On 2025-10-04 21:10:39, user Kim Wagner wrote:

The preprint has now been published in Scientific Reports:<br /> https://www.nature.com/articles/s41598-025-16222-y

On 2017-06-07 02:15:40, user Paul Katz wrote:

Hi Brett, Great summary!

On 2021-05-25 02:48:35, user Zhixing Feng wrote:

This paper is accepted by Nature Communications. The latest version is available at <br /> https://doi.org/10.1038/s41...

On 2021-06-07 21:57:11, user Fraser Lab wrote:

Summary:<br /> Within this manuscript, authors describe the antigen specific T cell response to SARS-CoV-2 mRNA vaccines. Both vaccines induce robust antibody and memory B-cell responses after two doses, but the kinetics and nature of the antigen specific T cell response are less well-characterized. In this longitudinal study of SARS-CoV-2 naïve and recovered individuals, Painter et al. aimed to characterize the antigen-specific CD4+ and CD8+ T cells response by 1) comparing differences in the kinetics of the CD4 T cell induction in both cohorts, 2) characterizing the differentiation state of vaccine-activated T cells, and 3) performing an integrated analysis of 26 measures of antigen-specific immune response to better characterize the immunological underpinnings of mRNA vaccine-mediated immunity. Their findings elucidate a coordinated immune response in both SARS-CoV-2 naïve and recovered individuals following mRNA vaccination that mimics a response to natural infection. Additionally, these data highlight the importance of characterizing the antigen specific T cell response during the development of future booster shots.

Major Points:

• Although we are unfamiliar with the process of obtaining participants for SARS-CoV-2 vaccine studies, the study cohort seemed small and predominantly white. The SARS-CoV-2 naïve group had 29 participants and the recovered group had 10 participants. Of the 39 total participants, there was one Native-identifying candidate and two Black-identifying candidates based on Table S1. It would be helpful to address the study size and demographics to discuss whether you might expect these results to be representative of the US population.

• Along these same lines, the paper did not include details about the severity of illness in SARS-CoV-2 recovered participants or the amount of time since infection. It would be helpful to include these details as well as a sentence in the discussion about how these factors might be expected to influence the results.

• Based upon Table S1, it appears that SARS-CoV-2 naïve participants all received the Pfizer vaccine, while 30% of recovered individuals received the Moderna vaccine. It would be useful to add a brief commentary about how authors accounted for different vaccines in their analysis and if there were any observed differences based on the vaccine administered.

Minor Points:

• PBMCs are collected at four time points: pre-vaccine baseline (timepoint 1), two weeks post- primary vaccination (timepoint 2), the day of the booster vaccination (timepoint 3), and one-week post-boost (timepoint 4). A short explanation rationalizing why these time points were selected may be beneficial for those who are less familiar with the kinetics of T cell responses.

• We would have liked more explanation of the markers used in the characterization of AIMS (activation induced marker expression) in the introduction. It would be useful to explain what each of these markers detects and why it is correlated with the activation state, perhaps with a reference to a previous publication.

Submitted by James Fraser on behalf of the anonymous reviewer(s) as part of https://fraserlab.com/peer_...

On 2019-10-03 19:11:12, user Simon Moore wrote:

Can verify this is a nice E. coli cell-free protocol modification as results repeatable - two new MRes students in my lab got it working first time

On 2019-07-10 15:44:28, user Travis L. Wright wrote:

Is there a free version of the BRAIN test online?

On 2015-11-30 10:16:16, user Marc Haber wrote:

Will you post more details on the methods and results? The paragraph on genetic discontinuity with modern Anatolians and Levantines is very vague.

On 2017-04-12 21:15:12, user Tim Vaughan wrote:

This article is now published in Bioinformatics: https://doi.org/10.1093/bio...

On 2022-01-11 08:36:31, user Dr-Asif Ali wrote:

This article has been accepted in Journal of advanced Research. <br /> Please see the latest Published version for reading and citation.<br /> 10.1016/j.jare.2022.01.003<br /> A putative SUBTILISIN-LIKE SERINE PROTEASE 1 (SUBSrP1) regulates anther cuticle biosynthesis and panicle development in rice

On 2020-10-05 12:20:04, user UAB BPJC wrote:

Review of Labana et al., “Armeniaspirols inhibit the AAA+ proteases ClpXP and ClpYQ leading to cell division arrest in Gram-positive bacteria” by the University of Alabama at Birmingham Bacterial Physiology & Pathogenesis Journal Club

Summary:<br /> This study focuses on elucidating the mechanism of action of the Gram-positive antibiotic armeniaspirol. Using a competitive proteomics strategy, this group identifies AAA+ proteases ClpXP and ClpYQ to be the target of armeniaspirol. Proteomics performed on clp mutants then shows that targeting of Clp proteases by armeniaspirol leads to dysregulation of important divisome and elongasome proteins, leading to cell cycle arrest. This is a novel mechanism of antibiotic activity, making armeniaspirol a promising compound for further drug development.

Overall, we found this to be a very interesting paper with valuable data and insightful conclusions. This paper was well-written, and the authors did a great job of focusing on both the chemical and biological perspectives throughout. With that said, we have some comments that may be beneficial for the authors to address, particularly in regard to data organization.

Introduction:<br /> May be beneficial to discuss Clp homology in other species either in the intro or elsewhere in the text to further demonstrate the relevance/importance of this antibiotic.

Figure 1:<br /> Including the abbreviations (1 and 2) in the Fig 1 A direct text would be useful for reference. Also, the use of various strains of staph shows a knowledge of the strains, which is appreciated.<br /> Showing the individual structures of each compound in 1A may be easier to understand visually.<br /> Might be beneficial to more clearly state in Results section “synthetic 5-chloro-armeniospirol will be referred to as ‘1’ throughout this paper.”

Figure 2:<br /> Figures 2B and 2C could be moved to the supplement to make room for more data, as the data can be interpreted without these figures in the main text.<br /> The volcano plot in Figure 2D is lacking context, and it is unclear if the highlighted genes/pathways are changed from published data or just from the drug. Side-by-side volcano plots, a heat map, or a table of the comparisons and functional analyses would make this data easier to interpret.<br /> The authors include some functional clustering (via coloring the proteins) but a table should be included at least in supplemental to better identify the relevant functional clustering which they use to support their conclusions later on (e.g. translation).

Figure 3:<br /> The goal of the electrophilic center noted in 3B (to covalently modify proteins, or covalent pull-down) should be explained better in the text...and why the competitive pulldown was surprising. (it is in the discussion but should be in the main figure-related text).<br /> Figure 3 could benefit, much like Figure 2, from a clearer functional clustering or a table which would clarify their rationale for choosing ClpP. <br /> Panels 3B and 3C could be moved to supplemental to make room for more data in the main text. For example, including data from the proteomics which supports their substrate modification or competition claims in the main text would be beneficial. Clarity on the rationale behind the path to looking at ClpP based on these data could be added so that the connection is more prevalent in the text.

Figure 4:<br /> The schematic to detail the methods can be moved to supplemental so actual data from supplemental (e.g. Fig S2 and Fig S3) can be moved to the main text as they are relevant to the main conclusions. If schematics are included, reducing the white space or making them less prominent in the figure would allow for more data to be included in the main text, rather than the supplemental.<br /> An E. coli protein is used, but it is shown previously that 1 is not active against E. coli. This is worth mentioning (it could be unable to enter the cell and therefore not function in this way for intact E. coli).<br /> Addressing the biology along with the chemical synthesis is important and adds to the impact of the results. We commend the authors for addressing both throughout. Considering the compounds as building blocks rather than THE answer, as comes across in the discussion, would couch the high IC50 and potential biological limitations.

Figure 5:<br /> Including the data on the divisome from Figure S4 seems important for the overall conclusions and could easily fit into Fig 5. <br /> The use of asterisks to denote "not significant” in 3B is confusing.<br /> The authors use data from supplemental and previous figures to support the hypotheses or conclusions of Figure 5. These previous figures should be referenced in the text with the citation (e.g. "the DivIVA expression with low transcript relating to the decreased proteolysis can be related to Fig S2 and S3 showing decreased proteolysis”).

On 2022-10-26 13:04:17, user Diana Camila Gómez De La Cruz wrote:

There appears to be no strong conservation of either RE02 in Solanaceae, or RLP30 in Brassicaceae. Perhaps the authors could go more into the phylogeny of these receptors, which might highlight putative receptors in Brassica and tomato involved in the recognition of SCPSs. Also, in the Arabidopsis accessions that do recognize SCPSs, is there sequence variation in RLP30?

On 2018-12-11 18:26:44, user Dietrich Jonathan wrote:

First: THANK YOU!! This type of work is really important! Also FYI in line 62-63, I've used this approach in both fungi and bacteria based on principle, but never validated it like this (can provide the full text if you want: https://www.nature.com/arti... "https://www.nature.com/articles/s41559-017-0123)").

On 2019-04-11 12:09:55, user Yiheng Hu wrote:

Nice paper! I am just curious that did you also do the 16S copy number adjustments?

On 2020-05-05 19:24:07, user michael a zasloff wrote:

As expected, this report with its provocative title, only provides the media with more material to scare the population.<br /> As the authors say: "There was, however, no significant correlation found between D614G status and hospitalization<br /> status; although the G614 mutation was slightly enriched among the ICU subjects, this was not<br /> statistically significant (Fig. 5C)" A more appropriate title might have been: "No evidence that mutations arising in SARS-CoV2 result in more significant clinical disease..." But that would have not been picked up by the press.

On 2020-05-03 01:17:28, user Gianguido Cianci wrote:

Could you share a reference for this please? Thanks!

On 2020-04-26 08:32:14, user Mengqi Ji wrote:

This paper will appear in Nature Machine Intelligence soon.<br /> For the code and data, please refer to: https://github.com/mjiUST/V...

On 2015-02-27 10:26:56, user Balaji wrote:

The Admixture figure at K=8 shows that the Early and Middle Neolithic Europeans as well as the WHG lack the light green component that is the largest part of South Asian populations. The early neolithic Near Easterners who were the ancestors of Early European Farmers must also lacked this component. But Yamnaya, Corded Ware and modern Near Easterners and Europeans all have this component. The explanation that immediately suggests itself is an Out-of-India migration that took this component to the Near East and Europe which were lacking this component until a few thousand years ago. An Out-of-India migration would also have taken along some ASI. Is there evidence for ASI in the Near East and Europe? At K=6 and K=7, there is a purple component that is modal in Papuans, Australians and Bougainville people and also prominent is South Asia. It is also present in East Asian populations. Clearly it is an ASI-related component. This component is present in Yamnaya and Corded Ware people. It is found in modern Near Easterners and in traces in modern Europeans.

