On 2024-11-28 10:07:05, user Sholto David wrote:

Abstract: "Since 2009, successful hoaxes usually appeared at a year of one or more a year" - perhaps this should say "at a rate of one or more a year"? Current phrasing doesn't quite make sense.

On 2019-06-12 20:12:25, user Kayla R Nygaard wrote:

New version published in Autism Research: https://onlinelibrary.wiley.com/doi/full/10.1002/aur.2154

On 2025-08-13 17:31:55, user Ruslan Dmitriev wrote:

Now published in: <br /> ACS Nano<br /> Cite this: ACS Nano 2024, 18, 19, 12168–12186<br /> https://doi.org/10.1021/acsnano.3c12539 <br /> Published April 30, 2024<br /> https://pubs.acs.org/doi/full/10.1021/acsnano.3c12539

On 2017-10-20 03:19:52, user Grégoire Altan-Bonnet wrote:

A very nifty result linking cell-to-cell variability and sharpness of dose response in population of cells: this will find applications in tons of quantitative biological studies.

On 2020-08-26 16:46:04, user Jordan Berg wrote:

Metaboverse v0.2.0b is live!<br /> https://github.com/Metabove...

• Improved metabolite synonym mapping
• New time-course and multi-condition features added during motif exploration

See https://metaboverse.readthe... for a full description of updates.

On 2024-10-08 00:33:10, user Alexis Rohou wrote:

This comment is based on the version of the article published by Cell.

I have concerns about the validity of the cryoEM result presented in this paper.

The authors claim they obtained “a structure of the NINJ1 segment at 4.3 Ŕ. At this resolution, the following features should be clearly resolved in a cryoEM map of a protein containing alpha helices:<br /> • The pitch of alpha helices (~5.5 Å)<br /> • Large amino acid side residue side chains (e.g. Phe or Tyr, of which there are several in Ninj1)<br /> Neither of these features are demonstrated in the figures prepared by the authors.

Figure panels S2D and E are consistent with a resolution of ~ 8-10 Å, where alpha helices are resolved as tubular features. No regular indentation or ridging corresponding to the helical pitch is apparent in the figures, or upon visual inspection of the deposited map ( https://www.ebi.ac.uk/emdb/EMD-42301) "https://www.ebi.ac.uk/emdb/EMD-42301)") . No features corresponding to Phe or Tyr side chains are visible. At the resolution claimed by the authors, features corresponding to the side chains of residues Phe100, Phe117, Phe127 and Phe135, which are all located in alpha-helical segments (not in loops), would be expected to be resolved in the map, but they are not.

In my view, based on this map inspection the authors should not have made this resolution claim.

To support their resolution claim, the authors present Fourier shell correlation (FSC) curves from the cryoSPARC software in Figure S2C.

While the FSC curve shown by the authors does cross the 0.143 threshold at ~4.3 Å, the FSC curve exhibits pathologies, which should have alerted the authors to the possibility that the 4.3 Å resolution estimate may be unreliable and that the map should be interpreted with caution. Most notably the curve starts dropping off at around 15 Å, indicating that the signal-to-noise ratio in the map is significantly deteriorated at resolution of ~15 Å and beyond.

After completion of 3D refinement, cryoSPARC also outputs a second FSC figure, which includes an additional curve (“Corrected”), which accounts for effects of masking on resolution estimation by the FSC. It is unfortunate that the authors didn’t include this in their manuscript.

The most likely explanation for this pathological FSC resolution estimate and its mismatch with the features resolved in the map is that the 3D refinement failed (due to high noise, or preferred orientation, or other pathologies in the dataset or in the refinement parameters), leading to overfitting to a local minimum in the scoring function.

Indeed, the validation report for the deposited map and PDB (EMDB: 42301; PDB: 8UIP; https://files.rcsb.org/pub/pdb/validation_reports/ui/8uip/8uip_full_validation.pdf ) contains evidence of overfitting during refinement. The orthogonal projections of the raw map (Section 6.1.2 of the validation report) show overfitting artefacts, as do the orthogonal standard deviation projections in false color of the raw map (Section 6.4.2).

Such overfitting causes artifically inflated FSC values, which may help explain why the FSC-based estimate of resolution was wrong in this case.

Given a map of this quality, it’s unclear how the authors built an atomic model of Ninj1. In that respect, the publication’s methods section is not detailed enough. Table S2 indicates that the authors started from a computational structure prediction from AlphaFold2. Given the lack of any features in the map to help place any residue side chains, I assume the location of key residues mentioned in the paper originated from this computational prediction rather than from the cryoEM result itself.

The lack of support for the modeled atomic coordinates from cryoEM is also made evident in the EMDB/PDB validation report by the unusually poor Q and atom inclusion scores for a map/model of the claimed resolution. A Q-score of 0.04 is unusually low and shows very little map-to-model fit.

These metrics, together with a visual inspection of the map and model as deposited, suggests that a significant fraction of the sequence was built outside the map and that the cryoEM result does not support the authors’ atomic model beyond the general shape and relative orientation of parts of the alpha-helical segments. The positioning of individual amino acid residues is not directly specified by the cryoEM result and may have come mostly from the AlphaFold model the authors used as a starting point.

Note: the above observations should be not be taken as having any bearing on other key cryoEM-based observations in the paper, such as the curvatures of Ninj1 assemblies, which are supported by two-dimensional class averages of cryoEM images and not affected by overfitting in the later 3D refinement process.

On 2024-02-12 19:19:36, user Alejandro wrote:

The Supplementary Information is missing

On 2024-03-12 20:49:58, user Bennett Fox wrote:

Hi bioRxiv, this paper is now published: https://doi.org/10.1038/s41...

thanks!

On 2020-07-01 13:06:21, user Jan Nagel wrote:

Very nice paper! Ancestral state reconstruction is quite sensitive to missing data and taxa. Do you think the conclusion that homothalism is the ancestral state in Phyllostictaceae will remain unchanged if more species' thallism is determined and added to the reconstruction?

On 2021-10-01 02:23:20, user John McBride wrote:

Thanks for this work, it's good to see someone evaluating AlphaFold in this way.

Maybe I missed something in the article, but I fail to see why one should expect differences in pLDDT to correlate with either changes in stability, or fluorescence. To my understanding, changes in pLDDT can be either that the algorithm performs worse, or that the protein is predicted to be more disordered / flexible. My personal impression is that there is no robust rationale for expecting a priori that pLDDT should strongly correlate with either stability, or fluorescence. This point is pivotal to the entire paper, so I'd be more convinced if it were addressed.

On 2021-09-20 16:07:37, user Rath R. Weird wrote:

Not sure that entries like that would be sufficient to offset the advertisement machine unleashed in support of the AlphaFold, but I do welcome a systematic attempt to evaluate AlphaFold performance in realistic applications. There is lots of anecdotes out there, when people turned to AlphaFold for structural info, and got gobs of disordered strings in return. Personally I compared some of the 2-3 year old models built by I-TASSER to AlphaFold output and found no discernible difference - typical homology modeling performance, none have much in looking-ahead capabilities, but at least the guys behind I-TASSER don't claim to have it. Here we have a more deliberate evaluation of the heuristic AlphaFold imbalance between physical realism and template alignment. Template alignment wins, to the detriment of physical realism.

On 2023-09-14 13:49:29, user Benoit B. wrote:

We advise readers that our preprint has been published and a link will be forthcoming.

On 2020-03-30 12:26:25, user Afolake wrote:

Waoh! I have been working on this gene for five years now and is amazing and as well mixed feeling to see this paper published before we could get my manuscript out. I hope we will be able to collaborate with your research group on FAM111B proteins for future work. Congrats on this beautiful and thorough work.

On 2020-09-09 09:33:15, user Yorgo Modis ???????????????? wrote:

This article has been accepted for publication by Nucleic Acids Research, DOI: 10.1093/nar/gkaa785

On 2025-08-26 14:09:40, user Jiajun Zhang wrote:

This preprint seems to be published on 07 May 2025, but doesn't get the link updated yet.<br /> (Tavakoli, M.R., Lyudchik, J., Januszewski, M. et al. Light-microscopy-based connectomic reconstruction of mammalian brain tissue. Nature 642, 398–410 (2025). https://doi.org/10.1038/s41586-025-08985-1 )

On 2020-05-27 14:40:38, user Vaughn Cooper wrote:

Note: this was submitted on 20-05-11 but the journal delayed posting here.

On 2020-09-15 15:27:02, user Duggy wrote:

Can we get the exact link for the this dataset? <br /> I am unable to find the dataset in the provided link (http://gigadb.org/site/index) "http://gigadb.org/site/index)")

On 2018-10-30 20:44:03, user Michael Hoffman wrote:

This preprint seems to be missing a Methods section referred to within. Methods are not described with the minimum level of detail one might expect for a scientific publication.

On 2020-01-09 18:49:08, user Hannah Takasuka wrote:

Under the "SHIELD Processing" section under methods, I believe that the first reference should be 13 and not 3.

On 2020-10-10 18:34:08, user Kristen Panfilio wrote:

Now published in Communications Biology: https://doi.org/10.1038/s42...<br /> With RNA-seq data available at NCBI under BioProject #PRJNA645519

On 2020-06-23 16:05:55, user ???? Dr. Jennifer Glass ???? wrote:

What a fascinating study! Wonder if you had a chance to look at our recent study on a similar topic: https://www.biorxiv.org/con...

On 2020-06-05 16:46:53, user Mariana Mateos wrote:

The final version of this manuscript is now published and freely available at https://www.frontiersin.org/articles/10.3389/fmicb.2020.01080/full

On 2025-11-06 19:00:54, user Joyce Lee wrote:

https://www.nature.com/articles/s41467-022-31238-y

On 2020-05-12 11:33:05, user Taekjip Ha wrote:

Thank you very much for sharing your interesting manuscript!<br /> We used your preprint as one of the journal club papers in the Single<br /> Molecule & Single Cell Biophysics course for graduate students of Johns<br /> Hopkins University during the Covid-19 lockdown. Students also practiced peer<br /> reviews as the final assignment. I am submitting their formal reviews here <br /> and hope you find them useful.

Taekjip Ha

Reviewer 1.

In this study, Laprade et al. engineered Hela 1.3 based cell lines withCRISPR-Cas9,<br /> such that telomerase RNA (hRT) can be tracked with the MS2-MCP system.Fluorescence<br /> signals from single hrT molecules were used as readout of telomerase activity.Previous studies<br /> employing FISH identified hRT to be predominantly localized in Cajal bodies.Laprade et al.<br /> present results that contradicts this, showing that only 10% of hRT reside inCBs. The conflicting<br /> results can be attributed to the longer residence time of hRT in CBs as opposedto that in the<br /> nucleoplasm. This finding boasts the strength of studies interrogating thedynamics, and not<br /> static properties, of a biological system, made possible with technologicaladvancements in<br /> super resolution microscopy.<br /> The argument presented in this paper is that short interaction times are basedon<br /> TPP1-TERT interaction and RNA-DNA base-pairing is responsible for the longerinteraction<br /> times. These two types of interactions are correlated with the scanning andengaged diffusive<br /> behavior of telomerase bound to telomere. One important control construct tosupport this<br /> argument may be a TERT knockout cell line. The propensity for hRT alone tointeract with the<br /> telomere solely based on base-pairing can be probed, which seems plausible in aPOT1<br /> OB-fold deletion context. The observation that short interactions becomenonexistent as<br /> long-lasting co-localization and slow diffusive states persist would furtherstrengthen the model.<br /> Since the study infers endogenous TERT binding based on hTERT and hTERT-K78E<br /> overexpression assays, that telomere-bound hRT is accompanied by TERT is mostlyassumed,<br /> but confirmation studies of hRT dynamics without endogenous TERT would stillhelp the<br /> interpretation of telomerase diffusive and interactive behavior. On a relatednote, comparing<br /> telomerase diffusion coefficients with that of existing literature, especiallyfrom single particle<br /> tracking studies utilizing Halo-tagged TERT, can strengthen the authors’ claimsof the different<br /> diffusive states. Taking the logarithm of the diffusion coefficient seems to becustomary in this<br /> field, but I am curious whether the raw distribution of diffusion coefficientscan also resolve<br /> different diffusive states.<br /> One reservation I have about the mechanistic explanations in this paper is thatHeLa 1.3<br /> cells are known to have long telomerase. It’s tempting to ask whether it ispossible that the<br /> scanning behavior of telomerase differs based on the length of the telomerase,since the<br /> telomerase retention step has not been delineated in lower eukaryotes. It wouldbe interesting to<br /> test if the proportions or lengths of short and long interactions scale with thelength of the<br /> telomerase.<br /> Provided that cancer lines see inherent variability in TERT expression, it isperhaps<br /> unsurprising that only up to 50% of cells found hRT-bound telomeres. On top ofthat, it’s difficult<br /> to tell how often one would come across false negatives i.e. bound hRT withoutthe<br /> TRF1-mCherry signal. Some measure of the variation of the number of visibletelomeres would<br /> be good, just to have an idea of the error associated with “% telomeres withhTR” data points.<br /> Grace Taumoefolau<br /> Such multidimensional variability has implications for statistical power, sosome notes on the<br /> specific statistical test employed in relation to the number of measurements andeffect size in<br /> the methodology section would be nice. Notably, several analyses comparecategorical data of<br /> multiple categorical treatments, so a pairwise t-test would be insufficient,assuming that’s what<br /> the p-values are based on. It is also stated that the first decay rate term ofvehicle and<br /> GRN163L in Figure S5H is statistically dissimilar but the short interactionsstill seem somewhat<br /> impacted by GRN163L. I would like to see the exact p-values that were deemed<br /> “non-significant”. To take things further, perhaps a maximum likelihood estimateensuring that<br /> the survival probability curves are bi-exponential and not single exponentialwould raise the<br /> confidence that RNA-DNA base pairing leads to long retention times.<br /> One final minor comment I have is that there is a typo in the figure legend ofFigure S5. It<br /> should be Figure S5E (F) and Figure 5F (G).<br /> All together, the article presented compelling evidence for their final model oftelomerase<br /> dynamics and interactions, complementing and expanding known details abouttelomerase<br /> maturation and recruitment process.

