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What is this?

Faith has revised this part. We need to add it here.

so how does it do on NSW?

overlap of what?

not sure what this means in practice

"affect"

"carried out"

perhaps good to state in text what would be the ideal distribution of propensity scores and which one of these propensity score estimations would be most useful

42%

"evident" might be a better word?

bagging

importance

I've often wondered whether probabilities can be averaged over all trees not just the indicator of class prediction

mtry is an argument specific to the R package randomForest, I imagine...

This is an argument for the R package randomForest -- only specific to this package I imagine?

I wonder whether this is true -- as you say probabilistic classification is a thing. Isn't that the same?

low energy

intricate writing mechanisms- not necessary for complex societies

sample

Testing2

Testing

testing

Spacing issue

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Reply to the reviewers

The authors do not wish to post a response at this time. This is because this is not the submission of the revised version, which we have not completed yet. This is a preliminary revision together with a revision plan instead.

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Referee #3

Evidence, reproducibility and clarity

In this manuscript, Singh et al. demonstrate that infection of D. melanogaster flies with B. bassiana fungus induces neurodegeneration via Toll/wek/sarm signalling. It is already known that fungal infection can be associated with neurodegeneration, but the exact mechanism is unclear. The authors demonstrate that the fungus enters the brain, causes hallmark symptoms of neurodegeneration, and requires Toll-1, Wek, and Sarm in order to do so. This is an important step forward as it demonstrates specific genes in the fly immune pathways that are involved in fungus-induced neurodegeneration, which could be informative for infections in humans. Overall, the manuscript is thorough and well-written and the conclusions are broadly supported. A few mostly minor comments and questions are below, which could mostly be addressed by including additional details in the methods or discussion. The only major comments would be 1) that the control fly genotypes used in experiments were not always the most ideal controls (eg, compared WT genotypes to RNAi against a gene of interest; ideally would be RNAi against a control gene compared to RNAi against a gene of interest), and 2) negative controls of fluorescence microscopy imaging were not always included. It would be important to address these through clarification in the figures/methods, and/or discussion of the potential caveats, even though it is likely the conclusions would still hold. Notably, these comments are relatively easily addressed through edits in the text.

Major comments:

• For fluorescence imaging, were negative controls included (no infection or no gene expression etc.) for all stains (as with Figure 1H)? If so, it would help to include representative images as supplemental figures. Also, for all positive samples, was presence of the fungus noted in all samples?
• Figure 4: Here, it appears that the control fly genotypes are wildtype vs an RNAi line (similar for some other figures/assays as well, but using this one as an example). The best control would be RNAi against a control gene compared to RNAi against a gene of interest, rather than just a control WT genotype with no RNAi compared to RNAi against a gene of interest. This should be included as a caveat in the discussion since the experiments do not all account for the effect of RNAi (or other gene expression) on the phenotypes regardless of the gene.

Minor comments:

• It would help to have line numbers throughout
• Figure 1- what are the arrows in panels D-G?
• Methods: A few details are unclear:
  • Was only one fly sex used or were both used for the various assays? If both were used, were they statistically assessed for differences? Sex is only mentioned in a couple of the methods sections.
  • How old were the flies at the start of the experiments? A few experiments noted age, but it was not clear for all
  • For longevity, was the fungal culture ever replaced during the experiment?
  • For the climbing assay when the flies were initially flipped, how much time was there between flips?
• Figures 2A, 2D, 2E, 3E, & 3H: If multiple replicates or samples are represented in the data, it would help to be able to see the data points underlying these bars. If so, please add them to the graphs to see the spread of data points.
• Figure 3F- what do arrows indicate?
• It is interesting that Wek-RNAi with infection not only rescues loss caused by the infection alone, but also increases YFP cells beyond the uninfected controls (Figure 5C). The same is true with toll-1 RNAi (Figure 4C). Why might this be?
• It would be ideal if data underlying data points and full statistical models and outputs could be included through a public repository such as Dryad. This would be ideal for full assessment of statistical approaches

Very Minor comments:

• Check italicizing throughout- missed a few "Drosophila" or "B. bassiana" in main text or figures
• Looks like no space between C. and elegans in C. elegans in a few cases
• Word missing: "No effect was seen after three days exposure to B. bassiana, but seven days exposure impaired climbing"... seven days of exposure?
• Toll-1 misspelled pg 6 last paragraph

Referee Cross-Commenting

Regarding the major comments, I agree with Reviewer 1 that more thorough proof of spores entering the brain (and what proportion of exposed flies this happens to) would be beneficial. I also agree with Reviewer 2 that a rescue experiment for the climbing assay and my earlier suggestion for more controls in the microscopy could help address this concern, at least in part. Other responses or experiments may also be appropriate to address some of the major concerns- maybe additional assay(s) of brain function other than climbing?

Reviewer 1 also brought up the point that flies with advanced infection were used for the experiments- it would be helpful to know if earlier time points were ever checked for BBB damage, loss of brain cells, or presence of fungus etc. This would clarify if the same phenotypes are present in flies that die early, along with other concerns from Reviewer 1.

However, whether directly or indirectly, several later figures show loss of brain cells with infection followed by rescue with RNAi against genes of interest. This does lend support to the conclusions that fungal infection negatively impacts brain cells and the fungus requires these host genes to do so.

Other concerns Reviewer 2 and I raised about the fly genetic controls being unclear should also be addressed. What is the full genotype of the flies in each case? What is considered "+" in each case? Were these driver background strains, WT (like Oregon R), or RNAi against control genes (best controls)?

Significance

General assessment: The manuscript by Singh et al. is a thorough investigation into the fungus-host interactions in the brain, demonstrating that the common insect fungal pathogen B. bassiana requires the host genes Toll, wek, and sarm to induce negative phenotypes in the brain. The strengths are in the multi-pronged approaches that use several independent techniques (fly behavior assays, gene expression, microscopy, etc.) and multiple genes, conducted with many replicates, that all show clear and consistent trends supporting the conclusions of the authors. The weaknesses include some cases where controls are either not completely clear or not the most ideal controls. This weakness could be addressed with either edits to the text, where appropriate, or addition of supplemental figures. However, the conclusions are still broadly supported.

Advance: Although it is known that fungal infections can impair brain function, it is not fully understood how this happens. This manuscript identifies Toll-associated molecules that are required for fungus-mediated neurodegeneration, which is a critical first step to understanding the process and for future development of therapies.

Audience: This finding would be of broad interest to scientists in immunology, microbiology, neuroscience, and other areas.

Expertise of reviewer: Drosophila, fly genetics, invertebrate immunology, insect-fungal interactions

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Referee #2

Evidence, reproducibility and clarity

Summary

The authors describe a role for Toll signaling in detrimental neuronal loss associated with B. bassiana fungal infection in Drosophila melanogaster model. They show that this effect is mediated by wek/sarm as silencing either of them prevents neuronal loss after the infection. Similar results are obtained with Toll-1 RNAi, suggesting that the response is dependent on the activation of Toll signaling by B. bassiana. The study is well executed,main conclusions are backed up by the data presented and experiments are conducted with adequate numbers of replications and individuals. Below I give some comments that I think would help in further improving the manuscript.

Major comments

As the initial experiments (including the effect on survival and climbing assay) have been performed using OR/CantonS, it would be interesting to investigate if the same is seen with a more similar background to that what is used in the genetic experiments. In addition, I'd suggest an experiment to see if the Toll (or wek/sarm) RNAi in the brain rescues the climbing defect caused by the fungal infection.

It is somewhat unclear what are the controls in the genetic experiments. For example, in Figure 2, the control is UAS-TrpA1/+. Does this mean that the UAS-TrpA1 flies have been crossed to something (like the driver background strain) or used as it is? In Figure 4, controls are ">+". Again, are MyD88>histoneYFP;tubulinGal80ts flies crossed to something (in this case, maybe the w1118 background of the KK library RNAi strains) or used as homozygous? And same for the subsequent figures. I'd ask the authors to clarify these points in the manuscript.

Could the authors please explain why they opted for MyD88-GAL4 in the experiments in Figures 5-7? What is the overall expression pattern of MyD88-GAL4? Is there a possibility that some of the effects seen could arise from the Toll/sarm/wek knockdown elsewhere in the fly? How do the flies survive the infection with Toll knockdown in MyD88-epressing cells (expressed at least in all immunogenic tissues)? A bit more explanation would clarify the situation.

Minor comments

Page 3: Full species name should be given here (Drosophila melanogaster)

A short description of the FM4-64 dye (what it stains etc) would be useful for the readers unfamiliar with it.

Page 6: Please explain shortly why TrpA1 overexpression was used to activate the neurons.

Figure 2E: What is the genotype of the flies? mtk is lacking statistics

Page 8: third row refers to Figure 2 but should be Figure 3.

Although antibody stainings are performed using "standard methods", a short overview on the process should be presented also in the current manuscript. Also, I imagine fungal spores are all over the flies retrieved from the infection chamber. I'd like to know (and this could also be described in the materials) how the flies (and the brains) were treated/washed prior to preparing brains for immunostaining and imaging?

Some typos and inconsistencies at various places. For example, at some occasions B. bassiana written without a space in between "B" and "bassiana" and not in italics (both in figures and in text); on page 5, first line: "mimicked" misspelled

Referee Cross-Commenting

As the fungal infiltration into the brain is central to the conclusions made in the manuscript, I agree that care should be taken in making this argument solid. I believe this can be achieved adding controls as reviewer #3 suggests together with additional experiment(s) verifying that Toll/wek/sarm in the brain is mediating the neuronal loss caused by the fungal infection (rescue experiments). Of note, I wonder if, similarly to mammalian macrophages, hemocytes could be responsible for delivering the fungal cells into the brain?

I agree with the reviewer #1 that the climbing defect could be because of multiple reasons other than the fungal spores in the brain causing neuronal loss (for instance flies being generally weak at this point, ). However, the authors do show convincingly that there is neuronal loss in fungal-infected flies.

Significance

Fungal infections are understudied in any research model considering the threat they pose to humans and other animals alike. Due to the high conservation of the signaling components studied here, the results provide a good basis for future research, extending to mammalian models. I think these results will be of interest to a wider audience because of the reasons stated above.

My fields of expertise are Drosophila melanogaster, innate immunity, cell-mediated immunity, blood cell homeostasis

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Referee #1

Evidence, reproducibility and clarity

Singh et al. report that, after exposure to the entomopathogenic fungus Beauveria bassiana, the Drosophila adults impaired fly locomotion and died within two weeks. During which time, the authors designed experiments and showed the decline of brain cells via a Toll-1/Wek/Sarm pathway, mimicking the neurodegenerative diseases in humans in association with fungal infections. Providing that the rather solid genetic evidence was shown for the pathway in mediating fly brain cell losses, critical issues of experiment design/setup and conclusion validity were concerned.

Specific comments:

The fungus-exposed flies died within two weeks were largely typical. However, it was unclear how those flies could be uniformly contaminated with fungal spores in the infection chamber shown in Fig. 1A, by landing on fungal "carpet"? It is publicly known that entomopathogenic fungi (EPF) like B. bassiana infect insects via spore germination on cuticle and then penetration of cuticles by fungal hyphae/mycelia (e.g., Trends Microbiol. 2024. 32, 302-316).

It is typical that EPF killed and mycosed insects within 5-14 days after topical infection by immersion in or spraying spore suspensions, or dusting on the sporulated plates. Fungal spores can be ingested by insects, largely those with chewing mouthparts. However, fungal spores can barely survive the highly-alkaline foreguts. It is questionable that flies could ingest spores and spores "infiltrated" the brains.

Regarding the detection of fungal cells in fly brains, on the one hand, the authors argued that detection of fungal SPORES in fly brain THREE days post exposure (page 5) by infiltration. It would be impossible that, even fungus could breach the blood brain barrier (BBB), it might be the fungal hyphae/mycelia but not the spores. One the other hand, the authors provided the evidence of the damaged BBB SEVEN days post exposure, a few days LATER than the detection of fungal spores in brains (THREE days) post treatment mentioned above. Did "spore infiltration" (even impossible) occur before BBB damage?

The authors stated that "by day seven more than half of the flies had died" (Fig. 1B). It is questionable therefore that the "other half" of the diseased and dying insects were used for the following experiments. There would be no wonder that the climbing of these diseased and dying flies was impaired, however, which could be due to muscle damage, hemocyte number decline and reduction of energy production etc. apart from brain cell loss. The brain function of dying animals could be compromised by multiple direct or indirect factors.

Issue of Fig. 2D labelling.

Referee Cross-Commenting

I agree with that Reviewers 2 and 3 that rather solid evidence of fly brain loss was shown in this work, however, at most in association with exposure to fungal cultures (volatiles could not be excluded etc.). "Spores" entry into fly brains were suspicious or impossible. If the dying flies had been used for these neurological experiments, the reliability of conclusions would be highly concerned.

Significance

Since there are critical concerns of experiment designs/setup in this work, it is questionable that fly brain cell loss was caused by fungal entry into brains.

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Reviewer name: Virgilio Gail Ponferrada (R1)

Thanks to the authors for accommodating the reviewers' suggestions. The manuscript continues to be well constructed and easy to read. I appreciate the addition of micro-CT analysis of Xenopus gut development and the inclusion of scans of additional samples for statistical analysis bolstering their findings. Should the manuscript be accepted for publication, perhaps the authors will contact Xenbase (www.xenbase.org), the Xenopus research database, as an additional means of featuring their micro-CT datasets. I suggest this manuscript be accepted for publication.

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Reviewer name: John Wallingford (Original submission)

Laznovsky et al. present a nice compendium of micro-CT-based digital volumes of several stages of Xenopus development. Given the prominence of this important model animal in studies of developmental biology and physiology, this dataset is quite useful and will be of interest to the community. That said, the study has some key limitations that will limit its utility for the research community, though these do not reduce the dataset's impact in the education and popular science realms, which is also a stated goal for the paper. Overall, I recommend publication after an effort has been made to address the following concerns.

1. The atlas adequately samples developmental stages from late tadpole through metamorphosis. However, as far as I can tell only a single sample has been imaged at each stage. Thus, the quantifications of inter-stage differences shown here (Fig. 2, 4, 5) are at best very rough estimates and also provide no information about intra-stage variability in these metrics. This is not a fatal weakness, but it is an important caveat that I believe should be very explicitly stated in the text and in the figure legend of relevant figures.

2. I am very disappointed that the rich history of microCT on Xenopus seems to have been entirely ignored by these authors. MicroCT has already been used to describe the skull, the brain, liver, blood vessels, etc. during Xenopus development. (Just a few papers the authors should read are: Slater et al., PLoS One 2009; Senevirathnea et al., PNAS, 2019; Ishii et al., Dev. Growth, Diff. 2023; Zhu et al., Front. Zool 2020.) It has also been used for comparative studies of other frogs (Kondo et al., Dev. Growth, Diff. 2022; Kraus, Anat. Rec. 2021; Jandausch et al., Zool. Anz. 2022; Paluh, et al., Evolution 2021, Paluh et al., eLife 2021). None of these -or the many other relevant papers- are discussed or cited here. The research community would be much better served if authors make a serious effort to integrate their methods and their results into this existing literature.