Further evidence that an ENA component is present in modern Europeans and Near Easterners associated with the influx of ANE into these regions is provided by f3 statistics calculated by Davidski.

http://eurogenes.blogspot.c...

Europeans:<br /> f3(Greek;Dai,French_Basque) = -0.000365561, z = -2.41124<br /> f3(East_Sicilian;Dai,French_Basque) = -0.000569748, z = -3.09234<br /> f3(Tuscan;Dai,French_Basque) = -0.000432676, z = -2.8525<br /> f3(Portuguese;Dai,French_Basque) = -0.000915428, z = -3.77992<br /> f3(Bulgarian;Dai,French_Basque = -0.00118302), z = -8.17306<br /> f3(Romanian;Dai,French_Basque) = -0.00125626, z = -8.90695<br /> f3(North_Italian;Dai,French_Basque) = -0.0002888, z = -1.80649<br /> f3(West_Sicilian;Dai,French_Basque) = -0.000558259, z = -2.84604

Near Easterners:<br /> f3(Assyrian;Dai,Sardinian) = -0.00059462, z = -3.05822<br /> f3(Ashkenazi;Dai,Sardinian) = -0.000297346, z = -1.91756<br /> f3(Armenian;Dai,Sardinian) = -0.000435056, z = -3.04019<br /> f3(Cyprian;Dai,Sardinian) = -0.000721083, z = -4.49671<br /> f3(Druze;Dai,Sardinian) = -3.8934E-005, z = -0.227219<br /> f3(Sephardic_Jewish;Dai,Sardinian) = -0.00134829, z = -8.69989

On 2020-05-02 01:42:50, user Gizaw M Wolde wrote:

I appreciate the effort the authors have taken for the work. However, the finding is NOT convincing at all to draw such a conclusion ! First of all, not all transgene are driven by CaMV35s. For example, to date there are more than 33 and 20 different Transgenic corn and soybean varieties, respectively, where the transgenes are likely to be driven by different promoters. So why authors mainly focused on CaMV35s to make such broad sense conclusion that the corn being produced in Ethiopia are non-GMO ? Very confusing indeed. The authors aslo hardly presented enough data for a manuscript based on their preferred promoter, i.e. CaMV35s, used for detecting transgenes in the suspected crops (maize and Soybean). Does the varieties used in the study are really different varieties or just same but being cultivated in different places? What is the justification that they are indeed different cultivars ?

On 2021-03-25 21:39:56, user Madeline Ho wrote:

Hi Dr. Alkhatib et al.,

My name is Madeline and I was part of the group that chose your paper to look at for our Journal Club seminar. As 2 of us are involved with cancer research in our respective labs at UCLA, we wanted to look at a cancer research paper for the seminar. Your paper immediately caught our eye, and we all found it incredibly interesting.<br /> As my groupmates have already posted our constructive criticisms for the paper, I’d like to highlight the aspects of the research we were drawn to as you move forward with your research. The science behind the paper on TNBC was really intriguing and well thought out. The novelty and use of single cell surprisal analysis was well defended in the context of TNBC. Using both murine and human models in your experiments also bolstered the research and the proposition for using personalized, targeted drug therapy in addition to radiotherapy in TNBC treatment. Additionally, the various pictorial diagrams connecting the different models and the workflow were extremely helpful in understanding the experimental process. I found myself constantly going back to these figures whenever I got confused. Overall, your findings in regards to the heterogeneity of the TNBC tumor and the application of targeted treatment were exciting and I look forward to the day this research can be applied at a clinical level.

Thank you!

On 2021-01-04 21:17:35, user drjenniferomanilay wrote:

Our revised paper was just accepted for publication at the Journal of Immunology!

On 2020-12-09 12:20:22, user Guillaume Rousselet wrote:

Quick feedback about the correlation analyses. I think your conclusions need to be toned down to account for the likely very large uncertainty in the correlation estimates due to your small sample sizes:

https://garstats.wordpress....

The correlation tests are also performed in 2 groups, one being significant, the other not, but the two correlations are not explicitly compared:

Nieuwenhuis, S., Forstmann, B.U. & Wagenmakers, E.J. (2011) Erroneous analyses of interactions in neuroscience: a problem of significance. Nat Neurosci, 14, 1105-1107.<br /> https://www.nature.com/arti...

Gelman, A. & Stern, H. (2006) The Difference Between “Significant” and “Not Significant” is not Itself Statistically Significant. The American Statistician, 60, 328–331.

https://amstat.tandfonline....

There are bootstrap methods to make such explicit comparisons:

https://garstats.wordpress....

Finally, to assess how well one variable can predict another would require cross-validation:

https://journals.sagepub.co...

On 2020-07-17 15:40:17, user Martin R. Smith wrote:

Congratulations on this detailed re-analysis. I'd be interested to see the size of the distance between optimal trees in some form of non-arbitrary units: RF distances are difficult to interpret (not to mention the biases in the metric itself). I've recently proposed improvements to the RF metric that allow distances to be measured in terms of the total information content of the tree topology (Smith, 2020, doi:10.1093/bioinformatics/btaa614), which might provide a clearer quantification of the importance of model selection.

On 2017-04-18 20:04:38, user Arlin Stoltzfus wrote:

This is confusion piled on confusion piled on error. The "Modern Synthesis" in these debates is something that keeps getting redefined based on what certain people say. The people who defend it do not refer to a falsifiable scientific theory, but to a tradition or line of scholarly descent that is defined on the basis of persons who are thought to have been generally on the right side of things. There is no redeeming scientific purpose to this meta-scientific activity. Anything stated, or even vaguely suggested, by anyone connected with this line of descent, gets to be included in the "Modern Synthesis" if needed to support an argument. When the "Modern Synthesis" gets accused of not having enough randomness, defenders cite Sewall Wright, even though Wright himself said he was "left out" of the actual Modern Synthesis. If niche construction is invoked, defenders of the "Modern Synthesis" point out that Darwin worked on earthworms, or that Lewontin wrote something relevant to that 20 years after the original architects of the "Modern Synthesis" declared victory in 1959 with their complete theory of evolution that didn't have this principle. Obviously the words "Modern Synthesis" in this debate do not refer to a scientific theory. A different term should be used. There *was* a scientific theory promoted in the influential mid-century works of Mayr, Dobzhansky, Simpson, et al., but no one defends it any more.

On 2022-04-04 15:35:52, user Daniel Baldauf wrote:

Hi Xiaoxuan, nice study - congratulations! When reading about your results of sound segregation and grouping in speech-in-noise language processing I was wondering how this might relate to the concept of 'object-based' representations (see Shinn-Cunningham et al., 2015; Marinato & Baldauf, 2019)? Marinato used a very similar task, also with speech-in-noise evironments. Also DeVries et al., 2021 JN used such a task and could use sprectroanalytic signals in the MEG to decode the (non-spatial) focus of attention. I hope that might be useful.

On 2018-09-07 21:10:57, user Charles Warden wrote:

Thank you very much for your reply.

I may need to review the citations in your paper to understand why a correlation of 0 is ideal for this paper: if the correlation was between biological (or technical) replicates, I would normally expect to emphasis to be placed on processing methods with correlation coefficients closer to 1. I think one point of confusion for me is that Figure 1 used the Xenopus time series, but those samples weren't used in Figure 2.

Is it possible that it could be useful to have a measure of distance between samples of the same species (from the Figure 2 samples, or combination of samples in both Figure 1 and Figure 2)?

I think we are in more agreement about Figure 2, although I think there can sometimes be multiple valid intepretations about the positions of samples in a PCA plot. You have a good point about zFPKM causing greater separation of the dark blue Mouse (rodent) samples and most of the light blue Macaque (primate) samples, and closer clustering of the primate samples (light blue Macaque and red Human).

On 2022-06-22 12:18:53, user shashi shekhar singh wrote:

Highly significant research done by authors and this manuscript may encourage the researchers to develop precise strategies for treating the pan-resistant bacteria.

On 2020-02-01 15:57:16, user torque wrote:

Jason, I took a look at the blast results. The Wuhan seafood market virus does seem to match the bat coronavirus. However, if you click on the Accession (QHR63250.1 and QHR63300.1) you can see that both were submitted on the same day, 27-JAN-2020 by CAS Key Laboratory of Special Pathogens, Wuhan Institute of Virology. There are some subtle differences in "ORIGIN". It may be instructive to see what those differences are.

On 2020-02-01 00:06:29, user Cole Knight wrote:

Is part of the Abstract true? The part about " 4 insertions in the spike glycoprotein" I see the other post calling out the people who wrote this paper. I agree 100% of course with what they are saying, that these sequences show up in a large amount of life. <br /> But, <br /> Is it true that these sequences do NOT show up in any other Coronaviruses ?

"We found 4 insertions in the spike glycoprotein (S) which are unique to the 2019-nCoV and are not present in other coronaviruses."

Seems that is what is NOT being referenced by the others. They are saying that these 4 insertions are not found in any other Coronavirus. Is that true? If so, that seems like it is still "tampering" to me.

So, what I get from it is that these 4 insertions are all in this one 2019-nCoV, but they do not show up in any other known Coronaviruses. Correct? <br /> Think about that for a minute. <br /> I am definitely not the smartest person on the planet and I don't know it all, but this still seems suspect based on the actual "Aspect" if you read it.

On 2025-07-02 20:16:49, user Samreen Jatana wrote:

This article has been peer reviewed and published.<br /> https://pubmed.ncbi.nlm.nih.gov/37475852/

On 2020-12-24 03:58:23, user Tiago Lubiana wrote:

I guess this was published in Nature: https://www.nature.com/arti...

On 2020-05-06 22:29:44, user Leonardo Feldman wrote:

Could it be of interest to know if patients on IMATINIB treatment as a patient with chronic myeloid leukemia who have contracted COVID had a favorable evolution versus others?

On 2019-09-25 19:23:49, user Gabe Al-Ghalith wrote:

Hi Alex,

I don't think it's too much to ask that a resource that bills itself as a unified resource of 280,000 genomes actually provides [a feasible route to] those genomes. I don't think a SNP list or protein set can stand in for the actual genomes or account for the "complete diversity of this collection" in most ways. DNA sequence variants, BGCs, operons, regulatory elements, sensitive short-read alignment... The genomes themselves are indeed needed for anything other than broad species-level (or pangenome) surveys. Users can't cluster by subspecies, make a comprehensive strain database of a species (like for StrainEst), assess intra- or inter-species variation by co-currence of auxiliary genes, report strain variation if there are 2 strains per species or if variation occurs in non-core genes...

