dataesr/scientific-paragraphs-categorization huggingface dataset:
Effect size is calculated as the absolute effect size (the difference in mean between the two copy number groups in the t test-which, in this case, equals Cohen's D as the standard deviation of the normalized brain measures is one) and the estimate of beta in the linear regression. Plots were generated using R library ggplot2 v2. 2. 1 51. Regional cortical visualization was done with the R package ggseg v1. 5. 1. In a novel analysis, the independent Icelandic data were processed and analysed as the main dataset. We metaanalysed the results using the R package metafor v2. 0. 0, as previously  15.
Cognitive task performance data We downloaded behavioural performance measures on seven cognitive tests (the pairs matching task, the reaction time task, reasoning and problem-solving tests, the digit span test, the symbol digit substitution test and the trail making A and B tests) from the UK Biobank repository, performed by at least 10% of the participants. The results were processed following the general approach by Kendall et al. 16. For more details, see Supplementary Note 6. For the analysis of the seven cognitive measures, we set the significance threshold to α = 0. 05/7 = 0. 007.
Sample characteristics The main 1q21. 1 distal (146. 5-147. 4 Mb, hg19) brain structural dataset consisted of 28 deletion and 22 duplication carriers and 37,088 non-carriers (derived from the same scanner sites as the CNV carriers) from ENIGMA-CNV and UK Biobank (Table  1 , separate demographics in Supplementary Table  9 ). The age of CNV carriers was lower (41. 7 ± 19. 0 (deletions), 55. 4 ± 12. 7 (duplications), respectively) than that of non-carriers (61. 1 ± 12. 1) (Table  1 ). Eleven deletion carriers and seven duplication carriers had a known neurological, neurodevelopmental or psychiatric diagnosis or had been recruited in a clinical CNV study. The remaining carriers either did not have an established diagnosis or were recruited in studies from which diagnostic information was unavailable (Table  1  and Supplementary Table  10 ).
1q21. 1 distal CNV associated with global cortical surface structures For our main dataset, there was a significant positive association between the number of 1q21. 1 distal copies and ICV (β = 1. 47, P = 2. 8 × 10 -25 ) as well as cortical surface area (β = 0. 81, P = 1. 1 × 10 -8 ) (Fig. 1  and Supplementary Table  5 ) at a significance threshold of P < 0. 0014 after correction for age, age 2 , sex, scanner site and ICV. In contrast, a significant negative copy number dosage effect was identified for the caudate (β = -0. 49, P = 6. 9 × 10 -4 ) and hippocampal volumes (β = -0. 56, P = 1. 3 × 10 -4 ). T tests indicated a decrease in ICV (Cohen's D = -1. 84 (-17%), P = 1. 6 × 10 -22 ) for deletion carriers and an increase for duplication carriers (Cohen's D = 0. 90 (+10%), P = 2. 3 × 10 -5 ), respectively, compared to non-carriers (Supplementary Table  6 ).
For a raw value plot of ICV, see Supplementary Fig. 3. The cortical surface area dosage effect was primarily driven by the deletion carriers with a significantly lower total cortical surface area (Cohen's D = -1. 13 (-23%), P = 2. 1 × 10 -9 ) and the dosage effect on caudate and hippocampus was primarily driven by duplication carriers with significantly smaller caudate (Cohen's D = -0. 71 (-16%), P = 0. 0012) and hippocampal (Cohen's D = -0. 92 (-15%), P = 4. 1 × 10 -5 ) volumes than noncarriers (Fig. 1  and Supplementary Table  7 ). Adding an independent Icelandic dataset with two deletions, five duplications and 1150 non-carriers (Table  1 ) in a metaanalysis strengthened the majority of the dosage results (Supplementary Fig. 4  and Supplementary Tables  11  and 12 ) and revealed additional significant between-group differences in nucleus accumbens, caudate and putamen (Supplementary Table 12).