Reviewer 2

In this paper, Laplade and colleagues aim to explore how telomerase andtelomeres are<br /> spatiotemporally coordinated within the nucleus to enable telomereelongation. To this end, they<br /> examine the dynamics of telomerase assembly andits recruitment to telomeres at the single-molecule<br /> level. Their experimentalapproach is to use the MS2-GFP RNA tagging system, in conjunction with<br /> cleveruse of photoconvertible fluorophores and FRAP, to visualise and track individualRNA molecules<br /> of the telomerase ribonucleoprotein in living cells. The authorsare able to observe hTR (RNA<br /> component of telomerase) trafficking dynamics inand out of Cajal bodies, where the proposed site of<br /> telomerase holoenzymeassembly. They also measure the relative dynamics of telomerase colocalized<br /> totelomeres and propose a novel “Recruitment-Retention” model of telomerasetargeting. Finally, the<br /> authors apply their system to show that acancer-associated shelterin complex mutation, POT1, may<br /> elongate telomeres bypromoting retention of telomerase.

Given that telomerase dysregulation is a common feature of cancers and animportant potential target<br /> for therapy, the direct measurement of individualmolecular dynamics provides valuable experimental<br /> data that furthers ourunderstanding of telomere homeostasis mechanisms. The authors make<br /> thoroughefforts to validate their system and characterise the dynamics of differentbinding states<br /> before manipulating conditions. Through their live-cellhTR-targeted single molecule approach, the<br /> authors make several novelobservations. Some of these challenge previous assumptions in the field<br /> that hadbeen based on fixed bulk IF-FISH data. Notably, they show telomerase and S-phasetelomeres do<br /> not interact within CBs, making the widely accepted “handovermodel” of CBs bringing together<br /> telomerase and telomere sequences highlyunlikely. They also show that contrary to IF-FISH reports,<br /> only a small subsetof hTRs within the nucleus localise to the CB. Most importantly, the authorsfind<br /> that telomerase oscillates between an initial high-mobility “scanning”state and a low-mobility<br /> “engaged” state at the telomere, in a manner dependenton template RNA-ssDNA base-pairing<br /> interactions. Moreover, the two modes aredifferentially targeted by drugs and mutations, suggesting<br /> novel functions thathave not been characterised before.

Overall, the authors use appropriate controls and are careful to validate theirmethods. However, one<br /> major issue present throughout the paper is a lack ofproper statistical information. While the<br /> figures do show p-values, the authorsnever indicate what statistical tests were used to obtain those<br /> p-values.Without this crucial information, it is difficult to determine how appropriateor accurate<br /> the statistics are for these data.

Another important point is that the telomerase holoenzyme requires both thecatalytic hTERT and the<br /> RNA template hTR. Although some experiments involvemanipulation of hTERT expression levels or<br /> activity, this study largely followsonly the single-molecule dynamics of hTR. Although the authors<br /> mention in theirdiscussion that several key findings (including the “scanning” vs<br /> “engaged”behaviours at the telomeres) diverge from previous single-molecule dynamicsstudies using<br /> Halo-tagged hTERT and offer possible explanations for thesedifferences, the fact remains that we<br /> cannot ascertain if the dynamics describedin the paper actually reflect those of telomerase or just<br /> the isolated hTR. Asuggestion for revision would be an experiment where both MS2-tagged hTR<br /> andHalo-tagged hTERT are expressed together in the same cell, enabling the trackingof both enzyme<br /> components and determining where the dynamics do not coincide.

A minor suggestion is that the authors may wish to edit their introduction forgreater clarity,<br /> coherence and cohesiveness. For example, the authors mentionthe possibility of a “potential… backup<br /> pathway during telomerase assembly” withregards to coilin loss, but this is never again referenced<br /> within the main bodyof the paper. A substantial section of the introduction also focuses on<br /> theinteraction between hTERT and TPP1; while this interaction provides therationale for some of the<br /> deletion experiments in the paper, it is not the mainfocus, and a more organised overview of ideas<br /> in the field concerning telomerasetargeting to telomeres would be more useful for the reader.

On 2016-08-22 05:17:31, user Rajat Rohatgi wrote:

Check out our recent 3.2 Angstrom crystal structures of near-full length Smoothened in its active, cholesterol-bound state in comparison to an inactive state bound to the anti-cancer drug vismodegib:<br /> http://www.nature.com/natur...

On 2017-10-11 20:27:16, user Keith Robison wrote:

Some typographic notes. TBLASTN should not be written 'tBLASTn', particularly in a variable space font. "CLUSATLX" probably should be CLUSTALX. Default parameters is never a reproducible description; these can be changed in future versions.

In introduction, it is incorrect to say "Homology can be divided into orthology and paralogy according to whether they are present in the same species" -- two genes can be different species yet still be either orthologs or paralogs, depending on whether their ancestral relationship involves a gene duplication event.

On 2024-07-02 11:48:52, user Hervé Acloque wrote:

The revised and peer-reviewed version of this paper is now available in the special issue of Genomics dedicated to FAANG:<br /> https://doi.org/10.1016/j.y...

On 2019-04-25 03:42:07, user Dawen Cai wrote:

This manuscript is published on:<br /> https://academic.oup.com/bi...

On 2017-03-27 15:13:48, user Casey Dunn wrote:

Great paper! A very minor comment - "although" should be capitalized on line 105.

On 2024-06-28 09:25:30, user Helle Jorgensen wrote:

Published after peer review<br /> Lambert, J., Oc, S., Worssam, M.D. et al. Network-based prioritization and validation of regulators of vascular smooth muscle cell proliferation in disease. Nat Cardiovasc Res 3, 714–733 (2024). https://doi.org/10.1038/s44...

On 2023-07-04 06:56:22, user Eduardo Oliveira wrote:

Would it be possible to share the sequences of the 228 generated proteins?

On 2021-10-20 09:46:31, user Shenghong He wrote:

This study has now been published in Journal of Neuroscience (https://doi.org/10.1523/JNE... "https://doi.org/10.1523/JNEUROSCI.0770-21.2021)").

On 2016-05-26 03:25:27, user Charles Simmins wrote:

Two observations. As to method of transmission, is Zika conjunctivitis infectious? Many forms of viral conjunctivitis are highly contagious and I would suggest this may be the case with Zika.

My second point has to do with the reported numbers of Zika cases. The numbers for laboratory verified cases are quite low in Brazil. Most diagnoses seem to be made clinically, and that is a problem.

Dasgupta, et al, found that 85 percent of patients with at least one Zika symptom tested negative for the virus. <br /> http://dx.doi.org/10.15585/...

Dirlikov and colleagues looked at a similar set of patients in Puerto Rico, http://dx.doi.org/10.15585/...

The total for both studies was over 10,000 subjects with good reason to believe they had been exposed to Zika. Combining both studies, less than 15 percent of those tested had Zika. It appears that diagnosing Zika through signs and symptoms produces a serious rate of error. I would suggest that the strong possibility exists that the number of Zika viral illnesses in Brazil is much smaller than first believed, due to incorrect diagnosing.

On 2017-08-25 05:52:23, user C. Ghang wrote:

Does hydrogen peroxide damage the RNAs and effect the result of sequencing?

On 2018-03-25 16:50:43, user Sven Bradler wrote:

Great study,but one major reference has been overlooked that already reported the horizontal gene transfer of pectinase genes from bacteria in Phasmatodea: Shelomi et al. 2016 https://www.nature.com/arti...

On 2019-03-19 06:33:52, user Xing Xu wrote:

I will suggest putting APOE somewhere in the title. It will give the article a huge boost, considering how notorious APOE4 is in Alzheimer's community.

On 2020-04-25 13:17:54, user David Sherman wrote:

Nice work and glad to see you had success with various megaenzymes, including PikAIII. We found significant value in our recent paper <br /> DOI:10.1021/acssynbio.0c00038 using Orbitrap proteomics to confirm presence of the key biosynthetic enzymes. Nice analysis of phosphopantetheinylation of the PCP/ACP. Any sense of IVTT system to handle proteins that express poorly or are insoluble from traditional E. coli production methods?

On 2020-03-11 03:02:04, user V Zenkov wrote:

Summary of the paper:

The authors propose a new deep learning framework, NetFLICS-CR, which speeds up hyperspectral lifetime imaging. It is an extension of a previous product, NetFLICS (from https://arxiv.org/abs/1711.... "https://arxiv.org/abs/1711.06187)"). Both strategies use single-pixel imaging, which is typically based on an inverse problem that reconstructs 2D images from measurements featuring a series of patterns. They speed up the process using compression sensing. NetFLICS is 4 orders of magnitude faster than the previous solution and requires less photons, but it has a compression ratio of 50% for 32x32 images. NetFLICS-CR improves by staying fast, and works on 128x128 images. For perspective, NetFLICS-CR reduces the acquisition time from 2.5 hours at 50% compression to 3 minutes at 99% compression while using a single-pixel Hyperspectral Macroscopic Fluorescence Lifetime Imaging (HMFLI) system.

Comments:

• is there a reference for TVRecon? There is a summary in the supplement, perhaps it should be referenced in the text.

• there should be a consistent numbers of iterations. Why are there sometimes only five? Some justification would be appreciated.

• it would be very helpful to have the code on GitHub for reusability.

• what are the limitations of this research?

• Figures 1,2,3,4: The legends would benefit from having summary sentences and conclusions. (The current legends only guide the reader to what is in the figure.)

• Figures 1,3: If the paper is printed in gray scale, the lines in the graphs look largely similar.

• Figure 1b: What are the units? Also, why is mean absolute error (MAE) used instead of mean squared error (MSE)? Mean squared error is traditional, as far as I know.

• Figure 1b: The top right of both graphs shows a "zoomed in" view of the bottom right rectangles. However, it is not immediately clear that that is what is happening because the rectangles have different proportions. One way to help would be to add numbers on the axes to show that the epoch number / MAE are "zoomed in".

• Figure 1b: It could help to add the title "compression ratios" next to the compression ratios at the top of the figure.

• Figure 1b: The legend could say "six different compression ratios" to be even clearer.

• Page 2: The text below Figure 1b defines Compression Ratios as #acquired patterns / #full patterns, but the examples in the text are instead the ratio (#full patterns - #acquired patterns) / #full patterns. I believe the latter is used throughout the paper.

• Figure 2b, especially the top graph: The legend may be confusing because all the lines seem to overlap, with the only visible color in the graph being yellow. This might be fixable with smaller points.

• Only one metric was used (Structural Similarity Index Metric) in the paper, which could lead to bias. The optimization is then based on the same metric used to assess the performance of the model, which could be biased toward that metric. Having multiple metrics / measures of performance could help remove this possible bias.

• Figure 3c: I would find 3 panels easier to read than the 3D graph.

• Figure 3c: P and I/R are labeled backwards.

• Figure 3c: The legend says NetFLICS, but it seems like it should say NetFLICS-CR. (This is confusing because NetFLICS is also a thing!) The legend says "per CR" and that would make more sense in the context of the paper.

• Figure 4a: Next to the red images, why is External CCD turned 90 degrees to the left while other text in the figure is turned 90 degrees to the right? If all the text is turned in the same direction then it will be more consistent.

• Figure 4b: TVRecon and NetFLICS-CR are not next to each other in the graph, which makes it difficult to compare them.

• Figure 4b: Why is this the only figure with error bars?

• There is no actual labeled conclusion section - the conclusion begins with "in conclusion" in the text. Having a set-off conclusion section would make it easier for someone to quickly read the conclusion.

