dataesr/scientific-paragraphs-categorization huggingface dataset:
Variants in CHMP1A cause pontocerebellar hypoplasia type 8 (OMIM:614961), with similar phenotypes to patients with VPS4A variants: severe psychomotor retardation, pontocerebellar hypoplasia, decreased cerebral white matter, thin corpus callosum, abnormal movements, hypotonia, spasticity, and variable visual defects. High gene expression levels are present across all tissues as measured in humans (GTEx data), both disease and non-disease related. Similarly, high levels of expression were also seen in WT mouse embryos from 4 to 36 somites according to Deciphering the Mechanism of Developmental Disorders (DMDD)  42. In addition, we found two de novo variants reported in denovo-db database  40  : a nonsense (stop-gained) variant with an associated intellectual disability phenotype and a missense variant in a patient with autism.
Combined OMIM-ORPHANET data was used to compute the number of disease genes in each FUSIL bin. Odds ratios were calculated by unconditional maximum likelihood estimation (Wald) and confidence intervals (CIs) using the normal approximation, with the corresponding adjusted P values for Fisher's exact test. b Distribution of disease-associated genes according to mode of inheritance. Disease genes with annotations regarding the mode of inheritance according to the Human Phenotype Ontology 8. c Haploinsufficient genes. Known haploinsufficient genes curated by ClinGen (percentage with respect to the total number of disease genes in each bin). d Age of onset as described in rare diseases epidemiological data from Orphanet (Orphadata). The earliest age of onset associated with each gene was used. Bar plots representing the percentage of disease genes associated with each age of onset for each FUSIL category.
e Distribution of the number of physiological systems affected. The phenotypes (HPO) associated with each gene were mapped to the top level of the ontology to compute the number of unique physiological systems affected. f Enrichment analysis of developmental disorder genes. The Developmental Disorders Genotype-Phenotype Database (DDD-DDG2P) set of genes was used to compute the number of developmental disorder genes in each FUSIL bin. These genes were compared against non-disease genes (OMIM, ORPHANET and DDD-DD2GP). Odds ratios were calculated by unconditional maximum likelihood estimation (Wald) and confidence intervals (CIs) using the normal approximation, with the corresponding adjusted P values for Fisher's exact test. g Distribution of disease genes. Percentage of distribution of Mendelian and developmental disorder genes among the different FUSIL categories.
h Distribution of disease genes by mode of inheritance. Percentage of distribution of Mendelian and developmental disorder genes among the different FUSIL categories according to the mode of inheritance reported in the HPO (set of Mendelian disease genes) and DDD (developmental disease-associated genes). CL cellular lethal (pink), DL developmental lethal (orange), SV subviable (yellow), VP viable with phenotypic abnormalities (light blue), VN viable with normal phenotype (dark blue), DDD/DDD-DDG2P Deciphering Developmental Disorders database of genes that are likely causative of developmental disorders. For e, centre line, median; notch, CI around the median; box edges, interquartile range, 75th and 25th percentile, respectively; whiskers, 1. 5 times the interquartile range. protein-protein interactions with known developmental disorder genes, further supported these associations.
Methods Main data sets. In this study, we examined genes with viability data for null homozygote mice produced by the IMPC (https://www. mousephenotype. org). We obtained high-to-moderate-confidence human orthologues and integrated selected human genetic data. In particular, we incorporated gene viability data obtained in human cell screens (initially 11 cell lines corresponding to three studies)  18  ; in this study, we used the Project Achilles Avana data set comprising 485 cell lines (release 18Q3 of August 2018), which is part of the Cancer Dependency Map (DepMap) project  19. We also incorporated constraint scores (shet, Residual Variation Intolerance Score (RVIS), pLI and o/e LoF), disease information (OMIM, ORPHANET, HPO, DDD) and DMDD annotations (see below for details). We used HGNC and Mouse Genome Informatics (MGI) as stable identifiers in our analysis to avoid problems associated with gene symbol changes (synonyms and previous symbols, which may lead to ambiguous identifiers) [https://www.
genenames. org/download/ statistics-and-files/; HGNC protein-coding gene file, downloaded 18. 11. 14]. IMPC primary and embryo viability assessment. We conducted an analysis on 4934 genes with primary adult viability data currently available in IMPC Data Release (DR) 9. 1. This release included the 1751 genes analysed in Dickinson et al. 21 and the 4237 genes analysed in Munoz-Fuentes et al. 18  , corresponding to DR4. 0 and DR7. 0, respectively, and additional data collected since then. Viability data generated by the IMPC was analysed as defined in IMPRESS (the International Mouse Phenotyping Resource of Standardised Screens, https://www. mousephenotype. org/impress/). A minimum of 28 pups were genotyped before weaning, and the absence of knockout (null) homozygote pups would classify the gene as lethal.
