On 2019-06-06 18:27:31, user Amrit Singh wrote:

Hi Y-h. Taguchi, this is interesting. The datasets in the mixOmics R-library are a small subset of the original data used in the manuscript (https://www-ncbi-nlm-nih-go... "https://www-ncbi-nlm-nih-gov.ezproxy.library.ubc.ca/pubmed/30657866)"). Please see https://github.com/singha53... for the entire breast cancer data (train and test). I wonder if there is a sparse version of HOSVD, in order to perform variable selection which is already implemented in DIABLO using soft-thresholding.<br /> Best,<br /> Amrit

On 2020-04-30 18:50:08, user Marine Ecology Lab wrote:

THIS PREPRINT HAS BEEN PEER-REVIEWED AND PUBLISHED IN "NORTHEASTERN NATURALIST" IN 2020:<br /> Scrosati, R.A. 2020. Effects of intertidal elevation on barnacle recruit density and size in wave-exposed habitats on the Atlantic Canadian coast. Northeastern Naturalist 27: 186-194.<br /> http://pdfpapers.weebly.com...

On 2017-01-29 19:50:04, user Jenner Little wrote:

Extracting relationships between genes based on vector space was also done a few years ago at a National Lab https://www.kaggle.com/c/wo...

On 2024-09-09 13:34:38, user Tien Vu wrote:

The preprint has been published at Nature Plants ( https://www.nature.com/articles/s41477-024-01786-w ), and its link will be added to this page soon.

On 2019-06-28 18:15:59, user Fraser Lab wrote:

This manuscript by Rocchio and co-workers investigates the structural basis for the interaction between the molecular chaperone Spy and the client protein Im7. Although the Spy-Im7 complex crystallizes, only the Spy portion is readily modelable and there is only residual electron density for the Im7 molecule. This attribute of the cyrstal system means that investigating the structural basis for the interaction is challenging for traditional X-ray crystallography methods. In a previous publication (Horowitz et al., 2016), an approach was designed to overcome this challenge; 4-iodophenylalanine (pI-Phe) was introduced into the Im7 peptide at one of eight positions, and anomalous data was collected for the Spy-Im7 complexes. The Spy-Im7 interaction was mapped using a combination of the anomalous signals from pI-Phe, the residual electron density from Im7, with plausible Im7 conformations generated using molecular dynamics simulations. The original work was the subject of criticism (Wang, 2018), which focused mainly on whether or not the interpretation of the weak anomalous signal was valid. The authors have acknowledged aspects of the criticism (Horowitz et al., 2018), while emphasizing the cross-validation build into their approach, and that other biophysical methods (e.g. NMR) corroborate their proposed model for the Spy-Im7 interaction. The major successes of the present manuscript are that: i) it increases the sensitivity of pI-Phe assignment, validating parts of the previous paper, and ii) it contributes to the development of anomalous scattering as a tool for interrogating protein structure and function.

In the present work, Rocchio and co-workers focus on optimizing data collection and analysis for anomalous signals from pI-Phe. They placed pI-Phe at one of three positions (only one of which cleanly overlaps with the previous publication) in the Im7 peptide and collected diffraction data around the iodine L absorption edge (? = 2.38 Å and 2.76 Å). Because the iodine scattering factor is 3.9-fold lower at the longer wavelength, peaks in the anomalous density map that are present at 3.28 Å but absent at 2.76 Å, were assigned to iodine. To test signal reproducibility, they adjusted the goniometer ? angle, and collected additional datasets from the same crystals, and also collected repeat datasets from different crystals. The reproducibility was moderate; three signals were observed in all the datasets, one signal was observed only in one of the datasets, and two signals were observed in three out of four datasets. The authors used a cut-off of 6 ? for assigning peaks. Why was this cut-off chosen? Were peaks present at site I3 (e.g. Fig. 1A/C) at lower electron density thresholds? We suggest that showing the anomalous difference maps at various thresholds is a good idea for a supplemental figure to appreciate the sparsity of the signal at high sigma values and the choice of the cut-off at 6 ? .

The manuscript could be strengthened if the authors addressed the lack of reproducibility for some of the iodine signals in more detail. They state that it is “…clear that the ability to detect low intensity signals is highly dependent on the crystal and the resolution.” Could it be that different crystallization conditions have shifted the binding mode of Im7? I noticed that the concentration of zinc acetate was listed as between 70-270 mM – could this have contributed to the difference? The authors also mention that three out of the four binding sites were identified in the previous work. To make a fair comparison, it might be worth showing all of the proposed binding sites from the previous paper (Horowitz et al., 2016) (e.g. on Fig. 5). On a related note, how confident are the authors in the refined occupancies for the iodine atoms? Is it possible to perform refinement with different initial occupancies to see if they converge on a particular value?

The major weakness of the manuscript lies in its failure to extend the increased sensitivity of iodine assignment to an improved understanding of the Spy-Im7 interaction. The authors begin the manuscript by introducing the READ algorithm – why hasn’t the more sensitive assignment of pI-Phe been fed back into READ? It would be interesting to see if whether the ensembles generated by the two anomalous data collection strategies agree with each other.

Overall this is a well written article that will be of interest to a wide section of the structural biology community. The improved capability of modern beamlines to collect anomalous data at long wavelengths, as described in this paper by Rocchio et al., may help to interrogate the structure and function of macromolecules that were previously intractable to traditional approaches.

Minor points

1. Consider revising the manuscript title – it is possible for a residue to be partially occupied and not dynamic - “conformationally heterogeneous” is probably more accurate. Or leave it as is, but clearly define the differences and the ability of crystallography to inform between dynamics and heterogeneity somewhere in the manuscript

2. It might be helpful to add a couple of sentences in the introduction to describe what is known about the Spy-Im7 interaction from orthogonal methods (see point 6).

3. Please include a section in the methods describing how the ITC experiment was performed.

4. Page 4 line 31: Typo - “we reasoned that we should be able to specifically distinguish iodine anomalous signals from other with elements”

5. Page 2 line 14: Shouldn’t it be Residual Electron and Anomalous Density not Residual Anomalous and Electron Density?

6. Please either label Fig 1D with the location of the “crook of Spy’s cradle” or include a new figure. It might be helpful to have a “cartoon” type schematic in the introduction to illustrate what is known about the Spy-Im7 interaction.

7. Fig S4-6: Please label the iodine peaks with the labels used in Fig. 1 (e.g. I1, I2, I3, I4).

8. PDB 6OWX: Is imidazole 214 in chain A modelled correctly?

9. It’s notable that they have co-crystalized Spy with 10 peptides (8 in the previous publication, 1 overlapping in both, and 2 novel ones in this publication) - have soaks been attempted? This could also add an interesting experimental control where the anomalous signal should be displaced by excess unlabeled client peptide but maintained (or enhanced) by labeled peptide.

10. Peak height is a good measure and relatively unbiased, but the anomalous maps have the potential to inform (in a relatively model unbiased manner) on the occupancy and B-factor directly. Showing multiple concentric contours or plotting density as a distance from peak center along a pseudo atomic radius will help to clarify the differences in the profiles. For example, in table 4, the peak heights for the 4th and 5th rows are the same, but the occupancy is 2-fold different.

11 - Figure 4 is pretty difficult to follow, even for us. We could do with a bit more annotation of where the disordered peptide backbone is predicted to trace. It also seems like the aromatic group attached to the I is a fairly strong modeling constraint that could help guide the eye in this figure.

12 - These data are especially valuable for methods developers and given the issues raised by Wang on the previous paper, it is especially important to put as raw data as possible out into the public. Papers like this should be held to a higher data deposition standard (mtzs are not enough!): the authors should deposit their raw diffraction data at SBGrid Data Bank (https://data.sbgrid.org/) "https://data.sbgrid.org/)") or an equivalent resource to enable future re-use and validation.

We review non-anonymously, James Fraser and Galen Correy (UCSF), and will have posted this review as a public comment on the preprint: https://www.biorxiv.org/con...

References<br /> Horowitz, S., Salmon, L., Koldewey, P., Ahlstrom, L., Martin, R., Quan, S., Afoine, P., van den Bedem, H., Wang, L., Xu, Q., Trievel, R., Brooks, C. & Bardwell, J. (2018). Nat. Struct. Mol. Biol. 25, 989–991.<br /> Horowitz, S., Salmon, L., Koldewey, P., Ahlstrom, L. S., Martin, R., Quan, S., Afonine, P. V., Van Den Bedem, H., Wang, L., Xu, Q., Trievel, R. C., Brooks, C. L. & Bardwell, J. C. A. (2016). Nat. Struct. Mol. Biol. 23, 691–697.<br /> Wang, J. (2018). Nat. Struct. Mol. Biol. 25, 989–991.

On 2021-06-23 07:38:24, user Alex Crits-Christoph wrote:

This work by Professor Jesse Bloom details a phylogenetic analysis of a set of SARS-CoV-2 genomic sequences originally submitted to the Sequence Read Archive, likely by the authors of Wang et al. 2020, in February - March, 2020. Both a preprint, and a later paper (by Wang et al.), were publicly available describing these sequences, including key aspects of their genomic features. However, independently of the preprint or paper, which make no direct reference to an NCBI submission, the sequences been marked for deletion on NCBI, but were re-obtained by Bloom via internet backups of the data. While sharing more genomic data from the early epidemic can be valuable, I believe the current version of this work makes several errors that are important to address in both scientific content and critically, in scientific communication.

In general, the work is vague or remiss about extremely important context and details about the sequences in question. To begin, the genomes obtained are consistently referred throughout the title and abstract (and indeed most of the text) as being from the "early" epidemic. This terminology is too vague, as it may invite the reader to assume that the genomes include the earliest cases known - in fact, they are not, and were originally reported by Wang et al. 2020 to be from January 2020. Over 25 genomes had previously been obtained during 2019 for SARS-CoV-2. To properly interpret the context of this work, an emphasis on the timing is critical and should not be avoided in the abstract or title.

Secondly, the abstract and title both strongly give the inaccurate impression that the re-obtained sequences alone newly demonstrate that the known and reported genetic diversity of viruses in the Huanan market was not representative of all circulating SARS-CoV-2 variants at the time. This is alluded to in the title, which says they "shed more light" on the early epidemic. Yet, this is not supported by the data in the manuscript: it was already known that the genetic diversity of viruses obtained from Huanan market cases was not representative of all genetic variants circulating at that time. As can be seen most clearly in Figs 3 and 5, the new sequences obtained were not completely unique in sequence or unusual in case timing. There were already several reported publicly available similar genomes with for the most part identical sequences from the early epidemic. The author is certainly aware of this, as it is evident from the data presented and from the works cited (both Garry 2021 and Kumar et al. 2021 discuss this at length), but this key point is not emphasized in the title or abstract, and indeed is a weakness of the importance of the work itself.

Thirdly, in the introduction the work makes several key omissions about the timing of cases. The introduction directly suggests that the Huanan market is unlikely to be a site of zoonosis because a number of early cases could not be confirmed to be connected to the market; However, this was true of SARS-CoV-1 in Fushan as well. The author also neglects to mention that a significant fraction of cases were connected to *other* markets in the city, including the earliest cases (as described in the WHO report). And indeed, if there were multiple spillover events, e.g. from a shared wildlife supply chain that connected multiple markets throughout the city, even with perfect contact tracing data one would not expect all cases to be connected to one market.

Fourthly, I believe the inclusion of Figure 2 in this paper is highly unprofessional and misleading. This is a screenshot of an email from a scientist who is not associated with the papers or data described in the data; indeed, the only relevance between them seems to be a shared nationality of the author(s). Additionally, as of June 2021 the data described as 'deleted' in this screenshot is publicly available again on NCBI. While I understand that Bloom likely did not realize this mistake, it is difficult to justify the inclusion of this figure in a scientific work.

Fifthly, and perhaps most importantly, I think the previous point happens to provide a fortuitous lesson about a key error that Bloom makes in assuming misconduct on the part of the Wang et al. 2020 authors. He writes "It therefore seems the sequences were deleted to obscure their existence". It is implicitly assumed that Bloom believed that to also be true of the data described in Figure 2 - hence its inclusion - but its recent re-publication on NCBI makes it abundantly clear that was not the case. To be honest, this is not surprising. Claims of scientific misconduct made from a distance, without knowledge of the details and circumstance surrounding the issue, have the potential to appear convincing despite having perfectly ordinary (or perhaps slightly less than ideal, but not nefarious) scientific rationale.

Similarly, there is also evidence that scientific misconduct is also highly unlikely to be the case with the dataset that is described in this paper. Primarily, this is because the paper describing these sequences was published in a journal several months later. If the authors were indeed trying to obscure their data, they would simply also not publish their paper in a public journal, or request it to not be published after submitting for review. If these sequences were removed for the purpose of obscurity, it is also worth noting that such an effort clearly flopped - because as described above, these sequences do not immediately provide any completely new knowledge about the genetic diversity of SARS-CoV-2 in the early pandemic. In both the preprint and the paper (the first submitted before data removal; the second published likely after), no mention of a BioProject accession is made, and instead it is said that the data is available upon reasonable request from the authors. If the writers initially intended for the data to be made public, standard practice would have included the BioProject reference (or even a placeholder) in the text. The fact that there was never such a reference makes it quite evident that there likely was a miscommunication between co-authors about whether the data was intended to be released to NCBI.

