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    1. On 2023-02-18 15:30:01, user Latané Bullock wrote:

      Hi, nice work. I think this paper could benefit from reporting raw power spectral densities (perhaps from both the baseline period and the trial window for each of the types of recordings) for the following reason; I was not convinced that the 40-50 Hz band is a true oscillation based on the spectrograms presented in Figures 2-4. Your analyses rest on the assumption that the 40-50 Hz band has a phase; and to have a phase, the power in the band should have power beyond the 1/f falloff. You could easily show this with FOOOF (https://fooof-tools.github.... "https://fooof-tools.github.io/fooof/)") or something similar.

    1. On 2022-02-24 23:09:27, user Keshav Singh wrote:

      Please see other related publications<br /> Singh KK et al Decoding SARS-CoV-2 hijacking of host mitochondria in COVID-19 pathogenesis. Am J Physiol Cell Physiol. 2020 Aug 1;319(2):C258-C267. doi: 10.1152/ajpcell.00224.2020.

      Ajaz S, McPhail MJ, Singh KK, Mujib S, Trovato FM, Napoli S, Agarwal K. Mitochondrial metabolic manipulation by SARS-CoV-2 in peripheral blood mononuclear cells of patients with COVID-19. Am J Physiol Cell Physiol. 2021 Jan 1;320(1):C57-C65. doi: 10.1152/ajpcell.00426.2020. .

    1. On 2022-04-28 17:35:30, user Charles Warden wrote:

      Hi,

      Thank you very much for posting this preprint!

      I think it is interesting, and I believe that it is good to see multiple possible assemblies assessed for the same sample.

      I have some questions / comments:

      1a) I could successfully find the SRA reads for PRJNA602938.

      However, I couldn't find the reads for PRJNA770127 or PRJNA781109.

      I get similar results if I start the search from Bioproject, instead of the SRA.

      Am I misunderstanding something, and/or do some of the projects need to be released to the public?

      1b) I apologize if I am overlooking something, but I noticed that the "Data Availability" statement also says that "final assembly files" were deposited.

      I was curious where I could find the varying assembly results for the same sample. If you make a decision about how to combine the results in a representative sequence, then I guess that would be more along the lines of what might be deposited in something like NCBI Nucleotide / GenBank. While PRJNA602938 is recognized when I search NCBI Nucleotide, I am not finding any assembly sequence results.

      For the project where I can see the SRA reads, I also don't think I am seeing additional sequence under the genome for snow leopard.

      If you expected some refinement in the draft assemblies (and/or you wanted to avoid confusion with the separate assemblies used for evidence), then I guess maybe those could be deposited in some place like Zenodo. However, my understanding is that the assembly sequence was deposited in some other way with NCBI.

      In general, I see that there is some "analysis" data that can be viewed from SRA Trace, but I found it hard to find an assembly sequence if I didn't already know the "analysis accession."

      Is there something that I am overlooking in terms of being able to find the assembly data deposited with NCBI?

      2) As a minor comment/question, if I am understanding that there was African leopard sequencing UGA Genomics and Bioinformatics Core, then should the acknowledgements say "We would also like to thank the University of Georgia Genomics and Bioinformatics Core for their assistance in HiFi sequencing."?

      Thanks Again,<br /> Charles

    1. On 2023-02-20 19:30:25, user Banksinoma spinifera wrote:

      This is an interesting study about the mechanisms behind fructose-induced ER stress in the liver that promote NAFLD. This study is heavily based on Western Blots, but there are no molecular weight markers or raw blots. That would help the reproducibility of the results found in this study and help discern between very similar band profiles, such as PERK, Vinculin, and mTOR, in Figure 4, before peer-reviewed publication.

    1. On 2016-11-10 15:43:29, user Todd wrote:

      Thanks for a great analysis! In the manuscript, it mentions that APE version 3.6 contains the updated code for correct treatment of support values. I noticed on their website, the updated code already available in the "testing version": 3.5-0.10. Presumably, this will also be available in an upcoming 3.6 release. See release notes: http://ape-package.ird.fr/NEWS

    1. On 2017-05-17 09:37:27, user Geog marin wrote:

      Not bad. Maybe they will discard even more hunter-gatherer input on the near future with sequences that have been thought to be brought by other people (or as more recent), being pre-neolithic. That has been happening all the time, recently, and there are some very good new candidates. Another proof that other Iberians than Basques (such as West ones on this case) can give us nice surprises

    1. On 2018-05-04 14:45:02, user Grimm wrote:

      I like the idea and set-up.

      But why do you restrict your comparison to parsimony (MP) and Bayesian analysis (BI)? I reckon that they are the most commonly used optimisations for non-molecular data, but this doesn't mean that the other two optimisation criteria, maximum likelihood (ML) and least-squares (LS), are not worth testing.

      I would like to see at least ML included, too. MP and BI differ in two fundamental properties when inferring the tree. The way the best-fitting topology is optimised and whether change is considered a uniform (all changes are equal) or variable process (i.e. allowing different probabilities for a change). <br /> Maximum likelihood would hence be a logic bridge between the two. It also doesn't use a (MC)MCMC chain like MP, but does infer a most-probable topology like BI.

      And I think it would be very interesting to see how it performs against BI. For network-affine people like me, it'd be interesting to know, because we can use bootstrap support patterns for EDA – exploratory data analysis, but PPs often mask internal data conflict in real-world matrices (examples for EDA can be found here, including some using real-world morphological and simulated binary data: http://phylonetworks.blogsp... "http://phylonetworks.blogspot.fr/search/label/EDA)").

      You could even test (if you like to) whether correcting for the ascertainment bias (since we usually have no invariants in morphological data sets) makes a difference, at least RAxML has an according implementation (don't know for the other ML inference softwares)

      LS (e.g. trees generated via the BioNJ algorithm) would be a fun thing to add. I would expect that it performs somewhere in-between MP and the probability methods (bootstrap-wise, for real-world data, it usually has a better fit with the PP than MP-BS). But I understand that this would fulfil a rather particular interest.

      Cheers, and good luck with the (usually anonymous) peers,

      Guido

    1. On 2021-12-04 23:51:27, user Raghu Parthasarathy wrote:

      Fascinating work! You note (p. 22) preservation of membranes by trehalose and other sugars and comment on the surface glycans in HA possibly playing a similar role. You might find interesting the ability of trehalose-decorated lipids from mycobacteria to protect membranes from dehydration: <br /> Christopher W. Harland, David Rabuka, Carolyn R. Bertozzi, and Raghuveer Parthasarathy. "The M. tuberculosis virulence factor trehalose dimycolate imparts desiccation resistance to model mycobacterial membranes," Biophys. J. 94: 4718-4724 (2008). http://www.cell.com/biophys...

      Dehydration-resistance is fascinating, and (I think) under-studied.

    1. On 2017-03-03 05:54:37, user Davidski wrote:

      Hello authors,

      I'm seeing a couple of issues with your paper. Firstly, I'm confused how you came to this conclusion?

      "Furthermore, the presence of a genetic component associated with <br /> Caucasus hunter-gatherers and later with people representing the Yamnaya<br /> Culture in Eastern hunter-gatherers and Estonian CCC individuals means <br /> that the expansion of the CWC cannot be seen as the sole means for the <br /> spread of this genetic component, at least in Eastern Europe."

      If this is based on ADMIXTURE output, then it would be useful to see it <br /> backed up with some robust formal stats, and modeling software like <br /> qpAdm. That's because ADMIXTURE is not a formal mixture test. In other <br /> words, if your conclusion is indeed based on ADMIXTURE, then it might be<br /> the wrong conclusion.

      Moreover, I note that you used the projection (P) option in ADMIXTURE, which, in my experience, can result in projection bias and skewed results, especially in fine scale intra-continental tests. Why not run the ancient samples without the P option using transversion sites only and see how that goes?

      Indeed, it looks to me as if your PCA suffers from projection bias, because the ancient samples are being pulled into the middle of the plot. One of the Estonian foragers almost clusters with modern-day Lithuanians, while the Estonian CWC appear too western.

      I understand that you used the lsqproject option when running your analysis. Please note that lsqproject doesn't solve problem of projection bias; it just makes sure that missing markers don't skew the projection.

      Cheers

    1. On 2018-11-30 07:51:51, user Thomas Felzmann wrote:

      Dear colleagues,

      I congratulate you on this timely and potentially important study. However, I seem to detect a contradiction. You state, as is generally accepted, that cortisol has anti-inflammatory properties. Not by accident it is used therapeutically to treat autoimmune diseases, allergies, or transplant rejection. How would you explain your observation, that the levels of pro-inflammatory cytokines match the levels of cortisol rather than mirroring it, such that high cortisol levels correlate with low pro-inflammatory cytokines and vice versa.

      Kind regards, Thomas<br /> - -<br /> Thomas Felzmann, MD, MBA<br /> Associate Professor of Immunology

      AUSTRIAN RED CROSS, CENTRAL BLOOD BANK<br /> Research & Development

      Wiedner Hauptstrasse 32, 1040 Vienna<br /> T: +43 1 58900 <br /> M: +43 664 1317106<br /> E: thomas.felzmann@roteskreuz.at<br /> W: www.roteskreuz.at

    1. On 2023-10-09 12:16:03, user Taise Gonçalves wrote:

      First, I would like to congratulate the authors, because this article is very well done. The diagram presented at the end of the introduction (Figure 1), exemplifying the expected results, adds a lot to understanding the text and it is an enriching differentiator for the manuscript. The data analyzed were obtained from environmental monitoring for 3 decades, which the authors were able to synthesize in very concise and accurate results. It certainly represents the best assessment to the hypotheses of pre-adaptation and limiting similarity to date.

      Taíse Gonçalves - Master's student - Fungi, Algae & Plant Biology Program - UFSC - Brazil (On behalf of the PLENTBio Journal Club; plentbio.wixsite.com/alcant... "plentbio.wixsite.com/alcantaralab)")

    1. On 2016-10-27 11:21:09, user Jordi Faraudo wrote:

      Thanks for sharing. On the technical side, there are a couple of important points: (1) Why do you use the Berendsen thermostat? As it is well known, it does NOT reproduce correctly the canonical NVT ensemble while other algorithms implemented in the GROMACS program you use do it correctly (for example Nosé-Hoover) see https://en.wikipedia.org/wi... and refs therein or Chapter 6 in Frenkel & Smit textbook "Understanding Molecular Simulation" (2) The LJ parameters for gold in CHARMM can be greatly improved using those proposed by Heinz in 2008: J. Phys. Chem. C, Vol. 112, No. 44. (9 October 2008), pp. 17281-17290. These "new" parameters make a difference...

    1. On 2022-02-28 11:25:34, user Mattia Deluigi wrote:

      Congratulations on these very interesting results. Together with the functional data, the new structures described in this preprint greatly contribute to a better understanding of neuromedin U receptors (NMURs) and potentially related peptide-binding GPCRs.

      Here, I add a short, constructive comment regarding one of the NMURs’ related peptide-binding GPCRs, i.e., the neurotensin receptor 1 (NTSR1).

      In wild-type NTSR1, residue 3.33 is Asp, not Glu. Therefore, the labeling of residue 3.33 in the panel “JMV449-NTSR1” in Supplementary Fig. 5d should be corrected. In addition, the sentence in lines 119–121 of the main text PDF beginning with “Noteworthily, a conserved salt bridge between...” should be slightly changed; e.g., “E3.33” should be changed to “E/D3.33” or to “acidic residue at position 3.33”.

      The shorter D3.33 side chain in NTSR1 compared to E3.33 in the related NTSR2, NMURs, ghrelin receptor, and motilin receptor probably affects the strength and possibly the role of the ionic interaction between the acidic residue at position 3.33 and R6.55 in NTSR1 compared to the above-mentioned related receptors.

      In the ghrelin receptor, the salt bridge between E3.33 and R6.55 has been proposed to play a role in the receptor’s constitutive activity [https://doi.org/10.1038/s41...], and the same could also apply to NTSR2. In contrast, NTSR1, which bears D3.33, has almost no constitutive activity. The shorter D3.33 side chain probably plays a role in agonist-induced activation of NTSR1 [DOI: 10.1126/sciadv.abe5504 (ref. 47 in this preprint)] and is related to the “R6.55-mediated activation mechanism” that the authors of this preprint point out in the Discussion section.

      All the best,

      Mattia Deluigi

    1. On 2021-12-08 22:57:38, user Nicholas Gladkov wrote:

      Hello,

      Thank you very much for the fascinating paper about the sex differences in the metabolism of glutamine. The paper was very much an interesting read, and I have learned much about the subject area. Below are some comments and suggestions for the paper:

      Figure 1A/B: It may be beneficial to include the original data to the supplemental figures. If there are limitations by converting the data down to z-scores, possible data may not be lost.

      Figure 1D: Clarification as to what “number of metabolites” means.

      Figure 2A: A comment as to if the male and female brain tumors being on different hemispheres should be ignored or is of importance to the paper.

      Figure 3B: The paper mentions that male cells incorporate 20% more nitrogen from [13C5 15N2]Gln into nucleotides than female cells (Fig. 3B). It may be helpful to add an explanation as to how the graph displays this increase in 20% (explaining how it is quantified). An overlay of the two graphs may make it easier to compare the two. The graph is also missing a y-axis.

      Figure 5E: In the paper, it is mentioned that NAC significantly restored cell number in male and female BSO treated cells (Fig. 5E). But in figure 5E, the females do not show significant differences between the control and the MAC treated cells.

      Overall, the experiments were well conducted, and I enjoyed reading about additional evidence supporting that anti-cancer therapies may have sex specific differences.

      Thank you very much for taking the time to read my suggestions for your paper, and I hope they may be of help.

      All the best in your future research.

    1. On 2023-06-30 07:49:21, user Arnauld Sergé wrote:

      Interesting work, but with one annoying concern to me: how can you say right from the abstract that “Analysis of SPT data can be challenging due to the lack of comprehensive user-friendly software tools” while many algorithms have already been published? In fact, you even cite several of them in your references. All these works have been published upon peer review, each with its own field of application, performances and limits of course, based on classical approaches or, more recently, on artificial intelligence. It's certainly worth discussing, but I wouldn't mention it, certainly not in that way, in the abstract.

    1. On 2020-01-31 23:40:09, user Song Yang wrote:

      In the supplemental Fig S2, the author mentioned that “the Bat-SARS Like CoV in the last row shows that insert 1 and 4 is very unique to Wuhan 2019-nCoV”. In fact, the Bat-SARS Like CoV, discovered in 2013, contains all the four insertions. Insertion 2 and 3 of 2019-nCoV are identical to Bat-SARS Like CoV. Insertion 1 involves two synonymous and one Thr-Ile mutation. Insertion 4 contains one synonymous mutation and a 12-bp insertion. Therefore, the two viruses have very high sequence similarity, and are likely evolutionarily related, naturally. Indeed, the four identified insertions are the result of the pair-wise comparison of Wuhan 2019-nCoV and 2013 SARS virus (Fig 1), and are an artifact when comparing only two viruses. The ‘insertions’ regions also appear in other coronavirus, as indicated in the multiple comparison in Fig.S1, possibly functionally important.

    1. On 2016-05-27 17:28:49, user drdetroit wrote:

      Page 14 second half tells you more about the "big picture" here than anything else.

