SciScore for 10.1101/2022.03.01.22271684: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

Limitations include the small number of studies with viral culture and serial viral load estimates among transplant patients, high variability in study design and reporting and impossibility to pool results due to the well-known variability in sensitivity across essays21. Case series are conventionally considered low in the evidence hierarchy, as they may entail inherent bias in the selection of study participants and therefore have limited generalisability; however, here they are essential in providing the detailed reports needed for this unusual patient group. The case reports included here comprise some of the most detailed longitudinal reports of this patient group for whom data are needed. The evidence base is limited, however, by heterogeneous design and reporting within the studies with, for example, different observation windows for reporting of viral burden and culturability or clinical characteristics of patients. In addition to providing appropriate care for the individual patient, ongoing transmission of SARS-CoV-2 is a concern, and immunosuppressed individuals may pose a challenge by experiencing prolonged carriage of the virus that could lead to forward transmission. Based on our findings we would offer the following general guidance to clinicians: Physicians who are experienced with these immunosuppressed patient populations should work with public health to direct their isolation and quarantine requirements. Patients with immunosuppressive treatment following ...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Reply to the reviewers

1. General Statements [optional]

This section is optional. Insert here any general statements you wish to make about the goal of the study or about the reviews.

We are grateful to the reviewers for their honest opinion regarding this work and plan to address the majority of the comments in a revised version either through new analysis or revision to the text, as we believe these will improve the manuscript by making some of the details clearer. There were few suggestions that will lead to substantiative changes to the findings. Here, we address the most salient critiques, the primary one being related to novelty.

We respectfully disagree, as our detailed analysis of the DNA methylome in Octopus bimaculoides represents a significant advance to understanding how the epigenome is patterned in non-model invertebrates in general, and cephalopods in particular. We acknowledge that the previous report that the octopus methylome resembles the few other invertebrates where low DNA methylation has been found, the finding was part of a multi-organism study last year (de Mendoza et al., 2021), which lacked any detailed investigation. Our study provides the first in depth analysis on methylation patterning, the relationship with transposons and gene expression, and reports the finding of other key epigenetic marks in O. bimaculoides, and in other cephalopods.

In short, we believe our study to be highly novel and that it represent the first analysis of this kind in cephalopods and one of the few existing in non-model invertebrate organisms. In addition, we identify the conservation of the histone code in cephalopods. While this may be expected, this is the first experimental evidence in this class and represents an important step forward to understand the epigenetic regulation of genes and transposons in invertebrates. Finally, we plan to provide an updated transcriptome annotation for O. bimaculoides that will be available for the scientific community as a new valuable resource. We believe these features will make this study highly cited.

We believe that findings like ours will complement several recent studies that extend the epigenetics field out of the current narrow focus on model organisms to understand how epigenetic mechanisms function in diverse animals. This provides new insights regarding the epigenetic mechanism of gene regulation in an emerging invertebrate model.

2. Description of the planned revisions

Insert here a point-by-point reply that explains what revisions, additional experimentations and analyses are planned to address the points raised by the referees.

Reviewer 1 raised the following points that we are planning to address:

*- It is unclear why the authors did not use the original gene models of O. bimaculoides or tried to improve them. By only relying on adult tissue (but the relatively late hatchling stage), they would have omitted most developmentally expressed genes, that are incidentally also the ones that are subjected to extensive spatiotemporal gene regulation (which is also a problem to assess the role of methylation). I think more comparisons with existing gene models and how the newly generated stringtie models should be provided. *

We agree that using as many tissues and developmental stages as possible will expand the octopus transcriptome.

We plan to:

• Add RNA-seq data from stage 15 embryos to improve this.
• Compare the gene model used in the original version of the manuscript (Stringtie model to use in Trinotate for improving the annotation of the genes) to the existing annotation model and report on which has superior performance for annotating the * bimaculoides* transcriptome.
• Extend the annotation of the transcriptome which we undertook in a focused fashion in the first iteration of this manuscript. Reviewer 2 raised the following points that we are planning to address:

*- It is not exactly clear to me why the authors look for expression clusters in the first part of the manuscript? This information, while interesting, does not seem to be used in the methylation analysis. It is also somewhat contradictory because the authors first claim that, based on their GO-term enrichment analysis, that different expression clusters are associated with "complex regulatory mechanisms, potentially based in the epigenome". Yet at the end they conclude that, due to the global and tissue-overarching nature of methylation, this "argues against this epigenetic modification as a player in the dynamic regulation of gene expression". *

We thank the reviewer for pointing out this issue and we plan to clarify the point through changing the text and additional analysis. Since we found that the methylation pattern was stable across tissues, and that it corresponded to gene expression levels regardless of tissues, we concluded that the methylation pattern is not likely relevant for the tissue-specific gene expression pattern reported in Figure 1.

We plan to:

• Ask whether there is a correlation between the gene clusters generated in Figure 1 and the DNA methylation patterns identified in Figure 4. *- At least for the trees that are shown in the main figures it would be great to show support values. *

We thank the reviewer for this request.

We plan to:

• Add full Supplementary information regarding the support values in Supplemental Files for all the trees present in the main Figures. Reviewer 3 raised the following points that we are planning to address:

*- It would be great to see more data on cephalopod TET and MBD structure. For example, it would be interesting to know whether octopus TETs have a CxxC domain or whether MBD proteins harbor functional 5mC - binding domains. *

We agree that it would be of interest to examine the conservation of TET genes to expand upon the initial analysis by Planques et al 2021 showing that O. bimaculoides have one TET homolog, one MBD4 homolog and one MBD1/2/3 homolog. Detailed analysis of MBD4 protein has been already performed in de Mendoza et al. 2021 by using the protein sequence of O. vulgaris, as the MBD4 gene in the O. bimaculoides genome appears truncated.

We plan to:

• add the PFAM domain analysis for TET proteins This will be added as a new figure panel.
• Update the text to include the reference to the identification of MBD4/MECP2 as the invertebrate homologs of vertebrate MBD4. *- Even though RRBS provides limited insight into DNA methylation patterns, the authors could have done more to explore read-level 5mC information. For example, by studying single reads, the authors could deduce the numbers of fully methylated, unmethylated or partially methylated reads. Such analyses might provide valuable insight into potentially different modes of epigenetic inheritance in different tissues i.e are there tissues that favor fully methylated or unmethylated stretches of DNA vs tissues that favor partial methylation? *

We think this is a really interesting point. This has been partially addressed in a previous work (de Mendoza et al., 2021) which found limited to no partially methylated reads in whole-genome bisulfite sequencing from O. bimaculoides brain.

We plan to:

• Use Proportional Discordant Reads on the RRBS data we generated from 30 dpf hatchlings to assess the presence of discordant methylated reads across different tissues to assess possible different modes of epigenetic inheritance. We will use “WSHPackage” in R: https://academic.oup.com/nar/article/48/8/e46/5760751?login=true https://github.com/MPIIComputationalEpigenetics/WSHPackage/blob/master/vignettes/WSH.md#x1-50003.1 This will be added as a new figure panel.

3. Description of the revisions that have already been incorporated in the transferred manuscript

Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. If no revisions have been carried out yet, please leave this section empty.

Reviewer 1 raised the following points that we have already addressed:

We addressed all the comments raised by this Reviewer by revising the text, fixing references, typos and improving clarity.

Reviewer 2 raised the following points that we have already addressed:

We addressed all the minor Comments raised by this Reviewer regarding spelling errors and Supplementary Figures.

- The finding that less than 10% of all possible sites are methylated is surprising. I could not (easily) find statistics of RRBS experiment read mapping to the genome.

We have now provided this data and new Supplemental Table 1 (refereed in the text as Table S1).

*- It is very exciting to see methylation of gene bodies and some correlation to their expression levels, but the authors may need to include a disclaimer that the methylation of TEs may go undetected due to the gapness of the genome. In fact, the authors may try to map their data onto a somewhat closely related Octopus sinensis genome sequenced with long reads available at NCBI to confirm overall pattern. It is likely though that due the evolutionary distance only gene bodies will have mapping. *

The thank the reviewer for this suggestion and we included a sentence in the Result session indicating that methylation of TEs may go undetected due to the poor annotation of the octopus genome.

*- The statistical reasoning (and methodology) behind how clusters in Figures 1 and 4 were defined is unclear. In particular, in Figure 4, it seems that the authors had asked the program to give four clusters in total - why was this number chosen? It seems that using the same generic clustering approach as in Figure 1 may benefit or confirm the results in Figure 4. *

We clarified the rationale in the Material and Methods session to describe the bioinformatic analysis. We will put the full code used in the manuscript in our GitHub page (https://github.com/SadlerEdepli-NYUAD/) to have a more comprehensive understanding of the Method used.

Reviewer 3 raised the following points that we have already addressed:

We addressed all the minor comments in the text and figures raised by this reviewer regarding typos and clarity.

*- There is little info on the generated 5mC data. To bolster its value as a resource, the manuscript should have a link to the table describing RRBS metrics. This should include: non-conversion rates, numbers of sequenced and mapped reads, read length and other info that the authors deem useful. *

We have now provided this data in a new Supplemental Table 1 (refereed in the text as Table S1).

4. Description of analyses that authors prefer not to carry out

Please include a point-by-point response explaining why some of the requested data or additional analyses might not be necessary or cannot be provided within the scope of a revision. This can be due to time or resource limitations or in case of disagreement about the necessity of such additional data given the scope of the study. Please leave empty if not applicable.

Reviewer 1 raised the following points that we are not planning to address:

*- The newly sequence RNA-seq samples are using a ribodepletion protocol (RiboZero) while the other ones are using a polyA selection. This might be a slight problem to compare them quantitatively. Actually in the Figure 1, all 4 newly generated samples group together in the hierarchical clustering. *

We acknowledge the reviewer’s point here and agree that heterogeneity in library prep and batch is a common issue when comparing public available with newly generated datasets. This could account for the clustering of the Ribosomal RNA depleted (i.e. RiboZero) from polyA selected RNA libraries. While this could potentially introduce bias, we do not believe that it substantially alters any of the main findings or the interpretations of this data. Our purpose for carrying out the cluster analysis of transcriptomic data from multiple tissues was to identify distinct gene patterns that defined different tissue types. This was accomplished regardless of the potential confounding variable introduced by different library preparations. In addition, we used TMP which seems to help in the comparison across different samples when used for qualitative analysis such as PCA and cluster analysis (Zhao et al. 2020; DOI: http://www.rnajournal.org/cgi/doi/10.1261/rna.074922.120). Therefore, even if not ideal we think that this approach is still valuable.

