dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods Samples. We performed a series of CWAS using individuals from five data sets (Table  1  and Supplementary Information). (1-2) Two genetic-first cohort (recruitment based on the presence of a genetic variant, regardless of any DSM diagnosis): 16p11. 2 deletion and duplication carriers (29. 6-30. 1 MB; Hg19), and extrafamilial controls from the Simons Variation in Individuals Project (VIP) consortium  61. 22q11. 2 deletion and duplication carriers (18. 6-21. 5 MB; Hg19) and extrafamilial controls from the University of California, Los Angeles. (3) Individuals diagnosed with ASD and their respective controls from the ABIDE1 multicenter dataset  23. (4) Individuals diagnosed with SZ (either DSM-IV or DSM-5) and their respective controls. We aggregated fMRI data from 10 distinct studies.
(5) Individuals diagnosed with ADHD (DSM-IV) and their respective controls from the ADHD-200 dataset  62, 63. Imaging data were acquired with site-specific MRI sequences. Each cohort used in this study was approved by the research ethics review boards of the respective institutions. Signed informed consent was obtained from all participants or their legal guardian before participation. Secondary analyses of the listed datasets for the purpose of this project were approved by the research ethics review board at Sainte Justine Hospital. After data preprocessing and quality control, we included a total of 1,928 individuals (Table  1 ). Preprocessing and quality control procedures. All datasets were preprocessed using the same parameters with the same Neuroimaging Analysis Kit version 0. 12. 4, an Octave-based open-source processing and analysis pipeline  64.
Statistical analyses were performed in Python using the scikit-learn library  67. Analyses were visualized in Python and R. Code for all analyses and visualizations is being made available online through the GitHub platform https://github. com/ surchs/Neuropsychiatric_CNV_code_supplement. Connectome-wide association studies. We performed seven CWAS, comparing FC between cases and controls for four CNVs (16p11. 2 and 22q11. 2, either deletion or duplication) and three idiopathic psychiatric cohorts (ASD, SZ, and ADHD). Note that controls were not pooled across cohorts. Within each cohort, FC was standardized (z-scored) based on the variance of the respective control group. CWAS was conducted by linear regression at the connectome level, in which zscored FC was the dependent variable and clinical status the explanatory variable.
9). Whole-brain FC-similarity between idiopathic psychiatric conditions and CNVs. We tested the similarity between dysconnectivity measured across idiopathic psychiatric conditions and CNV. This similarity was tested by correlating individual whole-brain connectomes of cases and controls of one group to the whole-brain FC-signature (group level) of another group (Fig. 3 ). The group-level FC-signature was defined as the 2080 β values obtained from the contrast of cases vs. controls. This was repeated 21 times between all CNVs and conditions and in both directions (n = 42 similarity tests). Individual connectomes of cases and their respective controls were used after independently adjusting for sex, site, head motion, age, and average group connectivity for each of the datasets. Similarity scores were derived by computing Pearson's correlations between the whole-brain connectomes.
We asked whether cases compared to their respective controls had significantly higher (or lower) similarity to whole-brain FC-signature of another group using a Mann-Whitney U test. We reported significant group differences after FDR correction accounting for the 42 tests (q < 0. 05). Similarity of regional FC-signatures between idiopathic conditions and CNVs. The same approach described above was performed at the regional level. Each of the 1705 connectomes of individuals with idiopathic psychiatric conditions and their respective controls was independently adjusted for sex, site, head motion, age, and average group connectivity for each dataset. We calculated a similarity score between these individual connectomes and the FC-signatures of the 16p11. 2 and 22q11. 2 deletions and duplications.
To investigate the transcriptomic relationship of altered FC in each deletion, we aligned gene expression values in the adult human brain from the Allen Human Brain Atlas (AHBA) dataset  16  to the MIST64 brain parcellation following previously published guidelines for probe-to-gene mappings and intensity-based filtering  71  and adapting the abagen toolbox  72. We normalized expression values within each brain sample across genes for each of the 6 donors and then for each gene across samples for each donor using a scaled robust sigmoid normalization  71. We computed the mean of the normalized values of all samples encompassed within each functional region of the MIST64. This was performed for each donor and then averaged across donors. A leave-one-donor out sensitivity analysis generated six expression maps.
