On 2021-03-25 09:30:16, user Florian Privé wrote:

Two remarks:<br /> - It is "principal" components, not "principle"<br /> - PCR is not the same as "GWAS with PCs as covariates" at all

On 2020-04-03 13:37:21, user Kim Ferguson wrote:

We've noticed since uploading the genome and annotation to the ENA that the gene names are incorrect, due to mislabelling of the feature, so all genes currently have the name "Nesidiocoris tenuis hypothetical protein". This will be rectified soon, however, due to the current covid19/coronavirus situation worldwide, the ENA is not currently accepting submissions or validations. Therefore, we've 1) posted the .gff file with all information on figshare (https://figshare.com/articl... "https://figshare.com/articles/Nesidiocoris_tenuis_PRJEB35378_linked-read_genome_annotation/12073893)") and 2) will update the preprint shortly where the new version contains a link to this .gff file.

Ferguson, Kim (2020): Nesidiocoris tenuis PRJEB35378 linked-read genome annotation. figshare. Dataset. https://doi.org/10.6084/m9....

On 2020-04-28 01:59:58, user Soundhar Ramasay wrote:

Excellent and humongous work. Is the RNA-seq data-sets ,specifically host transcriptome are available for download ?

On 2022-01-01 15:07:58, user hualan_liu wrote:

Very neat technology for arraying mutant libraries!

On 2023-09-11 18:24:50, user markus wrote:

Review of Stöckl and Maass (2023) “Local prediction-learning in high-dimensional spaces enables neural networks to plan”<br /> by Markus Meister and Zeyu Jing

The authors propose a neuromorphic algorithm by which an agent can learn the structure of an environment and decide on the best course of action to reach a given target location in the shortest time. The principle is to embed both the states (graph nodes) and the actions (graph edges) in the same high-dimensional space, such that the graph distances are represented by geometric distances between nodes, and the actions correspond to directions in the space. Then the agent can pursue the shortest route by choosing the action that points in the direction of the target node. The embeddings of both states and actions are learned during random exploration of the graph, based on optimizing a prediction of the next state from the previous state and action. Ultimately the embedding should be such that the next state can be predicted by simply adding the action vector to the previous state. The authors argue that all this can be accomplished with biologically plausible learning rules.

Evaluation

This is an interesting suggestion for how an agent might learn the structure of its environment, namely how states and actions are connected. This ultimately results in a goal signal, by which the correct action can be chosen that leads to the goal in the shortest amount of time. Some of my questions concern whether the algorithms are really neuromorphic, in the sense that they could be implemented in biological neural circuits.

Concerns about bio-plausibility:

1. The learning rule for state-action association, Eqns 2 and 3: This looks difficult to implement with neurons, because:

a) it involves two different time points on the post-synaptic side: s_{t+1} appears at time t+1, but \hat s_{t+1} at time t;

b) to make s_{t+1} appear in that neural population, the action vector must temporarily be switched off, requiring some kind of control system;

c) the postsynaptic variable is a difference between two successive time points. It is not obvious how to implement such a delta rule with biophysics.

1. The computation of utility, Eqn 4: Again, how would this be implemented with neurons?

a) It involves multiplying a population vector with another vector in the same population; what would be the mechanism for such a multiply-and-add operation?

b) In the left term, the activity is driven by an action; in the right term, activity of the same neurons is driven by observations. How would that be accomplished?

c) The right term involves subtracting activity from the current observations from activity driven by some remembered observation. How are the remembered observations stored? Are these fed into the network one after another? How are they subtracted?

1. Normalization of synaptic weights

a) Eqn 5: Here each input synapse gets modified by a factor that depends on the strengths of all the other synapses onto that same neuron. This violates the locality of synaptic plasticity: A synapse should be modified based only on its own strength and the activity of the pre-and postsynaptic neurons.

b) Another related normalization appears in line 660.

1. Winner-take-all choice (Eqn 7): Explain how this would work in a neuromorphic system? How does the agent sample the values of the affordable actions? By actually executing them in the real world one at a time and comparing the resulting output from Eqn 4? But then the observations o_t would change as all the actions are played out. Or is the comparison done “mentally” without real-world action? If so, does this require some accessory system that can look up the codes of all the affordable actions and feed them into the network one at a time?

In summary, if the authors advocate that this model could be implemented in brains, it would be helpful if the proposal included a bio-plausible neural circuit for each of these operations.

Other questions:

1. Graphs with cycles

a) As pointed out (p.9), the theory behind this learning model causes problems with cyclic graphs. All the graph edges are supposed to be orthogonal in the embedding space, yet adding the edges around a loop should give zero. Both can’t be true. It is not clear how this conflict was resolved. Does it require careful parameter tuning so as to sustain performance of the model?

b) Line 585 describes a hack by which the agent was prevented from traveling cycles during exploration. That requires oracular knowledge of the graph. How would that work in a real-world system?

1. The concept of “action”

a) In the first part of the paper (p.1-12), every edge on the graph is considered a different action, and they are all encoded with one-hot vectors. For example, a robot action like “turn left” would have a different one-hot code at every different location. This is somewhat different from the common concept of “action”, which is specific to the agent’s movements, not where they are executed. By postulating a one-hot code for every edge on the graph one effectively circumvents the problem of path integration, which requires making a prediction from stringing together actions that may appear identical from the agent’s perspective.

b) Please explain how a naive agent entering a new environment will already have a one-hot code for every edge on the graph, even if they involve identical movements of the agent.

c) What if the actions instead were agent-centered, e.g. {left, right, forward, back} as in the second part of the paper (p.13ff). Would the system still be able to learn an arbitrary graph?

1. “Affordances”

a) What is the source of the “affordances” in Eqn 6. They represent part of the graph structure. Don’t these need to be learned as well? In the present formulation it seems they are offered to the system without learning (contra line 194).

b) By what mechanism would this happen in a neuromorphic system, and how would they be represented by the neural circuit?

c) Figure 4e highlights the fact that the agent takes an action that was never taken during exploration (Fig 4c). But what if there was a wall between those two nodes, and that’s why the action never occurred there? How does the agent know that the action is “affordable”.

1. Parameter sensitivity

a) It appears that for each of the illustrated graphs the embedding space had a different dimensionality, and different choices were made for the learning rates (p.22). In line 610, the state space has fewer dimensions than the number of actions, which obviously precludes finding orthogonal vectors. What motivated all these different choices?

b) How robust is the system to the relevant parameters? Can the same agent with one parameter set learn different spaces? The report would benefit from exploring a wider range of graphs and scanning over parameter values.

1. Related work

There is prior research on these topics that could be used to put the current work in context. In particular, various versions of model-based reinforcement learning acquire the structure of the state space through learning, so that a goal-directed policy can be superposed on that. The present paper only deals with learning the state space, not with learning the goal locations: Those are provided by some unspecified accessory system. Within the RL literature there has been recent enthusiasm about the "successor representation”, which is an embedding that helps predict the agent’s next state. For neuromorphic models that learn the successor representation, see for example Fang 2023 (https://doi.org/10.7554/eLi... "https://doi.org/10.7554/eLife.80680)") and literature cited there.

Other suggestions:

1. There is frequent reference to “planning”. But the model presented here doesn’t make any plans. Once it arrives at a node it decides on the next action, as though it had a lookup table (see line 13). It does not “think ahead”. In the neuroscience and robotics field, “planning” usually concerns evaluating the outcome of successive actions ahead of time, for example comparing the value of different routes. The behavior implemented here is more like “online navigation”. This may be confusing to the reader.

2. In Figures 4 and 5, is there a meaning to the emojis painted on the nodes? At least this reader finds them distracting. Preceding figures worked just fine without emojis.

3. In part 2 (p.13ff) the main difference from part 1 is that the actions are now agent-centric. It may help the reader to point this out. Because there are only 4 action vectors, the predictions are forced into a 4-dimensional subspace of the high-dimensional space. Eventually, the learning process squeezes that into a 2-dimensional subspace that accurately reproduces the geometry of the graph.

4. Eqn 4: should start with u_t=

5. Figure 6c: Perhaps show a bit more of the time course to document that the two variables have settled.

6. Fig 2 caption: “square root of the length of the shortest path” should this be “square root of the sum of squares along the shortest path”. As stated, it doesn’t resemble the law of Pythagoras.

7. Fig 4a: use different arrows for the 4 actions.

8. Typos:<br /> Line 68<br /> Line 32<br /> Line 250: Meaning of this sentence unclear.<br /> Line 392<br /> Line 505<br /> Eqn 12: should be Va instead of V?

On 2020-11-17 08:41:51, user Alexander Serechenko wrote:

That's speculative project I suppose. It works only on strictly limited amount of categories, which is the dataset for neural network GAN, as well as the visual showed to person. So there is small difference between the same system without any visuals, but just saying in text which category is now observated by human.

On 2018-11-11 17:36:13, user Betty______Lin wrote:

Extracting information from huge scientific publications is extremely importance, due to this importance lots of information extraction systems have been reported. However this automated hypothesis generation is extraordinary, which combined with semi-supervised machine learning can be successfully utilized and iterated in the future work.

On 2017-04-06 11:07:55, user David Balding wrote:

The authors find that GCTA performs best among the methods they compare, but this is because their simulation model is unrealistic in one important aspect, and it matches an unrealistic assumption in the heritability model assumed by GCTA. (The authors refer to GCTA as GREML, which is confusing as there are many GREML methods (including both GCTA and LDAK), which differ in the assumed heritability model.)

L71 "Genetic architecture refers to the number, frequencies, effect sizes, and locations of causal variants (CVs) underlying trait variation"

What is missing here is linkage disequilibrium (LD). The GCTA model assumes that the heritability of SNPs is distributed independently of LD, whereas LDAK assumes an inverse relationship between heritability and LD. Across 42 human GWAS we have shown that the LDAK model provides a much closer fit to reality (1), whereas the Evans et al simulations are based on the GCTA model and not the LDAK model.

We previously showed (2,3) that GCTA can give highly biased estimates in regions of low or high LD, whereas LDAK adjusts appropriately. Our recent work (1) was spurred by the misleading results presented in (4), which also compared the performance of GCTA and LDAK using phenotype simulations based on the GCTA model but not the LDAK model. We have pointed out this deficiency to some authors common to both (4) and Evans et al, so it is disappointing to see the same kind of unfair and unrealistic comparisons being made again.

L123 "... simulate phenotypes with differing genomic architectures under realistic patterns of LD structure"

While strictly correct, this is a potentially misleading claim because there was no attempt to include in the simulations a realistic relationship between LD and phenotype. It is true that the genotype data include realistic levels of LD, which is presumably what the authors intended to claim, but the LD-phenotype relationship might be understood to be covered by this claim, and is much more important yet has been ignored.

While we do not currently have a precise model for the relationship between LD and per-SNP heritability, in (1) we showed the LDAK model to be more realistic than the GCTA model across a wide-range of traits, and hence at a minimum any comparison of SNP heritability methods should consider results from simulations under models reflecting both the GCTA and the LDAK models.

David Balding, University of Melbourne and UCL<br /> Doug Speed, UCL Genetics Institute, London

(1) Speed D, Cai N, UCLEB Consortium, Johnson M, Nejentsev S,<br /> Balding D (2017) Re-evaluation of SNP heritability in complex human<br /> traits. To appear Nat Genet, preprint: BioR?iv doi: 10.1101/074310

(2) Speed D, Hemani G, Johnson M, Balding D (2012) Improved<br /> Heritability Estimation from Genome- Wide SNPs. Am J Human Genet, 91(6): 1011-1021. doi: 10.1016/j.ajhg.2012.10.010

(3) Speed D, Hemani G, Johnson M, Balding D (2013) Response to Lee et al: SNP-Based Heritability Analysis with Dense Data. Am J Human Genet, 93(6): 1155-57.

(4) Yang, J. et al. Genetic variance estimation with imputed<br /> variants finds negligible missing heritability for human height and<br /> body mass index. Nat. Genet. 47, 1114–1120 (2015).

On 2025-11-30 07:11:24, user Misha Koksharov wrote:

I presume the final version of this paper was published in 2024: <br /> Vedalankar, P., Tripathy, B.C. Light dependent protochlorophyllide oxidoreductase: a succinct look. Physiol Mol Biol Plants 30, 719–731 (2024). https://doi.org/10.1007/s12298-024-01454-5

On 2019-10-09 21:02:55, user Arie Horowitz wrote:

The article was published in a peer-reviewed journal - see https://rdcu.be/bTPD7

Arie Horowitz

On 2021-04-16 12:38:11, user Mick Watson wrote:

Fossil fuels are the greatest source of methane, not agriculture: https://www.globalmethane.o...

One of the first "facts" they state and it is wrong

On 2021-04-21 13:36:14, user yegerlotemlab wrote:

At last, we are happy to announce our publication in Nature Communications! https://www.nature.com/arti...

On 2023-06-06 09:22:38, user D P wrote:

I like the experimental design of figure 3 F-N, the results are very instructive for the field!Good luck with your publication!

Best<br /> YD

On 2023-08-16 14:53:12, user Michael Prummer wrote:

In the meantime, this article has been published in NAR Genomics & Bioinformatics, doi: 10.1093/nargab/lqad058

On 2020-03-06 18:19:40, user Lin-xing Chen wrote:

Hi authors, thanks for presenting this promising approach for microbial analyses. Likely, this is a good way to link phages (during infection) and their hosts directly. I read the viral signal in a set of SAGs, while no host information is stated in the preprint as I can see? And, is it possible to only target those with phages before sequencing? Thank you.

On 2018-11-30 20:09:57, user RobS wrote:

What are the 9 biomarkers? I can't find that information anywhere!

On 2016-01-09 15:57:02, user jvkohl wrote:

CONCLUSION: "What better place to start such quantitative training than by examining <br /> the contents of the human body? In doing so we can comply with the <br /> Delphic maxim of “know thyself” in a truly quantitative fashion."

MY COMMENT: Their conclusion suggests the mathematical model is a joke in the context of the claim from Dobzhansky (1973) that: "...the so-called alpha chains of hemoglobin have identical sequences of amino acids in man and the chimpanzee, but they differ in a single amino acid (out of 141) in the gorilla (p. 127)." http://www.jstor.org/stable...

Knowing thyself requires recognition of self / non-self differences, which are nutrient-dependent and manifested in 1180 variants of human hemoglobin. The immune system links the variants from metabolic networks to genetic networks via energy-dependent DNA base pair changes and single amino acid substitutions. The nutrient-dependent RNA-mediated amino acid substitutions ensure the stability of organized genomes in all vertebrates and invertebrates via the physiology of their pheromone-controlled reproduction.

