dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analysis Quantitative variables were expressed as meanAESD or median (range), and categorical variables as frequencies or percentages. The Kolmogorov-Smirnov test was used to test for normal distribution. Comparison of affected and unaffected coronary segments was performed using Student t test for continuous variables with normal distributions and the Mann-Whitney U test for continuous variables with nonnormal distributions. Fisher exact or the v 2 tests were used for categorical distribution comparison. All analyses were performed with SigmaStat V3. 5 (Dundas Software, Germany). A 2-tailed P value of <0. 05 was deemed significant.
INTRODUCTION The idea of conducting strategies of digitization programs with cultural heritage documents has emerged since the early 1960s. The primary goals of these digitization programs, which were related to the tremendous growth and spread of the Internet technologies, were not clearly identified (e. g. providing digital copies of historical document images (HDIs), sharing databases of document images between many libraries, designing a computer-assistance tool for textual data handling, etc. ). Nevertheless, the rapid growth of digital libraries has become a serious hindrance to promote wide efficiency and effectiveness in the management of this cultural heritage resources (i. e. quick and relevant access to information contained therein) due to the huge amount of digital high quality reproductions of fragile books and digital copies of rare collections.
To meet the need to reinforce the enrichment and exploitation of heritage documents in addition to make it electronically available for access via the Internet, many research projects has been set up with the support of public funding provided through the European and American governments. The main goals of these projects are to provide a computer-based access and analysis of cultural heritage documents, searchable and browseable HDI databases, and an automatic indexing, linking and retrieval semantic-based systems of HDIs  (Coustaty et al. , 2011). In this work, we are interested in historical document image layout analysis (HDILA). HDILA starts by segmenting a document in order to find and classify homogeneous regions or zones, such as graphic and textual regions  (Okun and Pietikäinen, 1999).
CORPUS AND PREPARATION OF GROUND-TRUTH Our experimental corpus contains 1000 groundtruthed one-page document images which have been collected from Gallica, encompassing six centuries (1200-1900) of French history. The HDIs of our corpus have been selected from several printed monographs and manuscripts across a variety of disciplines (e. g. novels, law texts, educational books (history, geography, nature), xylographic booklets, etc. ) to provide a broader range of document contents. They are gray-scale/color documents which have been digitized at 300/400 dpi and saved in the "TIFF" format, which provides a high resolution of digitized images. Our dataset has been structured into four categories of real scanned HDIs differentiated by their content (cf. Figure  2 ), reflecting the challenges of our work to determine which texture features can be more adequate for segmenting graphical regions from textual ones on the one hand and discriminate text in a variety of situations of different fonts and scales on the other hand.
This distribution of this dataset points out whether or not the proposed algorithm is firstly adequate for segmenting graphical regions from textual ones, and secondly if it can discriminate between texts with a variety of fonts and scales. The results of accuracy metrics are presented in Table  1. First, in this study the F-measure (F) is computed to evaluate the different steps of post-processing of the proposed algorithm for extraction of homoge-neous regions in HDIs. The overall pixel labeling results are reasonably promising, i. e. we obtain 67% and 70% of overall F-measure rates, without taking into consideration the topological relationships of pixels and their labels for "Overall * " and "Overall * * ", respectively. We conclude that the best performance is obtained for documents containing graphics and single text font (81%).
Such diversity can first be explained by the aim, shared among all producers, to produce a healthy and diverse food for the family while Downloaded by [santiago javier sa] at 06:43 02 July 2015  selling the excess of food when there is some. Feeding animals is also a need that they integrate into their food balance. Nevertheless, some producers also cultivate to make an income, in order to pay for school for children or land taxes. When the objective is to supply local markets, products diversity is strongly linked to consumers' tastes and demands. Some producers also take into account their mission to manage a natural heritage and its diversity. Data were collected in 2012 and 2013 with a comprehensive procedure. Data collection was based on at least two semistructured interviews (along with walking around farm fields) touching upon the following topics: farm history and family, objectives, products (diversity and use), technical practices, knowledge sources, and networks.
