dataesr/scientific-paragraphs-categorization huggingface dataset:
We assembled a list of 26 reported CNV associations to SCZ, where an odds ratio and p-value were available. At each CNV locus, we list the odds ratios and p-values from the largest sample collection available in the literature. Results from all CNV loci metaanalyzed inRees et al. (2014), when available, were used. Throughout this article we refer to this entire list as "previously reported" loci. Reported p-values for nine loci shown in bold surpass the multiple testing threshold drawn from the current dataset using a Cochran-Mantel Haenszel test stratified by genotyping platform. Throughout this article we refer to these nine loci as "previously implicated".
Programme (FP7/2007-2013) under grant agreement n° 279227, a fellowship to JW from the MRC/Welsh Assembly Government and the Margaret Temple Award from the British Medical Association. We thank Novartis for their input in obtaining CLOZUK samples, and staff at The Doctor's Laboratory (Lisa Levett/ Andrew Levett) for help with sample acquisition and data linkage and in Cardiff (Kiran Mantripragada/Lucinda Hopkins) for sample management. CLOZUK and some other samples were genotyped at the Broad Institute (which has a separate acknowledgment) or by the WTCCC and WTCCC2 (WT (083948/Z/07/Z). We acknowledge use of the British 1958 Birth Cohort DNA (MRC: G0000934) and the Wellcome Trust (068545/Z/0/ and 076113/C/04/Z), the UK Blood Services Common Controls (UKBS-CC collection), funded by the WT (076113/C/04/Z) and by NIHR programme grant to NHSBT (RP-PG-0310-1002).
Our work was supported by: Medical Research Council (MRC) Centre (G0800509; G0801418), the European Community's Seventh Framework Programme (HEALTH-F2-2010-241909 (Project EU-GEI)), the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 279227, a fellowship to JW from the MRC/Welsh Assembly Government and the Margaret Temple Award from the British Medical Association. CLOZUK and some other samples were genotyped at the Broad Institute (which has a separate acknowledgment) or by the WTCCC and WTCCC2 (WT.
Acknowledgements Core funding for the  Psychiatric Genomics Consortium  is from the  US National Institute of Mental Health  ( U01 MH094421 ). We thank  T. Lehner  and  Anjene Addington  (NIMH). The work of the contributing groups was supported by numerous grants from governmental and charitable bodies as well as philanthropic donation. Details are provided in the Supplementary Notes. Membership of the  Wellcome Trust Case Control Consortium and Psychosis Endophenotype International Consortium  are provided in the Supplementary Notes.
Acknowledgements Data Processing and Statistical analyses were carried out on the Genetic Cluster Computer (http://www. geneticcluster. org)hosted by SURFsara and financially supported by the  Netherlands Scientific Organization  ( NWO 480-05-003 ) along with a supplement from the  Dutch Brain Foundation  and the  VU University Amsterdam. The GRAS data collection was supported by the  Max Planck Society , the  Max-Planck-Förderstiftung , and the  DFG Center for Nanoscale Microscopy & Molecular Physiology of the Brain (CNMPB), Göttingen, Germany. The Boston CIDAR subject and data collection was supported by the  National Institute of Mental Health  ( 1P50MH080272 , RWM; U01MH081928, LJS;  1R01MH092380 , TLP) and the  Massachusetts General Hospital Executive Committee on Research (TLP). ISC -Portugal: CNP and MTP are or have been supported by grants from the  NIMH  ( MH085548 ,  MH085542 ,  MH071681 ,  MH061884 ,  MH58693 , and  MH52618 ) and the  NCRR  ( RR026075 ).
CNP, MTP, and AHF are or have been supported by grants from the  Department of Veterans Affairs Merit Review Program. The Danish Aarhus study was supported by grants from  The Lundbeck Foundation , The  Danish Strategic Research Council, Aarhus University , and The  Stanley Research Foundation. Work in Cardiff was supported by  MRC Centre  ( G0800509 ) and  MRC Programme  ( G0801418 ) Grants, the  European Community 's  Seventh Framework Programme  ( HEALTH-F2-2010-241909  (Project  EU-GEI )), the  European Union Seventh  ( 083948/Z/07/Z ). We acknowledge use of the British 1958 Birth Cohort DNA (MRC:  G0000934 ) and the  Wellcome Trust  ( 068545/Z/0/  and  076113/C/04/Z ), the  UK Blood Services Common Controls (UKBS-CC collection) , funded by the  WT  ( 076113/C/04/Z ) and by  NIHR  programme grant to  NHSBT  ( RP-PG-0310-1002 ).