*- I am not so sure about the way the authors used z-score normalized logTPMs and applied hierarchical clusters, this most likely would not fully alleviate the impact of expression level on the outcome compared to more advanced form of normalization and clustering. *

We agree with the reviewer that applying z-score or a logTPMs normalization would not fully resolve the technical variance in the direct comparison of libraries generataed with different RNA selection methods. We did not apply z-score on logTPMs but these 2 methods were applied separately: z-score on TPMs in Figure 1B to define the gene clusters and log2(TPM+1) in Figure 4E. We have clarified the text to reflect this.

*- I am not convinced that differences in western blot for histone modification could really provide a clear insight into their regulatory role. *

We agree with the reviewer that Western blotting for histone modifications does not provide deep insight into their regulatory role. However, this is the first description of these marks in any cephalopod, and we believe that reporting a finding from experimental evidence is important, even if the result is aligned with the existing paradigm. Moreover, the marked difference in levels of distinct histone marks across tissues supports the hypothesis that they play a regulatory role. We observed this in mice where difference abundance in western blot correspond to different abundance and enrichment also by ChIP-seq (Zhang et al., 2021 DOI: https://doi.org/10.1038/s41467-021-24466-1). Considering the limited tools available in this species, we still consider this an important finding.

Reviewer 2 raised the following points that we are not planning to address:

*- The finding that less than 10% of all possible sites are methylated is surprising. I could not (easily) find statistics of RRBS experiment read mapping to the genome. I also wonder how much the gap-richness of the genome may affect the overall methylation estimate. If assembly permits, would it make sense to limit the sampled sites to areas where no flanking gaps are present (and sufficient scaffold length is available, maybe excluding very short scaffolds)? *

We added all the statistical values regarding the RRBS in a NEW Supplemental Table 1. We used a single base pair analysis approach (not tiling windows), so the data we extracted is not biased by the length of the scaffolds. This is confirmed by the fact that the DNA methylation value obtained in our RRBS data matches the findings observed in Whole Genome Bisulfite Sequencing (WGBS). Moreover, global DNA methylation values assessed by Slot blot analysis as a technique independent from genome assembly confirmed what observed with RRBS.

SciScore for 10.1101/2022.02.28.22271615: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

Limitations: Our study has several limitations. According to our knowledge, this is the first study that assesses the validity of the “COVID Stress Scales”. We found that reliability and validity of the scale was excellent but further studies in different populations and settings should test the psychometric properties of the scale. Also, our sample comprised 200 participants, thus studies with larger and more representative samples could add more information regarding the “COVID Stress Scales”. More sophisticated analysis such as confirmatory factor analysis could be applied to confirm or not our results.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

不能随便用terminate方法

SciScore for 10.1101/2022.02.24.481866: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

There are clearly a number of limitations for this kind of analysis. Unavoidable is the issue of single copy orthologues, which comprised 63-77% of genes identified by RNA-Seq in lung tissues. This issue is less of a problem for pathway analyses when using programs such as IPA that accept both human and mouse gene nomenclature. The different sources of tissues and the different technologies used to generate gene expression data (Table 1) likely add to non-biological variability, although this was perhaps mitigated herein by combining multiple human and mouse studies. The large differences in viral loads between some groups (Figure 2A) would appear to play a role in the poor concordance in gene expression profiles, particularly for human groups and down-regulated genes. However, analyses of K18-hACE2 (high viral load) and mACE2-hACE (lower viral loads) argued that the difference in viral loads was not a major player in the poor overlap in up-regulated orthoDEGs for mouse vs. human groups. In summary, the analyses herein argue that overlap in orthoDEG expression in the lung tissues of hACE2-transgenic mice and humans after SARS-CoV-2 infection is generally poor. In contrast, the concordance in immune and inflammation pathways was high, arguing that the transgenic mouse models provide relevant and pertinent models in which to evaluate new interventions for SARS-CoV-2 and COVID-19.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

The author gives multiple examples from multiple different states about the discrimination and how awful it is, by showing all these different examples it makes the reader understand how big of a problem this was.

This shows the brutal reality of how black people were treated. They never asked for harm and whenever they got it a little altercation life blew up for them. Black people constantly lived in fear.

• how to : shutdown wsl

start up command on windows

do wsl --list

motif of eyes - windows to the soul, Nadia's eyes containing worlds

Make sure to open the Application (Application/SceneBuilder on mac, /Users/???/AppService or something on Windows) when it asks you for a path, and not the file path to your .fxml document.

$ git config --list http.sslcainfo=C:/Program Files/Git/mingw64/ssl/certs/ca-bundle.crt http.sslbackend=openssl diff.astextplain.textconv=astextplain core.autocrlf=true core.fscache=true core.symlinks=false credential.helper=manager user.name=xxj user.email=hea@exa.com credential.helper=store credential.username=xxxj core.repositoryformatversion=0 core.filemode=false core.bare=false core.logallrefupdates=true core.symlinks=false core.ignorecase=true remote.origin.url=ssh://@192.168.2.172:29418/hea_storage_model.git remote.origin.fetch=+refs/heads/:refs/remotes/origin/ branch.master.remote=origin branch.master.merge=refs/heads/master

此种配置下， git push 出现如下提示：

fatal: Could not read from remote repository.

Please make sure you have the correct access rights and the repository exists.

检查后发现，有问题的配置项：

remote.origin.url=ssh://@192.168.2.172:29418/hea_storage_model.git

remote.origin.url=ssh://@ 缺少了 username

she is seeing nr everywhere! her regression of thought is represep

Value of objects/commodities over other lives

This connects with my group project because it relates to the nypd. If the bail reform laws were in place during this time all of the people arrested in this would be out on bail.

安装多版本 Python

Windows 安装 Python 2.7 和 Python 3，如何设置环境变量中的系统变量，而可以分别执行不同的版本？

1、安装 Python 2.7 后 1.1 先将 python.exe 命名为 python2.exe。 1.2 将python2.exe 所安装的路径，添加到系统变量中.

2、安装 Python 3.x 后 2.1 重命名，将安装路径下的 python.exe，命名为 python3.exe 2.2 将该 python3.exe 所在的路径，添加到系统变量中

SciScore for 10.1101/2022.02.16.22271075: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

Our study has several strengths and limitations. A strength of our study is the relatively large and unselected study cohort recruited during the first and second pandemic waves in Norway. The time for the antibody test was related to the PCR test, and not the onset of symptoms. Another strength is the longitudinal design with a relatively long follow-up time. Validated serology assays were used, and the participants answered the questionnaire on the same day as the serum sampling. A limitation of our study is that changes in antibody levels above the assay’s upper limit value 10 Index could not be determined, but it is probably in the group with low antibody levels that significant changes will appear. Direct comparison of the current study’s results with those of other studies is difficult because assays targeting different antigens are used in the available studies [10]. Further, differences in study populations, severity of illness, and frequency of antibody sampling make comparisons across studies challenging [31]. In our study, the same assay was used for all participants and as a quality control, all PCR-participants with detectable antibodies and all PCR+ participants with low levels of antibodies were retested with a second assay. Using two different assays was considered useful in low seroprevalence countries like Norway because of the possibility of false-positive tests [14]. The use of a self-reported questionnaire may be considered a limitation of the study becau...

Results from TrialIdentifier: We found the following clinical trial numbers in your paper:<br><table><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Identifier</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Status</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Title</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04514003</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Recruiting</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">The Corona and COVID-19 Study in Telemark and Agder</td></tr></table>

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a protocol registration statement.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

Challenging Cozzo's argument about crosspieces; unable to support corbels

Don't need this

cut

both these links opened in separate windows

Creating alternative ways for all students to have access to their work over online tools is awesome for students. It isn't fair that students with disabilities have to figure out access to work on their own, being mindful of that is really important.

Description of Arches national park.

13 and 14. No instruments used for data analysis

SciScore for 10.1101/2022.02.14.22270965: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

This study had several limitations. It was done in a single-center, so it is not representative of the scenario of the whole country. It was a retrograde study, so there might have been some recall bias. The hospital admission and the test capacity are bed or slot limited. So, the hospital admission rate and the number of tests in different surges might not be the actual representation of the nation’s scenario. A multi-center study is required to get a representative picture.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.02.11.22270504: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

Data collection through self-report reflects another limitation as self-assessment of COVID-19 knowledge can introduce biases. Another limitation was access to unvaccinated individuals. Most of the population surveyed had already received the vaccine or were receptive to getting it. We can not discount that some participants holding strong anti-vaccine beliefs [15] were less likely to participate in this voluntary study. Another limitation was the few native Spanish-speaking participants in our sample. Future studies should seek to include sizable proportions of unvaccinated individuals [15] along with the addition of bilingual research assistants and interpreters to their team. Future investigations that include an appreciable number of unvaccinated participants may further clarify the overall profile of unvaccinated minorities, allowing for the development of a more nuanced strategy to address their concerns and encourage vaccine and booster acceptance among all ages [19].

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

Neither of these included taking over a large part of the city for 3 weeks or shutting off trade routes on a mass scale though. I agree the potential implications and justification for the use of the emergency act are questionable and unjustified, but this is a false comparison.

Reviewer #3 (Public Review): 

In this study, Donnelly and colleagues quantified sleep oscillations and their coordination in in young people with 22q11.2 Deletion Syndrome and their siblings. They demonstrate that 22q11.2DS was associated with enhanced power the in slow wave and sleep spindle range, elevated slow-wave and spindle amplitudes and altered coupling between spindles and slow-waves. In addition, spindle and slow-wave amplitudes in 22q11.2DS correlated negatively with the outcomes of a memory test. Overall, the topic and the results of the present study are interesting and timely. The authors employed many thoughtful analyses, making sense out of complicated data. However, some features of the manuscript need further clarification. 

1) Several interesting results of the manuscript are related to altered sleep spindle characteristics in 22q11.2DS (increased power, increased amplitudes and altered coupling with slow waves). On top of that the authors report, that the spindle frequency was correlated with age. I was wondering whether the authors might want to take these individual (age-related) differences into account in their analyses. The authors could detect the peak spindle frequency per participant and inform their spindle detection procedure accordingly. This procedure might lead to an even more clear cut picture concerning altered spindle activity in 22q11.2DS. 

2) The authors state in the methods section that EEG data was re-referenced to a common average during pre-processing. Did the authors take into account that this reference scheme will lead to a polarity inversion of the signal, potentially over parietal/occipital areas? This inversion will not affect spindle related analyses, but might misguide the detection of slow waves and hence confound related analyses and results. 

