On 2020-05-07 04:03:26, user GetOffMyLawn wrote:
None of this is really new news there has been two real known strains one weaker and one stronger. Seems the European one is the same that arrived in the US - the weaker version would be the one that many people had and never knew it almost like a weak or somewhat inert strain. It doesn't seem like there is any known differences in the US outside of the two?
On 2020-05-05 20:08:57, user Robert I. Price wrote:
Classical chaos is child's play compared to Q.M. grounded stochasticism. Most biologists I have met process their subject classically. I have been staring at this situation for a bit of time and the progression of events is striking like tracking events at the most elementary of levels.
The lack of "what will happen next" predictability suggests that we might not be able to create a testing protocol before the target form mutates.
On 2019-11-01 13:24:46, user Anna wrote:
Hey, cool method! Where can I find the supplementary tables? Thanks!
On 2020-04-03 12:14:32, user pedrobeltrao wrote:
Could you please share the list of interactions as a supplmentary file as well ? or provide a link in the comments section to a file containing this information?
On 2020-12-16 16:03:37, user Mattia Matasci wrote:
We are pleased to announce that this manuscript has been accepted for publication in "Experimental Biology and Medicine"
On 2021-02-25 04:32:39, user Miles Davenport wrote:
For a study using mixed effects to model SARS-CoV-2 immunity see:
On 2021-02-26 08:28:41, user Sharon wrote:
Really nice information. amino acids is actully a big source of protein. I also like this information about bcaa-<br /> https://www.ajinomoto.com/a...
On 2023-03-23 13:49:31, user wonderfulponderfulponds wrote:
Good analysis. All points agreed. A valuable contribution to the field. Except the attribute given to the causal factors such as feeding, I could not grasp: (a) how and where the authors quantified bioturbation or uprooting effects of foraging cyprinids?; (b) where and how the authors quantified repeated feed application in littoral zone/ bed made them unsuitable for recurrent aquatic macrophyte growth?; (c) what is the redfield ratio of feed input and how it dilutes or upconcentrates the same ratio in littoral bed making them unsuitable?. The authors show nicely temporal trends of littoral machrophyte zone shrinking (OKAY), but do not show direct causal relationship with attributes for which they are blaming the reason (e.g., feeding or no feeding). One can say, the same inference could be drawn for climate change too (perhaps stronger after evaluating a multi-decadal water stress in the territory). If fishpond management is to blame, why chasing feed application alone, when there are bigger causes such as farmers preferring not to keep them as they interfere with netting/ harvesting operations. Why not discuss these? Lastly, an intuitive point to consider, higher the littoral macrophyte vegetation (especially the emergent ones), more intense is the evapotranspiration by the pond body; because there is more surface area which actively evapo-transpires. This is further compounded by water stress (climate change), see the IPCC report published recently. Is not the relationship between shrinkage of littoral vegetation cover and a good littoral vegetative cover, universally juxtaposed forever? As a matter of fact, if recent IPCC report is true, one could question if keeping dense macrophyte beds (littoral) is even reasonable to combat water stress, and not making green belts around the pond instead. Which is efficient and not counter-productive? To conclude, the publication has a strong logic, but it could easily discuss its results on a broader context; instead of localizing its focal point with things that are more socio-politically charged and less of science. I recommend the study to be published; but also request the learned authors (with good legacies of work behind their names) to consider this request. The writing is good.
On 2020-04-02 17:59:16, user ricky wrote:
Are there followup studies to define the interaction maps either:<br /> -using less-pathogenic strains of coronaviruses (like the seasonal cold)<br /> -using SarsCov2 proteins in cells from a species that does not get severe disease (such as bats)
On 2017-02-24 16:40:53, user Matthew Gemmell wrote:
Hello
I though the manuscript was very. It gives a lot of reasons why preprints are good and I will definitely look into submitting a preprint in the future. You asked on Twitter and I would not call the tone too fanboy.
I would say that their are some fears that people may have of the idea of preprints that could be addressed in updated versions possibly. These are some suggestions.
As you hint at in the preprint the engagement of scientists commenting on preprints is important but can be hard. How will scientists be encouraged to do this?<br /> I think one answer is that preprints would be a good avenue for new collaborations to occur and start. If people comment on an author's work and they gain a good rapport and work in similar fields or complementary ones they could collaborate on future papers. It may be possible that an individual may be able to vastly improve on a preprint by an idea or skills and co author it together.
On the topic of scooping. You could mention that research coming out earlier will help scientists to not start something that is near final publication at another research group.
A major hurdle for the acceptance of preprints i think is the fear that it will open the floodgates of poor science and so good research will be obscured. Although you have good examples of quick preprints to post publication peer review a person may rush publishing a preprint which is wrong and another scientist or even the media may run with this. Unfortunately it can be very hard to get the wrong information out of people's heads. I think this is unfortunately an issue of places where anyone can post stuff. <br /> This is a hard issue to counteract and i unfortunately don't have an answer to it but I think it would be good to acknowledge this fear and possibly give suggestions to try and prevent this.
There are 2 main preprint servers. In most online forums there eventually reaches a point were two main groups have such different opinions on the use of the forum a schism occurs and the one forum becomes two separate ones. Considering this is still early days for preprints do you think this will occur for preprint servers? Do you think there is ways to prevent this or do you think this is just a natural process that needs to occur. This point may be beyond the scope of this paper but it might be interesting to address.
Thanks for writing this preprint I think it will be a great resource to show collaborators who are hesitant to use preprints and it definitely showed me the benefits of it. I hope my comments will be useful to you.
Thanks for reading<br /> Matthew Gemmell
On 2015-11-03 00:15:16, user Qiongshi Lu wrote:
This paper (with a minor change in title) has been published in Bioinformatics. http://bioinformatics.oxfor...
On 2018-05-03 23:23:05, user Utku Perktas wrote:
An important consortium produced an interesting paper that includes pool seq of D. melanogaster. A wide geographic coverage is very important for these kind of studies. This paper looks have a very good sampling coverege. That is certainly worth reading.
On 2020-03-29 07:31:19, user Robert George wrote:
with regard to enigmatic Chermuchek people, the text states ''This model fits even when ancient European farmers are included in the outgroups, showning that if the long-distance transfer of West European megalithic cultural traditions to people of the Chemurchek culture that has been suggested in the archaeological literature occurred, it must have been through<br /> spread of ideas rather than through movement of people. '''
However, a possible link entails the documented movement of Eur-farmer ancestry toward steppe (e.g. GAC groups - Mathieson 2018). In turn, Yamnaya groups (putative parental source of Afansievo) have ~ 10% EEF admixture c.f. Progress steppe Eneolithic (Wang et al 2019). Thus, we have more than a diffusion of ideas.
On 2025-02-13 19:40:38, user Pick Up Litter wrote:
This paper will help move science away from studying "cute" species and to be objective. Perhaps the most severe example is the housecat-- still portrayed as cute in media, but in the feral form a worldwide destroyer of birds. One critique concerning the Ivory-billed Woodpecker-- the number quoted by Troy et al, 20 million dollars spent for studies, is misleading. Most of the efforts are university and private, not governmental, and if the bird is proven to be extant, will have conservation benefits since it is a wide-ranging and keystone species. I help with research for the Ivory-billed Woodpecker. The growing body of evidence that this species exists is more rational than the critique-- see other BioRxiv papers and the work of Mike Collins for example. John D Williams
On 2019-04-03 16:41:34, user Ariane Nunes Alves wrote:
This is a very nice paper! I loved it!
I have some comments:<br /> - for figures 2, 3 and 4: I did not like the representation of the data in the y axis. It is a bit hard to make comparisons for different tracers with different diffusion rates. Maybe you could change it to deltaL/L0, or Dtrans/(Dtrans at infinite dilution);<br /> - it would be nice to know the fraction of occupied volume for the amount of crowder you used in the experiments;<br /> - I saw figure S5, and I am not convinced that charges do not play a role in the enhancement in the transport of tracers. What are the net charges of the tracer particles and of the crowder? If charge really does not affect the enhancement in the transport of tracers, this contradicts the results of ref. 15 in the main paper. You could discuss possible reasons for such contradiction in the discussion.
On 2019-08-22 08:55:34, user Felix Heeger wrote:
The article is now published in Methods in Ecology and Evolution: https://doi.org/10.1111/204...
On 2016-07-19 21:47:27, user Francisco De La Vega wrote:
It would be very useful for the community to have a site allele count summary across the genome, like the open tier of ExAc. Hope this becomes available.
On 2019-10-23 15:16:41, user Alessia Indrieri wrote:
Nice work! We recently published a paper in@EmboMolMed demostrating that miR-181a and miR-181b control mitochondrial biogenesis and mitophagy in neurons. This is our paper https://www.embopress.org/d...
On 2024-12-07 20:21:15, user Amir Rahmani wrote:
Now published in Optics Express https://doi.org/10.1364/OE.520845
On 2017-11-22 06:33:42, user Partha Ray wrote:
Thank you for downloading and viewing our Abstract/Manuscript. We look forward to your questions, comments and thoughts on this and other related research directions. Partha S. Ray, MD
On 2018-11-10 15:11:38, user Michael Hoffman wrote:
This manuscript is incomplete as it is missing a Methods section referred to within. Methods are not described in the usual level of detail for a scientific publication.
On 2017-02-12 19:58:16, user Gary Miller wrote:
Just about 4 months after posting the original version to bioRxiv an extensively revised version has been accepted at PNAS. We will update soon.
On 2020-09-04 14:52:39, user zack troilo wrote:
Seems like this is the best in class vaccine. Little Arcturus Therapeutics is going to come out one of the big COVID vaccine winners
On 2020-10-22 08:32:28, user Saverio Brogna wrote:
The data suggesting a role of Upf1 in transcription, Figure 5 of this preprint, is now published in microPublication Biology - https://micropublication.or...
On 2018-06-28 09:25:08, user Julien Gagneur wrote:
See also the companion manuscript: <br /> A deep proteome and transcriptome abundance atlas of 29 healthy human tissues<br /> https://doi.org/10.1101/357137
On 2016-01-06 17:07:26, user Barry Jacobson wrote:
I have now posted the second paper on this topic titled, "Frequency Transformations and Spectral Gaps in Cochlear Implant Processing", available on BioRxiv. Currently at work on a third, to address some remaining issues, which should be completed shortly.
On 2024-08-01 16:35:16, user John McCusker wrote:
I just saw ( https://www.science.org/content/article/bad-agar-killing-lab-yeast-around-world-where-it-coming ). Many years ago, I had a similar problem with S. cerevisiae and C. albicans (but not E. coli) growth on agar. (Multiple vendors/suppliers, none of whom found anything wrong.) I eventually found that exposure of agar plates to light while drying (or incubating) caused the problem. Dried/incubated plates in dark and no problem.
On 2023-10-09 11:26:07, user Arda Sevkar wrote:
It appears that an incorrect strain was used for the alignment of Mycobacterium leprae. As indicated in the supplementary materials, the MRHRU-235-G strain was utilized for this purpose. Notably, the NCBI genome page designates this strain as the "reference strain". Unfortunately, that's not true. In the literature, alignment and genotyping are consistently carried out using the strain labeled as "TN" (NCBI Genbank accession number: AL450380.1)."<br /> Additional information regarding this subject is available in the following sources: Pfrengle2021, Krause-Kyora2018, Schuenemann2018/2013, Benjak2018, Monot2009
On 2022-05-31 06:39:26, user Prof. T. K. Wood wrote:
Original JBac 1996 manuscript that discovered phage inhibition by toxin/antitoxin systems should be cited:
doi:10.1128/jb.178.7.2044-2050.1996.
On 2025-09-13 04:10:37, user duckman wrote:
Using the score_interface function in BindCraft (with binder_chain set to A), I tried to reproduce the AF3 Rosetta metrics for several binders, but the values did not match those in final_data.csv (e.g., Motif0030_ems_3hC_714_0001_0003_6877_0001 and Pdl1_binder_AF2_54). However, I did succeed in reproducing the af3_ipSAE_max and af3_ipSAE_min metrics for these binders. Are there any caveats I should be aware of when generating AF3 Rosetta metrics?
On 2018-03-31 19:11:56, user Richard Stevens wrote:
Sorry to say this, but I think you may have a problem with some of the y-dna haplogroup assignments in your spreadsheet, but maybe you can clear it up. You have ZVEJ27 from Latvia (I4628) listed as R1b1a1a2a1 (L51), and in the original paper it was listed specifically as xR1b1a1a2a1.
You have I5235 from the Iron Gates in Serbia, a Mesolithic HG, listed as R1b1a1a2a1a1b1a1a, which is R1b-S5741, well downstream of U106 (S21), even though I5235 was listed as xR1b1a1a, xR1b1a1a2 in the original paper.
You also have I8998, dated 1000-800 BC, from the Swat Protohistorical burials in Pakistan, listed as R1b1a1a2a1a1c2b2b1a2, which, according to ISOGG, is R1b-S21728, downstream of R1b-Z9, i.e., a U106 subclade. Anything is possible, I guess, but does that seem likely?
On 2024-07-10 07:25:49, user Constantinos Constantinides wrote:
Published version: https://onlinelibrary.wiley...
On 2020-01-04 22:25:11, user Leonardo wrote:
Yeah I've always said that about "genome-wide association studies" - so is there a stupid patient-level explanation of this?
On 2024-06-28 16:26:14, user Yat Ho Chan wrote:
Hi everyone,
We are excited to share that our article has been peer-reviewed and published in Nature Communications! You can find the article at the link below.
On 2019-09-02 08:53:55, user Mika Gustafsson wrote:
Thanks for an interesting article, which in many ways are related to our 2014 article (Gustafsson et al Genome Med 2014, PMC4064311) were we also proposed the same principle of shared and specific disease modules that could stratify patient responses. I recommend you read also that. //Thanks Mika
On 2024-01-02 10:08:48, user Anita Bandrowski wrote:
I am looking for the mouse that you got from MMRRC, but you state that you got the PG00171_Y_4_H09–Nfkbia vector from them. I don't think that is possible because they don't sell vectors. Can you check your lab records? Usually Addgene sells vectors, MMRRC sells mice. This is really odd.
On 2025-08-12 07:47:30, user ME de Jong wrote:
This preprint has now been published in Behavioral Ecology: https://doi.org/10.1093/beheco/araf071
On 2020-06-30 16:50:46, user Bruno Lopes Abbadi wrote:
Dear, I would like to congratulate you on the manuscript and say that I have some observations to make. In the methodologies section on cell culture, limiting dilution and isolation, the final volume in each well after adding the medium, virus, and cells is not very clear; would it be 150 uL? What was the final FBS concentration in each well? In addition, it was not very clear why you performed serial dilutions of the virus along lines 2-12. What is the purpose of this? Another question: from which well was the collection of 50 uL to infect the cells in the 24-well plate, since several wells may have a cytopathic effect? Finally, what is the purpose of infecting cells in 24-well wells? Did you use them only to count plaque-forming units, or to increase the virus titer? I hope that these considerations can further improve the quality of your manuscript. All the best.
