dataesr/scientific-paragraphs-categorization huggingface dataset:
The processing with FreeSurfer was performed on the  Dutch e-Science Grid through BiG Grid project  and  COMMIT  project  Be-Biobanking  with imaging for healthcare^, which are funded by the  Netherlands Organization for Scientific Research (NWO). The  Barcelona OCD study  was supported by project grants no. PI09/01331 ,  PI10/01753 ,  PI10/01003 ,  CP10/00604 ,  PI13/01958  and  CIBER-CB06/03/0034  from the  Carlos III Health Institute , grant no. 2014SGR1672  from the  Agency for Administration of University and Research (AGAUR) , and a 'Miguel Servet' contract ( CP10/00604 ) from the  Carlos III Health Institute  to  Dr. Soriano-Mas. The  Brain Imaging Genetics (BIG ) database was established in Nijmegen in 2007. This resource is now part of Cognomics, a joint initiative by researchers of the  Donders Centre for Cognitive Neuroimaging , the  Human Genetics and Cognitive Neuroscience departments of the Radboud University Medical Center , and the  Max Planck Institute for Psycholinguistics.
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). The research on BIG also receives funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013 ) under grant agreements # 602450  (IMAGEMEND) and # 602805  (Aggressotype) and from the  National Institutes of Health (NIH) Consortium  grant  U54 EB020403 , supported by a cross- NIH  alliance that funds Big Data to Knowledge Centers of Excellence. We would also like to thank  Hans van Bokhoven  for his contributions to the Cognomics initiative and to all persons who kindly participated in this research.
In addition,  AF Marquand  gratefully acknowledges support from the  Language in Interaction  project, funded by the  NWO  under the  Gravitation Programme  (grant  024. 001. 006 ). The CIAM and OCD-SU datasets were supported by the  Medical Research Council of South Africa. CLiNG and HMS studies were partially supported by a research grant from the  Competence Network Schizophrenia  to  Oliver Gruber. The FBIRN study was supported by the  National Center for Research Resources at the National Institutes of Health  (grant numbers:  NIH  1  U24 RR021992  ( Function Biomedical Informatics Research Network ) and  NIH  1  U24 RR025736-01  ( Biomedical Informatics Research Network Coordinating Center ; http://www. birncommunity. org). FBIRN data was processed by the  UCI High Performance Computing cluster  supported by  Joseph Farran ,  Harry Mangalam , and  Adam Brenner  and the  National Center for Research Resources  and the  National Center for Advancing Translational Sciences, National Institutes of Health , through Grant  UL1 TR000153.
FBIRN thanks  Mrs. Liv McMillan  for overall study coordination. The HUBIN study was supported by the  Swedish Research Council  (grant numbers  K2009-62X-15077-06-3  and  K2012-61X-15077-09-3 ), the  Karolinska Institutet  and the  Knut and Alice Wallenberg Foundation. The IDIVAL-PAFIP study was supported by  Instituto de Salud Carlos III ,  FIS 00/3095 ,  01/3129 ,  PI020499 ,  PI060507 ,  PI10/00183 ,  PI14/00639 , the  SENY Fundació Research Grant   CI 2005-0308007 , and the  Fundación Marqués de Valdecilla   API07/011. We thank  IDIVAL Neuroimaging Unit  for its help in the technical execution of this work. The IMpACT study was supported by a grant from the  Brain & Cognition Excellence Program  and a  Vici grant  (to Barbara Franke) of the  Netherlands Organization for Scientific Research (NWO , grant numbers  433-09-229  and  016-130-669 ) and in part by the  Netherlands Brain Foundation  (grant number,  15F07[2]27 )and the and  BBMRI-NL  (grant  CP2010-33 ).
