On 2023-04-25 10:47:11, user Juan Rodriguez Vita wrote:

A peer-reviewed version of this manuscript has been published in Cancer Research (doi: 10.1158/0008-5472.CAN-22-0076). For your information, we have outlined a list of the changes introduced:<br /> • Longer time point for our animal model for metastatic EOC<br /> • Subcutaneous tumor model for Lewis lung carcinoma cells<br /> • Bioinformatic analysis of patient prognosis<br /> • Lipid raft staining by flow cytometry<br /> • Tissue microarray of patients suffering ovarian cancer showing the correlation between Notch activity and myeloid cell infiltration.<br /> • Determination of CXCL2 levels in vivo upon Rbpj deletion<br /> • Experiments to determine the receptor involved in CD44 regulation

On 2019-03-01 17:41:05, user James Chataway wrote:

Hi there, I am planning on doing a two-sample MR study using these MS SNPs as instrumental variables. However, I'm confused on which SNPs to use from your supplementary tables 6/7. In supplementary table 6 the "discovery SNP" and "effect SNP" differ from one another as do their alleles. Which should I use and should I use the OR(joined) and P(joined). Or should I be using the SNPs from supplementary table 7? Any help appreciated, thanks

On 2021-03-26 22:02:23, user Toby Samuels wrote:

This manuscript has since been published in Frontiers in Marine Science on the 27th January, 2021. https://www.frontiersin.org...

On 2016-12-09 21:57:49, user Chris Brown wrote:

Here is the link to our published manuscript: http://dx.doi.org/10.1038/n....

On 2017-08-02 19:30:51, user Erik Svensson wrote:

You are welcome to publish a response - once this paper gets published. Such responses are usually peer-reviewed, and unless you have something more substantial to say than just marking words, I doubt you will get it published. Opinions are easy to air - especially if you are anonymous and do not have to go through peer-review, but only as a comment on Disqus. With that, I end this "debate", which has been completely meaningless and waste of my time.

On 2016-06-14 18:30:47, user PornHelps wrote:

Without controlling for individual differences in sexual desire or <br /> sexual shame, which <br /> dozens of studies have shown better account for these differences, these<br /> results are meaningless. Further, it is irresponsible of the authors to<br /> circumvent peer review with science too poor to survive it. Finally, <br /> they fail to cite the repudiation of this work in multiple publications,<br /> pretending that the critiques do not exist does not make them go away.

On 2021-03-08 19:15:21, user Anne-Karina Perl wrote:

This article has been accepted in Thorax https://pubmed.ncbi.nlm.nih...

On 2020-01-15 19:00:39, user Joshua Shaevitz wrote:

Please not that there is a typo in the URL for the source code. It should be: https://github.com/talmo/leap

On 2018-03-22 18:17:21, user Capra internetensis wrote:

Great stuff, and some surprises there.

Could the qpAdm models benefit from additional West Asian outgroups (ancient or modern)? Might help it distinguish between Yamnaya, EHG, and LBK.

On 2019-05-30 08:21:59, user Martin Modrák wrote:

Liked the preprint and great that all the code is available. Good job! Should my main takeaway be that amplicon sequencing of the V4 (or other regions) in 16S is not the best investment of time and money and we should change to other marker genes? Or would you caution against that interpretation of your data?

A few nitpicky suggestions follow:

• I would use heatmaps or contour plots in Figure 1 instead of the 3D surface which (to me) was very hard to decipher.

• I feel that when you combine recall and precision as in Figure 3b, it might be better to use F1 than their sum (not that would likely change your conclusions much) - if that was a conscious decision, might be worth discussing in the paper.

• One thing that is not clear is whether you used the whole 16S rRNA as a marker gene or just the region used in common amplicon sequencing protocols (from Figure 3c I would guess you used whole length).

• If the regions of 16S used in amplicon sequencing are not included separately, it would IMHO be nice to add them - I can see reasons why they would be better than full-length 16S as well as reasons why they would be worse.

On 2019-07-18 16:46:55, user Pasha Takmakov wrote:

Great work by Neuralink Team! <br /> I'm very curious to see how these devices can survive in the body for a long time. <br /> Implants are attacked by a immune system that can lead to device failure and it was particularly notorious problem for a miniaturized devices made using microfabrication technologies. <br /> For more details on how hard it is to make small electronic devices not to fail in the body: http://interface.ecsdl.org/...

It may be possible to mitigate the immune response by eliminating damage to the blood vessels, which Neuralink cleverly does by having a very small device and implanting it precisely between the densely spaced blood capillaries.

It is exciting to see long-term performance studies and animals and evaluation of the explanted devices!

On 2019-08-12 00:11:23, user shams wrote:

There is no link for the package in the article or searchable in the web!.

On 2020-04-28 13:15:19, user Lukasz Plóciennik wrote:

We are pleased to introduce a research article entitled “Detection of epistasis between ACTN3 and SNAP-25 with an insight towards gymnastic aptitude identification”. We follow up on experiments originally performed by Luigi Galvani in 1782 and 1786 but from a genetics perspective. We demonstrate that a gene involved in the differentiation of muscle architecture – ACTN3 and a gene, which plays an important role in the nervous system – SNAP-25 interact. The results obtained from the analyses revealed a remarkable crosstalk between ACTN3 – SNAP-25 polymorphisms. We found that for ACTN3 * SNAP-25 moderation effect, the homogenous derived genotype carriers exhibit the lowest chance of classification to the athlete group – gymnasts. Our observation of rs1815739 * rs362584 interaction may be applicable in advanced talent identification procedures. The ACTN3 – SNAP-25 interaction has never been reported before but based on the model and empirical evidence presented here, the two genes are undeniably interlinked.

PS. Please, look in on Supplementary Material and explore the graphical abstract developed by Prof. Marek Józwicki (Department of Architecture and Design, <br /> Academy of Fine Arts, Gdansk, Pomorskie Voivodeship, Poland).

On behalf of the authors.

On 2017-01-27 17:09:58, user German Dziebel wrote:

mtDNA hg X doesn't show up in European aDNAs until the Neolithic. Not in Paleolithic, not in Mesolithic but quite frequently in Neolithic remains. This pattern can be interpreted as product of an agricultural migration from the Near East where hg X is found at higher frequencies than in modern Europeans. Ancient DNA from Paleolithic sites in Europe (including Solutrean) is overwhelmingly hg U, which is not found in Native Americans (ancient or modern). re: Y-DNA R1b in Amerindians. It's commonly thought of as a result of post-1492 European admixture in Native Americans.

On 2020-05-14 04:28:56, user Wylied wrote:

"Testing at the point of care was not practical."

Point-of-care testing what Abbott ID Now is all about, hence the name. It's a strange study that doesn't attempt to replicate the conditions the test was intended for. That said, it appears that Abbott, at least initially, was unclear about how to obtain optimal results.

On 2016-10-03 16:13:45, user Shiquan Sun wrote:

There is no supplementaries S1 and S2

On 2020-08-26 07:18:45, user Kyoshiro Sasaki wrote:

This is a good attempt. Thank you for this interesting paper! We have submitted a comment on this paper. https://psyarxiv.com/fqmce

On 2020-09-21 22:34:33, user Javier Rasero wrote:

Hi there,

This is one of the authors of this manuscript. If you have any question, comment, suggestion about our work, please, do not hesitate to post them here or contact us. We would be more than pleased to address them as having (any kind of) feedback would be really helpful. Thanks!

On 2021-03-10 14:17:42, user Steve wrote:

Why hasn't this been published somewhere following peer review? Are there issues with the immortalized cell line model?

On 2024-09-21 04:17:05, user Zach Hensel wrote:

I have a short response to the two comments on this preprint, which, of course, we took into consideration while revising the manuscript, which is now published following peer review here: https://doi.org/10.1016/j.cell.2024.08.010

One commenter, David Bahry, has taken to social media to call myself and co-authors "frauds" who are "trying to pretend" and made some vulgar comments that can't be repeated in this comments section because, he says, we "ignored" his comments. This is not true, so it's appropriate to respond here.

Both commenters note that low p-values for relative risk maps (Figure 2 and Figure 4) require sufficient sampling density to obtain a low p-value. Of course, this is correct. Both commenters argue that this is misleading. I disagree. No co-author, peer reviewer, or editor involved was misled. News articles on the paper thus far have almost all portrayed the data and analysis accurately. We do not argue in the paper that SARS-CoV-2 could not have been found in places that were not sampled; I'm unaware of anyone making this argument based on our paper; it's not misleading if no one is misled.

On top of this, the proportion of positive samples in each location is displayed along with the relative risk p-value. The n=1/1 sample that Bahry complains "shows little heat" is clearly indicated as a sampled location with high positivity in Fig 2A. And the underlying data is available in supplementary figures and tables. It's demonstratively not misleading.

The other arguments the two commenters make consist of (1) arguing against a different paper published years ago, (2) a demand for a citation of an irrelevant paper, (3) arguing for an alternative analysis method without demonstrating that it would have more statistical power, (4) an argument that implicitly assumes all expectations for all coronaviruses are identical regardless of their hosts and modes of transmission, and lastly (5) a typo in a citation. We addressed the last one -- showing that, in fact, we didn't ignore these comments.

On 2019-09-23 18:15:50, user quagmire wrote:

It is well written piece of work. The way the experiments are planned<br /> and executed was dope. I never thought HDAC6 can do this much changes <br /> when it gets secreted by neurons. I can see that a lot of investments <br /> have gone into this work, totally worth it. The way you connected <br /> neurite extension results, GSK3 beta and ubiquitination was zonked. Some<br /> supplementary data on gene knock out or western blots would be like <br /> cherry on top.

On 2015-04-14 13:47:13, user Dan McGlinn wrote:

This article was peer-reviewed and published at GEB, here's the open access link: http://onlinelibrary.wiley....

On 2020-04-17 23:18:54, user Charles Warden wrote:

Thank you for posting this preprint.

However, I think there may be some sort of formatting issue in Figure 5B. I was not sure what the categories were supposed to be, but they look like errors due to the presence of symbols like "&" and "!".

I was admittedly skimming the paper, but I think these are supposed to relate to RNP reassortants? I also only see a description for the "A)" section in the legend for Figure 5.

On 2022-05-21 10:01:09, user KU Lab Hirase wrote:

Plasmids for green/red-FP blood plasma probes are now available @Addgene:<br /> https://addgene.org/browse/...

On 2019-05-19 23:04:36, user Charles Warden wrote:

Thank you for putting together this pre-print.

I am sure that there are some situations where higher read coverage can be beneficial. Admittedly, I think other applications like mutation calling would have a relatively greater need for more reads (and that would depend upon the evenness of coverage for your library type, and possibly what genes you have the greatest need to check mutations in), but I think it is perfectly reasonable to focus on the differential expression part for one paper.

That said, when I saw the tweet mentioning "We find > 70% published studies would have benefitted from increasing number of reads sequenced", I was a little worried about the influence it could have on readers for the following reasons:

1) If somebody is considering purchasing a Desktop sequencer for RNA-Seq analysis, I think 2-6 Million reads to cover genes with above average expression may be a better option than using targeted gene panels. For example, if you do re-analysis (with unique read counts and updated differential expression methods, like DESeq2, limma-voom, etc.), I think the MiSeq data from the cuffdiff2 paper shows reasonable results (for treatments with clear gene expression changes).

2) In most cases, I am more concerned about people having replicates than needing more reads (at least for gene expression).

I apologize that I think it may be a little while before I can focus more on point #1, but I tried to take a quick look at this paper.

I think that it is great that you performed benchmarks with DESeq2, edgeR, and limma-voom (although maybe you want to change “limma” to “limma-voom” in the abstract?). I apologize for not being able to find this on the superSeq page (although I did find the reminder for the previous biocLite() command for dependencies to be helpful), but are tables of pre-processed counts (and their gene lists with all 3 methods) readily available for the 1,021 contrasts?

I am also glad that you are looking at differentially expressed gene counts (and not just unique read sequences) for your rarefaction plot, since I think that is a more relevant measure for whether you get functionally relevant results. However, in terms of the vignette example, I think the difference between 1338.968 “Estimated number of discoveries” at read depth of 1 and 1888.286 “Estimated number of discoveries” at read depth of 3x is within the range that could be achieved from changing the p-value method and/or changing the FDR cutoff (from 0.05 to 0.25 or 0.50, for example).

Similarly, I am concerned about some of the maximum gene counts in the pre-print, which look like pretty much the entire genome in Figures 2 (and are already above 2000-4000 genes in the theoretical example in Figure 1). I think the best balance for functional enrichment is often around 1000-2000 total genes (~5-10% of genes). So, I would be interesting in knowing if your framework can answer a question like “What is the range of reads needed to identify 1000 or 2000 differentially expressed genes?.”

While some treatments have greater effects, I think 10-20 million reads for a human polyA library is probably usually OK (and perhaps double that for a ribosome-depleted library, with a lower exonic percentage). I think that is pretty much what Figure 1A shows (although that looks like close to 30 million reads), but I am wondering if there is a figure derived from your ~1000 comparisons (and/or a parameter that can be added to plot pre-computed values in the R package).