On 2017-03-30 13:05:18, user Mateusz Iskrzynski wrote:
Just a small spotted misprint :
Figure 1:<br /> "A and D are unweighted", while in the picture A and B are unweighted
On 2020-02-26 16:34:05, user Sophie wrote:
Exciting work. I found it particularly relevant to a work we published in 2017 (link below), which showed the interaction of BCL-XL to KRAS mutant was necessary to maintain the expression of stemness genes. At the time, we thought that BCL-XL promoted a full RAS signalling because the expression of only some clusters of genes decreased after BCL-XL KD. Now, with this work and the recent work of Amendola et al. in Nature showing that KRAS4A specifically interacts with proteins at the mitochondria membrane, I think that by knocking down BCL-XL (a mitochondrial protein), we were only disrupting the signalling from KRAS4A and maybe not KRAS4B. <br /> https://www.nature.com/arti...
On 2024-02-07 11:17:19, user Mark Hahn wrote:
Now published: https://onlinelibrary.wiley...
On 2020-09-06 19:55:07, user Erik Welk wrote:
Hello,
library(lcvplants)<br /> LCVP("Hibiscus vitifolius")<br /> Error: 'tab_position' is not an exported object from 'namespace:LCVP'
Best wishes<br /> Erik Welk
On 2021-09-30 17:05:14, user Jin Yu wrote:
Published with MSDE from Royal Society of Chemistry:<br /> https://doi.org/10.1039/D1M...
On 2014-09-02 13:03:12, user Jomar Fajardo Rabajante wrote:
New abstract:
The well-known Waddington’s epigenetic landscape of<br /> cell-fate determination is not static but varies because of the dynamic gene regulation<br /> during development. Mathematical models of bistability cannot fully characterize<br /> the landscape’s temporal transformation because of limited number of state<br /> variables and fixed parameters. Here we simulate a model of gene regulation with<br /> more than two state variables and time-varying repression among regulatory factors.<br /> We are able to show sequential multi-lineage differentiation at different timescales<br /> that portrays the branching canals in Waddington’s illustration. We also show<br /> that a repressilator-type system activates suppressed genes by producing sustained<br /> oscillations in a flattened landscape, hence providing an alternative strategy<br /> for cellular reprogramming. The time-varying parameters governed by gradient-based<br /> dynamics dampen these oscillations resulting in dedifferentiation. The<br /> high-dimensional model integrates the theories of branching and oscillations in<br /> cell-fate determination, which further explains the mechanisms of cell differentiation<br /> and associated diseases, such as cancer.
On 2022-09-23 12:58:38, user Laura Rossini wrote:
Pleased to announce that the final updated and peer-reviewed version of this manuscript was published in Frontiers in Plant Science. Laura Rossini
Bretani G, Shaaf S, Tondelli A, Cattivelli L, Delbono S, Waugh R, Thomas W, Russell J, Bull H, Igartua E, Casas AM, Gracia P, Rossi R, Schulman AH and Rossini L (2022) Multi-environment genome-wide association mapping of culm morphology traits in barley. <br /> Front. Plant Sci. 13:926277. doi: 10.3389/fpls.2022.926277
On 2021-06-11 01:06:47, user Sriharsha Talapaneni wrote:
For Figure 2, no WT shown which would be good for a reference. In 2C, it is unclear what is happening. A suggestion is to zoom out for a better image or do a zoomed out pic with a zoomed in figure beside it. Additionally, are both prox1a and tfa necessary? One subfigure could potentially be put in supplemental. Labels for arrows would also be helpful for 2D, F, and H. There also appears to be an inconsistency with dpf in the figure. Is the prox1a expression gone by 4 dpf? The reasoning for only showing 2 dpf might be needed. Concerning Figure 2F, a suggestion is to have a graph show foxa2 expression over time (1.5 dpf to 2.5 dpf). There also seems to be a resolution issue throughout the figure. A potential fix would be for 2F, to possibly trace it. To support claim and significance, we would also like to see more animals and different time points.
A potential future experiment could be the use of an organoid.
Moreover, I understand that the main idea of the paper is prove that zebrafish can be used as model system by conducting the experiments in various organ systems. But I believe that this point is kind of understand and people would normally get lost in the details of the figure and forget why you conducted experiments in different organ systems. Therefore, for this it would be better to mention and repeat it so that we understand the reason for the transition.
On 2016-02-09 23:05:52, user roedert wrote:
test2
On 2018-11-18 13:13:33, user Erlembaldo wrote:
Very interesting paper. I was wondering if they have been released the raw data about the samples used.
On 2021-12-09 13:30:13, user R. Rocca wrote:
You have misstated that haplogroup I2a2 is most common in Serbia. In fact, it is most common in places like Germany. See the SECOND frequency map on this link for reference: https://www.eupedia.com/eur...
On 2021-06-02 19:55:27, user Charles Warden wrote:
Hi,
Thank you for posting this preprint.
As a minor point, I think there is some grant information missing:
"National Institutes of Health grant X (CBB)"
Best Wishes,<br /> Charles
On 2025-07-24 21:32:08, user Matthew Shoulders wrote:
Please refer to v3 https://www.biorxiv.org/content/10.1101/2023.10.19.562780v3 for the correct version of the preprint at this DOI and ignore this v4. A member of our team mistakenly uploaded this v4 as a revision to the wrong preprint. It was instead intended to be uploaded here https://www.biorxiv.org/content/10.1101/2024.11.07.622468v2 where it now also resides. bioRxiv informed us they could not correct the upload error by simply removing the mistaken v4 from this DOI location and instead their administrators suggested adding this comment to v4 to help clarify any resulting confusion from readers.
On 2017-07-25 01:24:22, user nextgennacrri Cornell wrote:
Genome-wide association mapping and genomic prediction unravels CBSD resistance in a Manihot
On 2017-05-02 19:34:19, user Jonas Richiardi wrote:
We have performed several additional experiments, and replied to this commentary in detail at https://doi.org/10.1101/132746. In brief, contrary to what is claimed here, spatial distance alone does not explain our results, which still stand.
On 2022-05-12 10:40:58, user Ramon Crehuet wrote:
very nice and clean work. I have a technical question. From what I understand, in general you use AF monomer except for the case of 1 peptide competing for MDM2/MDMX, is that right? In detail, you use:
https://colab.research.goog... for all cases, except the the MDM2/MDMX competition, where you use:<br /> https://colab.research.goog...<br /> Is the reason why you don't use AF-multimer in all cases the clashes found in version 1? If so, do you expect version 2 to work better and be the best option for these competition assays?
On 2022-10-28 22:50:28, user Nathan Bowen wrote:
what output from gnomix and additional software did you use to produce figure 8b. Chromosome painting of a Xoloitzcuintle (Xolo) dog ? thanks.
On 2022-09-16 12:07:25, user EM wrote:
This is a very important paper. It indicates that classifying and understanding the crystal polymorphisms that occur during protein/enzyme reactions with its ligand in crystals can lead to a detailed understanding of protein reaction mechanisms. This paper will become increasingly important with the development of the 4th generation synchrotron radiation facilities.
On 2023-11-28 16:09:01, user Fraser Lab wrote:
Deep mutational scanning has revealed the impact of individual point mutants on protein function and improved computational predictors of mutational effects. However, many mutations observed in evolution and disease are the result of insertions or deletions (indels) and the impact of these mutations are poorly predicted computationally relative to missense mutations. While a few other papers have profiled InDels in a systematic way, the major contributions of this paper are: 1) profiling indels across many different proteins (9), 2) profiling InDels for both stability and function, 3) introducing the concept of high throughput profiling of DelSub mutations (Mutations that remove an aa and substitute an aa in a single event)
The authors make a library of substitutions, insertions and deletions of 9 protein domains. They carry out deep sequencing coupled to fragment complementation assay (aPCA) that is well correlated with expression/stability. They then go deep on two peptide binding domains, comparing functional mutational scans as well. This provides a rich set of data to compare to computational predictions, where notable limitations in current prediction methods are identified. The major limitation of the paper is in the presentation - there is a lot of data and the figures are quite complex - but the text is brief and difficult to follow in parts. An expanded text and breaking up the figures into more figures would likely improve the ability to extract insights from these impressive datasets. Additionally further discussion of the results within the context of past literature would be helpful in guiding interpretation of the study.
**Major points:**
The authors included 9 domains that span classical motifs to include in their indel scan. From our reading, it is unclear what rationale the authors used to include these domains. What makes these a diverse set of domains (a/b content? size? eukaryotic/prokaryotic origin? other topological features? etc)? This will help the reader understand how to generalise the results.
In section “Evaluating indel variant effect prediction”, authors can comment on why PROVEAN is better suited to predict insertions and deletions relative to substitutions. In contrast, why CADD predicts substitutions better than PROVEAN? What design choices can distinguish PROVEAN from CADD? Is there a way a model could be trained that could perform well on both?
In the section “Structural determinants of indel tolerance,” the authors mention multiple features that seem potentially important for the effects of insertions and deletions. Currently specific patterns are discussed for the substitutions but the main draw of this manuscript it contains indel mutagenesis across many proteins and the discussion in this section regarding indels are vague beyond that indels are more tolerated at the N and C termini, that the secondary structure is important, and where the indel is seems to have an impact. Currently in our reading these are vague descriptions and perhaps it would be possible to describe general trends? What specific lengths are tolerated vs not? Which secondary structural elements are more sensitive? It would be helpful to clarify these trends with existing literature. For example, previous work in a potassium channel kir2.1 (Macdonald, CM, Genome Biology 2023) found that deletions were more disruptive than insertions in beta sheets especially. Is that also seen?
The authors train a model as described in the ‘Accurate indel variant effect prediction’ section to predict the effects of indels within all the proteins that are contained within the screen and another manuscript Tsuboyama et al. However, there are other previous indel scans that have been done including one within a viral AAV capsid protein (<https: <a href="www.science.org" title="www.science.org">www.science.org="" doi="" 10.1126="" science.aaw2900="">), a potassium channel kir2.1 (<https: <a href="genomebiology.biomedcentral.com" title="genomebiology.biomedcentral.com">genomebiology.biomedcentral...="" articles="" 10.1186="" s13059-023-02880-6#citeas="">), and an amyloid protein that involved the senior author (<https: <a href="pubmed.ncbi.nlm.nih.gov" title="pubmed.ncbi.nlm.nih.gov">pubmed.ncbi.nlm.nih.gov="" 36400770=""/>). It may be useful to test performance of the model on these datasets that were not generated within the same study and discuss where the model performs well vs those that do not perform as well.
The authors find that insertions can generate gain-of-function molecular phenotypes at higher rates relative to deletions and substitutions. Overall, the manuscript presents the results of deep indel mutagenesis on several protein domains, but lacks thorough discussion of the results. A discussion addressing the possibility of non-native ligand binding following indel mutations would provide an evolutionary perspective that contextualises this research.
The authors mention that some gain-of-function mutations occur due to short insertion mutations. While some domain insertions have been shown to have stabilising effects (<https: <a href="doi.org" title="doi.org">doi.org="" 10.1371="" journal.pcbi.1006008="">, <https: <a href="doi.org" title="doi.org">doi.org="" 10.1038="" s41467-018-08171-0="">, <https: <a href="www.nature.com" title="www.nature.com">www.nature.com="" articles="" s41467-021-27342-0="">), the findings presented here are novel for being short insertions but prior work on domains and deletions of varying type being beneficial would be useful. Emphasising this in the “Insertions generate gain-of-function molecular phenotypes” section and considering how the insertions in the PSD95-PDZ3 domain might increase stability would enhance the understanding of the underlying mechanisms driving these gain-of-function phenotypes.
**Minor points:**
Several of the figures are very information-dense. This manuscript would benefit from breaking up these figures and reorganizing them to make them easier to understand. It may be helpful also to focus the figures on the main points the authors would like to make within the manuscript as currently the sheer amount of data and analyses makes it difficult to follow the narrative. Additionally, figures could benefit from having secondary structure representations above or below heatmaps to aid in interpretation.
Figure 1 contains A-C labeled sections but contains 9 comprehensive experiments with 6 subpanels each. It is very difficult to evaluate the data when it is so densely represented. aligning an “unfolded” secondary structure below the heatmap for all domains might be clearer than colouring by secondary structure. We find secondary structure coloring to obscure the patterns.
Figure 2 would benefit from significant reorganization perhaps around SH3 and PDZ domains specifically. The spacing within this figure makes it difficult to follow the immense amount of comparisons contained here. Perhaps it would be worth separating this figure up or moving some of the comparisons to the supplement. As in Figure 1 secondary structure above or below the heatmaps would be helpful.
In figure 5E data is shown for GRB2-SH3 in which some mutants show high binding and low abundance. Additionally these mutants are most prevalent in the core and surface. What could be an explanation for such a phenotype for core mutants, since they are bound to have the most destabilizing effect. For the surface mutants, are those residues close to the binding site? Additionally the distributions of these two plots look fundamentally different (with a much higher correlation between binding and abundance for PDZ and a distinct change in the pattern of binding residues being gain or loss of function across the two domains). What does that say about the baseline stability of GRB2 vs PSD95? Or is this more representative of some methodological aspect of the dynamic range and sensitivity within respective assays when run on these specific proteins?
In the heatmap figures the coloring is confusing. Currently they are colored from red to white to blue - in the corresponding color bars next to the heatmaps white is not centered at 0. Presumably 0 is wildtype fitness and blue and red are greater and less than wildtype fitness, respectively. This should be explicitly stated within all figure legends. White should correspond to 0 otherwise it makes it difficult to determine the effect of a mutation.
Figure 3E contains violin plots however a boxplot is missing from these that indicates the median, interquartile range, and whiskers to represent non-outlier distributions. This would be useful in comparing across these variant types
In ‘Materials and Methods’ section, we were confused by the filtering steps taken in the ‘sequence data processing’ section. It would be helpful to include the minimum read cut-off.
Figure 4b should be explained more completely in the figure legend. It is very difficult to make out what the difference in colour for each panel means.
