dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This work was supported by the  Fincyt  Project  N 140-FINCYT-IB-2013 , the  Peruvian-Argentinian   321-2009-CONCYTEC OAJ Project  and  MINCYT-CONCYTEC PE/09/01 ,  UBACyT   20020100100636 ;  CONICET GI-PIP 11220110101020 ;  PICT-2012-1167. MCM, RJC and SAB are members of  CONICET. Dimitry Aldakov  is gratefully acknowledged for careful reading of the manuscript. One of the authors,  J. Rodriguez , acknowledges the  Regional French Cooperation for the Andean countries of the French embassy in Lima , the  Nanoandes network  and the  Puya de Raimondi association  for partial financial support.
2, essentially as described previously  (10, 19). Briefly, parental and transfected HEK293 cells were exposed either to an outwardly directed 20 mEq NH 4 ϩ at 15 °C (ammonium transport) or to inwardly directed 10 meq HCO 3 Ϫ /CO 2 and outwardly directed 67. 5 meq Cl Ϫ gradients at 30 °C (Cl Ϫ /HCO 3 Ϫ exchange). When indicated, the anion transport inhibitor DIDS was added to the cells at 10 M 30 min before analysis. The pH-dependent fluorescence changes of BCECF were monitored at a 485-nm excitation wavelength, and the emitted light was filtered with a 520-nm cutoff filter. Data from three to four time courses were averaged and fitted to a monoexponential function using the simplex procedure of the Biokine software package (Bio-Logic). Determined alkalinization rate constants could be compared in the different samples because cell sizes were identical in all preparations.
The D3-D4 domains of ankyrins associate with a number of membrane proteins, for example: Na ϩ ,K ϩ -ATPase  (26) , voltage-gated sodium channels  (27) , nervous system cell adhesion molecules (28)), and particularly RhBG (1) and eAE1  (29). Of note, eAE1 has been shown to also interact with the D2 domain of ankyrin-R, and the region involved in this binding was located in the extreme N terminus of eAE1, which is actually lacking in kAE1 protein (i. e. amino acids 1-65)  (29, 30). Mapping of ankyrin-G binding determinants in the N terminus ϩ gradient at 15 °C. pH idependent fluorescence changes were monitored using a stopped flow spectrofluorometer. Data from at least three time courses were averaged and fitted to a monoexponential function using the simplex procedure of Biokine software (Bio-Logic), from which alkalinization rate constants (k, s Ϫ1 ) were determined.
Acknowledgments-We thank  Nicole Boggetto  ( Flow cytometry platform, Institut Jacques-Monod, Paris, France ) for cell sorting experiments using a fluorescence-activated cell sorter Influx 500 ( BD Bio-Sciences ), and  Julien Picot  and  Sylvain Bigot  ( Institut National de la Transfusion Sanguine, Paris, France ) for flow cytometry analysis. We also thank  Dr. Wassim El Nemer  ( INSERM U1134 ) and  Dr. Jean-Philippe Semblat  ( Institut National de la Transfusion Sanguine, Paris, France ) for helpful advice on PLA experiments and immunofluorescence confocal microscopy, respectively.
* This work was supported by institutional funding to the INSERM  U1134  and  Institut National de la Transfusion Sanguine. This study was supported by  Laboratory of Excellence GR-Ex  Grant  ANR-11-LABX-0051. The labex GR-Ex is funded by the  program Investissements d'Avenir of the French National Research Agency  through Grant  ANR-11-IDEX-0005-02. 1 These authors contributed equally to this work.
Measurements of comb size and comb mass Adult animals were weighed at the age of 300 days. Blood sampling was performed as part of the routine monitoring of the lines to get 1 ml of whole blood for DNA extraction and storage. Pictures of the head of live chickens were taken from each side. This was performed with a Nikon Coolpix L110 set in Macro mode with a 15 X Nikkor lens (Optical Zoom Wide VR 5. 0-75. 0 mm 1:3. 5-5. 4) with a 6X magnifying power and a distance of 27 cm between objective and target. At the end of the reproduction period, i. e. at the age of 390 days, animals were sacrificed and individual combs were dissected, cleaned if necessary, and weighed at the nearest 0. 1 g. Since some birds had died in the meantime between comb size measurement and comb mass determination, or were still needed for other experiments, the dataset for comb mass was reduced to 150 observations (Table  1 ).