Also, am I correctly understanding that you downloaded pre-processed counts? Did you look at some of the most extreme differences and test reprocessing the samples to see if that helped the differentially expressed gene counts become more similar? There are situations where I would prefer to start from FASTQ files and process all samples the same way.

For example, 60,000 in Figure 5 seems like it probably includes transcripts – is it possible to only look at unique gene-level counts (that is admittedly what I would be interested in checking)? Or, are there outliers that can be excluded if you only look at human and mouse experiments (trying to control for annotation effects)? Also, I’m not hugely concerned about the annotation in model organisms like yeast or fly, but the total number of genes in the genome is going to have some effect (both in terms of the effect on the differential expression models, as well as having very different genome sizes and maximum gene counts).

Finally, going back to my original point #2, I would expect replicates should help reduce false positives. With large enough sample sizes, I would expect to pick up more subtle effects. However, with 1-3 replicates, I think fewer genes to narrow down candidates may be beneficial (rather than increasing the number of genes identified). For example, at an estimated FDR of 0.05, how many genes are identified between biological replicates for the same group (to see if increased sensitivity may actually be affecting the accuracy of the estimation to allow more false positives, which seems likely if you are identifying >20% of the genome, in my opinion).

Or, it is a slightly different point, but I think 6 replicates are used in Figure 3A. If 6 replicates exist for an experiment, what is the effect of having 3 replicates at the current coverage versus 6 replicates at halved coverage? Sometimes, getting people to even do comparisons with triplicates can be a challenge.

I apologize that this is kind of a long comment, but that is because I think this is an important topic. When I get the point of being able to post some pre-prints, I realize that answering questions from long commenters can take time, but I think that is very important for the scientific community (in terms of helping put together the best possible paper for peer-review).

On 2017-09-15 17:19:03, user L Kushner wrote:

very cool!

On 2025-01-19 17:40:19, user Thomas Munro wrote:

It's a remarkable achievement to go from virtual screening to subnanomolar affinity and bound structures in one paper. On a minor point, I would recommend adding "neutral" to phrases such as "antagonists and inverse agonists", and in the title. The present wording could be taken to imply two separate categories, but almost all antagonists are inverse agonists . For instance, naloxone and JDTic are indeed antagonists as noted here, but both are also inverse agonists.

On 2019-03-12 08:46:38, user Christine Gee wrote:

Also see work from long-ago e.g. https://www.ncbi.nlm.nih.go... or https://www.ncbi.nlm.nih.go... and don't forget endogenous opsins (such as OPN3)

On 2019-10-20 15:36:40, user Maddie Moore wrote:

Hi Julie Meyer, I'm a 7th grader and doing a report on the epidemic Skittle D and found this website while reading and article on it. I am so interested in this and would love to know how the coral is in Florida and in ST.Thomas, Its ok if your busy but It really did peak my curiosity

On 2020-05-26 10:14:14, user Ben Berman wrote:

Are the Hi-C maps from CAST or BL6 strain? It seems like you should be able to compare CTCF sites specifically not present in the strain of the Hi-C, and they should be less associated with TAD boundaries (assuming there are strain-specific TAD boundaries)

On 2019-12-20 20:07:10, user Concerned reader wrote:

It is important to give correct credit where credit is due in citations of prior work: The authors talk about PCR-based random access yet completely ignore the original work that proposed this scheme, published in 2015 (Tabatabaei Yazdi et al, Nature SR). Citing follow-up papers and not the original contribution is not appropriate.

On 2018-02-23 17:53:41, user Laxmi wrote:

It's really great paper !! I really like your efforts to science. I would like to ask you the web site which you written in the paper is not working any more . Cloud you please help me to get that one for my sgRNA design studies

On 2023-08-02 18:51:59, user William Matchin wrote:

The authors claim that that certain "fictional conlangs... were created to differ from natural languages", yet this is false. There is abundant evidence that each of these languages are based on a variety of natural languages, as attested by the creators of the languages themselves. See e.g. https://www.daytranslations... regarding the creation of Dothraki by David Peterson. Here are some relevant quotes:

"Using words from obscure and exotic languages and the words originally written by Martin, Peterson developed several words and sentences, including attaching prefixes and suffixes to many of the words. He followed how nouns are formed in Swahili and studied the negative forms of verbs in the Estonian language among other languages he used as references, such as Russian, Turkish and Inuktitut. From his initial list of 1,700 words, he was able to expand the Dothraki vocabulary to 10,000."

"According to Peterson himself, you can see the influences of Arabic and Spanish languages in Dothraki language."

This is true for each of the languages assessed in this paper. The grammatical structures are all based on the kinds of structures found in natural languages - thus, there is nothing to be gained from this paper that hasn't already been demonstrated by a wealth of papers documenting brain activations for non-native languages in roughly the same areas as native languages.

On 2018-09-14 18:04:38, user deanna.church@gmail.com wrote:

As an update: we've combined the data in this manuscript with another manuscript: https://www.biorxiv.org/con...

On 2022-11-01 16:18:06, user Joel Boerckel wrote:

Journal club review of:<br /> Toll-like receptor 4 signaling in osteoblasts is required for load-induced bone formation in mice.<br /> Rajpar et al. bioRxiv 2022<br /> doi.org/10.1101/2022.08.05....

We reviewed this preprint as a part of Arcadia Science's preprint review initiative. Collated comments follow:

In this preprint, Rajpar et al. identify a novel role for Toll-like receptor 4 (TLR4) in mechanical load-induced bone accrual. The authors conditionally deleted TLR4 from Ocn-Cre-expressing cells (which primarily target mature osteoblasts and osteocytes). Ocn-conditional TLR4 deletion had negligible effect on baseline bone phenotype, but abrogated the effects of ulnar loading on periosteal and endosteal bone formation in both male and female mice. Prior papers from the lab demonstrated that nerve growth factor (NGF) expression by periosteal cells is upregulated by loading. Here, they show that periosteal NGF expression in loaded bones is reduced by the NF-kB inhibitor, BAY 11-7082, and that loading increases the number of TLR4+ periosteal cells in WT mice. Complementary in vitro experiments in MC3T3 cells and primary osteoblasts, showed that both NFkB and TLR4 inhibition abrogated the increase in NGF expression induced by in vitro mechanical stimulation (by fluid shear stress). Finally, the authors use bulk RNA seq to compare the transcriptomic profiles of loaded or non-loaded limbs in TLR4 knockouts and wildtype mice. <br /> Overall, these new data are exciting and implicate a novel role for a classic inflammatory signaling cascade in bone mechanoadaptation. However, we found that the structure of the paper, written with NGF as the starting point, is challenging to follow for naïve readers unfamiliar with the prior NGF studies and obscures the key finding (viz., TLR4). The authors could adapt the flow described above to make it an easier read and to emphasize the novelty and impact.

Several experiments in the paper feature insufficient sample size or missing data, whose addition would improve the strength of the conclusions that can be made.

Specific comments:<br /> 1. Immunostaining in Fig 1 shows NGF:eGFP expression in the periosteum. This is qualitative; it would be better to quantify the number of eGFP+ cells and show this as a percentage of the total number of cells in the region of interest, for both loaded and non-loaded bones. While built on prior results, display and quantification of both loaded and non-loaded bones is important to demonstration the extent to which the BAY inhibitor reduces NGF expression to non-loaded baseline levels.<br /> 2. The qPCR data in Fig 1 C-F are not adequately powered. A minimum of 3 mice per treatment group must be analyzed for statistical analysis. <br /> 3. It is not clear how the ??Ct values in Fig. 1 C-F are normalized. This information appears to be missing.<br /> 4. An explanation of the kinetics of NGF and TRL4 analysis would help. NGF-EGFP expression is analyzed 3 hours post loading and TLR4 positivity is analyzed at 7 days.<br /> 5. Figure 2 is presented as relative values, and non-loaded images are not shown. It took us a while to understand this figure. It would be clearer and more rigorous to show all four groups – Non-loaded WT, Loaded WT, Non-loaded cKO and loaded cKO on the same graph, along with corresponding representative images. These data are included in table 2, but tables are always harder to interpret compared to the main figure. Statistical comparison using repeated measures ANOVA would preserve matching and account for animal-animal variability.<br /> 6. Scale bars are missing on the images in Fig 2. <br /> 7. Figure 3 provides important support for the TLR4-NGF connection, especially with TAK-242 inhibition. The use of two orthogonal NFkB inhibitors to show the same effect is robust. Adding figure labels or illustrations to clarify the cell types used in each panel will add clarity. <br /> 8. The timeline is missing from Fig 3A. <br /> 9. The sample size for experiments in Fig 3 is unclear. Showing individual data points for independent samples, including for controls, is important. <br /> 10. Addition of a diagram/illustration to Figure 3 to indicate the fluid shear stress conditions would add clarity. “Load” should be changed to FSS (Fluid Shear Stress) in this figure.<br /> 11. The RNA-seq analysis in Figures 4, 5, 6 is unclear and does not show the comparisons most relevant to the study. We recommend re-analysis using the following comparisons:<br /> A. Effect of load: Loaded WT vs Non-loaded WT. Which pathways are regulated by loading?<br /> B. Effect of TLR4-cKO on pathways identified in (A) as load-induced pathways: Loaded WT vs Loaded cKO. Are the same pathways that were up/down due to loading still up/down after the KO? <br /> C. NGF-signaling: the in vitro data show NGF expression is abrogated by TLR4 inhibition. But in the RNA-seq data, NGF remains significantly upregulated by loading in cKO mice. Whether this upregulation in the knockouts is due to the heterogeneity of the lysed cells in the tissue or is actually relatively lower than the upregulation of NGF by loading in WT mice is not shown. Comparison of the effect of loading on NGF induction in WT and cKO mice could answer this. If NGF signaling is reduced in cKO mice, compared to WT, RNA-seq would be the ideal method to look for signatures of altered signaling downstream of NGF.

Reviewed by: Boerckel Laboratory, University of Pennsylvania, Oct. 14, 2022.

On 2020-05-06 15:28:18, user Charles Warden wrote:

I agree that using imputed values from a SNP chip can be a problem, and I would say medical decisions should never be made using an imputed value (whether that is from a SNP chip or lcWGS data).

I have some general notes (and opinions related to some comments), but I thought I should move those to a blog post, in order to keep the commentary more focused.

In terms of this specific paper:

1) The choice of array can affect the results. For example, while the conclusions are similar to concordance sections of this paper, the GLIPSE paper shows better performance with the Infinium Omni 2.5 compared to other SNP chips (in these interactive plots for EUR and ASW individuals, as well as the Rubinacci et al. 2020 pre-print). This should also be possible to compare with the 1000 Genomes samples, and this may be different than the GSA results?

For example, I checked the manifest files, and it looks like Infinium Global Screening array would be a “medium” density array versus a “high” density array (with the categories defined from that other study).

2) In the context of this paper, the comparison that I am interested in is not imputed SNP chip genotypes to imputed lcWGS genotypes, since I would agree that it is likely to see some (although possibly subtle) improvement for lcWGS imputations versus SNP chip imputations.

Instead, what I would like to see is directly assayed SNP chip genotypes versus imputed lcWGS genotypes. The ability to provide results without any imputations is the main reason I prefer SNP chips over lcWGS (if those were my only 2 options), so I would want to compare the SNP chip genotypes where all variants were directly measured by the SNP chip versus the lcWGS imputations.

This would mean you could only compare PRS values among probes present on the SNP chip, for example. However, it looks like you selected a CAD PRS with 1,745,179 variants (225,667 were directly typed on the GSA) and a BC PRS with 313 variants (75 were directly typed on the GSA). The BC PRS is closer to the number of variants in PRS that I have tested on myself. So, if you can find a PRS with between dozens and 1000s of variants, where 100% were directly typed on either the GSA or Omni-2.5 SNP chip (or both), then maybe that can help with providing the comparison that I would like to see?

As I mention below, maybe using public SNP chip + WGS data can help you identify a custom array where the probes were designed to cover everything for a PRS? I would guess/hope that this would be a requirement if getting FDA approval for a clinical test (and this is why I don’t consider a PRS with imputed SNP chip values to be equivalent), but maybe you can also find this for some research-level PRS (like the 23andMe diabetes example that I described in my blog post above, which I think uses 1,244 loci, even though other risk factors were more likely to predict whether you got diabetes)?

Or, removing the PRS results would be another option that would reduce concerns from myself. For example, it looks like the error rate in this paper was noticeably higher when the BC PRS used 100s of SNPs (instead of a PRS with >1 million variants). There are also factors that could cause me to prefer removing the PRS results (which I moved to the blog post).

3) I agree that batch effects (like “index hopping”) could cause down-sampling to underestimate the error rate for lcWGS (which I would guess is more of an issue for smaller libraries on higher throughput machines). In the “Experimental Overview”, it sounds like you used cell lines for 1000 Genomes individuals for the new sequencing experiments? While it is hard for me to say exactly what could cause a problem, am I correct that previous Gencove developments considered 1000 Genomes data? Is it not possible to have more independent test datasets for your estimates (for a set of ~120 individuals)? I myself am one individual from the Personal Genome Project that has public high-coverage and low-coverage WGS from different companies (along with SNP chip genotypes).

