On 2013-12-11 21:20:10, user jipkin wrote:

A couple thoughts as I'm reading:

In 2.3.1, I'm curious about using 2us for the ballpark calculation, since I've seen datasets that have acceptable quality for synapse ID and segmentation with 0.5 us dwell times.

For the discussion of development costs, it should be mentioned that the technology to reliably and uniformly stain large volumes of tissue (like the mouse brain) is still under development. Talk to Shawn Mikula from the Denk lab for more.

It seems strange to speak about the capital costs of the microscopes as if these machines are going to be used once for the mouse brain and then put out to pasture. It may be worth mentioning that these machines have lifespans hopefully longer than 3 years and can therefore be used for other projects, increasing their value.

Finally, while I understand the focus of this piece on the mouse connectome as an example (and the current Holy Grail in the field), this seems like the perfect venue for cross-species comparisons. Wouldn't it make a sweet table to see the costs for species like drosophila, larval zebrafish, leech ganglion, stomatogastic ganglion, larval ciona intestinalis, maybe even the human brain?

Jason Pipkin<br /> Kristan Lab<br /> UCSD

On 2016-02-27 02:35:36, user Seth Bordenstein wrote:

Looks like great work. Mikhail and I talked over twitter a bit and I<br /> wanted to briefly summarize a point that I made to him. The hologenome <br /> is "defined as the sum of the genetic information of the host and its <br /> microbiota" by the Rosenbergs. It itself is an incontrovertible entity <br /> like the word genome or chromosome. It can not be "misleading" itself. <br /> What is debatable and testable is how the hologenome assembles? What <br /> levels of selection are operating - host level, symbiont level, or both?<br /> Hope this is of some help and maintains some clarity in the nascent <br /> field that has gotten a dose of confusion recently. The hologenome is an<br /> entity, which is different from the evolutionary processes that affect <br /> its variation.

On 2021-02-25 13:13:47, user Takeoka lab wrote:

As a part of course assignment for Hot Topics in System and Cognitive Neurosciences [Eo3N5a; Neuroscience Masters Research Track], Faculty of Biomedicine, KU Leuven, Leuven, Belgium: students' peer review

Summary: <br /> This study investigates learning and experience dependent adaptation in the mouse vibrissae system. The authors look for a relationship during sensory learning in a head-fixed standard Go/No-Go detection task, between controlled whisker inputs, primary somatosensory cortex (S1) activity and behaviour output. Firstly, during basic detection learning and secondly, during flexible adaptation to changing sensory contingencies. They performed chronic wide-field imaging of S1 activity with the genetically encoded voltage indicator (GEVI) ‘ArcLight’ in behaving mice. It seems that in response to changing sensory stimulus statistics, mice adopt a task strategy that modifies their detection behaviour in a context dependent manner as to maintain reward expectation. The neuronal activity in S1 shifts from simply representing stimulus properties to adaptively representing stimulus context in an experience dependent manner. They found that during basic learning, the neuronal sensitivity is mostly stimulus driven and does not change. The S1 seems to provide a stable representation over the course of learning. Furthermore, they found that the neuronal sensitivity can change when subjects already adapted its behavioural strategy before. They suggest that neuronal signals in S1 are part of an adaptive and dynamic framework that facilitates flexible behaviour as an individual gains experience.

Major concerns: <br /> 1. Figure 2c: It is unclear why the authors chose numbers 0.8 and 1.5 for ‘naive’ and ‘acquired’? It is not mentioned in the text.

1. Figure 2: Are three mice enough to prove the results about the S1 responses during basic learning? The statistics to calculate the power are missing.

2. Figure 2 and 3: The authors say basic learning happens first followed by the adaptive learning. But why are different mice used for basic and adaptive learning; i.e., mice 1-3 for figure 2 and mice 4-7 for figure 3?

3. Please define the downstream areas. It would also be interesting to see the experiments repeated with measuring neuronal activity in these downstream areas, since they do seem to explain some part of the behavioural activity.

4. To draw a causal link between neural activity and behaviour, impairing the animal’s performance due to an area inactivation seems necessary, especially when authors compared their findings with lesion studies in the discussion.

5. In order to further optimize the text, it could be useful to explain some terms briefly in the result section instead of only in the materials and methods, so that these terms are clear while reading the result section without having to search for the meaning of these terms in the materials and methods or figure legends. Examples include: Catch trial (page 17, line 348), false alarm rate (page 18, line 368) and response threshold (page 21, line 433).

Minor points: <br /> Figure 1e: Punishment is not clearly illustrated.<br /> Figure 1g: Using a histogram to show the distribution would be clearer. <br /> Figure 2c: Put the signs in a more structured way so it does not overlap, by making the bars bigger, so the signs are smaller compared to the bars.<br /> Figure 3a: It would be easier to interpret this figure, if the different values of the stimuli amplitudes would be mentioned in the figure itself or at least in the legend.

Figure 3a and figure 1g are presenting the same and can be emerged.<br /> Figure 3b: It would be clearer if the black dotted curve on top of the magenta curve is more prominent, since this dotted curve is barely detectable that it is positioned on top of the magenta curve. <br /> Figure 3e: The abbreviation PSTH is not explained in the legend.<br /> Figure 3f and 3g: Please clarify the downstream criterion c.

Figure 3f: The abbreviation CR is not explained in the legend.<br /> Figure 4 is difficult to understand. Please clarify the different panels with its results and methods.

Supplemental figure 2b: Use a clearer colour distinction instead of the grey colours.

Page 3, line 16-17: Rather: "While much is known about how and where in the human and non-human brain sensory signals are processed."<br /> Page 5, line 68: Injection no capitol letter needed.<br /> Page 7, lines 110,111: The closing square bracket ‘]’ seems to be missing.<br /> Page 8, line 145: delete ‘)’. <br /> Page 10, line 196-197: “A condition was always kept constant within and across multiple behavioural sessions before the task was change.” Changed instead of “change”.<br /> Page 14, line 278-279: “Ideal observer analysis. To quantify the fluorescence signal over the course of learning a metric ????’???????????????????? was computed” Comma after learning, otherwise this sentence implies the mice are learning the metric.<br /> Page 19, line 388: Replace “(Black)” with “(Grey)”.

Page 22, lines 472 and 474: Replace “(left panel)” with “(top panel)”; and “(right panel)” with “(bottom panel)”.

Page 29, lines 639: Add ‘the’ before cortex.

Please be consistent with the space before and after the ‘=’, as in lines 170, 264, 267, 435, 436, 482, 483, 484, 556, 557, 561.

Please be consistent with the ‘-‘ in between the number and the unit, as in lines 72, 74, 125, 126.

Please be consistent with repeating the unit. As in page 7, line 122: 69um x 69um vs 11.1 x 11.1 mm.

Please be consistent with the use of double and single quotes.

Please be consistent with the units at the axes in the figures.

On 2025-02-06 02:55:28, user Tom Liang wrote:

Excellent contribution to understanding the microscopic mechanisms of anion transport across Band 3! I would like to draw the author’s attention to a highly relevant recent publication in Langmuir: https://doi.org/10.1021/acs.langmuir.4c00216

On 2025-05-08 00:54:36, user Anonymous wrote:

This is an interesting preprint. It is noted that the method identifies some known Xenon sites, but not others. Assuming the known sites are identified using X-ray crystallography, I wonder what the results would look like if the simulations were run at very low temperature, since most crystal structures are determined under cryogenic conditions.

On 2019-12-11 11:22:31, user Ramon Casero wrote:

Hi, I thought you may want to cite DeepCell (2016)

Van Valen, D.A., Kudo, T., Lane, K.M., Macklin, D.N., Quach, N.T., DeFelice, M.M., Maayan, I., Tanouchi, Y., Ashley, E.A., Covert, M.W., 2016. Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments. PLOS Comput. Biol. 12, e1005177. https://doi.org/10.1371/jou...

Although they work with other cells instead of adipocytes, and fluorescent and phase contrast microscopy, the idea of pixel-wise classification of cell interior vs. boundary/background with a CNN is already there. (They also proposed active contours for CNN post-processing, as an alternative to thresholding).

On 2023-08-04 16:27:45, user Edward Holmes wrote:

The algorithm used to infer recombination break points - GARD - is prone to false positives such that we can all but guarantee the 27-31 recombination breakpoints vastly overestimate recombination in this lineage. The algorithm's greedy methodology for finding incongruence in phylogenetic trees under a gamma site heterogeneity model means the algorithm will misclassify punctuated equilibria and variable rates of evolution as recombination events.

To illustrate this, the authors need only run this algorithm on mammalian mitochondrial DNA or SARS-CoV-2 sequences collected after 2021. Using their methodology, it wouldn't surprise me if they estimate humans & chimps diverged 100,000 years ago or SARS-CoV-2 arose in late 2020. If you reconstructed a recombinant common ancestor for mammalian mitochondrial DNA that clearly do not recombine, you would greedily construct a common ancestor that appears more like humans than the actual common ancestor by allowing the human genome to define its closest relatives at every small segment of the mitochondrial genome, thereby reducing the genetic distance between humans and its "RecCA".

Like Pekar et al.'s use of an HIV model of superspreading and unbiased case ascertainment to claim two basal polytomies implies two spillover events, this paper is an unstable stack of methods poorly understood by the authors applied to achieve the desired conclusions, when a modicum of attention to detail can quickly reveal the fatal limitations of their analysis.

There are ~1,100 substitutions separating RaTG13 - collected in 2013 - from SARS-CoV-2 in late 2019. SARS-CoV-2 acquired ~25 mutations per year when it was spreading in the far larger global human population and there is little to no evidence that bats suffer chronic infections that would accelerate this rate. Consequently, there are ~44 years of evolution separating SARS-CoV-2 and RaTG13, slightly fewer for BANAL-52. The authors' complex stack of models, each with clear limitations and biases known to those who make such models, hides this obvious arithmetic fact that contradicts their conclusions.

On 2021-03-08 23:10:15, user Amelia Andrews wrote:

Hello! My classmates and I chose your paper to discuss during our journal club. We found the use of TAK1 as a therapeutic target to prevent retinal neovascularization very interesting and relevant. I would like to share some of the comments we had that could help improve the paper, as well as highlight parts that we thoroughly enjoyed. We thought the figures, especially Figures 4 & 5 were well put together. The color scheme was consistent throughout the paper, which aided in data visualization. In Figures 4 & 5, we recommend labeling on the figure whether TIME cells or HRMECs were used to limit any confusion. Specifically, in Figures 4C & D, we found the x-axis to be a bit busy, so we thought the addition of a legend or structuring the labels similar to the labeling in Figures 5B & C could increase readability. In Figure 5, my classmates and I thought the images were very well done, especially in the wound healing assay. We did recognize throughout the paper that the addition of the non-significant p-values made the figures more crowded, so a suggestion we had was to only include the p-values if they are leaning towards being significant. We also wondered if in Figure 5F the image could be color-contrasted to aid in visualization. Overall, we appreciate the methodology and flow to the paper as we progressed from figure to figure. I look forward to reading more about this research and future work.

On 2024-12-07 11:02:50, user Thu wrote:

This work is published in American Journal of Botany https://doi.org/10.1002/ajb2.16429

On 2018-01-17 12:50:50, user E Rees wrote:

Many thanks for your interest and comments on our paper. We completely agree with you that our analyses must control for ancestry. Regarding the issue of the Finnish component of the Swedish dataset that you raise, we used principle components analysis to remove individuals from each dataset with non-European ancestry, and we also included the first 10 PCs as covariates in our logistic regression tests. Additionally, our filtering criteria excluded variants greater than a given allele frequency in any ExAC subpopulation, which included the European Finnish component of ExAC. With regards to our main novel finding of association between LoF and paralog conserved missense alleles in sodium channels, as an additional check, I have repeated the analysis of the Swedish dataset excluding samples with substantial Finnish ancestry (using PCs 5 and 3 to track Swedish/Finnish ancestry (Finnish samples identified using 1000 genomes data), as per Genovese et al 2016, SM Figure 12). After excluding the Finnish samples, association between schizophrenia and sodium channels is actually marginally more significant. In the next version of our manuscript, we will aim to present a sensitivity test excluding Finnish samples, and address the issue of fine-grained population stratification more generally.

On 2020-06-16 17:52:25, user Aaron wrote:

I'd suggest changing figure 1b (case fatality vs proportion of D or G at residue 614) to consider case fatality over time in countries with rising prevalence of G614. As is, the panel is a bit misleading as the case fatality rate for each country is going to be a product of D614 and G614 infections due to the shifting proportions of the two variants over time. Rising CFR in multiple countries with rising G614 prevalence would be more suggestive of this connection. However, it's still worth considering the fact that as prevalence of G614 has risen, so has general case load, which in and of itself can lead to overwhelmed healthcare systems and higher case fatality.

