dataesr/scientific-paragraphs-categorization huggingface dataset:
Alternative nonmicrobiological techniques such as Nucleic Acid technologies are now accepted by the EP and the FDA and therefore regarded as promising but are still not fully validated  [11]  despite several recent developments  [8, 9]. In contrast to this, the efficacy of HTS for the testing of biologicals has been demonstrated for viral testing  [21]  and is increasingly considered a replacement for in vitro and in vivo tests currently required by the guidelines and pharmacopoeia  [22] [23] [24]. Application of HTS for bacterial testing, however, needs to overcome the concern of the abundant background of bacterial sequences that are found in NGS datasets which do not correspond to live bacteria. Live and inactivated bacteria can be differenciated by different means  [25, 26]  including the integrity of bacterial bodies, viability PCR using inhibiting dyes specific for non protected DNA, cellular metabolism, Raman and fluorescence spectroscopy  [27]  or RNA transcription.
7  using the Organ Procurement and Transplantation Network database. In previous work  8  , we performed an external validation of this score on our database, as well as the Eurotransplant-Donor Risk Index (ET-DRI)  9  , according to the methodology proposed by Royston and Altman  10. We showed that neither the DRI nor the ET-DRI could be validated  8. In order to qualify the grafts, we then developed a new donor risk score, the Donor Quality Index (DQI), for which both internal and external validation have been performed  11. In France, the "Agence de la Biomédecine" (ABM) is responsible for managing the WL and allocating the grafts (see Supplementary Information for more details). The French allocation system, as in most countries, is based on a "sickest first" policy, according to the "Score foie"  12.
The rate of discard and non-recovery rates remains high and the appropriate use of grafts could therefore reduce the consequences of organ shortage. What kind of matching would be optimal? Would it be beneficial to remain on the WL and possibly later receive a "better-quality" or "lower-risk" graft? However, this practice would expose a candidate on the WL to clinical deterioration or even death. Using the French LT database, we explored the optimal matching between grafts' quality defined by the DQI, and recipients' disease severity status determined by the MELD, in the whole LT population studied, along with specific indications, namely, decompensated-cirrhosis, HCC and MELD exception. The matching was assessed by the survival benefit (SB) defined according to a sequential stratification modeling  [14] [15] [16] [17].
Authorization was also obtained from the "Commission Nationale de l'Informatique et des Libertés" (agreement No. 915206). Data were obtained from ABM without identifiers. LT candidates under 18 years of age or with multiple organ transplants were not included. Candidates with incomplete data were not retained as specified in the flow diagram presented in Fig. 1. In total, 8387 patients waiting for a deceased donor LT were analyzed (Table  1 ). Decompensated-cirrhosis was identified using the MELD score and the Child-Pugh score. All patients presenting with cirrhosis, without HCC or non-HCC liver cancer, a MELD score ≥ 16 and a B or C Child-Pugh score were considered as decompensated-cirrhosis. Diagnosis of HCC was performed according to Amin et al. 18. MELD exceptions were identified and resulted in extra points while on the WL  19.
Sequential stratification: survival benefit by MELD and DQI categories. A stratum was created that matched one transplanted patient (called the "index patient", Fig. 3 ) with all eligible control candidates (according to the criteria detailed in the Methods section), for each of the 5753 LT in the dataset. Control patients were chosen among all candidates, excluding the index patient of the retained stratum (N = 8386). A patient can therefore be the index in one stratum and a control in another. If no control patient was matched to an index patient, the stratum was dropped, and the index patient was not included in the analysis as an index patient (though this patient may have been included as a control patient in another stratum). In all, 4754 strata were created, which were combined and fit with a stratified Cox regression model.
