dataesr/scientific-paragraphs-categorization huggingface dataset:
64 % and 3. 13 %)  (Waidyanatha et al. , 2019). Oral bioavailability is the product of fraction absorbed, fraction escaping first-pass gut-wall elimination, and fraction escaping first-pass hepatic elimination. For the BPs mainly eliminated by urine (BPS, BPA, BP4-4, BPB, BPF, BPZ), the urinary excretion of total BPs over 24 h are relatively close for the two routes of administration, indicating a rather high absorption (not to be confused with bioavailability) following gavage as already observed for BPA and BPS  (Gayrard et al. , 2019). The great extent of absorption of these BPs may be explained partly by their relatively low molecular size (less than400 g/ mol) and their rather high lipophilicity  (Varma et al. , 2010). Using a database of 309 drugs in humans,  Varma et al. (2010)  show that although lipophilicity favors absorption, the fractions of drugs escaping gut-wall and hepatic presystemic elimination decreases with increasing lipophilicity, with compounds having logD values greater than 3 demonstrating higher gut and hepatic first-pass extraction.
BPS (respectively 18, 29 and 17 fold differences). By contrast, the relative clearance of BPFL vs BPS predicted by HTTK package is disproportionately low by comparison with the plasma clearance experimentally determined. Surprisingly, the clearances computed in HTTK package (0. 0007 L/kg per h for BPFL, 0. 0062 L/kg per h for BPS and between 0. 1 and 0. 18 L/kg per h for BPAF, BPA and BPB) are far below the plasma clearances measured here in pig (in vivo/prediction ratio ranging from 25 to 3552) and those estimated in human for BPA and BPS [respectively, 1. 54 L/kg per h  (Collet et al. , 2015)  and 0. 57 L/kg per h  (Khmiri et al. , 2020) ]. A great difference has already been reported for BPA, with a clearance estimated from the in vivo data obtained in a rat model approximately 100 fold higher than the predicted clearance using HTTK data  (Wambaugh et al.
The volumes of distribution calculated using the HTTK package for BPS, BPFL, BPA, BPAF, and BPB (between 0. 21 and 6. 34 L/ kg) are of the same order of magnitude as those determined in piglet (ratio measured/predicted from 0. 4 to 9. 3). The extent of distribution of a substance in the body is dependent on the tissue partitioning determined by the relative fraction unbound in plasma and outside plasma and tissue affinity (see equation (  10 )). The determinants of the overall unbound in a given tissue can be both nonspecific (e. g. , partitioning into tissue phospholipid) and specific (binding to proteins such albumin). The extent of membrane/tissue binding is also dependent on the lipophilicity of the molecule (Log P)  (Smith et al. , 2015). However, for the investigated BPs, the visual inspection of the Fig.
67 L/kg), despite a lower clearance. Terminal Half-life (HL), corresponding to the rate of elimination during the terminal phase, is a hybrid parameter reflecting both the plasma clearance and the volume associated with this terminal phase (Varea). HL, as directly measured in piglets, range between 0. 4 and 3. 6 h and are close to those of MRT, confirming that the body distribution of this class of phenols is rather moderate. The high BPs HL predicted by HTTK package (between 6 and 216 h) results from the gross underestimation of the plasma clearance, in particular for BPFL, BPS and BPA. In the pig model, for the same amount of BP ingested, BPA results in the lowest systemic exposure to unconjugated, active bisphenol in comparison to 11 structural analogs (2 to 4 times for 3-3BPA, BPAF, BPB and BPZ, 7-20 times for BP4-4, BPAP, BPP, BPFL, BPF, BPM and much lower, 150 times for BPS).