The reality is that minor scientific missteps and less-than-ideal circumstances surround the sharing of scientific data all of the time. The process of publishing on scientific data is fraught with tiny details and hard work, the trainees responsible for it are often overworked, and there are often unfortunate but relatively ordinary scientific incentives to avoid making as much data public as should be. The rationale behind what data is and is not shared for scientific works are often nuanced, and sometimes quite personal. The scientific process would be better if it were not the case, but these forces are almost universal, and everyone encounters them in scientific work. In this circumstance, like in the circumstance outlined in Figure 2 of this work, these ordinary forces of scientific circumstance are almost certainly more likely the case than direct censorship or misconduct. It is unfortunate and alarming that this work as it currently stands baselessly contributes to an environment where many readers assume the latter depending on the country of origin of the original authors.

Minor point:

At the bottom of page 5: "But although there are unusual aspects of RatG13's primary sequencing data". This statement is vague, the language is unscientific, and many interpretations of it would not be supported by the citations provided. I would recommend that Bloom change this sentence to present a coherent scientific statement if indeed there is a point about genome quality to be made there that is relevant to the text.

On 2017-03-14 14:26:22, user AdamMarblestone wrote:

-"Improved multitask learning through synaptic intelligence" https://arxiv.org/abs/1703....

On 2017-02-24 17:08:01, user AdamMarblestone wrote:

-"Bidirectional Backpropagation: Towards Biologically Plausible Error Signal Transmission in Neural Networks" https://arxiv.org/pdf/1702....

-"FROST: A Distributed Neurocomputational Model of Working Memory Maintenance" https://www.ncbi.nlm.nih.go...

-"Neural Episodic Control" https://arxiv.org/abs/1703....

On 2020-04-13 21:24:43, user Mutasem Taha wrote:

The supplementary table of this preprint raises doubts about the findings, for example, Enoxacin is given a high index of 1.18 while its very close analogue Norfloxacin is considered inactive and given index of 0.07. Similarly, Prothionamide is considered potent with an index of 1.14 while its close analogue ethionamide is totally inactive according to the study with activity index of 0.12. <br /> The preprint considered Nitazoxanide to be inactive (index 0.6) although it is currently in clinical trials as treatment for COVID-19 (https://clinicaltrials.gov/... "https://clinicaltrials.gov/ct2/show/NCT04341493)") and was reported to have anti SARS-Cov-2 IC50 of 2.1 microM in a peer reviewed paper (Cell Research (2020) 30:269–271; https://doi.org/10.1038/s41... "https://doi.org/10.1038/s41422-020-0282-0)").

On 2018-02-06 14:26:19, user Anthony Mathelier wrote:

Note that the link to the Zenbu genome browser configuration with miRGeneDB annotation and ENCODE CAGE data in human given in the manuscript is not working anymore. Please use: http://fantom.gsc.riken.jp/...

On 2024-12-06 17:54:14, user Malte Elson wrote:

The remarks below are a summary of the points discussed during the Cake Club of the Psychology of Digitalisation lab at University of Bern ( https://www.dig.psy.unibe.ch/studies/cake_club_/index_eng.html ). They do not reflect the opinions of each individual journal club participant. Any responses to these points should be addressed to Malte Elson.

In their preprint, Spiess et al. (2024) illustrate the impact of influential data points on statistical significance in linear regression analyses. The authors reanalyzed data from three high-impact journals by searching for the term "linear regression” and digitizing graphs of the included papers (due to the absence of raw data). Their findings revealed that excluding influential data points often rendered previously significant results non-significant. The simulations included in the study largely confirmed expected outcomes, supporting the overall argument for incorporating leave-one-out analyses in data analyses practices. The authors ultimately advocate for broader adoption of such methods to enhance the robustness of statistical conclusions.

We found the paper to be interesting and an illustrative contribution to statistical education, both in terms of the potential fragility of published claims and as an illustration of an intuitive but underused outlier detection method. We identified points that might allow the authors to strengthen future versions of the manuscript, including some critical points about potential weaknesses or absences in the current version of the manuscript.

1) TERMINOLOGY CONFUSION AND REPORTING ISSUES<br /> * Graphs vs. Papers: There is some confusion regarding the unit of analyses, and probably some reporting errors: On p. 4, l. 115, the paper states that the sample was 24 + 30 + 46 = 100 graphs, whereas on p. 6, l. 170 the authors state they examined 100 publications (going by Table 1, this is a simple clerical error, and should say graphs).

* Similarly, the description of the columns in Table 1 (p. 11) is confusing, and we think has at least one reporting error:

* It is unclear what “Hits” represent: Are these unique papers, or do the search engines of Science/Nature/PNAS return the same paper multiple times for each instance of the search term (“linear regression”)?

* What does "number of graphs that were not shown" mean? We think these are instances of linear regressions that simply were not reported with a corresponding graph in the original publication, but they could also be graphs missing, inaccessible, or excluded <br /> * The “Articles” column is described as “number of Articles in which the analyzable graphs were found” (p. 11, l. 314), but we think these are the 21 articles in which the 29 “influential variables” were found. The number of articles with analyzable graphs is not reported. It thus remains unclear how many papers were included, and how many graphs were analyzed from each paper.

* On p. 6, the authors report having identified 29 graphs in 21 papers in which the removal of one datapoint changes the result of a linear regression (see also Figure 1). On p. 6, l. 179 the “incidence” (should be prevalence instead) of changes in papers is reported as ~20%. However, this puts papers (21) in the numerator and graphs in the denominator (100), which underestimates the prevalence. On the graph-level, it should be 29/100 = 29%. The paper-level prevalence cannot be calculated because the authors do not report the number of papers with analyzable graphs (see above).

* We strongly recommend reporting a Prisma flowchart to clarify the inclusion/exclusion of graphs and papers. In the same vein, the paper lacks basic information about the included studies, such as sample sizes or the distribution of p-values. Other information would also help emphasizing the importance of the present study, e.g. citation metrics.

* The authors refer to “Supplementary Data 1” (p. 4, l. 121) but provide no link.

2) SAMPLING STRATEGY <br /> * The study focuses on digitizable graphs without overlapping data points, inherently excluding studies with (1) larger samples and (2) homogeneous effects, where overlapping data points should be more frequent. This selection skews the included papers towards studies with smaller samples and p-values near 0.05 (due to lower power and publication bias / p-hacking), which are more susceptible to the illustrated effects. This is not a problem per se, but means the findings (including the prevalence rate) are about a narrower population of studies. Either way, the selection effects should be discussed in the paper.

* It is not fully clear how it was decided which graphs are analyzable and which are not. Moreover, on p. 4, l. 127-130 the authors state that the obtained regression parameters match those reported in the paper closely, but they do not further explain what exactly this means, or what happened when they did not match

3) ANALYSES AND CONCLUSIONS <br /> * The analysis does not account for dependencies when multiple graphs from the same paper, which will likely be based on the same data (which are then susceptible to the exclusion effects), are included.

* In a way, the susceptibility of findings to the removal of a single data point is a restatement of issues related to small samples. Small samples are inherently more fragile, and larger sample sizes are more robust to the influence of removing (or adding) single data points and render p-values (and other estimates) more stable. This is not to say that the findings reported are not interesting; however, we were wondering whether a table of all included studies sorted by observed p-value and sample size would have flagged the same fragile papers. This is also not to say that dfstat is redundant, and we absolutely see the pedagogical value in being able to point at individual data points that “cause” a finding to be significant. Rather, we would be interested to what extent dfstat converges with common heuristics.

* Relatedly, the authors decry that influence measures such as dfstat are largely ignored, even by statisticians (p. 4, l. 139). This may well be, but of course, statisticians (and non-statisticians) are obviously aware of issues related to low power and small samples, and one of these issues is the problem of spurious findings (e.g. due to few, extreme data points).

* The authors largely blame frequentist statistics, particularly on p. 10, where e.g. they state that “[a]s long as stating significance or not is still based on the ubiquitous ? = 0.05 threshold, these statements can be sensitive to the presence of a single data point.” (l. 282-284). However, it is unclear how this follows from their findings. Any inference (not just ? = 0.05) could be susceptible to the influence of single data points when the estimate is close to the criterion. Moreover, particularly when the sample size is low, any metric’s value (e.g. point estimates) will vary as a function of the removal of individual data points, regardless of whether the inference is threshold-based or not. This is simply a property of statistical models fit to a limited amount of data. So again, the issue seems to be with small sample sizes.

4) RECOMMENDATIONS AND FUTURE DIRECTIONS<br /> Things we would have liked to see:

* Additional analyses, such as leave-two-out or leave-k-out methods. The leave-one-out analyses are providing a good intuition of how fragile some small-sample study results are. Additional leave-k-out analyses would provide further information about the fragility of the entire sample.

* So far, the authors are concerned with the fragility of results as an outcome of removing data points. An additional study exploring the reverse scenario would be valuable. Specifically, it could investigate how extreme an additional data point would need to be to alter results, and how adding non-extreme data points could mitigate the relative weight of extreme data points.

* Discussing dfstat as a robustness metric (“How many individual data points would have to be removed/added to render a significant result nonsignificant or vice versa”)

* A discussion of how dfstat could be used for p-hacking by showing researchers which data points they would have to remove to turn a nonsignificant study result into a significant one.

* The authors graciously and immediately shared data and code with one of us who requested it, and we thank them for this. We would like to see this data and code provided in a public repository and linked to in a future version of the manuscript.

* We note that the authors chose to anonymise their data so that the reader cannot tell which original study’s results are robust or not. Personally, we think that meta-scientific interests are best served by making this information public; that is, we would like this data to not merely be used to illustrate the method but also inform the reader about the fragility or robustness of those publications’ results. Of course, not everyone agrees with this practice - perhaps the authors could comment on their perspective on this issue in a future version of the manuscript.

On 2022-03-15 10:42:31, user sagar khare wrote:

Thank you, Roberto and James for taking the time to engage with our work and for these helpful comments! We are revising the manuscript in the light of these comments (among others). We will post a detailed response to your comments in time.

Re. why we used AF2 when exptl structures are available: we generated these models in early December and posted this preprint on Dec 13 before any experimental data were available. When the first structures came out in early Jan, we performed a comparison with our models and submitted to journal in mid-January. (what can I say, that's the pace of scientific publishing :))

On 2016-08-20 15:44:28, user Raquel Tobes wrote:

It is good to know new things about bacterial intergenic regions!<br /> Escherichia coli, Salmonella enterica and Klebsiella pneumoniae are plenty of REP (Repetitive Extragenic Palindromes) in their intergenic regions. Staphylococcus aureus and Streptococcus pneumoniae have many intergenic tandem repeats and Mycobacterium tuberculosis has REP-like intergenic sequences and some tandem repeats. <br /> Even considering that probably the short reads assemblies in many cases collapse the regions with repeats, it would be interesting to find some analysis about repeats in your study.

On 2018-06-25 08:50:18, user AlbrechtLab wrote:

final version at Comms Biol (open access) includes optical data<br /> https://www.nature.com/arti...

On 2018-06-06 10:27:19, user Balaji wrote:

I am posting the following jointly with Jaydeepsinh Rathod.

It is clear that the people of the Turan were a mix of people from Iran and India. From Iran came Anatolian agriculturalist ancestry and from India AASI as well as ANE. You have found that BMAC people had 60% Iranian agriculturalist-related ancestry, 20% Anatolian<br /> agriculturalist-related ancestry and 13% ANE-related ancestry and 6% AASI. Suppose from Iran came people with 60% Iranian agriculturalist-related ancestry and 40% Anatolian agriculturalist-related ancestry and from India came people with 62% Iranian agriculturalist-related ancestry, 26% ANE-related ancestry and 12% AASI and suppose they mixed in equal proportions, the resulting population would have the ancestry that was found in BMAC.

There is additional evidence that ANE found in Turan may have come from India and not from Kelteminar hunter-gathers or from West Siberian hunter-gathers. Figure S3.10 shows that other than the three Indus_Periphery outliers (Gonur2_BA, Shahr_I_Sokhta_BA2 and Shahr_I_Sokhta_BA3), it is Sarazm_EN and Gonur1_BA_o which share most alleles with Birhor. The high allele sharing of Indus_Periphery with Birhor is explainable by their high AASI. But Sarazm and Gonur1_BA_o have no AASI. Their high allele sharing with Birhor must be due to the West Eurasian alleles of Birhor being similar to those in Sarazm_EN and Gonur1_BA_o. Sarazm_EN has been modeled as 75% Ganj_Dareh_N, 2% Anatolia_N and 23% West_Siberia_N and Gonur1_BA_o as 61% Ganj_Dareh_N and 39% West_Siberia_N. In spite of the mainstream BMAC people having about 6% AASI and Sarazm_EN and Gonur1_BA_o having 0% AASI, the latter share more alleles with Birhor. This must be because the mainstream BMAC people have half their Iranian agriculturalist-related ancestry from Iran and only half their Iranian agriculturalist-related ancestry from India. The “Iranian agriculturalist-related” ancestry in Iran and India must be different as suggested by the Metspalu et al. 2004 finding that “mtDNA haplogroups which are shared between Indian and Iranian populations exhibit coalescence ages corresponding to the early upper paleolithic” and that they are present in India "largely as Indian-specific sub-lineages". Therefore Sarazm_EN and Gonur1_BA_o are likely to have been emigrants from the Indus civilization – they could not have come from Iran since they have almost no Anatolian agriculturalist-related ancestry.

