dataesr/scientific-paragraphs-categorization huggingface dataset:
HD-sEMG signal was acquired using a Quattrocento amplifier (OT Bioelettronica) at 2048 Hz sampling frequency, while force profiles were sampled with a Phidget load cell at 10 Hz. A Matlab 2019b (The MathWorks, Inc) program was used to synchronyse both modalities. The HD-sEMG signal was then decomposed into motor neuron activity using convolutive blind source separation  53. For the purpose of training and testing the supervised decomposition pipeline, motor neuron activity was accepted if it was present for at least 80% of the contraction window. For each participant the HD-sEMG signal and accompanying decomposed motor neuron activity (as a sparse binary matrix) was then split into a 4-s training window and a 1-s testing window. A gated recurrent unit (GRU) neural network was used as the deep learning model due to previous studies showing good performance with this data type  27.
Training, validation and test data was z-score standardised using the mean and standard deviation calculated from the training set. During training the input signal was augmented with noise of standard normal distribution. To account for the high sparsity of the output matrix, samples containing motor neurons were artificially oversampled, with each input batch of 512 time instants containing at least 20% motor neuron activation. All machine learning was implemented using the PyTorch library in python. Final performance was assessed using the rate of agreement metric (RoA). The optimised architecture of the GRU network was used for pretraining, which was conducted using multitask learning in a hard parameter sharing paradigm  56. Four digital twins were created for simulation using different model parameters (tissue conductivities, MU distribution, fibre properties, etc.
Acknowledgements For this study, DF was sponsored by the  European Research Council (ERC)  under the  Synergy Grant Natural BionicS  ( 810346 ) and the  EPSRC Transformative Healthcare for 2050 project NISNEM Technology  ( EP/ T020970/1 ). AC and IMG are sponsored by the  Engineering and Physical Sciences Research Council (EPSRC) -Centre for Doctoral Training in Neurotechnology for Life and Health and Meta.
Data availability Surface geometry of muscles, bones, subcutaneous tissue and skin data used for arm modelling were taken from BodyParts3D, The Database Center for Life Science (http://lifesciencedb. jp/bp3d/). The simulated MUAPs data that was used to train the neural network in the deep learning experiment have been deposited in the Science Data Bank database under https://doi. org/10. 57760/sciencedb. 07548 and CC BY-NC-SA 4. 0 licence. The raw experimental data that was used in the deep learning experiment has also been deposited in the Science data Bank database under https://doi. org/10. 57760/sciencedb. 07586 and CC BY-NC 4. 0 licence.
Replicating earlier work, we found that cases carried a significantly higher burden of rare protein-truncating variants (PTVs) among evolutionarily constrained genes (odds ratio = 1. 48; P = 5. 4 × 10 -6 ). In meta-analyses with existing datasets totaling up to 35,828 cases and 107,877 controls, this excess burden was largely consistent across five ancestral populations. Two genes (SRRM2 and AKAP11) were newly implicated as SCZ risk genes, and one gene (PCLO) was identified as shared by individuals with SCZ and those with autism. Overall, our results lend robust support to the rare allelic spectrum of the genetic architecture of SCZ being conserved across diverse human populations.
Letter https://doi. org/10. 1038/s41588-023-01305-1 rare was defined as a minor allele count of ≤5 among the entire cohort. To maximize power, PGC3SEQ was further meta-analyzed with SCHEMA data (Supplementary Table  4  and Supplementary Fig. 7 ) and sequencing datasets for bipolar disorder and autism. We performed two broad types of analysis: (1) a global enrichment of all constrained genes on the custom panel (n = 80 genes) to investigate the overall role of rare disruptive variants in diverse ancestries; and (2) gene-level burden tests to identify novel SCZ risk genes. PGC3SEQ SCZ cases carried a significantly higher burden of rare PTVs among the 80 constrained genes after adjusting for rare synonymous variant counts and five ancestry principal components (odds ratio (OR) = 1. 48; P = 5.