The metadata file you mentioned does not provide a reasonable route to download the genomes. It contains over 2500 unique study accessions, and it's not practical for users to manually crawl through all of them, gain access to the original publications, figure out the particular ways to get at the data dumps referenced in those, and then understand the structure of each dump to parse out just the genomes that comprise this set. The sample accessions in the metadata also don't provide the genome assemblies, just raw reads. At this point it becomes almost as much work for users to start a new search for the latest gut microbial MAGs from scratch, negating the point of a unified resource.

To your other points, the data shouldn't be massive in size. 280,000 genomes by 1MB compressed fasta is 280GB, which is less than a 100-sample metagenomics shotgun dataset routinely deposited into the SRA. But no need to host on your own servers if not feasible -- you can add the records to other public resources such as NCBI, or direct link to download each genome like they do via assembly_summary file (a model unified genome data resource).

Hence it is important to clarify that this resource doesn't actually comprise 280,000 genomes, but in actually 4,644 -- with the caveat (in the methods section, perhaps) that these 4,644 were selected and enhanced using a larger internal set that was put together but not shared by the authors. Absence of a clear route to the genomes also makes repeating your analysis and independent validation of the representative set virtually impossible.

On 2021-03-24 14:16:41, user Irene Leclercq wrote:

I am not able to find the supplementary tables:(

On 2025-05-02 10:36:23, user David wrote:

This paper was published in Scientific Reports. Please add the link:<br /> dx.doi.org/10.1038/s41598-021-84833-2

On 2025-10-15 17:54:53, user Pui-Yan Kwok wrote:

This paper is now published in Nature with an updated title: "The Taiwan Precision Medicine Initiative provides a cohort for largescale studies”<br /> DOI: 10.1038/s41586-025-09680-x<br /> URL: https://www.nature.com/articles/s41586-025-09680-x

On 2018-06-01 15:34:58, user libertyhamilton wrote:

Here's the peer-reviewed version of this manuscript, with lots of new information and new analyses, out in Current Biology: https://www.cell.com/curren...

On 2022-11-09 10:40:50, user Ben Kleinstiver wrote:

Nice work!<br /> It would be very appropriate here to cite the work from Scott Bailey's lab in 2014, which originally described the observation that shifted PAMs can lead to altered nicking site selection:<br /> https://www.jbc.org/article...

On 2024-05-29 08:20:26, user Alexey Belogurov Jr. wrote:

Manuscript has been published Chernov AS, Rodionov MV, Kazakov VA, Ivanova KA, Meshcheryakov FA, Kudriaeva AA, Gabibov AG, Telegin GB, Belogurov AA Jr. CCR5/CXCR3 antagonist TAK-779 prevents diffuse alveolar damage of the lung in the murine model of the acute respiratory distress syndrome. Front Pharmacol. 2024 Feb 21;15:1351655. doi: 10.3389/fphar.2024.1351655. PMID: 38449806; PMCID: PMC10915062.

On 2025-05-21 15:59:16, user Hindra H wrote:

Please link this preprint to the published article: https://doi.org/10.1128/msystems.01368-23 <br /> The title is different after peer-review process.

Requested by Hindra (hindraf@mcmaster.ca)

On 2015-12-19 16:21:54, user Peter Menzel wrote:

The manuscript was updated to reflect the changes in Kaiju version 1.1, which uses a new implementation of the sparse FM-index. Furthermore, the suffix array size can now be adjusted by the user on index creation, allowing for trading off memory vs. speed.<br /> These changes increased the classification speed, especially<br /> in MEM mode. Memory usage is 5.6 GB with the suffix array size (using option -e 3 for mkbwt) used for the speed benchmark in the manuscript. The classification accuracy remains the same.

On 2020-09-28 01:25:22, user G wrote:

A version of the pre-print with a working download link is available<br /> https://www.biorxiv.org/con...

On 2020-05-15 18:14:41, user Pandu Bano wrote:

The test method described in this article is flawed. They used VTM eluted sample swabs that were presented to ID NOW after 1-2 hours of collection.

Swab samples eluted in viral transport media (VTM) are not appropriate for use in the Abbott ID NOW - SARS-COV-2 Test according to the product insert sheet that comes with the ID NOW test kit.

ID NOW is supposed be used by patient bed side or closeby with fresh sample swab directly without elution in VTM which will result in decreased delection of low positive samples that are near the limit of detection of the test.

On 2024-12-06 16:47:49, user Nick wrote:

Incredible work! I was looking through the supplemental data and it appears that the A549_Compartment tab in Table S2 is duplicated from the HeLa_HPLM_Compartment tab rather than containing the 2,320 compartment hits for this cell line.

On 2017-04-24 08:07:17, user Francesco Meneguzzo wrote:

The article has been accepted for publication, after regular peer-review, in the journal LWT - Food Science and Technology:<br /> Albanese, L., Ciriminna, R., Meneguzzo, F., & Pagliaro, M. (2017). Gluten reduction in beer by hydrodynamic cavitation assisted brewing of barley malts. LWT - Food Science and Technology. https://doi.org/10.1016/j.l...<br /> The link to access the published article (currently as "In Press, Accepted Manuscript") is as follows: http://doi.org/10.1016/j.lw...

On 2021-02-09 08:33:27, user Florian Privé wrote:

Please note that this work is now included as a Supplementary Note in https://doi.org/10.1101/202....

On 2022-12-06 01:23:16, user Anne Boullerne wrote:

Nice original work among few reports on CD8+Tcells in leukodystrophies. It would be informative to know whether in the model CD8+ are accompanied by elevated immunoglobulins, and whether IgM are present along IgG. This would allow further comparison with other demyelinating diseases such as multiple sclerosis (characterized often by both IgG and IgM oligoclonal bands), and other autoimmune diseases like lupus with also IgM and IgG.

On 2017-05-11 22:12:30, user asademiloye wrote:

Please cite as:<br /> A.S. Ademiloye, L.W. Zhang, K.M. Liew, Does temperature change worsen or mitigate the effect of malaria infection on erythrocyte deformability?, Proceedings of the 8th WACBE World Congress on Bioengineering, Hong Kong, 2017. doi:10.1101/136796.

On 2020-05-12 20:34:06, user SciScore Test wrote:

Hi, we're trying to improve preprints using automated screening tools. Here's some stuff that our tools found. If we're right then you might want to look at your text, but if we're not then we'd love it if you could take a moment to reply and let us know so we can improve the way our tools work. Have a nice day. Specifically, your paper (DOI:10.1101/2020.03.29.013490); was checked for the presence of transparency criteria such as blinding, which may not be relevant to all papers, as well as research resources such as statistical software tools, cell lines, and open data.

We did not detect information on sex as a biological variable, which is particularly important given known sex differences in COVID-19 (Wenham et al, 2020).

We also screened for some additional NIH & journal rigor guidelines:<br /> IACUC/IRB: not detected ; randomization of experimental groups: not detected ; reduction of experimental bias by blinding: not detected ; analysis of sample size by power calculation: not detected.

We found that you used the following key resources: antibodies (3) cell lines (1). We recommend using RRIDs to improve so that others can tell exactly what research resources you used. You can look up RRIDs at rrid.site

We did not find a statement about open data. We also did not find a statement about open code. Researchers are encouraged to share open data when possible (see Nature blog). We also found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

More specific comments and a list of suggested RRIDs can be found by opening the Hypothes.is window on this manuscript, direct link https://hyp.is/JskapI-tEeqh...<br /> References cited: https://tinyurl.com/y7fpsvzy

On 2020-04-13 21:41:12, user Aaron wrote:

I could be mistaken, but I believe that the authors actually mean Washington state (home of University of Washington) rather than Washington DC.

On 2025-02-13 22:20:55, user Amer Alam wrote:

The peer reviewed version of this manuscript has been published in the EMBO journal:

Structural insights into binding-site access and ligand recognition by human ABCB1

PMID: 39806099 DOI: 10.1038/s44318-025-00361-z

On 2020-05-26 13:02:35, user OxImmuno Literature Initiative wrote:

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

On 2020-11-25 19:13:11, user KAORI SAITO wrote:

The figure legends for Figure 4a and 4b should be switched.<br /> Also, in Figure 4a and 4b, the p-value is shown in the upper lefthand corner but it wasn't clear on which groups this p-value is representative of.

On 2023-03-14 18:44:24, user Charles Warden wrote:

Hi,

Thank you for posting this preprint!

I think you have some typos that might be good to revise in a "v2" version in Figure 1:

Current: Cadidates (for "Fusion" or "Isoform" or "SNV/Index")<br /> Corrected: Candidates

Current: resuts (for "Fusion" or "Isoform" or "SNV/Index")<br /> Corrected: results

Current: Amino acid suquence<br /> Corrected: Amino acid sequence

Best Wishes,<br /> Charles

On 2025-01-20 13:30:27, user Paula Quiroga wrote:

Very interesting intepretation!

On 2020-09-12 18:26:35, user Kresten Lindorff-Larsen wrote:

This manuscript is now published as:<br /> Classifying disease-associated variants using measures of protein activity and stability<br /> MM Jepsen, DM Fowler, R Hartmann-Petersen, A Stein, K Lindorff-Larsen<br /> Protein Homeostasis Diseases, 91-107<br /> https://doi.org/10.1016/B978-0-12-819132-3.00005-1

On 2018-03-06 10:51:10, user Wouter De Coster wrote:

Dear authors,

This looks like very interesting work and I'll spend more time later to read the manuscript. Meanwhile, I cannot find a link to your software/repository? Did I miss it in the text or could you make this available too?

Kind regards,<br /> Wouter De Coster

On 2025-03-12 17:30:11, user xyz wrote:

I read your paper the study is nice but I have certain questions regarding your paper<br /> 1. In figure 5 why the percentage of macrophage with phagocytosis and trogocytosis in case of suspended cells same.<br /> 2. In figure 6 can you please confirm what was the effect on macrophage trogocytosis on increasing the cell adhesion<br /> 3. Also what was the cell nature adherent or suspended when you din different integrin knockout<br /> Hope to hear from you back

On 2025-08-27 22:27:02, user Telomere Biology Lab wrote:

Published in PNAS: https://www.pnas.org/doi/10.1073/pnas.2318438121

On 2022-07-11 12:29:29, user João Duarte wrote:

Peer-reviewed published version in Neuroimage, here: https://www.sciencedirect.c...