On 2023-11-13 18:38:19, user Curtis Loer wrote:

This paper needs to adopt standard C. elegans nomenclature for strains / mutants. For example, the strain designation for cat-1(ok411) is RB681 (from the CGC, if not outcrossed). 'UA57' IS a proper strain designation, but it would likely be clearer to indicate overexpression of TH in dopaminergic cells. In the abstract it would be appropriate to use the 'vesicular monoamine transporter / VMAT' terminology for the protein encoded by the cat-1 gene, and also simply state this is a cat-1 mutant (familiar to C. elegans biologists). Also, simply making a double mutant by routine genetics methods does not warrant calling a new genetic construct (MBIA) as a 'novel strain.'

On 2025-03-19 18:15:47, user Ana Nascimento wrote:

The peer-reviewed version of this pre-print can be found at: https://www.nature.com/articles/s41598-020-69944-6

On 2022-07-20 18:37:37, user Ariel Mundo wrote:

This paper has been published in Statistics in Medicine

On 2018-02-14 02:26:29, user James Lloyd wrote:

First of all, thank you for this really useful resource, I look forward to being able to get my hands on it as it will help with ongoing work.

One thing I noticed, in Table S2, the plasmid pCK020 (Pro+5U) is described as WUSCHEL but then the rest of the description describes the gene ID (At3g111260) and function (root stem cell niche / quiescent center-specific promoter) of WOX5. I assume what is present in this plasmid is the promoter + 5' UTR of WOX5 and not WUS. Is that correct?

Also, Table S2 has multiple asterisks after the plasmid names but this is not defined within the Tables legend.

Thanks again for creating this terrific resource.

On 2019-02-27 16:08:28, user HYC wrote:

Excellent work, congrats Lorenzo @lorenzpasquali, Mireia @mireia_bioinfo and all the team!!

On 2020-06-01 04:19:59, user Lachlan Coin wrote:

Hi authors, nice work! However this statement: " However, prokaryotic transcriptomes have not been characterized on the genome wide level by native RNA-seq approaches so far as prokaryotic RNAs lack a poly(A) tail, which is required to capture the RNA and feed it into the nanopore. " is not correct. We did something similar in K. Pneumoniae : https://doi.org/10.1093/gig...

On 2022-10-24 20:02:15, user Maiwenn KERSAUDY-KERHOAS wrote:

Supplementary videos are available from https://www.youtube.com/pla...

On 2023-08-09 18:40:04, user Jiahua Tan wrote:

The perspective of tackling the ratio compression caused by the isolation interference in this paper is interesting. It seems that the tool is designed for single plex experiment if I am correct. Are there some ways to run the tool for multiplex experiments simutaneously so that we can make use of all available cores in parallel processing to reduce the run time?

On 2019-03-20 16:25:43, user Rosa Fasching wrote:

That's why the pioneers (#KrebstherapiederAltmeister) said long time ago, no matter what kind of diet it is (Bircher-Benner, Gerson, Mayer, Kuhl etc.) it all depends on the microbes in your gut. They are the key players and "chefs".

On 2017-09-28 23:52:51, user Josie Briggs wrote:

Interesting study. Lectin sorting isperhaps a better way to get cell or segment specific isolates. Certainly beats the hard work of microdissection.

On 2018-04-12 23:08:18, user Pedro Duarte wrote:

(121-2) ..."while both I and P exclude the many Hymenoptera that are parasitic on other parasitoids (“hyperparasitoids”)."<br /> So, hyperparasitoids are in... C?

On 2020-01-27 10:40:55, user New CoV wrote:

the generation time is fixed for the new coronavirus to be the same as for Sars in the analysis and r0 for new coronavirus is somewhat higher - an alternate explanation would be shorter generation time for the new coronavirus?

On 2024-01-14 22:54:39, user Keji Zhao wrote:

Very interesting study --- providing insights into how MutSb and CNG cooperate to drive the expansion of trinucleotide repeats in Huntington's and other relevant diseases.

Do the authors know how well these trinucleotide repeats form nucleosome structure in cells?

On 2020-12-04 18:48:54, user Reza and Nasim Bekheirnia wrote:

Very interesting work. Congratulations! I was wondering if FACS is a required step? It seems that scRNA-seq could be done w/o FACS?

On 2019-08-09 22:58:37, user microbial_minded wrote:

I would like to thank the authors for uploading this interesting manuscript. I am hoping the authors can provide some additional comments and clarification. In figure 1, Ralstonia is indicated present in the basal plate from collected placentae based on fluorescence signal from a Bacterial-specific probe (panels C and H) and a Ralstonia-specific probe (panels D and I). DAPI counterstaining is shown in panels E and J. Arrows denote the location of Ralstonia in panels C, D, H, and I.

However, there are several punctate foci (bottom right of panels – two in C/D and one in H/I) that are not denoted as being Ralstonia in these same panels despite having signal in both channels. These same unlabeled points are also present in composite image panels G and L. There are other points that appear in the panels with the bacterial-specific probe and Ralstonia-specific probe, but those points do not appear to overlap, unlike the above mentioned three points and the arrow-labeled points. Of further note, DAPI staining of total nucleic acid does not appear where the Ralstonia cells are denoted, even in cases where the Ralstonia cells do not overlap with the DAPI-stained host cells. This is especially pronounced in panel L, with the bottom point being ~5um away from the nearest stained nuclei. This is a curious observation given that DAPI has been used to stain Ralstonia in the past (https://doi.org/10.1128/AEM... and http://dx.doi.org/10.1016/j... "http://dx.doi.org/10.1016/j.colsurfb.2012.09.044)") and the authors clearly demonstrate the ability of DAPI to stain Ralstonia in culture in supplemental figures S1 and S2.

I have three questions related to these data:

1a. Do the authors consider those unlabeled points in Fig. 1 to be Ralstonia? <br /> 1b. If not, what differentiates the unlabeled points from the labeled points?

1. Why is there lack of DAPI staining where the Ralstonia cells are in Fig. 1, as DAPI should also stain bacterial DNA?

Thank you in advance for your time and response.

On 2024-11-06 01:23:49, user sun zhen wrote:

The results of this paper are not credible.

Based on the gel result, we can see that only introns are removed by oligo dT purification, lots of precursor and nicked RNA still in the products. Obviously, there will be a decrease in dsRNA compared with RNA kit purification which removed nothing from the crude circRNA products.

When it comes to in vitro expression test, I am surprised to see even the expression of RNA kit circRNA is better than HPLC purified circRNA.We have also tested and compared RP-HPLC purified circRNA (circEGFP and circFLuc from GenScript) before, they got much higher expression than RNA kit purified circRNA or RNase R treatment crude circRNA.

As the purity of circRNAs is very important for their expression (which can reduce their immunogenicity) based on previous reports, the in vitro or in vivo data in this paper are not convincing.

On 2022-02-23 07:50:47, user Stefan Oehlers wrote:

This manuscript has been published in Scientific Reports with a slightly different title:<br /> Morris S, Cholan PM, Britton WJ, Oehlers SH. Glucose inhibits haemostasis and accelerates diet-induced hyperlipidaemia in zebrafish larvae. Sci Rep. 2021 Sep 24;11(1):19049. doi: 10.1038/s41598-021-98566-9. PubMed PMID: 34561530; PubMed Central PMCID: PMC8463691.

On 2018-03-26 20:25:53, user stephen shepherd wrote:

My excellent reviewers at Frontiers pushed for a reliability assessment of our behavioral scoring. Since this is the foundation for behavioral analysis, this is a very reasonable requirement.

Unfortunately, after conducting a partial rescoring of the data, the reliability was substantially lower than I had hoped. Mismatches reflected both uncertainty in timing of events and soft category boundaries around behaviors like 'facing' and 'looking'.

While I believe the gist of the scoring, and the subsequent analyses, are correct, please take these findings with a large grain of salt pending reliable rescoring of subjects' behaviors by multiple observers. This will be a large undertaking, and I am not sure when it will be completed.

On 2019-11-07 16:10:35, user Ashot Margaryan wrote:

The paper is currently in review, so it might take few more months after the resubmission.

On 2024-07-17 15:41:07, user Matilda wrote:

https://www.cell.com/iscience/fulltext/S2589-0042(24)01665-1

On 2019-05-19 03:06:39, user Mingye Wang wrote:

Now http://doi.org/10.1093/infd...

On 2021-03-15 16:17:39, user Kai He wrote:

We also found the Qingling-Daba Corridor in small mammals <br /> https://onlinelibrary.wiley...

On 2024-04-24 14:37:31, user Bonnie Thiel wrote:

This manuscript has been peer reviewed and published: https://journals.plos.org/p...

On 2023-10-05 22:27:50, user VK Ulaganathan PhD????????, M.Tech ( wrote:

Chimeric Cytokine Receptor expressing T cells (CCR-T cells)??

On 2021-07-01 04:07:56, user Javier Rasero wrote:

Hi,

First of all, congratulations for this amazing manuscript.

Could it be possible to also show the predictive accuracies using the coefficient of determination R2? Maybe in a supplementary material? This is usually the recommended metric for quantifying regression accuracy [1]. Pearson correlations for estimating out-of-sample performances can sometimes be biased.

In any case, wonderful and insightful paper!

[1] Poldrack RA, Huckins G, Varoquaux G. Establishment of Best Practices for Evidence for Prediction: A Review. JAMA Psychiatry. 2020;77(5):534-540. doi:10.1001/jamapsychiatry.2019.3671

On 2022-08-25 15:11:03, user Sam Nooij wrote:

I would like to thank the authors for sharing an updated version (v3) of such a fascinating manuscript. I enjoyed reading it and I have a couple of questions/points of feedback that I would like to share.<br /> 1. Do I understand correctly that in lines 133-134 the authors state that higher percentages of read recruitment to donor MAGs suggest bacterial colonisation? This seems to me very indirect evidence and not a justifiable conclusion based on this observation alone.<br /> 2. In figure 1, Canada is shown slightly bigger than the other countries. Is this because the donors and patients from the current study are also from Canada? I could not find this information in the text.<br /> Furthermore, I like that the authors made a distinction between industrialised and less industrialised countries. Would it be possible to change the colours slightly (e.g. make the red magenta) to make it easier for colour blind people to see?

Also, the blue and purple rows are somewhat difficult to distinguish for me. Is this intentional, as the donor and post-FMT recipient microbiota are supposed to be similar?<br /> 3. I find it interesting that the authors found that only 16 and 44% of donor microbial genomes were detected in all donor metagenomes, suggesting that only a minority of bacteria is stably present over longer periods. (Lines 180-182.) Have the authors considered doing similar analyses on these genomes to find if they are HMI and LMI bacteria?<br /> 4. I find the comparison made between short-read taxonomy and donor population detection (lines 184-188) a little difficult to follow. Would it be possible to rephrase this part or add a little explanation of what exactly is compared?<br /> 5. Lines 190-194 are also fascinating! Even with such small numbers, it is striking to see that more bacteria colonise from pills that from colonoscopic transfer. Could the authors speculate or provide additional info on why this may be the case?<br /> 6. From the final conclusion (lines 431-435) I gather that FMT or similar microbiota therapeutics are unlikely to (temporarily?) cure IBD. Do the authors have suggestions as to what might work better, and would they like to share their perspectives on promising new treatment options?<br /> 7. And finally, what do the ellipses in supplementary figures 2 and 3 represent? I suppose they show some sort of area around each cluster centroid. A few extra words of explanation in the figure captions would be nice. This information is also not easily found in the analysis scripts. (And by the way, it is wonderful that the authors share all code and instructions on how to reproduce the analyses!)

On 2024-10-15 18:37:28, user Jacek Majewski wrote:

This article has been published in Cell Reports:

Chromatin dysregulation associated with NSD1 mutation in head and neck squamous cell carcinoma

Cell Rep. 2021 Feb 23;34(8):108769. doi: 10.1016/j.celrep.2021.108769

On 2019-06-27 01:34:16, user Marcello wrote:

The corresponding author of this paper was mistakenly listed as Marcello Rosa. Please send any correspondence to the first author, Dr. Hsin-Hao Yu, at the address: hhyu00{at}gmail.com.

On 2021-05-24 22:36:53, user Howard Salis wrote:

Readers note: The authors make the following assumptions in all their analysis, which critically undermines several of their most important (and controversial) conclusions:

1. The authors are treating their ribosome profiling experiments as a measurement of translation rate. That's incorrect. Ribosome profiling measures the relative density of ribosomes bound to mRNAs. It is not a direct measurement of translation rate nor is it a direct measurement of translational efficiency.

Let me be clear with these terms: Translation rate is the number of proteins produced per mRNA per time. Translational efficiency is the number of proteins produced by a single transcript before it is degraded. Ribosome density is the translation initiation rate divided by the mRNA's translation elongation rate.