On 2022-05-13 19:06:36, user Allan-Hermann Pool wrote:

Hi Jenny! Very valid concern - I did use the 10x prefiltered gtf file as a starting point as most users probably use that as the default option. So all improvements are made based on the latest 10x Genomics default human and mouse genome annotations/reference transcriptomes. Will clarify that in the Methods.

On 2021-05-03 08:52:40, user Umberto Lupo wrote:

I wonder if the authors could further clarify some aspects of the validation setup for the experiments conducted in Section 4.1.

I understand that each set of PFAM families (in a given clan) is partitioned into five buckets, and that in turn each bucket is artificially shortened in depth. However, despite my best efforts reading and interpreting Appendix C.3 and the main body of text, I still am not sure exactly what data the independent Potts model and the NPM is fitted on during each of the five rounds of "cross-validation".

To be completely explicit, suppose we are at round i so that bucket i is being reduced. Then, is the NPM fitted on the reduced version of bucket i alone, or on the latter plus the other four buckets in their entirety? Or on something else? Similarly, what are the independent Potts models trained on exactly during each round?

If both models are fitted on the reduced version of bucket i alone, then what exactly is the validation set? Is it the rest of bucket i, i.e. the part discarded from the training set? (Otherwise, one may worry about trivial overfitting.)

Thanks in advance for your reply!

On 2016-07-19 13:56:27, user Terry Burke wrote:

Errr... am I missing something? Why don't we just use a JIF based on the median, not the mean? Everyone knows that the no. of citations for every journal ranges from zero to a large number. Seeing the plot doesn't really help, and a summary statistic is (obviously) always going to be more comparable. But at least the median summarises something usefully meaningful, while the mean can bounce around wildly according to a few highly cited papers. The mean must also exaggerate the difference between the "high impact" and the rest, as just a few journals carry the few most distorting hyper-cited papers. It has always perplexed me why Garfield went for a JIF based on the mean.

It's inevitable that journals will have reputational differences and an impact factor (however calculated, but let's do it better) is inevitably going to feed into that. The error of judging a paper according to its location would remain, with or without a JIF.

On 2016-07-08 18:14:40, user Hamed Seyed-allaei wrote:

Nice job!

I have a suggestion regarding Fig 4. This figure is the spotlight of your work. But it is noisy, especially at the tails. This is natural, because there are few highly cited works. This can be improved using one of the following methods:

1. You can use logarithmic bins to construct the histograms: 0,1,2,4,8, ...
2. You can use cumulative density/histogram instead.

This reduces noises at the tails of the distributions so one can compares the performance of journals around highly cited works.

On 2024-09-30 15:29:51, user KU Lab Hirase wrote:

Vittani, M. et al. (2024). Virally Induced CRISPR/Cas9-Based Knock-In of Fluorescent Albumin Allows Long-Term Visualization of Cerebral Circulation in Infant and Adult Mice. In: Rusakov, D. (eds) Fluorescence Imaging of the Brain. Neuromethods, vol 209. Humana, New York, NY.<br /> https://doi.org/10.1007/978-1-0716-4011-1_6

On 2023-11-16 18:41:06, user Vernita Gordon wrote:

The Langmuir DOI for this article is 10.1021/acs.langmuir.3c01637.

On 2016-12-16 13:13:08, user Devin O'Connor wrote:

We welcome your comments and questions on this manuscript here or @oconnord.

On 2018-09-22 08:59:52, user hasnain wrote:

Its a new technique and I think if any person read these articles may be improve the level of education and these articles implement on reports

On 2025-11-17 13:48:16, user Robertson, Andy wrote:

My thoughts on this are that using Bayesian models are probably not the most efficient method of computing 1.8 million floating points using 2D binomial mathematics. It seems a very antiquated model in terms of dealing with that many data points. The reality is we don't know much about living biological organisms that can potentially process that amount of data, human beings probably can't, let alone deal with anything moving faster than tradition UV wavelengths of light (with frequencies of about 400 nanometers). X-Rays obviously move faster than our ability to see them but that does not necessarily mean in curved space time, that theoretically an electron can't move faster than an X-Ray. Is it possible that Cesium Iodide, has the exact magnetic resonant frequency as x-rays, possibly. The fundamental problem seems to be understanding nature as it relates to cyber security. A whale and most marine life, are capable of generating a variety of different magnetic resonant frequencies to communicate as well as being able to swarm / flock around a central point in 3-dimensional space.

Seems to me the central rotational point of a swarm / flock whether, birds, bees or fish as defined by AI swarm mathematics, such that the central node, is moved in virtual curved space dimensions of n where Riemannian manifold geometry no longer apply (say on a forwarded edge cache header of a content delivery network) this could be moved through this space at theoretically faster than x-ray speeds where c tends towards infinity. Ie move the theoretical swarm response center faster than light.

This must be where modern cybersecurity must invariably end up such that AI ends up controlling the response to

1.8million malicious data points, or:

conversely;

1.8million requests of benign agents, in terms of bandwidth limiting / load shedding.

The notion of adversarial AI in either context largely becomes irrelevant.

It will simply lie to protect itself and humanity at the same time.

Any perceived threat against any aspect of humanity itself while it processes a faster response and solution to the entire 1.8 million data points of malicious attempts at denial of service, than could ever be neutralized in under 7 seconds

It becomes naturally obvious that it is working in humanities interest and always will.

As to the connection to whale brain functions and mice. I think it might be interesting to pose a question to the marine biologists of this world about their view regarding whale brains and how they generate sound, and magnetic resonant frequencies beyond the speed of sound, and potentially x-rays and yet they remain an endangered species?

On 2020-04-23 20:18:15, user Eric Johnson wrote:

By "high-risk groups", I'm referring to elderly, HTN, COPD, CKD, DM, and obese patients. There are data from studies in all these groups showing elevated baseline IL-6 levels.

On 2018-07-17 08:04:25, user Thom Thum wrote:

Interesting study!<br /> However, I miss some details: what are the <br /> genotyping PCR conditions (i.e. how many cycles were performed) for <br /> analysing the HDR-mediated integration of EGFP in the tyrp1b and h3f3a <br /> loci (Fig. 6) or mitfa rescue (Fig. 4)?

Could you further verify the HDR events by Southern blot analysis?

On 2019-02-20 19:21:09, user Arjan Boonman wrote:

Correct, however, an infinite number of scatters (a lawn would have a lot) would lead to a white noise spectrum, so no deep troughs anymore. Our paper only calculates up to 300 scatterers (effect on spectrum shown in figure 2). Leafy bush would limit the number of scatterers, so still give rise to deeply modulated spectra. We're looking into that at the moment. However, the pulses of bats closer to vegetation tend to be no longer narrowband so this topic departs from the subject of the article which is optimization of echo detection in noise by means of bio-sonar. Many narrowband echolocators (open space hawkers) still modulate (by 3-8kHz) their pulses even when foraging very high in the sky (incl species of Emballonuridae) (Table 1 this paper). We hope to be able to confirm this behavior in more species of bat that fly above sonar contact with the ground (as revealed by combined GPS and onboard recordings).<br /> The Doppler effect at 8.5m/s (likely max speed) gives rise to 5% increase in bandwidth so Figure 3 in our paper can be used to assess the small additional beneficial effect such increases may afford.<br /> For all clarity to any other reader: of course the extremely narrowband CF signals used by Rhinolophus and Hipposideros are NOT optimized for detection in open space, but for detection of Doppler shifts and wing-flutter in cluttered-space (see review by Denzinger and Schnitzler 2011).

On 2020-05-11 17:40:30, user Pablo Carravilla wrote:

Dear authors,

First, I would like to congratulate you for your nice article, I enjoyed reading it and I found the results very interesting. I am also investigating Env-mediated HIV entry and have a few questions about your work. I hope they can help improve your article!

-I always found fascinating that HIV entry takes minutes from attachment to fusion as reported by live fluorescence microscopy (e.g. Mamede et al 2017 PNAS, Iliopoulou et al 2018 Nat Str Mol Biol, Markosyan et al 2005 Mol Biol Cell), but the fusion process could not be detected by electron microscopy until now. Can you detect these attached but not fused virions? If so, what are they up to?

-Regarding Env distribution, I found it interesting that in your experiments Env is distributed randomly. I have performed STED Env distribution experiments with three different Envs (NL4-3, JR-CSF and PVO) and many different antibodies and all of them show mainly a one focus distribution (VRC01, 2G12, b12, PGT145, 4E10 and 10E8; see Carravilla et al 2019 Nat commun, Fig. 2A).

Of course, electron microscopy provides superior resolution to fluorescence microscopy. Still, I would not agree that "cryo-ET reconstructions revealed random spike distributions rather than a single cluster of spikes [53, 54]." In these two papers (Zhu et al 2006 Fig. 2b-d; Liu et al 2008 Fig S1b) Env does not seem to be randomly distributed. In fact, Zhu et al mention "some Env clustering" in the abstract. Moreover, these clusters would look like a single focus in a STED microscope with ca. 35 nm resolution.

Since the "Env trimers on HIV-1 virions are difficult to identify conclusively by ET", do you think these "other" molecules in your virions might be host proteins derived from the plasma membrane (for example reviewed in Burnie and Guzzo 2019 Viruses)?

I wonder whether within these putative clusters, closely located molecules participate together in fusion and form the spokes you detect. But as you discuss it seems strange that you rarely detect more than three spokes (thanks for making the raw data available).

-Finally, why did you link T1249 to Fc? Is it to reduce its potency?

Congratulations again for your beautiful work,

Pablo Carravilla

On 2021-03-10 19:29:01, user Luis David Alcaraz wrote:

The much improved reviewed & published version is available here https://www.sciencedirect.com/science/article/abs/pii/S0944501321000380?via%3Dihub if you want a copy contact me @ldalcaraz or ldalcaraz@ciencias.unam.mx

On 2022-02-03 20:41:57, user Investock Real wrote:

Yes, I am also interested. I guess that pollution will have some effect, it would not be the same in the country side that in a big city such a New York. Radiation is also a powerful mutagenic, places such as Hiroshima or Chernovil would increase the probabilities of mutation, right?

On 2022-07-20 16:07:55, user Laboratório Biofísico-química wrote:

Final version available at https://www.tandfonline.com...

On 2019-07-06 06:07:10, user Andre wrote:

A potential gamechanger!

On 2021-07-12 14:58:26, user @hugospiers wrote:

This is an exciting experiment with fascinating results. The discussion could be enhanced by referencing a range of human fMRI studies that have shown ramping activity to goals in VR. The authors (including me) assumed this was purely a neuronal-BOLD signal correlate, but your data suggests the fMRI BOLD may be tracking integration by astrocytes, and provides a novel perspective on those papers. See. Spiers and Barry, 2015, Curr Op Behav Sci, and Patai and Spiers, 2021 TICS. Exciting to have a new perspective. Bravo on the great work.

On 2018-01-29 19:05:59, user Hosein Fooladi wrote:

Dear Mukul,<br /> Thank you so much for your comment. We found diffusion of BMP4 and Noggin are very important parameters in our model and changing them can significantly change the emerging pattern. We have checked previous studies and I can say diffusivities of BMP4 and Noggin in our model are within meaningful biological ranges. But, Unfortunately I do not have access to experimental setup to measure these parameters directly myself.

On 2020-03-02 14:12:16, user Jonathan Wells wrote:

Cool paper, it seems pretty convincing, particularly given the clear pattern of 5' truncations shown in figure 2. Given that there are not many full-length elements remaining, it might be worth checking whether or not the transcripts map exclusively to the TEs, or if they also contain upstream non-TE sequence. The latter would be indicative of read-through transcription from neighboring genes. If you don't get any reads mapping exclusively to 5' end of the LINEs it might be harder to say that they are still currently active. I enjoyed reading this anyway, thanks!

On 2020-06-18 07:36:53, user Ole Herman Ambur wrote:

Great work in describing a likely selective pressure for eukaryotic sex I have been wondering about.