To be fair, this may be less important than some of the other points, especially if sections / content is removed. For example, if you reduced the focus to variability in technical replicates derived from 1000 Genomes subjects and different technologies (and remove the PRS application), then I don’t think this extra analysis needs to be added.

4) If the goal is to show a general principle, then maybe you could show if open-source programs like STITCH, GLIMPSE, etc. can achieve similar performance with lcWGS data? I think this would make disclosing the conflicting interests less important, even though I think that still needs to be done. This would be good to show for readers that might usually prefer to use open-source options, unless Gencove is changed to become open-source (and I think showing performance of alternative programs is common, even if that was true).

5) I think the most important issue has been fixed in the link for revision 2 (I previously had issues accessing the content in s3://gencove-sbir/, but I can see the data in https://gencove-sbir.s3.ama... "https://gencove-sbir.s3.amazonaws.com/index.html)"). Nevertheless, in order to match the current 1000 Genomes data deposits, is it possible to deposit the data (derived from 1000 Genome subjects) into public genomics databases like the SRA? Or, if you have already done so, can you please provide accessions that don’t require an extra step (or steps) to access the data?

Summary:

I think this is an interesting topic of research, and I think lcWGS imputations can be useful for certain applications (such as relatedness and broad ancestry). However, I have concerns about the clinical utility of Polygenic Risk Scores from imputed genotypes in lcWGS data. That said, I think these results could be presented with less controversy if the PRS section and Figure 4 was removed, and that is a possible solution to some concerns that doesn’t require extra work (taking out results, rather than adding in new results).

I think testing 1000 Genomes Omni 2.5 SNP chip concordance (and/or only comparing “directly assayed” SNP chip genotypes) and potentially removing the PRS results are what I am most concerned about.

Thank you very much for putting together this pre-print. I believe that it is important to see independent presentations of results from different groups. I can also tell that a lot of work was put into this paper (with a several pages of supplemental information), so I appreciate this.

On 2023-01-17 19:28:20, user John wrote:

Great paper, just a minor comment reference 9, which is cited in support of the following "fluctuate proportionally in response to nutrient flux through the hexosamine biosynthetic pathway (HBP)" actually concludes the opposite i.e., "HBP flux does not respond to acute changes in glucose availability".

On 2020-02-02 05:25:34, user Babu M wrote:

Dear authors,

Amazing findings, Congrats. Out of curiosity, authors has collected and cryopreserved <br /> Human PBMC's first; and performed experiments later on. Based on published literature, cryopreservation has significant role on adhesion molecules of cells. Either way, is there any possibility of correlation in between cryopreservation with cell-cell complexes modulation on this article context ? <br /> Thanks in advance.

Best regards, <br /> Babu Mia<br /> mdbabumia777@gmail.com

On 2020-08-27 01:27:50, user Alan Buckler wrote:

We read with great interest this preprint from Coffey and colleagues (the results of which were recently presented at the annual HD Therapeutics Conference organized by CHDI; see https://chdifoundation.org/... "https://chdifoundation.org/2020-conference/#carroll)"). The authors describe reductions of somatically expanded CAG repeats in liver cell populations upon prolonged peripheral administration of a potent HTT-lowering ASO to mice harboring either HttQ111 or Atxn2Q100 alleles. In addition, these investigators evaluated somatic expansion in a new lac-O-Q140 mouse model in which only the mutant allele is selectively and systemically suppressed by withholding exposure to IPTG. Further, the ability of an HTT-lowering ASO was assessed for its ability to prevent somatic CAG repeat expansion in medium spiny neuron containing cultures derived from iPSCs harboring a mutant HTT allele.

While these results led the investigators to conclude that HTT lowering can slow/lower the rate of somatic expansion of mutant Htt and Atxn2 alleles in mice, and that this may point to additional potential therapeutic benefits of pan-Htt lowering approaches. However, we raise herein some important points and concerns that may require resolution before the HD and broader Repeat Expansion Disorder (RED) communities accept such conclusions that are associated with important clinical implications for therapeutic approaches aimed at treating these devastating diseases.

These points and concerns are:<br /> 1) The effects of HTT lowering on these large, inherently unstable alleles is certainly evident in the Expansion Index and Peak scanner traces in each of the models tested. However, there remain prominent populations of somatically expanded repeats of shorter length in the liver cells present at the end of the study. While it seems intuitive that the observed effects implicate HTT in further expansion of these large repeats, we believe there are alternative and equally plausible explanations for the results. An obvious and testable possibility is a synthetic lethal interaction between very large repeats and ASO-mediated lowering of HTT expression, both of which could create chronic stress on liver cells. Over the course of these long treatment periods, this ‘large repeat’ cell population could be lost, which would generate a shift of the CAG repeat profiles similar to those observed in these studies. As such, we do feel that this and other alternative explanations should be tested before drawing the currently proposed mechanistic conclusion.

2) The HttQ140 results and cell model data are less convincing, and the Q140 model results are somewhat puzzling in that the mutant allele is selectively suppressed by 50% in these mice, leaving the normal allele fully operational. This would mean that the effects on somatic instability are mediated by a 25% reduction in total Htt (i.e., 75% Htt ‘activity’ remains) and that the 25% reduction is exclusively mutant Htt. These results would benefit from further explanation or additional characterization of the model before they can be considered to be consistent with the conclusions of the preprint. In particular as the data, as presented, show less effect of longer-term mutant HTT lowering in striatum compared to liver.

3) We have generated results, albeit in different animal and cellular models, that are inconsistent with the observations made by these investigators (see https://chdifoundation.org/... "https://chdifoundation.org/2020-conference/#bettencourt)"). We found no effect of siRNA-mediated Htt lowering on somatic expansion of a human HTT transgene in liver of R6/2 model mice. In contrast, siRNA-mediated lowering of selected DNA Damage Response (DDR) genes had a profound effect on expansion. Similarly, in HD patient-derived iPSCs (HD109 cells), we again fail to see any effect of siRNA-mediated HTT knockdown on somatic expansion. Consistent with the R6/2 result, siRNA-mediated knockdown of selected DDR genes has a pronounced impact on somatic expansion in this cellular model, consistent with the hypothesis originating from genetic modifier analysis of HD and other RED populations and as shown by Wheeler and Pinto in HD animals https://chdifoundation.org/... "https://chdifoundation.org/2019-conference/#wilkinson)"). We have proposed to work with Coffey et al. so that we can understand and resolve our disparate observations.

4) Importantly, given the investigators’ proposal that HTT may play a role in affecting integrity of the genome around expanded CAG repeat sequences, we were surprised by an omission in this preprint. Namely, the related, albeit preliminary, observations by the same authors that full loss of HTT function causes the accumulation of various types of somatic mutations in vivo (see https://chdifoundation.org/... "https://chdifoundation.org/2020-conference/#carroll)") were neither discussed nor alluded to by Coffey et al.. Given the possible clinical implications of this equally surprising observation, we feel that it is important to raise this point to readers and it will be very important to verify and further characterize these preliminary findings.

In summary, while the results presented in this preprint and the associated presentation are surprising and interesting, we feel that the mechanistic conclusions are premature, the preliminary observations of HTT loss-of-function effects on mutation burden in cells have important implications for approaches that target HTT, and that these issues merit discussion in the manuscript. <br /> We look forward to working with these investigators to further explore our observations and our interest in understanding the underlying mechanisms of HTT disease allele somatic expansion that clearly underlie the onset and progression of Huntington Disease.

Alan Buckler, Irina Antonijevic, Nessan Bermingham, Brian Bettencourt, Peter Bialek<br /> Triplet Therapeutics, Inc.

On 2017-11-24 19:21:57, user Anton Kulaga wrote:

where is the source code?

On 2021-11-18 19:04:36, user S. Olschewski and M. Rosenthal wrote:

This is a very interesting and timely study investigating the host interactome of Lassa virus L protein. Bunyavirus proteins need to interact with the host cell in order to get access to the

translation machinery, transport systems etc. The L protein contains the viral polymerase and is thus central to the viral replication cycle. Together with viral RNA and the nucleoprotein NP it constitutes the viral ribonucleoparticles, which are the structural and functional units for genome replication and transcription. So far only nucleoprotein and Z protein interactomes have been reported. The authors address this gap by inserting a

biotin ligase internally into the L protein, a position previously reported by Vogel and Rosenthal et al. 2019 (PMID: 30926610), and biotinylating all proteins in proximity to the L protein in a mini-replicon experiment, which recapitulates the steps of viral genome replication and transcription. The authors complement this dataset by silencing experiments using siRNAs. One of the proviral factors identified, GSPT1 – eucaryotic peptide chain release factor subunit 3a – was further validated by co-immunioprecipitation, co-localization and mini-replicon experiments as well as in infection experiments. This study will be of high interest for the scientific community and suggests GSPT1 as a potential drug target against Lassa virus infection. <br /> We have a few comments on the manuscript we hope the authors find useful and might want to consider:<br /> 1. We would appreciate mentioning, if in the constructs linkers have been used before or after the tags and which sequence those linkers would have.<br /> 2. In Figure 1B the authors used the term “polymerase activity” to label the y-axis while in Figure 5B its “minigenome activity”. If it’s the same assay and the same readout the<br /> terms should be consistent.<br /> 3. In line 135 the authors describe a slightly lower expression level of L-407-HA-TurboID. However, Figure 1B shows at least less than 50% expression level which is significantly<br /> lower. Was the detection of L performed with the same samples as the minigenome readout? If yes, the authors might want to discuss this discrepancy between<br /> expression and measured activity. <br /> 4. Only a small fraction of NP was biotinylated. The conclusion from the authors is that this “may reflect that only a low percentage of the total NP participates in the formation of a functional vRNP, or that in the vRNP, the majority of NP was not accessible to biotinylation or remained insoluble under the lysis conditions we used to prepare the proteomic samples”. This could easily be tested with Western blot analysis of the insoluble fraction after cell lysis.<br /> 5. In Fig. 2c it is not clear which factors occurred in more than one screen. A supplementary figure in which the hits in Fig. 2c are labeled (+ zoom into graph) would help to understand which of the hits are highly enriched.<br /> 6. Why haven’t the authors used a control in which TurboID-HA was transfected separately from the tagged L protein in the minigenome assay? <br /> 7. In the abstract and text 6 factors that influenced LASV infection are mentioned but in the figure 3C & S2B there are actually 7 factors labeled with “siRNAs significantly affected infection across two experiments”<br /> 8. In the two figures 3 and S2 (siRNA screen experiment, one for MOI 0.5 and one for MOI 1) it is unclear if for both MOIs biological repeats have been performed or if these were single experiments (in technical triplicates) that were analyzed together (MOI 0.5 and 1). It’s also not clear if the infection rate was similar for both MOIs. The authors might want to discuss the differences between the MOI 0.5 and MOI 1. <br /> 9. In the two figures 3 and S2 it is unclear why some dots have a white outline and other don’t. In Fig. S2 the upper UPF1 bubble is not completely with color.<br /> 10. For the siRNA experiments, LAMP1 and DDX3X knock-down as controls were only tested or displayed for MOI 1 (Fig S2) and not discussed at all. How do the authors explain that these siRNAs didn’t show any effect? DDX3 showed an effect in LCMV siRNA knockdown studies and effects upon knock-out for LASV and LCMV (PMID: 30001425). Although LAMP1 is described as an entry factor, Lassa pseudovirus infection studies with LAMP1 knock-down (approx. 15% remaining expression) showed no differences compared to wildtype cells (PMID: 29295909). The authors might want to discuss their results regarding LAMP1 and DDX3X.<br /> 11. The authors should confirm knock-down of their 6 or 7 hits via Western blotting.<br /> 12. In figure S2 the caption lists “C” instead of “B”.<br /> 13. The authors might want to compare their results also to the LASV NP interactome dataset available (PMID: 30001425). It seems strange that they compare to the LCMV NP interactome dataset but not to the LASV one. In Addition, also the LASV Z AP-MS dataset could be used for comparison since it is known that L and Z interact (PMIDs: 34226547, 34697302, and https://doi.org/10.1038/s41... "https://doi.org/10.1038/s41564-021-00916-w)") and although in the replicon system Z is of course not present it would have been interesting to have a look at possible commonalities.<br /> 14. In the validation experiments for GSTP1 via CoIP (Figure 5) the “Input” amount of L-HA differs strongly between the different samples. Problematic here is that the input for the control transfection without FLAG-GSPT1 shows a lower expression of L compared to the conditions with FLAG-GSPT1 and after FLAG-IP there is also IP of L detected in absence of FLAG-GSPT1. Like this it is hard to reliably conclude anything from these blots. The Co-IPs could be also repeated via pull-down of LASV L-HA.<br /> 15. As the bands in the blots of Figure 6c are quite smeary, the knock-down effect of GSPT1 compared to NSC is not clearly visible. Therefore, it is hard to conclude that the inhibitory effect is due to the GSPT1 knock-down if the knock-down isn’t confirmed. Similarly, the smear for LASV GP2 makes it hard to compare the GP2 levels. Therefore, to conclusion that GP2 levels have decreased 72 h.p.i. (lines 231-236) is not convincing. Since the authors have a functional NP antibody for Western blot, the blots from Fig 6c could be repeated additionally detecting NP. This would also help investigate if the effect the authors seem to observe is limited to the secreted GP or also valid for cytoplasmic proteins such as NP.<br /> 16. For the inhibitor studies in figure 6G, the actin control bands are also less intense in presence of the inhibitor, this makes conclusions about the specific targeting of GSTP1 difficult. This should be discussed. Also, (G) is not mentioned in the respective caption, instead (D) is listed twice.<br /> 17. In their hypothetical models the authors refer to NP as the cap-binding protein despite the fact that the respective reference (PMID: 21085117) fails to provide any hard evidence<br /> for a cap-binding function of LASV NP and other groups could not confirm a role of the proposed cap-binding residues during viral transcription (PMID: 21917929).<br /> 18. Since L, NP and the viral genome are sufficient for viral genome replication, transcription and viral protein translation – viral protein translation can’t depend on the eIF4E-Z interaction the authors propose in Fig S5. Also, the authors didn’t mention the role of L- eiF3CL interaction in any of their model.