      "Historical control rate for malignant glioma in Harlan Sprague Dawley rats from other completed NTP studies is 2% and ranges from 0-8% in individual studies. The 2.2%-3.3% observed all of the GSM modulation groups and in the 6W/kg CDMA modulated group only slightly exceeds the mean historical control rate and faills within the observed range." <br /> If you take only the female rats into account, the rate of gliomas is LESS than the historical rate for controls.

      Thus the meta-analysis would make it seem that the jury is still out on whether or not these types of RF exposures are associated with higher rates of brain cancer in rats.

    1. On 2024-03-08 13:17:33, user Tainara Duarte wrote:

      Dear authors,

      My name is Tainara, I am an undergraduate student in Biological Sciences at the Federal University of Minas Gerais and affiliated with the Plant Interaction Laboratory (LIVe). My research is focused on studying the interaction between plants and bacteria. Our laboratory has activities that include reading articles related to the areas of knowledge we study, called “Preprint Club”.<br /> For this activity, I selected your preprint called “Culturable approach to rice-root associated bacteria in Burkina Faso: diversity, plant growth-<br /> promoting rhizobacteria properties and cross-comparison with metabarcoding data” for reading and evaluation.<br /> In your manuscript you carried out tests with isolated bacteria and this was very interesting, studies like yours are very important to clarify the processes involved in the plant microbiome.<br /> However, I would like to make some observations about your manuscript:

      In the introduction I thought you covered several topics, which are important to the topic, but are not specific to your research so they are not that relevant.<br /> On the other hand, how do you make a direct comparison of your results with the study referring to your bibliographic reference (60) (Barro, et al 2022) I thought I could address aspects of this study in its introduction.

      Regarding the captions, in general I would suggest that the authors write more details when describing the figures so that it would be better to understand the results. This is important for the reader.<br /> Specifically in the caption of figure 4, we note that there is no caption for some images (specifically figure 4d)

      Regarding the figures in general, I would also like to suggest that the authors increase the size of the details in the figures. Specifically in figure 8, where the species included in the heat map are so close together and because they are very small, it is not possible to read them and this hinders the understanding of their results.<br /> And finally, I would like to suggest more photos and data on the results of the in vivo tests of the two rice cultivars.

      These are some notes that I thought were important to write, I hope they are useful for the authors. It was a pleasure to read the preprint of your research.<br /> All the best,

      Tainara Duarte.

    1. On 2022-01-17 17:30:09, user Sebastien Leclercq wrote:

      Dear authors, this study 's objectives are good, we indeed need a benchmark tool to evaluate the various AMR gene detections programs in a metagenomic context.

      But I am more doubtful about the methodology. I have 3 main comments.

      First, it would be useful to provide the AMR genes detected in the assembled genomes of the selected isolates. If only the reads are available, assemble them with SPADES. Do the search with the differents tools and combine detections which match at the same locus.<br /> This will helps the reader to know what exactly is expected from the artificial mock metagenome. For instance, I can't believe that fARGene finds 713 distinct genes after condensation, and even the 33 genes for RGI is suspicious. Comparing detections from the mock with the expected number from assembled genomes will provide some new useful metrics.

      This will help to provide more details on what is detected/not detected (which genes) and why. Related to the reference database/models ? To the defaults matching parameters ? Other ?

      Second, the way the mock community is constructed is inadequate. Metagenomic data reflect complex microbial communities (of hundreds of taxa) for which a small minority are AMR pathogens. Putting 9 highly multi-resistant pathogens together at equal proportion is not consistent with real metagenomes. In this regard, reducing the coverage from 100x to 50x and 5x doesn't help because AMR genes represent the exact same relative abundance in terms of reads in the 3 datasets. I would suggest to get a bunch of other isolate's sequences, from sensitive bacteria and include them in excess to simulate the whole bacterial community. The proportion of pathogens could then be modulated (e.g. 1/50, 1/100, 1/1000) to simulate the various conditions (healthy / infected patients).

      Last and more scientifically speaking, I don't really get for which kind of purpose the AMR gene detection will be used, and how the connection with phenotypic resistance will be treated (inference ?). The manuscript refers several times to clinical diagnosis use of metagenomic data, but I don't understand how.

      For instance, several E. coli strains concurrently exist in the gut, so detecting a bla-TEM gene in the metagenome will not help to infer which E. coli is resistant. And it may actually not be a E. coli at all since most AMR genes can be found in different species.

      I hope these few comments will help to improve this work, I don't have time to proceed to a full review.

      Sebastien.

    1. On 2022-11-25 01:33:18, user H Zhang wrote:

      Please note that this is the primary version of our manuscript. Now, our this work has been published in PNAS (https://www.pnas.org/doi/ep... "https://www.pnas.org/doi/epdf/10.1073/pnas.2214313119)"). During the review process at PNAS, we were requested to do some major changes. Please download, read and cite our formally published PNAS paper (https://www.pnas.org/doi/ep... "https://www.pnas.org/doi/epdf/10.1073/pnas.2214313119)"), if you are interested. Thanks.

    1. On 2025-10-02 18:31:42, user stephen roper wrote:

      An updated version of this preprint is now in press in JoVE:

      Fleites, I. R., Morales, K., Roper, S. D. Battery-Powered Homeothermic Warming Pad for Maintaining Core Temperature in Mice and Rats. J. Vis. Exp. e69074, In-press (2025).

    1. On 2021-10-04 09:26:16, user Fernando Racimo wrote:

      In "Ancestral contributions to contemporary European complex traits", Marnetto et al. look at enrichments of ancestral contributions to the genetic component of particular traits to modern individuals. They apply a newly-developed method to do so to a large cohort of individuals from the Estonian Biobank and find different contribution from ancestral populations on variants associated with pigmentation, anthropometric traits and blood cholesterol levels, among others. They also look at patterns of positive selection on a subset of these variants. The manuscript presents an interesting and extensive set of analyses informing on a fascinating question about the evolution of traits over time and adaptation, as well as the genetic make-up of trait variation in Estonia. The authors also introduce a useful statistic to measure these contributions. I detail some comments below that I think might help improve the text and analyses:

      • While covA is extensively explained in the Methods, it has too brief of a description in the beginning of the Results section, given its importance to the associated conclusions. I would dedicate a few more sentences to explaining the reasoning behind covA, before assuming that the reader will automatically know what one means as they continue reading the results. I was a bit confused at first as to what covA was exactly measuring, and I think it's a smart way of measuring ancestral contributions to traits, so a bit more motivation for its use would be useful.

      • I think it might be good to move the text describing the connection between covA and f-statistics to the Methods section, for people familiar with f-statistics to motivate its use this way. I also wouldn't say that "covA(i,j) has no interpretation in terms of branch length because of the fictitious nature of pA, an allele frequency which only serves as balanced comparison for the ancestries under analysis". After all, the "populations" used in f-statistics analysis are also artificial groupings of individuals that share more or less history, depending on the analysis at hand.

      • It might be useful to try to separate the Eastern Hunter-Gatherer and Caucasus Hunter-Gatherer into 2 ancestral candidate groups, rather than treating Yamnaya as an ancestral source. EHG, CHG and WHG have quite differentiated component ancestries, so this might help solve some of the correlation issues mentioned at the bottom of page 3? See, e.g. Lazaridis et al. 2017.

      • The study would greatly benefit from examining the behavior of the covA statistic using simulations of a phenotype (say, in SLiM, see, for example, the recent preprints by Yair and Coop (2021) and Carlson et al. (2021)). The authors mention the interdependence inherent to the fact that the same reference populations are used, and I am concerned about what other factors might contribute to the behavior of the statistic, including, for example: 1) varying levels of negative selection operating on a trait, 2) the bias inherent to the fact that some ancestral populations might be closer to the present-day population on which the trait was measured, 3) effective size of the ancestral contributing population under study, 4) heterogeneity of the average "reference" populations used as baselines for comparison and 5) sample sizes of the ancestral populations (e.g. low sample size for the Siberians).

      • The authors say that "those counties for which the covA distribution is significantly different than the rest of Estonia (two-tailed Wilcoxon-Mann-Whitney test, p <=0.001)." It seems like a large number of counties have an asterik. I wonder if *all* counties would have an asterisk if one had chosen a more standard p-value cutoff? Why was this cutoff chosen? What would be the null sampling distribution here for what one would call significant? That each county would have the same distribution of this statistic as the whole country?

      • Following up on this, it seems a bit strange to say that one intends to control the pleiotropies that exist between traits by avoiding overly stringent multiple testing corrections as Bonferroni. Without an alternative way to control for multiple testing (e.g. FDR, or looking at genetic correlations between traits), I don't think the best course of action is to err on the side of anti-caution and go with a vanilla P-value cutoff designed for single tests. What does "significance" in this context tell one about the importance of different ancestral contributions?

      • I commend the authors for adding a city/countryside residency covariate in their model, to control for socio-economic effects. I wouldn't suggest, however, that this covariate allows one to entirely control for other socio-economic effects (for example, socioeconomic or cultural effects that might not be well explained by place of residence). This is a very hard problem and the subject of extensive research (see for example Mostafavi et al. 2020). Maybe add some cautionary statements along these lines?

      • I'd be happy to also share this review on other preprint peer review venues like Review Commons, PCI or Peerage of Science if the authors so desire. Thank you for posting this very interesting manuscript on bioRxiv and giving me the chance to review it!

    1. On 2020-05-12 18:29:40, user mikezamb wrote:

      Triple cocktails and or duo combos of these drug targets u identified, must be mentioned and discussed and added in your paper, because like other viral diseases it may take the complex interference of three different medicines to halt the machinery of sars-cov2. I hope that u write about this and change your paper to include verbiage about Triple cocktails and or duo combos of these drug targets u identified, as well as testing them in various combos to see the effectiveness. Single drug regiment may not be adequate. In a triple cocktail you must consider the Quantum mechanics effects and implementation in drug-design and use QM to create a computational model of drug combos, and also applied to proteins, DNA, carbohydrates, and lipids, as well as molecules that are involved in drug transportation, binding, and signaling. Also understand the quantum nature of an active compound (e.g. reactions involving radicals or bond formation/breaking) force-field parameterization of bonded and nonbonded (e.g. partial atomic charges, Lennard-Jones parameters) terms for molecular mechanics, molecular dynamics, and docking calculations. And when i say Quantum mechanics effects i also mean the electro-magnetic dynamic effects of the drugs in combo. it may take the complex interference of three different medicines simultaneously to halt the machinery of sars-cov2.

    1. On 2017-07-24 17:50:11, user Jon pierceshimomura wrote:

      It is unfortunate that Keays et al could not reproduce some of our experiments providing behavioral evidence that C. elegans is magnetoreceptive (Vidal-Gadea et al eLife 2015). We also found physiological evidence for magnetoreception in C. elegans that Keays et al do not address. As stated in our original paper, we agree with Keays et al that the basis for how C. elegans strains orient at different angles to and with different propensities to magnetic fields remains mysterious and necessitates further study. Members of the Vidal-Gadea and Pierce labs continue to study the molecular, cellular, and behavioral bases for magnetic orientation behavior in C. elegans. Please contact us directly for advice on studying this fascinating topic. Sincerely, Jon Pierce (jonps@austin.utexas.edu) and Andres Vidal-Gadea (avidal@ilstu.edu)

    1. On 2020-05-04 14:02:05, user Chad Yost wrote:

      There is no genetic evidence to suggest that there was a human population bottleneck between 50–100 Ka, yet you write this manuscript from the position that there was. Your results aside, papers like this continue to perpetuate the myth of the Toba catastrophe hypothesis by ignoring recent studies and citing egregiously out-of-date works that lack empirical evidence. I suggest you digest the recent literature that directly address the Toba catastrophe hypothesis as well as those that reconstruct effective population size for modern humans over the past 200 ka. It’s hard to fully appreciate your findings when your background sources are cherry-picked to dramatize your narrative.

    1. On 2020-05-27 15:07:11, user David Curtis wrote:

      I looked at the exome-sequenced subjects in UK Biobank and did not find an overall excess of variants in TMPRSS2. The preprint is here: https://www.medrxiv.org/con...<br /> In that small sample the MAF of rs12329760 was 0.16 in those who had tested positive (so were seriously unwell) but 0.23 in those not tested. This is consistent with your hypothesis. However I just took a look at the whole UK Biobank sample and it looks as though the MAF in the 636 subjects who tested positive is 0.21, which is about the same as the background frequency of 0.23:

      CHR SNP A1 A2 MAF_A MAF_U NCHROBS_A NCHROBS_U<br /> 21 rs12329760 T C 0.2131 0.2274 1272 973644

      So I think that if this variant does have any effect on susceptibility to COVID-19 infection then the effect size is probably fairly small.

    1. On 2021-11-18 02:24:08, user Geetika Aggarwal wrote:

      Very interesting paper by Krauter et al! This helps in understanding the regulatory mechanisms of PMP22 and PTEN in myelin formation. My main concern is Akt/mTOR signaling. Akt/mTOR signaling is involved in controlling biology and pathogenesis of many other pathways. Using their inhibitors could lead to non-specific effects and side effects of therapeutic. Showing these inhibitors effect on pathways where Akt/mTOR is involved, would be helpful to understand the potency of side effects. This would be very interesting to see if PTEN inhibition would help in restoring myelination in other myelin related neurological disorders. Experimentally, small portion of whole protein lysate was used as a control but it would be appropriate to show expression of any constitutive protein like vinculin or GAPDH as a loading control.

    1. On 2022-10-15 11:39:10, user René Janssen wrote:

      Dear authors,

      In my opinion this study is very well done, well written and with very interesting outcomes. You are mentioning pollution by grooming already. Please add that this pollution is coming by timber conservation methods; now stays the pollution route unclear.

      The concept of the memory test is comparable with of McFarland (1998) (see https://doi.org/10.32469/10... "https://doi.org/10.32469/10355/67055)") for Permethrin and as you stated by Hsiao et al (2016) for Imidacloprid; Wu et al (2020) shows further that Imidacloprid has also effect on the echolocation for bats. It would be good to state this effect more clear in your paper, now this stays is a bit vague. It would be worthwhile to compare and contrast the results of these three very same outcomes in three different studies with three very different pesticides together. This would make the study more valuable than it is now already.

      Many thanks for doing this excellent study and well written paper.

      All the best,

      René Janssen<br /> The Netherlands

    1. On 2024-02-20 16:27:03, user Diego del Alamo wrote:

      (The comments below are my own thoughts and aren’t meant to serve as a substitute for peer review)

      This manuscript presents a much-needed quantitative examination of structures of LeuT fold transporters, which are helical membrane proteins that import and export a wide variety of substrates in and out of cells. In the context of protein dynamics, this superfamily is characterized by a diverse range of conformational changes amongst its members, with some helices staying fixed in some representatives but not others throughout their respective transport cycles. In this analysis, the authors break down these conformational changes between pairs of structures using a rotationally- and translationally-invariant method for tracking helical movements (distance difference matrices, or DDMs). From these movements, the authors conclude that bundle-hash rocking is the foundation defining all conformational changes in proteins in this superfamily.

      The results are compelling, but my enthusiasm is somewhat dampened by the use of a comparatively small dataset and relative absence of mathematical rigor. I think this can easily be addressed with a bit of additional analysis.

