dataesr/scientific-paragraphs-categorization huggingface dataset:
The authors acknowledge funding by  Agence Nationale de la Recherche (ANR)  grant ( program  Ln23  ANR-13-BS07-0007-01 ),  Swiss National Science Foundation  grant ( 200021-182369 ),  Deutsche Forschungsgemeinschaft , ( SH 87/1-1 ) and the  Max Planck Society. This work used the platforms of the Grenoble Instruct center (ISBG;  UMS 3518 CNRS-CEA-UJF-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). We thank  ESRF  synchrotron (id29 beamline) and SOLEIL synchrotron (PXII beamline) for beam time allocation and the respective beamline staffs for assistance with data collection. We also acknowledge access to the  ESRF MX high-pressure laboratory (HPMX). IBS acknowledges integration into the  Interdisciplinary Research Institute of Grenoble (IRIG, CEA ).
Data management Apolipoprotein concentrations were calculated using calibration curves plotted from standard solutions and expressed in micromoles, assuming that 1 mol of peptide was equivalent to 1 mol of protein. The concentrations were then converted to standard units (milligrams per deciliter) using the molecular masses (Table  1 ). Unlike the apoB and apoL1 isoforms  (22, 23)  (Fig. 1 ), the apoE phenotypes and isoform concentrations (E2/E3/E4) were determined from different peptide combinations (supplemental material)  (12, 24). The mean size of apo(a) was also estimated by LC-MS/MS from two proteotypic peptides (supplemental material)  (25).
Statistical analyses Graphics and analyses were achieved with GraphPad Prism software (version 6. 0, GraphPad Software Inc. , La Jolla, CA). The D'Agostino-Pearson test was used to estimate data distribution and select the most appropriate statistical test for data comparisons (significance at P < 0. 05). Network analysis based on Spearman correlation analysis was also performed. The Spearman's rank correlation coefficient was computed (R software) and displayed using the "corrplot package. ".
Apolipoprotein concentrations The circulating apolipoprotein concentrations are provided in Table  2. Several plasma apolipoprotein concentrations were slightly but significantly different between males and females. To ensure assay repeatability, EDTA plasma measurements were repeated over five distinct experiments. The calculated CVs ranged from 1. 8% to 13. 7% and were within acceptable limits for MS experiments. Of note, intra- and inter-assay CVs calculated from the QC samples and the plasma pool never exceeded 15%, validating the reproducibility of the measurements and the quality of the preparation and analytical runs. The apolipoprotein concentrations were significantly over- or underestimated in serum, Li-heparin, and citrate plasma, in comparison to EDTA plasma. The plasma apolipoprotein concentrations were also compared with other biochemical data (supplemental Fig.
This work was supported by grants from the  Fondation de France  and by the  French national Cholesterol Personalized Innovation (CHOPIN)  project, funded by the  Agence Nationale de la Recherche  ( ANR-16-RHUS-0007 ) and coordinated by the  Centre Hospitalo-Universitaire of Nantes. V. B. received scholarships from the  Région Réunion  and the  European Union  ( European Regional Development Fund INTERREG V). Additional financial support was provided by the  Biogenouest Corsaire core facility. The authors declare that they have no conflicts of interest with the contents of this article.
Linked Data (LD) is a set of best practices to publish reusable data on the web in RDF format. Despite the benefits of LD, many datasets are not published as RDF. Transforming structured datasets into RDF datasets is possible thanks to RDF Mappings. But, for the same dataset, different mappings can be proposed. We believe that a tool capable of evaluating the quality of an RDF mapping would make the creation of mappings easier. In this paper, we present EvaMap, a framework to assess the quality of RDF mappings. The demonstration shows how EvaMap can be used to evaluate and improve RDF mappings.
Introduction and Motivation Linked Data (LD) is a set of best practices to publish reusable data on the web in RDF format. Despite the benefits of LD, many datasets are not published as RDF. Transforming structured datasets into RDF datasets is possible thanks to RDF Mappings. An RDF mapping consists in a set of rules that map data from an input dataset to RDF triples. Languages like R2RML 3 and RML 4 are widely used to define machine-readable mappings. In this work, we use YARRRML, a humanreadable representation of RDF mappings. Making a relevant RDF mapping for a dataset is a challenging task because it requires to answer several questions: 1. What are the different resources described in the dataset (e. g. , cars, persons, cities, places, etc. )? 2. What are the attributes of these resources (e.