Written by<br /> Silke Olschewski and Maria Rosenthal

On 2021-10-08 12:04:39, user Karel Muller wrote:

Very nice work, indeed. Although, the title sounds to "weak". :) I really appreciated the focus on role of individual GH3 genes in plant development. And I believe that there is still much to understand between inactivation of GH3 activity of the overall phenotype. I would like to know whether authors tested levels of other auxin metabolites in their material as well as for example transriptomic profiles of those plants. Thank you.

On 2019-02-05 09:32:34, user Dr Emmanuel G. Reynaud wrote:

The paper used an acronym TC-LSFM already published 4 years ago by Francesco Pampaloni... for Tissue culture instead of Tissue Clearing

https://www.ncbi.nlm.nih.go...

On 2020-02-04 16:05:19, user PZM Diagnostics wrote:

This article has been accepted for publication in Fetal & Pediatric Pathology, published by Taylor & Francis"

On 2021-05-07 11:30:56, user Ammar Zaghlool wrote:

Any data on lateral diffusion rates?

On 2019-10-21 16:58:11, user Christian Bjerggaard Vægter wrote:

Very interesting work (and scary, actually). Two questions:<br /> 1) There is a clear positive effect of 5 days TAM vs 3 days TAM. I wonder if this effect can be extrapolated for a more effective TAM administration protocol (e.g. 10 days of TAM)? Have you, or others, looked into this?<br /> 2) Somewhat related to the above; do you think (or know) if non-inducible Cre results in full expression of both reporters in the same cells?

On 2021-04-06 06:17:04, user Antonelli Lab wrote:

Check out our response to this critique: "Unbiased clade age estimation using a Bayesian Brownian Bridge" https://biorxiv.org/content...

On 2016-03-28 01:05:32, user Ethan White wrote:

This is a great presentation of some of the benefits of preprints and the concerns of scientists considering using them. It's great to see this kind of reflection and discussion coming out of ASAPbio.

We wrote a related paper a couple of years ago that folks interested in preprints in biology may be interested in as well: https://doi.org/10.1371/jou....

On 2017-10-07 08:47:42, user Francois Nedelec wrote:

This is now published in Molecular Systems Biology:<br /> http://msb.embopress.org/co...

On 2018-04-03 01:41:28, user Jo Wolfe wrote:

This is now published in Nature Ecology & Evolution, doi:10.1038/s41559-018-0513-7

On 2025-04-29 11:23:26, user Jérémie Marlhens wrote:

I think you meant Robert Rosen, not Nathan Rosen. Best, Jérémie

On 2017-12-28 09:15:26, user Donald wrote:

Wonderful and enlightening paper:<br /> -You should discuss in the discussion the implications/discrepances <br /> of this results in the DSM5 diagnosis criteria.<br /> -In the Figure2, you missed the ADHD result. Further, you should explain<br /> (in the figure or in the text) how exactly is the correlation with cronotype <br /> (morningness, eveningness, sleep duration, disrupted circadian rhythms...)

On 2021-12-12 23:19:28, user Jorge Eduardo Chang Estrada wrote:

General comments:

The work presented by Harris; Nekaris and Fry investigates a possible event of coevolution between primates and venomous snakes, focusing on the resistance of the ?-1 nicotinic acetylcholine receptor against ?-neurotoxins. This hypothesis is based on the finding that members of the Cercopothecidae and Ponginae families of primates possess ?-1 nicotinic acetylcholine receptor alleles that are more resistant against neurotoxins of Naja sp venoms, snakes from the Elapidae family that have a similar evolutionary history: evolving initially in Africa and later spreading throughout Asia. I believe that the hypothesis is very interesting and the authors provide an interesting overview of it in the manuscript. However, it is not clear to me whether the authors can conclude an event of coevolution based on the data presented: a deeper analysis of it may be needed. I also think there are some limitations in the analysis presented (as pointed below), and I hope my observations help the authors to improve this work.

Major comments:<br /> 1. The authors provide a series of dendrograms without time divergence data. It would be useful to provide a time divergence data showing a temporal coincidence between primates and snakes evolutionary events to support the hypothesis. <br /> 2. A sequence alignment of the ?-1 nicotinic acetylcholine receptors of different primates is presented showing different mutations between the receptors from different species. It would be interesting if the authors could discuss how (at least some of) these mutations could help in the resistance mechanism, based on what is known about the binding of the neurotoxins to the ?-1 nicotinic acetylcholine receptor. <br /> 3. As your work presented, the clades of Platyrrhini and Lemuriformes were more susceptible to the venom of Naja then the other species that live in common areas with them. Both clades are separated from Africa and Asia and from the pressure from the Naja sp venom. Is there a possibility that these clades coevolved with neurotoxins found in venoms from Elapide members sympatric to these clades ?<br /> 4. Venomics studies showed that snake venoms may vary depending on the environment even within the same species. It was not very clear to me whether the venom source was from individuals collected from the same or different locations, and how this variable may affect the coevolution with primates? See the work of Calvete 2008 Snake venomics and antivenomics of the arboreal neotropical pitvipers Bothriechis lateralis and Bothriechis schlegelii. J Proteome Res. 2008 Jun;7(6):2445-57. doi: 10.1021/pr8000139.<br /> 5. The uses of the techniques may be more described to clarify the importance of the essays. I am not sure if this essay may demonstrate coevolution. I consider that, more experiments maybe apply in a more specific way. The limitations of the assay may generate a not clear conclusion. <br /> 6. Is there any evidence that other toxins from the venoms showed a similar pattern of sensitivity/resistance?<br /> 7. In order to propose a coevolution event, I would expect to observe a reciprocal effect between the two groups. What would be the characteristics evolved by the snakes that could have been impacted by the primates? Is there any evidence of such event? If not, that may be an adaptation of primates to the selection pressure of the snakes, and not necessarily a coevolution event.

Minor comments:<br /> 1. The authors report to have statistically analyzed the data, but that analysis is not presented in the figures. <br /> 2. I could not find the supplementary material. Access to this material may help readers to better understand the results.<br /> 3. The figure 4 have the indication of figure 2<br /> 4. The clade Lorisiformes present a high susceptibility to almost all venoms, except that from the venom of Naja kaouthia. How do you interpret this result? The clade was very susceptible to the venom of Naja siamensis, which is also an Asia cobra.

On 2018-04-05 02:41:00, user flavius.aettius@gmail.com wrote:

This portal looks to becoming a great resource, but that's only if extensive genomic data, in a searchable format is actually included. As I see it right now, there is some genomic data, for example, major oncogenes and tumor suppressor point mutations, but that's too limited to be useful. In a perfect world, this portal should eventually look like cBioportal in terms of what parameters are searchable.

On 2016-05-27 16:39:58, user dcx_2 wrote:

What was actually "powerful" were the electronic amplifiers that were in the TV. Those amplifiers are susceptible to extremely tiny fluctuations in electromagnetic radiation. Properly designed hardware should shield sensitive amplifier inputs from external RFI. However, less expensive units will often skimp on the shielding as a way to cut costs. The distortion in your TV was more an indictment of your TV's manufacturer than the power of the cell phone.

It's similar to looking through a microscope at a slide, placing a drop of water on the slide, and then observing that the drop of water was so huge that it flooded your field of view. The microscope in this example is equivalent to the amplifiers in the TV.

For what it's worth, I did some napkin math once to determine what would be necessary. FCC defines ionizing radiation as 10 eV. A single photon of 2.4 GHz is only 0.00001 eV (or 10 micro-eV). A 900 MHz photon like the one used in this study is even lower, about 3.7 micro-eV. That means it would take about one million photons with an average frequency of 2.4 GHz striking the same DNA atomic bond simultaneously in order to cause damage (and about 2.7 million of the 900 MHz photons). A tall order when the antenna radiates energy in all directions and the body does not absorb all of it and a DNA bond is a rather small target.

In contrast, the light from the sun contains UVA with a single photon containing over 3 eV. It only takes about 3 UVA photons striking the same bond at the same time to ionize it - not such a tall order when you're exposed to almost 1 W/m^2 of UVA in direct sunlight. And unlike this study, there actually are people who stand in direct sunlight for up to 9 hours a day...

On 2025-05-04 20:09:50, user Xiaoiqang Sun wrote:

This manuscript has been published <br /> Genome Res. 2025. gr.279857.124<br /> https://doi.org/10.1101/gr.279857.124

On 2024-12-06 01:19:33, user xPeer wrote:

Courtesy review from xPeerd.com

Summary

The preprint "Cell based dATP delivery as a therapy for chronic heart failure" proposes using genetically modified human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) to deliver deoxy-ATP (dATP) to improve contractility in chronic heart failure. This strategy involves overexpressing ribonucleotide reductase in hPSC-CMs, enhancing their dATP production. Key outcomes include increased left ventricular function, greater exercise capacity, improved cardiac metabolism, and reduced symptoms of heart failure in animal models. The approach combines remuscularization with enhanced contractility, offering a novel therapeutic direction for chronic heart failure.

Major Revisions

1. Validation of Claims:
2. Doubts: The study's claims about the efficacy of dATP-producing CMs in treating chronic heart failure need stronger validation in larger and diverse animal models and human studies.
3. Critique: The majority of the experimental data involve rodent models. Larger animal studies and eventual human trials are crucial for assessing translative potential and variability in responses (e.g., differences in metabolism and immune reactions).

4. Example: "Our goal was to improve regenerative strategies by genome-editing hPSC to make dATP-donor cells… Our results indicate that dATP donor CMs can… persistently improve the function of the chronically injured heart".

5. Mechanisms of dATP Delivery:

6. Clarification: Detailed mechanisms on how dATP is transported through gap junctions from donor to host cardiomyocytes require further elucidation and quantification.
7. Critique: While the study mentions gap junction-mediated dATP transfer, the precise dynamic and extent of this transfer across intercellular connections in vivo are not fully described.

8. Example: "In vivo, dATP-producing CMs formed new myocardium that transferred dATP to host cardiomyocytes via gap junctions, increasing their dATP levels".

9. Long-Term Safety and Efficacy:

10. Skepticism: Long-term safety data and potential adverse effects of continuous dATP elevation, such as risks of arrhythmogenicity, were not adequately addressed.
11. Critique: Although the study indicates beneficial dATP effects, continuous high levels of dATP need further investigation to rule out chronic side effects including arrhythmias or maladaptive cardiac remodeling.
12. Example: Concerns: "Interventions to increase the contractility of the failing heart have been sought for decades…our novel strategy of cell therapy…".

Minor Revisions

1. Typos and Errors:

2. There are a few minor typographical errors and ambiguities in phrasing that can be corrected for better readability.

3. Figures and Diagrams Consistency:

4. The figures and diagrams should uniformly represent the data and should be referenced consistently within the text (e.g., Fig 6 referenced properly with aligned legends and labels).

5. Formatting and Style:

6. Standardize font sizes and alignments, particularly in figures and tables. Ensure that equation formatting, subscripts, and superscripts are consistently applied.

Recommendations

1. Include Larger Animal Studies:

2. Conduct larger and more diverse animal studies to establish translational efficacy and safety across different species. This would bridge the existing gap between rodent models and potential human applications.

3. Detailed Mechanistic Studies:

4. Expand mechanistic studies on the biophysics of dATP transfer and integration into host cells, detailing the kinetics of dATP movement and concentration gradients across different heart zones.

5. Extended Safety Profiles:

6. Investigate long-term safety profiles of dATP elevation in vivo, focusing on electrical stability of myocardial tissues and potential non-target effects on other tissues/organs????

7. Human Trials:

8. Initiate phase I clinical trials after thorough preclinical validations to evaluate safety, dosage, efficacy, and delivery mechanisms in human heart failure patients.

9. Data Sharing and Reproducibility:

10. Provide access to raw data and methodological details to enhance reproducibility and allow independent verification of results.

In conclusion, the preprint presents a promising approach to treating chronic heart failure using genetically engineered hPSC-CMs. Nonetheless, further work on validation, safety, and translational studies is essential to move toward clinical applications.