Survival benefit in decompensated-cirrhosis, HCC and MELD exception patients. We studied MELD-DQI matching in three main indications: decompensated-cirrhosis, HCC and MELD exception, using the method described above with a supplementary matching based on LT indication (see Methods section for more details). Of the 4381 low and medium MELD recipients (Fig. 2 ), 1793 were HCC (without MELD exception component) and 1002 were MELD exceptions. From these datasets, 1723 and 669 strata were created for HCC and MELD exception sub-analysis, respectively. Decompensated-cirrhosis patients (without MELD exception component) accounted for 1863 of the 3459 medium and high MELD recipients (Fig. 2 ). From this subset, we created 1288 strata. A description of index patients by sub-groups is presented in Tables 4S, 5S and 6S in Supplementary Information.
fr/annexes/ bilan2016/donnees/organes/05-foie/synthese. htm#) were: -Low MELD: 6 ≤ MELD ≤ 15, -Medium MELD: 15 < MELD ≤ 30, -High MELD: MELD > 30. A stratum was created for each LT observed in the dataset (Fig. 3 ). Each stratum included the transplanted patient (who is the index patient) and all the matched "control" patients, who: -were active on the WL (not grafted, still alive, not removed from the WL, not under temporary contraindication, not lost to follow-up); -were in the same MELD group as the index patient at the time of LT (the MELD is calculated at the time of the LT of reference for the index patient and the control patients); -were aged within ±5 years of the index patient; -had the same high emergency status (i. e. acute liver failure, primary non-function) as the index patient; -and were ABO compatible with the graft received by the index patient.
Controls were matched on the index patient's diagnosis. All analyses and Figs. 2 and 4 to 7 were produced using R software, version 3. 3. 0 32 with the survival package (https://www. R-project. org/). Independent ethics committee. The data was provided by the "Agence de la Biomédecine" (ABM) in the context of the OPTIMATCH program. This program has been authorized and funded by the Ministry of Health. In France, the ABM is in charge of managing the waiting list and distributing the grafts (http://www. agence-biomedecine. fr/). It hosts the data relating to donors and recipients. The study was conducted according to the approval given by the "Comité de Protection des Personnes" of Paris Ile-de-France which is an independent Ethics Committee. It was also approved by the "Comité Consultatif pour le Traitement de l'Information en matière de Recherche dans le domaine de la Santé".
Acknowledgements We warmly thank  Lewellyn Andrada  and  Sarah Luery  for their constructive and sensitive editorial assistance. We thank all the professionals who contributed in gathering the information collected in the  LT Cristal database , the  "Agence de la Biomédecine ", and the members of the  OPTIMATCH Clinical Research Hospital Program. This work was funded by a grant of the  French Ministry of Health and Social Affairs. The present study is part of the  "OPTIMATCH" program  funded by the  French Ministry of Health  within the framework of the national  Clinical Research Hospital Program.
Genome sequencing, assembly and annotation The genome sequencing and assembly yielded an assembly of 1. 15 Gb (1. 00 Gb in gap-free scaffolds) with a contig N50 of 17. 7 kb and scaffold N50 of 802 kb. The overall genome size was estimated to be 1. 143 +/-0. 019 Gb (n= 4) and 1. 095 +/-0. 023 Gb (n=4) for the female and male, respectively, using flow cytometry (see Additional file 1). The data have been deposited in the NCBI as Genbank assembly accession GCA_000696795. 1. The Official Gene Set halhal_OGSv1. 1, reflecting automated and manually annotated genes, comprises 24,450 proteincoding gene models. The BUSCO completeness assessment tool  [19, 20]  searches assemblies and annotated gene sets for genes that are expected, based on comparisons to similar species, to be present as single-copy orthologs in order to assess completeness in terms of expected gene content.
halys through a series of species-specific tandem duplications (Additional file 1: Figure  S5 ). While hemipteran genomic resources for comparative analysis are growing  [14] , the Pentatominae in particular will benefit from greater sampling of additional species. Preliminarily, tBLASTn alignments of a recent, unpublished assembly for the fellow pentatominid Euschistus heros (GenBank accession GCA_003667255. 1) does support a potential tandem expansion of mannosidase genes in this polyphagous lineage (see Additional file 1: Figure  S6 ). Using the same methods as in Panfilio et al. (2019)  [26] , we identified a set of five additional candidate LGT events in H. halys (see Additional file 1). These include two independent LGTs of Wolbachia ankyrin-repeatbearing genes, one of which has expanded into a fourmember gene family and the other of which has duplicated once.