All interviews were recorded, transcribed, and structured in a database using NVivo computeraided qualitative data analysis software. This information was then coded and inductively analyzed using a strategy close to the "grounded theory" consisting of making a "systematic comparison of small units of data and gradual construction of a system of categories"  (Langley 1999: 700). Farming practices were first analyzed with a within-case approach  (Miles and Huberman 1994)  to abstract the "realized strategy" of each farmer, that is, the combination of practices implemented  (Mintzberg and Waters 1985; Girard and Hubert 1997). In a second step, we compared the practices with a crossfarm analysis  (Miles and Huberman 1994) : Within an inductive approach and an abstraction process that is deeply rooted in what farmers currently do, and not in a priori literature-driven categories, we thus characterized management principles that were common among all studied farms.
In the following discussion, all voltages are peak to peak. The setup is placed on an inverted microscope (Eclipse Ti-U, Nikon) connected to a high-speed camera (Phantom v7. 3) with a resolution of 800 × 256 px 2 when operated at an acquisition rate of 10 4 fps. The jet diameter, d j , and jet length, l j , of at least 100 images are measured via image processing (Matlab, Mathworks and ImageJ).
A possible reason for the instability is that the jet at the center of the channel is a situation potentially unstable. The jet is attracted to both electrodes, due to the voltage difference with respect to them, and any nonsymmetrical perturbation can be amplified if the electric field is high enough. The appearance of this instability at a certain frequency and below lacks of a theoretical explanation. Figure  6  shows the jet length, l j , as a function of signal frequency for the three water conductivities. The jet length is measured from the begining of the downstream electrodes. If we plot the dimensional quantities l j versus f /κ, all data points collapse onto a single master curve as depicted in the inset of figure  6. The figure also shows the best-fit curve with power -1/2. This key clue provided by the experimental data will be confirmed by theoretical analysis (see section 4).
Sampling of soils BCR 481 (Community Bureau of Reference) supplied from Sigma-Aldrich (BCR 481, Sigma-Aldrich, Saint-Louis, USA) was used as a reference soil. Moreover, nine soils were sampled during a collection campaign which took place in France in 2013. First 5 cm of oils originating from five distinct areas known as contaminated accordingly to the French public database inventorying contaminated soil, BASOL  (MEDDE, n. d. ) , were sampled (March to Fig. 1. Experimental design of the present study. Amounts of soil (D1, D2, D3, D4, D5) for each sample-soil group were chosen to fit in the range of tested doses in oil group. Spacing between doses within each soil-group is the same. Amounts of BCR 481 were set to correspond to those of oil and test if the dose-response relationship is quadratic.
Furthermore, the protocol was approved by the Ethical Committee of Lorraine (Permit Number: 00344. 01, from MAAR). Particular attention was paid to refining and reduction part of the principle of 3R's. Thus, the required number of piglets per group was assessed using available data on bioavailability of NDL-PCBs in artificial soils  (Delannoy et al. , 2014b). As noticed in a previous publication  (Delannoy et al. , 2014b) , 5 piglets for each soil sample were considered sufficient to allow an adequate calculation of RBA values and suitable 95% fiducial limits (corresponding to ±20% of RBA). Although, in order to properly establish the linearity of the dose-response relationship it was necessary to use 16 animals for the reference group. Furthermore, instead of using stressful multiple blood sampling to determine bioavailability of ingested NDL-PCBs, we used organs sampling after sacrificing of animals.
Analysis of biological samples Pericaudal adipose tissue was collected. All analytical steps for preparation of adipose tissue samples (freeze-drying, grounding) and chemical analyses are described in details elsewhere  (Delannoy et al. , 2014a). In brief, these analyses involved three successive steps (Fig. 1 ). ASE from freeze-dried and milled samples of adipose tissue was first performed (toluene/Acetone, 70:30 vol/vol; 120 °C, ASE 350, Dionex, Sunnyvale, USA) using 13 C-labelled NDL-PCBs from LGC Standard (Molsheim, France) as internal standards. Finally, quantification was achieved by using a GC-MS device (7 890A, Agilent Technologies, Santa Clara, USA). All chemicals used in this study were pesticide residue analytical grade. 2. 7. Data analyses 2. 7. 1. Quality control of the data set and data treatment Concentrations in soil were expressed in lg per g of dry soil and those in adipose tissue in ng per g of fat.
