On 2017-09-21 23:45:21, user L Kushner wrote:

Any correlation or link between de novo variants at probands and the types of damage observed in the recent nature autism paper by Powell? Amazing what a contributing factor all of these are!

On 2017-12-19 23:04:26, user chloe goose wrote:

This is fascinating. Check it out. Clinically important sex differences in GBM biology revealed by analysis imaging, transcriptome and survival data https://t.co/z7PmAHDVr2

On 2021-10-25 18:16:45, user Peter Rogan wrote:

This article is in press in the International Journal of Radiation Biology. DOI: 10.1080/09553002.2021.1998709

On 2021-04-20 12:55:09, user Nelii cheps kips wrote:

Our recent work on bacterial attachment in high salt environment #brunlab #Caulobacterales #holdfast #polysaccharides

On 2018-05-16 12:54:35, user Michael Monaghan wrote:

When searching for primer and fragment-length information (I could not find either) I notice that (line 128) Sherwood and Presting (2007) is not in the References.

On 2023-11-08 20:29:54, user P. Bryant Chase wrote:

Molecular basis for the "Abbott effect"? Bud Abbott was thrilled to know it was still being investigated in the 1980's, and would surely be thrilled to see this work if he was still living.<br /> Abbott BC & Aubert XM. (1952). The force exerted by active striated muscle during and after change of length. J Physiol 117, 77-86.

On 2017-02-06 10:39:29, user B C M Ramisetty wrote:

Kindly note an error made by us regarding disk diffusion assay results in the text. Thanks to Dr. Xavier Charpentier for spotting the errors.

Until a correction is posted, kindly read it as "We observed that zone of inhibition of MG1655 with ciprofloxacin (10 ug) was 3 cm (averages) while that of ?10 strain was 3.6 cm (Figure 3B)."

"With ampicillin (10 ug), the zones of inhibition for MG1655 and ?10 strain were 2.1 and 2.45 cm, respectively."

"With nalidixic acid, the zones of inhibition for MG1655 and ?10 strain were 1.78 and 1.98 cm, respectively."

Thank you

On 2021-03-29 19:30:31, user PaleFlesh wrote:

Interesting study. However I am concerned with the usefullness of these findings since only two species were tested, which is too small of a number to definitively say these findings are universal. It would be interesting to see if these results could be replicated with trees, grains, or even algae.

On 2021-06-04 18:54:07, user jasonrasgon wrote:

We are aware of an error in this manuscript regarding the small RNA analysis, the data have been reanalyzed and a corrected version will be uploaded shortly.

On 2022-03-17 17:00:47, user Joram wrote:

This work is now published in Frontiers in Digital Health: https://doi.org/10.3389/fdg...

On 2017-04-10 01:52:09, user AdamMarblestone wrote:

-"Superficial layers of the medial entorhinal cortex replay independently of the hippocampus" http://science.sciencemag.o...

On 2017-10-21 19:00:44, user AdamMarblestone wrote:

-"Top-Down Feedback Controls Spatial Summation And Response Gain In Primate Visual Cortex" https://www.biorxiv.org/con...

On 2021-04-09 16:25:37, user Cyril Falentin wrote:

interesting paper, is it possible to get Supp Fig/Table and Supp Material 1 please ? can't find it.

On 2019-07-09 10:18:30, user Peter Manning wrote:

Hi Noemie- you're probably aware of it but we took a similar approach, but no SEM, in an old Ecotron experiment. Details here: https://www.sciencedirect.c...

On 2023-10-26 14:53:28, user Jordan Hamm wrote:

This paper is now published in PNAS. https://doi.org/10.1073/pna...

On 2020-06-25 16:59:55, user Jitin Singla wrote:

Please refer latest version

On 2022-05-13 17:10:25, user Prof. T. K. Wood wrote:

May wish to cite the literature relevant to MqsR/MqsA since we discovered it in biofilms, characterized it as a TA system, and got the structure for the toxin, antitoxin, and antitoxin binding DNA (all not cited here). Moreover, we linked it to resistance to bile acid in E. coli.

On 2022-09-30 22:29:03, user MIT Microbiome Club wrote:

It is know that the effect of a drug, and therefore perhaps a bacteria, can be non-linear with concentration, and that dose-additivity (Bliss) predicts drug combinations better than effect-additivity (DOI: 10.1038/s41564-018-0252-1). How might this impact the results? Of course, it can hard to half the concentration of the bacteria in the setup to create the Bliss model (although maybe something with lower glucose might help?) Might the fastest grower in each pair/trio reach the highest concentration, limit the growth of other species, and therefore provide an effect quite similar to itself in isolation? How would a "fastest grower" model compare to a "strongest" model? The authors note that growth rate did not correlate with effect size for "some" focal species, but might such a model work for the remaining focal species?

On 2023-07-20 09:13:24, user Dmitrii Kriukov wrote:

This is a great paper I found so long! Thank you for your work! You express many thoughs I had and even more.

Minor comments to your work:

• Fig 3: "Black indicates observed variance; grey unobserved". - there is no grey entities, only black circles.

• Fig 10: "MRL" in the legend

• I would be excited to see also the experiment with KD method versus real data and its comparison to different MLRs.

On 2016-12-01 10:47:51, user Torbjørn Ergon wrote:

This paper has now been published as Open Access in Journal of Evolutionary Biology. Download at https://www.researchgate.ne...

On 2022-10-02 17:56:40, user Carrie Partch wrote:

The labs of Seth Rubin and Carrie Partch at UCSC jointly reviewed this preprint. This manuscript examines how the two transactivation domains (TADs) of ?-catenin interact with several domains of CBP/p300 to potentially control transcriptional activation. A combination of biochemistry, NMR, and ITC studies narrow down several binding sites for TAZ1 and TAZ2 domains. Overall, the manuscript is well organized and provides new details on these interactions that may play a role in ?-catenin function. We have some suggestions that might enhance the clarity of the work below. Thanks for an enjoyable read!<br /> Figure 1: <br /> • The schematics do not depict consistent widths/domain lengths and CBP/p300 is missing some domains, including one implicated in ?-catenin binding (Emami et al. 2004, PNAS).<br /> • It would be helpful if your schematic also illustrated all of the constructs used in the study and these names were used consistently.<br /> • If space allows, a simplified diagram of the pathway described in the text could be helpful.

Figure 2:<br /> • It would be helpful to add dashed line for predicted secondary structure cut-off at 0.3 in panel B.<br /> • It would enhance the rigor of the work to show expression levels for the constructs used in panel C.

Figure 3:<br /> • Labeling the MW markers, light and heavy IgG chains, and proteins (does the 666-781 fragment overlap with the light chain?) in panel A, along with the input, would make this figure easier to read.<br /> • The results of the pulldown seem pretty straightforward so quantification from n = 2 experiments seems unnecessary. If you do so, please define what ‘relative’ means in the quantification and make sure that your statistical methodologies are appropriate for this low n.

Figure 4:<br /> • There is some concern that the ITC data might be overfit to a two-site binding model without more information on the fits. Additional rationale or evidence that justifies use of the two-site model would also be welcome.

Figures 5 & 7:<br /> • It might be easier to interpret the binding interface if you used surface representation for the TAZ domains instead of ribbon.

Figure 8:<br /> • It was a bit confusing to show an analysis of the NMR data from the construct used in Fig. S3 in panel A, but then use data in panels B – E from a larger construct. We struggled a bit throughout the manuscript to match domain names and fragments (see Fig. 1 comment above) with the data.<br /> • It could be helpful to conclude with a cartoon or schematic that illustrates what was learned here.

Other:<br /> • Discussion text mentions a possible role for phosphorylation of serines; if citations for this exist, please add them or perhaps broaden this to a possible role for PTMs in general.<br /> • Consistent labeling of ITC data throughout the paper would help clarify which fragments of b-catenin were used in each experiment.

On 2017-03-02 23:39:01, user Stephen Williams wrote:

FYI the supplemental data is in a .tar ball not .txt.

On 2023-11-24 14:29:41, user eldad-a wrote:

Afik, E., Liu, T.J.B. <br /> & Meyerowitz, E.M. Macroscopic waves, biological clocks and <br /> morphogenesis driven by light in a giant unicellular green alga.<br /> Nat Commun 14, 6204 (2023). https://doi.org/10.1038/s41467-023-41813-6

On 2018-08-07 08:16:05, user matthewcobb wrote:

As an alumnus (1992, I think - the year Seymour Benzer came on the course as a student...), I thought this was a really interesting article. However, I think it could be strengthened quite a bit by being more critical/taking the long view and exploring some of the following questions:

How has *what* is taught changed over time, and *how* it is taught?

In retrospect, did the course get obsessed with certain techniques that turned out to be dead ends? How were the topics chosen? A summary of the topics taught in a table, together with some analysis, even hand-wavy, would be really useful.

How did the course contribute directly to the spreading of techniques, or did it only teach things that were well-established?

You describe how students are recruited, but what about the staff? You say where they are from, but what kind of 'social network' underpins staff selection? Has this changed over time? Why (not)?