If the Indus population already had 26% or more ANE instead of the 10% ANE based on your assumption that Indus_Periphery was typical of the Indus population, then there is no<br /> room for Steppe_MLBA to bring in additional ANE and WHG ancestry.

On 2018-04-02 05:54:08, user Arun wrote:

"...Using admixture linkage disequilibrium, we estimate a date of 107 ± 11 generations ago for Iranian agriculturalist and AASI-related admixture in the Palliyar, corresponding to a 95% confidence interval of 1700-400 BCE assuming 28 years per generation."

118 generations at 28 years per generation gets us to 3300 years before present, i.e., 1300 BCE, not 1700 BCE. This would kill the idea that the Palliyar admixture with Iranian agriculturalists originated with the collapse of the Indus Valley Civilization.

Moreover, I would take P. Moorjani's establishment of 26-30 years per human generation since the Neanderthals with a big pinch of salt. The !Kung are measured to have a generation time of 25.5 years. This would further kill this idea about Iranian agriculturalist - AASI admixture. That extra 2.5 years is significant.

On 2018-03-31 20:18:53, user Richard Stevens wrote:

You guys have Mal'ta Boy listed as R1b1a1a2 (R1b-M269), among other errors.

On 2025-02-19 09:08:52, user Gennady Gorin wrote:

This is a novel and interesting consideration, though I believe less fundamental than 1. the definition of "neighbor" is somewhat arbitrary because the metric is arbitrary and 2. the definition of "neighbor" is ill-specified because the uncertainty in (imperfect) data is not properly propagated.

On 2023-04-19 08:47:03, user Jakub wrote:

Hello, <br /> The improvements brought by pulse repetition rate reduction while holding pulse characteristics constant for in vivo imaging has been shown in the following work. Maybe you will find that interesting. <br /> https://www.pnas.org/doi/ab...

On 2018-11-29 09:06:32, user Conrad Mullineaux wrote:

Speculative hypothesis papers can be fun and good for stimulating debate. But, to be useful, I think they need to present a plausible and coherent scenario (something that at least has a chance of being true) and they need to pay reasonable attention to the facts. I’m not sure that’s the case here. My main concerns are:<br /> 1. Fig. 3. The feedback loop looks neat, but it ignores the fact that the local [O2] around the nitrogenase need not correlate to any significant extent with the global atmospheric [O2]. Huge discrepancies could occur, due to local environmental conditions, and also due to the metabolic activity of the cell itself. Considering only the latter factor, the intracellular [O2] could be much higher than ambient (due to PSII activity) or much lower than ambient (due to respiration). If the nitrogenase doesn’t actually see the global atmospheric [O2], such a feedback loop could not clamp global [O2] at any particular level as proposed.<br /> 2. P.5 “If diazotrophic cyanobacteria are grown under conditions where they have sufficient CO2 and light, and with N2 as the sole N source, then they grow and accumulate no more than 2% oxygen in their culture atmosphere (16). The 2% O2 remains constant during prolonged culture growth because this is the O2 partial pressure beyond which nitrogenase activity becomes inhibited. With greater O2, nitrogenase is inactivated and there is no fixed N to support further biomass accumulation. With less O2, nitrogenase outpaces CO2 fixation until the latter catches up, returning O2 to 2% in the culture.” The outcome of this experiment will come as a surprise to anyone who has observed diazotrophic cyanobacteria happily growing without a combined nitrogen source at 21% ambient O2 (it depends on the cyanobacterium, of course). The result is a key plank of the authors’ argument, but it’s not clear if, when or how the experiment has been carried out. It’s not as straightforward as it seems, and nothing like that is described in the cited reference (16: Berman-Frank et al 2003). The nearest thing in that paper is a statement that a specific cyanobacterium, Plectonema boryanum, is unable to fix nitrogen above certain ambient [O2] levels. The limits are actually rather lower that the 2% quoted: 0.5% in the light and 1.5% in the dark (16). Plectonema is a specialist for microaerobic environments, and most other diazotrophic cyanobacteria are not so susceptible to O2 inhibition. <br /> 3. P.5 “Cyanobacteria have evolved mechanisms to avoid nitrogenase inhibition by oxygen, including N2 fixation in the dark, heterocysts or filament bundles as in Trichodesmium. Critics might counter that any one of those mechanisms could have bypassed O2 feedback inhibition.” Indeed they might. The authors go on to brush aside their imaginary critic on 3 grounds, none of which seem valid. “First, evolution operates without foresight”. Foresight isn’t needed: there would have been an immediate selective advantage to acquiring an O2 protection mechanism. “Second, the mechanisms that cyanobacteria use to deal with modern O2 levels appear to have arisen independently in diverse phylogenetic lineages, not at the base of cyanobacterial evolution when water oxidation had just been discovered”. Very likely so, but what about the next 2 billion years? “Third, the oldest uncontroversial fossil heterocysts trace to land ecosystems of the Rhynie chert”. It may or may not be the case that heterocysts evolved late, but, in any case, heterocysts are not significant contributors to marine nitrogen fixation: in extant cyanobacteria it’s the other protection mechanisms that allow cyanobacteria to make a huge contribution to oceanic nitrogen fixation even in the presence of 21% atmospheric O2. What about those other mechanisms? The fact that different lineages of cyanobacteria have independently come up with at least 3 different ways to protect their nitrogenase from O2 indicates that evolving such mechanisms is not really such a big deal. The authors’ scenario suggests that for a period approaching 2 billion years there was a nitrogen-limited biosphere with cyanobacterial nitrogenase operating right up against an inhibitory concentration of O2. There would have been intensive selective pressure for adaptations to protect the nitrogenase from oxygen. The scenario depends on the assumption that no cyanobacterium was able to develop a protection mechanism that would allow nitrogen fixation at >2% O2, despite selective pressure operating over a period of about 2 billion years and the availability of multiple solutions to the problem, as seen in extant cyanobacteria. I’m afraid that’s implausible, and I suggest that we need to look elsewhere for an explanation of the low O2 level through the Proterozoic.

On 2017-03-29 06:40:36, user Ginny Barbour wrote:

Many thanks to everyone who has commented. We are following the comments and plan to respond to them as a group - there are some clear themes emerging. Keep the comment coming...

On 2020-07-08 13:37:25, user Anders Vahlne wrote:

Some comments about the reactivity of the blood donors from 2019 (BD2019) and 2020 (BD2020):

The results are shown in Figures 3A and 4B, F and G showing interferon-gamma-producing cells after stimulation with SARS-CoV-2 peptides.

In Figur 3A one can see that six BD2019 react with S (spike) and four with M (membrane) but none with N (nukleocapsid). Tthe cut-off may have been chosen high enough to get a desired outcome. In the histogram to the right the authors decide that in order to be T-cell reactive the cells have to react with N plus S or M. There is no explanation why. One might suspect that the reason is to have all BD2019 negative and still have BD2020 positive.

In Figur 4B they depict those that are CD4+ and CD8+. Now two CD8+ BD2019 are positive also with N.

In Figur 4F they depict those those that are antobody positive and antibody negative. The BD2019 were not tested and thus omitted in Fig 4F.

In Figur 4G there are two histograms. The one to the right summarizes T-cell positives in respective patient group. Again, the authors decide that in order to be T-cell reactive the cells have to react with N plus S or M so they get zero reactivity with the BD2019s.

On 2021-03-26 09:24:57, user Tijawi wrote:

Fascinating stuff, need to read through again. Note that UBP9 is now class Xenobia. Also, I hate to be pedantic, but be sure to check that the names of your nereid-based taxa are correctly combined: Eudoromicrobium, Autonoimicrobium, and Amphithoimicrobium.

On 2023-11-23 05:59:01, user Ying Cao wrote:

How can a mathematical model be generated to simulate a process/event about the change of unknown cellular states?

On 2019-07-09 09:29:52, user Martin Steppan wrote:

A very fascinating study and topic with interesting implications! If I understand the results and design correctly, the genetic instruments quantifying the risk for early menarche also grasp a considerable amount of socioeconomic information (by being associated with educational attainment). Would it not be straight forward to control outcome variables (like age at first birth) for socioeconomic differences on the observational level first, before conducting Mendelian Randomization? MR is powerful, but if you have real confounding variables, is there a rationale not use them?

On 2020-12-02 15:40:43, user Ryan wrote:

NE 598 Group 2<br /> Social isolation impairs the prefrontal-nucleus accumbens circuit subserving social recognition in mice. https://doi.org/10.1101/202...<br /> Ryan Senne, Evan Mackie, Patlapa Sompolpong, Anthony Khoudary

Introduction

We are a group of Boston University students enrolled in a course focused on understanding neural circuits, including cortical information processing, guided behavior and cognition. To further engage with current research in the field and to gain experience in the process of peer-review, we present the following critique of the currently unpublished manuscript from Park et al. posted on biorxiv.org on November 12, 2020.

Summary <br /> The medial prefrontal cortex (mPFC) has been shown to activate in response to social behaviors in both humans and rodents. Recent studies have revealed a corticothalamic circuit affected by social isolation; however, whether social isolation affects mPFC projections to other subcortical regions involved in social behaviors remains unclear. To this end, Park et al. investigate the role of projections from the mPFC to the nucleus accumbens shell (NAcSh) in the social recognition deficit observed in mice following social isolation. Through retrograde viral tracing, electrophysiological, chemogenetic and behavioral experiments they identified a novel circuit projecting from the prefrontal infralimbic cortex (IL) to the NAcSh affected by early social isolation. They found IL neurons to have decreased excitability in single housed (SH) mice compared to normally group housed (GH) mice. NAcSh-projecting IL neurons were activated when the GH mice interacted with a familiar mouse, but this activation was not observed in SH mice. Furthermore, inhibition of IL neurons in GH mice impaired social recognition without affecting social interaction in GH mice. Similarly, activation of IL neurons rescued social recognition in SH mice. These findings corroborate the social recognition defects observed in models of ASD and schizophrenia, which may reflect problems in human patients. Overall we recommend comparison of results to data collected before the re-socialization period, non-parametric data analysis and improved IHC imaging. Additionally, we recommend consistency between figures in the manuscript and the extended data, alternative anxiety measurements and in vivo electrophysiology recordings. We believe these recommendations will strengthen the argument for the role of this novel circuit subserving social recognition.

Figure 1 serves to establish the experimental timeline and demonstrate the social recognition deficit induced by social isolation. Mice were housed either singly or in groups for 8 weeks post weaning. SH mice were then regrouped for 4 weeks with their littermates. At the end of this 12-week period, experiments were conducted. Mice from both cohorts were subjected to three chamber tests assessing social preference and social recognition. Both GH and SH mice spent significantly more time with a novel mouse than an inanimate plastic mouse; indicating no change in social preference due to isolation (Fig. 1c). GH mice spent significantly more time with a novel mouse compared to a familiar one in the social recognition test. Constratingly, SH mice spent comparable time with both the novel and familiar mouse suggesting a deficit in social recognition (Fig. 1d). Both cohorts showed no significant deficits in general recognition memory or hippocampal dependent memory (Fig. 1e, f). SH and GH mice also showed similar body mass changes, basal locomotor activity and anxiety levels (Extended Data Fig. 1).

The authors hypothesized that projections from mPFC to NAcSh may be involved in social recognition. To test this the authors injected a retrograde enhanced green fluorescent protein (eGFP) virus into the NAcSh. Neurons in the deep layer of the IL were heavily labeled with eGFP. There was a significant difference in the number of eGFP+ cells in the IL compared to the PL (Fig. 2b). This observation led the authors to focus their study on mPFC-IL projections. Ex vivo brain slice whole cell patch clamp recordings revealed a significant decrease in excitability of NAcSh-projecting mPFC IL neurons in SH mice compared to GH mice (Fig. 2c). This decrease in excitability was not observed in mPFC PL projections to NAcSh, suggesting cell specific modulation of this circuit by social isolation (Fig. 2d). Other electrophysiological properties of NAcSh projecting IL/PL neurons were similar in both GH and SH mice (Extended Data Fig. 4).

