dataesr/scientific-paragraphs-categorization huggingface dataset:
We thank  J. -L. Bessereau ,  I. Katic  and members of the  Bessereau team  for helpful discussion,  M. Jospin  for comments on the manuscript, and  Manuela D'Alessandro  for constructing pMD8. pCFJ90 was a gift of  Christian Frøkjaer-Jensen. We thank Wormbase, which is supported by  National Institutes of Health (NIH)  grant  U41 HG002223. This work was supported by a research grant from  Fondation Fyssen  (T. B. ) and an  ERC Starting Grant  ( Project Kelegans ) (T. B. ).
ACKNOWLEDGMENTS We thank  D. Chalker  for DLIA5 strains and the  NGS unit of Vienna BioCenter Core Facilities  for sequencing. This work was supported by an  NIH   R01 Grant  ( GM087343 ) to Y. L. ; an  ERC Starting Grant  ( 204986 ) under the European Community's  7th Framework Program  to K. M. ; a  Stand-alone Grant  ( P26032-B22 ), a  Doctoral Program grant  ( W1207-B09 ), and a  Special Research Program grant  ( F4307-B09 ) from the  Austrian Science Fund  to Cell Reports 18,  2494-2507 , March 7, 2017 2505.
Material and Methods We locally irradiated primary tumors (MDA-MB231 cells and 4T1 cells), subcutaneously implanted to SCID and BalbC mice, with two NeoRT schedules (5x2Gy and 2x5Gy). We surgically removed tumors 4 or 11 days after the end of RT and kept the mice alive for the metastatic growth. Non-irradiated control tumors were also surgically collected at the same time. For metabolomic study, tumor samples were homogenized in deuterated phosphate buffer and supplemented with maleic acid and TMSP before Nuclear Magnetic Resonance (NMR) analyses. Data were analyzed with powerful statistical tool (supervised and multivariate analyses).
Acknowledgments:  José Alberto Luna  collaborated in the fabrication and measurements of the photodetector. Godofredo García  participated in the measurements of the EQE of the UV photodetector. Héctor Juárez  collaborated in the fabrication of the UV detector. Jesús Carrillo  wrote the proceedings. The authors would like to thank  Vicerrectoría de Investigación y Estudios de Posgrado (VIEP)-BUAP  for providing support for this work. The funds for covering the costs to publish the work will be provided by  Instituto de Ciencias-BUAP  and VIEP.
Data acquisition Anatomical and functional magnetic resonance imaging (MRI) was performed using a 3T scanner (Magnetom Skyra, Siemens) with the 20-channel head coil. Highresolution, anatomical images were acquired using T1 MPRAGE sequence (sagittal slices; 1 × 1 × 1 mm3 voxel size; TR = 2200 ms, TE = 2. 43 ms). Functional images were acquired using an EPI sequence; scan parameters were as follows: TR = 3000 ms, TE = 21 ms, flip angle = 90°, voxel size 2 × 2 × 2. 5 mm3, FOV 192 × 192 mm2, GRAPPA acceleration factor 2, phase encoding A > > P. Whole brain image (excluding cerebellum) was covered with 48 axial slices taken in an interleaved fashion. There were five functional runs; acquisition time for each run was 10′48″ (216 volumes). Due to magnetic saturation effects, the first four volumes (dummy scans) of each run were acquired and then discarded by the scanner.
Pupillary data analysis First, pupil diameter was converted from arbitrary units to millimeters. Then, a linear interpolation algorithm was applied to estimate pupil diameter during periods when data was lost due to eye-blinks as well as when the pupil diameter was smaller than 1. 5 mm or the difference between two subsequent samples was larger than 0. 2 mm. The average amount of interpolated data was 19% (SD = 9). One participant was excluded from all further analyses due to 51% pupillary data loss. The pupil dilation index was calculated for each trial and participant by subtracting the mean baseline (1 s before stimulus onset) from mean pupil size in a window from 2 to 5 s following stimulus onset, analogously to  Bradley et al. (2008)  and  Henderson et al. (2014). The resulting pupil dilation index was examined using repeated measures ANOVA with factors of noise level (5 levels) and valence category (negative and neutral).
fMRI data analysis Functional data were analyzed using the FEAT FMRIB Expert Analysis Tool version 6. 0 (http://fsl. fmrib. ox. ac. uk/ fsl/fslwiki/). The standard preprocessing steps included brain extraction using BET  (Smith 2002) , slice timing correction, motion correction using MCFLIRT  (Jenkinson et al. 2002) , spatial smoothing with a Gaussian kernel of full-width at half-maximum of 5 mm, and high-pass temporal filtering with 100 s cut-off. Next, a whole brain General Linear Model (GLM) analysis was conducted for each of five runs separately. Three regressors for each valence category were entered into GLM analysis, that is image onset and two mean centered parametric regressors, one representing noise level and another pupil dilation. Onsets of the indoor/ outdoor diagnostic questions were added as a regressor of no interest.