Among the remaining 4614 genes, 1185 (26%) genes had a lethal phenotype, 443 (9%) a subviable phenotype, and 2986 (65%) a viable phenotype. Of these, 4446 genes had human cell viability data (Avana data set; see next section). Essentiality in human cells. In our previous study  18  , we used viability data as reported for 11 cell lines from 3 studies  [15] [16] [17]. Here we used the Project Achilles Avana data set and CRISPR-Cas9 proliferation (essentiality) scores, which is part of the Cancer Dependency Map (DepMap) project  21. This data set comprises viability data on 17,634 genes in 485 cell lines (https://depmap. org/portal/achilles/; release 18Q3 in August 2018), with lower values indicating more intolerance (i. e. more essential). The Avana data set presents several advantages as compared to the previous studies.
It is a larger data set, viability is measured using the same units for all cell lines (allowing us to obtain a mean per gene) and the data are corrected for copy number variation  20. Gene identifiers were provided as Entrez identifiers (NCBI) and converted to HGNC identifiers. Determining functional bins. For each gene, we obtained a mean of the Avana proliferation scores and integrated with our data set of mouse genes with viability data based on the mouse-to-human orthologues (obtained as described above). We observed that, for genes with an Avana mean score < -0. 45, the mouse null homozygotes were lethal in almost all cases, while genes with an Avana mean score >-0. 45 presented lethal, subviable or viable phenotypes (Supplementary Fig. 1a , Supplementary Fig. 1b ). A similar pattern was observed when a different resource for cell essentiality-based on 11 cell lines from 3 different studies-was used (methods and threshold criteria explained in Munoz-Fuentes et al.
All the subsequent analysis focussed on the main five FUSIL bins: CL, DL, SV, VP, and VN. Sankey diagrams representing the mappings between mouse and human cell essentiality categories were plotted with the R package alluvial  50. GO BP enrichment was conducted with the R package category 51 , with the entire set of IMPC genes as the reference set. The Benjamin and Hochberg (BH) method was applied for multiple testing correction  52  and an adjusted P value < 0. 05 was considered significant. The results were plotted using the REVIGO algorithm for semantic similarity and redundancy reduction  53. The algorithm selects representative GO terms out of the significant results, maximising semantic representation and enriched/statistical significance (settings: SimRel semantic similarity measure, medium similarity, Homo sapiens database).
Significant results were only found for the CL and DL gene categories. In Fig. 1b , the bubble size is proportional to the frequency of the term in the database and the colour indicates the significance level as obtained in the enrichment analysis, after correcting for multiple testing. Reactome pathway enrichment analysis was performed by means of the R/ Bioconductor package ReactomePA 54. The same approach for multiple testing correction followed in the GO analysis was applied. Previous knowledge on mouse viability. The IMPC is an ongoing project. Given that about ¼ of the mouse genome has been screened for viability to date, we decided to compare our results with previous annotations from MGI  55. We found a total of 4599 mouse genes with embryo lethality annotations (50 Mammalian Phenotype Ontology terms as described in Dickinson et al.
Gene expression. Human gene median transcripts per kilobase million (TPM) values by tissue were downloaded from the GTEx portal [https://gtexportal. org/ home/datasets; accessed 18. 12. 02, file "GTEx_Analysis_2016-01-15_v7_RNA-SeQCv1. 1. 8_gene_median_tpm. gct. gz"]  61. Gene symbols were mapped to HGNC ID identifiers. Spearman correlation coefficients between gene expression values across tissues were estimated, only TPM values for relevant (non-correlated) tissues were considered for further analysis. Protein-protein interaction data. Human protein-protein interaction data were downloaded from STRING website [accessed 18. 11. 18]. Only high-confidence interactions, defined as those with a combined score >0. 7 were used for further analysis  62. Ensembl protein IDs were mapped to HGNC IDs using Ensembl bio-maRt (Ensembl Genes 94)  60.