CSHL has a tradition of highly influential summer courses (the Phage course in the 50s and 60s would be the obvious example). How does the fly course compare to this? Has it been as influential (probably not, although maybe in the early years), and if so how do you measure this?

Finally, I think you need to be a bit more critical about the claims for the role of the course in future careers. Understandably, you don't have a proper control group – as you explain, the students you recruit are generally the best/most motivated (I exclude myself from this ), so you can't compare their success with the overall grad school figures. You can make the comparison, but your suggestion that the course has 'a dramatic impact on trainee outcomes' isn't supported by it.

Good luck with the publication of this!

Matthew Cobb

On 2019-09-11 15:31:45, user Vesta Bahrami wrote:

Hi,

A question! What about those Zorastrians who converted to Islam recently? I also have a comment: I am from Iran and I have been told my family (from my mother side) were practicing this religion until 1900. My mom is from a place close to Hamedan in central Iran. My grandpa told us that they were from a big family (Bahrami) and they practiced persian religion not Islam until recent. They do not practice Islam now anyway, but are registered as muslims. They are from Persian_A group (I guess) since they live in that area. And also another comment. In Iran, in old days, people mixed mostly with local people who they shared similar genes with. Do u know if it is the same in other groups that Iranian Zartoshtis? And I know some Bahai people are mixed with zoroastrians in Iran.

On 2020-01-27 01:34:19, user Petting My Dog wrote:

So according to the formula a 1 year old dog would be aged to 31 Years right?

By Petting My Dog

On 2022-10-24 23:44:43, user CDSL JHSPH wrote:

Thank you for giving us the opportunity to review your preprint article! I enjoyed reading the article and it was fun to learn a little more about whale songs and their potential influences. Understanding how vocal learning and conformity is especially important as the noise environment continues changing in the ocean. Overall, the article had a lot of information that supported how much fin whales depend on vocal learning and conformity.

I felt that your abstract and introduction requires additional information to understand the paper. There was a clear definition for vocal learning, but conformity and what a singing season is was not well defined. Additional information on fin whales would have also been nice to better understand their behavior not in the context of song. I liked how you included other examples of species to gain a better understanding and it also helped show that this study could potentially translate to those species as well. It was clear what questions you were trying to answer with your study, and you defined your results clearly without going too deep into it.

For the most part your methods and results were clear to understand even for someone who has no background in what was studied! In Figure 2, I thought that I had was to potentially add a comparison in Panel C to the 1998/1999 season. It would be interesting to see the change that occurred in all the locations in the ONA region instead of just seeing the one shown in Panel B. Figure 3 was clear to understand and it supported most of the claims that were made in the introduction. I thought it was very cool to see how many ways these figures could be interpreted. An additional suggestion that I have is for Figure 4, I felt like the figure caption was bare and was missing some information to make it easier to understand and there was not much detail into what this figure was supporting so I had to make my own inferences into what was being shown there. Additionally, frequency of note was spoken throughout the article so the frequency on the y-axis was confusing so potentially changing or clearly defining that axis title would be beneficial.

The discussion section in your paper went into a lot of detail and at times felt like too much. The discussion of the results that you obtained were lost in a lot of the extra information that was in it and at times were confusing since it felt like it was jumping around too much. At times it felt like I was reading a review article on animal songs instead of results from a study, but some of this information may be beneficial to have in the introduction section instead. Overall, I thoroughly enjoyed getting to understand whale songs a little bit better and the results that came out of your work are very interesting and hopefully this can form a basis for future studies in other animals that use song.

On 2025-03-17 14:47:47, user Gabriele Scheler wrote:

It looks like you omitted this paper https://pubmed.ncbi.nlm.nih.gov/29071065/ and the earlier paper by Koulakov etal., which showed that Hebbian learning alone - for instance in a homeostatic setting - is sufficient to result in lognormal distributions of synaptic strengths, also intrinsic strengths and spike rates. This was first discovered by SchelerSchumann2006, taken up by Hromadkaetal2008 and then explained by Scheler2017. With the exception of Buzsaki, the whole discussion is missing.

On 2021-02-05 18:43:15, user Morgan wrote:

Nice work from the Stavrou lab! I do have a question about the statement that the MARCH proteins addressed in this study target viral glycoproteins for degradation. Do you think MARCH proteins could be targeting various viral GPs through different mechanisms? For example, I noticed levels of cell lysate EBOV-GP2 was assessed in the presence of MARCH1/2/8, but did you assess the level of EBOV-GP0? Other studies on MARCH antiviral activity suggest EBOV-GP sequestration to the golgi and inhibiting processing of GP0 to GP1/2. How might you explain or reconcile conflicting reports? Also curious, do you have localization data or inhibitor experiments performed not only with MLV but also HIV-1, EBOV-GP, IAV, and the other viral GPs assessed in this study? I think those data would be interesting to see! Thanks for your time and efforts!

On 2020-05-18 13:31:12, user Jessica C Kissinger wrote:

My research group and collaborators are pleased to share our research on the complex & novel mitochondrial genome of Toxoplasma and related parasites with the larger parasite and evolution communities @WiParasitology @ISEPprotists @CTEGD. We welcome your feedback.

On 2019-04-01 07:40:24, user Jubin wrote:

The authors have missed to mention/compare a related tool called SVAMP that has already got a nice and user-friendly GUI: https://github.com/raeece/s...

The original paper for the same can be found here: https://www.ncbi.nlm.nih.go...

On 2019-03-31 00:38:04, user Gokul Rajan wrote:

Nice work. :) It's really interesting that the microbes were necessary for this learning task; but isn't it an overstatement to call them sufficient for cognition?

On 2021-03-18 21:47:49, user Raghu Parthasarathy wrote:

This looks useful, and I'm glad you found my work valuable! I strongly feel, though, that science doesn't need more acronyms (see e.g. here: https://elifesciences.org/a... "https://elifesciences.org/articles/60080)"), and simply combining my radial symmetry localization with FISH doesn't warrant a new term.

Also, by the way, I generalized my algorithm to 3D many years ago, but never formally published it (https://pages.uoregon.edu/r... "https://pages.uoregon.edu/raghu/Particle_tracking_files/radial_symmetry_3D.html)"). Other people also made a 3D version, as noted in the link above. If you had contacted me, I'd have happily told you about this and saved you some work.

On 2021-03-08 14:39:24, user @DrySci wrote:

Nice article @benjraphael et al... are there possibilities to apply in targeted sequencing data ?

On 2021-11-05 16:17:02, user Alizée Malnoë wrote:

The manuscript by Seydoux et al. investigates the role of proton potassium antiporter KEA3 in diatoms. The authors first demonstrated the pH dependence on photoprotection, specifically non photochemical quenching (NPQ) and showed that NPQ can be induced in the dark by acidic pH. They found that KEA3 modulates NPQ by impacting the proton motive force (PMF); indeed generated kea3 mutants showed increased partitioning into deltapH. Importantly they showed that diatom KEA3 in contrast to plant KEA3 possesses an EF hand motif which can bind Ca2+ and proposed that it controls KEA3 activity. The role of KEA3 and pH in affecting the NPQ response has been previously shown in other photosynthetic organisms however the novelty of this study lies in the demonstration that NPQ can be induced in the dark by acidic pH and the proposed role of Ca2+ in regulating KEA3 function.

Major comments<br /> - Page 5, you state that pH-induced quenching in the dark was accompanied by the conversion of DD into DT. Please provide de-epoxidation state (DES) at t15 time (Fig. 1B) to substantiate this statement. Starting DES would also be informative to ensure there was no retention of DT/zeaxanthin in the dark. <br /> - Also to ensure there is no sustained NPQ (and/or damage or disconnected antenna) at t0, please provide Fo and Fm levels for all NPQ kinetics experiments. Assessing PSII accumulation by D1 immunoblot could be done to ensure PSII damage does not occur.<br /> - In Fig. 2F, it is not clear which data points represent HL or ML treatment as well as which ones come from light or dark period. Please indicate them in different colors or symbols. Also clarify whether you have averaged data from the kea3 mutant alleles.<br /> - To confirm that lack of complementation by deltaEF is not due to mislocalization, please show whether deltaEF accumulates at the thylakoid membrane.

Minor comments<br /> - Page 3, Introduction, specify qE after NPQ response; PSBS should be written PsbS<br /> - Page 4, DD-dependent NPQ should be DT-dependent<br /> - Page 4, we suggest changing “crucial” to “Given the unknown role” if pH-dependence of NPQ in diatoms hasn’t been fully established before<br /> - Page 8, KEA3 most likely homolog, were there other homologs than the two shown in Fig. S5? also discuss conservation of other ion channels (is Phatr J11843 thylakoid-localised?) and if they could compensate for the absence of KEA3 in KO mutant (by being upregulated for instance).<br /> - Fig2B, comment on the band at ~80kDa in OE, is that from cleavage of GFP?<br /> - Fig2G, shouldn’t you expect a lower dpH in the OE? Please comment.<br /> - Page 13, for the statement that only dpH can modulate NPQ, we would suggest to tone down or specify that this is the assumption made here as it could be that dpsi modulates NPQ but has yet to be shown!<br /> - Most of the protein analyses were performed loading samples based on protein content, when possible please provide proof that chlorophyll levels are comparable between the genotypes (at least for the native gels)<br /> - Abstract, extra ‘of’ between capacity and via; page 23, extra ‘being’ between likely and less important<br /> - Define acronyms when used for the first time<br /> - There is a lot of ‘peculiar’ in the text ;-)<br /> - Fig. 2D, star symbol instead of square symbol, check consistency of symbols

Pushan Bag, Pierrick Bru (Umeå University) - not prompted by a journal; this review was written within a preprint journal club with input from group discussion including Alizée Malnoë, Maria Paola Puggioni, Jingfang Hao, Jack Forsman, Wolfgang Schröder, Emma Cocco, Jianli Duan.