A number of sensitivity analyses were run on the main dataset, namely: (a) Matching each carrier with one non-carrier for age, sex, scanner site and ICV or age, sex, scanner site; (b) including only: (i) non-affected individuals (i. e. excluding individuals with a known neurodevelopmental or neurological disorder (e) including firstand second-degree relatives (see Supplementary Note 5 for methods). These analyses validated the overall effects (Supplementary Tables  5  and 6 ).
These findings provide insight into molecular mechanisms involved in critical stages of human brain development and mapping of gene dosages to brain structural fingerprints. programme under grant agreement no. 643051 (MiND) and from ERC-2010-AdG 268800-NEUROSCHEMA. In addition, the work was supported by a grant for the ENIGMA Consortium (grant number U54 EB020403) from the BD2K Initiative of a cross-NIH partnership. deCODE genetics: deCODE genetics acknowledges support from the Innovative Medicines Initiative Joint Undertaking under grant agreement nos. 115008 (NEWMEDS) and 115300 (EUAIMS), of which resources are composed of EFPIA in-kind contribution and financial contribution from the European Union's Seventh Framework Programme (EU-FP7/2007-2013), EU-FP7-funded grant agreement no. 602450 (IMAGEMEND) and EU-funded FP7-People-2011-IAPP.
Acknowledgements 1000BRAINS: The 1000BRAINS study was funded by the  Institute of Neuroscience and Medicine, Research Center Juelich, Germany. We thank the  Heinz Nixdorf Foundation (Germany)  for the generous support of the  Heinz Nixdorf Recall Study  on which 1000BRAINS is based. We also thank the scientists and the study staff of the  Heinz Nixdorf Recall Study  and  1000BRAINS. Osaka: Osaka study was supported by the  Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS : Grant Number  JP18dm0207006 ), Brain/MINDS& beyond studies (Grant Number  JP20dm0307002 ) and  Health and Labour Sciences Research Grants for Comprehensive Research on Persons with Disabilities  (Grant Number  JP20dk0307081 ) from the  Japan Agency for Medical Research and Development (AMED) ,  Grants-in-Aid for Scientific Research  (KAKENHI; Grant Numbers  JP25293250  and  JP16H05375 ).
Some computations were performed at the  Research Center for Computational Science, Okazaki, Japan. PAFIP: The PAFIP study was supported by  Instituto de Salud Carlos III ,  FIS 00/ 3095 ,  01/3129 ,  PI020499 ,  PI060507 ,  PI10/00183 , the  SENY Fundació Research Grant   CI2005-0308007  and the  FundaciónMarqués de Valdecilla   API07/011. Biological samples from our cohort were stored at the  Valdecilla Biobank  and genotyping services were conducted at the  Spanish 'Centro Nacional de Genotipado' (CEGEN-ISCIII ). MCIC/COBRE: The study is funded by the  National Institutes of Health  studies  R01EB006841 ,  P20GM103472  and  P30GM122734  and  Department of Energy   DE-FG02-99ER62764. PING: Data collection and sharing for the  Paediatric Imaging, Neurocognition and Genetics (PING) Study  ( National Institutes of Health  Grant  RC2DA029475 ) were funded by the  National Institute on Drug Abuse  and the  Eunice Kennedy Shriver National Institute of Child Health & Human Development.
A full list of PING investigators is at http://pingstudy. ucsd. edu/investigators. html. QTIM: The QTIM study was supported by the  National Institute of Child Health and Human Development  ( R01 HD050735 ) and the  National Health and Medical Research Council  ( NHMRC 486682 ,  1009064 ),  Australia. Genotyping was supported by  NHMRC  ( 389875 ). Medland is supported in part by an  NHMRC  fellowship ( APP1103623 ). SHIP: SHIP is part of the  Community Medicine Research net of the University of Greifswald, Germany , which is funded by the  Federal Ministry of Education and Research  (grant nos. 01ZZ9603 ,  01ZZ0103  and  01ZZ0403 ), the  Ministry of Cultural Affairs  and the  Social Ministry  of the  Federal State of Mecklenburg-West Pomerania. Genome-wide single-nucleotide polymorphism typing in SHIP and MRI scans in SHIP and SHIP-TREND have been supported by a joint grant from  Siemens Healthineers, Erlangen, Germany  and the  Federal State of Mecklenburg-West Pomerania.