The research leading to these results also received funding from the  European Community 's  Seventh Framework Programme  ( FP7/ 2007-2013)  under grant agreement no. 602805  (Aggressotype), no. 278948  (TACTICS), and no. 602450  (IMAGEMEND). In addition, the project received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement no  643051  (MiND), the  NIH   BD2K U54 020403  grant for the  ENIGMA  Consortium, and from the  ECNP Network ADHD  across the lifespan. IMAGEN 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  FP7  projects  IMAGEMEND ( 602450 ;  IMAging GEnetics  for MENtal Disorders),  AGGRESSOTYPE  ( 602805 ) and MATRICS (  603016 ), the  Innovative Medicine Initiative Project EU-AIMS  ( 115300-2 ), a  Medical Research Council Programme Grant BDevelopmental  pathways into adolescent substance abuse^(93558), the  Swedish funding agency FORMAS , the  Medical Research Council  and the  Wellcome Trust  ( Behavioural and Clinical Neuroscience Institute, University of Cambridge ), 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), the  Deutsche Forschungsgemeinschaft (DFG  grants  SM 80/ 7-1 ,  SM 80/7-2 ,  SFB 940/1 ), the  National Institutes of Health, U.
S. A. (Axon, Testosterone and Mental Health during Adolescence ;  RO1 MH085772-01 A1 ), and by  NIH Consortium  grant  U54 EB020403 , supported by a cross- NIH  alliance that funds Big Data to Knowledge Centres of Excellence. LBC1936: Data collection was supported by the  Disconnected Mind project , funded by  Age UK. J. M. W. is partly funded by the  Scottish Funding Council  as part of the  SINAPSE Collaboration. The work was undertaken by  The University of Edinburgh Centre for Cognitive Ageing and Cognitive Epidemiology , part of the cross-council Lifelong Health and  Wellbeing Initiative  ( MR/K026992/1 ). Funding from the  Biotechnology and Biological Sciences Research Council (BBSRC).
, Fig. 3, and Supplemental Information S5 Dataset N Median age Dataset N Median age (years) (years) Males Females Males Females BIG 1. 5 T 1 733 728 23 OCD-Kunming 3 T 27 27 68 BIG 3T 2 579 729 22 OCD-Kyoto 1. 5 T 28 25 23 BIL & GIN 3 221 232 24 OCD-Kyoto 3 T 29 20 22 BP-Houston 4 79 94 19 OCD-London 30 12 21 CIAM 5 16 14 27 OCD-Shangai 31 21 17 CLiNG 6 132 191 24 OCD-SNU A 32 53 26 FBIRN 7 129 54 37 OCD-SNU B 33 97 59 HMS 8 21 34 41 OCD-SNU C 34 115 72 HUBIN 9 69 33 46 OCD-SU 35 11 18 IMAGEN 10 735 847 15 OCD-VUmc Amsterdam 1. 5 T 36 16 38 IMpACT 11 61 80 32 OCD-VUmc Amsterdam 3 T 37 20 22 LBC-1936 12 282 274 73 OCD-Zürich 38 15 23 MAS 13 224 280 78 Osaka 1. 5 T 39 206 231 MCIC 14 103 60 28 Osaka 3 T 40 131 106 Meth-CT 15 50 13 25 PAFIP-IDIVAL1 41 51 30 MüNC 16 327 420 32 PAFIP-IDIVAL2 42 69 45 NCNG 17 105 222 54 PAFIP-IDIVAL3 43 13 21 NESDA 18 23 43 41 QTIM 44 169 422 NeuroIMAGE 19 180 208 17 SHIP-2 45 538 572 OATS 20 87 153 69 SHIP-Trend 46 994 1046 OCD-AMC 21 9 18 14 STROKEMRI 47 19 33 OCD-Barcelona 22 30 36 33 TCD|NUIG 48 116 145 OCD-Fukuoka 23 16 25 37 TOP 49 159 144 OCD-India 1.