3) I have some issues understanding the reported slow wave - spindle coupling results. Figure 5A indicates that ~100 degrees correspond to the down-state of the slow wave. Figure 5E shows that spindles preferentially clustered at fronto-central electrodes between 0 and 90 degrees, hence they seem to peak towards the slow wave downstate. This finding is rather puzzling given the prototypical grouping of sleep spindles by slow wave up-states (Staresina, 2015; Helfrich, 2018; Hahn, 2020). Could it be that the majority of detected spindles represent slow spindles (9-12 Hz; Mölle, 2011)? Slow spindles are known to peak rather at the up- to down-state transition (which would fit the reported results) and show a frontal distribution (which again would fit to the spindle amplitude topographies in Fig 3E). If that was the case, it would make sense to specifically look at fast spindles (12-16 Hz) as well, given their presumed role in memory consolidation (Klinzing, 2019). In addition, is it possible that the rather strong phase shift from fronto-central to occipital sites is driven by a polarity inversion due to using a common reference (see comment 2)? <br /> Apart from that I would suggest to statistically evaluate non-uniformity using e.g. the Rayleigh test (both within and across participants). 

4) Somewhat related to the point raised above. The authors state that in the methods that slow wave spindle events were defined as time-windows were the peaks of spindles overlapped with slow waves. How was the duration of slow waves defined in this scenario? If it was up- to up-state the authors might miss spindles which lock briefly after the post down-state upstate, thereby overrepresenting spindles that lock to early phases of slow waves. Why not just defining a clear slow wave related time-window, such as slow wave down-state {plus minus} 1.5 seconds? 

5) The authors correlated the NREM sleep features with the outcomes of a post-sleep memory test (both encoding and an initial memory test took place pre-sleep). If the authors want to show a clear association between sleep-related oscillations and the behavioural expressions of memory consolidation, taking just the post sleep memory task is probably not the best choice. The post-sleep test will, as the pre-sleep test, in isolation rather reflect general memory related abilities. To uncover the distinct behavioural effects of consolidation the authors should assess the relative difference between the pre- and post-sleep memory performance and correlate this metric with their EEG outcomes.

Right here what I took from it was that although she was talking about the window, the window was resembling her eyes and not only about how they closed but also how she might have felt disconnected from the outside world. Another thing I referenced was that since sometimes people say that "eyes are the windows to the soul" I feel like she indirectly played off of that as well.

Here she describes light and how the windows failed and how she couldn't see this easily describes how she couldn't see any more like her eyes are shutting down because she is dying, she describes her eyes as windows when a window is shut or covered you can't see thru it. This is what she relates her vision too

I think this is important to consider because many students have access to varying forms of technology that may or may not be synonymous with the technology that is being used in the classroom. In addition, some students do not have internet access at all which can pose problems if technology-based projects/activities are assigned outside of class time. I think a way to combat this is to keep technologically related assignments in the classroom and hard copies for homework when possible.

I question how are you supposed to test user interface when you might not have access to all the different systems when finding a source or before a lesson can happen.

Making sure that the tool is operating on all kinds of devices and browsers is so important. Throughout my education experiences, there have been times where a certain website or program would not work the same way on a school computer than the one at my house. I also think that the point about making sure the tool works on more devices is important because some students may not have access to a computer at home. Considering all of these factors allows for all students to have the opportunity to learn at home and at school.

This is such a true point. I am currently in a stats class and we needed to download a software and he said it mainly works for mac and windows, everyone else needs to go to the library computers- which can be so inconvenient!

I learned the hard way (I cancelled the first attempt after 2 hours) to simply wait it out. After 3 hours of "Working on updates 48%", the update was complete and the laptop rebooted.<br /> MS programmers should revise this process to increment the % regularly (as long as the update is working).

SciScore for 10.1101/2022.02.08.22270685: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

Limitations of our data include the use of self-collected swabs in the familial cohort, which may not have been collected as reliably as those collected by a healthcare provider in the cross-sectional cohort. RT-PCR assays were performed with a common assay (Cepheid GeneXpert) for MT and OP samples while saliva was assayed based on the SalivaDirect protocol. Neither assay has EUA approval as a quantitative test and this may limit conclusions, particularly from comparison of Ct values between saliva and the other specimens. Additionally, the quantity of cellular material and mucous may be inherently different between sample types with different collection methodologies, particularly with saliva collected as a pooled drool specimen compared to MT and OP samples obtained with collection swabs eluted in buffer. Despite this, the pragmatic question of viral yield for diagnostic samples can be investigated in this manner particularly because a consistent sample volume and a common assay was used for nucleocapsid measurements across all sample types. Thus, our data appear to suggest that higher antigen levels may be found in saliva specimens, but calculation of PPA failed to show benefit in diagnostic yield. Conclusions as to the utility of saliva for antigen testing may be limited by missing specimens from some participants and a small sample size. The observation of OP- and MT-predominant phenotypes is a key finding in our data in addition to the absence of an effect of days of sy...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.02.09.22270717: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

Our study has several limitations, such as the fact that we only collected samples from hospitals and the garment industry, but the results would be more representative of the community if we included other groups. We could not compare immunological responses produced by different COVID-19 vaccine brands at the same post-vaccination interval since distinct COVID-19 vaccines were licensed and supplied to CMA at different times. We did not reveal the type and name of COVID-19 vaccines, whereas a sufficient fraction was not covered under the vaccination program, and we were concerned about an infodemic.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

User accountability is very important. If students are able to actively understand the software, and can engage with it in a way that enhances their learning, this tool will be very helpful to their learning experience.

SciScore for 10.1101/2022.02.04.479171: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: Thank you for sharing your data.

Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.02.05.22270453: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

NIH rigor criteria are not applicable to paper type.

Table 2: Resources

No key resources detected.

Results from OddPub: Thank you for sharing your code and data.

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

A limitation of this analysis is that we had to extrapolate from wild-type data to estimate the ARs for Delta infections given that there was insufficient direct data. There is also likely a degree of underestimation in the DRDC database of the IRs. It is difficult to accurately model underreporting rates and how they change over time because one is trying to model something where there are no data. This is shown by the extreme variation in underreporting estimates [4, 23]. Local context/knowledge is required to estimate underreporting rates in a region over time, which is not available on a global scale. Estimating the NNIs required using publicly available data in published and preprint reports, such that several included studies have not yet been fully peer-reviewed. This was unavoidable due to the newly emerging evidence base on this topic and the multiple month lag from the peer-review process. It is reasonable to ask why we did not use a risk metric to estimate the IR which uses a longer period of time (e.g., incidence proportion, period prevalence) since longer time windows would increase the IRs and thus lower the NNIs. Point-prevalence is the more appropriate metric for IR than incidence proportion and period prevalence for four reasons. First, the infection risk depends not just on new cases, but existing ones too. Second, incidence proportion and period prevalence depend on the time at risk. In general, shorter time windows will lower these metrics than longer time...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

I ought to spend some more time learning some of these keyboard shortcuts.

https://en.wikipedia.org/wiki/Windows_key

https://support.microsoft.com/en-us/windows/keyboard-shortcuts-in-windows-dcc61a57-8ff0-cffe-9796-cb9706c75eec

https://www.wired.com/story/quick-select-keyboard-shortcuts-no-mouse/

SciScore for 10.1101/2022.01.31.22270058: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

Some of the limitations of this study include the reliance on reported infections and deaths, hence it’s impossible to estimate how many cases were missed by non-reporting. As an emerging research area in the current pandemic, there are other factors worth considering. For example, the effect of time of hospitalisation on disease severity and mortality. As a secondary analysis, we were unable to analyse this variable. Future studies to investigate this variable is essential.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

This is the directory right above "src". Sometimes Windows creates a "cake151" directory with a "cake151" directory inside. Open the subdirectory.

should they measure what car students' parents have, if they take the bus, or some other manner that could effect their exposure? Additionally, looking at factors like air conditioning, where windows face, and the amount of outdoor time during schools could also effect exposure. Those students in street facing classrooms could be exposed to more NOx than those in non-road facing classrooms.

Reviewer #2 (Public Review):

The manuscript by Huisjes et al presented an open-source platform for the storage and processing of imaging data, particularly for single-molecule imaging experiments. Compared to sequencing data, which have a more standardized format for data storage, imaging data have more diverse formats due to the fact that different research labs tend to use different instruments and software (either commercial or home-built) for data collection and analysis. Manual input is almost always necessary at certain steps of data analysis. All these create difficulties in data storage and reproducibility. The authors provide a practical solution to this problem by the molecular archive suite, "Mars". This platform is integrated into imageJ/Fiji, and can be used for storing detailed description of experimental settings, performing standard imaging processing steps, and recording manual input information during data analysis. I judge this platform, if fully functional and generalizable, will be very useful to many labs who are using single-molecule imaging methods in the research.

Strength:

1. The work presented a fairly user friendly interface (using Fiji directly), and fairly detailed protocol and other documentations in a very nicely designed website. I was able to download and use it based on the tutorial.<br> 2. It is integrated very well with Fiji, and some analysis modules are directly from existing Fiji analysis/plugins.

Weakness:

I invited one of my students to co-test the suite. We tried on both Mac and Windows systems, using the example FRET data set described in the manuscript and one of our own single-molecule images. We encountered some technical issues.

SciScore for 10.1101/2022.01.24.22269745: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

The high drop-out rate is also a limitation that should be addressed. When asked by phone, the main reasons for dropping-out included lack of time, to work overtime (to replace co-workers infected by SARS-CoV-2), to take care of family members, and fear of leaving their house and being re-infected by SARS-CoV-2. Future studies and exercise programs addressing to overcome these barriers are thus welcome.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

It's interesting he says this. This statement shows how far psychology has come since this was written. I've seen many arguments that we value things of "no practical value" and call them beautiful before. Some psychologists would argue that this idea of beauty (really, creativity) is necessary for us to invent. If something is of no practical value, then we are free to invent the purpose of it. But a deeper point that lies in Freud's statement is the relativity of "practical value." One man's tool is another man's pleasure.

They focus on the violence.

To who is also struggling with this step, here is some tips: 1, download the Docker, you might need to update your WSL 2 if you use windows. 2, pull the pic by docker docker pull poldracklab/fmriprep:latest. You'd better to go to the contrainer to check the system first. 3, install the fmriprep-docker by pip first if you haven't. Remember to add the PATH to enviornment variable. 4, register a FreeSurfer license from the link given in the section. These might use up 4-5 GB. Have fun!