In the last paragraph of the introduction, it would provide additional support and context if you referenced other work with similar findings. These papers (<https: <a href="doi.org" title="doi.org">doi.org="" 10.1186="" s13059-023-02880-6="">, <https: <a href="doi.org" title="doi.org">doi.org="" 10.1101="" 2023.06.06.543963="">) would support the statement, “In general, indels are better tolerated in protein termini than in secondary elements.”
Reviewed by Priyanka Bajaj, Karson Chrispens, James Fraser, Willow Coyote-Maestas
On 2019-05-31 23:25:09, user Charles Warden wrote:
Very interesting paper:
What if you plot precision (for breast cancer), with pathogenic variants for BRCA1 versus BRCA2 (versus current PRS predictions)?
On 2023-10-05 12:53:24, user Matteo Brilli wrote:
I was checking the multialignment provided for download on the website. I am not an expert of supermatrix approach, but I have 20 years experience with phylogenetics, even if it is not my main occupation. Now, it turns out the multialignment has NO positions where all sequences have a non-missing character. The median number of Ns per sequence is around 32k and the alignment has 36327 sites. Across those sites, the minimum number of Ns is over 700, with a median number of Ns per site around 4000. Now, my question is, is ML able to reconstruct a satisfactory phylogenetic tree in these conditions? I understand missing data can be accounted for during reconstruction, but I suspect that if there are only (or almost only) missing data, the approximation will be far from reality.
On 2020-08-14 12:38:44, user qx jiang wrote:
The structures are pretty good and record-making. <br /> The channel behavior, ion selectivity sequence, and electrical recordings, esp. S2 appear strange without matching regular opening/closing events.
On 2016-06-23 17:34:00, user Simon Schultz wrote:
Please note this paper was published as:
S. Reynolds, C. S. Copeland, S. R. Schultz and P. L. Dragotti, "An extension of the FRI framework for calcium transient detection," 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), Prague, Czech Republic, 2016, pp. 676-679.?doi: 10.1109/ ISBI.2016.7493357.
On 2025-05-20 21:39:39, user Chase Clark wrote:
https://github.com/medema-group/BiG-SCAPE/tree/master/Annotated_MIBiG_reference
Doesn't exist in the current master branch, needs to be updated to reference the permalink
On 2015-02-07 15:47:52, user Peter L Borst wrote:
For openers, the English in this paper is absolutely atrocious. But beyond that, the authors have no idea what CCD is. The seem to conflate it with death of the colony by any factor. Colonies that are poisoned do NOT exhibit symptoms concomitant with CCD, which has never been clearly defined nor have any actual cases of it been documented. Honey bee colonies are very susceptible to poisoning, but this does not prove that colony die-off is correlated to widespread use of insecticides, especially since colonies have been failing in non-agricultural regions at near the same rate. The most likely cause of widespread die-off is honey bee viruses, exacerbated by the stress of varroa mites and seasonal dearth.
On 2017-12-07 09:24:36, user Bayazit Yunusbayev wrote:
Very interesting! I wonder if you can also report average Fst between simulated parental populations?
On 2019-05-28 21:15:48, user Kristof Theys wrote:
All R code will be shortly posted on https://github.com/ktheyss/..., to be used in future studies or to be improved.
On 2021-10-04 08:50:43, user Ramon Crehuet wrote:
I find it intriguing that AF2 outperforms experimental structures (Fig. 3A) when used to predict variant effects. That comes from the regions where AF2 has high confidence but where it could not use a template (Fig 2B)! Is this reporting on the quality of experimental structures? And does it depend on the experimental method? I guess this could be obtained from data in Fig. 6, but it is not straightforward.
The fact that the template reduces the quality when pLDDT also suggests that AF2 could be improved by allowing it to discard a bit experimental templates, right?
On 2018-02-21 11:00:04, user Benjamen White wrote:
My unpopular opinion would be to rename your tool to avoid confusion and increase uptake. I know a lot of people are moving onto Canu now, but the FALCON assembler is still very prevalent. https://github.com/PacificB...
On 2019-04-14 08:05:38, user Arpan Parichha wrote:
I was thinking of using the SABER technique in our system and wanted use its multiplexing feature for our purpose. I am unable to use the oligominer pipeline. Can anybody tell me how to design the PER primers?
On 2020-05-18 16:18:00, user Anita Bandrowski wrote:
"Hi, we're trying to improve preprints using automated screening tools. Here's some stuff that our tools found. If we're right then you might want to look at your text, but if we're not then we'd love it if you could take a moment to reply and let us know so we can improve the way our tools work. Have a nice day.
Specifically, your paper (DOI:10.1101/2020.04.03.023846); was checked for the presence of transparency criteria such as blinding, which may not be relevant to all papers, as well as research resources such as statistical software tools, cell lines, and open data.
We did not detect information on sex as a biological variable, which is particularly important given known sex differences in COVID-19 (Wenham et al, 2020).
We also screened for some additional NIH & journal rigor guidelines:<br /> IACUC/IRB: not detected ; randomization of experimental groups: not detected ; reduction of experimental bias by blinding: not detected ; analysis of sample size by power calculation: not detected .
We found that you used the following key resources: cell lines (1) . We recommend using RRIDs to improve so that others can tell exactly what research resources you used. You can look up RRIDs at rrid.site
We did not find a statement about open data. We also did not find a statement about open code. Researchers are encouraged to share open data when possible (see Nature blog).
More specific comments and a list of suggested RRIDs can be found by opening the Hypothes.is window on this manuscript, direct link https://hyp.is/U4WZvo-tEeqx...<br /> References cited: https://tinyurl.com/y7fpsvzy"
On 2022-02-22 16:06:32, user Shahan Derkarabetian wrote:
The paper has been published:
On 2022-06-21 13:17:37, user Davidski wrote:
Hello authors,
Thanks for making the genotype data available so quickly. A few points after running the data, copy pasting from my blog...
in terms of fine scale ancestry, the Erfurt Jews show enough variation to be divided into three or four clusters, as opposed to just two as per Waldman et al.
some of the Erfurt Jews show excess "Mediterranean" ancestry, while others excess "North African" ancestry, and this cannot be explained with ancestral populations similar to Lebanese and/or South Italians, but rather with significant gene flow from the western Mediterranean and possibly North Africa
several of the Erfurt Jews show relatively high levels of "East Asian" ancestry that cannot be explained with admixture from Russians, or even any Russian-like populations, because such populations almost lack this type of ancestry, and instead show significant "Siberian" admixture
as far as I can see, there are no correlations between any of the observations above and the quality of the samples. That is, low coverage doesn't appear to be causing the aforementioned excess "Mediterranean", "North African" and/or "East Asian" ancestry proportions.
More at this link:
https://eurogenes.blogspot....
Cheers, David Wesolowski
On 2019-10-05 15:54:37, user Dr. David Ludwig wrote:
BMJ STUDY CO-AUTHOR RESPONDS
In the multiple versions of their preprint here, and in a final version in International Journal of Obesity, Hall & Guo criticize our BMJ study that showed higher energy expenditure on low- vs high-carbohydrate diets.
We now respond in full in the same journal (full text linked here):<br /> https://www.nature.com/articles/s41366-019-0466-1
We show that all these criticisms are fundamentally misleading or simply wrong. Specifically, we consider why:
The post-weight-loss baseline is the most appropriate for studying metabolism during weight-loss-maintenance.
The change in our registry was proper.
Non-adherence would not plausibly account for our findings, based on several sensitivity analyses.
In summary, there is substantial support for the carbohydrate-insulin model of obesity, and assertions to have disproven ("falsified") the model are without merit. At the same time, admittedly, neither is the model proven. Pending additional high quality studies with complementary design, scientist on both sides of the debate would do well to avoid premature conclusions.
On 2015-03-12 19:12:25, user paulhummerman wrote:
Nice paper. 2 minor comments. It would be nice (though unsurprising) to show that strengths of synapses made by the same axon on different dendrites of the same postsynaptic neuron were also highly correlated, but of course this would require more extensive 3D reconstruction. Second, since most connections are made of several synapses, it seems likely that the bit resolution of connections is even higher than 4.6
On 2017-03-02 14:27:17, user Steve Shea wrote:
I posted a comment on this paper to my blog: https://idealobserverblog.w...
On 2018-07-10 20:54:23, user DHelix wrote:
Very helpful. Thanks a lot for sharing the information!<br /> I was wondering how your HEK293 ATAC-Seq tagmentation looked like on DNA gel or Agilent profile. I'm trying to perfom ATAC-Seq on HEK293 cells. I could see bands but it also had quite high background (the bands were not very clear and sharp). Any ideas? Thanks!
On 2023-04-21 15:46:40, user Manolis Kellis wrote:
We thank Murphy, Fancy, and Skene for their attention to our manuscript, and for their efforts in reprocessing and analyzing our data. We acknowledge that there can be advantages to pseudobulk methods, and that no single method is likely to outperform all others in all settings. As the technologies and methods used in our paper are now over four years old, in a rapidly moving field, we expect many newer analyses will be able to clearly outperform previous papers in the field, including ours. More broadly, we encourage students and practitioners to revisit previous papers as a general practice of open science, and to share the results in open, respectful, and transparent ways, which can move the field forward collaboratively and constructively.
That said, in this particular case, Murphy et al.'s results fall short of their claims, and they fail to demonstrate any advantages of their approach compared to our manuscript, due to several technical and logical mistakes in their analysis, which we outline here: http://compbio.mit.edu/scAD...
Briefly: <br /> * Murphy et al. do not compare to our actual 1,031 reported differentially expressed genes (DEGs), they exclude our mixed-model results from their comparison, thus misinterpreting our work as cell-level-only with 14,274 DEGs, which is a misrepresentation of our results.
* They interpret any differences between their results and ours as improvements (conflating false positives with true predictions and lack of power with specificity) without providing any independent support – by contrast, our paper validated our DEGs using both targeted experiments and comparison with previous human and animal studies.
* Their central claim that our reported DEGs are false positives lacks any substance: indeed, they are not found by their pseudobulk method, but they could simply be missed due to lack of power, and Murphy et al. provide no independent evidence that their method is sensitive or accurate.
* Conversely, the 16 DEGs that they do report lack any independent evidence that they are biologically meaningful, and Murphy et al. provide no evidence that their method is specific. In fact, their 16 DEGs show unrealistically-high ~1200% median changes, and only implicate the rarest cell types, suggesting they may represent false positives stemming from unaddressed statistical artifacts due to outliers and small cell counts.
* Murphy et al. also make general claims about the advantages of pseudobulk methods by applying only one method, erroneously, to only one dataset, without any benchmarks, and without comparison to current state-of-the-art methods, while misusing statistical terminology.
* Lastly, they overstate their previous results from a similar Matters Arising commentary, which also advocated for pseudobulk using the same pattern of only analyzing a single dataset, methodological shortcomings, overstated conclusions, and not addressing corrections suggested by the original authors.
This is a helpful example to discuss potential pitfalls in comparing bioinformatic pipelines, and to recommend perhaps more constructive strategies for such comparisons, including: applying candidate methods to more than one dataset, comparing to the actual results of the original paper without misrepresenting them, using independent validation metrics to evaluate differences instead of assuming that they are all improvements, using well-established and rigorous benchmarks, including comparisons with other state-of-the-art methodologies, scrutinizing their own predictions for potential confounders, and seeking to understand the biological or technical drivers of potential differences.
We provide a more detailed response here: http://compbio.mit.edu/scAD...
On 2018-08-04 13:38:26, user Peter Rogan wrote:
Great paper.
"Indeed, although not identified by haplotype, allelic differences in accessibility and volume have been noted at the level of individual genes (47)" <br /> Thanks for the citation, however, we did identify DA by haplotype...
A haplotype is a group of genes within an organism that was inherited together from a single parent, and this precedes the current genomic definition based on SNPs and CNVs. Our preceding paper (PMID 25520753): showed that differential accessibility was allele specific by marking chromosomes in which the DA loci were linked unrelated structural abnormalities (translocations) and an instance of familial transmission of a linked structural abnormality (microdeletion). So we have demonstrated DA based on haplotypes. We suggest that you add this reference as well and remove the incorrect qualifier during peer review..
On 2020-11-22 03:46:09, user Jingyi Jessica Li wrote:
Thanks for pointing us to the SERGIO paper. In fact, scDesign2 and SERGIO require different inputs. While scDesign2 learns gene correlation from a real gene-by-cell count matrix, SERGIO requires a user-specified GRN as input and does not estimate gene correlations from real data.
A quote from the SERGIO paper: "It is worth noting here that several existing single-cell expression simulators employ a probabilistic model whose parameters are directly estimated from a real dataset and then sample synthetic data from the model. This approach is not feasible in SERGIO since the true GRN underlying the real dataset is unknown and notoriously hard to reconstruct, and the explicit use of a GRN is a crucial distinguishing feature of SERGIO."
Thanks to the comment, we have revised our manuscript and updated it on bioRxiv. Please check out our next version.
A feature of scDesign2 is to guide experimental design by mimicking input real data and varying cell number and sequencing depth.
On 2018-04-17 16:30:35, user gwern wrote:
"However, the individual effect sizes of these variants are small, and all variants identified to date jointly explain only ~20% of the heritability of height."
50%. See "Accurate Genomic Prediction Of Human Height" https://www.biorxiv.org/con... , Lello et al 2017.
On 2020-04-03 02:13:57, user Tamara Terzian wrote:
Here is the podcast on the article from Chris Casey at the CU Anschutz newsroom:
On 2019-04-01 03:12:11, user Stephan Anagnostaras wrote:
neat paper
On 2020-04-24 22:25:46, user Xander de Haan wrote:
“In conclusion, we have discovered that GAGs can facilitate host cell entry of SARS-CoV-2 by binding to SGP in the current work.” This study shows binding, not enhancement of infection. MHV-a59 also has a furin-cleavage site at S1/S2. Binding of this virus to HS probably has a negative effect on entry, which can be overcome by PBS-DEAE. MHV/BHK does depend on HS for entry (PMID: 16254381). Its furin cleavage site at S1/S2 is no longer cleaved in the producer cell (similarly for FCoV ref 28, ref does not deal with IBV or MHV btw). So cleavage at the furin/multibasic cleavage site appears inversely correlated with HS binding. To what extent were the S proteins in this study processed by furin? Furthermore, binding to HS may not necessarily be helpfull for infection, as HS may probably also function as a decoy receptor.
On 2015-11-01 16:08:23, user Keith Robison wrote:
While writing a blog entry on this paper, http://omicsomics.blogspot...., I noticed that the imaging buffer recipe is probably incomplete, as catalase would be needed to complete the oxygen scavenging reaction series
On 2018-09-07 17:18:34, user Alexander Predeus wrote:
Congratulations to the authors on this preprint - very promising result, which could be useful for numerous applications. Would it be possible to publish the R code used for finding the associations on github? Thank you in advance!