The goal of the next experiment was to determine if IL-NAcSh projections were activated by familiar mice in a different behavioral paradigm. Mice from both cohorts were habituated to a target mouse (Fig 3a). Interestingly, both GH and SH mice spent significantly less time interacting with the target mouse on consecutive social habituation trials (Fig. 3b). In the social recognition test SH mice again spent comparable time interacting with both novel and familiar mice, indicating the social recognition deficit (Fig 3c). Post mortem slice histology was used to quantify the activity of IL-NAcSh projections in response to a familiar or novel mouse. A retrograde eGFP virus was injected into NAcSh in both GH and SH mice; eGFP+ cells co-labeled with c-Fos staining were used as a proxy for activation of this circuit (Fig. 3d, e). Quantification of this labeling revealed that GH mice had a significant increase in the ratio of c-Fos+/eGPF+ cells after interacting with a familiar mouse compared to a novel mouse (Fig. 3f). This increase in activity was not observed in SH mice, supporting the claim that this circuit is activated by a familiar conspecific.

To confirm the findings in Fig. 3, the authors conducted chemogenetic experiments in normal GH mice. A retrograde eGFP-Cre virus was injected into the NAcSh and a Cre dependent hM4Di receptor virus or mCherry control vector was injected into the IL (Fig. 4a, b). Intraperitoneal injection with CNO confirmed the inhibitory effect of hM4Di (Fig, 4d). Mice were then subjected to the social preference and social recognition tests following CNO injection. Inhibition of IL-NAcSh projections did not affect social preference, but did result in a significant decrease in social recognition (Fig. 4e, f). To further investigate this effect, mice were subjected to the social recognition test with the choice between a cage mate (in place of a target mouse) and a novel mouse. When IL neurons were inhibited, mice were unable to distinguish their cage mate (Extended Data Fig. 5). These findings support the claim that activation of this IL-NAcSh circuit is necessary for social recognition.

In an attempt to solidify this claim, further chemogenetic experiments were conducted in SH mice. The previously mentioned experimental approach was used; however, a Cre dependent hM3Dq or mCherry control vector injected into the IL (Fig. 4a, b, c). CNO injections confirmed the activation of IL neurons (Fig 4d). Activation of IL-NAcSh projections did not affect social preference but did rescue social recognition (Fig. 4e, f). These findings demonstrated that activation of this IL-NAcSh circuit is both necessary and sufficient for social recognition.

Major Criticisms

The authors claim that regrouping SH mice in the model is insufficient to rescue social recognition. White the first experiment showed that SH mice spent relatively similar time with both the novel and familiar mouse, suggesting a social recognition deficit, all behavior tests were done following resocialization of SH mice (Fig. 1d). Adding another SH cohort without resocialization prior to administering behavioral tests would be beneficial to determine whether there is a significant change between the performance of regrouped SH mice and non-regrouped SH mice.

In the second experiment, the authors found the projections from the prelimbic cortex (PL) to the NAcSh to have less neuronal density when compared to IL-NAcSh projections, therefore decided to conduct subsequent experiments only looking at the IL (Fig. 2b). Relatively less dense neuronal density in the PL does not equate to low activity in the PL and is not sufficient to rule out the role of the PL in social behavior, especially because previous papers have found projections from the PL to contribute to social behavior. There was no information on how eGFP-positive cells in the IL and PL were quantified. The cell numbers in the IL and PL were compared using an unpaired t-test, however, the IL cells appear to have a normal distribution while the PL cells do not. Using a parametric test may therefore be inappropriate for comparing the two populations. In Figure 2, there was also minimal physiological data to confidently conclude that excitability in the IL of SH mice is significantly reduced (Fig. 2c). Incorporating in vivo data would be beneficial.

In the third experiment, c-Fos immunohistochemistry was performed as a proxy of recent synaptic activity. The ratio of quantified c-Fos+ cells in the IL to GFP+ cells was used to prove that GH mice show a significant increase in c-Fos positive NAcSh-projecting IL neurons while encountering familiar conspecifics. The method behind quantifying the overlaps are unclear in the paper. The major issue with this approach is that separately quantifying c-Fos+ cells and comparing it to the quantified number of GFP+ cells is that there is a possibility that there are quantified neurons that are not co-labeled with c-Fos and GFP. A one-way ANOVA and Tukey's multiple comparisons test was used to analyze the data, however, all of the data does not appear to follow a normal distribution (Fig. 3f).

Apart from data in Figures 2 and 3 that are not appropriate for parametric statistical tests, data from other figures such as Figure 1 exhibit a binomial distribution and also do not fit the criteria for parametric tests (Fig. 1c). The distribution of the data in all experiments should be taken into consideration when running analyses <br /> While the viral stain in Figure 2 appears to be non-nuclear, the stains in Figures 4 and 5 appear to be nuclear (Fig. 2a; Fig. 4b; Fig.5b). It would be more standard to use the same virus for labeling throughout the experiments. The figures state that a retrograde adeno-associated virus (AAVrg) expressing eGFP was used, but the expression patterns are not consistent with this.

Minor Criticisms <br /> Many of the summary bar graphs in the figures have error bars that are obscured by the individual data points, specifically figures 3b and 3f, 4e and 4f, and 5e and 5f. Changing the color of the error bars would help with better visualization of the data and its distribution. Additionally, in figure 1b and all three chamber tests, it would be worth noting whether or not the tests were counterbalanced with the stimulus mice in different chambers. This would control for the SH mouse simply memorizing the location of a preferred stimulus rather than true social recognition or preference.

In the first experiment, it would have been worth titrating the length of juvenile isolation in order to find the critical period where its effect is strongest. The referenced paper determined 8 weeks to be effective, but an experiment to prove that 8 weeks is ideal would have been beneficial to the study as a whole. Another useful tool would have been in-vivo electrophysiology to selectively measure activity in IL-NAcSh projecting neurons during socialization and confirm the results shown by the c-Fos immunohistochemistry. Optogenetics also could have been used to measure social preference or recognition during the inhibition of these IL-NAcSh projections.

Merits <br /> The panels in Figure 1 are incredibly well made and very easily communicate the experiments and data. The heatplots used throughout the paper are incredibly parsimonious in their representation.The novel object and object place controls on the three-chamber test often get ignored so this experiment was very well controlled. <br /> The behavioral schedule in Figure 3 is incredibly erudite and can be recycled by other researchers for these types of experiments. <br /> One of the biggest mishaps in chemogenetic experiments is a lack of proper controls. The researchers were incredibly thorough in their DREADD’s experiemnts and included all the necessary control groups including CNO in WT mice and using a saline vehicle in a DREADD injected animal. This type of comprehensive experimental schedule ensures that the data has a considerable level of confidence attached to it.

In the supplemental figures the authors chose to include several controls which are necessary for the confidence of their results. Their inclusions of anxiety controls, often overlooked electrophysiology metrics, object controls, and cagemate controls inspires confidence in the results. Overall, a very well controlled paper.

Future Directions <br /> One of the most important future experiments could involve dissecting between cell subtypes within the IL. A recent paper has shown that somatostatin interneurons house social memory within the PL and such cells could be necessary and sufficient for proper memory expression. The authors coils also determine the receptor subtypes the pyramidal neurons they focused on contained. For example, a recent article showed that Pl neurons which projected to the NAc shell were D1R+ and it would be interesting to see if similar neurotransmitter systems were prevalent in both mPFC areas.

With respect to Figure 2, outside of the IL, the PL, and vCA1 have also been shown to be necessary for the expression of proper social cognition and behavior. These other areas have been shown to project tohe NAc shell. A follow up study that highlighted the unique contributions of these distinct areas and how possible neural circuitry links them together would be a valuable funding for the social neuroscience community. The electrophysiology in this figure is solid from a technical standpoint but whether this difference in excitability translates to meaningful behavioral phenotypes isn’t characterized. To this end, in vivo physiology during epochs of social interaction may more aptly furnish the narrative that Il cells are preferentially affected by social isolation.

With respect to Figure 3, one of the most crucial aspects of this paper is that socially isolated mice have functioning social recognition on a short time scale as shown in 3A and 3B. The authors supply two reasonable hypotheses that this then could be a deficit in consolidation or retrieval memory mechanisms. This would be a crucial discovery for the field of social and memory neuroscience. One possible set of experiments the authors could pursue in a future paper would be to use the TRAP2 or tet-tag viral system to tag cells active at the encoding of a social epoch with ChR2 and eYFP within vCA1 or the PL, two areas shown to be important for the social engram. The next day the researchers could perform a 90-minute transcardial perfusion and quantify overlap. If there is an above chance overlap between the “tagged” cells and endogenous c-Fos this would rule out consolidation as the faulty mechanism. In this hypothetical scenario the researchers could then use a subsequent cohort to see if chronic activation of this memory ensemble could be enough to rescue the behavior if it were a failure of retrieval.

Works Cited

1.)Yamamuro K, et al. A prefrontal–paraventricular thalamus circuit requires juvenile social experience to regulate adult sociability in mice. Nature Neuroscience, (2020).<br /> 2.)Murugan M, et al. (2017) Combined Social and Spatial Coding in a Descending Projection from the Prefrontal Cortex. Cell 171(7), 1663-1677.<br /> 3.)Cummings K. and Clem R. (2020) Prefrontal somatostatin interneurons encode fear memory. Nature Neuroscience 23(1):61-74<br /> 4.) Xing B. et al (2020) A subpopulation of Prefrontal Cortical Neurons Is Required for Social Memory. Biological Psychiatry in press.<br /> 5.) Okuyama T et al. (2016) Ventral CA1 neurons store social memory. Science. 129:17-23.

On 2018-11-30 13:12:24, user theempiricalmage wrote:

And of course, lack of Iranian samples from the paleolithic. "Caucasus", nowadays, has become the choice euphemism for Iranian. Note the lack of 25kybp R1a diversification in the Caucasus compared to Iran (likely origin). Difficult to reconcile that with this study. The Iranian anthropological record is impeccable, and it has long been established that the plateau was well populated by that time.

On 2016-11-09 02:34:34, user James Hane wrote:

An interesting read, but excluding scaffold N50s and using only contig N50 in assembly comparison tables versus the other paired-end/mate-paired assemblies is misleading. Adding scaffold N50 and total unknown(N) bp to those tables would address this.

Fleshing out a discussion on how the nanopore platform resolves heterozygous regions would also be appropriate.

On 2016-08-14 16:13:44, user Christopher Mason wrote:

Nice to see out! FYI, the detection of m6A in single-molecule RNA-seq was first published in 2012:<br /> http://www.ncbi.nlm.nih.gov...

On 2018-02-26 16:40:49, user imroze khan wrote:

Our recent work identifies evolutionary ecological parameters that influence insect immune memory in natural populations

On 2023-11-22 00:43:31, user Andrew Jiang wrote:

Apologies for the correction, the correct site should be https://launch.icarus-scrna...

An updated manuscript version will be accessible soon.

On 2019-02-05 10:21:11, user Sascha Rösner wrote:

https://uploads.disquscdn.c...

The results of this study are very interesting. Especially as we are dealing here with a long lived habitat specialist with very limited dispersal abilities. The ecological or conservation consequences might be crucial in the case of e.g. increased habitat fragmentation. In a study, where similar questions were asked, we found somewhat similar result in terms of relationship with significant closer relationship of males up to 5km I guess.

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

On 2018-10-04 14:35:17, user Nicole Dubilier wrote:

Host dependency also influences symbiont transmission, check out this excellent paper by Roberta Fisher, Lee Henry, Charlie Cornwallis, Toby Kiers, Stu West:<br /> https://www.nature.com/arti...

On 2019-01-30 17:06:55, user Camila Fernandes Nascimento wrote:

Hello Jurjen Luykx and co-authors, very cool manuscript! How could we have access to the supplemental content.<br /> Thanks<br /> Camila Nascimento

On 2020-02-07 11:42:41, user mlhnrca wrote:

"Among acylcarnitines, acetylcarnitine (C2) and the hexanoylcarnitine (C6-DC/C8-OH) showed the greatest absolute differences in baseline values between cases and controls (7.54 µM versus 9.92 µM (p=0.54) and 0.12 µM versus 0.08 218 µM, respectively)."

Those p-values are not significantly different, so it's misleading to state that there were concentration differences.

On 2024-02-26 20:40:13, user marcinkortylewski wrote:

The final version of this manuscript is published after peer-review at Molecular Therapy Nucleic Acids - doi: 10.1016/j.omtn.2024.102137.

On 2021-06-01 06:06:21, user Prof. T. K. Wood wrote:

Unfortunately, much literature is missing from this manuscript:

Manuscript should cite the first persister proteome study; one that even used a similar technique, overproducing toxin YafQ (doi:10.1111/1462-2920.12567).

Manuscript should cite the first single cell work showing the importance of ribosomes and heterogeneity in persistence resuscitation (doi:10.1111/1462-2920.14093).

Manuscript should cite the first resuscitation study with single cells which showed the importance of HflX (https://doi.org/10.1016/j.i... "https://doi.org/10.1016/j.isci.2019.100792)").

Manuscript should cite the persister formation work with single cells showing the importance of RaiA (https://doi.org/10.1016/j.b... "https://doi.org/10.1016/j.bbrc.2020.01.102)").