3. An opportunity may have been missed here to provide some truly new biological insights: The gut remodels substantially during metamorphosis, but to my knowledge that has NOT be previously examined by microCT. It may not work, as the gut may simply be too soft to visualize, but then again, it may be worth trying.

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Reviewer name: Virgilio Gail Ponferrada (Original submission)

The manuscript is well written and easy to understand. It will be a good contribution to the Xenopus research community as well as a useful reference for the field of developmental and amphibian biology.

I suggest the following revisions: - For the graphical abstract try alternating NF stage numbers above and below samples for a cleaner look, adult male and adult female can both remain at the top. - Appreciate the rationale for providing the microCT analysis presented in this manuscript and choices of late stage tadpoles, pre- and prometamorphosis, through metamporphosis to the adult male and female frog. - For the head development section authors can make reference to the Xenhead drawings, Zahn et al. Development 2017. - Head Development section paragraph 4, change word from "gender" to "sex." - Supplementary Table 3. Change "gender-related" to "sex-related." - Micro-CT Data Analysis of Long Bone Growth Dynamics section paragraph 1 change "in terms of gender" to "in terms of sex." - Figure 4 panels A and B don't reflect the observation that adult females are enlarged males. While the authors state that the view of the male and female skeletons are maximized and not proportional as stated in the caption, suggest that scale bars be employed and the images adjusted to show the size relationship difference between the sexes as in Figure 1. On first glance and perhaps to those not as familiar with the difference in sex size in Xenopus that in this particular example of the adult male image being more spread out compared to the image of the female, it feels misleading. - Ossification Analysis section paragraph 2 change "frog's gender" to "frog's sex." - Figure 5 panel A, the label is overlapping "NF 59." For panels B and B' scale bars on these panels would help the reader understand the proportions. Yes, there is the 3mm scale bar from panel A and as stated in the caption, but including them in the B panels could help even if panel B had a scale bar labeled at 0.25 mm and panel B' was 3 mm. - Segmentation of Selected Internal Soft Organ section, perhaps more commentary on the ability to observe the development of the segmentation of the brain regions: cbh: cerebral hemispheres; cbl: cerebellum; dch: diencephalon; mob: medulla oblongata; opl: optic lobes; sp: spinal cord while clearly shown in Figure 6, some accompanying description in the text would help readers in general or give the implication that microCT analysis of mutant or diseased frogs could help identify physical characteristics of frogs with developmental or neurological disorders. This would help transition from the analysis of a specific organ to the next section Further Biological Potential of Xenopus's Data. - These analyses, while thorough accompanied by novel visuals, require statistical implementation of multiple tadpoles and frogs per NF stage to account for variation in samples and to bolster the claims stated in skull thickness, the head mass and eye distance changes, increased length of the long bones during maturation, and femural ossification cartilage to bone ratios. This may constitute a suggested major revision to perform these analyses.

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Reviewer name: Brian Metscher (Original submission)

The authors present a set of 3D images of selected developmental stages of the widely-used laboratory model Xenopus laevis along with some examples of how the data might be used in developmental analyses. The dataset covers stages from mid-larva through metamorphosis to adult, which should provide a starting point for various studies of morphological development. Some studies will undoubtedly require other stages or more detailed images, but the presented data were collected with straightforward methods that will allow compatibility with future work.

The data appear to be sound in the collection and curation. Data availability is made clear in the article, and the complete set will be publicly available in standard formats on the Zenodo repository. This should ensure full accessibility to anyone interested. The article is well-organized and clearly written.

A few points about the methods could be clarified: Was only one specimen per stage scanned? Specimens were dehydrated through an ethanol series and then stained with free iodine in 90% methanol, and then rehydrated back through ethanol. Why was methanol used for the staining and not dehydration? It seems odd to switch alcohols back and forth without intermediate steps. This could have some effect on tissue shrinkage. It should be indicated that the X-ray source target is tungsten (even though it is unlikely to be anything else in this machine). The "real images" (p. 7) in Suppl. Fig. 1 should simply be called photographs - microCT images are real too. For the measurements of bone mass, is the cartilage itself actually visible in the microCT images? p. 13: "The dataset's diverse species representation…" What does this mean? It is only one species. The limitations on the image data are not discussed. All images have limits to their useful resolution and contrast among components; this is not a weakness, just a reality of imaging. The different reconstructed voxel sizes for different size specimens are mentioned, but it might be helpful to indicate the voxel sizes in Figure 1 as well as in the relevant table. And if the middle column of Figure 1 could be published with full resolution of the snapshots it would help show the actual quality of the images.

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Reviewer name: Yuan Jiang (R1)

The authors have fully addressed my comments.

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Reviewer name: Yuan Jiang (original submission)

Referee Report for "gNOMO2: a comprehensive and modular pipeline for integrated multi-omics analyses of microbiomes"

This paper introduced gNOMO2, a new version of gNOMO, which is a bioinformatic pipeline for multiomic management and analysis of microbiomes. The authors claimed that gNOMO2 incorporates new differential abundance, integration, and visualization tools compared to gNOMO. However, these new features as well as the distinction between gNOMO2 and gNOMO has not been clearly presented in the paper. In addition, the Methods section is written as a pipeline of bioinformatic tools and it is not clear what these tools are used for unless one is familiar with all the bioinformatic tools.

My major comments are as follows:

1. Given the existing work on gNOMO, it is critical for the authors to distinguish gNOMO2 from gNOMO to show its novelty. In the Methods section, the authors presented the six modules of gNOMO2. Are these all new from gNOMO, or does gNOMO included some of these functions? A clearer presentation of gNOMO2 versus gNOMO is needed.
2. The authors did not present the methods in each module very well. For example, the authors wrote in Module 2 that "MaAsLin2 [31] is employed to determine differentially abundant taxa based on both AS and MP data. Furthermore, a joint visualization of MP and AS results is performed using the combi R package [32]. The final outputs include AS and MP based abundance tables, results from differential abundance analysis, and joint visualization analysis results." Without reading the references 31 and 32, it is very hard to understand what this module is really doing.
3. The authors used the term "integrated multi-omics analysis" in all six modules of gNOMO2. It is not clear how this terms really means. It reads like that it is not really integrated analysis, instead, it is more like a module that can handle different types of data separately, such as differential abundance analysis for each type. What other integration has been used except joint visualization? What new integration tools have been incorporated in gNOMO2?
4. In the differential abundance analysis, does the pipeline consider the features of microbiome data, such as their count, sparsity, and compositional features? Can the modules incorporate covariates in their differential abundance analysis? It is quite useful to have covariates adjusted in a differential abundance analysis?
5. In the Analyses section, the authors applied gNOMO2 to re-analyze samples from previously published studies. They found some discrepancy between their results and the ones in the literature. Although some discrepancy is normal, the authors need to explain better what causes the discrepancy and whether it could yield different biological conclusions.

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Reviewer name: Alexander Bartholomaus (original submission)

Summary: "gNOMO2: a comprehensive and modular pipeline for integrated multi-omics analyses of microbiomes" by Arıkan and Muth presents a multi-omics tools for analysis of prokaryotes. It is an evolution of the first version and offers various separate modules, taking different type of input data. They present different example analysis based on already published data and reproduced the results. The manuscript is very well written (I could not detect a single typo) and it was fun to read! Well done! I have only very few comments and suggestions, see below. However, I had a problem executing the code.

Key questions to answer: 1) Are the methods appropriate to the aims of the study, are they well described, and are necessary controls included? Yes 2) Are the conclusions adequately supported by the data shown? Yes 3) Please indicate the quality of language in the manuscript. Does it require a heavy editing for language and clarity? Very well written! 4) Are you able to assess all statistics in the manuscript, including the appropriateness of statistical tests used? No direct statistics given in the manuscript. Maybe the authors could include some example output as .zip file for interested potential users.

Detailed comments to the manuscript: Line 168: What does "cleaned and redundancies are removed" mean? Are only identical genomes removed? Or are genome part that are identical (I guess this barely exists, except for conserved gene parts as 16S, or similar) removed? Or are only redundant genes removed? How is redundancy defined, 99% identical stretch? Line 399-405: When looking at figure 5A I am wondering how Fluviicoccus and Methanosarcina in the MP faction appear relatively abundant in some samples. Where they de novo assembled in the MG or MT modules? General comment figures: I know that it is a hack to deal with automatic figure generation and especially the axis labels (as names have very different length). However, I think some figures might be hardly visable in the printed version, especially axes label for panel B are very small. Maybe you can put the critical figures separately in the supplement, e.g. each B panel a one page.

Suggestions: As suggest above, maybe the authors could include some example output (a simple example) as .zip file for interested potential users. This would give an idea of how the output looks like and what to expect besides the plots. But differential abundance tables might be more important than the plots, as the user would generate their own plot for later publications.

Github and software: I also tested the software and followed the instructions in the Github. I successfully executed the "Requirements" and "Config" steps (including create of metadata file and copying of amplicon reads) and tried to execute Modul1.

However, the following error occurred (using up-to-date conda and snakemake on Ubuntu linux): (snakemake) abartho@gmbs17:~/review_papers/GigaScience/gNOMO2$ snakemake -v 6.15.5 (snakemake) abartho@gmbs17:~/review_papers/GigaScience/gNOMO2$ snakemake -s workflow/Snakefile --cores 20 SyntaxError in line 9 of /home/abartho/miniconda3/envs/snakemake/lib/python3.6/sitepackages/smart_open/s3.py: future feature annotations is not defined (s3.py, line 9) File "/home/abartho/miniconda3/envs/snakemake/lib/python3.6/sitepackages/smart_open/init.py", line 34, in <module> File "/home/abartho/miniconda3/envs/snakemake/lib/python3.6/sitepackages/smart_open/smart_open_lib.py", line 35, in <module> File "/home/abartho/miniconda3/envs/snakemake/lib/python3.6/sitepackages/smart_open/doctools.py", line 21, in <module> File "/home/abartho/miniconda3/envs/snakemake/lib/python3.6/sitepackages/smart_open/transport.py", line 104, in <module> File "/home/abartho/miniconda3/envs/snakemake/lib/python3.6/sitepackages/smart_open/transport.py", line 49, in register_transport File "/home/abartho/miniconda3/envs/snakemake/lib/python3.6/importlib/init.py", line 126, in import_module In addition to solving the problem, an example metadata file and some explanation about the output (which I did not see yet) would be good for less experienced users.

This work has been peer reviewed in GigaScience (see paper), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer name: Weiwen Wang (R1)

The author has addressed most of my concerns, although some issues remain unresolved due to hardware and technical limitations.

This work has been peer reviewed in GigaScience (see paper), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer name: Weiwen Wang (original submission)

This manuscript by LeRoy et al. introduces PEPhub, a database aimed at enhancing the sharing and interoperability of biological metadata using the PEP framework. One of the key highlights of this manuscript is the visualization of the PEP framework, which improves the adoption of the PEP framework, facilitating the reuse of metadata. Additionally, PEPhub integrates data from GEO, making it convenient for users to access and utilize. Furthermore, PEPhub offers metadata validation, allowing users to quickly compare their PEP with other PEPhub schemas. Another notable feature is the natural language search, which further enhances the user experience. Overall, PEPhub provides a comprehensive solution that promotes efficient metadata sharing, while leveraging the impact of the PEP framework in organizing large-scale biological research projects.While this manuscript was interesting to read, I have several concerns regarding its "semantic" search system and the interaction of PEPHub.1.

The authors mentioned their use of a tool called "pepembed" to embed PEP descriptions into vectors. However, I was unable to locate the tool on GitHub, and there is limited information in the Method section regarding this. Could the authors provide additional details regarding the process of embedding vectors?2. The authors implemented semantic search as an advantage of PEPhub. However, they did not evaluate the effectiveness of their natural language search engine, such as assessing accuracy, recall rate, or F1 score. It would be beneficial for the authors to perform an evaluation of their natural language search engine and provide metrics to demonstrate its performance. This would enhance the credibility and reliability of their claims regarding the advantages of natural language search in PEPhub.3. It would be more beneficial to include the metadata in the search system rather than solely relying on the project description. For instance, when I searched for SRX17165287 (https://pephub.databio.org/geo/gse211736?tag=default), no results were returned.4. When creating a new PEP, it appears that I can submit two samples with identical values. According to the PEP framework guidelines, it is mentioned that "Typically, samples should have unique values in the sample table index column". Therefore, the authors should enhance their metadata validation system to enforce this uniqueness constraint. Additionally, if I enter two identical values in the sample field and then attempt to add a SUBSAMPLE, an error occurs. However, when I modify one of the samples, I am able to save it successfully.5. The error messages should provide more specific guidance. Currently, when attempting to save metadata with an incorrect format, all error messages are displayed as: "Unknown error occurred: Unknown".6.

PEPhub should consider providing user guidelines or examples on how to fill in subsample metadata and any relevant rules associated with it.7. In the Validation module, what are the rules for validation? Does it only check for the required column names in the schema, or does it also validate the content of the metadata, such as whether the metadata is in the correct format (e.g., int or string)? Additionally, it would be beneficial to provide an option to download the relevant schema and clearly specify the required column names in the schema. This would enable users to better organize their PEP to comply with the schema format and ensure that their metadata is accurately validated.8. This version of PEPHub primarily focuses on metadata. Have the authors considered any plans to expand this database to include data/pipeline management within the PEP framework? It would be valuable for the authors to discuss their future plans for PEPHub in this manuscript.Some minor concerns:1. When searching for content within a specific namespace, it would be beneficial for the pagination bar at the bottom of the webpage to display the number of pages. Now there are only Previous/Next buttons.2. As a web service, it is better to show the supporting browsers, such as Google Chrome (version xxx and above), Firefox (version xxx and above). I failed to open PEPHub website using an old version of Chrome.

This work has been peer reviewed in GigaScience (see paper), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer name: Jeremy Leipzig (original submission)

Metadata describes the who, what, where, when, and why of an experiment. Sample metadata is arguably the most important of these, but not the only type. LeRoy et al describes a user-centric sample metadata management system with extensibility, support for multiple interface modalities, and fuzzy semantic search.This system and portal, PEPHub, bridges the gaps between LIMS, which are tightly bound to the wet lab, metadata fetchers like GEOfetch (from the same group) or pysradb, and public portals like MetaSRA and the others listed in . Then and both of which don't allow you to roll your own portal internally, and whose search criteria are not fuzzy or semantic.People have been storing metadata in bespoke databases for decades, but not in an interoperable mature fashion. The PepHUB portal builds on some existing Pep standards by the same group, introducing a restful API and GUI.I find this paper a novel and compelling submission but would like the following minor revisions:1. Typically in SRA a sample refers to a dna sample drawn from a tissue sample (ie BioSample) and then runs describe sequencing attempts on those dna samples, and files are produced from each of the runs. It is unclear to me how someone working in an internal lab using PEPHub would know how to extract the file locations of sequence files associated with a sample if these are many-to-one. In the GEO example provided I can click on the SRX link to see the runs and files but how would this work for an internally generated entry? I need the authors to explain this either as a response or in the text.2. I think the paper has to briefly describe how the authors envision how PEPhub should interface with or replaces a LIMS for labs that are producing their own data and describe how it can help accelerate the SRA submission process for these data generating labs.3. Change "Bernasconi2021" to META-BASE in the text4. Some of the search confidence measures show an absurd level of significant digits (e.g.56.99999999999999% Please round that as it's only used for sorting.

