dataesr/scientific-paragraphs-categorization huggingface dataset:
Specificity and Sensitivity Evaluated on Separate Data Sets Sensitivity and specificity were evaluated for 8000 patients from every hospital for both the direct and DLD-based algorithms. Sensitivity (or recall) was evaluated for patients who died between January 1, 2001, and December 31, 2020, and were registered as the gold standard for each BDW. The algorithm classified patients as deceased if they were linked by at least one FNMD record. Specificity was evaluated for patients alive on 1 January 2016 (ie, patients with at least 1 hospital encounter between January 1, 2001, and December 31, 2015, and another hospital encounter between January 2, 2016, and December 31, 2020). The algorithm classified a patient as alive if the patient was not linked to the FNMD. The same patient could be present in both the sensitivity and specificity data sets (eg, a patient who died on May 13, 2017).
Acknowledgments This work was supported in part by the cluster  DELPHI -NExT  financed by  French National Research Agency  ( Agence Nationale de la Recherche, ANR ), integrated to France 2030 plan, by  Région Pays de la Loire  and by  Nantes Métropoles. We thank  Drs David Lair ,  Romain Casey , and  Florance Molinié  for their suggestions.
The 3D velocity fields have been employed for the Lagrangian AL LA estimation (further details below). For the AL SST reconstruction, annual mean near-surface temperature taken at the vertical model level 3 (~16 m depth) has been used instead of real SST, to allow for a better imprint of ocean dynamics  10. Sea surface height (SSH) anomalies were used to calculate geostrophic velocities for the AL LA-SSH reconstruction using cdftools, a fortran package for analysis and diagnostics on NEMO ocean model output. The strength of the AMOC was computed at each latitude and for each time step as the maximum of the annual mean AMOC streamfunction in density coordinates: AMOC σ ðt; yÞ ¼ max σ Ψðt; y; σÞ; Ψðt; y; σÞ ¼ where σ is the potential density referenced to the surface, V is the annual mean meridional transport, and x w and x e are the western and eastern boundaries of the Atlantic.
Consequently, for this study, we focus not on the mean transport, but on the variability of Agulhas leakage. Ocean reanalysis. The ocean reanalysis product BRAN2020 (here referred to as REA BRAN ) is a near-global ocean model constrained by ocean observations via data assimilation  19. The ocean model is the Ocean Forecasting Australian Model, version 3 (OFAM3 66 ) with a horizontal resolution of 1/10°based on the Modular Ocean Model version 5 and forced with the JRA55-do atmospheric dataset  61. The data assimilation system is a two-step, multiscale optimal interpolation method that uses satellite sea surface temperature and sea level anomaly as well as in situ temperature and salinity from e. g. , Argo floats, surface drifting buoys, moorings, and ship-borne surveys. BRAN2020 is available at daily resolution from 1993 to present, but the vertical velocity field is only provided from 1998.
The Agulhas Current system is realistically simulated by BRAN2020, but the representation of Agulhas leakage has not been analyzed in detail  67. It is noteworthy that, in contrast to the non-assimilative ocean models, BRAN2020 features a mean leakage transport that falls within the lower range of the observational estimates (16. 9 ± 1. 2 Sv). Lagrangian analyses. The Lagrangian leakage estimation was performed offline by making use of the software Parcels  21, 22  and following the recipe published by Schmidt et al. 17  , with the specifications outlined above in the section "How can Agulhas leakage variability be estimated?". For the AL LA estimation, particles were advected with the simulated 5-day mean 3D velocity field, for the new AL LA-SSH estimation with the 2D horizontal geostrophic velocities obtained from 5-day means of simulated and observed SSH anomalies.
This is normalized by dividing by the temporal average for each release year of the total transport in the whole region with the transport of particles captured repeatedly as long as they remain in the region. Eddy detection. We detected eddies in the simulated 5-day mean SSH anomaly field, using an established algorithm  68. A python implementation of this algorithm was obtained from https://github. com/ecjoliver/eddyTracking and further modified to include NEMO model output and improve the required computational time. The algorithm searches closed contours in spatially high-pass filtered SSH anomaly fields (Gaussian filter to remove wavelength scales larger than 20°). A closed contour is classified as an eddy, if the following criteria are met: (i) the contour contains more than 100 and less than 5000 grid points, and a local maximum (anticyclonic eddy) or minimum (cyclonic eddy); (ii) the eddy amplitude, that is the difference between the local extreme value and the SSH anomaly averaged along the perimeter of the last closed contour of the eddy, exceeds 5 cm; (iii) the eddy size, measured by the maximum distance between any pair of points within the contour, must be smaller than 300 km.