On 2020-02-25 04:25:13, user Hsiao-Huei Chen wrote:

More than E/I... Rethinking Autism? See how 2 neurons tango! PV-SOM compensatory interaction alter information filtering @ the ACC!

See Chen's lab work @ Nat Commun 2020 Feb (https://rdcu.be/b16IK) "https://rdcu.be/b16IK)").<br /> "Hyperactivated PTP1B phosphatase in parvalbumin neurons alters anterior cingulate inhibitory circuits and induces autism-like behaviors"

On 2020-12-17 16:53:38, user Raffi Aroian wrote:

This article is now published: https://aac.asm.org/content...

On 2020-10-02 18:57:09, user ravi chandra wrote:

Is there a control for this experiment? tried to look for SARS COV2 sequences in a healthy and COVID negative individuals? What is the confirmation that the amplified rna sequences originated from SARScov2?<br /> Which was the first SARScov2 virus sample or gold standard that was considered in this study ? Any reference cited as such ?

On 2016-12-21 15:00:29, user ???? wrote:

Published as a part of proceedings BIBE2016<br /> https://doi.org/10.1109/BIB...

On 2018-11-14 15:56:42, user dalloliogm wrote:

This is an interesting method, however I don't understand the application to GWASes. Isn't the p-value in a GWAS dependent on the allele frequency?

On 2018-05-14 14:23:32, user Eduardo J. Villablanca wrote:

Intriguing! Interesting Figure 4F, in which ileum and C1 draining lymph nodes are removed and 7B8 cells proliferate in all LN (e.g. D1, D2, etc). Wondering if the SFB is spread through out the GI tract or ileal DC now can migrate to D1?

On 2021-01-24 12:48:14, user alejandro wrote:

Please cite this paper

https://www.nature.com/arti...

On 2018-09-26 14:38:20, user Natsuhiko Kumasaka wrote:

Dear authors,

I think the conditional distribution just above the equation (6) in p.25 is flipped. Correctly b|\hat{b}, but not \hat{b}|b.

On 2018-02-26 16:01:24, user Helder Maiato wrote:

I must apologize to Dr. Paola Vagnarelli for not mentioning in my previous discussion her recent paper (De Castro et al., Oncotarget, 2018) where, in addition to data supporting a role for Aurora B in the regulation of proper nuclear envelope reformation on lagging chromosomes in human cells, they show that “core” nuclear envelope components (Lamins A/C) are uniformly recruited to lagging chromosomes.

On 2020-08-13 12:06:59, user kdrl nakle wrote:

It would be great if you would have any real manufacturing support.

On 2019-10-22 22:09:58, user DKF wrote:

Great to see anything re genetics in relation to France - considering the present official attitude towards DNA testing ("recreational" or otherwise). None the less, Y chromosome male line and mtDNA female line uniparental markers are the most informative for understanding the origins of regional groups - when combined with data from history and archaeology. Apparently none presented here. Perhaps in a subsequent publication? In addition, until there are ancient DNA studies of key French sites (e.g., LaTene Celtic) we are flying blind in many respects since migration for example during the Industrial Age will have had a strong impact on the population of today. We need to know "what is under our feet". Why is it that neighboring countries are flooding the literature with immensely informative ancient DNA studies? We need to integrate this data with similar work from France before we can make conclusions about how history and prehistory have affected the population of France today. More broadly, there is an expanding body of knowledge from Spain, Italy and Germany concerning for example Bronze Age Bell Beaker sites. Those from France would help to tie things together coherently so that we can provide an accurate story of Europe through the ages.

On 2022-03-01 07:08:40, user Mark Fisher wrote:

Interesting work, but where is the supplementary information? I can't seem to find it.

On 2018-02-23 10:15:21, user Ferran Aragon wrote:

Dear authors,<br /> Congratulations for these very interesting results. We are struggling to get good yields for long ssDNA (3-8Kb). I don`t understand very well how the yielding result indicated in the text when doing aPCR with AccuStart HiFI (695+/-35 ng in 50 ul, thus 14ng/ul aprox) fits with the purification results shown in Fig.S7 where the most efficient purification method gave a yield of about 250ng/ul. Could you clarify this? Thanks a lot!

On 2018-02-28 18:19:51, user Leslie Vosshall wrote:

The Vosshall Lab discussed this pre-print at our journal club on 2/28/2018. We agreed that it was a very exciting series of observations.

The following discussion points came up:<br /> 1. The experiments refer to control as 100% humidity, but was ambient relative humidity measured directly? It is very difficult to get a room to 100% humidity!<br /> 2. Figure 1A, how does the time on the X axis relate to the entrained circadian cycle of these animals? Is the big peak at 40 hr modulated by circadian time or is it an absolute peak dependent only on dehydration?<br /> 3. Figure 1A, what are the dehydration levels of the animals in this experiment? How tight is the correlation of dehydration to that activity peak? We were interested in comparing the curve in Figure 1C to the activity data in Figure 1A.<br /> 4. Figure 1C, is this behavior specific to blood-feeding? Or if you gave the animals the option of drinking water or sucrose would you see the same increase in feeding correlated to dehydration?<br /> 5. Figure 1C, what happens if you repeat this experiment with gravid females who previously blood-fed, who have a lower drive to blood-feed? i.e. can you disentangle the drive for blood from the drive to rehydrate from any source, including blood?<br /> 6. Figure 3D-E: it would have been good to include the GFP dsRNA control that was used in the sugar measurements in Figure B-C.<br /> 7. Figure 4A refers to n=8, but there are many more than 8 data points. Is this a typo in the legend?<br /> 8. Figure 4A, can the authors clarify what “wet conditions” and “dry conditions” refer to? Did they measure relative humidity?<br /> 9. Figure 4B, the X-axis is very confusing. Do you mean 1.94 cases/10,000? Or do you mean 19,400 cases? Or is 10[4] a typo given that there are closer to 2000 WNV cases per year – did you mean 10[3] as a multiplier? Also starting the axis at a non-zero value is misleading.<br /> 10. Do male mosquitoes have some of these phenotypes? It would be good to see how much of this is a general effect of dehydration vs a specific drive of females to seek blood sources<br /> 11. Given how low the blood-feeding rate on the Hemotek devices was, we wondered why the investigators did not use live hosts, especially in Figure 4.

On 2023-05-12 17:40:58, user Maurice Franssen wrote:

Fascinating research and results. One comment: the moth shown in Supplementary Figure 1i is not Noctua pronuba but its sister species Noctua fimbriata. I do not think that species will behave differently but the authors better show a specimen of N. pronuba.<br /> Maurice Franssen, amateur entomologist, Wageningen, the Netherlands

On 2020-02-05 11:14:40, user Pei-Hui Wang wrote:

Forward to you friends who needs. Prof. Wang Pei-Hui Lab in Shandong University, China, all orf clones of 2019-nCoV / SARS in pcDNA6B-FLAG can be freely requested, please contact him. Email: wphlab@163.com covering the following orfs: nsp1-16, S, 3, E, M, orf6, orf7a/b, orf8, orf9, orf14, N; and ACE2

On 2020-06-14 08:47:04, user Ivan Ansia wrote:

Please find companion manuscript at https://doi.org/10.1101/202...

On 2016-06-24 16:37:11, user Peter wrote:

We appreciate all the comments and will address them individually in due time.

One comment was that we are measuring stable gene transcripts rather than the up regulation of genes. As stated in lines 905 to 925, one would not expect a gene transcript to be stable at one time, unstable at another time and then stable later on (see Figure S3). We see this pattern in many of the transcriptional profiles. This is difficult to explain by the 'stable gene transcript' idea. Moreover, many of the gene transcriptional profiles show putative feedback loops. This is also difficult to explain by the 'stable gene transcript' idea. It cannot be attributed to 'noise' because the behavior of each probe is calibrated and 'noisy probes' are not used.

We are not the first to report the postmortem up regulation of genes as pointed out on lines 927 to 947 (using a different technology) and the other paper mentioned by mit_opinion below. The difference in our study from the others is that we measured the precise abundances of hundreds of genes through post-mortem time.

Lastly, one comment stated that we were measuring absolute transcript abundances. We never said anything about absolute concentrations. Our measurements are always relative to the live control.

On 2019-08-18 18:27:53, user A wrote:

This has been peer-reviewed and published, with significant modifications: https://www.mdpi.com/2072-6...

On 2020-02-10 16:30:13, user Virginia Savova wrote:

Now published with Springer Nature in Scientific Reports https://rdcu.be/b1sYv

On 2023-08-13 02:56:12, user Takanobu A Katoh wrote:

This preprint is published from DOI: 10.1126/science.abq8148

On 2022-03-21 23:11:00, user Soso wrote:

This paper has so much information and all the data looks great. However, I do have a few questions and suggestions on the methods and materials section of your paper.

First, fecal sample collection was mentioned but not the process of how the samples were collected. Was the feces collected after it passed from the monkey or was there some process to collect upper GI samples? The difference in collection methods may affect the presence of certain bacterial species, such as anaerobic species.

Second, I did not see internal controls for DNA extraction, PCR, or the sequencing process. Were there any internal controls used to verify the results of each step? So, DNA extraction for example, known bacterial species grown in the lab can be included at specific concentrations and used to calculate how much DNA should be extracted from this known species, this way we can check to make sure the DNA extraction process worked as expected.

Third, were there any steps taken to minimize PCR errors (such as ambiguous bases, etc.) and chimeras? It would be beneficial to mention these steps and what programs were used to minimize them in the materials and methods section, for example ChimeraSlayer is often used for removing chimeras. Also, it was mentioned that analysis was performed on samples after rarefaction to 10000 sequences/sample, but how many sequences/sample were there before this rarefaction?

Last, in the Microbiota analysis section for 16S amplification, the link for the earth microbiome project does not seem to work for me. I apologize if this is an error on my end, but it says the page cannot be reached. Is there another way I can find this information? It may be beneficial to also include PCR steps and cycles you followed in the methods and materials section so that if anything ever happens to the link and it no longer works, you still have the steps you followed in the paper.

Overall, I really enjoyed your paper. I hope you find these questions and comments helpful!

SHSU5394

On 2019-12-30 06:58:01, user Koki Tsuyuzaki wrote:

I'm a developer of scTensor.<br /> Thanks for adding scTensor in your experiment,<br /> but there are many misleading or wrong parts as below,

so please consider the modification.

1. LRBase

In Figure 1C and the caption, you use "the database of scTensor", but please use "LRBase" (the name of our L-R database) instead.

Please note that the scTensor is the name of algorithm or the software package, and not L-R database.

Actually, scTensor can be used with any L-R database.<br /> https://rdrr.io/bioc/LRBase...

I think you should separately consider the effect of selection of L-R database and CCI detection algorithm based on the database.

2. Which is the largest LR database?

In the INTRODUCTION, you said

to the best of our knowledge, it is the largest database of this kind.

but in the Figure 1C, LRdb seems not so large.

3. Blank elements in Table 1

There are many blank elements in Table 1 scTensor column.

If you will add our method in your experiment, you should investigate it more.<br /> I still do not understand your intention of

complete pipeline<br /> the other items are as follow:

Accept preprocessed data Y

Export types<br /> tables Y<br /> circualar plots N<br /> graphML or equiv. N

Perhaps the complete pipeline means the built-in call type calling using t-SNE, k-means, or SIMLR but I don't think such name is appropriate, any tool can perform such task by combinined with the other tools.

Besides, if the user want to use other dimensional reduction, clustering, and cell type identification methods not included in your tool, your tool can be used with them?

4. UniProtKB/Swissprot

In the Comparison with other tools, you said

Swissprot annotations (secreted/membrane) automatically

but UniProt/Swissprot is based on the manual annotation.<br /> https://www.uniprot.org

We also used UniProt/TrEMBL and this database is based on the prediction by cellular localization algorithms.<br /> So please explain more accurately.

5. No registraction of LRdb in Bioconductor

In the AVAILABILITY, you said

the LRdb package is submitted to Bioconductor

but I couldn't confirm the submitted R/Bioconductor package,

although I could confirm that a TSV file is put on the GitHub.

https://github.com/Biocondu...<br /> https://www.bioconductor.or...<br /> https://github.com/SCA-IRCM...

Where was the package published?

6. Benchmark design

In the manuscript, you compared your method with other methods,

but the benchmark is not well designed; each method use different L-R databases and different algorithms, so even if your tool showed the good performance, the reader cannot understand why the tool was good.

Again, you should separately consider the effect of selection of L-R database and CCI detection algorithm based on the database.

7. Criticism against scTensor

As the criticism against scTensor, you said

We found 2 reliable LR pairs whereas scTensor returned 14 pairs, none in common<br /> or<br /> we found significant discrepancies with PyMINEr and scTensor

but there are no detail explanation or no quantitative evaluation,<br /> and these parts are just your impression.

Acutually, some L-R pairs detected by scTensor (Supplementary Table 3) are still curated and not "none in common".<br /> https://string-db.org/cgi/n...

Besides, as you said, LRBase includes many purative (not known) L-R pairs,<br /> you cannot simply say which L-R pair is correct or not.