On 2023-11-17 10:21:46, user EML wrote:
Really interesting paper, will dive into this deeper. Quick comment and question already:<br /> - would it be possible to do segregation testing in pedigree 1? As the affected individuals are still alive it might be possible to obtain DNA (no large amounts needed).<br /> - It would be helpful if the pedigrees in figure 6 would be redrawn in such a way that the first generation does not appear to have offspring of two females...<br /> best wishes,<br /> Elisabeth Lodder
On 2020-10-19 19:02:04, user Marion06 wrote:
Calcium channel blockers also inhibit the effects of electromagnetic fields on cells. EMF increase calcium inside the cells.
On 2023-02-20 18:47:32, user Donald R. Forsdyke wrote:
Despite this feedback, the paper was published in Physics Reports. The authors have continued to disregard the feedback. A new paper in eLife has passed peer-review and is now formally accepted for publication. I have added a brief comment to the corresponding bioRxiv preprint.
On 2016-04-21 13:03:39, user Kyung Mo Kim wrote:
This preprint manuscript is an extended version of an eLetter posted by Harish et al. commenting on the article by Nasir and Caetano-Anolles, "A phylogenomic data-driven exploration of viral origins and evolution”, which was published in Science Advances 1(8): e1500527 (2015). A response to this eLetter by Nasir et al. entitled “No ‘small genome attraction’ artifact” can be found at http://advances.sciencemag..... An extended version of this eLetter is forthcoming.
On 2016-05-27 16:08:26, user dcx_2 wrote:
Yeah, 6W/kg and it was _whole-body_ SAR, too. The researchers upped the wattage as necessary whenever the animal gained weight. Meanwhile, the FCC measures the SAR on the one-gram mass absorbing the most signal.
Further, they were blasting these animals with the radiation in-utero *from day 5*. I don't know any blastocyst that has a cell phone...
There's also this gem on page 9 of the study: "At the end of the 2-year study, survival was lower in the control group of males than in all 2 groups of male rats exposed to GSM-modulated RFR." In other words, the animals *not* exposed to the radiation were more likely to die.
On 2016-05-29 19:56:45, user guy wrote:
90 seems like a pretty small sample size to me, but I'm not a biomedical researcher or a doctor.
On 2021-03-15 09:25:36, user Remi wrote:
Hello, may be a stupid question but, how the virus can change/mutate without a replication cycle? Because they use plasma, no cells are available for the virus to replicate. Does it mean these mutants ( E484K & F140) are already present from the beginning, all the other are neutralized but only them remain at the end?
On 2022-04-08 10:59:36, user Amos Bairoch wrote:
The paper indicates "Caco-2 and HT29-MTX cells (ATCC)" but the HT29-MTX cell line is not distributed by ATCC. If you are using HT29-MTX you need to indicate where you really got it from. Alternatively if you are using HT-29-MTX-E12 distributed by ECACC (see the relevant Cellosaurus entry (https://web.expasy.org/cell... "https://web.expasy.org/cellosaurus/CVCL_G356)") you need to indicate that and not just HT29-MTX.
On 2024-06-06 12:01:12, user Elli wrote:
I am unable to see the suplementary tables the preprint refers to, where can I find these?
On 2020-04-13 17:39:45, user Charles Warden wrote:
Thank you for putting together this paper.
While it seems like it isn't essential for the main results, there were a couple things that I think you could note in Table 1:
1) The bumphunter function is used for the DMR analysis in minfi, which is described in Jaffe et al. 2017.
2) COHCAP (Warden et al. 2013) is another option for both site and region level analysis. It would be mostly like the summarization for IMA (which is listed), although newer updates allow refinement of region boundaries through a clustering step (in the Bioconductor package).
Since I am the author for point 2), I realize that I am not completely unbiased. However, I thought this might be worth mentioning, if you are not aware of that program.
On 2017-09-22 22:43:28, user Jessica wrote:
Need to include a citation ;) "First, the trio-sequencing results for four of the disorders (ASD, ID, EPI and CHD) came from datasets that are partially overlapping (include citations)."
Very clearly written though, this was a pleasure to read.
On 2019-02-11 15:42:17, user Casey Greene wrote:
Could someone please share a methods section? There does not appear to be one in the methods or in the supplement.
On 2021-02-17 04:55:24, user Brian Key wrote:
might want to look at Key and Brown (2018) Designing brains for pain. Frontiers in Physiology 9:1027
On 2021-12-10 15:56:23, user Leo G. wrote:
Molnupiravir is also carcinogenic, cytotoxic, & creates coronavirus mutations that are unlikely to happen naturally!
On 2023-02-10 02:33:03, user Ruby Tang wrote:
Hello! We selected your paper to read as part of our Journal Club seminar for the Biomedical Research Minor at UCLA. First of all, thank you so much for sharing your research; this was a fantastic paper and I learned a lot from it. I wanted to share some of the feedback we discussed during the seminar in the hope that some of it may be useful!
1) We were curious how you chose the sex of the mice to be used in each experiment, as it doesn't seem to be consistent throughout. Also, what were your inclusion/exclusion criteria for the young and aged mice cohorts?<br /> 2) Fig. 1: Very minor, but in Fig. 1I the image itself has 14dpl in the title but 10dpl in the figure legend.<br /> 3) Fig. 2: The pseudocoloring is really difficult to see, especially with DAPI as blue, OLIG2 as cyan, and CNP as grey. It could potentially be easier to see if OLIG2 and CNP could be changed to yellow and green. Also, for Fig. 2G, it would be helpful to clarify whether the cerebellar slices included in the sample size of n=7 are from the same mice or from different mice. <br /> 4) Fig. 3: We were curious if you had also considered doing the reverse experiment (young Treg injected into aged Treg-depleted mice). <br /> 5) Fig. 4-5 (and throughout the paper): We were wondering if you had done power analyses for this study given the highly variable sampling throughout the paper. For instance, it seemed like there were over 10 mice used in some experiments and as few as 3 in others, so we were curious about the reasoning behind these decisions.
Overall, though, this paper was really informative and exciting to read! We learned a lot from you and are very grateful to have gotten the opportunity to read about your research.
On 2016-06-11 13:39:16, user Eric Fauman wrote:
Nice work here.<br /> Could I suggest that it would be nice to know the ORs, CIs, effect allele and effect allele frequency from the meta-analysis in Table S2 for all 236 SNPs and not just the 26 newly significant SNPs.
On 2025-11-26 02:46:52, user Tania Perroux wrote:
This article is now published in Journal of Zoology<br /> https://doi.org/10.1111/jzo.13017
On 2018-03-04 19:44:17, user Ban darp wrote:
Figure S12 B appears to be incomplete; would like to see the rest of the argument for nested loops.
On 2020-10-31 12:02:44, user kdrl nakle wrote:
Important paper but highly disorganized.
On 2025-04-23 11:56:38, user Anonymous wrote:
There appear to be technical issues here (in particular the R0 measurements are nonsensical) but perhaps more concerning are the ethical / conflict of interest concerns of a group paid by a pharmaceutical giant (Gilead)— which is profiting off lifelong continual antiretroviral regimens—attempting to undercut a novel single-dose therapy strategy that may reduce their profits.
On 2017-01-16 07:53:15, user Christoph Nowak wrote:
Hi Brian - brilliant method!<br /> I'm not a born-and-bred bioinformaticians (but about to get my PhD in molecular epidemiology): Will you be making the code / SMAF package available open access at some point? That would be a huge help!<br /> Thanks for letting me know - <br /> Chris<br /> christoph.nowak@medsci.uu.se<br /> Medical Sciences Dept., Uppsala University, Uppsala, Sweden
On 2020-06-11 15:52:37, user Observer375 wrote:
If only sugar as sucrose at 30% sucrose with a control at 8% sucrose was tested, the characterization or generalization to sweetness may be overstated or misattributed. Were other caloric products such as fructose, high-fructose corn syrup (HFCS), glucose or sucralose tested? Was any less-caloric or non-caloric product tested, such as saccharin, cyclamates, acesulfame, neotame, xylitol, aspartame or stevia (leaf or extract)? The reaction may be to the caloric aspect or to some component of this particular formulation of sugar which is not common to other "sweeteners". Painting all sweeteners with one brush seems to lack rigor.
On 2017-09-05 19:07:23, user Ronald Holz wrote:
This is an experimental tour-de-force correlating membrane curvature with the kinetics of clathrin pit formation and endocytosis. Have the authors attempted to study receptor-mediated endocytosis? Timing of the curvature changes and clathrin accumulation to the addition of an appropriate endocytic ligand would be interesting
On 2022-09-20 13:48:52, user Galano Jean-Marie wrote:
Such an impressive work. How long did it take...? so good.
anyhow still reading but cannot get to the zenodo link for QcxMSin the supporting<br /> https://doi.org/10.5281/zen...<br /> congrats
On 2019-08-16 00:36:00, user Ashley Woodfin wrote:
The same method was already used for site-specific bioorthogonal labeling of macro molecular complexes in 2015 at Scripps https://doi.org/10.1016/j.j...
On 2020-04-10 17:37:28, user Sinai Immunol Review Project wrote:
Key Findings<br /> The authors of this study describe a common respiratory viral antigens microarray that can be used to assess the cross-reactivity of antibodies against the novel SARS-CoV-2 antigens and common human coronaviruses. Sixty-seven (67) antigens from across subtypes of coronaviruses, influenza viruses, adenoviruses, respiratory syncytial viruses and other viruses, were printed onto microarrays, probed with human sera, and analyzed. Five (5) serum samples collected before the SARS-CoV-2 outbreak were tested as part of a study that monitored acute respiratory infection cases. Four out of five (4/5) sera samples showed high IgG seroreactivity across the 4 common human coronaviruses. All sera showed low IgG seroreactivity to SARS-CoV-2.
The S1 domain of the spike protein is suggested to be subtype specific as it demonstrated very low cross-reactivity among the novel coronaviruses (SARS-CoV-2, SARS-CoV and MERS-CoV) and between the novel coronal viruses and common human coronaviruses. In contrast, the S2 domain of the spike protein and the nucleocapsid (NP) protein showed low levels of cross-reactivity between the coronavirus subtypes, suggesting the presence of more conserved antigenic domains. Therefore, the authors conclude that the S1 domain is an ideal candidate for virus-specific serologic assays.
Importance<br /> This study provides insights into the potential cross-reactivity of common human coronavirus antibodies for SARS-CoV-2 antigens. Understanding cross-reactivity is useful because it can help in the development of sensitive and specific serological assays that can test for COVID-19. Additionally, studies such as this can help inform vaccine development and indicate which antigens would be optimal to target.
Limitations<br /> This study is limited by a small sample size (n=5) of serum samples that all came from students who were part of the same college resident community at The University of Maryland. A greatest limitation is the lack of serum samples from COVID-19 patients. Additionally, the MFI of 2019-nCov from these naïve samples (in particular sera 4) is not zero. This indicates that while relatively low, there is some cross-reactivity of common human coronavirus antibodies for SARS-CoV-2 antigens, including the S1 domain. A larger sample size and samples from SARS-CoV2 infected patients may be needed to confirm the sensitivity and specificity of the assay.
Review by Jamie Redes as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.
On 2021-10-10 19:23:38, user Equi Epi Vet wrote:
On 2019-03-10 00:15:11, user eight oh wrote:
This is an interesting paper, but users should know that MAGIC induces extremely unrealistic correlations among genes. After running it, a large fraction of genes have a correlation > 0.99 across cells. If you run it on shuffled data (which should have no covariance structure) it still induces tons of correlations with extremely high coefficients.
I am surprised that these points either escaped the authors' attention or were dismissed by them. I have tried contacting them directly, but they did not seem to think it was a problem.
You can see for yourself by following their tutorial on GitHub:<br /> `# run MAGIC on their test dataset
library(Rmagic)<br /> data(magic_testdata)<br /> MAGIC_data <- magic(magic_testdata)
imputed_data <- MAGIC_data$result<br /> coefs = cor(imputed_data)<br /> round(quantile(abs(as.numeric(coefs)), na.rm=T), 2)<br /> 0% 25% 50% 75% 100% <br /> 0.00 0.69 0.91 0.98 1.00`
In other words, even on their GitHub tutorial, after running MAGIC on their own dataset using the exact commands they suggest, over 50% of the gene-gene correlations in their dataset have a magnitude exceeding 0.90. Over 25% of the gene-gene correlations have a magnitude exceeding 0.98. Therefore, it is not surprising that CDH1 and VIM are perfectly correlated - you will get this relationship for virtually any pair of genes.
Using MAGIC with published single cell datasets is not any better. I have tried running it on several datasets and for many sets of parameters - in every case, it returns extremely unrealistic results that are all but unusable for any application.
On 2020-05-13 13:28:01, user Bart Appelhof wrote:
About the fish model: How did it not work to raise a F0 generation? It should be standard to only analyse F1 (or further out-crossed) fish. What you currently are describing is a very standard toxicity response phenotype, seen often in zebrafish injected with something. I would suggest to invest in a better animal model, e.g. make F1 heterozygotes which produce homozygotes.
On 2018-11-21 09:57:49, user dalloliogm wrote:
Some comments about the paper:
Since there are many colors used in Figure 2 and 3, it is difficult to read which method is which (especially if you printed the paper in grey scale, like I did :-) ).
in Fig 4, it would be useful to have the maximum number of total rejection for each column. It took me a while to understand that the columns have different scale. For example, I would expect BH and q-value to have the same color intensity within each dataset (e.g. ChIPseq, etc..) as these methods do not make use of covariates. However the colors are different, even when the total number of tests is the same (e.g. in many scRNA datasets).
On 2015-11-08 19:11:25, user Katherine Smith wrote:
Great read! A few small points:
The legend for Figure 1F reads 'Compared to in-frame indels, frameshift variants ... are more common (have a lower proportion of singletons)', but the figure indicates that in-frame indels have a lower proportion of singletons.
Have the legends been swapped for Figures 2A and 2B?
Should Figure 2E have similar x axis groupings as 3F, i.e. all, PolyPhen, missense CADD, nonsense CADD?
On 2022-03-21 13:45:45, user todd primm wrote:
fascinating study. you should adjust your counts of your known strains by their 16S copy numbers to reflect true abundance.
On 2016-01-19 13:58:38, user MM wrote:
ok, put this in layman terms.
On 2021-05-07 01:18:39, user Neuroscience wrote:
The Okur-Chung Neurodevelopmental Syndrome (OCNDS) mutation CK2K198R leads to a rewiring of kinase specificity.