On 2024-06-11 13:08:39, user wonderfulponderfulponds wrote:

I feel the research design and some conclusion drawn is premature due to an overlook of the following aspects:

1. Lacking data on Fatty acids analysis of some representative wild capelin testis, semen or centrifuged spermatozoa: The regeneration of sperm and endurance of males largely relate to absence/ presence of enough "raw materials" (i.e., high quality lipids, phospholipids and long-chain polyunsaturated fatty acids to be precise). The original hypothesis tested would be dubious, irrespective of any male sub-cohorts or phenotypes, if the above basic requirement is missing.

2. Lacking data on a gastrosomatic index (gut/ visceral weight divided by body weight and expressed in %) or gut content analysis: if some capelins are not feeding in between their spawning, it is highly unlikely they would replenish depleted energy reserves, and bioenergetically channel 50% of such intake energy in food to invest in gamete/ milt production. As such the original experimental design does not take into account a careful bioenergetics point of view either. Some whole-body carcass analysis of some representative capelin would have been beneficial as an alternative.

I urge the authors to consider these in future work and good luck with the revisions.

On 2020-05-28 07:27:37, user Benjamin Gagl wrote:

Thanks you for this comment. We are currently including a newly collected dataset that resolves this issue for a revised version of the manuscript. I will inform you when the new version is online.

On 2020-09-25 14:59:07, user Michelle Momany wrote:

Very nice work! The increased propensity of fly septins to bundle in liquid vs yeast septins and interaction of fly w PIP2 and PS containing membranes vs just PIP2 for yeast are striking. The displacement of bundles along the membrane in your AFM studies (Fig5 suppl 4) reminded me of septin bundle dynamics we observed in Aspergillus nidulans. https://ec.asm.org/content/... Wondering how common bundles are in nonyeast organisms.

On 2014-04-04 08:56:40, user Davidski wrote:

Thanks for the update.

I have a couple of questions and/or comments:

• were the PCA corrected for projection bias? See here for more info...

http://arxiv.org/abs/1211.2970

• in regards to the comments about correlations between geography and genetics in PCA, I'd say there's no need to work out the correct amount of samples from each subregion to get a high correlation with geography for European and West Eurasian PCA. The best thing to do is to pack the PCA with as many samples from as many subregions as possible. If there's no correlation with geography after that, then the dataset is too limited. Here's an example of a PCA of West Eurasia:

https://docs.google.com/fil...

PCA of Europe with very thorough sampling also show a high correlation with geography. I don't have an example right now online, but with enough samples from enough subregions it's actually possible to produce more geographic-like results than in Novembre et al. 2008.

• I would also love to know the relationship between ANE/WHG/EEF and ANI/ASI. More specifically, does ASI contain ANE admixture? And what are the levels of ANE like from the Hindu Kush to South India?

Cheers

On 2016-03-22 16:04:37, user David Coil wrote:

I think this is an awesome paper and I'm really excited to see more and more PMA studies out there highlighting the importance of the live/dead question. However, I have to take a bit of issue with the first sentence of the abstract... it seems like most people are aware of the live dead issue? I was say more that non-PMA studies (e.g. most of them) accept that getting DNA from dead cells is just one more potential problem in their study... not that they assume all the DNA comes from live cells?

On 2020-04-05 04:36:18, user leon wrote:

I was hoping that SARS CoV had the answers for this new virus, similar to how the Smallpox vaccine was found from cowPox, im a noob in this area but im glad there are new findings each day.

On 2020-03-24 01:10:36, user Brian Osborne wrote:

Where are the sequences of the 28 proteins that they expressed? These experiments can't be reproduced without the sequences. Didn't see these in the paper, or in the supplementary material.

On 2020-05-14 00:17:38, user Nadine Powell wrote:

While coming to this 3 years on I will still comment. As with the other commentor my experience with online family trees is not positive. Even the more seasoned researcher with a family tree uploaded to Ancestry frequently has not verified connections they encounter in another family tree. With the "explosion" of amateur genealogists in recent years this has only become an exponentially worse problem. I have just wasted an entire afternoon trying to hunt down an original source for a death date, going from tree to tree (on Ancestry) only to encounter the source of the One World Tree which is no longer a viable, searchable source. Even more frustrating is the fact that the tree that listed OWT as their source attributed it to a completely different person. I could go on and on of the countless errors in family trees whose only source is some other uploaded family tree. I would love to know what the algorithms were that they used to catch all these errors! I'm afraid I would have little trust in any work being derived from unvetted online family trees.

On 2022-12-27 11:43:52, user David Curtis wrote:

This work is very much in the same vein as other papers I have published on this topic using the same dataset. I think it would be appropriate to cite my papers in order to put your findings in context. Two examples are here:<br /> https://www.sciencedirect.c...<br /> https://jmg.bmj.com/content...

On 2017-10-28 16:49:57, user Lionel Christiaen wrote:

In this experiment, I assigned three papers from the BioRxiv to first and second year graduate (PhD) students in the Developmental Genetics and Stem Cell Biology program at NYU Biology and NYU Langone Medical Center. <br /> I asked students to review the preprints following a simple structure: 1) summarize the work, 2) highlight the key merits, 3) point out major issues, focusing on whether the data support the conclusions, and propose constructive suggestions.<br /> I also suggested to highlight minor problems, as is often done in reviews, and to dissect the paper and paragraphs reporting on 1) the background, 2) question(s)/hypothesis, 3) Approach and methods, 4) observations/analyses/results, 5) interpretations (what happens in the experiment?), and 5) conclusions (how does the system work?)<br /> We then discussed the papers in class, and I did let students amend their reviews following the discussion.<br /> Also, because we discuss three preprints in total, the students were influenced in their evaluation of each paper by the other papers. <br /> The three preprints were:<br /> - Chen, H., Fujioka, M., Jaynes, J.B., Gregor, T., 2017. Direct visualization of transcriptional activation by physical enhancer-promoter proximity. bioRxiv.<br /> - Crocker, J., Tsai, A., Muthusamy, A.K., Lavis, L.D., Singer, R.H., Stern, D.L., 2017. Nuclear Microenvironments Modulate Transcription From Low-Affinity Enhancers. bioRxiv.<br /> - Mir, M., Reimer, A., Haines, J.E., Li, X.-Y., Stadler, M., Garcia, H., Eisen, M.B., Darzacq, X., 2017. Dense Bicoid Hubs Accentuate Binding along the Morphogen Gradient. bioRxiv.

(note: we noticed the Mir et al. paper was published in Genes & Dev the same week we discussed it, but focused on the preprint)

Below I paste the unedited reviews students shared with me, knowing that I was going to post them online.

Lionel Christiaen<br /> NYU Biology

PS: Due to server error, I have to upload the students' reviews one by one.

On 2024-12-05 22:37:23, user Cecylia Olivo wrote:

The DNA Damage Repair (DDR) pathway, including ATR/ATM, have previously been linked to metabolism and mTORC1 regulation, however the key players and mechanisms, especially in unperturbed cells, in this downstream signaling pathway are currently unknown. This manuscript, authored by Aird et al., demonstrates that in both p16 knockdown cells and unperturbed cells, ATR increases lanosterol synthesis through de novo cholesterol synthesis, which promotes mTORC1 activity by lysosomal localization. They determined that this pathway consists of ATR, not ATM, and is independent of the CHK2 and TSC2 processes.

The paper contains several major concerns, detailed below, that must be addressed before the data can be properly evaluated. Until these concerns are resolved the findings within the paper are unable to be thoroughly assessed, delaying our understanding of how ATR influences mTORC1 during DDR.

The first major concern identified within the paper is the lack of orthogonal validation, specifically for Figure 4 A & B. This is a concern because, in order to validate the findings, different methods should be applied to confirm reliability, reproducibility and robustness. In Figure 4 A & B we are specifically relying on the visual trends to make a conclusion, which could be misinterpreted. The authors should perform Radiolabeled Cholesterol Uptake Assays which measure cell cholesterol absorbance by labeling cholesterol compounds with radioactive isotopes which would contribute to their orthogonal validation and help support their results. The second major concern is that phosphorylation of S6K is not limited to mTORC1. This is a major concern because S6K is a direct substrate for other kinases, such as JNK1 and PKC. Multiple validations are required to show that mTORC1 activity leads to the decrease in phosphorylation of S6K. One validation that can be conducted is to overexpress ATR to observe if phosphorylation of S6K increases, which would further support the direct link between mTORC1 activity and S6K phosphorylation. An additional validation is to conduct an in vitro kinase assay with mTORC1 and S6K to eliminate the possibility of confounding variables. The third major concern is that p16 expression can vary significantly between cell types. This is a major concern because HeLa cells have high levels of p16 expression, HEK293 cells have low levels of p16 expression unless under stress and MEFs have significantly higher levels of p16 as cells approach senescence. Quantifying the basal levels of p16 in each type of cell line is crucial since the focus is on ATR’s effect under basal conditions. This can be done by quantifying the levels of p16 through western blotting and testing if ATR affects mTORC1 similarly across varying expression levels.

The first minor concern is that the quantification of the western blots is needed throughout the paper in order to substantially improve the clarity of the figures. The second minor concern would be to provide justification about the selection of the specific cell lines which would provide clarity on the major concerns related to varying p16 expression. The third minor concern is that the verbiage of mTORC1 should be consistent throughout the whole paper to increase readability and reduce confusion. The final minor concern is that Figure 3’s title is unclear; replacing “decreases” with “knockdown” would be more effective.

On 2021-06-27 07:33:40, user Luca Jovine wrote:

Of direct relevance to the work presented in this preprint is the SAXS and HDX analysis of human SUFU (both with and without IDR2) that accompanied the crystal structures of the apo and GLI peptide-bound protein described in Cherry et al., 2013. Regrettably, although the latter publication is cited in the present preprint, Makamte et al. neither mention its SAXS/HDX results nor discuss them in relation with their own interesting findings.

On 2017-03-07 16:03:46, user Jean Manco wrote:

Thank you for the additional results for the European Neolithic. I am noting discrepancies as I go through the list.

GEN68 is given as mtDNA K1a in extended data table 1, but K1a26 in supplementary table 1. Which is correct.<br /> KO2 is given a location different from the one in Gamba 2014 and Mathieson 2015. Looks like a mix-up between KO2 and KO1.

I will note anything else I come across.

On 2024-07-08 10:19:50, user Frederica Theodoulou wrote:

This paper is now published at: doi: 10.1093/plcell/koae117

On 2015-03-03 03:39:54, user Páll Melsted wrote:

Thanks for the comment. The single end case is on my todo list.

If you have a list of fastq files that are used to construct a single bam file a simple workaround is to compute the fingerprint for each fastq file and add them up. This works because the fingerprint is constructed as a sum of the fingerprints of all the reads.

On 2016-06-18 12:07:27, user Lank wrote:

Fantastic study and data!

Just one note on the admixture modeling. Natufians were not tested as the source population of Eurasian ancestry in East Africa, because they predate Mota and the inferred admixture dates in East Africa. Yet, this also goes for the Levantine Neolithic, who were the closest to the source population among the tested groups.

It may be worth considering that the timing of Eurasian migration into Africa may predate Mota (from SW Ethiopia) in other parts of Africa.

On 2017-09-14 03:15:20, user Yuri Lazebnik wrote:

Dear Naomi,

Thank you very much for your insightful comment and for your interest in the R-factor.

Please let us reply point by point.

“The idea of classifying hypotheses as supported or refuted by ongoing works, as a means to identify "strongly supported" or "strongly refuted" claims is an interesting one. I would like to see further discussion of how this could be applied.”

Thank you for considering our idea interesting! We will be happy to discuss it.

We would prefer to avoid qualifiers, such as “strongly”, because what is strong evidence for one scientist can be nonsense to another, as many a scientific discussion or a set of opposing reviews would testify.

“Namely, it seems the R-factor is something that should be applied to a specific scientific claim, as opposed to a whole research article. Being able to quickly identify the evidence that supports (green), refutes (red) or relates unclearly (yellow) to a claim, directly from the claim in said literature, could aid comprehension (not to mention discoverability) of the surrounding literature, and highlight claims that are well-supported or lacking in independent replications. Do the authors feel that one paper is sufficiently related to one central claim for application of the R-factor the paper? Alternatively, I would argue that judging the "veracity" of component evidence presented within an article could be more informative.”

We agree completely and tried to emphasize the focus on a claim as a unit of evaluation in our preprint. We will update the preprint to articulate this focus explicitly. Whether an article would have one claim or more depends on the report. In the latter case, applying the R-factor to all claims would be reasonable.

In the examples mentioned in our preprint and in our current research we deal with the main claims because these claims are commonly articulated in the titles of the articles by their authors. This choice minimizes the possibility of misunderstanding what the authors concluded and facilitates the automation of identifying what an article claims.

“Further, limiting these data to the "cited by" literature from that paper could skew the perspective, depending on which article you are viewing the claim in - to understand the overall "veracity" of a claim, it seems the reader would need to navigate back to the first mention of that claim in order to find the longest chain of evidence. Instead, I would be interested to explore the feasibility of a claim-centric (as opposed to paper-centric) count, and to understand whether this is already achieved by existing practises (such as meta-analyses of the literature). Perhaps an alternative approach would be to ensure that meta-analyses that include an article are more clearly visible from that article (e.g. highlighted in a "cited-by" section), and an extension to that would be to link that more recent work to the specific assertions that it relates to in the current article.”