On 2018-11-24 15:37:21, user Klaus Fiedler wrote:

The manuscript version no. 1 page 13 should read in the last paragraph: Further inspection shows, that also N-glycans of TMED proteins shown to be modified by carbohydrates<br /> within the GOLD-domain, had already been further analyzed (65,75). For TMED7 and TMED9, N-glycans processed to complex and high mannose/complex N-glycans, respectively, had been found. Comparison of N-glycosylation sequons of each TMED of Mus musculus suggests that TMED4 and TMED11 could be N-glycosylated within, and likely TMED6 and 10 exterior to the GOLD-domain putative glycan binding pocket (data not shown). Asn103 in TMED7 is not located within the binding site of complex/hybrid N-glycans as gleaned from the structural comparative sequence analysis (Fig. 3A, Fig. 4B and Suppl. S5). The three TMED proteins TMED4, TMED9 and TMED11 may thus be impeded in putative glycan binding to the concave lectin surface if themselves covalently glycosylated within the GOLD-domain. It is possible that all other TMED proteins are free to interact with ligands via their concave patch GOLD-domain without steric hindrance.<br /> The file should be updated-

On 2018-12-28 15:31:06, user leszek.kleczkowski wrote:

Impressive work! By the way did you find any effect of the loss of peroxisomal HPR1 on the activities of cytosolic HPR2 and GR1? (e.g. was there any compensatory increase?)This was something I wish we did when I worked on barley mutant (Plant Physiol. 94, 819-825, 1990).

Regards<br /> Leszek

On 2022-02-03 00:35:40, user Ragnhild Eskeland wrote:

All code can be found at https://github.com/Eskeland...

On 2017-02-04 19:21:51, user Anshul Kundaje wrote:

Very nice work. Didn't see a link to the code. Could you share. We'd like to compare to our Deeplift method. Also a quick suggestion. I think the paper would be more complete with a systematic comparison to other existing methods such as in-silico mutagenesis, Simonyan et al, LRP and Deeplift. We'd be interested in benchmarking as well on simulations and real data.

On 2021-01-26 23:29:47, user Edwin Alberto Pinilla Salazar wrote:

Dear Fan, below I am sending you the link of a document that correlates a lot with the research you have been carrying out and that resolves some concerns that you had, related to tests on animals and on human beings.<br /> https://www.ncbi.nlm.nih.go...

On 2021-03-13 21:22:34, user Givi Koberidze wrote:

Has this paper been peer-reviewed and published?

On 2025-08-26 14:13:25, user Darren Thomson wrote:

Can you please provide more details about your image analysis pipeline? Simply citing the software won't help anyone reproduce this assay.

On 2018-04-04 16:53:18, user bennedose wrote:

Here are some further thoughts on the findings presented in the paper. I will first quote lines 276 to 282 from the paper:

"Third, between 3100-2200 BCE we observe an outlier at the BMAC site of Gonur, as well as two outliers from the eastern Iranian site of Shahr-i-Sokhta, all with an ancestry profile similar to 41 ancient individuals from northern Pakistan who lived approximately a millennium later in the isolated Swat region of the northern Indus Valley (1200-800 BCE). These individuals had between 14-42% of their ancestry related to the AASI and the rest related to early Iranian agriculturalists and West_Siberian_HG. Like contemporary and earlier samples from Iran/Turan we find no evidence of Steppe-pastoralist-related ancestry in these samples."

The paper clearly states that earlier samples from Swat (taken 1200-800 BCE) did not show steppe ancestry, but had AASI. This must be correlated with what is known from history. By 500 BC Emperor Darius had erected the Behistun monument in Iran which was recorded in Old Persian - which developed after the earlier "Iranian" language Avestan. Avestan was spoken by early Zoroastrians and dates back to 1000 BCE in the Punjab region. Scholars like Darmetester and Mary Boyce show that the Zend Avesta - the holy Zoroastrian book post dated the Vedas and was almost identical to the Atharva Veda. That means that Indo European languages were already there in North West India (Gandhara/Swat area) by 1000 BC and earlier (since the Vedas are dated earlier)

Now the genetic picture uncovered by this paper shows that the people of the Swat area DID NOT have steppe ancestry in that period (1200-800 BCE). But Indo European Languages were already present in the area. This rules out the connection between steppe ancestry and Indo-European languages. In India, Indo-European languages pre-dated steppe ancestry.

On 2020-10-01 15:17:11, user Muhammad Zakiruddin Chowdhury wrote:

This research was conducted by members of Globe Biotech and Globe Biotech is presenting this as a publication in International Journal. They conveniently skipped mentioning that it has not been peer reviewed. Please google Bangladesh English News portals on 01 and 02 October and you will find more.

On 2021-07-21 14:37:48, user Taras Oleksyk wrote:

A modified and improved <br /> version of this article has been published in GigaScience. doi: 10.1093/gigascience/giaa159

On 2021-06-08 02:23:10, user Daniel J Paluh wrote:

The peer-reviewed paper is now published at eLife: https://doi.org/10.7554/eLi...

On 2022-09-19 07:58:28, user zhljude wrote:

Hi Thomas Burger:

This article is a nice work. However, the low resolution of the Figures makes it confused to understand the content of the article. Could you provide clearer Figures ?

Best regards<br /> Jude

On 2017-11-10 18:07:39, user James Lloyd wrote:

I would like to thank you for the interesting pre-print and tool. I really enjoyed reading about this tool for fast quantification of alternative splicing events, including complex events not often captured by other approaches.

Comments:<br /> Page 5: I wanted to know more about the identification of novel AS events. There is mention of "pseudo-de novo AS event identification (see below)" but then it is not discussed again, as far as I could see. I think discussing what sort of de novo events, with junctions not explicit in the transcriptome annotation provided Whippet can find would be really helpful.

Page 10: "HeLa whole cell, nuclear, and cytosolic fractions, as well as mono- and polysomes", it is worth pointing out that the RNA in the polysomes used in study #5 were from HEK293T cells.

Page 30: "Additional nuclear and whole-cell HeLa fractions originating from a different paper were also analysed as a comparison" what paper is the HeLa fractions from?

PS, as a Yorkshireman, I appreciate the name Whippet.

On 2020-12-24 18:56:48, user Charles Warden wrote:

Thank you for posting this preprint.

I noticed that there was highlighting of sentences throughput the manuscript. Was this intentional, or should those be removed after editing and discussion?

On 2020-10-02 16:28:20, user David Ross wrote:

This preprint is related to an earlier preprint with the same title (https://www.biorxiv.org/con...: "https://www.biorxiv.org/content/10.1101/2020.07.10.197574v1):") We received feedback that the earlier manuscript contained too many ideas for one paper. So, we split the story into two parts. The part contained in this manuscript includes a description of the measurement and a discussion of what the results can tell us about LacI allostery. A subsequent manuscript will focus on the use of the results for precision engineering of genetic sensors.

On 2015-08-22 15:37:34, user Edouard Debonneuil wrote:

format suggestion: at the end of the first sentenceof thediscussion, list many articles rather than only 2 (eg list of references embedded in Sven Bulterijs and David Gems' articles)

On 2021-09-01 00:46:56, user Tom J wrote:

Please check Fig 1d relative to the text on page 4...seems contradictory. If the Alpha spike/Delta backbone replicated less efficiently than Alpha, the ratios should be >1. Is the y-axis in the figure correct?

On 2017-03-12 18:31:48, user Jean Manco wrote:

@ David Smurkovic. I am not one of the authors, but I have entered BOVO1b in my online table as H27. The markers are 263G 750G 1438G 4769G 8860G 15326G 16093C 16519C<br /> http://www.ancestraljourney...

On 2017-06-10 08:42:33, user imroze khan wrote:

We provide the first experimental evidence for the rapid evolution of immune memory in insects

On 2017-05-21 20:53:25, user Carl Mesarich wrote:

The tomato accession names/numbers are now correct (May 21, 2017).

On 2024-06-28 13:20:02, user Jo Wolfe wrote:

Interesting preprint! Regarding the intro, indeed the oldest direct fossil evidence is Jurassic...but we recently found that the crown group of Brachyura are probably Triassic<br /> https://academic.oup.com/sy...

Also, in our 2021 Bioessays paper, we did suggest the pleon folding in metamorphosis may be due to Abd-A repression, so it's cool that you found support for that result

On 2014-02-14 23:56:27, user Casey Brown wrote:

This is a very nice paper.

A have a few questions/comments:

1. On the classifier. Did you attempt any other<br /> approaches? Could you provide more details on how the cell type specific probes<br /> were selected? Were they simply the 58 most correlated probes? Were they<br /> selected in any way to maximize independent signals? Did you consider the NNLS<br /> approach used by Battle et al.?

2. Limiting search to previously identified,<br /> additive cis-eQTLs. As the authors note,<br /> limiting the search for interaction-eQTLs (as opposed to searching the full set<br /> of cis-SNPs) almost certainly downwardly biases the fraction of<br /> interaction-eQTLs identified, because they previously selected for SNPs that<br /> explain a large proportion of the variance in an additive model. Of course, this effect will be even more<br /> dramatic for the eQTLs with larger effects in the minority cell<br /> population. Could the authors perform<br /> the analysis on the full set of SNPs on at least a subset of the data?

3. Characteristics of interaction-eQTLs. The analysis of GWAS overlap is nice, but it<br /> would be great if the authors pushed this a bit further. Are these genes enriched for genes that are<br /> differentially expressed between cell types? Are the distributions of SNP to<br /> TSS distances different between the different classes? Are there cell type<br /> specific regulatory element (e.g. DHS?) datasets that can be used to interpret<br /> these findings?

4. Cell type gene expression signature. Are there<br /> genetic variants that are associated with the proportion of neutrophils? When<br /> you include the cell type term in the model, do you increase the number of<br /> identified additive cis-eQTLs? (I.e., does heterogeneity add noise to the<br /> standard analysis?) Relative to uncorrected expression data? Relative to covariate-naïve latent variable<br /> removal?

5. Examples. It would be nice to see a few plotted<br /> examples of particular gene-SNP combinations that have significant interaction<br /> eQTLs. In particular, it would be nice to see the data from one or both of the<br /> datasets where the cell type counts were directly quantified.

<cross posted="" on="" haldane's="" sieve,="" bc="" i'm="" not="" sure="" what="" the="" protocol="" is="" in="" the="" pppr="" world="">

On 2020-07-21 16:05:48, user OxImmuno Literature Initiative wrote:

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

On 2017-04-24 21:18:41, user Benjamin Zinszer wrote:

Published version of this article (with substantial revisions) is available from PLoS ONE: http://journals.plos.org/pl...

Emberson LL, Zinszer BD, Raizada RDS, Aslin RN (2017) Decoding the infant mind: Multivariate pattern analysis (MVPA) using fNIRS. PLoS ONE 12(4): e0172500. https://doi.org/10.1371/jou...

On 2017-05-05 11:53:48, user Harry wrote:

Quiet an interesting investigation. Looking forward for your future works.

On 2020-05-09 23:10:52, user Dima Shvartsman wrote:

Excellent and thorough work. Limiting the proliferation of non-committed cells is very important for the safety of transplanted cells and a reduction of heterogeneity in the cell population.

On 2018-08-26 12:56:55, user Maier Lab wrote:

Detailed, important analysis on protein denaturation at air-water interfaces - and rescue options. Critical for cryoEM sample preparation but important also beyond cryoEM.

On 2019-11-15 21:12:39, user Tyler Square wrote:

Cyclostomes (lampreys and hagfishes) are not established as being 2R, and they are unaddressed here. It seems like you are actually addressing gnathostomes and the gnathostome common ancestor, not "all vertebrates" and their ancestor (per the first sentence of your abstract).

On 2021-04-05 03:48:58, user Zohreh Khosravi wrote:

Congratulations with your amazing paper. I have a question and really appreciate you if you could help me in this matter.<br /> when You performed a whole genome CRISPR screen in cancer cells to identify regulators of T cell killing. One of your hits leads to increased killing of your cancer cells in presence of T cells but it represents an uncharacterised protein. What is the hypothesis and which three experiments would you carry out to elucidate the function of your hit?<br /> Best Regards,<br /> Zohreh

On 2019-02-12 18:23:11, user Eve Wurtele wrote:

In Arabidopsis, in silico predictions followed by experimental evidence indicate that the de novo orphan QQS gene was quickly integrated into the metabolic network affecting carbon and nitrogen partitioning, and into a network conferring broad-spectrum resistance; most significantly, because QQS interacts with conserved network elements, introduction of QQS into other plant species confers the same effects (Li et al .,2015, PMID: 26554020 ; Qi et al., 2019, PMID: 29878511 : Li et al., 2009, PMID: 19154206; Arendsee et al, 2014, PMID: 25151064)

On 2018-04-05 15:18:51, user Andrew Millar wrote:

Smallwood et al. tested lipid synthesis in nutrient-limited conditions with added glycerol, in a preprint published a day before this one, https://doi.org/10.1101/293704. Sadly we did not see this or their January preprint on lipid droplet release prior to our publication, so will take their work into account during revision.

