dataesr/scientific-paragraphs-categorization huggingface dataset:
org/, accessed on 28 November 2020). Default settings were used, i. e. , medium confidence of 0. 4 and 4 criteria for linkage: co-occurrence, experimental evidence, existing databases, and text mining. As the bovine Gene Ontology (GO) had limits, orthologous human Uniprot IDs, following the procedure by Gagaoua et al. [14] , were used for this analysis to take advantage of the most complete annotations available.
Statistical Analyses Statistical analyses of protein abundance were performed with XLSTAT 2018. 2 (AddinSoft, Paris, France), as well as the online tools NormalyzerDE and MetaOmGraph, mainly for data standardisation. Raw data were scrutinised for data entry errors, any missing data, or outliers. Log2 transformation and mean normalisation were performed on protein abundance among replicate samples. For the comparison of protein abundance between the tender (low WBSF values) and tough meat samples (high WBSF values), a one-way analysis of variance was performed for each protein. Differences in protein abundance between the tender and tough groups were considered significant at p < 0. 05, and significant proteins were considered as candidate protein biomarkers. Pearson correlations were computed between the individual WBSF values and protein abundances for those proteins significant following ANOVA.
A total of 34 proteins were different (p < 0. 05) in their abundance between the tender and tough groups (Table  2 ). These 34 proteins belonged to five major biological pathways (Table  2 ), these being: (i) muscle contraction, structure, and associated proteins (n = 17; 50%); (ii) energy metabolism and associated pathways (n = 5; 15%); (iii) heat shock proteins (n = 4; 12%); (iv) oxidative stress (n = 2; 6%); and (v) other pathways including regulation of cellular processes, binding, apoptotic, and transport proteins (n = 6; 17%). The 34 proteins were then compared with a database of beef tenderness biomarkers by Gagaoua et al. [13] , of which 23 overlapped with the database (Table  2 ). The regression model built for WBSF is presented in Table  3. The model explained 79% of the variability in WBSF (p < 0.
Dominant Pathway Related to WBSF of Young Limousin-Sired Bulls Muscle contraction and structure were identified as the most important pathway associated with WBSF in this study. Most of the proteins from this pathway were localised in the sarcomere. Of these, compared with the database of beef tenderness biomarkers of Gagaoua  [13] , 13 were already identified, and 4 proteins (BIN1, FHOD1, CORO6, and RDX) were reported for the first time in this study. Myosin and actin were critically important to textural changes in muscle that occurred post-mortem during meat ageing through the weakening of the actin/myosin complex in the myofibril  [22]. As the major component of the thick filaments of the myofibril, molecular myosin consisted of two heavy and four light chains. This study revealed, for example, MYLPF (Myosin regulatory light chain 2) and MYL1 (Myosin light chain 1/3) to be negative biomarkers of beef tenderness.
The impact of HSP70 proteins on meat tenderness was thought to be mainly because they obstruct pro-apoptotic factors such as Bcl-2 in apoptotic pathways  [13, 55]. HSPA8 is grouped in the response to unfolded protein (GO:0006986) and protein refolding (GO:0042026) in Table  5. In this sense, HSPA8 played an important role in response to cellular stress  [56]. Moreover, this protective role might be based on its interaction with structural proteins or by regulating cell signalling pathways (Figure  3 ). STIP1, known as a stress-induced-phosphoprotein, is a co-chaperone whose negative relationship with tenderness, found here, was consistent with the findings of Picard and Gagaoua  [8]. As for the small HSPs, HSPB1 was identified as a robust biomarker of beef tenderness (referring to the database by Gagaoua et al.
Funding: This research was funded by  Teagasc  and the  Walsh Scholarship programme (Walsh Scholarship 2016022 ), the  BreedQuality  project ( 11/SF/311 ), which is supported by The  Irish Department of Agriculture, Food , and the  Marine (DAFM)  under the National Development Plan 2007-2013. Mohammed Gagaoua is a  Marie Skłodowska-Curie Career-FIT Fellow  under project number  MF20180029 , grant agreement No. 713654. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
620 ** P19879 OGN b Mimecan -0. 64 0. 017 0. 589 * Q2HJF7 CAMK2D Calcium/calmodulin-dependent protein kinase -0. 20 0. 038 0. 510 * Q6EWQ7 EIF5A Eukaryotic translation initiation factor 5A-1 -0. 45 0. 046 0. 574 * Q3SYR3 APOBEC2 Probable C->U-editing enzyme APOBEC-2 -0. 66 0. 047 0. 530 * a Significance of the correlations: * p < 0. 05; ** p < 0. 01; *** p < 0. 001. b Proteins identified as Quantitative Trait Loci (QTL) of shear force using ProteQTL tool included in ProteINSIDE (http://www. proteinside. org/, accessed on 28 November 2020) from the Animal QTL Database (https://www. animalgenome. org/QTLdb/, accessed on 28 November 2020). c Proteins identified as QTL of sensory tenderness.