Image analysis was performed on one picture per bird, corresponding to the one where the comb was the most straight, in order to maximize the surface available for the analysis. Geometric morphometry relies on the Thin Plate Splines approach (TPS) in order to quantify the shape of the comb, and to separate 'size' from 'conformation' in a second step. The surface of the comb was first outlined on each picture by defining 150 anatomical landmarks between the proximal and the distal ends of the comb (Fig. 1 ), using the tpsDig2 software. Then, images were analyzed by the Procruste process following 3 steps: (i) translation of all objects to superpose them at their center of gravity (ii) normalization to a reference scale for each object (iii) rotation of each object to minimize the distances between its landmarks and the landmarks of the consensus object.
Statistical analysis Elementary statistics were first calculated for comb size, comb mass, body weight and RQ. The individual data for these traits are provided in S1 File. The distribution of RQ values was plotted according to line, genotype and sex. For each line by sex combination, phenotypic correlations were calculated between comb size, comb mass, body weight and RQ values. For both comb size and comb mass, we fitted models including the fixed effects of line, sex, and the line Ã sex interaction, as well as body weight and RQ as covariates, to three independent subsets of the data: the full dataset, a subset containing only the WL-DJ and NOE lines, and a subset made up only of the heterozygous carriers of the Pea-comb mutation. We also considered a set of reduced models where the RQ covariate was removed from each of the full models described above; this allowed the calculation of the amount of variance explained by RQ in each case by comparing the proportion of the total variance explained by the full and reduced models.
Images and videos in Images in Figure  5D -F, 6D were acquired using a Nikon TE2000-E inverted microscope with a Yokogawa spinning-disk confocal head (CSU-10), a 60× 1. 40 NA Plan Apochromat lens (Nikon, Tokyo, Japan) and an EMCCD camera (iXon DV887ECS-BV, Andor Technology, Belfast, United Kingdom). Imaging parameters were controlled using the Andor iQ2 software. Images in Figure  1 -figure supplement 5B was acquired using the same system as above except that a 100× 1. 40 NA Plan Apochromat lens (Nikon) was used. Images in Figure  2F  and Figure  2 -figure supplement 5 were acquired using a Nikon eclipse E800 microscope with a 60× 1. 40 NA Plan Apo lens (Nikon) and a Hamamatsu ORCA-ER camera (Model C4742-95-12ERG, Hamamatsu photonics). Imaging parameters were controlled using the Metamorph software (Molecular Devices, Sunnyvale, CA).
Image analysis EB1 comet counting was performed using imageJ software (FIJI) in a macro-aided semi-automatic fashion. Tiff images were first subject to 'Gaussian blur' (Sigma = 1 pixel), 'subtract background' (rolling ball based, r = 20 pixels) and 'threshold' (manually adjusted parameters to retain most comets). Effective area (excluding nuclear area) was obtained by subjecting images to 'threshold' so that only the cytoplasmic region was highlighted. EB1 comet density was calculated by dividing the number of EB1 comets by the effective area. Quantifications of GIP-2::GFP intensity were performed automatically using an imageJ (FIJI) macro script. For measuring the intensity of GIP-2::GFP puncta in the epidermis, tiff images were first subjected to 'Gaussian blur' (Sigma = 10 pixels) and 'setThreshold' (lowThreshold = 0, highThreshold = 205) to get the total imaged area (Image Area).
Acknowledgements We thank  K Laband  for assistance in making the NOCA-1 antibody;  R Khaliullin  for help with python scripts and image analysis;  N Hattersley  for providing the purified GFP nanobody;  S Dehenau  for help with the palmitoylation prediction;  D Starr  for NOCA-1h identification;  H Fridolfsson  for nuclear migration assay training;  JS Han ,  W Lan , and  DH Kim  for providing insect cells and baculovirus support;  M Chuang ,  S Xu ,  A Chisholm ,  M Hendershott , and  R Vale  for plasmids; and  A Chisholm  and OD lab members for helpful discussions. The  Caenorhabditis Genetics Center and NemaGENETAG  provided strains. KO and AD receive salary and other support from the  Ludwig Institute for Cancer Research. This work was supported by an  NIH  grant ( GM074207 ) to KO.
Methods Treatment-naïve patients with EGFR mutation-positive stage IIIB/IV lung adenocarcinoma (LL3=345; LL6=364) were randomised (2:1) to afatinib or chemotherapy, stratified by EGFR mutation (Del19/L858R/other) and race (LL3). Mature OS analyses, including pre-specified subgroup analyses, were planned in the intent-to-treat population after 209 (LL3) and 237 (LL6) events. Individual patient data from these studies were also combined for post-hoc exploratory OS analyses. These ongoing (active, not recruiting) studies are registered with ClinicalTrials. gov: NCT00949650 (LL3), NCT01121393 (LL6).
