dataesr/scientific-paragraphs-categorization huggingface dataset:
All of them have been corrected. We have also carefully proofread the manuscript for other remaining typos. Competing Interests: No competing interests were disclosed. In the present manuscript Riquelme-Perez and coll. are presenting the DEVEA software aimed at helping biologists with no or poor programming skills to analyse their Omics data (mainly RNAseq). The rationale for the development of a new software is clearly explained, additionally in the manuscript, the authors compare their software to other web-based software and highlight that DEVEA is higher flexibility (see Table  1 ). The software is available as a web-based application but can also be installed locally. The description of the software is technically sound. As a matter of facts, it is based on the several well-known and highly used R packages aimed at analysing RNA-seq data.
Interestingly, differential expression analysis (DEA) is performed using DESeq2 package on the most reliable tool for DEA. DEVEA is also flexible as it accepts input data from different format and at different stage of the analysis. Thus, DEVEA can be used for initial analysis of the data, but can be used at later stage of the data analysis. One very interesting feature of the DEVEA software is that it enables Miriam Riquelme-Perez Thank you for taking the time to review our paper, and for the thoughtful suggestions. In the revised version, we have modified the main text to refer more clearly to the method information provided on the interface, and improved the clarity of the text in several places, according to your suggestions. In the same way, we have included a few enhancements in the application tool itself.
html or https://shiny. imib. es/simpleDevea/tutorial. html. The tutorial was mentioned in the 'Software availability' section, and now this is mentioned in the 'Operation' section. -Could the authors specify if already normalized counts can be used as input in CM+SI type of analysis? + The counting matrix (CM) and the sample information (SI) elements will be used as raw data to be internally analyzed through the various functions of the DESeq2 package. To build the final DESeqDataSet object, DEVEA uses the data as obtained from the counting process, presented in the form of an array of integer values. Decimals will not be accepted. The DESeq2 model internally corrects for library size and generates its own normalization, so transformed or normalized values must not be used in the CM + SI input type (as detailed in the official bioconductor DESeq2 vignette section "Why un-normalized counts?" http://bioconductor.
org/packages/deve l/bioc/vignettes/DESeq2/inst/doc/DESeq2. html#why-un-normalized-counts). A sentence that specifies this point is now included in the 'Data requirement' section, 1. CM + SI mode. -Could the authors specify whether the software support both ". " and "," as decimal separators? + Thank you for raising this point. Decimals must be indicated with ". " as a separator. "," will not be properly recognized by the program. -When uploading the GL with gene symbols, these latter are automatically converted to Ensembl identifiers, but some symbol are not recognized. It is unclear which genes are considered for EA analyses: all genes with symbol, only genes with an Ensembl identifier, only annotated genes with an Ensemble identifier? + The KEGG, GO, and GSEA libraries used in DEVEA need as input a list of genes coded in their ENTREZ ID format.
This point was discussed in the 'Enrichment analysis (EA) and visualization' subsection in the 'Implementation' part. -In some of the EA graphs, deregulated pathways/gene sets are referred to by their url rather than by the pathway name. This complicates the reading of the graphs; + We agree that using only the URL is cumbersome since it is necessary to go back to the table to link with the actual name. Due to space limitations in some plots, pathways or functions that have a very long name cannot be shown in full. However, in most of the images where the full name cannot be displayed, it will be shown in full by placing the mouse arrowhead on top of the interactive graph. -Demo data are downloadable, but the link given in the manuscript does not work; + We apologize for this problem. The link has been corrected in the manuscript and now redirects to the appropriate page to access the demo data on GitHub.
It has also been implemented in the plot as a red line indicating the extent of what we consider the leading edge genes. This is also mentioned in the text 'Enrichment analysis (EA) and visualization' section. -Further improvements that would be interesting to make : Enrichment analysis: In addition to GO and KEEG analyses, it would also be interesting to add other databases, such as Wiki Pathways for example. For the moment, we aim at offering a stable and robust version and creating a docker container with these basic functionalities efficiently working in the server. We think that including these significant improvements is beyond the scope of this first DEVEA version. However, these are exciting suggestions to be considered in the following versions. Our goal is to further develop DEVEA according to the needs of the community and bio-informatics developments.