Intriguingly, the three, unknown proteins that are highlighted as being up-regulated in nutrient-limited conditions in their paper were also up-regulated in prolonged darkness in our results (their RefSeq protein identifiers below), suggesting that what we propose as a quiescent 'dark state' might be similar to their lipid-release state:

XP_003078347 is now ostta03g04500, an abundant protein in our Fig. 2a, Fig. 4B.

XP_003078347 is now ostta09g00670, an unknown protein, in Fig. 4A.

XP_003078347 is now ostta02g03680, unkown protein with a putative BAR domain, in Fig. 4A and EVFig. 7a,7b.

On 2021-04-19 14:22:39, user Milka Kostic, PhD wrote:

Dear authors,<br /> Thank you for sharing this preprint with the community. I read the preprint with interest, and I am sharing the comments below in hope they will be helpful to you as you go forward with publishing and sharing your new findings further.<br /> Kind regards,<br /> Milka

Comments to the authors:<br /> In this preprint by Shao, Yang, Ding et al. the authors describe an expansion of the PROTAC (Proteolysis Targeting Chimera) concept into a new direction: using chimeric molecules that combine a DNA sequence fragment (used here to recruit DNA binding proteins) and an E3 ubiquitin ligase binding warhead (used here to recruit either VHL or cereblon (CRBN)). The resulting hybrid molecules are referred to in this preprint as O’PROTAC, short for oligonucleotide PROTACs. The big motivation for this work is the lack of strategies to target majority of transcription factors (TFs). <br /> TFs represent a large and diverse class of proteins that are critical for many different aspects of biological regulation. They could also be viewed as essential targets for drug development; and yet, outside targeting nuclear receptors (NRs), which represent a subfamily of TFs that are endogenously regulated through small molecule binding and have therefore evolved to bind drug-like molecules, efforts to develop chemical tool compounds and/or drug leads that target TFs has been difficult. <br /> Couple of relatively recent breakthroughs in this are IMiD compounds (immunomodulatory imid drugs) - small molecules that serve not to inhibit protein-protein interactions, but rather to promote complex formation between an E3 ubiquitn ligase and different TFs (such as SALL4, IKZF1, IKZF3), resulting in fully functionally competent E3 complex that marks these TFs for proteasomal degradation. In many ways this is similar to the effect of PROTACs, another kind of small molecule degraders, that feature two warheads connected via a linker. One warhead binds an E3 ubiquitin ligase (usually VHL or CRBN) and the other is a ligand for a protein of interest. By now, PROTACs targeting many different targets have been developed, but when it comes to TFs finding the ligand that can be transformed into the PROTAC warhead remains a major bottleneck.<br /> Enter Shao, Yang, Ding et al. - these authors exploit the fact that TFs bind specific DNA sequences, usually short-ish oligonucleotide sequences. They design O’PROTACs to include double-stranded oligonucleotides, on one hand, and a VHL or a CRBN warhead on the other. The two proof of concept target TFs they focused on are ERG transcription factor and Lymphoid enhancer-binding factor 1 (LEF1), both clinically relevant.

Dealing with nucleic acid based reagents requires special delivery methods (which is the down side of this strategy), so the authors used lipid-mediated transfection. They were able to observe:<br /> - degradation of exogenously expressed HA-ERG in 293T cells as monitored by western blotting<br /> - CRBN-based O'PROTACs had a stronger effect than VHL-based ones<br /> - ERG degradation could be achieved in prostate cancer cell line VCaP that overexpresses ERG as well as its truncated form (TMPRSS2-ERG)<br /> - degradation of ERG has the expected downstream effect on its transcriptional targets<br /> - similar behavior was noted for LEF1, and LEF1 targeting O'PROTACs were able to inhibit prostate cancer cell line proliferation; however some of the validation steps done for ERG O'PROTACs (ERG pulldowns, and proteasome dependence) do not seem to be included for LEF1.

I think this is an important proof-of-concept work, albeit a bit preliminary. What authors could have done a bit differently is:<br /> - try to be more quantitative (it's unclear how large the observed effects are)<br /> - use a negative control (create O'PROTACs that don't bind to the ligase, or feature an oligonucleotide that has no target binding); negative controls are essential and some work around developing a high quality negative controls for O'PROTACs would be useful<br /> - have the authors tried to see if their O'PROTACs have an effect on cells where ERG (or LEF1) have been deleted? These experiments are important when validating new modalities.<br /> - provide some commentary about and/or evidence that they affect their target cleanly (selectively). Are there any other TFs that would potentially bind to the oligonucleotide motifs they used here?<br /> - provide a more useful discussion of the design consideration for O'PROTACs, potential limitations of this strategy, and how to get the most out of using them as a research tool; in the current form the Discussion is not necessarily all that useful for anyone interested in using this technology. <br /> - (in the future) it would be cool to see what happens in cells that don't overexpress ERG. Have the authors tried those experiments?

Congratulations on driving forward this interesting new concept of O'PROTACs and I hope my comments help you strengthen your technology further.

On 2020-05-12 08:46:49, user Theodor Sperlea wrote:

The findings reported this preprint - and some additional results - have now been published in Scientific Reports (https://www.nature.com/arti... "https://www.nature.com/articles/s41598-020-63424-7)").

On 2022-07-06 11:54:24, user Luca Jovine wrote:

Now published: https://doi.org/10.1038/s41...

On 2019-05-10 17:15:35, user Leslie Vosshall wrote:

We discussed this interesting paper at the Vosshall Lab Olfaction and Behavior Journal Club on May 8, 2019. The use of GCaMP for peripheral antennal imaging is really exciting because it opens up Anopheles mosquitoes to comprehensive investigation of mechanisms of olfaction. The paper also takes on the thorny question of the mechanism of action of insect repellents. The big ideas out there are: 1. DEET smells bad and repels insects. 2. DEET soaks up human body odor, making you invisible to them. 3. DEET scrambles the mosquito odor code so that humans smell like pizza-vomit-coffee-gasoline or something rather than just humans. This paper provides more evidence for model 2. Syed and Leal (PMID: 18711137) first pointed to DEET as binding odorants as the mechanism for blocking mosquito biting (e.g. if you coat yourself with DEET you become invisible) (model 2). Syed and Leal also provided evidence that DEET smells and repels, by activating olfactory neurons (model 1). Our group published a response to this and showed contrary evidence that at the concentrations we tested that DEET did not prevent odorants from volatilizing (PMID: 21937991). Our current data in fly and mosquito are consistent with model 3. This paper shows that DEET does NOT activate olfactory neurons but bind odorants (model 2), so neither model 1 nor model 3.

So which model is correct? DEET is a seductive molecule to study scientifically; we are still no closer to closure on its mechanism of action and this paper adds an additional wrinkle that is worthy of further investigation by the field.

We had the following feedback and questions (in no particular order):

1. In Figure 1, is it possible to do an overlay to estimate which of the 7 identified cells reliably respond to which of the 6 tested odorants? This would extract more information from the figure and give some initial glimpses into mosquito odor coding.
2. Scale Bar missing Figure S1
3. The graphics would be easier to “read” if the Tufte “chart junk” of background grids were removed to let the data take center stage (example Figure S3a)
4. The 1-octen-3-ol in Figure 1d and Figure 2a appear to be the same image, duplicated in different figures. It would be ideal to provide a new image or disclose this in the Figure 2 legend.
5. We wondered if the odor activation code is really consistent across all segments, such that focusing on one segment would give a universal answer for all antennal segments? Or is there some zonal nature or functional specification that would alter the conclusions? The whole antenna images in Figure S1b suggest some variability in how many neurons are activated in a given segment.
6. Do the natural repellents block/change odorant responses as DEET does to 1-octen-3-ol? We could not find this experiment in the paper.
7. The paper focuses on one odor 1-octen-3-ol to build the case that DEET acts merely to reduce drastically the volatility of this odorant, thus reducing/eliminating the delivery of this odorant to olfactory neurons. Is this the case for other odorants? How would this one DEET molecule be able to reduce, mechanistically, the volatility of the hundreds of different molecules emitted by the skin? We are not chemists but DEET does not seem to be particularly reactive. Is it a covalent attachment promiscuously to every odorant or more hydrophobic van der Waals mechanism that blocks odorants from volatilizing when mixed with DEET? How could this work given the enormous range in the chemistry of human odor volatiles?
8. Is Anopheles coluzzi repelled by DEET behaviorally?
9. PIDs measure bulk ionized molecules but cannot identify them. What are the prospects for repeating this with GC-MS?
10. Finally, if DEET acts by binding odorants on our skin rather than acting to repel (model 1) or confuse (model 3) wouldn’t you be bitten if you had a swath of skin that was not coated with DEET that was giving off human odor fumes?

On 2017-06-21 20:15:29, user Lindsay Cowell wrote:

Nice paper. You may be interested to see this paper which has prediction of shared sequences as a use case PMC4698203.

On 2022-04-10 11:06:58, user uv wrote:

More credit could have been given to the CATCH method. this is basically exactly the same enrichment method

On 2019-02-06 18:02:11, user Wen Li wrote:

cool! our recent shows the alpha tACS effects can last up to 24 hrs. Mostly in alpha connectivity. https://academic.oup.com/sc...

On 2018-11-15 22:28:30, user Ravishankar Palanivelu wrote:

The Supplemental information is not loading up. I get an error message.

On 2023-08-15 21:00:41, user Elijah Taeckens wrote:

This has now been published as part of the conference proceedings for the IEEE Conference on Neural Engineering.

DOI: 10.1109/NER52421.2023.10123745 <br /> Link: https://ieeexplore.ieee.org...

Please update accordingly.

On 2024-05-10 08:11:47, user Stefano Vianello wrote:

Dear Dr. Blotenburg,

I'm Stefano, the author of REF 20 re endoderm-rich gastruloids. In the Discussion section of your manuscript you write that

[REF20] maintained mESCs in 2i-medium and reported faithful emergence of endoderm cells

. Given the importance of mESC culture conditions in your analyses and possible future interpretations (at least, re endoderm), I wanted to point out that — following the practice of the lab I was working in at the time — mESCs were not grown in the classic 2i medium (2i in N2B27), but in fact in a 2i in ES+LIF medium (exact recipe in REF20's Materials & Methods > Cell culture). Based on gastruloid end-phenotype alone (of those shown in FigS1), I would guess this atypical mESCs culture medium is most closely matched by your culture condition 3 (and possibly condition 4), and that those conditions (though they were not selected for scRNAseq) are giving rise to endoderm-rich gastruloids.

Sincerely,<br /> Stefano Vianello

On 2025-05-06 17:52:12, user Alizée Malnoë wrote:

In this manuscript, Miyazaki et al. studied the interactions between EcLptM and EcLptD/E, by performing mutagenesis experiments, immunoblotting, crosslinking assays and solving the structure of the EcLptD/E/M complex via cryoEM, to further understand the role of LptM in LptD assembly and maturation. This study revealed that LptM has an essential region (C20GLKGPLYF28) within its N-terminal domain that interacts with LptD, although possibly slightly longer as discussed below. Consistent with the resolved cryoEM structure of EcLptD/E/M, in vivo disulfide crosslinking experiments revealed that LptM residue F28 is involved in the interaction with the LptD barrel domain. Additionally, residue G21 was shown to be critical for the function of LptM in the maturation of LptD. Also, the authors revealed the timing at which LptM interacts with LptD, showing that it acts at the late maturation step of LptD, after the action of BepA. The manuscript is well-written, and the conclusions made are supported by the data presented. We provide major and minor comments to help clarify some of the data and interpretations made.

Major comments<br /> - Figure 1C. Could you explain why erythromycin sensitivity increased in the ?bepA strain, while it resembled that of WT in the ?lptM strain? Does it mean that maintaining a well-folded ?-barrel domain is sufficient to maintain OM integrity? This is beyond the scope of this study, but do you think that would also be the case in ?dsbA strain?<br /> - Figure 1E-F. Narita et al., 2016 concluded that BepA might play a role in facilitating the interaction of LptD and LptE at the BAM complex. This conclusion was based on the observed suppression of erythromycin sensitivity when overexpressing lptE in a ?bepA strain which you also observed. Consider including this hypothesis as part of the discussion.<br /> - Figure 2B. Could you explain the accumulation of LptDC in K33amb and A35amb strains? Could it mean that LptM becomes less stable, or are these residues essential for LptM function? <br /> Lines 174-183 and lines 248-250: The interpretation that LptM in the cryoEM model stabilizes the folding of LptD due to the observed tight closure of the ?-barrel junction is not fully convincing. It was based on comparisons made with the crystal structure of S. flexneri and an Alpha-fold model. Would it be possible to perform the comparison with a cryoEM structure of EcLptD/E without LptM instead, to see whether the junction is tightly closed in the absence of LptM or not? Or maybe do the comparison with the solved crystal structure (only ?-barrel domain) of E. coli (PDB ID: 4RHB)?<br /> - Figure 4B. Consider showing the +ME lane between 15 and 20 KDa with ?FLAG antibody to control that LptM is present.