      The basis for the main finding, stated above, derives from a principal component analysis (PCA) of these DDMs. If my understanding is correct, the authors use distance differences in 22 pairs of structures across nine proteins and arrive at six distinct motions that can explain most of these changes. While the authors show the reconstruction error when different numbers of principal components (PCs) are used in Fig 3D, I did not see a mathematical justification for selecting six components specifically. It might help to compute a statistical criteria such as the Akaike or Bayesian Information Criterion to verify that six PCs is the appropriate number.

      By the same token, it would be beneficial to run some cross-validation on some intentionally left out structural pairs. A low reconstruction error on proteins left out during parametrization would go some way toward supporting the authors’ conclusion that these movements are shared. For example, can the conformational dynamics of NSSs like SERT and LeuT be explained entirely using PCs derived from structures in other families?

      Finally, I would strongly encourage the authors to expand their analysis to include new structures deposited after mid-2021. I understand that this would add a lot of work, as I suspect that segmentation and assignment of residues to helices is done manually. But given the rapid clip at which these LeuT-fold structures are being deposited in recent years, it could significantly increase the size of the dataset. Off the top of my head, this would add NKCC1, KimA, and SGLT2, and probably others.

      Beyond that, a few things here and there stood out:<br /> • It isn’t clear based on Fig 3F or the text if the PCA itself is segmented by specific steps in the conformational cycle, or if the structural pairs are unlabeled during analysis (I suspect the latter from the text).<br /> • On the use of pymol cealign to align pairs of structures, it is a little strange given that this is a sequence-independent method intended to align proteins with little to no sequence homology. However, given the low RMSD between their pairs of structures, and that the paper’s bulk focuses on alignment-independent analysis, this is unlikely to affect the conclusions much at all (I've also tested it on a few pairs and the results look more or less identical to other sequence- and structure-based alignment methods). With that in mind though, I wouldn't state RMSD values in the text if they were calculated this way, unless they are supplemented by other metrics, such as TM-score<br /> • The name MntH is used throughout the text, except at the very end where the name DraNramp is used. I assume these are the same protein?<br /> • I just want to say that including an analysis where the membrane serves as a reference plane for a structural analysis is a great idea and very much appreciated, and I hope others follow your example and do the same thing

    1. On 2020-06-10 22:42:33, user Carrie Partch wrote:

      This is an interesting paper that provides much needed in vivo data on the role of the CBP KIX domain in circadian rhythms. However, it's unfortunate that they missed reading and citing a paper from 2015 showing how the CBP KIX domain competes with binding directly to the BMAL1 transactivation domain (TAD) with the circadian repressor Cryptochrome (from Andrew Liu and my lab). Glad you caught the recent paper from Eva Wolf's lab (ref #92 in the discussion), but the scientific foundation upon which this study rests is broader than one is led to believe.

    1. On 2022-09-01 18:11:32, user Matt Wersebe wrote:

      Hi Miguel,

      We haven't yet quantified this but we have done some preliminary life table experiments with some of these clones. Anecdotally, high tolerance clones may have a smaller "r" or intrinsic rate of increase. This may reflect a trade off in salt tolerance versus fecundity.

    1. On 2024-01-04 16:19:04, user Manuel Théry wrote:

      This manuscript has not yet been published in a peer-reviewed journal yet because we noticed that our engineered epithelial cell line, expressing ZEB1 under the control of doxycyclin, was contaminated with mycoplasma. We currently don't have the human ressources to make a new cell line, and repeat the key experiments in order to validate (at least) the main conclusions.

    1. On 2015-11-24 00:26:39, user Graham Gower wrote:

      I'm glad to see this topic getting some attention, there are definitely some improvements that can be made to software quality. I have some comments based upon a cursory reading.

      1)<br /> The malloc usage errors section in the main text gives the wrong impression about what you actually check for. Upon reading, I assumed you were checking type casts relating to the return value of malloc() - which should not be type cast in C. From the SI, I see this is not the case, you are instead referring to parameters being passed to malloc.

      Specifically you discuss multiplying by a signed integer, which can result in the wrong parameter being passed, and suggest to cast the integer prior to multiplication. In such cases it would also be more appropriate to use calloc().

      Table 3 shows "No-Error" as a value in the malloc column, which I assume to mean the return value was not checked for error. Perhaps the meaning of this should be mentioned in the text.

      2)<br /> Valgrind may falsely report invalid memory accesses when the code is built using highly optimised memcpy/strcpy functions, e.g. sse optimised versions are inlined by gcc. You should be able to compile with -O0 to avoid these. However, certain compiler warnings (at least with gcc, I don't know about clang) are also dependent upon the specified optimisation level. It was not obvious to me what level of optimisation was used, or if this was uniformly applied to all programs. In addition, it would be useful to know which versions of gcc and clang were used, as warning certainly change over time between versions.

      3)<br /> I'm curious to know more about the memory leaks you report. E.g., is a given leak of constant size, or does it change with the size of the input data set. It was also not obvious to me which leaks were likely innocuous because they are in a short running program, or if they are more severe due to many occurrences of the same leak in successive iterations of the program's main loop.

    1. On 2021-02-19 12:41:50, user Ekaterina Shelest wrote:

      The are two major concerns in regard to the aims and the main idea of this work, and they are interconnected. <br /> The first is the concept of co-evolution of the BGC genes. I agree that the genes belonging to the same biosynthetic pathway will most likely co-evolve; the question is, do we expect that ALL of them will co-evolve? We can see that the cluster regions are mostly syntenic (not always, by the way), but we can also see that genes appear and disappear from the syntenic clusters. So why should we expect that a BGC co-evolves as a whole? Some new genes can be recruited to the cluster much later than it has been formed; in this way, the organism can start producing a new substance. You just add, e. g., a tailoring enzyme and get something new. However, the authors take for granted that all genes in BGCs are cemented together forever and from the beginning of time. <br /> In addition to possible recruitment of biosynthetic genes we have a cohort of non-biosynthetic ones that have a good chance not to co-evolve but be just recruited. TFs, transporters, and other auxiliary genes are more likely (than some enzymes) to be recruited to an existing cluster not having shared evolutionary history with it. They are more numerous in genomes and “at hand” to be taken into a cluster. However, they are crucial for the production of the SM even though they don’t synthesise anything. To start with, a cluster will not be even expressed without its TF (if it happens to have one). <br /> So I would expect that some genes co-evolve, the other don’t. This, by the way, is illustrated by high number of false negatives in your analyses.

      In any case, if there is some major idea of an approach or a phenomenon, on which this approach is based, and if it is not self-evident, it must be justified, explained, and referenced. There are zero references to articles about BGC genes co-evolution; not too surprising as I haven’t found any papers in PubMed as well. This means, that you have first to substantiate your idea. In the way it is written now, the article causes a major question that is not answered, discussed or anyhow addressed. I dare to suggest that the paper would gain a lot if it were not about the tool per se but about application of this<br /> method to investigation of cluster gene co-evolution.

      The second serious problem for me is what the authors actually understand as a cluster and what they aim to identify with their tool. A BGC contains not only biosynthetic genes. Transcription factors, transporters, other auxiliary genes are also indispensable parts of a cluster. However, in the discussion of the “exemplary analysis” of Lov, the authors with a light heart tell us that LovE does not cluster with necessary genes in PCA for any distance measure but it’s fine because it’s just a TF and does not participate in the biosynthesis.<br /> I am left with the impression that the authors are happy not to find a TF as a part of the detected cluster. So what is a BGC, in this case? What do the authors plan to find? Only the genes immediately involved in the synthesis? But this is NOT the definition of a BGC. If the tool is indeed oriented on identification of only biosynthetic genes, excluding all other BGC genes, this must be explicitly stated and discussed. In this case, although it is an interesting idea, I doubt that it will be very useful for BGC annotation and especially for practical applications, as in many cases it’s crucial to know the cluster-related TF (e.g., to overexpress it). It will also not help refining cluster borders and excluding gap genes, which are two aims formulated in the beginning of the paper. For the borders, it’s obvious: if you don’t aim at finding all cluster genes, you risk losing flanking ones. For the gap genes, why are you so sure they will not co-evolve together with the cluster? As a<br /> part of landscape? <br /> This second concern is mainly caused by the way how the authors serve their ideas. If the Introduction did not make such a great emphasis on the necessity of better detection of the BGCs and in particular their cluster borders, and on the practical purposes of the BGC identification (support of the lab work, etc.), I wouldn’t be left with the impression that their major aim is to detect the whole clusters. As it turns out, the aim is to detect only the co-evolved genes; in fact, this is said directly in the title: “Identification of essential biosynthetic genes” but the introduction changes the expectations, and the way how the whole paper is written does not add clarity to this. As I’ve mentioned above, if cleverly applied, the tool is a great way to investigate the cluster evolution. The paper will gain a lot if you change the angle under which you describe it.

      I have many more remarks and concerns. I’ll add them in the next comment.

    1. On 2021-09-21 10:36:04, user Martin R. Smith wrote:

      This is an interesting approach; always good to see exploration of aspects of data that can be recovered before the alignment step.<br /> One small comment on the use of RF distances to compare to a reference topology: the RF distance has a number of biases and limitations that make ill suited to this purpose. I review and propose some alternatives in Smith, 2020, Bioinformatics: https://doi.org/10.1093/bio...

    1. On 2020-04-07 07:23:14, user David Posada wrote:

      Nice work Will et al! A minor comment, mainly nomenclature ...as usual for me ;-). Normally, we use the term stabilzing selection to refer to phenotypes, not to genotypes. For example stabilizing selection maintains human height around a certain mean. I would say here you are mainly seeing the action of negative selection. Indeed negative selection can result in the maintenance of stable phenotypes, but is it the case here? Take care!

    1. On 2023-09-28 21:27:59, user LabTerra wrote:

      Dear authors,

      First and foremost, we would like to congratulate you on your work. The text is relevant given the context of climate change and despite the inherent complexity of this subject and the extensive analysis conducted, you effectively guided the discussion in a clear and compelling manner. We also found the research idea and the results obtained to be quite intriguing, showing how different climate variables influence beta diversity and its components. In particular, the taxonomic diversity being more aligned with phylogenetic and functional diversity in the tropics as opposed to temperate and polar regions, is a very interesting finding.

      That being said, we believe that some of the results could have been further explored in the discussion section and that the introduction could use additional information to highlight the work's importance and clarify some of the choices made, such as more details on why the LGM was chosen for comparison.

      Below, we provide a list of specific suggestions that we hope could contribute to your work, especially for the clarification of some of the results and methods used and for a more comprehensive discussion section.

      List of specific comments:

      The importance of your work and how it relates to current climate change could be further emphasized in the introduction section.<br /> It is mentioned that precipitation seasonality was the main variable explaining total beta-diversity. This result could be better explored in the discussion, as it was only briefly mentioned in the results section.<br /> Consider integrating some of the limitations identified in the methodology section in the discussion as well. For example, the explanation on how the gaps in the functional traits dataset could affect your results.<br /> As it is mentioned in the discussion section, the climate changes that are happening now are different from the changes that happened in the last glaciation. We believe the comparisons made between them and the conclusions reached could be expressed with less certainty.<br /> While the importance of conserving a network of protected areas in regions with rare species is indicated in the discussion section, this subject could have received more attention. It's also worth emphasizing in the text that such actions will likely not be enough to stop climate change-driven extinctions on their own.<br /> The high beta-diversity in regions such the Sahara transition and the USA is an intriguing result that could be investigated.<br /> In the models, the r² values are high for the combined variables, but relatively low for individual variables. The study focuses on the effects of the LGM anomaly, but we believe that a more in-depth exploration of the interactions among the studied variables would be beneficial.<br /> We found it unclear what corresponds to a “species” (used in taxonomic diversity) in the context of phylogenetic and functional diversity. Do these correspond to functional and phylogenetic groups?<br /> Figure 1a caption is somewhat confusing, as it mentions beta diversity but only shows LGM anomaly.<br /> In figures 2 and 4, we recommend that all sub-figures share a consistent scale to facilitate comparisons between them.<br /> Figures 2 (j, k and l) and 4 could use a different color scale. Light yellow is especially difficult to discern from the background white.<br /> The inclusion of a table with the description of all environmental variables used to compare with beta-diversity might be beneficial to the understanding of the reader.

      We hope that our suggestions will help in further improving the quality of your work.

      Kind regards,

      The LabTerra team

    1. On 2017-11-27 16:13:13, user Emily Stephen wrote:

      Interesting article! I'm curious how you distinguish between propagating oscillations vs. event-related potentials. Any ERPs in the data could have frequency content in the theta and alpha range that would be short-lasting, and traditionally considered separately from oscillations.

    1. On 2021-05-12 22:35:17, user KW wrote:

      Great to see this study for these charismatic, but enigmatic, plants!

      Are the points in Fig. 3 colored by taxon identity or by DAPC cluster? Are these two exactly congruent?

      Adding some location or taxon labels to Fig. 4 may be helpful, particularly for the domensis/nevadensis populations (e.g., the pie that is 75% purple appears to be domensis, but is actually one of the nevadensis populations [right?]).

      Could the authors offer more justification for choosing K=7 in the STRUCTURE analysis? They say it is "most biologically relevant," but the existence of these 7 genetic clusters is then used to justify recognition at the species level, when really STRUCTURE indicated another grouping (K=5 or K=14) to be more appropriate. Why perform the Evanno and Pritchard tests in the first place?

      The authors support recognition of all of these taxa at the species level, but the evidence doesn't look as strong for each taxon. Recognition of P. maguirei looks well-warranted, but less certain for domensis and nevadnesis.

      I think some discussion of the species concept(s) used by the authors would be helpful. Are genetic clusters the primary means to delineate these taxa? If so, what about domensis/nevadensis/cusickiana, which group together to varying degrees at different K values. What is the difference in genetic clustering for different populations of the same taxon, different taxa at the varietal level, and different taxa at the species level?

      I think this study does a good job of taking a close look at these taxa. It's important that this show's the distinctiveness of P. maguirei, and it opens up new avenues to investigate the other Great Basin Primula taxa.