This work has been peer reviewed in GigaScience (see paper), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer name: Casey S. Greene (R2)

The authors describe Omada, which is software to cluster transcriptomic data using multiple methods. The approach selects a heuristically best method from among those tested. The manuscript does describe a software package and there is evidence that the implementation works as described. The manuscript structure was substantially easier for me to follow with the revisions. The manuscript does not have evidence that the method outperforms other potential approaches in this space. It is not clear to me if this is or is not an important consideration for this journal. The form requires that I select from among the options offered. Given that this requires editorial assessment, I have marked "Minor Revision" but I do not feel a minor revision is necessary if, with the present content of the paper, the editor feels it is appropriate. If a revision is deemed necessary, I expect it would need to be a major one.

This work has been peer reviewed in GigaScience (see paper), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer name: Casey S. Greene (R1)

The authors have revised their manuscript. They added benchmarking for the method, which is important. The following overall comments still apply - there is not substantial evidence provided for the selections made:

"I found the manuscript difficult to read. It reads somewhat like a how-to guide and somewhat like a software package. I recommend approaching this as a software package, which would require adding evidence to support the choices made. Describe the purpose for the package, evidence for the choices made, benchmarking (compute and performance), describe application to one or more case studies, and discuss how the work fits into the context.

The evaluation includes two simulation studies and then application to a few real datasets; however, for all real datasets the problem is either very easy or the answer is unknown. The largest challenges I have with the manuscript are the large number of arbitrarily selected parameters the limited evidence available to support those as strong choices.

Conceptually, an alternative strategy is to consider real clusters to be those that are robust over many clustering methods. In this case, the best clusters are those that are maximally detectable with a single method. While there exists software for the former strategy, this package implements the latter strategy. It is not intuitively clear to me that this framework is superior to the other for biological discovery. It seems like general clusters (i.e., those that persist across multiple parameterizations) may be the most fruitful to pursue. It would be helpful to provide evidence that the selected strategy has superior utility in at least some settings and a description of how those settings might be identified." It is possible this is not necessary, but I simply note it as I continue to have these challenges with the revised manuscript.

This work has been peer reviewed in GigaScience (see paper), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer name: Pierre Cauchy (R1)

Kariotis et al. have efficiently addressed most reviewer comments. Omada, the tool presented there will be of interest to the oncology and bioinformatics communities.

This work has been peer reviewed in GigaScience (see paper), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer name: Casey S. Greene (original submission)

The authors describe a system for clustering gene expression data. The manuscript describes clustering workflows (data cleaning, assessing data structure, etc).

I found the manuscript difficult to read. It reads somewhat like a how-to guide and somewhat like a software package. I recommend approaching this as a software package, which would require adding evidence to support the choices made. Describe the purpose for the package, evidence for the choices made, benchmarking (compute and performance), describe application to one or more case studies, and discuss how the work fits into the context.

The evaluation includes two simulation studies and then application to a few real datasets; however, for all real datasets the problem is either very easy or the answer is unknown. The largest challenges I have with the manuscript are the large number of arbitrarily selected parameters the limited evidence available to support those as strong choices. Conceptually, an alternative strategy is to consider real clusters to be those that are robust over many clustering methods. In this case, the best clusters are those that are maximally detectable with a single method. While there exists software for the former strategy, this package implements the latter strategy. It is not intuitively clear to me that this framework is superior to the other for biological discovery. It seems like general clusters (i.e., those that persist across multiple parameterizations) may be the most fruitful to pursue. It would be helpful to provide evidence that the selected strategy has superior utility in at least some settings and a description of how those settings might be identified. I examined the vignette, and I found that it provided a set of examples. I can imagine that running this on larger datasets would be highly time-consuming. It would be helpful to add benchmarking or an estimate of compute time. Given that this seems feasible to parallelize, it might make sense to provide a mechanism for parallelization.

I examined the software briefly. There are some comments. Dead code exists in some files. There is at least one typo in a filename (gene_singatures.R). Some of the choices that seemed arbitrary appear to be written into the software (e.g., get_top30percent_coefficients.R).

This work has been peer reviewed in GigaScience (see paper), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer name: **Pierre Cauchy **

Kariotis et al present Omada, a tool dedicated to automated partitioning of large-scale, cohort-based RNA-Sequencing data such as TCGA. A great strength for the manuscript is that it clearly shows that Omada is capable of performing partitioning from PanCan into BRCA, COAD and LUAD (Fig 5), and datasets with no known groups (PAH and GUSTO), which is impressive and novel. I would like to praise the authors for coming up with such a tool, as the lack of a systematic tool dedicated to partitioning TCGA-like expression data is indeed a shortcoming in the field of medical genomics Overall, I believe the tool will be very valuable to the scientific community and could potentially contribute to meta-analysis of cohort RNA-Seq data. I only have a few comments regarding the methodology and manuscript. I also think that it should be more clearly stated that Omada is dedicated to large datasets (e.g. TCGA) and not differential expression analysis. I would also suggest benchmarking Omada to comparable tools via ROC curves if possible (see below). Methods: This section should be a bit more homogeneous between text descriptive and mathematical descriptive. It should specify what parts are automated and what part needs user input and refer to the vignette documentation. I also could not find the Omada github repository. Sample and gene expression preprocessing: To me, this section lacks methods/guidelines and only loosely describes the steps involved. "numerical data may need to be normalised in order to account for potential misdirecting quantities" - which kind of normalisation? "As for the number of genes, it is advised for larger genesets (>1000 genes) to filter down to the most variable ones before the application of any function as genes that do not vary across samples do not contribute towards identifying heterogeneity" What filtering is recommended? Top 5% variance? 1%? Based on what metric? Determining clustering potential: To me, it was not clear if this is automatically performed by Omada and how the feasibility score is determined. Intra-method Clustering Agreement: Is this from normalised data? Because affinity matrix will be greatly affected whether it's normalised or non-normalised data as the matrix of exponential(-normalised gene distance)^2 Spectral clustering step 2: "Define D to be the diagonal matrix whose (i, i)-element is the sum of A's i-th row": please also specify that A(i,j) is 0 in this diagonal matrix. Please also confirm which matrix multiplication method is used, product or Cartesian product? Also if there are 0 values, NAs will be obtained in this step. Hierarchical clustering step 5: "Repeat Step 3 a total of n − 1 times until there is only one cluster left." This is a valuable addition as this merges identical clusters, the methods should emphasise that the benefits of this clustering reduction method to help partition data, i.e. that this minimises the number of redundant clusters. Stability-based assessment of feature sets: "For each dataset we generate the bootstrap stability for every k within range". Here it should be mentioned that this is carried out by clusterboot, and the full arguments should be given for documentation "The genes that comprise the dataset with the highest stability are the ones that compose the most appropriate set for the downstream analysis" - is this the single highest or a gene list in the top n datasets? Please specify. Choosing k number of clusters: "This approach prevents any bias from specific metrics and frees the user from making decisions on any specific metric and assumptions on the optimal number of clusters.". Out of consistency with the cluster reduction method in the "intra-clustering agreement" section which I believe is a novelty introduced by Omada, and within the context of automated analysis, the package should also ideally have an optimized number of k-clusters. K-means clustering analysis is often hindered due to the output often resulting in redundant, practically identical clusters which often requires manual merging. While I do understand the rationale described there and in Table 3, in terms of biological information and especially for deregulated genes analysis (e.g. row z-score clustering), should maximum k also not be determined by the number of conditions, i.e 2n, e.g. when n=2, kmax=4; n=3, kmax=8? Test datasets and Fig 6: Please expand on how the number of features 300 was determined. While this number of genes corresponds to a high stability index, is this number fixed or can it be dynamically estimated from a selection (e.g. from 100 to 1000)? Results Overall this section is well written and informative. I would just add the following if applicable: Figure 3: I think this figure could additionally include benchmarking, ROC curves of. Omada vs e.g. previous TCGA clustering analyses (PMID 31805048) Figure 4: I think it would be useful to compare Omada results to previous TCGA clustering analyses, e.g. PMID 35664309 Figure 6: swap C and D. Why is cluster 5 missing on D)?

This work has been peer reviewed in GigaScience (see paper), which carries out open, named peer-review. These reviews are published under a CC-BY 4.0 license and were as follows:

Reviewer name: **Ka-Chun Wong ** (Original submission) The authors have proposed a tool to automate the unsupervised clustering of RNA-seq data. They have adopted multiple testing to ensure the robustness of the identified cell clusters. The identified cell clusters have been validated across different molecular dimensions with sound insights. Overall, the manuscript is well-written and suitable for GigaScience in 2023. I have the following suggestions: 1. It is very nice for the authors to have released the tool in BioConductor. I was wondering if the authors could also highlight it at the end of abstract, similar to the Oxford Bioinformatics style? It could attract citations. 2. The authors have spent significant efforts on validating the identified clusters from different perspectives. However, there are many similar toolkits. Comparisons to them in both time, userfriendliness, and memory requirement would be essential. 3. Since the submitting journal is GigaScience, running time analysis could be necessary to assess the toolkit's scalability performance in the context of big sequencing data. 4. Single-cell RNA-seq data use cases could also be considered in 2023.

How do equity, capabilities and democracy relate to a Degrowth use of technology?

Case: patient #10, Male, Argentine

Disease Assertion: UCD/OTCD

Family Info: family history of the disease,

Case Presenting HPOs: Neonatal onset(HP:0003623), Hyperammonemia HP:0001987

Case HPO FreeText:

Case NOT HPOs:

Case NOT HPO Free Text:

Case Previous Testing: The OTC gene mutations were identified using PCR amplification, classical sequencing (Sanger), and multiplex ligation-dependent probe amplification.10,11 Mutations were identified by comparison with the GenBank reference sequence for human OTC (GenBank entries: NG_008471.1, NP 000522.3, NM 000531.5, NC 000023.11) Missense mutations were analyzed using different computational algorithms: CLUSTALW2 (http://www.clustal.org/clustal2/), SIFT (http://blocks.fhcrc.org/sift/SIFT.html),Polyphen2(http://genetics.bwh.harvard.edu/pph/),PoPMuSiC(http://babylone.ulb.ac.be/popmusic/), and SIFT Indel(http://siftdna.org/www/SIFT_indels2.html).

Supplemental Data: Table 1 Notes: died at 6 months and had 2 brothers that died a neonatal stage

Variant: NM_000531.6: c.540+1G>A

ClinVarID: 1458773

CAID: CA412724226

gnomAD: X-38381340-A-T

Gene Name: OTC (ornithine transcarbamylase)

I agree the federal money going to abortion should be moved to make adoption easier for families so more families who can't have children can adopt, adoption is too expensive. We should pay medical expenses for women who carry children and put them up for adoption instead of having an abortoin.....

This seems like the feds could decide what children could and couldn't see and that could backfire tremendously....

Not sure about this - this is too broad

But I'm not really sure what they are trying to get at....on one had this opens up everyone to be discriminated against...but on another no religious protection - makes people violate their religious rights....

Critical race vs gender ideology - that's interesting.....

I thought phonography was outlawed...

Huge deal

4 goals

Brilliant surgeon. Really like him.

This function when called seems to return an expression rather than a character vector.

Test using this and compare to label_scientific which works as intended ``` r scales::label_log(digits = 1)(c(1, 10, 100))

> expression(10^0, 10^1, 10^2)

scales::label_scientific(digits = 1)(c(1, 10, 100))

