dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2001) , discovEHR  (Dewey et al. , 2016) , 1000 Genomes Project (1000GP; The 2015 Genomes Project Consortium, 2015), Exome Variant Server (Exome Variant ), and GnomAD exomes  (Lek et al. , 2016)  databases. The latter three databases also provide information on specific populations (e. g. , Caucasian, Asian, African American, etc. ), which can be selected for improved causal variant prioritization. Information on evolutionary conservation is incorporated from PhyloP  (Rhead et al. , 2010) , and PhastCons  (Hubisz, Pollard, & Siepel, 2011) , including conservation scores for primates, mammals, and vertebrates. Precalculated scores for functional impact of variants have been integrated based on the algorithms SIFT  (Kumar, Henikoff, & Ng, 2009) , PolyPhen-2  (Adzhubei et al. , 2010) , Mutation Assessor  (Reva, Antipin, & Sander, 2011 ), Condel (González-Pérez & López-Bigas, 2011 ), Eigen (Ionita-Laza et al.
eDiVA-Score is built by training a random forest (RF) model using the R "randomForest" package with 1000 binary classification trees  (Breiman, 2001; Hastie, Tibshirani, & Friedman, 2009)  and five-fold cross validation. Eleven features were selected to train the RF model: (a) the maximum minor allele frequency (MAF) of 1000Genomes and GnomAD databases; (b) four conservation measures (conservation in primates and mammals using the PhastCons  (Hubisz et al. , 2011)  and PhyloP  (Pollard, Hubisz, Rosenbloom, & Siepel, 2010) ; (c) four functional impact predictors: Condel (González-Pérez & López-Bigas, 2011), Phred-scaled CADD score  (Kircher et al. , 2014) , Eigen  (Ionita-Laza et al. , 2016) , and Mutation Assessor  (Reva et al. , 2011) ; (d) the likelihood to be in a segmental duplication, which correlates with false-positive variant calls  (Ho, Tsai, Chen, & Lin, 2011) ; and (e) an in-house estimator of systematic sequencing errors called ABB-score  (Muyas et al.
, 2019). Note that Condel, Eigen and CADD are combination scores integrating several features also included in eDiVA-score, namely evolutionary conservation (PhastCons and PhyloP in mammals and primates) and Mutation Assessor scores. The RF model has been trained using 15,000 random pathogenic and likely pathogenic variants from the ClinVar database  (Landrum et al. , 2014)  as positive cases. We then built a control set composed of 15,000 nonpathogenic variants from ClinVar, and 100,000 random variants from GnomAD  (Lek et al. , 2016)  not contained in ClinVar. The vast majority of variants in both positive and negative training set are rare (allele frequency [AF], <1%; Figure  S2a, b ), thus circumventing that AF dominates the classification model. Following the neutral theory of molecular evolution  (Kimura, 1983)  missing data is generated using expected values for nonpathogenic (neutral) variants (Figure  1 ).
| Performance evaluation using semisynthetic cases To assess the performance of eDiVA and several competing methods, we implemented a semisynthetic benchmark based on real WES data from a trio in which we spiked-in known pathologic variants from the ClinVar database  (Landrum et al. , 2014). We chose a publicly available CEPH trio sequenced within the framework of the 1000 Genomes Project composed of samples with European ancestry NA12878 (daughter), NA12891 and NA12892 (parents), downloadable from https://public_docs. crg. es/sossowski/MicrobeGenomes/human/eDiVA/ insilico_simulation_data/, and we called variants and generated a multisample VCF file using eDiVA-Predict. For the purpose of this benchmark study, all 138,705 variants found in the original trio are considered true negatives, that is, variants not associated with the disease.
Next, we embedded known disease variants in the trio following segregation patterns expected for Mendelian diseases. This positive set, containing variants associated with the diseases, consists of all variants from ClinVar  (Landrum et al. , 2014)  database labeled as "pathogenic" or "likely pathogenic", having an OMIM reference in the database and that had not been used for training of eDiVA-Score. For each pathogenic variant, we extracted: chromosome, position, reference and alternative nucleotides, dbSNP identifier, gene name, inheritance mode of the associated disease (where available, randomly assigned otherwise), and HPO terms for the disease. Variants without HPO annotation have been excluded from the benchmark set. We have simulated three inheritance patterns: autosomal recessive homozygous, autosomal recessive compound heterozygous, and dominant de novo, as these are the most likely patterns found in parent-child trio based rare-disease diagnostics.