In H. halys, we found singleton orthologs of the Rh7 and C-opsin subfamilies (Fig. 3  and Supp. Additional file 1: Figure  S10 ). No sequence evidence of the A-opsin subfamily was detected in H. halys (although this subfamily has been found in the water strider and milkweed bug)  [12, 26]. A Hemiptera-wide search in the NCBI NR protein database for C-opsins and arthopsin homologs detected three hemipteran species with singleton orthologs of both C-opsins and A-opsin (Lygus hesperus: JAG03839, JAG63746; Bemisia tabaci: XP_ 018896152. 1, XP_018897455; and Diuraphis noxia: XP_ 015365906. 1, XP_015372008. 1). Moreover, all three nonretinal opsins, including Rh7 opsin, have been found in the water strider genome  [12]. Taken together, these data constitute unambiguous evidence for the presence of all three non-retinal subfamilies in early hemipterans.
(2014)   [163]. This colony was established in 2007 from adults collected in Allentown, PA, USA and was supplemented annually with several Beltsville, MDcollected individuals until 2011. The sibling-sibling mated individuals from this colony, from which the genome originates, are the Beijing haplotype, as confirmed using primers and haplotype conventions from Xu et al. (2014)  [164]. Additional sequencing and assembly details are provided as Additional file 1. The resulting assembly has been deposited in the NCBI Genbank as assembly accession GCA_000696795. 1. Automated gene annotation was performed both with a MAKER 2. 0 annotation pipeline  [165]  tuned specifically for arthropods and NCBI's Eukaryotic Genome Annotation Pipeline, Gnomon  [166, 167]. Manual annotation was enabled by the Apollo manual annotation and JBrowse viewing software  [168, 169]  hosted at The i5K Workspace  [170].
Existing RNA-Seq datasets available for H. halys  ([42, 131, 171] ; see also Additional file 1: Table  S1 ), in combination with RefSeq and GenBank protein sets from Diaphorina citri, D. melanogaster, A. pisum and other insects, were utilized as extrinsic evidence in preparing automated gene calls and in assisting expert annotators with refining gene models. The Additional file 2 presents gene expression levels observed within each sample reported in the aforementioned H. halys transcriptomics studies; RNA-Seq reads were mapped to gene models using bowtie2  [172]  and expression levels (conveyed using the Transcripts Per Million (TPM) measure) were estimated by RSEM  [173]. Gene annotations are distributed with the genome assembly at NCBI and are available under accession number GCA_ 000696795.
1. The Official Gene Set halhal_OGSv1. 1 is available at the i5K Workspace (https://i5k. nal. usda. gov/ data/Arthropoda/halhal-(Halyomorpha_halys)/Hhal_1. 0), as well as the Ag Data Commons (doi: 10. 15482/USDA. ADC/ 1504240). Detailed information for all annotation-related topics is available in the supplement.
Assembly and annotation completeness assessments Completeness in terms of expected gene content of the H. halys genome assembly and annotated protein-coding gene set was assessed with the Benchmarking Universal Single-Copy Orthologs (BUSCO) tool, v3. 0. 2  [19]. The Insecta BUSCO lineage dataset (insect_odb9) was used, which consists of 1,658 single-copy orthologous genes present in at least 90% of insects at OrthoDB v9  [174]. For comparisons with other hemipterans, the same assessments were performed on the assemblies and gene sets of the pea aphid, A. pisum (downloaded from AphidBase  [175] ); the bed bug, C. lectularius; and the kissing bug, R. prolixus (obtained from VectorBase  [176] ). For all gene set assessments, protein files were first filtered to select only one protein per gene when alternative transcripts were annotated, always selecting the longest protein product as the representative sequence.
The same length criterion was applied to gene fragments thought to represent otherwise fulllength genes (with some exceptions in the GR family; see below). Many gene models were joined across scaffolds, mostly based on spliced RNA-Seq reads, but sometimes on the appropriateness of gene fragments on either ends of two scaffolds. Every effort was made to complete partial gene models by repairing gaps in the genome assembly using raw RNA-Seq and/or genomic reads. Multiple alignments for each family were used to reveal problematic models, which were then manually improved. All are modeled as best as possible in the Apollo browser at i5K and were incorporated into the Official Gene Set (OGS). Their protein sequences are provided as supplementary data (see Additional file 4), as they include many genes modeled across two scaffolds and others for which the genome assembly was repaired, as well as translations of pseudogenes, none of which are available from the OGS.
