dataesr/scientific-paragraphs-categorization huggingface dataset:
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  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 ). VCU investigators were supported by  NIMH  grants  R01 MH083094 ,  R01 MH041953 , and  R01 MH068881 , and  WTCCC2  grant  WTCCC-084710.
218251);  EU   7th Framework Programme  (PsychDPC; Grant agreement nr. 286213). The  Wellcome Trust  supported this study as part of the  WTCCC2  project. E. Bramon  holds a  MRC New Investigator Award  and a  MRC Centenary Award. The  TOP Study  was supported by the  Research Council of Norway  (# 213837 , #  217776 , #  223273 ), South-East Norway Health Authority (# 2013-123 ) and  K. 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  Stanley Foundation (AKM) ,  NARSAD (AKM) ,  NIMH  ( MH065580  to TL;  MH001760  to AKM), and  NIMH   RC2 MH089964  and  R01 MH084098. SynSys ,  EU FP7-242167 ,  Sigrid Juselius Foundation , The  Academy of Finland  (grant number:  251704 ),  Sohlberg Foundation.
2. 0304. 11-0312 );and  FP7  grant  313010. MM was supported by  CZ. 2. 16/3. 1. 00/24022OPPK , NT/13770-4and 00064203  FN Motol. Funding from the  National Medical Research Council  ( NMRC/TCR/003/2008 ) and the  Biomedical Research Council (A*STAR)  is acknowledged. Genotyping of the Swedish Hubin sample was performed by the  SNP&SEQ Technology Platform in Uppsala , which is supported by  Uppsala University ,  Uppsala University Hospital, Science for Life Laboratory , and the  Swedish Research Council  ( Contracts 80576801  and  70374401 ). The Swedish Hubin sample was supported by  Swedish Research Council  (IA, EGJ) and  Stockholm County Council  and the  Karolinska Insititutet (EGJ). B. J. M. , V. J. C. , R. J. S. , S. V. C. , F. A. H. , A. V. J. , C. M. L. , P. T. M. , C. P. , and U.
S. were supported by the  Australian Schizophrenia Research Bank , which is supported by an  Enabling Grant  from the  National Health and Medical Research Council  (No. 386500 ), the  Pratt Foundation ,  Ramsay Health Care , the  Viertel Charitable Foundation  and the  Schizophrenia Research Institute  and the  NSW Department of Health. C. P. is supported by a  Senior Principal Research Fellowship  from the  National Health and Medical Research Council (Australia). The Perth sample collection was funded by  Australian National Health and Medical Research Council  project grants and the  Australian Schizophrenia Research Bank. The Bonn/Mannheim sample was genotyped within a study that was was supported by the  National Alliance for Research on Schizophrenia and Depression (NARSAD)   Independent Investigator Award  (to A.
M. M. ), and data collection was supported by the  Health Foundation Clinician Scientist Fellowship. BIG: This work makes use of the  BIG (Brain Imaging Genetics ) database, first established in Nijmegen, The Netherlands, in 2007. This resource is now part of Cognomics (www. cognomics. nl), a joint initiative by researchers of 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  is supported by the participating departments and centres and by external grants, i. e. the  Biobanking and Biomolecular Resources Research Infrastructure (Netherlands) (BBMRI-NL) , the  Hersenstichting Nederland , and the  Netherlands Organisation for Scientific Research (NWO).
We wish to thank all persons who kindly participated in the BIG research. The research leading to these results also receives funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013 ) under grant agreements # 602450  (IMAGEMEND) and # 602805  (Aggressotype), and from  ERC -2010-AdG 268800-NEUROSCHEMA. B. Franke is supported by a  Vici grant  from the  Netherlands Organisation for Scientific Research (NWO ; grant #  016. 130. 669 ). Brain Genomics Superstruct Project (GSP): Data were provided [in part] by the  Brain Genomics Superstruct Project of Harvard University  and the  Massachusetts General Hospital , with support from the  Center for Brain Science Neuroinformatics Research Group , the  Athinoula A. Martinos Center for Biomedical Imaging , and the  Center for Human Genetic Research.
