On 2020-11-19 11:29:30, user Martin R. Smith wrote:

This is a very interesting study. I'm not sure how representative the trees in Fig. 4 are of the dataset more broadly, but the rather pectinate nature of Tree 1 makes me worried that the high Robinson-Foulds distance might be unduly amplified; it's clearly not a 'good' tree by any measure, but in a pectinate setting, misplacement of a single leaf or pair of leaves can make an outsized contribution to the RF distance. Is there a chance that the shapes of the curves in Fig. 4a/b would change if a more sensitive measure of tree distance was employed – perhaps a generalized RF distance or a quartet-based measure? I have found that these approaches give more representative measures of distance between trees than the RF (Smith 2020, Bioinformatics, https://doi.org/10.1093/bio... ).

On 2020-11-19 10:46:16, user Martin R. Smith wrote:

A note of caution on equating Robinson–Foulds distances with 'accuracy': the RF distance conflates both accuracy _and_ precision, unless a suitable normalization is conducted; see Smith (2019, Biology Letters, https://doi.org/10.1098/rsb...

Another way to separate out the contribution of accuracy would be to calculate the mean accuracy of each (perfectly resolved) tree in the posterior sample (weighted according to probability, i.e. sampling frequency). To some extent, precision is a function of your choice of consensus method, which is perhaps less interesting than the innate agreement in the data.

Separately, the RF's reliance on splits has other consequences that make it, in my view, less than optimal for this sort of study; I'd suggest considering Generalized RF distances (e.g. Smith 2020, https://doi.org/10.1093/bio... as well perhaps as a quartet-based measure, in order to more accurately represent different aspects of tree accuracy.

On 2020-11-19 10:38:55, user Martin R. Smith wrote:

A thought on the use of the Robinson-Foulds distance: I wonder whether flaws in the metric, such as its sensitivity to long SPResque moves, might be responsible for the high rf-distances between the ASTRAL and IQ-TREE topologies, despite the perceived similarities at the subfamily level?<br /> It might be interesting to explore whether a Generalized Robinson–Foulds distance (e.g. Smith 2020, Bioinformatics, https://doi.org/10.1093/bio... or a quartet-based distance would report distances that more intuitively match your perception of true underlying similarities between trees.

On 2020-11-19 10:33:58, user Martin R. Smith wrote:

A thought on the use of the Robinson-Foulds distance – because this metric is so readily saturated, random trees are likely to obtain a value unreasonably close to the maximum, meaning that they don't add much context to the distance between the PepM tree and genome phylogeny. The RF distance has other issues too. There are various other tree distances that might be more suitable, some of which which I've discussed and reviewed in Smith (2020, Bioinformatics): https://doi.org/10.1093/bio....

On 2020-11-19 09:43:13, user Gilles Vergnaud wrote:

Previous work is being ignored. The "new lineage" was described eight years ago in reference 8 quoted by the authors in relation with the discovery of L7. We specifically discussed in 2012 the deep branching of two of the five strains (Percy119 and Percy34 renamed G00074 and G00075 in this report) which define here the so-called L9 (Blouin et al. https://journals.plos.org/p... ). The strains were not "related genomes that had not been classified into any of the known MTBC lineages" as stated here. Giving a specific name to this group is highly discutable, and this is indeed why we did not do it. We also described a similar deep branch within L5 (Percy717) which the authors have overlooked and did not include. Please read or re-read the literature and correct this.

On 2020-11-18 20:29:11, user Marco Calderone wrote:

Immediately after performing the nasopharyngeal swab, swabs should be stored in the transport medium and frozen at -20?

On 2020-11-18 18:42:13, user Taras K Oleksyk wrote:

There is a new version of this article on biorxiv https://www.biorxiv.org/con...

On 2020-11-18 18:07:27, user SUE HEINTZEN wrote:

Suppl Fig1 which has temperature data is missing.

On 2020-11-18 14:28:45, user lotrus28 wrote:

This project was published in Cell iScience.<br /> It contains an improved version of the model presented in this preprint

https://www.cell.com/iscien...

On 2020-11-18 14:18:48, user Olga Matveeva wrote:

High quality paper. There are two recently published papers with similar messages "The genetic structure of SARS‐CoV‐2 does not rule out a laboratory origin" https://doi.org/10.1002/bie... and " SARS-Cov-2 Natural or Artificial? That is the Question" <br /> DOI: 10.31487/j.CMR.2020.01.06

On 2020-11-18 06:44:18, user kdrl nakle wrote:

Very slim results. 3 types of produce only and nothing to do with SARS-CoV-2. Even the formatting was done in a lazy way. Looks like a paper my students would do.

On 2020-11-18 06:34:32, user Chaitanya wrote:

Im curious to see how the authors could distinguish between crowding effects and viscosity. Certainly a fascinating question, since if crowding doesnt reduce dynamics is it drag? <br /> - chaitanya athale

On 2020-11-18 01:36:32, user Peter K Rogan wrote:

MedComm, in press.

On 2020-11-17 20:05:02, user matthew scholz wrote:

It may be too late to ask this, but looking at your figshare data, it doesn't look like the kraken2 databases have bracken indexes included. Is it possible to generate them?

On 2020-11-17 18:26:38, user Patrick Shaw Stewart wrote:

Typical body temp of minks is 39C. Many promoters are temp-sensitive and "RNA thermometers" are important in many species.

Any mutations in "promoter" regions & regions affecting RNA secondary structure could have profound effects on viral pathogenicity.

Easy intro: https://youtu.be/l_jmqGvdeB...

Review: https://www.douglas.co.uk/f...

On 2020-11-16 23:18:09, user Fraser Lab wrote:

This manuscript details the efforts of a team of structural biology computational experts to cross-validate the proliferating SARS-CoV-2 structures emerging during the COVID-19 pandemic. Over the past five months, as soon as each new SARS-CoV-2 structure is made publicly available, the authors have subjected it to a barrage of validation metrics as well as residue-by-residue manual inspection. When they were able to get a hold of the raw data, they analyzed that as well for several of the most commonly occurring pathologies. Re-refined structures were sent back to the structures' original authors for reupload to the PDB via the recently available versioning option that preserves the PDB code (although it would be nice to quantify how many authors were contacted and what the “re-versioning” rate is after contact). In this manner, the structural biology community has simultaneously benefitted from an increased number of experimentalists' single-minded focus on the coronavirus (even where these efforts fall partly outside their areas of expertise) and these experts' careful curation of the resulting structures.

The manuscript represents an incredible effort. As the authors call attention to in a few places, the errors in data processing and modeling are not only inevitable (especially under the circumstances) but tolerable, as long as they can be identified and corrected in a timely manner — the goal is not to gatekeep so that only experts are permitted to do this work, but to tag-team as effectively and efficiently as possible. Furthermore, there is the separate issue of pathologies resulting from decisions during data collection that cannot be corrected after the fact. It is critical that fixable and unfixable issues are extremely clearly distinguished from each other. We suggest the authors rewrite some of these narratives with the deliberate aim of identifying the origins of pathologies that can be mitigated or corrected in full, again differentiating between these, and taking care that the wording is as charitable as possible toward the researchers responsible.

There are a few cases of oversimplified concepts that we believe can be succinctly expressed more accurately. For example, where the authors describe data as being "incomplete due to radiation damage," they could instead take the time to explain the difference between incompleteness resulting from a poorly chosen collection strategy, incompleteness in higher resolution bins, and radiation-induced damage that renders some reflections (and some real-space features) self-consistent but inaccurate. The "lower quality" of datasets suffering from these pathologies could be separated into uniformly low resolution datasets, which are more easily recognizable, and seemingly high-resolution datasets with serious systematic errors.

