dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , 22q11. 21  84  , 15q11. 2  85  ) that can even show population stratification (e. g. , 16p11. 2  86  ). More accurate and complete (pangenome) references will be needed to determine the exact breakpoints of such complex rearrangements  87, 88  , even in the case of sequencing-based CNVs discovery. Lastly, we performed joint epilepsy/seizures and cross-disorder meta-analyses in individuals with minimal clinical information. Future studies with access to rich clinical metadata, such as electronic health records, will likely identify additional seizureassociated CNVs. It is important to consider the inclusion criteria for this cohort and the definition of cases and controls when interpreting associations and their relevance to a patient. Our phenomic analysis cohort was performed using the years 1-3 data of the Epi25 Collaborative, predominantly recruited from academic epilepsy centers and of European ancestry (92.
9%, see Online Methods). Additionally, we screened cases to exclude those with brain trauma, meningitis, or encephalitis. Thus, our clinical associations should be considered most valid in individuals of European ancestry with likely genetic or unexplained epilepsies attending specialist epilepsy centers. Future data analyses from subsequent years of Epi25 will provide data more applicable to other populations. Large-scale collaborations that enable the aggregation of massive datasets have greatly advanced epilepsy and the discovery of genetic factors through GWASs. Here, we have extended this framework to CNV discovery by meta-analyzing epilepsy and seizure disorders, followed by additional meta-analyses in neuropsychiatric disorders and traits to explore pleiotropy. We also identified fine-grained genotypephenotype associations and clinical profiles for each CNV.
Study cohorts Each center's ethics committees/institutional review boards approved data collection and use. For the Epi25 cohort, patients or their legal guardians provided signed informed consent/assent according to local IRB requirements; as samples had been collected over 20 years in some centers, forms reflected standards at the time of collection. For Epi25 Consortium samples collected after 25th January 2015, forms required specific language according to the NIH Genomic Data Sharing Policy.
9  91  and GCTA  92. European individuals were defined as individuals clustering with the 1000 Genomes Project  93  European samples. We created GC wave-adjusted LRR (Log-R ratio) intensity files for all samples using PennCNV, generated a custom population B-allele frequency file, and employed PennCNV's CNV calling algorithms  2, 94  to detect CNVs in our dataset. The post-CNV calling QC included the following steps: (1) CNV calls of the same type (deletion or duplication) were merged if the number of SNP/intensity markers between them was <20% of the total number when both segments were combined; (2) CNVs supported by <20 markers, <20 kb long, and with a SNP density <0. 0001 were excluded from subsequent analyses; (3) CNVs that overlapped other CNVs in ≥1% of all samples within the Epi25 dataset were excluded to remove potential platform-specific artifacts, (4) CNVs with >50% overlap with telomeric, centromeric, and immunoglobulin regions of the hg19 reference assembly were excluded; (5) CNVs with ≥50% overlap with reported common CNVs (allele frequency >1%) in two independent CNV reference catalogs (DGV Gold Standard Dataset  95  ; DECIPHER Population Copy-Number Variation Frequencies 96 ) were excluded.
Individuals with seizures or neuropsychiatric phenotypesneuropsychiatric disorders cohort A large CNV dataset from individuals with a range of neuropsychiatric disorders (including seizure disorders) was aggregated from 17 different sources by Collins et al. 97. The contributors of each cohort provided the specific clinical phenotypes. The aggregated individuals were grouped into 54 partially overlapping disease phenotypes standardized through the Human Phenome Ontology  98. The 54 different phenotypes of Collins et al. 97  were obtained through a recursive hierarchical clustering that defined a minimal set of nonredundant primary phenotypes, each including a minimum of >300 samples in at least three independent cohorts, >3000 samples in total across all cohorts, and had less than 80% sample overlap with any other phenotype.
Of the 54 phenotypes, we only selected neurological and psychiatric HPObased phenotypes (N = 23, excluding Seizures, Supplementary Table  2 ). The architecture of these HPO-based phenotypes allows the identification of associations at different levels, from broad to narrow phenotypes, providing the opportunity to distill between pleiotropic and specific associations. This data set also included the Epi25 cohort from our previous CNV GWAS study  20. This previous (outdated) Epi25 cohort was excluded from the neuropsychiatric cohort for crossdisorder meta-analyses in the present work. All the considered cohorts are listed in Supplementary Table  1. This aggregated CNV dataset comprised 248,751 individuals affected by at least one of 24 neuropsychiatric disorders, including 10,590 individuals with seizures and 483,779 population controls.