1a ). We assigned samples to five ancestral super-populations, as defined in the 1000 Genomes Project (Methods). At the aggregate level, four of the five populations displayed a higher burden of rare disruptive variants (PTV + MPC > 3 missense) in SCZ cases compared with controls (P < 0. 05; Fig. 2b  (left) and Supplementary Table  6 ). Although we did not find a nominally significant enrichment in the fifth ancestral population (SAS), the magnitude of enrichment was similar to that in the African (AFR) population (OR = 1. 5), indicating that nonsignificance is probably a power issue (Supplementary Note and Supplementary Fig. 9 ). When considered separately, PGC3SEQ and SCHEMA provided independent support for the ancestry-stratified enrichments (all ancestries had OR > 1 in both datasets; Supplementary Table  6 ).
2 × 10 -7 ) and AKAP11 (P = 4. 2 × 10 -7 ). In previous work, SRRM2 has been shown to play a role in the tauopathy of Alzheimer's disease  [36] [37] [38]  , and de novo mutations in this gene have been linked to developmental disorders  39  , while AKAP11 was suggested as a trans-gene linking to a SCZ GWAS locus in a recent study  40  , which, together with our results, adds to examples of the convergence of common and rare variant associations in the same gene. A recent meta-analysis of SCHEMA and a bipolar disorder dataset also found exome-wide significance for AKAP11 (ref. 41 ), suggesting a role for this gene in the shared etiology of SCZ and bipolar disorder. The current study consolidates the role of AKAP11 in SCZ, independent of other psychiatric disorders. Lastly, we meta-analyzed gene-level rare disruptive variant statistics from SCZ, autism spectrum disorder (ASD)  42  and bipolar disorder  41  to identify pleiotropic risk genes that are not detectable at the sample sizes attained by studies of any single disorder.
We assessed the impact of such indels on our findings via a sensitivity analysis and found that excluding them would not change our conclusions (Supplementary Tables  10  and 11  and Supplementary Fig. 11 ). We used an interim version of the SCHEMA results for PGC3SEQ panel design, and this version is different from the published results due to changes in SCHEMA analytical strategy. Specifically, the interim SCHEMA statistics  31, 32  did not include de novo mutations from trios, used a different strategy to combine PTV and missense variants and were compiled before the incorporation of Genome Aggregation Database (gnomAD) controls. Comparing the interim and published SCHEMA results, gene ranks underwent nontrivial changes, with only 27 overlapping genes between the top 100 lists in the two versions.
Consequently, our panel probably included more random noise than it would have if panel construction had waited until SCHEMA was complete. As WES studies of other diseases approach the sample size achieved for SCZ, and strategies are considered for how to increase power, the current report offers valuable lessons, and we note that results on datasets as large as 24,000 cases and 50,000 controls can still change substantially as more samples are added. The possibility of such changes makes the targeted panel approach vulnerable, and perhaps WES and WGS are the safest strategies despite their cost. In summary, rare PTVs have a robust role in SCZ, and across ancestries their effect is consistently concentrated in genes under strong evolutionary constraint. The deconvolution of this overall contribution into individual genes that may have ancestry-specific effects will require the sequencing of more individuals of diverse backgrounds.
Finally, an additional 24 genes were chosen for inclusion by taking the best 24 gTADA rankings of the remaining genes with a burden P value < 0. 05. Based on the observation that gene-level rare single-nucleotide variant burden statistics have been consistent across ancestries in a wide range of diseases  [18] [19] [20] [21] [22] [23] [24]  , our targeted panel was expected to have broad utility across ancestries, even though its construction used EUR-dominant datasets. This was further consolidated by findings from our own ancestry-stratified analysis (Fig. 2b ).
Genotype-level quality control We interrogated the call set with respect to a variety of quality control metrics and implement procedures to ensure rigorous quality control standards. In the absence of well-established quality control procedures specifically for Ion Torrent data, we drew on the idea of GATK's variant quality score recalibration technique and developed a machine-learning genotype-level filter based on 177 quality metrics and annotation profiles, including Ion Torrent sequencing metrics such as QUAL, FMT/GQ and FMT/DP, allele-related metrics such as AF, HRUN and MLLD and coverage and allele frequency from the gnomAD database version 2 (https://gnomad. broadinstitute. org). Considering that the majority of SCHEMA data with which we meta-analyzed were generated on the Illumina platform, we calibrated our Ion Torrent targeted sequencing data using a subset of the control samples (n = 1,347) with available Illumina WES data.