20 individual investigators at  Harvard  and MGH generously contributed data to GSP. GIG: The  GIG (Genomic Imaging Göttingen ) sample was established at the  Center for Translational Research in Systems Neuroscience and Psychiatry at Göttingen University. We thank  Maria Keil ,  Esther Diekhof ,  Tobias Melcher  and  Ilona Henseler  for assistance in MRI data acquisition, and  Elisabeth Binder  and  Holger Mohr  for their valuable help with genotyping. We are grateful to all persons who kindly participated in the GIG study. IMAGEN: IMAGEN was supported by the  European Union -funded  FP6  Integrated Project IMAGEN (Reinforcement-related behaviour in normal brain function and psychopathology) ( LSHM-CT-2007-037286 ), the  FP7  projects  IMAGEMEND  ( 602450 ) and MATRICS ( 603016 ), and the  Innovative Medicine Initiative Project EU-AIMS  ( 115300-2 ), the  Medical Research Council   Programme Grant  " Developmental pathways into adolescent substance abuse " (93558), as well as the  NIHR-biomedical Research Center "Mental Health".
We thank the staff of the  Center of Applied Genotyping (CAGT)  for generating the genotypes of the MARS cohort. The study is supported by a grant of the  Exzellenz-Stiftung of the Max Planck Society. This work has also been funded by the  Federal Ministry of Education and Research (BMBF)  in the framework of the  National Genome Research Network (NGFN) ,  FKZ 01GS0481. NCNG: this sample collection was supported by grants from the  Bergen Research Foundation  and the  University of Bergen , the  Dr Einar Martens Fund , the  K. G. Jebsen Foundation , the  Research Council of Norway , to SLH, VMS and TE. NESDA: Funding was obtained from the  Netherlands Organization for Scientific Research  ( Geestkracht program  grant  10-000-1002 ); the  Center for Medical Systems Biology (CSMB, NWO Genomics), Biobanking and Biomolecular Resources Research Infrastructure  ( BBMRI-NL ),  VU University's Institutes for Health and Care Research (EMGO+)  and  Neuroscience Campus Amsterdam, University Medical Center Groningen, Leiden University Medical Center, National Institutes of Health (NIH ,  R01D0042157-01A ,  MH081802 ,  Grand Opportunity  grants  1RC2 MH089951  and  1RC2 MH089995 ).
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  BiG Grid , the  Dutch e-Science Grid , which is financially supported by  NWO. NeuroIMAGE: The NeuroIMAGE was supported by  NIH  Grant  R01MH62873  (to  Stephen V. Faraone ),  NWO   Large Investment Grant   1750102007010  (to  Jan Buitelaar ) and grants from  Radboud University Nijmegen Medical Center, University Medical Center Groningen and Accare , and  VU University Amsterdam. The research leading to these results also receives funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013 ) under grant agreements n° 602450 (IMAGEMEND), n° 278948 (TACTICS) and n° 602805 (Aggressotype).
ADNI data are disseminated by the Laboratory of Neuro Imaging at the University of Southern California. BETULA: this sample collection was supported by a Wallenberg Scholar grant from the Knut and Alice Wallenberg (KAW) foundation and a grant from Torsten and Ragnar Söderbergs Foundation to LN, a grant from HelseVest RHF (Grant 911554) to SLH. Bipolar Family Study (BFS): The Bipolar Family Study wishes to thank the Scottish Mental Health Research Network for research assistant support, the Brain Research Imaging Centre Edinburgh, a center in the Scottish Funding Council Scottish Imaging Network-A Platform for Scientific Excellence (SINAPSE) Collaboration, for image acquisition and the Wellcome Trust Clinical Research Facility for genotyping. Genotyping 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.
Quantitative Real-Time PCR The relative expression levels of the afeI, afeR, zwf, AFE_0233, and AFE_1339 genes were compared to that of the 16S rRNA rrs gene used as a reference standard by quantitative real-time PCR. RNAs were extracted from planktonic cells grown on S 0 prills after 2, 3, and 4 days of growth and from sessile cells after 3 days of growth on sulfur prills, as described below. The realtime PCR analysis was performed on a CFX96 real-time PCR detection system with the C1000 TM thermal cycler (BioRad) with the "SsoFast EvaGreen Supermix 2X" kit (Bio-Rad) following the manufacturer's instructions and as described in  (Slyemi et al. , 2013). The results were analyzed with the Bio-Rad CFX Manager Software 3. 0. The real-time quantitative PCR experiments were performed on RNA extracted from at least three independent cultures and duplicated for each RNA preparation with the oligonucleotides listed in Supplementary Table  S1.