The authors could also be more clear with a couple choices of wording around concepts of correctness. They write, "While the deposited structures are often improved by PDB-REDO, they need to be checked and should not be viewed as 'more correct' purely on [the] basis of a lower R value." In this and several other instances, we challenge the authors to replace any terms assigning value (improved, correct, error, bad, misidentified) with descriptions of what metrics they are examining and what they mean for the model and data. This publication is an opportunity to instill readers with a stronger sense of how to use the existing validation tools, and what to do when they turn up serious issues. It would be highly useful to go into some explanation of what constitutes model bias and how this is detected in crystallographic and EM data, what metrics we traditionally use to detect it, what happens when we refine against those metrics (!), and how the tradeoff between agreement with priors (geometry, clashscore) and agreement with data (real space CC, FSC) should vary with map quality. If the authors are willing to go as deep as explaining how the available validation metrics were devised, the average reader might learn quite a bit!

A separate but closely related issue is the identification of real features that conflict with prior knowledge. Under what circumstances do we accept "bad" geometry is actually the right way to model something? These are often information-rich and functionally relevant discoveries, such as Hoogsteen base pairing or very strained geometries at a catalytic site. This is worth calling attention to.

We read the opening of the "manual evaluation" section as a framing of structure solution as tedium that should be automated as much as possible, but whose results nevertheless fall short in the absence of an expert's intervention. This is unfortunate. We would rather laud both the amazing efficiency (and thereby throughput) that automating routine steps has made possible and the important role of the researcher in guiding the process and interpreting the results.

On the topic of data not deposited in the PDB, the authors describe a case of a severely radiation damaged dataset and how it was necessary to reprocess the raw data to improve it. We strongly agree that raw data should be made publicly available for exactly these sorts of reasons. Once again, separating this administrative barrier from the researchers' decisions during data collection would be helpful in setting a positive tone. The authors point out the amazing proteindiffraction.org resource and should call for more deposition there (or to SBGrid DataGrid). In EM, the EMPIAR database plays a similar role (with greater proportional adoption) and the reprocessing potential of datasets deposited there should be highlighted and celebrated.

The "supplying context", "summary" and especially "outlook" sections bring up some extremely important points that could bear to be repeated at the beginning of the manuscript to help frame this work. The tradeoff necessary under the present circumstances in particular — the fact that imperfect "first draft" structures are still useful, and much more useful when they can be quickly updated with any corrections — deserves greater emphasis, and perhaps further discussion of how the field should go about addressing and documenting problems with models and data after the pandemic. We are overall very excited to see this work in print alongside the resources already publicly available at insidecorona.net. Collectively, that resource and this manuscript represent an exciting development in peer review away from gatekeeping and toward continuous improvement!

Finally, we note a handful of points that we suggest would improve readability:<br /> SARS-CoV is now also known as SARS-CoV-1. We strongly suggest using this term throughout the manuscript to differentiate it from SARS-CoV-2.<br /> The phrase "not by experimentalists, but scientists from other fields" suggests a false dichotomy. We recommend rewording so as to recognize the existence of experimentalists in other fields. <br /> The rationale for annotating secondary structures with the Haruspex neural network is not yet clear.<br /> The COVID-19 pandemic is "unprecedented" in very recent history, but arguably not unique even in recorded history — we would favor a different term here.<br /> The abbreviation RdRp is not defined.<br /> "fulfil" is a typo.<br /> “Structures solved in a hurry to address a pressing medical and societal need _are_ even more prone to mistakes.” - suggest "may be"

James Fraser and Iris Young (UCSF)

On 2020-11-16 21:30:35, user Michael Bok wrote:

Interesting manuscript! I will need to read it in detail.

Regarding mantis shrimp UV color vision, we have shown behaviourally that they can discriminate different wavelengths of UV light independent of intensity. But being mantis shrimp, things are a bit convoluted and we chose to describe it as "polychromatic UV sensitivity" rather than "UV color vision".

On 2020-11-16 16:29:03, user Wilhelm wrote:

I was unable to find the uniprot data for A7YK11 and A7YK09. From the available data on A7YK10, it is a murine norovirus strain. As Human immune responses have been show to be highly genotype specific, there is a significant loss of relavence in regards to characterizing the human immune response to a human norovirus infection. I suggest performing these experiments with the predominant Norovirus strain GII.4.

On 2020-11-16 14:14:59, user Melissa wrote:

Hi everyone

Very interesting paper, looking forward to the publication.

Could you upload the data onto EMPIAR?

Regards,<br /> Melissa

On 2020-11-16 13:58:07, user Valeriya Rudomanova wrote:

This article is now published in the Journal of American Society of Nephrology <br /> https://doi.org/10.1681/ASN...

On 2020-11-16 03:10:15, user Alex Tilley wrote:

Paper now out in PLoS One - find it here: https://journals.plos.org/p...

On 2020-11-15 20:24:05, user Liangcai Gu wrote:

The final version is published in ACS Synthetic Biology:<br /> https://pubs.acs.org/doi/10...

On 2020-11-15 18:52:51, user Raghu Parthasarathy wrote:

This is a fascinating paper, and it's great to see new data of a sort I've never encountered before, from a wide variety of species. Could you please include, in the supplementary material, the actual raw data of killed bacterial fraction for each bacterial species, mammal, and dilution? This would be a straightforward text data file, and would allow the reader to replot and think about all the relationships. It would be great, for example, to apply various models to the data of figure S8, but for all data sets. (The spline doesn't tell us much.) Thanks!

On 2020-11-14 11:55:11, user Sal Peralta wrote:

Prunella vulgar is not just a Chinese medicinal herb. The herb has a worldwide distribution and has been used medicinally across the world for millennia.

On 2020-11-14 02:58:55, user Radostin Danev wrote:

Very exciting work!<br /> I would recommend replacing "Near-atomic resolution" with "Cryo-EM" in the title. Also, replace "unbiased" with "reference-free" in "Unbiased cryo-EM image processing" in the methods.

Best wishes,

Rado

On 2020-11-14 02:54:31, user kdrl nakle wrote:

115F for 12 hours? And how is that practical? Where exactly could we do this?

On 2020-11-13 23:24:04, user anna bernasconi wrote:

Please note that this preprint has now been published in the Proceedings of the 39th International Conference on Conceptual Modeling (ER 2020).<br /> The version on bioRxiv (last posted on May 1st, 2020) is not the final version, but the first submitted version. Readers should view the published LINK for the final version of the paper, for further reference and citation:

Bernasconi A., Canakoglu A., Pinoli P., Ceri S. (2020) Empowering Virus Sequence Research Through Conceptual Modeling. In: Dobbie G., Frank U., Kappel G., Liddle S.W., Mayr H.C. (eds) Conceptual Modeling. ER 2020. Lecture Notes in Computer Science, vol 12400. Springer, Cham. https://doi.org/10.1007/978-3-030-62522-1_29

On 2020-11-13 19:48:02, user Marcel Ramirez wrote:

manuscript published in Frontiers

On 2020-11-13 15:05:43, user Abhay Sharma wrote:

Notably, a recent clinical trial (U. Padmanabhan, S. Mukherjee, R. Borse, S. Joshi, R. Deshmukh. Phase II Clinical trial for Evaluation of BCG as potential therapy for COVID-19. medRxiv 2020.10.28.20221630) has found that moderate adult COVID-19 patients administered a single dose of intradermal BCG achieve faster resolution of hypoxia, and significant radiological improvement and viral load reduction, without showing evidence of BCG induced cytokine storm (31). The epidemiological transcriptomic evidence of persistently upregulated antiviral defense response and downregulated myeloid cell activation in BCG vaccinated subjects (the present preprint) is in line with this new finding.

On 2020-11-12 19:34:33, user Craig Kaplan wrote:

This preprint would have a greater impact if the methods section were added to allow the experiments to be better understood.

On 2020-11-12 13:27:35, user Pere Puigbo wrote:

This preprint has been accepted for publication at Journal of Hazardous Materials and a link will be forthcoming.

On 2020-11-12 09:02:43, user Chih-Ting wrote:

Hi, it's an interesting topic. Is there any information about the exact position of the leader sequence in the Figure 1 ? Thanks!