Sampling design and data collection Fish assemblage surveys were conducted at each MPA during 3-4 consecutive days in the warm season under optimal visibility conditions, respectively in summer 2013 for the Tavolara and Cabo de Palos MPAs, and in summer 2014 for the Scandola MPA. At each MPA, on rocky habitats between 5 and 15 meters depth, four sites inside the no-take zone and four sites outside the MPA were randomly selected ! 500 m apart from each other. Visual census transects of three different surface areas were used in order to account for different fish groups (Table  1 , S1 Table ) and to allow assessments on the whole fish community, as required by our second objective. The rope used for UVC surveys had 5-m sections coloured alternatively in red and white. This allowed the observer to calibrate their estimate of 10-and 2.
Fish wet weight was estimated from size data by means of length-weight relationships from the available literature, selecting coefficients referring to Mediterranean samples whenever possible (www. fishbase. org). At each site, four replicates of each transect size were randomly located within homogenous rocky habitat: first the large transect was surveyed, followed by the medium transect at approx. 25 m distance. The small transect was surveyed on the way back from the medium transect, while rewinding the transect rope. A new set of transects was then started following the same order at approx. 50 m distance.
Preliminary data preparation. Every single fish, depending on its taxon and body-size, was assigned to one of the three sampling methods, "Large and Medium transects combined", "medium transects" or "Small transects", and to one of the six functional groups based on trophic functions following  [35] , and on the different micro-habitat uses specific to taxa and/or size class (as a proxy of life stage). Functional groups were: high trophic level predators (HTLP), necto-benthic carnivores, juveniles of necto-benthic fish (which have a crypto-benthic behaviour pattern), crypto-benthic carnivores, shoaling planktonivores and herbivores (S1 Table ). As in the previous analyses, the statistical unit was the site, density variables (at the scale of species, functional groups or the whole fish assemblage) were expressed as (decimal) number of fish per square meter, biomass variables were expressed as grams per square meter and taxa richness was the number of taxa observed per site (by pooling replicates).
Analysis 2. In order to assess the reserve effect on the trophic structure of the whole fish assemblage, we computed the mean absolute biomass (g/m2) and the mean relative biomass (% of total biomass) for each functional group at each MPA and protection level. The two variables were then averaged over the three MPAs, and compared between protection levels using 95% CI. All analyses and graphical visualizations were performed in R Environment  [37]  using libraries plyr  [38] , reshape2  [39] , coin  [40]  and ggplot  [41]. All data are provided as Supporting Information (S1 Database).
Acknowledgments The authors gratefully acknowledge  Paolo Guidetti  for the support provided during the development of the study. Special thanks to  Pier Panzalis , JoseAntonio  Garcia Charton ,  Emna Ben Lamine  and the  Porto San Paolo Dive Center  for invaluable assistance during field work. The authors also wish to thank  Augusto Navone  (director of the  Tavolara-Punta Coda Cavallo MPA)  and his staff, and  Jean-Marie Dominici  (Scandola natural reserve) for helpful assistance. The present study was supported by  MMMPA -Training Network for Monitoring Mediterranean Marine Protected Areas , which has received funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013 ) under Grant Agreement no. 290056. The authors wish to thank two anonymous reviewers and  Prof.
MetaMorph (Molecular Devices) image analysis software was used to process the raw images and obtain the average intensity of three ROIs, which were used for further normalization: the bleached ROI, the whole nucleus ROI and a ROI corresponding to background signal. EasyFRAP software  (Rapsomaniki et al, 2012)  was used to normalize the obtained background-subtracted fluorescence values. With the latter software, the signal of the bleached ROI was normalized to the average value of the last 50 prebleach frames, which was set to 1. Next, average fluorescence intensity of the whole nucleus at each time point was used to correct for signal loss in this ROI resulting from observational photobleaching and fluorescence loss during photobleaching. The obtained curves were normalized according to the full-scale normalization method  (Ellenberg et al, 1997) , in which the value of fluorescence intensity in the bleached ROI for the first post-bleach frame is set to zero.