Specifically, we used XGBoost version 1. 3 (ref. 54 ) in Python version 3. 7. 3 to train the classifier in 70% of the Ion Torrent-Illumina paired data using Illumina as the ground truth. In the remaining 30% test set, the classifier achieved an area under the curve of 0. 95, an accuracy of 95. 3% and a false discovery rate of 4. 4% for SNPs and an accuracy of 99. 0% and a false discovery rate of 6. 4% for indels. Applying the trained classifier to the test dataset improved the concordance between Ion Torrent and Illumina calls from 83. 1 to 95. 7%. We also compared our machine-learning classifier with a set of conventional hard filters and confirmed that the classifier performs unanimously better in all metrics considered (sensitivity, specificity, accuracy and false discovery rate). Applying the machine-learning filter to the entire dataset, 83.
Sample relatedness We used the population structure-adjusted relatedness estimation methods PC-AiR and PC-Relate to estimate pairwise relatedness between samples. In addition to the quality control steps performed per previous sections, we further performed linkage disequilibrium pruning on the dataset and removed indels before relatedness estimation. Considering that the conventional kinship coefficient ranges for varying degrees of relatedness may not be appropriate when the estimates are from targeted sequencing data covering only a small fraction of the genome, we derived empirical boundaries based on the clustering of sample pairs on an identity-by-descent kinship scatterplot (Supplementary Fig. 3 ). The unrelated and related pairs were clearly separated into two clusters with distinct patterns (unrelated pairs: lower oval-shaped cluster; related pairs: upper left).
Ancestry assignment The genetic ancestry assignment of the PGC3SEQ participants was done by calculating principal components jointly with 1000 Genomes phase 3 participants (n = 2,501), followed by a K-nearest-neighbor classification using the top three principal components. We restricted the analysis to 1,372 linkage disequilibrium-pruned common SNPs (MAF > 1%) that were present in both the study dataset and the reference dataset (1000 Genomes). The reference data were first cleaned and quality controlled using PLINK by filtering for missingness per individual (<10%) and missingness per SNP (<10%) and then subsetted to the variant set that passed all of the quality control filters in the PGC3SEQ cohort. The cleaned reference and study datasets were harmonized, combined and pruned for linkage disequilibrium, then input into PLINK for principal component analysis with default settings.
Variant annotation We employed the same variation annotation workflow as was used in SCHEMA for ease of replication and comparison. Specially, annotation by LOFTEE (as implemented in the Variant Effect Predictor)  56  was applied to variants that passed all quality control filters, and the analysis was restricted to the canonical transcript with the most damaging annotation. The three broad types of coding variants analyzed were: (1) PTVs, defined as any mutation that introduced a stop codon, changed the frame of the open reading frame or introduced a change at a predicted splice donor or splice acceptor site; (2) missense variants, which included any single-nucleotide variant that caused an amino acid change; and (3) synonymous variants, which resulted in no amino acid change, as a negative control.
Use of SCHEMA data SCHEMA is a large multisite collaboration aggregating, generating and analyzing high-throughput exome sequencing data of individuals with SCZ and controls to advance gene discovery. We accessed the post-quality control data of a subset of SCHEMA case-control samples with appropriate sharing permissions at the time of this work and did not reperform genotype-and sample-level filtering. Specifically, the controls from gnomAD, as included in SCHEMA, were not used in the current study due to data sharing restrictions. After excluding 216 samples detected as genetic duplicates with a PGC3SEQ sample, the available SCHEMA datasets contained 19,108 cases and 18,001 controls (Supplementary Table  4 ). We used the genetic ancestry label for each individual determined by the SCHEMA analysis team and, within each ancestral group, calculated population-specific principal components using linkage disequilibrium-pruned SNPs with a MAF of >1%, a call rate of >95 and a Hardy-Weinberg P value of 1 × 10 -6.
Statistical approaches for global enrichment across constrained genes We defined rare variants as those with a minor allele count of ≤5 in the entire sample for any ancestry-combined analysis and lifted this threshold to MAF < 0. 1% in ancestry-stratified analysis to preserve power. We counted the number of rare variants by annotation type observed in each participant in individual genes and added up the counts across the 80 constrained genes. The association between rare variant burden in the gene set of interest and SCZ status was tested using logistic regression with Firth's penalized likelihood method to account for sparse data  57  , while adjusting for ancestry principal components and baseline rare variant burden. The first five global principal components were used in the ancestry-combined analysis and the first four principal components calculated within each ancestry were used in ancestry-stratified analysis.