Minor comments<br /> - Introduction (Lines 80-88): Could you add to the LptM paragraph that it interacts with the LptD/E translocon by mimicking LPS binding?<br /> - Figure S4A (middle). The crystal structure presented (PDB ID: 4q35) is for Shigella flexneri and not E. coli.<br /> - Lines 176-177: The crystal structure solved in Qiao et al., 2014 is for Shigella flexneri and not E. coli.<br /> - Figure 5 and Lines 208-209: Could you clarify why alanine, cysteine and tryptophan were chosen for the mutagenesis experiment? Is it because they represent a non-polar (small), polar and non-polar (bulky) amino acids, respectively? <br /> Also, the data shows that only when G21 was mutated to Trp (and not to Ala or Cys), was the LptM activity affected. Could you explain why? And whether it has to do with steric hinderance or that the bulkiness of Trp obscures an essential interaction between other two amino acid residues? Could you show with what residues of the ?-barrel domain G21 interacts in the cryoEM structure?<br /> Also, could you show an alignment of the conserved region with LptM homologs in the Enterobacteriaceae family and show whether G21 is conserved?<br /> - Lines 243-245: “Considering that this short essential region tightly interacts with the ?-barrel domain of LptD (Figure 3, 4), it is unlikely to serve as a recruiter for the disulfide oxidase DsbA or disulfide isomerase DsbC to the LptD intermediate”, Could you explain why being in tight interaction with the ?-barrel domain rules out the possibility that LptM recruits either DsbA or DsbC? Also, could you re-type “Considering that this short essential region....” to “considering the short essential region of LptM ....”, because it is not clear to the reader if you were referring to LptM. <br /> - Section 5 (Lines 218-250): Since this section describes the model, could you make the model a main figure instead of a supporting figure?<br /> - Lines 261-263: Could you remove “consisting almost entirely of signal sequences”, because, for example, LptM is not made entirely from a signal sequence or clarify that you are referring to the secreted proteins.

Sally Abulaila, Kim Kissoon and Habib Ogunyemi (Indiana University Bloomington) - not prompted by a journal; this review was written within a Peer Review in Life Sciences graduate course led by Alizée Malnoë with input from group discussion including Michael Kwakye, Madaline McPherson, Madison McReynolds, Mandkhai Molomjamts, Octavio Origel and Warren Wilson.

On 2025-08-26 09:30:10, user Constant VINATIER wrote:

Feedbacks about your preprint : https://doi.org/10.1101/2025.08.01.668136

About registration: <br /> We could not find any information about the pre-registration of your study in the pre-print. Pre-registration involves documenting the hypotheses, methods, and/or analyses of a scientific study prior to its conduct (10.1073/pnas.1708274114; 10.1038/s41562-021-01269-4). If your study was pre-registered, we strongly encourage you to include the registration number in the pre-print, ideally in the abstract make this important information easy to retrieve, as this practice enhances transparency and reproducibility. If the study was not pre-registered, this should be acknowledged as a limitation. For future studies, we recommend pre-registering on an appropriate repository.<br /> About Protocol Sharing: <br /> We did not find the protocol for your study. If you have one, we encourage you to share it as supplementary material or deposit it in a publicly available repository such as the Open Science Framework ( https://osf.io ) or Zenodo ( https://zenodo.org/) "https://zenodo.org/)") . You can then include a statement in the Methods section indicating that your protocol is openly available (e.g., 'The protocol for this study is available at (link)/ in the supplementary'). Sharing your protocol will help readers better understand your study and enable them to reproduce it if they wish to test it.<br /> About the Statistical Analysis Plan Sharing: <br /> We did not find the Statistical Analysis Plan (SAP) for your study. If you have one, we encourage you to share it as supplementary material or deposit it in a repository such as the Open Science Framework ( https://osf.io ). You can then include a statement in the Methods section indicating that your protocol is openly available (e.g., 'The SAP for this study is available at (link)/ in the supplementary'). Sharing your SAP will help readers better understand your study and enable them to reproduce it if they wish to test it.<br /> About Deviations and/or changes<br /> We could not find any information about potential deviations or changes to the protocol in your pre-print. Since such deviations are common, if this applies to your study, we strongly encourage you to include a subsection titled Changes to the Initial Protocol in the Methods' section and discuss these changes as a potential limitation of your results. If any deviations occurred during your study, please specify them in this new subsection.<br /> About Data sharing / FAIR Data<br /> We found insufficient information about your data sharing approach. Data should be findable, i.e. data are to assigned a globally unique and persistent identifier (for instance there is a DOI assigned to the dataset, or data are registered or indexed in a searchable resource). Data should also be accessible, i.e. data are retrievable by their identifier and can be accessed following an open, free, and universally implementable protocol. As your data id not sensitive data, we encourage you to share it openly on a data sharing repository (Dryad, etc.) and include the Digital Object Identifier (DOI) in the Methods section. If you want more information about good practices of data sharing, visit https://www.go-fair.org/ <br /> About Code sharing<br /> We could not find any information about your (statistical) code. Sharing code is important for enhancing transparency and reproducibility, especially since it does not contain sensitive information. We encourage you to openly share it on a code sharing platform (Github, Codepen, CodShare, etc.) and include the Digital Object Identifier (DOI) in the Methods section. If you want more information about Code sharing https://fair-software.nl/ .

On 2017-12-19 22:04:47, user Chris Gorgolewski wrote:

Thanks for your reply. This is a great conversation to have.

I remain skeptical about Point 3 - especially if Bob decides to test a hypothesis not reported in the original paper. Such analysis might not yield expected results even if the hash and and order was correct indicating a problem with data and experimental design which would be then conflated with academic fraud (an accusation one should be very careful with).

As for the range of possible pseudo random sequences please mind that fMRI design efficiency will impose some constraints on the item sequence that would be usable in a given experiment. This combined with the fact that two different sequences could yield very similar results (making it very difficult for Bob to be able to say if differences he sees in his results are due to differences in processing pipeline, differences in hypothesis he is testing, or fraud). I'm afraid that limiting the entropy of the sequence generator will be the easiest way to game this system.

BTW I could not find the part of the example where Bob evaluates entropy of the sequence generator in your manuscript.

PS Love the photoshop rebuttal.

On 2017-07-20 20:08:48, user Néstor Saiz wrote:

Nice work in looking at the role of transcription/protein degradation in this fate decision.

However, it would be good if you discuss on the paper how your treatment regimes compare to those we did in our Nature Comms 2016 paper. Especially since a lot of your conclusions seem to confirm our observations, although this is not acknowledged. We already propose that it is the double positive cells (NANOG+, GATA6+) the ones whose fate is affected by FGF signaling modulation, and not the single positives. As a consequence of their progressive specification towards either epiblast or PrE, the response to the treatment at the population level changes over time.

Also, regarding your quantifications in the methods, it isn't currently clear how cell populations were scored - I seem to both understand that cell types were assigned manually but also that there was some sort of segmentation to measure protein? At this point, it would be worthwhile elaborating on how measurements were done, whether any correction or transformation was done to the data, etc., as this affects the statistical analysis. Also, maybe automate the fate assignment based on measured protein expression, or justify why not.

Nestor

On 2018-08-07 15:42:17, user cplaisier wrote:

Very cool and intersting work. Had one issue and one suggestion:

Issue. Adding a spike-in won't help with determining the fraction of RNA-seq reads coming from ambient RNA in the soup. The spike-in would be generated from a known distribution because you spike-in a given amount. The ambient RNA in the soup would be as you say experiment and channel specific. The two distributions are not related and as such a spike-in would not be a great solution to the determining the amount of ambient RNA contamination. Am I missing something?

Suggestion. Could you use the transcripts which show up in the less than 10 UMI cells to catalog transcripts which will have contamination. Coming up with a genes likely to be unexpressed is potentially difficult and dagerous if the assumption is in correct. It seems like coming up with a more standardized approach for this would be highly beneficial.

On 2022-01-01 13:22:23, user ??? wrote:

Please check out this paper also: https://www.osapublishing.o...

On 2019-10-24 16:35:01, user Ian Fiebelkorn wrote:

Traveling waves and rhythmic sampling during attention. The authors describe traveling waves in the marmoset visual cortex (area MT) associated with both elevated neuronal responses and elevated perceptual sensitivity during a difficult visual detection task. I very much enjoyed reading this excellent manuscript, which describes a perceptually important phenomenon. Near the end of the manuscript, the authors speculate that this traveling wave phenomenon might explain previous reports of attention-related relationships between pre-target theta (3-8 Hz)/alpha (9-14 Hz) phase and behavioral performance (e.g. Busch and VanRullen, 2010, PNAS; Fiebelkorn et al., 2018, Neuron; Gaillard et al., 2019, BioRxiv; Helfrich et al., 2018, Neuron; Fiebelkorn & Kastner, 2019, TICS). This is an intriguing possibility that remains to be tested. Here, I will point out some of the differences between what is described in the present manuscript and the attention-related effects that we recently described based on recordings in the fronto-parietal network.

(1) The authors contend that these traveling waves are much more predictive of perceptual sensitivity than previous reports of links between visual-target detection and either pre-target alpha or theta phase. First, it is difficult to compare the overall magnitude of effects given differences between the tasks, differences in recording techniques, differences in species, differences in brain regions (sensory cortex vs. higher-order cortex), and differences in how the data were analyzed. For example, we used a broad phase window (of either 90 or 180 degrees, see Figures 3 and S7 in Fiebelkorn et al., 2018, Neuron) when calculating phase-dependent hit rates. This smoothed the data (i.e., phase-detection relationships), biasing the behavioral effects downward. Second, whereas the present manuscript measures the phase of a wideband signal (5-40 Hz), we measured frequency-specific phases. Moreover, our results demonstrated that higher-frequency activity (e.g., in the beta band) was modulated by the phase of theta-band activity. When we accounted for the phase of both higher and lower frequencies there was a stronger link to the likelihood of visual-target detection. Third, whereas performance in the present task was at approximately 50% detection, performance in our task was at approximately 80%, leaving less room for behavioral modulation.

(2) Unlike the present manuscript, which describes a link between visual-target detection and the phase of spontaneous events. We describe a link between visual-target detection and what appear to be ongoing theta-dependent changes in neural activity. That is, we report evidence that higher-frequency activity is associated with visual-target detection at various time points (from -500 to 0 ms) prior to target presentation (Figure S11 in Fiebelkorn et al., 2018, Neuron), not only the time period just prior to target presentation. To be more specific, our data show, e.g., alternating periods when either higher or lower beta-band activity is associated with better behavioral performance. That is, higher beta-band activity occurring at either -500 or -250 ms (relative to target presentation) is associated with a higher likelihood of detection, while lower beta-band activity occurring at either -375 or -125 ms is also associated with a higher likelihood of visual target detection. In our data, higher beta-band activity (in FEF) just prior to target presentation is generally associated with better detection, so why would lower beta-band activity, e.g., at -375 ms also be associated with better detection? Because the strength of beta-band activity oscillates at a theta frequency, and a period of lower beta-band activity at -375 ms indicates that the strength of beta-band activity will oscillate back to a high point just prior to target presentation. This is just one example, but our data generally indicate behaviorally relevant, theta-dependent structure in the ongoing neural activity, rather than spontaneous events (see also, e.g., Figure 2 in Helfrich et al., 2018, Neuron).

(3) The present manuscript links behavioral performance to pre-target phase in a wideband (5-40 Hz) signal, finding no relationship to behavior when the data were filtered to isolate either alpha- or beta-band activity. In comparison, we report links between behavioral performance and narrow-band activity, in the theta, alpha, and beta bands (Figure 3 in Fiebelkorn et al., 2018, Neuron). Moreover, we show that these frequency-bands are functionally distinct by linking them to specific, functionally defined cell types.