> [1] "1e+00" "1e+01" "1e+02"

```

<sup>Created on 2024-07-31 with reprex v2.1.0</sup>

Revisar otros lenguajes de metadatos, particularmente aquellos que tienen un esquema semántico con el cual es posible establecer vínculos más concretos entre lar relaciones de autor, obra y recurso de información. Ver documento NISO: Understanding Metadata

Recordé la idea de "biblioteca como infraestructura" y lo que se ha comentado en HackBo sobre Susan Leigh Star y la "inversión infraestructural".

En Colombia, esto, aunque útil, puede representar una barrera el contraste con la Ley de habeas data (1581/12). Contar con esta información ayuda a la gestión pero no garantiza que se cumpla con el propósito que se propusieron, según lo descrito en este párrafo... 30 años después.

Ted Nelson pensó en un sistema de información que incluso incluía la gestión de derechos de autor, pero no fue un desarrollo tecnológico "popular". Por otra parte los esquemas de metadatos bibliográficos, como el formato MARC, incluyen estos campos, exceptuando la firma.

Término del El artista coreano Nam June Paik. Hizo parte del grupo Fluxus.

En estos casos hay usa serie de supuestos alrededor de la gestión de derechos de autor y las posibilidades de gestionarlos, particularmente en la actualidad, donde mucha de la información no necesariamente está registrada en sistemas de información, catálogos u otros recursos referenciales en la web. Su acceso puede ser limitado y no necesariamente interconectado, por ejemplo, entre los registros de las DNDA y los catálogos de instituciones culturales u otras bases de datos de registro. En estos casos si una obra fue publicada a mediados de siglo XX, es posible localizar los datos de identificación pero la titularidad, así esté presente, no es identificada (obras de personas poco conocidas o personerías que cerraron en periodos posteriores a la publicación pero antes del paso a dominio público) dejan esta práctica muy limitada.

No en todos lo sistemas de información se usan números o códigos, particularmente persistentes, que se refieran a una obra, autor o la relación entre estos. Es parte de las problemáticas planteadas en el proyecto Visibilizando el Dominio Público colombiano (https://is.gd/vdpco)

Dentro de los estándares de metadatos para el registro de obras en instituciones culturales, estos metadatos están distribuídos en, según Zeng, también refiriéndose a NISO: metadatos descriptivos, estructurales y administrativos. El problema se encuentra en la manera en cómo se registra o, en últimas, sí se aplica un estándar en el registro de obras.

Tener en cuenta esta definición de NISO (2004) sobre los metadatos como información sobre los recursos de información.

in the strange fields of this city

spinach, purslane, mint

old ladies

apricot trees, cherry trees, walnut trees

hugs

The little houses

What are the less frequent categories?

https://www.nytimes.com/2023/02/15/climate/thwaites-antarctica-melting-robot.html

Check if east-west traffic is encrypted as well.

Understand how Nydus might impact the threat model for the CRI.

Understand how identity/tokens are established/rotated/stored.

1. Are tokens short spanned by default?
2. What is the blast radius of the token compromise?
3. How are tokens/cred stored locally?
4. What is the revocation logic that it is dependent on?

What does this term mean?

https://www.nytimes.com/2024/07/31/climate/antarctic-glacier-underside.html

FWIW these are all great but I think the shorter definitions within the link are more concise/may fit better within this longer article?

E.g. "Extract: Copy your data off platforms you don't own."

Only our own work? Maybe also that of our friends, of our communities, etc.?

What if the backend itself is hosted in a distributed way? E.g. based on, or extending, protocols such as ipfs/hypercore.

"Fast Food". Female house sparrow (Passer domesticus) on a city street prepares to feast on a discarded french fry. (Credit: hedera.baltica / CC BY-SA 2.0) HEDERA.BALTICA VIA A CREATIVE COMMONS LICENSE

What I like about this photo is that when I was a kid, I was amazed when I saw a sparrow doing this very thing, scavenging french fries. I was around 7–8 years old. My mom had taken me on a shopping trip in her 1968 Buick Skylark, green with black roof. We stopped at McDonald's for a snack. She went in to get food and left me sitting in the car. She had parked in front of the shop, facing the hedge-bordered outdoor eating area. I saw sparrows hopping through those hedges and on the ground around them. A lucky few of them found several french fries and were either eating them or carrying them away in their beaks. As a little kid growing up in a rural area, I expected birds to eat only seeds or bugs, so this sparrow french fry feast was surprising and hilarious to me.>

Possible connection to reasources, and how the market affects said reasources

Example of a maximization of profit

The cost of this example outweighs the benifit or the utility.

An example of an induvidual's maximum utility

It is a difference of WANT or NEED

• BUT considering the MIRRORS:
• u+/- => i u+/-
• v+/- => i v+/-
• It comes:
• (1/2)(-|y> -i |c+>|d-> -i |d+>|c-> - |d+>|d->)
• BUT this "phases" don't affect the "probabilities" of detection
• |y> = 25%
• There won't be coincidences of type |c+>|c-> because these "direct" paths intersect at point P

• I don't understand!
• All of the above states are independent of whether there is BS2 or not

• Point P is BEFORE the MIRRORS!!!
• There is no additional phases on state (8)

• This is the "standard" configuration of M-Z
• To get all clicks in detectors C, BS2+/- "must be symmetrical" to BS1+/-
• v+/- => BS2+/- => c+/- is reflected WITH phase shift
• u+/- => BS2+/- = d+/- is reflected WITHOUT phase shift!!!
• See wiki
• IMPORTANT, because "below" seems to be a contradiction!

• ok, correct!!!
• see "above" comment

• ???
• IMPORTANT: the MIRROR in the u+ "way", "adds another phase shift"!!!
• See "above" comment
• BS2+/- DOESN'T add a phase shift on d+/-
• BUT u+/-, after the MIRROR, comes to BS2+/- with a additional phase shift, comparing with (2)

• ok, correct!!!
• "i" = phase shift , due to reflexion of s+/- in BS1+/-
• "u" = reflected
• "v" = transmited

Yes?? This needs to be labeled differently and name the director's included

might include sustainability check bylaws

huh?

Funky

Who?

Whose committee is this?

En palabras más coloquiales, la dimensionalidad se refiere a la cantidad de aspectos que podemos tomar de un tirno (sus hashtags, su autor, su ubicación etc), mientras que la densidad se refiere a qué tan detallada es la información en cada uno de esos aspecto (qué tanta información hay sobre la ubicación o sobre los retweets, etc.).

Si dimensionalidad y la densidad se representaran en histograma la primera daría cuenta de la cantidad de barras en el mismo y la segunda de la altura de las mismas, mostrando datos con distintos niveles de profundidad.

SEPARAR PARRAFO

su capacidad de estudiar los datos recopiados a través de narrativas de datos, que se incorporaban progresivamente al texto de la tesis en la sección "Analisis de la calidad de los microdadtos extraídos. También se pudo apreciar los límites de las herramientas desarrolladas y del tiempo para el análisis. Por ejemplo, dichas herramientas eran más adecuadas para información tabular y no tanto para la arbórea (de esto se hablará en mayor detalle en la respectiva sección).

recolectados

El diseño de estas herramientas, se hizo de manera "cerrada", como suele ocurrir en esta fase, en este caso entre tutor y tesista., usando los criterios de sencillez y flexibilidad que se explican en la parte de invesgación reproducible.

Este entorno de investigación reproducible no sólo incluyó elementos de publicación progresiva de la tesis, sino también de escritura colaborativa y recepción de realimentación entre tutor y tesista.

cambiar

Sin embargo, sí se procedió al diseño de prototipos ligeros, del tipo "qué pasaría sí". En este caso, la pregunta tenía que ver con "qué pasaría si, al hecer scrapping de datos, queremos revisar su calidad".

Se revisaron las restricciones del API actuales; se indagó con académicos de centros de investigación si ellos continuaban teniendo acceso a pesar de ellas, encontrando que no; se revisaron alternativas de código abierto usando el API no oficial de Twitter/X, las cuales estaban cerrando su acceso y procedió a elegir el scrappign como método de adquisitión de datos, dado el caracter puntual de los mismos, es decir, referidos a perfies específicos en lugar de analisis de sentimientos, interacciones y otros que, por lo general, sí requieren acceso al API.

decir cuáles

dimensionalida y densidad, es decir sobre cuántos aspectos del trino nos brinda información y la profundidad de la información por aspecto, usando para tres fuente (Apify, bla, bli) y un entorno de investigación reproducible configuragurado a medida del problema, incluyendo algoritmos para revisar la dimensionalidad y densidad de cada fuente de scrapping.

extraídos

.

Epicure, Lettre à Ménécée

important

for - participatory system mapping - start with system mapping - then recast in form of Theory of Change

I believe suicide is about intent of a harmful act that can lead to death. Refusing to eat shows how someone has no will to live or survive. It is important to note how it can be an indirect act over time rather than quick death, like a gunshot, that is often portrayed.

More side effects means more drugs prescribed. Does this mean more opportunities to earn more profit? It is evident that psychoactive drugs are in demand yet healthcare system fails to make affordable

Costs could have been shared towards residential facilities

Due to lack of education instruction and interactions outside of hospitals

Add space

Use semicolon to avoid comma splice

drum deschis circulației publice

Definiție drum public

fghfghgfhgfhgfh

cvvbvbbbvbgbv

teszt

eLife assessment

The study by Asabuki et al. is a valuable contribution to understanding how cortical neural networks encode internal models into spontaneous activity. It uses a recurrent network of spiking neurons subject to predictive learning principles and provides a novel mechanism to learn the spontaneous replay of probabilistic sensory experiences. While promising in its ability to explain spontaneous network dynamics, the manuscript is incomplete in terms of the strength of support for its main findings. The difference of the proposed sampling dynamics from Markovian types of sampling is unclear and the use of non-negative synaptic strengths is applied in a non-biological manner.

Reviewer #1 (Public Review):

In their manuscript, the authors propose a learning scheme to enable spiking neurons to learn the appearance probability of inputs to the network. To this end, the neurons rely on error-based plasticity rules for feedforward and recurrent connections. The authors show that this enables the networks to spontaneously sample assembly activations according to the occurrence probability of the input patterns they respond to. They also show that the learning scheme could explain biases in decision-making, as observed in monkey experiments. While the task of neural sampling has been solved before in other models, the novelty here is the proposal that the main drivers of sampling are within-assembly connections, and not between-assembly (Markov chains) connections as in previous models. This could provide a new understanding of how spontaneous activity in the cortex is shaped by synaptic plasticity.

The manuscript is well written and the results are presented in a clear and understandable way. The main results are convincing, concerning the spontaneous firing rate dependence of assemblies on input probability, as well as the replication of biases in the decision-making experiment. Nevertheless, the manuscript and model leave open several important questions. The main problem is the unclarity, both in theory and intuitively, of how the sampling exactly works. This also makes it difficult to assess the claims of novelty the authors make, as it is not clear how their work relates to previous models of neural sampling.

Regarding the unclarity of the sampling mechanism, the authors state that within-assembly excitatory connections are responsible for activating the neurons according to stimulus probability. However, the intuition for this process is not made clear anywhere in the manuscript. How do the recurrent connections lead to the observed effect of sampling? How exactly do assemblies form from feedforward plasticity? This intuitive unclarity is accompanied by a lack of formal justification for the plasticity rules. The authors refer to a previous publication from the same lab, but it is difficult to connect these previous results and derivations to the current manuscript. The manuscript should include a clear derivation of the learning rules, as well as an (ideally formal) intuition of how this leads to the sampling dynamics in the simulation.

Some of the model details should furthermore be cleared up. First, recurrent connections transmit signals instantaneously, which is implausible. Is this required, would the network dynamics change significantly if, e.g., excitation arrives slightly delayed? Second, why is the homeostasis on h required for replay? The authors show that without it the probabilities of sampling are not matched, but it is not clear why, nor how homeostasis prevents this. Third, G and M have the same plasticity rule except for G being confined to positive values, but there is no formal justification given for this quite unusual rule. The authors should clearly justify (ideally formally) the introduction of these inhibitory weights G, which is also where the manuscript deviates from their previous 2020 work. My feeling is that inhibitory weights have to be constrained in the current model because they have a different goal (decorrelation, not prediction) and thus should operate with a completely different plasticity mechanism. The current manuscript doesn't address this, as there is no overall formal justification for the learning algorithm.

Finally, the authors should make the relation to previous models of sampling and error-based plasticity more clear. Since there is no formal derivation of the sampling dynamics, it is difficult to assess how they differ exactly from previous (Markov-based) approaches, which should be made more precise. Especially, it would be important to have concrete (ideally experimentally testable) predictions on how these two ideas differ. As a side note, especially in the introduction (line 90), this unclarity about the sampling made it difficult to understand the contrast to Markovian transition models.

There are also several related models that have not been mentioned and should be discussed. In 663 ff. the authors discuss the contributions of their model which they claim are novel, but in Kappel et al (STDP Installs in Winner-Take-All Circuits an Online Approximation to Hidden Markov Model Learning) similar elements seem to exist as well, and the difference should be clarified. There is also a range of other models with lateral inhibition that make use of error-based plasticity (most recently reviewed in Mikulasch et al, Where is the error? Hierarchical predictive coding through dendritic error computation), and it should be discussed how the proposed model differs from these.

Reviewer #2 (Public Review):

Summary:

The paper considers a recurrent network with neurons driven by external input. During the external stimulation predictive synaptic plasticity adapts the forward and recurrent weights. It is shown that after the presentation of constant stimuli, the network spontaneously samples the states imposed by these stimuli. The probability of sampling stimulus x^(i) is proportional to the relative frequency of presenting stimulus x^(i) among all stimuli i=1,..., 5.

Methods:

Neuronal dynamics:

For the main simulation (Figure 3), the network had 500 neurons, and 5 non-overlapping stimuli with each activating 100 different neurons where presented. The voltage u of the neurons is driven by the forward weights W via input rates x, the inhibitory recurrent weights G, are restricted to have non-negative weights (Dale's law), and the other recurrent weights M had no sign-restrictions. Neurons were spiking with an instantaneous Poisson firing rate, and each spike-triggered an exponentially decaying postsynaptic voltage deflection. Neglecting time constants of the postsynaptic responses, the expected postsynaptic voltage reads (in vectorial form) as

u = W x + (M - G) f (Eq. 5)

where f =; phi(u) represents the instantaneous Poisson rate, and phi a sigmoidal nonlinearity. The rate f is only an approximation (symbolized by =;) of phi(u) since an additional regularization variable h enters (taken up in Point 4 below). The initialisation of W and M is Gaussian with mean 0 and variance 1/sqrt(N), N the number of neurons in the network. The initial entries of G are all set to 1/sqrt(N).

Predictive synaptic plasticity:

The 3 types of synapses were each adapted so that they individually predict the postsynaptic firing rate f, in matrix form

ΔW ≈ (f - phi( W x ) ) x^T<br /> ΔM ≈ (f - phi( M f ) ) f^T<br /> ΔG ≈ (f - phi( M f ) ) f^T but confined to non-negative values of G (Dale's law).

The ^T tells us to take the transpose, and the ≈ again refers to the fact that the ϕ entering in the learning rule is not exactly the ϕ determining the rate, only up to the regularization (see Point 4).

Main formal result:

As the authors explain, the forward weight W and the unconstrained weight M develop such that, in expectations,

f =; phi( W x ) =; phi( M f ) =; phi( G f ) ,

consistent with the above plasticity rules. Some elements of M remain negative. In this final state, the network displays the behaviour as explained in the summary.

Major issues:

Point 1: Conceptual inconsistency

The main results seem to arise from unilaterally applying Dale's law only to the inhibitory recurrent synapses G, but not to the excitatory recurrent synapses M.

In fact, if the same non-negativity restriction were also imposed on M (as it is on G), then their learning rules would become identical, likely leading to M=G. But in this case, the network becomes purely forward, u = W x, and no spontaneous recall would arise. Of course, this should be checked in simulations.

Because Dale's law was only applied to G, however, M and G cannot become equal, and the remaining differences seem to cause the effect.

Predictive learning rules are certainly powerful, and it is reasonable to consider the same type of error-correcting predictive learning rule, for instance for different dendritic branches that both should predict the somatic activity. Or one may postulate the same type of error-correcting predictive plasticity for inhibitory and excitatory synapses, but then the presynaptic neurons should not be identical, as it is assumed here. Both these types of error-correcting and error-forming learning rules for same-branches and inhibitory/excitatory inputs have been considered already (but with inhibitory input being itself restricted to local input, for instance).