Acknowledgements This study has been funded by the  German Federal Ministry of Education and Research  through the  SPACES-II CASISAC  project (grant no. 03F0796A ) and by the  European Union 's  Horizon 2020 research and innovation programmes  (grant agreement no. 818123 , iAtlantic). It has also received funding from the  Initiative and Networking Fund of the Helmholtz Association  through the project " Advanced Earth System Modeling Capacity (ESM) ". R. S. acknowledges funding through the project  DEEPER  ( ANR-19-CE01-0002-01 ) from the  French National Agency for Research (ANR). The ocean model simulations were performed at the  North German Supercomputing Alliance (HLRN)  and on the ESM partition (JUWELS) at  Jülich Supercomputing Center (JSC) ; the Lagrangian trajectory simulations as well as all postprocessing was conducted at the  Christian-Albrechts-Universität zu Kiel  (system NESH).
Data availability The station-based annual SAM index can be downloaded from https://legacy. bas. ac. uk/ met/gjma/sam. html. The HadISST 1. 1 data is made available through the Met Office, Hadley Center and can be downloaded from https://www. metoffice. gov. uk/hadobs/ hadisst/data/download. html. Altimeter satellite gridded sea surface height anomalies as well as derived geostrophic currents are provided by E. U. Copernicus Marine Service Information: https://doi. org/10. 48670/moi-00148, https://doi. org/10. 48670/moi-00149. The updated AL SSH timeseries are publicly available on zenodo: https://doi. org/10. 5281/ zenodo. 7022671. Output from the BRAN2020 reanalysis is available from NCI (Australia National Computational Infrastructure) OPeNDAP servers at https://doi. org/10. 25914/ 6009627c7af03.
Code availability The Lagrangian software Parcels is available at http://oceanparcels. org/. The code for Lagrangian leakage estimation in ocean models can be retrieved from https://hdl. handle. net/20. 500. 12085/b704e917-09dd-4a73-b6a1-ea24a549920c. The cdftools can be obtained from https://github. com/meom-group/CDFTOOLS. The scripts necessary to reproduce the figures of this manuscript are available through GEOMAR: https://hdl. handle. net/20. 500. 12085/3b8db9da-5aa0-476c-81bc-9b5d1d53186e.
Acknowledgements KKL-H was supported by a  Carl Tryggers postdoctoral  grant. JKA was supported by  ERC-Stg-678148  and  VR-2015-04680. SAOA was supported by a  Heisenberg fellowship  ( AR 872/4-1  and  AR 872/7-1 ) from the  Deutsche Forschungsgemeinschaft (DFG). MAMK was supported by the  DFG  through project  AR 872/1-1 , awarded to SAOA as part of the  priority programme 1399 "Host parasite coevolution ". Funding Open access funding provided by  Lund University.
Disease gene discovery on chromosome (chr) X is challenging owing to its unique modes of inheritance. We undertook a systematic analysis of human chrX genes. We observe a higher proportion of disorder-associated genes and an enrichment of genes involved in cognition, language, and seizures on chrX compared to autosomes. We analyze gene constraints, exon and promoter conservation, expression and paralogues, and report 127 genes sharing one or more attributes with known chrX disorder genes. Using a neural network trained to distinguish disease-associated from dispensable genes, we classify 235 genes, including 121 of the 127, as having high probability of being disease-associated. We provide evidence of an excess of variants in predicted genes in existing databases. Finally, we report damaging variants in CDK16 and TRPC5 in patients with intellectual disability or autism spectrum disorders.
15 Disease gene discovery on chrX is thus associated with greater challenges, including male-female patient selection and variant interpretation biases, compared to autosomes. ChrX is often omitted from genome-wide analyses in a research context due to the difficulty of dealing with sex dichotomy in bioinformatics pipelines. Identifying novel gene-disease associations on chromosome X requires dedicated studies of families with multiple affected males  18, 19  or multiple subjects with matching phenotypes. 9  The interpretation of X-linked variants in sporadic cases and small families remains difficult in absence of extended segregation in the family, which is rarely available. Furthermore, the presence of damaging variants at relatively high frequency in large databases (ExAC, gnomAD) led to question previously established gene-disease associations.