(4) While it remains to be tested whether traveling waves are coordinated across brain regions, we have shown (Fiebelkorn et al., 2019, Nature Communications) that attention-related rhythmic sampling is characterized by theta-dependent changes in between-region functional connectivity (across cortical and subcortical nodes of the attention network).

On 2019-07-19 00:23:12, user Guillermo Parada wrote:

In the discussion you said "There is no evidence for RNA editing to modify splice sites yet", however at least in vertebrates there is evidence of GT-AA introns that are activated by ADAR and transformed to GT-AI (read by the spliceosome as GT-AG). In 2014 we found 7 putative splice sites that can be activated by A-to-I editing and one of them, located at ADARB1, was previously found by other researchers (see Table 2; https://academic.oup.com/na... "https://academic.oup.com/nar/article/42/16/10564/2903109)"). It might not be a very frequent event as non-canonical introns are very rare, but it's very interesting how different RNA processing events interplay during transcription.

On 2024-10-07 14:40:02, user BindCraft Enjoyer wrote:

I like the ‘Design-Until’ architecture of the BindCraft pipeline, but one thing I couldn’t find in the paper is any quantification of BindCraft’s in silico design success rate. In the Introduction, you note that one drawback of RFDiffusion/MPNN-based pipelines is the need to screen thousands to tens of thousands of designs in silico before finding the 10-100 that pass the quality metrics and can be tested experimentally with good success rates. Does BindCraft also require screening of thousands to tens of thousands of designs, or is it more efficient in silico than an RFDiffusion pipeline? You mention in the paper that BindCraft outputs statistics from each design run, and that biasing away from alpha helical binders reduces the in silico design success rate; so it sounds like you have the statistics ready to hand, at least for the targets reported in the paper. I’d love to see these design success rates added to a table, either in the main paper or the SI.

Another thing I’d like to see is some quantification of the compute time and cost required to run the 4-step pipeline until 100 designs pass the in silico filters. I understand this cost scales with target/binder size and target difficulty, but I would imagine you have the data required to calculate these metrics at least for the design campaigns reported in the paper. I saw on Twitter that you’re working on a direct BindCraft / RFDiffusion pipeline comparison; I hope you’ll include the computational hardware and total CPU/GPU time for each side of that design campaign.

Great work!

On 2019-11-01 10:13:27, user Rudolf Meier wrote:

We used two different primer pairs to check for consistency. With regard to the pig DNA, it is consistently found with both primers for the products of a certain supplier. Upscaling is certainly needed, but it's arguably even more important to have a sampling scheme that is tied to specific targets (e.g., volume of sales; sale of endangered species; preventing fraud in mixed-species samples through regular checking, etc.). MinION is a technique that is quite versatile and one can now design with regulatory targets in mind instead of having to think too hard about how to sequence it all with Sanger sequencing without spending too much money (and Sanger sequencing would fail for mixed-species samples).

On 2020-04-21 08:04:39, user AspiringPolymath wrote:

Citation 45 (https://www.nature.com/arti... "https://www.nature.com/articles/nature12426)") is cited and used within this article as microfossil evidence — it is not...

On 2024-01-03 17:20:48, user Francesco Iorio wrote:

Now published in The CRISPR Journal:<br /> https://www.liebertpub.com/...

On 2020-11-17 18:26:38, user Patrick Shaw Stewart wrote:

Typical body temp of minks is 39C. Many promoters are temp-sensitive and "RNA thermometers" are important in many species.

Any mutations in "promoter" regions & regions affecting RNA secondary structure could have profound effects on viral pathogenicity.

Easy intro: https://youtu.be/l_jmqGvdeB...

Review: https://www.douglas.co.uk/f...

On 2025-10-02 06:19:23, user Wolfgang Graier wrote:

This is a thrilling work! Thank you for sharing it with the community. Did you had a chance to test for differences in mitochondrial dynamics and motility upon various ratios of L-MFN2 and S-MFN2? Good luck with publishing.<br /> Best, Wolfgang

On 2020-06-10 18:25:05, user Simon Drescher wrote:

To add some more literature which is not mentioned in the manuscript:

A review about asymmetric phospholipids: Huang and Mason, 1986 BBA 864, 423-470; "Structure and properties of mixed-chain phospholipid assemblies".

And some original work about the SDPC including x-ray studies and freeze-fracture electron micrographs: Hui et al. 1984 Biochem. 23, 5570-5577; "Acyl Chain Interdigitation in Saturated Mixed-Chain Phosphatidylcholine Bilayer Dispersions".

Finally, McIntosh et al. 1984 Biochem. 23, 4038-4044; "New Structural Model for Mixed-Chain Phosphatidylcholine Bilayers" - a whole paper about SDPC using x-ray.

Hence, the biophysical properties of SDPC are known for >35 years ...

On 2020-11-25 14:39:12, user Richard Zimmermann wrote:

Dear Sarah and Steve, congratulations, that’s a really cool and timely study with milestone potential for both drug screening strategies and fight against viral infections.<br /> We all have been following news on the search for small molecules as potential antivirals in the fight against COVID-19 and related clinical trials with great interest for almost the entire year 2020. We realize that there are trials with RNA-polymerase- and viral protease-inhibitors and that there are great efforts under way to develop e.g. TMPRSS2-inhibitors as well as new viral protease-inhibitors. What we have not yet seen to be systematically addressed as potential small molecule SARS-CoV-2 antivirals, however, are the Sec61-inhibitors. The last decade has led to the discovery of a constantly growing list of Sec61 targeting small molecules and toxins, including cyclic heptadepsipeptides (such as cotransin 8 and CAM741), eeyarestatins, apratoxin A, mycolactone, Ipomoeassin-F, and Coibamide A, which can be expected to inhibit the biogenesis of viral membrane proteins in infected human cells. I am convinced that it is worthwhile to pursue Sec61 inhibitors as potential antivirals. Therefore, it is fantastic that you have started to tackle this subject with your study on the biogenesis of CoV-2 membrane proteins in a cell free system. I realize that there is a long way to go with this project, as you correctly point out in your exciting manuscript. But even if it does not lead to a therapeutic approach or combination therapy in the current crisis, it may well do so for the next viral pandemic.

On 2017-10-16 22:04:21, user Tamara Bidone wrote:

VERY interesting paper

On 2020-05-21 22:35:56, user GG Anderson wrote:

excellent work. Is the inhibition mechanism known? It seems that ORF3b could either block transcription by binding to TF region that controls IFN-1, or impede translation by binding to mRNA.

On 2016-09-13 15:50:19, user Andrew Jaffe wrote:

The software is currently available in the sva-devel branch of SVA and will hopefully be available in the next Bioconductor development branch release:<br /> https://github.com/jtleek/s...

On 2019-04-01 18:29:51, user Stefan Stender wrote:

Thank you for sharing this interesting work. One question: MARC1 p.Arg200Ter appears to be about 70-fold more common in Ashkenazi Jewish than in European populations: https://gnomad.broadinstitu...<br /> Do the 5 principal components of ancestry used in the analysis account for this?

On 2024-01-10 09:49:46, user Todd Vision wrote:

The peer-reviewed version is available at Methods in Ecology and Evolution 14, 2531-2540.<br /> https://doi.org/10.1111/204...

On 2021-01-20 12:09:56, user S Ramasubramanian wrote:

It's very promising to see non injectable intra nasal single dose vaccine for covid-19, which will be boon to india..hope to see it in use very soon..

On 2019-05-01 21:54:01, user Brian DeVeale wrote:

Awesome work! Perhaps the wrong forum, but it looks like the command for converting v2 objects to v3 is 'UpdateSeuratObject' in R and listed as 'UpgradeSeuratObject' in the FAQ on your website.

On 2020-03-16 22:12:55, user Laura Sanchez wrote:

Dear Yu and Petrick, this preprint was discussed in a lab meeting and we would like to offer the following for review. Thank you for posting this very interesting manuscript. Best, The Sanchez Lab:

As frequent users of molecular networking tools which are based on tandem MS data, we enjoyed reviewing a method not commonly used in our lab. It is clear that reactomics networks may help to identify analogs of compounds that are not easily matched by MS2 networking, especially for molecules with poor fragmentation patterns. We are very interested to see the application of linking enzymes to disease states through metabolomics approaches. That said, we have also included a list of critiques and suggestions for the preprint.<br /> Major:

• Networking tools incorporating mass shift and KEGG integration such as MetaNetter 2 (Cytoscape plugin) and MetaMapR (R package) exist. The manuscript could benefit from clarifying what makes this approach unique.

• Overall, the manuscript assumes a high level of knowledge. There are processes that go unexplained, such as the full extent of pre-processing accomplished in CAMERA and RAMclust, and why the Pearson’s correlation coefficients threshold was set to 0.6, etc. The pre-processing is extremely important for this technique to work due to the issues that were stated in the text. E.g. how is the difference between an isotope and a PMD determined and how do you differentiate between in-source fragments and neutral losses and the PMDs reported.

• As it stands, self-loops in figure 1 are confusing and add a lot of visual clutter. Based on the source code, it appears that retention time is taken into account when defining peaks, so we are assuming what’s happening is two peaks with the same accurate mass have different retention times. However, this should be explicitly stated within the text.

• In “Source appointment of unknown compounds”, the second paragraph is very difficult to understand. It sounds as if all compounds plotted are carcinogenic, but then “carcinogenic compounds were not connected by high frequency PMDs”? This then doesn’t seem to align with the next sentence discussing the average degree of connection, for which the values of 8.1 and 2.3 arealso not explained. The carcinogenic 1, 2A, and 2B could be briefly explained or perhaps just calling them level 1, level 2A etc. would help. In the same vein, the Figure 3 legend could benefit from changing labels from “Endogenous compound 2” to “Level 2 endogenous compound” or something else to clarify that it is referring to the toxicity level. Even then it is not sure if including that information is relevant to the figure.

• The final example in “Biomarker Reactions” feels underwhelming in its current state. Even though it’s claimed to have a high significance, it is not convincing that the +2H mass shift is a significant biomarker when it’s such a common reaction. If this could be tied to a specific enzyme that has been implicated in lung cancer, or some other biological evidence to support this claim, it would be more convincing. Otherwise, it could be worth making networks based off of the biomarker metabolites found in the original publication e.g. NANA?

• Figure 4 does not effectively communicate the significance of the data points as it is explained, the plots look very similar to the naked eye. Perhaps there is a statistical visualization that would make the point more clearly.
• Finally, it might be worth adding a discussion regarding the resolving power needed and limitations. The described workflow seems to highly depend on the ability to accurately gather three decimal places. This will take a higher mass resolving as the molecular weight of the analytes of interest increase. It is unclear right now what the targeted mass ranges were for the measured compounds and what resolving power may have been used. Given that this is an experimental variable users can designate on an Orbitrap for instance, is there a recommended number or do the authors envision leaving this to the mass spectrometrist to define? Depending on the target audience this could greatly aid in implementation and general understanding. <br /> Minor:

• The first sentence of the manuscript defines metabolomics using specifically an untargeted metabolomics definition. Some clarification of targeted vs untargeted or a focus on untargeted would make this clearer.

• Methylation, oxidation, are common artefacts from the extraction process and analysis of biological samples. These are unavoidable facts of metabolomics, but perhaps the authors could comment on accounting for sample degradation processes.

• As a proof-of-concept, we would have liked to see orthogonal identification for some molecules to prove that the molecules are related. An exact mass is only a “Level 5” identification (doi/10.1021/es5002105).

• The two matrices might be clarified by being presented as tables or figures with abbreviations as an element of explanation. For instance, adding in “Ethylnitronate (S1), Oxygen (S2)” would help clarify and remove the necessity for the sentence starting in “For KEGG reaction R00025..”. The following sentence could be made into the table caption.

• Use of the phrase “Topological structure” in the section “PMD Network Analysis” is confusing when it’s used in proximity with a compound’s chemical structure. It is unclear whether stating that chemicals with similar topological structure have similar biological activity refers to the topological properties of the network representation or if it is referring to the chemical structure itself. The reference to Figure 1 does not clarify this and either way this is a statement that requires further experimental or literature support.

• Figure 2 is very low resolution and the yellow and peach colors are hard to make out. Overall we’d suggest changing edges to have a text reference to what reaction is being used as opposed to color. Addition of the chemical structures for TBBPA and some of the analogs into this figure would help to visually make the point that what you are seeing is a representation of molecular analogs. We also offer that the node color could be based on what has been confirmed in the previous study and what was newly annotated by this analysis to more clearly make the point that there is new information to be gleaned by running this analysis

• Would it be possible to re-examine tandem data collected from the original paper to match fragmentation and confirm that the secondary network is related to the confirmed network?

