dataesr/scientific-paragraphs-categorization huggingface dataset:
A biobank must rely on a dedicated IT-infrastructure (database) to manage samples and associated data, equipped with a user-friendly interface to make them available to the scientific community. Since the amount of data linked to individual specimens determines the value of the biological samples for future research, biobanks gain value if sample information is connected to the medical records of the participants and their updated clinical data  [71]. Carefully annotated human biospecimens stored in a biobank are invaluable not only for human genetic research, but also to support a wide range of new scientific approaches to advances in the field of such rare diseases. A virtuous example of biobanking is represented by the Telethon Network of Genetic Biobank (TNGB), which interconnects 11 Italian Rare Diseases Biobanks, hosting ~ 127,000 biospecimens from people affected with 950 genetic diseases, available for distribution.
Acknowledgments The European Forum on Visceral Myopathy (EFVM 2022) was funded by the  European Joint Programme for Rare Diseases-Networking Support Scheme-Round 3, and by 'POIC e dintorni ' patient advocacy organization (https:// poic-e-dinto rni. org/). ROH is supported by the  Irma and Norman Braman Endowment , the  Suzi and Scott Lustgarten Center Endowment, NIH   R01 DK128282 , and  The Children's Hospital of Philadelphia Frontier Program Center for Precision Diagnosis and Therapy for Pediatric Motility Disorders.
9. 1 version of the software developed by the ESRF  [33]. The elemental areal mass concentration was calculated using the Fundamental Parameters (FP) approach implemented in PyMca software package. A reference material containing elements of certified concentration (RF7-200-S2371 from AXO, Dresden, Germany) with nanoscale uniform mass depositions (ng/mm 2 range) was used. The resulting elemental areal mass density maps were visualized with ImageJ 1. 54f version of the software.
Atomic Force Microscopy (AFM) Nanowizard II AFM (JPK, Berlin, Germany) mounted on a ZEISS 510 confocal microscope (Zeiss, Cambridge, UK) was used to obtain the force-indentation curves. During AFM, cells were kept alive in a pH indicator-free and serum-free culture medium at 37 • C in a petri dish during analysis lasting 3 h at maximum. The inverted optical microscope was used to position the tip on the cell and force volume, conducted using borosilicate colloidal (Novascan, Oxford, UK) cantilevers, with a nominal spring constant of 0. 35 N/m and a radius of 2. 5 µm. JPK Data Processing program (https://jpk. software/, accessed on 1 October 2020 was used to process the acquired force curves as detailed in  [22]. In order to resolve the membrane architecture, cells were fixed for 30 min in 4% PFA (Merck, Feltham, UK) diluted in PBS at room temperature prior to imaging.
Acknowledgments: We acknowledge the  Diamond Light Source (Didcot, Oxfordshire, UK)  for providing beamtime at the I14 Hard X-ray Nanoprobe facility through proposals sp17733 and I14 staff, particularly  D. ,  Cacho-Nerin , for their assistance. We acknowledge the  European Synchrotron Radiation Facility (ESRF)  for the provision of beamtime through proposals LS2696, FAME-UHD, and ID16A, and the staff for their help during experiments. We acknowledge Labex OSUG@2020 (Grenoble, France,  ANR-10-LABX-0056 ). The  FAME-UHD  project is financially supported by the  French "grand emprunt" EquipEx  ( EcoX ,  ANR-10-EQPX-27-01 ), the  CEA-CNRS CRG consortium , and the  INSU CNRS institute. We thank  L. H. Winkel ,  ETH Zürich  for ICPMS access. The electron microscope image was supported by the  Rhône-Alpes Region ,  FRM ,  FEDER ,  CNRS ,  CEA , the  University Grenoble Alpes ,  EMBL ,  GIS-IBISA , and the  Grenoble Instruct-ERIC Centre  ( ISBG :  UMS 3518   CNRS-CEA-UGA-EMBL ) with support from  FRISBI  ( ANR-10-INSB-05-02 ) and  GRAL  ( ANR-10-LABX-49-01 ) within the  Grenoble Partnership for Structural Biology (PSB)  platform ( D.