For each study, the main comparison of OS between treatment arms was performed using a stratified log-rank test adjusting for EGFR mutation type (LL3 and LL6) and race (LL3); the combined OS analysis was adjusted for study (LL3/LL6) and EGFR mutation type. Cox proportional-hazard models were used to derive HRs and 95% CIs comparing the two treatment arms, and examining patient subgroups of interest. The proportional hazards assumption was checked via a test for proportionality  21  along with visual checks of the log-cumulative hazard plots. Kaplan-Meier (KM) estimates were used to construct survival curves and calculate median OS. Median follow-up was calculated using the reverse KM method. 22 Statistical analyses were performed with SAS (version 9. 2).
Role of the funding source Boehringer Ingelheim managed the clinical trial database. JC-HY, Y-LW, LVS, and the Boehringer Ingelheim study team analysed the data according to the statistical plan and decided upon exploratory analyses. JC-HY had full access to the study data and prepared the manuscript draft, and all authors participated in the manuscript development and made the final decision to submit the manuscript for publication. In subgroup analyses examining EGFR mutation type in both LL3 and LL6, a significant improvement with afatinib compared to chemotherapy was observed in patients with tumours harbouring the EGFR Del19 mutation (figure  3D ). In LL3, median OS for these patients was Patients with disease progression or intolerable AEs discontinued assigned study medication (first-line afatinib or chemotherapy) and received subsequent standard treatment at their physician's discretion.
Acknowledgements We thank the patients, their families, and all of the investigators who participated in these studies. Medical writing assistance was provided by  Katie McClendon, PhD , of GeoMed, part of  KnowledgePoint360 , an  Ashfield Company , and was supported financially by  Boehringer Ingelheim  during the preparation of this manuscript. SP acknowledges NHS funding to the  Royal Marsden Hospital/Institute of Cancer Research NIHR Biomedical Research Centre.
For the virtual-postural analysis, DELMIA ® software was used to virtually model a workstation, and records of activities and the postures associated with various repetitive actions were gathered in a non-invasive manner with cameras and video cameras. CaMAN ® software developed by the researchers from the Department of Industrial Engineering in Salerno (Italy) was used to calculate comfort indexes. An analysis of the comfort indexes was used to make as the basis for suggestions to correct workers' postures and for plans to redesign the workstations in order to improve ergonomics and allow workers to perceive them as more comfortable.
g. , body posture and muscle activation) and the perceived effects on several body parts (based on the range of rest posture [RRP]). It was demonstrated that anthropometric parameters could be used to evaluate users' well-being level (comfort). Papers  [41]  and  [42]  present a new software named CaMAN ® that was developed by authors and is, based on posture analysis. This software allows making quantitative evaluations of postural comfort. The aim of this study is to re-design and improve manual assembly workstations through the use of this last mentioned quantitative method.
Follow-up screening of GABRG2 was done by Sanger sequencing in the 43 probands of the Italian cohort as previously described. 13 Screening in the cohort of 64 probands from French families was performed using a targeted epilepsy gene panel (SeqCapEZ custom design, Roche NimbleGen) including GABRG2, GABRA1, STX1B, PRRT2, CHRNA4, CHRNA2, CHRNB2, LGI1, DEPDC5, KCNT1, and EFHC1. Quality of raw sequencing data was assessed with the FASTQC tool. Reads were trimmed with Trimmomatic. BWA was used to align reads on human genome (hg19). Finally, GATK best practices were applied to call variants. Variants with low quality (,20) were excluded. Variants with a read depth. 30 and allele read frequency. 0. 30 were filtered. We selected heterozygous nonsynonymous variants with frequency ,1% in the exome variant server, affecting the protein-coding sequence (nonsense, missense, frameshift, or essential splice-site) and predicted to be damaging by at least 1 of 2 in silico prediction tools (Sift, http://sift.
jcvi. org/; PolyPhen-2, http://genetics. bwh. harvard. edu/pph2/). We followed the guidelines for assigning causality of sequence variants in human disease as outlined. 14 overage of GABRG2 was satisfactory (. 2503) except for exon 6, which was subsequently analyzed by traditional Sanger sequencing. Filtered variants were validated by Sanger sequencing, and segregation was analyzed in all available family members. Impact of mutations was assessed with Alamut version 2. 4. 3 (Interactive Biosoftware). GABRG2 mutations were mapped on the immature peptide, according to the current convention. 15, 16 We compared the frequency of rare nonsynonymous GABRG2 variants (including missense, nonsense, essential splice-site, in-frame Indels) identified in patients with epilepsy and those present in 33,370 European cases from the publicly available Exome Aggregation Consortium (ExAC) database (http://exac.