On 2020-05-20 12:39:54, user Crayfarmer wrote:

There is no market for such a small crayfish except in developing countries. Marbled crayfish is on average considerably smaller than Procambarus clarkii. Therefore, culture of marbled crayfish in closed systems will never be profitable.

On 2019-10-20 03:57:17, user David Acton wrote:

A very interesting study that sheds some light on the cutaneous information that gates itch. However, more work needs to be done to discriminate between pre and postsynaptic effects, particularly with reference to GRPR+ neurons. I would also like to point out that animals were not shaved in the study by Acton et al., as stated in the discussion.

On 2025-03-04 21:05:38, user Simone Picelli wrote:

Hi, I think there is a mistake in the name of the company used to make the modified TSO. It's not Biosyn Corporation ( http://biosyncorp.com ), as you wrote, but rather Bio-Synthesis ( http://biosyn.com ). <br /> Moreover, in the TSO sequence: "/5Biosg/" is the acronym used by IDT for a 5' biotin. The "g" has nothing to do with deoxyguanosine (G), but you write in the paper "5BiosG/" and this can be confusing. The standard 10x TSO sequence is, in fact: 5’-AAGCAGTGGTATCAACGCAGAGTACATrGrGrG-3’<br /> so no G at the 5' (like there was never a G at the 5' of the SMART-seq oligo, from which 10x took their sequence).<br /> The code for biotin at Biosyn is [Btn] (standard C6 spacer, which I assume is the one you mean here).

On 2016-09-15 21:52:20, user Francisco De La Vega wrote:

Please indicate the version of RTG tools that you used to compare varmatch with vcfeval. These tools are in active development and your timing and comparison results may change if you use a different version and thus the reader (and I assume the reviewers) need to know that.

On 2024-11-15 07:55:21, user Giovanni Bussi wrote:

Authors of the review

Olivier Languin-Cattoën, Giovanni Bussi

Summary

The authors use Molecular Dynamics with enhanced sampling techniques to gain insight in the directional catch-bond mechanism of Vinculin tail (Vt) interaction with F-actin. They construct two models of the Vt-actin complex that are hypothesized to represent a weak state and a strong state in a force-activated allostery model of the catch bond. They use enhanced sampling techniques to estimate the free-energy landscape of the unbinding process in each state, as well as the unbinding kinetics, in absence and presence of pulling forces of variable intensities and directions along the actin filament axis. Their results demonstrate higher kinetic stability for the strong state with respect to the hypothesized weak one, with unbinding kinetics in range of experimental expectations, confirming the viability of a “3-state” (2 bound states, 4 unbinding pathways) kinetic model of the bidirectional catch bond observed in single-molecule experiments. They additionally observe an increase in bond lifetimes for moderate constant pulling force (10-20 pN) in both directions, indicating an intrinsic catch-bond behavior within each “allosteric” state that may superimpose to the overall allosteric one. They show how an external pulling force affects the positioning of the H1 ?-helix believed to act as a regulatory motif of the weak-to-strong transition, providing a compelling structural hypothesis for a force-induced allosteric mechanism. Finally, they provide molecular insight on the difference in stability between both states, highlighting the role of a C-terminal extension (CTE) and the redistribution of Vt-actin contacts under force.

Comments

* We wonder if the nomenclature “Holo” can be confusing at first glance for the reader, given the historical usage of the holo- and apo- prefixes to designate protein constructs with and without their constitutive prosthetic groups (usually non-proteic cofactors)

* We suggest that the authors make clearer the composition of the Holo and Aligned protein sequences given the different numbering in 6UPW and 1QKR (that we guess is due to the presence of Metavinculin instead of Vinculin), that in our understanding are identical except for the absence of the leading H1 helix.

* In FES calculations, since the constant force (that is, a linear bias) is applied on the same coordinate Q_?, we expect that the resulting FES could be entirely predicted from the FES at zero force by simple addition of the linear slope, given sufficient exploration of the Q_? direction during the OPES-MetaD sampling. This fact could be used by the authors to assess the consistency between the FES computed at different forces. Alternatively, one may want to first aggregate the OPES-MetaD simulations at all forces using appropriate reweighting, and then estimate minimum free-energy paths and free-energy barriers at arbitrary force using the aggregated FES. This approach might lead to a better statistical use of the vast amount of simulations and a smoother estimate of the FES at all forces within the studied range. Finally, the multiple trajectories (20 replicates x 9 force values) could be used in a single bootstrap to assess the statistical uncertainty of the results (see below).

* Given the coarse nature of the set of chosen CVs (Q_? and Q_?) it is unclear whether the Vt is able to regain its canonical binding site during OPES-MetaD, notably because of free rotation with respect to the actin filament. <br /> - The authors acknowledge the difficulty in the methods (sec. 5.5) without explicitly stating whether such rebinding events happen at all in their simulations. We believe this is an important piece of information for proper understanding of the FES presented. We would suggest showing time series to clarify how many binding/unbinding events are observed.<br /> - One might expect that the absence of recrossing lead to a poor estimate of the free energy difference between the bound and unbound states as well as the height of the binding free energy barrier. On the bright side, the estimate of the unbinding barrier – which is the one they are the most interested in – can still be expected to be reliable.<br /> - The authors suggest to run multiple (20) separate OPES-MetaD simulations to compensate for this limitation. It should be acknowledged that independent runs starting from the bound state will not correct for the systematic bias caused by the absence of rebinding events. A bootstrap on these replicas would anyway estimate their statistical error.<br /> - We wonder if the use of the more specific CV Q_contact might allow for such recrossing to happen within OPES-MetaD, without the need of aggregating a high number of independent trajectories. In our understanding the authors only used Q_contact to assess the robustness of the free-energy barrier height to the precise choice of the projection space, but did not try to perform OPES-MetaD directly on this CV space, which could be instructive.

* The authors analyze the FES by determining a minimum free-energy path using the “String method” as a post-processing method. <br /> - The Methods section might benefit from some information about the use of the method, in particular that it is directly applied on the 2D CV-space projected FES (as opposed to a search of a minimum energy path on the full potential energy surface as originally proposed in [50]), and provide details about initialization (choice of end points for the string, number of nodes, initial interpolation) and robustness to these parameters in the converged paths and corresponding barrier estimates.<br /> - Since the FES are aggregated from 20 independent OPES-MetaD runs, it might be relatively straightforward to estimate errors (for example using bootstrapping) and provide error bars on Fig 2c. We believe this would strengthen the significance of the observed barrier difference.

* One may be concerned about the significance and reliability of the constructed “Aligned” state, since this state was constructed by aligning Vt to another conformation (in what we could refer to as “docking-by-homology”) with little experimental confirmation that such a state is stable in vitro. We understand that the model constitutes the core hypothesis of the whole computational approach, and that the consistency of the computational outcomes with single-molecule experiments themselves validates its plausibility. Nevertheless, it could be argued that lower binding affinity and lifetime are to be expected from a suboptimal binding partner in a suboptimal binding pose. This raises the question of whether the proposed model corresponds to a specific binding mode in reality, or if the results could be reproduced with a different alignment. Is this ruled out by the stability observed in the 500 ns simulations shown in Fig S1?

* In Sec 3.2 §4, the authors say “these results [...] do not quantitatively explain the observed experimental results, since the experimental changes in lifetime shown in Fig. 1D reflect a net 1.4 kcal/mol change in barrier in the negative direction, and 0.6 kcal/mol in the positive direction (if one assumes a constant prefactor the kinetic rate constant)”. It was somewhat unclear to us where these values come from (Are they computed from the fitted 3-state model in SI S1? At a specific value of the pulling force?) and how exactly they are compared to the computed barriers for Holo and Aligned to conclude to a discrepancy (Overestimated?)

* In Sec 3.4 §3, the authors convincingly remark that in two out of five simulations of Holo+H1 state pulled towards the barbed end, the conformation of H2–H5 becomes more similar to the Aligned (unbound Vt) structure, suggesting a first step in the strong -> weak allosteric transition. We wonder if (i) the specific contacts made with actin and (ii) the specific intra-domain contacts of H1 with the H2–H5 bundle are also indicative of a displacement toward the Aligned state, since this would be an even stronger argument validating the proposed model.

* Given the relative simplicity of the supposed allosteric motif and the suggestive results of the Holo+H1 simulations, we cannot help but wonder whether the authors also tried to "unfold" the H1 helix in the Aligned model with a N-terminal pulling force since this seems a very natural test to look for equally suggestive indications of a weak -> strong allosteric transition under force.

* Typos or writing remarks:<br /> - Fig 1B: The caption is inconsistent with the figure. In the caption, p1 denotes the COM of Vt helices H2–H5, p2 the COM of actin A1/A2 and p3 the COM of actin A4/A5. On the figure instead (p1, p2, p3) -> (p3, p1, p2).<br /> - Some repetitions that might be elegantly avoided<br /> x “capture the difficult-to-capture” (abstract)<br /> x “protein of interest [...] on our molecule of interest” (introduction §1)<br /> x “takes into account a Boltzmann weighted average over all possible configurations [...] a Boltzmann weighted average over all possible configurations” (introduction §4)<br /> - Typos<br /> x “FimH-manose” -> “FimH-mannose” (introduction §8)<br /> x “which is the the direction” (3.1 §1)<br /> x “These approximate one-dimensional free energy pathways also give us a way to define when the system has crossed into the unfolded state” -> Maybe the authors meant “unbound state” (3.1 §1)<br /> x “not all of the catch bond need come from” -> “need to come from” (3.2 §3)<br /> x “we note that these results are suggestive, they do not quantitatively” -> We wonder if the authors intended a formulation along the lines of: “we note that despite being suggestive, these results do not quantitatively” (3.2 §4)<br /> x “a constant prefactor the kinetic rate constant” -> “a constant prefactor for the kinetic rate constant” (3.2 §4)<br /> x “grafted to our Holo structure random orientation” -> “grafted to our Holo structure with a random orientation” (3.4 §2)<br /> x “TIP3 water” -> “TIP3P water” (5.1 §2)

Acknowledgements

This report was written after a journal club given by OLC in the bussilab group meeting. All the members of the group, including external guests, are acknowledged for participating in the discussion and providing feedback that was useful to prepare this report. The corresponding authors of the original manuscript were consulted before posting this report.

On 2018-05-02 16:13:26, user Alan VanArsdale wrote:

The record for Homo erectus now appears to be badly truncated. The dating here coincides with the timing of first appearances in the fossil record of Homo erectus in Eurasia and Africa, which likely was a major expansion from some unknown place for Homo erectus. During this expansion morphologies from encountered popualtions of Homo (with just one species of Homo ever existing at any one time), were picked up. There is no evidence in the fossil record of any lineages of Homo erectus being "dead end" morphologies. Instead it appears early appearances of Homo erectus in both Asia and Africa were morphologically African from the hip and below, and Asian above the neck, as intraspecific hybrids. .........................The best candidate for denisovans in the fossil record, and the best match morphologically for the known denisovan teeth, is Homo sapiens heidelbergensis, which is now known from Asia and South Asia (the cranium). The closest match morphologically for the trachiolus foot prints / track ways, reliably dated at about 5.7 mya, is Homo floresiensis (so H. floresiensis could have walked to Flores when last thought to have been connected to Asia about 5 mya). With Homo floresiensis being the closest known morphological match to whatever unknown Asiatic grades of Homo "erectus" contributed morphologies to AMH not seen (usually, except in a few early neandertal females), in African origins neandertals. Especially relatively small nasal bones and eye ridges, P4 relatively small compared to the molars, high domed heads, and presumably ancestral ARHGAP11A (which is thought to have enhanced intelligence but not led yet to increased brain size). .................................................. That populations such as Red Deer Cave people, Homo tsaichangensis and Homo floresiensis are late survivals needs to be allowed for. However, there is no evidence that as late survivors they in some way became genetically isolated from the single species existing in the genus Homo. Selective pressure not only maintains genetic diversity (as with neandertal genes as they enter Africa to be selected out there), they also maintain morphological diversity within interbreeding populations spread over great distances. With great morphological diversity, by about 1.9mya Homo was able to occupy diverse environments. Asia for the genus Homo long being more arboreal in morphology relative to Homo in Africa, on average, as reflected in lower body morphology when known. In Africa and Europe australopithicinae Western gorillinae and Pan, unlike in Asia, at first gave strong competition in all environments, and over time retreated more to arboreal environments limiting arboreal adaptations in Homo in Africa. Gigantopithicines (Asiatic gorillinae), may have been somewhat environmentally restricted in their competition with Homo and late surviving Lufengpithecus. ''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''Nor is the Asiatic stream of morphology coming into AMH limited to a little less than 2 mya, it goes back at least 10mya, just it can not be seen with what is known in genetics back more than about 2mya (for now). The "remarkable" convergence of Homo tsaichangensis with Javanese Gigantopithecus is not convergence at all, this is shared primitive character states (from initial divergene of Homo and gorillinae about 9mya), just as the lingual groove of gigantopithecus is a shared primitive character state with orangs. And in modern humans (as was published in the 1952 paper), Gigantopithecus dental character states are seen, with highest frequencies and strength in those places where Gigantopithecus is known in the fossil record. This can seem incredible, to those who imagine typologically all hominins at some time are derived from just one morphology or holotype. However, this is not supported by the evidence in primates. Instead, it is supported that in hominin evolution there has at any time always been high morphological diversity. With morphological diversity usually being preserved in the phenotype (even if not seen in the spotty and intermittent fossil record), or sometimes hidden to be expressed later in the genotype.