Genome analyses. The genomes were sequenced by Eurofins, using NanoPore GridION platform with Flow Cells version R9. 4. 1 (Oxford Nanopore Technologies, UK) and assembled using the softwares Flye (https:// github. com/ fende rglass/ Flye) and Medaka (https:// github. com/ nanop orete ch/ medaka). The presence of plasmids was verified using PlasmidFinder (https:// cge. food. dtu. dk/ servi ces/ Plasm idFin der/). The genomes were annotated using RAST-Tk from BV-BRC (https:// www. bv-brc. org/ app/ Annot ation) and EggNOG-mapper (http:// eggnog-mapper. embl. de). Variations between the reference genome WT and the variants 7259, 7292 and 7358 were identified using two approaches. Large variations were verified using Artemis Comparison Tool v18. 1. 0 (http:// sanger-patho gens. github. io/ Artem is/ ACT/).
The sections were then treated with trihydro-chloride trihydrate (0. 5 mg/ mL Hoechst 33342, Invitrogen, ThermoFischer Scientific) in PBS. Subsequently, the slides were mounted using Fluorescent Mounting Medium (Dako, Agilent Technologies). Tissues were visualized using a high-capacity digital slide scanner (3DHISTECH Ltd. ) and Panoramic and Case viewer software (3DHISTECH Ltd. ). Reverse transcription (RT) and quantitative real-time PCR (qPCR). Reverse transcription was performed with the applied biosystems high capacity cDNA reverse transcription kit (ThermoFisher, France). For this, reverse-transcription of 1 μg of total RNAs was performed and the resulting cDNAs were then quantified using a Nanodrop (ThermoFisther Scientific Inc. , France). Samples were diluted (10x), duplicated, and subjected to quantitative real-PCR (qPCR) with a Step one plus detection system (Applied).
The cells were analyzed using flow cytometry (Accuri, BD) and CFlow Sampler software (BD Biosciences) as described previously  30. Gut microbiota analysis. Fresh fecal samples were collected at two time points (D13 and D23) and stored at -80 °C. DNA was then obtained via mechanical lysis (Fastprep FP120 [ThermoSavant]) and phenol/chloroform-based extraction as previously described  31. Amplification was performed using the primers V3-V4 for 16S rRNA (forward:CCT ACG GGNGGC WGC AG, reverse: GAC TAC HVGGG TAT CTA ATC C). The samples were loaded into flow cells in an Illumina MiSeq. 300PE Sequencing Platform in accordance to the manufacturer's instructions. An adaptation of the QIIME v2. 0. package was used to analyze the sequence data (Caporaso et al. 2010). Briefly, read pairs were demultiplexed and trimmed (q > 20) before being merged using QIIME.
Merged reads with q > 25 over a window of 15 bases, no ambiguous bases and a minimal length of 300 were retained. Taxonomic assignment was performed using the RDP classifier against the SILVA_ Silva_138 database, the results of which sequences were aggregated to different taxonomic levels. Reads with eukaryotic assignments, as well as reads with a low relative abundance up to 0. 0005% in all samples were excluded from further downstream analysis. Reads with eukaryotic assignments, as well as reads with a low relative abundance up to 0. 0005% in all samples were excluded from further downstream analysis. All analyses were performed using R version 3. 6. 0. Samples were rarefied using phyloseq library in R. R package Vegan (function richness) was used to calculate alpha diversity metrics (Shannon's).
Differences between changes of alpha-diversity and Bray-Curtis dissimilarities from baseline were evaluated using the Mann-Whitney tests for specific comparison, without adjustment for multiple testDESeq2 package (version 1. 26. 0) was used to identify genera differentially abundant within groups and between groups (~ Group + Group:Day + Mice_ID). Significant fold-change differences between doses at each time point were evaluated with the negative binomial model-based Wald test implemented in DESeq2 (FDR < 0. 1). Sequences have been deposited at ENA accession number PRJEB56457.