On 2021-12-20 19:58:34, user Carles Fèlix Calatayud Aristoy wrote:

Brilliant idea! We are currently giving it a try in the lab!

On 2018-04-01 17:29:36, user Modou wrote:

Hi I am very honoured to be a participant and one of the Ambassadors

On 2017-09-18 04:17:00, user JocelynSP wrote:

I have just uploaded an edit to add the data accessions. Sequences are now available in SRA, and OTU tables in Figshare, if anyone wants to replicate or critique.

On 2020-03-31 21:48:04, user Brett Pike wrote:

Is CBCAS more likely to produce THC than CBDAS?

On 2024-01-30 05:25:01, user Sergey Ovchinnikov wrote:

See additional reply concerning RfaH pLDDT here:<br /> https://twitter.com/sokrypt...

Lauren used different alphafold models. When you correct for this, the conclusions in AF-cluster hold.

On 2015-11-17 02:30:57, user Daniel MacArthur wrote:

Thanks everyone for the comments so far! We will make these changes in the next revision.

On 2019-01-11 10:59:25, user PTRRupprecht wrote:

This is a really useful resource!

Something I did not fully understand: "To allow easier and faster access to the exposed brain, all pipettes are positioned on one side of the preparation." Also from Fig. 1B, I cannot see immediately why one should not position the pipettes one all sides of the craniotomy. What am I overlooking?

And it would be really cool to have (in the supplementary material) a video which shows both the 2P image and the oscilloscope (or something similar) during the procedure.

Kind regards,<br /> Peter

On 2016-08-31 11:54:15, user Aleksey Belikov wrote:

All attempts of field normalization for citation indices are basically useless, because this is a non-issue. Nobody in his right mind would compare a biologist with a mathematician based on a citation index, and then give preference to the one who has a higher index. Indices are used for hiring and promotion for a particular open position. If this position is for a mathematician, would anybody hire a biologist, even if his citation indices are 20 times higher?

On 2023-07-14 23:30:00, user Zach Hensel wrote:

The revised manuscript overlooks the dispositive analysis first suggested to the authors, to my knowledge, in the first week of September 2022. The manuscript’s hypothesis of an endonuclease “fingerprint” of a synthetic origin in the SARS2 genome makes a testable claim: if regions around the sites composing the “fingerprint” are sampled in nature, engineered nucleotides will stick out like a sore thumb.

Authors were told about this test in the first week of September 2022 when people independently noted the recombinant evolutionary history and that almost all elements in the “fingerprint” are sampled in a handful of the most closely related genomes. Others rephrased essentially the same test, with Francois Balloux commenting to Alex Washburne on September 5, 2022:

Assuming we wished to follow up on this, the next step would be to test if high homology can be found to different Sarbecoviruses for (some of) the 6 fragments defined by the restrictions site (ie. there's no reason to expect natural breakpoints to match restriction sites).

This step was not taken. And it was not a difficult step. Shortly after the manuscript’s publication, Crits-Cristoph and colleagues rigorously showed that the hypothesis fails this test: https://github.com/alexcritschristoph/ancestral_reconstruction_endonucleases – the conclusion is noteworthy considering the public record, which demonstrates bias in site selection and post hoc selection of statistical tests. In fact, this manuscript’s hypothesis gained attention only after Justin Kinney, who is acknowledged for his assistance on the manuscripted, prompted the discussion by suggesting a different hypothesis about a different restriction endonuclease, BsaXI.

In the comments section of V1 of this manuscript, Alex Washburne proposed a second test of his hypothesis, claiming that “the rapid loss of this pattern is indicative of its evolutionary instability, suggesting what we observe in the SARS-CoV-2 ancestral state is not a stable pattern resulting from recombination, but a transient, unstable pattern that perhaps went against selection and reverted back once the infectious clone was subjected to selection from considerable onward transmission.” While this statement makes some dubious claims and another test is not needed, this comment shows that Washburne considers fitness changes in mutations at these sites to be another test of his hypothesis. This is a test that Washburne can conduct based upon published analysis of the fitness impacts of mutations: https://github.com/jbloomlab/SARS2-mut-fitness – as Washburne and co-authors have not published the results of this test, I will briefly do so here.

The mean, median, maximum, and minimum fitness change estimated for point mutations in the “fingerprint” of the 5 BsmBI or BsaI sites in SARS2 are -1.7, -1.4, 2.2, and -6.5. The same calculations for 1000 random samples of 30 nucleotides give -1.7, -1.5, 1.8, and -6.4 (see link above on interpreting these numbers, or simply note their similarity). A search on https://cov-spectrum.org/ shows that point mutations or deletions for one or more of these 30 nucleotides have been reported in 0.75% of sequences sampled in the most recent 3 months. Point mutations or deletions for one or more of 30 random nucleotides (a single random sample; results will vary) have been reported in 0.96% of sequences in the same period. All in all, the main point of interest in these 30 nucleotides is the attention given a hypothesis of a “fingerprint” of synthetic origin that was effectively disproven before this manuscript was published.

Finally, considering the countless number of equivalent hypotheses, I suggest that a better effort would be immune to these tests (and I can think of at least one example myself). It is critical that a manuscript of this type demonstrate that there is an unbiased rationale behind the hypotheses tested and that is plainly not the case here. One simply needs to observe that “longest fragment” is referred to 20 times in the manuscript, while “shortest fragment” goes unmentioned.

On 2018-12-12 21:01:26, user Michael Neel wrote:

Thanks for the really interesting paper! I recently reviewed this paper for a class assignment and decided to share my comments with you.

Paper Summary:<br /> This paper investigates mechanisms that help to establish centriole number in multi-ciliated cells (MCCs). The authors investigate this using an ex vivo airway culture model that produces mouse tracheal epithelial cells which are MCCs. They investigate whether the parental centrioles (PCs) are involved in regulation of centriole abundance. Using centrinone, the authors ablate PCs from their cell cultures and present data they claim shows PCs loss does not inhibit centriole amplification, deuterosome biogenesis, or affect amplification dynamics. The authors also presented data they claim shows that PIk4 levels do not affect centriole abundance, although it may delay amplification. Lastly, the authors investigated the relationship between cell surface area and centriole abundance. They present data that suggests centriole abundance correlates with cell surface area and that they were able to affect centriole abundance by manipulating cell size. Overall, the authors propose that cell surface area is a determining factor of cilia abundance in MCCs.<br /> Overall, I liked the authors experimental approach and the amount of quantification attempted. In particular, I like how they not only investigated PCs, but also PIk4 and surface area as possible regulators of centriole abundance. I also deeply appreciate their attempts to quantify many of their immunofluorescent images. However, the paper contains a number of issues predominantly including insufficient sample sizes, and graph choices which I address in more detail below.

Comments and suggestions <br /> 1. In many of the graphs (2b-g, 3b, 5c,d,f,g, SF3b) the authors present data that include error bars and statistical tests based on averages of 2 independent experiments. This means that most of the data have an n of 2. While an n of 2 is not technically insufficient for statistical testing, data with n=2 lack statistical power and presenting SEMs with n=2 can be misleading. I would advise the authors to perform addition independent experiments to increase their n and possibly a power analysis to determine a sufficient sample size. <br /> 2. For fig 2d-g, authors show bar graphs depicting percentages of cells with 0, 1, 2, 3, 4, >4 centrioles from cultures stained for markers of various centriole assembly stages. They claim these graphs show that loss of PCs did not affect the overall timing of centriole amplification stages, but the graphs shown do not appear to be appropriate for this type of analysis. I would suggest the authors instead include graphs quantifying the % of cells positive for the various markers in graphs similar to fig 2 b and c.<br /> 3. In their results section on page 6, authors say that deuterosomes are lost by ALI8 in control cells. However, fig 3a clearly shows some Deup1 immunostaining at ALI8 and fig 3b shows 20-30% of control cells are positive for Deup1 at ALI8. Authors should amend this statement.<br /> 4. On page 8, authors claim that manipulating PIk4 protein levels does not alter deuterosome number, which contradicts data in fig 5e that shows increased deuterosome number when PIk4 is knocked down. <br /> 5. Authors mention in results that the centrinone concentration used is roughly 3-8 times higher than needed in most cells, but do not provide their rationalization for using such a concentration. This can be addressed by including a sentence or two explaining why such a concentration was used<br /> 6. On page 7, authors state that fraction of MCCs at ALI12 show no overall difference between control and PIk4-depleted cells, but do not reference any data. Authors can address this by including a bar graph displaying this data.

On 2020-10-14 13:10:38, user Brian wrote:

In your methods, you note using 4 ul of collagenase I, however the catalog number provided refers to a solid. Could you provide the concentration of the stock solution you used?

On 2020-08-13 12:25:42, user kdrl nakle wrote:

That will too complex and too complicated to treat people that are days away from dying. It is OK for cancer but I bet it is not going to work effectively for acute infections. It is interesting so try to prove me wrong.

On 2020-03-20 03:52:25, user Shiya Song wrote:

Still confused on how feature vectors for x_v1 is calculated? Does it use b_v1 to b_vm?

On 2021-08-15 09:17:23, user David Pellow wrote:

Will the performance of this tool be compared to the standard tools for metagenomic plasmid assembly?

On 2023-11-12 00:05:45, user Elizabeth Duncan wrote:

Recently, a group of trainees read and discussed this preprint as part of a journal club at the Markey Cancer Center at the University of Kentucky. We thought the findings suggesting that SETD1A may be driving the increase in H3K4me3 in MLL1 mutated cells (and possibly leukemic cells with MLL1 translocations) were very intriguing. However, we have several questions and suggestions:

In figure 2B (metagene analysis) and C (pie charts), you plot the mean read counts from H3K4me3 ChIP-seq. We interpret the unexpected lack of enrichment of H3K4me3 at gene TSSs in the WT sample as a reflection of the relatively significant increase of H3K4me3 at new gene loci in the MT1 and MT2 cell lines. Is this correct?<br /> If so, we believe this point could be made stronger by adding, for example, a Venn diagram of the genes with MAC2 peaks in the WT cells and those with peaks in the MT1 and MT2 cells. You could also create two separate metagene plots based on the data in Figure 2B: one looking at H3K4me3 in all three cells lines at genes with MACS2 peaks in WT, and one looking at H3K4me3 at genes with MACS2 peaks in MT1+MT2.<br /> Given that there is likely variability in the chromatin state in different iPSC lines, we also wonder if you performed these experiments and/or analyses using a separate iPSC line?<br /> It is unclear how you performed the differential expression analyses in figures 3, 4, and 5. The heatmaps show changes in both the WT and the mutated cell lines, even though we assume the differential expression is in relation to the WT cells? We appreciate there are many ways to perform these analyses, however we would like to understand the details of how they were done here to better understand their implications.<br /> What happens if you knock down SETD1 expression in the MLL1-R3765A cells?<br /> Do you see the same effects if you KO or KD MLL1? Versus this mutation that prevent association with WRAD?

We look forward to seeing your paper in publication.

On 2022-04-10 10:59:21, user Yutaro Ueoka wrote:

Thank you for interesting article.<br /> Fig4a and Fig4b seems to be completely same graph. mistake?

On 2021-05-12 20:17:50, user Ujváry István wrote:

Interesting report on the effect of the 7-hydroxylated metabolite of CBD. However, experimental evidence in humans for your statement "7-OH-CBD is the active ingredient for the treatment of epilepsy" has been lacking. For a hypothesis I advanced recently, see:<br /> https://www.liebertpub.com/...<br /> http://innovationinfo.org/a...

On 2018-02-14 10:38:49, user Benoît Girard wrote:

A purely formal comment: on fig 3A, it is quite difficult to spot the lonely additional blue spike (I had to read the legend to learn about its existence, and to subsequently search the figure to find it).

On 2016-08-05 05:12:03, user Jason Chin wrote:

Try this one: [https://downloads.pacbcloud...](https://downloads.pacbcloud.com/public/dataset/PhasedDiploidAsmPaperData/FUNZIP-PhasedDiploidAssemblies.tgz "https://downloads.pacbcloud.com/public/dataset/PhasedDiploidAsmPaperData/FUNZIP-PhasedDiploidAssemblies.tgz")

We will correct it in the revision.

On 2022-10-22 00:15:10, user CDSL JHSPH wrote:

This is interesting research, not only because it corroborates past findings, but also because it confirms and arouses mixed reactions concerning microbial diversity in equal measure. It is my pleasure to make these remarks. The work is well structured, well researched, and properly presented, easy to read even for non-scientific audiences. When going through the details though, I could not keep the concepts of hospital-acquired infections, antibiotic resistance, and the emergence of novel diseases out of my mind, particularly because of how they are linked to the overall concept of microbial diversity and adaptability. For antimicrobial resistance, for instance, the underlying factor has everything to do with the transfer of mobile genetic elements (MGEs) between two genomes. When MGEs access the chromosomes of new bacterial hosts, the outcome is phenotypical alteration. If the MGEs contained antibiotic resistance then novel or ongoing pathogenesis may result. Nevertheless, your study has demonstrated that bacterial species rarely cross environmental barriers. However, it is interesting to note that this is not the entirety of the results because there are distinct transitions between aquatic biomes, which, noteworthy, are ancient, rare, and often directed towards the brackish biome. At the same time, there are frequent transitions into brackish sites, which are harder to explain. I am just concerned, are there tests that can ascertain these claims? Previous studies have identified that bacteria are opportunistic and may manipulate any loophole to establish supremacy. The concern is further aggravated by your additional findings, that brackish bacteria often exhibit enriched gene functions for various physiological responses, including transcriptional regulation, which is integral in the re-writing genetic information, further begging the question should there be a cause for worry.