On 2020-11-11 21:40:52, user Ridhi wrote:

This paper conducted a thorough analysis of the role of Smpd3 (and other genes) in neural crest EMT. A strength is the meticulous experiments conducted in order to identify Smpd3 as a significant gene of interest. Hybridization chain reaction was performed to amplify and detect Smpd3, and Figure 1c and 1d clearly show the strong expression pattern of this gene at both pre-migratory and migratory stages, supporting your hypothesis that the gene is critical to EMT. Figure 2c shows a similar pattern, in which nSMase2 knockdown via electroporation displays a clear reduction in neural crest cell migration, relative to the negative control (a non-binding MO). However, for the electroporation experiments, it would have been beneficial to include a positive control, such as an MO designed against a highly expressed neural crest gene to ensure binding specificity. Next, the Wnt/BMP signaling quantification in Figure 3c is a bit unclear. You state that the nSMase2 knockdown reduced both Wnt and BMP output; however, the image does not show a convincing decrease in GFP intensity to support a significant reduction in Wnt signaling. The BMP image, on the other hand, paints a more convincing picture, as the GFP fluorescence is almost negligible compared to the control side. Finally, you state that endocytosis inhibition phenocopies nSMase2 knockdown, citing Figure 4c as evidence. However, the image shows that the reduction in migration after endocytosis inhibition is clearly not as significant as that observed after nSMase2 knockdown. Further experiments to compare the reduction in migration would be beneficial.

On 2020-11-10 07:21:09, user Eddie wrote:

I enjoyed reading this paper and finding out more about epithelial to mesenchymal transition in correlation with the membrane. I found figure 1 to show a strong and clear introduction towards your paper. The labeling of your figures also helped in understanding the structure and components. From what I gathered, figure 2b is supposed to show the reduction of ceramide expression leading to decreased migration. You add dots to specify the region where the ceramide expression is located and the migration, however it is unclear how you determine what to include within this dotted area. It would be nice to add, within your methods, your reasoning to incorporate certain regions within the dotted area as well as how you quantified your results. I enjoyed the side by side comparison for Figure 2c allowing for a direct comparison for your results. Connecting the points in the graph with the control and morpholino was a great addition to help understand and visualize your results. However, as mentioned before, you use dotted lines to assist in visualizing, and, for 3c, it is unclear how you determined these areas and what to include and not include. Additionally, it will help strengthen your argument if you clarify how you quantified your results for 3b and c. An explanation on your quantification for these figures will help better understand these figures and the reasoning. For the Wnt data, it is harder to visualize the change for Wnt signaling compared to the BMP signaling. Providing data that shows the normal expression of Wnt and BMP would help understand the change in expression for Wnt and BMP. Overall, this paper was a great read that presented nice evidence to support your findings. With a few adjustments clarifying your results, this will make the paper stronger.

On 2020-11-10 02:09:02, user Mitchell wrote:

I am pleased that the paper opened with such strong evidence through the data from figure 1. Both parts a and b show clear distinctions between the premigratory and migratory cells, and the fluorescence imaging for part c is also very clear. This sets up a very interesting research paper with a targeted focus, but for some of the other figures I would have changed a few things. For figure 2b the paper can say that ceramide expression is reduced, but that doesn’t necessarily show cell migration. It would be better to specify the direct implications from this data instead of implying migration. For figure 2c I feel like an additional control is missing. It would have been nice to see a control that binds to highly expressed genes so we have a baseline comparison to make sure the target gene actually binds. You could then knockout the gene and see if the pathway has the predicted effect. Showing this, I feel, would strengthen the evidence. For figure 3, I’m not convinced there is a reduction in Wnt signaling given the way the dotted line was drawn. I’m wanting to know exactly what Wnt and BMP expression levels are or how they change. Finally, for figure 4b, I’m not quite sure why all the yellow dots are included on the left but not on the right. I think this should be made more clear. Overall the paper tracks a logical flow of experiments and data, but I feel a few adjustments would make the work much stronger.

On 2020-11-11 14:48:22, user Leo Wang wrote:

I think the Figure S1 is the MRI of prostate, not CT

On 2020-11-11 14:48:11, user Pratik Jagtap wrote:

Our paper was published in PLOS One: Survey of metaproteomics software tools for functional microbiome analysis. https://doi.org/10.1371/jou...

On 2020-11-11 05:43:34, user Bảo Quốc wrote:

Although it is not conclusive, it is also possible to consult and expand knowledge about SARS-CoV-2 and prevention is also very good. Thanks the author.

On 2020-11-10 21:10:16, user Svetlana Kozmina wrote:

An excellent review. Inhibition of TNF-a and IFN-g will decrease an excessive inflammation by complete CD40 neutralization, decreasing level of TLR7,9, ROS, IgE/Histidine, interleukins, increasing oxygen by elevation of EPO, transferrin, and RBC levels. However, from my point of view for increasing therapeutic approaches we need to consider combination of this medical therapy with:<br /> 1. Monitoring of the mineral/vitamins balance. Normalization levels of Iron (association with an increasing thrombotic, Hb), Zn and Mg (suppressed Th17/ STAT3, histidine, alcohol, increase of serum transferrin level),vit B6,vit B9 (tryptophan/melatonin balance), vitD and vitE(regulate of Ca+2 and phosphate, reduce IL6), IgE(associate with pulmonary thromboembolism, reduce histidine/histamine, regulate neutrophils production);<br /> 2. Decreasing IL6, IL10 will increase of EPO/ RBC/Hb and oxygen, increase IgA, decrease Th17, IL4, 5,13, IL1, and decrease activation of pathways: inflammation/IFN-g, STAT3, IgE/Histidine,ROS/NF-kB, and TLR7,9/NF-kB;<br /> 3. Increasing intracellular/extracellular oxygen by increasing RBC count, decreasing IL10 level, TNF-a, and IL6/EPO.<br /> 4. Reduction of defect of glycosylation by suppression high levels of HSP70(will decrease chronic inflammation, IFN-g, CD40/CD40L and ROS) and HSP90(will decrease TLR3,7, 9, IL6/STAT3, TNF-a, and increase intracellular oxygen). The molecular chaperones HSP70 and HSP90, stabilize partially unfolded proteins, help maturation, transporting proteins across membranes within the cell and increase level of oligosaccharides with inaccessible proportion congenital disorder of glycosylation. Desialylated ACE2 containing altered N-glycans with 4, 2, and 0 sialic acids has change pH with following reduction of its expression. Present of intracellular alcohol lack sialic acid residue in protein and form mannosylated patterns. Proteins contained N-glycans with mannosylated patterns bind to carbohydrates on the surface of a wide range of pathogens. Melatonin treatment will decrease formation of mannosylated sites of N-glycan by decreasing effects of ethanol on the glycosylation.

On 2020-10-31 20:24:40, user Janet Lee wrote:

This is the first study to link cytokine mediated inflammation and cell death pathways in COVID-19 infection. STAT1/IRF dysregulation appears to be a common signature observed across studies this study by the Kanneganti lab opens up therapeutic possibilities for patients exhibiting cytokine storm phenotype in severe COVID-19 disease.

On 2020-11-10 16:21:23, user Joachim Moser von Filseck wrote:

Co-author Martin Lenz' ORCID is 0000-0002-2307-1106<br /> https://ocid.org/0000-0002-...

On 2020-11-10 13:50:01, user Darren Norris wrote:

Interesting and timely. <br /> But maybe useful to expand the Discussion in relation to the importance of examining population demographics (what % of the comprehensively assessed species have population trend data available?), particularly as different life stages can be more or less sensitive to "sustainable" or "unsustainable" uses e.g. timber vs non-timber forest products.<br /> Our findings from a global scale analysis of turtles showing early stages (eggs) are perhaps potential candidates for use whereas adults are unlikely to be so may be of interest:<br /> Population dynamics and biological feasibility of sustainable harvesting as a conservation strategy for tropical and temperate freshwater turtles: <br /> https://doi.org/10.1371/jou...