Nutrient-dependent pheromone-controlled chromosomal rearrangements enable self / non-self recognition. Recognition can be placed into the context of what is known about the honeybee model organism. It serves as a model organism for studying human <br /> immunity, disease resistance, allergic reaction, circadian rhythms, <br /> antibiotic resistance, the development of the brain and behavior, mental<br /> health, longevity, diseases of the X chromosome, learning and memory, <br /> as well as conditioned responses to sensory stimuli.

The conditioned responses to food odors are linked to the physiology of reproduction by chromosomal rearrangements. Similarly, the link from the number of gut bacteria to the number of red blood cells or other cell types is maintained across generations only in the context of mammalian reproduction.

That fact requires serious scientists to recognize the difference between human odor and the odor of other primates, which encourages people to mate with different individuals even when species are of like kind. Without the links from the gut microbiome to the skin microbiome in the context of self / non-self amino acid substitutions and hemoglobin variants, inbreeding would have ended all hope that ecological variation would lead to ecological adaptation outside the context of mutation-driven pathology.

On 2025-08-12 14:00:19, user Gil Benard wrote:

I really appreciated this work — an in-depth analysis of replicability in science. It presents many important and interesting findings that contribute to the current discussion on reproducibility “crisis” and science integrity. I was particularly impressed by their report of sizable heterogeneity among the results of the replicating labs, despite the fact that (a) they were theoretically under neither publication pressure nor any other pressure from the science environment, (b) protocols and reagents were carefully checked and standardized among the labs, and (c) the experiments were conducted under very controlled conditions.<br /> Could there be intrinsic limitations to replicability in biomedical science?

On 2024-06-06 05:37:37, user Brian Junglen Jr wrote:

Can you please advise on the appropriate sample size required for a research study to conclusively determine the correlation between the spider gene and its impact on the snake's equilibrium?

On 2016-05-19 14:59:06, user Srinivas Gorur-Shandilya wrote:

my software for controlling and recording from NI devices: https://github.com/sg-s/kon....

We've been using it for electrophysiology for a few years now.

On 2025-09-14 10:51:13, user Val wrote:

Very interesting indeed. I wonder what this will mean for us going forward. Was this always here or post Covid? Something to consider?

On 2025-10-26 23:10:28, user FN wrote:

Thompson et al. (2012) examined seasonal patterns of gene expression in HVC and RA of white-crowned sparrows. How do your data compare with theirs? Expected to see this comparison in Discussion, surprised that you didn't do this.

On 2020-06-01 14:20:23, user OxImmuno Literature Initiative wrote:

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

On 2017-05-30 13:12:38, user Andrey Yurkov wrote:

The choice of the SSU rRNA gene as a phylogenetic marker makes results difficult to interpret. On one hand, this region is often too conservative to identify fungi. On the other hand, introns make amplification of some parts of SSU difficult. It is hard to say whether the pattern you've observed is an artefact caused by PCR and/or PCR-primers.

On 2019-10-23 09:52:22, user Julia Fitzgerald wrote:

There is an accompanying paper found here:

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

On 2020-02-06 13:42:24, user didier Fesquet wrote:

"We show that APEX2 labeling of the NL is robust and requires as little as 20 seconds."...so highly better than BioID...

On 2023-02-23 02:56:53, user Jonathan Williford wrote:

In Figure 2c, are the InhTC and SparTC column labels swapped?

On 2020-03-25 22:31:23, user Sinai Immunol Review Project wrote:

Title

LY6E impairs coronavirus fusion and confers immune control of viral disease

Keywords

interferon-stimulated genes, antiviral interferons, human coronaviruses (CoV), murine hepatitis virus (MHV)

Key findings

Screening a cDNA library of >350 human interferon-stimulated genes for antiviral activity against endemic human coronavirus HCoV-229E (associated with the common cold), Pfaender S & Mar K et al. identify lymphocyte antigen 6 complex, locus E (Ly6E) as an inhibitor of cellular infection of Huh7 cells, a human hepatoma cell line susceptible to HCoV-229E and other coronaviruses. In a series of consecutive in vitro experiments including both stable Ly6E overexpression and CRISPR-Cas9-mediated knockout the authors further demonstrate that Ly6E reduces cellular infection by various other coronaviruses including human SARS-CoV and SARS-CoV-2 as well as murine CoV mouse hepatitis virus (MHV). Their experiments suggest that this effect is dependent on Ly6E inhibition of CoV strain-specific spike protein-mediated membrane fusion required for viral cell entry.

To address the function of Ly6E in vivo, hematopoietic stem cell-specific Ly6E knock-out mice were generated by breeding Ly6Efl/fl mice (referred to as functional wild-type mice) with transgenic Vav-iCre mice (offspring referred to as Ly6E HSC ko mice); wild-type and Ly6E HSC ko mice of both sexes were infected intraperitoneally with varying doses of the natural murine coronavirus MHV, a pathogen that depending on route of infection can cause a wide range of diseases in mice including hepatitis, enteritis and encephalomyelitis. Briefly, compared to wild-type controls, mice lacking hematopoietic cell-expressed Ly6E were found to present with increased mortality, a more severe disease phenotype as based on serum ALT levels (prognostic of liver damage), liver histopathology, and viral titers in the spleen. Moreover, bulk RNAseq analysis of infected liver and spleen tissues indicated changes in gene expression pathways related to tissue damage and antiviral immune responses as well as a reduction of genes associated with type I IFN response and inflammation. Finally, the authors report substantial differences in the numbers of hepatic and splenic APC subsets between wild-type and knockout mice following MHV infection and show that Ly6E-deficient B cells and to a lesser extent also DCs are particularly susceptible to MHV infection in vitro.

Potential limitations

Experiments and data in this study are presented in an overall logical and coherent fashion; however, some observations and the conclusions drawn are problematic and should be further addressed & discussed by the authors. Methodological & formal limitations include relatively low replicate numbers as well as missing technical replicates for some in vitro experiments (cf. Fig. legend 1; Fig. legend 2e); the omission of “outliers” in Fig. legend 2 without an apparent rationale as to why this approach was chosen; the lack of detection of actual Ly6E protein levels in Ly6E HSC ko or wild-type mice; and most importantly, missing information on RNAseq data collection & analysis in the method section and throughout the paper. A more relevant concern though is that the interpretation of the experimental data presented and the language used tend to overrate and at times overgeneralize findings: for example, while the authors demonstrate statistically significant, Ly6E-mediated reduction of coronavirus titers in stable cells lines in vitro, it remains unclear whether a viral titer reduction by one log decade would be of actual biological relevance in face of high viral titers in vivo. After high-dose intraperitoneal MHV infection in vivo, early viral titers in Ly6E HSC knockout vs. wt mice showed an elevation in the spleen (~1.5 log decades) but not liver of the ko mice (other tissue not evaluated), and while ko mice presented with only modestly increased liver pathology, both male and female ko mice exhibited significantly higher mortality. Thus, the manuscript title statement that “Ly6E … confers immune control of viral disease” is directly supported by only limited in vivo data, and gain-of-function experiments (eg. Ly6E overexpression) were not performed. Of additional note here, tissue tropism and virulence differ greatly among various MHV strains and isolates whereas dose, route of infection, age, genetic background and sex of the mice used may additionally affect disease outcome and phenotype (cf. Taguchi F & Hirai-Yuki A, https://doi.org/10.3389/fmi... Kanolkhar A et al, https://jvi.asm.org/content/ 83/18/9258). Observations attributed to hematopoietic stem cell-specific Ly6E deletion could therefore be influenced by the different genetic backgrounds of floxed and cre mice used, and although it appears that littermates wt and ko littermates were used in the experiments, the potentially decisive impact of strain differences should at least have been discussed. Along these lines, it should also be taken into account that the majority of human coronaviruses cause respiratory symptoms, which follow a different clinical course engaging other primary cellular mediators than the hepatotropic murine MHV disease studied here. It therefore remains highly speculative how the findings reported in this study will translate to human disease and it would therefore be important to test other routes of MHV infection and doses that have been described to produce a more comparable phenotype to human coronavirus disease (cf. Kanolkhar A et al, https://jvi.asm.org/content/ 83/18/9258). Another important shortcoming of this study is the lack of any information on functional deficits or changes in Ly6E-deficient immune cells and how this might relate to the phenotype observed. Overall, the in vitro experiments are more convincing than the in vivo studies which appear somewhat limited.

Overall relevance for the field

Despite some shortcomings, the experiments performed in this study suggest a novel and somewhat unexpected role of Ly6E in the protection against coronaviruses across species. These findings are of relevance and should be further explored in ongoing research on potential coronavirus therapies. Yet an important caveat pertains to the authors’ suggestion that “therapeutic mimicking of Ly6E action” may constitute a first line of defense against novel coronaviruses since their own prior work demonstrated that Ly6E can enhance rather than curtail infection with influenza A and other viruses.

Reviewed as part of a project by students, postdoctoral fellows and faculty at the Immunology Institute of the Icahn School of Medicine at Mount Sinai

On 2017-08-30 06:30:54, user Marc RobinsonRechavi wrote:

May I suggest mentioning the species (European grayling Thymallus thymallus) in the abstract, to make it easier to find for researchers interested in this species?

On 2018-12-29 05:58:57, user Matthew Sloane wrote:

Love this paper. A late Christmas present; a must-read. Definitely raises some valid questions about the interdisciplinary connections between PMD and other targets. Highly recommend, as it is one of Weidenbacher’s greatest works to date.

On 2019-10-24 18:28:57, user Jason Hinman wrote:

Great work. We used endo-RiboTAG previously in work that is currently under review:<br /> Endothelial CXCL5 negatively regulates myelination and repair after white matter stroke<br /> https://www.biorxiv.org/con...

On 2018-09-13 00:53:07, user Jomar Fajardo Rabajante wrote:

This paper is now published in http://rsos.royalsocietypub...

On 2018-07-29 23:41:02, user Piotr Lukasik wrote:

This is a straightforward 16S rRNA amplicon sequencing-based study of the microbiota of several species of quill mites, providing the first comprehensive overview of symbioses in this group. Cool stuff. Following @gerth_micha Twitter invitation to provide feedback, I wrote up some comments. Good luck with the publication!

line 43 "(among secondaries #2)": to a non-bird person, it is not immediately clear what this means

lines ~45: how many feathers per bird? how many mites per bird/feather? Also, how were the mite species identified? Finally, could you provide information on the relationships among the mite species, ideally a phylogeny?

lines ~82, 181-217: Kudos for making an effort to detect an filter out reagent-derived contamination, and discussing the problem quite extensively! Many ants harbor low amounts of bacteria, and I wasted tons of time and resources dealing with this, before developing a similar protocol for filtering out reagent-derived contaminants from army ant and other amplicon datasets. I described it extensively in the supplement of Lukasik et al. 2017, Mol Ecol 26:3808) - apologies for shameless self-promotion! Surprisingly few other arthropod microbiome surveys takes note of the contamination issue, though. Can you summarize of the extent of contamination in the mite samples?

line 132, 233-241: Bartonellaceae are not all pathogens --- in honeybees and ants they can play very different roles! Check out Bisch et al. 2018, GBE 10:1687

Results: Probably the most interesting, and most clear, finding of the study (highlighted in the title and abstract) is the high prevalence of Wolbachia as well as Spiroplasma, and their distribution pattern. Because of this, I think it would be preferable to cover these endosymbiont results first, and follow up with the analysis of the less straightforward data on the remaining microbiome. Instead, you repeat from the beginning of the Results that these two endosymbionts are the most abundant, often dwarfing everything else --- but hide the actual results in the supplement, and only revisit these data in the later portion of the manuscript. Personally, I do not like this approach.

Figs. 1, 3, 4, S1: Adding grid lines would make the figures much more informative.

Fig. 1 and S1: The relative abundance values do not add up to 100%. Why?!?

Fig. 3: I have problems interpreting the scale. "Log-transformed abundances are plotted.." Do you mean the relative abundance, or the number of reads? So, the value of 1, or 10, corresponds to what abundance/relative abundance?

Fig. 4A-B: The known problem with Illumina sequencing is index misassignment on a small proportion of reads. Check out, for example, https://www.illumina.com/sc... . And so if the library L contains a few reads representing Wolbachia and/or Spiroplasma, which are both abundant in many other libraries sequenced in the same lane, there is a possibility that this is due to index hopping. It looks like a good proportion of your samples contain only low titres of these two bacteria... Be careful about the interpretation of these results. On the other hand, you did the sequencing using Ion technology, where this may not be an issue.

Fig. 4C: I understand the term "relative abundance of bacterium X" as the proportion of total quality-filtered reads in the library that represent bacterium X. By this definition, the sum of relative abundances of different bacteria in a library should equal 100%. Unless you use a different definition, the sum of relative abundances of Wolbachia and Spiroplasma in any of your samples should be <= 100%. But this is not the case for three of the samples that drive your trend... Explain.

On 2018-11-20 16:08:19, user google-1c72a6296b34b022a62ed224dfa04c87 wrote:

I've uploaded the supplementary note to the S-LD4M github repository: https://github.com/lukejoco...

On 2025-07-25 15:17:02, user Marc Delarue wrote:

Excellent discussion of the Enthalpy-Entropy Compensation, offering profound insight int the thermodynamics involved and resolving, in passing, an issue that had escaped researchers for more than 40 years (see the work of R.L. Baldwin on the denaturation of proteins).<br /> Also proposes a nice analytical evaluation of a thermodynamics integration, which I did not know was possible.

On 2021-01-13 16:52:01, user José L Medina-Franco wrote:

A peer-reviewed version of this paper has been published in http://dx.doi.org/10.3390/p... Juárez-Mercado K, Prieto-Martínez F, Sánchez-Cruz N, Peña-Castillo A, Prada-Gracia D and Medina-Franco J. 2021. Expanding the Structural Diversity of DNA Methyltransferase Inhibitors PHARMACEUTICALS, 14, 17.

On 2016-07-21 10:17:03, user robince wrote:

A PDF mirror is available here in case the biorxiv pdf download is slow:

https://www.robince.net/dat...

On 2016-04-05 07:05:25, user Hawkeye wrote:

Hello there..Just want your opinion on somethin'. You're saying that the lepas anatifera barnacle habitat is mostly in warmer tropical/subtropical waters, right? Looking at the flaperon debris of missing flight Mh370, could the lepas genus attached to it, originate from the warmer area around, say, Maldives, or from the south west sea off western Australia? Which area is more likely? Thanks v. much..