The principal components of these six expression maps were highly correlated (average Pearson correlation of 0. 993). The same high correlation was observed for the differential Stability score (average Pearson correlation of 0. 987). Normalized gene expression value was available for each of the 15663 genes and for each of the 64 functional brain regions. Gene expression analyses. For all analyses, we used a dataset including one expression value per gene and per functional region. Expression values were associated with the average connectivity alteration of the corresponding regions (specifically, we computed the mean of all 64 beta values of each region, a measure of global connectivity). PLSR method was used to investigate the association between spatial patterns of gene expression (of the 37 and 24 genes encompassed in the 22q11.
Acknowledgements This research was supported by  Calcul Quebec  (http://www. calculquebec. ca) and  Compute Canada  (http://www. computecanada. ca),the  Brain Canada Multi investigator research initiative (MIRI) , funds from the  Institute of Data Valorization (IVADO). S. J. is a recipient of a Canada Research Chair in neurodevelopmental disorders, and a chair from the  Jeanne et Jean Louis Levesque Foundation. C. S. is supported by a fellowship from the  Institute for Data Valorization. Kuldeep Kumar  is supported by  The Institute of Data Valorization (IVADO)   Postdoctoral Fellowship program , through the  Canada First Research Excellence Fund. This work was supported by a grant from the  Brain Canada Multi-Investigator initiative  (S. J. ) and a grant from The  Canadian Institutes of Health Research  (S.
J. ). Dr P. Bellec  is a fellow (" Chercheur boursier Junior 2") of the "Fonds de recherche du Québec-Santé ", Data preprocessing and analyses were supported in part by the  Courtois foundation  (P. B. ). We are grateful to all of the families at the participating Simons Simplex Collection (SSC) sites, as well as the principal investigators ( A. Beaudet ,  R. Bernier ,  J. Constantino ,  E. Cook ,  E. Fombonne ,  D. Geschwind ,  R. Goin-Kochel ,  E. Hanson ,  D. Grice ,  A. Klin ,  D. Ledbetter ,  C. Lord ,  C. Martin ,  D. Martin ,  R. Maxim ,  J. Miles ,  O. Ousley ,  K. Pelphrey ,  B. Peterson ,  J. Piggot ,  C. Saulnier ,  M. State , W. Stone,  J. Sutcliffe ,  C. Walsh ,  Z. Warren ,  E. Wijsman ). We appreciate obtaining access to imaging and phenotypic data on SFARI Base. Approved researchers can obtain the Simons Variation in Individuals Project population dataset described in this study by applying at https://base.
sfari. org. ABIDE I is supported by  NIMH  ( K23MH087770 ),  NIMH  ( R03MH096321 ), the  Leon Levy Foundation ,  Joseph P. Healy , and the  Stavros Niarchos Foundation. Data in the schizophrenia dataset were accessed through the SchizConnect platform (http://schizconnect. org). As such, the investigators within SchizConnect contributed to the design and implementation of SchizConnect and/or provided data but did not participate in the data analysis or writing of this report. Funding of the SchizConnect project was provided by  NIMH  cooperative agreement  1U01 MH097435. SchizConnect enabled access to the following data repository: The Collaborative Informatics and Neuroimaging Suite Data Exchange tool (COINS; http:// coins. mrn. org/dx). Data from one study was collected at the  Mind Research Network  and funded by a  Center of Biomedical Research Excellence (COBRE)  grant, ( 5P20RR021938/ P20GM103472 ) from the  NIH  to  Dr.
Vince Calhoun. Data from two other studies were obtained from the  Mind Clinical Imaging Consortium Database. The  MCIC  project was supported by the  Department of Energy  under award number  DE-FG02-08ER6458. MCIC is the result of the efforts of co-investigators from the  University of Iowa, University of Minnesota, University of New Mexico , and  Massachusetts General Hospital. Data from another study were obtained from the  Neuromorphometry by Computer Algorithm Chicago (NMorphCH)  dataset (http://nunda. northwestern. edu/nunda/data/projects/NMorphCH). As such, the investigators within NMorphCH contributed to the design and implementation of NMorphCH and/or provided data but did not participate in the data analysis or writing of this report. The  NMorphCH  project was funded by  NIMH  grant  RO1 MH056584.