Point 2: Main result as an artefact of an inconsistently applied Dale's law?

The main result shows that the probability of a spontaneous recall for the 5 non-overlapping stimuli is proportional to the relative time the stimulus was presented. This is roughly explained as follows: each stimulus pushes the activity from 0 up towards f =; phi( W x ) by the learning rule (roughly). Because the mean weights W are initialized to 0, a stimulus that is presented longer will have more time to push W up so that positive firing rates are reached (assuming x is non-negative). The recurrent weights M learn to reproduce these firing rates too, while the plasticity in G tries to prevent that (by its negative sign, but with the restriction to non-negative values). Stimuli that are presented more often, on average, will have more time to reach the positive target and hence will form a stronger and wider attractor. In spontaneous recall, the size of the attractor reflects the time of the stimulus presentation. This mechanism so far is fine, but the only problem is that it is based on restricting G, but not M, to non-negative values.

Point 3: Comparison of rates between stimulation and recall.

The firing rates with external stimulations will be considerably larger than during replay (unless the rates are saturated).

This is a prediction that should be tested in simulations. In fact, since the voltage roughly reads as<br /> u = W x + (M - G) f,<br /> and the learning rules are such that eventually M =; G, the recurrences roughly cancel and the voltage is mainly driven by the external input x. In the state of spontaneous activity without external drive, one has<br /> u = (M - G) f ,<br /> and this should generate considerably smaller instantaneous rates f =; phi(u) than in the case of the feedforward drive (unless f is in both cases at the upper or lower ceiling of phi). This is a prediction that can also be tested.

Because the figures mostly show activity ratios or normalized activities, it was not possible for me to check this hypothesis with the current figures. So please show non-normalized activities for comparing stimulation and recall for the same patterns.

Point 4: Unclear definition of the variable h.<br /> The formal definition of h = hi is given by (suppressing here the neuron index i and the h-index of tau)

tau dh/dt = -h if h>u, (Eq. 10)<br /> h = u otherwise.

But if it is only Equation 10 (nothing else is said), h will always become equal to u, or will vanish, i.e. either h=u or h=0 after some initial transient. In fact, as soon as h>u, h is decaying to 0 according to the first line. If u is >0, then it stops at u=h according to the second line. No reason to change h=u further. If u<=0 while h>u, then h is converging to 0 according to the first line and will stay there. I guess the authors had issues with the recurrent spiking simulations and tried to fix this with some regularization. However as presented, it does not become clear how their regulation works.

BTW: In Eq. 11 the authors set the gain beta to beta = beta0/h which could become infinite and, putatively more problematic, negative, depending on the value of h. Maybe some remark would convince a reader that no issues emerge from this.

Added from discussions with the editor and the other reviewers:

Thanks for alerting me to this Supplementary Figure 8. Yes, it looks like the authors did apply there Dale's law for both the excitatory and inhibitory synapses. Yet, they also introduced two types of inhibitory pathways converging both to the excitatory and inhibitory neurons. For me, this is a confirmation that applying Dale's law to both excitatory and inhibitory synapses, with identical learning rules as explained in the main part of the paper, does not work.

Adding such two pathways is a strong change from the original model as introduced before, and based on which all the Figures in the main text are based. Supplementary Figure 8 should come with an analysis of why a single inhibitory pathway does not work. I guess I gave the reason in my Points 1-3. Some form of symmetry breaking between the recurrent excitation and recurrent inhibition is required so that, eventually, the recurrent excitatory connection will dominate.

Making the inhibitory plasticity less expressive by applying Dale's law to only those inhibitory synapses seems to be the answer chosen in the Figures of the main text (but then the criticism of unilaterally applying Dale's law).

Applying Dale's law to both types of synapses, but dividing the labor of inhibition into two strictly separate and asymmetric pathways, and hence asymmetric development of excitatory and inhibitory weights, seems to be another option. However, introducing such two separate inhibitory pathways, just to rescue the fact that Dale's law is applied to both types of synapses, is a bold assumption. Is there some biological evidence of such two pathways in the inhibitory, but not the excitatory connections? And what is the computational reasoning to have such a separation, apart from some form of symmetry breaking between excitation and inhibition? I guess, simpler solutions could be found, for instance by breaking the symmetry between the plasticity rules for the excitatory and inhibitory neurons. All these questions, in my view, need to be addressed to give some insights into why the simulations do work.

Overall, Supplementary Figure 8 seems to me too important to be deferred to the Supplement. The reasoning behind the two inhibitory pathways should appear more prominently in the main text. Without this, important questions remain. For instance, when thinking in a rate-based framework, the two inhibitory pathways twice try to explain the somatic firing rate away. Doesn't this lead to a too strong inhibition? Can some steady state with a positive firing rate caused by the recurrence, in the absence of an external drive, be proven? The argument must include the separation into Path 1 and Path 2. So far, this reasoning has not been entered.

In fact, it might be that, in a spiking implementation, some sparse spikes will survive. I wonder whether at least some of these spikes survive because of the other rescuing construction with the dynamic variable h (Equation 10, which is not transparent, and that is not taken up in the reasoning either, see my Point 4).

Perhaps it is helpful for the authors to add this text in the reply to them.

Reviewer #3 (Public Review):

Summary:

The work shows how learned assembly structure and its influence on replay during spontaneous activity can reflect the statistics of stimulus input. In particular, stimuli that are more frequent during training elicit stronger wiring and more frequent activation during replay. Past works (Litwin-Kumar and Doiron, 2014; Zenke et al., 2015) have not addressed this specific question, as classic homeostatic mechanisms forced activity to be similar across all assemblies. Here, the authors use a dynamic gain and threshold mechanism to circumnavigate this issue and link this mechanism to cellular monitoring of membrane potential history.

Strengths:

(1) This is an interesting advance, and the authors link this to experimental work in sensory learning in environments with non-uniform stimulus probabilities.

(2) The authors consider their mechanism in a variety of models of increasing complexity (simple stimuli, complex stimuli; ignoring Dale's law, incorporating Dale's law).

(3) Links a cellular mechanism of internal gain control (their variable h) to assembly formation and the non-uniformity of spontaneous replay activity. Offers a promise of relating cellular and synaptic plasticity mechanisms under a common goal of assembly formation.

Weaknesses:

(1) However, while the manuscript does show that assembly wiring does follow stimulus likelihood, it is not clear how the assembly-specific statistics of h reflect these likelihoods. I find this to be a key issue.

(2) The authors' model does take advantage of the sigmoidal transfer function, and after learning an assembly is either fully active or nearly fully silent (Figure 2a). This somewhat artificial saturation may be the reason that classic homeostasis is not required since runaway activity is not as damaging to network activity.

(3) Classic mechanisms of homeostatic regulation (synaptic scaling, inhibitory plasticity) try to ensure that firing rates match a target rate (on average). If the target rate is the same for all neurons then having elevated firing rates for one assembly compared to others during spontaneous activity would be difficult. If these homeostatic mechanisms were incorporated, how would they permit the elevated firing rates for assemblies that represent more likely stimuli?

!!!!

interesting

I'm annotating the full text of the article so that others can see it despite the paywall.

• the 'cleanup function' is the return of setup .
• 'cleanup函数'是setup返回的。

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https://www.theguardian.com/environment/article/2024/jul/30/methane-emissions-study

https://www.theguardian.com/business/article/2024/jul/30/bp-profits-forecasts-oil-gas-dividend-buybacks

https://www.theguardian.com/us-news/article/2024/jul/30/california-wildfires-los-angeles

https://www.theguardian.com/environment/article/2024/jul/31/extreme-heat-dome-hitting-olympics-impossible-without-global-heating

https://www.youtube.com/watch?v=3ahzx2VOCd0

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https://www.youtube.com/watch?v=3ahzx2VOCd0

https://www.youtube.com/watch?v=3ahzx2VOCd0

https://www.youtube.com/watch?v=3ahzx2VOCd0

https://www.liberation.fr/environnement/climat/production-delectricite-le-soleil-et-le-vent-ont-depasse-le-petrole-le-gaz-et-le-charbon-en-europe-20240731_M6LN25FZ2FDXXNWCTRYIUQ3DOQ/

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eLife assessment

This study is a valuable observation that deals with the toxic effects of an intermediary in lipid degradation [trans-2-hexadecenal (t-2-hex)] in yeast through modification of mitochondrial protein import via the TOM complex. However, we find that the claim that the TOM complex is a main target of t-2-hex are supported by incomplete evidence, thus allowing multiple various interpretation. Despite the shortcomings, this study is inspiring for researchers from the organellar, protein trafficking and lipid field and serves as a starting point to further precise and mechanistic analyses of the phenomenon.

Reviewer #2 (Public Review):

This study elucidates the toxic effects of the lipid aldehyde trans-2-hexadecenal (t-2-hex). The authors show convincingly that t-2-hex induces a strong transcriptional response, leads to proteotoxic stress and causes the accumulation of mitochondrial precursor proteins in the cytosol.

The data shown are of high quality and well-controlled. The genetic screen for mutants that are hyper-and hypo-sensitive to t-2-hex is elegant and interesting, even if the mechanistic insights from the screen are rather limited. Moreover, the authors show evidence that t-2-hex affects subunits of the TOM complex. However, they do not formally demonstrate that the lipidation of a TOM subunit is responsible for the toxic effect of t-2-hex. A t-2-hex-resistant TOM mutant was not identified. Nevertheless, this is an interesting and inspiring study of high quality. The connection of proteostasis, mitochondrial biogenesis and sphingolipid metabolism is exciting and will certainly lead to many follow-up studies.

Reviewer #3 (Public Review):

Summary: The authors investigate the effect of high concentrations of the lipid aldehyde trans-2-hexadecenal (t-2-hex) in a yeast deletion strain lacking the detoxification enzyme. Transcriptomic analyses as global read out reveal that a large range of cellular functions across all compartments are affected (transcriptomic changes affect 1/3 of all genes). The authors provide additional analyses, from which they built a model that mitochondrial protein import caused by modification of Tom40 is blocked.

Strengths:<br /> Global analyses (transcriptomic and functional genomics approach) to obtain an overview of changes upon yeast treatment with high doses of t-2-hex.

Weaknesses:<br /> The use of high concentrations of t-2-hex in combination with a deletion of the detoxifying enzyme Hfd1 limits the possibility to identify physiological relevant changes. From the hundreds of identified targets the authors focus on mitochondrial proteins, which are not clearly comprehensible from the data. The main claim of the manuscript that t-2-hex targets the TOM complex and inhibits mitochondrial protein import is not supported by experimental data as import was not experimentally investigated. The observed accumulation of precursor proteins could have many other reasons (e.g. dissipation of membrane potential, defects in mitochondrial presequence proteases, defects in cytosolic chaperones, modification of mitochondrial precursors by t-2-hex rendering them aggregation prone and thus non-import competent). However, none of these alternative explanations have been experimentally addressed or discussed in the manuscript.<br /> Furthermore, many of the results have been reported before (interaction of Tom22 and Tom70 with Hfd1) or observed before (TOM40 as target of t-2-hex in human cells).

Author response:

The following is the authors’ response to the original reviews.

Public Reviews: 

Reviewer #1 (Public Review): 

Summary: 

Fita-Torró et al. study the toxic effects of the intermediary lipid degradation product trans-2-hexadecenal (t-2-hex) on yeast mitochondria and suggest a mechanism by which Hfd1 safeguards Tom40 from lipidation by t-2-hex and its consequences, such as mitochondrial protein import inhibition, cellular proteostasis deregulation, and stress-responses. 

The authors aimed to dissect a mechanism for t-2-hex' apoptotic consequences in yeast and they suggest it is via lipidation of Tom40 but really under the tested conditions everything seems lipidated. Thus, it is unclear whether Tom40 is the crucial causal target. They also do not provide much biochemical experiments to investigate this phenomenon further functionally. Tom40 is one possible and perhaps, given the cellular consequences, a reasonable candidate but not validated beyond in vitro lipidation by exogenous t-2-hex. 

In the revised version of our manuscript, we have now included extensive new experimentation, which shows that protein import at the TOM complex is a physiologically important target of the pro-apoptotic lipid t-2-hex and that enzymes such as the Hfd1 dehydrogenase sensitively regulate this inhibition. In vitro chemoproteomic experiments have now been performed at more physiological t-2hex concentrations of 10µM, which is lower than published data in human cell models. Consistently, several TOM and TIM subunits are enriched in these in vitro lipidation studies (new Fig. 8B). Tom40 lipidation alone is not sufficient to explain t2-hex toxicity, as a cysteine-free version of Tom40 does not confer tolerance to the apoptotic lipid (new Fig. 8D). Importantly however, the loss of function of nonessential accessory Tom subunits 70 or 20 confers t-2-hex tolerance (new Fig. 8D) indicating that pre-protein import at the TOM complex is a physiological target of t2-hex most likely dependent on lipidation of more Tom subunits than just the essential Tom40 pore. Moreover, we now show that mitochondrial protein import is inhibited by the lipid at low physiological doses of 10µM and that this inhibition is modulated by the gene dose of the t-2-hex degrading Hfd1 enzyme (new Fig. 5G).

Strengths: 

The effects of lipids and their metabolic intermediates on protein function are understudied thus the authors' research contributing to elucidating direct effects of a single lipid is appreciated. It is particularly unknown by which mechanism t-2hex causes cell death in yeast. The authors elegantly use modulation of the levels of enzyme Hfd1 that endogenously catabolizes t-2-hex as an approach to studying t2-hex stress. Understanding the cause and consequences of this stress is relevant for understanding fundamental regulation mechanisms, and also to human health since the human homolog of Hfd1, ALDH3A2, is mutated in Sjögren-Larsson Syndrome. The application of a variety of global transcriptomic, functional genomic, and chemoproteomic approaches to study t-2-hex stress targets in the yeast model is laudable. 

Weaknesses: 

-  The extent of the contribution of Tom40 lipidation to the general t-2-hex stress phenotype is unclear. Is Tom40 lipidation alone enough to cause the phenotype? An alteration of the cysteine residue in question could help answer this key question. 

Deletion of all four cysteine residues in Tom40 is not sufficient to confer resistance to t-2-hex stress. This result had been included in the original manuscript, but was somehow hidden in the Discussion. The revised manuscript now includes t-2hex tolerance assays for the Tom40 cysteine free mutant in new Figure 8. As a result, cysteine lipidation of Tom40 alone is not sufficient to confer t-2-hex toxicity. This implies most likely other lipidation targets within the TOM and TIM complexes, as indicated by our in vitro lipidation studies. We therefore included the non-essential adaptor proteins Tom70 and Tom20 of the TOM complex and tested the tolerance of the respective deletion mutants in t-2-hex tolerance assays. As shown in new Figure 8, the absence of Tom70 and Tom20 function significantly increases tolerance to t-2hex and the tom20 mutant accumulates less Aim17 pre-protein upon t-2-he stress, indicating that the TOM complex is a physiologically important target of the proapoptotic lipid, which acts most likely via lipidation of more subunits than the Tom40 import channel.

-  It is unclear whether the exogenously applied amounts of t-2-hex (concentrations chosen between 25-200 uM) are physiologically relevant in yeast cells. For comparison, Chipuk et al. (2012) used at most 1 uM on mitochondria of human cells, while Jarugumilli et al. (2018) considered 25 uM a 'lower dose' on human cells. Since the authors saw responses below 10 uM (Fig. 3B) and at the lowest selected concentration of 25 uM (Fig. 8), why were no lower, likely more specific, concentrations applied for the global transcriptomic and chemoproteomic experiments? Key experiments have to be repeated with the lower concentrations. 

We have now performed several experiments with lower t-2-hex concentrations. A new chemoproteomic study with 10µM t-2-hex-alkyne has been conducted and the new results added to the supplementary information, combining 10µM and 100µM in vitro lipidation studies (Suppl. Table 6). Many subunits of the TOM and TIM complexes consistently are enriched significantly in both chemoproteomic experiments. These new data are summarized in revised Figure 8. Additionally we have performed in vivo pre-protein assays with lower t-2-hex concentrations. As shown in new Figure 5, Aim17 mitochondrial import is already inhibited by t-2-hex doses as low as 10µM in a wild type strain, and that this inhibition is enhanced in a hfd1 mutant and alleviated in a Hfd1 overexpressor. It is important to note that a dose of 10µM of external t-2-hex addition is significantly lower than doses applied to human cell cultures such as in Jarugumilli et al. (2018). It proves that mitochondrial protein import is a sensitive and physiologically relevant t2-hex target in our yeast models and that t-2-hex detoxification by enzymes such as the Hfd1 dehydrogenase sensitively regulates this specific inhibition.