g. , price, age, etc. )? 3. How should the IRI of resources be defined? 4. What are the possible relations between the different resources (e. g. , the city is the birthplace of the person)? 5. Which ontology, classes, and properties should be used? In addition to possible errors by the user, different answers are possible for some of these questions and, thus, different RDF mappings are possible for the same dataset. For example, Figure  1  represents two possible mappings for the dataset in Table  1. Unlike mapping 1(a), mapping 1(b) does not include a class description in resource IRIs and does not reference the Birth Province column. Given a structured dataset, how to help users to create RDF mappings without errors automatically, and how to choose the best mapping from a set of RDF mappings? We believe that a tool capable of evaluating the quality of an RDF mapping would make the creation and the choice of RDF mappings easier.
[1]  proposes a framework that assesses and refines RML mappings. However, authors focus on logical errors due to incorrect usage of ontologies (e. g. , violation of domain, range, disjoin classes, etc. ). [3]  proposes a framework to assess the quality of RDF datasets through metrics. Metrics are organized in dimensions evaluating different aspects of a dataset (e. g. , availability, interlinking, etc. ). But,  [3]  does not propose to assess the quality of an RDF mapping. In our work, like in  [1] , we evaluate metrics on the RDF mapping instead of on the resulting RDF dataset. This choice allows us to identify errors at the beginning of the publishing process and saves time. Based on the framework proposed in  [3] , we propose EvaMap. EvaMap is a framework to Evaluate RDF Mappings. The goal is to control the quality of the resulting dataset through its mapping without having to generate the RDF dataset.
2 EvaMap: A Framework to Evaluate RDF Mappings EvaMap uses a set of metrics organized in 7 dimensions. Each metric is evaluated on the RDF mapping or on the resulting RDF dataset when instances are needed. For example, the available resource IRIs metric needs RDF dataset to check if generated IRIs are dereferenceable. In this case, EvaMap generates a sample such that applying each mapping rule to the entire input dataset is not necessary. Table  2  describes each dimension of EvaMap. These dimensions are based on  [3].
Demonstration We implemented EvaMap to evaluate YARRRML  [2]  mappings for datasets of the OpenDataSoft's data network 5. Our tool is available as a web service at https://evamap. herokuapp. com/. The source code of our tool 6  and web service 7  are available on GitHub under the MIT license. During the demonstration, attendees will be able to select different mappings and use EvaMap to compare them. For each the global quality score will be computed as well as the quality score for each dimension. Our tool will also give feedback to improve RDF mapping. In our tool, users can assess two mappings for the dataset football-ligue. Users can see that the mapping football-ligue obtains a worse global score than the mapping football-ligue-fixed. In the detailed report, users can analyze by dimension why these scores are different.
Both strains are accessible at the Roscoff Culture Collection website 1  with the following accession numbers: ST147 = RCC1627 for S. acuminata and A120 = RCC4398 for the Amoebophrya-like parasite. We used continuous light in order to minimize circadian variations of the host metabolism, such as pigment cell content and composition. We have found that continuous light does not seem to stress phytoplanktonic organisms, as illustrated by significant increases in growth rates under this condition (data not shown). Consequently, S. acuminata cultures were acclimated to continuous white light in exponential growth phase under 85 µmol photons m -2 s -1 for over 3 weeks prior to the experiments. We used fluorescent tubes (Sylvania Luxline plus) and adjusted the irradiance using a quantamer (Biospherical Instrument Inc.
Transcriptomics Host Transcriptome and Proteome We downloaded and reanalyzed a recently published transcriptomics dataset from the internal development of two Amoebophrya strains (ENA database project ID PRJEB26803) following a full infection cycle  (Farhat et al. , 2018). We reconstructed the host organellar energy-related metabolic pathways for which part of the genes are encoded by either the mitochondrial (mt) or the chloroplast (cp) DNA, and monitored their expression. To do so, a reference transcriptome (Step 1 in the bioinformatics workflow; Supplementary Figure  S1 ) was created for the host S. acuminata (host-T0) from reads of the uninfected host using the Trinity assembler v. 2  (Grabherr et al. , 2011). We created a reduced version of this reference transcriptome using companion scripts from the Trinity package  (Haas et al.