Several network parameters for every node in the network were computed with Cytoscape (v3. 5. 1) plug-in Network Analyzer  63. Spearman correlation coefficients between different network parameters were estimated; only non-correlated parameters were considered for further analysis. Protein complexes. A core set of human protein complexes was downloaded from Corum 3. 0 64 (http://mips. helmholtz-muenchen. de/corum/#download; accessed 18. 12. 03). Gene symbols were mapped to unambiguous HGNC ID identifiers corresponding to protein-coding genes. Paralogues. Paralogues were retrieved with biomaRt (https://www. ensembl. org/ index. html;Ensembl; Genes 95 version [data accessed 19. 02. 18])  60. Only genes with HGNC IDs were considered, and only those protein-coding paralogues with an HGNC ID were kept for downstream analysis.
Gene-disease associations. Disease-associated genes curated by OMIM (https:// omim. org/) and Orphanet (https://www. orpha. net) were analysed through our PhenoDigm pipeline  67  [Data accessed 19. 01. 16]. DECIPHER developmental disorders genes are defined as those reported to be associated with developmental disorders, compiled by clinicians as part of the DDD study to facilitate clinical feedback of likely causal variants. The DDG2P 3 is categorised into the level of certainty that the gene causes developmental disease (confirmed or probable), the consequence of a mutation (LoF, activating, etc. ) and the allelic status associated with disease (monoallelic, biallelic, etc. ) [DDG2P version: DDG2P_19_2_2019. csv; https://decipher. sanger. ac. uk/ddd#ddgenes]. Given that OMIM contains a certain number of genes involved in susceptibility to multifactorial disorders and other non-Mendelian gene-disease associations and that DDD-DDG2P also includes probable and possible gene-disease associations, we decided to investigate additional sets of curated gene-disease associations.
org/ download/current/Ensembl2Reactome. txt; data accessed 19. 07. 31] and protein interactors from STRING  62  as explained above (STRING ppI annotations with a combined score >0. 7). We compared our selected set of DL genes with those in the different FUSIL bins that are not associated with disease and with the entire set of genes in each FUSIL bin. For each one of the three features (protein families, pathways and interactors), we computed the percentage of genes in each category sharing a PFAM protein family, a Reactome pathway (lowest level) or directly interacting with any monoallelic developmental disease gene. Deciphering developmental disorders (DDD) research variants (found in ~2000 genes) were downloaded from https://decipher. sanger. ac. uk/ddd#researchvariants [accessed 18. 11.
05]. This is a set of variants of unknown significance found in 4293 children with developmental disorders who participated in the UK DDD study. It includes functional de novo and rare LoF homozygous, compound heterozygous and hemizygous variants in genes that are not associated with developmental disorders and are not reported in OMIM in children who remain undiagnosed in the DDD study. CMG 1 Tier 1 and Tier 2 level genes (~2000 genes) were obtained from http://mendelian. org/phenotypes-genes [accessed 19. 28. 02]. No information about individual variants is provided. De novo variants (<0. 1% in the 100KGP and public resources such as gnomAD and predicted to have effect on the coding region of genes) in undiagnosed patients with intellectual disability from the 100KGP and associated clinical phenotypes (HPO terms) were extracted by querying the data available in the GeCIP research environment [accessed 18.
10. 20] and intersecting with our set of 163 prioritised genes. Denovo-db database, a resource containing human de novo variants identified in the literature  40  was also explored. Mainly focussed but not limited to neurodevelopmental phenotypes and congenital heart disease, it also includes published de novo variants associated with other phenotypes, e. g. autism, Tourette syndrome or schizophrenia or controls) [denovo-db, Seattle, WA URL: denovo-db. gs. washington. edu; data accessed 19. 07. 24]. Only those variants with a predicted effect on the coding region were considered for the purpose of this study (synonymous variants were excluded). Research candidates were identified in 82 of the 163 prioritised genes (highly LoF intolerant not previously been associated with Mendelian disease).
An additional set of haploinsufficient genes from ClinGen 68 (https://www. clinicalgenome. org/)(n=295)wasused for the analysis [https://github. com/macarthur-lab/gene_lists;dataaccessed19. 02. 19]. Genomics England PanelAPP also contain information about the mode of inheritance. We restricted the analysis to those genes associated with one of the following modes of inheritance: monoallelic, biallelic, or both with consistent annotation across different panels [https://panelapp. genomicsengland. co. uk; data accessed19. 06. 02]. Age of onset. The age of onset was obtained from rare diseases epidemiological data (Orphadata) [http://www. orphadata. org/cgi-bin/epidemio. html; data accessed 19. 02. 13]. The earliest age of onset associated with each gene was selected for downstream analysis.