, 2017)  and Germany  (Kasper-Sonnenberg et al. , 2019) , however, to date, no data regarding DINCH exposure in the French general population is available. Studies describing phthalate exposure in the general population usually assessed their metabolites in spot urine samples. Due to the short half-life of phthalates and the temporal variability in exposure-prone behaviors (e. g. , use of personal care products, food intake), a high within-subject variability has been reported in phthalate metabolite urinary concentrations (reviewed by  Casas et al. , 2018). To improve exposure assessment in biomarker-based studies about such compounds, it has been advocated to collect repeated urine samples per individual  (LaKind et al. , 2019; Perrier et al. , 2016). To limit assay cost, one can pool these repeated samples within subject for each period of interest.
Study population The SEPAGES cohort is a French couple-child cohort that recruited 484 pregnant women between July 2014 and July 2017  (Lyon-Caen et al. , 2019)  in the Grenoble area. Inclusion criteria were being pregnant by less than 19 gestational weeks at inclusion, older than 18 years old, reading and speaking French fluently, being affiliated to the French national health security system and having planned to deliver in one of the four maternities of the area. Twin or multiple pregnancies were excluded. Our study population was restricted to the 479 mothers with at least one urine collection during pregnancy. The SEPAGES study was approved by an ethics committee (CPP: Comité de Protection des Personnes Sud-Est V) and the French data privacy committee (CNIL: Comité National Informatique et Liberté).
In addition to the metabolites classically assessed, we measured urinary concentrations of DINCH and DPHP metabolites. The parent compounds of these two metabolites are used in replacement of high molecular weight phthalates such as DEHP  (Koch et al. , 2013; Schmidtkunz et al. , 2019; Schutze et al. , 2014) , and no data on exposure to these chemicals was available so far in the French population. While their medians were among the lowest observed in our study population, the high frequencies of detection for these metabolites (99-100% in the pregnancy and twelve-month infant samples, and up to 64% at two months of age) suggested widespread exposure to these new plasticizers.
Acknowledgements The SEPAGES study group includes  E. Eyriey ,  A. Licinia ,  A. Vellement  ( Groupe Hospitalier Mutualiste, Grenoble ),  I. Pin ,  P. Hoffmann ,  E. Hullo ,  C. Llerena  ( Grenoble University Hospital, La Tronche ),  X. Morin  ( Clinique des Cèdres, Echirolles ),  A. Morlot  ( Clinique Belledonne ,  Saint-Martin  d'Hères),  J. Lepeule ,  S. Lyon-Caen ,  C. Philippat ,  I. Pin ,  J. Quentin ,  V. Siroux ,  R. Slama  ( Inserm, CNRS, University Grenoble  Alpes IAB research center). We thank  Dr Holger Koch  for sharing his knowledge on phthalate metabolism and DEHP metabolite ratios. SEPAGES biospecimens are stored at  Grenoble University Hospital (CHU-GA ) biobank (bb- 0033-00069 ); we would like to thank the whole CRB team, led by  Pr. P. Mossuz  and  Mr. P. Lorimier , and in particular the technicians for the huge work of biospecimens processing and pooling:  Mrs.
Funding This work was funded by the  French National Research Program for Environmental and Occupational Health of ANSES (CNAP ,  EST-2016-121 ) and the  French Research Agency -ANR  ( EDeN ,  19-CE36-0003-01 ,  ANR-15-IDEX-02 ). The SEPAGES cohort was supported by the  European Research Council  (N •  311765-E-DOHaD ), the  European Community 's  Seventh Framework Programme  ( FP7/2007-206 -N • 308333-892 HELIX ), the  French Research Agency -ANR  ( ANR-19-CE36-0003-01 ;  ANR-12-PDOC-0029-01 ,  ANR-14-CE21-0007 ,  GUMME  project ( ANR-17-CE34-0013 ),  ANR-18-CE36-005 ,  ANR-15-IDEX-02  and  ANR-15-IDEX5 ), the  French National Research Program for Environmental and Occupational Health of Anses  ( 2019/1/039 ;  2018/1/264 ,  EST-2016-121 ), the  Plan Cancer (Canc ' Air  project), the  French Cancer Research Foundation Association de Recherche sur le Cancer -ARC , the  French Endowment Fund AGIR for chronic diseases -APMC, PRENAPAR) , the  French Endowment Fund for Respiratory Health  and the  French Fund -Fondation de France  ( CLIMATHES -00081169 ).