Sanger Sequencing. Samples that were cPCR-amplifed and visualized on a 1% agarose gel under ultraviolet light were then Sanger sequenced by a service provider (Genoscreen, Lille, France) using the same primers employed in the cPCR. ChromasPro (version 2. 6. 6) was used to assemble nucleotide sequences that were at least 330 bp in length and compatible with the genus Leptospira. Each contig was queried using the nucleotide Basic Local Alignment Search Tool (BLAST) in the NCBI database (https://blast. ncbi. nlm. nih. gov/) to determine Leptospira species assignment. A phylogenetic tree was then generated based on the partial 16S gene rDNA sequences obtained from our blood sample amplicons and reference Leptospira DNA sequences provided by the "Laboratoire des Leptospires"  [42]. Te tree was constructed using Muscle version 5  [43]  with IQ-TREE 2.
Molecular Analysis. β-actin gene expression was reported by Real-Time PCR from all 187 DNA samples that were extracted from whole blood. Te 16S rRNA gene sequence of Leptospira was detected by Real-Time PCR in 7 of 135 local cattle (representing 5 of the 14 herds) and in 1 of 52 imported cattle. For six of the seven Real-TimePCR-positive local animals (representing the same fve herds) and the Real-TimePCR-positive imported animal, cPCR amplifcation revealed a 330-bp fragment compatible with the genus Leptospira  [45]. All seven sequences demonstrated 100% nucleotide afnity with a published sequence corresponding to Leptospira kirschneri (GenBank accession number MK726123. 1). Te fve PCR-positive herds were geographically distributed as follows: two were located in the north, one in central ML, and two in the south (Figure  1 ).
Acknowledgments Te authors express their gratitude to  VetAgro Sup , specifcally to  Prof. Angeli Kodjo , director of the  Laboratoire des Leptospires et d'Analyses Vétérinaires, VetAgro Sup, France , for fnancial support, and to  Prof. Zoheira Djelouadji  for supporting molecular information. Te authors also acknowledge the support of  USEK , particularly  Dr. Samar Azzi ,  M. Mira Mrad , and  M. Boushra Jazzar , as well as the support of the  French Embassy in Lebanon  and the  Directorate General for Food in Paris. Finally, we acknowledge the support of  Dr. Jirji Maasri , owner of the veterinary company, for the provision of samples. Tis work was supported by  VetAgro Sup , EH PhD candidate grants from  AUF ,  USEK , and  CNRS-L , and from  PHC-Cèdre  (project code  47858PK ). Te funders had no role in study design, data collection and analysis, the decision to publish, or the preparation of the manuscript.
Material and methods Data supporting this research were extracted from the SCImago Journal and Country Rank, "an open access scientometric directory" (Guerrero-Bote and Moya-Aneg on, 2012, p. 675) that includes the journals and country scientific indicators developed from the information contained in the Scopus database. SCImago covers the 27 major thematic areas divided into 313 specific subject categories (also referred to as disciplines in this study), comprising over 34. 000 journal titles from more than 5. 000 international publishers and country performance metrics from 239 countries worldwide.
In addition, we admitted only one journal from each publisher under the same subject area, avoiding results based on the same editorial policy. The analysis assumed the data contained in SCImago Ranking as shown within the database in the period covered by the data gathering  [2]. The sample took the date of 31 October 2019 as an upper-bound reference from which the most recent journal issues fully attending the selection criteria were selected. Articles published between 1 October 2019 and 31 October 2019 (or the most recent equivalent period for journals not publishing articles in this interval) were considered for selecting the journals in case they did not organise their articles to any issue. Special issues were not regarded as eligible for composing the sample since the editorial policies with which they are submitted may differ from those applied to regular issues.
For the same reason, articles that were not original research communications, like special articles, letters to the editor and book reviews, were not considered eligible for the composition of our sample. SCImago's thematic scheme is based on The All Science Journal Classification (ASJC), developed by Elsevier and adopted by the Scopus database, which was considered by this analysis for grouping journals according to thematic categories and subcategories to support approaching related disciplines journals data. Each journal is represented by five articles published in the most recent issue between October 1st and October 31st, 2019. For journals not releasing any issue in this period, the sample considered the immediately previous issue published before October 1st. For issues containing more than five articles, the selection considered a probabilistic systematic random sampling technique based on the average number of articles published by the journal in the period above.