On 2022-10-29 16:04:44, user Prashant wrote:

I see the logic of the 'presence' of the type IIs sites as an indication that the genome was prepared for in-vitro assembly but those sites were not yet used for inserting variant fragments. So the paper should also comment on any restriction sites that they believe have been used up by adding a variant fragment thereby removing any type IIs sites present there.

For example I would do this by aligning a fragment such as the furin site containing fragment to multiple viral genomes, look for regions where homology shifts from one genome to another anook into those genomes for any restricti

On 2023-06-15 17:28:22, user Grace Donnelly wrote:

This paper brings to light some interesting new information about RNA G-quadruplexes and their associated proteins. The overall flow of the paper, namely the shift from theoretical predictions to experimentation was a good segway into your web application. However, I did have some critiques, mostly regarding the figures. One general comment is that all schematics with red and green should be recolored for colorblind readers.

Fig. 2a) Perhaps an overlay or neighboring graph showing what the expected CD specter for G4A4 RNA would be helpful to readers that this does in fact resemble what is expected.<br /> Fig. 2b) Indication of significance needed.<br /> Fig. 2c) This hierarchical clustering schematic should be much larger, and possibly contain a legend for interpreting the opacity of the colors.<br /> Fig. 2d) The label at the top is unnecessary and slightly misleading, and the labels at each axis should be larger.<br /> Fig. 3b,c) Pie charts should include percentages/quantities for all portions.<br /> Fig. 4d) Placement of p-value is confusing, perhaps place it over the area where the differences become significant.<br /> Fig. 5a) The x-axis labeling here is confusing. One solution could be to just put “neutral” and “very high,” or assign values to them, like “neutral” as 0 and “very high” as 1. I also noticed that your graph lines are closer towards the middle, however this could have been missed and should be made to appear more obvious to the reader.<br /> Fig. 5b, 6c) Color-coordinating the proteins listed in these figures based on classification/function would provide a richer understanding of the trends observed in G4 binding propensities. While this would interrupt the current color scheme in 5b, perhaps bold font would be sufficient to indicate controls.<br /> Fig. 6a,b) Data points are too clustered in these graphs; differently colored dots would probably work better.<br /> Fig 6c) This graph has some distortion (vertical stretching).

On 2017-05-16 07:02:32, user Gopal J wrote:

"Given the redundancies present in the network of PQC genes and the large number of connected components, it is expected that the network easily adapts to altered demands" - although this statement holds in the context the biological systems are inherently able to compensate for inadequacies, it is not evident if single PQC deletions can cause a problem in the steady state proteome maintenance. Substrate flux through chaperone networks are perhaps handled in a manner which does not require a response from the cell. For instance, there is no formal evidence if deletion of single chaperone shifts the abundance of (it's) client proteins into an insoluble/pellet fraction.

On 2014-04-23 23:34:46, user Ross Tellam wrote:

Suggestion: H3K27me3 has strong modification subtypes i.e. near chromosome wide on Xi of females, broad regional modifications (multiple genes or whole gene bodies eg HOX loci), and promoter specific (eg gated ion channels). In particular there is highly conserved broad regional modification for about 300-500 genes (eg HOX genes). The latter would be a good group for a separate analysis as these genes are often involved in early developmental programs particularly in relation to body patterning. One might expect to see a few, but very important H3K27me3 differences between species for these genes.<br /> Ross Tellam<br /> ross.tellam@csiro.au

On 2016-05-27 03:02:38, user Andrea Kessel Fabry wrote:

This is truly a game changer. Thanks to Louis Slesin of Microwave News for first bringing this to the public's attention.

On 2024-12-05 03:18:42, user Arianna wrote:

The mechanism driving miRNA load and 3’UTR lengthening and its subsequent effect on mRNA half life, and the significance of mRNA stability mechanisms shaping gene expression and 3’UTR usage during human neurodevelopment are well documented. The manuscript by Ellis et al. investigates an alternative approach to systematically quantify transcription rate and mRNA stability using RATE-seq and SLAM-seq to further validate the relevance of miRNA loads among induced pluripotent stem cells (iPSCs), neural progenitors (NPC), and neurons (Neu). This approach expands on the role of mRNA stability in shaping transcriptional buffering and driving 3’UTR lengthening via miRNA load. <br /> The paper suggests a comprehensive approach towards understanding the regulation of mRNA stability and transcription rates during human neurodevelopment. The use of RATE-seq as a technique for measuring half-life and transcription rates was a powerful and novel approach to dive deeper into the current understanding of this field, and could be applicable to future studies. The authors also explore the idea of a mechanistic link between miRNA regulation and transcriptome changes. The reproducibility of results between cell types was reinforced that accumulation of miRNAs during neurodevelopment potentially leads to preferential degradation of short 3' UTR-containing mRNAs in neurons.

The manuscript outlined the following findings about neuronal differentiating cells: i) mRNA stability and transcription rate play an equal role in establishing steady-state levels of the transcriptome, ii) buffering genes controlled by pluripotency transcription factors are regulated by mRNA stability, iii) increased miRNA load corresponds to mRNA degradation of most genes in neurons, and iv) preferential degradation of neuronal short 3’ UTR-containing mRNAs resulting in decreased RNA stability. These are not novel findings to the scientific community but the reproduction of previous research is not without merit. Reproducibility is a hallmark of quality scientific discovery. However the novelty of this research is demonstrated in the unique sequencing protocols and statistical techniques utilized. Despite this achievement the paper doesn’t sufficiently expand the literature in such a way that qualifies it for publication. Given the issues outlined below, the manuscript must address the following concerns prior to acceptance for publication.

One of the most pressing major concerns is the lack of a discernable attempt to close the gap in knowledge regarding the relationship between mRNA stability and transcriptional regulation during neurodevelopment. Specifically, the manuscript demonstrates the shifts in mRNA stability and 3’UTR lengthening. These results have already been documented in previous literature thus leaving unclear how this research advances the field and our understanding beyond known mechanisms. The question of whether miRNA load is sufficient or necessary to drive 3’UTR lengthening should be addressed in any future iterations of this paper. The authors could address this concern by designing an experiment to validate the causal relationship between increasing miRNA load and 3’ UTR lengthening. This can be achieved experimentally by overexpressing miRNA in human cell lines and tracking subsequent increases or decreases in 3’UTR lengthening.

A second major concern that should ideally be remedied by the authors is the use of a single experimental cell line. To strengthen the quality of the research performed in this manuscript experiments should be orthogonally validated in other human cell lines. Justification of each cell line used should be explicitly mentioned in the manuscript. Additional tissue types can also be explored experimentally to strengthen the mechanistic link.

The minor concern that needs to be addressed by the authors of Ellis et al. is the addition of a justification of the time points used during data collection. This context will allow readers more insight into the overall logic of the experiment. It is generally understood that the chosen time points are typical when working with neural cells but this logic may not be clear to an individual not actively involved in the field.

On 2019-06-05 14:40:21, user David Posada wrote:

Thx, will think about 1) but the important things is relative performance, so running all callers in the same way, as far as possible.

Multiregional means multiple spatial samples from the same tumor...

On 2022-03-28 21:55:53, user Fraser Lab wrote:

In this paper, the author uses innovative SVD rotation methods to analyze previous serial crystallography datasets with the aim of delineating retinal isomerization in bacteriorhodopsin (bR). This analysis reveals novel intermediate states that lend insight into how the transition takes place as well as the role played by residues in bR’s active site. The author hypothesizes that a non-specific Coulomb attraction force triggers sampling of multiple cis conformations and single bond rotations in retinal, but the selection of the 13-cis conformation is achieved by the stereochemical constraints in the protein environment. The author lays out the major questions of the paper very clearly in the introduction and the results and discussion sections remain well-focused on answering those questions. The mathematical and computational methods presented here demonstrate how they can be used to tease out the intermediates from “multi” datasets (as shown by the author in his other publications). We share the author’s frustration that “never-observed dataset reconstituted from a collection of experimental datasets does not match the well-established crystallographic template of PDB” - there are interesting new directions in joint refinement across datasets that are considered siloed by the PDB - and this paper contributes well to that frontier. Overall, the paper is well written and the methods section is particularly instructive in the general approach, but lacking details that would allow us to follow the exact implementation used for this paper.

Major Points:

1. Overall: no statement on data/code availability is given. Even if proprietary software is used, it is essential to know what options are used and to have a detailed protocol to reproduce the work.

2. The author comments on how timepoints from the Kovacs et al drive the vectors of interest (U10, U14, U17), yet it’s challenging to differentiate the insights gained from the SVD over FO-FO maps from Kovacs. We suggest a quantitative comparison showing signal-to-noise or goodness of fit which may help the reader interpret the benefits gained from SVD.

3. While the methods section is quite detailed in describing how SVD is performed and how the Ren rotations retain the orthonormal properties of the SVD, the details of how the angles for rotations are chosen are not given. The author says “SVD analysis presented in this paper employs rotations extensively” but what angles of rotations were sampled? Which were the good rotations and which were the bad rotations? A read-through of the earlier papers of the author (Ren 2019, Ren 2016) did not provide answers for these questions either (with large portions of the text in the methods sections being nearly identical between these papers) We infer this is using the software “Dynamix” based on the Acknowledgements. While it is understandable that decisions on what rotations need to be done are subjective, it is imperative that the author guides the reader through these decisions to help in reproduction/application of this method. Without these details it is difficult to know, for example, whether the oscillatory vectors are related to particular choices in the SVD procedure or are relatively without bias.

4. Majority of datasets are clustered in the current SVD iteration - did another Ren rotation not separate Kovacs datasets according to an interesting piece of metadata? This seems like an opportunity to rotate the SVD and further demonstrate the utility of the Ren rotation method.

5. The author shows the oscillatory behavior of 5 pairs of components in figure S3. But how did he arrive at these 5 pairs? Did other pairs not show such kind of oscillatory behavior?

6. The author goes into fine details of the intermediate conformations of retinal and shows differences in distances in the range of less than an angstrom. Is there a goodness-of-fit metric that can be used by the author to show that the atomic positions determined by the author are indeed the best/only possible positions? What if there is another intermediate conformation of retinal that fits equally well into the densities but does not have the S-shaped creasing? How do these conformations navigate the need to fit to density without being too high energy?

Minor points:

1. Labeling of “inboard” and “outboard” terms can be done in main Figure 1 rather than referring reader to S1

2. Figure 2(b): overall trend is useful, might there be an easier way to visualize? Some sort of 1D - distance plot of integrated intensities?

3. Panel C in figure 2 is cited extensively in the text. Rather than mentioning the sub-panels as the 1st, 2nd, 3rd panel, etc., the author could just split panel c into d,e,f, etc.