      -Kevin Weitemier

    1. On 2022-01-17 19:43:52, user innabiryukova wrote:

      This study investigates mRNA transfer at the transcriptome-wide level (the transferome) between heterogeneous human-mouse cell populations in cell culture using RNA-sequencing. Unlike the intensively studied transfer of small regulatory RNA and fragmented RNA by extracellular vesicles, relatively little is known about full-length mRNA transfer between adjacent cells via direct cell-to-cell tunneling nanotubes (TNTs). Dasgupta et al provide a comprehensive set of the high depth bulk RNA-sequencing experiments and downstream bioinformatics analysis to determine overall levels of the most abundantly transferred RNA. They show that RNA transfer to the acceptor mouse cells is non-selective and the amount of transferred RNA strongly correlates with the endogenous level of gene expression in the donor human cells. They validate these results using a variety of orthogonal approaches (e.g. qRT-PCR, smFISH together with advanced imaging techniques, spatially separated cell culture) that overall support: 1) The relatively low level of endogenous mRNAs and lncRNAs transfer via TNTs. 2) TNTs as the predominant route for transcriptome-wide mRNA transfer. <br /> In addition, both cis-RNA motif-enrichment analysis and synthetic RNA reporter assays in cell culture back-up the authors’ conclusions on non-selective RNA transfer via TNTs. Lastly, the authors observed stark changes in the acceptor mouse cell transcriptome in response to co-culture with the donor human cancer cells, including the upregulation of cancer-associated genes as result of oncogenic-like transformation. This is a carefully performed study based on a simple and quantitative method. Overall the conclusions are well justified and supported, limitations of the study are clearly stated.<br /> The only minor comments and suggestions that I have are:<br /> 1. The authors performed polyA(+) RNA-sequencing. How abundant could be the 3’ end partially decayed mRNA transferred to the acceptor cells?<br /> 2. The direct correlation between RNA and protein levels in MCF7 is shown previously in (Edfors et al 2016). Is it known that the TNT-transferred MCF7 mRNAs undergo translation in the acceptor mouse cells?<br /> 3. TNTs are also shown to transfer proteins. Is there any overlap between the TNT-transferred mRNA and proteins? <br /> The points 2-3 are very challenging to address but could be discussed.<br /> 4. Fig 3C. Is mRNA half-life in K562 consistent with the mRNA half-life rates in MCF7?<br /> 5. I find the ability of the transferred RNA to down-regulate cell immunity-related genes interesting. Could this be an indirect effect? <br /> The authors used unique mapped reads in downstream bioinformatics analysis. Is there any estimation of the multi-mapped reads in the TNT-transferred RNA fraction?

    1. On 2020-12-02 22:44:20, user Emma Leshan wrote:

      Thank you for this wonderful work! This seems very promising.

      In figure 1D, it seems that some of the data points don't have error bars, and it would be good to be consistent with the inclusion of error bars in all data points. It would also be nice to see a ribbon diagram showing how the spike protein and HRC peptides interact if possible.

      In figure 2A, it would be good to label the x-axis as well so it is more clear that you are comparing different peptide concentrations.

      I also wondered why you used DMSO in the buffer given to the ferrets, when using sucrose would be more relevant for translational potential in humans.

      It would also be interesting to discuss the relevance of this technology now that vaccines are on the horizon. It would likely be applicable for use in cancer patients, babies, the elderly, etc.

    1. On 2019-07-16 10:02:39, user sylvain garciaz wrote:

      Great work from Sebastian Müller and Raphaël Rodriguez, linking iron uptake by the glycoprotein CD44 and epigenetic plasticity. This paper paves the way for new comprehensive therapeutic interventions involving iron regulation in cancers.

    1. On 2022-10-24 01:42:04, user CDSL JHSPH wrote:

      This was a fascinating and well written paper. Additionally, the analysis in the results and discussion were logical and easy to follow. I do think at times there is some confusion/ambiguity regarding the sample sizes for some tissues. In the methods section, you mentioned that 3 brain samples (2 day 10, 1 day 14) and 1 heart sample (day 10) were excluded from analysis. When talking about Figure 2A, you said that the initiating VSG was detectable in 23/24 tissue samples from day 10. I was wondering if that was supposed to be 20/21 tissue samples? I had a similar comment with figure 3C and figure 4, where the legends say that n = 4 for each tissue. I think it would be helpful to mention that n=2 for day 10 brain samples and n = 3 for day 10 heat/day14 brain samples in the legends of figures 3 and 4 in case a reader did not catch that in the methods section. I also had two minor comments regarding figures. For 2A and 2C, since you are comparing the blood to tissue spaces collectively, I don’t think having the tissues being different colors is necessarily useful. It might be visually beneficial if all tissue samples were the same color (i.e. blue) like they are in 3B. Additionally, for 2A, 2C, and 5, the Y-axes say Log10(% parasites), but the tick marks show actual per cents.

    1. On 2020-05-14 16:33:21, user Anita Bandrowski 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:10.1101/2020.01.31.929042); 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: cell lines (2) . 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

      Thank you for sharing your data.

      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/Yt9Z0o-sEeqF...<br /> References cited: https://tinyurl.com/y7fpsvzy"

    1. On 2022-07-27 18:58:54, user Moritz Oberlander wrote:

      There is an opportunity to discuss the infectious study in the paper J Galmès, 2013 and her French thesis: https://tel.archives-ouvert... with TTMV-Ly1,-Ly2,-Ly3 virus particles in lung epithelia or embryonic kidney cells. It would be interesting to compare them but it seems to me, there is not significant difference in the infection of the TTMV-Ly1, Ly2, Ly3 viral particles (Figure 45).

      I looked at the TTMV-Ly2 because it has two characteristic repeats in the 5’-noncoding region, the second repeat was probably created by the insertion of about 66nts (insert: gccggaaaaccacataatttgcatggctaaccacaaactgatatgctaattaacttccacaaaaca). I searched (Blast) for a homologues direct lineage of TTMV-Ly2 (to exclude a recombination) based on this insert and found several homologues of Ly-2. I wanted to see if they hold a similar spike structure as well. Some lineage-homologs seem to be similar but safia-668-2 and 314-17 may show more changes in spike region of Ly2:

      YTGANLPGDTTQIPVADLLPLTNPRINRPGQSLNEAKITDHITFTEYKNKFTNYWGNP TTMV-ly2 2979nts<br /> YAPGNPPTTPDNIQIADLIPLTNPRENKPGMSLNEAKIGGKTDFNNYKTNYKNYWGNP safia-367-10 2991nts<br /> YAPGNPPTTPDNIQIADLIPLTNPRENKPGMSLNEAKIGGKTDFNNYKTNYKNYWGNP safia-692-0 2992<br /> YAPGNPPTTPDNIQIADLIPLTNPRENKPGMSLNEAKIGGKTDFNNYKTNYKNYWGNP safia-418-10 2941<br /> YAPGNPPTTPDNIQIADLIPLTNPRENKPGMSLNEAKIGGKTDFNNYKINYKNYWGNP safia-569-10 2849<br /> YAPGNPPTTPDNIQIADLIPLTNPRENKPGMSLNEAKIAAKTDFNKYKTNYKNYWGNP safia-388-14 2991<br /> YAPGPPIPTAENLKVGDLIPLTNPRDNVSGESFFEQQTTTHETWKQYFTNYKKHWGNI safia-668-2 2977<br /> YAPGPPIPTAENLKVGDLIPLTNPRDNVSGESFFEQQTTTHETWKQYFTNYKKHWGNI safia-314-17 2977

      FNKHIQEHLDMILYSLKSPEAIKNEWTTENMKWNQLNNAGTMALTPFNEPIFTQIQYN ly2<br /> FNVEIQEHIQDILYSLKSPEAIKSAWTTENMTWKQLDNAGQMALTPFNEPIFTQIQYN safia 367-10<br /> FNVEIQEHIQDILYSLKSPEAIKSAWSTENMTWKQLDNAGQMALTPFNEPIFTQIQYN safia-692-0<br /> FNVEIQEHIQDILYSLKSPEAIKSAWTTENMTWKQLDNAGQMALTPFNEPIFTQIQYN safia-418-10<br /> FNVEIQEHIQDILYSLKSPEAIKSAWTTENMTWKQLDNAGQMALTPFNEPIFTQIQYN safia-569-10<br /> FNVEIQEHIQDILYSLKSPEAIKSAWTTENMTWKQLDNAGQMALTPFNEPIFTQIQYN safia-388-14<br /> FNVHTSEHLEDLLYSLKSPEAIAKKALENENKTDLKWSELDNAANMALTPFDQPIFIP safia-668-2<br /> FNVHTSEHLEDLLYSLKSPEAIAKKALENENKTDLKWSELDNAANMALTPFDQPIFIP safia-314-17

      In the phylogenetic tree, the TTMV5-TGP96 (no the second insert) is the closest homolog to TTMV-Ly2:<br /> YTGTNPPSDTSQIKVADIIAVTDKKNNKPGESYHDQQTTSNKNWQQYFENYQQFWGNP TTMV5-TGP96

      It seems to be difficult to find a direct lineage for TTMV-Ly1 or its homologs with similar structure and discuss that......

    1. On 2018-04-03 03:59:19, user sandeep chakraborty wrote:

      I dont get how that you say:

      "CRISPR may not introduce numerous, unintended, off-target mutations"

      when 4545(12%) variants are diff (3 CRISPR vs 1 ctrl)

      Also, why variants when you know know where to look, CRISPR is not random.Here are the off-targets, just these need to be checked - and I have a feeling you will be vindicated !!

      https://sanchakblog.wordpre...

    1. On 2019-04-22 21:21:08, user Irilenia Nobeli wrote:

      Thank you for bringing this to our attention. I have to say that in all honesty I did not know of its existence (nor do I see the similarity to our name, but that's possibly because I'm not a native speaker). When we looked at similar tools, we searched the literature covering bacterial transcriptomic data, not eukaryotic data (for which the search for non-coding expressed regions is probably harder but more common). We did not aim for a comparison of our tool to all other existing solutions (as you have probably guessed from the length of the paper this was meant to accompany the release of a software tool, not to cover a full-scale comparison of software); we picked Rockhopper for the comparison because it is the tool that gets most cited in the bacterial transcriptomic analysis literature and hence, we assumed it is the one that is mostly used by people analysing bacterial RNA-seq data. <br /> Having said this, I think I would like to try it out. and will as soon as I find some time. <br /> Thanks once again for your comment.<br /> I. Nobeli

    1. On 2020-01-24 17:56:49, user Wang Gang wrote:

      Our work develops a novel and practical strategy that achieves highly efficient genome editing in large animals. We used this approach to generate a selective germline genome edited pig (SGGEP)and xenotransplantation in several animal models, including NHPs were performed to test whether these genetic modifications combinations are justified for translational medicine applications and launching the clinical trial.

      Our results showed that SGGEP skin graft could survive functionally on NHP up to 25 days without the administration of immunosuppressive drugs. Considering that a pig skin graft does not affect the success of a subsequent allograft, or vice versa, therefore, this is a major milestone for skin xenotransplantation and serves as a proof of concept to initiate investigator-initiated clinical trials (IITs) in severe, life-threatening burn patients.

      As the skin is considered the vital, unique and immunogenicity organ, our preliminary success in skin xenotransplantation using the combination of multi-gene modified pig in NHP provides the approval of the concept, paves a way to initiate the other organ preclinical trial and clinical trial, implies a success of these organs’ xenotransplantation. Therefore, SGGEP could have the potential to become an unlimited organ source for future clinical transplantation.

      Furthermore, our work provided a conceptual framework for selective genome editing for other large animals with other important purposes such as human diseases modeling, establishment of the disease resistant-large animals, etc.

    1. On 2024-04-29 15:59:37, user Amber Gonzalez wrote:

      Additional comments<br /> Hello! I am a Sam Houston State University graduate student enrolled in a microbiome course, BIOL5394. My overall impression of the manuscript is that it is excellent, and the information presented is helpful for forensic science. <br /> Abstract and Introduction<br /> The introduction is very clear and to the point. Does this study consider where the individual's death occurred, indoors or outdoors? I believe the environment in which a death occurred could alter the microbial community succession and potentially influence your given results. What ethical guidelines were followed when handling the cadavers?<br /> Methods<br /> Quality control <br /> • Need specific PCR protocol, like # cycles, temps, polymerase, etc. https://journals.asm.org/do... <br /> • There is no mention of the correction of the 16S copy number and variances in genome size for taxa identified ASVs. https://journals.plos.org/p... <br /> • Was coverage of communities measured and representatively sampled equally?<br /> o Include coverage measures such as Good’s coverage or Chao1.<br /> • Line 115 explicitly states the protocol for USA samples. What about the samples from Finland and Italy?<br /> • Lines 113-114 mention sections of the dissected internal organs but fail to mention the specific section. Is there a measured area region that was dissected? This could help with consistency in sampling.<br /> DNA Extraction and Sequencing <br /> • Lines 130-131 state the Greengenes database used to assign taxonomy was last updated in May 2013. More accurate identification results would come from a more recently updated 16S gene database.<br /> Statistical analyses<br /> • Good practice to include the complete list of packages and codes used for the analysis.<br /> Results<br /> Figure 3<br /> • PCoA assumes normal distribution of data. Need to show normality test or use NMDS.<br /> Figure 4<br /> • The taxa in this figure show excessive “unknowns.” I believe updating the database could improve this.<br /> Figure 5<br /> • Lines 256-259 mention the findings of ASV family are in class Bacteroidia, however after reviewing I believe the correct class is Betaproteobacteria.<br /> o Bacteroidia, Betaproteobacteria, and Clostidia are color-coded with very similar colors. Consider making the jump to the next class a bit more distinctive.<br /> Additional comments<br /> • The article mentions the sample size was 265, but when I add up the four category sample sizes, I get a sum of 262. Is this intentional, or a typo?<br /> • Of the 20 Finnish cadavers, why was the liver the only organ supplied?<br /> • Overall, it was a very interesting study!

    1. On 2019-10-31 20:29:46, user Charles Warden wrote:

      I had previously heard of CellProfiler (and CellProfilerAnalyst), but I think this title and abstract is a little confusing to someone new to the area.

      In other words, you are providing a pipeline of scripts to use CellProfiler for a specific application, but you didn't develop CellProfiler itself.

      If you re-word this, then I think that is fine. Being able to create new modules and specific protocols to expand applications for open-source software is important!

    1. On 2018-01-12 15:34:49, user jvkohl wrote:

      You can block my comments here, but not my tweets. Exogenous vitamin C (from ingestion of sago palm-like leaves) was linked from increased endogenous vitamin C to the protection of organized genomes in a population of China via the mouse to human model of an EDAR V370A amino acid substitution. The claim that "Loss of APOBEC1 RNA-editing function in microglia exacerbates age-related CNS pathophysiology" https://t.co/Vt6FbXnW6j links the virus-driven degradation of messenger RNA to all pathology in all living genera.

    1. On 2019-09-12 13:52:30, user Ryan Bell wrote:

      Great preprint! Dr. Huveneers please do check your email for a message from Excision Editing. It contains some important information on some changes we highly recommend to the Abstract. If you can't find it please email editor@excisionediting.com. Again, great work!

    1. On 2022-07-18 20:55:54, user Gabriela K. Popescu wrote:

      We discussed this article in our group meeting today; my students appreciated the transparent reporting of methods, data points, and analyses, and the rigor in the experimental approach. We learned a lot.

    1. On 2020-06-18 03:12:46, user Aaron Wilk wrote:

      Some details concerning the PBMC dataset published by Wilk, Rustagi, Zhao, et al. as reported in this preprint are incorrect. That dataset profiles 7 patients, not 8. Additionally, this preprint reports that 4 of the patients from the original study are "mild." While this preprint does not report which patients are labeled "mild", all 7 patients in the original study were hospitalized and need to be classified as severe. In fact, all but one patient were in the ICU. Therefore, this dataset cannot be used to support this preprint's conclusions regarding phenotypic differences across the spectrum of disease severity.

    1. On 2020-07-05 21:33:08, user Billy Bostickson wrote:

      Does this mean that SARS-COV-2 contains artefacts of insect cell expression systems or baculovirus viral particles or does it mean it may have partly evolved via recombination events involving viruses in insects and bats in a cave for example? Just can't understand how insect sequences could be found in a virus which supposedly emerged from bats.