2a ). We did not pursue a meta-analysis of the PTV and MPC > 3 variants because the extremely low number of MPC > 3 variants prohibited a reliable estimation of their effect magnitude, which would be used as weights in a meta-analysis. Although Fisher's exact test is not able to accommodate covariates such as ancestry principal components and baseline burden, this did not adversely affect our analysis as the Q-Q plot showed no sign of inflation in the statistics (Supplementary Fig. 10 , top row). In the gene-level analysis of SCHEMA, case-control cohorts and trio cohorts were meta-analyzed, and rare variants found in both types of cohort were not double counted. We combined gene-level P values from PGC3SEQ and SCHEMA (summary statistics obtained from the SCHEMA publication) using signed Stouffer's method, with the sign of the Z scores being the effect direction of the PTVs and the weights of each study calculated as: The above equation applies equal weight to the case-control data and trio data.
Since only a subset of genes had de novo mutations in SCHEMA trios and the number of trios was small relative to the case-control sample size, fine-tuning weights would not meaningfully change our results. This meta-analysis totaled 35,828 SCZ cases and 107,877 controls, representing the largest SCZ sequencing dataset to date. The exome-wide significance level was determined to be 0. 05/(23,321 tests performed in SCHEMA + 161 tests performed in PGC3SEQ) = 2. 13 × 10 -6. As expected, the meta-analysis P values deviated substantially from the null (Supplementary Fig. 10 , middle left), consistent with an enrichment of risk genes in the targeted panel. Gene-level synonymous variant P values displayed the expected null distribution (Supplementary Fig. 10 (middle right ) and Supplementary Table  9 ), assuring that the gene-level PTV results were free from technical or methodological artifacts agnostic to variant annotation.
We then combined the two SCZ cohorts with the WES datasets of two other psychiatric diseases to identify genes shared across diagnoses. The two studies from which we obtained summary statistics were: (1) the latest release of the Autism Sequencing Consortium (ASC)  42  (and we further converted the gene-level q values to P values); and (2) the WES of bipolar disorder by Palmer et al. 41. Meta-analysis was performed similarly as above and the same exome-wide significance threshold was also applied (2. 13 × 10 -6 ). We noted some degree of control overlap between these studies (for example, SCHEMA and ASC both included Swedish controls from the same collection). As the overlap between SCHEMA and ASC consists only a small fraction of the entire sample, our analysis (and the discovery of PCLO) should only be minimally affected.
Data availability We describe all of the datasets in the Methods and Supplementary Information. The raw PGC3SEQ genotype and phenotype datasets are permitted to be distributed at the individual level and we have deposited the data in the database of Genotypes and Phenotypes under accession number phs003138. v1. p1. We provide the aggregated variant counts at the gene and gene set level in Supplementary Tables  1 2 3 4 5 6 7 8 9. SCHEMA summary-level data are available online for viewing and download (https://schema. broadinstitute. org). SCHEMA individual-level whole-exome sequence data are hosted on the controlled-access Terra platform (https://app. terra. bio/) and shared with the collaborating study groups. Requests for access to the controlled datasets are managed by data custodians of the SCHEMA Consortium and the Broad Institute and are sent to sample contributing investigators for approval.
Acknowledgements A. W. C. is supported by the  National Institute of Mental Health (NIMH ;  R01MH109536 ). L. M. H. is supported by the  NIMH  ( R01MH118278 ,  R01MH124839  and  U01MH109536 ). M. C. O. , M. J. O. and J. T. W. are supported by  Medical Research Council Centre  grant number  MR/L010305/1  and program  MR/P005748/1. For sample acquisition, curation and preparation, we are grateful to  Leyden Delta ,  Magna Laboratories  and their staff ( M. Helthuis ,  J. Jansen  and  A. King ). We also thank  L. Hopkins  and the core laboratory team at  Cardiff University. B. N. is supported by  1R01MH124851. K. E. B. is supported by  R01MH100125  and  1I01CX000995. J. D. B. is supported by  P50MH066392. J. M. F. is supported by the  Janette Mary O'Neil Research Fellowship and Australian National Health and Medical Research Council (NHMRC)  Project Grant  1063960.