The point about the “trees” of evidence for a claim is indeed excellent! We envision that these trees will be extractable from the R-factor resource and would be one of its most powerful features, enabling the user to grasp quickly the history of the claim and thus the novelty or the lack thereof of the articles referring to it. We are beginning to build a prototype of the “tree viewer”, which we call the Linker: http://bit.do/mock_up (you can zoom in and out, click on the links and nodes, and move around the graph). We keep in mind the century long history of ignoring the claim that the ulcer disease is caused by a bacterium as an example of how a timely reconstruction of the “trees” of evidence could help accelerate discovery.

“I would also be interested in whether the authors' have any thoughts on the reporting bias towards positive results (it may be hard to judge replicability, if failed replications remain in desk drawers), as well as on more nuanced evaluations of related evidence: is some evidence stronger than others? Is it feasible to define a scientific claim, or is it dependent on context/species/other factors?”

Indeed, the R-factor can only reflect what scientists have published, which means that the results that are now in the drawers would not be considered. However, we anticipate that the use of the R-factor and the increasing popularity of preprints can change this. Currently “negative” results stay in the drawers because the value of reporting them is uncertain while the effort of reporting them is substantial. We think that the opportunity to affect the R-factor of a praised paper that everyone in the field knows is wrong and the ease of reporting the results through a preprint service like bioRxiv would help to keep the drawers empty.

We would like to emphasize that the R-factor of a claim does not measure the replicability of the study that reported it, but whether the claim has been confirmed. For example, testing a claim in a different experimental system, which is a common practice, is not a replication by definition and thus the result of such testing would be missed by the replication approaches, but would be included in the R-factor. Likewise, a replication study can test whether the reported result is reproducible, but not whether it is misinterpreted. The R-factor evaluates the chance that a scientific claim, which is an interpretation of the results, is correct, irrespective of whether this claim is based on valid results, a guess, or misunderstanding, which all have their role in science.

“Finally, I would be concerned about applying such a metric to individual researchers. An examination of unintended consequences for such a metric would be useful to discuss.”

We welcome this discussion, but the R-factor of researchers will be derived from the R-factors of their reports by extension. We do not see how this extension can be blocked and question whether it should be blocked. We would suggest that an open and transparent score could be better than a reputation based on grapevine, the membership in the old boys clubs, or on unqualified praise in the media. We envision that once the dust settles, people will see in numbers what they already know intuitively, namely that no one is perfect in their scientific judgment and that some outliers on both sides of the distribution are present. We would also like to emphasize that the R-factor will be but one of the measures used to evaluate scientists and hope that non-quantifiable evaluating criteria will also stay in place.

Thanks again for your insightful comments, which made us think and wish to discuss the issues you raised further.

Best regards,

The Verum team.

On 2018-01-16 18:08:05, user AdamMarblestone wrote:

-"Spatial Information in a Non-retinotopic Visual Cortex" http://www.cell.com/neuron/...

On 2017-11-09 15:42:44, user AdamMarblestone wrote:

-"Deep Learning Predicts Correlation between a Functional Signature of Higher Visual Areas and Sparse Firing of Neurons" https://www.frontiersin.org...

On 2019-09-16 17:11:24, user Lindsey Young wrote:

The authors of this preprint are ecstatic about the preprint process as a way to make scientific findings accessible to as many people as possible, as early as possible, and to foster constructive and transparent dialog.

While I appreciate these comments, they relate to a supplementary experiment, which as the commenter says, "is not critical to interpreting the manuscript." The HDX in the supplement to this cryo-EM paper was included to show that the new constructs used in this paper behaved similarly to ones that we previously characterized by HDX in Young et al. PNAS 2016.

A second important point I would like to raise is one of transparency, as the commenter does not disclose that they are a previous direct competitor (Ohashi et al., Autophagy, 2016) with our own previous HDX work on this same topic (Young et al., PNAS, 2016). Masson's prior role is relevant information that we believe should have been disclosed as part of their posted comment.

On 2019-09-06 13:38:12, user Glenn Masson wrote:

Very interesting paper, with beautiful EM data. I know this is a preprint, and not the finished article - however, the reporting of HDX-MS data leaves a lot to be desired.

As it currently stands, the amount of information provided on how the experiments were conducted is too sparse to allow for the repetition of experiments, and the data is reported in such a manner to prevent a complete interpretation of that data. For example - the statement "The deuteron content was adjusted for deuteron gain/loss during pepsin digestion and HPLC.", is insufficiently detailed, was this achieved using a fully deuterated control? How was that produced? How are deuterons potentially gained during the HPLC/digestion process, which is conducted (presumably) in H2O? Not a single peptide's exchange data is presented, and there are no reports of the overall coverage of the protein subunits, nor the redundancy of the data collected. From my understanding of Figure S1E/F(?), there could be as few as 16 peptides covering the entirety of Beclin 1, and 10 covering ATG14.

Additionally, I have serious concerns about how the data collected. There is no explanation offered on how a single 10-second timepoint can sample the exchange kinetics of an entire complex. The experiment was carried out in duplicate (only), with no mention of the error or variability associated in these measurements.

The HDX-MS data is not critical to interpreting the manuscript, and, as I stated, the manuscript as a whole is intriguing and worthwhile - but serious attention should be given to the HDX-MS data. Please see the HDX-MS community agreed guidelines paper for the minimum standards for reporting and conducting HDX-MS experiments: https://www.nature.com/arti...

On 2020-12-21 05:53:06, user deloris vandivort wrote:

in the discussion of the cancer patient dying from the so called mutation has too many variables to prove accurate. why were other treatment modalities not attempted over the remdesivir that has failed in other pandemic outbreaks and also this was a cancer patient that RNA is used for treatment could also alter outcomes I am personally am disappointed in the narrow sightedness of treatment given to patients or lack of treatment. I do agree that mutation usually doesn't occur at the rates they are being stated here. I also would like to know that those they found with the so called mutated virus ever had the virus in the first round or not. I am skeptical of a lot of the information given on all of this because I am missing the use of control groups so necessary in proper scientific method. I have yet to hear about reinfection of a person with the virus and if so I am sure it is a very minimal number. I am still showing antibodies at six to seven months post the virus that is not suppose to happen.

On 2021-08-11 06:50:33, user Ticklicker wrote:

Are your planning to share your data? I would like to know the exact date of collection, the county & state, and map coordinates (lat, long) or approximate location (distance & directiion from nearest town) for the 4 "positive' deer samples that were collected in 2019 (1) and January 2020 (3). Also, have you considered that deer samples that tested "positive" for SARS2 may represent instead cross-reaction detection of tick-borne viruses; for example, the "Heartland Virus", which may be spreading eastward since its 2009 discovery at 2 locations in NW Missouri?

On 2022-12-08 22:57:33, user Abbie Hall wrote:

Hello! I really enjoyed your paper about alpha-ketobutyrate effects on lifespan and healthspan in C. elegans and mice. I thought the results were fascinating! I thought your experiments were well laid out and supported your claims. I also appreciated how you provided qualitative and quantitative data and included two model organisms.

Here are some general comments I have about your paper. I think it could be helpful to split the paper up more by different sections, like materials and methods, discussion, etc, to make it more organized. In regards to your figures, I think it would be helpful to include the data being statistically significant and the p-value. For example, I really liked how figure 4 did this and I think it could be useful for the other figures as well. Additionally, I think it could be helpful to explain why you chose your sample sizes, such as figure 1. You suggest that you included the sample size because of previous research so it would be helpful to cite this research or even perform a power analysis to justify your sample size.

I really appreciate how you included male and female mice in your study. Additionally, I appreciated how you still included female mice in your studies on healthspan even though there was not a statistically significant increase in lifespan for these mice. In regards to figure 4e, I think it would be helpful to perform this study on male mice as well. It was interesting how alpha-ketobutyrate was able to decrease the decline of motility for female mice; however, it would be interesting to investigate if these results are also applicable to male mice, as you have displayed in this study, there is a difference between male and female mice. Additionally, I noticed how male and female mice were grouped together in 4f when studying hepatology. As the other experiments separated the two by sex, I also think it would be helpful to keep them separated when studying this. I noticed how there was only one female mouse included in this study so it would be helpful to include a larger sample size if possible.

Overall, I found your paper really interesting! Really great job!

On 2022-05-23 13:38:43, user Yaqing Wang wrote:

See updated version on https://journals.biologists.... Also https://li.me.jhu.edu/publi...

On 2023-01-11 08:55:55, user kamounlab wrote:

I don’t think “inspiration” in the title is the correct word. It doesn’t mean anything in this context. Better say impact or influence.

On 2023-10-24 17:32:53, user Jianhua Xing wrote:

It is nice to see more efforts on learning the governing equations of gene regulatory networks from single cell data, and thanks for mentioning our dynamo work. Congratulations on the work. I notice that some discussions on dynamo are not accurate --unfortunately it has happened repetitively in the literature such as stating dynamo requires data with metabolic labeling only and the vector field gives only lear relation between a regulator and its target gene. Related to what discussed here, with the dynamo vector field one can predict cell states NOT covered by the data. That is, dynamo is a generative model. So the criticism on using embedding is not justified. One uses low-dimensional manifold embedding (e.g. in Dynamo) to simplify the model (with reduced number of parameters to specify), and it is well-established that a dynamical system typically falls to a low-dimensional manifold after a transient period of time. A famous example is the 3-variable Lorenz model. Starting from any initial state, it falls to a strange attractor with dimensionality 2.06

On 2021-07-08 18:10:00, user Alex Alex wrote:

PMID: 30582748 on oxygen dependence

On 2016-06-14 13:25:27, user A. Mesut Erzurumluoglu wrote:

I have made PowerPoint slides for the manuscript available at: https://mesuturkey.wordpres...

On 2020-07-26 13:33:26, user UAB BPJC wrote:

Review of Zhang et al., “Crash Landing of Vibrio cholerae by MSHA pili-assisted braking and anchoring in a viscous environment” by the University of Alabama at Birmingham Bacterial Physiology Journal Club

Summary<br /> Mannose-sensitive hemagglutinin pili and flagellum are important for the attachment of V. cholerae to surfaces. V. cholerae frequently live in viscous environments such as the human intestines, where the pathogen causes cholera. This paper characterizes the landing dynamics of V. cholerae through cysteine substitution labelling for single-cell visualization and through resistive-force-theory based hydrodynamic modelling. Overall, this paper demonstrated that V. cholerae cell landing includes three phases- running, lingering, and attaching. MSHA pili are proposed to repeatedly attach and detach from the surface, acting as a braking and anchoring mechanism to facilitate cell landing in viscous solutions.<br /> Overall, we found this to be an interesting paper with insightful conclusions. The use of cysteine substitution-based labelling provided valuable information about V. cholerae landing dynamics. With that said, we have some comments that may be beneficial to address and some additional questions for the authors.

General Comments<br /> • There is no explanation for why 2% LB is the media of choice or why methylcellulose is chosen to increase viscosity. Is methylcellulose commonly found in viscous environments in which V. cholerae live?<br /> • Who is the target audience for this paper? Will this be going to a biology-focused journal or a physics/mathematics journal? If it is the former, the language of this paper will need to be tailored for biologists to understand more easily, and more insight should be given as to why the conditions used to study V. cholerae landing are biologically relevant.

Figure-Specific Comments<br /> • It is difficult to distinguish what is happening in some of the microscopy images; increasing the resolution for these images would be helpful.<br /> • Figure 4D: Would be nice to see the angular velocity of more than one cell. <br /> • Include the r value for correlation data.<br /> • Placing Figure 5 earlier in the paper would be a great way to catch the reader’s attention. <br /> • Figure 5D would look better visually as two separate graphs rather than an inset.

Future-Specific Comments/Questions<br /> • It would be interesting to contextualize this paper by providing some of the rheometer data for their two groups (2% LB and 2% LB + 1% MC) and compare them to ocean water, mucin, etc.<br /> • It would be also nice to see coverslip binding experiments with different molecules coating them. <br /> • How many pili are needed for irreversible attachment? How would overexpression of MSHA pili change cell trajectory/attachment?<br /> May be beneficial to use multiple methylcellulose concentrations to observe how different levels of viscosity affect landing dynamics.

On 2021-03-17 00:35:55, user Cassandra Franco wrote:

We recently discussed your paper in our journal club, and we wanted to share a few thoughts and questions.

Overall, we would have liked to see more data from the male cohort of mice, as it brought up interesting conversations when it was included. We also felt that it might be beneficial to establish one method for indicating significance as it varied from figure to figure.

Starting with Figure 1, we would like to see a quantification of the size change in the livers in Fig. 1F. While discussing Fig. 1G we wondered why there was such a large discrepancy in the movement of PDE9-I mice when compared to the placebo group. We felt that adding the control data for this experiment may alleviate this comment.