StrokeMRI: StrokeMRI  was supported by the  Norwegian ExtraFoundation for Health and Rehabilitation ( 2015/FO5146 ), the  Research Council of Norway  ( 249795 ,  262372 ), the  South-Eastern Norway Regional Health Authority  (2014097, 2015044, 2015073) and the  Department of Psychology, University of Oslo. Sydney MAS: The  Sydney Memory and Aging Study (Sydney MAS ) is funded by  National and HealthMedical Research Council (NHMRC)  Programme and Project Grants ( ID350833 ,  ID568969  and  ID109308 ). We also thank the Sydney MAS participants and the  Research Team. SYS: The  SYS Study  is supported by  Canadian Institutes of Health Research. TOP: Centre of Excellence: RCN # 23273  and RCN # 226971. Part of this work was performed on the TSD (Tjeneste for Sensitive Data) facilities, owned by the  University of Oslo , operated and developed by the  TSD service group at the University of Oslo ,  IT-Department (USIT)  (tsd-drift@usit.
uio. no). The research leading to these results has received funding from the  European Union Seventh Framework Programme  ( FP7-PEOPLE-2013-COFUND)  under grant agreement no. 609020-Scientia Fellows; the  Research Council of Norway  (RCN) # 276082 -A lifespan perspective on mental illness: toward precision medicine using multimodal brain imaging and genetics. Ida E. Sønderby and  Rune Bøen  are supported by  South-Eastern Norway Regional Health Authority  (# 2020060 ). Ida E. Sønderby and  Ole A. Andreassen  have received funding from the  European Union 's  Horizon 2020 Research and Innovation Programme  under Grant agreement no. 847776  ( CoMorMent project ) and the  KG Jebsen Foundation  ( SKGJ-MED-021 ). UCLA_UMCU: The UCLA_UMCU cohort comprises of six studies which were supported by  National Alliance for Research in Schizophrenia and Affective Disorders (NARSAD)  ( 20244  to  Prof.
Funding was also granted by the  Initiative and Networking Fund of the Helmholtz Association (Caspers)  and the  European Union 's  Horizon 2020 Research and Innovation Programme  under Grant Agreement 945539 ( Human Brain  Project  SGA3 ; Amunts, Caspers, Cichon). Brainscale: The Brainscale study was supported by the  Netherlands Organization for Scientific Research MagW   480-04-004  ( Dorret I. Boomsma ),  51. 02. 060  ( Hilleke E. Hulshoff Pol ),  668. 772  ( Dorret I. Boomsma  and  Hilleke E. Hulshoff Pol );  NWO/SPI 56-464-14192  ( Dorret I. Boomsma ), the  European Research Council  ( ERC-230374 ) ( Dorret I. Boomsma ),  High Potential Grant Utrecht University (Hilleke E. Hulshoff Pol)  and  NWO Brain  and  Cognition 433-09-220  ( Hilleke E. Hulshoff Pol ). Betula: The Betula study was funded by the  Knut and Alice Wallenberg (KAW) foundation (Nyberg).
The Freesurfer segmentations on the Betula sample were performed on resources provided by the  Swedish National Infrastructure for Computing (SNIC)  at  HPC2N  (in  Umeå, Sweden ), partially funded by the  Swedish Research Council  through grant agreement no. 2018-05973. Brain Imaging Genetics (BIG ): This work makes use of the BIG database, first established in Nijmegen, The Netherlands, in 2007. This resource is now part of Cognomics (www. cognomics. nl), a joint initiative by researchers from the  Donders Centre for Cognitive Neuroimaging , the  Human Genetics and Cognitive Neuroscience departments of the Radboud University Medical Centre  and the  Max Planck Institute for Psycholinguistics in Nijmegen. The Cognomics Initiative  has received support from the participating departments and centres and from external grants, that is, the  Biobanking and Biomolecular Resources Research Infrastructure (Netherlands)  ( BBMRI-NL ), the  Hersenstichting Nederland  and the  Netherlands Organization for Scientific Research (NWO).