On 2020-11-10 10:56:03, user Merci B wrote:

recreate the wheel? You find the original here: https://science.sciencemag....

Use Planet now - otherwise such work is not needed. Code sharing?

On 2020-11-10 04:09:22, user Adam Alexander Thil SMITH wrote:

Dear authors,

I do not have an issue with using the gene permutation approach, however the 3rd sentence of the current version of the introduction incorrectly qualifies the original GSEA implementation as being based on gene permutation, instead of sample permutation. C.F. "step 2" of the method in the referenced paper (Subramanian 2005):

Step 2: Estimation of Significance Level of ES. We estimate the<br /> statistical significance (nominal P value) of the ES by using an<br /> empirical phenotype-based permutation test procedure that pre-<br /> serves the complex correlation structure of the gene expression<br /> data. Specifically, we permute the phenotype labels and recompute<br /> the ES of the gene set for the permuted data

The later implementations cited do indeed use gene permutation.

Best regards,

-- Alex

On 2020-11-10 01:02:40, user DHelix wrote:

A very interesting paper! I was wondering if you had removed duplicate fragments when you calculated FRiP and FRiTSS (Figure 1f)? Thanks!

On 2020-11-09 21:31:18, user Clemantine wrote:

Why would they use aborted fetal tissues lines to experiment with? Using any cell line after the 1947 Nuremberg code requires the informed consent of the Subject and the ability to withdraw from the experiment, both denied to aborted human babies. All cell lines derived after 1947 using aborted fetal tissue is immoral, unethical and illegal and must be discontinued!

On 2020-11-09 17:55:31, user Vishal Koparde wrote:

Thanks for providing links to the raw data. This raw data is in FASTQ format. Is it possible to get access to the FAST5 files directly?

On 2020-11-09 16:37:20, user anon wrote:

Very surprising to see some of the most basic controls missing here. A simple "(LNF+mRNA) without exosomes" control is nowhere to be found, and all of the results shown can easily be due to treatment with liposomal mRNA on its own with exosomes spiked in. There's no evidence that the exosomes are helpful or do anything at all. The expression of the SARS-CoV-2 mRNA in cultured cells using patient sera is also unusual, as a Western blot from cell lysates with a monoclonal antibody would give more information, including confirmation that the full-length antigen is expressed.

I guess this is a pre-print, but some of the basic experimental design, materials/methods, and controls leave quite a few open questions.

On 2020-11-09 16:20:16, user Neil Blumberg wrote:

You also should be considering the risks of graft versus host disease due to transfusion, which is universally fatal. Perhaps could irradiate the T cells so they cannot replicate, which might of course minimize benefit. Transfusion of allogeneic white cells suppresses host cellular immunity and inappropriately activates innate immunity so this may increase the chances of nosocomial bacterial/fungal infection, increase the risk of inflammation and thrombosis and multi-organ failure. The transfusion literature suggests that removing white cells (the opposite of what is proposed) minimizes morbidity and mortality so this is a reason for concern about this therapy.

On 2020-10-27 16:34:10, user Kamran Kadkhoda wrote:

What about cross-reacting memory cells from past CoV infections and also what about HLA compatibility issue? These two, among others, will cast doubt on the usefulness of this approach...

On 2020-11-09 15:46:54, user Joe wrote:

Are the authors suggesting that everyone use this peptide prophylactically until the end of the pandemic? What is the cost for widespread use of this type of therapy?

On 2020-11-09 15:44:51, user Marsden wrote:

Is there any therapeutic efficacy, or is the peptide only protective when used prophylactically?

On 2020-11-09 15:43:05, user Robert wrote:

How immunogenic is this peptide? Can you use it without developing an immune response to the peptide? How long after you start using the peptide, will your own immune response block the peptides from working?

On 2020-11-06 22:14:54, user MaximumBoo wrote:

Is COVID only inhaled through the nose? I was under the impression one could inhale though the mouth. How would this help in that regard?

On 2020-11-06 17:41:25, user Patrick Martin wrote:

Perhaps this could be incorporated into masks, encouraging mask use as well as delivery.

On 2020-11-06 09:42:26, user n nicraith (nnicraith) wrote:

If this is successful in humans, it could be used to protect Covid-19 caregivers and first responders. If it scales up well, it could also be used in conjunction with contact tracing to limit spread in exposed individuals.

On 2020-11-06 08:51:35, user Williamiain wrote:

Very interesting-brilliant idea, congratulations. Presumably the drawback with humans is that many of them are mouth breathers and you can’t use the spray there, and additionally humans blow their noses, which would I imagine expel the spray?

On 2020-11-09 14:28:32, user Amartya Sanyal wrote:

This article is now published: Please cite:<br /> Khalil, A.I.S., Muzaki, S.R.B.M., Chattopadhyay, A. and Sanyal, A. Identification and utilization of copy number information for correcting Hi-C contact map of cancer cell lines. BMC Bioinformatics 21, 506 (2020). https://doi.org/10.1186/s12...<br /> Share this article: https://rdcu.be/b98I1

On 2020-11-09 12:37:00, user michela riba wrote:

Hi, what about connecting to the QIIME2 community and propose a Kraken2 plug in to approach the part of feature classification?

I have had this suggestion in the forum<br /> https://forum.qiime2.org/t/...

I am using qiime1, qiime2 and Kraken in my experience

On 2020-11-09 12:32:02, user Niko wrote:

Hello,

did you test different anitbodies for the dot blot and do you have a reason, why you chose the antibody from Abcam?

Thanks for your help!

On 2020-11-09 11:41:12, user David Curtis wrote:

"???? is a shared component with distribution ????~????(0,r2????????/2/2)"<br /> Does this ignore any shared liability due to genetic effects not captured by PRS? E.g. schizophrenia CNVs? And such residual effects would make embryos more similar and your strategy less successful?

On 2020-11-09 11:21:13, user David Curtis wrote:

Khera et al exaggerated the value of PRS for CHD. The important familial hypercholesterolaemia variants confer a much higher risk than the top decile of PRS.

https://onlinelibrary.wiley...

On 2020-11-09 10:25:45, user Camila Fonseca Amorim da Silva wrote:

Is the neutralising action of iota-carrageenan on the receptor-binding domain?

On 2020-11-09 10:25:09, user Shilpa Yadahalli wrote:

The related codes are made available in my github repository:<br /> https://github.com/shilpasy...<br /> -Shilpa Yadahalli (Primary and corresponding author of this paper)

On 2020-11-09 01:28:16, user Lila Gierasch wrote:

This structure is smashing! It answers questions and poses others. So excited to dig deeply into this...

On 2020-11-08 12:20:58, user Vinx wrote:

I tried to download the dataset from https://archive.ics.uci.edu... but there is no data available.

On 2020-11-08 09:49:12, user Namratha Rao wrote:

I would like to have the code of the above method

On 2020-11-08 01:10:39, user Ricardo Rodriguez de la Vega wrote:

Hi,

I read your preprint with interest, I am very much looking forward for the realease of the sequencing datasets.

I have several questions that I suppose are addressed in the not available supplemental methods, any chance you will make it available?

Nonetheless, I have a couple of outstanding questions about your phylogenomic and (scorpion toxins) gene trees, that you might want to consider.

1. How could it be that your least stringent criteria (103 or more species out of 120) will provide a matrix smaller than more stringent criteria (115/120). 2. If I understand it correctly, your gene models for species tree reconstruction are based on your previous phylogenomics of quelicerates, shall we interpret that you find them all in the large number of taxons stated in your matrices (i.e. all 3000+ in at least 115 taxins for matrix 1)?
2. I understand you obtained a scorpion toxin gene tree for all CSab, ICK and DDH you have identified (1394). Why did you based your tree on an alignment of gene families that are not homologous? While some experts think DDH and ICK are homologous, none of these folds is believed to be in any evolutionary meaningful way related to CSab.