Acknowledgements Authors would like to thank  Christian Chervaux  and  Pierre Renault  for their help on the genomic analyses. RMR was the holder of a postdoctoral contract in the framework of a collaborative project with Danone Research ( 2014-2017 ). We thank the histology facility and aBridge platform of  UMR 1313  GABI and the MIMA2 platform for access to the virtual slide scanner (Pannoramic SCAN,  3DHISTECH ). We acknowledge the staff of the  INRAE Infectiology of Fishes and Rodents Facility (IERP-UE907, Jouy-en-Josas Research Center, France ) in which animal experiments have been performed. IERP Facility belongs to the  National Distributed Research Infrastructure for the Control of Animal and Zoonotic Emerging Infectious Diseases through In Vivo Investigation.
Note: The thromboxanes TXB1, TXB2, TXB2-d4 and TXB3 display peak tailing caused by tautomerism (Figure  4 ). Following sample measurement, raw data are processed using the MassHunter analysis software (or similar) that integrates the respective peaks and provides areas for each detected compound. Figure  5  shows examples of peak integration (area calculation) for four compounds (13-HODE, 13-KODE, AA, and eicosapentaenoic acid). In the frame of the present protocol we have analyzed fresh livers from mice. Table  3  outlines the compounds that were identified and quantified in those samples (see also Table  S3 ). Absolute quantification of selected molecules is also possible provided a calibration curve has been constructed. Figure  6  shows some examples of calibration curves. Additional examples are given in the Figure  S1.
Using the calibration points, a linear or quadratic equation can be established in a given concentration range. This curve outlines the relationship between area of the signal and its quantity for a specific metabolite. Example: The 13-HODE calibration curve linear equation is y = 89629x + 363. 7 (Figure  6 ), where y is the peak area of 13-HODE and x is the concentration in mM. In Table  3 , for 13-HODE, we measured an average peak area of 1434. 267. When this value is inserted in the equation, it gives: x= (1434. 267-363. 7)/89629 and thus, a value of 0. 012 mM. To illustrate the analysis of different conditions in a typical metabolomics experiment, we have re-analyzed the previously published  4  PUFA data using MetaboAnalystR package and PUFAMetaboR script. Our test dataset contains the results of the PUFA analysis in breast cancer cell xenograft samples in which the MYOF gene was silenced (shMYOF) or not (negative control; shNT).
This dataset includes five biological replicates per condition. For details on the script and to download the test dataset see: https://github. com/DirtyHarry80/ PUFAMetaboR. git. Briefly, ISTD normalized data were loaded in PUFAMetaboR. ''Normalization'' parameters were set to: ''QuantileNorm'', ''NULL'', ''AutoNorm''. Following data normalization, the Grubbs' test showed that the shNT4 replicate behaved as an outlier. Therefore, it was removed from the analysis. The remainder of the script contained standard parameters as described in MetaboAnalystR tutorials. The PLS plot shows a clear distinction between the individual experimental conditions, indicating the presence of differences between sample groups (shMYOF and shNT). Following MYOF silencing, the concentration of several PUFAs showed variations above the 2-fold change.
These types of analyses are most efficiently done using dedicated R language packages, for example MetaboAnalystR. 8  To provide an example on how to use MetaboAnalystR, we deposited an R script and a test dataset in the public repository GitHub, accessible at the following address: https://github. com/DirtyHarry80/PUFAMetaboR. git. Note: To be usable in MetaboAnalystR, data need to be saved as tab-delimited text files (decimals separated by point) and must follow a number of criteria. The R code lines as outlined in the PUFAMetaboR will only work with the following data table format (Table  4 ; measurements are in columns, ''Label'' indicates the condition group): Depending on the type of data pre-normalization performed, the function ''Normalization'' needs different parameter adjustments (see MetaboAnalystR User's Manual).