    1. On 2019-07-25 08:46:32, user Andrew wrote:

      Over 200 pages of supplementary information without a table of contents makes a lot of work for your readers. Why are the Orkney sites, Oxford and Weymouth listed as UK, not England or Scotland, but Llyn as Wales not UK. The Isle of Man is not in the UK.

    1. On 2020-05-06 09:53:17, user Anna Birke wrote:

      Hi all, super cool piece of science. Just some comments and feedback <br /> that came out of the Hoski Journal Club (as you will have seen on <br /> Twitter, we dedicated some time on Friday afternoon to chatting about <br /> your paper :-)):

      1) in general, the paper is really well written <br /> and flows nicely, however, it would be great if there was a flow <br /> chart/diagramme at the start of the results section to give the readers <br /> an overview of the different culture conditions (continous flow, static <br /> broth and colony biofilms) and transformation experiments (strain to <br /> strain, plasmid, eDNA, gDNA to strain etc.) you carried out.<br /> 2) right at the start of the results section it says<br /> ...were obtained from broth the biofilm biomass and biofilm effluent (Figure 4a) --> should be Figure 1a. <br /> 3)<br /> the use of gDNA and eDNA was a little confusing. Maybe clarify that by <br /> gDNA you mean chromosomal genomic DNA isolated from cells and by eDNA <br /> you mean exogenous DNA that was extracted from culture supernatant (it's<br /> in the M&Ms but would be good to have that distinction in the <br /> results too). <br /> 4) one paragraph down it says that the concentration of gDNA or eDNA was 0.5 mg/ml...did you mean 0.5 ug/ml?<br /> 5) paragraph at the bottom of page 10: ...we set out to determine if this process with also occuring (no with)<br /> 6) there aren't any error bars on figure 3b? <br /> 7)<br /> it might also be a good idea to go into more detail about the strain <br /> and time-dependent differences in transformation efficiency in the <br /> discussion. So like why is the number of transformants so much higher <br /> for PAK than for PAO1? Also might be cool to culture PAK and PAO1 in <br /> seperate cultures of the course of 24 hours, take out a small volume at 5<br /> or 6 timepoints and transform to get an understanding of how <br /> transformation efficiency varies over the course of a culture. Maybe <br /> you've already done something like this, if so, it would be nice to see <br /> that in the results. <br /> 8) another set of experiments which would be <br /> really nice for the future (maybe you're already on it) is to look at <br /> the expression of genes presumably involved in transformation, <br /> especially in a time-dependent manner. <br /> 9) in the discussion it would<br /> have been really nice to hear your thought on why Pseudomonas is such a<br /> pain to transform in a lab setting if you're work clearly shows that, <br /> at least with some strains, transformation occurs naturally and also <br /> what's your hypothesis why eDNA is so inefficient at transforming (maybe<br /> also include the eDNA gDNA gel you mention in the results) if it's the <br /> source of DNA that is naturally available in a biofilm?

      We hope you find these comments constructive and helpful. :-) <br /> Cheers from the Hoski Journal Club

    1. On 2023-02-03 22:42:59, user Miles Markus wrote:

      The authors of this interesting article correctly refer to the malariological dogma that frequently, the majority of Plasmodium vivax infections in human populations are the result of activation of dormant liver stages. Some figures exceeding 80% appear in the literature. As is mentioned in the article, these parasite forms are known as "hypnozoites", a term I coined 45 years ago [1].

      This hypnozoitophilic dogma is, entirely logically, based on extrapolation from the results of drug treatment of patients with P. vivax malaria. However, the idea does not make sense to me parasitologically. It is not necessarily correct, for reasons explained elsewhere [2,3].

      The point is that a proportion of relapse-like P. vivax malarial recurrences might originate from concealed merozoites (a recently confirmed parasite reservoir), as opposed to hypnozoites [3]. The suggestion was originally made 12 years ago [4]. It is not so much a question of why would these merozoites be a source of recrudescent P. vivax malaria but, rather, why would they not be? Luckily, evidence one way or the other should soon be forthcoming from drug-related experimentation using humanized mice [2].

      Following on from the novel research concerning inhibition of hypnozoite and hepatic schizont activity that is reported in this bioRxiv paper, perhaps the authors could consider investigating, in addition, what might inhibit non-circulating P. vivax asexual stages (they did include P. falciparum blood stages in their study), such as occur in vast numbers in bone marrow and the spleen. I.e. if feasible. These hidden merozoites may be responsible for numerous clinical malarial episodes and it is possible that they are at least as problematic as hypnozoites. But this remains to be determined.

      REFERENCES:<br /> 1. Markus MB. 2011. The hypnozoite concept, with particular reference to malaria. Parasitology Research 108 (1): 247–252. https://doi.org/10.1007/s00...<br /> 2. Markus MB. 2022. How does primaquine prevent Plasmodium vivax malarial recurrences? Trends in Parasitology 38 (11): 924–925. https://doi.org/10.1016/j.p...<br /> 3. Markus MB. 2022. Theoretical origin of genetically homologous Plasmodium vivax malarial recurrences. Southern African Journal of Infectious Diseases 37 (1): 369. https://doi.org/10.4102/saj...<br /> 4. Markus MB. 2011. Origin of recurrent Plasmodium vivax malaria – a new theory. South African Medical Journal 101 (10): 682–684. http://www.samj.org.za/inde...

    1. On 2018-09-28 20:54:03, user BU_Fall_NE598_Group2 wrote:

      NE 598 Group 2

      Summary:<br /> Reinhard and colleagues report interesting findings regarding circuitry between the superior colliculus and two projection sites: the pulvinar and the parabigeminal nucleus. The authors demonstrate that circuitry for these two sites develops from their own respective set of retinal ganglion cells. Cell types were classified based primarily on morphology. To accomplish this, researchers took advantage of transsynaptic tracing, both rabies and herpes viruses, and antibody staining. The most novel finding appeared to be the clustering of cells into circuit-dependent groups based on dendritic stratification. The identification of these different clusters were then used to align putative function to each retinal ganglion cell type.

      While these findings are interesting, this manuscript would be significantly improved with a more comprehensible introduction. We found it difficult to interpret the concept of “hard-wired circuits” vs. “flexible networks” (Line 24) and how they connect to the bigger picture of “different behaviors” (Line 42). Further, additional discussion regarding the importance of the pulvinar and the parabigeminal nucleus would strengthen the introduction and help to elucidate the motives of this paper. Why were these two regions of interest selected? What is the role of these in the overall visual system? More discussion of the relevant literature would assist in showing how the findings of this paper can be integrated into the current understanding of visual system circuitry; consider the works of KG Usunoff et. al 2007, H Cui et. al 2003, and E Benarroch 2015. Merits, discrepancies around methodology, and supplementary minor concerns are detailed below.

      Merits:<br /> The study utilizes retrograde viral tracers which effectively show neural pathways and connections of interest. Reinard et. al’s usage of NSTR1-GN209-Cre mouse line effectively labeled neurons projecting uniquely to the lateral pulvinar. The study also uses SMI32 and CART staining to identify retinal ganglion cell subtypes. Using a tSNE algorithm, the authors identified 12 distinct subtypes of retinal ganglion cells and corroborated these results with morphology.

      Specific Critiques: <br /> Reinhard et. al. injected rabies ENVA-GCaMP6s virus into the superior colliculus along with a herpes TVA-mCherry virus into the lateral pulvinar or parabigeminal nucleus to specifically target retinal ganglion cells that project to each circuit (line 60-64). It was unclear why the authors used GCaMP6s instead of just GFP to label these cells, as there are no calcium dynamics shown in the paper. Since GCaMP6s already labels these cells, however, it would be interesting to see if there are cell-type specific patterns in calcium activity that align with each circuits (as in Figure 6).

      Since both herpes and rabies viruses have been shown to have toxicity in mice as compared to AAV, toxicity assays demonstrating long-term cell health after viral injection would be a more convincing reason to use these viruses instead of adeno-associated viruses. In addition, further clarification of purification and concentration steps used during virus preparation is needed to eliminate the possibility of coat contamination across viruses.

      Instead of the negative control presented in Supplementary Fig1A, a more comprehensive test of the system would be to inject the pulvinar and PBG with a HSV-mCherry virus not expressing TVA and co-inject the SC with RV-ENVA-GCaMP. This would confirm the effectiveness of the TVA-ENVA system, as well as ensure that no contamination between viruses (or viral coats) occurred. In addition, the manuscript would be significantly improved if Reinhard et. al. were more specific about where they injected. They are unclear about their injection location given that the colliculus has several layers and observed retinotopy.

      The authors labeled 3 out of 4 ON-OFF retinal ganglion cells with CART and following identification, dendritic stratification morphology was used to classify each of the 3 specific subtypes. Supplementing the morphological classification with more robust distinction methods (e.g. genetic markers, functional verification) would significantly add to this paper. Furthermore, the morphological factors distinguishing the circuit groupings (Figure 6, Supp. Figure 2) seem to be inconsistent, as the dendritic arborizations and overall structure of these cells vary widely. More discussion of specific criteria used to determine these groupings is needed for more comprehensive analysis.

      Future Directions: <br /> It would be interesting to investigate whether or not projections from the superior colliculus to the pulvinar and parabigeminal nucleus come from distinct or identical populations of neurons. Injections of two different CTB retrograde tracers into the pulvinar and parabigeminal nucleus, with imaging of the superior colliculus for overlap would allow researchers to determine this. Additionally, we would be interested in the implications of silencing the projections from the superior colliculus to either the pulvinar to determine behavioral implications.

      Minor Concerns: <br /> Regarding surgical procedures, although Reinhard et al. acknowledge their goal to “cover as much as possible of the superficial layer of the superior colliculus,” (line 366), there is no verification of accurate targeting. Not providing targeting verification gives rise to concerns of viral targeting bias. Providing images to verify “even and accurate” bilateral targeting of the superior colliculus would strengthen this manuscript.

      It was unclear why parvalbumin-positive cells were chosen to compare the identified cell types to. More discussion for why this particular marker was chosen or extensive comparison to other cell types is needed.

      Figure 1, Figure 3, and Figure 4 would benefit from larger, higher-resolution confocal images. The current images provided are difficult to visualize.

      The authors state that they use mice “of either sex” (line 321) to complete these experiments. Please address whether or not the mice used in these experiments show sex differences.

      Please review manuscript for grammatical, syntactical, and spelling errors. Including “innervated” (line 17), “labels” (line 66), “weraree” (line 125), “are innervating” (line 167), “understanding” (306).

    1. On 2020-01-10 11:38:26, user jvkohl wrote:

      ICYMI: Their significance statement links natural selection for food energy-dependent codon optimality from the physiology of pheromone-controlled reproduction to biophysically constrained viral latency and healthy longevity via epigenetically effected microRNA-mediated sympatric speciation in all mammals.

      Fixation of the EDAR V370A amino acid substitution is the example I used in my mouse-to-human model.

    1. On 2017-01-25 15:46:02, user Christiaan Henkel wrote:

      Thank you for the suggestion and link! Indeed there are some similarities, especially in the ultimate goals of reducing the computational costs of long-read assemblies, and minimizing coverage. If I understand correctly, DBG2OLC does this by mapping short-read contigs to long reads, and using these contigs as anchor points in an overlap graph. The long reads are then used to sort the contigs, so this hybrid approach is basically a sophisticated scaffolder - at least, I think the idea is to use all available contigs?

      Much like the DBG2OLC contigs, TULIP uses 'seed sequences' to sort the long reads. The key difference is, that TULIP has no intrinsic reliance on short-read data, and especially not on pre-assembled short-read contigs. For now, we use Illumina reads as seeds (anchors), but I expect that we will soon be able to replace them by bits of long-read data (the current quality of Oxford Nanopore data is probably already sufficient). To us, it is important to avoid short-read contigs altogether, because we are aiming for those very large, repetitive genomes for which De Bruijn-graph-assemblers are simply not well suited. Another difference is that TULIP does not use an actual read (overlap) graph.

      Although the TULIP algorithm itself is not the main point of our paper, it would be interesting to run a direct comparison at some point. I am especially interested in the correction mechanisms for structural errors in the long reads. DBG2OLC apparently detects chimaeric long reads using the short-read contigs. TULIP does not explicitly try to detect chimaeric reads, but will remove suspect connections between seeds, based on the topology of the graph: chimaeric reads will lead to the third situation shown in Fig 2c. Apparently, in the case of the eel genome, this is sufficient to avoid misassemblies (Fig. S2-S7).

    1. On 2017-11-02 14:59:18, user Kyle Fletcher wrote:

      Interesting article. With regards to how SuperNova was run, the syntax provided indicates that only six indices were used.

      Could you please elaborate why so few of the millions available were used and how those six indices were decided upon?

      Thanks

    1. On 2018-03-06 10:51:57, user Roland Malli wrote:

      Unfortunately, we have to indicate that almost all findings in this posting largely replicate and confirm our previously published work (https://www.nature.com/arti... "https://www.nature.com/articles/s41467-017-01615-z)"). <br /> Hence, we are wondering why this manuscript of Yi Shen and colleagues is not published as “Confirmatory Results”. It is not showing lot of new results!! <br /> Moreover, it is disappointing that our work has not been adequately cited. Our paper is mentioned as reference 44 out of 49. Just few sentence late in the discussion refers to our study. <br /> We disagree with introducing a new name for the FRET based genetically encoded potassium probe. We have published the principle first and named the constructs GEPIIs, genetically encoded potassium ion indicators. Our achievements are largely ignored by the authors particularly by renaming an almost identical construct. In addition, renaming will be confusing for others interested in the novel potassium sensor. <br /> In summary, we feel quite disappointed about the fact that our work has been largely ignored by this BioRxiv posting. This is unfair. The manuscript should be changed accordingly or retracted.

    1. On 2023-10-02 00:05:18, user LEVIn - Unicamp wrote:

      We discussed this preprint in our journal club and enjoyed reading it. We collectively agreed it's an exciting contribution towards a more efficient and comprehensive use of camera traps as a powerful tool to monitor biodiversity. We appreciated the concerns the authors had to create a user-friendly and flexible approach, which will certainly appeal to a diverse public of ecologists and wildlife biologists. As tropical ecologists, most of the questions that came up during our discussion were related to applying this approach to tropical, species-rich ecosystems. For instance, we wondered whether models would perform similarly (e.g., classification accuracy) in megadiverse communities where the local pool of species is larger and morphological variation (both across and within species) may also be amplified. Would this imply having a more robust training set? We understand this is not something trivial to predict based on the current set of species, but a deeper exploration of how species' traits influence prediction accuracies would be very welcome in this direction. For example, does the model have lower performance for species with smaller body sizes and/or coloration more similar to the background? Or perhaps within groups of species that are more similar in size? We also wondered whether detection and classification accuracy varies across diurnal and nocturnal records, which could bias predictions. Congrats on developing such a powerful tool for ecologists and wildlife biologists, and good luck with the next steps of this work!

      Laboratório de Ecologia das Variações Individuais (LEVIn), State University of Campinas (Unicamp), Brazil

    1. 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.