Ricardo

On 2020-11-07 17:34:28, user giusppe nanni wrote:

Is it possible to treat symptomatic COVID patients with high D-dimer without heparin, that it was already in the protocol of COVID therapy since april?

On 2020-11-07 15:40:28, user Célio Dias wrote:

This paper was published in Microbiome:

https://microbiomejournal.b...

On 2020-11-07 09:41:54, user Sebastian Quilo wrote:

Nice paper in the Biomarker field!<br /> I have a suggestion, it would be better if you add references for the tools used in your work like scikit-learn and PyCM :

scikit-learn :

@article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011} }

PyCM :

@article{Haghighi2018, doi = {10.21105/joss.00729}, url = {https://doi.org/10.21105/joss.00729}, year = {2018}, month = {may}, publisher = {The Open Journal}, volume = {3}, number = {25}, pages = {729}, author = {Sepand Haghighi and Masoomeh Jasemi and Shaahin Hessabi and Alireza Zolanvari}, title = {{PyCM}: Multiclass confusion matrix library in Python}, journal = {Journal of Open Source Software} }

On 2020-11-07 09:32:44, user N-fixer wrote:

Nice work! One small query - why do you state that "B. rhizoxinica" is a single lineage? It's clearly in Mycetohabitans, and is sister to M. endofungorum (Estrada de los Santos et al. 2018). Or perhaps you have additional evidence to refute this?

On 2020-11-07 00:56:03, user Charles Warden wrote:

Thank you for posting this interesting preprint.

I myself do not use the MGISEQ-2000 data, and there are some applications where I think erring on the side of safely would be better than trying to recover the run.

However, it is interesting and important to think about how base calling and the sequencers work. So, I am glad that you shared your experiences. Thanks again.

On 2020-11-07 00:51:18, user Charles Warden wrote:

Thank you for posting this preprint.

I am not sure what the expectations are for describing the methods in the Abstract (for your communication goals as a preprint and/or if you have a specific eventual peer-reviewed journal in mind). When I saw the abstract, I thought it was a bit strange because some steps were left out (such as your alignment using STAR).

So, I might consider re-wording the "Methods" within the Abstract (either adding some more information like the aligner, or perhaps providing a broader description in the Abstract and the full details in the Methods section)? However, that is just my individual opinion.

If possible, you could provide a link to code for maximum reproducibility. Am I overlooking that in the main text?

On 2020-11-06 17:54:19, user Colin Kretz wrote:

Very nice work. A useful library for identifying novel protease substrates, which is currently a very challenging problem!

On 2020-11-06 17:48:42, user Miao YU wrote:

This paper has finally published on Communications Chemistry with title "Untargeted high-resolution paired mass distance data mining for retrieving general chemical relationships". The title changed due to the comments from peer-review. Thanks a lot for the reviewers of this paper to improve the quality.

On 2020-11-06 16:09:33, user Dragana Stojković wrote:

Atmospheric N2 (nitrogen) is transformed into NO (nitrogen monoxide) in the alveolar pericytes of the pulmonary capillaries. The SARS-CoV-2 virus also brings N2 (nitrogen) from the atmosphere to the alveolar capillary pericyte.

On 2020-11-06 11:18:38, user Nabil Hajji wrote:

Since we placed our paper in the BioRxiv we have received huge number of emails from editors of distinct journals. We are please with that, however, our paper is novel and great research story with substantial data and international collaboration. Therefore, our manuscript should be published in well respected and major journal.

On 2020-11-06 09:24:59, user Katsushi Kagaya wrote:

This preprint is published: https://www.life-science-al...

On 2020-11-05 20:20:35, user Tiago Ribeiro wrote:

Very cool work! I have one question, towards the end of the discussion it written "We calculated each of those statistics from our simulated data and, as expected, the variance of each increased with recombination rate (Supplementary Figure 4)." Is that correct or is it the contrary? The FST variance is negatively correlated with recomb, and looking at the pictures I would interpret that is true for the other statistics as well.

On 2020-11-05 14:27:03, user Luping2020 wrote:

This paper has been accepted for publication in Experimental Biology and Medicine.

On 2020-11-05 10:58:56, user Luca Venturini wrote:

Interesing article, thank you.

How does SICILIAN measure against our previously published Portcullis? <br /> At a first reading the main difference seems to be the building of the model across multiple samples (portcullis performs this operation per-sample).

https://pubmed.ncbi.nlm.nih...

On 2020-11-05 08:38:52, user Bryce wrote:

data cannot be found from website provided

On 2020-11-05 08:19:08, user Rita Zhang wrote:

Impressive work with promising drug candidates! I am really thrilled to learn about how ML100, a ketoamide inhibitor, could be a potential drug for SARS-CoV-2. Especially in the absence of an effective vaccine, it was thoughtful to investigate an antiviral drug that is safe to be administered orally for patients in an outpatient setting. I like how the methods were well designed and the experimental outcomes were neat. One suggestion I would like to make is to keep the figure representation simpler. For example, in figure 6, it would be nice if only one variable, either color or the shape of data points, changes among the plots for different groups. This would allow a more direct comparison visually. Another potential improvement could be try to address certain the variation for different experiment groups. For instance why specific cell lines are used in certain methods. Also, in figure 2b, an explanation on why different concentration of telaprevir and boceprevir was used would make this experimental setup clearer to the readers. Overall, this is a stellar paper and I look forward to read about any follow-up paper within this area.

On 2020-10-30 20:55:30, user Angel Ni wrote:

Beautiful paper! I thoroughly enjoyed reading this because of how well thought out the methods were. For example, the cell lines were very good fits for the specific experiments they were involved in. HEK293A was a model for kidney cells, Huh7 for liver cells, and Caco-2 for intestinal cells.

I would have appreciated a figure in the introduction that outlines the role of Mpro in virus replication. After a quick search on Google, I was unable to find any descriptions that covered the mechanisms relevant to this paper in good detail. In addition, it would be nice to have a figure with the structures of all the inhibitors that were studied. This would allow easy and direct comparison.

This paper makes a strong case for the therapeutic potential of SARSCoV2 Mpro inhibitors with ketoamide warheads and cyclic structures. I look forward to seeing further research in this area.

On 2020-11-05 01:24:50, user Gyeongdeok kim wrote:

I want to know whether the spiked mutations into normal sample(HCC1395BL) is from real tumor sample(HCC1395) or just synthetic thigs....

On 2020-11-04 23:32:54, user Nataliia wrote:

It's bad news. Now we need to look for anti-idiotypic antibodies with the same characteristics as the spike protein. Possibly it could lead to the exaggerated inflammatory responses too. What about the key to "long COVID"? In this case any spike protein based vaccine may be a nonstarter.

On 2020-11-04 17:45:18, user Angela Cuervo wrote:

Final accepted version is available in: https://journals.plos.org/p...

On 2020-11-04 16:14:40, user Marie wrote:

Peptides numbers 2, 3 and 4 give homology with Sodium-coupled neutral amino acid transporter 1 isoform X2 (ref|XP_011537088.1|) and Dynein heavy chain 2, axonemal isoform X10 (ref|XP_011521972.1|) from Homo sapiens

On 2020-11-04 15:21:16, user Morgan Sobol wrote:

Hello Grünberger et al.,

It was stated "The efficiency of poly(A)-tailing was low."

Do you know approximately what percentage of the total RNA could be polyadenylated?

Thanks,<br /> Morgan

On 2020-11-04 13:51:19, user Antonio Hohn wrote:

Published!!!<br /> https://doi.org/10.1371/jou...

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 2020-11-03 17:53:18, user Zia Mehrabi wrote:

Please ignore this pre-print and directly download the post-print for free here: hdl.handle.net/2429/70224 . In addition to correcting some errors the post-print includes a lot of new interesting analysis too.