1. The authors assume that all mRNAs have the same translation elongation rate in order to convert ribosome densities into translation rates. For example, in their Equation 5, epsilon (translation elongation rate) is just a single constant, used throughout to relate translation rates to protein synthesis rates and protein concentrations. This is incorrect. mRNAs do not all have the same translation elongation rate. There is a whole genre of literature on synonymous codon usage, the effects of synonymous codons on ribosome translocation speeds, and the use of codon optimization to increase heterologous protein expression levels. Epsilon is not a single constant. At the very least, if we only care about protein concentrations, one must consider the CDS-averaged translation elongation rate for each mRNA transcript in the transcriptome.

The authors' conclusion "Translation initiation rates are similar across mRNAs and growth conditions" needs to be revisited without assuming that all translation elongation rates are the same. Likewise, the conclusion that "Total mRNA abundance matches the translational capacity" depends on the veracity of this conclusion. The conclusion that "Constancy of ribosome spacing across mRNA and nutrient conditions" is more accurately viewed as "Constancy of *ribosome density* across mRNA and nutrient conditions". Finally, the conclusion that "Total mRNA synthesis flux is adjusted to match translational capacity" is again dependent on the veracity of the first conclusion.

Concluding that all mRNAs have the same translation rate is bold. But it's not supported by data. If you see someone using ribosome profiling to make this claim, be skeptical!

On 2019-10-03 22:50:47, user Laura Hayward wrote:

The Supplementary Information will be posted later on. Meanwhile, the manuscript is largely self-contained

On 2017-09-05 13:36:00, user Stephen Buratowski wrote:

This is our first time using Biorxiv, so it's a bit of an experiment. I'm very excited about the model we propose here, which I believe explains a lot of previously puzzling TFIID findings. That's why I wanted to get it out to the field early. All I ask in return is that you cite us (this preprint or eventually the published paper) if you use our ideas. I look forward to hearing about any other data that support (or argue against) our model!

On 2021-09-08 08:19:11, user Rony wrote:

Published version: https://doi.org/10.3390/ijm...

On 2021-02-19 13:48:13, user robin khosla wrote:

Good Work. Very Informative app to survey the target reserach groups for initial stage researchers.

On 2021-05-11 06:38:22, user Christophe Leterrier wrote:

The manuscript refers to numerous Supplementary Figures and material, but they are not provided next to the preprint. Would it be possible to upload them as well? Thank you.

On 2022-01-12 14:21:34, user Rafael Najmanovich wrote:

Review of Improving AlphaFold modeling using implicit information from experimental density maps. https://doi.org/10.5281/zen...

On 2017-01-07 00:22:56, user Manuel Corpas wrote:

Please note that the 23andMe chip has a number of versions, and not all the analysed SNPs come from the Illumina HumanOmniExpress-24 format chip.

Hence you may find customers that joined 23andMe before this chip version whose SNPs may be slightly different, hence affecting results.

On 2017-09-04 10:57:08, user Sotirios Tsaftaris wrote:

To all interested readers, I also bring to your attention Phenotiki and the Phenotiki software (http://phenotiki.com/) "http://phenotiki.com/)"), which was published a year ago in the plant journal (http://onlinelibrary.wiley.... "http://onlinelibrary.wiley.com/doi/10.1111/tpj.13472/abstract)"). It is open, free, based in Matlab, but also available as executable, and relies heavily on award-winning machine learning to segment plants and count leaves, making it more robust to environmental variations and laboratory conditions.

On 2017-06-20 13:11:48, user @hugospiers wrote:

Would be great if the model could predict the distortions in judgments of space and time found in our new article in Cognition: http://www.sciencedirect.co...

On 2023-01-31 18:53:04, user Marco Incarbone wrote:

Pre-print review for Nielsen et al, bioRxiv 2023 <br /> (doi: 10.1101/2023.01.10.523395)

The manuscript by Nielsen and colleagues proposes a novel function of Arabidopsis DCL2 as a sensor of double-stranded RNA (dsRNA) that activates innate immunity through NLR receptors. This immune function of DCL2 is independent of its canonical function in RNA interference through small RNA. The strength of the study is in the genetic dissection of the autoimmune phenotype caused by DCL2 that was previously described in literature. The authors use phenotypic analysis combined with RNA sequencing in Arabidopsis to show that DCL2 triggers activation of immunity and an autoimmune phenotype through at least two NLR proteins (L5 and RPP9). This is a novel and important discovery. They show that this activity is cytoplasmic and not nuclear. Interestingly, immunity is also activated in tomato by DCL2 and moss by DCL3, suggesting this mechanism is evolutionarily highly conserved. These conclusions are well supported by data and well presented.<br /> However, the evidence for an antiviral function of this pathway is not sufficient for the strong claims made in this paper. References to virus are made in the title, the abstract, results and conclusions, and are an important part of the paper, so major concerns need to be addressed. These are listed below, with suggested experiments.

• The virus infection results are not conclusive. For example, in Fig 4A dcl4/l5 line 1 shows more virus accumulation than dcl4/l5 line 2 (which in turn accumulates as much virus as the dcl4 single mutant), while in Fig 4B the opposite is true. In fact, in Fig 4B the l5 mutation is not additive to dcl4 mutation in terms of antiviral defense. In Fig 4B the dcl2 mutant shows significantly less accumulation than WT, which is not the case in Fig 4A and in literature. All this points to high stochastic variability between samples, which can be expected when working with pathogens. It is highly appreciated that the authors show conflicting results between experiments, but these results need to be robust given their importance for the conclusions of the paper. One possible explanation for this variability could be the use of labile in vitro transcripts as inoculum. This could be solved by either randomizing the plants when infecting (not one genotype after the other, if this wasn’t done) and/or use systemically infected tissues from inoculated dcl2/dcl4 mutants as inoculum for rub inoculation (although this TCV clone has no CP, which could be a problem). In any case, the infection experiments need to be repeated with more biological replicates per genotype, in the key genotypes Col, dcl4, l5, rpp9, dcl4/l5, dcl4/rpp9.

• A VSR-impaired virus should not be used to test whether DCL2 has an RNAi-independent function. As this virus is very sensitive to RNAi, in this experimental setup this pathway cannot be uncoupled from the proposed dsRNA-DCL2-NLR sensing pathway. In addition, the P38 VSR is also the viral coat protein, loss of which could impact the viral life cycle and accessibility of dsRNA structures in vivo. On the other hand, the use of wild-type TCV, encoding a very efficient VSR, would reliably show whether DCL2 has a small RNA/AGO-independent antiviral function. In this case WT, dcl4, l5 and rpp9 should show similar levels of virus accumulation, while dcl4/l5 and dcl4/rpp9 should show increased accumulation, if the hypothesis is valid. As above, several replicates should be analyzed.

• A central conclusion in this paper is that DCL2 is a sensor of dsRNA that activates innate immunity through NLRs. While the fact that DCL2 is an RNAseIII strongly suggests that it senses dsRNA, there is limited experimental evidence for this (the knock-out of RNA decay factors in literature and the experiments with TCV-deltaP38 - see comments above). Two experimental approaches would significantly strengthen these conclusions. (1) Treatment with a dsRNA analogue such as poly I:C of the various dcl/NLR mutants described in the paper. Activation of immunity can be assessed by quantifying expression of immune-responsive genes as in Supplementary Figure 1 or as described in literature (DOI: 10.1111/pbi.13327, DOI: 10.1042/EBC20210100). Performing this experiment on WT, dcl2, l5, rpp9, dcl2/l5 and dcl2/rpp9 should genetically determine whether DCL2 activates innate immunity through L5 and RPP9 NLR receptors upon detection of dsRNA. (2) Perform infection experiments with phylogenetically distant RNA viruses, and test activation of immunity as in (1). Available and routinely used in Arabidopsis are, among others, Cucumber mosaic virus (CMV), Turnip mosaic virus (TuMV), Tobacco rattle virus (TRV), Turnip yellow mosaic virus (TYMV), Tomato bushy stunt virus (TBSV) and Turnip crinkle virus (TCV – see above). These virus species have very different proteomes, replicate on different organelles, have different capsid shapes, etc. If several of these activate immunity in a DCL2/NLR-dependent fashion, the case for DCL2 being a sensor for dsRNA in vivo would be far more robust. Using these viruses to infect Col, dcl4, l5, rpp9, dcl4/l5, dcl4/rpp9, then assessing viral accumulation, would indicate that in addition to activating immunity, this mechanism also has antiviral activity.

If these points are not addressed, I believe that claims of immune activation by viruses and antiviral activity should not be present as major conclusions in the paper. They could be suggested in the discussion. In addition, conclusions regarding dsRNA as the trigger for immunity should be tempered.<br /> It is my hope that these experiments will be conducted, and if the current conclusions of the authors are substantiated, this is a very significant discovery and advancement in plant virology and immunity.

Next are listed some minor points to address.

• The authors refer to antiviral RNAi function of DCL2 as being dependent on the genetic abrogation of DCL4. The authors state more than once that DCL4 is the main antiviral Dicer, with DCL2 dicing only when DCL4 is somehow overwhelmed by viral dsRNA. This view is not up to date, as more recent studies have shown that in WT plants DCL2 can be an active antiviral RNAi player, depending on virus species and/or tissue (e.g.: doi: 10.1038/nplants.2017.94; doi: 10.1093/nar/gkab802), suggesting that precise function and context of DCL2 in antiviral RNAi remain poorly understood. This should at least be mentioned.
• A whole section in the middle of the manuscript delineates the model proposed by the authors, while describing some of the results. In my opinion this interrupts the flow of the manuscript. The results should be presented as in the rest of the manuscript, while the model should be built in the discussion or at the end of the results. A partial model is also presented in Figure 2, taking away space for data.
• The authors end the final paragraph of the results with “when RNAi has been abrogated by mutation of DCL4”. KO of DCL4 does not abrogate antiviral RNAi, it reduces its efficacy. This should be corrected.

On 2019-02-19 13:39:41, user Marco Tripodi wrote:

Matsuyama et al. have made an early report in bioRxiv on a technical concern regarding the self-inactivating rabies (SiR) virus vector on which we recently published. In the absence of any peer-review we feel obliged to respond directly to Matsuyama et al. in <br /> order to give a context to their report and to obviate any concerns regarding SiR that others may have.

Further, although it is clear that Matsuyama have been investigating this issue for some time, we only learned of it a few days ago. We shall produce experimental data to illustrate our case in due course, but for now, with time pressing, we offer a reply of concept.

It appears that Matsuyama et al. received from a third party batches of the SiR in which mutations targeted the PEST domain, a critical regulator of self-inactivation of the vector. As the authors point out, these mutations (which are absent in the Addgene deposited plasmids) can naturally emerge during the viral amplification process due to the high rate of replication errors of rhabdoviruses. In particular, the characterised mutants carry a nonsense mutation just upstream to the PEST site, effectively reverting the SiR to a wild-type (WT), and hence toxic, ?G Rabies. The rest of their findings, and the observed cytotoxicity, are therefore the obvious consequence of the presence of the reported PEST mutation that transforms the SiR into a WT ?G Rabies.

Indeed, what they refer to as “SiR” in the manuscript is, in fact, a “SiR mutant PEST”.

The results of Matsuyama et al. confirm the requirement for an intact PEST sequence to sustain the self-inactivating behaviour of the SiR. This is perfectly in line with our model and the results of our published work. Once the PEST is rendered ineffective (via a mutation or otherwise) the SiR reverts functionally to a first generation, and hence toxic, <br /> SADB19.

Matsuyama et al. also claim that the substitution of a Cherry reporter with the CRE in a wild type SADB19 ?G Rabies would be, in itself, sufficient to eliminate cytotoxicity, albeit via an unspecified biological mechanism. However, when the SiR PEST mutant (i.e a wild type revertant SADB19 ?G-CRE-Cherry Rabies) is compared to a wild type SADB19 ?G-CRE Rabies, the former would appear to be more toxic than the latter. Given that the two <br /> viruses are functionally identical (since the SiR has lost the PEST domain and since both express the CRE) these findings appear to defy logic.

In fact, it is most likely that both viruses are equally toxic. However, the lack of an early fluorescent reporter in the ?G-CRE Rabies used by Matsuyama et al. probably does not allow them to observe most of the early cytotoxicity elicited during the early stages of the infection, due to the delay in the expression of the transgenic CRE-driven reporter. Instead, for the SiR PEST mutant (i.e a wild type revertant SADB19 ?G) they are able to <br /> track neurons for the entire infective period, given the early expression of the CRE-independent Cherry reporter. Once that is taken into account, cytotoxicity will likely result to be present, and identical, for the two strains, which are, in fact, wild type SADB19 ?G <br /> viruses.

Overall, these findings show that revertant mutants can arise during viral amplification of the SiR virus and that, therefore, care must be taken during SiR production to avoid selecting mutant variants with advantageous replication kinetics (such as the reported PEST mutants). The key issue is to be aware of this and to implement quality-control <br /> procedures that prevent the emergence of such mutants during viral <br /> production.