, 2013)  by filtering out poorly covered transcripts (FPKM < 1) and rare isoforms (<1%), then predicted a proteome (host-T0p) with TransDecoder v. 3. 0. 1 2. Both the reference transcriptome host-T0 and the predicted proteome host-T0p were automatically annotated using a combination of similarity searches with Diamond v. 0. 8. 34  (Buchfink et al. , 2015)  against the UniProtKB/Swiss-Prot (version October 2019) and the GenBank nr (version October 2019) databases, hmmscan from HMMER v. 3. 1b2 3 , SignalP v4. 1  (Petersen et al. , 2011) , and TMHMM v2. 0 4. Finally, GO terms annotation and KEGG mappings were performed and all the annotations were integrated with the Trinotate pipeline 5.
Manual Curation of Energy-Related Genes We used the KEGG Pathway reference database 6  to identify all recorded genes whose products are involved in the electron transport chains (ETC) of the "Oxidative phosphorylation" or OXPHOS (ko00190) and the "Photosynthesis" (ko00195 and ko00196) pathways, and searched for them in our transcriptome and proteome annotations (Step 2 in the bioinformatics workflow; Supplementary Figure  S1 ). For each gene, a set of reference protein query sequences was downloaded from the UniProtKB/Swiss-Prot database  (UniProt Consortium, 2019)  and used as queries in similarity searches with the blastp and tblastn scripts (e-value = 1e - 10  ) from the BLAST package v2. 6  (Altschul et al. , 1990 (Altschul et al. , , 1997) )  to capture homologs from the predicted proteome (host-T0p) and reference transcriptome (host-T0) of S.
acuminata. We checked positive hits by reverse BLAST against the UniProtKB/Swiss-Prot database. A combination of gap4 v. 4. 11  (Staden et al. , 2000)  and gap5  (Bonfield and Whitwham, 2010)  from the STADEN package 7  , MITObim v. 1. 8 (Hahn et al. , 2013) , Bowtie2 v2. 2. 9+  (Ben and Salzberg, 2012) , and scripts from the Samtools v. 1. 3. 1 package  (Li et al. , 2009)  were used to manually curate and extend positive matches. For each gene, we used the ExPASy Translate tool from SIB ExPASy Bioinformatics Resources Portal  (Artimo et al. , 2012)   8  with the Standard Genetic Code to translate the selected transcripts into predicted peptides. Gene transcripts were retained when predicted peptide matched homologs on public databases (blastx using the Standard Genetic Code against nr and the UniProtKB/Swiss-Prot databases), and preferably included both a start and a stop codon with a portion of the untranslated regions (UTR) at the 5 and 3 ends of the CDS region (excluding those genes encoded by organelle genomes).
Transcriptomics of Infection We created a modified transcriptome for the host (host-T0new) by identifying and replacing contigs from the host-T0 transcriptome that matched the curated energy-related gene sets identified above (Step 3 in the bioinformatics workflow; Supplementary Figure  S1 ) using tblastn (e-value = 1e -5 ). An artificial "hybrid" reference transcriptome (hybtrans) was created by combining the modified host transcriptome host-T0-new and the parasite predicted mRNAs (that include both the 5 and 3 UTR regions) for the Amoebophrya A120  (Farhat et al. , 2018). We used the DESeq2 differential expression analysis tool from the Trinity v2 package  (Haas et al. , 2013)  and custom scripts to monitor the abundance of host and parasite genes in hybtrans throughout the infection. In short, filtered RNA-seq reads for each replicate (triplicates sampled every 6 h of a 36 h infection cycle in experiment 2) were separately mapped with the Bowtie2 aligner, and gene expression matrices were computed using the RSEM method.
This heterogeneity was further exemplified by different patterns of AR expression following ADT across the different models (decreased in 22rv1 when compared to CWR22res, increased in LNCaP AI compared to LNCaP, and strongly increased in VCaP-CR tumours in comparison to VCAP, Figure  1d ). Finally, we took advantage of our proteomic approach to generate a proteomic signature characteristic of CRPC, irrespective of tumour type or AR status. We compared our proteomic datasets to identify proteins that were commonly modulated in all three models of CRPC. Interestingly, only 8 proteins were commonly regulated across the different models (FC = 2; p-value < 0. 05; Figure  1b ). Among these candidates, Adducin-1 (ADD1) and Schlafen Family Member 5 (SLFN5) were significantly more abundant in all CRPC tumours.
Moreover NDNF, STRBP, UBAP2 and SLC7A5 transcript levels strongly correlated with SLFN5 expression in PCa patients (Figure  5f , TCGA dataset), thus supporting a potential transcriptional regulation by SLFN5. Of note, SLC7A5 and SLC3A2 are the functional components of the AA transporter LAT1 which has recently gained interest as a molecular target for cancer therapies  22. We therefore sought to explore the link between SLFN5 and LAT1 in PCa.