73% of journals sample), followed by the APA Reference Style (6th ed. 2010), which achieved a rate of 8. 05% of adoption across disciplines also showed to be the most adopted within Social Sciences' journals (52. 94%). The most adopted reference style in Life Sciences journals was Chicago (no edition specified), with 8. 69% adopting journals and 4. 69% adopting journals across all subject categories. In total, we found 31 guidelines on formatting citing and referencing data and metadata provided or indicated by publishers in their journal's webpages. Fifteen different reference styles were detected within journals composing the Social Sciences subject category, disregarding the multiple editions of a single reference style. Indeed, Social Sciences was the subject category with the most comprehensive range of reference styles, followed by Health Sciences (14 reference styles), Physical Sciences (11 reference styles), Life Sciences (8 reference styles), and lastly, the Multidisciplinary subject category in which only one reference style was detected (i.
An analysis of citing and referencing habits Our analysis identified two citation systems within articles composing the sample, according to data represented within Figure  4 , i. e. the citation-sequence system (which uses numbers to refer to bibliographic references within the text, e. g. "[3]") and the author-date system (which uses the names of some authors of the article plus a date, e. g. "(Doe et al. , 2020)"). On average, the citation-sequence system is adopted by 51. 37% of the article's sample, against 48. 41% adopting the author-date system. This scenario prevents assuming one of the citation systems as the most adopted overall. However, by considering the subject categories perspective, we can say that Health Sciences, Physical Sciences and Multidisciplinary journals tended to adopt citation-sequence system (respectively 79%, 57% and 51% of journals, on average), Social Sciences and Life Sciences tended to adopt author-date system (respectively 86% and 52% of journals, on average).
An analysis of citing and referencing habits The core objective of systematically structuring bibliographic metadata as in-text reference pointers and bibliographic references is to assure the identification of a cited content, i. e. a work or a passage, by providing (at least) the minimum elements supporting the reader in seeking tasks within a bibliographic catalogue. Of course, such minimum elements may change depending on the particular type of the publication. For instance, a bibliographic reference to a journal article should include at least the authors, title, year of publication, journal name, volume, issue and pagination. Instead, a reference to a born-digital publication, such as a dataset, should include authors, the year of publication, the version number and the repository where it has been deposited.
41 of journals indicate the AMA 9th edition, 11. 11 indicate the AMA 10th edition, and 7. 41 do not provide accurate instructions concerning which particular edition of such reference style authors should consider. 1 Bibstyle is a reference style adopted by some academic journals 2 BibTeX is a fomatting tool used in LaTeX documents. And LaTeX is a marking system or program for publishing high quality typographic documents, specific for the elaboration of scientific texts Source(s): Table  by authors   Table 3. JD 79,7 5. 2 The disadvantages of reinventing the wheel Analysing Figure  5 , we can speculate about a direct relation between journals adopting reference styles authored by their respective publishers and the rate of reference styles not providing clear formatting guidelines for citing and referencing metadata compared with those widely adopted ones.
CSR Assays CH12 cells were cultured for 72 h in vitro with IL4 (5 μg/μL; Peprotech), TGF-β (1 μg/μL; R&D Systems Europe), and anti-CD40 antibody (200 ng/mL; eBioscience) for 72 h. CSR to IgA was assessed by staining with an anti-IgA-PE antibody (1/500; Southern Biotech). Before analysis, DAPI was added to exclude dead cells. Cells were analyzed by flow cytometry (Celesta BD) using the FlowJo software and following the flow cytometry guidelines. Gating strategy is shown in Supporting Information Fig. S1D.
Acknowledgements: We thank members of the  Reina-San-Martin laboratory  for discussions. C. Ebel  and  M. Phillips  for assistance with cell sorting. B. Heller  for cell culture. This work of the  Interdisciplinary Thematic Institute IMCBio , as part of the  ITI 2021-2028 program  of the  University of Strasbourg ,  CNRS  and  Inserm , was supported by  IdEx Unistra  ( ANR-10-IDEX-0002 ), by  SFRI-STRAT ' US  project ( ANR 20-SFRI-0012 ), and  EUR IMCBio  ( ANR-17-EURE-0023 ) under the framework of the  French Investments for the Future Program  and by grants to B. R. S. M. from  Fondation ARC, Institut National Contre le Cancer (INCA)  and  Fondation Recherche Médicale (FRM).