As noted above, we shall soon be able to address this and the other points made <br /> by Matsuyama et al. in a follow up experimental paper.

On 2019-07-07 00:03:23, user Buert Sohrrem wrote:

The amounts of CDNF released from ischemic/ reperfused hearts are sufficient to generate protection in another heart?<br /> Ischemic preconditioning is able to increase CDNF secretion? Does the activation of the KDEL receptor by the CDNF have any genomic effects?

On 2020-02-03 09:31:09, user Douglas Philip Dyer wrote:

Interesting paper, we had a couple of recent (ish) papers that might be of interest to you in respect to the interaction between CCL5 and GAGs and the biophysical effects of this interaction.<br /> https://www.ncbi.nlm.nih.go...<br /> https://www.ncbi.nlm.nih.go...

On 2021-04-26 12:10:00, user Thomas D Alcock wrote:

Very nice work! Could I please ask if you would expect Si to be taken up through OsNIP2;1 as silica or as silicic acid? I am curious, as you state in the introduction that silicic acid is the naturally occurring bioavailable form of Si, but in later figures, it looks like silica is represented as transiting through NIP2;1. Is silicic acid transformed to silica prior to plant uptake/translocation? Many thanks, Tom

On 2016-05-27 21:46:03, user K. wrote:

Two years of daily 9 hour exposure in rats is equivalent to about 56 human years. <br /> What if the exposure had been more than 9 hours? Would that have changed the outcome? Many people are exposed day and night.<br /> Just looking at the group with low-level exposure: with 1 in 15 male rats developing cancer or pre-cancerous lesions when exposed to LOW levels common from cell phones, and when levels may be similar or higher in conjunction with other wireless devices (utility meters, Ipads, routers, etc.)--what does this mean for public wireless and liability?

On 2018-06-07 21:15:24, user Brendan O'Fallon wrote:

Nice work! You should consider providing a breakdown of the sizes (number of overlapping enrichment targets) for the CNV calls, since larger CNVs are much easier to detect than smaller CNVs. Grouping them all together has the potential to obscure reduced sensitivities for smaller CNVs

Also, it might be important to clarify the statement that "A true positive (TP) call is defined as a Genalice CNV call that overlaps an MLPA CNV call." Right now it sounds like ANY Genalice CNV call that overlaps the true CNV will be labeled as a correct call. Other studies have used a 50% or 80% reciprocal overlap rule to determine the correct calls (this prevents really large called CNVs that just happen to overlap a couple true CNVs from contributing to sensitivity)

On 2018-12-06 06:25:17, user Sam Diaz-Munoz wrote:

**<br /> This review was conducted by the Díaz-Muñoz Lab, as part of a pre-print journal club (inspired by Prof. Prachee Avasthi’s journal club: https://asapbio.org/preprin... "https://asapbio.org/preprint-journal-clubs)"). We provide the review in the best spirit of open science and to contribute to the virology field.<br /> -Sam Diaz-Munoz, Ilechukwu Agu, Sari Mäntynen, Alex Wilcox, Ivy José<br /> **

This manuscript aims to quantify the contribution of influenza virus genetic variation to the observed heterogeneity of virus infection outcome. The experiments first enriched for interferon activated cells (which are rare in normal infections), then infected with well-characterized viral stocks, and finally characterized the full genotypes and transcriptomes of all viral genes present within single cells. This transcriptomic data is used to derive the viral transcriptional burden on cells and to quantify innate immune activation (IFN, ISG) on a per-cell basis. The genotype data provides information on mutations, deletions, and other changes in the viral genome(s) that infected those same cells. The results from these two data sets show that a majority of cells infected by viruses with mutations are IFN+ compared to cells infected by wild type viruses, but this difference was not statistically significant. Further analysis shows statistically significant associations with IFN+ cells and specific mutations, particularly in segments PB1 and NS1. Some of these mutations were engineered into viruses and experiments showed that 5/8 of these mutations increased the percentage of IFN+ cells relative to wildtype, with the greatest effect mutation yielding ~17% IFN+ cells.

This paper presents leading edge methods to investigate the outcomes of viral infections at the single cell level. A major strength of the text is the explanation of the methods, which briefly and very clearly explains the overall methodology with plenty of supplementary information. The presentation of results and data is clear, with increasing levels of detail in the multiple supplements. The data in Figure 4 certainly represent a milestone in the influenza virus field, made all the more impactful by the exquisite presentation.

This manuscript could be improved by contextualizing the statistical significance and magnitude of the observed effects with regard to the role of viral genetic variation in the heterogeneity of innate immune activation. In a few sections, some words could be interpreted by readers as an overstatement of the support of the data for this topic. We present specific suggestions (see Major Comment 1) to improve this aspect of the text. The abstract could clarify the background of interferon in the context of influenza infection for readers not familiar. Finally, a summary figure tracking the overall results (see Major Comment 1) might benefit the reader.

Overall, we find this manuscript highly stimulating and useful for the innovation in the methods. We anticipate some aspects of this paper (esp. Figure 4) will become textbook knowledge in influenza virology. This paper is an exciting contribution to the influenza field, and indeed the broader virology community, which is currently characterizing viral infection dynamics in increasing detail.

Major comments:

1. One of our major points of our discussion reviewing this paper centered around contextualizing the statistical significance and magnitude of the observed effects with regard to the role of viral genetic heterogeneity in innate immune activation. Particularly given that the activation of IFN is so rare to begin with and that the data seem to support modest effects that are not always statistically significant. We have some suggestions to this effect.

First, some of the language in the discussion and abstract should be qualified to benefit the reader’s understanding (e.g. the word “crucial” in Line 286-287). Second, the data in Figure 3G can be used to provide some context showing that viral infection indeed increases the proportion of IFN+ cells, relative to uninfected cells. This result can also be highlighted with a statistical test of these proportions and the discussion could expand on how cell state could influence spontaneous IFN activation. Third, it may be helpful to conduct a post-hoc power test of data in Fig 5B to assess whether there was enough power to detect a significant difference between these proportions (0.184 and 0.306) given the sample size imposed by the single cell methodology. Fourth, it may be worth placing more emphasis on the fact that although the stated NS1 and PB1 mutations were well known to activate immunity, this is (if we are not mistaken) the first characterization at the single cell level. Finally, the discussion should place more emphasis on the fact that although IFN activation is rare, it could affect the course of the entire infection when founding infection size is small.

Additionally, it would be very useful to have a single figure that summarizes the major results addressing the central question of the paper. This figure could summarize the infected/uninfected proportions, immune activation/genotypes, and specific mutations that impact IFN, thus tracking the broad outlines of the full experiment. Readers may get lost in the rich data and helpful figures, which could dilute the main message of the text.

1. We find Figure 4 a very interesting and effective figure. It is an accomplishment in both experimental methods and data visualization. I (Sam Díaz-Muñoz) anticipate routine use of this figure in the classroom and we expect it will become textbook knowledge in virology.

2. There are a couple of terms used throughout the paper that could be substituted or clarified to benefit the reader. Viral infection outcomes is used throughout, but it is not always clear what this term refers to. In the context of the paper, the two measured infection outcomes we found were mRNA levels and immune state. If this is the case, “expression and immune state” is only one more word and provides flexibility to refer to one or the other.

The second term is the reference to mutations, deletions, and other modifications from “wild-type” as defects. While this is certainly commonplace in genetics and is convenient shorthand, it does have a connotation of being uncommon and negative. As Figure 4 so exquisitely shows (among many, many other studies), this heterogeneity is probably the norm for influenza viruses. As Brooke (2014) and many others have advocated, as a field we perhaps should be moving towards investigating and recognizing this heterogeneity as a standard part of influenza virus biology.

1. We find it very interesting that the coinfection rate is estimated to be 63% given a rough, “effective” MOI ~ 0.241 (290/1200). While the text explained this could be due to methodological biases, it may be worth indicating if there were any relationships between coinfection and expression or immune activation.

Minor Comments:

Line 16 - This line could be confusing to some readers if they do not have the background (stated well in the Introduction line 32) that influenza virus is very good at preventing IFN induction.

Line 37 - For the introduction, this statement could be written with more accessible language, paralleling the wording in the discussion (Lines 325-332), which is very understandable.

Line 41 - This paragraph could perhaps more appropriately start by discussing NS1’s well known role in suppressing IFN, setting a baseline for what the expectation should be. This could better contextualize the results for readers not familiar with influenza virus immunology.

Line 87 - To benefit the reader, the text could indicate (as done in Fig1 Supp4) that the Sendai infection was done at high MOI to validate the IFN activation of cells. As written may not be clear if text is still referring to Steuerman data.

Line 106 - May want to spell out IFN negative, instead of IFN-, some readers found it easy to miss the - sign

Line 201 - This is a really nice summary of the criteria used to call mutations and a frank assessment of the limitations of the approach

Line 216 - A very nice figure, with a wealth of information, indeed

Line 226 - A “favorable outcome for the virus” could be interpreted as increased viral replication, which doesn’t necessarily follow from the proportion of viral mRNA expressed in the cell.

Line 235 - “Also” is confusing in this context. Delete if retains intended meaning, otherwise should be rephrased

Line 325 - This paragraph is a really good discussion of the importance of IFN+ cells in light of the fact that they are very rare in influenza infection.

Line 431 - This section is a very detailed description of the methods. Highly useful for the community.

Line 608 - The computational analyses are helpfully described and the full pipeline is open and available in GitHub and explained in several Jupyter Notebooks

On 2025-07-15 08:25:43, user Patricio Fuentes Bravo wrote:

Nice and insightful article!

Have you explored alternative protocols for generating organoids, such as those developed by Pasca or Muotri?

I’m curious whether your criteria or determinants might be generalizable across different methodologies.

Many thanks.<br /> Patricio

On 2020-12-17 16:02:34, user Lamya Ghenim wrote:

The text has been improved as well as some of the figures, and that a link will be forthcoming to the accepted version shortly. The paper has been accepted in Scientific reports (Nature).

On 2022-09-21 03:31:40, user Daniel E. Weeks wrote:

What a fun and interesting paper! It even applies Student's t test to Student's data!

I am interested in your variable selection (Table S2) with the disparate results between the different methods.

One minor suggestion would be to merge Table S3 into Table S2, as it would be nice to be able to see these metrics at the same time as we're seeing which variables were selected.

Regarding variable selection, I really like this discussion of variable selection issues:

Heinze G, Wallisch C, Dunkler D. Variable selection – A review and recommendations for the practicing statistician. Biometrical Journal. 2018;60(3):431–449. DOI: https://doi.org/10.1002/bim...

I like their recommendation to "assess selection stability and model uncertainty", which is what we ended up doing in this recent paper:

Heinsberg LW, Carlson JC, Pomer A, Cade BE, Naseri T, Reupena MS, Weeks DE, McGarvey ST, Redline S, Hawley NL. Correlates of daytime sleepiness and insomnia among adults in Samoa. Sleep Epidemiology. 2022 Dec;2:100042. DOI: https://doi.org/10.1016/j.s...

We had originally wanted to use a lasso where we forced in a few variables that we thought had to be in all models, but couldn't get existing software to work in our hands to enable proper post-selection inference. When no variables are forced in, proper post-selection inference after variable selection via lasso can be done using these approaches:

1. Taylor J, Tibshirani R. Post-Selection Inference for l1-Penalized Likelihood Models. Can J Stat. 2018 Mar;46(1):41–61. PMID: 30127543 PMCID: PMC6097808 DOI: https://doi.org/10.1002/cjs...

2. Lee JD, Sun DL, Sun Y, Taylor JE. Exact post-selection inference, with application to the lasso. The Annals of Statistics. Institute of Mathematical Statistics; 2016 Jun;44(3):907–927. DOI: https://doi.org/10.1214/15-...

On 2020-07-06 01:13:30, user Brent Lengel wrote:

From the study's methodology:

"...***We acknowledge that this study was conducted with untrained individuals and not transgender athletes.*** Thus, while this gave us the important opportunity to study the effect of the cross-hormone treatment alone, and as such the study adds important data to the field, it is still uncertain how the findings would translate to transgender athletes undergoing advanced training regimens during the gender-affirming intervention. It is also important to recognize that we only assessed proxies for athletic performance, such as muscle mass and strength. Future studies are needed to examine a more comprehensive battery of performance outcomes in transgender athletes. Given the marked changes in hemoglobin concentration in the current study, it is possible that gender-affirming treatment also has effects on endurance performance and aerobic capacity. Furthermore, since the TM and to some extent also the TW demonstrated progressive changes in muscle strength, and strikingly some of the TW individuals did not lose any muscle mass at all, follow-ups longer than 12 months are needed to better characterize the long-term consequences and individual responsiveness to gender affirming interventions. Future studies should also include age- and body size-matched cisgender control groups undergoing the same assessment timepoints without the therapies."