broadinstitute. org) using Fisher exact tests (accessed July 2015). Linkage analysis. Pairwise LOD scores were calculated using the MLINK program of the FASTLINK package version 4. 1 assuming an autosomal dominant trait for FS with disease allele frequency of 0. 01 and incomplete penetrance of 85%. Because the frequency of FS in the general population is approximately 2%-5%, we used a phenocopy rate of 3% as previously reported. 17 We considered as affected all individuals with FS. CGH array. CGH array was performed in the 43 probands of the Italian cohort using a custom 105K Agilent CGH array containing 44K backbone genome-wide probes and approximately 60K high-density probes spanning 440 genes encoding for ion channels (list available on request) according to previously described protocols and bioinformatics pipelines.
Originally, a 20 cM spaced genome-wide microsatellite analysis was suggestive of a locus on chromosome 18qter and a probable second locus on chromosome 1q25-q31 with maximum pairwise LOD scores of 3. 04 and 2. 33, respectively. 19 We first focused the analysis on these 2 regions, then on the rest of the exome dataset. According to our filtering criteria, only 7 variants were common to all 3 siblings, but none of the corresponding genes (ABL2, BOD1L1, B3GALT4, EXO1, HERC4, OR5K1, or SSH2) were functionally candidate for epilepsy or expressed in the brain. We therefore subsequently restricted the analysis to the exome datasets of individuals A/II-1 and A/II-4, who belong to the same branch of the family. We identified a heterozygous   1 ). The variant created a frameshift at codon Glu402 leading to a new reading frame that ended in a stop codon 2 positions downstream (p.
665-161. 067. 144, and affecting coding exons 1-4 of GABRG2. The proband (F/II-1) was an 8-year-old boy who had recurrent episodes of simple FS between ages 12 and 36 months, which remitted spontaneously. From age 5 years, he developed absence seizures and 2 GTCS, successfully treated with valproate. His mother (F/I-1) had 4 episodes of simple FS during her first 2 years of life. Both individuals had normal cognition. No additional affected relatives were reported. Gene panel sequencing performed in the probands of family B, C, D, and E did not identify other rare variants in known epilepsy genes targeted in our panel. None of the GABRG2 mutations of this study were reported in the Exome Aggregation Consortium (Ex-AC) database, which includes the 1000 Genomes project database and the 6,503 exomes listed in the exome variant server.
Most patients Disease-causing point mutations in GABRG2 include 8 missense mutations,  7, 22, 24, 26, 27, 30, 31  4 nonsense mutations, 20,23,29,41 4 frameshifts,  25  and 2 splice-sites. 28, 31 Mutations indicated by a blue star were identified in this study; those indicated by a red star were previously reported. All mutations are reported on the immature peptide. Protein structure was designed according to UniProt database information (NP_000807. 2). carrying a GABRG2 mutation had a reported history of FS (22/23), associated with TLE in 1 family (family A), generalized epilepsy with absences and GTCS in 2 families (families E and F), and undefined epilepsy with TCS and episodes of loss of consciousness in 1 family (family D). GABRG2 mutations segregated predominantly with FS, indicating that GABRG2 mutations are drivers of the FS phenotype, rather than epilepsy.
ACKNOWLEDGMENT The authors thank the patients and families for their participation in the study and the sequencing platform of ICM ( Y. Marie ), the bioinformatics/biostatistics core facility of ICM (J. Guegan), the DNA and cell bank of ICM ( C. Dussert  and  S. Forlani ) and  Genethon  ( J. Debusne ) for providing technical assistance, and  J. Lemke  for discussion.
Even cells expressing wtRANTES at low levels (based on a very weak signal) showed a filamentous RANTES pattern (data not shown). Moreover, we electroporated HUVECs with low amounts of plasmid DNA encoding wtRANTES (4 mg compared to 20 mg in the standard protocol) which resulted in a dramatic reduction in protein expression and fewer positive cells. Despite the low level, wtRANTES organized in filaments (data not shown). Finally, we performed ELISA on supernatants harvested from HUVECs electroporated with plasmid DNA encoding the various constructs. Both Y3A and E66A were present in higher amounts than wt, whereas E26A and  44  AANA  47  were found in approximately two-thirds and half the amount, respectively, of that measured for wt (Fig. 5D ). Altogether, these data confirmed that the different patterns were not a result of various expression levels per cell or by the culture.