The final multiple alignments for each family included the members of the three other heteropterans noted above, as well as relevant proteins from other insects, and were generated with CLUSTALX v2. 1  [179]. Alignments were trimmed with TRIMAL v1. 4  [180] , using the "gappyout" option for the ORs and GRs, which are of generally similar length, and the "strict" option for the IRs, which commonly have highly length-and sequencevariable N-termini. Phylogenetic analysis was conducted using PHYML v3. 0  [181]  with default parameters. Trees were arranged and colored using FIGTREE v1. 4. 2 (http://tree. bio. ed. ac. uk/software/figtree/).
Odorant-binding proteins Odorant-binding protein (OBP) family members were searched in the H. halys genome scaffolds through BLAST in Apollo/JBrowse in The i5K Workspace. The OBP gene search used 30 putative HhalOBP transcripts mined in the antennae of females and males through RNA-Seq  [42] , as well as H. halys Gnomon-predicted proteins and six-frame translation products of the H. halys RNA collection screened against Classic, Plus-C and Atypical OBP motif-patterns (bit score 40. 0). The OBP motif-patterns were built up using a set of 6,064 OBPs as a reference retrieved from NCBI by querying for "odorant binding protein". In addition, OBP transcripts in other closely related heteropterans were also used, such as from the Miridae  [182]  and Pentatomidae  [54]. OBP gene annotations were directly performed in the Apollo genome browser.
Vision and light detection genes For preparation of the global opsin gene tree, H. halys sequences were collected by tBLASTn searches against the genome sequence draft version 1. 0 (GCA_000696795. 1). A multiple sequence alignment was generated with Clustal Omega  [183]  and variable sites were removed with Gblocks at least stringent settings  [184]. A bootstrapped maximum likelihood topology was generated with RAxML on the Cipres platform  [185, 186] , with the cutoff for showing support values in the trees being set to 75. For preparation of the long wave-sensitive opsin gene tree, H. halys sequences were collected by tBLASTn searches against the NCBI TSA database; multiple alignment, variable site clearance and bootstrapped maximum likelihood analysis were performed as described above.
Cysteine peptidases The evolutionary history among cysteine peptidases was inferred by the Maximum Likelihood method (using the JTT matrix-based model  [187] ) as implemented in MEGA7  [188]. Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using a JTT model, and then a topology maximizing the log-likelihood score was selected. The analysis involved 53 amino acid sequences and a total of 145 positions in the final dataset. All positions with less than 95% site coverage (i. e. , those containing at least 5% alignment gaps, missing data and ambiguous bases) were eliminated from consideration.
Salivary effector genes Salivary effector genes in H. halys were identified using a reciprocal best BLAST hit approach as described earlier for effector identification  [99, 189]. Initially, aphid effectors  [90, 93, 94, [190] [191] [192] [193] [194]  were used as queries in a BLASTp search (E-value cut-off 1e-20) against H. halys predicted proteins. Top hits in the H. halys genome were BLAST searched against an A. pisum protein dataset to identify false positive candidates. If H. halys genes had a top hit different from the A. pisum query used in the first step, these were excluded from further analysis. Retained H. halys candidate genes were validated based on following three criteria: 1) presence of secretion signal peptide as revealed by signalP (version 4. 1)  [195] , 2) absence of transmembrane domain as revealed by TMHMM (version 2)  [196] , and 3) presence of signature domains and/or conserved sites as revealed by an InterPro output  [197] , which was inspected manually.
Insect Immunity Manual annotation efforts were organized around a list of genes involved in the innate, humoral immune response contributing to recognition, signaling and response to bacteria and fungi in arthropods. Genes were found using a combination of approaches. Immunity genes with the same gene id names (e. g. , PGRP) and identified as belonging to the phylum Arthropoda (taxid: 6656) were downloaded from the UniProt database and used to create an HMM profile to search against proteins identified in the genome assembly (HVIT v. 1. 0). Proteins with similar domains to the HMM-constructed protein families were ranked by similarity using the lowest E-values (min cutoff 1e-20; HMMER 3. 1b1 May 2013  [200] ;) and then BLASTed against the raw genome fasta file to recover scaffold coordinates of the original protein match and any potential paralogs.