On 2020-11-30 09:16:49, user Niko Popitsch wrote:

Nanopanel2 is now released on github: https://github.com/popitsch/nanopanel2

On 2021-08-07 12:30:57, user ????????? ??????? wrote:

There are 0 not predicted proteins, not 29. Please fix it.

On 2021-12-06 11:51:44, user Martin R. Smith wrote:

Congratulations on this very useful test between these approaches, which is clearly a very important thing to do! Apologies if I've missed something in my quick read of the paper, but one concern I have about the interpretation of the findings is that low RF distances might be reflecting a lack of precision (i.e. more nodes collapsed to polytomies) rather than higher accuracy; see Smith 2019, Biol Lett, 10.1098/rsbl.2018.0632 – would this really make a tree "better"?

And I didn't quite follow whether collapsing nodes at random with -R might resolve nodes in a fashion that is not consistent with the original analyses; if so, this could potentially inflate RF distances.

Presumably the size of the simulated trees precluded the use of any of the more robust alternatives to the RF distance (e.g. Smith 2020, Bioinformatics, 10.1093/bioinformatics/btaa614)?

On 2025-08-20 14:02:09, user hinata wrote:

no supplementary, no methodology part

On 2024-10-20 07:36:15, user XKM wrote:

This study has now been published in PLoS Biology.<br /> https://doi.org/10.1371/journal.pbio.3002615

On 2020-03-30 18:24:04, user Mohamed Diwan wrote:

The title of this paper is misleading and incorrect. 'high-throughput screening' in the title should be replaced with 'virtual screening' or 'docking'. There is no experimental work in this paper.

On 2020-07-21 17:22:55, user Huanglab wrote:

It has been published on JMC. See link: https://pubs.acs.org/doi/10...

On 2019-11-11 20:58:38, user Kristen Sakura wrote:

Curious how these findings will be used in regards to understanding addiction (referenced at very end of discussion section). Are there going to be more efforts from this research team regarding addressing the "broad implications" for addiction mentioned therein? Thanks in advance.

On 2017-12-21 03:09:57, user BenjaminSchwessinger wrote:

And I forgot figure 4A would be better as an upset plot e.g. http://vcg.github.io/upset/....

On 2024-12-26 11:37:01, user Francisco Rodriguez-Valera wrote:

Did you include in the sample the microbiome of microscopic animals (e.g. nematodes)?

On 2022-07-12 11:01:20, user Gregor Gorjanc wrote:

Nice work @Yanyu! See also https://www.biorxiv.org/con...

On 2018-07-18 02:48:03, user Jason D. Yeatman wrote:

General summary:

This is an important paper that carefully examines mechanisms that contribute to MRI-based measures of cortical thickness. The main question at hand is whether developmental decreases in MRI-based cortical thickness are picking up on synaptic pruning or are, in fact, driven by myelination of the underlying white matter. With hundreds of studies using MRI-based cortical thickness to study development, this is an extremely important methodological question to work out and this paper has important implications for the vast literature that uses these MRI-based measures to develop theories of cortical pruning during childhood.

The paper demonstrates that multiple mechanisms contribute to MRI-based measures of cortical thickness and that one of the primary mechanisms is myelination as oppose to the thickness of cortex per se. Since MRI voxels are millimeter scale measurements, myelin in the superficial white matter drives the voxels on the gray/white boundary to become brighter on a T1-weighted image and this change pushes the gray/white boundary closer to the pial surface leading to an apparent thinning of cortex. The findings and analytic approach is quite elegant, combining conventional T1-weighted images with quantitative MRI measures and post-mortem histology. However, while the paper has important implications for the MRI-based literature on development, it should not be taken as redefining what post-mortem studies have shown about pruning. Since the paper argues that MRI-based measures of cortical thickness are not in fact sensitive to synaptic pruning, then it does not make sense to interpret these data as ruling out (or testing) the pruning hypothesis. In summary, the paper is a major and important contribution to the MRI literature, but I think that some of the interpretations and assertions need to be revised/clarified and that there are a few additional analyses that would help generalize the findings.

Specific comments:

-There is an extensive literature on synaptic pruning over development that is based on careful post-mortem measurements. Some of the claims in the paper seem to contradict that literature. The paper acknowledges that the various hypotheses about development are not mutually exclusive (introduction page 3), but then goes on to interpret the data as if it invalidates post-mortem studies on pruning. For example, paragraph 4 asks how MRI measures can differentiate these hypotheses, but I do not think that the paper shows that MRI measures can. Since the hypotheses are not mutually exclusive, it is possible that myelination and synaptic pruning happen together and that MRI is not sensitive to synaptic pruning and, thus, is primarily driven by myelination. MRI (at least the methods employed here) jusr might not be appropriate for differentiating these three potential mechanisms. To be clear, this point does not make the findings here any less important - The results clearly demonstrate that myelin and other factors contribute to apparent thinning of cortex measured with MRI. However, these findings should not be presented as refuting the idea that synapses are pruned during development – only that pruning is unlikely to be the mechanism that is driving MRI-based measures of cortical thinning. I think that this is an important distinction that should be carried throughout the paper and could be accomplished through some small changes in wording. For example, the fist sentence of the abstract asserts that the mechanisms of cortical thinning during development are unknown – this assertion seems contrary to a multitude of careful post-mortem measurements (e.g. see work by Huttenlocher and others). If the statement were revised to be specific to the MRI literature then it would be true and consistent with the results of the paper.

-The analyses showing that T1 values predict much of the variance in cortical thickness is a nice finding, elegantly demonstrating how individual differences in T1 relaxation rates (likely driven by myelin) affect segmentation algorithms. To generalize this finding it would be extremely useful to generate maps of variance explained across the whole cortical surface. In other words, expanding beyond the functionally defined ROIs, how general is this finding? Can most of the variance in cortical thickness be predicted across the whole cortical surface? If so, we should probably refrain from using the term “cortical thickness” and come up with a new term that more accurately conveys the mechanisms driving this measure.

-The post mortem data is a beautiful example demonstrating how myelinated fibers entering the cortex can influence MRI-based measures of cortical thickness. To generalize this finding, synthetic T1-weighted images could be generated at millimeter resolution from the histology data and then passed through the freesurfer segmentation algorithm. This analysis would make it possible to directly test how differences in myelin push the gray/white boundary closer to the pial surface across the brain. If this is not possible, a similar analysis could be done based on the high resolution (50 micron) T2* weighted images.

-Rather than simply dilating the ROIs into the white matter, would the results be the same if the surface normal from freesurfer were use to extend the white/gray surface into the white matter by a few millimeters?

-Discussion page 14 – “First, we found no evidence of pruning after age 5 in any region of VTC.” I would suggest revising this sentence to state “First, we found no evidence that MRI-based measures of cortical thickness are sensitive to pruning in any region of VTC” or something along these lines. My read of the Results is that it is unlikely that pruning is a major driving mechanisms for these MRI measures but the fact that we are not measuring pruning does not mean that it isn’t occurring.

-Discussion page 15 “Our data provide evidence that increased myelination of axons during childhood is a key source of cortical thinning in VTC after age 5”. I would suggest revising this sentence to “Our data provide evidence that increased myelination of axons during childhood is a key source of cortical thinning in VTC after age 5 measured with MRI.”

-The carefully designed analytic approach in this paper would be useful to many other investigators. Moreover, given the complexity of the analysis it is difficult to ascertain how various methodological choices contribute to the overall findings. The impact and reproducibility of this work would be increased if the authors posted the code in an open repository. If it is too early to release all the data then single example dataset would suffice to demonstrate how these various measures are extracted and summarized.

On 2020-03-01 11:02:30, user Stefano Campanaro wrote:

Dear Jennifer Lu and Steven Salzberg,<br /> Thnaks for posting the preprint, it describes a really interesting analysis with useful software to check potential misassemblies. I am checking a genome of a Cyanobacteria species that we have recently sequenced using hybrid Illumina/Nanopore approach.<br /> By checking the supplementary materials provided, I realized that the Supplemental Table 2 file contains links to other files and columns "D" and "E" cannot be visualized.<br /> I hope you can manage to revise the file.<br /> Additionally, in git documentation, it could be useful to remember the users to install biopython before running the scripts. You can suggest the users to create a conda environment including python 2.7 (or similar), and then to include in this environment both python 2.7 and biopython using:<br /> conda create --name your_env_name python=2.7<br /> conda activate your_env_name<br /> conda install -c bioconda biopython

Hope these suggestions will be useful.<br /> Sincerely<br /> --<br /> Stefano Campanaro<br /> Department of Biology<br /> University of Padova

On 2022-03-22 18:09:31, user Cheyenne wrote:

This study is interesting in that it suggests that some of the unknown aspects of drug addiction could be attributed to the microbial community within the gut. The use of rats to model addiction was well designed with the employment of the increased electric shock with the increased self-administration of the drug. While looking at your methods for this study, I have a few suggestions that a reviewer might mention in the publication process. <br /> Firstly, the insertion of the catheter in the rats included flushings with a saline solution that contained antibiotics. It is unclear if these antibiotics were given throughout the course of the study or just initially during the insertion of the catheter. If there were any preliminary assays performed that showed what or if any changes occurred to the gut microbiome with the administration of the Cefazolin, they should be included in the study. <br /> Furthermore, you state that you performed 30 cycles of PCR. There are studies showing that an increase in cycles increases the likelihood of PCR errors, https://journals.asm.org/do.... If the study were to be repeated with fewer cycles, could it result in a similar population census? Just something to keep in mind. I would also suggest performing qPCR on the entire 16S gene to determine the initial abundances of the varying taxa. This real-time quantification is able to correlate the number of cycles with the number of amplicons to determine the initial abundance of the fragments in the sample. With regular fragmentation PCR, there is at best, an establishment of relative abundance. Were there any controls used during PCR and sequencing steps? It is ideal to run a simultaneous amplification and sequencing of known samples to determine if they were successful. These controls should be briefly mentioned during these procedures.<br /> As far as describing quality filtering of the amplicons, it would be prudent to specify what exactly is being filtered out, be it chimeras, PCR errors, etc. It is also important to state how many sequences were collected before and after this filtering so that it is apparent if the number of errors is what is to be normally expected. You show that you used QIIME, however, this is an older system and QIIME2 would be more up to date. It is unclear what the inclusion criteria are and what percent of the taxa found are considered in the study. It would be understandable to include the top 15 OTUs in the study, or to only include taxa that make up the majority of the population, so those taxa that are dominant. Also something to note would be what level of identification are you aiming to determine. In your figures, some of the taxa are inconsistently identified at either the genus, family, or order level. Greengenes is a database that is also not as up to date as others, and using a newer program could reveal better taxa ID, preferably species. Alpha diversity is only briefly addressed and should be further examined by determining Shannon and Simpson’s indexes. <br /> When assessing the relative abundance of taxa based on 16S gene amplification, it is important to address those taxa that possess multiple copies of the 16S gene and adjust the abundance accordingly. Another important piece of information to include would be if you assessed sampling coverage. If you did not, it would be good to determine the Goods’ index of the sampling or Chao1 to compare to the number of OTUs found to assess coverage and richness of sample collected. The graphs in the supplemental figures appear to still be showing the generation of new OTUs, whereas if coverage is adequate, there would be a decrease in the number of new OTUs being established. <br /> If any of these processes or assays were further explored, the information should be added to the article. It is important to show not only exactly what was done to collect the data and analyze it, but also to verify the conclusions that are being made from the data. Studying any microbiome, especially that of the gut, brings with it much uncertainty. Many steps in the process can result in the loss of data or the introduction of errors, explaining all details in the study will contribute to the confidence in the results you present.

SHSU5394

On 2020-07-14 18:01:15, user Timothy S Jarvela wrote:

Interesting paper. I like the experimental setup. I wonder how quickly newly synthesized GRASPs are degraded in the presence of IAA, or conversely how quickly the GRASP levels recover after removing IAA from the media.

Overall it shows similar results to previous work with actue inactivation of the GRASP proteins. https://www.molbiolcell.org...

On 2021-01-15 16:09:07, user Oliver Pescott wrote:

The statement in the discussion that "model-averaging is an empirical shrinkage estimator and it is quite easy to show using simulation that under conditions of small to moderate signal-to-noise ratio, model-averaging, which averages over incorrectly specified models, outperforms OLS or ML estimates of the correctly specified model" is very interesting. Is it possible to provide a reference for this, or to give some indication of what is considered to be a "small to moderate signal-to-noise ratio"?

On 2020-08-21 19:34:36, user Enrique Medina-Acosta wrote:

Please follow the comment posted by Borsa, M., and Mazet, J.M. (2020). Attacking the defence: SARS-CoV-2 can infect immune cells. Nature Reviews Immunology doi: 10.1038/s41577-020-00439-1: "As monocytes and lymphocytes do not express ACE2, it remains to be seen whether the virus uses an alternative entry strategy and whether circulating infected immune cells contribute to viral spread and COVID-19 disease progression."