FLIP was performed using the same hardware set-up as for FRAP. For the experiments described in this manuscript, a circular nuclear ROI of 17. 5-pixel radius, corresponding to ~46. 6 lm 2 , was bleached every 3 s at maximum laser power by a double bleach pulse for a minimum total period of 4 min. Like in FRAP, raw images were quantified by MetaMorph software. Average fluorescence values were extracted from three ROIs required for FLIP data normalization: the first corresponds to the whole nucleus, the second corresponds to a neighbouring cell that was always included to the field of view (used to normalize for observational photobleaching), and a third region corresponds to background signal. Normalization of FLIP data was done according to the formulas described in  Nissim-Rafinia and Meshorer (2011).
H Box-and-Whisker plot illustrating the comparison between the apparent diffusion constant values of the fast and slow component of each tested factor (black boxes) and the apparent diffusion constant of eGFP (green box). Values included in the box plots were calculated with the Maximum Entropy approach. The box represents values from the upper quartile (25% of data grater than this value) to the lower quartile (25% of data less than this value). The median (middle of the dataset) is indicated. Whiskers indicate maximum gratest value (top) and minimum least value (low) excluding outliers. The values included in the Box-and-Whisker plots include the pool of at least 3 independent experiments for each factor.
Acknowledgements We are grateful to  M. HT. Timmers  for TAF5 expression vector, to  W. Herr  for the anti-ASH2L antibody, to  M. Vigneron  for  GFP-RPB1  and to present and former members of the  Tora lab  for cDNA constructs. We thank  S. Bour  for the illustration,  J. Laporte  for support for PK,  F. El Saafin ,  D. Devys ,  S. Vincent  and T. Sexton for critically reading the manuscript and for helpful comments, the Imaging platform and the cell culture service for help and technical advice. N. V. was supported by an  IGBMC PhD fellowship  and a  Fondation pour la Recherche Médicale (FRM)  fellowship. This work was supported by funds from  CNRS, INSERM, Strasbourg University , the  EC Marie Curie-ITN  ( NR-NET ) and the  Agence Nationale de la Recherche  ( ANR-11-BSV5-010-02   Chromact ;  ANR-13-BSV6-0001-02   COREAC ;  ANR-13-BSV8-0021-03  DiscoverIID).
This study was also supported by the  European Research Council (ERC)   Advanced grant  ( ERC-2013-340551 , Birtoaction, to LT) and grant  ANR-10-LABX-0030-INRT , a  French State  fund managed by the  Agence Nationale de la Recherche  under the  frame program Investissements d'Avenir   ANR-10-IDEX-0002-02.
Since 2013, several beginner and advanced workshops have been made focusing on various ALA tools. Because the level of the community members has increased, some former trainees have become new trainers. Added to these technical workshops, we have been able to present and demonstrate our work during international conferences with always the main goal to show how re-using an existing software can be motivating and stimulating as well as be part of a living community. Indeed, ten institutions members to Living Atlases launched their national data portal. In the future, we expect more GBIF nodes to put their software in production (Andorra, Germany, Benin, Luxembourg, etc. ). During this introduction, we will present the community by showing past events as well as presentations done during previous conferences.
Image analysis and micro-PIV The images obtained by confocal microscopy were analyzed with MATLAB ® using a customized version of the particle image velocimetry (PIV) toolbox mPIV  (43). The correlation functions between adjacent frames were rescaled to correct for non uniform time steps between frames, and the resulting correlations functions were added over a pack of 10 to 30 frames before searching for peak correlations. This allowed to correct for Brownian motion uncertainties. For the time-relaxation experiments, the velocity profile in the chamber during the loading phase at high flow rate was obtained by measuring the mean length of the fluorescent particle streaks on the image and dividing by the exposure time.
01 µm. For all simulations with g < 1 mm, the maximum element size on the interface is 0. 05 µm. For simulations with g = 1 mm and g = 2 mm, the maximum element size on the interface is 0. 2 µm. Finally, for g = 5 mm and g = 10 mm, a coarser mesh is used in the central part of the interface, 1 mm away from the endpoints, with a maximum element size of 2 µm. In the bulk, the maximum element size is 10 µm for all simulations. To implement the model, we combine the Laminar Flow module with a Dilute Species Transport module of COMSOL for the transport equations in the bulk (1-3). The equation for the transport of surfactant on the interface (  9 ) is implemented through a General Form Boundary PDE, with a source term corresponding to the kinetic flux S. This flux also serves to implement the boundary condtion (8) at the interface for the Dilute Species Transport module.
Acknowledgments We thank  D. Page-Croft  and the engineers of the G. K. Batchelor Laboratory  for their technical support. We gratefully acknowledge financial support from the  Raymond and Beverly Sackler Foundation , the  Engineering and Physical Sciences Research Council , the  European Research Council  Grant  247333 ,  Mines ParisTech , the  Schlumberger Chair Fund , as well as from  Churchill and Magdalene Colleges, Cambridge.