Considering that the CEPH trio has been sequenced as part of the 1000GP we finally tested if the use of 1000GP allele frequency information for filtering biases the performance estimates of eDiVA. However, we found no difference when not using the 1000GP AF database (Figure  S7 ). Nonetheless, we cannot exclude the possibility that eDiVA (or the other methods) show reduced performance in understudied populations. The use of HPO IDs for prioritization further reduced FPs reported by eDiVA (label eDiVA-HPO in Figure  4c ). Overall, we observed a two-fold reduction in FPs across all inheritance modes tested. However, filtering by in silico gene panels also resulted in a reduction in recall (Figure  4b, c ). Finally, we observed improved prioritization ranks under all inheritance types, with the strongest impact seen for de novo variants (Figure  4a ).
eDiVA has been able to find disease-causing variants with higher recall, fewer false positives and better ranking than competing tools in three benchmarked modes of inheritance. Finally, we evaluated the use of phenotypic descriptors for optimizing the prioritization process. We found that adding HPO ID-based prioritization introduces a trade-off between recall and the number of false positives in the output list. Despite the marginal reduction in recall, focusing on known disease genes is often the preferred choice for diagnostic purposes. Our knowledge of genetic factors playing a role in disease is constantly growing, reflected in a rapid increase of genotypephenotype relations stored in various databases. Hence, it would be beneficial to reanalyze WES data sets once in a while (e. g. , every 6-12 months) to benefit from new knowledge and to facilitate identification of previously unknown/unreported causal variants.
Additional steps can be taken to improve the identification of disease-related variants from WES data. The availability of custom allele frequency databases with geographical specificity would help to reduce the number of false-positive genotype-phenotype associations due to population specific variants. To this end, institutes and hospitals with access to large cohorts of sequenced exomes may use in-house data to filter population specific variants, an approach we have pursued our self by collecting thousands of Iberian cases in an aggregated allele frequency database (http://geevs. crg. eu/, unpublished). Identification of extended homozygosity regions could in addition help to diagnose causal homozygous variants in consanguineous cases. Moreover, the integration of structural and copy number variants (SVs and CNVs) has been shown to increase recall rates substantially  (Gambin et al.
, 2017). Despite their frequent involvement in rare diseases  (McCarroll & Altshuler, 2007) , CNVs are often disregarded in WES analyses, and are rarely processed in combination with point mutations. Prioritization algorithms will have to be adapted to consider compound heterozygotes composed of a point mutation in one and a CNV in the other allele. CNV analysis is currently being integrated in eDiVA and will be available in the near future. Better use of phenotypic descriptors has the potential to improve both precision and recall of causal variant prioritization methods. We observed that HPO ID-based prioritization dramatically improved the precision of eDiVA. However, incomplete maps of known genotype-phenotype (or gene-phenotype) relations in public databases led to a mild reduction in recall.
GnomAD databases (not used in model training or present inClinVar) as TP and TN, respectively (Figure2d-f). Third, we measured the performance of all methods on HGMD data only, using the categories for damaging and likely damaging mutation (DM and DM?) as TP and any other HGMD category as false positives (FP) (Figure3g-i). Functional impact values for the benchmarked methods have been obtained from the respective publications. CADD, DANN, and eDiVA provide damage estimates for all positions of the genome, and Eigen for close to 70% of all positions, whereas Revel, M-CAP, and MetaLR are trained specifically for rare (AF, <1%) or known variants and are only available for a subset of ClinVar, HGMD, and GnomAD. We, therefore, performed three separate performance tests for each of the three benchmark sets, applying the following criteria (a) using only variants having Revel and M-CAP scores available (ClinVar: 3,887 TP and 10,494 TN; HGMD/GnomAD: 63,712 TP and 100,000 TN; HGMD: 63,712 TP and 1,892 TN); (b) random subset of all variants, assigning a default value of 0 to missing values(ClinVar: 19,888 TP and 16,694 TN; HGMD/GnomAD: 96,569    TP and 100,000 TN; HGMD: 96,569 TP and 7376 TN); and (c) using only rare variants (AF, ≤0.
eDiVA is a disease variant prioritization tool optimized for NGSbased genetic disease diagnostics in families and parent-child trios. It is composed of four components: eDiVA-Predict handles read alignment and variant prediction, eDiVA-Annotate performs functional annotation of variants, eDiVA-Score estimates the probability of variants to be pathogenic, and eDiVA-Prioritize filters and ranks variants according to various quality criteria, proper segregation, and likelihood to cause phenotypic changes. eDiVA is available as standalone software at https://github. com/mbosio85/ediva, and as a web service providing access to functional annotation, pathogenicity classification and causal variant prioritization modules (www. ediva. crg. eu). The eDiVA web service facilitates analysis of families or parent-child trios in a few clicks, requiring only a VCF file, and optionally a set of HPO IDs describing the disease phenotype.