Acknowledgements This work was supported by  NIH  grant  U54 HG006370. IMPC-related mouse production and phenotyping was funded by the  Government of Canada through Genome Canada  and  Ontario Genomics  ( OGI-051 ) for NorCOMM2 (to C. M. ) and the  National Institutes of Health and OD, NCRR ,  NIDDK  and  NHLBI for KOMP  and  KOMP2  Projects  U42 OD011175  and  UM1OD023221  (to C. M. , K. C. K. L. ),  Infrafrontier  grant  01KX1012 ,  EU Horizon2020: IPAD-MD  funding  653961  (to M. H. d. A. ); EUCOMM:  LSHM-CT-2005-018931 , EUCOMMTOOLS:  FP7-HEALTH-F4-2010-261492  (to W. G. W. );  UM1 HG006348 ;  U42 OD011174 ;  U54 HG005348  (to A. L. B. ),  NIH   U54706HG006364  (to A. L. B. );  Wellcome Trust  grants  WT098051  and  WT206194  (to D. J. A. ); The  French National Centre for Scientific Research (CNRS) , the  French National Institute of Health and Medical Research (INSERM) , the  University of Strasbourg  and the  "Centre Europeen de Recherche en Biomedecine ", and the  French state funds through the "Agence Nationale de la Recherche " under the  frame programme Investissements d'Avenir labelled  ( ANR-10-IDEX-0002-02 ,  ANR-10-LABX-0030-INRT ,  ANR-10-INBS-07 PHENOMIN  (to J.
Data availability The data that support the findings of this study are available in the BioStudies database under accession number S-BSST293. All other data supporting the findings of the study are included in the paper or its supplementary information tables. Additional information on mouse phenotypes is available through the IMPC web portal (https:// www. mousephenotype. org/) and the IMPC FTP repository (ftp://ftp. ebi. ac. uk/pub/ databases/impc/).
Specific corpora used for Continuous Lexical Sign Recognition Specific annotated SL corpora are used for this task, with lexical annotations only, with three of them standing out: The German Sign Language (DGS) SIGNUM Database  [10]  contains 780 elicited sentences, based on 450 lexical signs. In total, there is approximately five hours of video. Because of the rigorous elicitation procedure, it is safe to say that the level of spontaneity as well as the interpersonal variability in the observed SL are very low. Signers indeed repeat the original reference sentences with no initiative. The elicited gloss sequences have lengths ranging from two to eleven glosses. The RWTH-Phoenix-Weather (RWTH-PW) corpus  [11] [12] [13]  is made from 11 h of live DGS interpretation of weather forecast on German television, with nine interpreters.
Conversely to many corpora produced in laboratory conditions and/or strong elicitation rules, RWTH-PW has been described by its authors as real-life data  [11]. However, because of the specific topic, the language variability is necessarily limited. Furthermore, it is crucial to note that interpreted SL is a specific type of SL, quite different from spontaneous SL. There is a good chance that the translation will be strongly influenced by the original speech (in German), especially in terms of syntax, and make little use of the structures typical of SL  [14]. The Continuous SLR100 (CSLR100) dataset  [15]  is a continuous Chinese Sign Language (ChSL) corpus, with more than 100 h of recordings. However, the level of variability and spontaneity in the produced language is very low: the corpus is based on 100 pre-defined sentences, that are repeated five times by 50 signers.
Moreover, they also tested their model on the signer-independent version of RWTH-PW and obtained a WER of 44. 1%, which is a relative 65% higher, showing that the signer-independence is a challenge that should not be overlooked. Using two CNN streams-one for the hands and a global one-for feature extraction,  [15]  used a combination of Long Short-Term Memory (LSTM) and Attention  [23]  to tackle the temporal modality, with an encoder-decoder architecture, along with a Dynamic Time Warping (DTW) algorithm. They published results on RWTH-PW and the signer-independent version of the Continuous SLR100 dataset. Recently, 3DCNNs have proven effective for action recognition, and have progressively replaced traditional 2D convolutions. The LSTM encoder-decoder architecture with Attention is used by  [24, 25] , and Connectionist Temporal Classification (CTC) decoding by  [25] [26] [27] [28] [29].