SciScore for 10.1101/2022.01.19.22269391: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

However, it is also subject to several limitations. We have not taken into account the SARS-CoV2-variant causing infection and our data might not be generalizable to all new variants including omicron. Our samples are restricted to one university hospital and to not include healthy or asymptomatic individuals who could be expected to show comparable parameters to convalescent patients. It would be valuable to analyze the sphingolipid pattern during the course of the disease to study the dynamics and to correlate early patterns with progression and outcome to develop predictive markers. Typical risk factors for a lethal COVID-19 course including age, obesity or hypertension are also associated with the ASM/Cer system (Kornhuber et al. 2021). We were therefore cautious to include age as a cofactor in our statistical models because our groups taken from available hospitalized patients during early pandemic differed significantly in age. Due to a lack of availability, the body mass index as marker of obesity could not be included in our analyses. Future studies should pay attention to collect and integrate full medical data including biochemical laboratory parameters and clinical parameters of disease progression as well as medications which could have additional confounding effects such as FIASMAs. Due to lacking material, we did not analyze relevant cellular enzymes in peripheral blood mononuclear cells of patients such as lysosomal ASM as well as sphingomyelin synthase (Muhle ...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• No funding statement was detected.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

Gutes Beispiel für keine Email schreiben

he goes rogue. offers explanation of why celebs go crazy?

page 382 "What meetings he does have all seem to go awry: people get punched, phones are stolen, babies are dangled out of windows, drugs taken, cars crashed."

SciScore for 10.1101/2022.01.18.22269501: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2022.01.18.22269424: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

Creates an image of scenery for reader to see the setting

We may try this in this class

Weebly and Blogger, like many other web authoring tools, offer smartphone views during editing.

.

ok

SciScore for 10.1101/2022.01.17.22269081: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

At last, it must be noted that this trial has several limitations. Firstly, the number of subjects is rather limited and was based on a power analysis for the serological and IFN-γ release read-outs. Hence, this design is less suited to pick up rather unexpected events such as BTI. Larger studies that look at both arms of the adaptive immune response months after vaccination are needed to further finetune our findings and to characterize BTI more reliable. Secondly, only SARS-CoV-2 naive individuals were included in this study. The pronounced recall response due to vaccination in convalescent individuals has been described and even led to the discussion whether these individuals should receive one rather than two vaccine doses. Addressing this was beyond our scope as we specifically aimed to study the BNT162b2 vaccine-induced de novo immune response in a real-world setting. Thirdly, the emergence of new and potentially more dangerous SARS-CoV-2 variants leads the binding and neutralization assays available at any given time. In this perspective, running neutralization assays with VoC RBD could give deeper insights in the breadth of the vaccine-induced functional immunity. Finally, long-term sustainability of the vaccine-induced immune response can only be truly considered when looking even beyond the three month timepoint. This will be particularly relevant as we are slowly progressing from a global pandemic state to a genuine endemic circulation of the SARS-CoV-2 virus.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

Case study in equivocation.

JD

Reviewer #2 (Public Review):

The Authors J-J Lee et al., investigated cortical and subcortical brain networks and their organization in communities over time during evoked tonic pain. The paper is well-written, and the findings are interesting and relevant for the field. Interestingly, other than confirming well known phenomena (e.g., segregation within the primary somatomotor cortex) the Authors identified an emerging "pain supersystem" during the initial increase of pain, in which subcortical and frontoparietal regions, usually more segregated, showed more interactions with the primary somatomotor cortex. Decrease of pain was instead associated to a reconfiguration of the networks that sees subcortical and frontoparietal regions connected with areas of the cerebellum. The main novelty of the proposed analysis, lies in the resulting high performances of the classifier, that shows how this interesting link between frontoparietal network and subcortical regions with the cerebellum, is predictive of pain decrease. In summary, the main strengths of the present manuscript are:<br> • Inclusion of subcortical regions: most of the recent papers using the Shaefer parcellation in ~200 brain areas1, do not consider subcortical areas, ignoring possible relevant responses and behaviors of those regions. Not only the Authors smartly addressed this issue, but most of their results showed how subcortical regions played a key role in the networks reconfiguration over time during evoked sustained pain.<br> • Robust classification results: high accuracy obtained on training dataset (internal validation), using a leave-one-out approach, and on the available independent test dataset (external validation) of relatively large sample size (N=74).<br> • Clarity in the description of aim and sub-aims and exhaustive presentation of the obtained results helped by appropriate illustrations and figures (I suggest less wording in some of them).<br> • Availability of continuous behavioral outcome (track ball).

Even though the results are mostly cohesive with previous literature, some of the results need to be discussed in relationship to recently published papers on the same topic as well as justifying some of the non-standard methodological procedures adding appropriate citations (or more detailed comments). The Authors do not touch upon the concept of temporal summation of pain, historically associated with tonic pain, especially when the study is finalized to better understanding brain mechanisms in chronic pain populations (chronic pain patients often exhibit increased temporal summation of pain2). I would suggest starting from the paper recently published by Cheng et al. that also shares most of the methodological pipeline3 to highlight similarities and novelties and deepen the comparison with the associated literature. Here the main significant weaknesses of the study:<br> • The data analysis is entirely conducted on young healthy subjects. This is not a limitation per se, but the conclusion about offering new insights into understanding mechanisms at the basis of chronic pain is too far from the results. Centralization of pain is very different from summation and habituation, especially if all the subjects in the study consistently rated increased and decreased pain in the same way (it never happens in chronic pain patients). A similar pipeline has been actually applied to chronic pain patients (fibromyalgia and chronic back pain)3,4. Discussing the results of the present paper in relationship to those, could offer a more robust way to connect the Authors' results to networks behavior in pathological brains. Vice versa, the behavioral measure used to assess evoked pain perception (avoidance ratings), has been developed for chronic pain patients and never validated on healthy controls5. It might not be an appropriate measure considering the total absence of pain variability in the reported responses over forty-eight subjects6,7.<br> • The dynamic measure employed by the Authors is better described from the term "windowed functional connectivity". It is often considered a measure of dynamic functional connectivity and it gives information about fluctuations of the connectivity patterns over time. Nevertheless, the entire focus of the paper, including the title, is on dynamic networks, which inaccurately leads one to think of time-varying measures with higher temporal resolution (either updating for every acquired time point, as the Authors did in their previous publication on the same dataset4, or sliding windows involving weighting or tapering8,9). This allows one to follow network reorganization over time without averaging 2-min intervals in which several different brain mechanisms might play an important role3,10,11. In summary, the assumption of constant response throughout 2-min periods of tonic pain and the use of Pearson correlations do not mirror the idea of dynamic analysis expressed by the Authors in title and introduction. I would suggest removing "dynamic" from the title, reduce the emphasis on this concept, address possible confounds introduced by the choice of long windows and rephrase the aim of the study in terms of brain network reconfiguration over the main phases of tonic pain experience.<br> • Procedure chosen for evoking sustained pain. To the best of my knowledge, capsaicin sauce on the tongue is not a validated tonic pain procedure. In favor of this argument is the absence of inter-subject variability in the behavioral results showed in the paper, very unusual for response to painful stimulations. The procedure is well described by the Authors, and some precautions like letting the liquid drying before the start of the scan, have helped reducing confounds. Despite this, the measures in figure 1B suggest that the intensity of the painful stimulation is not constant as expected for sustained pain (probably the effect washes out with the saliva). In this case, the first six-minute interval requires particular attention because it encapsulates the real tonic pain phase, and the following ones require more appropriate labels. Ideally the Author should cite previous studies showing that tongue evoked pain elicits a very specific behavioral response (summation, habituation/decrease of pain, absence of pain perception). If those works are missing, this response need to be treated as a funding rather than an obvious point.

References<br> 1. Schaefer, A. et al. Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cereb. Cortex N. Y. N 1991 28, 3095-3114 (2018).<br> 2. Price, D. D. et al. Enhanced temporal summation of second pain and its central modulation in fibromyalgia patients. Pain 99, 49-59 (2002).<br> 3. Cheng, J. C. et al. Dynamic functional brain connectivity underlying temporal summation of pain in fibromyalgia. Arthritis Rheumatol. Hoboken NJ (2021) doi:10.1002/art.42013.<br> 4. Lee, J.-J. et al. A neuroimaging biomarker for sustained experimental and clinical pain. Nat. Med. 27, 174-182 (2021).<br> 5. Vlaeyen, J. W. S. & Linton, S. J. Fear-avoidance model of chronic musculoskeletal pain: 12 years on. Pain 153, 1144-1147 (2012).<br> 6. Asmundson, G. J., Norton, P. J. & Norton, G. R. Beyond pain: the role of fear and avoidance in chronicity. Clin. Psychol. Rev. 19, 97-119 (1999).<br> 7. Beebe, J. A. et al. Gait Variability and Relationships With Fear, Avoidance, and Pain in Adolescents With Chronic Pain. Phys. Ther. 101, pzab012 (2021).<br> 8. Hindriks, R. et al. Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI? NeuroImage 127, 242-256 (2016).<br> 9. Lurie, D. J. et al. Questions and controversies in the study of time-varying functional connectivity in resting fMRI. Netw. Neurosci. 4, 30-69 (2020).<br> 10. Allen, E. A. et al. Tracking Whole-Brain Connectivity Dynamics in the Resting State. Cereb. Cortex N. Y. NY 24, 663-676 (2014).<br> 11. Hutchison, R. M. et al. Dynamic functional connectivity: promise, issues, and interpretations. NeuroImage 80, 360-378 (2013).

• SELECT ALL: "CTRL" + "a"

• WHICH COLLECTION?

I did this too, even if I didn't have network issues earlier. I did it because I had network issues when installing docker on WSL1, so "better safe than sorry" kind of.

Among the tools we'll be using this Spring is Slack. It will be our primary place of collaboration and communication. I highly suggest you download and install the app, as it is much more convenient that way.

(3 of 3) Click the link below to join our class Slack Team. Slack will take you on a guided tour when you join - don't skip it! After the tour, go to the #introductions channel and follow the instructions in the final stage of our scavenger hunt.

RCA Spring 2022

• OK

El archivo host traduce el hostnames a direcciones IP. Cuando abrimos un sitio tipeando su URL en un navegador nuestro hostfile la IP correspondiente para abrirla.

1. En su primera parte el hostfile contiene nombres e IP de nuestra máquina local.
2. En su segunda parte se encuentra información sobre los host capaces de usar el protocolo IPV6 y difícilmente es editada por el usuario.

Don't use GPT. Use MBR (see below).