Quality control -neuropsychiatric disorders cohort The CNV harmonization procedure for the Neuropsychiatric cohort is described in the Supplementary Materials of Collins et al. 97  and included following steps: (1) CNV calls of the same type (deletion or duplication) were merged if their breakpoints were within ±25% of the size of their corresponding original CNV calls to avoid oversegmentation of large CNV calls; (2) CNVs not mapped to autosomes from the primary hg19 assembly were excluded; (3) Only CNVs between ≥100 kb and ≤20 Mb in size were considered; (4) CNVs that matched reported common CNVs (allele frequency >1%) in three independent CNV reference catalogs derived from genome sequencing (Abel et al. 99  ; Collins et al. 100 ; Sudmant et al. 81  ) were excluded; (5)  CNVs that overlapped other CNVs in ≥1% of samples within the same dataset or in any of the other array CNV datasets were excluded to remove potential platform specific artifacts; (6) We excluded all CNVs with ≥30% overlap with somatic hypermutable sites, segmental duplications, simple/low-complexity/satellite repeats, or N-masked bases of the hg19 reference assembly.
We required an overlap between the CNV and the genomic window of ≥10% to reveal the potential burden of small deletions or duplications (size ≥ 20 kb). We used the one-sided Fisher test as the test statistic for the CNVs collapsed for each segment. Cases/control CNV counts and the Fisher tests were performed using the CNV docker available at https://hub. docker. com/r/talkowski/rcnv and custom python (version 3. 7. 9) and R (version 3. 6. 1) scripts. The same procedure was applied to the cohorts of the neuropsychiatric disorder dataset, as detailed in Collins et al. 26.
Meta-analysis and fine-mapping Fixed-effects meta-analyses were performed using the metafor R (version 3. 6. 1) package with an empirical continuity correction 101 and a saddlepoint re-approximation of the null distribution used for inference. The meta-analysis procedure is detailed in Collins et al. 26. We meta-analyzed the effect sizes from 7 GWAS derived from the 17 cohorts of the neuropsychiatric disorder dataset with each segmentbased P-value of the Epi25 dataset. The threshold for genome-wide significance was set to α = 3. 74 × 10 -6 after Bonferroni correction for multiples testing corresponding to the number of independent, nonoverlapping 200 kb windows, calculated by merging all overlapping windows and dividing the sum of their sizes by 200 kb (effective N = 13,339. 6 independent windows; P = 3.
Detailed HPO characterization of Epi25 participants To identify phenotypic associations with each of the CNVs within a cohort of individuals with epilepsy, we translated clinical data from years 1-3 of the deeply phenotyped Epi25 Collaborative international cohort into Human Phenotype Ontology (HPO, version released 2022-02-14) concepts, following our optimization of the HPO for epilepsy phenotypes  105. We selected only individuals with CNV data and sufficiently detailed clinical data (as of 2022-01-25) to confirm the presence of seizures or epileptic encephalopathy with continuous spike-andwave in sleep (EE-SWAS, an epilepsy syndrome in which overt clinical seizures may not always be observed). Categorical clinical data were mapped to HPO concepts using a data dictionary. Free text data were annotated with HPO terms manually (D.
L. S. under the supervision of I. H. and R. H. T. )  25. Quantitative data related to the gestational age, weight, and head circumference at birth were categorized to match HPO definitions using sex-stratified distributions from the INTERGROWTH-21th Project using the R growthstandards package (version 0. 1. 5)  106. We inferred all HPO concepts applicable to each individual from those translated from the clinical data by propagation, following the is_a relationships between HPO concepts as previously described 107 , using the R ontologyIndex package (version 2. 7) 108. We excluded HPO terms that carried no information in the context of this cohort (those that were annotated ubiquitously) and modified the relationships of others, tailoring them to this analysis (Supplementary Table  6 ). Phenotypes were annotated as being explicitly present or not, without annotating any phenotypes as being explicitly absent.
Acknowledgements This research was funded in whole, or in part, by the  Wellcome Trust  [ 203914/Z/16/Z ], supporting D. L. S. For the purpose of Open Access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. I. H. was supported by  The Hartwell Foundation ( Individual Biomedical Research Award ), the  National Institute for Neurological Disorders and Stroke  ( K02 NS112600 ), the  Eunice Kennedy Shriver National Institute of Child Health and Human Development through the Intellectual and Developmental Disabilities Research Center (IDDRC) at Children's Hospital of Philadelphia  and the  University of Pennsylvania  ( U54 HD086984 ), and by the  German Research Foundation  ( HE5415/3-1 ,  HE5415/5-1 ,  HE5415/6-1 ,  HE5415/7-1 ).