Perspective https://doi. org/10. 1038/s41592-023-01987-9 While the focus of QUAREP-LiMi is on light microscopy in the life sciences, the principles may also apply to figures with other images (photos, electron micrographs, medical images) and to image data beyond the life sciences. The checklists are intended for all users, from beginners and laypersons who only occasionally work with light microscopy to experienced users and also experts, such as core facility staff or members of the bioimage community, who review image data or teach image processing. The checklists do not include principles for designing imaging experiments or recommendations to avoid image manipulation. Previous literature covers experimental design for microscopy images, including accurate and reproducible image acquisition and ensuring image quality  2, 28  , examples and recommendations for avoiding misleading images  1, [29] [30] [31] [32] [33]  , detection of image manipulation  [34] [35] [36] [37]  , appropriate image handling and analysis  7, 19, 38, 39  , guidelines for writing materials and methods sections for images  40  , and recommendations for general figure preparation  41.
Image formatting After exploring, processing and analyzing image data, authors then communicate insights in publications with figures as visual evidence. Preparing a figure begins with the selection of representative images from the dataset that illustrate the message. When quantitative measurements are reported in a chart, an example of the input image should be shown; when ranges of phenotypes are described, several images may be necessary to illustrate diversity. To quickly focus the audience on key structures in the image, it is permitted to crop areas without data or with irrelevant data (Fig. 4a ). As a rule, cropping, similar to selecting the field of view on the microscope, is allowed, as long as this does not change the meaning of the conveyed image. Next, specimens are often presented in consensus orientation in figures (for example, apical side of cells upward, tree top upward), which may require image rotation.
legends  12  , which are key to fully understanding the data. To ensure that images are presented in a clear, standardized, and reproducible manner, it is essential that the scientific community establishes unified and harmonized guidelines for image communication in publications. Images document biological samples and the ranges of their phenotypes. Increasingly, microscopy images are also a source of quantitative biological data, and variables are measured with a growing number of open source image-analysis software packages such as Fiji-ImageJ 13 , CellProfiler  14  , KNIME  15  and Python software libraries  16  (https://scikit-image. org (ref. 17)) and commercial software packages such as ZEN, LAS X, NIS-Elements, Amira-Avizo, Imaris, Arivis Vision 4D and Huygens  18. Image analysis is often a workflow of many steps, such as image reconstruction, segmentation, processing, rendering, visualization and statistical analysis, many of which require expert knowledge  19, 20.
A comprehensive publication of quantitative image data should then include not only basic specimen and imaging information, but also the image-processing and analysis steps that produced the extracted data and statistics. Toward fully reproducible image analysis, it is also essential that images and workflows are available to the community, for example, in image repositories or archives  [21] [22] [23]  , and code repositories such as GitHub  24. To ensure that image figures provide insights to their readership, any supportive experimental metadata and image-analysis workflows must be clear and understandable ('what is the pixel size?', 'what does the arrow mean?'), accessible ('are colors visible to color-blind audiences?'), representative (no cherry picking) and reproducible ('how were the data processed?', 'can one access and reanalyze the images?').
Image colors and channels Fluorescent light microscopes use a range of wavelengths to generate images of specimens. In the images, the light intensity for individual wavelengths, most often in grayscale, is assigned or mapped to a visible color scheme. In multicolored images, several channels are overlaid to compare data from several channels. Microscopy images often must be processed to adapt the bit depth to the visible range  2, 44. Usually, brightness and contrast are adjusted for each channel independently in many software platforms (for example, ImageJ-Fiji) by defining the minimum and maximum displayed intensity values before converting these into eight-bit images (for screen display, printing). Intensity-range adjustments should be monitored (for example, with the image histogram) and performed with care: too wide of an intensity range results in 'faded' images that lack details, while too narrow of an intensity range removes data (Fig.