Data from the  UCLA  cohort provided by C. E. B. (participants with 22q11. 2deletions or duplications and controls) was supported through grants from the  NIH  ( U54EB020403 ),  NIMH  ( R01MH085953 ,  R01MH100900 ,  R03MH105808 ), and the  Simons Foundation  ( SFARI Explorer Award ). Finally, data from another study were obtained through the  OpenFMRI  project (http://openfmri. org)from the  Consortium for Neuropsychiatric Phenomics (CNP) , which was supported by  NIH   Roadmap for Medical Research grants   UL1-DE019580 ,  RL1MH083268 ,  RL1MH083269 ,  RL1DA024853 ,  RL1MH083270 ,  RL1LM009833 ,  PL1MH083271 , and  PL1NS062410.
Data availability ABIDE1, COBRE, ADHD200, 16p11. 2 SVIP data are publicly available: http://fcon_1000. projects. nitrc. org/indi/abide/abide_I. html, http://schizconnect. org/queries/new, http:// fcon_1000. projects. nitrc. org/indi/adhd200/, https://www. sfari. org/funded-project/ simons-variation-in-individuals-project-simons-vip/. Around 1/3 of the SZ data (172 out of 484 subjects) can't be shared as participants did not consent to that at the time. For the 22q11. 2 sample, raw data are currently available by request from the PI. Beta maps from all Connectome wide association studies (16p11. 2 deletion and duplication, 22q11. 2 deletion and duplication, ASD, SZ, and ADHD) performed in this study are available in the Supplementary Data 1 and on GitHub. Expression data used in this study are also available in Supplementary Data 4.
Sensitivity analysis investigating medication effect in ASD cohort is presented in Supplementary Notes. Sensitivity analyses also showed that the FC-signature of 22q11. 2deletions is not influenced by a diagnosis of ASD or ADHD (Supplementary Fig. 1). Columns ASD, SZ, and ADHD represent the number of subjects with those diagnoses. One subject may have several diagnoses. For example, 9 subjects with ASD have also an ADHD diagnosis. Dataset Status n Age FSIQ Sex FD a ASD a ADHD SZ SVIP 16p11. 2 Cohort Del carriers 20 12. 7 (6. 8) 92. 5 (16. 1) 12 M 0. 18 (0. 03) 4 6 0 (2 sites) Controls 79 26. 7 (14. 7) 103. 6 (14. 8) 46 M 0. 17 (0. 04) 0 0 0 Dup 23 28. 2 (12. 8) 94. 1 (15. 1) 12 M 0. 19 (0. 05) 1 1 0 carriers UCLA 22q11. 2 Cohort Del 46 16. 8 (6. 1) 77. 2 (13. 8) 20 M 0. 17 (0. 06) 23 19 3 (1 site) Carriers Controls 43 13.
The availability of more data, more powerful modeling methods, and dramatically increased computing power led to the rise of systems biology as a compelling research theme around the turn of the millennium  (Kitano, 2000; Ideker et al, 2001). Though computational models were at first published as printed equations in journal articles, the desire to reuse an ever-increasing number of models called for digital formats that were interoperable between software systems and could be easily exchanged between scientists (topics of interest as early as the 1960s; c. f. Garfinkel, 1969). This drove efforts to create tool-ways of representing models that independent could avoid the potential for human translation errors, be stored in databases, and provide a common starting point for simulations and analyses regardless of the software used  (Goddard , 2001; et al Hucka , 2001; Lloyd , 2004).
Whether using reactions or not, values of model variables and their changes over time may be fixed or determined by mathematical expressions, either before or during simulation, continuously or in response to discrete events, with or without time delays. Units of measurement can be specified for all entities and values; in addition to adding a layer of essential physical knowledge (after all, how else could one interpret whether a time course is in milliseconds or years?), information about units can be used to verify the relationships expressed in a model. Units also facilitate reuse of models and components, interconnection of models, conversion of models between different frameworks, and integration of data with models. SBML does not dictate which framework must be used to analyze or simulate a model; in fact, it purposefully lacks any explicit way to specify what is done with a model whether to run simulations or other types of analyses, how to run them, or how to present the results because externalizing this information enhances model reusability and permits independent innovation in separate but complementary formats.