ACKNOWLEDGMENTS We acknowledge support of the  Spanish Ministry of Economy and Competitiveness, Centro de Excelencia Severo Ochoa   2013-2017 , and the  CERCA Programme/Generalitat de Catalunya. This project has received funding from the  "la Caixa" Foundation , the  CRG  emergent translational research award and the  European Union 's  H2020 Research and Innovation Programme  under the grant agreement No  635290  (PanCanRisk).
DISCUSSION In this study, we aimed to study RASs to DAAs in patients infected with HCV GT3 or 4 by NGS at a detection threshold of 1% which is more sensitive than that used in other clinical studies  [10]. This threshold allowed a better characterization of mutant dynamics before and after DAA failure and strong dynamics of RAS emergence could somehow reflect their potential impact on DAA response. In accordance with other studies about RASs on NS5B region, we detected at failure a low rate (1/13 patients) of RASs (S282T) to NS5B inhibitors or precisely to SOF. No RASs, including minority substitutions, were detected at baseline even by UDS. Similarly, a study on 1459 sequences (91% from DAA-naïve patients) extracted from the Nucleotide NCBI database by Chen et al. reported a low global prevalence of RASs to SOF (3.
Datasets Gene expression profiles of GBM cell lines. We downloaded GBM cell lines transcriptomes from different studies on GEO public repository  20  (list available in Table  S1 ) using the GEOquery R package. 21 We used transcriptomic data from all of the different cell lines of GBM that we found on the GEO web data base. All these cells lines come from five GEO datasets (GSE15824, GSE23806, GSE9171, GSE104291 and GSE8537). GSE12824 involves five GBM cell lines in duplicate from five different patients, GSE23806 involves cell lines or neurospheres or stem-like cells in duplicate in different stimulating conditions, GSE9171 involve cell lines from different patients (16 cell lines), GSE104291 involves six cell lines in triplicate from different patients and GSE8537 involves one cell line from a patient in four different conditions.
All cell lines samples were obtained through Affymetrix H133plus2 platform. This dataset was used to create three groups of cell lines. We then estimated for each patient the quantity of each cell line group in their tumour. Gene expression profiles of GBM tumours. We used two different cohorts for this study. The first one is the Affymetrix H133A TCGA GBM cohort. 22 Both clinical and gene expression profiles were downloaded with the TCGA2STAT R package. 23 The data were already normalised using the RMA algorithm. We removed patients with a 'G-CIMP' molecular subtype because they have a particular survival behaviour. We decided in both cohorts to exclude G-CIMP tumours because G-CIMP patients have better prognosis than other patients (median overall survival (OS) of 22. 7 months vs 10. 5 months for other patients).
This population is rare in the TCGA cohorts (39 patients among 520) and may introduce a bias in the analysis. In addition, it is known that such population is enriched in Isocitrate deshydrogenase (IDH) mutated glioma. 22 All these arguments support that G-CIMP is probably a secondary GBM, which is raised from IDH mutated low-grade glioma and thus needed to be excluded because of its different biology. The number of patients after removal of these patients is 481 in this cohort. The second cohort (Rembrandt) was downloaded from GEO under the accession number 'GSE68848'. Clinical data were downloaded from the GDOC website (https:// gdoc. georgetown. edu/gdoc/). The Rembrandt study gathers patients with different types of brain tumour. We only kept the GBM type of tumours resulting in 180 patients for this cohort.
24 ene expression profiles were obtained through the H133Plus2 Affymetrix platform. Gene expression profiles normalisation. Raw data were normalised using the RMA method from the affy package. 25 A batch effect was observed between the two GBM tumours datasets due to the different Affymetrix platforms. This unwanted effect was corrected using the SVA package. 26 We worked on the set of genes common to the two platforms representing 51% of genes for the H133plus platform and 86% for the H133A.