On 2021-01-22 19:22:12, user alejandro wrote:

To the interested we extended this methodology to other variants, while we wait for the laboratory results.

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

On 2020-12-13 21:07:59, user Nikhil M wrote:

Interesting work, Could you please tell me that this method work to model ssDNA structure which is not solved experimentally.? Also, the CMD doest shows mcuh folding of the structure, why?

On 2021-06-10 08:38:06, user Sebastian Dresbach wrote:

Dear Xingfeng Shao, Fanhua Guo, Qinyang Shou, Kai Wang, Kay Jann , Lirong Yan, Arthur W. Toga, Peng Zhang and Danny JJ Wanga,

We have discussed the manuscript entitled “Laminar perfusion imaging with zoomed arterial spin labeling at 7T” in the Maastricht layer-fMRI seminar on Monday June 7th. In this letter, we would like to share a summary of our discussion points.

The manuscript describes a sophisticated study about the implementation and application of functional layer-dependent CBF mapping in sensory and motor cortex. The authors use a pseudo-continuous ASL sequence with an optimized (relatively superiorly aligned) labeling plane and locally-focused 3D-GRASE readout at UHF. The method is validated with previously described “test-tasks” that evoke laminar-specific modulations in vascular responses.<br /> The study addresses one of the most pressing questions of the emerging field of human layer-fMRI. Namely, how to efficiently capture layer-specific signal changes that resemble laminar-specific neuronal activation changes. Thus, we believe that this manuscript will be of wide interest to the field.

The study increases an already long list of non-BOLD layer-fMRI method studies that are currently being published in the field. This study stands out in the sense that it provides more than “just” a usable MRI sequence with extremely clear interpretable layer-profiles. It also shows expected modulations of activation changes for subtle task modulations of sensory feedback into the primary motor cortex, as well as attention modulations in V1.

Some of the specific findings of the study are:<br /> -> The relative CBF change at these laminar resolutions can be as large as 150-200%. This is an extremely valuable piece of information to know in the field of laminar signal modeling. This will help with the interpretation of CBV results and it will help the with understanding of the vascular physiology in general. Until now, the field had to assume underestimated CBF-values from low resolution experiments (partial voluming), and from non-human animal experiments (anesthesia).<br /> -> pCASL is a usable sequence to study subtle cognitive modulations across depth within a conventional human neuroscience acquisition setting.

While there are a few specific points that the manuscript could be revised on, we are extremely enthusiastic about the manuscript.

Some aspects that lower our enthusiasm a little bit, refer to <br /> (i) the unclear influence of short-TI back-ground suppression, <br /> (ii) over-stated claims on novelty and superiority over other modalities, <br /> (iii) limited information about some methodological aspects, <br /> and (iv) the restricted data availability.

An itemised list of potential improvements is given below.

1.) Influence of background suppression is unclear.<br /> The authors use a single-inversion background suppression. Due to the long T1 at 7T, this background-suppression results in the fact that the CSF magnetisation is aligned along the opposite direction of the external magnetic field (negative phase). This results in signal cancellation at the superficial layers. While the control images (underlays of many figures) have a beautiful structural contrast, they exhibit a clear dark line of the transition between CSF and the superficial layers of GM. This might have substantial effects on the interpretation of the CBF profiles. With a net-negative phase of the z-magnetisation, an increase of CBF would result in a decrease of the MRI magnitude signal.<br /> Thus, for any voxel with partial voluming of CBF and GM, this might make the CBF quantification a bit tricky. For partial voluming of 50% and more, it might make the CBF quantification impossible? Given the nominal resolution of 1mm, this artifact might concern up to half of the cortical thickness. <br /> We would advise the authors to discuss potential influence of the background suppression as used here. <br /> Was the TI of the background suppression kept constant for all post-label-delays?

2.) Details about the “deblurring” in the partition direction can be extended.<br /> We applaud the authors’ efforts to acknowledge and account for the blurring in the partition direction of 3D-GRASE. Unfortunately, we are afraid that the method's description is not really sufficient to help us fully appreciate how appropriate and effective the method works.

On page 7, it is mentioned that partial Fourier sampling is applied in the partition direction and furthermore it is mentioned that variable refocusing flip angles are used. Furthermore, the spatial variance of non-180deg pulses will result in stimulated echoes and a sensitivity to T1 as well as local B1+. All of those features have substantial effects on the k-space signal evolution in the partition direction. However, based on the descriptions of the deblurring and based on the depiction of the simulated k-space signal (Fig. S5A), those effects do not seem to be incorporated in the deblurring model.<br /> We would advise the authors to comment on the limits of the used deblurring method and the potential of introducing artificial edge-enhancement features into the data. E.g. the ringing effect of the PSF in Fig. 4D might have the same spatial frequency as the layer-fMRI double peak in Fig. S5E. Is it possible that the edge enhancement-filter introduced layer-signatures across cortical depth based on the sharp border at the GM-CSF transition? Could the overcorrection of T2-blurring be responsible for the vertical stripes in the axial view of Fig. S5G?

3.) <br /> a) We don’t follow the claim about VASO’s lack of capturing absolute CBV changes.<br /> The authors claim that the proposed method is superior to CBV-based (VASO) methods because “VASO only measures relative CBV changes that may be confounded by different baseline CBV values across cortical layers” (page 3). This claim is repeated on page 7. <br /> We believe that this statement is untrue. VASO measures "absolute" CBV changes. VASO is sensitive to volume redistributions within the voxel. Thus, the percent VASO signal change refers to “absolute” physical units of ml of CBV change per 100 ml of tissue. The “relative” part about CBV changes does not refer to a normalization to baseline CBV, however it refers to the relativity of 100ml of tissue. This is identical to the “absolute” CBF quantification in ASL. The authors quantify their “absolute” CBF values in “relative” units (per 100ml of tissue). VASO’s lack of a baseline CBV quantification (without the use of multiple inversion times) should not be misunderstood as an inherent normalization of CBVrest.<br /> For more background about the “absolute” units of VASO in layer-fMRI, see Fig. 4 and section 4.4 in Huber et al., 2021, as well as Fig. 8 in Huber et al., 20215

Huber L, Poser BA, Kaas AL, et al. Validating layer-specific VASO across species. Neuroimage. 2021. doi:10.1016/j.neuroimage.2021.118195

Huber L, Goense J, Kennerley AJ, et al. Cortical lamina-dependent blood volume changes in human brain at 7T. Neuroimage. 2015;107:23-33. doi:10.1016/j.neuroimage.2014.11.046

There are plenty of VASO approaches from the Johns-Hopkins group, from NIH, and the Yale group quantifying absolute CBV changes by means of multiple TI’s.

Hua J, Qin Q, Pekar JJ, van Zijl PCM. Measurement of absolute arterial cerebral blood volume in human brain without using a contrast agent. NMR Biomed. 2011;24(10):1313-1325. doi:10.1002/nbm.1693

Ciris PA ksi., Qiu M, Constable RT. Noninvasive MRI measurement of the absolute cerebral blood volume-cerebral blood flow relationship during visual stimulation in healthy humans. Magn Reson Med. 2014;72(3):864-875. doi:10.1002/mrm.24984

Gu H, Lu H, Ye FQ, Stein EA, Yang Y. Noninvasive quantification of cerebral blood volume in humans during functional activation. Neuroimage. 2006;30(2):377-387. doi:10.1016/j.neuroimage.2005.09.057

b) Furthermore, the authors claim that due to the (wrongly presumed) CBVrest sensitivity of VASO, it fails to capture the fact that layers II/III have a stronger activation than layer Vb during finger tapping (page 5).<br /> This statement is not supported by the literature. A finger tapping task has been conducted in about a dozen layer-fMRI VASO studies and every single one shows a stronger activation in layers II/III compared to layer Vb. See the studies below, just to name a few:

Guidi M, Huber L, Lampe L, Gauthier CJ, Möller HE. Lamina-dependent calibrated BOLD response in human primary motor cortex. Neuroimage. 2016;141:250-261. doi:10.1016/j.neuroimage.2016.06.030

Beckett AJS, Dadakova T, Townsend J, Huber L, Park S, Feinberg DA. Comparison of BOLD and CBV using 3D EPI and 3D GRASE for cortical layer fMRI at 7T. Magn Reson Med. 2020:1-18. doi:10.1101/778142

Persichetti AS, Avery JA, Huber L, Merriam EP, Martin A. Layer-Specific Contributions to Imagined and Executed Hand Movements in Human Primary Motor Cortex. Curr Biol. 2020;30:1-5. doi:10.2139/ssrn.3482808

Chai Y, Li L, Huber L, Poser BA, Bandettini PA. Integrated VASO and perfusion contrast: A new tool for laminar functional MRI. Neuroimage. 2020;207. doi:10.1016/j.neuroimage.2019.116358

Huber L, Goense J, Kennerley AJ, et al. Cortical lamina-dependent blood volume changes in human brain at 7T. Neuroimage. 2015;107:23-33. doi:10.1016/j.neuroimage.2014.11.046

Guidi M, Huber L, Lampe L, Merola A, Ihle K, Möller HE. Cortical laminar resting-state fluctuations scale with the hypercapnic bold response. HBM. 2020;41:2014-2027. doi:10.1002/hbm.24926

c) In the paragraph on comparisons with VASO (first paragraph on page 7) the authors claim that “the proposed ASL fMRI is robust to potential BOLD contamination”. We believe that this claim needs further explanations and/or rephrasing. The authors use the CBF model in (Alsop et al., 2015), which does not account for T2/T2’ contaminations (and lacks any discussion of BOLD or functional imaging). The model in (Alsop et al., 2015) is in fact the one that was developed in (Buxton et al. 1998) and has been developed for much lower field strengths and different voxel sizes with assumed average vascular distribution. While we agree that the intra-vascular BOLD contamination is indeed negligible at 7T (maybe even with GRASE), we do not believe that the extra-vascular BOLD contamination around the microvasculature can be neglected at 7T. <br /> While layer-fMRI VASO studies account for such T2/T2’ contaminations, by means of a dynamic division, this extra-vascular BOLD contamination is not taken care of in this study. In the setup used here, the labeled water magnetization that permeated through the capillary walls (during the label condition and not during the control condition) will experience a T2/T2’ contamination during the readout and linearly scale the CBF signal.<br /> Maybe the authors can elaborate a bit about the reasoning behind the claim that their BOLD contamination is 1-2%. How was this number obtained?

Alsop DC, Detre JA, Golay X, et al. Recommended implementation of arterial spin-labeled Perfusion mri for clinical applications: A consensus of the ISMRM Perfusion Study group and the European consortium for ASL in dementia. Magn Reson Med. 2015;73(1):102-116. doi:10.1002/mrm.25197

4.) Claim about first in-vivo depth-dependent CBF dynamics. <br /> On page 5, the authors claim “this is the first time that the dynamics of labeled blood flowing from pial arteries, arterioles to downstream microvasculature is shown in vivo on the cerebral cortex”. This claim is repeated on page 3.<br /> Aside from our general hesitance to appreciate novelty as scientific value, we do not believe this statement is true. Please compare the reference below:

Zappe AC, Pfeuffer J, Merkle H, Logothetis NK, Goense JBM. The effect of labeling parameters on perfusion-based fMRI in nonhuman primates. J Cereb Blood Flow Metab. 2008;28(3):640-652. doi:10.1038/sj.jcbfm.9600564

Along those lines of claims on CBF dynamics, the authors might be interested in the fact that a number of other studies investigated and depict time courses of the temporal evolution of CBF. The manuscript at hand, however, does not show a single time course of the CBF dynamics.

Kim T, Kim SG. Cortical layer-dependent arterial blood volume changes: Improved spatial specificity relative to BOLD fMRI. Neuroimage. 2010;49(2):1340-1349. doi:10.1016/j.neuroimage.2009.09.061

Kashyap S, Ivanov D, Havlicek M, Huber L, Poser BA, Uludag K. Sub-millimetre resolution laminar fMRI using Arterial Spin Labelling in humans at 7 T. PLoS One. 2021;16(4 April):1-23. doi:10.1371/journal.pone.0250504

5.) The data are solely available given there is a MTA contract. Compared to other papers in the field, this is quite restrictive and highly unconventional. Without access to the sequence and the data, the impact of the manuscript on the field is substantially reduced. We would advise the authors to provide more details about the terms and conditions of the MTA.

6.) The nominal resolution of 1mm iso is unconventionally low for laminar fMRI. Even for non-BOLD laminar fMRI. We believe that the “proof is in the budding” and the shown specificity of the layer-profiles justify usability of the resolution used. However, we still think that the manuscript would benefit from a brief discussion on the laminar specificity across the two investigated brain areas (M1 and V1) with respect to the cortical thickness.