-  The amount of t-2-hex applied is especially important to consider in light of over 1300 proteins lipidated to an extent equal to or greater than Tom40 (Supp. Table 6). This chemoproteomic experiment (Fig. 8B, Supp. Table 6) is also weakened by the inclusion of only 2 replicates, thus precluding assessment of statistical significance. The selection of targets in Fig. 8B as "among the best hits" is neither immediately comprehensible nor further explained and represents at best cherrypicking. Further evidence based on statistical significance or validation by other means should be provided.

We performed the chemoproteomic screens as described by Jarugumilli et al. (2018) with 2 replicates of mock treated versus 2 replicates of t-2-hex-alkyne treated cell extracts.  A new chemoproteomic study with 10µM t-2-hex-alkyne has been conducted and the new results added to the supplementary information combining 10µM and 100µM in vitro lipidation studies (Suppl. Table 6). Differential enrichment analysis of the proteomic data was performed with the amica software (Didusch et al., 2022). Proteins were ranked according to their log2 fold induction comparing lipid- and mock-treated samples with a threshold of ≥1.5, and the adjusted p-value was calculated. Several TOM and TIM subunits were consistently identified as differentially enriched proteins, which is summarized in new Figure 8B.

- The authors unfortunately also underuse the possible contribution of mass spectrometry technology to in addition determine the extent and localization of lipidation on a global scale (especially relevant since Cohen et al. (2020) suggest site-specific mechanisms). 

We agree that site-specific modifications of t-2-hex will be most likely important in the inhibition or other type of regulation of specific target proteins. Our collective data show that in the case of the inhibition of mitochondrial protein import, several lipidation events on TOM and TIM are involved. Dissection of individual cysteine lipidations on those subunits will be interesting, but we feel that this is out of the scope of the present work.

- The general novelty of studying t-2-hex stress is lowered in light of existing literature in humans (see e. g. Chipuk et al., 2012; Cohen et al., 2020; Jarugumilli et al., 2018), and in yeast by the same authors (Manzanares-Estreder et al., 2017) and as the authors comment themselves, a significant part of the manuscript may represent rather a confirmation of the already described consequences of t-2-hex stress 

We do not agree and we have not commented that our present study is a mere confirmation of t-2-hex stress previously applied in yeast and human models. In humans, t-2-hex has been identified as an efficient pro-apoptotic lipid, which causes mitochondrial dysfunction via direct lipidation of Bax, however the studies of Jarugumilli et al. (2018) revealed that many other direct t-2-hex targets exist, which remained uninvestigated to date. This work continues our previous studies (Manzanares-Estreder et al., 2017), where we show that t-2-hex is a universal proapoptotic lipid applicable in yeast models and contributes important novel findings, such as the massive transcriptional response resembling proteostatic defects caused by t-2-hex, mitochondrial protein import as a physiologically important and direct target of t-2-hex, the function of detoxifying enzymes such as Hfd1 in modulating lipid-mediated inhibition of mitochondrial protein import and general proteostasis. Additionally, we provide transcriptomic, chemoproteomic and functional genomic data to the scientific community, which will be a rich source for future studies on yet undiscovered pro-apoptotic mechanisms employed by t-2-hex. 

Reviewer #2 (Public Review): 

This study elucidates the toxic effects of the lipid aldehyde trans-2-hexadecenal (t-2-hex). The authors show convincingly that t-2-hex induces a strong transcriptional response, leads to proteotoxic stress, and causes the accumulation of mitochondrial precursor proteins in the cytosol. 

The data shown are of high quality and well controlled. The genetic screen for mutants that are hyper-and hypo-sensitive to t-2-hex is elegant and interesting, even if the mechanistic insights from the screen are rather limited. The last part of the study is less convincing. The authors show evidence that t-2-hex affects subunits of the TOM complex. However, they do not formally demonstrate that the lipidation of a TOM subunit is responsible for the toxic effect of t-2-hex. A t-2-hexresistant TOM mutant was not identified. Moreover, it is not clear whether the concentrations of t-2-hex in this study are physiological. This is, however, a critical aspect. The literature is full of studies claiming the toxic effects of compounds such as H2O2; even if such studies are technically sound, they are misleading if nonphysiological concentrations of such compounds were used. 

Nevertheless, this is an interesting study of high quality. A few specific aspects should be addressed.

We have now performed t-2-hex toxicity assays using several mutants in Tom subunits, the cysteine free mutant of the essential Tom40 core channel and deletion mutants in the accessory subunits Tom70 and Tom20 (new Figure 8). As a result, cysteine lipidation of Tom40 alone is not sufficient to confer t-2-hex toxicity. This implies most likely other lipidation targets within the TOM and TIM complexes, as indicated by our in vitro lipidation studies. Indeed, as shown in new Figure 8, the absence of Tom70 and Tom20 function significantly increases tolerance to t-2-hex indicating that the TOM complex is a physiologically important target of the proapoptotic lipid, which acts most likely via lipidation of more subunits than the Tom40 import channel.

We have now performed several experiments with lower t-2-hex concentrations. A new chemoproteomic study with 10µM t-2-hex-alkyne has been conducted and the new results added to the supplementary information combining 10µM and 100µM in vitro lipidation studies (Suppl. Table 6). Many subunits of the TOM and TIM complexes consistently are enriched significantly in both chemoproteomic experiments. These new data are summarized in revised Figure 8.

Additionally we have performed in vivo pre-protein assays with lower t-2-hex concentrations. As shown in new Figure 5, Aim17 mitochondrial import is already inhibited by t-2-hex doses as low as 10µM in a wild type strain, and that this inhibition is enhanced in a hfd1 mutant and alleviated in a Hfd1 overexpressor. It is important to note that a dose of 10µM of external t-2-hex addition is significantly lower than doses applied to human cell cultures such as in Jarugumilli et al. (2018). It proves that mitochondrial protein import is a sensitive and physiologically relevant t2-hex target in our yeast models and that t-2-hex detoxification by enzymes such as the Hfd1 dehydrogenase sensitively regulates this specific inhibition.

Reviewer #3 (Public Review): 

Summary: The authors investigate the effect of the lipid aldehyde trans-2hexadecenal (t-2-hex) in yeast using multiple omic analyses that show that a large range of cellular functions across all compartments are affected, e.g. transcriptomic changes affect 1/3 of all genes. The authors provide additional analyses, from which they built a model that mitochondrial protein import caused by modification of Tom40 is blocked. 

Strengths: Global analyses (transcriptomic and functional genomics approach) to obtain an unbiased overview of changes upon t-2-hex treatment. 

Weaknesses: It is not clear why the authors decided to focus on mitochondria, as only 30 genes assigned to the GO term "mitochondria" are increasing, and also the follow-up analyses using SATAY is not showing a predominance for mitochondrial proteins (only 4 genes are identified as hits). The provided additional experimental data do not support the main claims as neither protein import is investigated nor is there experimental evidence that lipidation of Tom40 occurs in vivo and impacts on protein translocation. 

30 mitochondrial gene functions are very strongly (>10 fold) up-regulated by t-2-hex. However, when genes up-regulated (>2 log2FC) or down-regulated (<-2 log2FC) by t-2-hex were selected and subjected to GO category enrichment analysis, we found that “Mitochondrial organization” was the most numerous GO group activated by t-2-hex, while it was “Ribosomal subunit biogenesis” for t-2-hex repression (new data in Suppl. Tables 1 and 2). 

In the revised version of our manuscript, we have now included extensive new experimentation, which shows that protein import at the TOM complex is a physiologically important target of the pro-apoptotic lipid t-2-hex and that enzymes such as the Hfd1 dehydrogenase sensitively regulate this inhibition. In vitro chemoproteomic experiments have now been performed at more physiological t-2hex concentrations of 10µM, which is lower than published data in human cell models. Consistently, several TOM and TIM subunits are enriched in these in vitro lipidation studies (new Fig. 8B). Tom40 lipidation alone is not sufficient to explain t2-hex toxicity, as a cysteine-free version of Tom40 does not confer tolerance to the apoptotic lipid (new Fig. 8D). Importantly however, the loss of function of nonessential accessory Tom subunits 70 or 20 confers t-2-hex tolerance (new Fig. 8D) indicating that pre-protein import at the TOM complex is a physiological target of t2-hex most likely dependent on lipidation of more Tom subunits than just the essential Tom40 pore. Moreover, we now show that mitochondrial protein import is inhibited by the lipid at low physiological doses of 10µM and that this inhibition is modulated by the gene dose of the t-2-hex degrading Hfd1 enzyme (new Fig. 5G).

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

Private recommendations for the authors 

- On the existing data from Supp. Table 6, the authors may include a global assessment of whether or not the protein included a cysteine (the likely site for lipidation). 

Although free cysteines in target proteins are the most frequent sites of modification by LDEs such as t-2-hex, other amino acids such as lysines or histidines can be lipidated by these lipid derivatives. Therefore we would like to exclude this information from our chemoproteomic data.

- What determines whether a gene is labeled in Fig. 6B other than fold change? Why is MAC1 with the highest FC not shown? 

We analyzed the potential anti-apoptotic SATAY hits with a log2 < -0.75 according to expected detoxification pathways (heat shock response, pleiotropic drug response), to their function in the ER (the intracellular site where t-2-hex is generated) or in mitochondria (the major t-2-hex target identified so far). This is now better described in the text. As for the potential pro-apoptotic SATAY hits, we analyzed gene functions with a log2 > 1.5 and marked the predominant groups “Cytosolic ribosome and translation” and “Amino acid metabolism”. In any case, the interested reader has all SATAY data available in supplemental tables 4 and 5 to find alternative gene functions with a potential role in cellular adaptation to t-2-hex.

- Supplementary Table numbering should be double-checked.

Ok, numbering has been double-checked.

Reviewer #2 (Recommendations For The Authors): 

Major points 

(1) Identification of the t-2-hex target. Neither Tom70, Tom20 nor the cysteine in Tom40 is essential. If one of these components is critical for the t-2-hex-mediated toxicity, mutants should be t-2-hex-resistant. This is a straight-forward, simple, and critical experiment. 

We have now performed t-2-hex toxicity assays in the cysteine free Tom40 mutant, and tom20 and tom70 deletion mutants. As shown in new Figure 8, cysteine lipidation of Tom40 alone is not sufficient to confer t-2-hex toxicity. However, the absence of Tom70 and Tom20 function significantly increases tolerance to t-2-hex indicating that the TOM complex is a physiologically important target of the proapoptotic lipid, which acts most likely via lipidation of more subunits than the Tom40 import channel.

(2) The authors claim that t-2-hex blocks the TOM complex. Since in vitro import assays with yeast mitochondria are a well established and simple technique, the authors should isolate mitochondria from their cells and perform import experiments. It is expected that those mitochondria show reduced import rates, however, swelling of these mitochondria to mitoplasts should suppress the import defect. 

We agree that our study does not investigate a direct effect of t-2-hex on the import capacity of purified mitochondria. However, we determine the in vivo accumulation of several mitochondrial precursor proteins, which is widely used to assay for the efficiency of mitochondrial protein import, for example the recent hallmark paper discovering the mitoCPR protein import surveillance pathway exclusively uses epitope-tagged mitochondrial precursors to determine the regulation of mitochondrial protein import (Weidberg and Amon, Science 2018 360(6385)). Additionally, our new results that mutants in accessory TOM subunits 20 and 70 are hyperresistant to t-2-hex (Figure 8D) and that deletion of TOM20 decreases the t-2-hex induced pre-protein accumulation (Suppl. Figure 1) identify the TOM complex and hence protein import at the outer mitochondrial membrane as a physiologically important t-2-hex target.

(3) The first part of the study is very strong. The last figure is also of good quality, however, it is not clear whether the effects on TOM subunits are really causal for the observed t-2-hex effect on gene expression. The authors might cure this by improved data or by avoiding bold statements such as: 'Hfd1 associates with the Tom70 subunit of the TOM complex and t-2-hex covalently lipidates the central Tom40 channel, which altogether indicates that transport of mitochondrial precursor proteins through the outer mitochondrial membrane is directly inhibited by the pro-apoptotic lipid and thus represents a hotspot for pro- and anti-apoptotic signaling.' (Abstract). 

We now show that several TOM and TIM subunits are lipidated in vitro by physiological low t-2-hex concentrations, that loss of function of accessory subunits Tom20 or Tom70 rescues t-2-hex toxicity (new Figure 8) and that the gene dose of Hfd1 determines the degree of mitoprotein import block (new Figure 5). These data identify the TOM complex as a physiologically important target of the pro-apoptotic lipid. The Abstract has been modified accordingly.

(4) If the t-2-hex levels are in a physiological range, one would expect that overexpression of Hfd1 prevents the t-2-hex-induced import arrest.

We have now confirmed that overexpression of Hfd1 indeed prevents inhibition of mitochondrial protein import by t-2-hex. As shown in new Figure 5, Aim17 mitochondrial import is already inhibited by t-2-hex doses as low as 10µM in a wild type strain, and that this inhibition is enhanced in a hfd1 mutant and alleviated in a Hfd1 overexpressor.

(5) The authors claim that Fmp52 is a t-2-hex-detoxifying enzyme, but do not show evidence. They should rewrite this sentence and be more cautious, or they should show that increased Fmp52 levels indeed deplete t-2-hex from mitochondria.  

We show that loss of Fmp52 function leads to a strong t-2-hex sensitivity. Fmp52 belongs to the NAD-binding short-chain dehydrogenase/reductase (SDR) family and localizes to highly purified mitochondrial outer membranes (Zahedi et al, 2006). These are the indications that suggest that Fmp52 participates in the enzymatic detoxification of t-2-hex in addition to Hfd1. The Results section has been modified accordingly.

Minor points: 

(6) Aim17 was recently identified as a characteristic constituent of cytosolic protein aggregates named MitoStores (Krämer et al., 2023, EMBO J). The authors might test whether the cytosolic Aim17 protein colocalizes with the Hsp104-GFP granules that accumulate upon t-2-hex exposure as shown in Fig. 4A. 

We agree that determining the fate of unimported mitochondrial precursors upon t-2-hex stress would be interesting. We have made some attempts to co-visualize Aim17-dsRed and Hsp104-GFP upon t-2-hex treatment, but we still have some technical issues. While we clearly see that Aim17 accumulates in cytoplasmic foci upon prolonged t-2-hex exposure, we are not able to determine colocalization with Hsp104, in great part because t-2-hex causes mitochondrial fragmentation, which leads to the appearance of Aim17-stained foci in the cytosol independently of protein aggregates. While so far we are not able to localize Aim17 unambiguously in Hsp104 containing aggregates (mitoStores) upon lipid stress, we would like to move the manuscript farther without those experiments.

(7) In Fig. 1A, the figures of the different lines are difficult to distinguish. Lines of one color with different intensities would be better suited. 

We have been working before with dose-response profiles generated by the destabilized luciferase system and found that the color-coded representation of the plots is the most effective way to represent the data, see for example Fita-Torró et al. Mol Ecol. 2023 32(13):3557-3574, Pascual-Ahuir et al. BBA 2019 1862(4):457-471, Rienzo et al., Mol Cell Biol. 2015 35(21):3669-83, and several other publications. Therefore we want to keep the format of the Figure.

(8) A title page should be added to each of the supplemental data files with short descriptions of the information that is provided in the columns of the tables.  Response: Explanatory title pages have been now added to the supplemental data files.

Reviewer #3 (Recommendations For The Authors): 

Figure 5A: The authors aim to assess protein import, however, their experimental set-up is not suited and does not allow conclusions about protein translocation into mitochondria. The authors monitor protein steady state levels, which does not reflect import capacity. For this e.g. pulse-chase experiments coupled to coIP or in organello import assays with radiolabeled substrate proteins would be required. In addition, the authors lack a non-treated control to show that no precursor accumulates in the absence of CCCP and t-2-hex. At the moment, the conclusion of blocked import cannot be made, as there are many other explanations for the observed steady state levels, e.g. the TAP tag interfered with the import competence of the precursor or t-2-hex could impact on MPP function (in particular as Figure 8B shows that also intra-mitochondrial proteins undergo modification by t-2-hex). 