K p stands for the partition coefficient of the AITC between film and the simulant at equilibrium and was calculated using Equation (6). K p = C F,∞ C S,∞ (6) where C F,∞ and C S,∞ are the equilibrium concentrations of the AITC (mg L -1 ) in the film and in the food simulant, respectively. This model was previously adapted and applied by Benbettaieb et al. [50]  to experimental release kinetics (up to equilibrium). Modeling was performed using Matlab/Simulink software (MATLAB version R2021a, The MathWorks Inc. , Natick, MA, USA). Model calculation was analyzed in triplicate. From each calculation, both the coefficient of determination (R 2 ) and the root mean square error (RMSE, mg/L) were determined.
Results Literature review on the impact of maternal LCS consumption on baby's obesity risk. To examine if maternal LCS consumption is associated with metabolic malprogramming in humans, PubMed, Ovid Medline, and Web of Science databases were systematically searched and identified 130 records. After removing duplicates, 54 records remained. Outcome measures were parameters of the offspring's weight, BMI, and body composition. After excluding nonhuman studies, review articles, studies not in pregnancy/lactation, studies with incorrect outcomes, and duplicates, we identified 6 studies of interest to be included in the review (Supplemental Figure  1 ; supplemental material available online with this article; https://doi. org/10. 1172/jci. insight. 156397DS1) (9-12, 25, 26). A summary of these articles and their findings is shown in Supplemental Table  1.
In fact, in the principal component analysis plot, the fecal microbiota of LCS-consuming dams clustered together with that from control dams (Supplemental Figure  3C ). Additionally, the profile of the fecal microbiota of LCS-treated dams did not differ from that of control dams, since the relative abundance of the most abundant bacterial phyla (Supplemental Figure  3D ) and families (Supplemental Figure  3E ) were both similar. Taken together, these data indicate that maternal LCS exposure does not markedly shift the abundance or the composition of maternal fecal microbiota during the lactating period. We also assessed the gut microbiota diversity in the adult offspring of LCS-fed and control dams using 16S rRNA gene sequencing followed by dada2 analysis of fecal samples. We identified and classified 1,604 unique ASVs.
It also provided potential mechanistic insights into how perinatal LCSs exert their effects on hypothalamic development by identifying PAG as a potential mediator of LCS on neurodevelopmental and metabolic outcomes. All sections were counterstained with bis-benzamide (1:10,000, Invitrogen) to visualize cell nuclei. Image analysis. The images were acquired through the PVH and the DMH and pancreas using a ZEISS LSM 710 confocal system equipped with a 20× objective. For the quantitative analysis of β cell mass, images were acquired using a 5× objective. Quantifications were performed in 2-4 sections per animal. Slides were numerically coded to obscure the treatment group. The image analysis was performed using ImageJ analysis software (NIH) as previously described  (24). For the quantitative analysis of fiber density (for POMC, AgRP, VAChT, and TH fibers), each image plane was binarized to isolate labeled materials from the background and compensate for differences in fluorescence intensity.
Samples were stored at -80°C until analysis. Total DNA was extracted using DNA extraction QIAamp PowerFecal Pro Kit (QIAGEN, catalog 51804). The V3-V4 hypervariable region of the 16S rRNA gene (55) was amplified using Phanta Max Super-Fidelity DNA Polymerase (Vazyme, catalog P505), and the primers forward MS341F: 5′-CCTACGGGNGGCWGCAG-3′ and reverse MS785R: 5′-GACTACH-VGGGTATCTAATCC-3′ were used. They were selected from the Klindworth et al. publication  (56). Quality control, sequencing library construction, and sequencing (MiSeq Illumina) were performed using the Genotoul bioinformatics platform. The resulting sequences were analyzed using R workflow combining dada2 v. 1. 26 (57)  and FROGS 4. 0. 0 (58) software. Reads were filtered, merged, and assigned to ASVs with dada2, and the ASVs were assigned to species using the FROGS affiliation tool.