However, with this precision comes risk: the small statistical error bars that come from the large datasets involved will lead to potentially far-reaching and erroneous conclusions if the accuracy of the results is not correspondingly high. To achieve this requires very good control of systematic effects at all stages of the experiment, including in the statistical analysis of the data. In this report, we focus on weak gravitational lensing, and propose a rigorous statistical framework that can encapsulate essentially everything the data tell us, allowing for accurate and suitably precise inferences to be made, with full propagation of errors.
Statistical Analysis Data analysis and plotting were performed using Microsoft Excel (Microsoft Corporation, USA) and Sigma plot (v. 10. 0, Systat, Inc. , Point Richmond, CA, USA). Significant differences were assessed through paired t-test using the SPSS Statistics software (IBM China Company, Ltd, Beijing, China) as previously described  (Tu et al. , 2016). All experiments were performed using three biological replicates, with each biological replicate having three technical replicates.
Accession Numbers The accession numbers of the genes used in this paper are found in The Arabidopsis Information Resource 1  and the grape genome Sequence 2  : AtActin2 (AT3G18780), AtEDS1 (AT3G48090), AtPR1 (AT2G14610), AtPR2 (AT3G57260), AtPR5 (AT1G75040), AtPDF1. 2 (At5g44420), AtICS1 (At1g74710), VvActin1 (AY680701), VqWRKY52 (KY411919).
Cloning and Sequence Analysis of VqWRKY52 Gene specific primers were designed according to the VvWRKY52 (GSVIVT01028718001) cDNA sequence from the Grape Genome Database (12× 2 ) and used to amplify the VqWRKY52 ORF. The VqWRKY52 coding sequence (CDS) is 1092 bp, encoding a 364 amino acid protein, and the nucleotide sequence had 99. 27% identity to the V. vinifera homolog, with only eight single nucleotide polymorphisms (SNPs) found between the CDS from the two grape genotypes (Supplementary Figure  S1 ). The corresponding deduced amino acid sequences shared 99. 18% identity (Supplementary Figure  S2 ).
Gel Retardation Assays Gel retardation assays were essentially carried out as described before  (Singh et al. , 2012). Thus, different DNA fragments were amplified by PCR using pLS20cat as template. The resulting PCR fragments were purified and 170 ng of DNA [200 or 362 bp] (with or without 220 ng of control DNA [176 bp]) were incubated on ice in binding buffer [20 mM Tris HCl pH 8, 1 mM EDTA, 5 mM MgCl 2 , 0. 5 mM DTT, 100 mM KCl, 10% (v/v) glycerol, 0. 05 mg ml -1 BSA] without or with purified Aux1 LS20 or Aux2 LS20 to a fixed final concentration of 90 nM (Supplementary Figure  S3 ) or using twofold increasing concentrations ranging from 0. 09 to 5. 76 µM (Figure  3 ) in a total volume of 16 µl. The negative control, corresponding to bp numbers 63,774-63,950 of accession number NC_015148.
Identification of Mob L Members Rel LS20 was used as a query sequence to execute a psi-blast (version 2. 6. 1+) search against the NCBI nr protein database (July, 2017), allowing up to 10 rounds of reiteration with an e-value threshold of 1e-15  (Altschul et al. , 1997 (Altschul et al. , , 2005;; Schaffer et al. , 2001)  producing 1445 hits. The program "USEARCH" (version v10. 0. 240_i86linux32) was then used to identify and remove redundant sequences showing 100% identity  (Edgar, 2010) , resulting in 1249 unique hits showing high similarity to Rel LS20.
Identification of Putative Auxiliary Proteins Protein sequences of Aux1 LS20 and Aux2 LS20 were used as query against the NCBI nr protein database (July 2017) using psi-blast (version 2. 6. 1+), with an e-value threshold of 1e-6 and 1e-7, respectively, until no new hits were retrieved. The sequence identifiers obtained from psi-blast, were crossed with the sequence identifiers preceding the MOB L family relaxase members, obtained from the nucleotide entries from they were translated. Prediction of Secondary Structure for Aux1 LS20 and Aux2 LS20 Homologs Corresponding sequences were submitted to the RaptorX property web server  (Wang et al. , 2016)  and predictions for β-strands and α-helices along the sequences were plotted with "R" 1  ) (R Core Team, 2017).