    1. On 2019-06-28 18:36:39, user JAZLYN MOONEY wrote:

      I was wondering if any of the authors could comment on their reasoning behind using the unsupervised model of ADMIXTURE and then comparing with RFMix which requires haplotype data? It seems like you would want to use the supervised model of ADMIXTURE with the same haplotypes you used in RFMix to make the comparison fair.

    1. On 2017-05-01 03:34:29, user Rob Lanfear wrote:

      This is pretty amazing stuff, very much enjoyed the ms. I would love to see two things here:

      1. A discussion in the main ms (perhaps it is buried in the supps somewhere, which I didn't read in detail) of the power of the new method. E.g. roughly how many mutations would one have to measure to have a hope of using the models you propose (e.g. 192 rates in the trinucleotide model presumably requires a decent amount of variation as input; this is compounded by trying to estimate mutation rates for each gene, even using a binomial regression).

      2. Implementations of the methods, with READMEs etc to get people started. I notice that you mention this on Twitter, but I think the impact of the ms would be much higher with available implementations.

      A question related to point 1 on power. It seems like a standard model selection framework (e.g. hLRT or AICc) should work well here to decide how many parameters one can/should be estimating for a given dataset, and thus whether fitting a full 192 rate model is justified. Does this seem right to you?

    1. On 2017-12-07 20:59:40, user Neil Adames wrote:

      Kumar et al. describe how a yeast histone deacetylase (HDAC), Hos3, is involved in delaying the START transition specifically in daughter cells. This manuscript is very thorough and utilizes some elegant experiments. Based on its length and content, I suspect it is intended for Cell or Molecular Cell.

      Based on previous work on Hos3 localization and its role in G1 phase timing, Kumar et al. hypothesized that Hos3 specifically inhibits START in daughter cells. The authors demonstrate that Hos3 becomes associated with the nucleoplasmic side of nuclear pores of daughter nuclei during anaphase. Interestingly, Hos3 association with daughter NPCs requires that Hos3 localize to the mother-bud neck and that the nucleus traverse the neck. Daughter NPC association of Hos3 also depends on the karyopherin Mtr10.

      Kumar et al. propose that Hos3 maintains a high level of the START inhibitor Whi5 in daughter nuclei by altering Whi5 import dynamics and show that altering Hos3 localization and activity also alters Whi5 nuclear distributions and the length of G1. However, the authors importantly show genetically that Hos3 and Whi5 act synergistically, implying that Hos3 also affects another process important for START. Besides Whi5, Hos3 affects the localization of a number of proteins involved in nuclear transport of proteins and mRNA. Because the repressor of CLN3, Ace2, has been shown to asymmetrically accumulate in daughter nuclei, the authors checked if Cln3 abundance was affected by Hos3, but it was not. However, CLN2 expression is affected by Hos3, with expression occurring early in the absence of Hos3 activity.

      Since HDACs like Hos3 are instrumental in gene silencing, and CLN2 is relatively close to the telomere, the authors examined if CLN2 is silenced by Hos3. As one would expect for a silencing mechanism, CLN2 is associated (along with its telomere) with the nuclear periphery during G1, but not S, and more so in daughter nuclei. Moreover, this peripheral localization depends on Hos3 activity. Artificial tethering of CLN2 to the periphery resulted in delayed START in both mother and daughter cells independent of Hos3 activity, suggesting that this artificial tethering bypasses Hos3 function.

      Finally, Kumar et al. start to dig into the Hos3 silencing mechanism by showing that Hos3 deacetylates components of the NPC and their deacetylation is important for Whi5 asymmetry, CLN2 tethering to the nuclear periphery, and the G1 delay in daughter cells.

      Criticisms:

      1) The manuscript lacks a lot of background information needed for a reader not versed in yeast cell cycle regulation or nuclear pore function.

      The START transition in yeast is regulated by cyclin-dependent kinase (CDK) associated with the G1 cyclins Cln3, and Cln1/Cln2 (Cln1 and Cln2 are partially redundant). Cln3-CDK phosphorylates and inhibits Whi5, which binds to a major S-phase transcription factor complex called SBF. The balance between Cln3 and Whi5 is a major determinant of the critical cell size necessary to enter S-phase. Once Cln3 is able to overcome Whi5, SBF transcribes CLN2. Cln2-CDK is also able to phosphorylate and inhibit Whi5 in a positive feedback loop that results in rapid Whi5 phosphorylation and consequent export from the nucleus. Whi5 nuclear import is constitutive and dependent on the karyopherins Kap95 and Cse1, whereas Whi5 export is dependent on the karyopherin Msn5, which interacts only with phosphorylated forms of its substrates. Re-import of Whi5 occurs in anaphase when the mitotic exit network phosphatase, Cdc14, dephosphorylates Whi5.

      Yeast nuclear pore complexes (NPCs) have been shown to regulate many disparate cellular processes beyond that of nucleocytoplasmic shuttling. Relevant to this manuscript, association of genes with the nuclear pore is an antecedent for transcriptional silencing and activation (or memory), depending on the proteins that bind to the gene’s promoter (SAGA complex for activation, YKU70/80 and Rap1 for silencing). In the case of transcriptional activation (or memory), association of the gene with the NPC is thought to facilitate handing off of the transcript to the mRNA export machinery (of which Mtr2 and Mex67 are a part) and maintenance in a poised open conformational state. In the case of transcriptional silencing, the NPC facilitates association of the silenced gene with the inner nuclear membrane protein Esc1 (yeast don’t have an equivalent to the nuclear lamina, which is important for silencing in vertebrates). In general, gene silencing eventually involves histone deacetylation by recruitment of HDACs and heterochromatin formation.

      2) Presumably, Hos3 moves from the neck pool to the NPC pool by their close proximity, but the authors do not measure if there is a directly inverse correlation between the amount of neck-localized Hos3 and daughter NPC-localized Hos3 during transit of the nucleus through the neck. Alternatively, Hos3 could be moving from cytosolic pools to the NPCs.

      3) The authors don’t explain how cytoplasmic dynein (Dyn1) is involved in movement of the anaphase spindle into the neck. Dynein associates with the bud cortex and pulls on cytoplasmic (astral) microtubules emanating from the daughter-oriented spindle pole body, pulling the spindle into the mother-bud neck.

      4) The authors show that in cdc12-1 septin mutants, Hos3 is symmetrically localized to mother and daughter NPCs, while in hsl7? mutants Hos3 is still daughter-specific, but only accumulates to the spindle pole body, and in cdc15-1 mitotic exit mutants, Hos3 is localized to the neck and daughter spindle pole body. These observations beg for a better explanation than that provided – “We conclude that Hos3 recruitment to the bud neck is dispensable for its localization to the dSPB but is essential for its enrichment at the daughter cell nuclear periphery.” Clearly, close proximity of the NPCs to the neck isn’t important for Hos3-NPC localization because Hos3 is symmetric in the cdc12-1 mutants but the mother NPCs don’t traverse the neck (or do they?). The septin ring has been shown to be important for the asymmetry of numerous daughter or mother-specific molecules, so Hos3 may normally be prevented from diffusing into the mother by the septins. Moreover, it seems that Hos3 NPC localization to NPCs might depend on its phosphorylation state since loss of two different kinases maintain Hos3 asymmetry but prevent its daughter NPC association (SPB localization may be a weak one normally masked by NPC localization).

      5) The authors don’t explain why they tethered Hos3 to the pore interior protein Nup49 rather than the nuclear basket protein Nup60.

      6) The authors should have measured CLN2 expression in the experiments in which they artificially tethered CLN2 to the nuclear periphery (Fig. 6) and in which they used constitutive acetylation mimicry NPC mutants (Fig. 7).

    1. 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?

    1. On 2017-11-13 02:18:25, user Thomas Millner wrote:

      I want to add the following important information:

      In 1994, Stackebrandt and Goebel recommended a 97 % 16S rRNA gene sequence similarity threshold up to that an additional DNA-DNA hybridization (DDH) determination need not be conducted for confirming that two strains do not belong to the same species (Stackebrandt and Goebel, 1994).

      Now, this topic has already been revisited by Meier-Kolthoff et al. (2013) [1] based on a large empirical dataset, sophisticated statistics and a broad comparison of different approaches to calculate similarities among 16S rRNA gene sequences. A recommended approach was given as well. Their study ("When should a DDH experiment be mandatory in microbial taxonomy?") confirmed what microbial taxonomists had already assumed based off their day-to-day routine, i.e., the traditional 97% threshold was ultra-conservative and could safely be increased -- without a significant risk for wrongly differentiated species. Even better, they found out that the threshold varies from phylum to phylum and they thus used their model to also provide phylum-specific thresholds.

      I recommend citing their work, as it is highly relevant for your work.

      [1] https://doi.org/10.1007/s00...

      Free version of their article:

      https://www.researchgate.ne...

    1. On 2025-10-01 16:26:41, user Stephen Smith wrote:

      A major milestone! The underlying technology advanced here seems certain to have great impact in connectomics and beyond, combining efficiently as it does the imaging of dense tissue context with multiplex molecular annotation. This combination will address a vast set of needs presently unmet across a broad swath of neural and non-neural tissue biology and pathology. Kudos to the E11 Bio team! DISCLAIMER: I am a member of E11's Scientific Advisory Board.

    1. On 2020-04-02 00:00:54, user Aaron wrote:

      The authors should be careful in making a claim that the expression of ACE2 in cats and dogs would allow them to be links in transmission chains. While they discuss sequence similarity, they point out that even the closest ACE2 protein to humans still has 15% divergence. Regardless of receptor protein presence, a virus needs much more than a receptor to replicate within a host. Until there is biological evidence supporting such a claim, this should be removed at the very least from the abstract to avoid misleading future readers.

    1. On 2021-04-03 18:31:11, user smith wrote:

      Pablo, great work! There is flaw in the paper that may change your results. I think the SC heat map is inaccurate. Feet and Fingers must be red not blue. See this paper for a reference:Emotional sweating across the body: Comparing 16 different skin conductance measurement locations.

    1. On 2018-07-30 14:18:04, user Yoav Gilad wrote:

      I have two concerns about this study. First, as evident from table S1, there is a completely confounding batch effect that was introduced as part of the study design. All of the RNAlater samples were extracted at the same time (1/24/17 at 5:30pm), while the flash frozen samples were extracted over multiple days, without any overlap with the RNAlater samples. Because the extraction date/time are completely confounded with the method used to store the RNA, it is impossible to correct for this batch effect. My second concern is that even if one assumes that the batch effect is minimal and cannot explain the observed results (I am not willing to assume that, but it is required as context for the sake of raising this second concern) – there is another obvious technical difference between the RNA samples stored differently: the RIN (table S1). RNA samples stored in RNAlater are more degraded than RNA that was flash frozen – the effect is quite large. Thus, this study, at most, can report the comparison of storage methods on RNA quality, not the effect of storage methods on gene expression. We already know that different transcripts degrade at different rates. In summary, I believe that the question asked by the authors is important, but that the study is flawed and as a result, it is difficult to use these data to effectively address the question.

    1. On 2019-07-15 19:06:59, user Cathy Stein wrote:

      I would recommend the authors read recent papers from the Ugandan household contact study where resistance to M. tuberculosis infection is documented epidemiologically. PMID: 30165605 is another documentation of highly exposed individuals not acquiring Mtb infection. In addition, PMID: 29304247 shows that many TST conversion events happen 3-6 months after ascertainment of the TB index case, so declaring subjects as "non-converters" after only 3 months of observation runs the risk of misclassificaiton bias. Lastly, the Clin Infect Dis study was the basis of a recent immunologic study (PMID: 31110348 ) that would be an interesting comparison to the work presented here by Weiner et al.

    1. On 2018-03-15 21:43:59, user Eric J Feczko wrote:

      Very cool! <br /> Unfortunately, I think the method from our paper was referred to as an unsupervised algorithm; it is actually a hybrid approach that combines supervised and unsupervised methods. While the community (subgroup) detection is unsupervised, the inputs to the community detection are derived from a supervised model. The supervised model ties the subgroup detection to a central question, as a result, the identified subgroups are more likely to be associated with the question.

      If the supervised model does not accurately predict outcomes, then the unsupervised isn't used.<br /> We have an alpha release for our package available at:

      https://dcan-labs.github.io...

      Anyone is free to use, provided they cite our work :)

    1. On 2022-09-23 05:03:03, user Bela Toth wrote:

      Hi Michi,<br /> impressive work, although I slightly disagree with you on the basic assumption of evolution. Nevertheless, I have a technical issue with this manuscript: The number of mRNA transcripts contigs that you show for the different genes in Figure 5 are not well defined. You forget that genes can produce several transcripts by the process of alternative splicing. Therefore labeling the x-axis with the gene names is a bit misleading.

      Take care!

    1. On 2020-01-29 19:47:53, user Daniel Corcos wrote:

      A follow up of this article has been published in the New England Journal of Medicine: <br /> Corcos D, Bleyer A (2020). "Epidemiologic Signatures in Cancer". N Engl J Med. 382 (1): 96–97. doi:10.1056/NEJMc1914747. PMID 31875513.

    1. On 2022-11-28 18:49:11, user Connor Morozumi wrote:

      Neat paper and will be important that people incorporate these findings into their pipelines for mycobiome work! I have some line item comments that I will send the authors as it's too much for a comment. My main thoughts are: 1) that a bit more info will be beneficial in the methods regarding how studies were selected, 2) it would be good to speculate a little on why there are large divergences in some ecosystems and not others, and 3) give some recommendations in the discussion beyond just using an outgroup database, ASV tables should also be checked manually and anything assigned just to Kingdom probably should be discarded or held very suspect!

    1. On 2022-01-04 09:20:01, user Abraham De silva wrote:

      FRAP recovery curves provided in the manuscript and fitting done to estimate t ½ (s) are meaningless. Especially Figure 2b, 3b, and Figure 5f. By definition, t ½ is the time necessary for 50% recovery. It is surprising that in all exponential recoveries after the photo-bleach (0th time), the next time point corresponds to more than 60% of total recovery (Figure 5f). It is the same for all of them; how t ½ can be estimated and compared in this situation. Authors should work on the basics of FRAP data analysis and fitting before making any novel claim. Classical FRAP (at this current FRAME acquisition rate) is too slow to detect the differences. It is the same for some of the recoveries shown in Figures 3b, 2b as well. Authors should re-measure those systems with higher FRAME rates (faster data acquisition, will give more data points before 50% recovery happens) to correctly calculate the real t ½ or use a quicker FRAP method.

    1. On 2018-08-24 13:48:27, user Sulev Reisberg wrote:

      Please take a look at http://journals.plos.org/pl... We used two large GRS-s (both containing thousands of SNPs) and showed that the differences between GRS distributions (and therefore high/low risk estimations) for Europeans and Africans based on GRS can be even larger - the opposite to each other. We show that risk allele frequencies tend to be higher in African than European population, which is the cause of getting higher GRS values there. Moreover, the frequencies of the SNPs used for GRS calculation contain strong population component - even without applying any GRS weighing.

    1. On 2021-03-04 22:20:53, user James Mallet wrote:

      This is a wonderful paper! It shows the superior power of Drosophila for getting large sample size tests of mating behaviour.

      We did this in lab group today, and noted some typos: Fig 1 RH panel has mau mating 100% with sim, and 0% with mau. Fig 4C reference I think should refer to Fig. 5.