On 2019-12-19 20:48:48, user Swagatha Ghosh wrote:
Mystery solved! An interesting article from my former lab describing the structural and molecular basis of degradation of DMF, a human-made industrial solvent, by a naturally existing enzyme. This study will open up many new opportunities for evolution and design of enzymes and micro-organisms useful for bioremediation. Cheers to Chetan et. al. ! #cryoem #proteinfold #bioremediation
On 2019-03-12 18:24:14, user Rayna wrote:
I noticed that this manuscript references the Future of Research in the abstract, but none of the manuscripts published by the organizers of the Future of Research are cited. It would be great to include and cite some of their findings. The full list of publications can be found at https://www.futureofresearc...
On 2017-05-11 19:17:20, user Richard Wood wrote:
This is an innovative and important finding! Regarding HELQ interactions (lines 162-163), two papers (including one of ours) report that HELQ interacts by immunoprecipitation with BCDX2 but not with CX3, so these would be the citations to add on line 163 (PMID: 24005565 and 24005329).
On 2017-07-19 16:07:25, user Preprint Now wrote:
Dear Aviv,
thank you very much for your prompt and very kind response to our comment and for swiftly correcting your mistake by posting a complete version of your manuscript.
As mentioned in our original comment our goal was not to point the finger at you and your co-authors, but to initiate a debate about the need for certain standards for preprints. Judging by the discussion on social media this is indeed a topic that is of interest to many. While there seems to be a general consensus that preprints should contain sufficient detail to enable others to reproduce the reported advance, some have pointed out that trying to enforce such standards could amount to introducing peer review for preprints.
In our view preprint servers such as bioRxiv should refine their submission guide to spell out which standards submitted manuscripts are expected to meet. Affiliates screening preprints before they are posted should be encouraged to return manuscripts to authors if these standards are obviously not met. However, this can only be a cursory screen to spot the most obvious problems. In the end it will be for all of us reading and using preprints to take responsibility for assuring they meet the standards we expect from them. This will need a cultural shift, as currently most scientists refrain to engage in any kind of post publication/posting peer review (PPPR) for a variety of reasons. We hope that our comment and your kind response have given a good example of the value of PPPR.
Best wishes,
Preprint Now! (please)
On 2017-07-17 11:21:44, user Preprint Now wrote:
Preprints in biology have been hailed as a way to speed up scientific <br /> progress and enable scientists to receive feedback on their work at an <br /> early time point. To fulfil this promise preprints would need to contain<br /> all information necessary to assess the described experiments and to <br /> replicate the results. Unfortunately, this preprint by the groups of <br /> Feng Zhang and Aviv Regev does not contain any description of the <br /> material and methods used to obtain the described results. As such it is<br /> not possible to critically evaluate the manuscript. Moreover, without a<br /> detailed description of the microfluidics device central to this study <br /> it is not possible for others to use the reported advance in their own <br /> laboratory. As a result this preprint cannot accelerate scientific <br /> progress.
Currently, there exist no standards for what is a <br /> complete or incomplete preprint and Biorxiv does not require preprints <br /> to contain a material and method section. We believe that in order to <br /> realize the potential benefits of preprints for the biomedical research <br /> community it is important to introduce such standards. In our view <br /> preprints should be required to contain basic information necessary to <br /> independently replicate the reported experiments. Otherwise preprints <br /> will not speed up scientific discovery, but might instead become a <br /> mechanism by which scientists try to claim priority over certain <br /> discoveries without the risk of being “scooped”. Such a development <br /> would likely further intensify the current race to be the first to <br /> report a particular finding, which we believe is damaging for science <br /> and scientists alike.
We would like to point out that we do not <br /> want to speculate about the motives of the authors of this particular <br /> preprint for omitting the materials and methods from their preprint. <br /> They certainly had every right to post such a manuscript on Biorxiv <br /> under the current standards. We also would like to point out that they <br /> are not alone in posting such truncated preprints (see for example: http://www.biorxiv.org/cont... "http://www.biorxiv.org/content/early/2017/07/02/154476)").<br /> We simply hope that with this comment we can stimulate some discussion <br /> in the community about which standards should be introduced to ensure <br /> that preprints bring about the change in science communication many of <br /> us are hoping for.
On 2025-10-13 16:13:22, user Melanie McNally wrote:
I am the first author in this article, it was published as a peer-reviewed article last spring (PMID: 40297712), can you please update that in the bioRxiv system? Thank you! - Melanie McNally
On 2020-11-05 08:38:52, user Bryce wrote:
data cannot be found from website provided
On 2017-04-18 18:25:52, user Dan Bolnick wrote:
Accepted to Molecular Ecology
On 2017-07-18 06:41:19, user Jørgen K. Kanters wrote:
Using the I2((letter)(number))n convention alone is a bit dangerous, since the naming may change over years when/if new branches are identified in the Y-haplotree. Why not add the terminal SNP i.e. I2b1-M423. It would also be very interesting to know if the subject where M423 without any known SNPs below M423 or the further SNPs (Y3104,L621,L161) were not tested?
On 2018-05-07 01:37:16, user Tim wrote:
Thank you for sharing these results. This comparison will undoubtedly be highly useful to the scRNA-seq community! I had two questions:
1) What version of the 10X Chromium kit did you use?
2) For the 10X run 2 14,000 cells were loaded. Human-mouse mixture experiments conducted by 10X suggest that loading at this cell concentration should result in roughly 5-10% doublet cells. Was any analysis performed to address this possibility? (https://pdfs.semanticschola... page 9)
On 2023-10-30 21:19:17, user Kyle Sander wrote:
Published: Biotechnology and Bioengineering. DOI: 10.1002/bit.28532
On 2017-01-18 22:28:45, user John Hangasky wrote:
Dear Bastien,
These results do in fact indicate that LPMOs react with H2O2, but it is difficult to conclude at this point that H2O2 is the natural substrate for these enzymes. Are there estimates of the extracellular concentrations of H2O2? Although other oxidoreductases are secreted with LPMOs, would these enzymes be able to produce high enough concentrations of H2O2 that would effectively compete with O2?
Numerous enzymes utilize H2O2 and other reactive oxygen species as substrates, yet this would be the first example of an enzyme that generates a free hydroxyl radical as an intermediate. As hydroxyl radicals are extremely reactive, how does the observed activity account for the selective hydroxylation of only the C1 or C4 of the glycosidic linkage? The observation of four different protein residues being oxidized is indicative of the non-selective nature of hydroxyl radicals. Although the LPMO reaction with O2 is slower, it does not ultimately lead to enzyme inactivation or protein modification.
When AnGOX was used for in situ generation of H2O2, it appears the 10 minute and 30 minute time points for the first two AnGOX concentrations are similar, albeit slightly higher, to the control. These minor differences could be due to the O2 competition nature of the assay and glucose oxidase having a relatively low KM(O2) (~35 µM). Only after increased AnGOX concentrations and longer time points are taken, are the oxidized products significantly higher. At these time points, high concentrations of H2O2 are being produced.
John Hangasky and members of the Marletta Lab
On 2020-10-27 11:50:35, user Jale Özyurt wrote:
Dear Authors,
Most of the brain tumor patients treated surgically have implants, screws and plates which<br /> are often made of titanium. These devices induce severe artifacts, particularly<br /> in the EPIs. We are still seeking to find a way to adequately cope with those artifacts and<br /> could not find any information for this in your manuscript.
Thus, I would very much appreciate to get more information on this topic related to<br /> your patient sample. In my opinion, it should also be an important topic to be<br /> mentioned in your manuscript.
Kind<br /> regards,
Jale Özyurt
On 2023-09-27 09:22:07, user xibing wrote:
Hi, <br /> Really nice work on Ubl systems from bacteria. Just a kind reminder, ThiS-ThiF ubl-E1/2 of E. coli could attach ubl to some substrates in vivo and in vitro, please check doi: 10.1016/j.ijbiomac.2019.08.172.<br /> Best wishes,<br /> Xibing
On 2019-07-30 14:39:18, user Harvard2TheBigHouse wrote:
And my apologies, one additional suggestion inside the carrots:
These realities are explained by major genetic diversity (MGD), which posits that mutation levels reach saturation points that reflect selective environmental pressures.?
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On 2015-02-14 16:20:22, user Matt wrote:
Thanks.
Re: the modelling to fit the Kostenki 14 (K14) and Ust-Ishim samples in SI 8, did this modelling involve using either of the following stats?:
a) D(Mbuti,Ust-Ishim;Kostenki14,[Ancient Eurasians])
b) D(Chimpanzee,Mbuti;Ust-Ishim,[Eurasian])
Stats a) seem to show that K14 has the same distance from Ust-Ishim as the WHG samples, but is closer to Ust-Ishim than an LBK famer
Stats b) seem to show that all Eurasians were closer to Mbuti than Ust-Ishim was.
Finally, do the statistic D(Mbuti,[Han or Onge];[EHG],[WHG]) evaluate to 0? It could be useful to have an explicit reference to this in the phylogenic modelling section SI 8.
Re: the PCA, would it be possible to add a PCA with ancient individuals only then project modern variation onto it? As a complement to the PCA in the SI.
On 2025-11-04 14:10:40, user Luka wrote:
Article in Press available at https://doi.org/10.1016/j.csbj.2025.11.003
On 2019-01-21 14:40:18, user Dubayew wrote:
Dubayew: Seems the Secret Life of Plants is alive and well.
On 2025-05-29 08:29:11, user Jeffrey Ross-Ibarra wrote:
Podcast on this work: https://share.transistor.fm/s/bed2ffa6
On 2018-05-11 06:42:28, user Morten Rye wrote:
A revised version of this article is now published in BMC Cancer
On 2019-06-11 19:25:06, user Julius Adler wrote:
June 11, 2019: here are some changes of April 18, 2019
Drosophila Mutants that Are Motile but Respond Poorly to All Stimuli Tested<br /> Mutants in RNA Splicing and RNA Helicase, Mutants in The Boss
Lar L. Vang and Julius Adler
Decision making in Figure 1 has here been replaced by action selection because action selection is more widely used in the literature than decision making:<br /> https://uploads.disquscdn.c... <br /> Figure 1. The mechanism of behavior. Action selection (also called decision making) occurs when several sensory stimuli are encountered together. In the absence of sensory stimuli, the organism moves in straight flights alternating with rapid turns called saccades, see Fig. 2 of Lance Tammero and Michael Dickinson (2002). Central processing produces the response. The Boss controls this.
Tony Prescott (2008) says, “Action selection describes the task of choosing ‘what to do next’. The task is to decide what action to perform. Selecting between alternative actions has been a focus of research in ethology, psychology, neurobiology, computational neuroscience, artificial intelligence, and robotics." “Effective decision-making, one of the most crucial functions of the brain, entails the analysis of sensory information and the selection of appropriate behavior in response to stimuli; here, we consider the current state of knowledge on the mechanisms of decision-making and action selection in the insect brain,” say Andrew Barron et al. (2015).
Summary: Does The Boss exist?
Adler and Vang (2016) reported Drosophila mutants that lack all responses to external and internal stimuli at 34 degrees but at room temperature these mutants are not deficient. This could mean that activity by The Boss can be eliminated at 34 degrees but it is still present at room temperature.
Vang and Adler (2018) reported a Drosophila mutant that lacks responses to all stimuli at both 34 degrees and room temperature. That indicates that this mutant lacks action by The Boss.
Where does The Boss act?
One possibility for the action of The Boss could be at action selection of Fig. 1 above. How it would act there is not yet known. Further work is needed.
Another possibility for the action of The Boss could be at saccades described in the legend of Fig. 1 above. Again, how it would act there is not yet known. Further work is needed.
Relation between The Boss and consciousness
Many views exist about consciousness, as reported in Wikipedia by Marc Bekoff with seven others: Richard Frackowiak together with seven other neuroscientists (2004) felt that this was still too soon for a definition of consciousness, "We have no idea how consciousness emerges from the physical activity of the brain…At this point the reader will expect to find a careful and precise definition of consciousness. You will be disappointed. Consciousness has not yet become a scientific term that can be defined in this way. Currently we all use the term consciousness in many different and often ambiguous ways. Precise definitions of different aspects of consciousness will emerge…but to make precise definitions at this stage is premature…It has been defined somewhat vaguely as: subjectivity, awareness, sentience, the ability to experience or to feel, wakefulness, having a sense of selfhood, and the executive control of the mind.” Max Velmans and Susan Schneider (2005) wrote, “Anything that we are aware of at a given moment forms part of our consciousness, making conscious experience at once the most familiar and most mysterious aspect of our lives.” “Despite the difficulty in definition, many philosophers believe that there is a broadly shared underlying intuition about what consciousness is", according to John Searle (2005). See also “Animal Minds, Beyond Cognition and Consciousness, in Favor of Animal Consciousness" by Donald Griffin (2001). Wikipedia says, “Over the last 20 years, many scholars have begun to move toward a science of consciousness. Antonio Damasio (1999) and Gerald Edelman (2003) are two neuroscientists who have led the move to neural correlates of the self and of consciousness.” In summary, the term consciousness is widely used but it has not been experimentally defined.
In contrast to consciousness, The Boss is based on experimental results (see above, Summary: Does The Boss exist?). The Boss is the leader of the organism. But more is needed to establish this.
Adler J, Vang LL (2016) Decision making by Drosophila flies. bioRxiv March 24, 2016.
Barron AB, Gurney KN, Meah LFS, Vasilaki E, Marshall JAR (2015) Decision-making and action selection in insects: inspiration from vertebrate-based theories. Front Behav Neurosci 9:216 doi:10.3389.
Damasio A (1999) The Feeling of What Happens: Body, Emotion and the Making of Consciousness, Harcourt Brace.
Edelman GM (2003) Naturalizing consciousness: A theoretical framework. Proc Natl Acad Sci USA 100:5520-5524.
Frackowiak R and seven other neuroscientists (2004) 269 -301 chapter 16, The neural correlates of consciousness.
Frighetto G, Zordan MA, Castiello U, Megighian A (2018) Mechanisms of selection for the control of action in Drosophila melanogaster. bioRxiv April 17, 2018.
Griffin D ((2001) Animal minds: beyond cognition to consciousness. U Chicago Pres.
Heisenberg M (2019) Outcome learning, outcome expectations, and intentionality in Drosophila. Cold Spring Harb Lab Press 22:294-298.
Prescott TJ (2008) Action selection. Scholarpedia 3:2705, 1-14.
Searle J (2005) Consciousness in Honderich T (ed). The Oxford companion to philosophy. Oxford U Press.