Validation of putative novel disorder genes. To provide additional evidence supporting our predictions, we searched for evidence from databases or the literature that variants in the 235 NN-predicted genes could indeed result in new X-linked disorders. We first used HGMD and DECIPHER to retrieve the number of single nucleotide variants and indels of unknown significance reported in each gene and compared the number of variants in predicted versus non-predicted gene categories (Supplementary Table  9 ). This analysis revealed that the 235 NN-predicted disorder genes are enriched in point variants compared to non-predicted genes (p=5. 3×10 -6 ; OR=2. 2), suggesting that this excess is due to pathogenic variants. NN-predicted genes meeting at least one LME criterion had more point variants than predicted genes that do not meet any LME criterion (Fig.
4a-b ; Extended Data Fig. 6 ). Second, we used the SysID and Gene2Phenotype (G2P) developmental disorders databases to compare the overlap of predicted and non-predicted genes (Fig. 4c ). Nine of the 16 NN-predicted PMT genes were in SysID (Fig. 3a-b ). Three genes (PTCHD1, NLGN3, and KIF4A), indicated as confirmed SysID genes, were also in G2P. 5] [26]  A splice site variant in KIF4A was identified in a family with four affected males,  27  and this finding was recently strengthened by the identification of additional de novo or inherited variants causing a range of different phenotypic manifestations. 28, 29 Focusing on no-disorder genes, 21 (9%) of the NN-predicted genes and 14 (4%) of the non-predicted genes were in SysID (Fig. 3c-d ). Five predicted genes (CSTF2, FGF13, PLXNA3, OTUD5 and STEEP1) were confirmed SysID genes.
In addition, six genes (ARHGEF6, CDK16, FGF13, GSPT2, MMGT1 and ZDHHC15) had entries in G2P. Pathogenic variants in OTUD5 and FGF13 respectively cause a severe neuro-. developmental disorder with multiple congenital anomalies and early lethality,  30, 31  and developmental and epileptic encephalopathy,  32  both described in 2021. PLXNA3 have been associated with ID and autism spectrum disorders (ASD),  33  or hypogonadotropic hypogonadism. 34 itional evidence from the literature not yet reflected in OMIM or any other database include possibly pathogenic variants described in one or a few families in ZMYM3,  35  GLRA2,  36  and GPKOW. 37 Although some of these genes now have an OMIM disorder entry, their association with a monogenic disorder was not known at the beginning of our study. Third, we retrieved the number of de novo predicted damaging (truncating or CADD≥25) variants identified in these genes from the Martin et al.
Gene intolerance to variation Metrics related to intolerance of chrX genes to genetic variation (loss-of-function (LoF), missense and synonymous) were retrieved from the Genome Aggregation Database (gnomAD) v2. 1. 1  23  after conversion of gene identifiers to HGNC approved symbols. For genes with available data for multiple transcripts, the metrics of the one with lowest LoF observed/expected upper bound fraction (LOEUF) were kept.
Gene expression data Median gene-level transcripts per million (TPM) for 54 tissues from the v8 release were downloaded from the GTEx portal  52. The robust tissue specificity measure tau  53, 54  was calculated for chrX genes as previously described, aggregating the median expression of each gene for the different brain regions into two values: i) the median of the values for the two cerebellar regions and ii) the median of the values for the other brain tissues. 22 Genes with tau below 0. 6 are broadly expressed, while tau higher than 0. 6 indicates genes expressed in a restricted number of tissues. Additional expression data was downloaded from the BrainSpan Atlas of the Developing Human Brain. 55 The Developmental Transcriptome Dataset contains gene summarized RPKM (Reads Per Kilobase of transcript, per Million mapped reads) normalized expression.
Canonical transcript selection The criteria for canonical transcript selection prioritized: i) MANE (Matched Annotation between NCBI and EBI) Select transcripts, which were independently identified by both Ensembl and NCBI as the most biologically relevant; and ii) APPRIS-annotated transcripts  56. TSS, MANE, and APPRIS annotations of transcripts were retrieved from Ensembl Genes 102 via BioMart  57. For most genes (ca. 80%), the canonical transcripts belong to the MANE Select category. The longest transcript with APPRIS annotation was selected for ca. 20% of genes. In one case, the only existing transcript was kept.