On 2022-09-26 12:05:30, user Miha Moškon wrote:

The final version of this paper has been published at https://doi.org/10.1016/j.biosystems.2022.104778

On 2017-07-12 17:39:25, user guillemaud wrote:

PREPRINT PEER REVIEWED AND RECOMMENDED by PCI EVOL BIOL

This preprint by Clemente has been peer-reviewed by Michael D Greenfield and Joël Meunier and recommended by Vincent Calcagno for Peer Community in Evolutionary Biology. Peer-reviews, decisions, author's replies and the recommendation can be found here: https://evolbiol.peercommun...

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

On 2016-07-31 09:08:38, user Steven Wilson wrote:

This manuscript has been revised and accepted after peer-review, and should be available for open access reading in about a month!

On 2020-07-18 08:25:51, user Önder Kartal wrote:

Our paper has now been published by Genome Biology, <br /> see https://genomebiology.biome...

On 2023-09-26 21:25:26, user Victor M Faundez wrote:

Paper is now in press at Human Molecular Genetics<br /> https://doi.org/10.1093/hmg...

On 2018-12-11 22:42:03, user Andrew Leifer wrote:

Thank you for reading our manuscript closely and for sharing your comments with the community. We welcome a robust scientific discussion about our findings. In fact, this is one of the primary reasons why we post to the bioRxiv. Your group, in particular, has done pioneering work in this area and we value your thoughts. Below we provide a brief response to your note, highlight areas where we disagree, and discuss specific analyses to support our claims.

• The major concern expressed in your comment relates to noise in our measurements in (Scholz et al., 2018). The strongest argument that counters concerns about noise is that our neural recordings predict the animal's behavior in held out data, while control GFP recordings do not (Fig 2G). Thus, noise in our recordings are not sufficiently strong to swamp out relevant behavior signals in moving animals nor are they strong enough to mimic those signals in control animals.

Extrapolating from your pioneering work, we had expected to see a dominant behavior signal in the first three PCs of neural activity, but we did not find such a signal. You express concern that perhaps noise may be present to such an extent during movement that it precludes drawing any conclusions from our PCA analysis. We do not think that is the case. Nonetheless, it is worth imagining what such noise would have to look like for it to both invalidate our PCA analysis yet simultaneously preserve our ability to successfully predict behavior. To be consistent with our measurements, such noise would have to have the following properties: 1) Be comprised of at least three independent orthogonal components that are the most dominant features in the recording. 2) Be distributed across many neurons (because otherwise these signals would not dominate in PCA, which involves z-scoring each neuron's signal). 3) Not overpower a signal that we observe in the first three PCs that is slightly predictive of the animal's velocity in GCaMP worms but absent in GFP control worms (Fig 2G) and 4) still preserve our ability to predict velocity and turning from the activity of a subset of neurons on held out data. While we cannot rule out noise with such unique properties, we think a much simpler explanation is that the first three PCs are not dominated by noise. We therefore merely conclude that the first three modes lack predominant behavior signals. In retrospect, it may not be surprising that moving worms have other signals dominating their neural dynamics. These could, for example, be related to sensory signals or to internal states.

• You also express concern about our ability to observe the manifold that you report in immobile conditions (Kato et al., 2015). We agree that perhaps the recording shown in Fig 1F is too short to clearly see multiple cycles on the manifold. We chose this recording because it allowed us to directly compare moving and immobile states in a single trial. Longer recordings provide a better example. When we look at longer recordings (BrainScanner20171017_184114 from Table 3) we clearly recover a very similar manifold to the one your group published (see Comment Figure 1, below, two views of same recording). Thank you for urging us to push this comparison further, we will include this plot in future versions of the manuscript.

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

• You also express concern that our velocity prediction is dominated by switches between positive and negative velocity. Comment Figure 2, below, shows that this is not the case (the same trace is shown here as is in Fig 2). The fit is not dominated by forward or backwards velocity, but rather accurately fits and predicts intermediary velocities. We will add these plots to future versions of the manuscript. Thank you for encouraging us to look more critically at this.

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

• We are aware that estimating neural IDs is extremely challenging. We have tried to be very transparent in our estimates. Table S2 and the supplementary methods give details. We are also happy to answer any specific questions you might have.

• Sparse models have been successfully used before in neuroscience (Pillow et al., 2008; Tankus et al., 2012). We are aware of potential concerns with fitting sparse models. We mitigate them by 1) using elastic net which is suitable for highly collinear datasets such as ours, 2) using cross-validation to evaluate the robustness of our fits (Roberts et al., 2017), and 3) assessing model performance on held-out data, which we note is a higher standard than typically used for linear regression in the field. <br /> In fact, reference (Wu et al., 2007) that you mention in your note claims that LASSO performs best for datasets like ours where the variables have correlations, and we note that our elasticnet model incorporates the LASSO penalty.

• Thank you for finding the typo in Fig 2D. We will fix it in future versions of the manuscript.

We appreciate your comments as they help us to strengthen the manuscript and anticipate reviewer comments.

Sincerely,<br /> Monika Scholz and Andrew Leifer

REFERENCES

Kato, S., Kaplan, H.S., Schrödel, T., Skora, S., Lindsay, T.H., Yemini, E., Lockery, S., and Zimmer, M. (2015). Global brain dynamics embed the motor command sequence of Caenorhabditis elegans. Cell 163, 656–669.

Pillow, J.W., Shlens, J., Paninski, L., Sher, A., Litke, A.M., Chichilnisky, E.J., and Simoncelli, E.P. (2008). Spatio-temporal correlations and visual signalling in a complete neuronal population. Nature 454, 995–999.

Roberts, D.R., Bahn, V., Ciuti, S., Boyce, M.S., Elith, J., Guillera-Arroita, G., Hauenstein, S., Lahoz-Monfort, J.J., Schröder, B., Thuiller, W., et al. (2017). Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography 40, 913–929.

Scholz, M., Linder, A.N., Randi, F., Sharma, A.K., Yu, X., Shaevitz, J.W., and Leifer, A. (2018). Predicting natural behavior from whole-brain neural dynamics. BioRxiv 445643.

Tankus, A., Fried, I., and Shoham, S. (2012). Sparse decoding of multiple spike trains for brain–machine interfaces. J. Neural Eng. 9, 054001.

Wu, Y., Boos, D.D., and Stefanski, L.A. (2007). Controlling Variable Selection by the Addition of Pseudovariables. Journal of the American Statistical Association 102, 235–243.

On 2024-03-27 20:38:47, user Chris Buck wrote:

I've posted some further thoughts about this manuscript on my Substack page:<br /> https://open.substack.com/p...

On 2021-09-14 14:35:27, user Donald R. Forsdyke wrote:

The final peer-reviewed version of this paper may be found in Computational Biology and Chemistry 94:107570

On 2014-05-15 07:55:24, user Daniel Klevebring wrote:

Very nice work.

For some reason, I can't see the figures in the PDF of the main paper. The suppl figs show nicely, but only white boxes in the main PDF. Any idea why that is?

thanks

On 2017-04-20 18:45:48, user Loren Hauser wrote:

Interesting, but unfortunately this doesn't tell me if the variation is planned or just due to mis<br /> takes by the splicing system in cells. You really need to combine this with proteomic measurements that tell which splice variants are actually translated and inserted into the membrane.

On 2019-05-01 16:07:38, user Bert wrote:

Interesting, but there is no evidence yet that there is actually a TonB-dependent (outer membrane) transporter that imports these compounds.

On 2023-07-06 12:26:49, user Felix Hernandez wrote:

This MS has been published in International Journal of Molecular Sciences - MDPI

https://www.mdpi.com/1422-0...

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

On 2018-05-09 17:37:56, user Uri David Akavia wrote:

Congratulations Dylan!<br /> Is the source code going to be available at some point?

On 2021-10-12 13:47:56, user Luisa Hugerth wrote:

The github link in the abstract leads to a 404 Page Not Found error. Maybe the repository is mistakenly marked as private?

On 2022-06-24 01:15:41, user Jichen Bao wrote:

The manuscript has been accepted by ACS synthetic biology. https://pubs.acs.org/doi/10...

On 2022-11-22 11:33:47, user Deepali Pal wrote:

Part of this study has been pre-print as below:<br /> https://www.cell.com/cell-r...

On 2020-07-22 19:33:46, user Richard Sanchez wrote:

interesting work from Ferdosi et al. It beautifully illustrates PTEN as a<br /> novel marker for neddylation inhibition as well as further exemplifying<br /> the integration of multi-omic data.