16S rRNA amplicon sequencing bioinformatic analysis was performed on R (v4. 2. 1) with package phyloseq (v1. 40. 0). Differential analyses were performed with DESeq2 R package (v1. 36. 0). To quantify the abundance of total bacteria, qPCR of 16S rDNA was performed using the primers F_Bact 1369: 5′-CGGTGAATACGTTCCCGG-3′ and R_Prok1492: 5′-TACGGCTACCTTGTTACGACTT-3′. PCR amplification was carried out as follows: 10 minutes at 95°C, followed by 40 cycles of 15 seconds at 95°C, 60 seconds at 60°C. Detection was achieved with the StepOne PLUS instrument and software (Applied Biosystems) using the MasterMix PCR Power SYBR Green (Applied Biosystems, catalog 4309155). Each assay was performed in duplicate in the same run. For construction of standard curves, 5-fold dilution series from E. coli genomic DNA preparations (MilliporeSigma, catalog D4889) were applied to the PCR.
The flow rate was 350 μL/min. The liquid chromatography gradient was 0 minute, 75% B; 3 minutes, 75% B; 4 minutes, 50% B; 5 minutes, 10% B; 7 minutes, 10% B; 7. 5 minutes, 75% B; 11 minutes, 75% B. The injection volume of the sample was 3 μL. Data analysis was performed with Compound Discover software (ver. 3. 2, Thermo Fisher Scientific), and data were corrected for multiple comparisons, based on the number of measured metabolites, via the Benjamini-Hochberg method, with an FDR cutoff at 0. 05 used to determine statistical significance. Heatmap construction was performed using Multiple Experiment Viewer from The Institute of Genomic Research. Cell viability assay. The embryonic mouse hypothalamic cell line mHypoE-N43/5 (Tebubio, catalog CLU127-A)  (60)  was cultured in DMEM (MilliporeSigma, D5796) supplemented with 10% fetal bovine serum, 100 U/mL penicillin, and 100 μg/mL streptomycin at 37°C in 5% CO 2 and a humidified atmosphere.
After 48 hours, the explants were fixed in paraformaldehyde, and neurites extending from the explants were stained with a mouse anti-β-III tubulin antibody (1:5,000, Covance catalog MMS-435P) as previously described  (24). Images were then acquired using a ZEISS LSM 710 confocal system. Sections of 5 regions of interest (100 × 100 μm) spaced at 100, 200, 300, 400, and 500 μm extending radially from the edge of the ARH explants were acquired with a 10× objective. The image analysis was performed using ImageJ analysis software, as previously described  (24). Briefly, each image plane was binarized to isolate labeled fibers from the background and compensate for differences in fluorescence intensity. The integrated intensity, which reflects the total number of pixels in the binarized image, was then calculated for each image.
The β-diversity was represented by multidimensional scaling using weighted UniFrac distance calculations on the rarefied count matrix (R package phyloseq). The data were normalized by rarefaction before diversity analysis. Distances were calculated using the package phyloseq/distance, followed by phyloseq/ordinate and phyloseq/plot_ordination. Statistical analysis was done using permanova/adonis, followed by a pairwise post hoc test. To compare the relative abundance of major bacterial phyla or families, nonparametric ANOVA (Kruskal-Wallis) was followed by Dunn's post hoc test to show the impact of the treatment on the relative abundance of each phylum/family.