We acknowledge that there is limited support for plant and animal species in the current version of DEVEA. We are planning to expand the number of species available in future versions. For this version, following the reviewer's suggestion, we have now included the model plant species Arabidopsis thaliana in DEVEA. A demo dataset has also been created compatible with DEVEA for this species, based on an RNA-seq study published recently (Lee et al. New Phytologist, 2020, 225(4):1715-1731).
In fact, DEVEA is a web interface with two R Shiny applications bundled together and due to R packages structural constraints, it is not so straightforward to package such an application. However, to facilitate the local installation of DEVEA, we have added a script on DEVEA's github that will install all the necessary R dependences for DEVEA (script_libraryApp. R). Then the user only has to clone the DEVEA repository from github and launch the application (2-steps process) after running the script. For future versions, we plan to develop a Docker image, rather than an R package. We have detailed the process in the supplemental information (https://github. com/MiriamRiquelmeP/DEVEA/blob/main/Supplemental-Information. txt) -Page 3: "Despite the great progress to facilitate transcriptomics computational analyses, some improvements are still possible for these tools.
August 2022 https://doi. org/10. 5256/f1000research. 135003. r142536 © 2022 Hirbec H. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Hélène Hirbec Institut de Génomique Fonctionnelle, CNRS, INSERM, Université de Montpellier, Montpellier, France.
Is the rationale for developing the new software tool clearly explained? YesIs the description of the software tool technically sound? Yes Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? YesIs sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? YesAre the conclusions about the tool and its performance adequately supported by the findings presented in the article? YesCompeting Interests: No competing interests were disclosed. Reviewer Expertise: Transcriptomics; Neuroinflammation; Alzheimer DiseaseI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.
It will also be interesting to enable evaluating over-representation for custom genesets (i. e. specific molecular signatures of interest in the research field). Regarding the first further improvement, adding more databases would be a significant extension to the current version of DEVEA, that we want to keep fast and operational. For the second suggestion, the specific custom genesets needed for the over-representation analysis could be very different between users and experiments, hence we prefer to only include the curated well known genesets from the databases. Nevertheless, DEVEA allows the definition of background genes for comparison in the EA part, avoiding the inappropriate inflation of statistical p-values and false-positive results from contrast against the whole genome/proteome.
Software availability 1. Software available at http://shiny. imib. es/devea/. Archived source code as at time of publication: https://doi. org/10. 5281/zenodo. 6657245. 2. Latest source code on https://github. com/MiriamRiquelmeP/DEVEA. 3. Test files for every input mode can be found also on https://github. com/MiriamRiquelmeP/DEVEA Data section. 4. Tutorial accessible from both DEVEA modules (DESeq DEVEA and Simple DEVEA) in the 'Tutorial' section from the top controls and independently on https://shiny. imib. es/DESeqDevea/tutorial. html or https://shiny. imib. es/simpleDevea/tutorial. html. License: Apache license 2. 0.
Reviewer Report 16 16 August 2022 https://doi. org/10. 5256/f1000research. 135003. r142536 © 2022 Hirbec H. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Hélène Hirbec Institut de Génomique Fonctionnelle, CNRS, INSERM, Université de Montpellier, Montpellier, France.
○○+ It will also be interesting to enable evaluating over-representation for custom genesets (i. e. specific molecular signatures of interest in the research field). Regarding the first further improvement, adding more databases would be a significant extension to the current version of DEVEA, that we want to keep fast and operational. For the second suggestion, the specific custom genesets needed for the over-representation analysis could be very different between users and experiments, hence we prefer to only include the curated well known genesets from the databases. Nevertheless, DEVEA allows the definition of background genes for comparison in the EA part, avoiding the inappropriate inflation of statistical p-values and false-positive results from contrast against the whole genome/proteome.