b Plot of the weighted regression of the age coefficients on each sample's median age. The dotted line represents the best linear fit (P = 0. 03). The size of a point is proportional to the square-root of a dataset's sample size contributions of other FreeSurfer versions to AI variability was limited. Variability in image acquisition is likely a substantial source of dataset AI heterogeneity. Structure Pooled effect Standard error P-value Total N (datasets) Cochran's Q (p-value) Higgins' I 2 Fail-safe N Nucleus accumbens 2. 1*10 -6 2. 1*10 -4 0. 99 12,073 (37) 229. 16(5. 8*10 -30 ) 88. 08 0 Amygdala -1. 8*10 -4 6. 9*10 -5 0. 009 12,287 (38) 74. 75(2. 3*10 -4 ) 54. 07 0 Caudate nucleus 5. 9*10 -5 5. 0*10 -5 0. 24 12,150 (36) 148. 03 (7. 5*10 -16 ) 78. 14 0 Globus pallidus -2. 0*10 -4 1. 8*10 -4 0.
, 2011)  and drive the movement of the cell layer. Our data showing that P-cadherin increases polarization and intercellular stress nicely confirm the predictive theory of soft active matter on force-velocity relationships in 1D and 2D living systems  (Marchetti et al. , 2013). This theory proposes that high polarization and high viscosity or stress would lead to rapid and cohesive expansion under high intercellular tension (fast-strong phenotype). Rose plot showing the distribution of angles of FA orientation calculated using the monolayer migration direction as the reference axis; 90° corresponds to the reference axis. More than 700 FAs were analyzed from 50 cells. Data represent the mean ± SEM of five independent experiments. (k) Cells were transfected with paxillin-GFP, and FA dynamics were analyzed as described in Fig.
Time-lapse imaging Cells were plated in Ibidi Culture-Inserts (BioValley; 20,000 cells per chamber) until they reached the desired density level. The insert was removed to allow the cells to migrate into the cell-free areas. The cells were imaged using an Axiovert inverted microscope (Carl Zeiss) equipped with a motorized stage, as well as a heated and CO 2 -regulated incubator. Phase-contrast images were taken every 5-15 min overnight using a 10×/0. 3 NA PH1 DIC1 objective and captured with a CCD MicroMax 1300 Y/HS camera (Roper Scientific) controlled with the MetaMorph 7. 0 software.
Particle velocimetry analysis The velocity field in monolayers was mapped via PIV analysis (available at http ://folk. uio. no /jks /matpiv /index2. html; Poujade et al. , 2007). Stacks of images were analyzed using the MatPIV toolbox (http ://www. mn. uio. no /math /english /people /aca /jks /matpiv /) for MAT LAB (MathWorks). We used an in-laboratory loop protocol in MAT LAB to analyze sequential time frames for velocity and angle measurements (see dataset in the online supplemental material). Velocities were plotted in GraphPad Prism and angles in Rose. net software. The time between successive analyzed images was 5 or 10 min. The window size was set to 32 or 64 pixels, with no noticeable differences.
Cell monolayer patterning and time-lapse microscopy To pattern the cells on top of the polyacrylamide gels, a PDMS membrane with a rectangular opening was deposited on top of the polyacrylamide gel. We then seeded 20,000 cells within the rectangle defined by the PDMS stencils; the cells were allowed to adhere and proliferate on the gel for a few hours. Before the time-lapse analysis, the PDMS membrane was carefully removed, enabling the cells to migrate toward the available substrate. The time-lapse imaging was performed using an automated inverted microscope (10× lens; Eclipse Ti; Nikon) equipped with thermal, CO 2 , and humidity control with MetaMorph (Universal Imaging) software. The recording started ∼30 min after removal of the PDMS membrane and lasted for 15 h. Images were obtained every 3 min over a period of 1-15 h.
Immunohistochemistry Cells were plated in Ibidi's culture inserts on 12-mm-diameter coverslips until they reached the desired density level. 5-10 h after insert removal, the cells were fixed in 3. 2% paraformaldehyde (in PBS) for 15 min, followed by a 2-min permeabilization with 0. 1% Triton X-100 (in PBS) and saturation with 2% BSA (in PBS). The cells were incubated with primary and secondary antibodies in PBS containing 2% BSA. Images were taken using a confocal SP5-SMD (Leica) with 40×/1. 3 or 63×/1. 4 oil HCX PL APO CS objectives (Leica) and captured with a hybrid detector (Leica HyD) controlled using C software. Colocalization data originating from multichannel fluorescence stacks were collected using a Leica SP5 microscope. For each stack, a single value of the Pearson's coefficient (ranging from -1 to 1) was measured at the CCJ, imposing a threshold value (calculated based on the algorithms by  Costes et al.
