dataesr/scientific-paragraphs-categorization huggingface dataset:
05% Tween 20; Bethyl Laboratories), and anti-SOD1 rabbit (Prestige 001401; 1:1,000 buffered in 1% BSA; in PBS containing 0. 05% Tween 20; Sigma-Aldrich). After washing steps in PBS enriched with 0. 05% Tween 20, the membranes (nitrocellulose membrane for SOD1 and PVDF for TDP43 and FUS) were subsequently incubated for 1 h at RT with goat anti-rabbit IgG-HRP-conjugated secondary antibody (1: 10,000 in PBS-Tween 20; Bio-Rad). After appropriate washing steps, the membranes were further treated with Western ECL-immunodetection buffer (Bio-Rad) and acquired using Image Lab 5. 0. ThermoScientific PageRuler Plus Prestained was used as a protein ladder. Samples were corrected for background, and densitometry analysis was performed using Image Lab software5. 0. To control variability in loading individual samples, the signal intensity of protein bands was normalized to housekeeping GAPDH protein.
Claudin-5 breaks quantification The loss of CLN-5 ribbon is defined by a lack of CLN-5 immunostaining under specific threshold by taking as reference the capillary wall stained with collagen-IV. The length breaks are measured and traced (in yellow) with ImageJ software. The quantification is performed by relating the total length of the single breaks and the total length of the vessels, outlined by collagen-IV, and expressed as % of breaks on the total vessels. Images are acquired as single tile scans, covering the ventral horn of the spinal cord, with specific Z-stack at the confocal microscope and analyzed as collapsed pictures. Single-molecule in situ mRNA hybridization Detection of mRNA in situ together with co-immunostaining was performed as previously reported (Olde  Heuvel et al, 2019)  and according to the manufacturer's recommendation (Acd Bio).
Acknowledgements F Roselli  is supported by the  Synapsis Foundation , the  Thierry Latran Foundation (projects "Trials" and "Hypothals") , the  Radala Foundation , the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation )-Project  ID   251293561-Collaborative Research Center 1149  and with the individual grant nos. 431995586  ( RO-5004/8-1 ) and  443642953  ( RO5004/9-1 ), the  Cellular and Molecular Mechanisms in Aging (CEMMA)  Research Training Group, and  Bundesministerium für Bildung und Forschung (BMBF)  ( FKZ 01EW1705A , as member of the  ERANET-NEURON consortium "MICRONET"). N Ouali Alami  and  B Commisso  are members of the  International Graduate School in Molecular Medicine, Ulm University. N Ouali Alami  is currently supported by the  Baustein  grant of the  Ulm University Medical Faculty.
Acknowledgments:  Conseil Départemental de l'Ordre des Médecins du Morbihan, Conseil Départemental de l'Ordre des Médecins du Loiret, Conseil Départemental de l'Ordre des Médecins du Nord, Conseil Départemental de l'Ordre des Médecins du Pas de Calais, Conseil Départemental de l'Ordre des Médecins de l'Ile et Vilaine, Conseil Départemental de l'Ordre des Médecins de la Seine Maritime, Conseil Départemental de l'Ordre des Médecins du Val d'Oise, Conseil Départemental de l'Ordre des Médecins des Vosges, Conseil Départemental de l'Ordre des Médecins des Yvelines, Conseil Départemental de l'Ordre des Médecins de l'Eure.
Population sample The SEVE (Suivi des Enfants Vuln erables sur le R eseau Elena; www. chu-st-etienne. fr/seve) Network is the French regional cohort of clinical follow-up dedicated to vulnerable children e i. e. born either at less than 33 weeks of GA or with neonatal encephalopathy e living in the catchment area of the Centre Hospitalier Universitaire de Saint-Etienne, France. Five hundred thirty-seven children among the 570 potentially eligible born between November 2011 and June 2015 were enrolled in the SEVE Network, providing an inclusion rate of 96%. They constitute the population sample of the present study. Each enrolled neonate had a free of charge and standardized clinical follow-up involving pediatricians, general practitioners, physiologists, and neuropsychologists. Data were longitudinally collected in the SEVE database using standardized questionnaires and clinical assessments throughout the follow-up.