On 2020-11-03 17:17:08, user Gerry Smith wrote:

On 8 September 2020, this paper was accepted for publication in Scientific Reports. The accepted version differs little from the version available here.

Gerry Smith

On 2020-11-03 17:08:45, user Kristiina wrote:

Is syntactic data on the differences between IE, Uralic and selected Altaic languages available? I would be interested in it.

On 2020-11-03 04:50:11, user Naveen v wrote:

We have recently published the structural work on SARS-COV-2 Nsp1 and 5'UTR complex. It is good to see the consistency in the analogy of work. https://pubs.acs.org/doi/fu...

On 2020-11-03 04:48:03, user Naveen v wrote:

We have recently published the structural work on SARS-COV-2 Nsp1 and 5'UTR complex. It is good to see the consistency in the work. https://pubs.acs.org/doi/fu...

On 2020-11-03 00:41:10, user Fraser Lab wrote:

Allostery is hard to comprehend because it involves many interacting residues propagating information across a protein. The Monod-Wyman-Changeux (MWC) and Koshland, Nemethy, and Filmer (KNF) models have been a long standing framework to explain much of allostery, however recent formulations have focused on the role of the conformational ensemble and a grounding in statistical mechanics. This manuscript focuses on the functional impact of mutations and therefore contribution of the amino acids to regulation. The authors unbiased approach of combining a dose-response curve and mutational library generation let them fit every mutant to a hill equation. This approach let the authors identify the allosteric phenotype of all measured mutations! The authors found inverted phenotypes which happen in homologs of this protein but most interesting is the strange and idiosyncratic ‘Band-stop’ phenotype. The band-stop phenotype is bi-phasic that will hopefully be followed up with further studies to explain the mechanism. This manuscript is a fascinating exploration of the adaptability of allosteric landscapes with just a handful of mutations.

Genotype-phenotype experiments allow sampling immense mutational space to study complex phenotypes such as allostery. However, a challenge with these experiments is that allostery and other complicated phenomena come from immense fitness landscapes altering different parameters of the hill equation. The authors approach of using a simple error prone pcr library combined with many ligand concentrations allowed them to sample a very large space somewhat sparsely. However, they were able to predict this data by training and using a neural net model. I think this is a clever way to fill in the gaps that are inherent to somewhat sparse sampling from error prone pcr. The experimental design of the dose response is especially elegant and a great model for how to do these experiments.

With some small improvements for readability, this manuscript will surely find broad interest to the genotype-phenotype, protein science, allostery, structural biology, and biophysics fields.

We were prompted to do this by Review Commons and are posting our submitted review here:

Willow Coyote-Maestas has relevant expertise in high throughput screening, protein engineering, genotype-phenotype experiments, protein allostery, dating mining, and machine learning.

James Fraser has expertise in structural biology, genotype-phenotype experiments, protein allostery, protein dynamics, protein evolution, etc.

On 2020-11-02 20:34:19, user Marco Trabassi wrote:

I’m not a doctor or researcher but i would like to have contact with you to show a case of tb probably activated by coronavirus that happened on my girlfriend in Italy. Please contact me at marco.trabassi@gmail.com

On 2020-11-02 18:26:41, user David Klinke wrote:

Based on a class exercise in reviewing pre-prints, students generated the following critique of this pre-print. We hope that you find these comments helpful.

Makaryan and Finley have submitted a pre-print of work relating to a gap in the field’s understanding of possible methods to combat NK cell exhaustion by developing a computational model that describes the dynamics of GZMB and PRF1, which showed that suppression phosphatase activity maximized GZMB and PRF1 secretion, but that this method depleted intracellular pools of GZMB and PRF1. As a result, they investigated further by modifying their model with a synNotch system. They found that the optimal synNotch system is dependent on the frequency of NK cell stimulation. The ultimate goal of the work was to provide insights that could be used in clinical applications for the engineering of robust NK cells resistant to exhaustion. Although this work is of interest to the field, there are some concerns that could be addressed in the next version. These are outlined below.

-What results did you find the most interesting and why?

The methods presented in this paper were of particular interest to me. As a researcher new to the field of computational modeling and Bayesian frameworks such as the Metropolis-Hastings algorithm, this reviewer appreciates the opportunity to read about what others are doing in the field using such methods.

This reviewer found the results relating to the optimal synNotch system and its dependence on the number of rounds of stimulation particularly interesting. Specifically, the fact that the inhibition of SHP is not a beneficial long term strategy because of the accumulation of phospho-proteins. From the model diagram, one would think that this would be effective long term by eliminating the inhibition coming from the pSHP node, but the interdependencies make for a more interesting optimal case.

This manuscript has the potential to open up opportunities for new work in the engineering of NK cells for use in immunotherapies, which is of particular interest in cancer research, however, this reviewer believes that there are some concerns that need to be addressed before the results can provide any actionable insight.

Major Concerns:<br /> - Considering that there are some assumptions that have assigned some random values for type of parameters, which can be called “hyper-parameters” in the paper. This reviewer would use some hyper-parameter optimization methods for finding the best one so that the model accuracy will be improved by this way. Literally, hyper-parameter tuning is just an optimization to find the set of hyper-parameters leading to the improvement of a model. Practically, we can specify a grid of acceptable values for the specified hyperparameters. Then train a number of models pertaining to each of the different hyperparameters. Finally, select the model that performs the best from the pool of many models.

• Regarding Figure 2, is there any assessment for accuracy of the model? What if add a test set to evaluate the performance of the model? Clearly, validation set is different from test set and it can be a part of training set, because validation set is used to build your model. It is always used for parameter selection and to keep away from overfitting in your model. If your model is non-linear that is training on a training set only, it is more likely to get highest accuracy and overfitting, then you will get very poor performance on test set. So, you choose a validation set such that it is not depends on the training set and is used for tuning the parameters of a model. Conversely, test set is going to be only used to evaluate the performance of a trained model.

• Significant concern lies in some of the assumptions made for this model. In particular, the setting of the upper bound of the initial value of synNotch receptor based on the CHO cells modified to produce IgG is questionable. While the manuscript already points out the dissimilarities between CHO and NK cells and between the synNotch receptor and human IgG, the specific value of 10 uM, which I assume was chosen because it was the approximate average of the range from the CHO experiment, also presents problems. The results presented in Figures 4B and 4C regarding the difference between the optimal amount of R0 for the two pathways was specifically dependent on this value of 10 uM that was arbitrarily chosen. What would have happened if you had arbitrarily chosen the minimal value of 0.3 uM in that range so that it matched the initial amount of NKG2D? Or the maximum value of 20 uM which would be closer to the initial value of CD16? The importance of this upper bound in the trends presented in the results section should warrant a more sound basis for the choice of value. Other important assumptions, such as the value of the weight constant used to determine the emphasis on minimizing exogenous material versus maximizing cytolytic molecules should have some literary backing and be further explored as opposed to being chosen for simplicity.

• This reviewer also believes this model requires further validation beyond that currently presented. At this point, all validation was done internally using a subset of the same data set used to train the model (from Srpan et. al.). A second data set, either from Srpan or preferably repeated in Finley lab should be used as validation to ensure the model is not highly specific to the single data set used, but that it can be generalized to the dynamics as a whole.

Minor concerns:<br /> - While the manuscript overall flows well and tells a cohesive story, there were small sections when reading that information would be unclear, only to be clarified later in the paragraph or in the next paragraph. One such instance was the discussion of the Akaike information criterion for the three different models that were tested. In the beginning of the paragraph as the addition of crosstalk and synthesis/decay reactions was discussed, it was unclear that you were forming multiple models. When arriving at the sentence “Excitingly, all candidate models demonstrated a good agreement with experimental observations”, it wasn’t understood that there were multiple combinations of parameters being investigated in different models, which caused confusion. The explanation of the AIC and Table 1 at the end of the paragraph helped to provide clarity, but if a reader choses to go back in the manuscript rather than reading forward to find their answer, it may cause further confusion. It may be helpful to clarify some of these basic pieces of information throughout the manuscript to ensure understanding.

