dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS GW was supported by a  NERC   GW4+  studentship hosted by the  University of Exeter  and  The Natural History Museum , with  CASE  funding from  Cefas. We also thank  NERC  for a  Standard Research Grant  ( NE/H009426/1 ) supporting DB and CB and a  New Investigator Grant  ( NE/ H000887/1 ) supporting DB. DB was further supported by  Defra  contract  C6560. RG was funded by Verkefnasj oður um sj avarranns oknir a samkeppnissviði (VSR). RG thanks  Erla Bj€  ork € Ornolfsd ottir (V€ or/H olar  University College)  for sampling and project management, and Snaed ıs Bj€ ornsd ottir ( Mat ıs/University of Iceland ) for assistance with 454 sequencing. SN and SC were funded by the  Austrian Science Fund (FWF)  grants  J3175-B20  and grant  Y801-B16. CB and SR were supported by the " Investissements d'Avenir" programme OCEANOMICS  ( ANR-11-BTBR-0008 ) awarded by the  French Government via Agence Nationale de la Recherche.
UTOFIA System UTOFIA is an active imaging system providing 2D and 3D images covering a volume limited by the field of view and the selected distance-range interval (Figure  2 , Table  1 ). A range from 1 to 9 meters could typically be scanned in less than 1/10 s. The volume is scanned by changing the laser firing relative to the camera shutter, and three-dimensional information is extracted by processing the time scanned series of images. The system is compact and weighs 9 kg in air and less than 2 kg in water. It is composed of an integrated camera sensor and laser illumination system within a sealed and pressure-resistant cylindrical casing, which also houses the drivers for the camera and the laser, as well as the electronics needed to let these parts talk with each other; a topside command box and a cable provide power and allow data transmission (Figure  2a ).
UTOFIA System UTOFIA is an active imaging system providing 2D and 3D images covering a volume limited by the field of view and the selected distance-range interval (Figure  2 , Table  1 ). A range from 1 to 9 m could typically be scanned in less than 1/10 s. The volume is scanned by changing the laser firing relative to the camera shutter, and three-dimensional information is extracted by processing the time scanned series of images. The system is compact and weighs 9 kg in air and less than 2 kg in water. It is composed of an integrated camera sensor and laser illumination system within a sealed and pressure-resistant cylindrical casing, which also houses the drivers for the camera and the laser, as well as the electronics needed to let these parts talk with each other; a topside command box and a cable provide power and allow data transmission (Figure  2a ).
Data Processing A software package is supplied with the system and consists of a graphical user interface (GUI) and a C# API. The GUI has been developed in MATLAB and it uses a C# API to connect and interface with the camera. The C# API is also available for the customer and can be used to develop user specific automated measurements. Through the GUI the operator is able to set operation parameters like scan range and field of view. There are several operation modes available. The user can find the best settings for their application by experimenting with the different options. Through the GUI, acquired range-gated data can be visualised, stored and processed. Length measurements can be done directly by manually selecting reference points on the screen. The GUI can also be used to re-play and post-process stored data.
Bone segmentation and PET quantifications Image processing was performed using the Beth Israel PET/CT viewer plugin for Fiji (Fiji Is Just ImageJ)  [18]. This software is developed as a free and open-source PET/CT viewer for research purpose (http://petctviewer. org)  [19, 20]. On the basis of Fiji/ImageJ, advanced image processing is allowed because of ImageJ services and plugins. To calculate the metabolic bone tumor volume (MBTV), bone tumor volume was assessed on CT images, which were presegmented using Weka Trainable  [21]  segmentation on the basis of a machine learning algorithm. The previous dataset of 10 learning patients was used to feed the machine learning algorithm by manual delineation of bone tissue using a patient's CT with no cancer disease. The bone classifier generator was applied to the CT series of the 18 F-FCH PET/CT.
Previous scoring systems related to quantification methods for bone tissue infiltration on planar bone scintigraphy using various automated systems rendering a bone scan index (BSI) as a percentage of the tumor burden on the total skeletal mass on the basis of identifying and quantifing hotspots as lesions on the basis of databases (e. g. EXINI bone  [29, 30] , BONENAVI  [31] ) enabled the prediction of overall survival after hormonal treatment  [32]  or docetaxel therapy  [33]  with decreasing BSI percentages. However, at present, there is only one study assessing BSI on initial bone scintigraphy assessment to predict hematologic toxicity before 223 Ra  [34]. Bone tumoral burden was also assessed on 18 F-fluoride PET/CT using a volumetric semiautomatic quantification of whole-body skeletal tumor burden and was identified as an independent predictor of overall survival in 42 patients with CRPC treated with 223 Ra  [35].