4. Figure 2(c) - lines are difficult to visualize. They could be made bold. The author could also reduce the panel to show only atomic displacement and torsion angle and move the rest to supplement if necessary, so that the two main plots could be made bigger for easier interpretation.

5. Figure 1(c) and Figure 3(a) & (b) are very difficult to interpret. The author can zoom in further and use translucent volume rendering rather than mesh

6. Figure 3(d) - The author could label as “helix A, B, …, G” so that it’s immediately obvious what’s shown in figure, otherwise a nice illustration of motion

7. Figure 3(e) retinal could be color coded or more easily separated from amino acids to make it easier and faster to interpret.

8. The maps of I’ (Fig S6), I (Fig S7), J’ (Fig S9) and J (Fig S10) are contoured at different sigma levels presumably due to the Fo contributions differing based on the reconstitution procedure. Is there any trend in the reconstitution that would guide us to calibrate what sigma levels to be equivalent across datasets, or is this purely visual?

Alexander Wolff, Ashraya Ravikumar, James Fraser (UCSF)

On 2023-08-21 16:16:34, user Fredy Barneche wrote:

Comment from the authors: a curated and modified version of this manuscript has now been published in Cell Reports (https://doi.org/10.1016/j.c... "https://doi.org/10.1016/j.celrep.2023.112894)").

On 2016-07-10 07:11:04, user Aleksey Belikov wrote:

Well, I think both single numbers and distributions are needed. Distributions can not be easily compared - you will anyway need to calculate the median or mode from it, which are single numbers. And it is not clear whether they really describe these distributions in a best possible way. When you look at the long lists of journals, or scientists, and want to sort them by impact, how would you do this without a single number for each journal/scientist? As you showed in your paper, current technology easily permits to create distributions to anyone interested in particular journal or scientist, but for quick sifting through large lists of candidates single numbers are unbeatable.

On 2021-06-02 04:23:36, user VINODKUMAR SARANATHAN wrote:

v2 Now Published in PNAS:<br /> https://www.pnas.org/conten...

On 2017-09-22 00:21:02, user Kisse Ellis wrote:

The first Europeans were Blacks who entered via Iberia- Gibraltar and crossed the Mediterranean - island hopping.

Its rather obvious if you look at the map. But centuries of propaganda obscure the issue

On 2017-08-30 01:11:54, user Elwyn Hegarty wrote:

As an 80-plus mother of several chikdren, and a retired scientist, I am aware that most mothers in my country, and probably elsewhere, tend to cradle bables, when not being fed, in the crook of their left arm which is presumably nearer to where they hear their mother's heartbeat most strongly. In noticing the summary of this paper in New Scientist, I wondered about newborns with limited experience sometimes being attracted to the array of black dots to their right side, while the looming image of their mother is still becoming distinct? Which would be the case if the child was already, or expected to be cradled in the left arm of the mother? Have I interpreted the summary text right?

On 2017-12-21 21:49:04, user Keren Lasker wrote:

K. L. and A. D. are co-first authors on this paper.

On 2025-03-15 04:05:42, user Min Zhu wrote:

The full version of this manuscript is online in Science Advances. https://www.science.org/doi/10.1126/sciadv.adt7274

On 2023-08-23 15:43:29, user Theo Sanderson wrote:

This work was published here: https://www.nature.com/arti...

On 2019-08-13 19:33:30, user Doniv Hgnis wrote:

Dear author, you mentioned that "Consistent with a previous report (Alexandrov et al. 2018), we find tobacco smoking (signature6) ", but if you look at the cosmic signature database, signature4 belongs to tobacco smoking.

1. in the statement "which presents high exposure for signature 2 (UV light) ", and signature 2 is associated with APOBEC reactions of DNA.

Please relook the manuscript once more

On 2023-09-29 08:18:43, user Marcos wrote:

Now published in Science DOI: 10.1126/science.adf2160

On 2020-08-31 22:21:33, user Rubayetul Alam wrote:

Could you please tell here that from where the accession no was derived?<br /> Thanks

On 2020-11-04 05:43:06, user som wrote:

This paper should be fortified with following references to be visible to broader scientific community:<br /> 1. Suslick, K. S. Sonochemistry. Science 247, 1439-1445 (1990).<br /> 2. Ragazzon, G. & Prins, L. J. Energy consumption in chemical fuel-driven self-assembly. Nat. Nanotechnol. 13, 882-889 (2018). <br /> 3. Hwang, I., Mukhopadhyay, R. D. et al. Audible sound-controlled spatiotemporal patterns in out-of-equilibrium systems. Nat. Chem. (2020) (DOI: 10.1038/s41557-020-0516-2)

On 2021-11-11 00:27:38, user Kenta Watanabe wrote:

This paper has been published in "PeerJ" after peer review (on 10Nov, 2021). https://doi.org/10.7717/pee...

On 2023-02-16 20:34:15, user Scott Glaberman wrote:

This is great work and we are excited to see others working on similar questions. We also have a preprint from last summer on this same topic:

https://www.biorxiv.org/con...

On 2017-10-12 10:55:02, user Samuel Terkper Ahuno wrote:

An alternative title could have been "The one-million project". Congratulations to you all!

On 2022-12-19 01:36:25, user Donald Milton, MD, DrPH wrote:

The detection method for viral aerosol described here appears to be a version of settle plate collection. It is poorly described. Petri dishes are "placed vertically" -- distance from the source is described but height above the floor is not. I think that by "vertically" the authors mean that the surface of the Petri dishes were oriented perpendicular to the floor. This is NOT an appropriate method for assessing aerosol concentrations. Collection using this method is determined by the velocity of air in close proximity to the plates and size and diffusion rates -- not concentration in the air. The apparent protection by high RH may be more an impact on diffusion than actual exposure. The authors should have used a standard method for aerosol collection (e.g., an Andersen Impactor or SKC Biosampler). Phi6 is known to be much less sensitive to UVC than coronavirus when tested in liquid. Given these methodological concerns and failure to assess whether the in-duct UVC system delivered a dose expected to be sufficient to inactivate the test organism, this preprint does not present data that support the conclusions made. The test of UVC was inadequate, poorly described, and only used the least effective type of UVC system on the market, UVC in a box rather than upper room conventional or direct far-UVC. The results regarding RH may be an artifact of the poor air sampling methods used. The generalization implied that high RH in cold winter climates is safe -- without accounting for mold growth, asthma exacerbation, and structural damage that it can produce -- is unwarranted.

On 2020-04-08 17:31:52, user Mikolaj Slabicki wrote:

NOTE: This protocol has not been validated with clinical samples. To facilitate collaborations<br /> with interested parties to jointly advance the fight against the current coronavirus pandemic, we have set up a public forum on www.LAMP-Seq.org.

On 2020-06-09 12:13:18, user Alex Mielke wrote:

Review<br /> While the idea behind this preprint – using modelling approaches to determine what an appropriate null model for behavioural variation between chimpanzee/orang-utan communities would be – is nice, the simulations fail in several ways in replicating the generative process that underlies the original Whiten et al 1999 paper. It is simply an insufficient null model because it ignores any information about ape behaviour except the number of cultural traits produced by that paper. The model simply does not approximate the system that it claims to simulate. It also feels like a strawman is attacked here by singling out one paper and ignoring everything else that is known about these species and their behaviour in the wild. It is telling that apart from the Whiten et al 1999 paper and van Schaik et al 2001 paper, almost no other papers on tool use in wild apes is cited. In the following, I will detail where the simulations fail to convince. This is often due to a complete lack of explanation as to why certain choices were made, making it impossible to replicate if one would start from scratch. There also seems to be a lack of humility in terms of what simulations can and cannot do.<br /> I will focus my criticism largely on the modelling approach and less on the concept of ‘socially-mediated reinnovation’, even though there is certainly enough to be said on the topic. The subheading for each paragraphs summarises the argument made. Mainly, this boils down to the following: the simulations ignore what we know about chimpanzee/orang-utan behaviour in general but also about the Whiten et al paper specifically. The simulations ignore that apes show hundreds of behaviours and exist in thousands of communities, and Whiten et al randomly picked subsets of both – picking 7 other communities might have led to having 70 or 90 cultural behaviours. By pre-selecting 64 behaviours as their only choice, the authors forego the conclusion of their simulations. They also ignore the fact that not all the behaviours in Whiten et al are the same in form, function, complexity, usage etc. The concept of ‘innovation’ seems so broad to be meaningless, because individuals can ‘reinnovate’ behaviours they already used in the past and seem to never be exposed to the behaviours of others until they innovate, at which point they suddenly take social information into account. Innovation rates seem excessively high. Innovation as defined here is meaningless for social behaviours. In the simulations, this leads to the fact that the rule that defines ‘innovation’ is essentially the same as one that would define ‘copying’. The simulations cover the entire range of possible results if one tweaks the parameters right; the authors then focus only on the simulation that contained the observed value in Whiten et al and claim that the parameters for that simulation were biologically the most meaningful, without giving any indication as to how this was decided.

Material and Methods<br /> Highly unnatural demographics and ignorance of the consequences of failed learning and of known learning biases towards specific group members<br /> First off, it is hard to see how the description of an oranzees life here follows ‘realistic demographic features’, as is stated by the authors. Citing the Hill et al 2001 paper here is somehow odd, because even in the best field site in that study, half of individuals died before 25 years of age. In most field sites, risk of death is highest in the first year of life, and continues to be high before individuals reach adulthood; in many sites, few males especially reach 25 years of age. We also have a good idea about inter-birth intervals in these species. This is non-trivial because individual survival will depend on mastering socially learned skills. Presumably, copying does not evolve mysteriously out of the blue – it is useful when the costs of failing to perform a task correctly are high. The model, and I would say the theory underlying it, ignore that an ape who fails to learn a skill correctly faces immense costs. Also, the weird age distribution of communities means that the number of individuals from whom an individual can learn is skewed: there is ample evidence that younger primates learn more, and they use older individuals and higher-ranking individuals as models (e.g. Kendal et al 2015, Horner et al 2010). Individuals will obviously learn more from their mother than any other group member, especially early on – that effect alone renders the simulations meaningless, because an individual will have all the skills it needs by age 8 and then just apply them. So, if learning probability in the simulations is based on the frequency a behaviour is observed in the population, treating all potential models evenly and not weighting the impact of potential models by their age (e.g. remove infants and juveniles) biases the outcome of the results. Any theory and model that is based solely on the frequency of behaviours in the population fails to account for all of these well-known effects.