ncbi. nlm. nih. gov/ 1500 bp upstream of the stop codon of the CDS. The program successfully designed specific oligonucleotide probes for 3044 protein encoding genes, representing 96. 7% of the total number of genes. Due to the high similarity with other sequenced regions of the ATCC 23270 T genome, 103 genes (3. 3%) failed to be represented by a specific oligonucleotide probe. When possible, each gene was represented by two distinct oligonucleotide probes separated by a minimum of 100 nucleotides. A total of 6294 probes from 3147 genes were thus designed. The probes were spotted twice on slides using the Agilent technology 2. The array design, the experimental design, and the data for all hybridizations are available in Array Express database under accession numbers A-MTAB-592 and E-MTAB-4896.
Transcriptome Assay Twelve independent hybridizations using total RNA obtained from three different cultures grown without or with 5 µM of tetrazole 9c were performed on Agilent microarrays. Total RNA was used for the synthesis of cDNA fluorescent labeled with Cy R 3 and Cy R 5 as previously described  (Quatrini et al. , 2006 (Quatrini et al. , , 2009)). Microarray hybridizations were performed at 42 • C for 16 h in a microarray hybridization chamber (Agilent G2534A) following the manufacturer's instruction. Slides were washed in washing buffer serial dilutions. Arrays were scanned for the Cy R 3 and Cy R 5 fluorescent signals using an Axion 4400A scanner (Molecular Devices). The data were analyzed with the image quantification software package GenePix Pro 6. 0 (Axon Instruments, Inc. ) as previously described  (Quatrini et al.
, 2006 (Quatrini et al. , , 2009)). Each gene expression ratio was calculated from 12 values calculated from three biological and four technical replicates and normalized using Acuity 4. 0 package (Molecular Devices). Only the four best hybridizations (in term of reproducibility) out of the six were taken into account. Genes with weak expression (median intensity <250) were discarded. A onefold deviation from the 1:1 hybridization (log 2 ) ratio (corresponding to twofold change) was taken as indicative of differential gene expression in the conditions analyzed. The values of one Sample t-test -Benjamini-Hochberg (Adv) ≤0. 05 (corresponding to 95% confidence) for at least one oligonucleotide were considered statistically significant. Only the genes filling the conditions described above were analyzed.
Introduction High throughput "omic" technologies have been revealed as a promising tool for investigating at the molecular level, physiological as well as toxicological mechanisms in many living species. Since its introduction  (Schena et al. 1995 ) , the DNA microarray technology markedly evolved and became very popular in life science: a search for the keyword "microarray" in the PubMed database at the time of writing the present report resulted in more than 70,000 citations. Despite their sensitivity and accuracy of detection of large fold-changes in gene expression, distinct microarray platforms used for the same set of biological conditions can result in inconsistent lists of differentially expressed genes  (Liu et al. 2012 ; Zhang et al. 2009  ). This issue can even be more pronounced for custom microarray designs in species for which commercial microarrays are not available.
Choice of target sequences and design of oligonucleotide probes Sets of transcribed sequences for A. nidulans (Emericella nidulans FGSC A4, taxon identifier 227321) were downloaded and compared from two sources, i. e. , GenBank at NCBI (http://www. ncbi. nlm. nih. gov/genbank/) and "Aspergillus comparative database" at Broad Institute (http://www. broadinstitute. org/annotation/genome/aspergillus_group /MultiHome. html), which provided 2003 and 2011 releases, respectively. The data provided by the Broad Institute database at the time of this work (now available in AspGD: http://www. aspgd. org/; Arnaud et al. 2012 ) , and allowed to link accession numbers from both sources. From UniprotKB (http://www. uniprot. org/help/uniprotkb), Entrez Gene IDs were used to associate annotation information, i.
e. , protein name and gene ontology (GO) terms with each CDS. The A. nidulans CDS from the "Aspergillus comparative database" was uploaded in FASTA format into the free software eArray (https://earray. chem. agilent. com/earray/), working with Java and web interface in Windows. Design settings were chosen for selection of 60-mer sense probes with 3′ bias from each CDS, according to the "base composition" methodology, which considers fusion temperature (Tm), GC %, and cross-hybridization potential of probes. To get the best quality level for A. nidulans probes, we selected "best probe" (BP) or alternatively "best distribution" (BD) options. The probe sequences were checked for vector masking and low complexity masking. To obtain a total probe set size suitable for 15 K array format, we designed either one or two probes per CDS.