[15]  to isolate the bone tumoral infiltration from nonmetastatic bone. This double segmentation (bone tissue segmentation and SUV threshold) requires around 15 min of calculation time per patient and does not require any manual intervention. The full software package is freely available on http:// petctviewer. org and is available for any operating system (Windows, Mac OSX, GNU/Linux). This approach enables a direct quantification on 18 F-FCH PET of the bone mass containing proliferating tumoral cells  [13, 14] , using MBTV and TLA parameters, also normalized by height, which we found to be associated with the occurrence of hematologic toxicity. This may enable identification of patients for whom 223 Ra therapy will be limited because of a high toxicity rate. Normalization by height of bone metabolic parameters might be interesting as 223 Ra activity is prescribed as a function of the weight, but tumoral burden and volume is also dependent on a patient's height.
These data were collected by face-to-face conversation during their hospital stay. Patients were divided into three subgroups as follows: ACP was assigned in terms of an alcohol intake of ≥80 g/ d for a male and 60 g/d for a female for at least two years in accordance with previous publications  29, 30  , irrespective of smoking status; among the remaining patients, those had smoked ≥20 cigarettes per day for at least two years were assigned to a "smoking-associated chronic pancreatitis (SCP)" subgroup; all the remaining patients were assigned to the "ICP" subgroup. A total of 1196 unrelated Han Chinese blood donors were used as healthy controls. The electronic medical records of each patient were systematically evaluated for the following clinical features: age at disease onset (this was defined as the age at onset of abdominal pain; in patients who did not experience prior symptoms, age at diagnosis of CP); age at diagnosis of pancreatic stones, diabetes mellitus and steatorrhea; and M-ANNHEIM clinical stages.
Variant inclusion criteria We focused on rare variants, defined as having a minor allele frequency of <1% in the control population  8, 9. Thus, we first excluded from consideration those variants with a minor allele frequency of ≥1% in the Chinese control dataset. Then, we divided the retained variants into known or novel categories by reference to data in the genetic risk factors in chronic pancreatitis database (http://www. pancreasgenetics. org/index. php) (for SPINK1, PRSS1, and CTRC variants) or CFTR2 (https://www. cftr2. org/) and CFTR-France (https://cftr. iurc. montp. inserm. fr/cgi-bin/ about_CFTR. cgi) databases (for CFTR variants). Of the known variants, those classified as pathogenic or diseasecausing were included in the final analysis, whereas those classified as non-pathogenic were excluded from further consideration, and those classified as being of unknown significance were treated as novel variants.
Statistical analysis The significance of the differences between variant or genotype carrier frequencies in patients and controls was tested by means of Chi-square or Fisher's exact test using GraphPad Prism (v5. 01). A P value < 0. 05 was considered to provide evidence of statistical significance. The following statistical analyses were performed using SPSS (v19. 0) software. The statistical significance of differences between mutation-positive and -negative patients in terms of gender, pancreatic stones, diabetes mellitus, steatorrhea, and M-ANNHEIM clinical stages was assessed by means of the Chi-square or Fisher's exact test; the odds ratio (OR) value with corresponding 95% confidence interval (CI) was calculated. The statistical significance of differences between mutation-positive and -negative patients in terms of the mean age at disease onset and the mean ages respectively at diagnosis of pancreatic stones, diabetes mellitus, and steatorrhea were compared using the t test under an equal condition by homogeneity test.
Acknowledgements We thank  Yan-Fang Gong ,  Jia-Yun Chen ,  Jing Jin ,  Hui Chen ,  Jie Chen ,  Feng Liu ,  Luo-Wei Wang ,  Yi-Qi Du ,  Dong Wang ,  Zhen-Dong Jin , and  Duo-Wu Zou (Shanghai, China)  for their assistance in collecting patient samples and/or clinical data.