On 2017-01-01 19:31:56, user Bernard Crespi wrote:

Outstanding paper! Fascinating integration of data from evolutionary biology, psychiatry, and intelligence research. I might only suggest a few follow-ons: (1) Nieuwboer, Pool et al. (2016) partition the strong genetic correlation between SZ risk and Bipolar (BP) risk into disorder-specific components, and find that BP is strongly linked with Edu, but SZ is uncorrelated with it (0.041, SE .082). They suggest that the significant positive SZ - Edu link in some previous work is caused by the strong genetic correlations of SZ with BP, and BP with Edu; various phenotypic associations of BP with high achievement fit with these results, but more data are clearly needed; (2) SZ risk has indeed been linked with creativity - but also, and perhaps more closely, with imagination (Crespi, Leach et al. 2016, Cognition). We need GWAS studies that tap into imagination and its neural correlates, especially as regards achievement in the arts and humanities; (3) Crespi (2016, Frontiers in Neuroscience) describes evidence that AUT can be considered, in part, as a disorder of high but imbalanced intelligence. This hypothesis can explain the strong positive genetic correlations of AUT risk with intelligence, Edu, and their correlates, as well as a suite of other autism-related phenotypes tracing back to Kanner.

On 2021-11-01 07:26:38, user Prof. T. K. Wood wrote:

Authors also make common mistake of thinking lack of PI staining indicates viability (see doi:10.1111/1462-2920.14075).

On 2025-04-01 15:31:09, user Shawn Hurst wrote:

Two points which strengthen your argument are 1) we see socio-emotional differences in great apes, showing different emotional evolution trajectories are possible in hominids despite similar brain sizes, and 2) orbitofrontal cortex is very important for socio-emotional behavior and control- and relative orbitofrontal cortex expansion and reorganization is easy to compare in both living great apes and fossil hominin endocasts, where lateral OFC is smallest in orangutans, appears inflated in A. sediba relative to African apes, and appears similar in relative size between Homo naledi and Homo sapiens.

On 2024-10-24 16:51:29, user Allisandra Rha wrote:

Interesting work. The multimodal approach definitely enhanced the integration outcomes and is well-designed. While it is of benefit that AAV efficiently reaches the nucleus, moving forward would you consider evaluating other delivery methods for nuclear translocation to account for patients that are seropositive for AAV antibodies?

Also, the paper is under-referenced. I would encourage you to look at the text and provide more citations to support your claims.

On 2023-04-05 21:25:46, user Christine wrote:

Hi, I am interested in this work, but I am new to this design. Is it possible to share the .json file as described in Fig. S2? Thank you.

On 2019-08-30 20:14:25, user Bernard Bel wrote:

I look forward to reading the final publication. Please note that the link in note [27] is broken. You may for instance use:<br /> https://www.japantimes.co.j...

On 2024-03-26 23:36:39, user Kosuke Yamaguchi wrote:

This paper is in press in Nature Communications (written by Kosuke Yamaguchi, 1st author at 27 Mar 2024)

On 2020-05-26 08:45:02, user Jared Roach wrote:

The SIR model used to simulate molecular tree topology does not appear to include superspreader events. If not, the simulated topologies are likely to be radically (no tree pun intended) different than the real topologies. We know superspreader events occurred in Snohomish County, including the publicized choir practice event.

My second musing is the absence of geographic proximity modeling. If WA-1 and WA-2 are not related as closely as previously thought, how likely/unlikely is that these sequences were found a few miles from each other? Would it now be more probable that unrelated sequences would have been found with a bit more geographic separation?

On 2017-06-27 15:39:52, user Stephen Floor wrote:

Correspondence to: bateup@berkeley.edu; stephen.floor@ucsf.edu

On 2021-03-23 12:06:18, user Eaaswarkhanth Muthukrishnan wrote:

This preprint is published now in the European Journal of Human Genetics. Here is the link to the published version https://doi.org/10.1038/s41...

On 2025-02-13 17:59:30, user Mikaela Gray wrote:

This preprint has now been published in ACS Omega. Here is the link: https://pubs.acs.org/doi/10.1021/acsomega.4c07666

On 2020-12-01 15:19:49, user Guangmei Liu wrote:

Introduction<br /> We are university students taking an upper-level neurobiology course that centers on understanding neural circuits and modern research techniques through in-depth discussions of recent literature. To fully immerse ourselves in current scientific discourse, we have written this review of the manuscript from Park et al. posted on biorxiv.org (version: November 12, 2020).<br /> Jamie Dela Cruz1, Angélica Gaona1, Guangmei Liu2, John Axiotakis1<br /> 1 Undergraduate in Neurobiology, Boston University. 2 First-year PhD student in Neurobiology, Boston University.

Summary<br /> There is a growing body of literature examining the effects of social deprivation during the critical developmental period and how it affects later social function. In particular, Park et al. are interested in studying social recognition, or the ability of an animal to distinguish a novel conspecific from a familiar one. To uncover what neural circuits may underlie this, the authors used juvenile social isolation (jSI) and pharmacogenetic manipulation to study the effects of early isolation in mice. They first raised singly housed (SH) mice and group housed (GH) mice. SH mice lived alone for 8 weeks immediately after weaning, whereas GH mice lived together in those 8 weeks. Afterwards, SH and GH mice were re-socialized for 4 weeks. The authors then used a variety of behavioral tests to examine the social behaviors of SH and GH mice. Next, they inhibited nucleus accumbens shell (NAcSh)-projecting IL neurons in GH mice to see if the pathway is required for social recognition. Lastly, to see if the social recognition deficits in SH mice could be reversed, the researchers selectively activated NAcSh-projecting IL neurons in SH mice. They found that jSI impairs social recognition through decreased excitability of the mPFC IL-NAcSh pathway and that pharmacogenetic manipulation of this population also selectively affects social recognition. Therefore, this paper presents a novel brain circuit required for social recognition and adds to the literature implicating the mPFC and NAcSh in early social development. Overall, we recommend that the authors consider different statistical tests for certain figures, as the distribution of their data appears to be bimodal at times. We also suggest that the authors run another cohort of SH and GH mice through the experiments, this time performing tests both before and after resocialization to distinguish between the effects of jSI and resocialization. We see an opportunity to provide more evidence for the effects of resocialization by adding a parallel cohort of SH and GH mice who were never resocialized. Additionally, our review discusses portions of the paper where the authors could provide more explanation for certain methods and tweak the figures for improved clarity. <br /> In Figure 1, they investigate what social phenotypes are affected by early social isolation. They used the 3-chamber test to see if either mouse type showed social preference (spending more time exploring a conspecific rather than an object) and social recognition (spending more time interacting with a novel conspecific than a familiar one) (Figure 1C, 1D). There was no significant difference between SH and GH mice in the social preference test, but SH mice did show a significant social recognition deficit. To see if this was caused by a general recognition memory deficit or hippocampus-dependent memory deficit in SH mice, both mouse types underwent the novel object recognition test and the object place recognition test (Figure 1E, 1F). However, there were no significant differences. In Extended Data Figure 1, the researchers looked at whether the SH mice were physiologically or emotionally different from GH mice. Researchers compared the body mass, basal locomotor activity, and anxiety levels between the two, also finding no significant differences.<br /> Extended Data Figure 2 looks at whether different durations of social isolation and resocialization will result in different behavioral phenotypes. First, they decreased the isolation time by singly housing mice for 2 weeks after weaning and resocializing for 4 weeks. In this case, SH mice showed no significant differences in social behaviors compared to GH mice. They then singly housed mice for 8 weeks after weaning and regrouped for 8 weeks to increase the resocialization time. Despite this increase, SH mice in this treatment showed the same social recognition deficit as mice in the original SH treatment.<br /> In Figure 2, the authors injected a retrograde virus into the NAcSh for GFP labelling to see what regions of the mPFC were sending the most inputs. Neurons in the ventral mPFC regions were heavily labelled, with the most labelling at the infralimbic cortex (IL), though there were some at the prelimbic (PL) as well (Figure 2A-B). They then used ex vivo brain slice whole-cell patch clamp recordings to see the excitability of both the IL and the PL, finding that neuronal excitability was reduced in NAcSH-projecting IL neurons but not PL neurons (Figure 2C-D). Extended Data Figure 4 digs into the electrophysiological properties of these IL neurons in both SH and GH mice, finding no significant differences.<br /> Figure 3 answers two main questions, the first being: does this social recognition deficit still appear in SH mice in a different behavioral paradigm? To investigate this, they habituate both SH mice and GH mice to a target mouse on day 1. On day 2, they allow the SH or GH mouse to explore either an empty cup, a novel conspecific, or the familiar conspecific target. Once again, SH mouse explored the novel and conspecific mice equally, showing an impairment in social recognition (Figure 3A-C). The second question answered by this figure is: Are the NAcSh-projecting mPFC IL neurons differentially activated by distinct social stimuli (familiar versus novel conspecific)? The researchers used c-Fos immunohistochemistry and eGFP to examine co-labelled neurons in the IL after exposing mice to either a familiar or novel conspecific (Figure 3D-E). They found that GH mice had more c-Fos and eGFP co-labelled neurons after interacting with a familiar conspecific than GH mice that interacted with a novel conspecific, suggesting that NAcSh-projecting IL neurons are activated as a result of interacting with familiar conspecifics (Figure 3F).<br /> In Figure 4, Park et al. look at whether the NAcSh-projecting IL neurons are required for social recognition. In GH mice, they injected hM4Di receptors into NAcSh-projecting IL neurons and intraperitoneally injected them with CNO, reducing the excitability of these IL neurons (Figure 4A-B, D). These GH mice then underwent the social preference test and social recognition tests (Figure 4C). With their NAcSh-projecting IL neurons inhibited, GH mice showed social recognition deficits similar to that of the SH mice in Figure 1. Extended Data Figure 5 checks whether inhibiting the NAcSh-projecting IL neurons affected the GH mice in other physiological or psychological ways. However, the GH mice showed normal performance in the novel object recognition test, object place recognition test, open field test, elevated plus maze, and forced swim test. Extended Data Figure 6 looks at whether inhibiting these IL neurons affect sociability itself. The researchers found that inhibited GH mice did not distinguish a novel mouse from its cagemate, but this did not affect the reciprocal social interaction with a novel conspecific.<br /> Lastly, Figure 5 answers: Does increasing NAcSh-projecting IL neuronal activity rescue the social recognition deficit in SH mice? To test this, they expressed the hM3Dq receptor in NAcSh-projecting IL neurons within SH mice and injected CNO 40 minutes before undergoing the social behavior tests (Figure 5A-D). The authors found that social recognition was successfully rescued in these SH mice (Figure 5E-F).<br /> In the conclusion, they tie in their findings with similar ones regarding the hippocampus’s connections to the mPFC and NAcSh and their impact on social memory. They also discuss research about the impact of social isolation on impaired motivation and drug-seeking behavior. They wrap up with a discussion of when they believe the critical period of social recognition is and how their results can contribute to the understanding of disorders like ASD.

Major Criticisms<br /> In Figure 1, we thought that there were a few places that could use improvement or clarification. To go into detail, we would like clarification on why isolation occurred only after weaning and not pre-weaning. Previous literature has been known to isolate mice pre-weaning, and we wanted more justification on why post-weaning isolation was done instead. In addition, we also feel as 8 weeks of social isolation is too long of a period and would like to see additional evidence on why the period could not have been shorter. We also wonder why there was no behavioral testing done before and after resocialization. If there was, we would like to see the data included in the paper. Otherwise, we would suggest that you run the same behavioral experiment on a separate cohort and carry out tests before and after resocialization. Perhaps then results of the behavioral tests run on unsocialized mice can then be depicted in panels C and D for comparison. Another criticism we would like to note is that the distribution found in panels D, E, and F emulates a bimodal distribution instead of a Gaussian distribution. If possible we would like to see a different statistical analysis run that is better fitting of the data. The same can be said for Figure 5 panel E and F. Another major issue noted is the assumption that resocialization is rewarding for the mice. In some instances, one could argue that resocialization is not rewarding as the mouse could be faced with aggressive counterparts. A measurement of anxiety levels during resocialization would help aid in your argument depending on the results. We think that one way you can approach this is by measuring cortisol levels in mice before and after they have been reintroduced. You could also quantify aggression levels before the mouse was reintroduced and once the mouse has been added back into the group. Lastly, for panels E, F, and B we are looking for a bit more clarification on what characteristics delegated a familiar and novel object, position, and mouse. For example, we were wondering if the target mouse was an age and sex match.<br /> Figure 2 looks at NAcSh-projecting IL neurons in the deep layer of the mPFC. However, we suggest that the authors clarify which layer it is. Additionally, to avoid criticisms about possible discrepancies between the number of cells counted and the slice image, we suggest that the researchers provide a high-magnification image of DAPI staining and eGFP to show that each green dot shows a nucleus.<br /> The social habituation/recognition tasks in Figure 3 were performed after 4-week regrouping. It is a good control to keep all behavioral tests after the 8-week group housing or single housing and 4-week regrouping paradigm. However, to more directly confirm the social deficit in the SH mice, we suggest the social habituation/recognition tasks also performed in parallel without regrouping.

Minor Criticisms<br /> In Figure 1 panels C and D, we would like a bit more clarification on where the objects were located in relation to the mouse. The heat maps suggest their locations; however, it is not directly stated in the writing or the figure. In addition, a legend distinguishing between GH and SH in the Social Preference, Social Recognition, Novel Object Recognition, and Object Place Recognition bar graphs would be helpful as it is only indicated in panel A. In addition, we wondered if the chamber placement of the familiar or novel object/mouse were counterbalanced so that they were not always placed on the same side of the mouse. We speculate if there was no counterbalancing done that the mouse may have preferred a certain chamber instead of a particular mouse or object. One last thing we would like to see is the exact age range in which the mice participated in the behavioral tests since it is not made completely clear in the Methods section. <br /> The Results section that discusses Figure 2 begins with an explanation of why the authors focused on mPFC-NAcSh connections in their study. However, we suggest that this is explained in the Introduction instead since it left us wondering why the authors focused on mPFC-NAcSh connections in their study; it was unclear whether there was literature supporting this decision. <br /> In Figure 3 panel B, we think it is an interesting finding that SH mice also showed significantly decreased interacting time with the conspecifics in the habituation session as GH mice did, given that these SH mice would later do poorly in social recognition tests. Thus, it would be better to notify the readers that it is an unexpected or interesting finding, and also propose the hypothesis of this phenomenon. For the panel E and F, you did a good quantification of neuronal activity in the NAcSh-projecting IL neurons by calculating the percentage of c-Fos and eGFP co-labeled neurons among total eGFP labeled neurons, because the number of NAcSh-projecting IL neurons may change in SH mice compared to GH mice. We would like to know whether the neural circuitry from IL to NAcSh is altered by social isolation. For this purpose, we suggest you inject an equal amount of AAVrg-eGFP-Cre in both GH and SH mice, and quantify the number of eGFP-labeled neurons in them. <br /> For Figure 4, the data is generally well organized and the experimental protocol was summarized well in the schematic. However, for Figure 4D (the comparison between spiking in baseline and in the presence of CNO), it would be valuable to show the effect of the control saline vehicle on spiking for both the eGFP mice as well as the hM4Di mice to illustrate that the presence of the IP injection or saline has no effect if it indeed had none. The preference index had also proved difficult to read; the way the lines overlapped made the connections between the data points on each bar hard to discern. Perhaps color coding them or using different shapes instead of uniform dots would be beneficial. <br /> Overall, Figure 5 seemed clear and straightforward. However, we did want a bit more clarification as to why 1mg/kg CNO was used for vehicle injection even though previously in Figure 4 there was 3mg/kg used. We suspect this may be due to an increase in sensitivity pertaining to excitation but would like to see that confirmed within the literature if it is the case. Another point to be made is that 4B and 5B appear to be inconsistent and like to see a bit more clarification or a comment on why that may be.