Faulty assumptions of base likelihoods of behaviours and ignorance to the generative process underlying Whiten et al<br /> I think the most fundamental mistake encoded in the simulations is that they completely fail to understand the process that generated the Whiten et al 1999 results, and rather set up a process that is designed to create exactly the same number of behaviours just to make a point. I could make a random model with each individuals having a 30% chance of showing each of 65 behaviours, and there would obviously be some solutions that could look similar to the Whiten et al results, but that would not mean that the model at all captures any underlying processes. Simulation studies are only useful in as much as they can actually represent the probabilities underlying the original study, especially in this case where the simulation is specifically design to invalidate one existing study. Whiten et al 1999 did not select 65 behaviours out of 65; they selected 65 behaviours out of the several hundred observed chimpanzee behaviours in each site (Nishida et al 2010). They never claimed that these were the only behaviours in which variation could occur, and in fact adding more field sites since then has brought to light many other variants of existing behaviours, as well as entirely new behaviours. Non-tool use behavioural variation is not even included. Also, that study used a very small subset of randomly selected field sites. Everything else aside, sampling out of 65 behaviours means that the number of ‘customary’ etc behaviours is bound in a certain range, which becomes quite obvious in the supplementary, where even random selection leads to similar results as the Whiten et al 1999 study. This does not seem to make the authors suspicious. Essentially, any model would have to explain not why there is variation in 7 group in 65 behaviours, but how likely it is that a random selection of 7 groups (out of thousands) with hundreds of different behaviours shows the patterns observed here. For example, while all the communities in the Whiten et al study drag branches during displays, there is no a priori reason to believe that this is a chimpanzee universal. Also, just because the Whiten et al study does not include group-specific gesture use does not mean it does not exist. The impact of this decision becomes obvious once the genetic parameter is included: if the 65 variable behaviours are a subset of several hundred genetically or environmentally fixed behaviours, then the genetic and environmental parameters would have fundamentally different functions in the simulations.

Ignoring that most problems in the wild can be solved in hundreds of different ways<br /> The other problem with this generative process is one that is also apparent in all experimental studies in captivity, when testing whether apes learn tool use socially: usually, in those experiments, apes have a limited number of different ways of performing an action – often 2 options. However, this is not the case in wild populations. There are usually a large number of options with equal success likelihood that are NOT used; the more detailed the analysis, the more possible options there are. This becomes apparent, for example, in the use of bark pieces of different sizes for termite fishing in neighbouring communities in Gombe (Pascual-Garrido 2019). Chimpanzees in all sites could use a whole lot of tools to fish for termites, comb their hair etc: sticks of different sizes, bark of different sizes, parts of leaves, full leaves, their fingers etc. There are hundreds of different ways to groom someone. Many of these options are not used by any of the groups in the Whiten et al 1999 paper, without good reason, which again speaks against the a priori reduction to 64 behaviours as highly artificial. Again, the generative process for the original paper includes a random selection of field sites that happen to result in 65 solutions. Adding an 8th field site would have added e.g. 10 more behaviours. By ignoring this, the base likelihood of each behaviour in the simulations is off, and the result of the simulations more or less decided before any model is run.

Reinnovation is meaningless for social behaviours and embedded behaviours in sequences<br /> Next point: for social behaviours, re-innovation is a rather pointless concept. A display is not successful if nobody in the group understands what the displayer wants to express – even though individuals could incorporate a fantastical number of potential elements into their displays. Play elements that nobody else knows will not lead to successful play. Hand-clasp grooming does not work if only one individual does them. Courting a female by building a ground nest, as some chimpanzee males do, only works if the female gets the idea. This is as if I would re-invent the handshake – what is the point if nobody understands its meaning? This is completely putting aside that the 65 behaviours in Whiten et al largely ignore social traditions and communication, and focus heavily on tool use, which was the best studied at the time. Apes have probably in access of 100 different play elements in each group (Nishida et al 2010; Petru et al 2009), and it can easily be expected that innovation and social transmission occurs in this context (Perry 2011). One non-tool use example in Whiten et al 1999, rain dancing, cannot conceivably be reinnovated by one individual – what would that even look like, given that it is a coordinated action of several individuals with no discernible physical function? Many of the described behaviours in Whiten et al 1999 are not simple behaviours that occur in a vacuum, but action sequences with several elements that have to be fulfilled in the exact right order and are embedded in sequential behaviour patterns; for example leaf clipping. The generating process of the simulation ignores this and reduces behaviours to independent, on/off instances that fulfil their function outside of a wider context.

Artificial number of states and artificial assumptions about the use of behaviours<br /> The lack of detail in the Whiten et al study also plays an important role. For example, even though ‘drag branch’ is a common behaviour in all field sites, detailed analysis will likely find that there are different ways of dragging a branch, as has been found for other behaviour on the list (e.g. digging for honey Estienne et al 2017). But for the simulations here, this means that achieving the wanted ‘state’ of an individual is directly bound to doing a behaviour exactly like the partner. Also, the basic assumption of the simulations (there are 8 different grooming/play/courtship behaviours that all have the same outcome) is thoroughly misleading, because this is again not the case for the generating process underlying Whiten et al: The 64 behaviours on that list largely fulfil different functions, so instead of simulating 8 categories leading to 64 behaviours, the simulations would have to address 64 ‘states’ that need to be fulfilled. Just because many of them are used to acquire food does not mean that they all serve the same function. Categorising the behaviours as done in the simulations also ignores that even though the form of behaviours might be similar, function can differ drastically. For example, two behaviours that would fall under ‘display’, are branch dragging and drumming. Both are used in displays, but at different times and have different messaging functions, and drumming is also used in some field sites for long-distance communication in other contexts. Function and context might be specific to one sex or age-group: drumming in juveniles, for example, is often part of play; female chimps will slap the ground in displays rather than drum, even though drumming is sometimes observed.

Almost no decision in the simulation process is justified<br /> In general, it would be fantastic if there was even a basic description of why any of the simulations was designed as described here; many of the choices seem to be arbitrary and could not be replicated by someone engaging in the same activity.

‘Innovation probability’ is meaningless for social behaviours<br /> The innovation probability of social behaviour, psocial, seems to have no correspondence in the real world, and it is unclear to the reader why this way of calculating the necessity to innovate was chosen. This again highlights the inability of this framework to account for social traditions. Chimpanzees groom every day, play every day, display regularly, etc; they also observe others do these behaviours, and are the recipient, long before they actively take part in social interactions in their group. Also, from a modelling perspective, it is not clear here what is being innovated: for example, if an individual already has one ‘play’ behaviour but none of the other categories, is the play behaviour potentially reinnovated? Why are these social categories treated as fulfilling one overwhelming urge for ‘social’? I would understand if individuals would be assigned a random number of behaviours in each category, and would have to reinnovate if these do not match those of other group members (seems biologically much more plausible), but the way this process is described here seems meaningless. Also, the state of an individual’s social behaviours cannot exist outside of the state of other group members.

Group members’ needs are not independent<br /> The same is true for food: all group members at a certain time point would obviously be exposed to the same need and availability of food resources, so why is the simulation assuming independence of these things?

Socially-mediated innovation<br /> Individuals lack memory and the concept of ‘innovation’ used here is meaningless<br /> Now we come to some of the most irritating decisions taken in the modelling process, and it feels hard for the reader to understand why they were taken. These seem to completely ignore anything we know about social learning or the life history of animals. Let’s start with the first one: what is the meaning of ‘innovation’ if innovation can happen every month over and over again? If I read this right, each individual can ‘reinnovate’ the same behaviour multiple times in their life? That seems nonsensical – individuals create a repertoire of skills that they apply when necessary. They don’t ‘reinnovate’ nutcracking every time they are hungry, this renders the idea of innovation meaningless. If they already have one ‘play’ behaviour, and their state tells them to play, they use that behaviour. Essentially, the simulation pretends that these behaviours are consistently in flux in a population and an individual, but we know that they are not in wild ape groups. Also, the concept of innovation makes sense for zoo-based apes who are exposed to a new tasks, but is completely nonsensical for wild individuals: for example, by the time any chimp starts nut cracking, they will have observed several millions of strikes by their mother and other group members – are we to believe that they did not in any way take this information into account when acting? They will observe these actions by others while they themselves are in a sensitive period for learning the skills. This is fundamentally incomparable to throwing some stones into a zoo enclosure and hoping that an adult chimpanzee will potentially bang them on a nut. That reinnovation is potentially possible does not rule out the most individuals in a community do not in fact reinnovate. For example, I could easily re-invent the handshake; that does not mean that I initially learned the form and function of handshakes by myself.

The simulation rules used are undistinguishable from copying <br /> The second confusing aspect of the modelling approach described here is, that it would look essentially look the same if copying was described; it is unclear for the reader how these two models would differ from each other in real life. This does not mean that you rule out copying – it means that you are essentially modelling the same effect and give it a fancy name. The assumed difference in the simulations between socially mediated reinnovation (I find a pattern, I check whether this pattern fits the group pattern, I adopt the pattern) and copying (I check the group pattern, I adopt the group pattern) is the order of action and social information. As they are here modelled in the same step, there is no difference. The frequency of ‘innovation’ for any behaviour depends on the frequency of occurrence in the group. That is the same for copying – I can only copy a behaviour that I can observe, and the more I observe them, the more likely I am to copy them. Let us say I am looking for a way to crack nuts. There are three different ways of cracking nuts in my community. I choose to use one of those ways. This is particularly pertinent if the frequency of most behavioural options for a state is zero in the population, and no real ‘innovation’ (new solution) occurs. In the example run in the additional information, many behaviours seem to have one fixed choice in the population. I am not ‘reinnovating’ anything – I make use of the information that is present in the group, which I have observed my entire life. What the authors call ‘innovation’ from an individual perspective is not an ‘innovation’ from an information perspective – in my opinion, this it is thoroughly misnamed, because it assumes that individuals only incorporate social information after they have found an individual solution, which seems wasteful. It is therefore unclear why ‘socially mediated reinnovation’ is supposed to be a simpler explanation for copying. The S factor indicates that sometimes, individuals do not copy faithfully; that does not provide any evidence that the rule described here differs from copying. There is also no accounting for the fact that we do not know at which rate chimpanzees and orang-utans innovate at all; the assumption of the modelling approach seems that each individual innovates whatever they need all the time, but this stands in complete contrast to the fact that chimpanzee communities seem to spend decades doing things the same way. I would urge the authors to somehow indicate why they think that their approach is not simply modelling exactly the same process that everyone else would call ‘copying’, except that they switch whether individuals first observe and then do, or first do and then observe. Because, the latter is meaningless for long-lived animals with long infancy.