The research leading to these results also receives funding from the  NWO Gravitation  grant ' Language in Interaction ', the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement nos. 602450 (IMAGEMEND), 278948 (TACTICS) and 602805 (Aggressotype), as well as from the  European Community's Horizon 2020  grant agreement no. 286213  (PsychDPC). Dublin: This work was supported by  Science Foundation Ireland (SFI  grant  12/ IP/1359  to  Gary Donohoe  and grant  SFI08/IN. 1/B1916-Corvinto Aidan C. Corvin ). ECHO-DEFINE: The ECHO study acknowledges funding from a  Medical Research Council (MRC)   Centre Grant  to  Michael J. Owen  ( G0801418 ), the  Wellcome Trust (Institutional Strategic Support Fund (ISSF)  to van den Bree and Clinical Research Training Fellowship to Joanne L.
Doherty), the  Waterloo Foundation  ( WF 918-1234  to van den Bree), the  Baily Thomas Charitable Fund ( 2315/1  to van den Bree),  National Institute of Mental Health  ( NIMH 5UO1MH101724  to van den Bree and  Michael J. Owen ), the  IMAGINE-2 study  (funded by the  MRC  ( MR/T033045/1 ) to van den Bree,  Jeremy Hall  and  Michael J. Owen ), the  IMAGINE-ID study  (funded by  MRC  ( MR/N022572/1 ) to  Jeremy Hall ,  van den Bree and Owen). The DEFINE study was supported by a  Wellcome Trust   Strategic Award  ( 100202/Z/12/Z ) to  Michael J. Owen. ENIGMA: ENIGMA is supported in part by  NIH  grants  U54 EB20403 ,  R01MH116147  and  R56AG058854. NIA T32AG058507 ;  NIH/NIMH 5T32MH073526. EPIGEN-Dublin: The EPIGEN-Dublin cohort was supported by a  Science Foundation Ireland Research Frontiers   Programme award  ( 08/RFP/GEN1538 ).
HUNT: The HUNT study is a collaboration between  HUNT Research Centre (Faculty of Medicine and Movement Sciences, NTNU-Norwegian University of Science and Technology) ,  Nord-Trøndelag County Council, Central Norway Health Authority  and the  Norwegian Institute of Public Health. HUNT-MRI  was funded by the  Liaison Committee between the Central Norway Regional Health Authority  and the  Norwegian University of Science and Technology , and the  Norwegian National Advisory Unit for functional MRI. IMAGEN: This work received support from the following sources: the  European Union -funded  FP6  Integrated Project IMAGEN (reinforcement-related behaviour in normal brain function and psychopathology) ( LSHM-CT-2007-037286 ), the  Horizon 2020  funded  ERC Advanced Grant 'STRATIFY' (Brain network  based stratification of reinforcement-related disorders) (695313), ERANID (Understanding the  Interplay between Cultural, Biological and Subjective Factors in Drug Use Pathways)  ( PR-ST-0416-10004 ),  BRIDGET  ( JPND: BRain Imaging, cognition Dementia and next generation GEnomics ) ( MR/ N027558/1 ),  Human Brain Project  ( HBP SGA 2, 785907 ),the  FP7  projects  IMAGEMEND ( 602450 ;  IMAging GEnetics  for MENtal Disorders) and MATRICS ( 603016 ), the  Innovative Medicine Initiative  Project  EUAIMS  ( 115300-2 ), the  Medical Research Council Grant 'c-VEDA' (Consortium on Vulnerability to Externalizing Disorders and Addictions)  ( MR/N000390/1 ), the  Swedish Research Council FORMAS , the  Medical Research Council , the  National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London , the  Bundesministeriumfür Bildung und Forschung (BMBF  grants  01GS08152 ,  01EV0711 ; eMED SysAlc01ZX1311A;  Forschungsnetz AERIAL 01EE1406A ,  01EE1406B ), the  Deutsche Forschungsgemeinschaft  ( DFG grants ,  SM 80/7-2 ,  SFB 940/2 ), the  Medical Research Foundation and Medical Research Council  (grants  MR/R00465X/1  and  MR/S020306/1 ).