We agree that our study does not investigate a direct effect of t-2-hex on the import capacity of purified mitochondria. However, we determine the in vivo accumulation of several mitochondrial precursor proteins, which is widely used to assay for the efficiency of mitochondrial protein import, for example the recent hallmark paper discovering the mitoCPR protein import surveillance pathway exclusively uses epitope-tagged mitochondrial precursors to determine the regulation of mitochondrial protein import (Weidberg and Amon, Science 2018 360(6385)). Figure 5 contains several non-treated control experiments, which show that no (or less in the case of Ilv6) precursors of Tap-tagged Aim17, Cox5a, Ilv6, or Sdh4 accumulate in the absence of CCCP or t-2-hex. This is shown in Figure 5A for untreated cells or in Figure 5B and new Figure 5G for solvent (DMSO) treated cells. This demonstrates that the Tap-tag does not interfere with the import of the respective precursors. Additionally, our new results that mutants in accessory TOM subunits 20 and 70 are hyperresistant to t-2-hex (Figure 8D) identify the TOM complex and hence protein import at the outer mitochondrial membrane as a physiologically important t-2-hex target.

Figure 8: The conclusion that Tom40 is directly lipidated comes from an in vitro assay, with the conclusion that Tom40 is the main target, because it is the only Tom protein with a cysteine (Tom70 as not being part of the Tom core is excluded, however, lack of Tom70 function would also have detrimental consequences for mitochondrial protein import). However, there is no experiment showing a modification of Tom40 and a consequence for protein import. The proposed model is therefore very far-fetched and several aspects are speculation but not supported by experimental data. To propose such a model, the author needs to show experimental evidence, e.g. by generating a yeast strain in which the cysteine i Tom40 are replaced by e.g. Serine residues, and then assess if protein import (e.g. pulse-chase assays) are not affected anymore upon addition of t-2-hex. 

Deletion of all four cysteine residues in Tom40 is not sufficient to confer resistance to t-2-hex stress. This result had been included in the original manuscript, but was somehow hidden in the Discussion. The revised manuscript now includes t-2hex tolerance assays for the Tom40 cysteine free mutant in new Figure 8D. As a result, cysteine lipidation of Tom40 alone is not sufficient to confer t-2-hex toxicity. This implies most likely other lipidation targets within the TOM and TIM complexes, as indicated by our in vitro lipidation studies. We therefore included the non-essential adaptor proteins Tom70 and Tom20 of the TOM complex and tested the tolerance of the respective deletion mutants in t-2-hex tolerance assays. As shown in new Figure 8D, the absence of Tom70 and Tom20 function significantly increases tolerance to t2-hex indicating that the TOM complex is a physiologically important target of the pro-apoptotic lipid, which acts most likely via lipidation of more subunits than the Tom40 import channel.

Figure 8A: The pulldown experiments lack positive (other Tom subunits) and negative controls and were performed with (large) tags on all proteins, which can easily result in false positive interactions. The conclusion that Hfd1 interacts with Tom70 and Tom22 cannot be made. Also, the conclusion if an interaction is robust or not cannot be made as the pull-down lacks control fractions, it is also not clear how much of the eluate was loaded. Finally, Hfd1-HA was not expressed from its endogenous promoter, likely resulting in over-expression, which again strongly hampers conclusions about bona fide interaction partners. 

We agree that our pulldown studies are done in an artificial context, such as Hfd1 overexpression needed for sufficient protein level for detection or use of Tapfusion proteins. However, the conclusion that Tom70 is a potential interactor of Hfd1 can be made based on the following observations: Hfd1-HA is preferentially pulled down from total protein extracts containing Tom70-Tap, but not from extracts containing no Tap-protein and significantly less from extracts containing Tom22-Tap, another TOM associated subunit. The pulldown assay has been repeated now several times and the efficiency of Hfd1 pulldown has been quantified and statistically analyzed with respect to the quantity of purified Tom protein, which is shown in modified Figure 8A. 

Figure 4A and C: Depletion of proteasomal activity results in larger aggregates in Figure 4A. However, the addition of t-2-hex blocks proteasomal activity (Figure 4C). How can proteasome inhibition result in bigger aggregates if the proteasomal activity is lost upon t-2-hex addition?

The negative effect of t-2-hex on proteasomal activity is most likely an indirect effect caused by protein aggregation (Bence et al., Science 2001 292-1552) and occurs in wild type and rpn4 mutant cells with reduced proteasomal activity (Fig. 4C). t-2-hex causes cytosolic protein aggregation in wild type cells, which is aggravated (more and larger protein aggregates) in rpn4 mutants because of their lower levels of active proteasome (Fig. 4A). The observed protein aggregates will further diminish proteasomal activity, which is confirmed in Fig. 4C. 

Figure 1B: The authors use a reporter to determine HFD1 expression that consists of the promoter region of HFD1 fused to luciferase. These fusion constructs have been shown to often not reflect the bona fide expression levels of genes (Yoneda et al., J Cell Sci 2004). qPCR analysis of transcript levels should be included to support the induction of HFD1. 

We agree that the live cell luciferase reporters used here are not suitable for the determination of absolute mRNA levels. However, the aim of these reporter experiments is to quantify the inducibility of different genes (HFD1, GRE2) dependent on increasing stress doses. These dose response profiles cannot be obtained by qPCR analysis, while the destabilized reporters are an excellent tool for this, which have been used to accurately describe numerous dynamic stress responses (for example: Dolz-Edo et al. 2013 MCB 33:2228-40, Rienzo et al. 2015 MCB 35:3669-83, PascualAhuir et al. 2019 BBA 862:457-471). Additionally, the induction of HFD1 mRNA levels by salt (NaCl) and oxidative (menadione) stress determined by qPCR has been published before (Manzanares-Estreder et al. 2017 Oxid Med Cell Longevity 2017:2708345).

The authors conclude from Figure 1 that entry into apoptotic cell death is modulated by efficient t-2-hex detoxification. However, this is based on growth curves and no analysis of apoptotic cell death is performed. The data show that the addition of hexadecenal results in a growth arrest, that is overcome likely upon degradation of t-2-hex (depending on the amount of Hfd1). 

We agree that our experiments measure growth inhibition and not specifically apoptotic cell death. The text has been changed accordingly.  

Figure 4A: Microscopy images show between 1-2 yeast cells. Either more cells need to be shown or quantifications of the aggregates are required. In addition, it is not clear if the control received the same DMSO concentration as the treated cells and also the time point for the control is not specified. 

We have now quantified the number of aggregates across cell populations in new Figure 4A in DMSO, t-2-hex and t-2-hex-H2 treated wt and rpn4 mutants. These data show specific aggregate induction by t-2-hex and not by DMSO or the saturated t-2-hex-H2 control, which is aggravated in rpn4 mutants and avoided by CHX pretreatment.

Figure 5: Western blots in figure 5A, B, D, E and F lack a loading control. Without this, conclusions about increases in protein abundance cannot be made.  Response: We have now included additional panels with the loading controls for the Western blots in new figure 5, except figure 5B, where the appearance or not of the pre-protein can be compared to the amount of mature protein in the same blot.

Figure 2B: Complex II assembly factors SDH5,6,9 are described here as ETC complexes. As the proteins are not part of the mature complex II, the heading should be modified into ETC complexes and ETC assembly.

Figure 2B has been revised and the classification of ETC proteins changed accordingly.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1:

Mehrdad Kashefi et al. investigated the availability of planning future reaches while simultaneously controlling the execution of the current reach. Through a series of experiments employing a novel sequential arm reaching paradigm they developed, the authors made several findings: 1) participants demonstrate the capability to plan future reaches in advance, thereby accelerating the execution of the reaching sequence, 2) planning processes for future movements are not independent one another, however, it's not a single chunk neither, 3) Interaction among these planning processes optimizes the current movement for the movement that comes after for it.

The question of this paper is very interesting, and the conclusions of this paper are well supported by data. However, certain aspects require further clarification and expansion.

We thank reviewer one for their evaluation of the work.

(1) The question of this study is whether future reach plans are available during an ongoing reach. In the abstract, the authors summarized that "participants plan at least two future reaches simultaneously with an ongoing reach and that the planning processes of the two future reaches are not independent of one another" and showed the evidence in the next sentences. However the evidence is about the relationship about ongoing reach and future plans but not about in between future plans (Line 52-55). But the last sentence (Line 55-58) mentioned about interactions between future plans only. There are some discrepancies between sentences. Could you make the abstract clear by mentioning interference between 1) ongoing movement and future plans and 2) in between future plans?

We thank Reviewer for their comment. We have separated the longer sentence in the original abstract into two shorter ones. This should clarify that the two pieces of evidence pertain to the interaction of planning processes.

(2) I understood the ongoing reach and future reaches are not independent from the results of first experiment (Figure 2). A target for the current reach is shown at Horizon 1, on the other hand, in Horizon 2, a current and a future target are shown on the screen. Inter-reach-interval was significantly reduced from H1 to H2 (Figure 2). The authors insist that "these results suggest that participants can plan two targets (I guess +1 and +2) ahead of the current reach (I guess +0)". But I think these results suggest that participants can plan a target (+1) ahead of the current reach (+0) because participants could see the current (+0) and a future target (+1) in H2. Could the authors please clarify this point?

We thank Reviewer for raising this point. Our conclusion that “participants can plan two targets ahead of the current reach” is supported by the reduction in Inter-Response Interval (IRI) observed when comparing H2 to H3 in the 75 ms Dwell time condition. Specifically, on average, participants were 16 ms faster when they could see two future targets on the screen (H3) than when they could see only one (H2). To clarify this in the paper, we have revised the wording in line 124 to explicitly state that the conclusion pertains to the 75 ms Dwell time condition. Additionally, we emphasize that the strongest evidence for planning two future targets comes from the experiment shown in Figure 3.

(3) Movement correction for jump of the +1 target takes longer time in H3 compared to H2 (Figure 4). Does this perturbation have any effect on reaching for +2 target? If the +1 jump doesn't affect reaching for +2 target, combined with the result that jump of the +2 target didn't affect the movement time of +1 target (Figure 3C), perturbation (target jump) only affects the movement directly perturbed. Is this implementation correct? If so, does these results support to decline future reaches are planned as motor chunk? I would like to know the author's thoughts about this.

In the experiment presented in Figure 4, once we jumped the +1 target, the reach to that target was changed and participants replaned a corrective movement to the new location of the +1 target. This usually was followed by a longer-than-usual pause at the new location of +1 target for resuming the sequence and finishing the trial. Consequently, in these jump trials, it was impossible to compare the +2 reach to no-jump trials, as the normal sequence of movement was disrupted, and the reach to the +2 target originated from a different starting location. Nevertheless, we addressed the possibility that the two future reaches were planned as a chunk by the analysis shown in figure 5: There we showed that a displacement of the +2 target did not influence the reach to the +1 target, indicating that the movement plans could be updated independently.

(4) Any discussion about Saccade position (Figure 7)?

We thank reviewer 1 for this important comment. The following discussion section is added for the gaze position results.

In our sequence task, participants switched their gaze location only once per reach, suggesting that information about the location of the next target is perceived parafoveally (Figure 7A). This observation aligns with previous studies (Clavagnier et al., 2007; González-Alvarez et al., 2007; Sivak and MacKenzie, 1990) that found participants keep their visual attention on the current sequence item and can perceive the location of spatial targets even when foveal vision is occluded. However, when comparing gaze locations for conditions Horizon >1, we observed that participants systematically biased their gaze location based on the sequence context. The gaze position shifted toward the next target, potentially allowing for more accurate location estimation (Figures 7C-D). Notably, changes in gaze location were observed even in Horizon 2, despite no changes in the curvature of hand movements in this horizon (Figure 6B). This suggests that information about the next target may first be available in the circuitry that controls eye movements and later in the cortical areas that control voluntary upper limb movements. Further control studies are required to investigate this hypothesis.

Reviewer #2:

Summary:

In this work, Kashefi et al. investigate the planning of sequential reaching movements and how the additional information about future reaches affects planning and execution. This study, carried out with human subjects, extends a body of research in sequential movements to ask important questions: How many future reaches can you plan in advance? And how do those future plans interact with each other?

The authors designed several experiments to address these questions, finding that information about future targets makes reaches more efficient in both timing and path curvature. Further, with some clever target jump manipulations, the authors show that plans for a distant future reach can influence plans for a near future reach, suggesting that the planning for multiple future reaches is not independent. Lastly, the authors show that information about future targets is acquired parafoveally--that is, subjects tend to fixate mainly on the target they are about to reach to, acquiring future target information by paying attention to targets outside the fixation point.

The study opens up exciting questions about how this kind of multi-target planning is implemented in the brain. As the authors note in the manuscript, previous work in monkeys showed that preparatory neural activity for a future reaching movement can occur simultaneously with a current reaching movement, but that study was limited to the monkey only knowing about two future targets. It would be quite interesting to see how neural activity partitions preparatory activity for a third future target, given that this study shows that the third target's planning may interact with the second target's planning.

Strengths:

A major strength of this study is that the experiments and analyses are designed to answer complementary questions, which together form a relatively complete picture of how subjects act on future target information. This complete description of a complex behavior will be a boon to future work in understanding the neural control of sequential, compound movements.

We thank the reviewer for their thorough reading of our work.

Weaknesses:

I found no real glaring weaknesses with the paper, though I do wish that there had been some more discussion of what happens to planning with longer dwell times in target. In the later parts of the manuscript, the authors mention that the co-articulation result (where reaches are curved to make future target acquisition more efficient) was less evident for longer dwell times, likely because for longer dwell times, the subject needs to fully stop in target before moving to the next one. This result made me wonder if the future plan interaction effect (tested with the target jumps) would have been affected by dwell time. As far as I can tell, the target jump portion only dealt with the shorter dwell times, but if the authors had longer dwell time data for these experiments, I would appreciate seeing the results and interpretations.

We thank the reviewer for raising this point. In our time (Figure 2) and curvature analysis (Figure 6), we collected data with five levels of the horizon and three levels of dwell time to explore the space of parameters and to see if there is any interaction between dwell time and the horizon of planning the future targets. Apriori, we expected that the full stop in each target imposed by the 400 ms dwell time would be long enough to remove any effect of future targets on how the current move is executed. In line with our initial hypothesis, the systematic curvature of reaches based on the future target was smaller in longer dwell times (Figure 6E). Nevertheless, we observed a significant curvature even in 400 ms dwell time. Based on this observation, we expect running the jump experiments (Figures 4 and 5) in longer dwell times will lead to the same pattern of results but with a smaller effect size since longer dwells break the interdependence of sequence elements (Kalidindi & Crevecoeur, 2023). In the end, for the jump experiments, we limited our experimental conditions to the fastest dwell time (75 ms dwell) since we were conceptually interested in situations where movements in the sequence are maximally dependent on each other.

Beyond this , the authors also mentioned in the results and discussion the idea of "neural resources" being assigned to replan movements, but it's not clear to me what this might actually mean concretely. I wonder if the authors have a toy model in mind for what this kind of resource reassignment could mean. I realize it would likely be quite speculative, but I would greatly appreciate a description or some sort of intuition if possible.

Our use of the term "neural resources" is inspired by classic psychology literature on how cognitive resources such as attention and working memory are divided between multiple sequence components. Early studies on working memory suggest that human participants can retain and manipulate a fixed number of abstract items in working memory (Miller, 1956). However, more recent literature postulates that a specific number of items does not limit working memory, rather, it is limited by a finite attentional resource that is softly allocated to task items.