Among these evaluations, a systematic face-to-face encountered with a specialized neuropsychologist was proposed to all families at 30 months of CA. The present work is a cross-sectional study from our regional cohort of clinical follow-up (SEVE Network). Exclusion criteria were established risk factors of unfavorable neurodevelopmental outcome in preterm newborns, namely: severe neonatal brain injury e defined as cystic periventricular leukomalacia  [6]  and grade III and IV cerebral hemorrhage  [5]  e, neonatal seizures  [20] , late-onset neonatal inflammation exposure e defined as late-onset neonatal infection  [11]  and necrotizing enterocolitis  [21]  e, and genetic abnormality. All these items were mandatory fields prospectively collected on standardized questionnaires in the SEVE database.
Neonatal seizures were diagnosed by standard electroencephalography. A genetic abnormality was defined by the presence of a pathogenic gene or chromosomal anomaly. Children were divided into two groups, either non-exposed or exposed to perinatal inflammation. Perinatal inflammation was defined as clinical chorioamnionitis or early-onset neonatal infection. These items were both mandatory in the SEVE database. Clinical chorioamnionitis was defined as maternal fever upper than 38 C with at least two additional criteria among the following: fetal tachycardia, maternal tachycardia, maternal leukocytosis, uterine tenderness, and foul-smelling amniotic fluid  [25]. Neonatal infection was defined by a positive culture collected from a normally sterile site for which clinicians prescribed at least five days of appropriate antibiotics  [26].
Early-onset neonatal infection was defined by a neonatal infection with onset within the first 72 h of life; late-onset neonatal infection was defined by a neonatal infection occurring after 72 h of life  [26]. Maternal and neonatal items recorded in the SEVE database also included maternal age at delivery, singleton versus multiplets, antenatal steroids administration, vaginal delivery versus cesareansection, gestational age, birth weight, head circumference at birth, sex, adrenalin administration at birth, Apgar score at 1 and 5 min, surfactant administration, duration of any ventilatory support, postnatal steroids administration, vascular filling or amine use during hospitalization, grade I and II cerebral hemorrhages, feeding at discharge, and parental employment status. Birth weight z scores were calculated according to sex and GA, using the INTERGROWTH-21st International Newborn Size at Birth Standards application version 1.
In line with our results, data from preclinical models showed that perinatal inflammation induced by inactivated or live pathogens e e. g. lipopolysaccharide, polyriboinosinic-polyribocytidilic acid, and group B Streptococcus e affects the brain development and leads to motor and social impairments without inducing severe brain injuries [38e41]. Altogether, these clinical and preclinical findings support the idea that perinatal inflammation might lead to neurodevelopmental issues affecting gross motor function and behavior without inducing severe neurological impairment such as CP  [42]. A limitation of our study is that exposure to clinical chorioamnionitis and early-onset neonatal infection were recorded in the database without detailed diagnostic criteria. Nevertheless, data recording was performed prospectively in the same way in both groups, using the same standardized definition.
All raw reads were submitted to the National Center for Biotechnology Information (NCBI) Sequence Read Archive under Bioproject accession number PRJNA556392. For all chimneys sampled (except ToMo), fluids were also sampled as close as possible to putative chimney fluid outlets and in ambient seawater (Supplementary methods). Fluids were analyzed using inductively coupled plasma-optical emission spectrometry (ICP-OES). All analytical protocols and conditions are detailed in Supplementary methods, including results of control experiments (e. g. , Supplementary Fig. S1 ).
Amplicon filtering and microbial diversity analysis High-quality sequences from tag sequencing were clustered into amplicon sequence variants (ASVs) by applying the minimum entropy decomposition algorithm  [23]  with default parameters but the minimum abundance set to 25 to remove noising features, while conserving relative diversities in each sample. Chimeras were identified in the representative ASV sequences with the FindChimeras algorithm implemented in the DECIPHER package for R  [24, 25]  and were then removed. Representative node sequences were submitted to the SILVAngs pipeline (https://www. arb-silva. de/ngs/) for taxonomic assignment of ASVs against the SILVA database release 132  [26]. As no DNA was amplified from procedural controls, we removed commonly identified contaminants  [27]  based on ASV taxonomic classification at the genus level (Supplementary Table  S2 ).