Externally analysed soil-samples were re-identified to allow randomized, operator-blind analyses. All parameters measured were realized according to COFRAC quality standard criteria, except black carbon because of the lack of standardisation of this methodology. For adipose tissue, regardless the treatment group, PCB 28 levels were below the limit of quantification. PCB 52 exhibited mostly quantifiable but low levels leading to highly variable results. Thus, presented results are focused on the most abundant congeners: PCB 101, PCB 138, PCB 153 and PCB 180. NDL-PCBs concentrations observed from one piglet (R, 1. 5D) were removed from the data set because of unexpected low levels of NDL-PCBs.
Acknowledgments The authors are grateful to the  French Ministry for Agriculture and Agence de l'Environnement et de la Maîtrise de l'Energie (ADEME)  for their financial support. They thank  S. Poinsignon ,  C. Grandclaudon  and  C. Soligot  ( URAFPA ) for NDL-PCBs analyses. The authors thank also  P. Hartmeyer  and  H. Toussaint  ( URAFPA ) for providing care to piglets. In addition, the authors thank  Y. Le Roux  ( URAFPA ) for statistical analysis.
Microarray BT-474 cells or SK-BR-3 cells were exposed to #hMADS-CM for 2 hours. RNA was extracted using the QIAamp RNeasy Mini Kit (QIAGEN, Valencia, CA, USA). After RNA amplification with the Illumina TotalPrep RNA Amplification Kit (Life Technologies, Carlsbad, CA, USA), cRNA was hybridized on HumanHT-12 v4 Expression BeadChips (Illumina, San Diego, CA, USA). Scanning was performed with an Illumina iScan microarray scanner, and data were analyzed with GeneSpring and Ingenuity software (Agilent Technologies, Santa Clara, CA, USA).
Acknowledgments This work was supported in part by the  Institut National du Cancer (INCA). We thank  Dr Christian Dani  for giving us the hMADS cell line,  Dr Emmanuel Delay  for providing us with the adipose tissue and  Delphine Demangel  from  ProfileXpert-LCMT  for performing microarray experiments. MND is a recipient of a doctoral fellowship from the  French Ministry of Higher Education and Research  and financial support from " La Ligue contre le Cancer ". The authors thank  Dr Catherine Muller  for critical reading of the manuscript. This work was supported by the  Institut National du Cancer (INCa-DGOS-4664 ). MND received financial support from  La Ligue Contre le Cancer.
The amplification efficacy was assessed using the Agilent DNA 1000 reagent kit and the Agilent 2100 expert bioanalyzer (Agilent, Les Ulis, France). The expected amplicon were obtained in plasma samples for all the patients and in PBMC for 18 patients out of 40 (45%). The amplicons were pooled equimolarly and purified using Agencourt AMPure XP reagents (Beckman Coulter, Roissy, France). A library on the amplicon pool was prepared using the GS Junior rapid library prep kit. An emulsion PCR was run with the GS Junior emPCR (Lib-L) kit. Sequencing was performed with a 454 GS Junior PicoTiterPlate to a target depth reading of 1,000ϫ. Files were sent to Genostar for data analysis. A mean of 1,070 Ϯ 51 (median, 890) reads per nucleotide was obtained with a median length of 457 nt. Sensitivity of UDPS for mutation detection.
For each patient, the genetic structure of the viral population was analyzed using phylogenetic and multivariate data analysis methods. A multiple-sequence alignment combining PBMC and plasma HCV RNA samples was built with MAFFT software and the FFT-NS-2 method for large alignments  (21). Multidimensional analysis was performed with the R computer package (R Core Team 2012, Vienna, Austria) after removal of RNA sites exhibiting Ͼ2% gaps. Principal component analysis was used to display a two-dimensional representation of the viral population genetic data set. To test for compartmentalization in the viral sequences from the two sources of HCV RNA, Wilks' lambda, a statistic used in multivariate analysis of variance, was evaluated based on the combination of genotypes. In addition, linear discriminant analysis was used to evaluate the rate of misclassification for PBMC and plasma samples  (22).