For instance,  [16]  insisted on the importance of space as a grammar component of French Sign Language (LSF). In another work,  [37]  mentioned complementary arguments, observing that the focus had been on conventional signs-gestures-leaving out the grammar of SL-primarily referring to the iconic characteristic of SLs, and the multilinear aspects of SL, like facial expression or body posture. Related to the fact that CLexSR has been the main concern of researchers, leaving out the three linguistic characteristics we just mentioned, specific types of SL corpora have become popular. Many of them consist of artificial elicited sentences, repeated several times, with eliciting material-and annotation schemes-consisting of sequences of glosses. This is, for instance, the case of the SIGNUM Database and the CSLR100 corpus.
2. 1. 2. Dicta-Sign-LSF-v2: A Linguistic-Driven Corpus with Fine and Consistent Annotation Mixing the best of both worlds, Dicta-Sign-LSF-v2  [44]  is a very natural dialogue corpus made by linguists, with fine and consistent annotation across the 11 h of recordings of the corpus. It features annotation data for PTSs, DSs, FSs, FBuoys, NSs and Gs. Furthermore, the corpus is publicly available on the language platform Ortolang (https://hdl. handle. net/11403/dicta-sign-lsf-v2 [44] ) (Ortolang is a platform for language, which aims at constructing an online infrastructure for storing and sharing language data (corpora, lexicons, dictionaries, etc. ) and associated tools for its processing). The recording setup can be seen in Figure  4. The elicitation guidelines consisted in having the participants discuss about nine different topics about travel in Europe, i.
Proposal for a Generalizable and Compact Continuous Sign Language Recognition Framework While end-to-end frameworks are easier to set up and do not require any prior knowledge on the signer representation, they require more data and may not be easily generalizable. When signer representation and learning model are decoupled, the generalizability with respect to new types of videos are introduced, and one does not need to retrain the whole network in case new linguistic descriptors are added to the model. The reduced demand on training data is also an important benefit of such models, as annotated SL corpora are not that large. Also, the black box architecture of end-to-end models does not enable one to get a straightforward feedback on which signer features are linguistically relevant for recognition.
Signer Representation Since available training data are limited in quantity, we have decided to partly rely on pre-trained models for signer representation, with a separate processing of upper body, face and hands-which are usually dealt with in very specific ways, whether in SL-specific or non-SL-specific models. Upper Body: Image ! 2D, Image ! 3D, Image ! 2D ! 3D CNNs have emerged as a very effective tool to get relevant features from images. OpenPose (OP)  [47, 48]  is a powerful open source library, with real-time capability for estimating 2D body pose. Widely used in the gesture recognition community, its Image ! 2D body pose estimation module is fast-close to real-time for 25 frames per second (fps) videos on a modestly powerful desktop computer-is easy to use and works well even when part of the body is missing from the image-we only kept the 14 upper body keypoints and leave out the leg keypoints.
This is a great benefit, as most SLs videos only show the upper body. Many other pose estimation models we have experimented on do not offer this feature. Although direct Image ! 3D models do exist (for instance  [49] ), we were not able to find one fitting our requirements. Indeed, as for Image ! 2D models, prediction usually fails when part of the body is missing from the image, or when the person is not centered with respect to the image. Another type of issue is related to the training data of these models. As they were not trained with SL data, our experience is that they do not perform well when fed with SL images. Fortunately, the 2D OP estimates have proven robust even on SL videos. Therefore, we decided to rely on OP in order to get good 2D estimates, then train a 2D ! 3D Deep Neural Network (DNN), reproducing the architecture from  [50].
In the end, a Image ! 2D ! 3D model was thus obtained, aiming to learn the function f that estimates the third coordinate for each landmark of the signer in frame t, that is, with n as the number of landmarks (n = 14 in our case): f : R 2n ! R n ⇥ (b x 1t , b y 1t ), (b x 2t , b y 2t ),. , (b x nt , b y nt ) ⇤ 7 ! [b z 1t , b z 2t ,. , b z nt ] (17) where b x it , b y it and b z it are a standardized version of the original coordinates x it , y it and z it , with respect to the whole training dataset. The training loss is defined as the Euclidean distance between predictions and ground-truth data. The training data we decided to use for training consisted of motion capture data from the LSF corpus MOCAP1  [51]. These data have been particularly valuable since it contains high precision 3D landmarks recording of LSF, from four different signers.