To drive this research forward, we need to understand the genetic basis associated with ecological and evolutionary processes in salmonids. Genetic studies of salmonids are complicated by a whole genome duplication (WGD) event that occurred in their common ancestor approximately 80-100 Mya (Fig. 1 ;  [8] [9] [10] [11] [12] ). Nevertheless, several important resources have been established through the efforts of consortia such as cGRASP (Consortium for Genomic Research on All Salmonids Program, http://www. sfu. ca/cgrasp/index. html), ICSASG (International Collaboration to Sequence the Atlantic Salmon Genome), and SalmonDB (http://salmondb. cm m. uchile. cl). These include expressed sequence tag (EST) databases, microarray gene expression platforms, and SNP arrays. Consortia efforts have generated extensive EST databases for Atlantic salmon (Salmo salar) and rainbow trout (Oncorhynchus mykiss)  [13] [14] [15] [16] [17] [18] , as well as on a smaller scale for other salmonid species such as chinook salmon (Oncorhynchus tshawytscha), sockeye salmon (Oncorhynchus nerka) and lake whitefish (Coregonus clupeaformis)  [13].
Thus data generated as part of small-scale projects, such as the four species transcriptome resources presented in the current study, provide a valuable contribution to the development of community resources. Given that there is currently no reference genome available for the vast majority of salmonids, transcriptomes are key to facilitating research on genomic variation and expression. Efforts by independent salmonid research groups have generated de novo transcriptome assemblies for Kokanee salmon (Oncorhynchus nerka: 11,085 transcripts,  [31] ), lake whitefish (Coregonus clupeaformis: 77,797 transcripts  [32] ), coho salmon (Oncorhynchus kisutch: 43,228 transcripts  [33] ), and Arctic charr (Salvelinus alpinus: 34,690 transcripts  [34] ). Furthermore, the recent release of the PhyloFish database (http://phylofish.
RNA-seq library construction and sequencing RNA-seq library preparation and sequencing was carried out by Glasgow Polyomics research facility at the University of Glasgow. Briefly, libraries were synthesised for each of the 32 samples using the TruSeq Stranded mRNA Sample Preparation kit (Illumina, San Diego, CA), according to the manufacturer's instructions. Paired-end sequencing (75 bp from each end) was then performed on the NextSeq 500 system (Illumina, San Diego, CA) at a sequencing depth of 20-25 million reads per library. The raw reads in bcl format were converted to fastq files with Illumina provided bcl2fastq v2. 15. 0 software, and quality was examined using FastQC v0. 11. 2  [38].
Assembly optimisation Prior to filtering our four de novo assemblies to remove redundant and poorly constructed transcripts, we performed an initial quality assessment of the transcript sets. We determined completeness by examining the number of input RNA-seq reads that were represented in our de novo assembled transcriptomes, as per the pipeline suggested in the Trinity package (http://github. com/trinityrnaseq/trinityrnaseq/wiki) [45]. Read representation was determined by mapping the cleaned reads back to their corresponding assemblies, for each of the four species individually, with Bowtie2 v2. 2. 6 (-local, -no-unal)  [46].
Removal of redundant transcripts To obtain sets of non-redundant transcripts we applied the following filtering steps: first, we used TransDecoder v3. 0. 0  [47]  to identify all likely coding regions within our assembled transcripts (for each species individually), and then filtered by selecting the single best open reading frame (ORF) per transcript, as per the TransDecoder pipeline (-single_best_orf). Any transcripts with ORFs less than 200 bp in length were removed before performing further analyses. Second, redundancy was further reduced in the remaining transcript sets by clustering highly similar sequences with CD-Hit v4. 6. 6  [48] , using an amino acid sequence identity threshold of 1. 00.
Full-length transcript analysis To determine how successfully assembled transcripts were reconstructed to full-or near full-length in each of the four assemblies, we calculated coverage against the NCBI Atlantic salmon proteins database (GCF_000233375. 1). Atlantic salmon proteins were used as the reference dataset because it is the salmonid species with the most genomic resources available. The non-redundant sets of transcripts were subjected to BLASTP searches (-max_-target_seqs 1, -evalue 1e-3)  [49]  against the Atlantic salmon proteins; we applied a relaxed e-value to avoid discarding good hits for very short sequences. We processed the BLAST hits using the 'analyze_blastPlus_to-pHit_coverage. pl' script from the Trinity package (http://trinityrnaseq. sourceforge. net/) [45]  to identify the number of transcripts that aligned to the salmon proteins across varying length thresholds.