Results<br /> The simulations cover the full range of possible values, and the authors simply pick the one they like best<br /> I am not going to go into much detail for the results, because I am not sure what they are supposed to show given the problems raised above. Just relating to Figure 1: It is clear from this figure that a) the result of the simulation is dramatically influenced by the ‘genetic variability’ parameter, which seems artificial and not anchored in any real-world research, even when ignoring the problems of preselecting a subset of behaviours raised above; you can basically achieve any distribution between 0 and 64 by varying this parameter, so some of them necessarily will cover the value from Whiten et al 1999. B) On top of that, the variation for each of the combinations of environmental and genetic factors is huge, all of them cover a range of 20-30 cultural traits (about half the possible values). So, again, what does it mean in this case that the value described in Whiten et al falls into this category? Every other value does as well under some conditions, and the authors simply pick a subset and argue that this is the one they were looking for all along. For example, the manuscript says that there is a good match for alpha_e = 0.8 and alpha_g = 0.2. What does this mean biologically? Is there any indication that this in any way represents that actual circumstances of these chimpanzee communities in the original paper? Is this a better representation of chimpanzee communities than the ones with alpha_g = 0 or alpha_g > 0.5, and what are the criteria to make this decision? If we assume that these chimpanzee communities share several hundred behaviours that were not included in the original 65 possibly cultural behaviours, then alpha_g for chimpanzees is probably very large; the picture is distorted by just using one specific subset. It is repeatedly stated that this simulation represents ‘realistic values for genetic propensity and ecological availability’; it seems a bit cartoonish to reduce genetics and ecology to one value each and call that ‘realistic’. Obviously models need to abstract, but then this should be presented as what it is.

Discussion<br /> I just want to very specifically point out this statement: ‘More generally, the results of our models suggest caution when deriving individual-level mechanisms from population-level patterns (see also (Acerbi et al. 2016; Barrett 2019)).’ However, the same thing is also true the other way round. This paper obviously produces some population-level patterns, and under certain circumstances and when one abstracts everything one knows about primates, they look like they might be similar to the ones reported in the wild. That does not mean that the individual-level process that was used to generate the data was biologically meaningful or represents the system you want to study; as described above, there are many unexplained decisions taken by the authors, and they fail to convince this reader that their choices are replicable and accurately describe chimpanzee or orang-utan behaviour.

On 2019-05-23 00:14:09, user Charles Warden wrote:

I apologize that I won't be able to look into the raw data in the immediate future (and was the data deposited into a public database?), there were a couple things that seemed strange about the report:

1) Table 1 seems too high for high-coverage variants (not to mention low-coverage variants). The variant counts also seem low. Is there something special about those regions? Do they tend to be at higher copy number, or of some other reason that makes them easier to detect?

For higher coverage data, I think you had to look within regions that were easier to call (which is not representative for a person's overall set of variant calls, even within CDS regions), to predictive statistics in the 99+ percent range: http://cdwscience.blogspot....

2) For the HLA genes, I was expecting more of a difference for the HLA-D genes than the HLA-A/B/C genes (Figure 1), but maybe that is because I need to check more samples processed with different technologies. For example, you can see my HLA calls with various strategies in the "HLA Analysis Results" section here: https://github.com/cwarden4...

On 2020-04-21 01:39:33, user Rajendra Kings Rayudoo wrote:

Hi<br /> Ivan Mercurio, Vincenzo Tragni, Francesco Busto, Anna De Grassi, Ciro Leonardo Pierri

From the above paper are saying that by stimulating the "" angiotensin converting enzyme 2 (ACE2)"" receptor, to produce antibodies against spike protein""

Can we modify the ace2 in human to avoid binding of any other disease protein<br /> . With Regards<br /> Rajendra

On 2025-02-11 15:27:41, user Barbara Cassone wrote:

This paper in now published in Human Brain Mapping doi: https://doi.org/10.1002/hbm.70125

On 2015-12-02 11:38:32, user Mark Laszlo wrote:

Splice their DNA to mine, i want to live forever! ;)

On 2021-10-22 09:59:50, user Marc Gielen wrote:

Interesting read, thanks!<br /> Just two quick comments on figure 6 suppl 4 :<br /> 1) there is a microscopic reversibility issue in the kinetic scheme, which would be solved by increasing the AD --> ADI by 10^3 fold (i.e. k'on.10^3 rather than k'on)<br /> 2) by increasing 1000 fold the desensitization on-rate, it seems you are decreasing the lifetime of the open state rather than stabilizing the desensitized state (of course, there is a thermodynamic shift in favour of the desensitized state, but it doesn't sound to me like a stabilization in a kinetic sense). My bet is that if you rather decrease the desensitization recovery rate (d-), akin what you did for the flipped state in fig 6 suppl 3, you will end up with a similar observation to what we had for our 2018 review (GLIC & DHA): pretty much no effect on the peak current following preapplication of the inhibitor.<br /> Best,<br /> Marc

On 2016-08-17 05:28:38, user Hyeshik Chang wrote:

Thanks for the very interesting work! Is the motor protein used here identical to the protein used in the ONT DNA sequencing R9?

On 2020-06-05 12:56:48, user Patrick Lemaire wrote:

Since the last version of this preprint, we have improved the software <br /> and modified the access to both software tutorials and data. All details<br /> can be found here: http://www.crbm.cnrs.fr/en/...

On 2019-02-16 12:32:52, user Bilal Kerman wrote:

Our paper is now published by IEEE at the 26th European Signal Processing Conference 2018 (EUSIPCO). You reach that publication at: https://ieeexplore.ieee.org...

On 2017-05-04 14:32:34, user Valentine Svensson wrote:

8 datasets, not 1,305..

On 2025-04-15 10:58:18, user ryhisner wrote:

This is amazing. Thank you so much for this work. The paper tantalizingly mentions more detailed images of the structure in supplemental figures, but they are not included in the PDF and do not seem to be available for download. Will they be posted soon?

Also, is there any chance your Alphafold models (and manual extension of the model to the full pp1a’-nsp4-nsp10 + pp1ab’-nsp4-nsp16) can be made available for others to view? I'm very interested in the proximity of specific AA residues throughout the complex, which may help explain unusual mutational patterns throughout the replicase complex that have emerged independently hundreds of times.

On 2018-08-10 16:18:15, user chandu reddy wrote:

Check this article that gels well along with this article.<br /> Mutant ataxin1 disrupts cerebellar development in spinocerebellar ataxia type 1<br /> https://www.ncbi.nlm.nih.go...

On 2020-12-01 16:55:46, user klevan wrote:

Thanks so much for posting, it's a good read!

We also greatly appreciate that you put the list of datasets used in the supplementary materials (it makes it easy for us to track use!). The citation guidelines (posted here: https://www.neonscience.org... "https://www.neonscience.org/data-samples/data-policies-citation)") recommend putting a reference in the references section along the lines of:

"NEON (National Ecological Observatory Network). DP1.10072.001, DP1.10092.001, DP1.10093.001. https://www.neonscience.org (accessed 21 October 2019)."

On 2019-09-16 05:05:44, user Andrew Brooks wrote:

Interesting article. I found it a little surprising that the context of reference 54 (Ferraro et al) is only mentioned once. It would be good if there was some further discussion of this article regarding this publication. In reference to this article the manuscript states "but this model suggests a separation of 120 Å or more between the receptor transmembrane helices". It would be great if this could be clarified further as I could see not the conclusion for the 120A. In Ferraro it al Fig 6b shows the EPOR Trp283 residues to be 44A and the Trp872 residues of LEPR to be 45A apart. Therefore I could not see the basis for a separation of 120 A for the transmembrane helices based on the data presented by Ferraro et al.

On 2023-07-09 21:48:22, user Stephanie Wankowicz wrote:

Summary: In this study, researchers used 3D variability analysis (3DVA) combined with atomistic molecular dynamics (MD) simulations to investigate the dynamic motions of human asparagine synthetase (ASNS). By solving the structure of ASNS and performing 3DVA, they suggest that a single side chain's dynamic motion (Arg142) regulates the interconversion between open and closed forms of an intramolecular tunnel. The opening of this tunnel allows for the translocation of ammonia, which is necessary for ASNS’s catalytic function. MD followed up on this initial finding to determine exactly how

The study highlights the power of cryo-EM in identifying localized conformational changes and demonstrates how conformational dynamics can regulate the function of metabolic enzymes with multiple active sites. However, the lack of experimental electron density shown in the figures (or available publicly) makes it difficult to assess the claims in this study. Additional forward tests of the importance of this blockage via mutagenesis may also uncover why it must be regulated. If this is out of the scope of the current paper, it should be hypothesized and speculated upon in the discussion.

Major Points:

1. In your figures, Please show electron density and all individual atomic positions. This includes Fig. 1d, 2a, and all of Figure 3.
2. Please show the PCA of the 3DVA. Clarify whether this was done on the entire structure or the tunneling residues. If done on the entire protein, please comment and show if other changes were seen elsewhere.
3. In the RMSD analysis, please clarify what EM coordinates you are using. Are you comparing all structures from the 3DVA? Please also provide raw values as well as normalized values.
4. Your results do not support the claim ‘Our results suggest that changes in the C-terminal active site are propagated over a distance of approximately 20 Å, leading to tunnel opening and ammonia translocation’. While the data here shows that the tunnel can move between an open and closed state in apo form as part of the native fluctuations (revealed by the PCA analysis). No information is presented on how this information is propagated nor how the active site or binding interacts with this motion. Please change the wording of this or explain the mechanism.

Minor Points:

1. Neither the PDB nor the map is publicly available, making it difficult to examine the structures and map independently. Please release them. Also, include information and metrics regarding map sharpening and map-to-model fit. Zenodo is a good option for the 100 structures from the PCA analysis.
2. In Figure 1d, please label the amino acids and chains. Please provide experimental density corresponding to the positions of these residues in this figure or a supplementary figure.
3. In Figure 2a, please provide a legend for what each color represents.
4. How did you determine 5 PCAs for the 3DVA analysis?
5. Please provide details on the normalized RMSF. How was this normalization done?
6. In Figure 4, please provide a legend for all colors of amino acids and tunneling.
7. Please deposit the coordinate files for the 100 structures used in the 3DVA study and (ideally also the two MD-derived trajectories on Zenodo or a similar repository).

Stephanie Wankowicz and James Fraser

On 2023-01-30 12:20:05, user Zdravko Odak wrote:

There are a few ways the research could be improved:

Larger sample size: This study used cells collected from only two patients, and a larger sample size could provide more robust results and increase the generalizability of the findings.

Comparison with other treatments: Comparing the efficacy of mifepristone to other treatments could provide a more comprehensive understanding of its potential as a treatment option.