On 2018-11-02 03:56:36, user Stuart Cook wrote:

Great study. The finding that IL-11 is specifically increased in activated fibroblasts in colitis and predicts resistance to anti-TNF therapy is consistent with a previous study (PMID: 19700435). Notably, OSM and IL-13 signalling - both also predicting response to anti-TNF therapy in this study - have an absolute requirement for IL-11 to stimulate fibroblast activation (PMID: 15699166, PMID: 29160304).

On 2019-03-26 20:29:36, user Vincent Denef wrote:

We read and discussed this preprint as part of an upper-level microbial ecology class at the University of Michigan (EEB 446, Winter 2019) that I am teaching and below I am posting some of the thoughts the students had after reading the preprint:

1. General thoughts. Brito (2019) poses the question, “Do we exchange oral and gut commensals with our closest family and friends?” in order to understand the impact of routine interpersonal contacts in shaping the microbiome composition. Addressing this question is important because the gut microbiome is so impactful to the normal health of humans, any information about how it is affected is important and useful, although the students would have liked to see a more explicit explanation of how knowledge of transmission impacts how we understand how microbiomes are shaped, function, and affect human health. The structure of the study focused on isolated, non-industrialized communities. This enabled the authors to focus in on interpersonal interactions while reducing potential influences of external factors. However, it also makes it challenging to relate findings to other communities that are industrialized. While this is acknowledged in the study, they felt that this should be treated as a case-study and the data should not be used to draw conclusions between other populations and geographic regions.

2. They had some thoughts regarding the authors’ definition of 'transmission'. Brito et al. loosely define transmission by shared inferred genotypes. Their work provides insight into the correlation of community patterns for individuals in a household or family unit. As it is difficult to determine the exact mechanism for transmission, the approach used allowed the authors to identify trends in oral and gut microbiome similarities across individuals without understanding how or why this might be. Yet, this does mean they can’t necessarily rule out similar environmental factors among close family members to lead to similar ecological selection rather than transmission, thus there is a risk of overinterpreting correlative data.

3. Use of social network data. Some students had some suggestions regarding the methodology used for social network construction and wondered if other analyses of the network structure could have been added to gain deeper insights. Specifically, within the social networks constructed, simplified approaches of defining an individual and establishing a single connection and then utilizing that network for analysis is very broad in nature and only skims the surface with respect to extracting information from a social network. Understanding centrality measures of individuals within the village i.e., degree of individuals tied to, geodesic betweenness (how often is an individual on the shortest path to another individual), closeness of an individual (how easily can they reach other actors within the network), and eigenvector measures (how well-connected are individuals that an individual is connected to) would all have allowed for a stronger utilization of microbiome sample data for the usage of predictive models. Each of these different centrality measures come with an attributable value associated with the strength of centrality. After determination of centrality measure values for each individual, correlation of network centrality values and similarity in both oral and gut microbiome compositions could be measured.

4. Ethical concerns. Student questions: Did the participants in this study give consent, and furthermore, did they have informed consent about the study that they were taking part in? There were also questions whether the exact form used to gain informed consent could be shared to preempt any of these concerns readers may have. Further questions they had were whether the participants in this study will benefit in any way from the knowledge obtained in this study? Also, one student group wondered whether knowing about the microbiota composition of certain populations could help drug companies target certain populations with certain products?

5. Other methodological, interpretation, or presentation concerns. The students wondered why they inferred the amount of years couples were married based on the age of their oldest children. It’s interesting why they didn’t just ask the couple how long they had been married also it’s not always true that children are directly related to amount of time lived together. Another question related to the data availability section, where they didn’t quite understand why the authors discuss mislabeling of a sample in the database. Could they change the database to reflect this labeling issue?

On 2021-03-03 23:23:32, user William Longabaugh wrote:

The above manuscript has been published as a book chapter: Desai R.M., Longabaugh W.J.R., Hayes W.B. (2021) BioFabric Visualization of Network Alignments. In: Yoon BJ., Qian X. (eds) Recent Advances in Biological Network Analysis. Springer, Cham. https://doi.org/10.1007/978...

On 2019-05-28 21:23:35, user Kristof Theys wrote:

We intend to make all code used for this study available to anyone that can benefit from it. We plan to use the repository https://github.com/ktheyss to share the framework.

On 2017-01-11 20:21:34, user Stephen Van Hooser wrote:

Question: is it certain the calcium activity in the bouton reflects only the presynaptic signals? If you patched a cortical neuron and fired it, is truly no signal observed in the presynaptic boutons? (One might imagine responses derived via presynaptic NMDA receptors, etc.)

On 2020-01-31 22:02:47, user Dave Baltrus wrote:

It is likely that the evolutionary relationships found between these two protein sequences of both viruses are due to a complete coincidence and, stepping back, do not appear "uncanny" to multiple experts that have also examined the sequences. In short, the authors base their analysis on a short sequence of the spike protein from 2019-nCoV, but a much more comprehensive search outside fo the viral sequences queried in the manuscript demonstrates that this sequence is also found in *many* *many* other places than HIV. Thus, while the 2019-nCoV strain does appear to have a sequence difference from other closely related viruses, there is not enough resolution to clearly demonstrate the evolutionary history of this change let alone trace it to HIV.

see analysis here for instance: https://twitter.com/trvrb/s...

On 2024-10-17 01:51:59, user Kazuki Iijima wrote:

This manuscript has been peer-reviewd and published in Communications Biology as "Erroneous predictive coding across brain hierarchies in a non-human primate model of autism spectrum disorder" after peer review. https://doi.org/10.1038/s42003-024-06545-3

On 2021-04-07 10:21:36, user Jake Wintermute wrote:

All the raw data and python scripts for this study are available on Zenodo: https://doi.org/10.5281/zen...

On 2021-03-23 00:06:38, user Clara B Jones wrote:

... thinking of Ants, all of which are classified, Eusocial [e.g., see Holldobler & Wilson 1990] ... [1] are we going to accept the distinction between "primitively" and "advanced" eusocial? ... [2] among ant species, traits are highly variable, for example, with re: presence or absence of "castes;" patterns of task, role, and/or morphological specialization; &/or "totipotency;" ... [3] however, "reproductive division-of-labor" is a universal ... [4] since Cooperatively Breeding taxa exhibit [a] "reproductive division-of-labor;" [b] 1 or a few "pure" breeders; and, [c] "helpers," can we classify "cooperative breeders," "primitively" eusocial? ... [5] social mole-rats exhibit "reproductive division-of-labor;" "pure" breeders; totipotency; "helpers" [role specialization]; and, unless i am mistaken, "temporal division-of-labor" ["age polyethism:" Damaraland mole-rats] and are, generally, classified, "primitively" eusocial [eusociality including "reproductive division-of-labor;" totipotency; but without (more or less) "sterile casts" and, usually, without morphological specialization] ... how does this new report deviate from a classification, "primitively" eusocial for social mole-rats or from the highly variable traits reported for Ants?

On 2025-10-04 19:58:19, user annonymous wrote:

The authors claim that fitting a DDPM to a single MD dataset and subsequently generating samples from the trained model results in ‘enhanced sampling’. This claim is highly dubious as the results seem to indicate that the ‘enhanced sampling’ they refer to is equivalent to adding small amounts of guassian noise to samples already present in the MD training data. In fact, this result is expected - when generating samples from a DDPM, a simple prior distribution is iteratively transformed to a sample from the data distribution through noisey purturbations in the direction of maximum likelihood. Consequently, it comes as no surprise that generated samples are highly similar but not exactly identical to samples from the training data. Upon additional training of a DDPM on a single dataset, one expects that these deviations should asymtotically decrease until the DDPM ‘memorizes’ the training data and generate nearly exact copies of the training data. In the context of MD, adding gaussian noise to pre-existing samples from long-time MD is generally not considered a useful enhanced sampling method, unless noise perturbed structures are subsequently evaluated with a potential energy function and are subject to some aceptance criteria, as is done in metropolis hasting / MCMC sampling methods. I do not belive that the ‘enhanced sampling’ the authors claim to obtain from over-fitting DDPMs on single MD datasets is of the same nature as that expected from standard enhaced sampling methods for MD like replica exchange or metadynamics, which aim to explore completely new regions of phase space that are otherwise difficult to access and not yet characterized. The authors present no evidence that their procedure is able to generate samples that are substantially different than those already present in the MD training data - therefore, I would not consider this an enhanced sampling method in any sense. Moreover, the DDPMs presented here are trained to maximize the likelihood of generating samples that are similar to the training data distribution - there is no incentive for the model to explore new regions of phase space and one could argue that if their model were producing samples highly dissimilar to the traing data - it would suggest that the model is either under-fit or systematically incapable of appropriately modeling the training data.

On 2020-11-09 21:31:18, user Clemantine wrote:

Why would they use aborted fetal tissues lines to experiment with? Using any cell line after the 1947 Nuremberg code requires the informed consent of the Subject and the ability to withdraw from the experiment, both denied to aborted human babies. All cell lines derived after 1947 using aborted fetal tissue is immoral, unethical and illegal and must be discontinued!

On 2017-10-26 15:34:38, user Allen Collins Zoologist wrote:

Another minor taxonomic edit: The correct name for Lucernariopsis campanulata is Calvadosia campanulata (Lamouroux, 1815). Cheers,

On 2019-08-18 23:35:24, user Hunter D. Young wrote:

Im very intrigued. And i also hope they start human clinical trials.

On 2019-02-27 16:55:56, user Huiwang Ai wrote:

The final version of this paper is availiable from ACS Biochemistry: https://pubs.acs.org/doi/10...<br /> Please refer to the ACS Biochemistry version for the most acurate presentation.

On 2023-03-01 11:05:33, user KS wrote:

Dear author, <br /> I attempted to access PyCalibrate, but received an error message indicating that the processing frame on the web page failed to load. I attempted to resolve the issue by trying multiple browsers and devices, but without success.<br /> If you could kindly check the frame, it may resolve the issue. However, if the issue still persists, please let me know, and I would be happy to help you troubleshoot the issue further.<br /> Best,

On 2025-03-26 08:51:39, user Shigeaki Saitoh wrote:

The revised version of this article is published in Journal of Cell Science ( https://doi.org/10.1242/jcs.263747 ).

On 2019-11-29 13:58:11, user Yang Yu wrote:

An earlier GoldCLIP paper by Hannon, Yu and colleagues with essential the same idea published last year should be cited (GoldCLIP: Gel-omitted Ligation-dependent CLIP. https://www.ncbi.nlm.nih.go... "https://www.ncbi.nlm.nih.gov/pubmed/29709556)").

On 2021-01-30 22:18:10, user Dittmer Lab wrote:

Would this be considered a gain of function experiment?

On 2025-02-04 22:16:48, user Bill Mallet wrote:

To assist interpretation, it would be helpful to plot the dose-response curves in Fig. 2a with a log10 X-axis.

On 2017-06-02 15:26:16, user Debbie Kennett wrote:

I think you have a typo in line 295. The third word should be "our" not "or".

On 2017-05-17 14:51:59, user Jean Manco wrote:

Another little typo, this time in the supplementary table. Pabbay Mor should be corrected to Pabay Mor, as in the Supplementary Info.

On 2017-08-29 13:38:48, user Bernard Secher wrote:

Good work for this new version of the paper!

On 2017-09-29 04:09:39, user Roy Q wrote:

Has this been accepted anywhere? I keep seeing blogs on it but cannot it anywhere. If someone has a link thanks, nothing on pubmed