Research reported in this publication was also supported by the  National Center for Advancing Translational Sciences of the National Institutes of Health  ( UL1TR001878 ), by the  Institute for Translational Medicine and Therapeutics' (ITMAT)  at the  Perelman School of Medicine of the University of Pennsylvania , and by  Children's Hospital of Philadelphia  through the  Epilepsy NeuroGenetics Initiative (ENGIN). R. L. C. was supported by  NHGRI   T32HG002295  and  NSF   GRFP  # 2017240332. We thank the Epi25 principal investigators, local staff from individual cohorts, and all of the patients with epilepsy who participated in the study for making this global collaboration and resource possible to advance epilepsy genetics research. This work is part of the  Centers for Common Disease   Genomics ( CCDG) program , funded by the  National Human Genome Research Institute (NHGRI)  and the  National Heart, Lung, and Blood Institute (NHLBI).
Data availability All genome-wide CNV association summary statistics are available at Zenodo (https://zenodo. org/record/7939126#. ZGK7yi-B29Y with https://doi. org/10. 5281/zenodo. 7939126). Individual-level CNV data for epilepsy patients are available from the Epi25 Consortium (http:// epi-25. org/) upon signing the Epi25 charter (See Epi25 page http://epi-25. org/) and submission and acceptance of a full research proposal. Furthermore, raw data is deposited at dbGAP https://www. ncbi. nlm. nih. gov/projects/gap/cgi-bin/study. cgi?study_id=phs001551. v1. p1. All HPO-based phenome-wide summary statistics are available in.
Code availability The code for the association and meta analysis is available and have been deposited at Zenodo (https://github. com/talkowski-lab/rCNV2/ tree/v1. 0, with https://doi. org/10. 5281/zenodo. 6647918). Also, we provided a Docker image hosted on DockerHub (https://hub. docker. com/r/talkowski/rcnv) and Google Container Registry (https://gcr. io/ gnomad-wgs-v2-sv/rcnv), which provides a controlled container environment containing all dependencies necessary to execute the code identically as presented in this study.
a numerical hindcast of the macrotidal Loire Estuary (France) has been generated to provide a longterm dataset (2008-2018) of estuarine hydrodynamics, temperature, salinity, and sediment dynamics. This hindcast is based on simulations coupling water motion, wave and mixed sediment models, forced with realistic conditions and extensively validated in the salinity gradient and turbidity maximum areas. These data represent extremely valuable information for diverse scientific communities, providing (i) environmental parameters for ecosystemic studies along the Loire River-sea continuum, (ii) a singular estuarine configuration for inter-comparison of estuarine functioning, and (iii) a ten-year synoptical view of a major estuarine environment of the North Atlantic Ocean.
Such hindcasts are especially useful: (i) to study the impacts of anthropogenic and climate changes on estuarine physics and ETM dynamics;  11, 15, 16, [21] [22] [23]  and (ii) to provide abiotic explanatory parameters for biogeochemical and ecological studies  [24] [25] [26] [27]. Numerical hindcasts have recently been realised for two of the three largest French estuaries (Seine 15 and Gironde 21 ), arousing interdisciplinary studies and tackling challenges on habitats and species distributions  [24] [25] [26]. The aim of this study is to complete these datasets with a ten-year hindcast (2008-2018) for the Loire Estuary (France; Figs. 1, 2 ). On the one hand, it offers environmental parameters for ecosystemic studies along the Loire River-sea continuum; and on the other hand, it provides a contrasted estuarine configuration for inter-estuary comparisons  5, [28] [29] [30] [31] [32].
1 ). Horizontal cell size ranges from around 50 m in the meanders to approximately 1. 3 km offshore while the vertical grid is divided into 10 equidistant sigma layers. The 114 main tidal components, extracted from the CST France database, https://maree. shom. fr (Service Hydrographique et Océanographique de la Marine, SHOM), are used to force the circulation at the open boundaries. Surges, provided by a configuration of the two-dimensional MARS2D model applied to a larger domain (i. e. , over the Bay of Biscay), are added to the water elevation at these same boundaries. Realistic freshwater discharges and sediment loads are prescribed at the upstream boundary of the Loire River (i. e. , Saint Florent-le-Vieil) and at the Vilaine Estuary mouth for the Vilaine River (see further details on river supplies in the "Data records" section).