Column names should be provided without special characters (i. e. GeneName, FC, padj). The values will be used without further modifications by DEVEA to set the threshold of expression change and the significance. This table can be uploaded in. CSV,. TXT or. XLSX file format. The column names in the CM and the first row names in SI must be equal, and the gene IDs in this file can be included in Symbol or ENSEMBL format. Both files can be in. CSV,. TXT or. XLSX format. 2. DO mode: based on a DeseqDataSet object (DO) generated by the DESeq() function from DESeq2 package. It is an object used to store the input values, intermediate calculations and results from a differential expression Figure 1. DEVEA global workflow. analysis.
G. Confocal microscopy. For most of the experiments here, samples were imaged in an LSM 780 laser-scanning microscope (Carl Zeiss Microscopy) fitted with an incubation chamber (EMBL Mechanical Workshop). A Plan-Apochromat 20x air objective with a numerical aperture (NA) of 0. 8 (Carl Zeiss Microscopy) was used for imaging, and the zoom was set to 0. 6. Three z-stacks (spacing: 8 um) were scanned for each sample every 10 mins to acquire timelapse movies. Imaging of multiple samples in multiple locations was done with a motorized stage, controlled using Zen Black software (Carl Zeiss Microscopy), and automated using a VBA macro developed by Antonio Politi  (69) , which is available at https://git. embl. de/grp-ellenberg/mypic. The dimension of the images was 512 pixels x 512 pixels, with a pixel size of 1.
Extracting timeseries from global intensity of 2D-assays for subsequent analyses To extract the timeseries corresponding to the segmentation clock (Figure  2    Monitoring period-locking and phase-locking Entrainment was evaluated based on period-locking and phase-locking of the signaling oscillations to the periodic drug pulses. Oscillatory components were extracted from timeseries using a wavelet analysis workflow that was developed by Gregor Mönke, which was recently implemented as a Python-based standalone software (71) available at https://github. com/ tensionhead/pyBOAT. In this workflow, timeseries was first detrended using a sinc filter and then subjected to continuous wavelet transform. Time-resolved frequency analysis was done by cross-correlating the signal to wavelet functions of known frequencies, generating a power spectrum.
This was quantified from the average phases of the final (old_phase,new_phase) pairs considered, and the circular standard deviation (circSD) was calculated using the formula: circSD = √ -2lnR where R is the first Kuramoto order parameter. As wavelets only partially overlap the signal at the edges of the timeseries, resulting in deviations from true phase values  (71) , the first and last pulse pairs were not considered in the generation of stroboscopic maps. Polar plots were also generated summarizing the instantaneous phase of replicate samples and their first Kuramoto order parameter as shown in Figure  4D , Figure  5C , and Figure  S4B. The Python code is available as a Jupyter notebook (. ipynb) at https://github. com/PGLSanchez/EMBL_OscillationsAnalysis/tree/master/ EntrainmentAnalysis.
This code uses Matplotlib  (72) , NumPy  (73, 74) , pandas  (75) , scikit-image  (76) , SciPy  (77) , and seaborn  (78). Detrended timeseries of replicate samples were in some part of the study represented as a heatmap using PlotTwist  (47) , as shown in Figure  4A. Average periods (from 650 mins to 850 mins after start of experiment) were meanwhile plotted using PlotsOfData  (79) , as shown in Figures  3C-D  and Figure  S2. These apps are available at https: //huygens. science. uva. nl/PlotTwist/ and at https://huygens. science. uva. nl/PlotsOfData/, respectively.
Generating wavelet movies Period and phase wavelet movies were generated using the Wavelet Processing and Export workflow developed by Gregor Mönke, which runs on the EMBL cluster and is implemented in Galaxy with technical assistance from Jelle Scholtalbers (EMBL Genome Biology Computational Support). This workflow extracts timeseries of every pixel in a timelapse movie and subjects them to sinc filter-based detrending and subsequent continuous wavelet transform  (71). This results in extraction of instantaneous period and phase of each pixel, recovering period and phase wavelet movies corresponding to the input timelapse movie (for scheme, please refer to Figure  S8A ). The workflow is available at https://github. com/tensionhead/SpyBOAT and can be used via a public Galaxy server at https://usegalaxy.