0 software. A ratio image of FRET/CFP was created to represent FRET efficiency, which correlated with the activity of Cdc42. Pseudocolored ratio images were generated from images from CFP and FRET channels as described previously  (Hodgson et al. , 2006). Background values were calculated from a mean intensity value within a region of interest outside the cell and subtracted from the entire image. We create a binary mask based on the intensity threshold performed on the FRET image. We applied this mask to CFP and FRET images. Image ratios were calculated by pixel-wise divisions of the masked CFP and masked FRET images. ImageJ Fire LUT was applied to a fixed dynamic range of the 32-bit ratio image.
FA quantification and orientation Confocal images of paxillin staining were taken 6 h after removal of the Ibidi insert. The images were binarized to create a mask based on a manually selected threshold level that enabled the visualization of all FAs containing paxillin using an in-laboratory protocol with Cell Profiler software (dataset in supplemental material). The subareas 0-10 µm or 10-40 µm from the leading edge were scored manually using masks, and the FA surface area was measured from binarized images using ImageJ. FA orientation was measured using ImageJ (dataset in supplemental material), and angle values were plotted using Rose. Net software. FA orientation was determined according to the monolayer migration direction as a reference axis.
FA dynamics Cells were transfected with the pBabe-paxillin-GFP vector (a gift from C. Albiges-Rizo, Université Joseph Fourier, Grenoble, France); after 24 h, the FAs were imaged during cell migration at 37°C using a confocal microscope (SP5-SMD) equipped with thermal, CO 2 , and humidity control. Images were taken using a 63×/1. 4 oil HCX PL APO CS objective (Leica) and captured with a hybrid detector (Leica HyD) controlled using Leica LAS AF software. Images were taken every 5 s at 5 h after Ibidi insert removal and lasted for 10 to 15 min. Confocal images of paxillin-GFP-expressing cells were binarized and thresholded using ImageJ. The rate of FA area gains and losses was quantified over 15 min, and mean values were normalized to the FA area in addition to the change in mean intensity. Ratio images were generated using the same procedure.
25, and activation time 30 ms) with an isolation window of 3 m/z. Data were acquired using Xcalibur software (version 2. 0. 7). For all full-scan measurements with the Orbitrap detector, a lock-mass ion from ambient air (m/ 445. 120024) was used as an internal calibrator. Raw data analysis was performed using MaxQuant software (version 1. 5. 0. 0). Peak lists from IP1 and IP2 were searched against the UniProt mouse database, 250 frequently observed contaminants, as well as the reversed sequences of all entries. Search parameters were the default parameters when using MaxQuant: the search precursor mass tolerance was set at 20 ppm and the main search (after recalibration) at 7 ppm. Enzyme specificity was set to trypsin, and a maximum of two missed cleavages were allowed. Carbamidomethylation was set as fixed cysteine modification and oxidation was set as variable methionine modification for searches.
Three datasets are provided for the MatPIV toolbox used to analyze sequential time frames for velocity and angle measurements, the method for FA detection using Cell Profiler software, and the ImageJ macro to measure FA orientation. Table  S1  lists all the proteomic data. Online supplemental material is available at http ://www. jcb. org /cgi / content /full /jcb. 201505105 /DC1. Additional data are available in the JCB DataViewer at http ://dx. doi. org /10. 1083 /jcb. 201505105. dv.