Processing of microarray data Data were extracted from images using the Agilent Feature Extraction 10. 7. 3. 1. software with default protocols and settings. Data were computed using Genespring GX software version 12. 6 (Agilent Technologies). Fluorescence signals were background-subtracted, and a filtering procedure excluded data points considered unreliable as they corresponded to probe sets associated with low-signal intensities or bad quality features. A within-array loess normalization was performed, followed by an inter-array scaling normalization. Normalized log intensity ratios for each spot were obtained. Fold-change and corrected p-values were computed following a moderated t test with Benjamini-Hochberg multiple testing correction.
RT-qPCR For quantification of mRNA expression, the first strand cDNA was synthesized from 1 µg of total RNA, with random hexamers and oligo-dT priming using the iSCRIPT enzyme (Bio-Rad), according to manufacturer's instructions. PCR primer pairs specific to five target genes and two reference genes (Table  3  ) were designed using Primer3Plus software (http://primer3plus. com/cgi-bin/dev/primer3plus. cgi or PrimerBLAST (NCBI, http://www. ncbi. nlm. nih. gov/tools/primer-blast/) tools. The cDNA synthesized from 4 ng of total RNA was amplified in a CFX96 real time thermal cycler (Bio-Rad) using the SSoADV Univer SYBR Green Supermix (Bio-Rad) reagent according to manufacturer's instructions, with 500 nM (final concentration) of each primer, in duplicate 10-µl reactions, by 45 two-step cycles (95 °C for 5 s, 61 °C for 10 s).
Steps of oligonucleotide probe design The design steps were guided by probe evaluation through the base composition scoring (BC score) associated to the eArray design software. eArray was first launched in "best probe" (BP) mode to design two probes per nuclear annotated CDS (5666), and the resulting probes exhibiting quality score lower than the highest BC1 level were discarded. Then, the 762 input CDS having only one probe after this selection was subjected to a new design session in "best distribution" (BD) mode, providing 762 new probes, which were all included. Unique probes were designed in BP mode for the 4894 not (4664) or poorly (230) annotated CDS. Each one of all the probes described above was synthesized once on the v1 microarray (Online Resource 2). Moreover, for 18 A. nidulans annotated CDS chosen as "markers" (because of their expected different expression levels in the studied model), one or two additional BD probes with BC1 score were replicated five times on the array.
Two additional control probes were designed in "tilling" (TIL) mode for the V00296 sequence (lacZ gene, marker status), encoding β-galactosidase from E. coli, and synthesized fivefold each. spots), one had four probes (ANID_00670, riboB marker gene, 12 spots), and the remaining ones had two probes (six spots for marker genes, two spots for the others). eArray did not produce any probe for ten of the input set of 10,560 transcript sequences, which were strictly identical to ten alternative sequences present in the input set. Consistently, within most of these entity pairs, one sequence was described as an alias of the other in AspGD database at the time of writing (i. e. , ANID_05382 and ANID_05381; ANID_05541 and ANID_05540; ANID_08518 and ANID_08515; ANID_09411 and ANID_04288; ANID_09459 and ANID_08193; ANID_09518 and ANID_02539; ANID_09527 and ANID_06576; ANID_10106 and ANID_00643; ANID_08685 and ANID_08686; and ANID_09240 and ANID_05252).
2 %), which were predicted to hybridize with 100 % identity to their aimed specific A. nidulans target only, besides the two E. coli lacZ-specific control probes (Table  2  ). BLAST of the v1 probe set against the whole set of input CDS showed non intended hits for 121 of the A. nidulans probes, of which 108 (targeting 96 unique transcripts, half of which without known function at the time of this work) showed more than 75 % identity with their non-target hit, thus generating a risk of cross hybridization  (Kane et al. 2000 ). Of those probes, ten had 78-98 % identity with their non-intented targets, while the remaining had 100 % identity, which might be explained by a high degree of sequence identity between the targeted transcript and another one in the input set (paralogues), by the presence of alternative transcripts produced from the same locus, and/or by issues in sequence identification within the CDS set.
