been useful is being able to determine whether certain journals have a prevalence for this behavior, as particular targets of further work. However, on the basis of this work, one thing that could potentially be very interesting is partnering with journals that people are likely to publish with, presenting them with this information and asking them to distribute a follow-up survey to their reviewers and people who have submitted manuscripts in the past. This would hopefully be very useful for the journals, and perhaps anonymous data could be passed across in a data collection agreement from a number of journals to investigate the phenomenon, without necessarily naming the specific journals in the results. This is purely as an idea to potentially partner with journals who are interested in reform, if one desired outcome of this work is to address this problem explicitly.

This review has been undertaken with a view to using the FAST principles (Iborra et al., OSF Preprints, 2022, https://osf.io/9wdcq/) "https://osf.io/9wdcq/)") for preprint peer review.

COI: I have no conflicts to disclose.

On 2021-11-12 09:09:25, user Jeroen Demmers wrote:

The paper is now published in PLOS ONE: https://doi.org/10.1371/jou...

On 2018-12-31 09:03:42, user Gianluca Polgar wrote:

just wow. Where was this submitted?

On 2021-03-23 15:35:35, user Anchi Cheng wrote:

Congratulation on completing a well-written manuscript. However, it is my duty to inform you that using low mag relative ice thickness to filter targets has been a feature in Leginon auto target finder since the original manuscript published in 2005(doi: 10.1016/j.jsb.2005.03.010). 2021 update only added the graph and math to correlate such values with the high-mag EF or ALS measurements. Best, Anchi Cheng

On 2025-01-01 19:28:27, user William DeGroat wrote:

This manuscript has been published in Genome Biology: https://doi.org/10.1186/s13059-024-03365-w

On 2024-08-13 08:10:59, user William Dee wrote:

This article has now been revised and the published version can be found at: https://doi.org/10.1016/j.isci.2024.110511

On 2021-12-06 14:59:51, user Jasper Michels wrote:

A somewhat revised version of this manuscript has now been accepted by <br /> the ACS journal Biomacromolecules. An official link will be forthcoming.<br /> Regards, Jasper J. Michels

On 2019-02-25 19:30:01, user Xuhua Xia wrote:

This paper is obsolete and contains lousy/erroneous statements. It is replaced by a more extensive study submitted to AIMS Genetics

On 2019-06-15 15:51:16, user Mitchell Thompson wrote:

We are currently getting an Open Access License for the code described in our preprint as required by DOE. As soon as the license has been approved we will provide the repository link.

On 2020-03-29 15:42:26, user Kevin Olivieri wrote:

What a great starting point for tailoring existing treaments to SARS-CoV-2! Are your collaborators exploring synergies with some the approved compounds and the potent antivirals ribavirin and remdesivir? It may allow you to focus only on drugs already in the clinic. If you give remdesivir, which targets NSP12, and ponatinib which targets the NSP12 interactor RIPK1, you might block viral replication more potently than either compound alone. Additionally, impeding viral protein function may enhance the relative effect of ribavirin targets. For example, we know ribavirin inhibits IMPDH1 potently, but the NSP14 interactor IMPDH2 less potently. Since, IMPDH2 interacts with 4 ORF8 interactors, perhaps using drugs that block ORF8s interactants from functioning completely would combine to block IMPDH2 more effectively. Of note, two of these proteins, NPC2 and OS9, are highly expressed in lung.

On 2024-02-07 14:06:01, user Antriksh Srivastava wrote:

This is a great work, it directly relates to my recent publication in PCE (10.1111/pce.14821), we tried to quantify the effects of stomatal conductance reduction both positive and negative from a modelling perspective.

On 2025-08-20 15:18:28, user Elin wrote:

Hi,

Nice manuscript! But I can't find the supplementary files that are referred to in the manuscript.

Best,<br /> Elin

On 2024-08-23 18:17:17, user Elisabetta Babetto wrote:

How well does CaMKII-mtPyronicSF colocalize with mitochondria in these neurons?