ACKNOWLEDGMENTS We thank  Caroline Ashford  for technical assistance and  Alison Foote  for English language editing. The study was funded by the  Direction de la Recherche Clinique du CHU de Grenoble , the  Rhône-Alpes region , and in part by unrestricted grants from  Janssen  and  MSD. S. L. has received research grants from  Janssen  and  MSD. J. -P. Z. has been a speaker and/or consultant for  Boehringer Ingelheim, Bristol-Myers Squibb, Gilead Sciences, Janssen Pharmaceuticals ,  Merck Sharp & Dohme ,  Roche , and  Siemens. V. L. has been a speaker and/or consultant for  Bristol-Myers Squibb, Gilead Sciences, Janssen Pharmaceuticals ,  Merck Sharp & Dohme ,  Novartis  and  Roche. The other authors declare no conflicts of interest. The authors' editor,  Alison Foote , works for the  Grenoble Clinical Research Centre  and is funded by the  Grenoble University Hospital.
To investigate the genetics of late-onset myasthenia gravis (LOMG), we conducted a genome-wide association study imputation of >6 million single nucleotide polymorphisms (SNPs) in 532 LOMG cases (anti-acetylcholine receptor [AChR] antibody positive; onset age ≥50 years) and 2,128 controls matched for sex and population substructure. The data confirm reported TNFRSF11A associations (rs4574025, P = 3. 9 × 10 -7 , odds ratio [OR] 1. 42) and identify a novel candidate gene, ZBTB10, achieving genome-wide significance (rs6998967, P = 8. 9 × 10 -10 , OR 0. 53). Several other SNPs showed suggestive significance including rs2476601 (P = 6. 5 × 10 -6 , OR 1. 62) encoding the PTPN22 R620W variant noted in early-onset myasthenia gravis (EOMG) and other autoimmune diseases. In contrast, EOMG-associated SNPs in TNIP1 showed no association in LOMG, nor did other loci suggested for EOMG.
Genome-Wide Imputation of SNPs Imputation was performed by using a 1000 Genomes Phase 1 V3 reference set and the University of Michigan Imputation Server (https://imputationserver. sph. umich. edu/start. html#!run/minimac). This method uses a computationally efficient implementation of the Markov chain-based haplotyper (MaCH) algorithm with prephasing using SHAPEIT  (22)  as previously described  (23). The program outputs the most likely geno-type and provides r 2 metrics for quality measurement. We performed imputation separately for set 1 and set 2 genotypes. A total of 6,175,472 genotypes showed r 2 >0. 8 common to both set 1 and set 2, and all QC criteria as described above that were applied to these joined set 1 and set 2 genotypes. (Note: testing of strongly suggestive associated SNPs (P values <10 -7 ) with genotypes that met lower r 2 thresholds in one or both sets showed P values >5 × 10 -5 after regional imputation and testing.
Regional Imputation of SNPs and Insertion/Deletion Polymorphisms For any SNP that showed suggestive association (P < 10 -5 ) in the screening imputation (described above), we performed regional imputation using Impute2 (IMPUTE v2. 3. 1) (https:// mathgen. stats. ox. ac. uk/impute/impute_ v2. html)  (24)  under recommended settings and the afore-noted 1,000-genome phase 1v3 reference set. This software provides genotype probabilities (rather than base assignments), enabling a more accurate assessment of statistical support. Overall errors compared favorably with other approaches  (24). The intervals examined (excluding the MHC) included at least a three order of magnitude falloff of significance from the association peak and a minimum of 250 kb. For the MHC, we examined a 4-Mb interval (chromosome 6, ~29-33 Mb, HG19/HG38).
Imputation of HLA Antigens To impute HLA antigens, we used a separate reference data set collected by the Type 1 Diabetes Genetics Consortium and the SNP2HLA software, as described by its developers  (25). The Beagle software package  (26)  was used for imputation in this data set (compatible with this software). Here, we also used genotype probabilities for association testing; our final data consisted of only antigens, AAs and SNPs with information scores >0. 8 in SNPTEST analyses.
Association Testing Statistics For all association results, analyses included covariates for sex, genotype set and the first three PCs (as discussed above). All association tests were done by using the combined imputed SNP data set as described above. For autosomal data, we used SNPTEST V2. 3 (https:// mathgen. stats. ox. ac. uk/genet-ics_software/ snptest/snptest. html) for the primary association analyses for the imputed genotypes, as well as for conditional analyses. For the X chromosome analyses we also used SNPTEST V2. 5. Analyses included an X inactivation model and a model that allows for heterogeneity (stratify_on option). With the exception of the screening imputed genotypes, genotype probabilities (and a min-imum information score of 0. 8) were used in all analyses.