Reports on volatiles involving the pine processionary moth Published works reporting on the activity of tree volatiles on PPM behavior and the influence of PPM herbivory on pine volatile composition were compiled using Web of Science search engine, in all available databases, using the keywords "volatile" and "Thaumetopoea" or "processionary moth". The eight reports retrieved dated from 1999 to 2020 and were published in journals specialized in the scientific fields of Plant sciences (63 %), Entomology (25 %) and Environmental Sciences (25 %). These works generated 267 citations by a total of 240 works, in an average of 33 citations per work. The reports that cited these works were published in journals specialized in the scientific fields of Environmental Sciences (88 %), Plant sciences (83 %) and Forestry (71 %).
The performance of metagenomic methods is heavily dependent on accurate bioinformatic analysis, and both classification algorithms and databases are crucial factors determining the overall performance of available pipelines  [6, 7]. A wide range of metagenomic pipelines and taxonomic classifiers have been developed, often for the purpose of biodiversity studies analysing the composition of the microbiome in different samples and cohorts. In contrast, when applying mNGS for patient diagnostics, potential false-negative and false-positive bioinformatic classification results can have significant consequences for patient care. Most reports on bioinformatic tools for metagenomic analysis for virus diagnostics typically describe algorithms and validations of single in-house pipelines developed by the authors themselves  [8] [9] [10] [11] [12] , stressing the need for high quality validation studies.
User-friendly software considerations are cloudbased platforms with web front-end interfaces which can facilitate direct uploading of the raw files from sequencing instruments and direct downloading of the final output analyses from the server. Examples of these interfaces and platforms are the Galaxy  [14]  and INSaFLU platforms (https://insaflu. insa. pt/) [15] , server hosting (i. e. Amazon web services, Microsoft Azure), or cloud-based software solutions which can be scalable on-demand and frequently at lower operational costs (see Table  2 ). Finally, "third-generation" small sequencers based on nanopores that have relatively low capacity for metagenomic runs and are currently used for research applications, may simplify and streamline both the laboratory and bioinformatics processes, allowing for real-time analysis on a laptop computer, and futuristically, potentially near the bedside  [16] [17] [18].
fastq" and the name of the FAST5 files containing nanopore (ONT) raw electrical signals typically includes flowcelll number, run id and a consecutive number of the files generated per barcode, for instance, "FAK96194_5138107d5a8425587f0828dd31f396e3ebd774c4_1. fast5" and need to be converted into FASTQ format using for instance GUPPY). Most of the tools for NGS data processing accept files in the compressed formats 'tar', 'zip', or 'gzip'.
Data analysis: version control Downstream mNGS data analysis may be restricted to taxonomic classification of sequence reads or may alternatively include de novo assembly of reads into contigs or scaffolds, followed by aligning to a set of genomes, which requires selection of the tools for particular tasks and targets  [30] [31] [32] [33]. Currently there are no optimal or golden standard tools and different approaches can produce different results for the same FASTQ file. In a recent ENNGS comparison of viral metagenomic pipelines, performance was impacted by the overall components of specific pipelines including algorithm, settings, and database  [34]. It is recommended to use version controlled pipeline tools and external databases used for NGS data analysis of clinical samples. For each tool used in the pipeline, at least the following parameters/options have to be described: date of analysis, name and version of the tools and external databases, as well as user-defined and default values of parameters used for each tool, e.
Reference database Selection of the reference database can significantly influence the results of taxonomic classification  [7]. The reference database should consist of genomes that cover the entire genetic diversity of relevant organisms and should be curated in order not to contain any artificial, low-quality or incorrectly named genome sequences (Recommendation 5). Poorly curated databases containing misannotated reference sequences will lead to false positive results due to incorrect assignment of ambiguously mapped/aligned reads or k-mers. Incomplete databases missing newly discovered or uncommon viral strains can lead to false negative results  [54]. Database compression by removal of duplicate sequences  [46]  is an effective way to save storage space, but compression can lead to a decreased performance in pathogen detection  [39].