On 2021-09-09 17:28:47, user Donald R. Forsdyke wrote:

This paper has been peer reviewed and is formally published (Sept. 2021) by Computational Biology and Chemistry.

On 2021-02-14 22:15:21, user Anthony Leung wrote:

Please note that part of the work is now published in PNAS https://www.pnas.org/conten...

On 2020-05-06 00:36:13, user K Edwards wrote:

I am in computer science and technical marketing. They hand me new stuff with incomplete manuals and I turn it into communication for both IT and average people.<br /> I am astounded at the number of people commenting who can not read this paper at even a basic level I understand just enough to know they have separated out both the strain in question and the outcomes very nicely.<br /> The statistical data shows that the new strain in the same time period spreads faster. And this is key, if you looked at number at two different times you would be dealing with a ton of behavior differences. But it is clear where both strains occur in the same area following the same rules like Washington State or NY. That the new strain spreads FASTER in the same rule environment.<br /> The next thing they break down is that the prognosis by strain in the same location / time so you are dealing again with same medical capabilities etc. They also do a nice job of braking it out by age and outcome. We see a pretty clear case for the G614 strain having a larger percentage of those needing ICU is 3% for 614D vs7% 614g. I did not read deeply enough to see if they have confirmed this in any other region which would be a key follow up.<br /> And finally if you looked that details related to the new mutation we find that the D614 variation has two structures that bind 1:1 while the 614G has a new "behavior" in that while one structures binds 1:1 the the other is now binding to BOTH 1:2 While I don't know what the relative strengths of these bonds is, that would indicate some increased ability for 614G to bind. <br /> Oddly enough one of the companies doing vaccine work at one point a few weeks ago let a photographer take a picture of a screen marked "Confidential" related to the selected CV-19 vaccine candidate mappings in the same area of the Virus as they are talking about. And its working in a similar area, but the encoding was different so I couldn't make a direct comparison to see if they had targeted the CC mutation that is common to all the 614G variation. <br /> Compared to the CRAP I normally see used to prove things in the news, I would say they are on to something. That there is a descent case for causation in spread rate and mortality based on the differences in these strains.<br /> I also read Quantum / M theory for fun and this is much easier to grasp than that.

On 2023-05-16 05:13:32, user Rachel Jiang wrote:

I really enjoyed reading your paper which included well-designed experiments and explored interesting novel therapeutic treatments targeting the KRAS-induced COX-2 immunosuppressive pathway in LUAD. Some general stats-related and stylist comments I had were:

1) In Figure 5h, the significance level of the difference between the Gzmb mRNA quantity of anti-PD-1 and celecoxib was shown as "0.05", which should probably have been "n.s." since the figure legend only defined significance levels for p-values less than 0.05. In addition, the significance level was shown for anti-PD-1 and the combination treatment for Ifng and Gzmb, but not for Prf1 and Cd40 (instead the significance level for celecoxib and the combination treatment was shown). I was a little confused by this inconsistency since the main conclusion of Figure 5 was that celecoxib increases efficacy of anti-PD-1 treatment, for which I thought it would be better to show the significance levels of the differences between anti-PD-1 and the combination treatment for all four anti-tumor genes (as you did in Figure 6f).

2) I was also a little confused by the visualization of Figure 5g, specifically what exact comparisons correspond to the significance levels shown. The figure legend mentioned that a two-way ANOVA was performed but I thought perhaps a one-way ANOVA would be more appropriate since it seems that only a single type of T cells for different treatments were compared with one another. The results section could also explain a bit more regarding this figure that could clear up any confusion.

3) For all the ANOVAs performed, it would be helpful to specify which post-hoc you performed.

4) Just stylistically, it would be helpful if each figure could be followed by its own figure legend to make it easier to interpret the figures.

But overall, the paper is great and your findings are really exciting!

On 2021-03-29 17:05:42, user Benjamin Glick wrote:

I'm happy to report that our preprint has been featured as a preLight by the Company of Biologists:

https://prelights.biologist...