On 2020-06-13 03:31:18, user Ray wrote:

Introduce both mutations into the virus and test if one can outcompete the other in cell culture. If yes repeat with model animals. Only then you can say that. Everything else is jumping on the COVID-19 train to get an easy publication. But I guess that's the only way to get money for your lab atm. Science has become a commercial product that is being milked by mass media and journals. The more scary the better it sells. This doesn't help. This just makes things worse.

On 2018-07-31 11:29:06, user H. Etchevers wrote:

Accepted in a special issue of the journal "genesis" (Wiley) for the 150th anniversary of the neural crest, to appear at http://dx.doi.org/10.1002/d...

On 2018-10-02 19:55:41, user BU_Fall_BI598_G3 wrote:

Reinhard et al. performed transsynaptic tracing of retinal ganglion cells (RGCs) from targets in the superior colliculus (SC) projecting to either the lateral pulvinar (LP) or the parabigeminal nucleus (PBg), in order to determine how visual features are routed to the two areas. Analysis of dendritic morphology showed that retinal inputs to the parabigeminal and pulvinar circuits differ in size and stratification, and labelled RGCs were also characterized by their molecular identity. Morphological and molecular data was then used to identify twelve clusters of RGCs; each cluster contained cells of similar dendritic profile and molecular makeup. The LP and PBg receive input from distinct clusters, and the authors argue that each cluster contains a single cell type, which may encode a unique feature of the visual environment. They show that the SC demonstrates a relatively high degree of regularity in its guidance of inputs to various targets.

Overall, this study offers an in-depth look at the pulvinar and parabigeminal pathways. Reinhard et al. were successful in identifying which RGCs provide input to the LP and/or PBg, and the identifications made in this paper are helpful in indicating what type of input each nuclei is receiving. Another key strength of these experiments is the ability to express GCaMP in specific RGCs based on their downstream projection pathways. Such a technique is very useful for future experiments to precisely characterize ganglion cell response to a visual stimulus. Finally, Table 1 offers a nicely detailed summary of the structural and functional information on the pulvinar and parabigeminal circuits gathered in this study as well as in other studies. This summary allows clear understanding of the pathways and hints at possible future studies.

While we appreciate this manuscript’s strengths, we identified a few areas of major critiques, addressed below.<br /> Some important controls to verify the validity of the experimental setup are missing. Although using Cre-inducible viral cargo in injections to the lateral pulvinar is a good experimental design, it is not proven that the floxed TVA-G-mCherry is expressed in pulvinar-projecting neurons only. To verify this specific expression pattern, in a NSTR1-GN209-Cre brain injected with HSV-flox-TVA-G-mCherry and stained for pulvinar-projecting neurons, all fluorescent cells should be double-labelled. Additionally, does the experimental technique label cells in the superior colliculus that project to both the lateral pulvinar and parabigeminal nucleus? Such cells would take away from the claim that visual information is, for the most part, routed separately to these two areas. To test this possibility, we suggest injecting HSV-GFP into the pulvinar and HSV-mCherry into the parageminal nucleus and subsequently studying the superior colliculus for any double-labelled yellow cells. Also, rabies virus is known to destroy cells about ten days after infection. Therefore, significant variations in cell morphology created by the rabies virus could be present in the later stages of infection . We would like the authors to show that at the time of retinal extraction, the RGCs are still healthy and not yet negatively impacted by the rabies virus. Finally, though it is important to be able to extract out individual dendritic trees for analysis, does choosing cells only with non-overlapped dendrites create bias in the morphological or molecular identities of the chosen cells?<br /> Throughout the paper, techniques and ideas could be explained more clearly. Confusing wording and sentence structure make the paper difficult to follow at times (for example, the sentences starting on lines 112, 115, and 205). Additionally, the authors’ experimental setup is not entirely clear from the first few paragraphs of the paper. More detail about the viruses used, their cargo, and the expected expression patterns would be helpful for the reader to understand the overview of the experiment. A cartoon or an improved diagram of injection locations and expected labelling of the cells within the circuits could help with clarity. In other areas of the paper, the authors’ rationale for experiments is ambiguous. For example, with the description currently given in the text (lines 145-158), Figure 5 is very confusing. A more complete explanation of the rationale behind clustering the cells and brief descriptions of the three validation indices would make Figure 5 significantly more clear and highlight its importance for the rest of the paper. Another area that could benefit from further explanation is the comparison of 7 clusters to groups of PV cells in Figure 7. I.e., why were only 7 clusters shown and why were PV cells used for the comparison?<br /> The content and style of various figures made assessing the experimental design and results challenging. Small image/diagram size and low contrast was a recurring problem in several figures. For example, confirming injection specificity in Figures 1B and 1F is difficult simply due to image size. However, specific verification that fluorescent cells shown are truly in either the parabigeminal (1B) or pulvinar nucleus (1F) should be included also. Additional images verifying the placement of the EnvA-RV-GCamp6 superior colliculus injection would also be beneficial for readers. Image size and contrast was also an issue in Figures 1C,1G, 4A, and 4D. Low image magnification in figures 3C, 3G, and 4B made it difficult to see the colocalization of fluorescent labels. Similarly, to identify the lack of colocalization, Figures 4E and 4F should have been merged. Figure 2B should show percentages of cells rather than number, to provide more context for the data. Figure 1A and 1E depicting injection sites were confusing due to the color scheme chosen to represent HSV-TVA-G-mCherry and GCamp6. While we appreciated that two different colors were used to represent HSV-TVA-G-mCherry for the pulvinar injection (green) versus the floxed version for the parabigeminal injection (orange), this became confusing because the word GCamp is highlighted in green in the figure. Additionally, Figure 4 could be further improved if a quantification of CART+ cells was included, similar to what was done in Figure 3 for SMI32+ cells.

Additionally, there were a few minor weaknesses that, if addressed, could improve the quality of the paper. <br /> 1. The RGC clusters and their projections to the pulvinar and/or parabigeminal circuits identified in these experiments are useful in identifying the clustering of cells and subsequent circuitry implicated in routing visual inputs through the superior colliculus. However, an important distinction should be made between the identification of clusters versus cell types, as it cannot be necessarily concluded that cluster is equivalent to cell type. The 12 clusters identified in these experiments are referred to as putative cell types (line 161), and we do not believe the data presented in this paper is enough to make such a claim. Clusters may very well contain different types of cells that had similar morphology resulting in them being grouped into the same cluster, and the molecular data is not robust enough to validate these clusters as cell types. Moreover, the 3 clusters that were found to project input into both the pulvinar and parabigeminal circuits were referred to as ‘3 visual features’ (line 243). While separate clusters may represent distinct features of the visual space, this conclusion cannot be inferred from the data in this paper. <br /> 2. For both Figures 5 and 6, adding a color legend that identifies each cluster by the descriptive name given to it in the text (and Table 1) would help clarify the figures for readers. <br /> 3. In Figure 5, a 3D plot of the clusters could be added in the supplement for better visualization of the clusters along all three axes. <br /> 4. Use of PV-Cre mice as wild type mice is not discussed or validated.

On 2015-12-04 13:27:31, user Hiren Ghosh wrote:

Have any one tested this algorith with his/her own dataset ???

On 2023-11-10 16:44:10, user KJ Benjamin wrote:

Interesting approach, but I'm confused why the authors would model population instead of genetic ancestry? The authors use ADMIXTURE to show a great degree of mixed ancestry, but do not examine the effect of genetic ancestry, but "population grouping". This would be extremely influenced by environmental factors that are differences across and within continental groups.

On 2016-05-13 00:51:27, user Donald R. Forsdyke wrote:

Please note that the case has been made that "BDM" should be "DM". See Heredity (2011) 106, 202 (http://post.queensu.ca/~for... ).

On 2025-07-15 22:07:24, user Åshild Telle wrote:

Links to movies (Supplementary Material):

https://www.dropbox.com/scl/fi/pa00vq7h29jlptegmkmp3/Patient1.m4v?rlkey=8xj6oula1ys68vdpzprhmlu61&dl=0 <br /> https://www.dropbox.com/scl/fi/7daf5rwkbh907lhxq03yk/Patient2.m4v?rlkey=8td1vwxhl4f70w2151owcmful&dl=0 <br /> https://www.dropbox.com/scl/fi/m0rd44brd5mhxllcm9cra/Patient3.m4v?rlkey=6t6er8qicg1t5pfgpuhnk05es&dl=0

On 2019-03-27 19:49:57, user Andrew Johnson wrote:

Nice work. A few minor comments: <br /> 1) the 1st report of IQGAP2 rare variant association with MPV was Ref. 49 - comment is that this was an Exome chip study rather than GWAS<br /> 2) rare variants in KALRN first reached genome-wide significance for MPV in Gieger et al., 2011 PMID 22139419 (not currently cited here), subsequently replicated in Eicher et al. Ref 49 and then Astle et al. Ref 29

On 2020-05-27 15:45:40, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2023-03-29 14:06:54, user Sarah Chellappa wrote:

Thanks for sharing these important insights. It is unfortunately not surprising that race/ethnicity and socioeconomic status are not frequently reported. Most sleep and circadian studies come from study samples comprised of individuals who are white, middle-class and from high-income countries. Hence, there is a historic imbalance that perpetuates until today. One of the perks of addressing this issue is that it will help foster more research into the socioeconomic determinants of health.