G. Jebsen Foundation. This work was supported by the  Donald and Barbara Zucker Foundation , the  North Shore -Long Island Jewish Health System Foundation , and grants from the  Stanley Foundation (AKM) , the  National Alliance for Research on Schizophrenia and Depression (AKM) , and the  NIH  ( MH065580  to TL;  MH001760  to AKM). SynSys,  EU FP7-242167 ,  Sigrid Juselius Foundation , The  Molecular Genetics of Schizophrenia studies ,  Dr. DF Levinson  received additional support from the  Walter E. Nichols , M. D. , Professorship in the  School of Medicine , the  Eleanor Nichols Endowment , the  Walter F. & Rachael L. Nichols Endowment  and the William and  Mary McIvor Endowment ,  Stanford University. This study was supported by  NIH   R01  grants ( MH67257  to  N. G. B. ,  MH59588  to B.
Funding for this study was provided by the  Wellcome Trust   Case Control Consortium 2 project  ( 085475/B/08/Z  and  085475/Z/08/Z ), the  Wellcome Trust  ( 072894/Z/03/Z ,  090532/Z/09/Z  and  075491/Z/04/B ),  NIMH  grants ( MH 41953  and  MH083094 ) and British 1958 Birth Cohort DNA collection funded by the  Medical Research Council  (grant  G0000934 ) and the  Wellcome Trust  (grant  068545/Z/02 ) and of the UK National Blood Service controls funded by the  Wellcome Trust. We acknowledge  Hong Kong Research Grants Council project grants   GRF 774707M ,  777511M ,  776412M  and  776513M.
Overview We assembled a CNV analysis group with members from Broad Institute, Children's Hospital of Philadelphia, University of Chicago, University of California San Diego, University of Michigan, University of North Carolina, Colorado University Boulder, and University of Toronto/SickKids Hospital. Our aim was to leverage the extensive expertise of the group to develop a fully automated centralized pipeline for consistent and systematic calling of CNVs for both Affymetrix and Illumina platforms. An overview of the analysis pipeline is shown in Extended Data Figure  1. After an initial data formatting step we constructed batches of samples for processing using four different methods, PennCNV, iPattern, C-score (GADA and HMMSeg) and Birdsuite for Affymetrix 6. 0. For Affymetrix 5. 0 data we used Birdsuite and PennCNV, for Affymetrix 500 we used PennCNV and C-score, and for all Illumina arrays we used PennCNV and iPattern.
CNV burden between sexes Following recent evidence that ostensibly healthy females carry an increased burden of rare CNVs 1 , we examined whether this increased female burden existed in the current PGC dataset. We used a logistic regression model predicting sex using CNV burden and controlling for study covariates, as well as the Wilcoxon rank-sum test comparing male to female CNV burden  1. Focusing on the significant findings in the previous paper, we examined the burden in autosomal CNV count and genes affected among PGC controls (9856 males and 10371 females). We do see an elevated CNV count in control females (1. 90 autosomal CNV rate) to males (1. 87 autosomal CNV rate), however this difference is not significant in either the regression model (OR = 1. 004, p = 0. 66) or the Wilcoxon rank-sum test (p = 0.
7. 7 calculate the SCZ residual phenotype, we first fit a logistic regression model of covariates to affection status, and then extracted the Pearson residual values for use in a quantitative association design for downstream analyses. Residual phenotype values in cases are all above zero, and controls below zero, and are graphed against overall kb burden in Extended data figure We removed three individuals with an SCZ residual phenotype greater than three (or negative three in controls). After the post-processing round of QC, we retained a dataset with a total of 41,321 individuals comprising 21,094 SCZ cases and 20,227 controls.