To solve this problem, the Generalized Retrieval of Atmosphere and Surface Properties (GRASP) algorithm was developed, which provides a statistically optimized strategy that allows all aerosol-related measurement data from multi-angle polarized sensors to participate in the retrieval  (Dubovik et al. , 2014). It points out that the measured redundancy provided by multi-angle polarized sensor is considered to be positive and useful, especially when the number of observations is larger than the unknowns  (Dubovik et al. , 2011). At present, the GRASP algorithm has been successfully applied to a variety of sensors, including POLDER, lidar, and sun photometers, to retrieve complex aerosol parameters  (Chen et al. , 2020; Li et al. , 2019; Lopatin et al. , 2021). In this study, we retrieved AOD from DPC observations by using the GRASP algorithm and evaluated possible error-influencing factors.
Acknowledgements. We are grateful to the  Moderate Resolution Imaging Spectroradiometer (MODIS) team , the  Aerosol Robotic Network (AERONET) organization  and the  GaoFen-5 Directional Polarimetric Camera (DPC)  development team for freely distributing their aerosol products and measurements. The numerical calculations in this paper have been done on the supercomputing system in the  Supercomputing Center of Wuhan University. Finally, we would also like to thank all reviewers for their constructive and valuable comments. Financial support. This research has been supported by the  National Key Research and Development Program of China  (grant no. 2018YFB0504500 ) and the  National Natural Science Foundation of China  (grant nos. 41875038 ,  42071348 , and  42001291 ). Review statement. This paper was edited by  Linlu Mei  and reviewed by  Stefan Kinne  and two anonymous referees.
Whether "end-to-end models" are recommended by marine scientists to study the combined effects of shing and climate change on marine ecosystems, using one of these tools was beyond the scope of the project [see methods B]. We looked for alternative ways and built on knowledge and data from the park management plan and on past research conducted on the area: ecosystem-based quality indexes (EBQI) describing the functioning of speci c ecosystems and mass-balance models analyzing the overall ecosystem structure and shing impacts (Ecopath with Ecosim) [see methods C]. We map four major park habitats (see gure 2): "sandy&mud" (31 species), "rock" (18 species), "posidonia" (17 species), "coralligenous" (15 species). Here (group of) species are represented in aggregate form (biomass density) and linked together with diet ratios (see table SM2 a-d).
We then developed a spatially explicit model for the main dimensions of the MPA described in the narratives. To set up agents and their environments, we used data from the ecosystem-based representation and geographic information systems (GIS) layers provided by the MPA team. To model space, we used a regular grid, the size of each cell being related to the average size of an arti cial reef village (0,25 km 2 ). In accordance with our prospective horizon, simulations ran by 2050 with an annual time step. The food-web model is located at the cell level with previous year's outputs as input data for each new year. Other human and non-human agents are also represented at the cell level. At this stage we model temporal dynamics but lack important spatial dynamics such as adaptive behaviors of human and non-human agents re-locating their activities as a result of management measures.
57%, sandy = 12. 23%, rocky = 1. 75, posidonia = 0. 23 and coralligenous = 0. 22%. For "rocky", "posidonia" and "coralligenous", we selected corresponding ecological groups and associated data (Ewe) from functional compartments (EBQI). For "sandy&muddy" we created an ad-hoc conceptual model of the ecosystem functioning from the Gulf of Lion trophic chain (Ewe). [D] Food-web modelling. For each related group of species, the variation of the average density results from the equal combination of two potential drivers on a yearly basis: the abundance of prey (bottom-up control, positive feedback) and the abundance of predators (top-down control, negative feedback). To do so, we use data from the EwE publications listed above: biomass density, food requirements (diet matrix), mortality by predation and by shing.