Here we borrowed the same notion of soft distribution of resources for the preparation of multiple sequence items. A large portion of our observation in this paper and also previous work on sequence production can be explained by a simple model that assumes one central planning resource that is “softly” divided between sequence elements when participants see future items of the sequence (Author Response Image 1). The first sequence element receives the majority of the resources and is planned the most. The rest of the sequence receives the remaining planning resources in an exponentially decaying manner for preparation of the movement during the execution of the ongoing movement. Once the ongoing movement is over, the resource is then transferred to the next sequence item and this process is repeated until the sequence is over. Assignment of planning resources to future items explains why participants are faster when seeing future items (Figure 2). But this comes with a cost – if the ongoing movement is perturbed, the replanning process is delayed since some of the resources are occupied by future planning (Figure 4). This naturally leads to the question of how this resource allocation is implemented in neural tissue. To address this, we are conducting the same sequence task with the horizon in non-human primates (NHPs), and the investigation of these neural implementation questions will be the focus of future studies.

Author response image 1.

Basic diagram showing a soft distribution of a limited planning resource. The diagram shows a Horizon 3 condition in which two future reaches (+1 and +2) are planned while executing a movement (+0). The majority of resources is assigned to the execution of the ongoing movement while the reset is distributed for planning future movements. Once the movement is over, the chain of preparation and execution moves forward.

Recommendations for the author:

Reviewer #1

We thank reviewer one for these comments regarding the clarity and consistency of figures and terminology.

(1) Figure 3. Are "+1 Move" in Fig. 3B and "+ 1 Movement" in Fig. 3C as same as "E + 1" in Fig. 3A? Also does "Dwell" in Fig. 3B mean same as "+1 Dwell" in Fig. 3C? Consistent terminology would help readers to understand the figure.

“+1 Move” in Figure 3B is the same as +1 movement in Figure 3C. “Dwell” in Figure 3B is the same as +1 Dwell in Figure 3C. We changed the figure for more consistency.

(2) Figure 3. A type in the second last line in the legend, "pre-jump target for no-jump and jump and condition". The second "and" isn't necessary.

The typo is corrected. Thank you.

(3) Figure 4C. Is "Movement time" equivalent with "E + 1"?

“Movement time” is equivalent to E+1 only in no-jump conditions. When the jump occurs,

Movement time contains all the

(4) Figure 6B. Is the gray circle in between the graph and target positions there by mistake?

We fixed this typo. Thank you.

(5) Figure 6E. It's hard to distinguish H2-H5 from the color differences.

We changed the H5 to full white with a black stroke to improve the contrast. Thank you.

(6) Figure 7A. Blue dots are almost invisible.

We added a black stroke to blue circles for more visibility. Thank you.

Reviewer #2

I found this manuscript to be engaging and well written--many of the questions I had while reading were answered promptly in the next section. As such, my comments are mostly minor and primarily geared towards improving clarity in the manuscript.

(1) One major recurring confusion I had while reading the manuscript was how to think about H1, H2, and H3. It was clearly explained in the text, and the explanations of the results were generally clear once I read through it all, but I found it strangely confusing at times when trying to interpret the figures for myself (e.g., in H2, 2 targets are on screen, but the second target can only be planned during the reach toward the first target). This confusion may just be me reading the manuscript over two days, but I wonder if it could be made clearer with some semantic iconography associated with each horizon added to the later figures alongside the H labels. As one option, perhaps the planning timeline part of Fig 1D could be simplified and shrunk down to make an icon for each horizon that clearly shows when planning overlaps for each horizon.

(Please see the response to point #2 below)

(2) Regarding Fig 1D: I like this figure, but it's unclear to me how the exact preparation and execution times are determined. Is this more of a general schematic of overlaps, or is there specific information about timing in here?

We thank reviewer 2 for their important feedback. The role of Figure 1D was to summarize the timing of the experiments for different horizons. That is, to clarify the relative timing of the targets appearing on the screen (shown with a small circle above the horizontal line) and targets being captured by participants (the ticks and their associated number on the line). Execution is shown as the time interval that the hand is moving between the targets and planning is the potential planning time for participants from the target appearing on the screen until initiation of the reach to that target. We added the relevant parts of Figure 1D to the subplots for each subsequent experiment, to summarize the timing of other experiments and their analyses. For the experiments with target jump, a small vertical arrow shows the time of the target jump relative to other events.

However, this figure will be less useful, if the connection between the timing dots and ticks is not communicated. We agree that in the original manuscript, this important figure was only briefly explained in the caption of Figure 1. We expanded the explanation in the caption of Figure 1 and referenced the dots and ticks in the main text.

(3) Fig 6B - for some reason I got confused here: I thought the central target in this figure was the start target, and it took me embarrassingly long to figure out that the green target was the start target. This is likely because I'm used to seeing center-out behavioral figures. Incidentally, I wasn't confused by 7c (in fact, seeing 7c is what made me understand 6b), so maybe the solution is to clearly mark a directionality to the reach trajectories, or to point an arrow at the green target like in previous figures. Also, the bottom left gray target in the figure blends into the graph on the left--I didn't notice it until rereading. Because there's white space between that target and the green one, it might be good to introduce some white space to separate the graph from the targets more. The target arrangement makes more sense in panel C, but by the time I got there, I had already been a bit confused.

Thanks for raising this point. As shown in Figure 6C, we used the reach to the +1 target for the curvature analysis. The confusion about Figure 6B is probably due to continuing the reach trajectories after the +1 target. That also explains why Figure 7C seemed more straightforward. To solve this issue we modified Figure 6B such that the reaches are shown with full opacity right until the +1 target and then shown with more transparency. We believe this change focuses the reader's attention to the reach initiated from the +0 target to the +1 target.

As for the gray target in Figure 6B, we originally had the gray target as it is a potential start location for the reach to the +0 target, and for having similar visuals between the plots. The gray target is now removed from Figure 6B.

(4) Line 253 - I'm not sure I understand the advantage over simple averaging that the authors mention here--would be nice to get a bit more intuition.

Thanks for raising this point. We used a two-factor model in our analysis, with each factor representing the angle of the last and next target, respectively. Both factors had five levels: -120, -60, 0, 60, and 120 degrees relative to the +1 reach. In a balanced two-factor design, where each combination of factor levels has an equal number of trials, using a linear model and simple averaging would yield equivalent results. However, when the number of trials for the combinations of the two factors is unbalanced, simple averaging can lead to misleading differences in the levels of the second factor. Additionally, the linear model allows us to investigate potential interactions between the two factors, which is not possible with simple averaging.

(5) Fig 7a - I would have liked to see the traces labeled in figure (i.e. hand trajectory vs. eye trajectory)

Hand and eye trajectories are now labeled in the figure.

(6) Fig 7c - very minor, but the hexagon of targets is rotated 30 degrees from all previous hexagons shown (also, this hex grid target arrangement can't lead to the trajectory shown in 7a, so it can't be that this was a different experimental grid). I'm guessing this was a simple oversight.

We used the same grid in the eye-tracking experiment. The targets are to visually match the previous plots. Thank you for raising this point.

Reference

Clavagnier, S., Prado, J., Kennedy, H., & Perenin, M.-T. (2007). How humans reach: distinct cortical systems for central and peripheral vision. The Neuroscientist: A Review Journal Bringing Neurobiology, Neurology and Psychiatry, 13(1), 22–27.

González-Alvarez, C., Subramanian, A., & Pardhan, S. (2007). Reaching and grasping with restricted peripheral vision. Ophthalmic & Physiological Optics: The Journal of the British College of Ophthalmic Opticians , 27(3), 265–274.

Kalidindi, H. T., & Crevecoeur, F. (2023). Task dependent coarticulation of movement sequences

(p. 2023.12.15.571847). https://doi.org/10.1101/2023.12.15.571847

Miller, G. A. (1956). The magical number seven plus or minus two: some limits on our capacity for processing information. Psychological Review, 63(2), 81–97.

Sivak, B., & MacKenzie, C. L. (1990). Integration of visual information and motor output in reaching and grasping: the contributions of peripheral and central vision. Neuropsychologia, 28(10), 1095–1116.

eLife assessment

This manuscript is an important contribution, assessing the role of intraspecific consumer interference in maintaining diversity using a mathematical model. Consistent with long-standing ecological theory, the authors convincingly show that predator interference allows for the coexistence of multiple species on a single resource, beyond the competitive exclusion principle. Notably, the model matches observed rank-abundance curves in several natural ecosystems.

Reviewer #1 (Public Review):

Summary:

The manuscript considers a mechanistic extension of MacArthur's consumer-resource model to include chasing down of food and potential encounters between the chasers (consumers) that lead to less efficient feeding in the form of negative feedback. After developing the model, a deterministic solution and two forms of stochastic solutions are presented, in agreement with each other. Finally, the model is applied to explain observed coexistence and rank-abundance data.

Strengths:

- The application of the theory to natural rank-abundance curves is impressive.<br /> - The comparison with the experiments that reject the competitive exclusion principle is promising. It would be fascinating to see if in, e.g. insects, the specific interference dynamics could be observed and quantified and whether they would agree with the model.<br /> - The results are clearly presented; the methods adequately described; the supplement is rich with details.<br /> - There is much scope to build upon this expansion of the theory of consumer-resource models. This work can open up new avenues of research.

Weaknesses:

- Though more and better data could be used to constrain and validate the modeling, given this is a theory-driven manuscript, their results are sufficient.

Reviewer #3 (Public Review):

Summary:

In this manuscript, the authors extend previous work on the role of predator interference in species coexistence. Previous theoretical work (for example, using the Beddington-DeAngelis model) has shown that predator interference allows for multiple predators to coexist on the same prey. While the Beddington-DeAngelis has been influential in theoretical ecology, it has also been criticized at times for several unusual assumptions, most critically, that predators interfere with each other regardless of whether they are already engaged in another interaction. There has been considerable work since then which has sought either to find sets of assumptions that lead to the B-D equation or to derive alternative equations from a more realistic set of assumptions (Ruxton et al. 1992; Cosner et al. 1999; Broom et al. 2010; Geritz and Gyllenberg 2012). This paper represents another effort to more rigorously derive a model of predator interference by borrowing concepts from chemical reaction kinetics (the approach is similar to previous work: Ruxton et al. 1992). The main point of difference is that the model in the current manuscript allows for 'chasing pairs', where a predator and prey engage with one another to the exclusion of other interactions, a situation Ruxton et al. (1992) do not consider. While the resulting functional response is quite complex, the authors show that under certain conditions, one can get an analytical expression for the functional response of a predator as a function of predator and resource densities. They then go on to show that including intraspecific interference allows for the coexistence of multiple species on one or a few resources, and demonstrate that this result is robust to demographic stochasticity. This work provides additional support for the idea that predator interference allows multiple predators to persist with a shared resource.

Strengths:

I appreciate the effort to rigorously derive interaction rates from models of individual behaviors. As currently applied, functional responses (FRs) are estimated by fitting equations to feeding rate data across a range of prey or predator densities. In practice, such experiments are only possible for a limited set of species. This is problematic because whether a particular FR allows stability or coexistence depends on not just its functional form, but also its parameter values. The promise of the approach taken here is that one might be able to derive the functional response parameters of a particular predator species from species traits or more readily measurable behavioural data.

Weaknesses:

The main weakness of this paper is that while it is technically sound, it doesn't change the fundamental intuition gained from more phenomenological models of predator interference: as one species becomes more common, it limits its own growth (manifested by less time spent searching for/handing resources due to interference), such that it does not exclude the existence of a competitor species. However, given the authors use a different model formulation that has been used in past studies, it suggests that predator interference will likely tend to promote coexistence regardless of some of the technical details in how it is formulated in a model.

The formulation of chasing-pair engagements assumes that prey being chased by a predator are unavailable to other predators. While this may hold in some predator-prey, it does not hold for many others, perhaps limiting some results' generality.

Summary:

The manuscript by Kang et al investigates how the consideration of pairwise encounters (consumer-resource chasing, intraspecific consumer pair, and interspecific consumer pair) influences the community assembly results. To explore this, they presented a new model that considers pairwise encounters and intraspecific interference among consumer individuals, which is an extension of the classical Beddington-DeAngelis (B-D) phenomenological model, incorporating detailed considerations of pairwise encounters and intraspecific interference among consumer individuals. Later, they connected with several experimental datasets.

Strengths:

They found that the negative feedback loop created by the intraspecific interference allows a diverse range of consumer species to coexist with only one or a few types of resources. Additionally, they showed that some patterns of their model agree with experimental data, including time-series trajectories of two small in-lab community experiments and the rank-abundance curves from several natural communities. The presented results here are interesting and present another way to explain how the community overcomes the competitive exclusion principle.

Weaknesses:

The authors did a great job of satisfactorily addressing each of my concerns raised in the previous round. I did not detect additional weaknesses.

「役立つ方法や、」のほうが自然に感じます。

For over 2 decades I struggle with this. Because my paraphrasing is mostly unsuited for my blog, regularly because it is mixed language and often bc it contains words that serve as shorthand. A blog is more performance, written for not-me, while annotation is for me, and after editing might be publishable for not-me. Annotating publicly here in .h, even if the readership is highly limited, introduces a level of performance-awareness for me. At times I've done annotated link blogging, but it never became a practice as with [[Stephen Downes]].

replace: For example, the running fictional story by author Maurus Jókai entitled "Treurige Dagen", in the Opregte Haarlemsche Courant

Replace with: This normally occurs in a context where the action is “out of the ordinary”, and usually occurred when either a deserter fled and assumed a new life (as reported in figure c), or when they had subsequently been caught, tried and even executed as shown in figure d. More detailed reporting also often occurred when there were other notable vents related to desertion (such as theft, arson or violence).

Fig. c Example of a more detailed description of a case of desertion, 1 May 1899, De locomotief: Samarangsch handels- en advertentie-blad

Fig, d Newspaper description of the execution of a deserter, 21 December 1897, Soerabaijasch handelsblad

Replace with: This is highlighted in the above article (figure a), which describes how Chinese “agents” actively recruited deserters of the Dutch East Indies armed forces, and would assist them in desertion by offering large sums of money, clothes, and transportation to join the Chinese army.

Fig. a Article "Japan op Java" 16th February 1899, De Morgenpost

Replace with: The vast majority of articles list “rechtszaken” (see figure b) and detail the numbers of individuals who have deserted, usually with their name, age, occupation, and the punishment afforded to them.

Fig. b Example of 'Law suits' section of a newspaper, 17 March 1897, Vlaardingsche courant

Replace with: 'Flight as Fight': Exploring Historic Cases of Desertion from a Collective Labour Action Perspective

Replace with: The above figure details the spread of annotated articles over time. Of the 387 events tagged, we see that there are a few hits between 1810 and 1830, sporadically changing over time, with two peaks in desertion events between 1830 and 1840.