Data Tables 2-4 are separate. xlsx sheets available upon request. ED Table datasets. xlsx -datasets and sample sizes used in the current study ED Table NeuroGeneSsets. xlsx -Gene sets investigated in the current study ED Table stepdown_withLegend. xlsx -Unique contribution of significant gene sets in step-down regression models Locus 4q35. 2 2q37. 2 CNV type Duplication FAM149A/CYP4V2 Gene or region Duplication AQP12A/KIF1A Reference (see ED table 1) 12 12 Z-score p-value 0. 69 0. 72 CMH p-value 0. 77 0. 14 12 CNV carriers 125 SCZ Case 5 count 72 Control 7 count 53 CMH test 0. 71 Odds Ratio 1. 34 17p12 Deletion HNPP 14 0. 82 0. 89 N = 41321 22 N = 21094 12 N= 20227 10 1. 06 22q11. 21 4q25 Deletion Duplication ELOVL6 Multigenic 1 12 6. 20E-13 0. 9 4. 40E-13 0. 99 58 13 58 7 6 NA 1 16p11. 2 10q11.
Goals of the three new MIPs The MIPs were designed with three main goals in mind. As in their predecessors  (ISOMIP, MISMIP and MISMIP3d) , the first goal of the MIPs is to provide a controlled forum for researchers to compare their model results with those from other models during model development. Furthermore, it is X. S. Asay-Davis et al. : Experimental design for three ice sheet-ocean MIPs hoped that researchers will publish their MIP results and/or submit them to the relevant MIP database when they introduce new ice sheet models, ocean models with ice-shelf cavities or coupled ice sheet-ocean models. Differences between models should be investigated, understood and explained. We have endeavored to keep the MIP setups relatively simple to make them relevant and accessible to the largest possible number of potential contributors and to make them easy to duplicate, while still capturing physical processes relevant to ice sheet-ocean dynamics.
Requested output MISMIP+ requested output is divided into compulsory and optional parts. The compulsory components will be used to a melt-rate perturbation instead because it requires the same model capabilities as Ice1. Optional: 100-year coupled run with dynamic calving, COLD initial conditions and WARM forcing MISOMIP1 IceOcean2ra Optional: 100-year coupled run from end of IceOcean2r with dynamic calving and COLD forcing write an analysis paper, along the lines of the MISMIP3d paper  (Pattyn et al. , 2013). The optional data will be included with the compulsory data in an open-access database. Participants are required to supply point data at the grounding line, along the same lines as MISMIP3d, as well as integrated quantities such as volume above flotation, at set times throughout the experiments.
-iceVolume(nTime) [m 3 ], iceVAF(nTime) [m 3 ], groundedArea ( -uBaseGL(nPointGL,nTime), vBaseGL(nPointGL,nTime) [m a -1 ]: the x and y components of the basal velocity; -uSurfaceGL(nPointGL,nTime), vSurfaceGL(nPointGL,nTime) [m a -1 ]: the x and y components of the surface velocity; -uMeanGL(nPointGL,nTime), vMeanGL(nPointGL,nTime) [m a -1 ]: the x and y components of the vertical mean of the velocity. Since the number of grounding-line points n(t) will vary over time, most of the slices xGL(:,t) will contain missing values, which should be filled with the default value NC_FILL_FLOAT. In Python, C and Fortran this can be achieved by writing data for each time step in turn into the first n(t) elements of the slice xGL(:,t). At the same time, the unlimited dimension nPointGL will be automatically adjusted by the netCDF library routines to the maximum value of n(t).
Two python programs are included in the Supplement: write_example. py creates a netcdf file given data in the MISMIP3d text file format, and plot_example. py reads example netcdf files, constructs a plot like Fig. 4 , and takes advantage of numpy's masked array class to show the changing shape of the grounding line. All submissions should include a brief model description, in a pdf file, which summarizes the stress approximation   2015 ) and  Schoof (2005)  basal-traction laws lead to similar initial states and rates of retreat, as do the SSA and SSA * stress approximations, given the same rate factor A 0 = 2. 0 × 10 -17 Pa -3 a -1. On the other hand the, the Weertman basaltraction law results in a grounding line some way upstream given the same rate factor, a closer grounding line when the rate factor is increased to A 1 = 2.
Ocean4: cold initial conditions and forcing with advancing ice-shelf topography Conceptually, Ocean4 is an extension of Ocean3. The icedraft topography from Ice1ra was produced by abruptly shutting off melting at year 100 and allowing the ice to readvance for 100 years (see Sect. 2. 2). Thus, Ocean4 begins with the final topography from Ocean3 (which is also the same topography as in Ocean2). This time, we prescribe both initialization and restoring to the COLD salinity and potential temperature profile, which should lead to very low melt rates, consistent with the lack of melting in the MISMIP+ run that produced the ice topography. As in Ocean3, yearly topography data at 1 km resolution are provided through  Cornford and Asay-Davis (2016). Once again, participants will need to apply the calving criteria to these data.