30 It uses a hierarchical clustering based on the principal components and on the Euclidean distance. It is well suited for data with a large number of variables. The number of clusters was automatically selected by the algorithm. The partition chosen is the one with highest relative loss of inertia. In order to cluster patients based on their cell line proportions estimated by the deconvolution strategy, we used a hierarchical clustering model, with the hclust function in R, using the Ward's agglomeration method based on Euclidean distance. The optimal number of clusters was chosen between two and six using the gap statistics method implemented in the FactoExtra package. 31 This package provides automatic selection of the number of clusters based on different parameters. Validation of the created groups.
3. The resulting model was then used to estimate a linear predictor value for each patient. Two risk groups of patients were built based on this linear predictor value, using the median value as the threshold. This model was built on the TCGA cohort and validated on the Rembrandt cohort. The validation was performed using the same coefficients on both cohorts and the same cut-offs. The last model built is composed of the linear predictor of the LASSO model and the clinical variable age. From this model, we built three groups of patients (low, medium, and high risk). The groups were determined using first and third quartile of the computer score with the linear predictor and the age of the patients. Software used for the study and available data Version 3. 3. 3 of R was used for the statistical analysis.
While the contribution of Mu opioid Receptors (MoRs) to hedonic aspects of reward processing is well-established, the notion that these receptors may also regulate motivation to gain a reward, and possibly other related cognitive dimensions, has been less investigated. the prefrontal cortex (pFC) is a critical site for these processes. our previous functional magnetic resonance imaging study found alterations of functional connectivity (FC) in reward/aversion networks in MoR knockout mice. Here we pursued voxelwise seed-based FC analyses using the same dataset with a focus on the pFC. We observed significant reduction of PFC FC in mutant mice, predominantly with the nucleus accumbens, supporting the notion of altered reward-driven top-down controls. We tested motivation for palatable food in a classical operant self-administration paradigm, and found delayed performance for mutant mice.
In an earlier report, MOR knockout mice showed lower levels of self-administration in FR3 and progressive ratio schedules  7  , for both regular chow and sucrose, suggesting a significant MOR implication in the motivation to eat  8. Another study used Pavlovian Instrumental Transfer to assess motivational processes in MOR and delta opioid receptor (DOR) knockout mice  9. Resting state fMRI data analysis. The acquisition of Resting-state fMRI images is described in our previous study  6  , and raw data from these acquisitions were used for the present analysis. The preprocessing pipeline involved the following steps: (i) denoising using Advanced Normalized tools (ANTs) (https://github. com/ ANTsX/ANTs/blob/master/Examples/DenoiseImage. cxx); (ii) motion correction using ANTs and FSL (https:// fsl.
MoR knockout mice show weakened functional connectivity between the prefrontal cortex (pFC) and the nucleus accumbens (NAc). Our previous Rs-fMRI neuroimaging study revealed predominant FC alterations in reward/aversion networks upon deletion of the MOR gene  6. Here, we undertook a hypothesis-based approach using the same Rs-fMRI datasets, and focused on the PFC in order to determine whether this brain area shows any FC modification with the rest of the brain. We generated the PFC seed using the 2011 Allen Mouse Brain Connectivity Atlas (Fig. 1A ), computed FC of the seed with all the voxels of the brain (voxelwise) for each subject individually, then performed group comparison (MOR knockout versus control) (see methods). Correlation analysis showed significant differences, mainly in frontal areas (Fig.
MOR-/-mice were measured for operant responding to highly palatable chocolate-flavored pellets under a fixed ratio-1 (FR1) and 3 (FR3) schedules. Our data show significant delayed latencies to reach criterion for both FR1 and FR3 and decreased seeking behavior during an extinction session (Supplementary Result and Supplementary Fig. S2 ). These initial results demonstrated an altered motivational state in MOR knockout mouse strain. We then undertook touchscreen-based behavioral tasks to study both motivational and cognitive aspects of this phenotype in great details.
Interestingly in humans, lower inhibitory control is generally associated with weaker fronto-striatal functional connectivity  44  , and G-allele carriers for the MOR gene showed stronger negative fronto-striatal functional connectivity during a single alcohol taste-cue trial, compared to the more frequent A-allele carriers  45. Our combined MRI and behavior datasets, together, could raise the possibility that MOR in the PFC help setting the motivational drive to obtain a reward. In support of this hypothesis, previous literature showed that in human ventromedial prefrontal cortex (vmPFC) play a major role in reward evaluation and specifically in decision-making with known outcome probabilities  46  and in mice PFC-NAc neurons orchestrate reward-learning by encoding reward-predictive stimuli  47.