Tammero LF, Dickinson MH (2002) The influence of visual landscape on the free flight behavior of the fruit fly Drosophila melanogaster. J Exper Biol 205:327-343.
On 2020-04-27 19:08:33, user gwern wrote:
This could use improvement. The discussion of 'missing heritability' is approximately a decade out of date: height PGSes are 10x larger than the 5% quoted, and there is no missing heritability for height or BMI (see either https://www.gwern.net/docs/... or https://www.biorxiv.org/con... ). The loss of predictive power in transracial GWASes doesn't mean what it's taken to mean, as it's driven by changes in LD & allele frequency and the causal alleles appear to largely remain the same. The discussion of the microbiome, as illustrated by figure 1, is also wrong: as noted in the paper itself about the heritability of microbiomes, host genetics *cause* microbiomes, and further, the microbiome is caused *by* environment and health, and is deeply confounded. Despite being so heavily reverse-caused & confounded, Rothschild (https://www.biorxiv.org/con... - cited for the least interesting parts of it?) shows that microbiome correlates very modestly at best, like BMI at 16%, which upper bound on the causal effect is consistent with the minimal effects we see in many of the human microbiome experiments. And if the microbiome hardly causes even BMI, it's not going to explain much of more distant traits (not, again, that there is any 'missing heritability' problem left these days for the microbiome to explain, and why would microbiome effects not fall into the shared or non-shared-environment components?).
On 2020-07-04 05:48:44, user zhiyanle wrote:
If the authors got to know the first patients in Europe and South-Asia, they should find out that the patients came from China (some of them are Chinese). That is, the virus re Beijing’s new wave is rooted in PRC.
On 2019-11-26 14:39:53, user Tomáš Hluska wrote:
Hi, just a short comment after quick look.
Please, use the standard abbreviation for trans-zeatin, i.e. tZ. <br /> In description of Figures I'd switch it to "Quantification of Proline, total flavonoid content (TFC), phenylpropanoids (PPs)", etc.<br /> You need to improve the English significantly. Right now, some of the sentences really do not make much sense.<br /> Good luck with your work.
On 2020-02-23 18:17:25, user Alfonso Martinez Arias wrote:
This is very beautiful and insightful work which clarifies a number of issues that the micropatterns (or 2D-gastruloids -2D-Gstlds if you wished) leave open.
First, it is interesting that here there is no Trophectoderm which suggests that, in the original work, this might be a response of the cells to the high compliance of the substrate i.e. another example of a mechanical response of the cells. Second, the cells being free, they are allowed to express behaviours that do resemble some of what they (Bra expressing cells) do<br /> during gastrulation. In this regard, this manuscript https://bmcbiol.biomedcentr... shows much that is complementary to this. Notice figures 2 and 5, in particular see in Fig 2D the fibronectin tracks and the supracellular actin cables.
I also wonder if the two Bra expressing populations that you see (the one that remains in the colony and the one that moves away -more like that of Figure 2 in the BMC manuscript), do not represent different mesodermal or, maybe even one of them, an endodermal population; it would be good to look for FoxA2 (endoderm).
All in all, clear that, for cells to exhibit some of the features of gastrulation they need to have the 'right level of freedom' and sense 'the right comliance'
Also very pleased to see the recognition of Emmanuel Farge’s work.
Much interesting to think about.
On 2024-06-12 06:14:00, user Ilia wrote:
I found this article very interesting! Looking forward to see it published.
I have noticed that in the Fig. 3 legend, the DeepLabCut is mentioned as a DLC with no other references being provided. I also noticed the DLC logo on the figure. In the Methods section, only SLEAP is mentioned. Do you use DLC to compare its experience with SLEAP? Is the DLC a necessary part of the pipline?
On 2025-01-25 17:37:27, user Andreas wrote:
Hello,
Congratulations on the mauscript, really interesting approach! I am a wet lab scientist and I would like to know if you have though of "grounding" your designs to folds which are less likely to cause immunogenicity (e.g. IgG folds) or that have been explored for drug design (e.g. VHHs, DARPins, ankyrins, etc).
On 2021-06-27 04:11:33, user Mel Symeonides wrote:
With this study, Patterson et al. present a potentially very significant finding: that SARS-CoV-2 antigen persists in non-classical monocytes from Long COVID patients up to 15 months after the initial infection. The data supporting this finding are of moderate to low strength, as presented, primarily due to a wide range of major and minor presentation issues that are listed below. Most of these can be addressed easily, though it is unclear if some additional controls may be required. Finally, some orthogonal approaches are suggested that could be potentially very valuable in terms of increasing confidence in the findings (namely microscopy and immunoblotting), though these are not essential for the interpretation of the results as shown.
The authors are to be commended for tackling Long COVID head-on and getting right to the heart of the matter in terms of finding the pathological cause of this disease. That said, unfortunately, this manuscript requires considerable revision in order to be interpretable and allow others to reproduce the findings (which will be of critical importance, given their potential significance).
Major issues:
Table 1 and the accompanying text seems to indicate that PBMCs were tested for the presence of viral RNA by ddPCR. However, in the Material/Methods section, it is stated that nucleic acids were extracted from plasma, not from PBMCs. Please clarify this point as it is of critical relevance. Indeed, both plasma and PBMCs should have been individually tested in order to determine whether viral RNA was solely intracellular.
It is very unclear what Figure 2 is presenting. Presumably each row represents a different subject, but it is not denoted which subject belongs to which group, making intepretation very difficult. I presume this was an ommission.
Supplementary Table 1 was not provided, making it very difficult to evaluate the flow cytometry data. Even if that table were present, the methods provided for flow cytometry are very sparse. What steps were undertaken to establish the specificity of the Spike antibody? Was the Spike staining done after fixation and permeabilization? Was PE conjugation of this antibody done in-house, and if so, using which kit, and how was it verified that the conjugation and quenching were successful and that staining was specific within the context of the entire antibody panel? Were FMO controls done in the context of this new panel that includes the S1 antibody? Was Fc block included? etc.
In general, the Figure Legends are very sparse and should be much more descriptive.
Minor/moderate issues:
Table 1 shows that one of the study subjects was asymptomatic. Where is this subject grouped in the subsequent analysis? ALso, "NS" is not defined, presumably it means "nasopharyngeal swab"?
In Figure 2, left column, the CD14/CD16 gates shown were not applied equally from sample to sample. Furthermore, in the middle column it looks like S1+ non-classical cells tend to have a low-SSC profile, while S1- cells have a high-SSC profile that clusters together with intermediate cells. This suggests that the intermediate/non-classical discriminating gates may not have been set appropriately.
The quantification shown in the middle column of Figure 2 is labeled "CD16+CD14+COVIDS1+", however no "CD16+CD14+" subset is defined. Presumably the authors refer to the aggregate of the "CD14++CD16+" intermediate and "CD14loCD16+" non-classical subsets. This should be clearly stated as it makes interpretation of the data shown very difficult. Additionally, the quantification is based on the aggregate population, whereas based on the color coding, one would expect individual quantification for each subset. Given the relatively very minor contribution of the intermediate subset to the observed Spike S1 signal, it is unclear why this was included at all in this plot - why not just show the non-classical subset and base the quantification solely based on that, or alternatively, show quantification of each subset rather than their aggregate?
The labeling in Figure 3 could be better, the angled X axis labels are very difficult to follow. Maybe just indicate the monocyte subset as a title above each plot, and/or label each plot as a subfigure?
No information is provided on the statistical analyses done.
I did not look into all the cited work, but in one case (ref. no. 19) was puzzled to see that a review article was cited in which the relevant information was in turn derived from a single primary research article. Surely it makes more sense to just cite that primary research paper rather than the review?
General comments:
Why was S1 the only SARS-CoV-2 antigen stained for? One would expect that you would have quickly tried to look for other viral antigens, particularly Nucleocapsid, in order to begin to understand whether there might be virus particles present, especially since you found viral RNA in some samples. Additionally, some microscopy data on sorted non-classical monocytes would have been very valuable to validate what you see by flow cytometry, also because one could then evaluate whether the Spike signal in these cells looks like the expected pattern for protein being actively synthesized by the cell and present on the cell surface, or whether it is captured antigen from some site of viral persistence and is sequestered in some intracellular compartment. Finally, a Western blot for Spike (and other viral antigens) in flow-sorted monocytes would be of immense value to further validate the presence of this antigen and observe the state of the protein - indeed, it is rather odd that you seemingly went for LCMS before trying either microscopy or a Western blot!
The potential connections with the CX3CL1 pathway mentioned in Discussion are very interesting. Unfortunately, the authors have not demonstrated any elevation of CX3CL1 associated with severe acute COVID or long COVID disease, nor the presence of CX3CR1 on the particular cells of interest. If such data exist, please present them, otherwise this Discussion is rather speculative and much more work will be required to frame it in the appropriate context for a primary research paper. Alternatively, this discussion might be better suited for a separate Review article.
Much of the published work on Long COVID and other post-COVID conditions such as MIS-C is omitted here, and should be cited and discussed as appropriate.
Mel Symeonides, Ph.D.<br /> Postdoctoral Associate<br /> Department of Microbiology & Molecular Genetics<br /> University of Vermont<br /> Burlington, VT
On 2017-10-30 10:37:16, user Sébastien Rigali wrote:
The paper is now accepted in Molecular Plant Pathology: http://onlinelibrary.wiley....
On 2022-02-01 14:20:28, user Phazaca_R wrote:
Hello authors, I am in no position to comment on the scientific aspects of this study but it seems really interesting and I am looking forward to it.<br /> I came across a small possible error. According to Zahiri et al., 2012 the genus Eudocima should be in the tribe Ophiderini, but here I see it placed in Phyllodini. Has there been some other revision that places it in Phyllodini?
On 2020-08-29 13:41:50, user Himanshu Singh wrote:
Thank you for a new information regarding the Mtb infections. However, my query is below:
In supplementary Table 6, there is difference between Entities found and Submitted entities found. How the Entities ratio is calculated? For instance in pathway RUNX1 and FOXP3 control the development of regulatory T lymphocytes (Tregs), #Entities Found is 4, and submitted entities found is: 2 (IFNG;IL2RA). Entities ratio: 0.0012.
Please explain.<br /> Thanks<br /> Himanshu<br /> Email: himanshu720@gmail.com
On 2017-08-30 10:46:13, user Juan Ramón Gonzalez wrote:
I was wondering whether MAD package is not cited in this preprint. MAD <br /> is the software used to analyze genetic mosaicisms using SNParray data <br /> and has been used in several Nat Genet papers to describe association of<br /> mosaicisms with aging, cancer, Alzheimer, .... (MADseq seems to be inspired in MAD acronym).
I also was wondering why mrmosaic (http://www.biorxiv.org/cont... "http://www.biorxiv.org/content/early/2016/07/07/062620)") is neither cited (they cited MAD). It is another method that also performs aneuploidy detection using NGS.
Thanks,
On 2016-06-14 21:30:13, user Mateusz Gola wrote:
Dear PornHelps, thank you for your comment.
bioRxiv is a pre-print server allowing to share results with scientific community and receive feedback on draft manuscripts before its publication in journals. Our manuscript is currently under revision in peer-reviewed journal. We do not want to publish anything without peer-review. Sharing this manuscript with scientific community through bioRxiv also serve to collect feedback and improve the final version of future publication in peer-reviewed journal.<br /> If this preprint will focus too much attention of general publicity before its publication in peer-reviewed journal than we will withdraw it from bioRxiv.
Thank you for your important commentary on other significant variables. It will help us improve this manuscript.
We were controlling for sexual arousability with Sexual Arousability Inventory (SAI) and there were no differences between males with and without PPU, what is presented in Table 1. We also controlled for amount of dyadic sexual behaviors and for religiousness (what may be related to shame and self-percived consequences of pornography among non-PPU but no PPU subjects, as we showed in http://dx.doi.org/10.1016/j... "http://dx.doi.org/10.1016/j.jsxm.2016.02.169)"). There were no signifficant differences in these variables between males with and without PPU. Actually I am at vacation till Jun 26th, but as soon as I will be back in the office, I will prepare a supplementary Table witch these important information.
I would love to cite all relevant references, and discuss all aspects of problematic pornography use. Unfortunately the journal we submitted our paper to has limits of words and references we have to keep. If reviewers will request to extend discussion and editor will allow for that than I would love to do it.
P.S.: Personally I'm not against pornography. For majority of people it seems to be an entertainment. But for some people pornography use is a problematic behavior similarly to alcohol - majority of people drink it, some have a problem). In my studies I try to understand for whom and why it is a problem (i.e. http://dx.doi.org/10.1016/j... "http://dx.doi.org/10.1016/j.jsxm.2016.02.16)") and I hope we will be able to help these people.
Sincerely,<br /> Mateusz Gola
On 2021-01-15 04:52:23, user Simon wrote:
Excellent paper! A couple small questions.<br /> - DS1 used probabilistic, DS2 used deterministic. why the difference?<br /> - What happens determines the nodes for a random walk? I.e., if there is no structural connection between two nodes, will that edge not be selected?<br /> - How do effects change as a function of the walk length l? Is it possible that a walk length of 20 in a very sparse graph (as structural graphs are generally >80% sparse) might bias towards unrealistic pathways, ignoring shorter paths?
On 2018-08-31 14:28:48, user Alistair Miles wrote:
In the first (16 May 2018) version of this article there is an error whereby two of the kdr haplotype groups - F3 and F4 - had got swapped relative to the groups we identified in the previously published Ag1000G phase 1 paper "Genetic diversity of the African malaria vector Anopheles gambiae", Nature volume 552, pages 96–100 (07 December 2017).
The second (6 August 2018) version of this article fixes this error, effectively swapping F3 and F4 in all supplementary data tables and figures where they had been incorrectly labelled, such that all haplotype groups are now concordant between this preprint and the earlier Ag1000G phase 1 paper.
Please note that due to small differences in the phasing methods used, there remain some small differences in the allele frequencies between the haplotype groups as defined in this preprint and in the earlier Ag1000G phase 1 paper. In particular, some SNPs that were perfectly segregating between F3 and F4 in the earlier analysis are now not perfectly segregating (although still at high allele frequency difference). So in sum there may be some small quantitative differences between this preprint and the Ag1000G phase 1 paper, although qualitative results are highly consistent.
Further technical details are here: <br /> - https://github.com/malariag...<br /> - https://github.com/malariag...
On 2019-09-18 05:48:21, user Johannes Soeding wrote:
A newer version that includes DNA double-strand break repair as an example for the localization-induction model is available from soeding@mpibpc.mpg.de.