The standard grid has a high vertical resolution ( z = 5 m) in an attempt to accommodate models with a variety of vertical coordinates. Participants are welcome to provide plots of their results on their model's native grid in addition to supplying the output on the standard grid. Participants are asked to provide the iceDraft and bathymetry, which are time dependent for Ocean3 and Ocean4, to show how topography has been modified (interpolated in time, smoothed, the ocean column thickened, etc. ). Two python scripts for plotting the contents of a properly formatted results file are available in the Supplement (plotMISOMIPOceanData. py and plotMISOMIPOceanMetrics. py). Plots of the example POP2x simulation results produced with this script are available for download (see Sect. 6). We ask participants to include a description of the result in a pdf file (using the same naming convention as the results, i.
Code and data availability The BISICLES ice-sheet model  (Cornford et al. , 2013)  was used to produce the example MISMIP+ results and is the icesheet component of the POPSICLES model, which was used for the MISOMIP1 example results. The BISICLES source code is available via subversion at https://commons. lbl. gov/ display/bisicles/BISICLES. The example results were duced with svn revision r2975. The source codes for the POP2x ocean model and the POPSICLES coupled model have not yet been made available to the public. The Supplement for this article includes BISICLES Example results from all MISMIP+ experiments as well as a python script demonstrating how these data are written (specifically how to handle variations over time in the number of points describing the grounding line). Also included are pdf files describing the example results from all three MIPs to be used as templates for the participants.
Finally, example python scripts are included for plotting the grounded area from MISMIP+ results as in Fig. 4  and various fields from ISOMIP+ and MISOMIP1 ocean results (similar to Fig. 8 ). The ice topography data required for ISOMIP+ are too large to be included in the Supplement and have been archived separately in NetCDF4 format  (Cornford and Asay-Davis, 2016). These data come from a simulation of Ice1r and Ice1ra using BISICLES (svn revision r2825) with SSA and the basal friction parameterization from  Weertman (1974). The MISOMIP website (http://www. climate-cryosphere. org/activities/targeted/misomip) includes links to both NetCDF files and movie files showing the evolution of the example BISICLES, POP2x and POPSICLES simulations. We firmly wish to avoid giving the sense that the example results should be treated as a benchmark for the MIPs, and for this reason we do not feel it is appropriate to submit the results on their own to a data repository.
Acknowledgements. This material is based upon work supported by the  US Department of Energy, Office of Science, Office of Biological and Environmental Research  under award nos. DE-SC0011982  and  DE-SC0013038. Support was provided through  NYU Abu Dhabi  grant  G1204. Simulation results were produced using resources of the  National Energy Research Scientific Computing Center , a  DOE Office of Science User Facility  supported by the  Office of Science of the US Department of Energy  under contract no. DE-AC02-05CH11231. This work has received funding from the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement number  299035. Work at the  Lawrence Berkeley National Laboratory  was supported by the Director,  Office of Science, Office of Advanced Scientific Computing Research , of the  US Department of Energy  under contract no.
Data on railway suicides and trespass Two major international databases concerning railway suicides and trespassing accidents were identified, namely ERADIS (European Railway Agency Database of Interoperability and Safety) and the UIC safety database. Based on the analysis of these two databases a stable trend was indentified in Europe over the last ten years: there are around 3. 000 railway suicides and roughly 800 trespassing accidents per year in Europe. The latest available data can be consulted in the most recent reports published by ERA  [1]  and UIC  [8]. Additional data were collected using forms or questionnaires that were completed by RESTRAIL partners, who typically acquired the requested data from documents or by interviewing national experts, and in some cases by organising workshops (see for e.
Information, situation management and decision support platform Second, the project focused on information sharing platforms and effective lines of communication between responding bodies, which are essential for effective and coordinated incident management  [27]. Data shared in real time included geodata on the incident location and track access points; information on the site of the incident and on possible involvement of third parties, on train data recording and essential actions as part of the responsesafety, assistance to passengers, evacuation. For the very first time, RESTRAIL proposed technical specification and prototype of the situation management system, intended to assist IMs and RUs to achieve the above goals, improve coordination among first responders and help reduce system shut-down time due to incidents with casualties.