On 2021-01-05 00:58:07, user Charles Warden wrote:

Hi,

Thank you for posting this pre-print.

I see that this pre-print has both supplementary material and a link to code:

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

https://github.com/OSU-BMBL...

However, the section for "Supplementary Materials" says "Supplementary Data are available at Science Advance online".

Is this intentional (perhaps part of an automatic submission from a journal?), or should the section say something else?

Best Wishes,<br /> Charles

On 2015-09-03 07:53:42, user Pierre Boursot wrote:

Spectacular finding. However I am surprised that the interspecific introgession hypothesis is not evoked. There are numerous examples of massive interspecific mitochondrial introgression not accompanied by any detectable nuclear introgression (and here with three nuclear fragments, the power to detect it is very limited anyway). This would in my opinion be a much more plausible explanation than long term high effective population size, which should anyway leave even more marked traces in the nuclear genome. There would remain to find the donor species, but they may be extinct. An analysis of the coalescent in each of the divergent mitochondrial lineages segregating in a given species might give hints about which lineage is likeky to be introgressed, since the most likely source of massive introgression is during range expansion into the territory of another species, and this is expected to leave a signature of expansion of the lineage that introgressed from the species whose territory has been invaded.<br /> Sorry for the self-citation, but you could read this paper and several references therein:<br /> Melo-Ferreira, J., L. Farelo, H. Freitas, F. Suchentrunk, P. Boursot, and P. C. Alves. 2014. Home-loving boreal hare mitochondria survived several invasions in Iberia: the relative roles of recurrent hybridisation and allele surfing. Heredity 112:265-273.

On 2018-04-11 02:12:06, user Emily Stephen wrote:

Great paper, thanks for sharing! I think you have a mistake in the methods: "Each 200 ms epoch was multiplied with a Hann taper, zero padded to 1 s, and Fourier transformed, resulting in an FFT spectrum with a frequency resolution of 1 Hz." -- actually, the frequency resolution should be more like 10 Hz. With 200 ms windows and one taper, the half-bandwidth will be 1/0.2 = 5Hz. Zero-padding doesn't affect the frequency resolution, just the interpolation of the frequency axis.

On 2018-07-06 11:00:09, user kamounlab wrote:

In response to some comments we received, here is a comparison of the sequence chromatograms corresponding to Fig. 3. They show that MoT3 reverse primer sequences were recovered in amplicons from M. oryzae isolates including Bangladeshi wheat and rice blast fungi.

We conclude that:

1. The reverse primer showed mis-priming despite the single mis-match at the 3' extension end (based on FR13 sequence), allowing WB12-like sequences to be amplified.

2. There could also be degradation of primer at the 3' extension end that enabled amplification in rare cases: there is only one putative evidence (the double peak in chromatogram from RB-11).

3. The wheat blast isolate BR32 lacks the WB12 region.

Joe Win and Sophien Kamoun

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

On 2019-04-29 17:55:26, user Charles Warden wrote:

I hope we can find a way to get more comments on bioXriv, so that they could be discussed prior to submission to a peer reviewed-journal:

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

I would much rather have a revised pre-print than a correction / retraction in a peer-reviewed paper.

You can see any comments for an individual from their Disqus account, but I think this worked well in terms of keeping a friendly tone and asking questions that I think could improve reader understanding: https://www.biorxiv.org/con...

On 2022-02-21 19:22:36, user MA wrote:

The supplements tables are NOT included?!

On 2025-01-14 04:06:08, user Yi-Cheng Yang wrote:

This study contributes to the establishment of personalized treatment, such as considering the use of EZH2 inhibitors combined with PSMA-targeted therapy for NE type II patients, providing new treatment options for cancer patients.

On 2018-11-14 09:36:55, user Carlos Santiago wrote:

Quite interesting paper. Do you plan to extend it to evoked-potentials <br /> as well? There's another pipeline that deals with evoked and <br /> resting-data that has been validated in both healthy and schizophrenia <br /> patients that you guys could discuss: "An automatic pre-processing <br /> pipeline for EEG analysis (APP) based on robust statistics".

Further, there are the new guidelines for EEG pre-processing on https://osf.io/a8dhx that you could discuss. Nice work!

On 2019-05-07 01:17:57, user Keith Robison wrote:

I've written a pair of analyses on this -- some key criticisms are the lack of technical detail and that the analysis of errors is much less detailed than desired. There's also the lack of specificity in the text as to which data was deposited publicly -- only the E.coli is available. Also, the phred quality scores are overestimated at the high end by perhaps as many as 5 points.<br /> Poking at Genapsys Preprint<br /> Genapsys' Base Caller: Mysterious, But Not Ideal?

On 2023-01-11 04:42:12, user Leslie Vosshall wrote:

Konopka et al. produce a valuable reagent for the Anopheles mosquito community – a QF2 knock-in into the bruchpilot (brp) locus. They use this strain to quantify the number of neurons in the major sensory appendages using an existing QUAS-CD8:GFP reporter strain. The images in the paper are beautiful and the cell counts are a valuable resource for the field. No one has attempted to do cell counts like this before, and this reagent made this possible.<br /> A few suggestions to improve the paper:<br /> 1) The authors have not formally shown that the brp-QF2w line is in fact pan neuronal. It would require significant additional work to prove this, e.g. double label antibody staining with RNA in situ hybridization against a pan-neuronal gene to show a 1:1 correspondence. I do not suggest that they do this, but it’s important to note the caveat early in the paper that the genetic reagent is assumed to be pan neuronal because the targeted locus is a neuronal gene.<br /> 2) Speculation on phenotypic/behavioral variability caused by variation in cell number (Line 283-299) seems premature to me. It could be variation in the expression of the driver or reporter, rather than underlying variation in the number of chemosensory neurons. More experiments would need to be done to confirm that what is seen with the genetic reagents reflects actual biological variability. I might suggest that this paragraph be toned down or removed.<br /> 3) Figure 2E-F, Figure 3E: could the authors clarify how the data are plotted and what the sample sizes are. I have the impression that the small dots are individual experiments, but it is not clear. For transparency, it might be best to not use bar plots and instead use dot plots where all of the data points are clearly visible.<br /> Review prepared by Leslie Vosshall

On 2015-04-04 02:08:22, user Rat_Fink_Forever wrote:

What percentage is showing up? I have 4.4% Denisovan according to the Human Genome Project and my near term history is from the Baltics, with 50% northern European and a hefty percentage of SW Asian.

On 2023-08-16 13:06:21, user Pierre-Luc Germain wrote:

Very interesting contribution, I'd just like to make two comments.

First, it's wrong to write that scDblFinder is "formerly known as doubletCells". They're two methods developed independently, and it's simply that doubletCells was moved to the same package, but still as an independent method.

Second, your results are in contrast with other benchmarks, which you explain by more "realistic scRNA-seq datasets". I'm obviously not entirely disinterested here, but I think this is very misleading: you don't show any evidence that the traditional benchmark datasets do have unrealistic patient or batch effects, and omit to mention the critical fact that, as far as I know, the fatemap samples are homogeneous cell lines, which is far from being more realistic (people do scRNAseq on complex tissues much more often than on cell lines). I think a fairer description would be to abandon the "realistic/unrealistic" labels, describe your data as it is, and hence that your observations are basically about homotypic doublets, which the tested methods are very bad at detecting (but also don't claim to do). The lack of real difference between adjacent/distant seems to indicate pretty clearly that you're essentially dealing with homotypic doublets.

On 2020-02-01 15:40:37, user Levi Yant wrote:

Magdalena Bohutinska and Mark Alston contributed equally.

On 2015-07-27 15:52:01, user Maria Zavodszky wrote:

Hi, Thanks for sharing this manuscript. I have found that the manuscript mentions supplementary tables that I could not find in the word document posted here under Data Supplements. I would be interested in seeing them. Is is possible?<br /> Thanks,<br /> Maria Z.

On 2018-04-12 18:47:01, user H. Etchevers wrote:

Because this paper is behind a paywall, I will try to upload a revised version that includes the five references that had been missed in the first authors' version.

On 2019-11-25 09:30:11, user Daniel Žucha wrote:

Dear readers, <br /> I would like you to be notified that this preprint has been already published in the Clinical Chemistry journal with minor changes. It is accessible under DOI: 10.1373/clinchem.2019.307835. The direct link to this site is forthcoming shortly.

On the behalf of authors,<br /> Daniel Zucha

On 2020-07-07 11:41:45, user Rhyothemis wrote:

"SARS-CoV is known to repress ACE2 expression [4]. ACE2 regulation involves chromatin remodeling and structural chromatin changes [19]."

Could this type of change persist post-infection?