      The topic of mate discrimination against hybrids and members of other species was popular around 2000 and called, I think correctly, "disruptive sexual selection". It occurs within species, but also may be important in "reproductive isolation" between species.

      We had a Heliconius example, Heliconius cydno and H. melpomene (much lower sample sizes, and somewhat weak evidence, but good nonetheless I think) which prefer not to mate with hybrids. See: Naisbit et al. 2001. Disruptive sexual selection against hybrids contributes to speciation between Heliconius cydno and H. melpomene. Proc R Soc B 268:1849-1854. There's a mini-review of articles on disruptive sexual selection up to that time.

    1. On 2023-06-01 19:01:23, user Jennifer Leo wrote:

      Having ground-truthed several of these areas along the US Gulf Coast, I can say that the map grossly misclassifies marsh area in these locations. If we can assume that it has missed marsh habitats consistently across the globe, the marsh estimates are consistently incorrect. Additionally, you can't equate tidal marsh with the generic moniker, "wetland".

    1. On 2023-02-23 14:01:41, user Andreas Martin Lisewski wrote:

      In their preprint (https://doi.org/10.1101/202... version 2), Jones et al conclude:

      "As the MERS-CoV RBD binds more efficiently to hDPP4 than known HKU4r-CoVs, and as the MERS-CoV S protein has the demonstrated capability of utilizing human cell proteases for mediating cell entry, the HKU4r-HZAU-2020+S(MERS) chimera appears to constitute enhanced potential pandemic pathogen (gain-of-function) research."

      However, as they directly observe, their bioinformatics analyzed sequencing data does not cover a 33 nt stretch between genomic nucleotides 23,908 and 23,940 of MERS HCoV-EMC/2012, which corresponds to amino acid residues 818EQLLREYGQFCS829 in MERS S.

      This missing sequence is at the N-terminus of the "upstream helix" (UH, MERS S residues 816-851) - a critical spike ectodomain scaffold structure that is extremely conserved among betacoronaviruses [1-4].

      Without this local 818EQLLREYGQFCS829 sequence, the ectodomain would be structurally unstable and the resulting mutant MERS S glycoprotein most likely functionally inactive [3,4].

      It is therefore incorrect to conclude that "HKU4r-HZAU-2020+S(MERS)" is a representative of "enhanced potential pandemic pathogen (gain-of-function) research", because the sequencing data presented by Jones et al probably does not constitute a functional MERS S glycoprotein.

      In the context of biosafety and biosecurity, extreme care must therefore be taken with specific statements about "enhanced potential pandemic pathogen" and "gain-of-function research". The authors and/or bioRxiv content editors should have moderated the above statements and conclusions before publication.

      Also, it is unclear if "HKU4r-HZAU-2020" itself (the proposed backbone) corresponds to an actual virus; and any experimental attempt to bioactively resurrect a putative viral bioinformatics sequence of unknown origin poses considerable biosafety and biosecurity risks.

      References

      1.Yuan Y, Cao D, Zhang Y, Ma J, Qi J, Wang Q, Lu G, Wu Y, Yan J, Shi Y, Zhang X, Gao GF. Cryo-EM structures of MERS-CoV and SARS-CoV spike glycoproteins reveal the dynamic receptor binding domains. Nat Commun. 2017 Apr 10;8:15092. doi: 10.1038/ncomms15092.

      1. Barnes CO, West AP Jr, Huey-Tubman KE, Hoffmann MAG, Sharaf NG, Hoffman PR, Koranda N, Gristick HB, Gaebler C, Muecksch F, Lorenzi JCC, Finkin S, Hägglöf T, Hurley A, Millard KG, Weisblum Y, Schmidt F, Hatziioannou T, Bieniasz PD, Caskey M, Robbiani DF, Nussenzweig MC, Bjorkman PJ. Structures of Human Antibodies Bound to SARS-CoV-2 Spike Reveal Common Epitopes and Recurrent Features of Antibodies. Cell. 2020 Aug 20;182(4):828-842.e16. doi: 10.1016/j.cell.2020.06.025.

      2. Sorokina M, Belapure J, Tüting C, Paschke R, Papasotiriou I, Rodrigues JPGLM, Kastritis PL. An Electrostatically-steered Conformational Selection Mechanism Promotes SARS-CoV-2 Spike Protein Variation. J Mol Biol. 2022 Jul 15;434(13):167637. doi: 10.1016/j.jmb.2022.167637.

      3. Walls AC, Tortorici MA, Snijder J, Xiong X, Bosch BJ, Rey FA, Veesler D. Tectonic conformational changes of a coronavirus spike glycoprotein promote membrane fusion. Proc Natl Acad Sci U S A. 2017 Oct 17;114(42):11157-11162. doi: 10.1073/pnas.1708727114.

    1. On 2019-07-10 21:12:46, user Tatiana Arias wrote:

      We would like to know you opinion about the paper in order to improve before we submit to publication. We are hoping to submit to GBE, do you think this is a good fit for our paper? do you recommend another journal? THANKS!

    1. On 2020-03-31 22:43:44, user Alexander Ljubimov wrote:

      The revised version of this paper has been published in Nature Communications: Anna Galstyan, Janet L. Markman, Ekaterina S. Shatalova, Antonella Chiechi, Alan J. Korman, Rameshwar Patil, Dmytro Klymyshyn, Warren G. Tourtellotte, Liron L. Israel, Oliver Braubach, Vladimir A. Ljubimov, Leila A. Mashouf, Arshia Ramesh, Zachary B. Grodzinski, Manuel L. Penichet, Keith L. Black, Eggehard Holler, Tao Sun, Hui Ding, Alexander V. Ljubimov & Julia Y. Ljubimova. Blood–brain barrier permeable nano immunoconjugates induce local immune responses for glioma therapy. Nature Communications volume 10, Article number: 3850 (2019).

    1. On 2024-10-18 18:57:00, user CDSL JHSPH wrote:

      I really like this article. Thank you for sharing your article. Malaria remains a major global health challenge, and understanding the biological rhythms of mosquito vectors and malarial parasites is crucial for improving control strategies. However, previous studies have only revealed that mosquitoes have daily rhythmic behaviors. You provided new insights. First, about half of the genes in the mosquito salivary gland transcriptome show 24-hour rhythmic expression, and second, the gene expression of sporozoites in the salivary glands also shows circadian rhythms (parasite movement and infection ability). In addition, you mentioned in the discussion that the parasites and mosquitoes have evolved in coordination with the circadian rhythms, and jointly affect host infection by regulating parasite movement and mosquito blood feeding time. This is very interesting, and your findings provide a new perspective for understanding the temporal regulation mechanism of malaria transmission. At the same time, you mentioned optimizing the insecticide spraying time strategy according to the peak period of mosquito activity. In addition, your findings may also have reference effects on other diseases.

      But I have some questions. First, it is good that you use 12h dark and 12h light to simulate day and night. But in the wild, the temperature and humidity vary between day and night. Perhaps the natural day-night cycle could be better simulated by changing temperature and humidity. Secondly, you mentioned that some observations may apply to uninfected mosquitoes, but you did not specifically discuss the potential differences in rhythms and behaviors between uninfected and infected mosquitoes.

      Finally, thank you again for your contribution and new ideas.

    1. On 2020-05-13 10:01:43, user Armindo Salvador wrote:

      Key points:<br /> * The two active sites in each Prdx2 dimer are not kinetically independent<br /> * Oxidation of the Cp sulfenate is 2.2-fold faster when the second site is in –SOH form than when it is in disulphide or thiol forms <br /> * Formation of the disulfide is 2.4-fold slower when the second site is a disulfide than when it is a sulfenic or a sulfinic acid<br /> * The sulfinylation rate is independent of the redox state of the second site<br /> * Reduction of the second disulfide by DTT is 1.7-fold faster than reduction of the first<br /> * To our knowledge this is the first report of an enzyme combining positive and negative cooperativity in its catalytic cycle

    1. On 2016-03-07 02:28:54, user Kasper Hansen wrote:

      It has been possible to read VCF files in R by using the VariantAnnotation package from Bioconductor. This package has been available for around 4.5 years and was described in a 2014 paper in Bioinformatics, see http://doi.org/10.1093/bioi....

      Based on this, I suggest scaling back some of the claims made here. But it would be interesting to do some comparisons between the two approaches; I have seen very large VCF files in the wild.

    1. On 2020-09-11 14:49:08, user Jake Aronowitz wrote:

      Hi Dr. Scharff,

      Thanks so much for your kind words on the manuscript. We are very excited about these data. I think your suggestion to include a greater discussion about the postulations from the Tokarev (young active and well connected) paper can only serve to strengthen our manuscript. It will put our data into a fuller context and tell a more complete story about new neurons in HVC

      Thanks again!

    1. On 2022-03-03 19:11:05, user Brett Chrest wrote:

      Misleading conclusion: "We showed that mRNA vaccine (Pfizer) changes mitochondria by downregulation of cytochrome c resulting in lower effectiveness of respiration (oxidative phosphorylation) and lower ATP production."

      • Respiratory function nor ATP production were not actually accessed.

      Given the highly dynamic nature of the mitochondria, is is not always clear that a reduction in Cyt c is pathologic. Given the fact that intensity hardly changed (and also not statistically significant) in normal astrocytes when incubated with mRNA, the concern for alternations is weak. As seen in Fig 4., this was seen once again seen in Fig 5A.

      *This point above should be highly emphasized in the abstract since this paper is already being circulated online in order to discredit the vaccine and intensify hesitancy.*

      The entire justification for the mRNA being in direct contact with brain cells/tissue is unclear and not based on scientific data:<br /> "COVID-19 mRNA has been recovered from the cerebrospinal fluid [11], suggesting it can cross the blood–brain barrier (BBB)"

      • Citation 11 links to a 2004 paper on SARS-CoV replication in the respiratory tract of mice.
      • Dr. Abraham Alahmad, who studies the BBB, discussed this idea back in 2021 (links below); the biodistribution to the brain was incredibly low in mice and unlikely to have any meaningful effect in humans. The mechanistic justification for directly exposing brain cells to, what is estimated to be, an entire dose of the vaccine is flawed.<br /> https://twitter.com/scienti...<br /> https://scientistabe.wordpr...

      The manuscript highly contradicts the methodology of the paper:<br /> "Also, mRNAs are short-lived molecules. The vaccine’s messenger does not stay inside the cell indefinitely, but is degraded after a few hours, without leaving a trace."

      • Why was mRNA incubated for 96 hours, or even 24 hours? The justification here for how in vitro incubation of the mRNA for 96 hours translates to human physiology is lacking.

      Highly misleading abstract sentence:<br /> "mRNA vaccine produce statistically significant changes in cell nucleus due to histone alterations."

      • It should be emphasized that this was only seen in cancer cell lines, NOT normal brain cells. Additionally, changes in astrocytoma intensity were only seen under supraphysiological incubation of 96 hours.
    1. On 2021-04-08 22:20:07, user Charles Warden wrote:

      Hi,

      Thank you for putting together this preprint.

      I wish Genome Research had a Disqus-style comment system, similar to some other journals. I am currently taking something that I wanted to post as a comment on a different Genome Research article, and I instead will post it on PubPeer. So, in general, that is an option, even though I do not think that it is ideal for something truly intended as a comment.

      I think I would have to take a closer look to develop more of an option on this specific matter, but I did have a couple thoughts:

      1) My impression from this Tweet was different than my impression from the beginning of this preprint:

      https://twitter.com/KVittin...

      I can see certain parts of the preprint where I think the tone is more like a comment or complementary study. However, that is different than a "Contradictory Results" preprint or a PubPeer posting (assuming that the intention is to flag something about the study that may need to be corrected or retracted). I am not sure if that can still be changed for this preprint.

      My preference would probably to be something closer to my impression of the Tweet (as positive as possible), but it is not immediately clear to me if that is the intention. Essentially, I am noting that I am not entirely clear about the severity of the message.

      For example, I think there are usually limitations to simulated data. I can also believe that there are viewpoints that may benefit from being made more clear, beyond what is within the paper. However, I believe that I would agree with the idea of limitations due to extra noise in the RNA-Seq data, and I am not sure why this has to be "contradictory" instead of "alternative" results.

      I am not sure if it helps, but I have some notes about the RNA-Seq DEG method limits here. While I could see how someone could call the discordance "contradictory," I would say it is more of an overall limit (where no 1 strategy is best, and you can't lock down everything in advance). In that sense, I would say different freely available methods complement each other, rather than detract from each other.

      It is certainly possible that I missed something, since I did not have a strong opinion when I first saw the Varabyou et al. 2020 paper. However, unless I am missing something important, I wonder if a slightly different tone might help?

      In terms of material linked from my blog post (and other experiences), I think what I have seen matches some extra noise in the Salmon gene lists (relative to the TopHat2 / STAR htseq-count gene lists), but not with a horrible amount of extra noise. So, in terms of what I see in the abstract, I think my experiences might agree with the last sentence in the abstract (even though I am talking about extra noise in differential gene lists and the authors are talking about detection of expression in that particular sentence). Is there something different that you are concerned about?

      2) You say that "All data and scripts are available upon request.". I am not sure how large the intermediate files are in this situation. However, I think code and perhaps some processed data can be made available through means like GitHub, Zenodo, etc. (similar to what you have on FigShare).

      In general, I think it is best to avoid providing materials upon request, if at all possible. So, I am not sure what is the limitation (or how much you are not providing already), and I think either making more available and/or making clear what subset you couldn't provide might help.

      Best Wishes,<br /> Charles

    1. On 2022-07-10 01:25:17, user Charles Warden wrote:

      Hi,

      Thank you very much for posting this preprint.

      I think this provides a number of useful references and comparisons.

      I also have a few comments:

      1) While rMATS and MISO are popular programs, I don't think they are necessarily my preferred starting point for splicing analysis.

      For example, I might consider the following as starting points for splicing analysis:

      a) QoRTS + JunctionSeq (an extension of DEXSeq with a separate dispersion estimate for junctions versus exons)<br /> b) Custom analysis of the tab-delimited splice junction counts (SJ.out.tab) from STAR.

      Perhaps b) is too open-ended. However, if you labeled the junctions like genes or transcripts, you could calculate Count-Per-Million (CPM) values and perhaps use traditional differential expression methods (DESeq2, edgeR, limma-voom, etc.).

      Unlike a), there would not really be specialized splicing visualization for b) without additional coding.

      However, both of these strategies do not fit the description of "alternative splicing events (ASEs) are conceptualized as binary choices". Instead, each junction is treated like a feature.

      So, I can see how that can complicate comparisons. Nevertheless, limma-voom and DESeq2 are used as part of this package. If I look at the ASE-methods.R code and the supplemental methods, I think the main difference is the "Treatment:Isoform" interaction variable.

      In other words, if you had 2 groups with 3 replicates and 2 junctions as part of splicing event, am I understanding the that the difference between SpliceWiz and what I would have otherwise expected is that you now have 12 count measurements (2 junctions) instead of 6 count measurements (1 junction)?

      So, if I am understanding everything, I am not sure if you have comments with respect to why a strategy more like JunctionSeq (or what I might write with custom code for individual junctions) is not preferable and not provided as an option within SpliceWiz?