Assessment of assembly completeness To provide a comprehensive and quantitative overview of the level of completeness achieved for our assemblies, we applied four approaches to assess overlap with transcriptomic or genomic resources across taxa  [50]. First, we quantified completeness by comparing our four assembled transcript sets against a set of highly conserved single-copy orthologs. This was accomplished using the BUSCO (Benchmarking Universal Single-Copy Orthologs) v2 pipeline  [51]  compared to the predefined set of 4584 Actinopterygian single-copy orthologs from the OrthoDB v9. 1 database  [52]. We calculated the number of complete (length is within two standard deviations of the mean length of the given BUSCO), duplicated (complete BUSCOs represented by more than one transcript), fragmented (partially recovered BUSCOs) and missing (not recovered) in each of the four de novo assemblies.
To further assess the completeness and utility of the resources presented here, we examined how successfully BUSCOs were recovered in our assemblies compared to the NCBI protein dataset for Atlantic Fig. 2  Schematic of the de novo transcriptome reconstruction and analysis pipeline used to generate the protein-coding transcriptome assemblies for Atlantic salmon, brown trout, Arctic charr and European whitefish salmon (GCF_000233375. 1) (48,602 transcripts; based on retaining only the longest isoform per gene), as well as against the PhyloFish brown trout and European whitefish assemblies (75,388 and 74,701 transcripts respectively)  [35]. Second, we used the program OrthoFinder v1. 1. 2 [53]  to identify orthologous groups of proteins amongst our four assemblies using BLAST all-v-all (self and reciprocal BLASTs simultaneously) algorithm and to further quantify assembly completeness.
OrthoFinder represents a novel method of orthogroup detection, by performing reciprocal best-hit BLASTs that are normalised for transcript length, removing transcript length bias in the ortholog detection. OrthoFinder analysis was conducted for all pair-wise comparisons, for all four species assemblies, as well as against the Atlantic salmon RefSeq proteins (GCA_000233375. 4) in order to identify putative orthologs within the current datasets and to provide a reference source of identified orthologs and their distribution relative to the existing high-quality protein set for Atlantic salmon. We used the outputs from Ortho-Finder to determine the number of overlapping (shared across species) transcripts across our four assemblies. Utilising the sister taxa in the present study provides a validation of the completeness of our de novo transcriptomes.
In addition to assessing completeness of the final assemblies, we also applied OrthoFinder to assess and control completeness of our assemblies at each stage of the filtering pipeline, by comparing orthogroup size distribution within our salmon de novo assemblies relative to the Atlantic salmon RefSeq protein set (GCA_000233375. 4). Third, we quantified the extent of overlap between our four assemblies and the recently published Atlantic salmon reference genome  [12]. Transcripts from our four assemblies were aligned to the Atlantic salmon reference genome ICSASG_v2 (GenBank: GCA_000233375. 4  [54] ) with GMAP (2016-11-07)  [55] , additionally using the '-cross-species' parameter for heterospecific mapping. Fourth, we compared completeness and similarity of the current assemblies for Arctic charr, brown trout and European whitefish to previously published transcriptomes for these three species.
We compared our Arctic charr assembly to the Magnanou et al. [34]  assembly of 34,690 transcripts (http://ngspipelines. tou louse. inra. fr:9021/, accession: E-MTAB-3522), and our brown trout and European whitefish assemblies to the corresponding species from the PhyloFish database  [35]  (75,338 and 74,701 transcripts respectively; http:// phylofish. sigenae. org/index. html). First, we assessed how well full-length transcripts were represented in our assemblies compared to the previous transcriptomes. Full-length transcript reconstruction in the previous assemblies for each of the three species was evaluated following the same protocol described above for the four assemblies we present here. Separate BLASTP searches were made against the Atlantic salmon protein database for each species, and coverage was analysed using the 'analyze_blastPlus_topHit_coverage.
pl' script. Second, we identified the transcript set overlap of the current compared to previously published conspecifics assemblies. To focus on protein coding sequences, we used TransDecoder to identify putative protein coding regions in the published assemblies  [34, 35]. We then created BLAST databases from the predicted protein sequences for each species individually using NCBI-blast 2. 2. 30+ and performed BLASTP searches (-max_target_seqs 1, -evalue 1e-5) of our assemblies against those databases. Transcripts were considered 'shared' between the current and previous assemblies where our transcripts had alignment scores greater than 90% identity and 80% coverage.