Acknowledgments We thank  Alexandre Corthay  for critical reading of the manuscript,  Aaste Aursjø ,  Kathrine Hagelsteen , and  Linda I. Solfjell  for excellent technical assistance, and the  Department of Obstetrics and Gynaecology, Oslo University Hospital -Rikshospitalet  for assistance in obtaining umbilical cords. The  Confocal microscopy Core facility Gaustad at Oslo University Hospital , and the  NorMIC Oslo imaging platform  at the  University of Oslo  are acknowledged for providing equipment for confocal microscopy. This work was supported by grants from the  Norwegian Cancer Society , and it was partly supported by the  Research Council of Norway  through its  Centres of Excellence funding  scheme, project number  179573/V40.
Assessment of CD45 + cells, in situ To estimate the number of CD45 + cells in the brain, in situ, we performed CD45 immunolabeling, as described above. From each brain, we used four slices with the following stereotaxic coordinates: approximately 0. 86, 0. 26, -0. 34, and -1. 34 mm (anterior-posterior, AP) in relation to the bregma. We counted CD45 + cells populating one cortical and/or one striatal infarct core region, each corresponding to a field of vision of about 1 mm 2 per section. Regions were sampled equivalently across mice. Cells were counted under a fluorescent microscope (Nikon Eclipse 80i, Nikon, Solna, Sweden) by an experimenter blinded to the genotype. In addition, CD45 + cells were imaged using Nikon A1 Confocal on a Ti-E microscope and data processed using the NIS-Elements software (Nikon).
Under these conditions, counting cells is suboptimal, particularly when using epifluorescence microscopy. We therefore quantified the fraction of Iba-1 + signal colocalizing with Gal-3 and vice versa by calculating Mander's coefficients of the acquired Z-stack images (for simplification, referred to as the percentage of Iba-1 + cells expressing Gal-3 and vice versa), using ImageJ software (National Institutes of Health, Bethesda, MD, USA),  [29]. Signal intensity thresholds were set manually after applying a denoise filter. Our approach minimized false positives by treating each optical slice of each Z-stack independently, instead of analyzing collapsed Z-stack images. Analysis of Gal-3 + cell morphology was carried out using Image J. For each brain, we selected 30-40 Gal-3 + cells within the infarct core and manually plotted their outline.
Assessment of blood-brain barrier integrity through IgG and claudin-5 immunoassays When investigating the presence of IgG in the parenchyma of WT and IFN-βKO brains, we used brain slices centered at 0 mm AP in relation to the bregma (equivalent slices were used across animals). The IgG-specific signal within the infarct core and peri-infarct region was, for each section, qualitatively and qualitatively assessed. Adjacent brain slices were used for analysis of claudin-5 immunoreactivity, as described before  [30]. Briefly, only claudin-5 + vessels showing a relatively strong and continuous (claudin-5) signal were analyzed. The area occupied by and average length of claudin-5 + vessels in a selected region of the peri-infarct cortex (1 mm 2 ) were quantified, for each section, using the Image J software.
Infarct volume IFN-βKO For each animal, a series of 30-μm-thick coronal brain slices of the entire brain (240 μm between consecutive slices) was used for NeuN immunolabeling; we used equivalent series among animals. Micrographs were acquired with a Nikon Eclipse 80i microscope, using the NIS-Elements software, under standardized conditions. For each slice, the areas corresponding to the contralateral hemisphere (Contra) and to NeuN + tissue within the ipsilateral hemisphere (Ipsi NeuN ) were encircled using ImageJ. The infarct area (IA) was estimated accounting for edema or tissue shrinkage: IA = Contra -Ipsi NeuN. Infarct volume was obtained by volumetric integration.
Statistics We present the data as mean ± SD, unless otherwise indicated. To compare two groups, we used Student's t test (two-tailed), and comparison of three or more groups was done by two-way analysis of variance (ANOVA), with the following exceptions. For the composite neuroscore and rotating pole scores, we used the Mann-Whitney U test. Mortality rates were assessed using Fisher's exact test. For IFN-βKO and WT mice undergoing tMCAo or sham surgery, differences in temperature and weight loss post-surgery were analyzed using a mixed model ANOVA, with multiple comparisons being performed for representative time points (24 h, 2, 3, and 7 days). Analysis of the weight before surgery was done using one-way ANOVA. For IFNAR-KO and WT mice subjected to tMCAo, temperature and grip strength asymmetry post-surgery were evaluated using repeated measures ANOVA.