      2) I thought Figure S4 was interesting and useful.

      However, the retained intron counts are quite different, as also mentioned in the text: "Of the 15,188 extra retained introns annotated by SpliceWiz, 15,144 (99.7%) of these would have been excluded due to this extra criterion."

      Identifying additional differential intron retention events might be helpful, but I also wonder if perhaps this also corresponds to additional false positives. For example, I am also not sure how often there might be pre-mRNA and/or pseudogenes where no introns are spliced (not a single retained intron event). For example, if there are confounding factors not sufficiently modeled in simulated data, then I would expect estimates of accuracy could be considerably different than in actual data.

      3) As a minor point, there is a typo in the description for Figure 5: "Heirarachical" instead of "Hierarchical".

      Thanks Again,<br /> Charles

    1. On 2021-05-15 10:20:44, user Alvaro M. Guimerá wrote:

      It turned out that the random network generation algorithm was systematically overestimating the instances of 'additive' responses in the presence of constitutive signals. The reason was because inhibition was being modelled as an increase in the rate of the reaction that reversed the activation. Eg. Bactive + NegReg --> Binactive + NegReg. This resulted in a large pool of inactive enzyme that provided a lot of substrate for the mass action reaction to leap forwards in the presence of a stimulus. When inhibition was modelled differently, for example as the molecule being sequestered and then regenerated: Bactive + NegReg --> Binhib + NegReg, and then Binhib --> Binactive, the instances of additive responses greatly diminuished to become a small minority of cases compared to a blunted response.

      Overall, it is a conceptually interesting idea that the necessity of biological systems to self-regulate might create an attractor state of lower sensitivity to which constitutive signals push the system into.

      What might ageing look like at the molecular level? According to the molecular habituation concept proposed, it would be something like this:

      https://github.com/amguimer...

      May any of this be of some use to someone, somewhere. Good luck!<br /> Alvaro Martinez Guimera

    1. On 2023-03-22 10:19:38, user Sam Roberts wrote:

      In the 50:50 BaTP:UTP competitive transcription experiment you can't tell from figure 2B whether U or B are being incorporated preferentially as stated in the text. You could potentially however tell this from the HPLC data in 2c iii). To do this you need to calculate the relative molar extinction coefficients on the HPLC using a concentration gradient of standard samples and measuring their relative absorbance. Then normalise your HPLC integrals from 2c iii) against those coefficients

    1. On 2021-09-16 22:05:04, user Dyche Mullins wrote:

      Vimentin Intermediate Filaments and Filamentous Actin Form Unexpected Interpenetrating Networks That Redefine the Cell Cortex

      https://doi.org/10.1101/202...

      Members of the Mullins laboratory read this paper together and discussed it during a recent journal club meeting. Following the meeting, we drafted the following set of comment with the aim of helping the authors revise the work for final publication..

      Briefly, this manuscript describes in vitro and live-cell experiments aimed at understanding the connection between intermediate filaments and the actin cytoskeleton. As the authors note, this is an understudied topic, with many fundamental unanswered questions. The experiments described in the paper employ a variety of approaches, including rheometry, fluorescence microscopy, and cryo-electron microscopy. The major results claimed by the authors include evidence for extensive interactions between vimentin IF and actin cytoskeletal systems in the cell periphery and effects of IF networks on actin monomer diffusion.

      Specific comments/concerns:

      1. In the Introduction the authors state that it is “...generally thought that F-actin and VIFs form two co-existing but separate networks.” This is not an entirely fair description of the field. There is a significant body of work on interactions between intermediate filaments and elements of the actin cytoskeleton [e.g. myosin II/IF interactions described in Svitkina et al. (1996). J Cell Biol 135(4):991-1007], and how the actin cytoskeleton can regulate IF network architecture [e.g. this review by Chang and Goldman (2004). Nat Rev Mol Cell Biol. 5(8):601-13; and numerous research papers, including Schoumacher et al. (2010) J Cell Biol. 189(3):541-56 and Serres et al. (2020) Dev Cell. 52(2):210-222]. A more inclusive discussion of previous work on the interaction of the two cytoskeletal components would improve the presentation.

      2. The title mentions the cell cortex, but there is almost no mention of the cortex per se in the Results or Discussion.

      3. The figure captions should contain more experimental details. For example, captions should contain information on cell types, microscopy methods, and molecular probes (e.g. is the actin in Figure 1 phalloidin stained?). They should also contain other relevant details, such as buffer conditions, temperatures, and how many times an experiment was repeated.

      4. The authors should consider combining Figure 1 and 2. It is a little unclear what is being highlighted by the arrows in Figure 1, as the two networks appear to be quite separate, even in this region of the cell. Figure 2 makes a better case for overlapping network elements.

      5. In Figure 2, it is unclear whether the two networks are actually interpenetrating or simply present in different focal planes. Because the raw data includes z-stacks, it would be useful to show an XZ or YZ or 3d projection of the data.

      6. Figure 2 should contain some (even rough) quantification of the percentage of observed cells that displayed such unambiguously interpenetrating networks.

      7. The electron microscopy in Figure 3 is beautiful and quite convincing. The minimum distance argument, however, does not rule out the possibility that the actin and intermediate filaments are linked by plectins. The crosslinks could be present in areas where the filaments are further apart. Also, to convince the reader that they can distinguish between filaments based on width, the authors should provide a histogram of all the observed filament widths. Does the histogram show a clear bimodal distribution?

      8. For Figure 4, please cite some previous studies using this indenter method and/or provide further details on the method: does it apply uniaxial or isometric stretch? Some raw images before, during, and after stretching would be more useful than the recovery plot shown in B.

      9. As noted above the caption (or description) of Figures 4&5 should contain the number of biological replicates. P values calculated with n=number of cells (rather than the number of replicates) are not appropriate. We recommend simply removing the P value calculations.

      10. Unfortunately, the data presented in Figure 5 are not convincing:<br /> a. Firstly, P values calculated on the number of cells instead of the number of biological replicates are artificially small and should not be reported. The trend is intriguing, but does it hold up over multiple rounds of experiment?<br /> b. Secondly, the FRAP bleaches both monomers and filaments. To look at only monomers, a method like FCS would be better.<br /> c. Instead of the curves in B, raw images would be more informative.<br /> d. When vimentin is knocked out, does the rest of the cytoskeleton change to compensate? Currently the explanation for these results is that there could be direct protein-protein interactions between Vimentin and G-actin which affect G-actin diffusion. The author favored this over the model where G-actin diffusion is limited by physical/steric obstruction of Vimentin networks. However the current data is unable to fully delineate these two models as the Y117L mutant forms a sort of intermediate filament (“intermediate” in length relative to the long filament), which intuitively could physically obstruct G-actin diffusion in an intermediate fashion (which is observed by this experiment). Perhaps using another mutant form of Vimentin that is simply unable to form any filaments but is present in cellular protrusions could help clarify the mechanism of G-actin diffusion limiting.

      11. It is unclear what precisely the reader is to take away from Figure 6. The concentration of actin filaments is so high that one cannot make out individual filaments or their interactions with vimentin. It does not seem very surprising that two proteins can polymerize in the presence of one another. Is the point mainly that these two proteins can mix? Are there examples of the literature of other filament-forming protein phase separating from actin, or other conditions where this mixing doesn’t occur? A negative control would make this data more convincing.

    1. On 2021-06-23 11:58:40, user Tom Jacobs wrote:

      Interesting. I like the work. Just as you write, folks generally do not include the Cas- or gRNA/crRNA-only controls anymore. This was done back when CRISPR took off but it has largely been dropped.

      I wonder about the sRNA sequencing. Did you observe the intact DR + spacer in the reads, or only the processed forms? It looks like your library prep protocol would have excluded larger fragments. You mention the reads are 21-24 nt in length, but which part of the DR+spacer does that contain? Is it a single species? Or spread out across the 28-nt spacer?

      sRNAs should be produced along the entire length of your TRV vectors. Maybe what you are observing with the viral work is just the production of a functional siRNA from the viral RNA (standard VIGS) and not from a crRNA? Unless I missed it, all the results show that the presence of the crRNA vector is sufficient, but is there evidence that expression of the crRNA is required? I'm curious what would happen if you removed the U6 promoter and/or the DR. Or change the initiating nucleotide. That could establish that GIGS requires crRNA expression and not just presence.

    1. On 2017-06-15 11:11:46, user Jacob H Hanna wrote:

      The last three papers from Smith group describing human transgene free Reset cells, including this one, have failed to describe ability to generate teratomas. Mouse naive pluripotent cells have the intrinsic "self organizing capacity" to enter a formative state after in vivo SC injection and make teratomas within 4-8 weeks. I find this stunning and wonder whether the human "reset" cells being induced do not qualify to be annotated as pluripotent at all. <br /> I hope the authors can clearly and directly address this in their finalized published paper.

    1. On 2020-06-27 17:28:58, user Daniel wrote:

      wonder if conditions such as visual snow syndrome (VSS) and ringing in ears (Tinnitus) could be treated by Neuralink. both conditions seem to be linked to neurological disorders which tend to coincide together and is hypothesised to be a sensory issue even though the disorders happen in different parts of the brain, visual and auditory but are somehow linked. Does anyone know if there is any possibility or potential ways to go about treating such complex conditions with Neuralink?

    1. On 2021-05-04 05:32:49, user Lavinia wrote:

      Could you also expand on why you don’t think these “granules” are RNaseL dependent, as they still seem to form independent of PKR. Are these granules you observe as a response to dsRNA CHX sensitive? Emetine sensitive? What about ISRIB? (Key characteristics that differentiate canonical SGs from RLBs)

    1. On 2017-03-30 14:10:20, user Seth Bordenstein wrote:

      Hey David,

      I've got a comment on the pp below. We've taken careful efforts in several papers to not muddle phylosymbiosis with coevolution, as the former is a measurable pattern and the latter is a process, namely one that is quite restrictive and may/may not drive phylosymbiosis. The two terms are not meant to be associated by a loose definition as the pp below intimates.

      "Somewhat looser still, and approached more from a phenomenological standpoint, work on animal microbiomes have shown that microbial communities can co-diverge with their hosts in a process termed phylosymbiosis[47]. A challenge when discussing both of these later situations is that, although both *could* describe a coevolutionary process, other evolutionary dynamics could give rise to similar population patterns."

      Here's what we write in PLOS Biology 2016

      "As this outcome is not likely due to coevolution, cospeciation, or <br /> cocladogenesis of the entire microbial community from a last common ancestor, "phylosymbiosis" was proposed as a new term that does not necessarily presume that members of the microbial community are constant, stable, or vertically transmitted from generation to generation [1,12]."

      I suggest the following change that will make it more accurate:

      "Work on animal microbiomes demonstrates that microbial community relationships can parallel the phylogeny of their hosts in a process termed phylosymbiosis[ 47]. Phylosymbiosis is different from strict or flexible definitions of coevolution because it does not assume congruent splitting from an ancestral species, and host-microbiota assembly mechanisms could vary in space and time and be newly assembled each generation.

    1. On 2020-07-11 08:09:59, user acidic_compartments wrote:

      Nice story. Its good that you have cited Finnigan et al., 2012 for identifying a sorting signal. But, look into Banerjee and Kane, 2017 MBoC, for how Stv1 is retained at the TGN. It is guided a lipid, Phosphoinositol-4-phosphate (PI4P) and the W83KY sequence in Stv1 is bound by PI4P and retained at Golgi. This is probably distinct from the retention mechanism thought by Bryant and Stevens 1997 which you have cited.

    1. On 2022-01-29 22:46:43, user Werner Hemmert wrote:

      Dear Authors,<br /> for magnetic stimulation the current through the coil is relevant, not the voltage. A description of the coil is completely missing (number of turns?) Also very important would be the generated B field and its gradient. Or an estimation of the induced E-field (and its gradient), at least an order of magnitude...

    1. On 2016-07-12 00:22:02, user Davidski wrote:

      There's a bit of an issue with your TreeMix analysis.

      You included northern Russians from Kargopol in your Eastern European set. These Russians come from a former Uralic speaking region, so they have relatively high Siberian admixture. At the same time, your TreeMix run doesn't include Siberians.

      As a result, the East European set gets an admixture edge from the Yoruba from Nigeria, which doesn't reflect reality very well.

      The Kargopol Russians aren't relevant to Ashkenazi population history, so they can be dropped from the analysis. This should clean up the TreeMix run a bit.

    1. On 2018-01-31 06:16:31, user Wouter De Coster wrote:

      Dear authors,

      This looks like an interesting study, although completely out of my field. I am a bit surprised by a sentence from the abstract: "two semi-professional cliff divers (both male, mean age 19.3 years)". I don't think the mean age is a good descriptor if you only have N=2. For all we know, one of your bungee jumpers could be 0.5y old and the other 38.1.<br /> With N=2 I don't think there is a good summary statistic. My background knowledge of the subject is too limited to judge, but this struck me as statistically odd.

      Cheers,<br /> Wouter

    1. On 2025-04-02 13:02:42, user Gaurav Verma wrote:

      Very comprehensive study and highlights the significance of mitochondrial health in the context of TBI and its potential role in the development of neurodegenerative diseases. However, the OCR values in pmol/min is very low and doesn't corroborates with the acceptable range from agilent which is from 20-160 pmol/min (Source: Characterizing Your Cells Using OCR Values to Determine Optimal Seeding Density). Would like to hear the thoughts. Good luck.

    1. On 2022-10-13 12:53:12, user Ryan Kessens wrote:

      It's amazing how intolerant the Pikm-2 allele is of changes to the sensor Pik allele. I'm particularly surprised that the RGA5 HMA domain in Pikm-1 was not tolerated by Pikm-2 considering the fact that nanobody domains can be incorporated into Pikm-1 and coexpressed with Pikm-2 with some success. Do these seemingly contradictory results surprise you? Do you think there is something special about the nanobody structure that makes it a good replacement for the HMA domain in Pikm-1? Do you think coexpression of Pikp-2 with Pikm-1 pikobodies would result in less autoactivity?

    1. On 2018-12-19 09:25:59, user sjones54 wrote:

      Dietary studies that move beyond correlative factors and address, in an experimental fashion, transgenerational effects are of critical importance and I commend the authors for their undertaking. However, I have some reservations regarding this study. One of which is that, to my knowledge, 'lifetime reproductive fitness' cannot be accurately determined in Drosophila melanogaster via the methods described. Given the highly stochastic nature of a 'fly lifetime', alternative methods are appropriate to avoid generation of what amounts to random noise. An examination of the authors’ data (specifically the bimodal distribution) appears to confer with this point. Perhaps the removal of the low points (0-10 offspring), indicative of vial collapse, could help, post hoc, to obtain a more accurate assessment. Moreover, I am uncertain of any work in D. melanogaster that demonstrates a clear link between body weight and fitness. As it stands I remain skeptical this paper represents a concrete contribution to the on going dietary dialogue.

      On a related note it is of concern that the phrase “To make sure that female reproduction was not limited by male quality, a new male was transferred into each vial every second week, or immediately if escaped during handling or found dead.” was borrowed, albeit modified with respect to the timing, from work by Pekkala et al 2011 without reference to this work. As the protocol also appears to have been taken from this work (although used there for Drosophila littoralis), convention dictates referencing would be appropriate.