On 2018-06-22 16:01:11, user Jun wrote:

Dear authors

Very outstanding works. But I have some concerned about the article.<br /> 1) You mentioned "Despite many attempts, and using several different versions of Cas9 under control of different promoters, we were never able to generate mutants showing altered pigmentation, among the few transformants which resulted from transformations with either vector." <br /> Any detailed information about this statement? Which promoter you guys already tried? Which version of Cas9 you guys already tried? It will be a very useful information for whom wanna work on CRISPR in Magnaporthe oryzae.

2) You mentioned Cas9 is toxic to Mo, but do the transformants without pigment change has Cas9 expression? Did you find any difference in the growth rate, sporation and virulence between the transformants even without pigement and WT?

Thanks

On 2017-03-08 22:08:46, user Kobi wrote:

I would suggest expanding the definition of reproducibility to include experimental work, not just computer code. This will help the definition be more generally applicable to all sciences.

On 2021-10-08 11:57:25, user Eric Fauman wrote:

There are many p-values listed as 0 in the supplementary tables. You need to either report the -log10(p), or include the standard errors and subject counts for each variant so researchers can calculate the p-values for themselves.

On 2018-10-10 13:34:39, user Tony Warne wrote:

PDBs will be released on 17th October. We would welcome comments and questions, also by email.

On 2019-01-16 18:36:27, user Xuanting (William) Hao wrote:

Good job!

On 2017-04-05 02:08:41, user Zhiyong Shen wrote:

hi everyone,<br /> I try to install the panX on my desktop, the process of installing is well.<br /> However, i always get the error report as follows when i run the example data!<br /> Did anybody run the demo data success?

warning: ./data/TestSet/geneCluster/GC_00001981_na.aln is not a core gene<br /> core_list============== []<br /> step07-call SNPs from core genes:<br /> 0 minutes 0 seconds (0 s)<br /> fasttree time-cost: 0 minutes 0 seconds (0 s)<br /> Traceback (most recent call last):<br /> File "./scripts/run-pipeline.py", line 128, in <module><br /> aln_to_Newick(path, params.raxml_max_time, params.threads)<br /> File "/home/shenzy/soft/pan-genome-analysis-master/scripts/SF08_core_tree_build.py", line 39, in aln_to_Newick<br /> resolve_polytomies('initial_tree.newick0','initial_tree.newick')<br /> File "/home/shenzy/soft/pan-genome-analysis-master/scripts/SF08_core_tree_build.py", line 6, in resolve_polytomies<br /> tree = Tree(newickString);<br /> File "/usr/lib/python2.7/site-packages/ete2-2.3.10-py2.7.egg/ete2/coretype/tree.py", line 218, in __init__<br /> read_newick(newick, root_node = self, format=format)<br /> File "/usr/lib/python2.7/site-packages/ete2-2.3.10-py2.7.egg/ete2/parser/newick.py", line 231, in read_newick<br /> raise NewickError('Unexisting tree file or Malformed newick tree structure.')<br /> ete2.parser.newick.NewickError: Unexisting tree file or Malformed newick tree structure.

i past part of the results from the directory of geneCluster as follows:<br /> -rw-r--r-- 1 root root 73369 Apr 5 01:51 GC_unclust006_1.fna<br /> -rw-r--r-- 1 root root 0 Apr 5 01:51 SNP_whole_matrix.aln<br /> -rw-r--r-- 1 root root 6 Apr 5 01:51 core_geneList.cpk<br /> -rw-r--r-- 1 root root 0 Apr 5 01:51 core_geneList.txt<br /> -rw-r--r-- 1 root root 18658 Apr 5 01:51 gene_diversity.cpk<br /> -rw-r--r-- 1 root root 16578 Apr 5 01:51 gene_diversity.txt<br /> -rw-r--r-- 1 root root 54 Apr 5 01:51 snp_pos.cpk

Did any one can help me, many thanks in advance!

On 2023-01-03 08:55:04, user Pustelny Katarzyna wrote:

Thank you. Currently, we have MS data confirming Tyr273 phosphorylation in the activation loop and it is also clearly visible on the electron density map. Detailed analysis of pTyr273 is on-going.

On 2018-02-15 17:45:44, user Gaurav wrote:

Hi

A quick comment about using symbols for isoforms.<br /> In the Methods section, reference isoform refers to A1 (Line 4 of Overall design and problem formulation) and alternatively spliced to A2, however Figure 1 represents A as reference and A1 as alternatively spliced.<br /> The entire text uses A1 for reference and A2 for alternatively spliced.

This is confusing.

On 2019-07-30 22:49:30, user Charles Warden wrote:

Thank you for putting together this paper.

I was a little concerned when I saw "We estimate that a sample sequenced to the depth of 70 million total reads will typically have sufficient data for accurate gene expression<br /> analysis." for a couple reasons:

1) For most gene expression projects, I think 10 million aligned reads is OK and 20-30 million total reads is often pretty safe. While the exonic percentage varies for library protocol, and I'm not sure about the unique read conversion (or if that conversation also varies between library protocols and sample types).

2) I think the specifics have to be figured out for specific protocols (and raw data can be used for research purposes in different applications, or to check the validity of processed data).

For 1), I think that was justified from both my own experience (with 50 bp single-end reads), as well as Liu et al. 2014 / Wang et al. 2011 / Tarazona et al. 2011. I noticed those papers while responding to this discussion.

For 2), I don't exactly have a paper to show this, but I would say differential expression between groups requires testing / optimization per-project. So, you couldn't really define criteria that will work in all possible gene expression projects. While kind of messy, I have some notes from a Twitter discussion this past weekend.

However, I think part of the discrepancy for b) is different interpretations for "differential expression," "over-/under-expression," and "outlier expression". I am mostly thinking of the 10-20 total million polyA reads for differential expression and genes with clear expression / over-expression. If you talking about a pattern that would more more likely to be a technical artifact, I can see how extra effort would be needed for gene expression analysis. For example, if you could have 2-3 biological replicates from slightly different sections of a sample (each with 10-20 million reads), that starts getting close to a total of 70 million total reads for that sample.

I think your Figure 1A and Figure 4C (and possibly Figure 3C) makes me think there is more agreement than I originally expected from the abstract (since that emphasizes something with a threshold of 10-20 million MEND reads). However, I would say 90% specificity may be more reasonable for sensitivity (instead of 95%), for whatever metric is captured by that test. In general, I think 80% accuracy for a genomic signature is pretty good, and I think you need to be careful about over-fitting. That was part of the Twitter discussion that I linked above, but that is also described in my genomics for "hypothesis-generation" blog post.

On 2017-12-21 22:01:25, user awcm0n wrote:

Mollie, great work, but I think there's a problem with your code for simulating from a fitted model in Appendix B:

simdatsums=lapply(simdatlist, function(x){ <br /> + ddply(x, ~spp+mined, summarize, <br /> + absence=mean(count==0), <br /> + mu=mean(count))<br /> + })

Error in .fun(piece, ...) : argument "by" is missing, with no default

On 2023-10-02 00:06:33, user LEVIn - Unicamp wrote:

We discussed this preprint in our journal club and enjoyed reading it. We collectively agreed it's an exciting contribution to understanding how biological invasions shape invasive species' trophic niche and functional morphology in new environmental contexts. We all think the manuscript is well written and the figures are excellent! During our discussion, a major point that came up deals with how the hypothesis (lines 88-90) is motivated and then connected with the results. A more conceptual contextualization of the hypothesis in the introduction (e.g., explaining the ecological release hypothesis in the 3rd paragraph) could help readers to generalize the results beyond the study system and attract a more diverse readership interested in niche variation and biological invasions. Also, as the results combine a substantial body of statistical analyses aiming to understand variation in functional morphology and trophic niches across species, ontogenetic stages, sexes, and invaded vs. native ranges, presenting predictions after the hypotheses could help readers to navigate the results. For example, in light of the ecological release hypothesis, what is expected regarding morphological and body size variation across native and invaded areas? Our final point of discussion is related to the interpretation of the observed niche contraction in the invaded range. As replicates representing invaded vs. native ranges are sampling sites in space (Fig. 1), clarifying whether observed niche contraction emerges via lower variability in resource use across sites and/or within sites would be interesting. This is a key point to connect the results with the ecological release hypothesis. I hope you find these comments constructive; discussing this manuscript in our journal club was great. Congrats on your work, and good luck with the following steps!

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

On 2016-05-14 10:53:56, user Arjun Raj wrote:

Interesting paper! Perhaps I missed it, but I'm wondering if there's any way to know whether the putative trans-splicing or circRNA events are not due to template jumping, which I believe reverse transcriptase is (or at least was) prone to.

On 2020-04-17 10:22:59, user Rajesh G wrote:

Great article. We would like to discuss this research paper feasibility on API 5500 triple quadruple. Please share our contact details or skype id so that we can discuss further.

On 2018-10-19 12:45:54, user E.K. wrote:

Great study! Really convincing data.

On 2020-06-10 08:25:42, user Renzo Huber wrote:

This is a timely manuscript of exceptionally high quality. <br /> I believe that the flexibility of this sequence approach is a quantum leap forward for the emerging field of high-resolution fMRI at high magnetic field strengths. <br /> The combination of multi-shot readouts and CAIPI-based undersampling will allow future neuroscientists to choose the matix-size and spatial resolution as desired. Despite T2*-constraints of conventional fMRI approaches.<br /> I am looking forward to using skipped-CAIPI in many of my future experiments.

On 2019-04-18 18:06:07, user Paul Schanda wrote:

This is a really useful development. We have used FLYA ourselves and managed to assign a 12 x 39 kDa protein from solid-state NMR data in a fully automatic manner - the largest protein so far assigned in solids. About a year of manual work -- or a few hours with FLYA, leading to the identical result. (https://www.biorxiv.org/con... "https://www.biorxiv.org/content/early/2018/12/16/498287)")

Having this tool now - finally - for methyl assignments is very welcome and I am sure it will boost solution-NMR of large proteins further.

On 2021-05-21 06:34:43, user phil_lefty wrote:

https://www.nature.com/arti...

We found complementary results with an RNA silencing effect without Cas13 in mosquito cell lines.

On 2023-10-03 21:24:35, user Herwig Walter Lange wrote:

It is not correct to state that the large interneurons are not lost in striatum. As i could show in 1981 (Verh. Anat. Ges. 75, S. 923-925), 52 % of the macroneurons are lost in HD, compared to a loss of 86 % of the microneurons.

On 2016-04-18 17:37:35, user Julie Dunning Hotopp wrote:

1. You should read and cite this paper by Aravind: http://biologydirect.biomed...<br /> His group already demonstrated some of the things you describe about the latrotoxin domain proteins. They talk about some other protein that might relate to these as well. We talk about it in the BMSB transcriptome paper at<br /> http://download.springer.co...

2. I think you need to address convergent evolution in this case. It might not be convergent evolution (I can't tell from what you present). But if you don’t bring up and address the topic head on, you could set yourself up for criticism.

3. The literature is becoming riddled with LGTs from X to Y, where X was found first and Y was found second (or vice versa). But really there are probably intermediates in between. For example, you could imagine a gene moving from a eukaryote to a Rickettsia endosymbiont to Wolbachia, or something like that. So I would look at your text to see if you can clean-up any references to that line of thinking. One of the benefits to working on recent transfers is that it isn’t an issue! ;)

On 2021-02-05 00:28:34, user Steve Are wrote:

And still there are reports coming in from credible, knowledgable people. There will be another very interesting release about the Thylacine coming out later this year. Stay tuned...

On 2016-06-22 21:47:00, user PornHelps wrote:

In addition, sexual images are not considered sexual rewards by anyone. You do realize they are jacking it to these images every single time they see them, don't you? Yet you pretend like sexual stimulation doesn't exist. You are comparing apples (money reward) and oranges (sexual images). Your use of the term "porn" also is very telling. No scientist uses that term. Sexually explicit media (SEM) is an actually non-based term. You show your true inability to think or write objectively about this media.

On 2019-08-23 18:40:05, user IJ wrote:

Interesting manuscript!