7.) The y-axis of %-perfusion changes in Fig. 2E and Fig. 3E are two orders of magnitude smaller than Fig. 4E. We assume there is a conversion to 100% missing? Maybe it does refer to a different baseline? Maybe %-perfusion in Fig. 2E and 3E are referring to % of units of M0? And %-perfusion in Fig. 4E refers to % of units of CBFrest?

8.) The figure key and the axes descriptions in Fig. S3 are hard to read in printouts of the figure. We had to zoom in quite a bit on the electronic version to be able to read them. We would advise the authors to align the panels vertically to cover more space on the page. The y-axis range of panel G has huge implications for the field and it would be a pity, if it stays unreadable.

9.) The qualification of M0 is unclear to us. The fact that the FAs are not exactly 180deg in the GRASE readout leads to stimulated echoes. While these stimulated echoes are helpful to obtain a better PSF and increase the signal efficiency, they introduce a T1 weighting into the final contrast. This means that (unlike single shot methods with very long TRs) the readout module itself makes it impossible to obtain a reference image without any T1-weighting (M0). We would like to encourage the authors to add a few details how they estimated the equilibrium z-magnetization with the 3D-GRASE radout used.

Scheffler K, Engelmann J, Heule R. BOLD sensitivity and vessel size specificity along CPMG and GRASE echo trains. Magn Reson Med. 2021:1-8. doi:10.1002/mrm.28871

10.) It is mentioned that the study would use a “segmented” readout. We find this terminology confusing. Are the authors referring to multiple excitation pulses per volume? If we understand the sequence correctly, we believe “partitioned” would be an alternative term.

11.) We are puzzled about the message of the depicted run-averaged motion traces in Fig. S7. What should the reader take away from this? Is it concerning that there are common motion patterns that are repeatable across runs? How many participants are averaged here? A more informative depiction of the subject-motion might be the average frame-wise displacement for all runs and participants? The manuscript might become clearer by revising this figure and/or removing it.

12.) We found Fig. S6 is quite puzzling too. While we are excited that the described methodology can be used for functional connectivity analyses, there are way too many open questions about the underlying processes and assumptions to just dump it in the supplementary material. This figure feels like a completely new study in itself that is pushed into a single figure caption. WE would advise the authors to provide more information about how this figure is generated and/or consider removing it?<br /> How should functional connectivity be interpreted, if there is no “resting-state”. Since the task data are used from M1 (dominated by the main effect), does it mean that the seed-timecourse resembles the block-design activation? Then, it refers more to a correlation-analysis of a block-design task than “functional connectivity”. How come that there are three slices shown sometimes with target regions, sometimes not? Where is the seed ROI? Does it span across cortical depth?

13.) We would advise the authors to be a bit more specific about the terminology of “BOLD”. Most readers might interpret this as the conventional GE-BOLD. Maybe the authors can rephrase it to “GRASE-BOLD” or “SE-BOLD”, or something similar? E.g. in the abstract introduction and some figure captions, if they agree?

14.) Performing pCASL is not trivial at 7T. While the authors circumvent many challenges with the superiorly aligned labeling plane, I feel that a successful replication of the experiment would require a more detailed description of the sequence parameters. What was the pulse shape, what was the pulse duration and inter-pulse interval? Gradient strengths? Does the yellow line width in Fig. 1B represents the bandwidth for a given flow velocity?

15.) The choice of ROIs. Both finger tapping and flickering checkerboard tasks usually evoke widespread signal changes along the hand knob of M1 and in the visual cortex, respectively. We appreciate that you show the corresponding activation maps in figures 4B and 5B. You also state on page 9 that you manually drew the CSF/GM and GM/WM outlines in the hand knob area of M1. In addition to that, it might be interesting how you chose your slices of interest or how the lateral extent of the ROI was defined. Is the pattern you show in the layer profiles in figure 4E to be expected along the entire length of the hand-know or only in certain segments?<br /> The same holds for the ROIs in the visual cortex for which the CSF/GM and GM/WM outlines were defined automatically.

Minor comments:<br /> PLD acronym is not introduced<br /> Page 6 reference to fig.6E - should be 5E?

With kind regards,<br /> Sebastian Dresbach, Omer Faruk Gulban, Renzo Huber

On 2023-10-31 15:58:49, user Diogo M. Lourenço wrote:

Hello!<br /> I would like to inform that my manuscript has now been peer-reviewed and published.<br /> You can find the published version here:<br /> https://www.sciencedirect.c...

On 2022-04-05 14:10:29, user Alizée Malnoë wrote:

The manuscript “The role of LHCBM1 in non-photochemical quenching in Chlamydomonas reinhardtii” by Liu et al. aims to elucidate how LHCBM1 is involved in non-photochemical quenching (NPQ) in Chlamydomonas reinhardtii. The Chlamydomonas mutant lacking LHCBM1 (npq5) displays a low NPQ phenotype. The authors found that the antenna size and LHCSR3 accumulation are not responsible for the lower NPQ phenotype in npq5. They also artificially acidified the lumenal pH to protonate LHCSR3 for NPQ induction and found that npq5 NPQ is still low. They propose that absence of LHCBM1 could alter the association of LHCSR3 with the PSII supercomplex or that LHCBM1 interacts with LHCSR3 which would enhance its quenching capacity. This work enriches the knowledge about the impact of lack of LHCBM1 on antenna size, PSII function, LHCSR1 and 3 proteins accumulation and NPQ capacity during a 48-h high light treatment.

Major comments<br /> - Fig. 1, it is stated that LHCBM1 (Type IV) does not accumulate but Fig. S1 and S2 show that Type IV accumulates between 7 and 9% of the total amount of LHCBMs. Could you comment on this accumulation, and whether it increases in high light? <br /> - Statistical test between WT and npq5 for all the data represented in bar graphs would be required to state that differences are significant. Consider showing all data points in addition to error bars.<br /> - Consider providing Fo and Fm values for all fluorescence measurements (and D1 accumulation) to discuss whether a possible increased Fo in npq5 in HL is due to disconnected antenna and/or loss of functional core complexes.<br /> - Discussion, discuss why there is much less LHCSR1 in the npq5 mutant? Is this regulation at the transcriptional or post-translational level? It could be that LHCBM1 interacts with LHCSR1 preventing its degradation. In the abstract, and discussion, as no interaction data is shown, consider toning down statements regarding LHCBM1 interaction with LHCSR3. <br /> - Discussion, discuss whether similar results were observed for the npq4 mutant (increased antenna size in HL and less functional PSII observed in npq5). Is this phenotype linked to the low NPQ capacity or specific to the lower level of LHCBM1 in the npq5 mutant?

Minor comments<br /> - Please give a title to summarize the first result in Fig. 1. In the result section, consider making the section subtitles more informative about the main result from that section (e.g., “Photoprotection capacity after high-light acclimation” could be “NPQ induction is severely hampered in npq5 after high-light acclimation”).<br /> - Page 3, method, actinic light of 1,500 umol photons.m-2.s-1 (and saturating pulses of 12,000 umol photons.m-2.s-1) are used for the fluorescence measurements. Comment on the choice of such a high light intensity (and high intensity pulses, actinic effect?). Why not use a light intensity closer to the HL treatment? Also state length of dark-acclimation prior to each type of measurement.<br /> - Immunoblot method, state dilutions used and secondary antibody type and dilution used.<br /> - Page 4, it is stated that “the level of the other LHCBMs is similar to that of WT (CC-425)” by referring to Fig. S1 and S2. It is difficult to see in Fig. S1 that the level of other LHCBMs is similar in npq5 and WT as the data is represented in percentage of total LHCBMs. Could you also represent the amount of the different LHCBMs in npq5 normalized to WT in a bar graph? For the Type III and Type II/I LHCBMs accumulation, a dilution series might be best for quantification to ensure that CP47 antibody signal is not saturated.<br /> In Fig. S1, npq5 should be italic. In Fig. S2, one of the labels is likely wrong: there are two 48h time points. By biological replica, is it meant three independent batches of grown and treated cells? What is the fourth band right below the upper band detected by the LHCBM5 antibody at 48h? <br /> - Fig. 5, please enlarge panels B and C to match panel A.<br /> - Fig. 6, specify in the legend that 24h and 48h refer to the HL treatment. <br /> - Discussion, “and confirmed here by analysis of the [npq5] mutant” (not npq4)

Jingfang Hao and Pierrick Bru (Umeå University) - not prompted by a journal; this review was written within a preprint  journal club with input from group discussion including Alizée Malnoë, Aurélie Crepin, Fadime Demirel, Jack Forsman and Domenica Farci.

On 2016-09-16 00:45:44, user Keith Robison wrote:

Figures 3 & 5 would be greatly strengthened by directly labeling the plots in the figure rather than legend in the text (Fig 3) or in a box (Fig 5) - there is plenty of white space and this style speeds compehension. Fig 5 in its current form is problematic for viewers with color vision issues; the data series should use different shapes for data points if the legend is retained so that color is not the only encoding strategy

On 2022-06-20 01:43:16, user Artem B wrote:

Hello! Has this work eventually been published?:)

On 2017-01-18 16:10:21, user AdamMarblestone wrote:

-"Associative Learning from Replayed Experience" http://biorxiv.org/content/...

On 2020-08-13 12:16:22, user kdrl nakle wrote:

Perhaps you should test it first in-vitro on a live virus to see whether it really does inhibit replication. There are so many various claimed inhibitors of this or that and nothing yet available for a real therapy. I am weary of these researches that are leaving real test to others, too many of them don't go anywhere.

On 2018-03-02 16:15:07, user Sarah Bourlat wrote:

I agree that the manuscript would benefit from more detailed explanation. I see in your methods in p.8 that the 'captured libraries were amplified, purified and quantified to be ready for sequencing'. Does that mean that captured libraries were amplified by PCR and how many cycles were used? This is important as the method is otherwise described as 'PCR free'.

On 2017-01-06 21:21:28, user Yuhao Lan wrote:

Do you know how to use Traitor? I download traitor in Linux. And I follow the instructions to handle it. But when I open traitor file and run "./traitor" . It says that

Traceback (most recent call last):

File "./traitar", line 4, in <module>

from traitar import modify

ImportError: No module named traitor<br /> .

Do you know how to solve it?

On 2024-02-18 12:34:22, user Michael Polymenis wrote:

The paper has been published. Mol Biol Cell. 2023 Dec 1;34(13):br20. doi: 10.1091/mbc.E23-05-0166. Epub 2023 Oct 4.<br /> PMID: 37792491

On 2020-05-27 11:13:47, user Shama Virani wrote:

"HPV16 variant lineage assignment was based on the maximum likelihood tree topology constructed in MEGA, including 16 HPV16 variant sublineage reference sequences" was cited with the Burk paper. However, the Burk paper only showed 10 HPV16 variant sublineage reference sequences. The Mirabello paper also said the same thing in its methods but in Table 1 of the Mirabello paper, showed the 10 sublineages from the Burk paper. Can you please clarify?

On 2021-02-13 22:26:04, user Xiaoqi Feng wrote:

If you like this story, you may also like another one, about a completely different DNA methylation reprogramming event in the male germline of Arabidopsis. We show nurse-cell derived sRNAs transcribed from transposons target genes with imperfectly matching sequences in the germline, regulate meiosis, silence transposons, and determine paternal inheritance via specifying the sperm DNA methylome.<br /> https://www.biorxiv.org/con...<br /> Nurse cell-derived small RNAs define paternal epigenetic inheritance in Arabidopsis

On 2022-02-27 18:16:50, user Christian M Schuerch wrote:

Great work, I'm interested to test the RNAscope protocol myself. Regarding Rhesus macaque antibodies, we have recently published an article about a panel here: https://www.frontiersin.org...<br /> Maybe this could be of interest for you! Best wishes, Christian

On 2015-04-05 07:26:52, user Hans van Buuren wrote:

Sadly another weak case based on presumptions and statistically low numbers as we often see in this kind of publication. While evolution is impossible over this short period we don't see people dieing before their fertility because of their brown colour. We cannot even be sure that certain markers in ancient DNA will lead to the same physical effect as they do today. This is not how DNA works. Would love to read some more fundamental research into this.

On 2018-08-09 13:24:10, user Andrew Jaffe wrote:

I'd be curious to see how this compares to our approach for both polyA+ but also RiboZero libraries: http://www.pnas.org/content... as the degradation profiles are very different for polyA+ compared to RiboZero . note that all of our degradation RNA-seq data from brain is available on SRA: https://www.ncbi.nlm.nih.go...

On 2019-01-23 15:36:38, user Karma wrote:

https://patents.google.com/...

These investigations need RF spectrum analysis. Audio recordings are irrelevant.

On 2021-09-21 14:45:24, user Christian Meesters wrote:

nice paper, but there are a number of issues with the software:

• no versioned release on github

• no precise requirement statements (just this or that is needed, but not any (minimum) version, which means the software might work with the current versions of the given packages)

• there is a claim about HPC-compatibility, but no information about scalability. It is questionable, whether a Perl script is able to carry the weight of "HPC-compatibility" at all.