On 2021-02-18 06:24:22, user Ruoying Zheng wrote:

I totally enjoyed reading this paper. The experiment designs are great. It would be better if some parts of the article can be improved. In Fig 5, the biological model should be always mice instead of using human erythrocytes in 5A and using infected mice in 5C. Having a consistent biological model can rule out unnecessary variables. If both mice and human biological models are used here, then a vitro experiment about mice erythrocytes malarial infection and related treatments should be added here. In 5C, infected erythrocytes should have a higher FITC-A fluorescent binding properties than uninfected erythrocytes. More detailed explanation should be added in 5C to clarify the difference between each group. In the linear graphs of Fig 6A and 6C, different groups should have different colors, which would make it easier to read. As for the cell pictures in Fig 6A and 6B, the color scheme is not unified, the colors of the parasites should be the same in each picture so it would be less confusing. Besides, more background information about DHA(active metabolite of all artemisinin compounds) and why the author set up experiments to test efficacy of DHA+Alisporia should be explained.

On 2018-11-28 10:17:24, user Tanai Cardona Londoño wrote:

Hi, interesting proposal. I like it. A couple of comments.

The fossil heterocystous cyanobacteria reported by Pang et al., (2018) are not just akinetes. They are entire filaments with cells that do resemble heterocysts. I spent all of my PhD studying heterocystous cyanoabcteria, purifying them, extracting their thylakoids membrane, staining them, seeing them in a variety of microscopes... I have to say that those filaments are excellently preserved and are virtually indistinguishable from extant heterocystous cyanobacteria. I would dare to say that the fossils presented by Pang et al., (2018) are unequivocal fossils of heterocystous cyanobacteria. But I'm not a paleontologist.

You cited the review by Butterfield (2015) to validate your statement that the best fossil heterocystous cyanobacteria are from the Devonian, but in that paper that is Butterfield's own assessment, which predated Pang et al.'s paper. The Devonian fossil's cited by Butterfield are reported in a 1959 paper that I was not able to access. Were you able? Are those truly better preserved that Pang et al.'s? Butterfield does not show the Devonian fossils in his review... So, your argumentation there can be strengthened. Don't be so quick to dismiss Pang et al.'s fossils!

Your proposal also makes me wonder about the light-independent protochlorophyllide reductase. It is a nitrogenase-like enzyme and it is also oxygen sensitive (http://www.plantphysiol.org... "http://www.plantphysiol.org/content/142/3/911.short)").

Could it be that the oxygen sensitivity of protochlorophyllide-reductase limited the rise of oxygen prior to the Great Oxidation Event, before the diversification and expansion of today's taxa of cyanobacteria, and before the origin of the light-dependent protochlorophyllide reductase?

Thanks.

All the best,<br /> Tanai

On 2023-12-15 19:31:50, user Jordan C Rozum wrote:

This preprint has been accepted in PRX Life under a new title "Models of cell processes are far from the edge of chaos" and is openly accessible here: https://doi.org/10.1103/PRX...

On 2020-02-08 07:31:36, user jean-claude perez wrote:

Please read https://www.preprints.org/m...

On 2020-02-01 15:50:25, user ??? wrote:

.. Is this suggesting that the primary Coronavirus infects to HIV patients in Wuhan and mutated with transferring from hiv gene ?? Very aweful scenarios but possible.

On 2020-04-13 08:00:30, user Tartaglia Lab wrote:

This work is interesting and we find quite useful that the authors shared it. Thanks!

In our work (https://www.biorxiv.org/con... "https://www.biorxiv.org/content/10.1101/2020.03.28.013789v3)") we studied protein interactions with SARS-CoV-2 RNA using advanced computational approaches.

Just focusing on the RNA binding proteins present in the two studies, we found a significant overlap of genes such as Janus kinase and microtubule-interacting protein 1 JAKMIP1 (Q96N16), A-kinase anchor protein 8 AKAP8 (O43823) and A-kinase anchor protein 8-like AKAP8L (Q9ULX6), which in case of HIV- 1 infection is involved as a DEAD/H-box RNA helicase binding protein (among others).

It is very curious that our list of protein- RNA binding partners contains elements identified also in this protein-protein network analysis. Yet, it must be mentioned that ribonucleoprotein complexes evolve together and their components sustain each other through different types of interactions.

On 2025-04-04 09:28:15, user Volkmar Lessmann wrote:

Great work by the authors!! High profile research disentangling the role of phospholipid metabolism in rescuing AD phenotype with FTY720. Congratulations!

On 2021-02-18 21:31:48, user Dr. Tyeen C Taylor wrote:

This article is in review at Frontiers in Forests and Global Change

On 2015-06-03 14:54:48, user Kasper Hansen wrote:

This preprint was revised June 3rd, 2015. The major change in the revision is analysis of single cell epigenetic data (both ATAC-seq and WGBS). This has led to a change of abstract, introduction, a new results section and discussion; these changes simply reflect the additional data types. We have also included a very short analysis of the overlap of A/B compartments with various types of methylation domains (as has been previously done in for example Berman et al (2012) Nature Genetics. Typos etc. have been corrected and three new figures have been added.

On 2019-03-11 10:00:25, user Justin Andrushko wrote:

Fantastic paper investigating the cortical underpinnings of fatigability.The things I really like about this paper - no salami slicing! The authors conducted multiple experiments to investigate the observed mechanisms. The authors also included effect sizes on all statistical measures, something more people should consider to include. Effect sizes really help with data interpretation and meaningfulness of results. Great paper!

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

On 2020-03-06 22:44:50, user Luis Yáñez wrote:

this is a veyr useful paper, hopefuly will be published soon

On 2022-06-30 16:45:44, user Tatiana Mikhailova wrote:

Is raw .idat data from this study available for download?

On 2014-08-03 13:59:42, user Richard Rocca wrote:

Can you please post what where European samples ETR2, ETR5 and ETR9 are from and their archaeological context?

On 2018-03-30 13:00:18, user Markku Varjosalo wrote:

This preprint was published in Nature Communications on 22th of March titled as “An AP-MS- and BioID-compatible MAC-tag enables comprehensive mapping of protein interactions and subcellular localizations”

On 2016-11-22 07:13:13, user ???? wrote:

Thank you for your work,here I had some questions when I installed the package.When I install the package by :<br /> source("https://bioconductor.org/bi...") <br /> biocLite("fgsea")<br /> I got some errors like :<br /> fastGSEA.cpp: In function ‘Rcpp::IntegerVector combination(const int&, const int&, std::mt19937&)’:<br /> fastGSEA.cpp:318: error: ‘uniform_int_distribution’ is not a member of ‘std’<br /> fastGSEA.cpp:318: error: expected primary-expression before ‘int’<br /> fastGSEA.cpp:318: error: expected ‘;’ before ‘int’<br /> fastGSEA.cpp:324: error: ‘uni’ was not declared in this scope<br /> make: *** [fastGSEA.o] Error 1

Could you help me ? Thanks.

My R version is 3.3.1,and bioconductor is 3.4

On 2017-11-22 10:54:04, user Tristan Croll wrote:

Nice work! It will be very interesting to see how the results compare with our modelling (DOI 10.1080/07391102.2016.1165631).

On 2025-06-17 16:33:06, user Olivia Fromigue wrote:

Hello,<br /> This manuscript is now published:<br /> C-terminal binding protein-2 triggers CYR61-induced metastatic dissemination of osteosarcoma in a non-hypoxic microenvironment.<br /> Di Patria L, Habel N, Olaso R, Fernandes R, Brenner C, Stefanovska B, Fromigue O.<br /> J Exp Clin Cancer Res. 2025 Mar 5;44(1):83. doi: 10.1186/s13046-025-03350-6.<br /> PMID: 40038783

On 2020-08-26 13:28:16, user Qingtian Guan wrote:

This manuscript is now accepted in the International Journal of Infectious Diseases. We have updated the manuscript in pre-proof format, please refer to the following link for citation for the details: https://www.sciencedirect.c...

On 2016-08-23 15:43:05, user Danny wrote:

Raw data now available at http://www.ebi.ac.uk/pride/...

On 2017-08-25 16:20:58, user kamounlab wrote:

"...a signature of positive selection in candidate effector genes in S. reilianum is a poor indicator for the identification of genes with virulence functions"

Please see https://twitter.com/KamounL...