Further support was provided by grants from:  ANR  (project  AF12-NEUR0008-01-WM2NA ,  ANR-12-SAMA-0004 ), the  Eranet Neuron  ( ANR-18-NEUR00002-01 ), the  Fondation de France  ( 00081242 ), the  Fondation pour la Recherche Médicale  ( DPA20140629802 ), the  Mission Interministérielle de Lutte -contre-les-Drogues-et-les-Conduites-Addictives (MILDECA), the  Assistance-Publique-Hôpitaux-de-Paris  and  INSERM  (interface grant),  Paris Sud University IDEX 2012 , the  Fondation de l'Avenir  (grant  AP-RM-17-013 ), the  Fédération pour la Recherche sur le Cerveau ; the  National Institutes of Health, Science Foundation Ireland  ( 16/ERCD/3797 ),  USA (Axon, Testosterone and Mental Health during Adolescence ;  RO1 MH085772-01A1 ) and by  NIH Consortium  grant  U54 EB020403 , supported by a cross- NIH  alliance that funds Big Data to Knowledge Centres of Excellence.
Lifespan: The study is funded by the  Research Council of Norway  ( 230345 ,  288083  and  223273 ). NCNG: NCNG sample collection was supported by grants from the  Bergen Research Foundation  and the  University of Bergen , the  Dr Einar Martens Fund , the  Research Council of Norway , to le Hellard, Steen and Espeseth. The Bergen group was supported by grants from the  Western Norway Regional Health Authority  (Grant  911593  to  Arvid Lundervold , Grant  911397  and  911687  to  Astri Johansen Lundervold ). NTR: The NTR cohort was supported by the  Netherlands Organization for Scientific Research (NWO)  and  The Netherlands Organisation for Health Research and Development (ZonMW)  grants  904-61-090 ,  985-10-002 ,  912-10-020 ,  904-61-193 ,  480-04-004 , 463-06-001 ,  451-04-034 ,  400-05-717 ,  Addiction-31160008 ,  016-115-035 ,  481-08-011 ,  056-32-010 ,  Middelgroot-911-09-032 ,  OCW_NWO Gravity programme-024.
Part of the genotyping and analyses were funded by the  Genetic Association Information Network (GAIN) of the Foundation for the National Institutes of Health. Computing was supported by  NWO  through grant  2018/EW/00408559 ,  BiG Grid , the  Dutch e-Science Grid  and SURFSARA. OATS: The OATS study has been funded by a  National Health & Medical Research Council (NHMRC)  and  Australian Research Council (ARC)   Strategic Award Grant  of the Ageing Well,  Ageing Productively Programme  (ID No. 401162 ) and  NHMRC  Project Grants (ID Nos. 1045325  and  1085606 ). This research was facilitated through  Twins Research Australia , a national resource in part supported by an  NHMRC   Centre for Research Excellence Grant  (ID No. :  1079102 ). We thank the participants for their time and generosity in contributing to this research.
3) 48 (4. 2%) Scanner sites 11 15 8 2 2 1 Datasets 9 13 7 1 1 1 ADHD attention deficit disorder, clinically recruited in clinical NDD study but without a diagnosis, MDD major depressive disorder, SCZ schizophrenia, del deletion carrier, nc non-carriers, dup duplication carrier, P P value, AvPD avoidant personality disorder, OCD obsessive-compulsive disorder, DPD dependent personality disorder, STPD schizotypal personality disorder, NS non-significant. P value is based on a χ 2 test for categorical values and ANOVA for continuous values. a First deletion carrier: agoraphobia, AvPD, OCD, DPD, other substance-related disorder, conduct disorder. Second deletion carrier: specific phobia, social phobia, MDD, AvPD, STPD. Duplication carrier: social phobia, OCD, MDD, AvPD.