Merits<br /> For Figure 1 panel A, the addition of the schematic was helpful in understanding the timeline of the study for both GH and SH mice. The same can be said for panels B,E, and F. The results and tests done of the mice seemed appropriate to further aid in the understanding of social recognition and preference. All in all, the authors used every panel in the figure to justify how they came to their conclusion regarding the social preference and recognition test GH and SH mice. <br /> Figure 4 is paramount in illustrating the nuanced effect of this circuit on social behavior—namely, the impairment of social recognition while retaining nominal social preference in mice which was derived from the clearly reported results of the inhibitive manipulation. This connection is no more salient in the paper than here.<br /> Figure 5 was imperative for our understanding in rescuing social recognition. Hand in hand with Figure 4, it clearly defines how social recognition is affected by the loss and regain of the NAcSh-projection IL neurons. In particular, the middle graphs in E and F do an excellent job in highlighting the effect of IL-NAc shell neurons activation in social preference and recognition tests for both GH and SH mice.

Future Directions <br /> The findings of this paper prove to have important impacts pertaining to acquisition of social familiarity. Although it was noted in the paper that future direction includes investigation of animal models of ASD, it would be also beneficial to look at animal models of schizophrenia. In specific, revaluation of what was found in Piskorowski et al. in comparison to this paper pertaining to the critical period. The discrepancy, that is 2 weeks compared to 11 weeks, seems way too large and should be further investigated to better understand why the critical period may be one or the other. As an alternative, it may be found that it may be neither and that there is another time period that better represents the critical time period for normal social recognition.<br /> It would also be interesting to look into why the excitability of NAcSh-projecting IL neurons had decreased. Because the neurons in SH mice showed no significant electrophysiological differences from those in GH mice, the decreased excitability is likely a result of morphological changes. Indeed, Silva-Gómez et al. (2003) found that a similar social isolation protocol in rats results in decreased dendritic spine density within the mPFC.<br /> It is interesting and surprising that both GH and SH mice showed significantly decreased interacting time with the conspecifics in the habituation session in Figure 3B, but the social recognition was impaired in SH mice only. It might be because the SH mice could not remember the familiar mice and recognized them as all novel ones, which would indicate that the processes of memory consolidation and memory retrieval were impaired in SH mice. Thus, we think it would be interesting to investigate social recognition with a perspective of memory in the future.

Works Cited:<br /> Silva-Gómez, A.B., Rojas, D., Juárez, I., & Flores, G. (2003). Decreased dendritic spine density on prefrontal cortical and hippocampal pyramidal neurons in postweaning social isolation rats. Brain Research, 983(1), 128-136.

Piskorowski RA, et al. (2016)Age-Dependent Specific Changes in Area CA2 of the Hippocampus and Social Memory Deficit in a Mouse Model of the 22q11.2 Deletion Syndrome. Neuron 89, 163–176.

On 2025-03-10 19:52:40, user Sr. Galán wrote:

A peer reviewed version of this article is now published in Ecosistemas, link here:<br /> https://www.revistaecosistemas.net/index.php/ecosistemas/article/view/2847

On 2025-09-20 11:45:15, user Chris Redfern wrote:

This paper has now been peer-reviewed and published online in Ibis: https://doi.org/10.1111/ibi.13444

On 2019-04-27 15:04:45, user Paul Schanda wrote:

This publication has been accepted in Nature Communications.

On 2017-03-07 20:58:55, user Robert Edgar wrote:

This is not a significant problem in practice, especially compared with other algorithms that have similar issues plus a stronger tendency to overpredict. In theory, the described scenario could happen, but classification errors are rare in the databases I recommend, and even if they are present the scenario usually doesn't happen. If there are other members of T1 that are similar, then the sub-sampling of the kmers in the bootstrapping should cause those members to be the top hit in some, perhaps most, of the bootstrap iterations, even if T2 is the most similar database sequence. The same comment could be made about the RDP Classifier, especially for longer sequences. As the sequence length increases, the RDP Classifier will give increasingly high confidence to the wrong prediction while SINTAX is more robust because it fixes the sample size at 32 words regardless of the sequence length.

On 2019-03-11 23:19:53, user Paul Macklin wrote:

Follow-up work just posted as a preprint here: <br /> https://www.biorxiv.org/con...

On 2019-08-19 21:33:40, user Charles Warden wrote:

While the main focus of the paper is a little different, I noticed this study shows an increased error in imputation for rare variants:

https://bmcgenomics.biomedc...

https://bmcgenomics.biomedc...

On 2025-10-03 21:41:30, user stephen roper wrote:

N.B., the specific bead thermistor listed for this device may no longer be available. Here is a replacement: Murata NXFT15XH103FEAB025, $0.52 each: ( https://www.mouser.com/ProductDetail/Murata-Electronics/NXFT15XH103FEAB025?qs=rkhjVJ6%2F3EKkXdsbAbp%252BOw%3D%3D )

On 2021-02-17 15:35:10, user Kas Steuten wrote:

This article is now published in ACS Infectious Diseases: https://doi.org/10.1021/acs...

On 2016-05-28 13:06:29, user endmathabusenow wrote:

Can the authors release their data on all metrics, diseases, and tumors assessed in all rats in this study, including null results? Are there similar unpublished studies that were abandoned or not published for various reasons?

Also, does the level of radiation used induce any heating of any surrounding objects or walls, vibrations, or electrical sensations that could effect rat behavior?

On 2016-05-29 02:42:23, user Sophia Redmiles wrote:

This study was designed to look at the possibility of carcinogenic effects from RF on rat population used in the study. The unanticipated results, such as longer life span after exposure has to be studied separately to come up with any conclusions as would hints that GMO in the rat food could have produced the tumors that resulted. What commentors are missing is that the types of tumors that formed are consistent with those found in humans who have consistent exposure to RF radiation from cell phones and are located in the neck and head consistent with using a cell close to the head.<br /> All devices, past and present, that use RF do emit radiation; however, TVs, radios, etc. were not personal devices. What is different now is that use of wireless RF devices is up close and personal, stronger signals and chronic usage, which the current extremely outdated FCC Guidelines (emphasis "Guidelines" they are not standards!) do not take into consideration. ALL radiation is harmlful, and all radiation is CUMULATIVE. DoD has known this since WWII and has completed over 1800 studies showing same health effects shown in non-industry-funded studies. Asking wireless industry to fund RF studies is like putting the fox in the hen house. So far, only industry studies show RF is safe.

On 2017-04-13 02:21:17, user Dave Baltrus wrote:

As the "big data revolution" progresses and biology is confronted with ever more complicated patterns to interpret, evolutionary terms are being increasingly invoked to explain perceived patterns. "Frequency dependence" is one of these terms. The purpose of this manuscript from Brisson is to begin to clarify when it is/is not appropriate to use the term "negative frequency dependent selection (NFDS)" in the context of evolutionary explanations. Brisson does a great job of laying out definitions and explanations for use of this term over the last century or so, and does so while describing how such selection regimes could help to explain the amount of diversity we see in the world. I'm a proponent of clearly laying out the case for when nuanced evolutionary terms are applied inappropriately, and Brisson does a good job of describing instances where patterns may suggest negative frequency dependent selection but where this specific evolutionary model doesn't apply. He makes this case throughout the manuscript and does so in a way that is clear and concise. I think this manuscript could go a long way towards clearing up some confusion in the literature if the right people see it at the right time.

I have no major qualms with this preprint, it's laid out and written quite well. However, I do think that it would make the case slightly more clear if, in cases where the pattern suggest NFDS falsely, if some examples were imagined that would allow the patterns to fall under the purview of NFDS. For example...what would need to happen to make the "killing the winner" scenario actually fall under NFDS? I'm not sure if there is actually a clear way to do this or if it would muddle things, but if possible it would be good to include additions that could make these situations fall under NFDS as counterpoints.

I really enjoyed this preprint both for its subject matter and clarity, and I hope to see it well received across communities.

On 2020-06-04 11:10:15, user Stefan wrote:

I carefully read your manuscript and like your method. I hope this can significantly improve RNA structure analysis.

Here are some thoughts of mine:

1) looking at the 71bp sequence in your supp Figure 2, I count 24 A's, i.e. 4 more than in your Figure 1C. Why are those 4As in the closing stem missing? This also applies to Panels A and B in Figure 2. Furthermore, if the structure in 2A should correspond to the MFE structure sin supp2, there seems to be an offset in the white circles, which I think denote A's

2) regarding Figure 1G: the three drawn structures together make up ~60%. I wonder how heterogeneous the remaining 40% are? Could you maybe classify them by their level 1 shape (https://bibiserv.cebitec.un... "https://bibiserv.cebitec.uni-bielefeld.de/rnashapes?id=rnashapes_view_submission)") to report a more coarse grain overview?

3) For better orientation, it would be nice to have extra ticks on every tenth nucleotide in Figure 2C, as you used for supp2.

4) I don't understand your sentence "Both bound and unbound TPP structures had similar modifications profiles (Supp. Fig. 7) indicating preformation of structure in the absence of the ligand." Doesn't "bound" imply ligand presence?

5) Am I right, if I understand you dAMI scores for multiple individual molecules as something similar to covariance for a set of ortholog sequences? Regarding Figure 2E: you nicely discuss findings for boxes I to III. Looking at the 3' end of the sequence, I see a lot of red. Wouldn't that imply further structuredness at the molecules 3' end in both states? The squiggle plots in 2C draw those areas as unpaired regions, but is this supported by crystallography? I am also trying to wrap by head around the red rectangle between positions 7 and 61 for the unbound state, but cannot see how they would form a stem.

On 2025-02-21 09:00:55, user Froy wrote:

A pity this citation is missing: https://www.cell.com/cell-reports/fulltext/S2211-1247(22)00444-2

On 2022-05-24 09:13:50, user NATTASIT PRAPHAWI wrote:

Hi! Your work is interesting.<br /> YAP seems to regulate the patterning of gastruloid.<br /> I wonder that YAP nuclear localization is differentially express through out the hESC colonies?

On 2025-04-03 11:46:02, user Schoolenberg, Machteld wrote:

Dear authors, really nice paper, pleasure to read. I'll use it for my current NFF project on pathways towards the GBF targets. I missed an important, additional reference though, which is the IPBES, 2023 document on zenodo. https://zenodo.org/records/8171339 It is an IPBES document, with the plenary approved description of the NFF and it's official figure (which has been modified since Pereira 2020) as well as a 1st version methodological guidance on using the NFF.<br /> I especially wanted to point out this reference, since your figure 5 is graphically so extremely similar to the approved NFF figure, but without reference to this document. That is either a coincidence, in which case to avoid confusion I'd suggest seeing how to make the two more dissimilar since conceptually they are different. Or on purpose, in which case I think adding 'adapted from IPBES, 2023' is appropriate.<br /> Happy to discuss if needed.<br /> Best wishes, Machteld

On 2023-03-20 12:17:23, user Jeffrey Ruberti wrote:

Readers interested in this paper should first read Trelstad Dev Biol 1982; 92:133-4 and then begin a long reading thread with Bouligand Y. et al. 1985 Biol Cell 54, 143-180 and Bouligand Y. et al. 1985 in Biology of invertebrate and lower vertebrate collagens pp 115-134. Then they should continue on with the rest of Marie Giraud-Guille's work on liquid crystal collagen and its relevance to animal structure.

On 2020-05-14 16:34:14, user Anita Bandrowski wrote:

"Hi, we're trying to improve preprints using automated screening tools. Here's some stuff that our tools found. If we're right then you might want to look at your text, but if we're not then we'd love it if you could take a moment to reply and let us know so we can improve the way our tools work. Have a nice day.

Specifically, your paper (DOI:10.1101/2020.03.22.002204); was checked for the presence of transparency criteria such as blinding, which may not be relevant to all papers, as well as research resources such as statistical software tools, cell lines, and open data.

We did not detect information on sex as a biological variable, which is particularly important given known sex differences in COVID-19 (Wenham et al, 2020).

We also screened for some additional NIH & journal rigor guidelines:<br /> IACUC/IRB: not detected ; randomization of experimental groups: not detected ; reduction of experimental bias by blinding: not detected ; analysis of sample size by power calculation: not detected .

We found that you used the following key resources: cell lines (3), software (3) . We recommend using RRIDs so that others can tell exactly what research resources you used. You can look up RRIDs at rrid.site

Thank you for sharing your data.

More specific comments and a list of suggested RRIDs can be found by opening the Hypothes.is window on this manuscript, direct link https://hyp.is/9Mu_XI-sEeqF...<br /> References cited: https://tinyurl.com/y7fpsvzy"

On 2017-04-01 10:27:57, user Giovanna Serenelli wrote:

Science uses specialized terms, of course.<br /> But science and understandability are in perfect agreement. A scientist should not be an 'affiliated with a cryptic' sect, a sorcerer, or an alchemist. We all use jargons in the laboratory, but when we write or explain to others (even scientists) because exceed in use? Our research does not improve proportionately with the increase in the number of jargon used.

On 2024-12-18 18:47:29, user xPeer wrote:

Courtesy review from xPeerd.com

Summary<br /> The manuscript introduces “semantic mining,” leveraging a 7-billion parameter genomic language model (Evo 1.5) to generate de novo functional proteins guided by genomic context. Applications include toxin-antitoxin systems and anti-CRISPR proteins, with experimental validation demonstrating significant activity. The introduction of SynGenome, an AI-generated database of 120 billion synthetic DNA base pairs, expands accessibility to sequence exploration. While the methodology is innovative, the work requires greater clarity in data validation, reproducibility, and assessment of model biases.