The evaluation of the CBT module was qualitativeassessing the CBT's capacity to achieve its objectives. In the case of FFCCTV, it was not possible to organise a field trial. Instead, a study was carried out focusing on the numbers and costs to the rail industry of rail fatalities; the application, costs and the effectiveness of FFCCTV; and how, by whom and for what purpose the available information is used. Furthermore, where possible the pilot tests included a socio-economic evaluation of the implemented measure(s) in the form of CEA (Cost-Effectiveness Analysis) and CBA (Cost-Benefit Analysis) (see  [31]  for more details). For each possible pilot test, cost data included those related to design, implementation, maintenance, whereas effectiveness data included one or several variables assessed before and after the test period.
Goal and development method The aims of the RESTRAIL toolbox are threefold: (1) to lead decision-makers through the process of selecting from the range of preventative and mitigation measures;  (2)  to provide more detailed guidance on the implementation of those measures; and (3) to provide a framework for collecting and structuring information in order to feed an accessible and documented database on measures implementation and efficiency across the rail community and beyond. These three objectives meet the needs and requirements indentified in the state-of-the-art phase and are an attempt to fill in the gaps (both theoretical and empirical). Thus, the toolbox was developed through a systematic process which began with inputs from the state-of-the-art reviews and the collection of international data and best practices, results of the assessment process and results from the field trials.
Discussion and conclusions In summary, the RESTRAIL project has covered at least five relevant issues which significantly contribute to the prevention of railway suicide and trespass, and mitigation practice: (1) collating details across a wide range of countries of what is happening in terms of prevention, data on incidents and processes for investigation and the management of suicide and trespassing incidents, etc. ; (2) developing and using methodology for the evaluation of extensive sets of measures; (3) providing recommendations for further examination of selected preventative measures; (4) looking for additional empirical support for a sample of selected measures; and (5) providing a toolbox with guidance materials and best practice examples to help IMs and RUs implement measures more effectively tailored to their specific needs.
Strengths and limitations The RESTRAIL frame was very efficient to collect and integrate international knowledge and examples of good practice, to develop an evaluation methodology and to assess the existing body of prevention and mitigation measures, to develop field tests, and to initiate a practical toolbox. However, there were some limitations mostly because of the inherent gaps which were impossible to overcome within the project's timeframe. For example, it was not possible to perform any preliminary economic analyses in the case of four pilot tests (the two gatekeeper programmes, CBT module, and Forward Facing CCTV), due to the: (1) lack of time to collect long-term and robust data; (2) missing data or unavailability of actual measures of effectiveness and/or of other effects and impacts on the network at the time of the trials.
As revealed in the RESTRAIL workflow, a high amount of data and evidence are still unavailable or difficult to obtain, thus requiring further relevant indicators from the field, improved collection procedures, and tools at a wider scale than it was expected to do in the RESTRAIL context. Furthermore, the study periods for pilot tests in RESTRAIL project were limited and did not allow collection of data on long term effects  [34]. However, the RESTRAIL field tests provided considerable added value to the overall knowledge on the effectiveness of measures against railway suicide and trespass.
Acknowledgments This work was carried out as part of the  RESTRAIL  project. This project has received funding from the  European Union 's  Seventh Framework Programme  for research, technological development and demonstration under grant agreement  N°2 85153. On behalf of the  International Union of Railways -UIC  (project coordinator and leader of  WP6 ), the authors would like to thank all the members of the  RESTRAIL consortium  for their efficient collaboration and contributions:  Teknologian Tutkimuskeskus VTT  ( WP1  leader);  Trafikverket -TRV  ( WP2  leader);  Institut français des sciences et technologies des transports, de l'aménagement et des réseaux -IFSTTAR  ( WP3  leader); MTRS3 Solutions and  Services LTD  ( WP4  leader);  Fundación para la investigación y Desarrollo en Transporte y Energia -CIDAUT  ( WP 5  leader);  Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH) ;  Karlstad University; Fundación de los Ferrocarriles Españoles -FFE ;  Turkish State Railway Administration -TCDD; Deutsche Bahn AG ;  Instytut Kolejnictwa -IK ; ProRail B.