Structural layout: the connectome We used a connectome for the structural layout of the brain network model, which is included in the neuroinformatics platform The Virtual Brain (TVB)  (Sanz-Leon et al. , 2015; Sanz Leon et al. , 2013). This particular connectome considered 37 cerebral areas for each hemisphere, as listed in Table  1 , resulting in a total number of N = 74 cortical areas. The area parcellation was a hybrid based on diffusion spectrum imaging data and data from the CoCoMac neuroinformatics database  (Kotter, 2004). The connectivity matrix (Fig. S1A ) weights each link with 0, 1, 2, or 3 for being absent, weak, moderate, or strong, respectively. To put these gradations in relation to the topology of the structural connectivity matrix and to ensure the dissipation of activity in the network, we normalized the connectivity matrix to a unity maximum in-strength.
3A ). The dynamic state depends on the incoming input pulse rate to the pyramidal cells of each area as well as on the initialization (at t = 0) of the four state variables (because of multistable regimes). The time course of u Py (t) of each area constituted the output of each area in the network and was propagated to a set of 62 EEG sensors by means of a propagation matrix, which was already included in the according TVB dataset. The propagation matrix was calculated using OpenMEEG implementing a boundary element method (BEM). The three-shell BEM model was constructed with the MNI Colin 27 dataset, registered with the respective cortical surface from the TVB dataset. Each area instantaneously transmitted the mean firing rate of its pyramidal cell population to other connected areas. To account for the influence of tDCS we relied on the fact that electric fields affect neurons in a geometry-dependent fashion  (Bikson et al.
4 S/m, 0. 43 S/m, 0. 025 S/m, 1. 79 S/m, and 0. 33 S/m  (Wagner et al. , 2014)  were defined for finite elements in electrode, scalp, skull, CSF, and brain compartments. The tDCS simulation of the current density in all elements was performed in the open source software SimBio  (Wagner et al. , 2014)  with an input current strength of 1 mA applying homogeneous Neumann boundary conditions at the scalp surface and inhomogeneous Neumann boundary conditions at the electrode surfaces  (Wagner et al. , 2014).
thevirtualbrain. org]. The script we used to perform this study will be part of a demo package for using TVB. Therefore, the results are easy to reproduce and ready to be used for parameterizing models in other modeling scenarios. In general, the catalog nicely indicates the sensitivity of areas with respect to perturbations. Several phenomena might indicate universal effects of networks regardless of whether connectome or dynamical description is used for the area. First, the network topology and the connectivity weights disperse the areas' state from being homogeneous (see Fig. S9 ). Secondly, this dispersion causes interactions among the areas in the network, which rebalance and thus reorganize the dynamics. By scaling all network weights equally, such reorganizing behavior undergoes critical phases and divides network states into qualitatively different regimes (see Fig.
, 2015). The directionality of connections in our large-scale brain network model stems from the CoCoMac database  (Kotter, 2004) , thus based on tracer studies in macaque. Moreover, the connectome crucially determined the observed spatiotemporal dynamics. Consequently, the quality of the connectome in terms of content and accuracy is of general importance  (Van Essen et al. , 2013). Each of the 74 cortical areas (i. e. , each network node) of the chosen cortex parcellation were modeled by a single NMM and featured the same default parameterization  (Jansen and Rit, 1995). However, a single NMM is a spatially abstract and dimensionless system. The complex processes within a biological neural area are abstracted by the interaction of the three considered subpopulations in each NMM. The spatial information is incorporated by the connectivity between the areas.
Acknowledgments TK performed this work during a practical training session at the  Institut de Neurosciences des Systèmes ,  Marseille from April  to November 2014. TK was supported by the  German National Academic Foundation  and the  Erasmus Program of the European Union. AH and JH were supported by the  German Federal Ministry of Education and Research  (Grant No. 03IPT605A ). VK and AS were supported by the  Brain Network Recovery Group  through the  James S. McDonnell Foundation  and the  European Union   Seventh Framework Program  ( FP7/2007-2013)  under grant agreement no. 604102  ( Human Brain  Project).