Promoter CpG density. CpG density was calculated for the 4kb region surrounding the transcription start site (TSS±2kb) of the canonical transcripts of chrX protein-coding genes following previous publications. 22, 58 Promoter sequences were downloaded through UCSC Table  Browser. 59 The CpG density, defined as the observed-to-expected CpG ratio, was calculated as follows: number of CpG dinucleotides / (number of C x number of G)) × N, where N is the total number of nucleotides in the sequence being analyzed.
Exon and promoter conservation across species Nucleotide conservation across 100 vertebrate species was calculated using the phastCons score obtained with the phastCons100way. UCSC. hg38 R package,  60  and represents the probability that a given nucleotide is conserved (range 0 to 1). Exon-conservation score was calculated for each gene on chrX as the average phastCons of all nucleotides belonging to the gene coding sequence. Coding coordinates for the canonical transcripts were retrieved from Ensembl Genes 102 57 using biomaRt R package. 61, 62 Promoter-conservation score for each gene on chrX was calculated as the average phastCons score of all nucleotides 4kb around the TSS of the canonical transcript.
Pre-classification of genes for the neural network Genes were pre-classified based on 1) the type of associations with disorders and/or traits (confirmed, PMT, no-disorder), 2) the association with a brain disorder and 3) the tolerance to loss-of-function (LoF) variants (Extended Data Fig. 5 ). The list of confidently LoF-tolerant genes based on the gnomAD dataset was retrieved from data files from Karczewski et al. (2020) and consists of genes with at least one homozygous LoF variants. 16 Genes showing no homozygous LoF variants were considered LoF-intolerant.
For  > 0. 5 we can consider a gene with label T0 to be false positive. Thereby for a given  the empirical FDR can be measured by 𝑇1 (𝑡) (𝑇0(𝑡) + 𝑇1(𝑡)) ⁄. By calculating the FDR threshold by estimations on ten different neural networks, each applied on unseen data, we ensure that the threshold is not biased by overfitting. FDR thresholds were calculated as 0. 8152034 (FDR 0. 05) and 0. 9745963 (FDR<0. 01). Finally, a full network  is trained on the full data  and applied on the complete data set with scaled column by the former min-max-scaling. The inferred score for each gene, represents the estimated probability for an association between the gene and a brain diseases. All computational operations were performed in python with sklearn  70  for normalization and keras for building and training the neural network.
Variant data and clinical information were shared anonymously. Only damaging variants (i. e. variants leading to a premature termination codon or missense variants with CADD PHRED scores  25) in NN-predicted genes meeting at least one LME criterion were kept for further analysis. thing that is roughly 'Nshaped'. Englerin A (100nM) was applied to the cell expressing WT and mutant TRPC5 without immediate current upon break-in to confirm its functional expression.
Statistics Fisher's tests were performed for each chromosome to determine associations between: i) genes being present in the specific chromosome and genes having an associated phenotype with Clinical Synopsis data (CS-genes); ii) CS-genes being in the specific chromosome and. CS-genes having non-specific or specific neurologic features described in the Clinical Synopsis data. For chrX, Fisher's tests were performed to compute the enrichment/depletion of confirmed disorder-associated genes in each decile of the distribution of continuous variables. Odds ratios were log2 transformed and indicate enrichment or depletion of genes, for positive or negative values, respectively  (10 -4  was added prior to transformation, whenever necessary to deal with log of 0 issue). P-values were adjusted for multiple comparisons using Bonferroni correction.
to gene group. Confirmed disorder genes (orange), PMT genes (blue), no-disorder genes (grey). Vertical dashed lines separate deciles of the overall distribution. Grey areas depict deciles for which confirmed disorder-as-PMT (a, b) and no-disorder genes (c, d) predicted by the neural network as possibly disease-associated, either fulfilling (a, c) or not LME criteria (b, d). Genes present in the SysID database as "ID candidate genes" (*) and "Current primary ID" or "ID data freeze" (**) are marked. e, No-disorder genes meeting LME criteria but not predicted by the neural network.
Known point mutations in no-disorder genes. Boxplot showing the number of known mutations reported in HGMD and DECIPHER (a) or their value normalized by coding-sequence (CDS) length (b) according to their predicted status. No-disorder genes meeting LME criteria but not predicted by the neural network were omitted from the analysis. Box plot elements are defined as follows: center line: median; box limits: upper and lower quartiles; whiskers:1. 5×interquartilerange;points:outliers. Related to Supplementary Table9. c,Number of nodisorder genes present in the SysID database according to their predicted status. CC-BY-ND 4. 0 International license It is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in (.