Over 400 Members of the MOB L Family of Relaxase Genes Contain Upstream Genes Encoding Homologs of Aux1 LS20 and/or Aux2 LS20 Previously, we reported that the pLS20cat-encoded Rel LS20 constitutes the founding member of a novel, large family of relaxases that we named MOB L , which contained 817 members that were almost exclusively encoded in bacteria belonging to the phylum Firmicutes  (Ramachandran et al. , 2017). We wanted to know whether other MOB L relaxase genes were also preceded by genes encoding putative homologs of Aux1 LS20 and/or Aux2 LS20. To study this we first determined the current number of MOB L relaxase genes, applying the same method as that used in our previous study; i. e. , we performed a psi-blastp search of the NCBI nr database using Rel LS20 as a query. After removing redundant sequences this search now resulted in 1,453 hits that showed high similarity with Rel LS20 (threshold value P = 1e-15).
Next, the corresponding DNA accession number of each identified MOB L relaxase was retrieved, which was subsequently used to generate a database that contains the accession number of each MOB L member together with that of the protein encoded by the gene located upstream and downstream of the relaxase gene. We then performed the same procedures for Aux1 LS20 and Aux2 LS20 ; i. e. , we identified proteins sharing a high level of similarity with Aux1 LS20 and Aux2 LS20 and generated databases that contained these accession numbers together with those of the proteins encoded by the flanking genes. Finally, the three databases were crossed to identify those MOB L members that are preceded by a gene encoding a putative homolog of Aux1 LS20 and/or Aux2 LS20. This approach revealed 387 MOB L FIGURE 3 | Auxiliary protein Aux1 LS20 and Aux2 LS20 are DNA binding proteins that preferentially bind to oriT LS20.
DNA binding activity of Aux1 LS20 and Aux2 LS20 was analyzed by EMSA. Each lane contained two DNA fragments; one corresponding to oriT LS20 (OriT LS20 , 362 bp, 170 ng), and a control fragment (neg control, 176 bp, 220 ng, corresponding to pLS20 positions 63,774-63,950 of accession number NC_015148. 1). -, loaded without protein. Increasing concentrations of Aux1 LS20 or Aux2 LS20 were prepared using twofold dilution steps, and ranged from 0. 09 (lanes "a") to 5. 76 µM (lanes "g"). Positions of unbound control DNA (neg control) and oriT LS20 (oriT LS20 ) are indicated. The single shifted oriT LS20 species observed for Aux1 LS20 is indicated on the left with a blue arrow. The different shifted oriT LS20 species observed for Aux2 LS20 are indicated on the right with purple arrows, and the shifted species of the control DNA fragment is indicated with an asterisk.
FUNDING Work in the Meijer lab was funded by grants Bio2013-41489-P and BIO2016-77883-C2-1-P of the Ministry of Economy and Competitiveness of the Spanish Government to WM, which also funded AM-A, CG-C, and JV-C. Part of the economic support of the two aforementioned grants was provided by the "Agencia Estatal de Investigación (AEI)" and "Fondo Europeo de Desarrollo Regional  (FEDER). " This research was also supported by institutional grants from the "Fundación Ramón Areces" and "Banco de Santander" to the Centro de Biología Molecular "Severo Ochoa". LW's work was supported by Wellcome Trust grant WT098374AIA to Jeff Errington. JL-O and CA were supported by grant BFU2014-52070-C2-2-P of the Ministry of Science and Innovation to CA. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
ACKNOWLEDGEMENTS We thank  Dr Julien Verrax  for fruitful discussion, and  Remi Selleslagh ,  Bouazza Es Saadi ,  Veronique Allaeys ,  Isabelle Blave  and the team of  Professor Gianello  ( Pole de Chirurgie Experimentale et Transplantation ,  UCL ) for technical assistance. At the time of the study, MS was a Televie PhD fellow, LBB and PEP Postdoctoral Researchers, PDC a Research Associate and PS a Senior Research Associate of the  FRS-FNRS  ( Fond National de la Recherche Scientifique ). NMD is recipient of FRS-FNRS grants. PDC is recipient of an  ERC Starting Grant 2013  ( European Research Council , Starting Grant  336452-ENIGMO ), a  Baillet Latour grant  for medical research 2015 and is supported by the  FRS-FNRS via FRFS-WELBIO  under Grant number  WELBIO-CR-2012S-02R. For this study, OF and PS were supported by  Interuniversity Attraction Pole (IAP)  grant # UP7-03  from the  Belgian Science Policy Office (Belspo)  and by an  Action de Recherche Concerte  ´e from the  CommunauteFranc¸aise de Belgique  ( ARC 14/19-058 ).