SciScore for 10.1101/2022.01.05.22268637: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Despite the fact that this study provides nursing administrators with evidence to assist nurses in pandemic scenarios, certain limitations were identified. First, the research was limited to a small group of nurses from a particular institution, as the findings cannot be generalized to nurses across the country or the globe. Furthermore, the research design has significant drawbacks, such as the inability to establish causal links between variables. In the future, researchers using both qualitative and quantitative research methods may be able to collect information from participants that cannot be obtained through self-report measures. To determine the effectiveness of resilience management programs and other strategies, rigorous studies using rigorous methods (such as randomized control trials) would need to be conducted. Moreover, during the epidemic, nurses are likely to benefit from mindfulness and/or cognitive behavioral therapy[28]. Ideally, future research should explore individual factors (e.g., self-efficacy, coping skills, and hardiness), supervisors’ factors (e.g., nursing schedules and leadership styles), and organizational factors (e.g., workload, healthcare staffing levels, resource availability, hospital size, and number) play a role in nursing burnout during pandemics.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

• TEAMS, FONDOS VIDEO
• TRUCO

Reviewer #1 (Public Review):

Pfeffer, Keitel et al. collected pupil dilation as a non-invasive proxy of cholinergic and noradrenergic neuromodulation. In a large sample of healthy human participants, they related spontaneous fluctuations in pupil-indexed neuromodulation to concurrently recorded changes in magnetoencephalographic activity.

First, they show that pupil size co-varies with power fluctuations, especially in the alpha-beta band at posterior sensors. Next, in subsequent cross-correlation analyses, they show frequency-specific associations of pupil dilation and band-limited power. Decreases in low (2-4 Hz) as well increases in high (64-128 Hz) frequencies preceded pupil dilations by > 500 ms. For intermediate frequencies (8-16 Hz), both positive and negative associations were found with a closer temporal proximity to peak pupil dilation. Analyses were repeated for the first derivative of pupil dilation, which may be more closely linked to noradrenergic (relative to cholinergic) neuromodulation.

The authors additionally performed pupil-MEG correlations in source-space to reveal the spatial profiles of pupil-linked power fluctuations (findings largely consistent with the sensor-level). For this, they shifted the pupil data in time with respect to the MEG data to account for the sluggishness of pupil response. The temporal lag was determined based on previous research (see below). In a second set of source-space analyses, they additionally tested for quadratic associations of pupil dilation and band-limited cortical activity, which were observed for the alpha-beta band mainly at posterior sites.

Finally, the authors linked pupil dilation to the aperiodic component of the power spectrum. They found that larger pupil dilations were associated with a shallower slope, suggesting a higher excitation to inhibition ratio. Notably, pupil associations with band-limited power remained reliable after removing the aperiodic component (not so for the first derivative).<br> Recently, pupil dilation was linked to cholinergic and noradrenergic neuromodulation as well as cortical state dynamics in animal research. This work adds substantially to this growing research field by revealing the temporal and spatial dynamics of pupil-linked changes in cortical state in a large sample of human participants.

The analyses are thorough and well conducted, but some questions remain, especially concerning unbiased ways to account for the temporal lag between neural and pupil changes. Moreover, it should be stressed that the provided evidence is of indirect nature (i.e., resting state pupil dilation as proxy of neuromodulation, with multiple neuromodulatory systems influencing the measure), and the behavioral relevance of the findings cannot be shown in the current study.

1. Concerning the temporal lag: The authors' uniformly shift pupil data (but not pupil derivative) in time for their source-space analyses (see above). However, the evidence for the chosen temporal lags (930 ms and 0 ms) is not that firm. For instance, in the cited study by Reimer and colleagues [1] , cholinergic activation shows a temporal lag of ~ 0.5 s with regard to pupil dilation - and the authors would like to relate pupil time series primarily to acetylcholine. Moreover, Joshi and colleagues [2] demonstrated that locus coeruleus spikes precede changes in the first derivative of pupil dilation by about 300 ms (and not 0 ms). Finally, in a recent study recording intracranial EEG activity in humans [3], pupil dilation lagged behind neural events with a delay between ~0.5-1.7s. Together, this questions the chosen temporal lags.

More importantly, Figures 3 and S3 demonstrate variable lags for different frequency bands (also evident for the pupil derivative), which are disregarded in the current source-space analyses. This biases the subsequent analyses. For instance, Figure S3 B shows the strongest correlation effect (Z~5), a negative association between pupil and the alpha-beta band. However, this effect is not evident in the corresponding source analyses (Figure S5), presumably due to the chosen zero-time-lag (the negative association peaked at ~900 ms)).

As the conducted cross-correlations provided direct evidence for the lags for each frequency band, using these for subsequent analyses seems less biased.

Related to this aspect: For some parts of the analyses, the pupil time series was shifted with regard to the MEG data (e.g., Figure 4). However, for subsequent analyses pupil and MEG data were analyzed in concurrent 2 s time windows (e.g., Figure 5 and 6), without a preceding shift in time. This complicates comparisons of the results across analyses and the reasoning behind this should be discussed.

2. The authors refer to simultaneous fMRI-pupil studies in their background section. However, throughout the manuscript, they do not mention recent work linking (task-related) changes in pupil dilation and neural oscillations (e.g., [4-6]) which does seem relevant here, too. This seems especially warranted, as these findings in part appear to disagree with the here-reported observations. For instance, these studies consistently show negative pupil-alpha associations (while the authors mostly show positive associations). Moreover, one of these studies tested for links between pupil dilation and aperiodic EEG activity but did not find a reliable association (again conflicting with the here-reported data). Discussing potential differences between studies could strengthen the manuscript.

Related to this aspect: The authors frequently relate their findings to recent work in rodents. For this it would be good to consider species differences when comparing frequency bands across rodents and primates (cf. [7,8]).

3. Figure 1 highlights direct neuromodulatory effects in the cortex. However, seminal [9-11] and more recent work [12,13] demonstrates that noradrenaline and acetylcholine also act in the thalamus which seems relevant concerning the interpretation of low frequency effects observed here. Moreover, neural oscillations also influence neuromodulatory activity, thus the one-headed arrows do not seem warranted (panel C) [3,14].

4. In their discussion, the authors propose a pupil-linked temporal cascade of cognitive processes and accompanying power changes. This argument could be strengthened by showing that earlier events in the cascade can predict subsequent ones (e.g., are the earlier low and high frequency effects predictive of the subsequent alpha-beta synchronization?)-

Cited references<br> 1 Reimer, J. et al. (2016) Pupil fluctuations track rapid changes in adrenergic and cholinergic activity in cortex. Nat. Commun. 7, 13289<br> 2 Joshi, S. et al. (2016) Relationships between pupil diameter and neuronal activity in the locus coeruleus, colliculi, and cingulate cortex. Neuron 89, 221-234<br> 3 Kucyi, A. and Parvizi, J. (2020) Pupillary dynamics link spontaneous and task-evoked activations recorded directly from human insula. J. Neurosci. 40, 6207-6218<br> 4 Dahl, M.J. et al. (2020) Noradrenergic responsiveness supports selective attention across the adult lifespan. J. Neurosci. 40, 4372-4390<br> 5 Kosciessa, J.Q. et al. (2021) Thalamocortical excitability modulation guides human perception under uncertainty. Nat. Commun. 12, 1-15<br> 6 Whitmarsh, S. et al. (2021) Neuronal correlates of the subjective experience of attention. Eur. J. Neurosci. DOI: 10.1111/ejn.15395<br> 7 Nestvogel, D.B. and Mccormick, D.A. (2021) Visual Thalamocortical Mechanisms of Waking State Dependent Activity and Alpha Oscillations. bioRxiv DOI: 10.1101/2021.04.14.439865<br> 8 Senzai, Y. et al. (2019) Layer-Specific Physiological Features and Interlaminar Interactions in the Primary Visual Cortex of the Mouse. Neuron 101, 500-513.e5<br> 9 Buzsáki, G. et al. (1991) Noradrenergic control of thalamic oscillation: The role of α‐2 receptors. Eur. J. Neurosci. 3, 222-229<br> 10 Buzsáki, G. et al. (1988) Nucleus basalis and thalamic control of neocortical activity in the freely moving rat. J. Neurosci. 8, 4007-26<br> 11 McCormick, D.A. (1989) Cholinergic and noradrenergic modulation of thalamocortical processing. Trends Neurosci. 12, 215-221<br> 12 Goard, M. and Dan, Y. (2009) Basal forebrain activation enhances cortical coding of natural scenes. Nat. Neurosci. 12, 1444-1449<br> 13 Rodenkirch, C. et al. (2019) Locus coeruleus activation enhances thalamic feature selectivity via norepinephrine regulation of intrathalamic circuit dynamics. Nat. Neurosci. 22, 120-133<br> 14 Totah, N.K. et al. (2021) Synchronous spiking associated with prefrontal high gamma oscillations evokes a 5 Hz-rhythmic modulation of spiking in locus coeruleus. J. Neurophysiol. DOI: 10.1152/jn.00677.2020

If this is so, why do we perpetuate this mention to close, rigid analysis when it comes to writing and AP English and Comprehension, the college level of reading?

commonly called Windows Search. Can we not use that?

Click Browse to open the Windows Search. Select the zip file......

Author Response

Reviewer #1 (Public Review):

The key question addressed of this MEG study is whether speech is represented singly or multiplexed in the human brain in the linguistic hierarchy. The authors used state-of-the-art analyses (multivariate Temporal Response Functions) and probablilistic information-theoretic measures (entropy, surprisal) to test distinct contextual speech processing models at three hierarchical levels. The authors report evidence for the coexistence of local and global predictive speech processing in the linguistic hierarchy.

The work uses time resolved neuroimaging with state-of-the-art analyses and cognitive (here, linguistic) modeling. The study is very well conducted and draws from very different fields of knowledge in convincing ways. I see one limitation of the current study in that the authors focused on phase-locked responses, and I hope future work could extend to induced activity.

Overall, the flow in the MS could be streamlined. Some smoothing in the introduction would be helpful to extract the main key messages you wish to convey.

For instance, in the abstract:

-Can you explain the two views in a simpler way in the abstract and to a non-linguistic audience? Do you mean to say that classic psycholinguistic models tend to follow a strict hierarchically integration (analysis only) but an alternative model is hierarchically inferential (analysis by synthesis)?

-Indicate early on in abstract or intro where the audience is being led with a concise message on how you address the main question. For instance:

To contrast our working hypotheses A and B, we used a novel information-theoretic modeling approach and associated measures (entropy, surprisal), which make clear predictions on the latency of brain activity in responses to speech at three hierarchal contextual levels (sublexical, word and sentence).

We have revised the Abstract and Introduction to reduce the amount of terminology and add additional explanations. Wherever possible, we now use general terms (“bottom up”, “predictions”, “context”, …) instead of terms associated with specific theories. We hope we found a balance between improving accessibility and retaining the qualities seen by Reviewer 2, who thought the Introduction was clearly written and well connected to the psycholinguistics literature.

All the models we compare are compatible with an analysis by synthesis approach, as long as the generative models are understood to entail making probabilistic predictions about future input. The generative models in analysis by synthesis, then, are one way in which “to organize internal representations in such a way as to minimize the processing cost of future language input“ (Introduction, first paragraph). We have clarified this in the first paragraph of the Introduction.