P. R. S. is supported by the  Australian NHMRC Program  Grant  1037196  and Investigator Grant  1176716. D. B. W. and S. G. S. are supported by  NHMRC  grant  513861. M. R. has been funded by  Instituto de Salud Carlos III  projects  PI18/00238  and  PI18/00467  (cofunded by the  European Regional Development Fund/European Social Fund A Way  to Make Europe/  Investing in Your Future ). C. A. has received support from the  Spanish Ministry of Science and Innovation Instituto de Salud Carlos III  ( PI19/024 ), cofinanced by  European Regional Development Fund Funds  from the  European Commission ,  A Way  of  Making Europe ,  Centro de Investigación Biomédica en Red de Salud Mental, Madrid Regional Government  ( B2017/BMD-3740 AGES-CM-2 ) and  European Union   Horizon 2020 Program  under the  Innovative Medicines Initiative 2 Joint Undertaking  (grant agreement number  115916  (Project  PRISM ) and grant agreement number  777394  (Project  AIMS-2-TRIALS) ),  Fundación Familia Alonso and Fundación Alicia Koplowitz.
We acknowledge the  Biobanc of Parc Sanitari Sant Joan de Déu and Centro de Investigación Biomédica en Red de Salud Mental  for samples and data procurement. J. G. -P. holds a  Sara Borrell  grant from  Instituto de Salud Carlos III  ( CD20/00118 ). B. R. has received support from the  Spanish Ministry of Science and Innovation Instituto de Salud Carlos III  ( PI18/00213  and  Miguel Servet  grants  CPII21/00008  and  MS16/00153 ), cofinanced by  European Regional Development Fund Funds  from the  European Commission. M. G. and the work at  IRCCS Centro San Giovanni di Dio Fatebenefratelli  is supported by the  Italian Ministry of Health  ( Ricerca Corrente ). The CommonMind datasets were generated as part of the  CommonMind  Consortium, supported by funding from  Takeda Pharmaceutical Company, F.
Code availability The software and code used in this study are described in the Methods. In brief, we used Torrent variantCaller version 5. 8. 0 to call variants from the raw sequence data. For quality control and preprocessing, we used XGBoost version 1. 3 in Python version 3. 7. 3, BCFtools version 1. 9 and PLINK version 1. 9. Reanalysis of the SCHEMA cohort was performed using Hail 0. 1 and 0. 2 (https://hail. is/). Main analyses of the PGC3SEQ data and their meta-analysis with SCHEMA were performed using R version 3. 6 with various libraries. Visualization was generated with ggplot2 version 3. 3.
Model implementation and solution The cardiovascular model has been implemented using OpenModelica  (35) , an open-source modelica-based modeling and simulation environment. The mathematical equations representing the system have been solved using a differential algebraic system solver, DASSL, with time step of 0:001 s and tolerance of 10 À6 , as in  (7). Exploiting the library PyFMI, a Python script has been written to call a functional mockup unit (FMU) generated by OpenModelica that can solve the cardiovascular model for specified input values. In order to ensure that the solution reaches a periodic behavior, the system is solved over a time interval of 8 cardiac cycles. The solution segment corresponding to the last cardiac cycle is then considered for analysis. Post-processing of results has also been implemented in Python.
Experimental data acquisition Six subjects were recruited for this study. Sex, age, height, and weight are reported in Table  3. Data collection was performed in a controlled laboratory environment, where subjects were asked to lie on the suspended bed described previously in  (7). ECG and BCG were synchronously recorded by a three-lead configuration and by a Kionix accelerometer with 1000 mV/g sensitivity placed on the suspended bed frame, respectively. An ADInstrument PowerLab 16=35 data acquisition system was used to collect the signals synchronously. ECG and BCG signals were both filtered via a 6th-order Butterworth bandpass filter to remove the highfrequency noise and the low-frequency respiration movement. ECG and BCG signals were filtered with a cut-off frequency of [0. 7-40] and  [1.