On 2025-05-17 06:44:22, user 11 wrote:

Summary<br /> The manuscript “An online GPCR drug discovery resource” describes an online resource of GPCR drugs, clinical trial agents, targets and disease indications. This resource offers unique reference data, analysis and visualization, and is availed as a new section, ‘Drugs and Agents in trial’ integrated in the GPCR database, GPCRdb. Furthermore, it includes a target selection tool for prioritization of receptors for future drug discovery.<br /> The major weakness of the paper is that the paper fails to include olfactory receptors in the database, the reliance on Open Targets disease association scores might miss novel hypotheses with weaker genetic backing. The validation of the newly introduced selection tools and visualizations, how the prioritization recommendations compare to existing approaches or human expert selection are unclear.<br /> This paper helps identify strategies and trends in current GPCR drug discovery and give insights into which already drugged, or yet untapped targets have the largest potential in specific diseases.<br /> Major Points<br /> 1. Validation of the Target Selection Tool<br /> The target selection tool offers a powerful yet swift means to prioritize GPCRs for future drug discovery based on disease indications, characterization, tissue expression and more. While there is no validation to show the tool’s performance. How does the tool perform when testing a now-successful GPCR target before it was clinically pursued? Consider revising by running the tool on historical data and show how it would have prioritized GIPR before the approval of tirzepatide.<br /> 2. Missing Method Comparison<br /> You described several new visualization methods (e.g., GPCRome wheel, intersecting Venns, Sankey plots) but how these tools are different from existing tools are not explained. Why are these tools better than existing tools such as those in Open Targets, ChEMBL, or DrugBank? consider adding a contextual comparison section to clarify the advantages and disadvantages of those tools, explain the innovations of the new visualization methods.<br /> 3. Disease Classification and Filtering<br /> The disease annotation relies on ICD-11 mapping from Open Targets (using EFO/HPO/MONDO). While manual curation of disease terms is not clearly defined. Adding a supplemental table listing these modifications would be great.<br /> 4.Exclusion of Olfactory Receptors Have Limitations<br /> Your research excludes olfactory receptors--a large family of GPCRs which play essential roles in cancer, reproduction, and metabolic regulation. Databas such as CORD (Comprehensive Olfactory Receptor Database) provides better annotations of olfactory receptors than those in GPCRdb, ligand binding and diseases linkage are also included. Including olfactory receptors in your research may reveal potential drug discovery opportunities.

Minor Points<br /> Technical Questions<br /> 1.Definition of "Agent" vs. "Drug" should be explained in the Abstract or Introduction section.<br /> 2.Explain why do you choose 0.5 as the association score cutoff from Open Targets.<br /> 3. The classification rules of “pharmacological modality” of agonist, antagonist and allosteric modulatorare not clear.<br /> 4. Non-human GPCR targets (e.g., mouse models) are not included.<br /> Stylistic Issues<br /> 1."the platform contains 516 drugs, 337 agents…" is later repeated again in Figure 1 text.<br /> 2. “bioactivity data” and “bio-activity” are used inconsistently.<br /> 3. PDSP Ki database is used without prior expansion.<br /> Unable to Assess:<br /> Data integration are made from tools like Pharos, GTEx, and the Human Protein Atlas, especially for expression-based filtering. I cannot offer expert feedback on the transcriptomic expression atlas validation and the correctness of the tissue expression normalization procedures.<br /> Final Reflection<br /> This paper made great contributions to the GPCRs field by offering an online resource of GPCR drugs, clinical trial agents, targets and disease indications. The platform will help streamline drug development pipelines, helps identify strategies and trends in current GPCR drug discovery and give insights into which already drugged, or yet untapped targets have the largest potential in specific diseases.

On 2018-08-22 14:18:07, user Skeptical Scientist wrote:

Interesting application of machine learning!

On 2023-04-17 07:08:00, user Lance wrote:

Hello,

I have looked through all of your dataset files, and did not see the cell type and subtype annotations (i.e., cluster labels) for the cells anywhere, including in the adata.obs field. Was there a mistake in uploading the data?

Also, I noticed that in Supp Table 2, all the entries are 0. Was this also a mistake?

Thank you! Looking forward to being able to access the actual cell type labels!

Best, <br /> Lance

On 2023-10-27 14:47:25, user Joseph H Vogel Beckert wrote:

"To add to this uncertainty, the pilot test coincided with international discussions on the fair and equitable sharing of benefits from the access and use of digital sequence information (i.e., genomic sequences) under the Nagoya Protocol adding increased uncertainty surrounding the legal compliance landscape57."

There should be some mention of "unencumbered access" through the proposed modality of "bounded openness over natural information". The sentence above references a Comment from Nature Communications that trumpets "de-coupling" access from benefit-sharing. "De-coupling" means independence and is probably not what its 41 authors meant. Similarly, any reference to a multilateral mechanism for ABS without recognition of the overarching implications of the economics of information, i.e. the justification of "economic rents", introduces bias and thus undercuts the presumed scientific neutrality of the manuscript..

On 2019-08-16 08:02:18, user WJR wrote:

Bug Report:

This (preprint) paper by Hickey and Golding relies on a software simulation. (Available at: https://github.com/gbgolding/evolutionSex)

There is a bug in the currently posted version of that software. It occurs in the file "sexual7.c" (seen August 16, 2019). The bug strongly decreases the computational efficiency of the simulation, and slightly affects the results.

In that file, there is a structure integer variable named '.sex', which is initialized to zero, and tested several times by IF-statements, but it is never changed. In other words, it does nothing. That is the first clue something is awry.

(Note: For some reason, when I try to post the offending code here, it gets displayed as an unreadable mess. I don't know why. I tried placing the appropriate code formatting brackets around it. So, instead I must here describe where the bug occurs.)

Lines 233 through 252 comprise a While-loop, which creates each progeny by a mating of some parent_i with some other parent. However, the loop then proceeds to OVERWRITE that progeny's allele data by a mating with EVERY OTHER parent. The original parent_i's allele data gets obliterated within that progeny, because it gets over-written many times. Each and every progeny is computed by mating each parent with EVERY OTHER parent. But only the last matings count, as the previous matings get over-written. This is exceedingly inefficient computationally. If the population size is n, then the computer time increases with n-squared, rather than n.

The end result is that each progeny is indeed the result of a random pairing of parents, but not the ones the simulation-writers intended. Moreover, the simulation aims that each parent be involved in AT LEAST a minimum number of reproductive events (given in the code by "FECUNDITY"), but that goal is not achieved. Due to randomness, some parents can mate numerous times, while others don't mate at all -- contrary to the stated design of the simulation. There is a disparity between what the code intends to do, and what it actually does, and this can affect the results.

I'm guessing the variable .sex is a vestigial remnant of code (now largely absent) that had originally matched each parent with exactly one other parent (i.e., for an obligate monogamy model). I imagine .sex was originally a flag, used to indicate that a given individual has (or has not) been used yet as a parent. This approach would be one of the simplest ways of guaranteeing that each parent has the fecundity claimed in the paper.

On 2022-03-08 02:07:09, user Jimin wrote:

Live demo of chromatin architecture prediction:<br /> https://nyu.c-origami-previ...

On 2022-03-08 02:03:59, user Jimin wrote:

Link to the project website:<br /> https://tanjimin.github.io/...

On 2020-06-13 20:44:48, user Henrique dos Santos Pereira wrote:

Would anyone answer my question, please? Would the variation in frequency of these more virulent variant of the virus correspond to a trade-off virulence x transmission over time and thus also explains why velocity of death declines (mortality) while morbidity keeps accelarating in the affected populations? henrique.pereira.ufam@gmail.com

On 2018-06-14 11:38:18, user GUILLAUME GAUTREAU wrote:

Little typo in algorithm 1, line 11: the indice and exponent ("y" at the top and "out" at the bottom) of delta are not coherent with the delta exponent and indice ("in" at the top and "i" at the bottom) in definition 1

On 2013-12-31 01:57:24, user Jacob G Scott wrote:

some discussion here: http://mathematicaloncology...

On 2019-12-29 08:15:34, user Levi Yant wrote:

Correspondence should go to James Higgins or Levi Yant.

On 2016-04-14 14:29:53, user lepisorus wrote:

if the authors can prove that the retrotransposon proliferation is one reason leading to their special reproduction way, that will be fantastic.

On 2017-01-20 19:39:06, user James Jun wrote:

Thanks for your interest all. The manuscript was uploaded in a hurry to meet the grant deadline and still there are some work left to do. JRCLUST will be professionally supported by Vidrio Technologies, creator of ScanImage, and will be maintained as free and open-source software based on Matlab.

On 2019-04-03 03:51:05, user Ann wrote:

The pitfalls are obvious, lacking in vivo validation and non-specific capturing.

On 2020-04-28 20:27:16, user Matthew Bogyo wrote:

This work is now published at Cell Reports Medicine : DOI:https://doi-org.stanford.id...

On 2021-05-05 11:07:02, user Milka Kostic, PhD wrote:

Dear authors, <br /> Thank you for sharing this interesting preprint with the community. Below are some more detailed comments that ay have others appreciate your work better. I congratulate you on an excellent piece of work. <br /> Kind regards, <br /> Milka

COMMENTS ON THE PREPRINT BY Dölle, Adhikari et al. <br /> Targeted protein degradation is a very active field at the moment. Many efforts in this area are focusing on transforming known ligands (binders, inhibitors) of proteins with a clear disease relevance into bifunctional (PROTAC-based) degrader molecules. Unlike the traditional antagonist/inhibitor based compounds in preclinical and clinical use that diminish (inhibit) activity of the target, these degrader molecules induce selective degradation of the target. Thus, they remove the target from the proteome. This type of pharmacological activity could be a real benefit when the target in question plays significant scaffolding roles, by engaging multiple binding partners using different regions and binding sites. In such a case, inhibiting individual protein-protein interactions would be highly impractical. However, if the target is degraded, all these PPIs would disappear together with the target!