In vivo validation: Although the 3D organotypic model provides a closer representation of the in vivo environment, validating the findings in animal models or clinical trials would further increase the reliability of the results.

Long-term effects: Studying the long-term effects of mifepristone on cancer cells and its potential for preventing recurrence would be valuable for its clinical implications.

Mechanism of action: Further investigation into the mechanism of action of mifepristone on cancer cells could provide a deeper understanding of how it inhibits their metastatic abilities.

Best regards,

Zdravko Odak

On 2019-02-20 14:06:22, user Lauriane de Fabritus wrote:

Dear Dr Chen,

First, congratulations for the very interesting paper! Hope you will soon publish these data!

We have been indeed really interested by your work, and we would like to ask some questions.<br /> -Concerning the tigh junction, did you test other markers than Claudin5b?<br /> -Concerning the TUNEL assay, we are surprised to not see TUNEL+ macrophages for example? How come?<br /> -We would be curious to see transversal sections of the larvae brains, to have an idea of how deep the lymphatics are going. Do you have these pictures?<br /> -Did you try the photothrombosis in your Cbee1 KO? Are the results similar?<br /> -Why did you work on larvae (we are not zebrafish specialists)? Can we expect to have similar results in adult brains?

Best regards

On 2023-07-21 08:26:14, user julien chiquet wrote:

Hello, thank you for your work. <br /> I was curious to know which version of PLNmodels you used for your simulations: I recently lowered the tolerance of the optimization algorithms, and corrected a typo in the objective function of one of the models that could have an impact on the results. <br /> On my side, the AUC and AUPR with my simulation parameters give a clear advantage to PLNnetwork, SpiecEasi and SparseCC over GLasso/NeighborhoodSelection, although they are not specifically designed to help compositional approaches win... A simple example of simus with AUC and AUPR results is available here, for your information. Would be happy to give PLNnetwork <br /> I'd be happy to help give PLNnetwork its best shot!

scripts simus<br /> AUPR<br /> AUC

On 2017-11-07 07:31:20, user Yan Zhang (zany) wrote:

Does it applicable for large panels? For example, for 500 target regions those sparsed in the whole genome, then we should design large amount of sgRNAs, and then perform CRISPR-DS?

On 2024-09-26 15:09:36, user pLM Enjoyer wrote:

An important application of pLMs is enhancing the efficiency of protein engineering by adding a classifier top model onto a foundation pLM (with or without fine-tuning), training on a small number (0-96) of experimental sequence/fitness datapoints, and then using this model to score and predict high-fitness sequence variants. This task also provides a good benchmark of pLM quality, since pLMs with ‘better’ embedded representations of sequences produce better variant scores/suggestions. See, for example, Zhou et al 2024 (Enhancing efficiency of protein language models with minimal wet-lab data through few-shot learning), and Jiang et al 2024 (Rapid protein evolution by few-shot learning with a protein language model). I think it would be really useful for you to benchmark AMPLIFY’s performance against ESM/SaProt/etc on few-shot and zero-shot variant fitness prediction using public deep-mutational scanning datasets as described in the papers above. If AMPLIFY really outperformed ESM2-15B on this task, that would be huge!

On 2017-04-20 14:35:18, user Michael K. Gilson wrote:

We'd be very much interested in readers' thoughts on these results! <br /> --Mike

On 2024-11-15 05:26:52, user Shigeaki Saitoh wrote:

The final version of this article is published in https://doi.org/10.1083/jcb.202404085 .

On 2020-08-28 03:16:36, user Elizabeth Molnar wrote:

This receptor's susceptibility to Ivermectin seems ubiquitous, with multiple CNS/Behavioural effects, perhaps via Catecholamines' actions on purinergic receptors?

On 2020-02-06 15:59:37, user Nathanael Rollins wrote:

Hi Surge and Grig, shortcutting hard experiments by leveraging natural sequences is a great goal- in that direction, I think you need to benchmark against unsupervised models that enable “no N”

There’s enormous data already available in natural sequences- so the burden of “high N” doesn’t necessarily fall on wet lab, it’s often satisfied by existing sequence databases. Unsupervised models with “no N” initial variants can be used to engineer novel proteins, e.g. Socolich (2005). And unsupervised training on just natural sequences can create accurate generative models for many proteins, see Hopf (2017), Reisselman (2018) & (2019-preprint).

Your model UniRep is also using natural sequences- maybe layering it behind a second supervised learning task is creating a false bottleneck? Not all sequence design faces the constraint you outline, so consider moderating the text. Likewise, I’m curious, see if these designs could be found using an unsupervised model and not even require supervised data points!

On 2020-07-01 12:37:09, user D Chon wrote:

Giuseppe Pezzotti is part of the Sintx team per Sintx as a lead investigator and consultant for the company. Sintx CEO Sonny Bal said this was an independent study, yet someone getting paid by Sintx was in on this. What’s up with that?

On 2025-08-16 21:57:06, user Eric Helms wrote:

You report a non-significant p-value of 0.97 in the text for the relationship between 1RM and MVC changes with muscle volume changes, but the figure correctly shows a statistically significant p-value of 0.032. The value in the figure is the correct one based on the reported sample size and r-score. Further, in your subsequent discussion, may aspects are based on this error.

On 2021-04-26 19:19:47, user Samuel Katz wrote:

Update from the authors:<br /> Our work has been published here https://www.cell.com/cell-s...

And our method is under a new name: SIGNAL.

The current URL to access this tool is signal.niaid.nih.gov

The design and analysis of the method have not been changed.

On 2021-09-30 03:22:27, user Neil Andrew Bascos wrote:

Thank you for your interest in our work.

An updated version of this preprint entitled

"Structural Analysis of Spike Protein Mutations in the SARS-CoV-2 Theta (P.3) Variant

by

Neil Andrew D. Bascos, Denise Mirano-Bascos,<br /> Kim Ivan A. Abesamis,Camille Anne S. Bagoyo, <br /> Owen Tito O. Mallapre, and Cynthia P. Saloma"

has been accepted for publication in the Philippine Journal of Science (https://philjournalsci.dost... "https://philjournalsci.dost.gov.ph/)").

The published article may be accessed through the following link :

https://philjournalsci.dost...

On 2016-08-24 03:36:46, user z wrote:

Is it possible to provide us with the supplementary?

On 2019-11-21 21:53:28, user Tommy Vo wrote:

Very nice and interesting work to actually see the gradient of chromatin based on compactness. The observation of a "return to compaction" near the TES is reminiscent of recent findings that transcription termination machinery can promote heterochromatin formation.

Vo, T., et al. (2019). CPF Recruitment to Non-canonical Transcription Termination Sites Triggers Heterochromatin Assembly and Gene Silencing.

Chalamcharla VR, et al. (2015). Conserved factor Dhp1/Rat1/Xrn2 triggers premature transcription termination and nucleates heterochromatin to promote gene silencing.

On 2020-12-15 11:05:02, user Dominik wrote:

As others pointed out, this paper is seriously flawed in many aspects and should be retracted, especially considering that many people are afraid of a new type of vaccine (mRNA) and conspiracy theorists certainly will take this paper to "proof" that mRNA vaccines can in fact alter your genetic code.

On 2019-04-05 10:30:05, user Andrew Bateman wrote:

clearly a great deal of work has gone into this<br /> but I flinched most when I got to "Questionnaire and task data were first summarised using means and 95% confidence intervals". My suggested further reading around how you link questionnaires to a latent trait, and handle specific items, requires I suggest a delve into the Rasch Model. eg see Bond and Fox https://www.routledge.com/A...<br /> Creating a total and mean score is making assumptions about Likert data that may not hold up throughout the proposed dimension.

Second point I might mention "To the best of our knowledge, no questionnaire exists that asks simple single questions about a range of cognitive functions." I guess might need qualification about context, but in the clinical world we could certainly highlight more than a few, the one that I have done most with is the European Brain Injury Questionnaire.

On 2020-06-29 03:36:51, user aarontay wrote:

Very nice. I was doing similar analysis for my institution and just wondering about how much the results vary when moving from WOS, Scopus, Microsoft academic as well as the range of years involved. Analysing with diff Unpaywall dumps at different time is also novel. However I wonder if the most important factor the use of unpaywall itself might be worth studying using other services like CORE discovery or Open access button APIs.

On 2020-05-26 12:59:17, user OxImmuno Literature Initiative wrote:

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

On 2020-07-05 07:15:48, user Fernando Mendez wrote:

The result is very interesting. I do strongly suggest, however, a change at least in the title "The major genetic risk factor for severe COVID-19...".This is the risk factor with the highest statistical significance in the populations for which the samples taken (from Spain and Italy) are representative. We don't know what the genetic factors are in other populations that are not appropriately represented in the GWAS, including Africans, Indigenous Americans, Melanesians, East Asians,... you get it.

On 2020-12-21 14:16:34, user DR.Muratt, MD wrote:

Virus-specific antibodies are considered antiviral and play an important role in the control of virus infections in a number of ways. However, in some instances, the presence of specific antibodies can be beneficial to the virus..Could Antibody dependent enhancement (ADE) be a major problem with any vaccine developed for coronaviruses?

On 2025-01-14 03:19:09, user Bryan wrote:

Are you able to share a version with high quality figures? The text in many figures is not readable. Thank you!

On 2024-02-02 11:19:21, user Jay Jayaraman wrote:

Nice paper! Just noticed that the plate photos in Fig 1A and Fig S1A appear to be identical.

On 2023-12-03 05:36:10, user Dovota wrote:

Nice preprint! are you aware of this recent work? https://www.science.org/doi...

On 2020-06-18 09:57:33, user Marisa Martin-Fernandez wrote:

An interesting article. Readers might be interested in seeing what the architecture of the ligand-independent oligomers looks like, in Zanetti-Domingues et al, Nat Comm 2018 (https://doi.org/10.1038/s41... "https://doi.org/10.1038/s41467-018-06632-0)"). In this paper we showed that the asymmetric kinase dimer is only involved in the formation of ligand-independent dimers but is not compatible with ligand-independent oligomer formation.

On 2018-11-30 17:51:43, user Josh Lï Spinoza wrote:

Here is the peer-reviewed paper:<br /> https://mbio.asm.org/conten...

On 2019-04-10 19:58:53, user Douglas wrote:

Several papers available, but no citation here about some of the biggest crocodylomorphs: Purussaurus, Gryposuchus, Mourasuchus...

On 2021-01-28 14:42:57, user Ligophorus mediterraneus wrote:

The package FuzzyQ is now available on CRAN (https://CRAN.R-project.org/... "https://CRAN.R-project.org/package=FuzzyQ)")