• Why did the authors consider that the evoked response is the proper signal to assess as opposed to oscillatory (or non phase-locked) activity?

The primary reason for our choice of dependent measure is the prior research we based our design on, showing that the linguistic entropy and surprisal effects are measurable in phase-locked responses (Brodbeck et al., 2018; Donhauser and Baillet, 2020). We have made this more explicit in part of the Introduction where we introduce our approach (“To achieve this, we analyzed …”).

As to oscillatory dependent measures, we consider them an interesting but parallel research question. We are not aware of specific corresponding effects in non-phase locked activity. Accordingly, analyzing oscillatory responses without a clear prior hypothesis would require additional decisions, such as which bands to analyze, which would entail issues of multiple comparison. An additional caveat is that the temporal resolution of oscillatory activity is often lower than that of phase-locked activity, which might potentially make it harder to distinguish responses based on their latency as we did here, to test whether the latency of different context models differ.

• Parallel processing with different levels of context (hence temporal granularities) sounds compatible with temporal multiplexing of speech representation proposed by Giraud & Poeppel (2012) or do the authors consider it a separate issue?

We consider our investigation orthogonal to the model discussed by G&P (2012). G&P’s model is about the organization of acoustic information at different time-scales, and does not discuss the influence of linguistic constructs at the word level and above. On the other hand, the information-theoretic models that form the basis of our analysis track the linguistic information that can be extracted from the acoustic signal. The temporal scales invoked by G&P’s model are also different from the ones used here, defined based on acoustic vs. linguistic units. Thus, the kind of neural entrainment as a mechanism for speech processing hypothesized by G&P is fully compatible with our account, but not at all required by it.

Methods:

• Figure 2: please spell out TRFs and clarify the measured response

We have done both in the Figure legend.

• The sample size (N=12) is very low in today standards but the statistical granularity is that of the full MEG recording. Can a power estimate be provided or clear justification of reliability of statistical measures be described.

We appreciate and share the reviewers’ concern with statistical power and have made several modifications to better explain and rationalize our choices.

First, to contextualize our study: The sample size is similar to the most comparable published study, which had 11 participants (Donhauser and Baillet, 2020). Our own previous study (Brodbeck et al., 2018) had more participants (28) but only a fraction of the data per subject (8 minutes of speech in quiet, vs. 47 minutes in the present dataset). We added this consideration to the Methods/Participants section.

We also added a table with effect-sizes for all the main predictors to make that information more accessible (Table 1). This suggests that the most relevant effects have Cohen’s d > 1. With our sample size 12, we had 94% power to detect an effect with d = 1, and 99% power to detect an effect with d = 1.2. This post-hoc analysis suggests that our sample was adequately powered for the intended purpose.

Finally, all crucial model comparisons are accompanied by swarm-plots that show each subject as a separate dot, thus showing that these comparisons are highly reproducible across participants (note that there rarely are participants with model difference below 0, indicating that the effects are all seen in most subjects).

• The inclusion of a left-handed in speech studies in unusual, please comment on any difference (or lack thereof) for this participant and notably the lateralization tests.

We agree that this warrants further comment, in particular given our lateralization findings. We have made several changes to address this concern. At the same time we hope that the reviewers agree with us that, with proper care, inclusion of a left-handed participants is desirable (Willems et al., 2014), and indeed is becoming more mainstream, at least for studies of naturalistic language processing (e.g. Shain et al., 2020). First, we now draw attention to the presence of a left-hander where we introduce our sample (first paragraph of the Results section). Second, we repeated all tests of lateralization while excluding the left-hander. Because this did not change any of the conclusions, we decided to keep reporting results for the whole sample. However, third, we now mark the left-handed participant in all plots that include single-subject estimates and corresponding source data files. Overall, the left-hander indeed shows stronger right-lateralization than the average participant, but is by no means an outlier.

• The authors state that eyes were kept open or close. This is again unusual as we know that eye closure affects not only the degree of concentration/fatigue but directly impact alpha activity (which in turn affects evoked responses (1-40 Hz then 20 Hz) that are being estimated here). Please explain.

Previous comparable studies variably asked subjects to keep their eyes closed (e.g. Brodbeck et al., 2018) or open (e.g. Donhauser and Baillet, 2020). Both modes have advantages and disadvantages, none of which are prohibitive for our target analysis (ocular artifacts were removed with ICA and oscillatory alpha activity should, on average, be orthogonal to time-locked responses to the variables of interest). Importantly however, both modes have subjective disadvantages when enforced: deliberately keeping eyes open can lead to eye strain and excessive blinking, whereas closing eyes can exacerbate sleepiness. For this reason we wanted to allow subjects to self-regulate to optimize the performance on the aspects of the task that mattered – processing meaning in the audiobook. We extended the corresponding Methods section to explain this.

• It would be helpful to clarify the final temporal granularity of analysis. The TRFs time courses are said to be resampled to 1kHz (p22) but MEG time courses are said to be resampled at 100 Hz (p18).

Thanks for noting this. We clarified in the TRF time-course section: the deconvolution analysis was performed at 100 Hz, and TRFs were then resampled to 1 kHz for visualization and fine-grained peak analysis.

• The % of variance explained by acoustic attributes is 15 to 20 folds larger than the that explained by the linguistic models of interest. Can a SNR measure be evaluated on such observations?

We appreciate this concern, which is indeed reasonable. In order to better clarify this issue we have added a new paragraph, right after Table 1. In brief, since the statistical analysis looks for generality across subjects, the raw % explained values do not directly speak to the SNR or effect size. Rather, the SNR concerns how much variability is in this value across subjects. The individual subject values in Figure 3-B, and effect sizes now reported in Table 1, show that even though the % variability that is uniquely attributable to information-theoretic quantities is small, it is consistently larger than 0 across subjects.

Results and Figures:

• The current figures do not give enough credit to the depth of analysis being presented. I understand that this typical for such mTRFs approach but given the level of abstraction being evaluated in the linguistic inputs, it may be helpful to show an exemple of what to expect for low vs. high surprisal for instance from the modeling perspective and over time. For instance, could Figure 1 already illustrate disctinct predictions of the the local vs. global models?

Thank you for pointing out this gap. We have added two figures to make the results more approachable:

First, in Figure 3 we now show an example stimulus excerpt with all predictors we used. This makes the complete set of predictors quickly apparent without readers having to collect the information from the different places in the manuscript. It also gives a better sense of the detail that is modeled in the different stimulus representations. Second, we added Figure 6 to show example predictions from the different context models, and explain better how the mTRF approach can decompose brain responses into components related to different stimulus properties.

• Why are visual cortices highlighted in figures?

Those were darkened to indicate that they are excluded from the analysis. We have added a corresponding explanation to the legend of Figure 3.

• Figure 2 Fig 2A and B: can the authors quantitatively illustrate "5-gram generally leads to a reduction of word surprisal but its magnitude varies substantially between words" by simply showing the mean surprisal and its variance?

Added to the Figure legend.

Fig 2C: please explain the term "partial response"; please indicate for non M/EEGers what the arrow symbolizes.

Added to the Figure legend.

• Figure 3:

p8: the authors state controlling for the "acoustic features" but do not clearly describe how in the methods and this control comes as a (positive) surprise but still a bit unexpected at first read. Perhaps include the two acoustic features in Fig2C and provide a short couple sentences on how these could impair or confound mTRF performance.

We thank you for pointing out this lack of explanation. We have added a description of all the control predictors to the end of the Introduction, right after explaining the predictors of main interest. We have also added Figure 3 to give an example and make the nature of all the controls explicit.

Have the same analysis been conducted on a control region a priori not implicated in linguistic processing? This would be helpful to comfort the current results.

The analysis has been performed on the whole brain (excluding the insula and the occipital lobe). Figure 4 (previously Figure 3) shows that generally only regions in the temporal lobe exhibit significant contributions from the linguistic models (allowing for some dispersion associated with MEG source localization). Although this is not shown in the figure, regions further away from the significant region generally exhibit a decrease in prediction accuracy from adding linguistic predictors, as is commonly seen with cross-validation when models overfit to irrelevant predictors.

Fig 3B-C-E: please clearly indicate what single dot or "individual value" represents. Is this average over the full ROI? Was the orientation fixed? Can some measure of variability be provided?

Explanation of individual dots added to Figure 4-B legend (formerly 3-B). Fixed orientation added to the methods summary in the Figure 2-C legend. To provide more detailed statistics including a measure of variability we added Table 1.

Fig3E: make bigger / more readable (too many colors: significance bars could be black)

We have increased the size and made the significance bars black.

• Figure 4: having to go to the next Fig (Fig5) to understand the time windows is inconvenient and difficult to follow. Please, find a work around or combine the two figures. From which ROI are the times series extracted from?

We have combined the two figures to facilitate comparison, and have added a brief explanation of the ROI to the figure legend.

Reviewer #3 (Public Review):

This manuscript presents a neurophysiological investigation of the hierarchical nature of prediction in natural speech comprehension. The authors record MEG data to speech from an audiobook. And they model that MEG using a number of different speech representations in order to explore how context affects the encoding of that speech. In particular, they are interested in testing how the response to phoneme is affected by context at three different levels: sublexical how the probability of an upcoming phoneme is constrained by previous phonemes; word - how the probability of an upcoming phoneme is affected by its being part of an individual word; sentence - how the probability of an upcoming phoneme is affected by the longer-range context of the speech content. Moreover, the authors are interested in exploring how effects at these different levels might contribute - independently - to explaining the MEG data. In doing so, they argue for parallel contributions to predictive processing from both long-range context and more local context. The authors discuss how this has important implications for how we understand the computational principles underlying natural speech perception, and how it can potentially explain a number of interesting phenomena from the literature.

Overall, I thought this was a very well written and very interesting manuscript. I thought the authors did a really superb job, in general, of describing their questions against the previous literature, and of discussing their results in the context of that literature. I also thought, in general, that the methods and results were well explained. I have a few comments and queries for the authors too, however, most of which are relatively minor.

Main comments: 1) One concerns I had was about the fact that context effects are estimated using 5-gram, models. I appreciate the computational cost involved in modeling more context. But, at the same time, I worry a little that examining the previous 4 phonemes or (especially) words is simply not enough to capture longer-term dependencies that surely exist. The reason I am concerned about this is that the sentence level context you are incorporating here is surely suboptimal. As such, could it be the case that the more local models are performing as well as they are simply because the sentence level context has not been modeled as well as it should be? I appreciate the temporal and spatial patterns appear to differ for the sentence level relative to the other two, so that is good support for the idea that they are genuinely capturing different things. However, I think some discussion of the potential shortcomings of only including 4 tokens of context is worth adding. Particularly when you make strong claims like that on lines 252.