Major Revisions<br /> Validation of Novelty and Utility:

Page 3–6: While Evo-generated proteins are novel, the manuscript lacks detailed benchmarking against state-of-the-art generative tools like ESMFold or AlphaFold for function-guided design. Comparing success rates and biological plausibility of sequences generated by Evo vs. other models would validate its unique contributions.<br /> Recommendation: Include a comparative analysis using publicly available benchmarks or direct competition against contemporary tools.<br /> Model Limitations and Bias:

Page 10–11: The manuscript notes the autoregressive sampling approach's propensity for repetitive or non-functional sequences but does not quantify failure rates. A robust statistical analysis of failures versus successes would strengthen confidence in Evo's predictions.<br /> Recommendation: Provide an error rate analysis for generated sequences, specifying how often Evo outputs unusable or irrelevant results.<br /> Experimental Validation Scope:

Pages 7–8: While experimental validations (e.g., toxin-antitoxin assays) are presented, the sample size of 10–15 per class may not generalize to broader applications.<br /> Recommendation: Increase experimental validations, particularly for diverse protein classes, or clarify why the existing sample size is statistically sufficient.<br /> Ethical Considerations of AI in Genomics:

Page 12–13: The paper touches on the novelty of AI-designed proteins but does not address the ethical implications of releasing 120 billion synthetic sequences, especially regarding misuse.<br /> Recommendation: Include a section discussing potential misuse (e.g., biosecurity risks) and measures to mitigate ethical concerns.<br /> Scalability and Practical Deployment:

Pages 8–10: SynGenome’s database is comprehensive but lacks a discussion on computational resources required for its generation and queries.<br /> Recommendation: Add a performance benchmarking section detailing the hardware and time required for large-scale queries and data generation.<br /> Minor Revisions<br /> Formatting and Presentation:

Figures 3D–G (Page 8): Improve resolution and annotation clarity. Figures lack consistent labeling for protein sequence lengths and functional annotations.<br /> Headings (Throughout): Ensure consistent capitalization for section titles.<br /> AI Content Evaluation:

Estimated AI Contribution: 20–25%.<br /> Identified Sections: Abstract, Methods (Page 13–15), and descriptive portions of Results show repetitive phrasing indicative of AI generation.<br /> Impact: Low epistemic risk as AI-assisted text remains aligned with scientific integrity but lacks nuanced argumentation.<br /> Recommendation: Revise abstract and descriptive content to improve readability and originality.<br /> Methods Reproducibility:

Pages 14–16: The methods section requires clearer step-by-step reproducibility guidelines for Evo training and sampling, including parameter settings and failure case management.<br /> Recommendation: Add a concise reproducibility checklist summarizing critical steps and dependencies.<br /> Recommendations<br /> Enhance comparative benchmarking to validate Evo’s claims of novelty and efficiency.<br /> Provide expanded experimental validation, ensuring sufficient generalizability across diverse protein functions.<br /> Address ethical concerns about AI-generated sequences to preempt potential misuse and establish guidelines for responsible use.<br /> Refine text clarity in AI-generated sections and enhance the formatting of figures for better readability.<br /> Strengthen reproducibility guidelines, particularly for model training and sequence generation, to facilitate adoption by other researchers.

On 2016-01-13 20:30:33, user Joseph Gogos wrote:

Very nice replication of our work on SETD1A (KMT2F) and schizophrenia [Neuron 2014 May 21;82(4):773-80]

On 2024-09-02 14:42:35, user Peer wrote:

Looks like a very interesting story. Although, since the authors did not include any methodology it's hard to evaluate the quality and reproducibility of this work.

On 2020-07-26 20:22:28, user Bartholdi C wrote:

Hi Aparajita, thanks for the paper. I am a little bit confused on how you encode the sequence. In the paper, "Input sequences are represented in the form of a 4 × N matrix comprising the flanking ‘N’ nucleotides in the upstream and downstream of both acceptor and donor splice junctions." However, I think the dimension should be 5*L after encoding (L is the length of my sequence), since there are "ATCGN".

On 2018-06-21 17:34:38, user Moriarity Lab wrote:

Please see our published, updated manuscript in The CRISPR Journal for citation and additional information!

https://www.liebertpub.com/...

On 2022-10-24 15:34:28, user James Saulsbury wrote:

The maps in this paper have been flipped vertically rather than rotated 180°, so the directions are wrong

On 2023-10-23 12:27:01, user Senthil-Kumar Muthappa wrote:

This preprint article is now published, please see: Priya P, Patil M, Pandey P, Singh A, Babu V, Senthil-Kumar M. (2023). Stress Combinations and their Interactions in Plants Database: A one-stop resource on combined stress responses in plants. The Plant Journal, https://doi.org/10.1111/tpj...

On 2014-05-29 18:31:29, user Giulio Caravagna wrote:

Hi all, this is to inform you that the paper finally appeared in PLoS ONE, with further analysis on clonal expansion.

http://www.plosone.org/arti...

On 2025-08-22 14:10:57, user Tobias Herrmann wrote:

I think supplementary figure after S5 are missing?

On 2021-06-23 10:16:32, user Andrea Degasperi wrote:

Congratulations for this wonderful work! May I just make sure that our approach implemented in the signature.tools.lib package is used correctly? From the supplementary notes you provide it seems that the guidance for using our method is not followed. We advise using at least 20 bootstraps with 200 repeats and filter of best runs with RTOL=0.001 (see “what parameters should I use for signature extraction”, https://github.com/Nik-Zain... "https://github.com/Nik-Zainal-Group/signature.tools.lib#faq)"). However, in the supplementary notes it is stated that 100 bootstraps with no filter is used. Also, the default parameters shown in our package, in particular repeats=10 is only meant for testing, not for practical purposes, as at least 100 repeats are in general necessary for small medium problems.

On 2017-03-24 16:20:28, user Guillaume Devailly wrote:

It's really just a web interface for heatmap2 and plot.ly heatmap.<br /> Unless I am missing something?<br /> Also, fastheatmap lack any clusterisation options, it's just an image of the uploaded matrix.

On 2017-09-16 15:44:33, user Sebastian Munck wrote:

Isn't it called open hardware solution?

On 2017-12-19 17:40:54, user AdamMarblestone wrote:

-"Coherent representations of subjective spatial position in primary visual cortex and hippocampus" https://www.biorxiv.org/con...

On 2018-07-29 00:47:40, user coip wrote:

Forced genital mutilation of infants is an egregious human rights violation and needs to be banned immediately, everywhere.

On 2019-07-15 09:28:55, user May-Britt Öhman wrote:

I wonder about this "Saami DNA" - what is the reference point for that distinction? The Saami have lived in the region during a very long time, at least 2000 years but probably longer, and of course mixed with others coming in. It is not mentioned in the article, so please help me understand this. Or refer me to an article where this is discussed further. Thanks in advance, best, May-Britt Öhman, Uppsala University

On 2024-08-12 09:57:17, user John Hawks wrote:

The authors have prepared a summary of revisions to this manuscript, which is available in the Supplementary Information section of the preprint. I have included the text of that summary across the following two comments in two parts (the whole is larger than the maximum comment length).

Summary of revisions to Evidence for deliberate burial of the dead by Homo naledi

We extend our sincere thanks to the editor, referees for eLife, and other commentators who have written evaluations of this manuscript, either in whole or in part. Sources of these comments were highly varied, including within the bioRxiv preprint server, social media (including many comments received on X/Twitter and some YouTube presentations and interviews), comments made by colleagues to journalists, and also some reviews of the work published in other academic journals. Some of these are formal and referenced with citations. Others were informal but nonetheless expressed perspectives that helped enable us to revise the manuscript with the inclusion of broader perspectives than the formal review process. It is beyond the scope of this summary to list every one of these, which have often been brought to the attention of different coauthors, but we begin by acknowledging the very wide array of peer and public commentary that have contributed to this work. The reaction speaks to a broad interest in open discussion and review of preprints.

As we compiled this summary of changes to the manuscript, we recognized that many colleagues made comments about the process of preprint dissemination and evaluation rather than the data or analyses in the manuscript. Addressing such comments is outside the scope of this revised manuscript, but we do feel that a broader discussion of these comments would be valuable in another venue. Many commentators have expressed confusion about the eLife system of evaluation of preprints, which differs from the editorial acceptance or rejection practiced in most academic journals. As authors in many different nations, in varied fields, and in varied career stages, we ourselves are still working to understand how the academic publication landscape is changing, and how best to prepare work for new models of evaluation and dissemination.

The manuscript and coauthor list reflect an interdisciplinary collaboration. Analyses presented in the manuscript come from a wide range of scientific disciplines. These range from skeletal inventory, morphology, and description, spatial taphonomy, analysis of bone fracture patterns and bone surface modifications, sedimentology, geochemistry, and traditional survey and mapping. The manuscript additionally draws upon a large number of previous studies of the Rising Star cave system and the Dinaledi Subsystem, which have shaped our current work. No analysis within any one area of research stands alone within this body of work: all are interpreted in conjunction with the outcomes of other analyses and data from other areas of research. Any single analysis in isolation might be consistent with many different hypotheses for the formation of sediments and disposition of the skeletal remains. But testing a hypothesis requires considering all data in combination and not leaving out data that do not fit the hypothesis. We highlight this general principle at the outset because a number of the comments from referees and outside specialists have presented alternative hypotheses that may arguably be consistent with one kind of analysis that we have presented, while seeming to overlook other analyses, data, or previous work that exclude these alternatives. In our revision, we have expanded all sections describing results to consider not only the results of each analysis, but how the combination of data from different kinds of analysis relate to hypotheses for the deposition and subsequent history of the Homo naledi remains. We address some specific examples and how we have responded to these in our summary of changes below.

General organization

The referee and editor comments are mostly general and not line-by-line questions, and we have compiled them and treated them as a group in this summary of changes, except where specifically noted.

The editorial comments on the previous version included the suggestion that the manuscript should be reorganized to test “natural” (i.e. noncultural) hypotheses for the situations that we examine. The editorial comment suggested this as a “null hypothesis” testing approach. Some outside comments also viewed noncultural deposition as a null hypothesis to be rejected. We do not concur that noncultural processes should be construed as a null hypothesis, as we discuss further below. However, because of the clear editorial opinion we elected to revise the manuscript to make more explicit how the data and analyses test noncultural depositional hypotheses first, followed by testing of cultural hypotheses. This reorganization means that the revised manuscript now examines each hypothesis separately in turn.

Taking this approach resulted in a substantial reorganization of the “Results” section of the manuscript. The “Results” section now begins with summaries of analyses and data conducted on material from each excavation area. After the presentation of data and analyses from each area, we then present a separate section for each of several hypotheses for the disposition and sedimentary context of the remains. These hypotheses include deposition of bodies upon a talus (as hypothesized in some previous work), slow sedimentary burial on a cave floor or within a natural depression, rapid burial by gravity-driven slumping, and burial of naturally mummified remains. We then include sections to test the hypothesis of primary cultural burial and secondary cultural burial. This approach adds substantial length to the Results. While some elements may be repeated across sections, we do consider the new version to be easier to take piece by piece for a reader trying to understand how each hypothesis relates to the evidence.

The Results section includes analyses on several different excavation areas within the Dinaledi Subsystem. Each of these presents somewhat different patterns of data. We conceived of this manuscript combining these distinct areas because each of them provides information about the formation history of the Homo naledi-associated sediments and the deposition of the Homo naledi remains. Together they speak more strongly than separately. In the previous version of the manuscript, two areas of excavation were considered in detail (Dinaledi Feature 1 and the Hill Antechamber Feature), with a third area (the Puzzle Box area) included only in the Discussion and with reference to prior work. We now describe the new work undertaken after the 2013-2014 excavations in more detail. This includes an overview of areas in the Hill Antechamber and Dinaledi Chamber that have not yielded substantial H. naledi remains and that thereby help contextualize the spatial concentration of H. naledi skeletal material. The most substantial change in the data presented is a much expanded reanalysis of the Puzzle Box area. This reanalysis provides greater clarity on how previously published descriptions relate to the new evidence. The reanalysis also provides the data to integrate the detailed information on bone identification fragmentation, and spatial taphonomy from this area with the new excavation results from the other areas.

In addition to Results, the reorganization also affected the manuscript’s Introduction section. Where the previous version led directly from a brief review of Pleistocene burial into the description of the results, this revised manuscript now includes a review of previous studies of the Rising Star cave system. This review directly addresses referee comments that express some hesitation to accept previous results concerning the structure and formation of sediments, the accessibility of the Dinaledi Subsystem, the geochronological setting of the H. naledi remains, and the relation of the Dinaledi Subsystem to nearby cave areas. Some parts of this overview are further expanded in the Supplementary Information to enable readers to dive more deeply into the previous literature on the site formation and geological configuration of the Rising Star cave system without needing to digest the entirety of the cited sources.

The Discussion section of the revised manuscript is differentiated from Results and focuses on several areas where the evidence presented in this study may benefit from greater context. One new section addresses hypothesis testing and parsimony for Pleistocene burial evidence, which we address at greater length in this summary below. The majority of the Discussion concerns the criteria for recognizing evidence for burial as applied in other studies. In this research we employ a minimal definition but other researchers have applied varied criteria. We consider whether these other criteria have relevance in light of our observations and whether they are essential to the recognition of burial evidence more broadly.

Vocabulary

We introduce the term “cultural burial” in this revised manuscript to refer to the burial of dead bodies as a mortuary practice. “Burial” as an unmodified term may refer to the passive covering of remains by sedimentary processes. Use of the term “intentional burial” would raise the question of interpreting intent, which we do not presume based on the evidence presented in this research. The relevant question in this case is whether the process of burial reflects repeated behavior by a group. As we received input from various colleagues it became clear that burial itself is a highly loaded term. In particular there is a common assumption within the literature and among professionals that burial must by definition be symbolic. We do not take any position on that question in this manuscript, and it is our hope that the term “cultural burial” may focus the conversation around the extent that the behavioral evidence is repeated and patterned.

Sedimentology and geochemistry of Dinaledi Feature 1

Reviewer 4 provided detailed comments on the sedimentological and geochemical context that we report in the manuscript. One outside review (Foecke et al. 2024) included some of the points raised by reviewer 4, and additionally addressed the reporting of geochemical and sedimentological data in previous work that we cite.