Bar, 15 µm. (i) Quantification of the FA area measured 0-10 µm or 10-40 µm from the leading edge. More than 700 FAs were analyzed from 50 cells. Data represent the mean ± SEM of five independent experiments. (j)Rose plot showing the distribution of angles of FA orientation calculated using the monolayer migration direction as the reference axis; 90° corresponds to the reference axis. More than 700 FAs were analyzed from 50 cells. Data represent the mean ± SEM of five independent experiments. (k) Cells were transfected with paxillin-GFP, and FA dynamics were analyzed as described in Fig. 2g. An inverted contrast image of paxillin-GFP is in gray. Ratio images (lower panels) were generated to illustrate FA dynamics, with magenta showing the extension and yellow the FA loss. Green represents the FA area maintained during the analyzed period.
Acknowledgments We thank  Virginie Georget ,  Sylvain de Rossi , and  Vicky Dialou  from  Montpellier Imaging ,  Halima Amnai  and  Edith Demettre  from  Montpellier Proteomic Facilities ,  Dimitris Liakopoulos  for critical reading of the manuscript, and  Corinne Albiges-Rizo ,  Albert Reynolds , and  Keith Johnson  for plasmids. This work was supported by the  Ligue Nationale contre le Cancer ("Equipe labellisée")  and the  Institut National du Cancer. C. Gauthier-Rouvière  was supported by  Institut National de la Santé et de la Recherche Médicale ,  C. Plutoni  by the  Association pour la Recherche contre le Cancer , and  M. Le Borgne-Rochet  by the  Ligue Nationale contre le Cancer ,  Spanish Ministry of Economy and Competitiveness  (grant  BFU2012-38146 ), the  Generalitat de Catalunya  (grant  2014-SGR-927 ), and the  European Research Council  (grant  StG-242993  and  CoG-616480 ).
2 ). This complex is typical of cycloheximide inhibition that happens after a first translocation step by blocking tRNA Met into the E-site  36. Hygromycin B that typically prevents the translocation induced by eEF2 (refs 37-39) was probably not bound in this complex. Sorting was also applied to the 80S/h4 1-142 data, revealing the same mass of density for the 5 0 core domain of h4, but with the 40S in different conformations (Supplementary Fig. 6 ). The resolution was estimated in Relion at 0. 143 FSC  34  , indicating an average resolution of 10. 2 Å (Supplementary Fig. 7 ). Map interpretation was done using Chimera  40  and Coot 41 starting from our human ribosome atomic model  16  which was fitted by rigid body and real-space refinement using Phenix  42. Figures were prepared using the software Chimera  40  and Pymol (The PyMOL Molecular Graphics System, Version 1.
5. 0. 4 Schro ¨dinger, LLC. ; DeLano, 2006). h4 RNA modelling. Helix h16 from the 3. 65 Å cryo-EM structure of O. cuniculus (PDB ID 3JAG) was superimposed onto h16 in the ribosome structures from O. cuniculus (PDB ID 4UJE) and H. sapiens (PDB ID 4UG0) used for map fitting. The mRNA from a partial 48S preinitiation complex in S. cerevisiae (PDB ID 3J81) was edited according to sequence differences and fitted in density using Coot and the UCSF Chimera package. The structure data file (. sdf) for 7-methyl-guanosine-5 0 -triphosphate was retrieved from PDB entry 3AM7 (ref. 43). The. pdb file generated from the. sdf file by eLBOW  44  in Phenix  42  was fitted in density using Chimera. Based on a comparative analysis of various tetraloops, we selected the tetraloop from h16 in a S. cerevisiae translation initiation complex (PDB ID 3JAM) to model base pairs involving U541-G28 and U542-A27.
Acknowledgements We thank  L. Schaeffer  for technical support, as well as  P. Auffinger  and  L. d'Ascenzo  for helpful discussions on tetraloops. This work was supported by  ANR-2011-svse8-02501  ( MITIC  project), the  European Research Council  ( ERC Starting Grant   N_243296 TRANSLATIONMACHINERY ) and the  Centre National pour la Recherche Scientifique (CNRS). The electron microscope facility was supported by the  Alsace Region , the  FRM ,  INSERM ,  CNRS  and the  Association pour la Recherche sur le Cancer (ARC)  and by the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INSB-05-01 , and Instruct as part of the  European Strategy Forum   on Research Infrastructures (ESFRI).