We strongly agree with the reviewer that the 5-gram model is not the ultimate model of human context representations. We have added a section to acknowledge this (Limitations of the sentence context model).

While we see much potential for future work to investigate context processing by using more advanced language models, a preliminary investigation suggests that it might not be trivial. We compared the ability of a pre-trained LSTM (Gulordava et al., 2018) to predict the brain response to words in our dataset with that of the 5-gram model. The LSTM performed substantially worse than the 5-gram model. An important difference between the two models is that our 5-gram model was trained on the Corpus of Contemporary American English (COCA), whereas the LSTM was trained on Wikipedia. COCA provides a large and highly realistic sample of English, whereas the language in Wikipedia might be a more idiosyncratic subsample. Thus, the LSTM might be worse just because it has been trained on a less representative sample of English. As an initial step we thus ought to train the LSTM on the superior COCA database, but this simple step alone would already be associated with a substantial computational cost, given the size of COCA at more than a billion words (we estimated 3 weeks on 32 GPUs in a computing cluster). Furthermore, while we acknowledge the limitations of the 5-gram model, we consider it very unlikely that its limitations are the reason that the more local models are performing well. In general, as more context is considered, the model’s predictions should become more different from the local model, i.e., a more sophisticated model should be less correlated with the local models, and should thus allow the local models to perform even better.

2) I found myself confused about what exactly was being modeled on my first reading of pages 4 through 7. I realized then that all of the models are based on estimating a probability distribution based on phonemes (stated on line 167). I think why I found it so confusing was that the previous section talked about using word forms and phonemes as units of representation (lines 118-119; Fig 2A), and I failed to grasp that, in fact, you were not going to be modeling surprisal or entropy at the word level, but always at the phoneme level (just with different context). Anyway, I just thought I would flag that as other readers might also find themselves thinking in one direction as they read pages 4 and 5, only to find themselves confused further down.

Thank you for pointing out this ambiguity; we now make it explicit that “all our predictors reflect information-theoretic quantities at the rate of phonemes” early on in the Expressing the use of context through information theory section.

3) I also thought some the formal explanations of surprisal and entropy on lines 610-617 would be valuable if added to the first paragraph on page 6, which, at the moment, is really quite abstract and not as digestible as it could be, particularly for entropy.

We appreciate that this needs to be much clearer for readers with different backgrounds. As suggested, we have added the formal definition to the Introduction, and we now also point readers explicitly to the Methods subsection that explains these definitions in more detail.

4) I like the analysis examining the possibility of tradeoffs between context models. I wonder might such tradeoffs exist as conversational environments vary - if the complexity of the speech varies and/or listening conditions vary might there be more reliance on local vs global context then. If that seems plausible, then it might be worth adding a caveat that you found no evidence for any tradeoff, but that your experiment was pretty homogenous in terms of speech content.

Thank you for this suggestion. We added this idea to the Discussion in the Implications for speech processing section.

• !!!

This is helpful that they make the UI similar across multiple things, so that there is consistency for those in the industry.

• IT WORKED!
• var w = window; var s = w.document.createElement("script"); s.type = "text/javascript"; s.src = "./file_to_include.js"; w.document.getElementsByTagName("HEAD")[0].appendChild(s);

SciScore for 10.1101/2021.12.30.21268538: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Suggested mechanisms in the literature include Immune- mediated damage to the autonomic nervous system during COVID-19 and a peripheral cardiac limit to exercise resulting from an oxygen diffusion defect [9,10] Limitations: Our study has some limitations, including the small cohort size and differences in baseline characteristics. However, the fact that we measure findings in individuals against their own predicted value adjusts for these differences to a large extent. A sub analysis excluding patients with co-morbidities did not change the findings (data not shown). Two of the patients were only partially vaccinated, possibly leading to a slight underestimation of the effect of vaccination. We did not evaluate blood gas, which could have revealed more about the main cause of exercise limitation for patients with reduced pVO2.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.12.30.21268236: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Limitations: Our study is limited by its retrospective observational design with all patients enrolled during the first wave of the pandemic, when no specific therapies for Covid-19 were known and prior to the availability of vaccines. Further exploration of the pathophysiological changes resulting from elevated ET-1 levels in critically unwell patients was not feasible in this period.

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

• about : Tauri App

Registry Explorer Shell Extensions

SciScore for 10.1101/2021.12.27.21268264: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

The main limitation is the low number of participants. It is important to consider that the study was a proof-of-concept trial powered to detect significant effects on leukocyte, lipid, and protein inflammatory biomarkers and designed to allow mechanistic exploration of n-3 PUFA metabolism and cellular effects (12). Larger studies are nevertheless needed to determine if the significantly improved NLR by n-3 PUFA translates into better clinical outcomes in COVID-19. The clinical outcomes were not statistically tested in the present trial according to the a priori study plan and were therefore only presented in descriptive analyses. The generalisability of identified lipid metabolites was not established in the present study. The trial was performed during the introduction of cortisone, which may have influenced the cytokine-release to prevent detecting n-3 PUFA-induced effects. The subgroups of n-3 PUFA and placebo groups with or without cortisone are small and were used only for the exploratory mechanistic experiments. The older study population with multiple comorbidities and moderate COVID-19 may limit the extrapolation of the results to younger patients and severe COVID-19 cases. In summary, the primary outcome indicated a beneficial cellular immune response by i.v. n-3 PUFA treatment of COVID-19 detected as lowered NLR. The significantly altered the plasma PUFA metabolite profiles with increased proresolving mediator precursor levels and decreased leukotoxin-diols support...

Results from TrialIdentifier: We found the following clinical trial numbers in your paper:<br><table><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Identifier</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Status</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Title</td></tr><tr><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">NCT04647604</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Recruiting</td><td style="min-width:95px; border-right:1px solid lightgray; border-bottom:1px solid lightgray">Resolving Inflammatory Storm in COVID-19 Patients by Omega-3…</td></tr></table>

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.12.28.21268472: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

• Visual Studio Code: no--auto-update.mode

see translator: URI.js

По сравнению с чем, не сказали. Наверное, надо добавить "Станет ли Windows 11 такой же популярной, как Windows 10,"

Hm, page annotation. Should come with Windows.

[[bits/stdc++.hは環境依存であり、開発用途で使うべきではない]]

SciScore for 10.1101/2021.12.21.21267898: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

SciScore for 10.1101/2021.12.16.473063: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

A potential limitation of these mouse adapted SARS-CoV-2 viruses is deletion of the QTQTN furin cleavage site flanking sequence, which impairs S1/S2 cleavage by furin (47). Prior S1/S2 cleavage is required for S2’ cleavage by transmembrane protease serine 2 (TMPRSS2) at the cell surface, which is important for entry in TMPRSS2 positive cells. The QTQTN deletion commonly arises after virus propagation in TMPRSS2 negative cell lines (such as Vero E6 and HEK293T), although this deletion is also evident in some human clinical samples (47). This deletion improves cleavage by Cathepsin L, which substitutes for TMPRSS2 by cleaving S2’ in endosomes and releasing viral RNA into the cytoplasm (48). Other studies have shown that deletion of the furin cleavage site attenuates replication in hamsters and K18-hACE mice (49) and reduces transmission in ferrets (50). Our data suggests that this deletion didn’t dramatically affect mouse adapted virus replication or tropism in C57BL/6J mouse lungs compared to other studies (11), although side-by-side comparison with virus containing the same spike amino acid changes as MA1 and MA2 but maintaining furin cleavage would be needed to determine if there is any attenuation. The D614G change, which is present in all SARS-CoV-2 variants (except the ancestral Wuhan strain), also increases virus stability and entry via the cathepsin L route (48). Cathepsin L is widely expressed in human nasal and lung epithelial cells (48), thus our mouse adapted viruse...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

WHERE??? LINK???

ok!! me too!

ok, me too!

In my browser (Firefox on Windows) the part/part is barely contained within the parenthesis, most of it is above the parentheses. Is there a way in the code to get it lower so that it appears within the parentheses or is this just a PreTeXt bug that we can't do anything about?

揭露，暴露

一览

漏出，展示

杂乱的

SciScore for 10.1101/2021.12.17.21267993: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Table 2: Resources

Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

Limitations include that the study was relatively small, limiting numbers for each individual isolated symptom and for subgroup analysis, and we did not have information on the severity of each symptom nor on reasons for testing in asymptomatic children. We did not include data on Ct values or viral loads in the positive samples, both due to missing data and because many different PCR assays were used for clinical testing, which would have made these data difficult to combine for analysis. We did not do sequencing to confirm that the delta variant strain was responsible for the SARS-CoV-2 infections in these children, but the time window selected is consistent with the majority having been due to this variant [9] (prior studies similarly used time windows as a proxy for variant circulation [6]). We had too few vaccinated children in this study to draw conclusions about symptom presentation in this subgroup of children (prior studies similarly included predominantly unvaccinated children [6]). The study was performed in a region with high prevalence of the delta variant and in a population with a high proportion of known or suspected close contact, with variable local testing requirements and testing availabilities, so the population who presented for testing may not be fully generalizable to other settings (including settings with lower disease/exposure prevalence or other circulating variants). Though the high exposure rates might suggest that some patients presented for tes...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

• Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
• Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
• No protocol registration statement was detected.

Results from scite Reference Check: We found no unreliable references.

About SciScore

SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

Note: This rebuttal was posted by the corresponding author to Review Commons. Content has not been altered except for formatting.

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Reply to the reviewers

1. General Statements

We thank the reviewers for their helpful comments. We believe that we will be able to address all of their concerns and suggestions. We have highlighted our responses in the revision plan and the changes we have already made to the manuscript in blue text. For figures where we have added data or analyses at the request of reviewers, we have highlighted the corresponding text in the figure legends.

2. Description of the planned revisions

Reviewer #1

2- In Figure 4, the two mutations appear to have statistically differential effects on Rab5 and Rab7 puncta even though the data mean and distribution seem very similar. Interestingly, in each case the non-significant effect is associated with a smaller sample size. Given that the overall sample sizes used are rather small for such highly variable data, this could easily cause a statistical anomaly due to sampling bias. The sample size should be made uniform across all genotypes and should ideally be at least doubled.

We will repeat this staining to increase the n to at least double this number, and adjust our conclusions if need be, in the revised manuscript..