Acknowledgments We thank  Sylvie Rabot  for her valuable input on the microbiota analysis of our manuscript. We indebted to the  GeT-Biopuces core facility of the Génopôle Toulouse Midi-Pyrénées  for the high-throughput sequencing of the fecal microbiota 16S rRNA genes. We thank  Meryem Tardivel  and  Antonino Bongiovanni  from the  BICEL Photonic Microscopy and Histology Facility  US  41 -UAR 2014 -PLBS  for the access to equipment and technical advice provided. We acknowledge support of  The Saban Research Institute at Children's Hospital of Los Angeles  and its  Core Pilot Program  for the use of single-cell sequencing, and CyOF (SC 2 ) Core for the 16S rRNA sequencing, on some of our samples. SGB is funded by the  Fédération pour la Recherche sur le Cerveau  and l' Agence National pour la Recherche  ( ANR-22-CE16-0007 , MATBIOTA).
Contrarily, Emmental consumption resulted in a reduction of peptidoglycan metabolism, agellar assembly, and bacterial chemotaxis (Fig. 4  and Fig. 5E ). Although non-signi cant, an increase in galactose, starch, and sucrose metabolism was also observed (Fig. 5E  and Table  S6 ). Individual taxa contribution analysis and metabolic pathway reconstruction in mice treated with the Emmental-type cheese in healthy and in colitis conditions: Akkermansia muciniphila is a major contributor to nervous system functions in healthy mice Metagenome Assembled Genomes (MAGs) were recovered from the Emmental Group (sample B238) and taxonomically classi ed based on Tetra correlation analysis (TCA) against Jspecies database: Akkermansia muciniphila (97,9% Completeness, 0,0% Contamination); Alistipes dispar (77,2% Completeness, 6,4% Contamination) and Muribaculum intestinale (46,5% Completeness, 3,4% Contamination) (Table  S7 ).
Microbiome Taxonomy And Pro ling FastQ le base quality was veri ed using FastQC v. 0. 11. 9 tool, and the low-quality reads (Phred < 30) were ltered using Fastp v. 0. 232 software. A 150pb read-based taxonomic classi cation was performed using kaiju v1. 9. 2 software  [30] , aligning the sequences to the NCBI BLAST nr + euk sequences database. Afterwards, the relative abundances of bacterial taxa in the samples were estimated and statistically compared using RStudio built-in functions and STAMP v2. 1. 3 software. A diversity analysis was conducted by estimating the Shannon index using the Vegan package for RStudio. The reads classi ed as Bacteria were extracted and submitted for a de novo assembly using the megahit v. 1. 2. 9 tool for functional analysis. Following the assembly, contigs were annotated using Prokka v1.
14. 5. An enrichment analysis based on orthologs was performed using the KEGG database, and the number of genes for each category/pathway was compared statistically among the groups using the same tools described above. A normality test was performed using the Shapiro-Wilk test for the comparative analyses performed among the groups (n = 6). ANOVA or Kruskal-Wallis's test was used for data presenting either Gaussian or asymmetric distribution, respectively, followed by Tukey or Welch's T-test for Post hoc analysis. A Chisquare hypothesis test was used to compare data from the DSS-Emmental group (n = 1) to other groups. For Principal Component Analysis (PCA) data, an Analysis of similarities (ANOSIM) was performed. Signi cant differences were considered when the P-value < 0,05.
Mags Reconstruction And Individual Taxa Contribution Analysis To evaluate the individual functional contribution of taxa, de novo assembled contigs (minimum length = 1000pb) from one sample selected from the group Emmental and one sample from the DSS-Emmental group was used for recovering metagenome-assembled genomes (MAG) based on a binning algorithm performed by the MaxBin2 v. 1. 2. 0 tool  [31]. The MAG completeness and contamination were veri ed using CheckM v1. 2. 2 software. The taxonomy at the species level was investigated by tetranucleotide pairwise alignment against the JSpeciesWS tool genomesDB database followed by a multi-locus (92 single copy orthologous genes) phylogeny using the Up-to-date bacterial core gene (UBCG) v. 3. 0 tool. The trees were built using the Maximum Likelihood method with 1000 Bootstrap replicates.