• In addition, supplementary file S3 is not available on the BioRxiv site. As this contains all of the supplementary figures, it is important that this be available with the manuscript for optimal clarity.

On 2020-11-02 14:34:03, user Think4OnceNurLife wrote:

A later version of this article is published in the Journal of General Virology

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

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

On 2020-11-02 10:09:04, user Alberto Baena wrote:

The final version of this manuscript was published in Development doi: 10.1242/dev.170811

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

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

On 2020-11-01 19:00:58, user Prof. T. K. Wood wrote:

It is a lot of important work showing the importance (p)ppGpp. I feel ppGpp (and cAMP to some extent) are the key for persister formation in many strains and it is great to see that for B. subtilis. The GTP part is less clear to me since some cells are probably not persisters in Fig. 2.

For E. coli, Ab-induced and starvation-induced E. coli persisters are the same, and there is no proof of different kinds of persisters. There terms just get in the way and persisters can arise spontaneously or arise as an elegant response to a stressful world.

L 62: I would argue that our single-cell work sheds light on the mechanism now, both on formation and resuscitation.

L 90 & Fig. S1BC: I would argue these are not persisters as the cells are dying (rather than plateauing) except for panel G for WT + CCCP.

L 102 & 258 & 317: this method of pretreatment to reduce ATP by CCCP was done first by us (and should be cited, as we used rif, Tet, and CCCP originally, in 2013, attached) 3 years before Conlon, et al. Great to see CCCP works with Bacillus.

Line 317: again, we showed this 3 years before Conlon et al., that ATP depletion converts nearly 100% of an exponentially-growing E. coli population into persisters.

L 1414 & Fig. 2d,e,f, and most likely g (need more time points for this simple experiment): not persisters since cells are dying (not bi-phasic) so I would not trust those conclusions about persistence (see “Are we really studying persister cells”).

L 234: if they do not regrow, they may have intact membranes but are dead due to a lack of cytosol, which we found was the cause of the whole VBNC area of research.

L 246: Of course, if we can convert nearly 100% of E. coli into persisters with a rifampicin or Tet pretreatment, we have shown Abs induce persisters. I wonder if GTP plays a role in E. coli persisters, beyond its use for translation.

L 290 & 390: Gladys Hobby predates Bigger by 2 years and showed they were dormant before Bigger (attached).

L 311: our 100S mechanism has added some clarity.

L 372: Balaban used a non-toxic toxin (HipA7).

On 2020-11-01 16:43:47, user Eve Wurtele wrote:

This provides an excellent resource to search SARS-CoV-2:human protein interactions. Thank you!!

On 2020-10-31 18:34:25, user pb wrote:

Congrats! (:<br /> As you mention in the end preoccupation with "colonial language", may I suggest you don't call the Karitiana and Paiter Suruí "very isolated Amazonian populations"? It not only sound weird, but it is also not true. Their lands are also very close to big cities (Porto Velho, a capital) and major highways connecting North-South Brazil. (: <br /> I would also avoid using the word "tribe". I understand it is ok in english, but generally disencouraged in spanish and portuguese.

On 2020-10-31 12:02:44, user kdrl nakle wrote:

Important paper but highly disorganized.

On 2020-10-31 11:26:50, user kdrl nakle wrote:

Quite a lot of conflict of interests here for just in-vitro claims.

On 2020-10-31 09:30:56, user AnonymouseReader wrote:

Interesting paper! Although it's not clear how long non-coding is defined here. From Figure 3, it looks like at least some of the "long non-coding" isoforms start at the same start site as the mRNA transcript. Without data to show that the isoforms are nuclear-trapped and/or can not be translated by ribosomes, it could be possible that these are just shorter coding mRNA variants (that are made by either activation of cryptic promoters inside the annotated gene body, or by activation of cryptic transcription termination sites also inside the annotated gene body).

On 2020-10-30 20:44:13, user Adrian Flierl ???????? wrote:

Just came across this and having spent several years working on this, can confirm some of these data, but not all the conclusions.

When one is talking about ANT4 (germ cells), one has to consider that the liver cells used for this study are transformed into a stem-cell like regenerative state. Hence one would expect to see ANT4 expression (similar to germ/stem cells).<br /> However, this may not be the case in adult, differentiated tissues, and it would be interesting to see if ANT4 is actually expressed in muscle cells aka myocytes or other finally differentiated cell types devoid of ANT1 and ANT2. <br /> Nevertheless, my data in myoblasts certainly support your findings in this study, as is the developmental aspect of ANT4 expression masking effects of ANT1/2 in stem cells.

Regards,<br /> Adrian

On 2020-10-30 18:54:45, user Lee Albee wrote:

I think the model for NNA migration is partially right. I think the data would be better represented by an early entry of NNA or AOS? populations. With spread throughout North and Part of south America. When exactly is open for debate. But before the Ice free corridor opened and definitely prior to the Younger Dryas. Then with the opening of the Ice Free corridor, SSA type native American (similiar to Anzick-1 , often mis-labled as Clovis) poured into North America through the eastern Rockies,western plains and displaced and mixed with populations as they went. Disrupting genetic geographical continuity of NNA. Leaving populations in the East (People who may have become Algonquin, Ojibwe and Cree ), a group(s) in the Northwest that became heavily mixed with other Asian like populations and some SSA genetics (peoples like the Yakima and a potential early example being Kennewick), and southern western populutions ( like the Diaguita, Chilote and Chono) with heavy SSA admixture. This would would help explain why these geographically diverse groups show relatedness in Treemix and Admixture models

On 2020-10-29 21:00:30, user beroe wrote:

Amazing work! ;^)<br /> Small typo at the bottom of page 3: "only a had single public comment"<br /> What do you think about the possibility that by focusing on the 60% with a single comment, you are preselecting against those where a fruitful discussion has taken place.

On 2020-10-29 11:49:51, user Kevin RUE wrote:

See also: https://f1000research.com/a...

On 2020-10-29 00:59:12, user John Bowman wrote:

The paper tries to make an important connection - bacterial numbers versus read numbers/proportions in a Illumina MiSeq dataset - commendable since microbiome data is often fustrating and overly descriptive due to the lack of connection to the reality of bacterial numbers and makes dataset comparisons objectively difficult. For comparison we did something similar to the paper here (see Kaur et al. 2017 https://pubmed.ncbi.nlm.nih... except in the form of bacterial communities growing on vacuum packaged red (lamb) meat. In this case we had total viable counts and also LAB counts collected over time that fitted growth models and we were confdent that the total population was fully estimated (VP lamb at -1 C is a rather constrained scenario as well).

The LAB counts versus TVC counts were used for in silico sequenced based population estimates since we knew and had confirmed that LABs dominated the meat community (mainly Carnobacterium and Lactococcus) and so we could then use that as a guide to estimate other taxon abundances, such as Clostridium species. We only did this at the genus level since the data was more robust. Using the numbers (log TVC, log LAB, proportion of LAB reads) we were able to estimate populations in situ to a log scale over time that could be fitted to a logistic model to estimate growth rates and maximum population densities. Further estimates using qPCR could be useful but I doubt they would show anything wildly different. This approach was useful for the dominant taxa by the time you get to proportions of 1:1000 (which for VP lamb was around 4 log10 CFU/cm2) the noise is fairly high and you get situations were samples lack reads due to the sampling limits of the MiSeq process.

Good luck with your research,

Regards,

John Bowman (Prof.), University of Tasmania, Tasmanian Inst of Agriculture

On 2020-10-28 20:58:09, user Puddin'Head wrote:

I'm curious if you have investigated the effects of solubility on your results. Looking at your in vitro data, you are reporting dose dependent responses up to 1 mM concentrations of the added terpenes, but a quick look at PubChem suggests that the limit of aqueous solubility for beta-pinene, for example, is ~ 36 µM at 25˚ C, while beta-caryophyllene is reported to be insoluble in water. How do you account for your reported changes in activity at doses that are well beyond the known aqueous solubility of these molecules?

https://pubchem.ncbi.nlm.ni...<br /> https://pubchem.ncbi.nlm.ni...