On 2018-10-08 12:57:21, user Reuben Rideaux wrote:
Hi,
I think this paper overlooks several studies that have already addressed this issue:
Mance, I., Becker, M. W., & Liu, T. (2012). Parallel consolidation of simple features into visual short-term memory. Journal of Experimental Psychology: Human Perception and Performance, 38(2), 429.<br /> Miller, J. R., Becker, M. W., & <br /> Liu, T. (2014). The bandwidth of consolidation into visual short-term <br /> memory depends on the visual feature. Visual cognition, 22(7), 920-947.<br /> Rideaux, R., Apthorp, D., & <br /> Edwards, M. (2015). Evidence for parallel consolidation of motion <br /> direction and orientation into visual short-term memory. Journal of vision, 15(2), 17-17.<br /> Rideaux, R., & Edwards, M. (2016). The cost of parallel consolidation into visual working memory. Journal of Vision, 16(6), 1-1.<br /> Hao, R., Becker, M. W., Ye, C., Liu, <br /> Q., & Liu, T. (2017). The bandwidth of VWM consolidation varies with<br /> the stimulus feature: Evidence from event-related potentials.<br /> Rideaux, R., Baker, E., & Edwards,<br /> M. (2018). Parallel consolidation into visual working memory results in<br /> reduced precision representations. Vision research, 149, 24-29.
On 2024-11-21 18:56:40, user Ding Quan Ng wrote:
Peer-review version published in Neurotherapeutics: https://doi.org/10.1016/j.neurot.2024.e00480
On 2018-08-09 22:28:17, user bioinfo clemson wrote:
Hi Colin,
Thanks very much for your interesting in DeepSignal! this DeepSignal is not ours. we have not release our source code yet, but will do this soon. I will keep you updated.
Best,<br /> Feng
On 2016-07-22 09:50:15, user Cian O'Donnell wrote:
MATLAB code to implement the method available here: http://github.com/cianodonn...
On 2020-06-05 23:40:58, user Allison Hamilos wrote:
Please note that the companion paper, "Dynamic dopaminergic activity controls the timing of self-timed movement," is available on bioarxiv at: https://doi.org/10.1101/202...
On 2025-01-18 08:03:05, user Leonardo Chicaybam wrote:
I peer-reviewed this preprint on ResearchHub: https://www.researchhub.com/paper/6692139/scalable-intracellular-delivery-via-microfluidic-vortex-shedding-enhances-the-function-of-chimeric-antigen-receptor-t-cells/reviews
On 2015-03-27 19:00:31, user LJ Zwiebel wrote:
thanks for sharing this comprehensive work prior to its coming out in Cell Reports, especially the very detailed methods in the supplement.
On 2020-11-03 00:41:10, user Fraser Lab wrote:
Allostery is hard to comprehend because it involves many interacting residues propagating information across a protein. The Monod-Wyman-Changeux (MWC) and Koshland, Nemethy, and Filmer (KNF) models have been a long standing framework to explain much of allostery, however recent formulations have focused on the role of the conformational ensemble and a grounding in statistical mechanics. This manuscript focuses on the functional impact of mutations and therefore contribution of the amino acids to regulation. The authors unbiased approach of combining a dose-response curve and mutational library generation let them fit every mutant to a hill equation. This approach let the authors identify the allosteric phenotype of all measured mutations! The authors found inverted phenotypes which happen in homologs of this protein but most interesting is the strange and idiosyncratic ‘Band-stop’ phenotype. The band-stop phenotype is bi-phasic that will hopefully be followed up with further studies to explain the mechanism. This manuscript is a fascinating exploration of the adaptability of allosteric landscapes with just a handful of mutations.
Genotype-phenotype experiments allow sampling immense mutational space to study complex phenotypes such as allostery. However, a challenge with these experiments is that allostery and other complicated phenomena come from immense fitness landscapes altering different parameters of the hill equation. The authors approach of using a simple error prone pcr library combined with many ligand concentrations allowed them to sample a very large space somewhat sparsely. However, they were able to predict this data by training and using a neural net model. I think this is a clever way to fill in the gaps that are inherent to somewhat sparse sampling from error prone pcr. The experimental design of the dose response is especially elegant and a great model for how to do these experiments.
With some small improvements for readability, this manuscript will surely find broad interest to the genotype-phenotype, protein science, allostery, structural biology, and biophysics fields.
We were prompted to do this by Review Commons and are posting our submitted review here:
Willow Coyote-Maestas has relevant expertise in high throughput screening, protein engineering, genotype-phenotype experiments, protein allostery, dating mining, and machine learning.
James Fraser has expertise in structural biology, genotype-phenotype experiments, protein allostery, protein dynamics, protein evolution, etc.
On 2020-06-16 15:19:32, user Jung Choi wrote:
After a quick skim of the paper, I did not see how many samples were analyzed for each fluid/tissue.
On 2021-04-27 15:33:26, user Matteo Donegà wrote:
This looks really interesting!! Nice work!! Do you know what kind of fibres within the VN where activated at the stimulation amplitudes used?
On 2022-10-21 19:59:23, user Billy wrote:
This was a well conducted study to identify a small molecule that could inhibit the development of antimicrobial resistance. The authors identified a small molecule (ARM-1) that inhibited the RNA polymerase-associated DNA translocase Mfd, a protein shown to be involved in the development of antimicrobial resistance, and inhibited its ability to dissociate stalled RNAPs from DNA and had a modest impact on intrinsic ATPase activity of Mfd. The assays used were well suited for the study and revealed ARM-1's ability to inhibit Mfd and reduce the rate of mutation in bacteria grown in culture and during the course of infection of a human cell line. The authors also highlight that there was no detectable resistance of the bacteria to ARM-1 and that ARM-1 is able to deter the development of anitmicrobial resistance in diverse bacterial species. Only minor edits to spelling and grammar would be suggested. For example, the figure caption for Figure 4 says that the "Concentration of ARM-1 used against S. enterica is 50 uM," however, S. enterica was not one of the species indicated in the figure. Overall, this manuscript represents a significant advance in the battle against antimicrobial resistance and opens the door to further studies that explore the effects of this or other similar molecules in a mammalian system during the course of infection.
On 2022-05-12 14:21:51, user Luli Zou wrote:
Hi Andy, great paper and talk at #BoG22, this looks like a really useful tool. I am curious if you did any experiments looking at how the accuracy of the model changes with sparsity in the real data, i.e. how many spatially variable genes are necessary to achieve a good alignment in the Slide-seq hippocampus or cerebellum? This could be relevant for those looking to apply this method to MERFISH or CosMx data where much fewer genes are profiled and at lower read counts. Thanks!
On 2016-07-03 18:36:49, user Nicole King wrote:
This paper has now been published in PNAS: http://www.pnas.org/cgi/con...
On 2018-03-05 21:38:33, user magdalena Montt wrote:
very interesting work! thanks.
On 2024-09-26 21:44:42, user Lauren Porter wrote:
Most of the contents of this preprint have been included in this publication: https://www.nature.com/articles/s41467-024-51801-z
On 2020-11-30 14:10:41, user UAB BPJC wrote:
Title: “Antisense inhibition of accA in E.coli suppressed luxS expression and increased antibiotic susceptibility”<br /> Authors: Tatiana Hillman<br /> source location: https://www.biorxiv.org/con...
Description of selection<br /> This pre-print was selected by the UAB Bacterial Pathogenesis and Physiology Fall 2020 Journal Club 1) as it discusses the relationship between metabolism and antibiotic susceptibility of a common human pathogen, and 2) as the associated data were collected in a community lab (a novel concept for a new era of public science).
Summary<br /> This study attempted to ascertain the relationship between the gene products of accA and luxS in Escherichia coli and how these metabolic sensors influence biofilm formation and antibiotic susceptibility. Notably, these genes play respective roles in the fatty acid biosynthesis and quorum sensing systems of E. coli and serve to link metabolic sensing with general cell morphology. The author shows that transcription of accA, a gene associated with fatty acid biosynthesis, a pathway necessary for the maintenance and formation of the inner lipid membrane of E. coli, is increased with the addition of glucose to cultures in a dose-dependent manner. Additionally, the author shows that inhibition of accA by antisense RNA reduces luxS transcription. Finally, the author shows that inhibition of accA by antisense RNA increased susceptibility to three classes of antibiotics (beta-lactams, chloramphenicol, and tetracycline).
Major comments<br /> • Figures 1 and 2 are replications of previously published figures. These could be combined into a larger figure to include the total relevant fatty acid biosynthesis pathway. Additionally, the abbreviations used for gene and protein names (e.g. BCCP, Carboxyltransferase, Biotin Carboxylase, accA, accD, etc.) are not consistent from the text to the figures.
• While this may be journal-specific, the results section is formatted more as a technical methods write-up and the manuscript lacks the rationale for each experiment, interpretation of the results, or the impact of the findings on the current field.
• Explanation of how carbon (specifically glucose) is limited in the base LB media prior to experimental glucose addition is lacking.
• Statistic-specific data should be moved to be combined with the related experimental data or moved to supplemental.<br /> o Figure 5 details the statistics for normality of the accA transcriptional measures and should be included with the relevant plots of the source data or moved to supplemental<br /> o Tables 1 and 2 detail the results of comparison between accA and luxS transcription (or RNA amounts) and should be included with a plot of the source data and relevant comparisons or moved to supplemental<br /> o Figure 8 compares the results from the antibiotic susceptibility shown in Figure 7 and should be combined or moved to supplemental.
• Transitions and flow of logic are not clear in the results section. For example, the transition to the section on luxS and accA is abrupt and not explained.<br /> • Antibiotic susceptibility should be assessed on the same plate (e.g. bacteria with and without the antisense RNA plasmid should be struck onto separate parts of the same plate as to verify comparisons are equivalent<br /> • The title and abstract draw conclusions on the biofilm formation resulting from the accA and luxS transcription in various metabolic conditions but the author does not assess biofilms in any way. These potential impacts can be part of the implications and future directions of the discussion section but should not be a main claim of the paper as they are not assessed within.
Minor comments<br /> • Rationale for the use of the strain TOP10, a cloning strain of E. coli, was not explained. The cloning-optimized genetics of this strain has implications on the interpretation of results and should be addressed.
• Growth rates between the antisense RNA-expressing strain and the controls should be plotted as to verify any potential defects of the transformant which may influence results of subsequent tests.
• The results section is written with numerous improper sentences or sentence fragments and should be edited to correct this.
• The figure labeling is not consistent from figure to figure (e.g. text size, font, panel letter labels, etc.).
• Not all abbreviations are defined the first time they are used in the text (e.g. asRNA).
On 2015-04-13 14:55:36, user Nick Schurch wrote:
Regardless of how good GRIT is, I think a comparison with both Trinity and Bridger would add to the paper, especially since the former is the 'market leader' in this business.
On 2016-06-20 16:49:48, user Tofazzal Islam wrote:
A fantastic collaboration of 30 researchers from 7 countries of 4 continents against a fearsome fungal disease.
On 2020-07-04 06:51:13, user lin white wrote:
Why so many studies talk about human arg1 is expressed? I can not repeat the result. So strange!
On 2020-02-28 14:20:53, user Jason Chapman wrote:
Just been an outbreak fifty miles from where I live in Wales. I can't understand how every containment plan put into place is failing.
On 2016-07-29 03:35:51, user Kresimir Josic wrote:
Code available on GitHub
On 2017-02-20 13:37:45, user Daniel Jeffares wrote:
The paper is now out in Nature Communications: <br /> http://www.nature.com/artic...
On 2022-01-07 07:07:34, user Hamim Zafar wrote:
Congrats on this article. Given the importance of these concepts, it will be a very helpful article for the community. Please consider citing MARGARET (see https://www.biorxiv.org/con... "https://www.biorxiv.org/content/10.1101/2021.10.22.465455v1)"), which is our new method for inferring cellular trajectory. MARGARET provides an end-to-end framework for trajectory inference and downstream tasks. It can automatically detect terminal states. Furthermore, it can compute cell fate plasticity (aka differentiation potential) along the inferred trajectory and it generalizes the computation of differentiation potential for disconnected trajectories (improvement over Palantir, only other published method that computes differentiation potential).
On 2017-04-07 08:58:15, user Jesse Veenvliet wrote:
Thanks for sharing these interesting findings. I wonder if it would be possible to post an update with Experimental Procedures & Extended Data?
On 2018-02-03 17:24:09, user A. Mesut Erzurumluoglu wrote:
This manuscript is now published in Scientific Reports as "Y Chromosome, Mitochondrial DNA and Childhood Behavioural Traits" (URL: https://www.nature.com/arti... "https://www.nature.com/articles/s41598-017-10871-4)")
On 2018-09-27 09:44:01, user David Schreiner wrote:
nice work! you mention that the mouse lung dataset can be explored (Supplemental Files 1 and 2) - but where is the dataset? In the supplements here I only see spreadsheets. Thank you!
On 2015-02-20 02:02:47, user Davidski wrote:
Hello authors,
I made some comments about your manuscript at my blog. Apologies if I sounded too abrasive, that wasn't my intention.
On 2017-04-19 13:37:47, user Luke Pilling wrote:
The abstract should also read "116,666" participants, as in the title... thanks to the keen eyed reader for pointing this out, it will be corrected in a later version
On 2017-10-02 22:09:50, user Nils Gehlenborg wrote:
We published a paper in 2013 (https://www.ncbi.nlm.nih.go... "https://www.ncbi.nlm.nih.gov/pubmed/23419376)") that describes a similar approach and R package called “Nozzle”. It has been used by TCGA for the last couple of years: http://gdac.broadinstitute....
The authors might find the Nozzle paper useful for their background section.
On 2023-10-17 21:05:04, user Ulrich HOFMANN wrote:
this is an interesting and troublesome study. Not sure where you have nanoroughness in vivo, except on implants... Yet, I only have one question: how did you "glue" down the Si Nanoparticles (12 layers spin coated!) In light of one of our own obervations: Those astrocytes might just have "eaten the NPs and then ran away with a stomach ache"….<br /> The EMs in Sup Fig 2 does not exclude loose NPs.
Did you try fluorescent NPs and a good STORM microscope for cell digestion?
On 2019-11-13 16:09:39, user Desseyn wrote:
The published paper in Biol. Open (https://bio.biologists.org/... "https://bio.biologists.org/content/8/11/bio046359)") is not exactly the same as the preprint posted on bioRxiv. The preprint has been modified slightly for publication in Biol. Open.
One author name is misspelled in the preprint (BioRXIV). Readers should read Marc Le Bert instead of Marc Lebert.
On 2020-06-19 14:24:38, user Rebecca D wrote:
It would be interesting to see the changes in the fecal microbiota between the first and second FMT for those patients that received a second FMT.