The top ten most intense ions were selected for fragmentation in the linear ion trap using Collision Induced Dissociation (CID) using a maximum injection time of 25 ms or a target value of 4000 ions. The MS raw data (378 raw data files) were processed with MaxQuant v. 1. 5. 2. 8  43  and searched with Andromeda search engine  44  , querying two different UniProt databases  45  : Homo sapiens (09/07/2016; 92,939 entries) and Mus musculus (20/06/2016; 57,258 entries). Protein hits coming from individual database were separated using "Split protein groups by taxonomy ID" option in MaxQuant. The "Re-quantify" and "Match between runs" options were also enabled. For quantitation, multiplicity was set to 2 and Arg0 and Lys0, and Lys8 and Arg10 were used for ratio calculation of SILAC labelled peptides.
The MS. raw files were processed with MaxQuant software 43 v. 1. 6. 3. 3 and searched with Andromeda search engine  44  , querying SwissProt Homo sapiens database (30/04/2019; 42,438 entries). The database was searched requiring specificity for trypsin cleavage and allowing maximum two missed cleavages. Methionine oxidation and N-terminal acetylation were specified as variable modifications, and Cysteine carbamidomethylation as fixed modification. The FDR for peptide and protein identification was set to 1%. MaxQuant proteingroups. txt output file was further processed using Perseus software v. 1. 6. 2. 3  46. The common reverse and contaminant hits (as defined in MaxQuant output) were removed. Only protein groups identified with at least one uniquely assigned peptide were used for the analysis.
Acknowledgements We would like to thank the  Core Services and Advanced Technologies at the Cancer Research UK Beatson Institute , with particular thanks to the  Metabolomics and Proteomics Units , and the  PRIDE team. This work was supported by  Cancer Research UK Beatson Institute core funding  ( C596/A17196 ) and  CRUK core group  awarded to HYL ( A15151 ) and to SZ ( A29800 ). This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  721746. PP and EH were funded by grants from  "La ligue Contre le Cancer", "la région Bourgogne Franche-Comté " and " Canceropole Grand Est ". RSM is a  Horizon ITN Early Stage Researcher. MS is a  Medical Research Council Clinical Research Fellow  ( MR/L017997/1 ).
In turn, all these factors can variously interfere with the osteoanabolic effects of physical activity. Personalized physical exercise certainly represents, in association with drugs targeting immunological checkpoints, an important tool in the so-called precision therapy of osteoporosis. In the present review, investigators searched PubMed (n = 1052), ScienceDirect (n = 131) and Google Scholar (n = 729) databases using the following strategy: (Rats) and (exercise/physical exercise) and (bone) and/or (bone mineral density/bone quality/microarchitecture) and/or (osteoporosis/osteopenia) and/or (ovariectomy/hindlimb) with English and French language restrictions. In total, 1912 publications were identified (Figure  1 ). After these initial criteria, a second discrimination was made to separate the relevant studies from the others.
Q-PCR analysis: cfDNA was quantified by q-PCR according to an innovative design of short (60-100 bp ± 10bp) and long (300bp ± 10bp) amplicons targeting the wild-type sequences of specific genes: the KRAS nuclear gene and the mitochondrial Cytochrome oxidase III gene, MT-CO3 (Table  S 1 ). Quantification of the short and long amplicons provides an estimation of the concentrations of the total nuclear circulating free DNA (NcfDNA) and the mitochondrial circulating free DNA (McfDNA) respectively. Q-PCR amplifications were performed in triplicate in a 25μL final reaction volume controlled by the CFX manager software of a CFX96 touch TM Real-Time PCR detection system (Bio-Rad). Each PCR reaction mixture was composed of 12. 5μL of SsoAdvanced™ Universal SYBR ® Green Supermix (Bio-Rad, Marnes-la-Coquette, France), 2.
Mitochondrion staining: The 16,000g pellets from both, the plasma without platelet activation (pool from 5 healthy individuals) and FSC-H, both in logarithmic mode. Collected events in the newly created gate were gated as singlet. Following confirmation that all events recorded in this gate were MitoTracker Green positive, the gate was applied to all samples. Plot histograms were created for both unstained and stained samples. Results were analyzed with Kaluza analysis 1. 5 software.