1 % of raw Hg(0) data was discarded, the Hg(0) record being very weakly impacted by pollution plumes. 0 ) concentrations (ng m -3 ) measured at DDU from January 2012 to May 2015. Missing data are due to instrument failure or QA/QC invalidation. Hg(0) concentrations were highly variable during the sunlit period as compared to wintertime (May-August) suggesting a photochemically induced reactivity at this period of the year.
Acknowledgements. We thank the overwintering crew:  S. Aguado ,  D. Buiron ,  N. Coillard ,  G. Dufresnes ,  J. Guilhermet ,  B. Jourdain ,  B. Laulier ,  S. Oros , and  A. Thollot. We also gratefully acknowledge  M. Barret  for the development of a  QA/QC software program ,  Météo France  for the meteorological data, and  Susanne Preunkert  who helped to validate contamination-free ozone data. This work contributed to the  EU-FP7  project  Global Mercury Observation System  (GMOS -www. gmos. eu)and has been supported by a grant from  Labex OSUG@2020  (Investissements d'avenir - ANR10 LABX56 ), and the  Institut Universitaire de France. Logistical  and financial support was provided by the  French Polar Institute IPEV  ( Program 1028 , GMOstral). Edited by:  N. Pirrone.
When all autosomal genes were considered (Fig. 1E ), the majority of the variability observed among our samples (56. 2%) was attributable to the proximal versus distal identity of the analyzed domains. Along this axis, the control and the Hox13 -/-mutant proximal limb data sets clustered together and were separated from both the control and Hox13 -/-mutant distal data sets, indicating that in the Hox13 -/-mutant situation, the distal part of the limb did not simply acquire a proximal identity. The control and Hox13 -/-mutant distal domains were nevertheless clearly differentiated along the second axis of the PCA (which accounted for 35% of the total variability), supporting the idea that both functions of Hox13 genes are required for the specification of normal autopods. In contrast, the proximal domains from control and Hox13 -/-mutant limbs showed very little difference, as expected from the lack of function of Hox13 genes in this territory.
2011) , the specific interactions with region III and Prox had virtually disappeared along with the transcriptional off state of the C-DOM. Interestingly, contacts established by Hoxd13 with the T-DOM were slightly increased (from 24% to 36%) (Fig. 5E ), in agreement with the weak expression of this gene in distal mutant cells (Fig. 1B, C ). As expected, the interaction profiles of Hoxd13 in control and mutant proximal cells were comparable. We also assessed the impact of HOX13 dosage on the control of this regulatory switch by looking at the interaction profiles of intermediate allelic combinations. We analyzed both Hoxa13 -/-;Hoxd13 +/-(data not shown) and Hoxa13 +/-;Hoxd13 -/-(Fig. 5 ) mutant limbs. In these mutants, autopods were also severely impaired, yet a distal domain devoid of the Hoxd8 transcript was still identified (Fig.
RNA extraction Total RNA was extracted from individual pairs of wild-type or mutant proximal and distal forelimbs using the RNeasy microkit (Qiagen) following the manufacturer's instructions. A total of 100 ng of pure total RNA was amplified following standard Illumina procedure for polyA-selected RNA and sequenced on a HiSeq sequencer with a read length of 100 base pairs (bp). An in silico mutant version of the genome, including both the Hoxd13/LacZ and the Hoxa13/Neomycin alleles, was assembled, annotated, and used as the reference genome to map the Hoxa13 -/-;Hoxd13 -/-transcriptomes with the mouse GCRm38 assembly. The mapping and FPKM (fragments per kilobase per million mapped fragments) calculations of expressed transcripts were performed using TopHat and Cufflinks implemented in a local Galaxy platform.
Accession numbers RNA-seq, ChIP-seq, and 4C-seq data sets are available from the NCBI Gene Expression Omnibus repository under accession number GSE79261. The control ChIP-seq data for H3K27ac in proximal limb can be found under GSM1104588. Any other information for HOXA13 ChIP-seq data and ChIP-seq data from Hoxa13 -/-;Hoxd13 -/-embryos is available on request.