In addition, the model is forced by wind stresses and pressure gradients obtained from the high-resolution meteorological AROME model (Météo-France): https://donneespubliques. meteofrance. fr. The simulated turbulence is based on a k-ε turbulence closure scheme. Waves are simulated with the WAVEWATCH III ® (WW3) numerical model 35 using the same computational grid as the one used by MARS3D in this study. The hourly free surface elevation and current velocity provided by the MARS3D hydrodynamic model, along with local winds and swell data extracted from a larger model, were used to force the WW3 configuration. However, the wave effects on hydrodynamic circulation are not taken into account because of the dominance of tidal currents over wave-induced currents. The bottom orbital velocities simulated by the wave model were used to compute the wave-induced bed shear stress.
Data records Hindcast repository. The data files containing the results of the Loire Estuary hindcast, i. e. , hydrodynamics, temperature, salinity, and sediment dynamics, are available on the CurviLoire Hindcast repository  51. The repository is structured in four directories: • /hydro (NetCDF files): 3D half-hourly results for water column variables: • Hydrodynamics: water depth 'H0' , water level 'XE' , horizontal current velocity 'U,V'; • Temperature 'TEMP' , salinity 'SAL'; • Waves: significant height 'hs' , direction 'dir' , bottom orbital velocity 'ubrx,ubry'; • Suspended sediment concentration: mud 'Mud' , very fine sand 'Veryfinesand' , fine sand 'Finesand' , medium sand 'Mediumsand' , and gravel 'Gravel'. • /sedim (NetCDF files): 3D half-hourly results for bed compartment variables: • Bed shear stress: wave-induced 'TENFONW' , current-induced 'TENFONC' , and total components 'TENFON'; • Sediment bed: total thickness 'EPTOT' , number of layers 'NBNIV' , layer thickness 'DZS'; • Bed sediment concentration: mud 'Mud' , very fine sand 'Veryfinesand' , fine sand 'Finesand' , medium sand 'Mediumsand' , and gravel 'Gravel'.
• Tides: 114 main tidal components extracted from the CST France database (SHOM); • Storm surges: MARS2D-MANGAE2500 (2. 5 × 2. 5 km 2 spatial resolution and hourly outputs); • Waves: WAVEWATCH III ® -NORGASUG 4 (5 × 5 km 2 spatial resolution and hourly outputs). • River discharge: • Freshwater: daily-measured runoffs of the Loire and Vilaine rivers (Q L and Q V , respectively); • Sediment load: suspended mud concentration (SMC, in g/l) associated with the river runoff following the following relationships: Surface SSC hf at (b,c) Bellevue 'Be' , (d,e) Le Pellerin 'LP' , (f,g) Cordemais 'Co' , and (h,i) Paimboeuf 'Pa' (see station locations in Fig. 1c ). (Left panels) Measurements (blue) and simulations (red), and (right panels) simulations versus measurements, from January to December 2018.
acknowledgements This work has been carried out in the framework of the  CAPTURE  project within the  "Inter-estuary Mission" (MIE)  funded by the  French Biodiversity Agency (OFB). The authors would like to acknowledge the  Groupement d'Intérêt Public Loire Estuaire  for providing the measurements used in the model validation (i. e. , in-situ data from the SYVEL high-frequency monitoring network).
py python script  (30) , embedding one copy of a PIM lipid in a 10 x 10 nm 2 phosphatidylcholine (PC) bilayer. The system was solvated with MARTINI water  (26)  and neutralized with 150 mM NaCl, followed by minimization using the steepest descents algorithm. The system was then simulated for 3 μs using a timestep of 20 fs at 310 K. The lipids, solvent and ions were temperature coupled separately. The velocity rescale  (31)  and Parrinello-Rahman (32) coupling methods were used with the time constants T = 1. 0 ps and p = 12. 0 ps for temperature and pressure, respectively. Simulations were run using GROMACS version 2021. 3  (33). The reaction-field algorithm  (34)  was used for electrostatics interactions with a cut-off of 1. 1 nm. A single cut-off of 1. 1 nm was used for the van der Waals interaction.