Epigenetic Markers To assess possible regulatory function, we used RegulomeDB  (27). This database (search engine) annotates SNPs for predicted and known regulatory region for DNase hypersensitivity, transcription factor binding sites and promoter regions. It includes the current Encyclopedia of DNA Elements (ENCODE) releases and chromatin states from the Roadmap Epigenome Consortium and uses multiple resources that include DNase footprinting, position weight matrices and DNA methylation information (for further description of RegulomeDB see http://regulomedb. org). For this assessment, we examined all SNPs that were in at least weak LD (r 2 > 0. 25) to the strongest associated SNP and had annotations that showed possible regulatory function. Conditional analyses were then performed, and the SNP with the strongest likelihood of function was presented in Results.
To measure MMP, tetramethylrhodamine ethyl ester (TMRE) was added to each well at a final concentration of 50 nM for 15 minutes at 37 o C. TMRE is a cell permeable, positively charged dye that accumulates in active negatively charged mitochondria. In inactive or (partially) depolarized mitochondria, membranes have decreased potential and fail to sequester TMRE. Cells were then observed under an inverted fluorescence microscope using a filter cube with an excitation wavelength of 532-587 nm. The photos of cells treated with either TMRE, Hoechst 33342 or PI were analyzed using ImageJ image processing software  [29].
In Silico 3-D affinity docking analyses The structures of DIM and the ring-DIMs were optimized in a Gaussian 09 program package  (Frisch et al. , 2004 ) by the Density Functional Theory (DFT) method at the B3LYP/6-31G level. The output file was translated to pdb and pdbqt formats using Open Babel  [30]  and AutoDock Tools  [31] , respectively. The 3-D structures of the proteins were downloaded from the Protein Data Bank (PDB) in pdb format  [32]. The structure of TrailR1 was obtained from ModBase because its crystal structure was not reported in PDB. Protein structures were then prepared using Sybyl-X 2. 0 (Tripos, St. Louis, MO). In this process, all ions, water molecules and other substructures were removed  [33]. We also fixed all side-chains, backbones and protonation types. Once prepared, proteins underwent a two-step optimization procedure using Sybyl-X 2.
0. The first step included the Powell method applying Kollman"s united force field AMBER (Assisted Model Building with Energy Refinement) charges, dielectric constant 1. 0, NB cutoff 8. 0, maximum interactions 1000 and termination gradient 0. 001 kcal/mol. The second step utilized Kollman's All Atom approach with the same parameters. The resultant structures were saved as pdb files and then converted to pdbqt format using AutoDock Tools  [31]. Kollman charges and polar hydrogen atoms were added to the 3-D structures of the proteins using the same software  [34]. Docking and docking-refinement experiments were carried out through a blind docking strategy by AutoDock Vina  [35]  to allow inclusion of the whole protein surface and all possible binding sites. The 3-D docking grid was centered on the macromolecule  [36]  and coordinates were calculated with a resolution of 0.
357 Å using AutoDock Tools  [31]. Docking analyses were performed with the AutoDock Vina 1. 1 molecular docking and virtual screening program  [35]  running on a Linux operating system using the following settings: energy range = 1. 5, number of modes = 20 and exhaustiveness = 25. The docking models of protein/DIM and protein/ring-DIM complexes with high-affinity scores (less than -8. 0 kcal/ mol) were refined with repetitions of 100 runs to increase accuracy and identify alternative in silico binding sites for each compound. Protein/compound complexes underwent conformational analyses to determine the contact residues and interaction type using LigandScout 3. 1 software with default settings  [37]. The interaction cutoff threshold of the pdb interpretation, which defines a sphere around the ligand, was set at 7.
Statistical analyses All experiments were performed in at least triplicate and results presented as mean ± SEM. Statistically significant differences (* P < 0. 05, ** P < 0. 01, *** P < 0. 005) were calculated using a two-tailed Student t-test. IC 50 values were calculated using nonlinear curve-fit analysis. All analyses were performed using GraphPad Prism v5. 03 (GraphPad Software, San Diego, CA).