In general, larger databases enable more accurate sub-typing/classification to isolate level. Several viral databases are available to the scientific community (examples are shown in Table  3 ). Use of complete NCBI's GenBank nucleotide database  [55]  containing sequences assigned to viruses (NCBI: txid10239) contains redundant sequences, requires a lot of computer resources and leads to a number of false-positive virus assignments  [7]  as the GenBank database entries are not curated. In contrast, the non-redundant NCBI's RefSeq database  [56]  is relatively small by providing one sequence per species accurately assigned based on ICTV taxonomy, and importantly, well-curated, significantly reducing the number of false-positive assignments to provisional sequences that can be inaccurate. Viruses recently discovered and virus variants highly divergent from NCBI's RefSeq reference sequence may be unidentified, the latter also depending on the stringency of the mapping criteria of the classification algorithm used as described above  [4].
In clinical diagnostic practice, NCBI's RefSeq database is commonly used for identification and classification of viruses and resulted in good overall performance in an international benchmark study  [34]. Curated vertebrate virus genome databases have been proposed, conveniently for clinical diagnostics lacking non-vertebrate viruses, for example Virosaurus (https://viralzone. expasy. org/8676 [57] ) with sequences that are clustered to remove redundancy. With the exponential growth of the number genome sequences in public databases, it is important to periodically update the reference databases used for taxonomic profiling, and to validate this update (Recommendation 6). The frequency of the update is dependent on the need to classify at subtype or isolate level, and on the appearance of.
Removal of contaminating sequences Contamination can be introduced in several steps of the workflow, including nucleic acid extraction kits, reagents and diluents, postsampling environment (i. e. airborne particles, index switching, crossovers from past sequencing runs) and misclassification related to the classification algorithms used and/or the reference databases available  [58, 59]. As mentioned in Part I of these guidelines, positive and negative controls should be included in the sequencing run so post-sequencing contamination removal can be performed either manually or using computational algorithms (Recommendation 7). Two examples of such tools include Recentrifuge  [59]  and the R package Decontam  [60]. These algorithms are based on different assumptions: while Recentrifuge classifies candidate contaminating taxa based on the relative frequency in the samples compared to controls and checks for crossover contamination, Decontam assumes that sequences from contaminating taxa are likely to have frequencies that inversely correlate with sample DNA concentration and are also likely to have a higher prevalence in control samples than in true samples (the contaminating species do not have to compete with true species in the negative control).
Furthermore, Recentrifuge takes into account the score level of the classifications in every single step provided by the taxonomic classifier, therefore, removing potential false positive taxa introduced by the taxonomic algorithm. It must be taken into account that (low level) sequences detected in the negative run control not uncommonly originate from highly abundance species present in patient samples (e. g. due to index hopping). Automated removal of contaminating sequences should be validated (Recommendation 7). Alignment of sequence reads against a contaminant database (using bwa) can also be useful.
Datasets for validation The bioinformatics pipeline should be evaluated using data from real samples, well-characterized by molecular diagnostic methods, which can be supplemented with analysis using in silico datasets  [63, 64]  (Recommendation 9). Artificial mNGS reads can be generated using the tools such as ART; CAMISIM  [65]  or other simulators, reviewed in  [66]. By using simulated datasets, the impact of variable amounts of background sequences (e. g. reads of human or bacterial origin), different mutation rates, detection rate of less-related viral genomes, as well as multiple combinations of settings (single vs paired-end reads and different read lengths) can be tested  [6].
Ring trials Benchmarking of a variety of pipelines  [63, 64]  has recently been performed by the ENNGS using datasets from clinical samples using RT-PCR as a gold standard  [34]. A wide variety of viral metagenomic pipelines was used in the participating clinical diagnostic laboratories. In the benchmark, detection of low abundant viral pathogens and mixed infections remained a challenge. Benchmarks are required for accreditation purposes, can reveal less effective components of a workflow, and moreover, can point out best practices with regard to the common aim of the participants, the use of mNGS for clinical diagnostics. A ring trial organized by the Swiss Institute of Bioinformatics encountered the performance of both the wet and the dry lab procedures  [71]. The QCMD has initiated a EQA scheme of metagenomic workflows in 2020 (Q4) using spiked samples.