For Figure 2, we thought it would be useful to include a signaling map to emphasize the relationship between PPARa and PDE9 as we felt this wasn’t intuitive. In Fig. 2D, we enjoyed the use of the volcano plot, as we felt it displayed the genes of interest clearly, but we were a bit curious how the cut-off for significance was determined. We thought it would be worthwhile to clarify how the cut-off was selected. For Fig. 2G, we felt that the significance symbols made the figure very busy.

Lastly, we felt that Fig. 3C would have been better utilized in figure 4 as it relates more to the results found in figure 4.

We’re looking forward to seeing more in the future!

On 2014-07-05 14:30:53, user Adam Siepel wrote:

Thanks, Casey. I just caught this comment. We will definitely try to clarify these points in the next version of the article. Thanks for the Jenkins et al. reference -- I did not know about that one. On point (3), our simulations are indeed from multiple loci and in fact with rate variation across loci. We will make this clearer. Regarding point (1), I am not sure I agree that the use of the term "neutral" for sites rather than alleles is a problem of any consequence but we can try to be precise that by a "neutral site," we mean a site at which all possible point mutations have no affect on fitness.

On 2022-10-31 10:53:55, user ROBERTA BANKS wrote:

Thank you for taking the time to submit this paper. Although tobacco smoking is a prevalent problem, not much research dives into how it affects human physiology on a cellular level. I appreciate your efforts to explore this topic on a deeper level.

The title of the paper and objectives of the study stated in the paper claim to explore the effects of cigarette smoke on neurodegeneration and reactive oxygen species, however, there is a less definite link between these topics and inflammatory markers. These topics are not fully explored in this paper. More specifically, reactive oxygen species are not explored to the depths in which EVs were explored, yet it was stated in the paper, “cigarette smoke induces a series of mechanisms that activate cell populations from both the innate and the adaptive immunity, which in turn promote the secretion of multiple inflammation-related molecules such as proinflammatory cytokines including chemokines, reactive oxygen species (ROS) and extracellular vesicles...” Reactive oxygen species were not explored sufficiently to claim that cigarette smoke can activate ROS. The lack of exploration of key topics mentioned in the beginning of the paper, make the overall study over-promising in combination with a lack of data to back up the paper’s claims. For future studies, it would be helpful to see how ROS is affected in EV-secreting cells post smoking a cigarette to have a better understanding of cigarette smoke on ROS.

The data that is available in this paper seems to be more descriptive than quantitative and has difficulty showing significance to claims that are being made. There is also a lack of controls in your data which make the existing data and claims unreliable. Perhaps in Figure 1 and 2, it would have been helpful to take blood from the smokers after smoking to compare the data and ensure what you are seeing is significant. In addition some of the claims that are in this paper are very generalized. It is important to understand how different demographics are impacted by cigarette smoke physiologically. Might I suggest, for a future direction, conducting more testing data to see if there are any statistically significant differences in physiological response for individuals of different demographics. (For example, age, BMI, gender, ethnicity/cultural background, diabetic/non diabetic, etc.

Once again, thank you for posting this paper. It allowed me to think deeper about the physiological effects of tobacco smoke.

On 2018-06-26 19:22:24, user bob cohen wrote:

Hi,<br /> An interesting paper, but the use of GST fusion proteins to characterize binding of polyubiquitin (K63 chains in particular) can introduce artifacts. You might want to have a look at the Sims et al. paper in 2009 Nat Struc Mol Biol (https://www.nature.com/arti... "https://www.nature.com/articles/nsmb.1637)").

On 2020-03-26 17:38:55, user Sinai Immunol Review Project wrote:

Summary<br /> The authors explore the antigenic differences between SARS-CoV-2 and SARS-CoV by analyzing plasma samples from SARS-CoV-2 (n = 15) and SARS-CoV (n = 7) patients. Cross-reactivity in antibody binding to the spike protein between SARS-CoV-2 and SARS-CoV was found to be common, mostly targeting non-RBD regions in plasma from SARS-CoV-2 patients. Only one SARS-CoV-2 plasma sample was able to cross-neutralize SARS-CoV, with low neutralization activity. No cross-neutralization response was detected in plasma from SARS-CoV patients.

To further investigate the cross-reactivity of antibody responses to SARS-CoV-2 and SARS-CoV, the authors analyzed the antibody response of plasma collected from mice infected or immunized with SARS-CoV-2 or SARS-CoV (n = 5 or 6 per group). Plasma from mice immunized with SARS-CoV-2 displayed cross-reactive responses to SARS-CoV S ectodomain and, to a lesser extent, SARS-CoV RBD. Similarly, plasma from mice immunized with SARS-CoV displayed cross-reactive responses to SARS-CoV-2 S ectodomain. Cross-neutralization activity was not detected in any of the mouse plasma samples.

Potential limitations<br /> The size of each patient cohort is insufficient to accurately determine the frequency of cross-reactivity and cross-neutralization in the current SARS-CoV-2 pandemic. Recruitment of additional patients from a larger range of geographical regions and time points would also enable exploration into the effect of the genetic diversity and evolution of the SARS-CoV-2 virus on cross-reactivity. This work would also benefit from the mapping of specific epitopes for each sample. Future studies may determine whether the non-neutralizing antibody responses can confer in vitro protection or lead to antibody-dependent disease enhancement.

Implications of the findings in the context of current epidemics<br /> The cross-reactive antibody responses to S protein in the majority of SARS-CoV-2 patients is an important consideration for development of serological assays and vaccine development during the current outbreak. The limited extent of cross-neutralization demonstrated in this study indicates that vaccinating to cross-reactive conserved epitopes may have limited efficacy, presenting a key concern for the development of a more universal coronavirus vaccine to address the global health risk of novel coronavirus outbreaks.

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 2020-04-20 09:40:14, user JiaHsin Huang wrote:

Warning!!! This preprint is not a full manuscript. In the current version (4/20), the authors only provide the partially descriptions about their long-read sequencing method. No results and further information support their research. I doubted that this preprint is qualified for submission in any of the peer-reviewed journal. Really disappointed to see such unfinished draft to be published in the BioRxiv.

On 2023-12-16 01:15:13, user Hovsep Sultanian wrote:

This paper was very enjoyable to read as it had a lot of experiments that were well-thought-through and designed in an effective way. I have some general comments regarding the introduction and some of the figures outlined in this paper:

Introduction: Tuberculosis is a dangerous and deadly airborne disease that can spread very easily. It would be nice to see statistics regarding the number of people infected in the world through the centuries or decades and more statistics of how many people die from this highly contagious strain, Mycobacterium tuberculosis (Mtb). Also, it would also be interesting to mention more information about the Mtb strain alongside the reason behind choosing this specific strain, instead of others.

Figure 2: In Figure 2B, it was not indicated if the set of data shows gene expression of BL/6 compared to C3H, or the opposite way around. Alongside that, the data only shows that there is a significant difference, but does not show the magnitude of the significant difference. The y-axis is also supposed to have a negative in front of the “log10(padj)”. Also, figure 2C data did not specify which mouse strain compared to which, and it would be helpful if the sizes of the dots were specified, too. In figure 2D, there wasn’t a color bar and scaling, so it wasn’t known what the red and blue represented and the magnitude was also not known. It would also be nice for a statistical test to be performed to see which genes were statistically significant for figure 2D as well.

Figure 3: Figure 3B-C should have a Two-way ANOVA done for its statistical test because the groups compared are not all independent of each other, where the different variables could affect each other. In addition, there could be a statistical analysis done for figures 3D-G between the two different doses in addition to a power analysis to measure the effect size. Figure 4: Figure 4B could use a time series to prove that the mice are losing weight statistically. Also, the numbers on the y-axis of figures 4L-Q are not the same in the same range, which can possibly throw off the reader.

Figure 5: Figure 5B does not have a statistical test performed between the different ETO concentrations. It would also be helpful to show the “+” and “-” signs. Figures 5C-D could use a control mice without infection to show that it is not just ETO that’s affecting the cell number.

Otherwise, the paper was engaging and enjoyable to read. Well done to all contributors!

On 2024-11-12 14:14:42, user Velyudhan Mohan Kumar wrote:

In this report, human sleep-related gene orthologs have been studied in the unicellular green alga Chlamydomonas reinhardtii, which had been earlier considered as a model organism for the study of photosynthesis and flagella/cilia. Comparative genomics, which is an essential tool in biological research, is a very potent method that yields biological insights that are not possible with any other approach. It could also help us understand the human genome study, which has the potential to help us comprehend human sleep and sleep disorders. Phylogenomics gives a more profound comprehension of the discoveries made possible by comparative genomics.<br /> The results of this study indicate that the calcium signaling and synaptic transmission involved in sleep regulation are conserved across phyla, and a simple model organisms like Chlamydomonas may be useful in elucidating higher-order, complex behaviors such as sleep.

Chlamydomonas do not possess the complexity of the sleep seen in vertebrates. However, knowing their genetic makeup can help researchers to understand general molecular processes that may be used to understand sleep in more complex organisms. Comparative phylogenomic studies are also important in understanding human sleep disorders, and they may open new avenues for developing new therapeutic approaches.

On 2019-05-09 09:59:17, user Koen van den Dries wrote:

The cartoons in Figure 1B and Supplementary Figure 1 seem to incorrectly depict the myosin IIB tension sensors (except for the S1 variant). The myosin IIB motor complex is composed of two myosin heavy chains which is why two modules should have been drawn if I am not mistaken. Same is true for the depicted point mutation and the N-terminal mTFP (which should be present at both heads in the dimer).

On 2019-03-08 13:34:36, user Laura Sanchez wrote:

Dear Geier et al, this preprint was discussed in a lab meeting and we would like to offer the following for review. Thank you for posting this very interesting manuscript. Best, The Sanchez Lab:

The manuscript by Geier at all explores an interesting application of MALDI imaging mass spectrometry with FISH to correlate the spatial distributions of specific microbial genera with metabolites from the same tissue section in an animal host. The transparency of a very thorough SI was greatly appreciated and necessary to allow other readers to evaluate the data. It is clear that this methodology will greatly enhance our ability to probe microbial-host chemistry by keeping the tissues intact to generate hypotheses about how metabolites might be utilized in specific microenvironments of a host. It was also appreciated that the authors use a biologically relevant system and were able to offer hypotheses to test about the metabolites that were observed in different spatial clusters and how this may relate to the biology of the system on the whole. We offer the following as major and minor critiques that may improve the manuscript:

Major<br /> MALDI- FISH previous applications. Can the authors better delineate how this is an improvement on the existing methods, it is briefly stated but if it could be more clearly laid out that would be more helpful in clearly evaluating the innovative aspect of this approach. For instance, can the authors comment on how they were able to achieve 3 micron crystals when others were limited to 50 microns, is the laser more finely tuned or is it simply the matrix application? Or why is separate sections be non-desirable? For the presented method of washing the matrix followed by FISH can the authors comment on whether they believe this may impact the FISH results at all?

This paper should be cited in the MALDI-FISH discussion as a previous application. https://www.pnas.org/conten...

MS1, network and identities. We had a brief misunderstanding over this section. It had seemed that the molecules were assigned based solely on MS1 and the molecular formula, but it was clear that the authors have used MS/MS to verify the metaspace annotations. If this was more clearly delineated with the use of terminology from the ‘Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI)’ could improve the transparency and all the hard work done in this section.

The MF ratio calculation was slightly confusing. How were these assigned in lines 146-150? How do you really know where the metabolites are coming from, is this all based on FISH? MF ratio is confusing to a non-computational specialist. It seems contradictory between the SI and main paper. If a cell is 1 micron, 3 microns could encompass way more than a microbial cell.

Figure legends.For example, figure 3B, grey scale, should this be magenta? Why was grey used here and magenta in other images? Consistency across the figures could enhance the readability of the manuscript more. For instance, Figure 1 has no color legend, but it appears that it is this supposed to match Figure 3? Can the authors please be more consistent across figures with color legends?

Figure 4 is difficult to interpret. With the large numbers of colors and shapes used, it is difficult to infer what the meaning is meant to be and when printed, the shapes can’t be distinguished at this definition.

GNPS networks. In general for the type of high resolution mass data presented here, a 2Da parent mass tolerance isn’t appropriate and the same for a 0.5 for the ion tolerance (MS2). This means the real windows being examined are 4 Da and 1 Da for HR-MS data likely leading to artificial connections in a molecular networking or poor consensus nodes. Is there also a reason why the full GNPS network was not included as a figure in the SI? Why were only selected networks highlighted?

Along these lines and the 577 and 800 compounds, were the authors able to find literature precedent for these outside of mass spectrometry databases such as Marinlit, Antimarin, NP Atlas, Dictionary of Natural Products, ect? GNPS doesn’t have everything. For the 800 compound- was the dereplication workflow attempted on gnps.ucsd.edu?

Last paragraph in the conclusion was arguably the strongest tie to how this method may be used and applied to other systems. The is a history of targeting the wrong organism is powerful to mention in the intro and could possibly bring the manuscript full circle. <br /> Final thoughts, this almost seemed like two papers, the discovery aspect is important, but as it stands, it is underdeveloped. It seems like some of the SI material and more clear discussion on how the technique expands on existing methods could strengthen the manuscript. For instance, with the molecular formula, as it stands, it seems there are between 12-13 degrees of unsaturation (the reported formula for the left over atoms does not yield a whole number which is slightly confusing) which would mean this metabolite does have a strong chromophore, the authors allude to the fact, but no UV-vis trace was in the SI, could this help with the dereplication process? This can be arguably more diagnostic for a molecule than the MS at times.