Our data demonstrate the presence of quintessential ancient symbiotic energy-providing component of all eukaryotic cells, the mitochondrion, as a previously unappreciated constituent of a critical organ, the blood. Further investigations are warranted to evaluate the impact and the potential implications of this discovery in the field of cell-cell communication, inflammation and in clinical applications. The electron flow experiment performed using a seahorse XF96 instrument. Like the positive control (Isolated mitochondria from DLD-1), OCR was detectable in both 16gP from DLD-1 cell media and plasma pool, in the presence of pyruvate and malate that drive respiration via complex I (A). There was a decrease in OCR when rotenone was injected, indicating an inhibition of complex I-driven respiration.
When complex II-driven respiration was initiated, by sequential addition of succinate, a slight increase in OCR was observed, indicating that complex II may be functional. The inhibition of complex III (antimycin A) and the stimulation of complex IV (ascorbate + TMPD), led to a detectable activity of cytochrome C/complex IV in all the samples. Each value shown was from a minimum of three replicates. The data are presented in (A) as mean (symbol) ± SD (line). The four injections are shown as dashed lines. Blue histograms were obtained from MitoTracker-stained samples, and grey histograms, from non-stained samples. Mitochondria singlets were selected using the gating procedure described in Methods section. (A) Differential centrifugations of plasma extracted by Ficoll and cell media supernatant.
Acknowledgments: The authors would like to thank  P. Blache ,  T. Salehzada  and  C. Jay-Allemand  for helpful comments. We are grateful to  J. Venables  ( Science Sense ) for help in editing the manuscript. We are grateful to the  MRI platform (Montpellier Bio campus ) for access to imaging facilities, and to METAMONTP platform for access to seahorse technology (M-L. Vignais). We thank  Beatrice Chabi  ( INRA, UMR 0866 DMEM Dynamique Musculaire et Métabolisme, Centre de recherche de Montpellier, Montpellier, France ) for her contribution to the oxygraph experiment. We thank  A. Turtoi  for providing us CCD-18Co cell line and reagents. ART is supported by the  INSERM (Institut National de la Santé et de la Recherche Médicale ).
Results Descriptive statistics and phenotypic correlations. In the present study we measured a set of 30 traits related with immune, haematological and stress parameters on a commercial Duroc pig line comprising 432 individuals. The data on descriptive statistics, as well as the abbreviated name of the analysed measured traits are shown in Table  1. Among the haematological traits, the erythrocyte-related traits presented the lowest phenotypic dispersion, with a coefficient of variation (CV) below 0. 1, while the leukocyte and platelet-related traits presented CV ranging from 0. 34 (leukocytes count) to 0. 63 (eosinophils count). Regarding the ITs, most phagocytosis traits presented limited dispersion (CV ≤ 0. 2), whereas the highest CVs were obtained for the acute-phase proteins CRP and HP (CV = 0.
For the percentage of γδ T cells, five SNPs located at 33. 51-33. 64 Mb of SSCX were found to be significantly associated to this trait (Fig. 3G ). In this region, we did not identify any candidate gene among the annotated protein coding genes. However, four lncRNA and ssc-mir-9786-1 were annotated in this region. Both types of RNAs have been directly implicated in the innate immune response  39, 40  , although there is still a lack of information about the mechanism of action of lncRNAs. In contrast, miRNAs are better characterized and there are tools and databases that allow to perform an in-silico target prediction. In this way, ssc-mir-9786-1 was predicted to target a total of 528 genes by RNAhybrid. Noteworthy, among the biological functions represented in the list of targeted genes there was T cell differentiation (Supplementary Table  S4 ).
Finally, two interesting candidate genes (PRDX1 and PIK3R3) were also identified in the region associated with both MCH and MCV. These red cell parameters are highly related, showing positive phenotypic and genetic correlations between them (r p = 0. 89, r g = 0. 81), which is concordant with the identification of this pleiotropic region. PRDX1 is a member of the peroxiredoxin family of antioxidant enzymes. Severe haemolytic anaemia characterized by marked decrease in haematocrit and haemoglobin in peripheral blood has been observed in mice lacking PRDX1  67. Remarkably, MCV is among the 15 traits with the highest number of QTLs identified so far, with 546 associations (PigQTLdatabase, release 41, April 26, 2020). Nonetheless, we only identified a previously published QTL region associated with MCH  27  , which was proximal to the region for MCH/MCV identified in our study.