Dölle, Adhikari et al. select one such target - WDR5, a protein that performs different scaffolding roles (i.e. binds different partners) in the context of epigenetic regulation. Because of this, WDR5 has been implicated as a target for drug development and couple of compounds that inhibit WDR5 mediated PPIs have been described. These compounds served here as a starting point for WDR5 selective degrader development. The authors used existing structures of WDR5 bound to the PPI inhibitors to identify surface exposed areas of the molecules that could be modified for degrader molecule development. In brief, each PROTAC (bifunctional degrader) includes a ligand for the target and a ligand that recruits an E3 ubiquitin ligase, connected via a linker. The linker is known to have an impact on the performance of PROTACs and the authors use three chemically distinct types of linkers (PEG based, aliphatic and aromatic). The nature of the E3 ligase is also a major factor that affects degraders' activity, and the authors start by incorporating ligands for cereblon (CRBN), VHL and MDM2. Altogether, they generate number of PROTAC-based degraders featuring different linkers and different ligases.

They describe detailed validation steps of their degraders which included: <br /> - Measuring in vitro (biochemical) affinity between WDR5 and degrader molecules using ITC, showing Kd values in low nM. They also tested binding via DSF and observed some differences between results from ITC and DSF, which they provide likely explanations for (I encourage you to read the preprint as the authors provide an important technical note).<br /> - The authors tested that degraders were cell permeable and that they engaged the target using BRET. This is an important step in validation as degrader molecules tend to be larger, leading to concerns that may have difficulty entering cells. <br /> - They provide evidence that their degraders induce target degradation in cells, including under endogenous conditions. Importantly, they show that negative control compounds (always critical to have on hand) show no activity, and that inhibiting proteasome rescues observed degradation. Additionally, they confirm that mRNA levels of WDR5 did not change, thus further validating that the reason for decrease in protein levels is due to degradation. (They also include additional pieces of evidence that effects on WDR5 protein levels are degradation dependent) <br /> - Also importantly, the authors show selectivity by quantitative proteomics and demonstrate that WDR5 is the only protein depleted out of more than 5800 identified after 9 hours of treatment (while treatment with individual ligands did not have this effect) - Lastly, they show anti-proliferative effects in MV4-11 cells of their best performing degraders (these compounds were VHL-based PROTACs). However, the concentration needed for cellular effects was high (10uM). The authors then showed that this is due to low levels of VHL present. When they overexpressed VHL, the growth inhibitory activity improved.

Overall, the work is of high quality and includes appropriate steps for degrader validation. This gives high confidence that WDR5 degraders described in this work are useful as probes for WDR5 biology. For example, what happens to histone methylation once WDR5 is removed? Does removal of WDR5 lead to destabilization (or stabilization) of some of its binding partners (proteomics results suggest that this may not be the case, but would be interesting to dig deeper into this question)? What happens to transcription? What effect does this have on MYC activity (MYC family is known to engage with WDR5)? I am sure the authors and the community have these and many other questions in mind, and I look forward to seeing what new biology they and others can discover with this new generation of tool compounds in hand.

On 2020-09-01 09:25:13, user Sean Munro wrote:

I not think that this paper reports an "interactome" - it reports a "proximityome". BioID will label many proteins that simpy happen to be in the same compartment as the protein tagged with the biotin ligase without neccessarily interacting with them. Thus a BioID version of a SARS-CoV-2 protein that is in the Golgi will biotinylate many Golgi residents even if it only interacts with a subset of with them.

On 2020-09-10 21:07:07, user F.Li wrote:

For cells mentioned in each figure, the figure legend provides hyperlinks to neuuPrint where more detailed information about these cells can be obtained. These hyperlinks work in the PDF version. So if you are interested, please check out the PDF version of this paper.

On 2024-01-03 20:27:28, user Peyton Lab wrote:

I think the links in the figure legends are broken - not working from the browser pdf.

On 2020-10-22 14:47:26, user Matthew Terry wrote:

Very interesting paper. Would it be possible to also take into account the cost of expressing the genome? I would imagine that this would also work in favour of gene transfer. For genes retained, is protein turnover as equally important as abundance in the energy equation?

On 2020-05-16 09:13:37, user standardito wrote:

A great paper about the spreading of a naturally selected variant of SARS-CoV-2.

On 2020-11-25 12:05:29, user Mark Hanson wrote:

The collaborative group of Huang et al. (in preparation) is now available online at the link below. This is a manuscript written by Jianqiong Huang and her supervisors and collaborators at the University of Strasbourg and Sino-French Hoffmann Institute, and also the Platform Biopark d'Archamps.

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

On 2018-10-20 04:51:55, user koooo wrote:

GWAS101: please share the list of GWAS hits in a table format.

On 2017-05-13 17:45:40, user Anthonie Muller wrote:

Outside mitochondria, but still in the cell, smaller temperature gradients of a few degrees have been detected, for instance:<br /> (1) Jui-Ming Y, Haw Y, Liwei L. Thermogenesis detection of single living cells via quantum dots. 2010:963-966.<br /> (2) Yang J, Yang H, Lin L. Quantum dot nano thermometers reveal heterogeneous local thermogenesis in living cells. ACS Nano 2011;5:5067-5071.<br /> (3) Okabe K, Inada N, Gota C, Harada Y, Funatsu T, Uchiyama S. Intracellular temperature mapping with a fluorescent polymeric thermometer and fluorescence lifetime imaging microscopy. Nature Communications 2012;3:705.<br /> (4) Kucsko G, Maurer P, Yao N, et al. Nanometre-scale thermometry in a living cell. Nature 2013;500:54-58.<br /> (5) Bai T, Gu N. Micro/nanoscale thermometry for cellular thermal sensing. Small 2016;12:4590-4610.<br /> (6) Qiao J, Mu X, Qi L. Construction of fluorescent polymeric nano-thermometers for intracellular temperature imaging: a review. Biosensors and Bioelectronics 2016;85:403-413.

Are these thermal gradients 'real'? Some argue that they cannot exist, given the known value of the thermal conductivity of water. So there is a controversy here:<br /> (1) Baffou G, Rigneault H, Marguet D, Jullien L. A critique of methods for temperature imaging in single cells. Nature Methods 2014;11:899-901.<br /> (2) Kiyonaka S, Sakaguchi R, Hamachi I, Morii T, Yoshizaki T, Mori Y. Validating subcellular thermal changes revealed by fluorescent thermosensors. Nature Methods 2015;12:801-802.<br /> (3) Suzuki M, Zeeb V, Arai S, Oyama K, Ishimata S. The 10^5 gap issue between calculation and measurement in single-cell thermometry. Nature Methods 2015;12:802-803.<br /> (4) Baffou G, Rigneault H, Marguet D, Jullien L. Reply to. Nature Methods 2015;12:803-803.<br /> The same arguments of Baffou et al may apply to this reported high temperature inside the mitochondrion.

On 2019-04-04 04:13:45, user Nimród Hunor Attila Árpád wrote:

Avars were the siberians, Hungarians were volga bolgars.

On 2023-01-17 21:10:46, user Gregory Way wrote:

Hi Daniel,

It was our pleasure to review. Thank you for posting!

Here are some comments regarding your very speedy reply:

1. Glad to hear your repo will be made public!
2. The LINCS dataset, which includes official access instructions, is detailed in our recent paper (DOI: 10.1016/j.cels.2022.10.001)
3. We include level 3 data in that resource, but I'd advise strongly against using level 3 data. They contain unnormalized CellProfiler features that are incompatible with standard distance metrics. My assumption is that a like-to-like comparison is more suited if the full standard approaches are compared (i.e. I don't think people use the level 3 data all that often, so benchmarking against it is less valuable). I think MOAProfiler is likely to still outperform, but at a lower margin.
4. I agree that testing the generalizability is a very exciting application. I think that for my lab to use MOAProfiler, I would need to see this trade-off.

Thanks again!<br /> Greg

On 2016-05-27 15:36:35, user Kevin Watson wrote:

6W/kg, 9 hours a day, for 2 years. With that level of exposure, I'm surprised their blood didn't boil first.

This relates to real-world circumstances how, exactly?

On 2018-12-05 09:19:23, user Julien Racle wrote:

Hi,<br /> great paper, a thorough and fair comparison of the deconvolution methods was indeed needed.

One important question that also arises in the field is to what extent the reference profiles that have been derived from tumor-infiltrating lymphocytes that infiltrate one tumor type (e.g. melanoma) can be used for samples from another tumor type (e.g. ovarian cancer), or even if reference profiles derived from blood are sufficient. In the paper from Schelker et al., Nat. Com., 2017, they argued that it was important to use reference profiles coming from the same tumor type than the bulk.<br /> But from your data it seems that it doesn't really matter so much (as most methods have reference profiles derived from blood or melanoma TILs and your analysis includes also ovarian tumors (in this optic, EPIC could additionally be tested with the blood-derived reference profiles)). To further verify this, it would therefore be interesting if you build some mixes where you take only the cells that originated from one of the tumor type at a time instead of mixing together the TILs from melanoma and ovarian.

Additionally, CAFs and endothelial cells have been less studied by the deconvolution methods, but due to their potential importance in cancer, it could be nice to include them also in your table 2, to help researchers interested by these cell types.

Best wishes,

Julien

On 2025-11-26 05:26:36, user Prof. T. K. Wood wrote: