On 2020-09-30 18:25:51, user Holger Claus wrote:

I am almost sure that the calculated UVC dose for the moving regimens of 2.13 and 0.66mJ/cm2 are wrong (much too low). The actual dose must be measured. Also deactivating 6log with 0.66mJ/cm2 is magnitudes outside of other research

On 2020-12-02 21:30:36, user Alexis Rohou wrote:

I was asked by a journal to review this manuscript. Below is my review

***

This manuscript explores the observation that Thon rings visible in amplitude spectra of micrographs decrease in amplitude as a function of spatial frequency (distance from the origin in F space) and that this decrease is more pronounced in micrographs collected with larger objective lens defocus.

Since the height of Thon rings from image of test specimens can be taken as an estimator of recoverable signal-to-noise ratio in experimental data recorded under identical conditions, this has led many practitioners to prefer to collect data as close to focus as possible. The dominant assumption in the field has been that the observed defocus-dependent contrast attenuation is due to imperfect spatial coherence of the electron source, but this manuscript provides compelling evidence that another phenomenon is responsible.

The authors note that a significant amount of signal is delocalized beyond the edges of the field of view and so cannot be recovered. Further, the authors point out that single-sideband (SSB) signal in the collected image (be it from features in the field of view but near its edges, or delocalized from features not present in the field of view), while it contributes power to the image, does not contribute to Thon rings because its amplitude is not modulated by the CTF.

I find the authors' evidence in support of this compelling:<br /> - experimentally, the nodes (local minima) between Thon rings to not reach the "noise floor" as would be predicted if all contrast in the image arose from phase contrast attenuated by a spatial-coherence envelope. Computationally, the authors show that this "Thon ring floor" is raised under conditions where more of the recorded image power consists of SSB signal (increased defocus or small field of view)<br /> - theory predicts that, at the fluencies normally used in cryoEM, the spatial coherence of the illumination supplied by modern eletron sources is such that one would not expect significant defocus-dependent attenuation effects<br /> - most compelling, the relative intensity of Thon rings in actual images is well predicted by the fraction of image features for which signal for both side bands is recorded (Fig 4)

My only significant reservation with this manuscript is about the "messaging", and specifically this sentence of the abstract: "The principal conclusion is that much higher values of defocus can be used than is currently thought to be possible". <br /> While the authors have convinced me that the negative effects of defocus were misunderstood and overstated, their claim that higher defocus could be used with no ill effect should be qualified (preferably in the abstract, and in the main text) to make it clear that they are only referring to the imaging part of the experiment, and not the image processing part of experiments, where high defocus values would force users of most packages to use very large box sizes at various parts of the process creating unusually large computational burdens, and/or other problems may occur. If the authors want to keep the claim as is, they should add experimental results that support it, e.g. high-resolution apoferritin reconstructions obtained from both low and high defocus datasets, along with characterization of the mean SSNR, ResLog plot, or similar, in each case. Probably better to keep the paper more or less as is and just qualify this claim, in my opinion.

Beyond that, I have more minor suggestions / questions.

(1) Abstract: I'd encourage the authors to consider removing the sentence remove about correcting mag distortion ("We also show (...) many orientation") - if I understood correctly, this becomes very significantly only at very large defocus, and only if averaging spectra to 1D curve before fitting. For these reasons, I think this is a rather minor point of the paper. In the context of the abstract, I think this aside distracts from the main message

(2) Abstract: "and Ewald sphere correction". Perhaps I missed it, but I don't recall reading in the main text an explanation of why defocus should allow for better Ewald sphere correction, or a demonstration that this is the case. I suggest removing this from the abstract, or adding text explaining this, or a citation to a reference that does (on that note, after a quick re-read of Russo & Henderson 2018, I also don't see an obvious demonstration there that higher defocus yields better Ewald sphere curvature correction, but I'd happily stand corrected).

(3) Page 3: "This is because compensating information, which unfortunately is of no use, may enter the image from features that are outside the field of view." On first read, this sentence confused me - I think because the phrase "compensating information" threw me off. How about something like "This is because unrelated single-side-band signal delocalized from features outside the field of view may enter the image."?

(4) Page 4: "Since delocalized (...) high defocus values to record images (Russo and Henderson 2018b)". I think readers who like me are not well versed in the optics and maths of SSB imaging, this statement is difficult to understand. Could it be explained a little further / clarified? To spell out my confusion: why does the feasibility of recovering SSB information even the absence of the Friedel mate mean that it should be advantageous to operate at higher defocus?

(5) Same paragraph ("We note that information in (...) become greatly reduced"). This whole paragraph argues (I think) that collecting highly-defocus images is OK, yet wasn't one of the points of Downing & Glaeser (2008), cited in this paragraph, that the larger the defocus the lower the more CTF correction schemes or Wiener filters fail at retrieving all of the information (due to the "twin image" problem). My apologies If I'm mis-understanding - if that's the case perhaps other readers will also need a bit more hand-holding through this paragraph.

I loved all the detail poured into M&M, so I suggest specifying further:<br /> (6) Page 5: "annular zones of 1 reciprocal-space pixel" - how was interpolation done here? Nearest neighbor?<br /> (7) Page 5: "floated" - I assume this means adding a constant so that the average value is zero?<br /> (8) Page 6: "Smooth curve" - fix capitalization. Also, what kind of smooth curve?

Results:<br /> (9) Page 6: "The integrated power at 2.35 Å" - measured how? In real space in the white box?<br /> (10) Page 6: "(67% of intensity)" - 67% of which intensity?<br /> (11) Page 6: "~0.23 nm" - to guide the eye, please add a second x axis in figure 2, or replace the existing one, so that we can look for the 0.23 nm feature.

(12) Page 7: "The mean value of this noise spectrum can be regarded as the "zero baseline" for the power spectra of images recorded with a specimen". This noise floor will rise as a function of the number of electrons incident upon the detector. The choice of illumination condition when collecting "no-object"/"beam-only" images for these experiments is therefore important. I assume that the authors used the same illumination conditions as had been used in the actual experiment with a specimen. Is this correct? Either way, could the authors briefly mention somewhere what illumination conditions were used for this? <br /> -- I expect that using the same illumination condition would lead to an overestimate of the height of the noise floor. Indeed, during experiments with specimens, some fraction of electrons will be lost to apertures, leading to an overall decrease in the average number of eletrons reaching the detector. One may thus expect the actual noise floor in "with-specimen" experiments to be even lower, perhaps making the authors' point even more striking.

Discussion:<br /> (13) Page 7: "did not prevent images at 8 um defocus from being recoded at a resolution of 1.44 Å". Is this shown somewhere? Fig 1C shows 1.3 um defocus, not 8 um.

(14) Figure 2a: could the X axis be re-labelled, or also labeled with spatial frequency in nm-1 or Å-1 - this would help locate the 3.5 Å bump mentioned in the discussion

(15) Suppl Figs 4 and 5: here also, having a second X axis, or a second set of labels with spatial frequencies would be helpful.

(16) Figures S4 and S5: The lower bound of the Thon rings is "raised" with increased defocus, as predicted by the increase in SSB signal, but why is this lower bound so much higher at around 0.5 Nyquist, while remaining low at the origin and edges of F space? Is this predicted by the model? Does it correspond to the FT of the shape of the circular mask used in generating the simulated images?

(17) Page 9: "to interference between the contributions (...) which is 2a". This sentence reads as though the two SSB beams are interfering constructively or destructively with each other. Unless I'm mistaken the interference is between the scattered beams and the unscattered beam, is it not? That's certainly what the next sentence seems to say.

(18) Page 9: "The persistence of lattice images within (...) displaced from the particle". Likely because of my lack of expertise, and specifically because I do not know what the "coherence diameter" is, this sentence was lost on me.

(19) Page 10: "We note that this behavior is different (...) envelope function". For completeness, how about adding a supplementary plot overlaying the observed behavior (as in Fig 4) and the prediction from the spatial coherence (at whatever beam characteristics best fit the data, to point out perhaps that an unrealistic illumination semi-angle would be needed to fit the data)? This would help readers like myself who are not quite certain what one would expect such plots to look like if spatial coherence were really at play here.

(20) On the subject of Figure 4, I am curious about why the last few points of the 2.3 Å series seem so far off the prediction. The authors made a point of saying that the power spectra were so oversampled that even at that frequency, they had 3 pixels sampling each ring. So why the discrepancy, if not undersampling/aliasing? This made me curious: what would an equivalent plot from the simulation data look like? Would the Thon ring amplitudes from this synthetic experiment be a closer match to the predictions (dashed lines in Figure 4)? If not, perhaps this mismatch is due to poor sampling of these very fine rings at high defocus after all?

Summary and conclusions<br /> (21) Here might be a good place to formulate some caveat about the practicalities of processing data collected at very large defocus.

Figures & supplements<br /> (22) Figure S5: this would seem to argue strongly against evaluating the power spectrum using patches - would the authors agree? if so, how about mentioning it in passing somewhere? The optimal way to compute power spectra for the purpose of CTF parameter fitting is still a topic being discussed in the literature of late, and this observation would seem to be relevant.

On 2016-09-12 13:33:31, user Margaret Kosmala wrote:

This paper has been accepted in the peer-reviewed journal Frontiers in Ecology and the Environment. It is expected to be published in early 2017.

On 2019-11-03 10:40:59, user Jubin Rodriguez wrote:

Very interesting study! I was curious to know from the personal experience of the authors if GRiD exhibits better accuracy than iRep with regards to estimation of replication rates (of course I understand from reading your paper that both methods apparently do not fare that great with slowly growing bacteria!)? The reason I ask is cos' I'm getting a lot inconsistent replication rate values between iRep and GRiD for the same MAGs. For example I have a MAG (75.1% completeness and zero contamination & strain heterogeneity; 12X coverage) for which iRep estimates the replication index value to be 1.95 while GRiD outputs a value of 1.24. My gut feeling is that the GRiD estimated value is more closer to reality here but there's no way for me to be absolutely sure here since I've not made any real-lab measurements like you've done for your study. I was hoping to know if you would have any sort of inputs here to share from your own experiences?

On 2021-03-26 09:06:21, user ??? wrote:

Whether I can use my own data(protein and peptide fasta file) to generate features for make predictions?

On 2019-10-29 08:38:39, user Fridtjof lund-johansen wrote:

The next thing is to use this for antibody-based interactomics!

On 2025-01-23 18:08:17, user Laleh Cote, Ph.D. wrote:

I am the corresponding author for this paper ( https://doi.org/10.1101/2023.05.08.539924 ), which has now been published at PLOS One, here: https://doi.org/10.1371/journal.pone.0317403

Thanks, bioRxiv team!

On 2024-10-19 18:49:33, user Alessio Ciulli wrote:

This comment is to advise readers that our preprint has been published in Nat Commun 15, 8885 (2024). https://doi.org/10.1038/s41467-024-52871-9

On 2021-05-27 17:09:25, user Allan Konopka wrote:

I found this work via Antonia Fernandez-Garcia’s blog post from summer 2020, and thought it very intriguing. As I have a deep interest in physiological microbial ecology, I have wondered for some time now “whither metagenomics?” and this approach that categorizes GC’s by their “knownness” is helpful. Muren asked me to make further comment on a tweet (https://twitter.com/Hamatsa... "https://twitter.com/Hamatsa50/status/1397586142178865154?s=20)") <br /> here, to hopefully start a conversation.

So first, what is the objective of applying metagenomics? Sometimes stated (at least in grant proposals) is to “develop a predictive understanding of microbial communities.” But this implies knowing the function of the relevant gene products in adequate specificity (i.e., what specific biochemical function they carry out). We could all come up with lists of important functions, but let me identify 3 which I think are particularly problematic re: the databases of information.

1. Premise: the instantaneous activity rates of microbes are limited by the fluxes of an essential resource (for chemoheterotrophic bacteria, this is most often the diversity and concentrations of organic energy substrates)<br /> Inference: the breadth, levels of expression, and biochemical affinities of specific transport proteins are critical to understand interspecific competition in natural habitats.<br /> Problem: inadequate specificity – if “known” as (for example) an ABC transporter, this isn’t helpful in predicting in which cases a microbe has a selective advantage. [please correct me if there is recent work that improves this issue]

2. Premise: microbes/microbial communities rarely (if at all) exist in steady-state conditions. Rather, there are both regular and stochastic environmental perturbations to which organisms may evolve different strategies in response. [Side note: my fav paper on this is Nature’s Pulsing Paradigm, Estuaries 18: 547-555 (1995) by the three Odum brothers. Although about estuaries, easy to think how it applies to other systems and down to microscale.]<br /> Inference: Genes for regulation will be key here. <br /> Problem: I haven’t found much metagenomics work that addresses these regulatory proteins [please correct if necessary, as I have not done an exhaustive search of literature]. Likely (?) similar problem to transporters – motifs identifiable, but specificity of binding site unknown.<br /> Although most genome-scale simulation models (generally of one organism) generate a steady-state solution (and hence less useful ecologically), one can apply heuristics to simulate what you think you know re regulation (but this is outside metagenomics itself)

3. Premise: The extreme end of the “Pulsing Paradigm” are microbes in highly spatially structured habitats (soils, deep sediments, etc) in which the resource pulses are temporally rare<br /> Inference: evolutionary strategies that favor low/very low rates of metabolism (dormancy) better than the “optimistic” one high macromolecular content in terms of maintaining viability until the next pulse<br /> Problem: relatively weak understanding by microbial physiologists of dormancy (going beyond endospores)

On 2019-03-14 01:10:12, user Charles Warden wrote:

I noticed that the earlier EDGE paper wasn't cited in this pre-print (or listed in the "software" section of the Storey website):

https://academic.oup.com/bi...

Perhaps this citation should be added?

I thought the "ODP in EDGE (Storey et al. 2005)" plot in Figure 2 of the previous EDGE publication reminded me of the mODP plots in Figure 3 this pre-print. It looks like the "UW Biostatistics Working Paper Series" citation in the previous EDGE paper later became the peer-reviewed citation [3] in this pre-print (and I'm sure you understand everything better than I do). However, I thought the main result that caught my eye was Figure 2 in the earlier EDGE paper, so I thought I should say something.

I hope everything is going great with you!

On 2021-01-11 12:03:54, user YC Foo wrote:

Hello! Many thanks for making this available here! May I know if it would be possible to gain access to the Supplemental Tables mentioned here in the article, specifically Sup. Table 2? I'm afraid I've not been able to locate them from the text. Many thanks!

On 2024-11-30 19:30:43, user avtrader wrote:

BioRxiv Violation

The author claims no competing interests, yet he and others have said he is the Science Director for Mission Ivorybill. Mission Ivorybill's stated goal--is to save the Ivory-billed Woodpecker. Mission Ivorybill or the related, The Louisiana Wilds do not seem to be federal non-profits via a 501-c3 data base search.

Mission Ivorybill raises money via various channels and seems to be a commercial entity; this violates BioRxiv's publication rules for authors in addition to being a non-disclosed conflict by the author. Mission Ivorybill also has Go Fund Me efforts, publications, etc. and the organization is IBWO-centric. The author participates vigorously in marketing Mission Ivorybill. He controls and/or participates in Mission Ivorybill's media and marketing efforts such as their Facebook page and Zoom public presentations. The author reviews evidence gathered by Mission Ivorybill yet fails to disclose the relationship tainting this prepaper's Abstract and Conclusions.

Mission Ivorybill is mentioned in the subject paper. The author is well known to exaggerate claims of Ivory-bills recently proselytizing a video of a Tufted Titmouse was an Ivory-billed Woodpecker is association with a Mission Ivorybill presentation and on social media. The founder of Mission Ivorybill quickly distanced himself from the false Ivory-billed claims by the author.

The author receives organizational support, professional and informal introductions, recognition, publicity and public face-time from Mission Ivorybill/The Louisiana Wilds perhaps in return for his often aggressive marketing efforts for Mission Ivorybill. His efforts have even included researching and contacting employers, to disparge and econimically damage people who disagree with him on the Ivory-bill's status.

The author may have purposely not disclosed this unambiguous conflict and his association with a possible commercial entity whose only product is the Ivory-billed Woodpecker. Related the subject BioRxiv prepaper has some hyperbolic, marketing-like claims that are not based in science.

On 2018-02-23 14:06:51, user Mikkel C. Vinding wrote:

Interesting finding. Surprising how lousy scientist is at keeping time. I will be sending links to this next time I have to arrange a conference. With this amount of people going over time, it is hard to blame the individual, conference organizers have to step up to their job.

To improve the paper, I would suggest that you report how you constructed the regression model that you use. For example, what are the fixed effects and what are the random effects (are there any random effects? There ought to be in this design), and what precise are the predictors in the model? It would also be nice to know what software you used for statistical tests. Perhaps due to not knowing how the regression was done, I have a hard time interpreting the p-values: Are they the result of an ANOVA or are they the p-values of the regression coefficients (beta)? In the latter case, it might be considered controversial to base conclusions upon the p-values (see https://stats.stackexchange... "https://stats.stackexchange.com/questions/185360/t-value-associated-with-nlme-lme4)"). Finally, it would be interesting to see whether there is an interaction between gender and career step - to see if the stereotype that old male professors just don't give a damn about anything holds up.

Keep up the good work.

On 2019-02-19 08:48:44, user Mike Fainzilber wrote:

The authors state in the introduction that "Successful generation of a long 3´ UTR knockout mouse using CRISPRCas9 in mice has not been yet been reported". I beg to differ. Apart from the Xu et al BDNF long UTR model cited in the manuscript, the following models have been generated and published with in vivo phenotypes and mechanistic explanations

Miller et al, Neuron, 2002: Targeted mutagenesis of CaMKIIalpha 3'UTR shows LTP and memory phenotypes due to loss of the protein in dendrites - https://www.ncbi.nlm.nih.go...

Perry et al, Neuron, 2012 and Cell Reports, 2016: A floxed allele of the long 3'UTR of importin beta1 was generated. Cre mediated excision of this allele removes the protein from sensory axons, with effects on regeneration (the 2012 paper - https://www.ncbi.nlm.nih.go... ) and on axon growth rates (Perry et al., Cell Reports, 2016, https://www.ncbi.nlm.nih.go... ).

Terenzio et al., Science, 2018: Crispr/Cas9 mediated deletion of mTOR 3'UTR has in vivo consequences for general local translation in axons, and for neuronal survival after nerve injury - https://www.ncbi.nlm.nih.go...

On 2020-09-04 12:42:15, user Sadegh Nabavi wrote:

The findings in this paper go well beyond learned fear responses. I wouldn't be surprised if similar changes in responses are observed in other defensive behaviors. It would be interesting to test Nlgn3-/y line in innate defensive behaviors. It is known that dPAG controls escape response to a looming stimulus.

On 2025-06-16 05:52:19, user Jack Durant wrote:

Potential bias for gram-negative bacteria introduced in the DNA isolation or amplification methods? There are several reports of the isolation of Gram-positive bacteria from the symbiotic cavities.

On 2021-12-17 19:21:28, user HY wrote:

This article has just been accepted. You can check the final version here. <br /> https://www.sciencedirect.c...

On 2016-07-27 10:33:29, user Vladimir Seplyarskiy wrote:

Dear Iain and David,

I am really like your very well-timed article.<br /> I would like to point out that different numbers of mutation on transcribed and non-transcribed strands may be interpreted in a slightly different manner.

Methylated cytosines in CpG context after deamination converted to thymine and thus do not cause polymerase stalling and cannot be detected by TC-NER. Moreover, nucleotide and trinucleotide composition vary between strands, therefore even the same mutation rate on transcribed and non-transcribed strands causing the different number of mutations.

Also, transcription-dependent asymmetry may be a consequence of different mutation forces on different strands, but not solely repair.

Good luck in journal.

With best regards,<br /> Vladimir Seplyarskiy

On 2014-10-20 20:23:41, user TRS wrote:

In supplementary matérial Saudi Arabia is with Morocco and other North African countries! An error?

On 2023-07-19 12:56:20, user Pat Schloss wrote:

A reader pointed out a small, but significant typo in the first version of this preprint. The sentence, "They then randomly select that many sequences, with replacement from each sample", should have "without" rather than "with". Thus the sentence would read "They then randomly select that many sequences, without replacement from each sample". This correction will be included in the next version of the preprint

On 2017-01-21 22:01:05, user German Dziebel wrote:

Suppl Fig S9 is interesting. The authors state that "Using the Y chr sequence of a ~49,000-year-old Neanderthal from El Sidron Spain (Mendez et al., 2016), we found indeed closer genetic distance to this Neanderthal for haplotype A0, A1a, B, E, D and C, in the order of low to high distance which happens to correlate with degree of African ancestry, relative to G and HIJK (Supplementary Figure S9). These results indicate that admixture of F AMH with Neanderthals may have resulted in African-like descendants with ABCDE megahaplotype who largely preferred to live in the Southern hemisphere." What stands out to me on this figure is the fact that A0, A1a and B are closer to the Neanderthal sequence than C,D, G and HIJK. Could the most divergent human Y-DNA sequences have introgressed from Neanderthals? At the same time, hg E, which is most frequent in Africa doesn't seem to be closer to the Neanderthal sequence than non-African C and D.

On 2024-12-31 19:17:23, user Kate Keller wrote:

This paper has now been accepted for publication:<br /> https://pubmed.ncbi.nlm.nih.gov/38809543/

On 2025-05-27 19:12:26, user Mehtab Singh wrote:

This paper is now published in Plant Biotechnology Journal. <br /> Link: https://onlinelibrary.wiley.com/doi/10.1111/pbi.70146

On 2019-01-08 05:55:31, user Isaul wrote:

Ok, now can you explain the China sounds?

On 2024-03-17 07:37:14, user Hiroshi Mori wrote:

In order to properly use this tool, users must purchase a KEGG FTP license. In the config.yml file of this tool, the following description exist. "KEGG_FTP_DATA_DIR: '/scratch/shared_data_new/KEGG_FTP/2023-10-11/kegg' # Replace with your KEGG FTP data directory".<br /> Authors should mention this restriction (i.e. KEGG FTP license required) in the manuscript.

On 2019-07-24 15:23:44, user Miguel Valvano wrote:

Very interesting manuscript. I would like to make the suggestion that the clusters called "cps" be separated between O antigen (genes located between GalF and gnd) and cps [proper] genes located opposite from GalF. I am not sure for Klebsiella, but I know the is valid for Escherichia, Salmonella, Shilgella, Enterobacter and many others. You cannot properly call cps (as defined in this paper) as group 1 and group 4 capsules.

On 2017-04-07 21:26:19, user Eran Halperin wrote:

In more details, we found several major fundamental statistical flaws in the analysis in this manuscript. This manuscript posits six main criticisms of the claims made in our earlier response letter that discusses advantages of the ReFACTor method (Rahmani et al., Nature Methods, 2017). This manuscript misses several crucial parts of our letter, most of which are in our paper’s supplementary file (http://www.nature.com/nmeth... "http://www.nature.com/nmeth/journal/v14/n3/extref/nmeth.4190-S1.pdf)"). The above is true both for their recent correspondence as well as this manuscript. We outline a few examples of these flaws below:

1. The first section of this manuscript incorrectly states that ReFACTor breaks down when applied to cancer tissue in an EWAS setting. They argue that the permutation test we performed in our analysis (Rahmani et al., Nature Methods, 2017) is not valid. However in our permutation analysis, we constructed the list of “true positives” based on the exact same criterion used by Zheng et al. in their correspondence (Zheng et al., Nature Methods, 2017). In particular, we required differences in methylation between the two “controls” and the remaining methylation levels to have over 50% difference in mean methylation. Most importantly, we instructed users in the original ReFACTor paper, in our response letter to Zheng et al., and in the software associated with ReFACTor, to apply the feature selection step of the ReFACTor algorithm on the controls when the number of true positives is expected to be large. However, this manuscript ignores these instructions as well as many of the other details we provided about the breast cancer analysis (Supplementary Note 1 and Supplementary Note 2 in Rahmani et al, Nature Methods, 2017).

2. The second section of this manuscript tries to respond to our claim about the inapplicability of the method RMT (Teschendorff et al., 2011) for determining the dimension of ReFACTor. However, the section does not acknowledge our key point; RMT can not determine how many signal components capture the cell composition variation in data. Moreover, as we demonstrated in our recent correspondence (Rahmani et al., Nature Methods, 2017), comparing the performance of the two methods using small datasets cannot provide significant evidence for the improvement of one method over the other. The remainder of issues raised in this section were already addressed in Supplementary Note 3 of our paper.

3. In the third section, Zheng et al.’s manuscript does not accurately reflect our experiment’s protocol. In our experiment, we divided the samples into two groups on the basis of their cell-composition. Our experiment did not change the methylation levels to any degree. Therefore, we did not consider a scenario for which ReFACTor was designed, as claimed by Zheng et al. We merely assigned a "case" or a "control" label to each sample. Assignment of a label was based on the individual's estimated cell composition. Since the methylation data were not changed, the ReFACTor components calculated for each dataset are precisely the same ReFACTor components one would use in a real study. The batch effects noted by Zheng et al. would only have effects on EWAS results when they are correlated with the phenotype. In our case, the phenotype (case/control status) was generated only on the basis of estimating the cell type composition. Therefore, batch effects could be introduced only if the reference-based method captures batch effects. For more details, please see Supplementary Note 3 of our response letter (Rahmani et al, Nature Methods, 2017).

4. The relatively low R^2 values in the GALA data are due to a time lag (four months) between procurement of blood samples for measuring methylation and procurement of samples for measuring cell counts. While not ideal, this time lag will not affect the comparison between methods, as the noise added due to the time lag affects all methods equally. Moreover, at the time of publication, there was no other public dataset that had both cell counts and methylation data of this scale.

5. In the fifth section, Zheng et al do not accurately reflect the assessment of performance we presented in our paper. For example, the Rheumatoid Arthritis EWAS in the original ReFACTor paper (Rahmani et al. 2016) was not designed to show that ReFACTor outperforms the reference-based approach. In contrast, in our recent manuscript (Rahmani et al., Nature Methods, 2017), we provide strong evidence that ReFACTor has better performance over the reference-based methods (see Supplementary Note 3).

6. The sixth section of this manuscript contains several problematic assertions and unjustified claims. In the original ReFACTor paper (Rahmani et al. 2016), we compared ReFACTor with all the reference-free methods that we were aware of by the time of writing the paper. We chose reference-free methods that correct for cell type composition in methylation; we also considered a few methods from the gene expression literature (e.g., PEER). We find surprising the accusation that we did not compare ReFACTor with RUV. The authors of this manuscript (Zheng et al.) have published other papers in which they did not compare their suggested methods with RUV (e.g., Houseman et al. 2014, Houseman et al. 2016, Koestler et a. 2016, Teschendorff et al. 2017). We do not argue that comparison to RUV or to other methods is not useful, however the claims made by the authors of Zheng et al. are double standard. Finally, the McGregor et al. analysis did find SVA to be more stable in comparison to the alternatives in some scenarios, however, this study did not consider ReFACTor in their benchmarking, as their study was conducted while the ReFACTor paper was under review.

On 2020-09-18 02:09:42, user Maria Ingaramo wrote:

Summary: for now, we recommend using the S11 tag at the N-terminus of target proteins.

Details:<br /> We'd like to thank Dr. Abby Dernburg for pointing out that our S11 fragment, which ends in two glycines, might act as a C-terminal degron signal (doi.org/10.1016/j.cell.2018... "doi.org/10.1016/j.cell.2018.04.028)"). We've successfully tagged proteins at both the N-terminus and the C-terminus, but we have not established that these yield similar expression levels. We take this concern very seriously, and we're checking this now. Results will be posted here and at andrewgyork.github.io/split_wrmscarlet. In the meantime, we recommend avoiding the potential issue by attaching the S11 fragment at the N-terminus. If C-terminus tagging is required, we suggest the alternative S11 sequence YTVVEQYEKSVARHCTGGMDELYK.

-Maria Ingaramo

On 2017-09-25 19:32:35, user Ignazio Maria Viola wrote:

The published version includes a significant enhanced analysis of the flow field including gamma_2 vortex detection criteron and q criterion.

On 2020-06-02 09:21:09, user Marcos wrote:

Published in Nucleic Acids Research <br /> https://academic.oup.com/na...

On 2019-01-27 11:18:19, user Interested_party wrote:

Excellent paper; very useful. Could you provide information as to how the FnCpf1 PS were cloned into the vectors? Looks like you using a TGAG overhang, did you modify pCRISPomyces 2 to remove the gRNA scaffold? Also, will you be making these vectors available?

On 2022-10-23 03:07:17, user Wenwu Wu wrote:

An interesting study. Genes associated with cuticular wax and flavonoid biosynthetic pathways are highly expressed in leafy bracts, likely shedding lights. I wonder whether these genes are among the convergent natural selected genes in the species.

On 2024-09-13 21:17:20, user Bennett Fox wrote:

out in PNAS: https://www.pnas.org/doi/full/10.1073/pnas.2221150120

On 2020-02-14 19:29:28, user Greg Gomes wrote:

Hi Guest,

One of the authors here. This is precisely the type of feedback/discussion that BioRxiv is great for! Thanks!

The smFRET distances in Piana et al., are inferred from smFRET transfer efficiencies using an assumed homopolymer model. In our manuscript (and others) we show that deciding which, if any, homopolymer model is appropriate for a particular IDP is difficult, and allows considerable flexibility in the inferred distances. Our results compare smFRET effeciencies directly rather than using derived values from the data via polymer theory assumptions.

That being said, I take the point that phrases such as "novel", "new", and "for the first time" could be (or are) claims of priority, which are difficult or impossible to assess - and should therefore be avoided. The updated version will cite the referenced paper, as it is work that should be acknowledged.

On 2016-01-13 16:31:58, user Natalie Davidson wrote:

In Figure 2a the description says SE-duplicates are orange and PE-duplicates are blue, shouldn't the labels be switched?

On 2016-01-14 13:55:48, user Natalie Davidson wrote:

In the Methods section "Model for the fraction of sampling and fragmentation duplicates", I am guessing that this explains the method for calculating the expected fraction of duplicates with sampling only (in red on figure 3b), how do you calculate it for sampling+fragmentation (in blue on figure 3b)?

On 2021-04-05 06:49:52, user Ajay K Pandey wrote:

Nice work! congratulations! <br /> but am not sure if authors have discussed in light of these two originals!<br /> https://www.frontiersin.org... <br /> and <br /> https://pubmed.ncbi.nlm.nih...

On 2023-03-20 22:38:16, user Axel Nimmerjahn wrote:

The peer-reviewed version of this paper was published on March 6, 2023: https://www.nature.com/arti...

On 2018-05-30 09:58:59, user Guillaume Rousselet wrote:

Unfortunately, because of the lack of control of image properties, not much can be concluded from this experiment. The analyses and graphical representations are also outdated. These papers might help you improve your experimental designs and analyses in future studies:

https://www.frontiersin.org...

https://www.frontiersin.org...

On 2020-12-30 19:11:26, user Jon Moulton wrote:

This is the first clear exploration of the upper thermal limit of zebrafish culture that I have seen. At 37C the zebrafish-parasite system can be a useful model for human parasitic disease (doi:10.1111/jfd.12393); now we know how close to the temperature limit that culture system really is.

On 2018-11-30 11:52:13, user Mubassherul wrote:

You did a nice work which makes NER faster. I really appreciate it. All the recognized name are disambiguated by identifiers which sources are from different databases. I am pretty curious that sometime "Acronyms are often homonyms".So, how the tagger manage this problem? It would be great if the user can get return of the results instead of returning the result to central BeCalm server. Great job. Thumbs up.

On 2021-02-23 15:13:18, user Jon Fisher wrote:

This is an interesting analysis, but I see two aspects that would make it hard for it to get traction. First, as noted in the abstract, poor enforcement is a major problem now, and rapidly expanding PAs into areas suitable for agriculture would make enforcement much harder (even if more resources were available). Second, it appears that this centers around the need of species to keep almost all of their range, but doesn't seem to consider the needs of people to grow enough food and have livelihoods. Starting solely with an ecosystem need without considering capacity for enforcement nor opportunity cost to human well-being makes this pretty non-convincing to anyone who isn't already strongly supportive of conservation as a top priority.

On 2019-07-11 17:42:25, user Vagner Benedito wrote:

Very nice work, folks!<br /> I'd like to offer a few remarks to improve the manuscript:<br /> It would be good though to describe which leaves exactly were used for extraction, how much plant material per unit of solvent was used in the leaf dipping (and confirm that the exuding trichomes were preserved after the dipping - so that you captured RNA from these structures in the analysis - I'd fear that most trichomes would break from the leaves during the ethanolic dipping).<br /> An additional supplemental table with locus ID correlations between Sopen and Solyc orthologs would be very helpful.<br /> In your model (Fig 8), I'd add an element for the import of metabolites from the subapical cell, since it is very possible that the apical glandular cells is not fully responsible for generating substrates for acylsugar biosynthesis!<br /> I really like the way this paper used a simple comparative RNA-Seq approach to identify novel candidate genes involved in acylsugar metabolism - great job!

On 2021-07-29 16:12:46, user Andrew Kropinski wrote:

I deeply regret that the 12 sequences which I analyzed using megablast are either Escherichia/Shigella phages or are essentially identical to Escherichia coli genome sequences.

On 2022-10-31 15:51:23, user Daniel Lüdke wrote:

We appreciate the schematic diagram of light conditions/treatments in Figure 5. Would it be possible to include similar diagrams for the other figures as this would make it easier to follow how plants have been treated. In the methods could it be described in more detail at what point after infection the light conditions were applied?

On 2024-12-27 03:11:02, user samuel Yi wrote:

Thank you very much for this remarkable work. While reading the article, I noticed a detail that warrants further discussion. The authors used Codex staining results from adjacent sections as the gold standard to evaluate the performance of different spatial omics technologies. However, Codex exhibited relatively strong edge staining effects in certain channels, such as CD20, which led to an abnormal accumulation of B cells at the periphery of the sections. This observation is inconsistent with the results obtained from hematoxylin and eosin (H&E) staining. Therefore, a more meticulous examination of the Codex data analysis may be necessary to address these discrepancies.

On 2025-10-29 12:16:24, user Teemu Turunen wrote:

Update (29 Oct 2025): This work has now been peer-reviewed and published in Nature Communications: https://doi.org/10.1038/s41467-025-64095-6

On 2017-08-17 19:19:17, user Blake Joyce wrote:

The BUSCO app is available on the CyVerse Discovery Environment: https://de.cyverse.org/de/?...

On 2019-10-17 15:43:29, user Adrienne wrote:

Really amazing resource! I have also found the T cell isolation bead kits (by negative selection) result in CD8 loss - probably due to NK receptors expressed on CD8 populations. CD8+ MAIT cells are particularly reduced following bead-based T cell isolation - this can inform what the composition of antibodies may be (which StemCell won't disclose).

On 2016-08-31 23:02:23, user Mike Gandal wrote:

Really interesting method. Very glad to see that you are correcting for variability in GC content and looks like 5'/3' bias is in the works. We find that technical factors related to RNA-sequencing (library preparation, read depth, 3' bias) contributes substantially to RNAseq expression variation and is poorly accounted for by current methods.

I liked your comparison to other methods using GEUVADIS and SEQC datasets. It would be helpful to see how a few other "standard" methods perform: tophat2 alignment with cufflinks quantification or STAR alignment and HTseq-counts quantification.

Also, most of the standard quality control packages for RNAseq data work at the .BAM/.SAM level (e.g., PicardTools or RSeqQC). It would be interesting/helpful for fast transcript abundance estimator methods like this to be able to interface with such quality control pipelines.

On 2021-09-05 15:00:48, user UAB BPJC wrote:

Review of Barrasso et al., “Impact of a human gut microbe on Vibrio cholerae host colonization through biofilm enhancement” by the University of Alabama at Birmingham Bacterial Pathogenesis and Physiology Journal Club

Summary

This lab previously showed that Paracoccus aminovorans could be found in higher abundance in Vibrio cholerae-infected individuals compared to non-infected individuals. This study demonstrates that V. cholerae colonization is increased by the presence of P. aminovorans in an infant murine intestinal model of infection. Through crystal violet staining and murine intestinal colonization with a ?vpsL mutant of V. cholerae, it is shown that Vibrio exopolysaccharide (VPS) is necessary for P. aminovorans-dependent enhancement of V. cholerae biofilm formation and intestinal colonization. Microscopy also reveals VPS enrichment in areas of the pellicle with higher P. aminovorans abundance. Lastly, with mutants in accessary matrix proteins RbmA, RbmC, and Bap1, the researchers show that the ability of V. cholerae to form a structurally intact biofilm is necessary for P. aminovorans-dependent enhancement of V. cholerae colonization.

Overall, this is a very interesting paper with insightful experiments that sparked a great discussion in our journal club group. This paper gives strong evidence that P. aminovorans promotes V. cholerae colonization in a VPS-dependent manner. With that said, we have some comments that may be beneficial for the authors to address.

General comments<br /> * May be beneficial to keep y-axis scales for CFUs the same among all figures for more consistency<br /> * One-way ANOVA may be a more appropriate test for figures in which multiple comparisons are being made; we advise you to consider consulting a statistician on the appropriate statistical tests to use<br /> * The Mann Whitney U test is not a t-test, but is referred to as such in some of the figure legends. <br /> * It is mentioned multiple times that crystal violet absorbance was measured at 570nm, although measurements were made at 550nm for all crystal violet figures in the paper. <br /> * Discuss possible limitations with Wheat Germ Agglutinin; could WGA also stain GlcNAc produced by P. aminovorans?<br /> * Would be beneficial to describe in more detail the purposes of matrix proteins RbmA, RbmC, and Bap1, and why these were chosen to be studied.<br /> * One statistical test is mentioned at the end of every figure legend; is the same statistical test being formed on all panels in a given figure? If not, this needs to be clarified.<br /> * More details about how CFUs are quantified from pellicles in the results section would be good to add; the steps taken are only briefly mentioned and are a bit difficult to understand.

Figure-specific comments<br /> * Story may flow better if in vivo data from Figure 2 is added to the end of the paper along with Figure 7 in vivo data; introduce enhanced Vc colonization phenotype with in vitro data first<br /> * Would be beneficial to show single and dual species P. aminovorans CFUs in Figure 2B and 2C and also single species P. aminovorans CFUs in Figure 3B to see whether P. aminovorans colonization also increases in the presence of V. cholerae <br /> * Address the purpose of the grid in Figure 4A<br /> * Figure 5A: Title claims that P. aminovorans increases V. cholerae biofilm formation according to data in Figure 5, but can only make this claim if CFUs are quantified in the in vitro model.<br /> * Figure 6 should have a Vc single species control to compare dual species pellicle to.

On 2024-04-30 20:15:37, user Austin McIlhany wrote:

Fascinating paper both on the topics of cutting edge field of microbiomes and still-ongoing issues of human-caused environmental crises, specifically oil spillages. Here are a few ideas and questions I have that I think could possibly improve this paper:<br /> 1. Since this study focuses on N and P levels and hydrocarbon degradation, then the levels of N and P in the collected seawater need to be quantified.<br /> 2. I understand that it must not be under lab-ready circumstances to extract AND analyze the samples of the artic waters at the same exact location for a more accurate sampling of the microbiome<br /> 3. The levels in the three growth conditions of high, low, and ambient. It would be helpful to list all ingredients and their final concentrations for the three conditions as well.<br /> 4. Perhaps in the future, the use of transposons could also be used to track the genes that correlate with biodegradation of crude oil, and which strains/taxa of bacteria they originate. Making a isolated, pairwise and community could also help narrow it down.<br /> 5. PCR conditions for the V4 amplification should be described.<br /> 6. In the “differential abundance analysis,” were there adjustments made for different 16S copy numbers in different taxa? Different genome sizes? If not, this must be mentioned as a limitation.

On 2024-11-21 16:07:10, user Phillip Gienapp wrote:

This manuscript has now been published as:

JJC Ramakers, TE Reed, MP Harris & P Gienapp (2023) Probing variation in reaction norms in wild populations: the importance of reliable environmental proxies. Oikos e09592 doi: 10.1111/oik.09592

On 2017-11-07 15:26:42, user guillemaud wrote:

PREPRINT PEER REVIEWED AND RECOMMENDED by PCI EVOL BIOL

This preprint by Chauve et al has been peer-reviewed by by Mukul Bansal, Alexandros Stamatakis and 2 anonymous reviewers and recommended by Tatiana Giraud and Toni Gabaldón for Peer Community in Evolutionary Biology. Peer-reviews, decisions, author's replies and the recommendation can be found here: https://evolbiol.peercommun...

On 2020-11-09 12:32:02, user Niko wrote:

Hello,

did you test different anitbodies for the dot blot and do you have a reason, why you chose the antibody from Abcam?

Thanks for your help!

On 2019-03-20 15:27:31, user Claudiu Bandea wrote:

Free-Living Chlamydiae?

The finding that new chlamydial lineages, identified as metagenome-assembled genomes (MAGs), dominate the microbial communities in sediment cores from a region surrounding Loki’s Castle hydrothermal vent field (1) is a remarkable discovery.

Based on estimates of genome replication rate using the iRep algorithm (2), and in context of results indicating the absence of putative eukaryotic host cells in the sediments, the authors proposed that their new MAGs are derived from actively dividing chlamydia not associated with eukaryotic hosts. If true, this represents the first examples of ‘free-living chlamydiae’, an extraordinary finding given that all previously studied chlamydial lineages, both pathogenic and environmental, are obligate intracellular organisms (3,4).

However, the authors’ interpretation of the iRep results and suggestion of actively dividing extracellular chlamydiae might be questionable. A more likely explanation is simply that the chlamydial EBs, the extracellular spore-like cells in the chlamydial life cycle, contain a chlamydial genome at various stages of replication.

It is highly conceivable that during the differentiation of the intracellular dividing chlamydial cells, the RBs, into EBs, the chlamydial genomes is in process of replicating, which is ‘arrested’ during the differentiation process. Addressing this hypothesis, which is relevant for the entire chlamydial field, is relatively straightforward: perform a MAG-like sequencing experiment and an iRep study on a population of purified EBs from chlamydial lineages that can be grown in culture.

One additional comment on authors’ interpretation of the results and their proposal of putative free-living chlamydiae. Apparently, the authors failed to consider that the genome size of the new chlamydial MAGs is intermediary between that of environmental chlamydiae and the pathogenic chlamydiae, all of which are obligate intracellular lineages. Given that thousands of intracellular parasitic or symbiotic lineages have a smaller genome/proteome compared to that of their free-living relatives (4), it is very likely that the newly discovered marine sediment chlamydiae followed a similar evolutionary pathway (see also Ref. 5).

References

1. Dharamshi J, Tamarit D, Eme L et al. 2019. Marine sediments illuminate Chlamydiae diversity and evolution. bioRxiv: doi: https://doi.org/10.1101/577767 ;

2. Brown CT, Olm MR, Thomas BC, Banfield JF. 2016. Measurement of bacterial replication rates in microbial communities. Nat Biotechnol. 34(12):1256-1263; https://www.ncbi.nlm.nih.go...

3. Subtil A, Collingro A, Horn M. 2014.Tracing the primordial Chlamydiae: extinct parasites of plants? Trends Plant Sci. 19(1):36-43; https://www.ncbi.nlm.nih.go...

4. Taylor-Brown A, Vaughan L, Greub G, Timms P, Polkinghorne A. 2015. Twenty years of research into Chlamydia-like organisms: a revolution in our understanding of the biology and pathogenicity of members of the phylum Chlamydiae. Pathog Dis.;73(1):1-15. https://www.ncbi.nlm.nih.go...

5. Bandea C. Evolution of giant viruses from larger ancestors. 2018. Comment in bioRxiv on “Virus genomes from deep sea sediments expand the ocean megavirome and support independent origins of viral gigantism”; doi: https://doi.org/10.1101/469... https://www.biorxiv.org/con...

On 2022-01-21 15:22:14, user Ritesh Aggarwal wrote:

This manuscript is now online at Biophysical Journal - Cell Press. Read the peer-reviewed version at https://doi.org/10.1016/j.b...

On 2021-10-08 19:43:51, user Natascia Marino wrote:

This is the first molecular landscape of the NORMAL breast

On 2018-11-06 02:55:07, user Joseph Kirschvink wrote:

We are open to any comments or criticisms.<br /> Joe K.

On 2020-08-24 11:27:31, user Roberto Albanese wrote:

Which GENCODE version was used for the annotation of genes in samples of Series 5 (from GSM4462336 to GSM4462341)? Thank you!

On 2022-05-18 22:14:12, user Davidski wrote:

Hello authors,

Unfortunately, there are some serious problems with the geographic concepts in your preprint:

• your Steppe region includes a large swath of Eastern Europe that is mostly forest and forest steppe. Only about a third or less of this region is actually a steppe (the Pontic-Caspian steppe). Calling this region Eastern Europe would be more useful and in tune with geographic conventions.

• what you call Eastern Europe is not generally, by itself, known as Eastern Europe, especially since the fall of the Iron Curtain. That is, Czechia, Hungary and Slovakia (often along with Poland) are nowadays more commonly described as East Central Europe.

• what you call SE Central Europe is actually much of the Balkans, and thus straight up Southeastern Europe.

Honestly, calling Bulgaria Central Europe, while, at the same time, calling Czechia (inc. Bohemia) Eastern Europe just doesn't look right.

On 2024-12-05 12:22:35, user xPeer wrote:

Courtesy review from xPeerd.com

Summary<br /> The study explores the development of human heart assembloids integrated with autologous tissue-resident macrophages to replicate physiological immuno-cardiac interactions. The research emphasizes the importance of this model for understanding heart development and disease. The authors provide a detailed methodology for generating the assembloids, coupled with multi-omic analyses and functional assays, demonstrating the model's capacity to emulate key cardiac and immune processes. However, while the study presents comprehensive data and a novel approach, certain areas require further clarification and detailed statistical analysis to strengthen the findings.

Major Revisions<br /> 1. Statistical Analysis: The study lacks detailed statistical information to support the presented data. Including p-values, confidence intervals, and statistical tests used for each dataset is crucial for validating the results (e.g., Figures and gene set enrichment plots).

1. Reproducibility of Methods: While the methods section is comprehensive, it would benefit from additional clarity on specific protocols to ensure reproducibility. Including precise details about reagents, equipment, and any variations in experimental conditions can aid reproducibility (e.g., generation of heart assembloids).

2. Integration of Findings: The discussion should integrate the findings more thoroughly with existing literature. Highlighting how the study advances the field and addressing possible discrepancies with previous studies will provide a stronger context for the research.

3. Limitations and Future Work: The discussion needs a more critical examination of the study's limitations, potential biases, and confounding factors. Proposals for future research directions should be specified to guide subsequent investigations.

Minor Revisions<br /> 1. Typographical and Formatting Errors:<br /> - Page 15, Figure 2 legend: The phrase "?ë ëë ë ëëë" is a clear formatting error that needs correction.<br /> - Verify consistent formatting of subheadings and figure legends throughout the manuscript.

1. Definition of Terms: Ensure all technical terms, abbreviations, and acronyms are defined upon their first appearance. This will enhance readability for a broader audience.

2. Reference Formatting: Ensure all references are correctly formatted according to the journal's guidelines. Cross-check for the latest updates in cited references.

3. AI Content Analysis: Based on language consistency and technical depth, the estimated percentage of AI-generated content seems minimal. No sections explicitly exhibit characteristics typical of AI-generated text.

Recommendations<br /> 1. Enhancing Data Presentation: Incorporate more statistical data into figures and tables, such as error bars and exact p-values, to reinforce the reliability of the results.

1. Detailed Protocols: Append a supplementary section with detailed step-by-step protocols for key procedures to facilitate reproducibility by other researchers.

2. Expanded Abstract: Enrich the abstract with specific quantitative results to provide a clearer snapshot of the study's impact and conclusions.

3. Broader Impact Discussion: Expand the discussion on the broader implications of the model for disease modeling, drug testing, and therapeutic applications, tying it back to the study's findings.

This autonomous review aims to provide a comprehensive evaluation of the study, pinpointing critical areas for improvement while acknowledging its scientific contributions.

On 2022-08-29 10:14:15, user David Curtis wrote:

I just wanted to point out that I performed a similar study, though on a smaller scale with fewer gene-trait combinations:<br /> Exploration of weighting schemes based on allele frequency and annotation for weighted burden association analysis of complex phenotypes.Curtis D. 2022. Gene. 809:146039. https://doi.org/10.1016/j.g...

I didn't test VARITY but in my analyses REVEL was not an obvious winner and some other predictors performed better on some genes.

On 2018-03-18 09:51:40, user Slumbery wrote:

The attached map is seriously bad. The article claims that the population split happened north of the Himalayas and emphasizes that it did not happen in India. Then we have the included map (b) that places the splitting south of the Himalayas and in the middle of India. It totally contradicts the text.

Also the other map (a) already shows the endpoint of the first expansion pretty much inside the mountain range that separates Central Asia and South Asia, instead of Central Asia as the text claims.

On 2021-03-25 04:15:19, user Aalok Varma wrote:

Our lab presented this preprint at our journal club, and we thought we could start an open discourse about this work. The characterisation of V1 connectivity was quite interesting, and recording from such a diverse set of cell types in the spinal cord and stimulating over so many segments while retaining recording quality is definitely impressive. Here are some questions we have for you:

1. Have you tried recording fictive swims, either spontaneous or evoked, with optogenetic stimulation of different segments? One would expect that V1 activation should increase swim frequencies (since knockouts reduce speed), but since some of them are Renshaw cells (in limbed animals) producing feedback inhibition, the result could be something else. Moreover, have you tried monitoring a behaviour in response to V1 stimulation? That could address what role these neurons play in motor control more directly and with more spatiotemporal control than knockout models provide.

2. What is the effect of V1 stimulation on motor neurons, without network blocking, i.e. what happens if you’re recording from motor neurons in current clamp mode, and you stimulate V1s?

3. One of the striking results is that almost none of rostral stimulations led to IPSCs. Is it possible that the caudal arm of V1s are actually not axons, but rather dendrites that receive input? Perhaps imaging the localisation of pre- and post-synaptic markers, such as PSD-95 and Glyt2, for instance, within the V1 arbour could address this.

4. In continuation with the previous question, what are the inputs to V1? Are there V1s that function as Renshaw cells and type Ia interneurons even in zebrafish?

5. Lastly, is there any definitive evidence of the order in which the various cell types get activated on the arrival of a motor command? The model, for instance, directly connects pacemaker neurons to V2a’s. which would excite motor neurons, which would then activate V1s for feedforward inhibition? Are we correct in understanding how the model is set up, and does that match what one would expect from real spinal cords?

This work is quite interesting and we sincerely look forward to your thoughts.

On 2020-05-13 17:16:47, user FELIPE MARASCHIN wrote:

I suppose that, in page 8, in line 139, the sentence "the gain-of-function pPIF4:PIF4-FLAG mutant line (PIF4OX; Gangappa and Kumar, 2017)" relates to an overexpression line? In the referenced paper is attributed to Nozue et al., 2007. Nozue however, describes 2 PIF4 OX lines, one with the native promoter, and another with 35S:PIF4-HA. Nozue reports: "In this PIF4 overexpression line, designated PIF4-OX in this paper, PIF4 is driven by its native promoter but is expressed approximately 25-fold higher than in the wild type, presumably owing to the insertion site of the transgene" which was originally developed by Khanna et al., 2004. Otherwise, the PIF4 line it is a complementation line, not a gain-of-function?

On 2022-05-29 22:25:18, user Andres Betancourt-Torres wrote:

Summary:

This work investigates the role of nerve growth factor (NGF) during bone repair. Previous work from these authors established that NGF is important for the reinnervation of injured bone tissue. Here, they explore a secondary effect of NGF on mesenchymal cell migration that appears to be mediated by NGF’s low-affinity receptor p75. Using a variety of genetic techniques, the authors demonstrate that NGF promotes stromal cell migration in vitro, and that knockout of p75 in vivo slows bone repair. Further, p75 appears to control the expression of genes associated with cell migration. Thus, in addition to its role in bone reinnervation, NGF may act via p75 to promote stromal cell migration during bone repair.

Major successes:

The major success of the paper is a clear and well established connection between p75-NGF signaling and an effect on mesenchymal cell migration in mice. Also established is the importance of p75 for other processes, such as NGF production in macrophages and ossification of stromal cells.

Major Weaknesses:

The authors do not establish whether the effects of p75 knockout are independent of other NGF signaling pathways, such as the Trk family of NGF receptors. Thus, it is difficult to determine the relative importance of p75 versus Trk signaling in many of their experiments.

Impact: This paper is the first to describe the dependence of calvarial bone repair on p75 signaling. This finding could have important clinical implications for treating bone injuries.

Major Points:

-A description of review committees overseeing the use of human samples for the research presented in this paper should be included.

-Much of the paper relies on understanding the genetic tools, particularly transgenic mouse lines. There is no explanation in the text of what many of these transgenic lines are or what the abbreviations/notation used for them mean (e.g. NgfLysM). Interpretation of the results would be made easier if the authors included one-sentence descriptions of each of these tools and the logic behind using them. The genetics should also be added to the figures when they are critical to the experiment (e.g. a diagram of how the Cre-ER system works in figure 2).

-In other contexts, p75 plays a modulatory role in neurotrophin signaling through its influence on other receptor pathways (e.g. Trks). The authors did not test here whether the effects of p75 knockout were via independent functional consequences of the p75 pathway, or relied on modulatory influences over other neurotrophin receptor pathways. For example, the results shown in figure 1 could be largely the result of TrkA signaling, with only partial dependence on p75. The additional finding that p75 knockout reduces overall NGF expression by macrophages (Fig 5) also further complicates the interpretation of many of the in vivo results. To address these complications, the authors could test whether p75 deletion from stromal cells influences the function of other neurotrophin receptors. This can be accomplished by using Trk inhibitors in conjunction with the p75 knockout/knockdown (these authors have demonstrated the ability to use such tools in their previous paper). This is an important experiment because it would determine whether the influence of NGF on p75 can be studied independently of its role in promoting reinnervation via Trk signaling, or if the two phenomena are better examined in relation to each other.

-The authors’ should acknowledge additional caveats of their data from human cells/tissue. An important role for p75 in human cell migration is demonstrated in vitro, but it is not fully established in vivo. The observation that p75 is expressed during human bone injury does not alone indicate its function in vivo. Further, the human samples used were taken from tibia and ribs, whereas the rest of the paper is focused on calvarial bone repair. The authors should address these caveats and adjust the sentence beginning on line 261 to better reflect the full range of possibilities.

-The paper emphasizes a role for p75 in cell migration. However, it is also clear that p75 likely influences a range of cellular functions beyond cell migration as well. For example, RNA-seq experiments revealed a wide range of genes whose expression changed following p75 knockouts. Functions relating to NGF translation in macrophages were also likely impaired. These findings suggest that p75 may be critical to a broad range of cellular processes during injury repair, rather than just migration. Devoting more text to the discussion of p75’s role beyond just coordinating cell migration may broaden interest in this paper beyond its current scope.

-The authors should include further details about calvarial defect procedures. Specifically, the authors should elaborate on why they choose this injury model over other available models that could recreate common fractures that skulls experience (e.g. of possible reasons: convenience for observations or less pain for animals). At a minimum, the authors should state a reason why they choose this injury model for their studies. This discussion could best fit in the Materials and Methods.

Minor Points:

-The number of observations appears to be underpowered for some experiments. Particularly, the results in Figs. 2K and 4C look to be trending towards significance, but include small sample sizes. A power analysis for these data, or increasing the number of observations per sample for these experiments, would strengthen the authors’ interpretations of the data.

-The authors state the macrophage populations show a minor shift in population distribution based on the single cell data, but their IF shows significant differences in the number of macrophages at the injury site in the p75fl/fl and p75PDGFRa mice. An explanation for this discrepancy should be included.

-The data in figure 5 demonstrate that p75 knockout depletes NGF expression in macrophages. This suggests a positive feedback loop between NGF expression and NGF signaling. The authors should explore the consequences of this finding in their discussion as it may be important for considering the interaction between p75 and Trk signaling during bone repair in vivo.

-The authors should consider testing tissue samples for the presence of osteoclasts, due to the hypothesis that these cells could modulate the activity of osteoblasts. Generating this data would reinforce Figure 2 and the argument that p75 deletion is driving the lack of bone repair.Alternatively, the authors could discuss how osteoclasts modulate the osteoblast activity they describe through the presented data. This topic could be addressed in the Discussion section, for example as either a study limitations or future projections of this project.

-The authors should consider generating new images for Figure 2 panel I, and see if they can observe osteoblasts in the fracture healing area, using a higher magnification. This could reinforce the comparison among the two conditions presented by demonstrating the presence and hence participation of osteoblasts in the fracture healing process.

Stylistic Points:

-The order of the figures is a bit confusing. The authors switch back and forth between in vitro and in vivo experiments. One possible order is: Fig 1, 4, 3, 5, 2, 6.

-Typo on line 77: “microdissection bone defect site”.

-Reorganize Figures 1 H and I as Figures 1 B and C; they validate the model, and it may help readers accept the model before reporting any further data.

-Each chart should have its own legend. Although the color coding is clear, this will help each graph to stand independently from each other and help readers interpret the data quickly.

-Remove “squares” from the test in the Materials and Method section, or substitute with any possible missing symbol.

Whitney Tamaki (Whitney.Tamaki@ucsf.edu)<br /> Scott Harris (Scott.Harris@ucsf.edu)<br /> Andrés Betancourt-Torres (Andres.Betancourt-Torres@ucsf.edu)

On 2020-10-07 22:50:26, user Federico Giorgi wrote:

How could I miss this? In vivo single cell analysis of neuroblastoma early development.

On 2019-01-17 12:48:50, user Michael Hiller wrote:

Great to see more well assembled lizard genomes, but it would have been nice to cite the more recent assemblies of Salvator and Lacerta (even if the N50 metrics of both are assemblies better)

On 2022-03-29 09:55:05, user Daniel Baldauf wrote:

Beautiful study! These are amazingly strong decoding results with 90+ percent accuracy. That's great. I wondered though whether this might have to do with the relatively easy auditory targets (yes/no)?Or is it that the two speech streams can be easily distinguished by low-level features such as the pitch? I would be curious what role you think high-level attentional processes might play in this task, particularly, for example, when it comes to parsing words in a even more noisy environment. For example, recently Marinato & Baldauf (2019, Sci.Rep.) used a speech signal mixed with an environmental 'sound-scene', and showed that top-down object-based attention has a strong effect on the parsing of the language stream. DeVries et al. (2021, JN) then also recorded MEG during such a task, showing that it is particularly the alpha band in a fronto-temporal network that mediates these functions of object-based attention to words, and that allows for successful trial-wise decoding of the locus of attention (but not on quite the same level as 90% accuracy). Best wishes!

On 2019-02-12 21:39:33, user systemsbiology wrote:

Now published in Bioinformatics, https://doi.org/10.1093/bio...

On 2023-08-07 14:09:23, user LUCIANO RODRIGO LOPES wrote:

Dear Professor Porter and colleagues,

I have read your scientific article with great attention and interest. The initiative to include new species of deer as an experimental model to verify susceptibility to SARS-CoV-2 is of remarkable importance. The susceptibility and virological surveillance analyses involving the white-tailed deer (WTD; Odocoileus virginianus) have demonstrated the potential spillover of SARS-CoV-2 into wildlife. However, this appears to be just the tip of the iceberg. By adopting new species of deer as a susceptibility model, similar to your approach, we can better predict new scenarios involving SARS-CoV-2.

According to your results, it is concerning to learn about the potential susceptibility of mule deer and their ability to transmit SARS-CoV-2 with real infection capacity, whereas we observed that elk have lower susceptibility to this virus.

In an analysis involving deer ACE2 protein sequences (https://doi.org/10.1007/s10... "https://doi.org/10.1007/s10393-023-01632-z)"), I compared the binding sites that SARS-CoV-2 uses to enter the host cell. The mule deer shares the same binding sites with the WTD, while the elk has a different site, in addition to the evolutionary distance with the deer of the genus Odocoileus. I argued that this variation in an ACE2 binding site could decrease elk susceptibility to SARS-CoV-2. It appears that our results corroborate each other.

I conclude my comment here by congratulating you on the interesting work and publication by your group.

Sincere regards

Luciano

On 2020-05-08 15:18:05, user Manish Kumar wrote:

Supp Fig. 6. Technical typo: f = 4 mm for 50x Nikon.

On 2020-07-21 07:40:53, user MCMF wrote:

Thanks for quoting Fischer et al. (2019). It should be easy to integrate our method into your workflow for evaluation. It is open source and also written in MATLAB: https://github.com/RWTHmediTEC/PelvicLandmarkIdentification. You can cite the program with the DOI 10.5281/zenodo.3384110.

On 2016-09-01 18:16:03, user Ami Tsuchida wrote:

Very interesting paper...! I think the main analyses are very sophisticated, but I wonder whether the final analysis truly captures the change in functional connectivity. It seems that the authors simply included the time course of anterior LH as a regressor to compare change in correlation with this seed before and after learning. Isn't it possible that the correlation change because of the patterns of activations change before and after learning (i.e. both anterior LH and PFC regions are recruited during the task at early learning phase, but this pattern changes after learning)? Why not use PPI, modelling both task regressors and the seed time course, as well as an interaction term to see whether the change in correlation is above and beyond the change in the pattern of co-activation? I think the results would be interesting regardless, but the precise interpretation of "functional connectivity" may be different depending on whether it's driven by the change in the responsiveness of these regions to the common task stimuli.

On 2016-01-17 18:40:56, user Anthony Olszewski wrote:

In this paper my impression is that they are counting bacteria suspended in the fluid of the colon. Are there bacteria on the surface of the large intestine? How many might be there?

The bacteria concentration in the small intestine contents is stated as 10 to the 3rd compared to the large intestine 10 to the 11th. The surface area of the small intestine is amplified 60 to 120 times. What is the number of bacteria living in this real estate?

Even if less than originally thought, the surface area of the gut is considerable:<br /> http://www.ncbi.nlm.nih.gov...

On 2024-09-17 08:21:43, user EDG wrote:

This article is now published in Nature Communications : https://doi.org/10.1038/s41467-024-51638-6

On 2020-08-03 13:48:39, user SUMAN MISHRA wrote:

Thanks for sharing your generous comment. Somehow we overlooked this point while submitting our preprint. Accordingly we will modify the competing interest section.

On 2016-09-09 22:47:56, user Afif Elghraoui wrote:

The link in the abstract redirects to <https: <a href="github.com" title="github.com">github.com="" gifford-lab="" cpgenie=""/>, where the jedi octocat tells us the page doesn't exist.

On 2017-08-25 02:34:46, user Keith Williams wrote:

Gefeliciteerd! This is a really wonderful result; I hope that it holds up.

On 2025-10-12 06:26:52, user Chenxi Sun wrote:

Overall, the paper makes a strong and interesting contribution by showing that macrophage mitochondria transferred to cancer cells don’t serve as energy sources but instead act as ROS-producing signals that activate ERK and drive proliferation. I’d rate its significance a 4 out of 5, since this finding changes how we think about mitochondrial transfer, giving it a new signaling role in tumor biology. The experiments are carefully designed — live-cell imaging, RNA-seq, and biosensors all support the conclusions — but most data are from in vitro models, so it’s still unclear how much this process matters in real tumors. The main limitation is the lack of in vivo validation, which would make the story more convincing. Overall, the methods are solid, the logic is clear, and the conclusions mostly follow from the data, even though more physiological testing would strengthen it.

On 2019-08-29 12:40:51, user JMS van der Schoot wrote:

Please find the published (and revised) version of this manuscript at Science Advances:<br /> https://advances.sciencemag...

On 2020-05-13 20:54:28, user Are Bayode wrote:

The article is now peer-reviewed and published in PLoS NTD: <br /> https://journals.plos.org/p...

On 2018-05-08 07:52:25, user Mike Fainzilber wrote:

Very interesting work. The discussion on how precisely MAPK-15 influences length might also consider the possibility that it is part of a motor-dependent length-sensing mechanism similar to that described by us for mammalian neurons (admittedly axon-centric in our case, but nobody's perfect...). See https://www.ncbi.nlm.nih.go... and https://www.ncbi.nlm.nih.go.... There is also earlier work from the Eickholt group on myosin and PTEN in control of somatodendritic compartment size in mammalian neurons that is likely highly relevant, see https://www.ncbi.nlm.nih.go....

On 2021-02-06 06:24:23, user Olabode Omotoso wrote:

This preprint has been peer-reviewed and thereby modified. The peer-reviewed version can be accessed open access via https://bjbas.springeropen....<br /> Thank you<br /> We also appreciate all healthcare workers and researchers at the forefront of finding a lasting solution to the COVID-19 pandemic

On 2018-03-17 10:29:04, user Lukasz Kozlowski wrote:

The web server has been moved to http://isoelectric.org

On 2020-03-04 10:28:04, user Nikol Reslová wrote:

Hello, I would like to ask, the database is not working for some time now, will it be available once again or did you close the websites for good? Thanks for your response in advance, NR.

On 2021-08-17 09:17:54, user passanger lost wrote:

Can not found the SUPPLEMENTARY INFORMATION files

On 2020-06-02 14:05:55, user Stefania Di Blasio wrote:

This is a pre-print of an article published in Nature Communications, on June 2nd, 2020. The final authenticated version is available online at: https://doi.org/10.1038/s41...

On 2019-10-14 09:02:13, user Habi wrote:

As the first author and 'producer' of the analysis code, I'm happy to answer any questions on the manuscript.

On 2024-07-05 12:57:48, user Thomas BN Jensen wrote:

Please see https://zenodo.org/records/... for the additional datafiles (metagenome assemblies, metagenome bins).

On 2023-06-04 22:27:01, user Dr Ros Jones wrote:

Thanks for this detailed study. You mentioned concerns about spike protein from infection and from vaccines. Do you have any way to distinguish which you are finding? Were the findings similar in postmortems of those dying post-infection who had also been vaccinated?

On 2025-08-24 15:25:49, user Tsvetoslav Ivanov wrote:

Now published: https://www.nature.com/articles/s42003-025-08297-0

On 2019-05-28 02:20:02, user Huiwang Ai wrote:

There is a relevant paper published on Nature Methods two years ago: https://www.nature.com/arti...

On 2018-02-09 23:24:19, user John wrote:

Forgive me - I only skimmed the paper, so this question may be answered therein. Is one able to compare the scikit-ribo translation efficiency estimates across conditions? (i.e. does the TE for a given transcript differ between conditions A and B?) Or can you only compare the TEs within a single condition? (i.e. does X have a higher TE than Y?)

On 2022-01-15 03:53:20, user pierre wrote:

global mapping with 2000 points is nonsense, but the geospatial modelling of this article is also nonsensical.

On 2020-03-19 18:43:36, user Michael Ward wrote:

Hi, I've searched the PDB for 6M71 and it is not available. Do you have an idea when it will become available

On 2020-03-23 23:29:20, user Anna Maria Niewiadomska wrote:

Regarding ORF10:CUL2. Interesting particularly since there's evidence of other coronaviruses interacting with proteins in the ubiquitination pathway. However, two groups performing direct sequencing of SARS-CoV-2 mRNA have not been able to detect ORF10 transcripts and no ORF10 homologues exist in other coronaviridae. Although you do mention there's no evidence for ORF10 expression in the supplementary material, you may want to consider that the ORF10 results may be an artifact of over-expression and mention in the main text.

On 2021-01-22 17:50:16, user Fraser Lab wrote:

This work reports two atomic models of the main protease (Mpro) from SARS-CoV-2 using serial femto-second crystallography (SFX). The goal of the paper was to use this structural information to assist drug repurposing efforts against SARS-CoV-2. To accomplish this, the authors used computational docking and molecular dynamics simulations to investigate the molecular basis for binding of three previously reported Mpro inhibitors. There is a substantial (and growing) body of work describing the structure, function and inhibition of the main protease from SARS-CoV-2, but the claims of how the current results will become part of that effort are overstated in the manuscript.

The major result of this paper is the efficient use of the new macromolecular femtosecond crystallography setup at LCLS-II: two high resolution structures were reported from <5 hours of instrument time (1.9 and 2.1 Å). The major weaknesses of this paper stem from both the disconnected nature of the primary results and the rigor of some of the analyses. Specifically: 1) the reported structures have little relevance to drug repurposing efforts (the stated goal of the paper), 2) the differences between prior conventional structures and the analysis of the molecular dynamics simulations lack rigour, by lack of comparisons of electron densities and estimations of convergence/significance 3) the implications of the new Mpro models and/or the molecular dynamics simulations for inhibitor design are not articulated, 4) the compounds highlighted have been reported as promiscuous covalent inhibitors.

Some of the results described in this manuscript may be of interest to the SFX community, particularly if revised to more solidly compare to existing data. However, to maximize the relevance to the wider structural biology and SARS-CoV-2 research communities, the manuscript should be revised significantly.

Elaborating on point 2 above:

1. The manuscript does not present a fair comparison of structural information obtained from SFX versus traditional crystallography. Electron density maps are only presented for the SFX structures (e.g. Fig. 3B). Claims of structural differences would be stronger if the structural comparison was performed using crystals grown from the same conditions and with data processed/refined/modeled in a consistent way. Isomorphous FO-FO electron density maps would be particularly helpful.

2. The authors restrict their comparison to a single previously reported structure of Mpro (e.g. 6WQF). They should extend this analysis to the ~240 previously reported structures of Mpro from SARS-CoV-2. The majority of these structures were determined using cryo-crystallography and have ligands bound, however the comparison would still be informative, and is required for their claims of novelty. In particular, active site flexibility upon ligand binding has previously been characterized (e.g. Figure 1C of https://www.nature.com/arti..., Figure 4 of https://www.nature.com/arti... "https://www.nature.com/articles/s41467-020-16954-7)") - it would be helpful to know whether the structural differences reported by Durdagi et al match those previously reported.

3. The structural differences involve alternative side conformations of non-catalytic residues in the active site (e.g. Figure 2B and D). Importantly, no specific link is articulated between the alternative conformations identified and Mpro function or inhibition. The authors suggest that the new structures will help modeling efforts (e.g. P5 L20, P11 L5). The authors present modeling efforts in this manuscript. How have the structural differences identified by the authors helped their modeling efforts (compared to the previously available structural information)? There are obvious computational analysis controls here that are missing.

4. The catalytic histidine is modeled with a flipped side chain in 7CWB compared to 7CWC, however, this difference is not mentioned in the manuscript. Are the authors confident in their modeling? Looking at previously reported apo coordinates (e.g. 6WQF, 6YB7), both conformations have been modeled. The conformation modeled in 7CWB seems more compelling, based on the ability of His41 to H-bond to Cys145, and the H-bond with Wat441. The authors claim that Wat441 (W5) plays a crucial role in catalysis (P5 L28). Flipping His41 disrupts the Wat441 H-bond network, however this is not mentioned. If the authors are confident in this difference, then they should summarize their reasons and the implications for Mpro function and inhibition.

5. A substantial and growing body of work (e.g. https://scripts.iucr.org/cg..., https://scripts.iucr.org/cg..., https://pubmed.ncbi.nlm.nih..., https://www.pnas.org/conten... "https://www.pnas.org/content/117/8/4142)") deals with radiation damage in X-ray crystallography. Carefully designed experiments, coupled with advances in detector technology, mean that problems associated with radiation damage can be mitigated, even at room temperature. Indeed, the authors of a recent paper reporting the room temperature model of Mpro (Ref 27 in Durdagi et al - https://www.nature.com/arti... "https://www.nature.com/articles/s41467-020-16954-7)") explicitly state their efforts to mitigate radiation damage: “We grew large crystals that could be used on a home source to ensure minimal radiation damage.” If Durdagi et al are suggesting that radiation damage was a problem with previously reported data, then they need to present evidence to support their claim. One option would be to collect cryogenic and room temperature data using identical crystallization conditions, then calculate isomorphous difference maps to test for radiation damage (clearly, careful experimental design is required, and problems of non-isomorphism may be encountered). Without this experiment, claims of issues with radiation damage at room temperature are not supported by evidence and should be removed.

6. Differences in dynamics were identified with simulations performed using 7CWB and 7CWC, despite the starting coordinates being almost identical. This strongly suggests that the simulations have not achieved equilibrium sampling. Perhaps the 7CWC model was simulated with the four residue addition at the N-terminus, and this can explain the differences? This should be mentioned in the main text.

7. Differences are also highlighted between the dynamics identified from simulations of 7CWB and a previously reported cryo model (6WQF) and room-temperature model (6Y2E). As with the previous point, these coordinates are almost identical, and if simulations of the same coordinates produce different results, how can the authors be confident that simulations with different compounds will produce useful results?

James Fraser and Galen Correy (UCSF)

On 2017-03-06 03:22:17, user ale wrote:

Everything I have read on this URL is so far from the truth it’s surreal. This boils down to history, science and statistics. It is the most plausable form of nailing down any logical assumption on accuracy of reducing the probability for your origin to a demographic locus. So first the brass tax: most of you are Italians or have a significant genotype that is from the Italo Peninsula origin. Sorry, it’s the truth. I have been told this story by too many orthodox Ashkenazi as well as paleoanthropologists whom specialize in gene characterization. Being told this was not enough, so I researched it. Being a decendant of a Hebrew Tribe on my mother’s Sephardic Side from a family whom immigrated to Naples from Spain, we were traced by the Nazi Germans during WW2. Thus my family resided in a potato cavern behind a wall of an old farmer for two years before the war ended and were allowed to come to America. I did my due diligence and upon returning to Abruzzo visited a cousin whom happened to be on the accounting staff of the Vatican. This allowed me some leaway to utilize the Vatican library during my summer visits to truly understand my origins as an Italian-Jew. And what I leaned was right in line with science and history. My family originated from Spain, they were merchants whom specialized in the fishoil business to light lanterns. They had lived in Spain for almost 700 yrs generation to generation. Many were knights, nobleman, schlolars, lawyers and artists. I had a sample of my blood DNA analyzed and asked my mother and a few of my cousins to do the same. We all had the id mtDNA mitochondrial of Ashkenazi. But I always thought they were eastern EU and Germanic so I was confused because we were expecting to confirm some Sephardic lineage. We were wrong and I ventured to the local temple in a town nearby my mom’s hometown for further elaboration and I heard the story from an orthodox rabbi whom then was 101 yrs old. It was something out of the movies, and I will never forget his hand on my shoulder and smile and words “we are all related, so be nice to all.” The Romans who finally destroyed the second Temple of Jerusalem during Exodus led many Jews to live amongst the Romans in Rome and surrounding towns. Many were men who took Italian wives as they garnered citizenship as members of Rome. Some of the Rabbi’s own family he could trace to Poland now and Lithiania. Many of these same Jewish lineages were Italian/Hebrew, of which a small population left Italy during the Gaulish Invasions and found refuge amongst Northern Europeans. The spread out as a gypsy flock of people to Germanic states to settle down and form sub demographic and distinct cultural diaspora. That aside, each and every one of them, including yourself if you are from the eastern EU will find N1b2, M1a1b, K1a9 and perhaps even the major K1a1b1 genotype in your makeup. Yes, all origins from the Ibernian peninsula…(aka Italy). It was Exodus, you see. And the first stop was Rome of course. Romans were efficient at using the spoils of war for work and where better than the big apple itself? So, “I am who I am”. But you and I and most other Jews share almost an 80% statistical probability that the remnant non middleastern 49% of our genetic makeup is of Italian origin. This is the truth, this is what I learned. We are all connected and we all originated from two locuses, Israel and Italy. It all makes sense, think about it. Really think about it.

On 2023-02-06 02:08:53, user Susan C Kandarian wrote:

1. page 13, line 6, "(as in Figure 3C)" please correct to "(shown in Figure 4J)"
2. please discuss implications of increased TLR4 and TLR5 in monocytes from patients. Are these gene expression changes responsible for the difference in the "DNA sensing pathway" indicated in the figures?
3. The "TNFalpha signaling via NF-kappaB" is plotted in 3 figures but it is not referred to anywhere in the text. Is this what is meant by the few text references to inflammatory signals? Please expand on which gene expression changes were strongest in calling out this pathway by the gene cluster analysis program.
4. Related to the above comments, it would be helpful (where possible) to state some major gene expression changes that likely caused the bioinformatics programs (KEGG or GSEA) to call out pathways such as TNFalpha signaling, IFNalpha response, etc)
5. If you search the dataset for individual genes, like other PRRs (besides the 2 TLRs found), are there others whose expression was changed in patients such as the RIG-I like receptors that can be found on monocytes?

I think this work on scRNAseq in different immune cell types is an important advance, but realize that scRNA data alone are difficult for making conclusions about biological processes. The monocyte data (normal vs diseased) are compelling, and they are consistent with other data indicating a problem in innate immune cell function (detection of PAMPs or DAMPS and/or an innate immune cell signaling loop error?). Perhaps it is triggered by exertion products since even ATP or ischemia-reperfusion are known activators of the PRR called NLRP3.

I have no ideas about how to understand the messaging to macrophages since they are mostly in tissues. Lymph node biopsies?

On 2019-03-19 18:10:04, user Pete wrote:

Glad it's finally out!

On 2022-12-18 11:06:58, user Scott Cameron ???????????????????????????????????????????????????????????? wrote:

Couple of corrections needed in your paper here folks. Reference 24 did conduct ADP stimulation of human AAA platelets (supplemental Figure 2 — no difference). Also GP2B3A activation was assessed in AAA (Figure 2C — increased).

On 2016-09-01 19:18:38, user Erik Lindahl wrote:

Hi Steven!

Thanks for the feedback. You are entirely right that we should have used newer CPU nodes - we just hadn't gotten our new machines delivered when we wrote the first version. However, we got a bunch of brand new dual Xeon E5-2690v4 nodes this summer (and also realized the GPU version needs two SSDs in RAID0 to really shine), so we have more up-to-date numbers now. Unfortunately, new x86 CPU cores are not significantly faster (in particular not if your code doesn't benefit hugely from FMA instructions in AVX2). You can get _more_ cores per socket (at higher price), but the performance per core will not be much faster, both because the architecture has not changed a lot, the clock frequency trends downwards, and most importantly - all those cores in a single socket share memory bandwidth (so the memory bandwidth per core is sadly dropping). When comparing ten such latest-generation x86 nodes with a total of 280 physical cores (560 threads with hyperthreading) against a single-socket workstation with 4 GTX1080 GPUs, the latter single machine is more than twice as fast as the ten nodes (see attachment). The cards should be in stock pretty much everywhere, and a colleague just got a quote for a complete workstation with GPUs for just over $6000, available now :-). In my (admittedly non-neutral) opinon the relative advantage when running actual refinement is remarkable, in particular in terms of price/performance.

Our goal is simply to achieve massive speedup for data processing, and we are not married to any particular architecture. Relion will support them all, long-term too, and of course we hope to extend the impact beyond GPUs when possible. All changes in the code that are applicable to the CPU side have been implemented there too; when it comes to floating-point models double precision turns out to be just-so-slightly faster on the CPU, so it seemed fair to use the fastest alternative (but the difference is small). We hope to look more into the CPU side too in the future (in particular to help older hardware), but to be honest: right now we don't see any scenario where CPUs can become anywhere close to competitive for new investments, no matter how much work we invest in CPU acceleration for image reconstruction.

On 2016-09-20 22:10:00, user Ross Walker wrote:

Given the discussion on hardware here I thought I'd add a link to the Exxact preconfigured Relion GPU systems for those that are interested: http://www.exxactcorp.com/r...

These can be fully customized and can include GeForce or Tesla GPUs. The machines (including GPUs) are warrantied for 3 years even if equipped with GeForce and the QA process includes validating all GeForce cards for numerical correctness which has been very successful in the MD and other Life Sciences fields.

On 2022-09-02 01:38:59, user Matthew Templeton wrote:

What similarities are there to the Myrtle rust (Austropuccinia psidii) genome, given that both organisms have broad host ranges and very large Tn-rich genomes?

On 2025-08-15 09:06:18, user Alex Zhavoronkov wrote:

Did you see this paper? <br /> Machine Learning on Human Muscle Transcriptomic Data for Biomarker Discovery and Tissue-Specific Drug Target Identification<br /> https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2018.00242/full

On 2025-01-23 21:00:35, user Nicole Blackstone wrote:

The published paper linked below and on the main page is a cultivated meat LCA, not a media LCA. Is this a mistake?

On 2015-11-06 15:30:45, user Benjamin Voight wrote:

FYI - not sure if this is due to the comment below, but The google spreadsheet link in the supplement (pg. 35, section 4.11)

to the 3230 pLI genes is broken (as of 11/06/2015, 10:26AM)

On 2022-01-28 14:10:47, user Riccardo wrote:

Also check our 3D videos in Supplementary Material

On 2020-08-10 09:32:41, user OxImmuno Literature Initiative wrote:

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

On 2019-03-18 16:33:25, user Stephen Floor wrote:

Very curious what diffusivity looks like using SMdM if you deplete ATP!

On 2020-04-23 16:06:54, user keyser soze wrote:

Supporting information file?

On 2019-12-02 04:42:29, user Andrew Brooks wrote:

Thanks for the update. How did you measure the distance to the membrane. There is no membrane in ref 35. Just asking if this would depend on the prediction of where the JAK2 surface is associating with the membrane.

On 2023-04-04 18:07:42, user mkarikom wrote:

the geo accession is wrong: GSE190004 is from a lung cancer study

On 2020-02-02 03:41:00, user jay wang wrote:

I BlastPed the region that spans the first 2 “insertions”, and found no so called insertions at all in the alignments with other bat-cov viruses. On the contrary the alignments showed very natural diversities around the two ”insertion” regions. The two “insertions” are obviously products of evolution,not engineered artifacts! Furthermore,even human has huge number of proteins that are homologous with those of vegetables,so why it is a surprise that there are short homologous regions between 2 viruses?

On 2020-02-01 07:48:14, user ngschen wrote:

This paper is fake. I aligned all 4 insertions. 3 of them share with BAT sars-like virus (GISAID no.: BetaCoV bat Yunnan-RaTG13 2013 EPI_ISL_402131). The fourth insertion (CTCCTCGGCGGG), which is the only one 2019-nCov specific insertion, has the best match to Marine virus AFVG_250M1136. Since the 2019-ncov outbreaks from the seafood market, the possibility of marine virus rcombination might be much more persuasive.

On 2022-10-15 09:35:56, user Iratxe Puebla wrote:

Review coordinated via ASAPbio’s crowd preprint review

This study has developed a tool to characterize small molecule modulators of RNA-protein binding events. Please see below a few points which may help strengthen the manuscript.

• The term “temporal” is used multiple times in the paper, to facilitate clarity for readers from different disciplines, it may be useful to provide some further explanation or context for the term.

• Introduction section, “independent datasets have failed to reach consensus”, please provide some brief explanation about those independent datasets mentioned.

• Introduction section, last paragraph “We apply TRIBE ID to profile cytoplasmic G3BP1-RNA interactions …” - further explanation of these three processes linked together would be helpful.

• Figure 1, please provide some further explanation for the difference between TRIBE and TRIBE-ID. Since the dimerization is forced by rapamycin, a control experiment to explain artifact binding would be helpful.

• In the section, “Rapamycin-mediated dimerization of G3BP1-FRB and FKBP-ADAR”, recommend adding some clarification about the goal of this experiment, which could be understanding either native processes or in a rapamycin-dependent manner.