To address these comments we have revised the sedimentary context and micromorphology of sediments associated with Dinaledi Feature 1. In the new text we demonstrate the lack of microstratigraphy (supported by grain size analysis) in the unlithified mud clast breccia (UMCB), while such a microstratigraphy is observed in the laminated orange-red mudstones (LORM) that contribute clasts to the UMCB. Thus, we emphasize the presence and importance of a laterally continuous layer of LORM nature occurring at a level that appears to be the maximum depth of fossil occurrence. This layer is severely broken under extensive accumulation of fossils such as Feature 1 and only evidenced by abundant LORM clasts within and around the fossils.

We have completely reworked the geochemical context associated with Feature 1 following the comments of reviewer 4. We described the variations and trends observed in the major oxides separate from trace and rare-earth elements. We used Harker variations plots to assess relationships between these element groups with CaO and Zn, followed by principal component analysis of all elements analyzed. The new geochemical analysis clearly shows that Feature 1 is associated with localized trace element signatures that exist in the sediments only in association with the fossil bones, which suggests lack of postdepositional mobilization of the fossils and sediments. We additionally have included a fuller description of XRF methods.

To clarify the relation of all results to the features described in this study, we removed the geochemical and sedimentological samples from other sites within the Dinaledi Subsystem. These localities within the fissure network represent only surface collection of sediment, as no excavation results are available from those sites to allow for comparison in the context of assessing evidence of burial. These were initially included for comparison, but have now been removed to avoid confusion.

Micromorphology of sediments

Some referees (1, 3, and 4) and other commentators (including Martinón-Torres et al. 2024) have suggested that the previous manuscript was deficient due to an insufficient inclusion of micromorphological analysis of sediments. Because these commentators have emphasized this kind of evidence as particularly important, we review here what we have included and how our revision has addressed this comment. Previous work in the Dinaledi Chamber (Dirks et al., 2015; 2017) included thin section illustrations and analysis of sediment facies, including sediments in direct association with H. naledi remains within the Puzzle Box area. The previous work by Wiersma and coworkers (2020) used micromorphological analysis as one of several approaches to test the formation history of Unit 3 sediments in the Dinaledi Subsystem, leading to the interpretation of autobrecciation of earlier Unit 1 sediment. In the previous version of this manuscript we provided citations to this earlier work. The previous manuscript also provided new thin section illustrations of Unit 3 sediment near Dinaledi Feature 1 to place the disrupted layer of orange sediment (now designated the laminated orange silty mudstone unit) into context.

In the new revised manuscript we have added to this information in three ways. First, as noted above in response to reviewer 4, we have revised and added to our discussion of micromorphology within and adjacent to the Dinaledi Feature 1. Second, we have included more discussion in the Supplementary Information of previous descriptions of sediment facies and associated thin section analysis, with illustrations from prior work (CC-BY licensed) brought into this paper as supplementary figures, so that readers can examine these without following the citations. Third, we have included Figure 10 in the manuscript which includes six panels with microtomographic sections from the Hill Antechamber Feature. This figure illustrates the consistency of sub-unit 3b sediment in direct contact with H. naledi skeletal material, including anatomically associated skeletal elements, with previous analyses that demonstrate the angular outlines and chaotic orientations of LORM clasts. It also shows density contrasts of sediment in immediate contact with some skeletal elements, the loose texture of this sediment with air-filled voids, and apparent invertebrate burrowing activity. To our knowledge this is the first application of microtomography to sediment structure in association with a Pleistocene burial feature.

To forestall possible comments that the revised manuscript does not sufficiently employ micromorphological observations, or that any one particular approach to micromorphology is the standard, we present here some context from related studies of evidence from other research groups working at varied sites in Africa, Europe, and Asia. Hodgkins et al. (2021) noted: “Only a handful of micromorphological studies have been conducted on human burials and even fewer have been conducted on suspected burials from Paleolithic or hunter-gatherer contexts.” In that study, one supplementary figure with four photomicrographs of thin sections of sediments was presented. Interpretation of the evidence for a burial pit by Hodgkins et al. (2021) noted the more open microstructure of sediment but otherwise did not rely upon the thin section data in characterizing the sediments associated with grave fill. Martinón-Torres et al. (2021) included one Extended Data figure illustrating thin sections of sediments and bone, with two panels showing sediments (the remainder showing bone histology). The micromorphological analysis presented in the supplementary information of that paper was restricted to description of two microfacies associated with the proposed “pit” in that study. That study did carry out microCT scanning of the partially-prepared skeletal remains but did not report any sediment analysis from the microtomographic results. Maloney et al. (2022) reported no micromorphological or thin section analysis. Pomeroy et al. (2020a) included one illustration of a thin section; this study may be regarded as a preliminary account rather than a full description of the work undertaken. Goldberg et al. (2017) analyzed the geoarchaeology of the Roc de Marsal deposits in which possible burial-associated sediments had been fully excavated in the 1960s, providing new morphological assessments of sediment facies; the supplementary information to this work included five scans (not microscans) of sediment thin sections and no microphotographs. Fewlass et al. (2023) presented no thin section or micromorphological illustrations or methods. In summary of this research, we note that in one case micromorphological study provided observations that contributed to the evidence for a pit, in other cases micromorphological data did not test this hypothesis, and many researchers do not apply micromorphological techniques in their particular contexts.

Sediment micromorphology is a growing area of research and may have much to provide to the understanding of ancient burial evidence as its standards continue to develop (Pomeroy et al. 2020b). In particular microtomographic analysis of sediments, as we have initiated in this study, may open new horizons that are not possible with more destructive thin-section preparation. In this manuscript, the thin section data reveals valuable evidence about the disruption of sediment structure by features within the Dinaledi Chamber, and microtomographic analysis further documents that the Hill Antechamber Feature reflects similar processes, in addition to possible post-burial diagenesis and invertebrate activity. Following up in detail on these processes will require further analysis outside the scope of this manuscript.

Access into the Dinaledi Subsystem

Reviewer 1 emphasizes the difficulty of access into the Dinaledi Subsystem as a reason why the burial hypothesis is not parsimonious. Similar comments have been made by several outside commentators who question whether past accessibility into the Dinaledi Subsystem may at one time have been substantially different from the situation documented in previous work. Several pieces of evidence are relevant to these questions and we have included some discussion of them in the Introduction, and additionally include a section in the Supplementary Information (“Entrances to the cave system”) to provide additional context for these questions. Homo naledi remains are found not only within the Dinaledi Subsystem but also in other parts of the cave system including the Lesedi Chamber, which is similarly difficult for non-expert cavers to access. The body plan, mass, and specific morphology of H. naledi suggest that this species would be vastly more suited to moving and climbing within narrow underground passages than living people. On this basis it is not unparsimonious to suggest that the evidence resulted from H. naledi activity within these spaces. We note that the accessibility of the subsystem is not strictly relevant to the hypothesis of cultural burial, although the location of the remains does inform the overall context which may reflect a selection of a location perceived as special in some way.

Stuffing bodies down the entry to the subsystem

Reviewer 3 suggests that one explanation for the emplacement of articulated remains at the top of the sloping floor of the Hill Antechamber is that bodies were “stuffed” into the chute that comprises the entry point of the subsystem and passively buried by additional accumulation of remains. This was one hypothesis presented in earlier work (Dirks et al. 2015) and considered there as a minimal explanation because it did not entail the entry of H. naledi individuals into the subsystem. The further exploration (Elliott et al. 2021) and ongoing survey work, as well as this manuscript, all have resulted in data that rejects this hypothesis. The revised manuscript includes a section in the results “Deposition upon a talus with passive burial” that examines this hypothesis in light of the data.

On 2020-11-23 06:15:57, user Max Atfield wrote:

The result is not surprising as it has been found to have very positive results when treating hmans combined with Zinc and Doxycycline in the USA and other countries

Max Atfield

On 2018-07-31 19:43:12, user RobertChase wrote:

The cicadas are being infected by Massospora sp. fungi (not "mushroom mycelium & spores", as the twitterers would have it).

On 2025-04-02 09:04:49, user Lee Berger wrote:

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

On 2015-11-02 19:23:57, user Kelley Harris wrote:

If our preprint intrigues you, please also check out "The Strength of Selection against Neanderthal Introgression" by Ivan Juric, Simon Aeschbacher, and Graham Coop (http://biorxiv.org/content/... "http://biorxiv.org/content/early/2015/10/30/030148)"). We learned a couple weeks ago that we were working on related manuscripts and decided to co-post without reading each others' work. We were stoked to find that our conclusions agree so well when we'd arrived at them via such different methodologies.

On 2020-04-29 19:10:11, user Sinai Immunol Review Project wrote:

Keywords: SARS-CoV-2, COVID-19, ACE2, TMPRSS2, TROP2, Liver, scRNA-seq

Main findings: The authors performed scRNA-seq on human liver tissue and identified an epithelial progenitor cell type that co-express ACE2 and TMPRSS2. ACE2 receptor was highly expressed in cholangiocyte-biased liver progenitors (EPCAM+) which also had a high expression for TROP2 gene. TMPRSS2 was expressed by both TROP2high and TROP2int liver cells. The authors reported that these cholangiocyte-biased progenitor cells had a high expression of TROP2 that correlated with high levels of ACE2 and TMPRSS2 in liver. This suggests that SARS-CoV-2 can infect TROP2high cells via ACE2 and TMPRSS2, contributing to liver dysfunction by compromising the ability of the human liver to regenerate cholangiocytes. The authors also note that ACE2 and TMPRSS2 positive cells are absent in human fetal liver.

Critical Analyses:<br /> 1. Normal tissue from hepatocellular carcinoma patients are tumor-adjacent tissues from these patients and might not be truly ‘normal’.<br /> 2. Tissues from liver cancer patients may have an altered phenotype from healthy controls.<br /> 3. ACE2 expression may be variable and inducible in other cell types in the context of inflammation.

Relevance: <br /> The apparent only cell population in liver that expresses ACE2 and TMPRSS2 are EPCAM+ liver progenitor cells, making them suitable hosts for viral infection and potentially resulting in liver dysfunction. This cell population of liver progenitors with a cholangiocyte fate bias may favor SARS-CoV-2 entry into liver, affecting cholangiocyte precursors, thus leading to liver comorbidities in COVID-19 patients.

Reviewed by Divya Jha, PhD and edited by Robert Samstein, MD PhD, as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.

On 2022-03-22 23:10:37, user Adalia wrote:

The target region of PCR is mentioned and the primers are given for qPCR, but there is no mention of the 16S primers or number of cycles carried out. The number of cycles done can introduce errors if it is too high, so a cycle number of around 20-25 is ideal. Did you use universal primers or were there specific ones used? Were any primer pads added to prevent hairpin formation? There is also no mention of a negative control used in the PCR step, or any other internal standards to prevent error. Were there any internal standards steps taken for PCR, extraction, or sequencing? The coverage of alpha and beta diversity is there and is sufficient, but there is also no statistical analyses of gamma diversity of the microbiomes. Gamma diversity is a measure of the overall diversity for the different ecosystems within a region.There is a mention of quality filtering for chimera removal, which is very good. Despite the coverage of chimera filtering, operational taxonomic units are not explained. Did you cluster your OTUs besides the mention of clustering of chimera and singleton ASV reads? If so, why is it not mentioned in the methods? The Good’s coverage was also not mentioned at all. Was any Good’s coverage calculations done? Or was there any other coverage done, such as chao1 or Shannon’s? Given that this study does use qPCR, that does help to better understand the microbiome as it quantifies the bacteria given instead of just stating the bacteria that is present. The sampling methods are well explained, but I do question if the fecal samples are accurate in determining the microbiome if you were unable to collect them after the race due to dehydration. Is one sample taken before the race sufficient enough to be able to ascertain the microbiome completely, or would there be missing information that could be important? What did you filter for effective sample size? I think it would be helpful to go more in depth with the quality filtering that was done. Is a 50% completeness for MAGs high enough? When you mention normalizing the abundance for sequencing depth, how was this done?<br /> The flow of the paper is good, but the explanations in the methods section could be better. For example, the DNA sequencing steps are not thoroughly explained in this paper, and instead are referenced in other papers, making it difficult to know exactly what was done with the samples prior to extraction without having to go digging for the other papers.. Some questions on extraction can be made, specifically when it comes to the fecal samples. For an accurate depiction of the microbiome of the horses, I believe samples should have been taken before and after the races to understand any differences that might be seen prior to the races instead of what is just present before the race. #SHSU5394

On 2024-08-12 13:08:40, user Yona Levites wrote:

This preprint is now published in Cell rep Med

DOI: https://doi.org/10.1016/j.xcrm.2024.101669

On 2015-09-07 11:35:30, user Pawel Labaj wrote:

First of all would like to say that this is really a great work. Congratulations! <br /> Was just wondering whether you have analysed in a similar way the data set used in papers by Oliver Stegle, especially that you are referring to these (ref. 4 and 8).

On 2020-06-09 23:21:24, user Philip wrote:

Line 136-139- In areas where this was not applicable due to the lack of organized road connections as commonly seen in semi-urban and rural areas, the polio-vaccination micro-plan was adopted, using the polio house markings as a guide.... was lifted from Mshelbwala et al 2017 https://onlinelibrary.wiley.... Authors should acknowledge that.

On 2021-01-13 18:30:29, user Nicholas Markham wrote:

I'm happy this manuscript was resubmitted to Infection and Immunity and has been accepted for publication! Thank you to Borden Lacy all the co-authors!

On 2019-10-07 12:09:51, user Antonio Tugores wrote:

Very Interesting. Thank you. We have now more patients with this mutation and indeed segregates with the disease, so we agree in that it is a mutation. I see that alternative splicing at this exon seems to be a "natural thing": figure 1 shows that both 5-6-7 and 5-7 alt splice forms coexist in a wt (1934T) setting. Is that right?

On 2024-09-18 12:17:23, user Hu Raphael wrote:

I don't think your znf804a antibody can stain signals in neurons, because the proteins of mice and humans are very different. Your genetex znf804a antibody recognizes the central region of human znf804a protein. By comparing human and mouse znf804a proteins, it can be seen that this antibody can only specifically recognize human znf804a protein.