Material and methods To achieve our aim, we explored all available materials collected by French, German, Irish, and UK scientists using diverse approaches and protocols during the last ∼30 years, from 1990 to 2021. During research surveys and sampling of landings, depending on the data collection protocol, squids might be measured to either within 1 mm, or to the nearest 5 mm below, whereby a squid of 27 mm DML would be allocated to the 25-mmsize class that contains squids from 25 to 29 mm DML. Because of this, all squid length data, regardless of how individuals were measured, were aggregated by 5-mm-size classes, and the size class of 30 mm DML encompassed all squids of 30-34 mm DML. When biological analysis was performed, maturity was assigned using either the ICES WGCEPH maturity scale  (ICES, 2010)  or modified Lipi ński's (1979) maturity scale  (Lipi ński and Underhill, 1995).
For the purposes of this study and to avoid confusing the different stages, all females containing some ovulated ripe eggs in the oviducts were considered mature. Presence / absence of different life stages related to spawning and nursery grounds (i. e. egg masses, paralarvae, juveniles, and mature females) in Loligo spp. were established on the basis of 13 037 separate fishing hauls carried out on research vessels from England, France, Germany, Ireland, and Scotland. As there was a variety of fishing gear deployed, we used these data only to confirm the presence of a particular life stage of either species in each area. These hauls resulted in captures of 6 008 paralarvae up to 20 mm DML (all assumed to be identified to genus level), 72 218 juveniles with a reported DML between 25 mm and 60 mm DML (3 419 L.
facebook. com/groups/774425619306338). Presence of mature females of a particular species was used for tentative allocation of unidentified egg masses to one of these two squids rather than for mapping spawning grounds because the distribution of adult females might be much wider than the area where they actually lay eggs. To map the position of spawning grounds, we used data on occurrence of egg masses, as observed by recreational divers and reported online. They were extracted from the Facebook pages of UK Cephalopod Reports (https://www. facebook. c om/groups/1772714999 700580/), Blekksprutobservasjoner i Norge (https://www. facebook. com/groups/669716393533 330), Inktvis waarnemingen Nederland/België (https://www. facebook. com/groups/1915579675410208) and Tintenfisch-Sichtungen Deutschland (https://www.
In addition, information on 176 egg masses of Loligo spp. observed in January-September 1995-2020 was downloaded from the Seasearch database (Seasearch, 2021a-f). Only observations identified as Loligo were taken into consideration, while "Loliginidae" were ignored as likely also containing Alloteuthis. Other posts on the web page were ignored. In total, 378 egg masses and groups of egg masses were photographed/reported by the public and shared to Citizen Science projects and Seasearch (2021a-f), four of which had large adult L. vulgaris inside the frame and one of which was with an adult L. forbesii. To this online information were added data on egg masses published during earlier studies, n = 57 including seven records from  1987 -1989 (Lum Kong et al. , 1992; Collins et al. , 1995; Martins, 1997; Craig, 2001; Emery et al.
, 2001; Lordan and Casey, 1999; Laptikhovsky et al. , 2021). While summarizing the data, we used the following approach: (1) Loligo egg masses that were not studied by professional scientists were not allocated to a particular species; they were combined into Loligo spp. , apart from those that were photographed with identifiable adult spawners. (2) Paralarval Loligo are very difficult to identify so all paralarvae were combined into Loligo spp. even if these had tentatively been identified in the database. (3) Early stages of squids identified as "Loliginidae" were excluded as likely containing Alloteuthis together with Loligo. (4) The juvenile stage included squids between 25 and 60 mm DML, in order to exclude all squids which might be close to maturity. (5) Females were considered mature if they had reached at least stage 3a of the ICES scale (ICES, 2010), which corresponds to an ovary containing a high proportion of large turgid amber-coloured oocytes (≥2 mm), with plenty of oocytes in the oviducts.