Acknowledgements We thank the staff at the animal facility of the  Neurophenotyping Center Douglas Research Center (Montréal, Canada ), for technical support and housing of the animals. This work was supported by  National Institute of Health (National Institute of Drug Abuse  Grant No. 05010  to BLK and  National Institute on Alcohol Abuse and Alcoholism , Grant No. 16658  to BLK), the  Canada Fund for Innovation  and the  Canada Research Chairs  to BLK.
Fluorescence activated cell sorting (FACS) Embryos were harvested at E12. 5 from timed-pregnant Gnrh1 <GFP> mice, previously anesthetized with an intraperitoneal injection of 80 mg/kg of ketamine-HCl and 8 mg/kg xylazine-HCl and sacrificed by cervical dislocation. Juvenile (P12) and adult female mice (3 months old) were anesthetized with 80 mg/kg of ketamine-HCl and 8 mg/kg xylazine-HCl before being sacrificed by cervical dislocation. Microdissections from embryonic nasal region and post-natal/adult hypothalamic preoptic region were enzymatically dissociated using Papain Dissociation System (Worthington, Lakewood, NJ) to obtain single-cell suspensions as previously described  (Messina et al. , 2016). After dissociation, the cells were physically purified using a FACSAria III (Beckman Coulter) flow cytometer equipped with FACSDiva software (BD Biosciences).
Quantitative RT-PCR For gene expression analyses, cDNA obtained from RT-PCR were reverse transcribed using Super-Script III Reverse Transcriptase (ThermoFisher, Invitrogen). Real-time PCR was carried out on Applied Biosystems 7900HT Fast Real-Time PCR System using exon-span-specific TaqMan Gene Expression Assays (Applied Biosystems, Carlsbad CA). The list of primers used for these experiments is the following: Amh (Mm00431795_g1), Gnrh1 (Mm01315605), Amhr2 (Mm00513847_m1); Acvr1 (Mm01331069_m1); Bmpr1a (Mm00477650_m1); Bmpr1b (Mm03023971_m1). Control housekeeping genes: Rn18s (Hs99999901-s1) and Actb (Mm00607939). Amperase activation was achieved by heating to 50˚C for 2 min, before denaturing at 95˚C for 20 s, followed by 40 cycles of 1 s 95˚C with a 20 s extension time at 60˚C. Gene expression data were analyzed using SDS 2.
Imaging 3D imaging was performed as previously described  (Belle et al. , 2014). An ultramicroscope (LaVision BioTec) using ImspectorPro software (LaVision BioTec) was used to perform imaging. The light sheet was generated by a laser (wavelength 488 or 561 nm, Coherent Sapphire Laser, LaVision BioTec) and two cylindrical lenses. A binocular stereomicroscope (MXV10, Olympus) with a 2 Â objective (MVPLAPO, Olympus) was used at different magnifications (1. 6Â, 4Â, 5Â, and 6. 3Â). Samples were placed in an imaging reservoir made of 100% quartz (LaVision BioTec) filled with DBE and illuminated from the side by the laser light. A PCO Edge SCMOS CCD camera (2560 Â 2160 pixel size, LaVision BioTec) was used to acquire images. The step size between each image was fixed at 2 mm.
Image analysis For confocal observations and analyses, an inverted laser scanning Axio observer microscope (LSM 710, Zeiss, Oberkochen, Germany) with EC Plan NeoFluor 10Â/0. 3 NA, 20Â/0. 5 NA and 40Â/1. 3 NA (Zeiss, Oberkochen, Germany) objectives were used (Imaging Core Facility of IFR114 of the University of Lille, France). Images, 3D volume, and movies were generated using Imaris x64 software (version 7. 6. 1, Bitplane). Stack images were first converted to imaris file (. ims) using ImarisFileConverter and 3D recontruction was performed using 'volume rendering'. Optical slices of samples were obtained using the 'orthoslicer' tools. The surface of the samples was created using the 'surface' tool by creating a mask around each volume. 3D pictures were generated using the 'snapshot' tool. ImageJ (National Institute of Health, Bethesda) and Photoshop CS6 (Adobe Systems, San Jose, CA) were used to process, adjust and merge the photomontages.