Taxa relative abundance in each sample was visualized using the ggplot2 package  [28]. Filtered ASVs were imported in R version 3. 6. 0 and normalized using scaling factors calculated by the meta-genomeSeq package  [29]. Similarities and dissimilarities between communities were respectively evaluated using Bray-Curtis index with the anosim function and PERMA-NOVA implemented in the adonis function of the Vegan package  [30] , with 999 repetitions each time. Potential correlations between chimney microbial communities and associated mineralogical and elemental compositions were evaluated with Mantel tests based on Pearson's productmoment or Spearman's rank correlations implemented in the Vegan package  [30].
Metagenome analysis Quality-filtered reads were assembled de novo with MEGA-HIT v1. 13  [31]  with a minimum contig length of 1,000 bp and default parameters. For each sample, reads were mapped to the assembled contigs with Bowtie2  [32]. Mapped reads were counted for each gene with the featureCounts function of the Rsubread package for R  [33]. Gene abundances were then normalized using the TMM normalization method implemented in the R package edgeR  [34]. To infer the taxonomy of all genes predicted from metagenomes, their sequence similarities were evaluated against the non-redundant NCBI database using Diamond blastp tool  [35]  with an e value cutoff of 10 -5. The most confident taxonomic rank assigned to each aligned gene sequence was predicted with the lowest common ancestor (LCA) algorithm implemented in MEGAN6  [36].
In addition to taxonomic annotation of all predicted genes, near full-length 16S rRNA-encoding genes were reconstructed for phylogenetic analysis (Supplementary methods). The relative abundance of bacterial and archaeal lineages in metagenomes was evaluated based on the relative abundance of (1) all retrieved genes, (2) 12 or 40 single-copy genes shared between all lineages as suggested by  [37, 38] , and (3) reconstructed near full-length 16S rRNA-encoding genes. A set of marker genes (Supplementary Table  S3 ) with normalized abundances was retrieved and imported in R for comparison and visualization using ggplot2 package  [28]. Functional annotation of contigs was performed with the PROKKA pipeline  [39]  against the integrated custom database. In addition, protein sequences of genes identified by Prodigal  [40]  were submitted to GhostKOALA server (http://www.
kegg. jp/ghostkoala/) to retrieve KEGG orthology  [41]  for each gene. Since some genes coding for hydrogenases are absent from the KEGG database, we also searched for hydrogenases domains  [42, 43]  in our metagenomes (Supplementary methods). All protein sequences of specific microbial lineages were finally retrieved and submitted to the GhostKOALA server in order to assess the completion of encoded metabolic pathways and to provide a more complete picture of the potential physiology of these lineages.
Average carbon oxidation state of proteins predicted from metagenomes The average carbon oxidation state (Z C ) of protein sequences predicted from PROKKA  [39]  was calculated as described in  [44]. Protein sequences were imported into R version 3. 6. 0 using the read. fasta function of the CHNOSZ package  [45]. Since we used complete protein sequences derived from co-assembled contigs rather than reads, the amino acid counts of each protein were normalized by the abundance of the corresponding encoding genes obtained with Bowtie2  [32]. This normalization yields abundanceweighted values for each protein and allows comparison between samples. To account for intra-sample variations, sequences were randomly subsampled 100 times to generate a set of 200,000 amino acids on average for each sample, and were normalized by their length.
Z C values were finally computed using the ZC(protein. formula(input)) function of R package CHNOSZ  [45]. Results from subsampled sequences were used to calculate mean Z C value and associated standard deviation for each sample, all plotted using ggplot2 package  [28]. To assess differences between samples in Z C mean values and variances, we respectively performed Welch's t-test and pairwise Wilcoxon rank sum test in R version 3. 6. 0. Note that the overall approach only highlights the Z C profile of encoded protein sequences and ignores potential differences between gene, mRNA and protein abundances [e. g. ,  46, 47].
Microscopic evidence for chimney microbial colonization CLSM shows that chimneys are colonized by microorganisms grouped in clusters of cells attached to minerals Fig. 2  Relative abundance of taxa obtained from representative ASV sequences retrieved from Old City chimneys (at the genus and phylum level for Archaea and Bacteria, respectively and using the SILVA database release 132  [26] ). Semi-quantitative X-ray diffraction results presenting the mineralogy of sampled chimneys are also shown as pie charts. The predominance of brucite is observed in most samples while ToMo is dominated by calcium carbonates (calcite and aragonite) and PiMo shows a similarly high relative content in calcite. Cand. Nitrosopumilus Candidatus Nitrosopumilus, MBG-A Marine Benthic Group-A, BHI80-139 candidate phylum BHI80-139/ NPL-UPA2.