Indeed, the removal of impediments preventing the integration of information obtained from numerous disparate sources is considered essential for the advancement of conservation biogeography  (Richardson & Whittaker, 2010). To date, bivalve body size-to-weight and weightto-weight relationships have not been documented for several widespread invasive freshwater bivalves, or are not readily accessible through systematic searches of major online databases (e. g. Scopus, Web of Science, Google Scholar). Although a number of studies have documented conversion relationships for body length to biomass, most only consider individual populations of Corbicula and Dreissena species (e. g. Aldridge & McMahon, 1978; Balogh et al. , 2019; Glyshaw et al. , 2015) ; while similar studies on other widespread freshwater invasive bivalves such as Limnoperna fortunei and Sinanodonta woodiana are rare or altogether non-existent.
| Relationships between body size and individual biomass Body size to biomass relationships were examined for each of the six bivalves using nonlinear regressions in the form of the power law equation (e. g. Eklöf et al. , 2017) : where biomass is a measurement of the individual mass category (i. e. LW, WW, DW, SW, SFDW or AFDW, in g) and size is a directional measurement of body size (L, W or H, in mm), while α defines a normalisation constant, and β is designated as the scaling constant (i. e. the allometric coefficient). As biomass typically scales upwards with body size, nonlinear regressions with heteroscedastic variance were modelled as power functions in relation to mean α and β values, as initial data exploration indicated a superior fit relative to linear and logarithmic exponentiated equations (sensu  Packard, 2014).
| Proportional weight-to-weight conversion factors Ratios between six biomass variables (all those measured except AW, which was only used to calculate AFDW) were calculated for each individual specimen. Proportional conversion factors between the biomass variables were then calculated for each population by computing mean and median values for these ratios. Standard deviation, standard error (SE) and 95% confidence interval (CI) values were also calculated for each conversion factor and population as an assessment of data variability, thus informing on the precision of conversion estimates. Using population mean values, an overall grand mean, associated median, SE and 95% CI values were obtained for each species. Furthermore, absolute error calculations were then Biomass = × size. used to assess the accuracy of the estimated conversion factors in relation to each individual specimen (i.
| Data analysis For each species, the individual variables for body size, biomass measurements and proportional biomass conversion estimates were analysed among all sampled populations using Kruskal-Wallis tests, for a conservative test of possible differences among populations. All data analyses were performed using the base 'stats' package in R v3. 4. 4 (R Core Development Team, 2018) while nonlinear models were created using the 'gnls' function from the 'nlme' package.
| Size and biomass variables We obtained average L for each population, as well as average W and H values with the exception of one population for each of D. bugensis, D. polymorpha and L. fortunei (Table  S1. 1). Average L, W and H across populations are presented in parentheses, while populationspecific means are summarised in Table  S1. 1: C. fluminea (21. 9, 14. 6 and 20. 3 mm), C. largillierti (16. 7, 9. 4 and 14. 6 mm), D. bugensis (21. 3, 10. 8 and 12. 0 mm), D. polymorpha (17. 6, 9. 4 and 8. 9 mm), L. fortunei (14. 8, 6. 0 and 7. 0 mm) and S. woodiana (107. 0, 41. 6 and 69. 8 mm). Similarly, we obtained data for the six biomass variables for all populations inspected. However, in some instances, only partial information was available for species populations. This was the case for C.
02-0. 18: Table  S1. 13). Regardless of direction, calculated linear slopes (m) for species were relatively minor. Calculated slopes show changes to the proportional measurement for AFDW/DW to body size, ranging from 0. 07% to 1. 33% (Table  S1. 13). Similarly, for all species, per-population slope values generally remained quite small for length, width and height (<1. 8%). However, D. polymorpha specimens obtained from Lake F I G U R E 2 Graphical summarisation of variability in precision and accuracy for selected proportional weight-to-weight conversion factors. All conversion factors obtained are presented in Appendix S1 in Supporting Information (Tables S1. 2-S1. 5). Note differences in scale for the sake of data visualisation among the inserted panels [Colour figure can be viewed at wileyonlinelibrary.