On 2023-04-12 22:21:48, user 603 Journal Club UTK wrote:

Summary. <br /> In this study, they investigate the significance of type 2 immunity as a means of protecting the host against pathogenic helminths. On the other hand, pathologic type 2 immune responses are responsible for the development and maintenance of allergic diseases. In order to facilitate the creation of efficient methods for avoiding or treating these very widespread illnesses, it is vital to decipher the mechanisms that guide type 2 immunity. In mucosal tissues, type 2 responses are responsible for regulating allergic reactions and immunity to helminths. However, the processes that govern these cells are not well known, despite the fact that tuft cells are critical immune system regulators for type 2 immunity. This study investigates the mechanism by which intestinal tuft cells are diminished by commensal bacteria that produce butyrate. Butyrate's ability to inhibit tuft cells required the presence of the epigenetic enzyme known as histone deacetylase 3 (HDAC3). This finding suggests that HDAC3 may play a role in the promotion of tuft cell-dependent immunity. Epithelial-intrinsic HDAC3 actively controlled the growth of tuft cells in vivo and was required for the induction of type 2 immune responses after helminth infection. Curiously, butyrate blocked stem cell differentiation into tuft cells epigenetically, and inhibition of HDAC3 was sufficient to suppress tuft cell proliferation in adult mice and human intestinal organoids. Both of these findings are intriguing. These findings show a novel level of control that commensal bacteria use to calibrate intestinal immunity and disclose an epigenetic route in stem cells that governs tuft cell development. In addition, these findings shed information on how stem cells regulate tuft cell differentiation. It has been shown that the composition of microbiota is linked to the onset of a variety of chronic inflammatory illnesses, such as inflammatory bowel disease (IBD), asthma, and allergic rhinitis. According to the data provided in the research, commensal bacterial-derived metabolites play an important role in epigenetically regulating type 2 intestinal immune responses through active regulation of tuft cell growth. The studies that are being addressed here have shed light on the significance of this function for metabolites that are generated from commensal bacteria. These cells are to blame for the increased sensitivity in the airways, and they can be discovered elsewhere in the body. It is noteworthy to note that HDAC3 is expressed everywhere, and that the levels of butyrate in extraintestinal areas mirror the bacterial colonization of those regions. Both of these facts are related to the fact that HDAC3 is expressed everywhere. As a consequence of this, it is not inconceivable that butyrate manipulation of HDAC3 may potentially alter immunological responses that are dependent on tuft cells in distant tissue areas. As a consequence of this, it is possible that treating pathologic inflammation throughout mucosal tissues using treatments that include modulating this system using pharmacological agents, methods based on nutrition, or ways based on microbiota might be useful. Using a variety of research approaches and points of view, such as mouse models, human biopsies, organoids, and the interactions of microbiota, this work investigates the linkages between tuft cells and expands our understanding of the influence that microbiota have, which is currently understudied, on type 2 immunology.

Positive feedback. <br /> I truly appreciate how in-depth the research was, as well as how well-written the paper itself was. In addition to doing research on tuft cells in mouse models, the researchers investigated the formation of human tuft cells in connection to butyrate. They also investigated the microbiome for polymicrobial interactions and biochemistry. The outcomes of the investigation were supported by a wealth of information and high-quality microscopy in the aforementioned study. Employing this helminth was a brilliant choice, in my opinion, given that Nippostrongylus brasiliensis normally colonized rodents as a host organism. I had never heard of using organoids in tuft cell studies before, so I thought it was a really novel approach. It was quite intriguing to see how the findings from mice applied to human organoid samples.

Things to Possibly Consider

The use of butyrate for therapy is not clearly explained. Is it concentration-dependent? The immune response is not well understood. ILC2 (frequency) is the only measurement available, although it is not a reliable indicator of immunological response. <br /> What about the IgE reaction after worm infection? the Th2 cytokines? actual (as opposed to merely their frequency) <br /> It would be interesting to look at the immune response's effectiveness under various settings in the mouse model. <br /> It's wonderful to see how HDAC3 affects the animals with a deficiency. I do, however, wonder whether there is an additional method, such as using drugs. <br /> Perhaps this additional experiment could offer more that additional evidence that HDAC3 is crucial to the mentioned events. <br /> There are inadequate controls in F. prausnitzii recolonization trials. For instance, a control using bacteria that do not produce butyrate as well as bacteria that have been destroyed would be valuable.<br /> A better data display is required. Please consider displaying data from individual mice on barcharts. Bar charts are no longer advised for use in scientific articles.<br /> Analysis of imaging data and how many tuft cells are detected per 1) unit area, or per 2) nucleated cell, must be done in addition to flow cytometry data. The number of cells in the entire tissue might then be extrapolated using this information.<br /> There are no examples of gating strategies for identifying cells (like ILC2s) in the whole study. Without them, it is impossible to determine if the percentages depicted in the numbers are accurate. Additionally, displayed flow cytometry plots (like Fig. 1B) should reflect the percentage of cells in each individual gate. <br /> There is insufficient evidence to prove that the Cre mice are true knockouts rather than knockdowns with a phenotypic. <br /> What takes place after eggs have been expelled for a period of more than ten days (Fig. 3E)?<br /> Is there a relationship between having poor control and having a low IgE response? Low numbers of eosinophils being recruited? It is likely that butyrate has more than one use. <br /> What evidence do we have that the key factor impacting HDAC3 expression actually does exist? What additional possibilities are there to consider?<br /> What consequences does the absence of HDAC3 in the gut have for macrophages, DCs, and neutrophils that have been recruited in response to an infection?<br /> This has to be broken down much more. During the course of a normal illness, does the amount of butyrate in the body change?<br /> It would be helpful to illustrate the variation in the datasets using barcharts that contain data points for each individual mouse. <br /> There is a possibility that organizing with many figures will be beneficial. For example, Figure 1 depicts three independent tests that are now being conducted.<br /> There is some ambiguity about the keys in Figure 2. It's possible that matching color to solid/empty will result in a more accurate translation of the information.<br /> Never brought up the L stage, which is important for when the worms were put subcutaneously if you can help it. Could include a life cycle to highlight the movement of the worm as well as the stage at when mice were initially exposed to it. If mice don't cough, how are the worms supposed to get out of the lungs and into the stomach? <br /> What was the purpose of the K9 exercise in figure 6? <br /> Please explain the reasoning behind your decision. Describe the steps that you take.<br /> Why was the promoter looked into only?<br /> 16S sequencing is not shown by the numbers, despite the fact that it is specified. Why stop at sequencing the 16S region when we could do the full genome? <br /> You should be able to demonstrate that levels of butyrate fluctuate or shift during an infection with helminths.<br /> Did not investigate how common HDAC3 was in the population. <br /> What kind of an effect did the Cre knockout have on the mouse? There is no information available on the flow.

On 2021-01-02 16:40:48, user Carlos Ayala Grosso wrote:

Reference number 7 is not properly quoted. Data from this report showed expression of amyloid in sustentacular-like cells in sections from biopsies collected from LOAD patients, not autopsies as was mentioned in the Introduction. Later reports have also showed expression of NFT in ensheating-like cells from OM tissue biopsies of LOAD patients.

On 2020-12-16 15:18:24, user Noriko Osumi wrote:

This work has come out in Molecular Brain.<br /> https://molecularbrain.biom...

On 2018-02-28 13:17:27, user Dominique S. Blanc wrote:

The difference in alcohol tolerance was established with a concentration of 23% isopropanol. Alcohol based Hand Rub have a concentration of 60 to 70% which killed all strains of Efm according to the authors. There might be an increase of tolerance to alcohol, but the impact<br /> on hand disinfection is far from being evident. The conclusion of the authors that their finding "complicate infection control recommendations" is wrong and falsely alarming !

On 2024-10-11 22:57:08, user aquape wrote:

Recent information, google<br /> "GondwanaTalks Verhaegen English"<br /> + see <br /> - M.Vaneechoutte cs 2024 Nat.Anthrop.2,10007 open access https://www.sciepublish.com/article/pii/94 <br /> "Have we been barking up the wrong ancestral tree? Australopithecines are probably not our ancestors” <br /> - id.2024 Nat.Anthrop.2,10008 https://www.sciepublish.com/article/pii/187 <br /> "Reply to Sarmiento E. “Australopithecine Taxonomy and Phylogeny and the Savanna Hypothesis…”"<br /> https://www.gondwanatalks.com/l/the-waterside-hypothesis-wading-led-to-upright-walking-in-early-humans/

On 2024-04-12 16:25:56, user Lori Passmore wrote:

COMMENTS FROM PASSMORE LAB JOURNAL CLUB:

In this manuscript the authors show how LEA proteins can improve protein behaviour in cryoEM. This has an advantage over using detergents such as CHAPSO, because the target protein concentration can remain lower. It seems like this would be a straightforward method to implement for challenging specimens and therefore should be of broad interest.

We would find it helpful if the authors could provide more methodological detail in the manuscript, especially given it is a methods-based paper. For example, when was LEA added to the samples? What concentration was the stock solution of LEA? Is glycerol necessary for LEA protein stability?

The manuscript would be strengthened by investigation into the mechanistic basis of how LEA proteins improve particle quality. The authors hypothesize that LEA coats the air-water interface and could further investigate this. The orientation bias of the samples in this study suggests that the sample proteins are still interacting with an interface. Tomography could help explain these.

It would also be interesting to know if the authors attempted to reconstruct crosslinked PRC2 in the absence of LEA (as a control, instead of comparing to other laboratories' work).

On 2021-03-10 00:49:29, user Larry Hunter wrote:

There is an error in the URL for the is-opposite-of pull request for PATO in the text. It should be https://github.com/pato-ont... (note addition of a hyphen).

On 2021-04-01 03:10:46, user Michael Ailion wrote:

This manuscript investigates the maturation of secretory granules (SGs) using the glue granules of the Drosophila larval salivary gland as a model system. The authors show that these SGs dramatically increase in size during maturation, in two different growth phases<br /> with distinct kinetics. An RNAi screen of Rab proteins identifies Rab1 and Rab11 as being important for the SG size increase, and both Rabs are shown to localize to SGs. Rab11 is localized to SGs during the entire maturation process from SG formation until release and is required to recruit Rab1, which only localizes to immature SGs. Finally, it is shown that Rab11 is required for SG exocytosis, acting upstream of actin recruitment in this process . Overall, the manuscript is clearly written, the data are convincing, and the work is significant, uncovering new roles for these Rab proteins in the SG maturation process. This work is expected to be of interest to people interested in secretory granule biogenesis, maturation, or exocytosis (in many systems, not limited to Drosophila glue granules), and to people more generally interested in the function of Rab proteins. Our major critique is that much of the data is qualitative or based on single images. Though the single images are generally convincing, the paper could be improved by better quantification of the data. We also suggest a few additional experiments that could enrich the paper and strengthen some of the conclusions. However, the key claims and conclusions of the paper are not dependent on any new experiments.

Major comments:

1. Much of the data is not quantified and many of the results are shown by single images. Specific examples follow with suggestions for improvement:

a. Number of times experiments are replicated and number of animals should be specified in all figures. For example, in Figs 1C and 4C, the number of granules is provided, but it is<br /> unclear if these are all from a single animal (or salivary gland) or whether data from several animals (or glands) is lumped together. The way the data are presented, there is no way to assess the variation within and between animals, or within and between salivary glands. There are also no statistics for the Rab1 and Rab11 RNAi data shown in Fig 1C.

b. Some panels present semi-quantitative data that do not have clear rationale for the different categories.

Fig 2A provides categories for a protein present on “most” or “some” granules (+ or +/-), but it is unclear how these categories were assessed. Were granules counted? If so, it would be<br /> better to provide the quantitative data. If these were subjective assessments, this should be stated explicitly and it should be better described how the experiment was performed. Were experimenters blinded to the genotypes? It is also unclear what criteria were used to consider a protein “on” a granule -- did it need to show a halo all around the granule?

Table S1 shows categories for a “strong” or “moderate” reduction in granule size, but criteria for assignment to these categories is not given, nor any quantitative sense of what a “strong”<br /> or “moderate” reduction represents. If granule sizes were measured, it would be better to provide the quantitative data. If subjective, more description of how assessments were made should be provided, as well as if experimenters were blinded, and how many experiments/animals/glands were scored. It is also unclear if granule size was scored at a single time point in this screen or at multiple times during the maturation process – this should be stated.

c. Much of the data in the paper for protein localization on granules or protein colocalization is provided as single images. Though most of these images are convincing, there should be<br /> explicit description of how many times each experiment was repeated, and how many animals and glands scored.

1. The model that the presence of Rab1 on immature SGs acts as a checkpoint to prevent<br /> the precocious release of these SGs is an interesting idea. However, there are no data to directly support this model other than the correlational observation that Rab1 leaves the granules before they would normally be released. It is fine to present the idea that Rab1 may act as a checkpoint on SG release, but it should be made clear that this is speculative at this point, and the conclusion should be toned down (e.g. in the Discussion: “Rab1 removal signals the completion of maturation”). Many other proteins have been described as being on immature SGs and then removed during maturation so Rab1 is not unique, and any of these could act as checkpoints of maturation or merely be markers of it. The statement in the Discussion that immature granules are not released during stimulation is not entirely correct –<br /> this depends on cell type, and immature secretory granules in some cell types have been shown to be released.

If the authors wish to strengthen the conclusion that Rab1 acts as a checkpoint on release, we would suggest performing an experiment in which Rab1 removal from SGs is impaired and assess whether exocytosis is inhibited. Possible ways to do this would be to express a GTP-locked Rab1, use a mutant in a Rab1 GAP (if this GAP is known), or artificially tether Rab1 to SGs so that it can’t be removed. Alternatively, we would suggest toning down the conclusion.

1. The data in Fig 1 convincingly show that Rab1 and Rab11 are required for the full increase in SG size during maturation. Interestingly, the terminal size of SGs in Rab1 and Rab11 appears to match the normal size of SGs at the 4-8 hr timepoint when SGs begin the second slower phase of SG growth. This suggests that Rab1 and Rab11 may be specifically deficient in this second growth phase, which would also be consistent with the observation that Rab1 only is found on SGs during this second phase. To assess this directly, a nice experiment would be to quantitatively assess SG size at all timepoints in Rab1 and Rab11 RNAi animals (as done in WT in Fig 1C) rather than just the final timepoint.

2. The data in Fig 4 showing that Rab11 is required for normal Rab1 recruitment to SGs are convincing, but are based on only a single Rab11 RNAi line. This conclusion would be<br /> strengthened by showing the same effect using a second way to perturb Rab11 such as Rab11 dominant negative or a Rab11 hypomorphic mutant. Given that Rab1 is still localized to SGs to some extent in the Rab11 RNAi line, it is unclear if this reflects incomplete Rab11 knockdown or suggests that Rab1 localization is only partially dependent on Rab11. Stronger Rab11 perturbation might distinguish these possibilities.

Minor comments:

1. The conclusion that Rab11 recruits Rab1 to SGs could be bolstered by additional experiments, such as showing that Rab11 recruits a Rab1 GEF (if such a GEF is known). It would also be interesting to see whether a GTP-locked Rab1 mutant localizes to SGs independently of Rab11. Such experiments could enrich the paper, but are not considered<br /> necessary for the major conclusions.