Statements about the physical dimensions of the entire image are acceptable alternatives to scale bars. Magnification statements should be avoided, as pixel size can be determined by a number of factors, for example, sampling rate or binning, and does not depend only on the objective magnification. Many images include further annotations such as symbols (arrows, asterisks), letter codes or regions of interest (ROI; dashed shapes), which must be explained in the figure or the figure legend (Fig. 5b ). Symbols that resemble image data should be avoided, and note that symbols with clear vertical or horizontal arrangement are easier to distinguish than randomly oriented symbols on busy backgrounds (for example, ref. 12). At times, additional annotations may help readers to interpret the figure when the anatomical section of a three-dimensional object or the imaging frequency for time-lapse data is provided too (Fig.
Zenodo, Open Science Framework and figshare are currently essentially free options also for image data; however, some of these have file size limitations (Fig. 5e-g  and Extended Data Fig. 1 ). OMERO servers (https://www. openmicroscopy. org/omero/institution/) enable institutions but also individual laboratories to host public or private (access-controlled) image-sharing databases (see Extended Data Fig. 1  for an overview of current repositories). In the long term ('ideal'), uploading images with all experimental metadata to dedicated, specialized or fully searchable image databases has the potential to unlock the full power of image data for automated image and/or metadata searches and the possibility of image data reuse. Databases that allow such functionalities and more include the BioImage Archive (a primary repository that accepts any image data in publications; https://www.
ebi. ac. uk/bioimage-archive/), the Image Data Resource (which publishes specific reference image datasets; https://idr. openmicroscopy. org/) and EMPIAR (a dedicated resource for electron microscopy datasets; https://www. ebi. ac. uk/empiar/). At present, most of the available options are free of charge for 20-50-GB datasets; however, dedicated image databases have strict requirements regarding file type and metadata (Fig. 5e -g and Extended Data Fig. 1 ). To be inclusive, we do not enforce the use of online repositories but require as a minimal measure that scientists are prepared to share image data. Costly and expert-driven storage solutions are at present not accessible to all scientists in the global imaging community.
For local storage, size limit is not applicable (N/A). e-g, Image availability. e, Currently, image data are often shared 'upon request'. f,g, More images along with the image metadata should be available for download in public databases (f) and, in the future, also should be archived in dedicated, added-value databases, in which images are machine searchable or curated (g). 55  , specialized plugins and easy-to-use scripts in ImageJ  [56] [57] [58]  and tools and plugins that solve generic image-analysis problems such as tracking  59  or pixel classification  60, 61. For these workflows, extensive expertise, documentation and tutorials already exist that allow others (for example, reviewers, readers) to reproduce the workflow and to judge the validity of the results. Scientists publishing images or image-analysis results processed with established workflows thus can focus on documenting key parameters only.
Recommended. Authors should state all settings in the methods or the supplementary material of the article. Providing data upon request is an ineffective method for data sharing  54. Thus, authors should provide example input and output data and any manual ROI via a public repository (see Image availability). Ideal. Documenting the use of software in the form of a screen recording or, in the case of command line tools, by reporting all executed commands in detail greatly facilitates understanding the workflow application and therefore reproduction. To avoid any variation arising from factors such as computer hardware or operating system, authors could provide cloud-hosted solutions  [62] [63] [64]  (kiosk-imageJ-plugin, https://github. com/vanvalenlab/kiosk-imagej-plugin) or the workflow packaged in a software container (docker, https://www.
New workflows New image-analysis workflows assemble components into a new sequence, for example, a macro in Fiji, a pipeline in CellProfiler or a workflow in KNIME in an original way. To ensure reproducibility of the analysis, it is essential to report the specific composition and sequence of such new workflows. Minimal. The individual components used in the new workflow must be cited, named and/or described in detail in the methods section along with the software platform used. It is essential that scientists specify or provide the exact software versions of the used components and software platform in the methods whenever possible. Authors must describe the sequence in which these components have been applied. Key settings (for example, settings that deviate from default settings) must be documented in the methods section.