Phylogenetic analysis Based on the metagenomic datasets, we have reconstructed 364 high-quality near full-length 16S rRNA-encoding gene sequences. As shown in Fig. 4  and Supplementary Figs. S5-S7, these sequences group with all the dominant taxonomic groups highlighted by tag-sequencing community analysis (Fig. 2 ) despite differences in abundance (e. g. , Actinobacteria and Acetothermia which are much more abundant in metagenomes; Supplementary Fig. S4 ). BaC and PiMo share many similarities regarding their Bacteria and Archaea phylogenetic affiliations. Many Bacteria of these chimney samples closely group with clones retrieved from LCHF (Fig. 4  and Supplementary Figs. S5  and S6 ) including Thermodesulfovibrionia (Nitrospirae), dominant Thiomicrorhabdus (Gammaproteobacteria), and some Sulfurovum (Epsilonbacteraeota) and Desulfobulbus (Deltaproteobacteria).
Environmental clone accession numbers are indicated in brackets with serpentinization-related ecosystems in bold. They correspond to the closest hits to the near full-length 16S rRNA-encoding gene sequences reported in this study using the SILVA database release 132  [26]. Bootstrap values over 70% support based on 1,000 replicates are indicated by black dots on their respective nodes. LCHF Lost City hydrothermal field, PBHF Prony Bay hydrothermal field, EPR East Pacific rise, JdFR Juan de Fuca ridge, GoM, Gulf of Mexico. (Fig. 5b ). Both genes are present in all proteobacterial lineages and in Thaumarchaeota (Fig. 6 ).
Acknowledgements The authors express their deep appreciation to the crew of  R/V Pourquoi  pas? and to the Genavir engineers in charge of the ROV Victor 6000 operations. They are also grateful to the scientific party of the ROVSMOOTH cruise and to shore-based collaborators. The authors thank  Hilary Morrison ,  Aleksey Morozov , and  Nicole Robichaud  ( Marine Biological Laboratory, Woods Hole Oceanographic Institution, MA, USA ) for performing the DNA sequencing, as well as  Sophie Nowak  ( Plateforme Rayons X, Université de Paris, France ) and  Pierre Burckel  ( IPGP, France ) who performed the XRD and ICP-MS analyses, respectively. Sequencing was supported by the  Census of Deep Life  of the  Deep Carbon Observatory  funded by the  Alfred P. Sloan Foundation. This study also received financial support from the  French National Agency For Research (ANR)  through the  deepOASES project  ( ANR-14-CE01-0008-01 ), the  "Mission pour les Initiatives Transverses et Interdisciplinaires" of the French CNRS (Défi Origines   2018 ) and the  LABEX UnivEarthS.
This work was supported by the  IPGP multidisciplinary program  PARI and by  Région Ile-de-France SESAME  grant No. 12015908. Finally, the authors would like to thank the  French Institute of Bioinformatics (IFB ,  ANR-11-INBS-0013 ) for providing storage and computing resources on its national life science cloud. This study contributes to the  IdEx Université de Paris   ANR-18-IDEX-0001.
The current paper aims to introduce CYBELE, a platform aspiring to safeguard that the stakeholders involved in the agri-food value chain (research community, SMEs, entrepreneurs, etc. ) have integrated, unmediated access to a vast amount of very large scale datasets of diverse types and coming from a variety of sources, and that they are capable of actually generating value and extracting insights out of these data, by providing secure and unmediated access to large-scale High Performance Computing (HPC) infrastructures supporting advanced data discovery, processing, combination and visualization services, solving computationally-intensive challenges modelled as mathematical algorithms requiring very high computing power and capability.
Thus, PA and PLF are currently being shaped by two major technological trends: big-data and advanced-analytics capabilities on the one hand, and robotics-aerial imagery, feeding and milking robots, intelligent sensors, sophisticated local weather forecastson the other. Towards this end, PA and PLF are driven by the need of 1) fusing a vast plethora of data sources which through various technological breakthroughs are currently available, 2) algorithms optimized for parallel execution that can exploit this multitude of data and harness their power, and 3) advanced infrastructural capabilities to handle the execution of these big data enabled algo-rithms efficiently time-wise, lowering responsiveness down to seconds so as to cater for time-critical decisions. The current paper aims to introduce CYBELE, a platform aspiring to safeguard that the stakeholders involved in the agri-food value chain (research community, SMEs, entrepreneurs, etc.