On 2020-11-29 15:29:35, user Sam Wheeler wrote:
I'm confused. Official Dentyl website only mentions the Dual, not Fresh as of 29th Nov 2020:
https://www.venture-life.co...
5 varieties! Which of the 5 was tested?<br /> At least one of the 5 contains double amount of fluoride.
Dentyl brand names and official websites outside UK? In Finland this is the official site, brand name is Apteq:<br /> https://apteq.fi/tuotteet/s...
You tested PVP-iodine 0.5%. In many countries it is sold as PVP-iodine 1.0%, could you please test that with humans when do the 12-week human study?
On 2021-12-08 16:38:14, user Victor Corces wrote:
It looks like the total intrachromosomal contacts >20 kb is around 160 million for the control. You can't say anything about changes with such a small number of reads
On 2020-12-02 14:10:53, user Anil wrote:
doi: 10.1038/s41598-019-52872-5.
On 2018-06-24 04:04:22, user Dale wrote:
“You have to believe in free will, you have no choice.” I.B. Singer ????
On 2019-06-10 15:31:44, user Chun wrote:
Remarkable to see the disruption of GPCR dimer with a single point mutation.
On 2023-07-25 11:52:08, user Bjarke Jensen wrote:
This is potentially a highly interesting study! It is also my opinion, however, that it is crucial to describe in more detail the evidence that links HCM to I467V and the evidence that links LVNC to I467T. Otherwise it is hardly substantiated how point mutations at the same residue in beta-myosin heavy chain lead to distinct cardiomyopathies.
My apologies if the salient information is already in the manuscript and I missed it.
Best wishes
Bjarke
On 2020-03-31 23:13:00, user Aaron wrote:
I have concerns with the authors' claim that the US has the most evolved viral population. The study only includes 95 genomes, 54 of which are from the US. Of the remaining countries, six of them (Australia, Brazil, Italy, Nepal, South Korea, and Sweden) are represented by only a single genome, two (India and Taiwan) are represented by two genomes, Japan is represented by three genomes, and China is represented by 30 genomes. These sample sizes are vastly too small to be making any claims of viral diversity or patterns of spread. While the NCBI database has been rather limited in the number of available genomes (only 320 full genomes available as of March 31, 2020), there are many, many more available through GISAID's Next HCoV19 App. If this resource is an option for the authors, I'd strongly recommend redoing the phylogeographic analyses to include the sequences available from GISAID, otherwise there is insufficient power to be making these claims.
On 2021-10-08 11:58:06, user Eric Fauman wrote:
There are also lots of typos in the supplementary tables: e.g.: "signnifcant"
On 2021-04-26 16:58:24, user Alex Sack wrote:
How is India owning the ip, or a portion thereof, after having funded Covaxin's research and it's creation by Bharat Biotech, not a "competing interest"?
When your team are India state scientists, in collabortion with V. Krishna Mohan, Ph.D., who works for Bharat Biotech?
With all the money at play, globally, and a quite vested interest within India, it would seem that there is a whole lot of competing interest. Which begs particular questions on your research here, given your finding here that India's state (funded) vaccine is the only one effective against B.1.617 - now found throughout the world.
Particular questions, indeed, as you did not disclose this significant conflict of interest...
On 2019-10-21 02:45:01, user Jean-Michel Ané wrote:
One more question... Why did not you consider dark septate endophytes and fine root endophytes? <br /> http://www.plantphysiol.org...
On 2019-11-06 20:42:01, user Gabriela Rodriguez wrote:
BI 598 Group 5: Stephanie Yemane, Alex Terzibachian & Gabriela A. Rodríguez-Morales
Review written by undergraduate and graduate students from Boston University as requirement from the BI598 class
Summary:
The complement system, pathway that works alongside the immune system, is activated by the deposition of C1q, a protein complex that binds antigen-antibody complexes tagging synapses for elimination by cleaving C3 into C3a, which recruits phagocytic cells, and C3b which facilitates phagocytosis via the microglia-specific complement receptor 3. The deposition of the complement system has been shown during disease, in this paper, Hammond et. al. tried to test whether there is excess production of the complement system in the hippocampus of a multiple sclerosis mouse model, EAE, and if complement-dependent synapse loss is a source of degeneration in EAE.
To answer this question, the authors first aimed to characterize the change in complement production through quantitative analysis of C1qa, C3, and mRNA in Figure 1. Using Western blot analysis, researchers found that EAE mice had significantly increased expression of C1q and uncleaved C3 protein compared to sham mice. Through qPCR analysis of mRNA expression in the hippocampus, EAE mice were found to have significantly increased C1qa and C3 expression. qPCR was also used to analyze the expression of complement proteins in CD11b+ microglia/myeloid cells, EAE mice displayed significantly increased C3 expression, but no difference for C1qa expression.
Afterwards, they wanted to localize the expression of C1q and C3 in the hippocampus of EAE mice using immunohistochemistry analysis of hippocampal sections. In Figure 2, EAE mice were found to have varied increases of fluorescence in the hippocampus compared to sham. EAE brains were identified to have C1q localized in high density punctate regions. Postsynaptic marker PSD95 was used, where it was found that both EAE and sham brains had co-localization of C1q to synapses and dendrites, but not all C1q had overlapping localization with PSD95. Next, they analyzed the expression of complement proteins in different hippocampal regions. EAE were found to have no insignificant changes in complement protein expression across the striatum radiatum, lacunosum moleculare, and dentate molecular layer.
To examine if loss of C1q or C3 could protect against the EAE-induced motor impairment, the authors used a pre-mixed emulsion containing MOG in CFA containing heat-activated mycobacterium tuberculosis H37RA in order to immunize WT, C1qKO and C3KO for EAE. Results showed a significant decrease in EAE-induced motor deficits on C3KO mice during the peak disease phase and chronic phase while the C1qKO showed no significant difference in motor deficits compared to WT mice. These results suggest that the alternative complement pathway plays an important role in EAE white matter.
The authors then tried to show that C1qa and C3 knockout mice have synapse loss that’s correlated with EAE in the CA1-SR region of the hippocampus. They only focused on the CA1-SR region because they were previously able to demonstrate that there was a significant synapse elimination in the CA1-SR layer. In figure 4, they look at how Homer1 and PSD95 puncta in the CA1-SR are affected in WT, C1q KO and C3 KO mice in both Sham and EAE transfected mice. They do so by immuno-staining both postsynaptic markers (Homer1 and PSD95). C1qa and C3 KO mice result in partial protection of against EAE-induced synaptic death. Data processed was a normalized amount of present PSD95 and Homer1 puncta. C1qa KO shows a larger loss of Homer1 in the EAE mice compared to sham mice. Whereas, C3 KO shows relatively no difference in loss of Homer1, when comparing EAE and sham mice. The same was seen when looking at PSD95 puncta density, as C1qa KO showed some protection against EAE-induced synaptic death, but C3 KO showed stronger protection. Hence, knockout of C3 proved to be a lot more efficient at preventing synapse loss.
Finally, they looked at decreased amount of microglia activation in C3 KO mice with EAE compared to WT EAE mice. Loss of C1qa had a slight effect on microglia activation induced by EAE. They did so by looking at morphometric parameters of microglial activation. To do so, they measure the surface area/volume ratio and the skeletal length/volume ratio in figure five. They immuno-stained the microglia protein IBA1. WT EAE mice displayed increased expression of IBA1 by microglia and thicker/shorter processes in microglial morphology, which is associated with a functional microglial phenotype. Both WT and C1qa KO EAE mice showed similar increases in IBA1 volume and intensity compared to their respective sham mice. Yet C3 KO EAE mice showed no significant increase in IBA1 volume or intensity when compared to the C3 KO sham mice. Similar results were obtained when looking at the surface value/volume ratio and skeletal length/volume ratio. C1qa KO and WT EAE mice showed similar decreases in microglial morphology measurements, when compared to their respective sham controls. However, C3 KO EAE mice showed an insignificant decrease in the two morphologies. In conclusion, this study provides evidence that may suggest that genetic loss of C1q and C3 provides protective effects against grey matter synapse loss and microglial activation, making the complement pathway a possible therapeutic target for MS.
Merits:
The authors were able to provide enough evidence to suggest that C3 might be a protein of interest when working with multiple sclerosis-related symptoms which opens a new door into possible therapeutic applications of C3 in multiple sclerosis. Another strength of the paper is the use of the EAE animal model, this model has been proven to replicate many of the clinical and pathophysiological features of multiple sclerosis, making it a better experimental design than a mouse model only exhibiting motor deficits.
Major Criticisms:
Figures 1B-D should include individual data points on the bar graphs as these figures all had an N that was 11 or less; error bars displayed are rather large, and it may give more information to also display individual data points.
There was no reference for how many mice had successful EAE immunization. How many mice were immunized? What percentage of immunized mice displayed this increase in complement expression?
In figure 2E there is no quantification of C1q or PSD95 puncta, or quantification of how many overlap; the panel displaying the merged fluorescence is quite unclear and without quantification is not supportive of the hypothesis. The panel showing an image of C1q KO mouse hippocampus is rather dark, doing a DAPI stain to show that the structural integrity is not compromised, and that the WT and KO brains are comparable.
Using the C3d antibody for figure 2G doesn’t quite make sense as it detects the active and inactive forms, the cleaved and full length forms of C3, respectively. Using a marker that identifies the inactive form of C3 doesn’t indicate a good marker for analyzing the activity of the complement pathway – as the protein must be cleaved in order to be active. Additionally, this figure should include DAPI staining as well to prove that the sections are comparable. Why were C3/C3d panels not magnified, but C1q were? Is there more significance in visualizing C1q fluorescence?For figures I and J, there is also no quantification of the individual puncta, and the percentage of overlapping puncta.
In figure 3, the authors failed to provide comparison of experimental EAE animals with the sham mice regarding the clinical score for motor deficits across days post immunization. Another major criticism for figure 3 is the fact that they failed to provide a measure for complement deposition levels, specially during the increase in motor deficits.
A major criticism for figure four is that their data only represents one time-point. This doesn’t allow for analysis of how Homer 1 and PSD95 are affected over a period of time. Another major error was that they only decided to look at SR. They had a reason to only look there, but it would have made it clearer that its only specific to SR if they also looked at other regions such as SP, SO and SLM of the hippocampus and noticed no difference. A third error was that the differences in Homer1 and PSD95 found were so minimal that they were basically insignificant. This makes their conclusions seem exaggerated, as the discovery barely had any evidence to back it up.
A major criticism for figure 5 is the fact that they only decided to tag IBA1 in order to measure microglia density. This would not be enough to measure microglia density, as EAE immunization is not the only thing that results in increased IBA1 expression by microglia.
Minor Criticisms:
Minor criticisms include some punctuation errors in addition to referring to protein C1q as “C1q” and C1qa”. As well as inconsistencies in test references (i.e. “t-test” v. “t test”). N sizes were rather small, and thus don’t hold enough power to display significant differences; include more mice in order to prove there is no difference – begin by doubling the N per gender group and see if significant differences arise. Otherwise, include in the manuscript that not enough mice were included in the study to determine if there was a significant effect.
In figure 3, the n values for all three experimental groups are quite different, C3KO EAE mouse cohort being the one with the least amount of subjects. In this experiment the C3KO EAE group have an approximate difference of 10 to 17 animals in comparison with the other two experimental groups. This difference begs the question if the robust decrease in motor deficits are actually due to the C3KO or to the power difference.
One minor criticism in figure 5 is the location of the figure. It would be better to put it before figure 2, as it would be good to look at the change in morphology prior to behavior. To do so, they could even integrate it with figure 1, as that’s where they first start looking into C1qa and C3.
Future Directions:
Using CD11b as a marker for microglia/myeloid cells is not necessarily the most accurate, as it is not specifically for microglia but it is also a marker for monocytes and macrophages. Use a different marker specific for microglia, like TM119, and see if the CD11b overlaps in order to determine that it is specifically microglia/myeloid that are being observed.
The results section mentions that the increased expression of complement proteins in EAE mice could be due to other proteins. But, no other markers were used in order to determine if they were different from microglia/myeloid cells. How do we know another inflammatory response had been upregulated, or a different mechanism was utilized in the absence of the complement proteins? Using markers like TNF-alpha, IL-2, and IL-6 – which are pro-inflammatory markers for microglia responses. Or markers Arg1 and Ym1 which are markers for maintained inflammation response, which can identify other response mechanisms. It would strengthen the hypothesis of the increased phagocytosis by microglia if general markers like Arg1 and Ym1 were used to identify if there were inflammatory responses that did not overlap with the CD11b+ microglia/myeloid cells. Additionally, analyzing complement levels in mice at more time points post-immunization would show the progression of degeneration.
Methods for this Figure 2 included confocal microscopy which has limitations in resolution, electron microscopy would provide the resolution needed to analyze the co-localization of these proteins. If complement protein levels at the synapse are to be analyzed, using synaptosome enrichment of both EAE and sham mouse hippocampi in order to selectively observe pre/post-synaptic cleft areas and the proteins expressed. Without quantification of the puncta in panels D-J, localization of proteins cannot be directly addressed or compared across brain regions, or across EAE and sham mice. For all experiments in figure 2 that examine co-localization of proteins, merged panels should be pixel shifted in order to confirm that the location of proteins is specific and ordered.
As a future direction for the experiment shown in figure 3, it would be useful to use a conditional C3KO on the brain areas related to motor activity, like the motor cortices, striatum and GP in order to better identify the area of interest for the C3-related protective effect that reduces motor deficits. Furthermore, providing a cognitive score curve for the different KO groups alongside the motor curve would provide further characterization of the possible protective effects of these conditions against MS-related symptomatology.
To strengthen the research shown in figure 4, the authors should have shown a progression of the synaptic density over the course of the 26 days post immunization. They could have done so by picking out three different dates and looked at how the density of Homer1 and PSD95 are in comparison to the previous days.
One way to build upon figure five is to use more markers other than just IBA1 to measure microglial density. This would have made their data be better supported. Another future direction that would allow for a better study of the morphological changes of microglia, they could have used done a sholl analysis. This would have allowed them to record the number of intersections at various distances from the cell body, which would look at how complex and arborized the microglia is.
On 2019-03-24 18:26:10, user Adi Lavy wrote:
Could the authors please publish Supplementary Tables 1-2 mentioned in the text, as there is no information as to which samples were actually used in this study?
On 2021-06-26 12:03:13, user Nicolò Cavalli wrote:
Thank you professor Bloom for your impressively clever and incredibly important work.
On 2016-05-03 20:20:11, user gkcn wrote:
Typo fix: Iris -> Irys