3- Perhaps the most important issue related to Figure 6 where the authors find that there is no sterol accumulation at 96h APF in the Vps50 mutant. However, even that the dendritic phenotype is slower to appear in this mutant compared to the Vps54, are the authors sure that the accumulation is not just slower? This should be examined using the same temporal sequence used for Vps54 shown in Future 6 C. In addition, the fact that sterol accumulation returns to normal in the Vps54 mutant at 1 day, supports the notion of a delay phenotype (see point 1 above). These issues should be experimentally addressed to see if the data fully support the initial conclusions, or if the conclusions should be modified to suggest differential contribution of the two complexes to the process being studied and to a developmental delay phenotype.

We had included the filipin staining for Vps50KO/KO at 1 day in Figure S4 A (which did not show a significant difference from control). We did not collect data for this genotype at 72hrs APF because the dendritic length phenotype didn’t appear until later, and so we did not include Vps50KO/KO in the full time-course in Fig 6 C. We will collect additional data so that we can include Vps50KO/KO at all timepoints in this figure in the revised manuscript.

. Reviewer #2

It is stated that loss of VPS50 and VPS54 only causes dendrite morphogenesis defects. However, the corresponding supplemental figure S2c (which is not referenced in the text), is not suited to address this question. Axonal arborization, in particular terminal arbors, are not visible in samples where multiple/all c4da axons are labeled simultaneously (Fig. S2c). Analogous to the dendrite analysis of c4da neurons single cell resolution is essential to examine this in a meaningful way. Likely, however, c4da neurons may not be a good choice to address this question.

We should be able to get single cell resolution of the c4da axon terminals using MARCM. We already have two of the knockout lines recombined with FRTs (Vps53 and Vps54) for this analysis and we will make the third recombinant line so that we can use MARCM for all three lines to examine single-cell axon morphology, as suggested.

Overall, I am concerned whether the data shown here can be generalized. The cd4a neurons are rather extreme cell types due to their very large dendritic compartment. It seems quite possible that many other neurons may not have a comparable sensitivity to the supply of lipids/sterols. This type of question can only be addressed if other types of neurons/dendrites are examined. Are class 2 or class 3 da neurons showing any defects in VPS mutants?

Given that we see the phenotype emerge during the pupal stage, we want to analyze neurons that persist from the larval to adult stages. However, not all of the dendritic arborization neurons survive into adulthood- class I and II persist, while class III die during metamorphosis (Shimono et al., 2009). As we do not have adequate tools to for studying the class II neurons, we will examine dendrite morphology of the class I neurons in larvae and adults in our knockout lines. We would be happy to look at class III neurons at the reviewers request, but our analysis will necessarily be limited to the larval stage.

Reviewer #3

• Some of the experiments include multiple genotypes and so it would be important to show all in all figures. For example, figure 4B,D show four groups but figure 4F, presumably from the same set of animals, shows only three. Addition of the rescue genotype to 4F is particularly important here so should be shown. The same concern is valid for figure 5, where puncta number and area must be available.

The data from Fig. 4 F (using a genetically encoded marker for lysosomes, UAS-spin-RFP) are not from the same samples as Fig. 4 B and D (staining). We did not include the rescue for Fig 4. F because the lysosome marker, the rescue transgenes and the neuronal membrane marker are all on the third chromosome. We will build additional fly stocks so that we can include the rescue in experiments looking at lysosome morphology.

• This concern is amplified by the images in figure 6 of the filipin staining, that are more obviously perinuclear. However, the two sets of images in 6A and 6D, where co-staining with Golgin245 is shown, look very different. Improved images are required and it may be helpful to use supplementary information to show additional examples of the staining.

The images in Fig. 6 A are maximum projections of z stacks while Fig. 6 D shows single confocal planes, making it easier to see the perinuclear Golgi ring. Because other reviewers wanted some additional experiments related to Fig. 6 that we plan to incorporate into this figure in the revised manuscript, we will address this comment in a future revision and include additional images in the supplement.

• For the lipid regulation experiments in figure 7, please use an orthogonal approach to show that the Osbp and fwd RNAi had the expected effects on lipid accumulation.

In addition to sterol, Osbp and fwd both affect levels of PI4P at the Golgi. We have obtained a transgenic PI4P sensor that we can use to show the effect of these manipulations on this lipid as well.

3. Description of the revisions that have already been incorporated in the transferred manuscript

Reviewer #1 While the data presented clearly support a role for GARP in regulating sterol levels to support dendritic growth, they do not inter current for suffice to exclude a role for EARP as important analyses to allow such a clear cut conclusion are either insufficient or missing. If the authors wish to maintain this claim - as suggested by the title of the manuscript - further analyses are essential.

We don’t mean to argue the EARP complex doesn’t contribute to dendrite development at all – we do show it contributes to development in Fig 3, and as we discuss in the text.. We want to argue that the GARP and EARP complexes contribute to dendrite development by distinct mechanisms. Losing the GARP complex inhibits dendrite development by means of sterol accumulation at the TGN, which is what we are trying to highlight with our title. The reduced dendrite growth that we observe in EARP deficient neurons must occur by some other as yet unknown means. We apologize for the confusion and have reworded the title to read “Sterol accumulates at the trans-Golgi in GARP complex deficient neurons during dendrite remodeling.”

1- Figure 3E shows that whereas both Vps50 and Vps54 mutations reduce dendritic complexity, the Vps54 phenotype appears earlier (96h APF). Furthermore, at 7 days dendrites appear to grow again but at a slower rate than controls. This begs the question of whether these mutations are causing a delay rather than a block in the regrowth after pruning and whether the growth will eventually be normal a few days later or whether it will stop at some point.

We have included data for an additional adult timepoint (21 days) in the new Fig. 3 E. We also included graphs in which we show the statistics for each genotype over time (new Fig. S2 D-F), and discuss this analysis in the text (lines 186-195). We have also included a table of the p-values for each comparison in the Supplemental Materials (Table S2). From this analysis, we conclude that there is not a developmental delay in the knockouts, but rather a decrease in growth during the 72-96hrs APF and 1-7 day windows when the control neurons grow. We are unable to draw conclusions about the rate of growth as we analyzed neurons from different samples at each developmental timepoint, and not the same neurons over time.

Reviewer #2

It would be important to know, whether the dendrite morphogenesis defect is indeed a developmental patterning defect or rather a "scaling" defect due to the fact that da neurons increase their size (but not necessarily their projection pattern) during larval maturation.

We have analyzed the larval data for coverage index – neuron area/hemisegment (receptive field) area as defined in (Parrish et al., 2009) to determine if there is a scaling defect at this stage in development. We do not observe a defect in scaling (Fig. S2 C) and discussed in lines 175-182.

Reviewer #3

• The statistical analyses generally look appropriate but it would be critical to clarify what N means in every case. For example in figure 2 the authors state n=8 without clarifying if this is n=8 animals or n=8 neurons. N should always be the number of animals, but then the n of independent cells counted should also be indicated. Typically, one would either pre-average per genotype or use a mixed model that includes N of animals and n of cells (or similar).

For experiments analyzing dendrite morphology, n represents the number of neurons, as we have clarified in our figure legends. As per another reviewer’s request, we will increase the n for the organelle and filipin staining in our planned revision and specify fly and cell number at that time.

• Please add details of how experiments were blinded to genotype

The researcher was blinded to genotype during analysis. We have included that detail in our Methods section (line 566).

• Some of the experiments include multiple genotypes and so it would be important to show all in all figures. For example, figure 4B,D show four groups but figure 4F, presumably from the same set of animals, shows only three. Addition of the rescue genotype to 4F is particularly important here so should be shown. The same concern is valid for figure 5, where puncta number and area must be available.

We address the first portion of this comment in section 2, for additional experiments involving generating new fly lines. We have included data on puncta area, and mean fluorescence intensity for Rab5 and Rab7 in the supplement (Fig S3). We had already included the data on puncta number and area in Fig 5, but we have added the data on mean fluorescence intensity as well.

• Related to figure 5, please provide validation of the staining of the TGN. Typically, one would expect trans Golgi to be close to the nucleus with at least some extended stacks. A Golgin245 knockout would be ideal.

The Golgi in most Drosophila cells is typically found as discrete puncta dispersed throughout the cytosol like what we see in the Golgin245 staining, as opposed to the ribbon “stack of pancake” morphology typically seen near the nucleus in mammalian cells. For reference, please see Figure 6D in (Ye et al., 2007), Figures 2,4,5 in (Rosa-Ferreira et al., 2015), and observations reviewed in (Kondylis and Rabouille, 2009).

The Golgin245 antibody was well characterized in the paper first describing it (Riedel et al., 2016) (colocalization with other Golgi markers, decreased staining with Golgin245 RNAi), but we would be happy to repeat this validation in the c4da neurons at the reviewer’s request. There do not appear to be Golgin245 mutant or KO lines available, so we would also use the Golgin245 RNAi.

• For figures 6F, G please show examples of staining for late endosomes and lysosome with appropriate validation.

Because several of our planned revisions relate to Fig. 6, we will include images for Fig. 6 F and G when we remake this figure to incorporate those planned revisions. To clarify, we used the same reagents to mark late endosomes and lysosomes in both Fig. 4 and Fig. 6. Like the Golgin245 antibody, the Rab7 antibody was developed by the Munro lab and characterized in (Riedel et al., 2016) (partial colocalization with the endosomal marker Hrs and with the lysosomal marker Arl8). Spinster (aka benchwarmer) is a known lysosomal transmembrane protein that colocalizes with Lamp1 (Dermaut et al., 2005; Rong et al., 2011). The fluorescently tagged spin transgenes were developed by the Bellen lab and have been frequently used to mark lysosomes. We would be happy to carry out additional validation experiments at the reviewer’s specification.

• The title of figure 2 is inaccurate, at least if I understand the experiment, as it does not show neuron-specific knockout but instead whole body knockout with neuron rescue. Please rephrase.

Because of the lethality of whole body Vps53KO/KO in adult flies, we analyze MARCM clonal neurons that are Vps53KO/KO in flies that are otherwise heterozygous (Vps53KO/+). To clarify this experiment, we have changed the title of Fig. 2 from “Neuron-specific knockout of Vps53 results in smaller dendritic arbors” to “Vps53KO/KO MARCM clonal neurons have smaller dendritic arbors”.

• Figure 8 needs examples of the TGN and late endosome morphology.

We have included these images in Figure

The order appears different in Fig. 4 B & D because we only included the rescue for the KO that shows a phenotype for each staining. The genotypes included in Fig. 4 B are: +/+, Vps50KO/KO, Vps50KO/KO + rescue, and Vps54KO/KO. The genotypes included in Fig. 4 D are +/+, Vps50KO/KO, Vps54KO/KO, Vps54KO/KO + rescue. We have changed the shading of the bars corresponding to these rescue genotypes throughout the manuscript to make it easier to distinguish the two rescue conditions.

4. Description of analyses that authors prefer not to carry out

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