2. The authors state that Fig 5A and Movie 1 demonstrate that Rab11 is transferred from the granule to the plasma membrane during the fusion event. This movie does show that Rab11 is present on granules up to, and possibly through exocytosis. However, as there is no marker for the plasma membrane, it is not clear that Rab11 actually transfers between these two membranes. More imaging experiments demonstrating the overlap of Rab11 and a PM marker after exocytosis would be required to show this convincingly. Alternatively, we would suggest toning down the statement.

3. It is unclear how directly Rab11 participates in SG exocytosis or if its defects are indirect and due to effects on other proteins that participate more directly. One experiment that<br /> might be interesting would be to perform live imaging of exocytosis in Rab11 RNAi or mutant animals and see if SGs approach the membrane, which might possibly indicate whether there are defects in SG tethering or docking. This experiment is not considered essential for the key conclusions of the paper.

4. In Fig 5A, many of the SGs do not seem to have Rab11 on their membrane, as compared to Fig 2C.

5. In Fig S5A, the granules look different from others shown elsewhere in the manuscript (e.g. Fig 2C). In Fig S5A, multiple separate mucin spots appear to be found within one SNAP-24<br /> labeled membrane instead of appearing as a contiguous blob of mucin occupying the entire granule interior.

6. Though it is a minor point in the paper, colocalization of Rab1 and Rab11 with ER exit sites and Golgi in Fig S2 is hard to assess. This could be improved by quantification of<br /> colocalization and by using arrows to point out colocalization in the images, perhaps by zooming in.

7. Given that Rab5 and Rab10 were identified as lower-confidence hits that may affect granule maturation, it might be worth pointing out that these two proteins were also identified in C. elegans in a screen for Rab proteins that affect neuronal dense-core vesicle secretion (Sasidharan et al. 2012, PMID: 23100538).

8. In the discussion, there is the statement that “Our unpublished observations suggest that the mucins secreted from these small granules are abnormal.” These observations should be described. What evidence is there that the mucin secreted is abnormal?

9. The title could be more specific. We would suggest something along the lines of: “Rab1 and Rab11 localize to secretory granules and function in granule maturation”

10. What is the Golgi marker? It is just referred to as “Golgi-RFP.”

Reviewed (and signed) by Michael Ailion and Amy Clippinger

On 2018-07-08 13:29:01, user Jake Bailey wrote:

Putative rTCA and CBB pathways are also both present in genomes from single cell genomes of Thiomargarita and Beggiatoa. See:<br /> - MacGregor, et al. (2013). Sulfide oxidation, nitrate respiration, carbon acquisition, and electron transport pathways suggested by the draft genome of a single orange Guaymas Basin Beggiatoa (Cand. Maribeggiatoa) sp. filament. Mar. Genomics 11, 53–65. doi: 10.1016/j.margen.2013.08.001<br /> - Flood, B.E. et al. (2016) Single-Cell (Meta-)Genomics of a Dimorphic Candidatus <br /> Thiomargarita nelsonii Reveals Genomic Plasticity. Frontiers in Extreme Microbiology. 7:603. doi: 10.3389/fmicb.2016.00603

On 2022-02-15 12:18:04, user Chengxin Zhang wrote:

In Fig 2a, each figure has only 6 lines; but the figure legend on the left hand side lists 8 methods. Are two methods missed in this comparison?

On 2020-08-18 03:42:43, user Lin Sonyan wrote:

How long does this gel remain attached to the nasal mucosa? I guess it needs to remain so for at least a few hours to be effective.

On 2020-08-20 08:37:54, user Yannick Wurm wrote:

Heads up that this is now published at eLife<br /> https://doi.org/10.7554/eLi... - huge thanks to the reviewers and editors who helped improve the ms.

On 2016-08-17 15:59:10, user Jeanne Thomas wrote:

If I'm reading correctly, the test did not separate the 'praise' from the visual of their human. So while the social contact is rewarding, I wonder if the scans would have been the same without the praise - just with the owner appearing.

On 2020-07-14 12:04:01, user Jasper Slingsby wrote:

"The authors have declared no competing interest", but it was funded by Trillion Trees. The World Conservation Society is one of the major partners in https://www.trilliontrees.org/ and likely draws much donor funding through this programme (in addition to the stated funding for this paper). Surely this is a conflict of interest? If this was a paper about tobacco and funded by BAT you'd spot the conflict of interest straight away.

I know planting trees may seem less damaging than smoking, but inappropriate afforestation can have social and economic impacts that can actually be far more harmful through the reduction of water resources, fire, opportunity cost relative to other land use, etc (see https://doi.org/10.1016/j.tree.2019.08.003). This IS an issue for this paper because the baseline forest map used includes large areas of exotic plantation and invasive alien trees. Mapping their "integrity" is completely erroneaous in the context of the stated aims of the paper and would lead to potentially damaging policy recommendations!

On 2022-02-12 04:05:27, user S. Ecret wrote:

This is a really well written and clear preprint. Given the subject matter, it is also written very fairly with regards to the critique of the labs whose work is being re-assessed. Well done to all those involved. I sincerely hope that the concerned labs will respond in a productive and open-minded manner.

I am not qualified to discuss most of the technical aspects of this preprint but had a suggestion that might be, if nothing else, fun to do with the Infernal/R-scape work:

(Writing from memory so please excuse any mistakes in names)

The authors aptly show that the Hox9a hTL shoes no evolutionary signal for a conserved RNA structure. They also discuss inconsistencies with cryo-EM data with regards to the P4-ES9S interaction.

Infernal/R-scape cannot natively work on trans interactions - but perhaps by stitching the P4 and ES9S sequences from the already studied ~550 genomes together with a synthetic, fixed stem loop sequence, Infernal/R-scape could be used to assess the likelihood of a evolutionary signal for the trans interaction? The idea would be that a P4 and ES9S sequence from a single genome would be stitched together and then subjected to assessment after alignment.

I'm not sure what the Eddy or Rivas groups would say about such a use of their tool..

On 2020-09-23 09:48:48, user Ed Rybicki wrote:

Great paper! But: <br /> 1. if there is energy production, it will surely only happen in virions inside cells, where the substrates are made<br /> 2. if it doesn't make ribosomes by itself, and makes particles without these to transfer its genome to new cells, it's still a virus B-)

On 2023-03-07 10:33:27, user Prof. T. K. Wood wrote:

Work provides further evidence of CRISPR-Cas spacers as regulatory units, controlling primarily CRISPR-Cas-related genes. As an extension, we showed in E. coli that CRISPR-Cas spacers repress lytic genes of cryptic prophages through RNAi (doi: 10.3390/ijms232416195). This should be cited.

On 2025-09-18 07:38:14, user Prof. T. K. Wood wrote:

Chemical induction of persistence by reducing protein production was discovered in 2013 and used now by over 50 labs; please cite the appropriate work: https://journals.asm.org/doi/10.1128/aac.02135-12

On 2016-07-15 09:05:13, user Adam Eyre-Walker wrote:

We have known since Seglen’s seminal paper in the 1990s that the distribution of the number of citations for papers published in a journal is highly skewed, that there is considerable overlap in the citation distribution between journals and that there is a poor correlation between the number of citations a paper receives and the journal IF. These observations have been used to suggest that the journal IF should not be used to assess the merit or quality of a particular paper. Usually it is suggested that either the paper is read or that article level metrics are used to assess the merit or quality.

Reading the paper may be considered the gold standard but it is impractical in many circumstances in which one is interested in assessing merit; if for example, you have 100 CVs to look through, you can’t possible read all their papers, or even the best three. Even the papers of those on the shortlist may be too many and you may not be an expert in the field under consideration.

As for citations, as all researchers know articles are cited for all sorts of reasons, often incorrectly. The only quantitative analysis I know of, concluded that the vast majority of the variation in the number of citations a paper receives is just noise, and has nothing to do with the underlying merit of the paper (http://journals.plos.org/pl... "http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1001675)"). I suspect the same is true for other article level metrics.

I find there is a strange disconnect in arguments about the IF. The journal IF must contain some information about the merit of the papers published in a journal because we, the scientific community, are the ones that determine where things get published and what gets cited. We don’t publish any old paper in Nature and Science; we publish what we believe is the best and most interesting science. Now sometimes, may be even often, we will get this wrong, but an informed decision is made to publish a paper in a particular journal. In a sense all the IF represents is someone else’s opinion about the merit of a paper. I think this might be one of the reasons people are uncomfortable with the IF along with the fact that the IF is clearly subject to error as a measure of merit. However, all measures of merit are subject to error and there is no evidence that the IF is any worse (http://journals.plos.org/pl... "http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1001675)"). I’m not suggesting that the IF should be used blindly to assess papers and researchers, but suggesting that it contains little or no information about the merit of a paper seems illogical to me.

On 2019-02-26 18:13:54, user Cory Sheffield wrote:

Also, there is some discussion on large bees with respect to Megachile pluto. So does IT distance reflect overall size (body mass) or just length? Is a long skinny wasps more likely to emerge later than a more robust, but shorter bee?

On 2020-03-21 17:33:57, user Gerald Carter wrote:

Published version here: https://doi.org/10.1016/j.c...

On 2020-04-14 15:37:58, user Benjamin Branoff wrote:

A peer-reviewed version of this manuscript has been accepted and is available here: https://www.tandfonline.com...

On 2023-04-19 03:44:40, user Fox wrote:

I am confused. Is this article really about quinolones or should the title refer to quinolines instead of quinolones?

On 2017-11-29 21:18:39, user Kirill Tokarev wrote:

Update: 'Bird Puffer' software is freely available on our website http://soundanalysispro.com...

On 2022-03-15 17:19:15, user Wendy JM Huang's Lab at UC San wrote:

This article has been peer reviewed and published on Cell Reports with a modified title "ROR?t phosphorylation protects against T cell-mediated inflammation". See article on:<br /> https://www.cell.com/cell-reports/fulltext/S2211-1247(22)00256-X

On 2021-08-20 22:49:34, user Evans wrote:

Interestingly, another preprint recently published on biorxiv identified Zona Incerta as an important "hub" in the stress response. https://www.biorxiv.org/con...

Seems like a really underexplored brain area!

On 2025-10-09 12:20:41, user Paul Macklin wrote:

Published in Journal for Theoretical Biology

https://doi.org/10.1016/j.jtbi.2025.112266

On 2020-11-28 10:50:06, user Marco van de Weert wrote:

I was made aware of this manuscript, as the authors cite a paper I wrote, in which I and my colleague Lorenzo Stella critically assessed the type of work presented in this manuscript. Unfortunately it appears the authors did not fully understand our paper, as we point out several issues with the procedure used. Most importantly, we discuss the double log equation the authors used for analysis of their data (page 4, line 100), pointing out several pitfalls. One important pitfall is that the equation assumes the complex is totally non-fluorescent, an assumption that is very questionable. Beyond this, the authors use an overly complex version of this equation (after all, -log 1/x is the same as +log x). Moreover, it contains an attempt to correct for the difference between added and free ligand concentration (through [L]-[P]*((F0-F)/F)), but this implicitly assumes 1-to-1 binding (as well as a non-fluorescent complex), meaning one could just as well use a slightly modified version of the Stern-Volmer equation, as the double log equation refers back to the Stern-Volmer equation if there is 1-to-1 binding. Note that the double log equation the authors used contains the Hill coefficient (n - notably not defined by the authors in the manuscript), which is a measure of cooperativity between binding sites. Such cooperativity obviously makes no sense if the authors in the same equation assume there is only one binding site.

The issues with assuming a non-fluorescent complex are not trivial, as they cast doubt on what binding the authors actually are investigating and what binding they can actually observe. HSA has one single Trp residue that is responsible for most of its fluorescence. If the ligand binds anywhere else but Sudlow Site I, it is far away from this Trp residue. Depending on how the ligand quenches the Trp fluorescence (direct interaction, FRET, or local structural changes), one would expect that each binding site has a different impact on the fluorescence quenching. In fact, it is my contention that the ligand used here likely cannot give any FRET-induced quenching (the absorbance, if real at all, is extremely low), and thus likely can only quench the fluorescence if it binds in Sudlow Site I, very close to the Trp, either through a direct interaction or by causing some local changes in structure. Whether it fully quenches the fluorescence when bound in Sudlow Site I is obviously unknown, but in my opinion it is unlikely to be the case - the authors themselves find no direct interaction between the Trp residue and their ligand in the molecular docking, so how would it completely quench its fluorescence?

Another part of the manuscript relates to the synchronous fluorescence. Some years ago I and colleagues pointed out that the chosen "deltaLambda" values of 15 and 60 nm may not be sufficient to separate Trp and Tyr contributions, and that there is a much easier and more robust way (https://doi.org/10.1016/j.m...: "https://doi.org/10.1016/j.molstruc.2014.01.004):") just use excitation at 280 and 295 nm to get Tyr + Trp and Trp-alone contributions, subtract the latter from the former, and you get the Tyr-alone contribution. An issue I have with the data presented in Figure 2A+B is that the authors do not indicate what wavelength is plotted on the x-axis; I assume it is the excitation wavelength in both cases. If a correct assumption, this then immediately shows a major problem: the "Tyr" fluorescence (Fig 2B) then supposedly comes with a maximum excitation wavelength around 295-300 nm. This would be very surprising, since Tyr does not absorb in this spectral region! In fact, its absorbance is essentially absent already above 290 nm, hence the common use of 295 nm as excitation wavelength to get Trp-specific fluorescence.

My final comment relates to the connection with pharmacological treatment. The authors indicate some level of concern that warfarin and DHLA bind to the same site, and that this means that patients on warfarin would not get the same benefit of DHLA as those without. This is based on the often-overstated impact of (changes in) albumin binding on the pharmacokinetics, in the mistaken belief that significant (i.e., >10%) binding to albumin has a major effect on the therapeutic efficacy, primarily through prolonged circulation. This is, however, incorrect. Several papers have been written in the past to debunk this myth, pointing out the many other factors that are of importance, and often much greater importance - see e.g. https://dx.doi.org/10.1002/... and https://doi.org/10.1067/mcp....<br /> The concern here is thus extremely speculative, even more so if one realizes that most humans have HSA concentrations in plasma of around 600 micromolar, and that warfarin is typically dosed at 10 mg per day for two days, followed by much lower maintenance doses (3 mg/day). This amounts to some 33 and 10 micromoles a day. Even if it were all to accumulate in the HSA, one would need weeks of treatment before a significant percentage (>10%) of the available HSA has warfarin bound.