Two of the most popular methods for timecourse simulation are commonly used: one is numerical integration of differential equations created from the reactions and other relationships affecting model variables, and the other is simulating the time evolution of the model as a stochastic system via algorithms such as the one developed by  Gillespie (1977). Alternative approaches are also in use, particularly when a model is enhanced with SBML packages. Any element of an SBML model can be elaborated using machine-readable metadata as well as human-readable notes. For metadata, two schemes are supported. The first is direct labeling of SBML elements with terms from the Systems Biology Ontology (SBO; Courtot et al, 2011), which allows the mathematical semantics of every element of a model to be precisely specified.
The community currently comes together twice a year within the context of meetings organized by COMBINE the Computational Modeling in Biology Network; Hucka (the et al, 2015). HARMONY Hackathon on Resources for Modeling in Biology) is a codefest that focuses on the development of software, in particular via the development of libraries, tools, and specifications; by contrast, the COMBINE Forum meetings focus on the presentation of novel tools and the discussion of proposed features. In addition to these general meetings, special SBML working groups are organized as needed to drive SBML package development. COMBINE's central activity is coordinating and harmonizing standardization in computational biology, and SBML is one of its core standards. FAIRsharing, a broader community network that covers life sciences more comprehensively (Sansone , 2019), maintains interconnected and orgaet al nized collections of resources in many areas, including curated links between SBML and many associated funders, databases, and standards.
3    Figure. A closer look at SBML. The SBML development process has helped shape the field partly by directly involving software developers and modelers. Frequent workshops have provided essential feedback for developers to help them better serve modelers' needs (e. g. , Waltemath et al, 2014). Workshops as well as resources such as the SBML Software Guide (see Box 2) helped raise awareness of existing tools, which in turn increased their use and the use of SBML. This helped create a culture of sharing models and building on existing work in systems biology  (Stanford , 2015). It also led to new activities centered et al on the models themselves, including automatic model generation, analysis of model structures, model retrieval, and integration of models with experimental data  (Dra ¨ ger & Palsson, 2014 ).
SBML's successful approach to community organization has led other standardization efforts (BioPAX, NeuroML, SBGN, SED-ML) to adopt some of the same approaches; SBML was also a founding member of COMBINE  (Hucka , 2015) , discussed above. Before the advent of SBML, it was challenging to exchange models because software tools used incompatible definition schemes. As models increased in size and complexity, manually rewriting them became more difficult, error-prone, and eventually   2016). The availability of a well-defined format has also facilitated the comparison of software tools to each other. Using SBML-encoded models has become the norm to assess the accuracy of modeling software: initially it is done manually using models from BioModels Database  (Bergmann & Sauro, 2008) , and now, it is more commonly done using the SBML Test Suite (Box 2).
Many journals, aware of the challenges surrounding the reproducibility of scientific results, encourage authors not only to describe their models but also to make their models available in electronic form. Molecular Systems Biology was the first supporter of submissions in SBML format (beginning in 2005 4,5 ). Today, most journals still avoid a requiring specific format, though some such as the BMC 6 and FEBS 7 journals do explicitly encourage authors to submit SBML files as supporting material for research where it is relevant. Others, such as Biophysical Journal (Nickerson & Hunter, 2017), recommend authors deposit models in repositories such as BioModels Database, which encourages the use of common standard formats such as SBML. Many funding agencies also now have policies related to data sharing, and some program announcements suggested the use of SBML where appropriate.
Distributions The distrib package (Smith , ) provides the means to encode information about the distribution and uncertainty of numerical values assigned " " et al 2020 to a model element. Biological models often contain elements that have inexact numerical values, since they are based on values that are stochastic in nature or data that contains uncertainty; however, core SBML has no direct support for encoding values sampled from distributions. The recently-finalized " " distrib package adds constructs for sampling of random values from probability distributions and describing uncertainty statistics about element values.
A) Fragments of the global structure of an SBML file. In this example, the use of several SBML packages is declared in the file header. Model elements in the file include the descriptions of model variables, as well as their relationships. Elements of the same type are collected into ListOf elements; model parameters are in the ListOfParameters " " element. SBML package elements can refer to elements in the SBML Core as necessary. (B) Model elements are linked through unique identifiers used in the mathematical constructs and the elements describing the reactions, the molecular species, and their localization. The full model for this example is available in BioModels Database (Malik-Sheriff , 2020) as the model with identifier MODEL1904090001. et al / Impact of SBML As contributors to developments in methods, software, and standards over the past two decades (Hucka et al, 2015), we can attest to SBML's profound impact on the field, both from our own first-hand experiences and from surveys (Klipp , 2007) that et al indicate SBML has become a standard.