However, I would like to point out a minor error. The manuscript incorrectly states that Li and Zhang have disputed the results of Jungreis et al. "Drosophila melanogaster has been shown to have significant functional (“programmed”) readthrough (Jungreis, et al. 2011). While this is disputed (Li and Zhang 2019)..." Li and Zhang found evidence that the readthrough extensions of most of the 307 Drosophila genes found to undergo readthrough via ribosome profiling by Dunn et al. are non-adaptive. However, this set has very little overlap (only 43 genes) with the 283 genes found by Jungreis et al. to show evolutionary signatures of adaptive readthrough. While Li and Zhang have found evidence that most stop codon leakage is non-adaptive, they do not dispute that it can be adaptive for some genes. They state, "That most read-through events are nonadaptive does not preclude the possibility that a small proportion of such events have been co-opted in evolution for certain functions." Thus, Li and Zhang 2019 did not dispute the results of Jungreis et al.

On a related note, the Dunn et al. 2013 ribosome profiling experiments on readthrough in Drosophila and yeast seem very relevant to your manuscript, and you might considering discussing, or at least citing, their work.

On 2020-12-11 17:34:27, user kfos wrote:

Just when I think I have a hold on one part of single-cell RNAseq analysis... Another cool new papers comes out... Overwhelming!

On 2024-07-16 00:37:36, user Lana wrote:

Nice work, I didn't know ENTs could have so many phenotypes. Interesting

On 2018-10-18 18:37:33, user SIYUAN ZHENG wrote:

The statement about PRADA not able to run on 100nt is incorrect. We ran all TCGA datasets including glioblastoma which has the read length of 101 bp. <br /> Siyuan ZHENG

On 2015-03-31 14:05:45, user Leslie Vosshall wrote:

Thanks, glad you found it useful! I also hope that our experience will encourage people to post pre-prints on bioRxiv without fear that the manuscript won't be accepted subsequently by a peer-reviewed journal (disclaimer: i am on the bioRxiv board)

On 2018-01-15 02:11:20, user anonymous wrote:

Is the definition of an "interference domain" in this paper, having an area of ~1/rho around it's focal point, equivalent in any direct way, with the definition of a "linkage block" from Good et al. 2014**? i.e., sites separated by a map length of r < 1/TMRCA?

Also, with respect to: "The transition [between interference regimes] occurs when the interference probability e^–? becomes of order 1; the transition point marks the onset of nonlinearity in the fitness variance."

How was this determined from the simulated data? For example, in figure 2a, it isn't completely clear from those data points that there is a "transition to nonlinearity" at ?=1. Kind of looks like it, but it's not absolutely clear. How did you determine that?

enjoyed this paper a lot, thanks!

**http://journals.plos.org/pl...

On 2017-11-06 23:47:42, user John Altin wrote:

Nice study. Are the TCR seqs available for download somewhere?

On 2020-01-27 15:38:01, user Tom Featherstone wrote:

I'm very intrigued by this concept. It has been a while since there has been any update on this project and I'm wondering whether the company has started considering looking at alternative methods for the study of neural transmissions. Rather than just using electrodes to measure the impulses from neurons , having just read a paper on scientists using trans genetic flies to study neural path ways it would be interesting to see if they start utilizing this technique to further understand the neural pathways. It may have already started taking place however there is no mention of such at the moment neither the use of viral vectors to transmit markers round neural pathways. Really looking forward to see developments of this project and the future to come. Good luck guys! :)

On 2020-09-16 15:30:36, user Raghu Parthasarathy wrote:

The observation of cool fungi is fascinating. However, I don't understand many aspects of the proposed "mushroom-based cooling device." Since the mechanism is evaporative cooling, how is putting mushrooms in a box any better than putting the equivalent amount of water in the box? Perhaps the argument is that the mushrooms have greater surface area, but this requires that the cooling be surface-limited rather than flow-limited, and this isn't discussed; moreover, if how is mushrooms-in-a-box better than water-soaked sponges in a box (or something else with a large surface area)? Clarification would be welcome!

On 2023-05-03 06:39:42, user Felipe G. Grazziotin wrote:

Dear Peter,<br /> This dataset doesn't include several taxa that are key to evaluate the taxonomy of Dipsadini. A more comprehensive analysis regarding that was recently published by Oliveira et al. in Syst. and Biodiversity (https://doi.org/10.1080/147... "https://doi.org/10.1080/14772000.2022.2153944)").

On 2020-09-12 14:29:24, user Philippe Henry wrote:

A peer reviewed version of this article can be found here:

https://jcannabisresearch.b...

On 2021-02-17 17:45:07, user chikheang Soeng wrote:

Hello,<br /> My colleagues and I recently chose to present your paper in a journal club. We think that Artemisinin-resistant malaria is a major health threat and that repurposing Alisporivir as an anti-malaria drug, as demonstrated in this article, is a promising solution. <br /> I would like to share some of the comments that were brought up during our discussion. In Figure 1 and Figure 2, the interaction between Alisporivir and Cyclosporin A was demonstrated using computer simulations. However, we believe that this conclusion could be better supported by conducting an in-vitro protein binding assay. Also, in Figure 2C, the colors of the graph and the figure legends do not match, making it difficult to interpret the results. Since the main focus of this article concerns the effectiveness of Alisporivir against Artemisinin-resistant malaria, it might be a good idea to move them to the supplemental figure section. In Figure 3C, we think that the third column was mislabeled; it should be DAPI + Cyp. Otherwise, the quality of the microscopy images was excellent. When reading the methods section, the sample size could not be found and we hope to see it included there.<br /> Overall, I think that this paper is very interesting to read. I like the fact that the result section was broken down into smaller sections which makes it easy to follow. I am looking forward to reading more in the future.

On 2022-01-20 16:38:23, user Mathurin Dorel wrote:

Just a few remarks that would improve an overall rather good paper:

• A multiplicity of infection of 0.3 is not "extraordinary" low.
• 25 guides per cell on average is really high, either your multiplicity of infection is miscalculated or those are sequencing artefacts (a substitution makes a spacer sequence look like another). You should check that. This is probably a signal picked up by tour neural network. Another reason could be your fixation and rehydration protocol that increases the ambient noise.
• with a multiplicity of infection of 0.3, you do expect ~25% of the cells to have >2 guides. If you find less there is a problem. However your expression vs guide assignment argument is convincing for the accuracy of scAR so it might be worth checking the expression of the candidate second guide targets.

On 2019-08-03 00:24:50, user Heteromeles wrote:

Anthropocene refugia is not a novel term or concept, as it was proposed it in 2015 in the book Hot Earth Dreams by Dr. Frank Landis, and others are pursuing the same concept with plants in California. The analysis is quite welcome.

On 2021-03-04 12:55:16, user S.c. Daniel Choi wrote:

Thank you for your RT! I am so excited that this work came out here.

On 2021-02-01 02:14:52, user Hamid Gaikani wrote:

GREAT JOB!<br /> HZE stands for high (H) atomic number (Z) and energy (E)<br /> I thought maybe the actors want correct that before the papers appears in a journal.

On 2022-10-31 05:04:22, user Ashraya Ravikumar wrote:

Summary:

In this work, the author asks how protein structures change based on analyzing the torsion angles. Through examples they show that the distribution of points in this representation correlates with resolution and data collection temperature of the structures. They also construct the RoPE space of a protein using time-resolved experiment datasets and show that minor changes in the linear coordinate space are clearly observed in the RoPE space. This work demonstrates the utility of a non-linear representation of the conformational space in visualizing changes throughout the structure which are originally considered subtle. This work is very interesting and can have significant impact on ensemble studies on protein structures and in crystallization/cryo-EM and fragment screening efforts by showing the impact of temperature and resolution. The manuscript is very concise (perhaps too concise?) and well written.

Major points:

1. In Page 3, para 2, the author states differences associated with data collection temperature is preserved across space groups for trypsin and lysozyme but Figure 1(a) and 1(b) marks different space groups only for lysozyme and not for trypsin<br /> 2.The section on carboxymyoglobin has some unclear statements:<br /> (a) “The RoPE space of these structures showed that, over the first three picoseconds, two torsion angle modes are sufficient to represent a clear trajectory during release of carbon monoxide”. Fig 1(e) does show a trajectory from -0.1ps to 3.0 ps but it is not clear how two torsion modes are sufficient to build the trajectory.<br /> (b)“The last three timepoints, 10 ps, 50 ps and 150 ps, are therefore beyond the biologically relevant timescales for CO dissociation in myoglobin and in-line with this, they did not strongly correlate with any other timepoints in RoPE space”. We are confused about which figure/data supports this non-correlation. Is it to be interpreted from Fig 1(e)? If yes, then the author should describe what is correlation and non-correlation in the context of this figure.<br /> (c) The section on “mapping motion back onto structure” in the methods makes it unclear why the scaling is normalized to 1degree and how that might bias the magnitude of motion observed in Figure 2a (+/- 0.3 A)
2. We tried running some analysis on the RoPE website but it was either unclear how to go about submitting a job or the website became unresponsive after clicking on “view conformational space”. The author can provide a run-through of the website usage with some examples.
3. It is unclear how important the vagabond refinement performed here is in the clustering. How would figure 1a, b look, for example, if the PDB or PDB-REDO models were subjected to ROPE without further refinement?
4. At the end of the SVD, it should be possible to project the contributions for each SV back onto the torsion angles most responsible for the differences. It would be interesting to plot that for BPTI and lysozyme to identify the torsions/areas leading to the greatest differences across temperatures.

Minor points:

1. There are some gray colored points in Figure 1(a) and 1(b) which are not accompanied by a legend and their significance not explained.

2. To highlight the advantage of RoPE space, the author can show clustering of the same protein chains when clustered based on RMSD. The crowding of points when using RMSD vs. the separation of points when using torsion angles can make the utility of RoPE space obvious to the reader.

3. Ashraya Ravikumar and James Fraser, UCSF

On 2024-01-21 18:02:51, user Joana Mourão wrote:

This preprint was already published on Microbiol Spectr. 2023 Aug 17;11(4):e0138623. doi: 10.1128/spectrum.01386-23.

On 2016-10-18 02:18:13, user Philip Ashton wrote:

Hello! Great paper. I could be being dense, but where is the supplementary info available from? Thanks!

On 2017-05-17 14:31:06, user Jóhannes Guðbrandsson wrote:

There is a linkage map for arctic charr available. Published this february in G3 http://www.g3journal.org/co...

On 2022-02-03 16:00:38, user Xiaofei Bai wrote:

We published a very similar story back in 2020.<br /> https://doi.org/10.1242/dev...

On 2018-06-21 06:27:08, user gcochran wrote:

Wasn't there a paper by Scott Williamson that hinted in this direction?

"Localizing Recent Adaptive Evolution in the Human Genome"

On 2019-09-10 00:30:16, user Holly Beale wrote:

Congratulations on your paper. I really enjoyed it.

A couple of notes: <br /> I'm working on something related in bulk RNA-Seq, and I also did subsetting of fastq with seqtk. The behavior wasn't exactly what I expected. If I used the same random seeds to take two subsets, one with one million reads and the other with two million reads, the second set included all the reads from the first set. I ended up using different random seeds for each subset.

I think I eventually got it, but I had trouble parsing figure 3. It might be easier to understand if you omitted 2/3 of the groups from each plot. You could include the full figures in the supplement.

On 2016-05-28 15:43:57, user Davidski wrote:

Aren't the stats f4 (Steppe, Neolithic Farmer; Pop1, Pop2) potentially confounded by geography?

Steppe has more hunter-gatherer ancestry than Farmer, so if Pop1 has more hunter-gatherer ancestry than Pop2, and hunter-gatherer ancestry is usually positively associated with higher latitude in Europe, then the stat might be significantly positive as a result of Pop1 living at a higher latitude.

No wonder then that more northerly UK populations score more Steppe affinity in this test. That's not to say that they don't have more steppe ancestry than the southeast English. But the question is whether these particular stats can pick that up specifically, as opposed to just picking up extra hunter-gatherer ancestry in more northerly populations.

On 2021-03-18 06:53:46, user Michele Nunes wrote:

Hello,<br /> A group of undergraduate students at UCLA had the pleasure of discussing this BioRxiv paper during one of our journal clubs. Many of us were fascinated by the background information on phosphodiesterase inhibitors in relation to lipid metabolism. However, since the background consists of all text, it was a bit difficult for some of us to truly understand the signaling pathway in regards to how natriuretic peptides, PPAR?, and PDE9 were all related. We thought that including a visual aid such as a signaling map in the first figure or a visual summarizing the entire introduction would be helpful to engage readers who are not as familiar with the field.

Specifically, in the liver photos (Figure 1f), some of us found it difficult to distinguish a change in size between the placebo and PDE9-I group solely based on the images. We thought that including a line with a known measurement or showing the livers in cylinders with weights attached would be a more helpful metric to justify the results.

In addition, you state that these experiments were done in both OVX-female and male mice, but the only figure that includes both data is Figure 2g. The rest of the male mice data is pushed to supplementary. We were curious if there was a reason for only including a portion of male mice data? The ability to easily compare the data to OVX-female mice could bring to light important differences.

Finally, one of my colleagues was quite interested in the notion of estrogen having a protective effect against cardiometabolic syndrome. They suggested a future rescuing experiment where if OVX-female mice with induced obesity-cardiometabolic syndrome were injected with estrogen, could estrogen reverse the effects? I thought this was a great suggestion for possible future research in this area.

Thank you for your time!