Acknowledgements The authors thank the patients with CDK16 and TRPC5 pathogenic variants and their family for their participation in this study. We kindly thank  Mrs Céline Cuny  for Sanger sequencing analysis of the patient with TRPC5 missense variant. Electrophysiological experiments were carried out at the electrophysiology core facility of  ICM  funded from the program " Investissements d'avenir "  ANR-10-IAIHU-06. We thank  Universitätsklinikum Essen, University Duisburg-Essen , the  Deutsche Forschungsgemeinschaft (DFG) , the  Tom-Wahlig-Stiftung (TWS) , the  Deutsche Stiftung Neurologie (DSN) , and  Assistance Publique des Hôpitaux de Paris (APHP)  for their financial support to the research studies conducted by the authors. E. L. and F. K. are associated with the FOR 2488 project (DFG, Project number  287074911 ).
Data availability All data are available in the main text or supplementary materials. We used data from HGNC (https://www. genenames. org/), OMIM (https://www. omim. org/), gnomAD v2. 1. 1 (https://gnomad. broadinstitute. org/), GTEx Portal (https://gtexportal. org/home/), BrainSpan Atlas of the Developing Human Brain (https://www. brainspan. org/), Ensembl (https://www. ensembl. org/index. html), UCSC Table Browser (https://genome. ucsc. edu/cgibin/hgTables), Uniprot (https://www. uniprot. org/), Human Gene Mutation Database (http://www. hgmd. cf. ac. uk/ac/index. php), DECIPHER (https://www. deciphergenomics. org/), SysID database (https://www. sysid. dbmr. unibe. ch/), Gene2Phenotype (https://www. ebi. ac. uk/gene2phenotype) and CADD -Combined Annotation Dependent Depletion (https://cadd.
Protein-coding genes Annotations of genes in all chromosomes were downloaded from the HUGO Gene Nomenclature Committee (HGNC) database in November 2020 focusing on protein-coding genes with approved status and present in the reference assembly. 51 MED14OS was excluded from chrX-focused analysis due to its encoded protein being curated in Uniprot as "Product of a dubious CDS prediction". Information concerning genes in PARs was also retrieved from HGNC.
3: Different evidence strength for the predicted disorder-associated genes. a-d, to gene group. Confirmed disorder genes (orange), PMT genes (blue), no-disorder genes (grey). Vertical dashed lines separate deciles of the overall distribution. Grey areas depict deciles for which confirmed disorder-as-PMT (a, b) and no-disorder genes (c, d) predicted by the neural network as possibly disease-associated, either fulfilling (a, c) or not LME criteria (b, d). Genes present in the SysID database as "ID candidate genes" (*) and "Current primary ID" or "ID data freeze" (**) are marked. e, No-disorder genes meeting LME criteria but not predicted by the neural network. perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted February 17, 2022.
External supporting evidence for the predicted disorder-associated genes. a and b, Known point mutations in no-disorder genes. Boxplot showing the number of known mutations reported in HGMD and DECIPHER (a) or their value normalized by coding-sequence (CDS) length (b) according to their predicted status. No-disorder genes meeting LME criteria but not predicted by the neural network were omitted from the analysis. Box plot elements are defined as follows: center line: median; box limits: upper and lower quartiles; whiskers:1. 5×interquartilerange;points:outliers. Related to Supplementary Table9. c,Number of nodisorder genes present in the SysID database according to their predicted status. CC-BY-ND 4. 0 International license It is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in (.
05) (Fig. 3C ). We extracted the net signature of APOBEC3G (SBS. A3G) using the mutational shift distance (MSD) method (Methods). SBS. A3G was characterized by C>T substitutions in the TCC, CCC, and CCT (Fig. 3D ). The TCC motif is consistent with the secondary motif preference of APOBEC3G for the TC context observed in the HIV genome  (18, 36, 42, 43)  (Supplementary Fig. S9 ). To confirm the SBS. A3G signature we identified, we employed two additional orthogonal analytical methods for mutational signature extraction that rely on different mathematical procedures. The SigneR package  (44)  provides a full Bayesian treatment to the non-negative matrix factorization (NMF) model. The HDP package  (45)  utilizes the hierarchical Dirichlet process (Methods). Both methods identified a mutational signature that was present in the hA3G(+) mA3(-/-) tumors but absent from mA3(-/-) tumors consistent with the SBS.