When HMM-constructed protein families were not successful in finding a match to an immunity protein of interest, a consensus sequence was manually constructed using protein sequences from UniProt containing the same gene identifier restricted to Arthropoda (taxid: 6656), then compared with the genome sequence using tBLASTn and the default parameters provided by The i5K Workspace's BLAST tool (https://i5k. nal. usda. gov/ webapp/blast/). If the original search failed, default parameters were relaxed to remove the low complexity filter. Upon determination of a genomic location using one of these two methods, genes were reviewed and manually annotated. If a gene model was successfully annotated, the putative protein was compared to the NCBI NR database for arthropoda (taxid: 6656) using the BLASTp algorithm to reconfirm the annotation and gene name.
Xenobiotic detoxification genes Identification of H. halys carboxylesterase (COE) and glutathione-S-transferase (GST) enzyme inventory was performed via keyword search of Gnomon annotated H. halys inferred proteome. For each protein family, the resulting protein sequences were then used as queries in a BLASTp search against the inferred proteome to identify any sequences that may have been missed during the Gnomon annotation process. Results were then manually filtered on alignment quality and biological relevance. Both curated COE and GST protein sets were multiply aligned using MUSCLE  [201]. SeqBoot  [202]  was used to create a bootstrapped data set of 100 replicates, from which a maximum likelihood-based phylogeny was generated using the method of Le and Gascuel  [203]  as implemented in PhyML  [181].
Phylogenies were then visually rendered using the R Phytools package version 0. 5-64  [204]. Cytochrome P450s (CYP) from H. halys were mined by batch BLAST of NCBI's NR database using 52 P450 sequences representative of insects. Results from each search were combined and filtered to remove duplicate hits. The results were 212 gene models predicted by Gnomon from the genome. Some of these were fusions of adjacent genes that had to be split. After further refinement to split fusions and remove variants of the same gene, 141 P450s remained. To look for any additional P450s, 126 of the 141 sequences were used to BLAST search the WGS section for genomic contigs. 38, 000 hits distilled to just 65 contigs, indicating P450 gene linkage. The 65 contigs were BLASTx searched against a database of named insect P450s to find all exons for P450s in the genome and to determine associated start and stop coordinates.
2015 , Charmantier et al. 2017 , Corsini et al. 2017 , Senar et al. 2017 , Seress et al. 2018). Importantly, these long-term datasets make it possible to answer questions that were not anticipated at the onset of data collection (e. g. influence of global warming on phenology,  Visser et al. 1998 ; effects of habitat fragmentation,  Dhondt 2007). The main asset of individual-based bird studies is not only the long temporal scale, but also the high degree of spatial replication provided by multiple studies conducted simultaneously  (Korsten et al. 2010 , Dingemanse at al. 2012 ). The amount of information available when studies are combined has the potential to bring our understanding of ecological and evolutionary processes to entirely new levels, and has, not surprisingly, led to a number of collaborative projects (e.
Accepted Article © 2020 The Authors. Journal of Animal Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society predictions and tackle global issues, such as habitat degradation, animal welfare, or global warming, and gain insights into reproducibility of findings based on ecological time-series. Over time, individual-based studies have become more complex and in addition to data on breeding parameters, other types of data have been collected (e. g. morphological, behavioural, physiological, genetic and genomic). Further, the number of potential relational links to other sources, such as biological samples, climatic data and individual movement data has increased. With the increasing extent and complexity of datasets we urgently need to address data archiving, standards and integration, not only for individual based-studies but in all branches where many independent research groups collect similar but differently managed, and consequently underexploited, data (the long-tail of science,  Box 2, Palmer et al.
2007 , Wallis et al. 2013). In these branches, transition to Findable, Accessible, Interoperable, and Reusable (FAIR) data  (Wilkinson 2016 ; Box 2) is more urgent, but also more challenging compared to fields where data standards have been set up at the very start (e. g. genomics). Scientific collaborations that involve large-scale sharing of standardized data, even when access to data is not fully open but restricted, have been shown to generate significant insights, but we can only guarantee this with adequate mechanisms in place to align, store, and advertise the data that are available for such endeavours. Examples of projects that successfully integrate animal data across a large number of studies are EURING Data Bank (https://euring. org/, du Feu et al. 2016 ) that stores encounter records of ringed birds, Movebank database (https://www.