• In section, “G3BP1 TRIBE analysis with human and Drosophila ADAR2 catalytic domains” - suggest commenting on the reasoning for ideal ADAR to possess characteristics like “high editing activity when dimerized or fused to G3BP1”. Are these characteristics important to increase signal/noise ratio in the assay? Also, an explanation of T375G mutation and control experiments with wild type ADAR for any inhibition effect for Figure 2 would be helpful.

• In the section, “Temporally controlled G3BP1-RNA interaction analysis with TRIBE-ID”, please clarify whether the experiment described in Figure 3 provides information about the time of interaction between RNA and G3BP1.

• A paragraph describing any limitations and other possible applications of this tool on other systems would add to the manuscript.

On 2020-04-07 11:29:26, user Romain Koszul wrote:

Thanks @GauthierJeremy and JM Drezen for the nice collaboration and congrats for the paper.

On 2018-04-09 19:56:36, user Marcus wrote:

Bhatia and Heisler (2018) has now been updated to include images for 40 replicate leaves and an additional source of mDII seed.

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

On 2013-12-24 21:36:22, user B. Schwessinger wrote:

In the introduction you are suggesting the following: <br /> 'If true, conservation of important effector-target interactions<br /> may form another important means besides convergent evolution, by which targeting of<br /> important host proteins by divergent pathogen effectors, is achieved.'<br /> Do you mean that effector-target interactions might be evolutionary conserved between fungi, phytoplasma and oomycete.This seems rather unlikely given the evolutionary distance between these pathogens.

On 2023-05-22 22:45:16, user Fraser Lab wrote:

Summary:

In protein engineering projects, it is always desirable to screen as efficiently as possible. Screening a relatively small number of variants becomes especially important when enzyme activity cannot be coupled to a high throughput sequencing readout. The major goal of the paper is to provide a proof of concept scoring and filtering system for selecting among proteins generated using computational methods to meet this challenge of efficient screening. They consider proteins generated using 2 machine learning methods and one phylogenetic method (ancestral reconstruction).

The end result is a scoring filter combining the language model ESM-1v (which uses only sequence information) and the deep learning method ProteinMPNN (which is trained directly to find the most probable amino acid for a protein backbone predicted by AlphaFold2). After accounting for some simple idiosyncrasies of merging generative models with reality (ensuring starts with Met, removing repetitive sequences, accounting for localization signals) with heuristics, their filtering steps results in an enrichment of active sequences.

The major success of the paper is a pipeline that actually works for selecting active sequences both in the experiments they conduct and (to some extent) literature examples. The table of potential protein failure modes is particularly useful as a baseline approach and reference for people designing sequences with computational methods. It is especially insightful to see how few deliberate filtering steps in the training process can have a big change in the outcome.

We expect that a combination of sequence and structure-based filters will be used for prioritizing screening resources in the future. This paper lights the way of how to do that. The next steps will be to take into account structural features beyond stability (which is presumably covered by the AF2/ProteinMPNN), such as catalytic residue positioning, pocket size complementarity to substrate, etc. These are presumably implicitly captured by ESM-1. The next logical step (beyond this paper) is to go beyond statistical combination of these two scoring features to account for such features explicitly or with a new integrated deep learning approach.

Major points:

We are a bit confused about the exact value and sequencing of each part of the selection/filtering pipeline. We interpret experiment 3 as:<br /> Apply ESM-1v and Quality Filters and then apply a ProteinMPNN filter on top of that. <br /> Select Negative Controls by selecting sequences that fail the first filter (ESM-1v and Quality Filters) but are within 1% sequence identity to the closest natural sequence for some positive.<br /> The quality checks discussed in the supplementary information seem to have substantial impact. If the selected control sequences failed this quality check, it’s not clear whether the success of the pipeline is due to these heuristic quality checks or due to the computational filtering. These filters are biologically simple such as starting with a methionine, removing long repeats and not having a transmembrane domain - and it is kind of amusing to one of us (JF) that generative models have these pathologies so commonly. More discussion on why these filters were applied and what the distribution of effects were for the quality filters vs the insilico filters would help clarify the impact of each stage.

This confusion then extends to determining how each of the two computational methods affect the selection. The authors contend that “no single metric would be sufficiently generalizable to screen against multiple sequence failure modes” and hypothesize that ProteinMPNN and ESM-1v “may capture distinct features.” However, because negative controls were selected only after failing the initial ESM + Quality Filters, its impossible to know what effect adding ProteinMPNN on top of ESM had. This is even more relevant given that the structures used to obtain proteinMPNN scores are first generated with Alphafold. Alphafold can be computationally intensive (expensive to run) and therefore it is imperative that we understand how much this part of the pipeline contributes to the overall success of the selection process. The authors themselves contend that “Structure-supported metrics, including Rosetta-based scores, AlphaFold residue-confidence scores, and likelihoods computed by neural network inverse folding models, take into account protein atom coordinates potentially directly capturing protein functionality, however, they can be impractical to compute, especially when evaluating thousands of novel sequences.” This is something that can potentially be teased out. In the case of the paper only 200 proteins were selected using ProteinMPNN, however, if many sequences end up passing the ESM filter and budget allows it would be within reason to expand this random ESM selection.

In summary, it is a bit hard to tell (without some ablation studies) which different pipeline components and filters drive the results. Additionally, it would have helped if these same quality filters were applied in Round 2 but that doesn’t seem to be the case? A deeper discussion on the selection of quality filters would also point the way forward with combining more “functional” structural features as outlined above.

Minor points:

1) The author’s generalize the results with a few literature examples: “similar results were obtained by independently validating COMPSS on previously published datasets of six enzyme families generated by models not considered in the present study.” Looking at the results in more detail reveals that some of these (including one that we generated!) are very small samples and this caveat should be discussed. In 3 out of the 6 studies, only 1 sequence was selected by their pipeline. In another of the 6, 2 were selected. In all 4 of these studies, a number of actives were missed. The limited number of selected sequences makes it hard to know how effective the pipeline really is in these 4 studies. Further, with such a stringent filter is not practical especially when we consider the fact that the authors don’t discuss the level of activity across positive and negative active compounds. It’s entirely possible that you could miss very active sequences and select only moderately active sequences. In one retrospective, the results were truly similar, however in the last other study, the filters worked far from intended.

Even more, my team has observed in its own work that the sensitivity of machine learning models for scoring can be heavily dependent on the sequences the models have seen before. It would have been useful for the authors to consider how the tested enzymes overlap with the model training data to understand whether these scorers generalize outside the models training distribution.

2) The authors largely discount natural sequence identity as a metric:<br /> “Surprisingly, neither sequence identity to natural sequences nor AlphaFold2 residue-confidence scores were predictive of enzyme activity.”

I think it’s important to qualify this with the fact that we are looking at sequences in the 70 to 90% range with very little dynamic range here. In their first experiment they looked at sequences in the 70 to 80 range. in their second they look at sequences in the 80 to 90 range. In their third experiment they looked at sequences in the 50 to 80 range but their filters end up selecting for sequences in the 70-80 range anyways. So it’s possible that locally, identity might not select for select for activity but globally, it could be a first filtering step on its own (which maybe is obvious and hence why it’s not more qualified?). Also to note is that sequence identity seemed to fare as well as or better than other metrics in identifying functional GAN-generated sequences and could be its own generative method:

More problematic I think is figure 3f and figure 3g:

It seems like the inactive controls are largely in a separate part of the tree compared to the active sequences passing and control. Does this have anything to do with the fact that these features failed the sequence based quality filters. Second,it suggests an approach where if you have some idea of where to focus on in the tree you could use sequence identity to those natural sequences as a metric for selection . Of course this information may not be readily available but the authors should discuss whether we could have hypothesized that the failing controls would have failed beforehand by considering their phylogenetic origins.

Technical points:

1) There is some problem with this sentence:

“CuSOD training sequences had only a single Sod_Cu domain, while MDH had an Ldh_1_N followed by an Ldh_1_C domain and no other Pfam domains that generally only rarely occur in 6.3% and 1.7% of sequences in both families, respectively.”

It’s much better captured in the supplementary material:

“For CuSOD, 1,632 out of 25,701 proteins (6.3%) had aberrant architectures. For MDH, 1,127 out of 65,639 (1.7%) had aberrant architectures.”

2) It’s not clear where/how they selected the natural test sequences for rounds 1 and 2. We assume it’s from the curated set of data but that’s not necessarily a given, further it seems that in round2, sequences were selected to span the range of esm scores. Was this done for the test natural sequences as well?<br /> “Only 13 test natural sequences were selected, as we had already screened five similar natural sequences in the remediation for Round 1.”

“Besides the identity range, the experimentally tested sequences were selected to span the entire range of scores on each metric (Supplementary Table 4)”

3) The authors should be more explicit on the natural sequence identities in each round. If you check the supplement you can find this information if you pay attention to the figures or check the supplement but I think that it should be explicitly stated in the section “Round 2: Calibration data for COMPSS” that sequences are selected in the 80-90 range and in Round3 that the filters resulted in sequences with >69% identity.

4) The following section is confusing:

“To further test the hypothesis that poor truncation selection was responsible for the lack of observed activity in the Round 1 CuSOD natural test sequences, we assayed an additional 16 natural SOD proteins (pre-test group)…”

It should be stated at the beginning that 14 of the 16 test sequences are CuSOD sequences and 2 of the sequences are FeSOD sequences vs letting the reader figure that out later in the paragraph. Additionally, it would help the audience to say explicitly that 3/7 bacterial sequences with clipping also passed or include the table from the supplement up front. 3/7 doesn’t seem clearly distinct from 4/5.

5) What’s the reason for changing the esm-msa sampling method in round 2? Did they observe some benefit or was this purely a computational choice?

6) I think the text for a and b are switched in the figure 2 description. a is the AUC figure and b is the correlation figure. Further for figure a If the test sequences are natural sequences, is the identity score meaningless here?

7) From the supplement: “We skipped the 'starts with M' filter because very few of the sequences in these sets start with M, and did not subset by identity to closest training sequence.” This modification to the pipeline should be mentioned in the discussion of the external validation tests. Or they should speculate what would happen if they just added a M at the beginning of every sequence?

8) Looking at the figures in the supplement e.g. Fig 30 it seems like they had quantitative activity values. It would have been nice to discuss if there was any correlation between scores and activity for ranking purposes. Was this not included because of variance in the assay?

Joel Beazer (Profluent) and James Fraser (UCSF)

On 2016-11-08 16:45:38, user Kasper Hansen wrote:

Skimming this, I am surprised there is no discussion and comparison to Finucane (2015) Nature Genetics. From my understanding Finucane et al basically solves the same problem, accounting for the same factors as GARFIELD attempts to account for.

On 2020-06-03 16:25:39, user tomas.blisset wrote:

Hi, there is a mistake in equation (11), The steady-state decay time:

t_eff = Tm*t_a / (Tm + <h>)

instead the reported equation is:

t_eff = Tm*t_a / (Tm + t_a) ( which is also incorrect dimension-wise)

Thanks for the paper, very nicely written and meaningful.

On 2017-03-30 13:13:47, user Jamie Timmons wrote:

Hi Benjamin

Thanks for your response to the discussion on the incorrect assessment of our work by Jacob and Speed.

I wondered if you could expand on your posting and update it with the essential information?

Jacob original stated that he ranked our training data set for differential expression by age and sampled "randomly" from the top 25%. No code was presented and the R code itself doesn't need to reflect the input data matrix or indeed be what was actually used.

Jacob and Speed have refused to answer any questions or share code for the past year.

We did carry out 10,000 random samples from the U133+2 with and without our 'model probe sets' and beyond any doubt their claims are false for tissue age classification. No random 150 remotely matches our signature.

The remaining analysis by Jacob is scientifically invalid as it 1) combines clinical groups that can't be combined and 2) it relies on 'other' gene lists out with any into all model. Anyone can fit a unique list to a given data set. No one has taken 1 model and made it work nicely across 7 independent data sets....hence our paper.

I'd like to learn more about the route you took to establish your opinions on Disqus and if you can share any materials with my team?

Thanks<br /> Jamie

Professor James A Timmons