Minor<br /> There were some issues with referencing or figures, please carefully proofread.

Line 291. Homolog is strictly a phylogeny term; should this be analogue or constitutional isomer?

Can the authors define why the range 400-1200 was chosen? Why not larger or smaller?

Why were lipids extracted from the gills? Were any other extractions used, could this have impacted the LC-MS/MS analyses?

Can the authors comment on whether bringing the mussels up from depth might have impacted any of the chemistry. This seems like a large potential variable.

The authors reference non-fragmented metabolites, we assume this refers to the in-source fragments that may arise during AP-MALDI ionizations. Can the authors more clearly delineate how they could tell in source fragments apart from intact molecules?

On 2017-02-14 19:00:19, user Casey Bergman wrote:

This preprint is a revised version of http://biorxiv.org/content/.... Please see http://biorxiv.org/content/... for details.

On 2020-01-27 10:58:37, user jean-claude perez wrote:

Please find here an updated release including 2 releases of wuhan coronavirus génome.

Using the following theoretical numerical approach of génomes data (pdf), I proove évidence of long range numerical standing waves structuring DNA genomics séquences:

https://www.google.com/url?...

THEN, <br /> I briefly analyzed the standing waves of 7 SARS genomes ranging between 2003 and Wuhan 2020:<br /> There is an evolution increasingly directed towards Fibonacci waves, as follows:

Fibonacci Wave Genomes:<br /> Sars2003. No. 5<br /> Sars2004. No. 5<br /> Sars2004b. No. 5<br /> Sars2015. 5 8. 13<br /> Sars2017. 5 8. 13<br /> Wuhan2020old 5 8 13 21<br /> Wuhan 2020. 5 8. 13. 21<br /> Where 5 8 13 21 are Fibonacci numbers numerical standing waves.<br /> That is a formal proof of an évolution increasing global structure of SARS whole genomes, probably linked with génome intégrity and coherence and, probably pathogeneciity.

On 2025-06-23 22:23:26, user Xiong Liu wrote:

A published version is available at Diabetologia https://link.springer.com/article/10.1007/s00125-025-06411-9

On 2020-09-01 15:05:58, user Ben Sprung wrote:

SI is currently available at https://www.dropbox.com/s/e...

On 2024-05-15 13:15:57, user Ruben Perez wrote:

This preprint has been published in Virus Evolution (10.1093/ve/veae031). The title has been slightly modified: “Highly pathogenic avian influenza H5N1 virus infections in pinnipeds and seabirds in Uruguay: implications for bird-mammal transmission in South America”.

On 2020-08-17 18:11:34, user OxImmuno Literature Initiative wrote:

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

On 2020-05-22 23:54:38, user CleoWang wrote:

????????Recommend my paper on Nature Communications (Chuchu Wang et al. 2020), which might give you some inspirations.

On 2019-12-12 12:26:37, user Mustafa wrote:

Please suggest is there tool for plant except eric script

On 2017-06-21 13:48:31, user Dmitry Kobak wrote:

Hi Kenneth! I have a follow-up question to your answer to Philipp, point #2. The methods section says that "Prior to many analyses (including clustering and 2d visualization) the expression vectors for each cell were normalized". Wouldn't this normalization turn integer UMI counts into non-integers?

Thanks a lot,<br /> Dmitry

On 2025-08-25 12:12:59, user Divyansh Gupta wrote:

Nice extension to the Zeta test. Any plans to apply this to single-trials? Would be great to determine reponse latencies to individual trials!

On 2022-07-07 08:44:20, user Francisco F. De-Miguel wrote:

Dear Professors Neher and Taschenberger,

We are writing you concerning our articles modelling subsequent priming states of vesicles and their consequences on quantal release doi: 10.3389/fnsyn.2021.785361. eCollection 2021.

Our article published earlier this year, in which we reached remarkably similar conclusions. Sin our paper is not quoted in yours, we wish to send you some comments about the similarities and differences.

We find rather interes ting that from the use of Non Negative Tensor Factorization (NTF) applied to your experimental data you reached conclusions that are similar to ours, which were obtained with a stochastic approach applied to classic published data from neuromuscular junctions of frog and mammals.

In brief, the similarities we find are:

1. The differences in synaptic strength are not primarily caused by variable probability of fusion but determined by the fraction of matured release machinery of docked synaptic vesicles.

2. Fusion reflects the resting distribution of mature and immature priming states.

3. We both estimate the numbers or proportions of vesicles in different pre-fusion states.

4. The values estimated for the forward resting rate constants for the vesicle priming are virtually the same in both studies.

5. The forward rate constants in both studies are accelerated by calcium in an activity-dependent manner.

6. In both papers we reach the conclusion only the forward rate constants are susceptible to change due to calcium.

There are also some differences:

1. You propose an additional parallel step to adjust the responses upon high stimulation frequencies. We do not require such step.

2. You adjust your model to facilitation at low frequencies and depression at high frequencies. We find a coexistence of facilitation and depression using the same theoretical approach.

In addition, our study suggests that:

1. Spontaneous and asynchronous release can be expressed as spontaneous forward transitions from primed to fusion.

2. In our study, the backward constants are much larger than the resting forward rate constants

3. In our case, the recycling of the vesicle pool contributes to depression. although you don´t deal with it in your model, you have done extensive work on the matter.

4. Your finest way to simulate calcium indicates that the calcium-dependence of the forward rate constant is linear; ours considers a non-linear dependence.

In addition to intrinsic differences in the type of synapse studied in each paper, another major difference is the use of differential equations in your study versus the stochastic approach in ours. Yours allowed the numerical solution and a finer calculation of the calcium concentration. The stochastic approach allowed us to predict accurately two additional behaviors, which are absent from your study: spontaneous release and asynchronous release.

Altogether, the common findings in our studies suggest that such sequential maturation process of the fusion machinery with certain adaptations may be common to different synapses.

Therefore, we wish to invite you to read our paper and to quote it in your article. Needless to say that your recognition tour study will bring its general impact up immediately. In addition both our findings will strengthen each other.

I wonder if exchanging each other´s data to be fitted with the different approaches will confirm general mechanisms and give light as to synaptic-specific ones.

I would also like to ask you if a joint review would be of interest to you?

As a brief introduction to myself, I contacted you some years ago when John Nicholls and I co-organized a pair of Royal Society meetings in 2015 on extrasynaptic release of transmitters, the topic in which I do most of my work. Our common friend Walter Stuhmer was there. Last year I coordinated the sixth edition of From Neuron to Brain, in which Walter reviewed some of the chapters.

Sincerely,

Francisco F. De-Miguel

On 2024-01-14 05:29:31, user Lihua Song wrote:

We realize this manuscript misleads readers to believe that the attenuated pangolin coronavirus GX_P2V(short_3UTR) posed a spillover risk to human brains, resulting in a 100% mortality rate, which sparked panic among the public. This virus has no pathogenicity in normal animals. This manuscript necessitates revision to accurately state the abnormal nature of this mouse model, and the fact that these animal outcomes cannot be applicable to humans.

On 2022-06-08 05:41:00, user simon LECLERC wrote:

This manuscript is finished at more than 70%.<br /> There are left a couple of experiment to conclude this research article.<br /> Feel free to comment here to improve this manuscript as much as possible!<br /> Thanks,<br /> Simon

On 2018-09-30 00:25:30, user Robert George wrote:

Thanks for this seminal data. <br /> In regard to the statement: ''In Europe, descendants of this lineage admixed with pre-existing hunter-gatherers related to Sunghir from Russia for the Gravettians and GoyetQ116-1 from Belgium or the Magdalenians""

Can you attempt a more proximate model ? - specifically modelling Magdalenians on the basis of preceding (western) Gravettians (sampled from Belgium in Fu et al). This is because the ''cultural predecessors'' of the Magdalenians were Gravettians, not Aurignacians, which were long gone by 18 ky BP. I realise it might be more difficult due to ancestry overlap, but perhaps worth a shot.

On 2023-07-19 16:49:38, user Madeleine Rostad wrote:

July 13th-15th at the 37th Annual Symposium of The Protein Society, ASAPbio conducted a series of 20 minute “Live Preprint Q&A” sessions. The following is a summary of our conversation with one of the authors of this preprint.

Dr. Lauren Jackson's discussion focused on her research on the interaction between ?’-COP and the ArfGAP, Glo3, and its role in maintaining post-Golgi cargo recycling. The key point of the preprint is the identification of how a key regulatory molecule (a GAP protein) regulates an important membrane trafficking co-complex. They knew these two elements interacted, but pinpointing where the interaction was happening among the seven subunits was challenging. They identified specific amino acids and tested predictions in a yeast model system.

The figure they are most proud of, Figure 4, was improved through peer review, as reviewers requested an additional experiment to be conducted in cells. This figure demonstrated the interaction from both perspectives.

Dr. Jackson was excited to get the preprint out there, as it allowed her to attend an online conference and gain visibility during a challenging time. She also found preprints to be beneficial for grant applications. Most of the feedback they received came from peer review, and the preprint story differs from the final published version as they took out a crystal structure and turned it into its own paper during the review process.

Potential areas of confusion might arise from the fact that they never nailed down the structure of the interaction between ?’-COP and Glo3. They faced technical challenges and some methods suggested by reviewers had already been attempted but were unsuccessful and were outlined in the correspondence with the reviewers coordinated by the journal.

Looking forward, they plan to do more structural biology to get the supercomplex (potentially by tomography or by moving in the nanodisc direction, having done some work on tubules).

Dr. Jackson shared her positive experiences with preprints, noting their importance and the benefit of having a pool of reviewers. She is open to using Review Commons in the future and suggested that increasing the interactions with scientific societies or funder mandates could drive further innovation in this area.

On 2021-12-12 22:00:12, user Abc wrote:

Nice paper, but what does "ca." mean in reductions? I can guess from inference, but would prefer an explicit definition. Google and Bing are useless for this term.

On 2020-05-13 16:51:02, user Paul Schanda wrote:

The link to the Mendeley Data file is this one here (the one in the PDF does not work any more):

https://data.mendeley.com/d...

On 2017-05-30 09:32:30, user Colin Tosh wrote:

Don't know if this paper is of any interest to you. It shows essentially that high performance modular attractors probably exist in state space but that natural selection finds it difficult to reach them. https://www.nature.com/arti...

On 2017-10-07 11:15:54, user Asena wrote:

The supplementary tables list sample I0061 as "R1a1a1". <br /> Is this a mistake in the pre-print? The supplementary information of the<br /> "Massive migration from the steppe" paper (2015) was very particular <br /> about I0061 (Karelia_HG) being haplogroup "R1a1*(xR1a1a)".

On 2022-10-14 14:54:32, user Kevin McKernan wrote:

The conflict of interest section is misleading. It should clearly spell out that these authors are competitors of the company that hosts the largest dataset they chose not to use (Medicinal Genomics). The reasons provided for ignoring this data are not compelling given their own manuscript cites many authors who have made use of such data in peer reviewed settings (Hurgobin, Henry, Allen, Joly). The comment about a single preprint using this data not being peer reviewed is disingenuous given the weight of the other authors peer reviewed work. The only other sequencing data, the manuscript does considers are from entities that have exited the Cannabis genomics services business and no longer pose a commercial threat to the authors (Phylos and Sunrise genomics). These commercial biases are important for the reader to understand.

On 2024-08-22 07:48:09, user przemyslawlatoch wrote:

It’s out! Our first B. subtilis paper, published at @NatureComms

Translation in Bacillus subtilis is spatially and temporally coordinated during sporulation. #subtiwiki #bacillus<br /> https://rdcu.be/dRBF4 <br /> https://www.nature.com/articles/s41467-024-51654-6 <br /> https://doi.org/10.1038/s41467-024-51654-6 <br /> Performed at @IBBPAS

Funded by @FNP_org_pl<br /> @EMBO_YIP

On 2019-02-21 20:49:58, user Leonida Fusani wrote:

Interesting work. You might want to have a look at our recent paper in Behavioral Ecology:

https://doi.org/10.1093/beh...

On 2021-01-29 07:35:19, user Sarah Harden wrote:

We are pleased to say our paper has been published!<br /> https://www.frontiersin.org...

On 2016-09-12 08:18:46, user serverhorror wrote:

A ".pyc" file? Can you make the actual source code available? Could you please package that with a setup.py and stated dependencies? Is it Python2 or Python3?

On 2019-08-21 04:27:54, user Cathy wrote:

Interesting~ especially directly demonstrated the RNAs function on heterochromatin formation

On 2018-10-29 20:15:31, user SohamGhosh wrote:

Our work of nuclear mechanics and chromatin reorganization

On 2020-11-02 10:44:16, user Alberto Baena wrote:

This manuscript has been published in EMBO Reports https://doi.org/10.15252/em...