For each tool used in the pipeline, at least the following parameters/options have to be described: date of analysis, name and version of the tools and external databases, as well as user-defined and default values of parameters used for each tool. Additionally, it is recommended to version the overall ensemble of tools, e. g. using a workflow tools/ docker containers. Preferably, software should be made available via GitHub or GitLab to automatically handle version control to a large extent. Reference database (3. 2. 6) 1. The reference database should consist of genomes that cover the entire genetic diversity of relevant organisms and should be curated in order not to contain any artificial, low-quality or incorrectly named genome sequences. 1. It is recommended to periodically update the reference databases used for taxonomic profiling, and to validate this update.
org Curated database of reference genomes of microbial sequences, for diagnostic use Curated database of sequences of exemplars for each classified virus species Curated database of annotated genomic, transcript, and protein sequence records: viruses (ca. 8500 complete viral genomes), prokaryotes, eukaryotes Uncurated database of all publicly available nucleotide sequences, annotated Curated database of virus nucleotide sequences, available as Unclustered (U-) and Clustered (C-) nucleotide sequence files. Sequences determined to be irrelevant for virus detection are removed. Protein version (RVDB-prot and RVDB-prot-HMM) of the curated U-RVDB described above. Curated database of annotated viral genomes generated by the Swiss Institute of Bioinformatics (SIB), including all sequences annotated as complete viral genomes (ca.
70,000) downloaded from GenBank (query "VRL[Division] AND 'complete genome' [ALL]") and subsequently screened for several criteria. Curated and annotated database of proteomic virus references (ca. 10,000 virus reference proteomes) In-house database download from GenBank (query "txid10239[orgn] AND ("complete genome"[Title] OR srcdb_refseq[prop]) NOT wgs[PROP] NOT "cellular organisms"[Organism] NOT AC_000001[PACC]: AC_999999[PACC]" Curated database of virus sequences for clinical metagenomics, clustered (non-redundant), vertebrate viruses (ca. 24,000 sequences) can be downloaded separately or combined with non-vertebrate viruses. Curated database of virus etc. ) Database, Protein Data Bank, UniProt, Immune Epitope External sources: GenBank, emerging infectious diseases. and those causing (re) A-C Priority Pathogen lists species) in the NIAD Category genomes from ca.
Participants We enrolled 31 patients in the Randomized Controlled Stem Cell Trial (ISIS-HERMES) at the stroke unit of Grenoble Alpes University Hospital from October 2010 to 2014 (ClinicalTrials. gov NCT00875654). Patients received standard medical care including thrombolysis and thrombectomy when indicated. Patients were randomized to receive autologous mesenchymal stem cells (cell-therapy group) or rehabilitation alone (control group) in the rst two weeks following stroke  (Jaillard et al. , 2020). Cell therapy was administered one month following stroke, after baseline clinical and MRI assessment. The ISIS-HERMES study was approved by the Institutional Review Board (CPP: 07-CHUG-25). Written informed consent was obtained from all patients before they participated in the study. The main inclusion criteria were: age 18-70 years, rst-ever unilateral infarct in the internal carotid artery territory, moderate to severe neurological de cit de ned as NIHSS ≥7, and the ability to follow a rehabilitation program.
MRI data acquisition The MRI protocol included structural and diffusion sequences. Patients were scanned at the IRMaGe MRI facility (Grenoble) on a 3T Philips magnet (Achieva 3. 0 TTX; Philips, the Netherlands) with a 32-channel head coil. High resolution (1 mm3) sagittal 3D-T1-weighted (TR 9. 9 ms, TE 4. 6 ms, ip angle 8°, TI 920 ms, inter shot time 1792 ms) and uid-attenuated inversion recovery (FLAIR) images (TR 8 s, TE 342 ms) were acquired. Diffusion-weighted images were acquired using single-shot echo-planar imaging (EPI) sequence (TR 11 milliseconds, TE 72 milliseconds, FOV 240 mm, slice thickness 2. 0 mm, 70 axial slices, SENSE factor 2, fold-over direction anteroposterior, fat shift direction P, fat suppression, and voxel size 1. 67*1. 67*2 mm). We acquired 60 noncollinear directions with a b value of 1,000 s/mm 2 and 10 directions with a b value of 0 s/mm 2 that were averaged to give 1 average direction.