Annotation and gene ontology analysis To provide comprehensive annotation of the final transcript sets, we compared our de novo assemblies against two annotation resources; the NBCI Atlantic salmon protein database and the SwissProt/UniProtKB [56] database. As described above, all four assemblies were BLASTP searched against the NCBI protein sequences for Atlantic salmon. Additional annotation was provided by aligning transcripts against the SwissProt database curated proteins using BLASTP (-max_target_seqs 1, -evalue 1e-3). SwissProt database alignments, and their corresponding UniProtKB accessions, were used to assign gene ontology (GO) functional annotation. All GO analyses were performed using the PANTHER (protein annotation through evolutionary relationship) classification tool  [57].
Identification of paralogous sequences We used two approaches to identify paralogous sequences in our salmonid assemblies. First, using the BUSCO tool, we determined the proportion of transcripts within each assembly that were likely paralogs, i. e. duplicated single-copy orthologs. Second, we used OrthoFinder algorithms that normalise all-v-all BLASTS for transcript length. This allows greater accuracy and recall of orthogroups compared to previous methods and therefore more precise detection of both orthologous and paralogous sequences  [53].
Results and discussion de novo transcriptome data and assembly In this study, we present new, high-quality, protein-coding transcriptomes for four salmonid species: Atlantic salmon, brown trout, Arctic charr and European whitefish. RNAseq libraries, generated from whole organism samples, yielded between 18 and 32 million paired-end reads per individual for eight individuals per species. Quality filtering (quality score > 30) removed approximately 11% of the raw reads. This resulted in high quality RNA-seq datasets, which contained between 180 and 210 M paired-end reads for each of the four species (Table  1 ).
Reconstruction of full-length transcripts A common problem in assembly of RNA-seq data is the high proportion of transcripts that are highly fragmented, due primarily to difficulties in determining accurate transcript boundaries  [58, 59]. To produce assemblies that were representative of comprehensive gene sets in the current dataset, we examined the number of transcripts that were reconstructed to full length (100% alignment) or near full-length (> 70% alignment) by alignment to NCBI protein sequences for Atlantic salmon. For all four assemblies, 33-37% (between 11,099 and 13,546) of transcripts demonstrated complete (100%) alignment over 100% of their length. Furthermore, ~60% of the query transcripts aligned significantly (-evalue 1e-3) to the Atlantic salmon reference sequences over more than 70% of their length (Fig.
3 ). We detected no evidence of mapping bias, as might have been expected considering the varying level phylogenetic divergence (same species, same genus, different genera, different subfamilies) of our focal species from Atlantic salmon. Rather, we found that the number of reads mapped to the salmon reference was highly comparable across all four species (Fig. 3 ). This full-length transcript analysis was used to filter out and exclude fragmented transcripts (< 30% coverage). After this filtering, the final assemblies contained 36,505 protein-coding transcripts for Atlantic salmon, 35,736 for brown trout, 33,126 for Arctic charr and 33,697 for European whitefish, which can be accessed through the NCBI transcriptome shotgun assembly database (TSA, https://www. ncbi. nlm. nih. gov/genbank/ tsa/) (Additional file 1: Table  S1 ).
As expected, given the Fig. 3  Cumulative number of genes with alignment to the NCBI protein database for Atlantic salmon (GCF_000233375. 1) at a given coverage: Atlantic salmon (yellow), brown trout (green), Arctic charr (blue) and European whitefish (red) high-quality of the dataset, recovery for both 'complete' and 'complete-duplicated' BUSCOs was considerably higher for Atlantic salmon reference genome proteins, compared to either the current or previous assemblies, with 97% of BUSCOs completely recovered, of which 67% were duplicated (Table  3 ). However, we found that BUSCO recovery reported for the assemblies generated here was comparable to recent transcriptome studies using de novo methods, where recovery ranged between 68 and 95%  [44, 60, 61]. BUSCO recovery tends to be highest when full organism and/or multiple developmental stages (e.