Acknowledgments by SBML community members B. A. 's work on SBML Level 3 was supported by the  National Institutes of Health  ( NIH, US)  and he is currently employed by  AstraZeneca. G. B. was supported by  National Resource for Network Biology (NRNB, US) , award  5P41-GM103504. M. L. B. was supported by grant  P41-GM103313  and  R01-GM095485  from the  NIH (US)  to the  University of Connecticut School of Medicine. J.  C. was supported by the  Czech Research Infrastructure Infrastructure for Systems Biology C4SYS , project  LM2015055 ;  GA  CR , grant  18-24397S ; and the  MEYS of the Czech Republic  under OP  RDE grant   CZ. 02. 1. 01/0. 0/0. 0/16_026/0008413 " Strategic Partnership for Environmental Technologies and Energy Production ". / D. D. was supported by grant iLite (Innovations for LIver and Tissue Engineering) from  Agence nationale de la recherche (ANR, France)  to  INRIA, Paris, France ; by grant LiSyM (Liver Systems Medicine) from  Bundesministerium fu ¨ r Bildung und Forschung (BMBF, Germany)  to  IfADo, Dortmund, Germany ; by grant  VLN  ( Virtual Liver Network)  from  Bundesministerium fu ¨ r Bildung und Forschung (BMBF, Germany)  to  IfADo, Dortmund, Germany ; by grant Physicancer from  Institut national de la sante et de la recherche me dicale (INSERM, France)  to  INRIA, Paris, France ; and by grant NOTOX (Predicting long-term toxic effects using computer models based on systems characterisations of organotypic cultures) from  EU 7 th Framework Programme  to  INRIA, Paris, France.
C. E. was supported by  CHARME ,  COST ,  Action CA15110 MoU (PI: Chris Evelo)  to  Maastricht University. R. F. was supported by the  U. S. Department of Energy, Offices of Advanced Scientific Computing Research and the Biological and Environmental Research  as part of the  Scientific Discovery Through Advanced Computing program , grant  DE-SC0010429. A. G. was supported by the  Japanese Society for the Promotion of Science. M. G. was supported by funding to HITS from the  German Federal Ministry of Education and Research (BMBF)  as part of the  Liver Systems Medicine Network LiSyM  ( FKZ 031L0056 ) and through the  MulticellML  project ( FKZ 01ZX1707B ), as well as from the  European Union Horizon2020  framework programme of the  European Commission  under Grant Agreement 825843 and from the  Klaus Tschira Foundation  (KTS).
M. G. G. was supported by  European Molecular Biology Laboratory (EMBL)  core funding. W. S. H. was supported by grant  R01-GM111510  from the  National Institute of General Medical Sciences  ( NIGMS, US)  of the  NIH. D. K. was supported by  Novo Nordisk Foundation  grant  NNF10CC1016517. E. K. was supported by  Deutsche Forschungsgemeinschaft (DFG ,  GRK1772 ,  GRK 2290  and  TRR175 ) and by  People Programme (Marie Skłodowska-Curie Actions   H2020-MSCA-ITN-2015-675585 ). F. K. was supported by the  Russian Foundation for Basic Research ,  17-00-00296 ,  Institute of Computational Technologies of SB RAS, Novosibirsk ,  630090 ,  Russian Federation  (PI: Fedor Kolpakov). J. K. was supported by grants  R35-GM119771  and  P41-EB023912  from the  NIH (US)  to the  Icahn School of Medicine. P.
2 ( 2 A) Fragments of the global structure of an SBML file. In this example, the use of several SBML packages is declared in the file header. Model elements in the file include the descriptions of model variables, as well as their relationships. Elements of the same type are collected into ListOf elements; model parameters are in the ListOfParameters " " element. SBML package elements can refer to elements in the SBML Core as necessary. (B) Model elements are linked through unique identifiers used in the mathematical constructs and the elements describing the reactions, the molecular species, and their localization. The full model for this example is available in BioModels Database (Malik-Sheriff , 2020) as the model with identifier MODEL1904090001. et al / Impact of SBML As contributors to developments in methods, software, and standards over the past two decades (Hucka et al, 2015), we can attest to SBML's profound impact on the field, both from our own first-hand experiences and from surveys (Klipp , 2007) that et al indicate SBML has become a standard.