) have integrated, unmediated access to a vast amount of very large scale datasets of diverse types and coming from a variety of sources, and that they are capable of actually generating value and extracting insights out of these data, by providing secure and unmediated access to large-scale High Performance Computing (HPC) infrastructures supporting advanced data discovery, processing, combination and visualization services, solving computationally-intensive challenges modelled as mathematical algorithms requiring very high computing power and capability. The structure of the paper is as follows: Section 1 introduced the scope of the paper and highlighted the rationale behind the proposed research. Section 2 analyzes the methodology followed, graphically illustrating and explaining the proposed architectural approach for detaching the design, development and execution of HPC empowered big data analysis processes.
and Liu C-Z  [18]  , van Henten E. J. et al. [19]  , Ge Y. et al. [20]  , Primicerio J. et al. [21]  , Zhang C. and Kovacs J. M. [22]  , Ye J. et al. [23]  , Srbinovska M. et al. [24]  , Ferrandez-Pastor F. J. et al. [25]  , Popovic T. et al. [26]  , and Sawant S. et. al. [27]. Nevertheless, CYBELE aspires to reach far beyond the currently offered services, and offer a holistic platform targeting both domains of PA and PLF, and offering services not only to farmers, but also to a plethora of stakeholders involved in the agri-food value chain (research community, SMEs, entrepreneurs, etc. ). CYBELE also innovates in providing to these stakeholders integrated access to a vast amount of very large scale open and proprietary datasets of diverse types and coming from a variety of sources (including sensor data, environmental and climate historical data, satellite and aerial images).
Last but not least CYBELE offers the technolog-ical tools to the aforementioned stakeholders for experimentation with these data assets, and for the composition of novel services through the corresponding modelling environments, while abstracting the infrastructure required to support the development and the delivery of these resource demanding services. CYBELE will capitalize on: 1) The computing capacity and efficiency potential delivered by HPC e-infrastructure and HPCempowered services, enabling the processing of large amounts of heterogeneous data, and boosting modern scientific discovery, solving computationally-intensive challenges modelled as mathematical algorithms requiring real HPC architectures to achieve the required efficiency. 2) Technological advancements in big data technologies and related services, a) facilitating the aggregation of very large scale datasets (and/or their metadata) of diverse types (sensor data, satellite and aerial image data, etc.
) from a multitude of distributed data sources; b) allowing the (semantic) alignment of the aggregated data and metadata to a common schema and data model; c) empowering the execution of advanced data analytics extracting hidden knowledge and insights and d) empowering the delivery of intuitive and adaptive data visualization services, providing to the CYBELE stakeholders with a more understandable and more easily evaluated interface to the results of the complex simulations. 3) The potential of cloud services for delivering simple and secure service provisioning, thus providing a bouquet of domain specific and generic services on top of the CYBELE virtual, industrial experimentation environment to both research organizations but mainly to industrial communities with focus on SMEs lacking access to HPC infrastructures and the competences necessary to fully exploit them, facilitating the elicitation of knowledge from big agri-food related data, addressing the issue of increasing responsiveness and empowering semi-automated / automationassisted decision making when the prediction window is narrow.
The proposed architectural approach for detaching the design, development and execution of HPC empowered big data analysis processes is depicted in Fig. 1. This layered approach aims at ensuring interoperability among all involved components, putting emphasis on the way that pipelining of information (from data query, to simulation formulation, to analysis and to visualization) is supported, safeguarding smooth interoperation of the aspired services. In order to achieve this, CYBELE consortium aims at designing and delivering 1) on the one hand standardized interfaces putting emphasis on exposing reusable functional primitives for the HPC and Big Data frameworks integrated within CY-BELE, so that pipeline reusability is supported, minimizing the need of reprograming core integration engines, and 2) on the other hand normative schemes and common data models featuring common semantics, annotating the information to be exchanged between components, thus ensuring information harmonization and enabling seamless communication amongst the various heterogeneous components.