Before assembling the membrane, the parameters for these lipids were modified by adapting the refined Ac2PIM2 acyl tail parameters and changing the existing tail. The CL parameters were transferred over from the MARTINI 3 beta force field  (21). Simulations of the bilayer were set up using a modified version of insane. py python script (30) using the composition shown in Figure  1A , where the area per lipid for the periplasmic membrane was set to 0. 92 nm 2 and the cytoplasmic leaflet set to 1. 13 nm 2. The systems were then treated the same as described for the initial CG systems with added equilibration steps as per the Membrane Builder workflow  (39)  before the production simulation. There were two types of system assembled to represent the asymmetric membrane previously mentioned  (13).
The number of lipids in each system is summarized in SI Figure  21  and their compositions in SI Figure  22  Behavior of the membrane To calculate the bending rigidities, the strategy proposed by Fowler et al. (43)  to extract the bending modulus from CG simulations was followed. The membrane midplane position in Monge representation was determined by extracting the coordinates of all CG beads at the extremities of lipid fatty acid chains. Using a builtin function of the Mathematica software package, the positions were interpolated to get a smooth function before Fourier-transforming with a fast Fourier transform algorithm. From the so-obtained Fourier modes, the spectral density (or power spectrum) was estimated for a tensionless membrane: 𝑆(𝑞) = 𝐿 ! 𝑘 " 𝑇 +1/(𝜅𝑞 # ) + 1/0𝜎 $ 𝑟𝑞 ! 34 where  ! is the projected area in the (, ) plane,  "  is the thermal energy and  $ ~0.
Analysis of the residue distribution in membrane proteins All predicted protein structures from Mtb (taxonomy id:83332) and E. coli (taxonomy id:83333) labelled as transmembrane on UniProt  (48)  were downloaded from the AlphaFold database  (49) , totaling 729 for Mtb and 1,229 for E. coli. Alphafold was used to obtain a workable dataset of structures, expanding upon the number of experimentally determined Mtb and E. coli membrane protein structures available from the PDB. Each of 1,958 membrane protein structures was orientated in a membrane using memembed  (50)  with the -n in flag, then converted to CG using martinize2  (51)  and inserted into either the asymmetrical mycobacterial (see Figure  1A ) or E. coli membrane (75% PE, 15% PG, 10% CL) using a modified version of the insane. py python script  (30).
The system with the protein in the asymmetric mycobacterial membrane (see Figure  1A ) was assembled using martinize2 (51), memembed  (50)  and the modified insane. py python script  (30) , with eight molecules of BDQ placed either in the periplasmic or cytoplasmic leaflet with five repeats in each membrane (SI Figure  2 ). Simulations were run for 10 μs using the same setting as The density of each constituent was measured using gmx density and plotted using Matplotlib  (47)  and the results are shown in Figure  4E  (with representative positions shown in Figure  4F ). The interaction of BDQ with the protein was calculated using PyLipID (58) (Figure  4G  and SI Figures  34, 35 ). The same simulation set up was performed with a model E. coli membrane (75% PE, 15% PG, 10% CL) and with the symmetric mycobacterial membrane (SI Figure  22A ).
The PMF mdp input files can be downloaded at https://github. com/chelsea-brown/PIMlipids/tree/main/PMF-files. The PMF profiles were then constructed using the weighted histogram analysis method in GROMACS (gmx wham (60, 61)), and employing 200 rounds of Bayesian bootstrapping to report statistical accuracy (Figure  4H ). For the membrane crossing PMFs, BDQ or ISZ was placed free in the solvent phase, 7 nm away from the membrane periphery in either an asymmetric mycobacterial membrane (see Figure  1A ) or E. coli membrane. The drug was then steered towards and through the membrane and into the solvent phase on the other side. Windows were extracted, simulated (1 µs for ISZ per window and 2 µs per window for BDQ) and analyzed as described above (Figure  4B  and SI Figure  29D  for BDQ neutral).
Small differences were seen for ISZ interactions at the interfaces of the mycobacterial and simple membrane periplasmic membrane. This could be due to the presence of certain PIM lipids in this leaflet. In our simulations, when the BDQ starting position was in the bulk solvent, the drug quickly associated with the membrane, as shown in SI Figure  31 , and displayed no strong preference for either leaflet in these simulations. BDQ has been shown to target the mycobacterial ATP synthase (Rv1304-Rv1311), a membrane protein complex. A cryo-EM structure from M. smegmatis (PDB: 7JGC) shows multiple binding sites for BDQ at the interface of the c-subunits and the interface between the a-and c-subunits  (25) , where a highly negatively charged area is located (SI Figure  32A, B ). Using the available cryo-EM data, we performed 10 simulations of 10 μs each to examine how Mtb ATP synthase complex behaves in our asymmetric mycobacterial plasma membrane model.