On 2015-04-10 09:01:32, user Jacob H Hanna wrote:

Please note "Manuscript Comment" section and Acknowledgements on pages 25-26 of the PDF file on bioRxiv ( http://biorxiv.org/content/... ):

Page 25- Manuscript Comment: <br /> This manuscript constitutes a corrected version of a paper retracted on April 1st, 2015, which was previously peer-reviewed and published in the Journal of Clinical Investigation<br /> by our group (Novel APC-like properties of human NK cells directly regulate T<br /> cell activation- J Clin Invest. 2004;114(11):1612–1623. doi:10.1172/JCI22787). This version of the manuscript, now published on bioRxiv, includes the following corrections that have not been peer-reviewed, but rather validated and/or newly generated by our group:

1) ANK and UaNK samples were mislabeled in the gel presented in Figure 1 in the original manuscript, which is now corrected, and we add red dotted line to highlight cropping. All other gels accurately reflect the experiments described as originally presented.

2) We realized that the FACS plots in Figure 8 were inadvertently generated from the wrong FACS experiment data folder (which involved many of the same surface markers – OX40ligand, CD70, CD86, CD80 - used to generate Figures 3 and 6 in Hanna et al. J Immunology. 2004 Dec 1;173(11):6547-63). This mistake happened by our team, while the first author had limited access to the data while being abroad and away from the lab writing and revising all three manuscripts simultaneously. To resolve this, we have validated and repeated this experiment and now provide a corrected Figure 8. (We note that all Ethical and Helsinki Approvals and Board permits are still maintained and valid to conduct these<br /> experiments).

3) Due to the same issue, 2 negative control FACS plots in Figure 3D were inadvertently misrepresented from the wrong FACS files during revision of this paper (+ HA (middle of bottom row), UaNK + HA (in right of upper row)). We repeated the experiment presented in Figure 3D, and a corrected Figure is now included where we chose to provide 2 validated and representative control FACS panels (rather than replace the entire panel, in order to keep<br /> changes to a minimum).

We apologize for this inconvenience and the mistakes in the original JCI<br /> manuscript (J Clin Invest. 2004;114(11):1612–1623. doi:10.1172/JCI22787), which we revalidate and correct herein. All conclusions reported in the original manuscript remain corroborated and valid following this correction.

Page 26 - Acknowledgements:<br /> We thank all of the authors of the original published version of this manuscript (Hanna et<br /> al. J Clin Invest. 2004;114(11):1612–1623. doi:10.1172/JCI22787).

Thank you

On 2019-10-24 14:14:46, user Molecular Virology wrote:

Excellent technique to study full/partial HBV life cycle. Just <br /> wondering, how authors ascertained that only pgRNA is transcribed, what <br /> are the precise nucleotide coordinates considered in each HBV genotypes ?<br /> Suppose, if one wish to understand the biology of HBeAg, like very <br /> recent paper came out "Repression of Death Receptor-Mediated apoptosis <br /> of hepatocytes by Hepatitis B Virus e Antigen" how can one be very sure <br /> that precore RNA be included (further on excluded, if required) in this <br /> whole process and which precise coordinates in each HBV genotype may be <br /> important.

On 2020-12-18 17:31:10, user Jan Lui wrote:

Hi all, this paper is now published at Cell!

https://www.cell.com/cell/f...

On 2023-05-15 17:11:44, user Karma Bertelsmann-Mohn, PsyD wrote:

I hope someone may still be following the progression of this research. If so, please reach out!

On 2023-09-05 11:45:27, user Alan Bridge wrote:

Dear authors,

would you consider citing this work as the source of ChEBI annotations for UniProt records?

Coudert E, Gehant S, de Castro E, Pozzato M, Baratin D, Neto T, Sigrist CJA, Redaschi N, Bridge A; UniProt Consortium. Annotation of biologically relevant ligands in UniProtKB using ChEBI. Bioinformatics. 2023 Jan 1;39(1):btac793. doi: 10.1093/bioinformatics/btac793. PMID: 36484697; PMCID: PMC9825770.

Many thanks and good luck with the submission!