Given that artifacts of insolubility are known to confound a wide variety of assays in drug screening campaigns (e.g. colloid formation is known to generate false positive results), it may be worth looking into the solubility and potential colloid forming behavior of your tested terpenes. Furthermore, since the "tetrad" is composed of fairly non-specific phenomena that do not clearly correlate to any particular physiological or therapeutic outcome in humans, and the addition of 10% Tween-80 might be expected to emulsify the administered terpenes, it may be worth looking to see if the mere presence of insoluble/emulsified lipophilc material in the intraperitoneal cavity might trigger such responses.

What were the i.p. injection volumes?

On 2020-10-28 20:33:42, user Gerben wrote:

According to data availability, the data should be available through SRA, but I can only find the ATAC-seq data and not the RNA-seq data. Are the Bioproject numbers correct?

On 2020-10-28 18:45:57, user David Holcman wrote:

The text of this paper is available for modification and reuse under <br /> the terms of the Creative Commons Attribution-Sharealike 3.0 Unported <br /> License and the GNU Free Documentation License. In particular, it can be used for Wikipedia.<br /> D. Holcman--the lead author.

On 2020-10-28 13:00:08, user Ludmila Prokunina-Olsson wrote:

The paper is now published in Nature Genetics, https://www.nature.com/arti...

On 2020-10-28 11:44:36, user Matt wrote:

Hello, how does HGDP compare in place of 1000 Genomes? e.g. from "Insights into human genetic variation and population history from 929 diverse genomes" - https://science.sciencemag....

On 2020-10-28 08:30:24, user Alexei Tsygvintsev wrote:

Accepted to publication. To appear in C. R. Acad. Sci. Paris Sér. I Math.

On 2020-10-28 07:55:40, user Martha Crockatt wrote:

Hi there. Having read your article, I have a question about the control sites in the studies included in the meta-analysis. When you refer to "forest" control, is that generally productive forestry, or could it be any type? I'd like to think that most studies would use productive forestry as a control! This may seem a minor point, but I think it's important to include, to fully understand the application of your findings: if natural woodland is used as a control site, rather than managed, productive woodland, then it's unsurprising that biodiversity is higher than in AF.

On 2020-10-28 07:49:36, user VIROPLANT H2020 wrote:

The same discovery of splipalmivirus has been recently published in virus evolution together with some more molecular details. <br /> https://doi.org/10.1093/ve/...

On 2020-10-28 04:12:28, user Todd McLay wrote:

For some reason the abstract has the github link with an en dash, when it should actually be a hyphen. The correct link is: https://github.com/chrisjac...<br /> This is correct in the pdf.

On 2020-10-27 15:41:27, user harleyk wrote:

Nicely done, team. I love the practical application of using ML to decipher the most important variables for uVS. Figure 1 is hard to read. It's an important figure so I'm glad it's here, but I can't make out the numbers.

On 2020-10-27 12:46:33, user Thomas Hall wrote:

I was wondering, is there a place to access any of the supplementary information that supports this paper, and its assertion that H3K4me3 is not instructive for transcription? Without the raw and processed data, it is difficult to fully understand what the effect of H3K4me3 depletion was, given the findings you have presented in this pre print.

On 2020-10-27 12:25:06, user Rocktim R Das wrote:

The paper has just been published. <br /> https://threatenedtaxa.org/...

On 2020-10-27 09:11:45, user Cristina Villafranca wrote:

A peer-reviewed version of this article is now available here: https://rdcu.be/b9i5D

On 2020-10-27 06:36:17, user DR. NELSON PHIRI wrote:

The Paper has been peer reviewed and published on the following link: https://doi.org/10.5430/jer....

On 2020-10-27 04:46:26, user Rocky Baker wrote:

Great work! I currently work with ticks on the cape of Massachusetts. I am very interested in tick thermoregulation. Your article did a great job of providing excellent background, and managed to get right to the point while providing all necessary elements for understanding of concept. The first main idea was that the spread of fluid over cuticular surfaces facilitates heat exchange. The authors also concluded that the intense activity of coxal gland during feeding on cuticle structure contribute to rapid dissipation of heat stress. These conclusions were mildly supported. More graphics and photographic details needs to be presented for proper verification of a novel correlation. The figures used in the paper to describe the methodology enabled the reader to directly decipher the terminology used in the article. I do wish this article expanded more on thermoregulatory mechanisms and provided more methodological strategies for basing conclusion. Providing this would make the article more convincing. It was concluded that this exothermic species had thermoregulatory abilities however no direct mechanism or biochemical pathways are presented as evidence. This paper however is significant because it gives insight into the strategies utilized by arthropods for thermoregulation. Discover of tick thermoregulation can be used to uncover paralleled mechanisms in many other tick species. Understanding these mechanisms could help with Public health strategies. It can also elucidate tick physiology. On a scale of 5(great) to 1 (muddled), I would grade this paper a 3. It's not the best portrayal of the thermoregulation strategies of ticks but, but this paper succeeded in providing a to get a basic and logical understanding of thermoregulation. I did not find myself having to re-read any section of the paper. Many questions are produced after reading this paper. Maybe there are other evaporative cooling mechanisms used? Is urinating on itself the only thermoregulatory mechanism the tick uses? Could utilization of yeast cells be a potential tool to uncover more thermoregulatory mechanisms used in these ticks?

On 2020-10-26 22:10:00, user David Ianova wrote:

The authors write "we did not recombine any species name in the LCVP" but I can see hundreds of "comb. ined.", e.g. Silene saxosa (A.P.Khokhr.) comb.ined.<br /> Also the automated matching seems to create many errors. e.g. Melandrium gracile Tolm. (from Siberia) is listed as a synonym of Silene gracilis DC. (from the Medit.), clearly a lot more work is needed to make this scientifically defensible.

On 2020-10-26 21:26:59, user Critical Dissection wrote:

Dear Authors,

I enjoyed reading this paper and look forward to reading future studies regarding SARS-CoV-2 and circadian rhythms. However there are certain major questions I still have. First, how was ZT(0) measured, and what environmental cue marked the beginning of the ZT(0) phase? Second, I am still puzzled about the application of Cosinor analysis as it relates to BMAL-1 & CLOCK expressions in isolated monocytes. The method is alluded to frequently and seems to play a key part in the results, however, its application is very brief and superficial. The visual data is appealing but there seems to be a lack of discussion regarding the analysis. Also, from how many healthy individuals were these monocytes isolated from? The findings are extremely interesting and relevant but I believe the analysis warrants further explanation and rigor.

On 2020-10-26 19:11:32, user Critical Dissection wrote:

Great work with the development of anuran. The application of the sponge microbiome was fascinating and exciting, but how do the advantages conferred by the anuran software translate into information relevant for microbiologists without in-depth network construction experience? I strongly recommend including more definition of the parameters used in your experiments and greater explanation of conclusions so that an even wider audience may be reached.

On 2020-10-26 16:25:21, user Ian S wrote:

This was a very interesting read, great work. The Anuran software sounds like it'll be a really useful tool for microbial network analyses. However, I am also interested in the potential drawbacks to using this program. Have you considered adding a section about limitations to the discussion? It may allow other readers to better grasp the full idea of the software.

On 2020-10-26 18:36:14, user Critical Dissection wrote:

Great, relevant study! If the structural integrity of SARS-CoV-2 particles is largely disrupted (as dramatically as what is demonstrated in your data) when exposed to temperatures as low as 34ºC, do you have any alternate explanations for the peak in new cases around mid-July? It would be interesting if this study looked into the comorbidity of other environmental factors or public health measures on the infectivity of COVID-19, especially as the largest peak of spread occurred in the summertime.