dataesr/scientific-paragraphs-categorization huggingface dataset:
The system is programmed to activate an alarm every time a nonauthorized attempt is made to remove library items (e. g. books and magazines) from the library entrance. A mobile cart unit, shown in Fig. 44(a) , enables book identification and localization on the library shelves. The mobile cart system is equipped with an RFID reader, an antenna, a laptop/tablet with touch screen and a UPS that provides power to the reader and laptop. The system uses the Wi-Fi connection to access the database server and provide valuable information to the user. The mobile cart can be pushed around the library while scanning the books on individual shelves. The ability of the system to effectively read and correctly identify the books residing on top of the shelf antenna depends on the electromagnetic coverage, the uniformity and strength of the field in the volume of the shelf, and the interference to nearby shelves.
For the operation of the OECTs, a National Instruments PXIe-1062Q system was employed. A source-measurement unit NI PXIe-4145 was used to bias the channel of the OECT (V DS ), while gate potential was applied and controlled using an NI PXI-6289, a multifunction data acquisition module. For frequency-dependent measurements, output currents of the drain (I d ) and the gate (I g ) were recorded using two NI-PXI-4071 digital multimeters. The bandwidth measurements were performed by applying a sinusoidal modulation at the gate electrode (ΔV gs = 20 mV peakto-peak, 1 Hz of o20 kHz) while keeping a constant bias at the drain (V DS = -0. 4 V). Measurements parameters were controlled using a customized LabVIEW program. For fitting of the frequencydependent measurements, a MATLAB script was used to extract the cell layer resistance and capacitance as reported previously  40.
Microfluidic fabrication and operation A multilayer approach for the fabrication of the microfluidic device was employed. Briefly, the microfluidic structure was designed with freeware software (CleWin5) and fabricated using a cutting plotter (Large Flatbed plotter -FC2250 series) in order to define the microfluidic channel in a 160 μm thick pressure sensitive adhesive (PSA) (AR8890, Adhesives Research, Limerick, Ireland). By peeling off one of the protective layer of the double-sided adhesive, the PSA was permanently bonded to the substrate by application of constant pressure over the tape. Subsequently, the substrate was plasma activated (25 Watt, 2 min) and a 0. 3 mg mL -1 collagen type I from rat tail solution was drop cast on the substrate. The substrate was then incubated for 30-45 min at 37 °C in a convection oven to induce crosslinking of the collagen and physical adsorption of collagen fibers on the substrate.
ACKNOWLEDGEMENTS V. F. C. acknowledges support from a  Marie Curie post-doctoral fellowship   FP7-PEOPLE-2013-IEF , Project No. 624673. A. -M. P. and M. B. acknowledge support from  Marie Curie ITN OrgBIO  Project No. 607896. We acknowledge  Dr P. Leleux  ( EMSE ) for LabView technical assistance.
A solution to such technical challenges may be found in the nature. Some forest-dwelling mushrooms are evolved to break down plant materials and release (poly)saccharides and (poly)aromatics from lignocellulose. Saprotrophic white-rot fungi are highly specialized to decompose dead plants and convert them into consumable carbon sources  [4]. Phylogenetic reconstructions showed that white-rot fungi evolutionarily gained specific groups of enzymes to break down recalcitrant lignocelluloses  [5, 6]. The gene repertoires of saprotrophic white-rot fungi are enriched in enzymes for oxidative degradation of lignocellulose, called auxiliary activity enzymes (AA) classified in the CAZy database  [7]. There are different types of Carbohydrate Active Enzymes (CAZymes) in the CAZy database including glycoside hydrolases (GH), carbohydrate esterases (CE), polysaccharide lyases (PL), glycosyltransferases (GT), and carbohydrate binding modules (CBM)  [8].
Metabolic adaptations to the lignocellulosic substrates We investigated fungal metabolic adaptations during the growth on the lignocellulosic substrates at day 3 and day 7 using the annotation databases Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and EuKaryotic Orthologous Groups (KOG)  [33] [34] [35]. The functional annotations found in all 456 nodes are provided (S4 File). A total of 150 out of 456 nodes contained statistically enriched functional annotations (adjusted p < 0. 01; S1 Table ). There were twenty nodes showing either up-regulation/high transcription on two or three lignocellulosic substrates, and also up-regulation/high transcription specific to the pine substrate (S4 and S5 Tables). The terms relating to putative hydrolases active on carbohydrates were enriched in nodes 7, 14, and 15 (S4 Table ).
Protein extraction and detection Secreted proteins were collected from the same cultures, dia-filtered and identified by ESI-MS/ MS as described in  Couturier et al. (2015). Briefly, short SDS-PAGE runs were performed, allowing 10 μg of proteins to migrate on a 0. 5 cm length. Each one-dimensional electrophoresis lane was cut into two slices of gel and protein identification was performed using PAPPSO "Plate-forme d'Analyse Proteomique de Paris Sud-Ouest" platform facilities. In-gel digestion was carried out according to a standard trypsinolysis protocol. Online analysis of peptides was performed with a Q-exactive mass spectrometer (Thermo Fisher Scientific), using a nanoelectrospray ion source. Protein identification was performed by querying MS/MS data against the genome P. coccineus BRFM 310 v1.
0, together with an in-house contaminant database, using the X!Tandem software (X!Tandem Cyclone, Jouy en Josas, France). All peptides matched with an E-value lower than 0. 05 were parsed with X!Tandem pipeline software. Proteins identified with at least two unique peptides and a log (E-value) lower than -2. 6 were validated. The secretomic information is provided (S5 File).
Data preparation and manipulations QCed reads from each library were aligned to the genome P. coccineus BRFM 310 v1. 0 using TopHat  [37]  with only unique mapping allowed. R was used for data manipulations using our customized scripts  [38]. Raw read counts obtained from HTSeq were used for the calculation using DESeq2  [39]. A total of 11,430 genes having more than five reads were selected for the analysis. The normalized log2 transformed read count of genes showed similar mean and distribution patterns. The log2 fold difference of the gene expression induced by cultivation on aspen, wheat straw, pine, compared to maltose, was calculated at each time point. The statistically significant genes were selected based on adjusted p value < 0. 05 (FDR and Bonferroni correction). Normalized read counts of the genes were produced with the function, counts in DESeq2, which were subsequently log2 transformed  [39].
Construction of integrated omics models Two-time-point transcriptome and secretome models were constructed using the SHIN pipeline (i. e. the ABCDEFGT workflow with improved capacity)  [20]. A SOM was trained with the normalised read count of all conditions using Rsomoclu  [40]  on the computer clusters at Mesocentre (https://equipex-mesocentre. univ-amu. fr/en/welcome-mesocentre-aix-marseilleuniversite/). The matrix of 24 x 19 (456) was used with four neighbouring nodes. The resolution of 25 genes per node was used, which was empirically determined. The epoch of 1000 times more than the map size was applied, i. e. 456,000 = 456 (map size) multiplied by 1000. The initial radius for SOM calculation was determined using neighbour distance function in R Kohonen package  [41]. The following outputs were visualized; 1) transcriptomic models of all biological replicates in four cultivation conditions at two time points; and 2) combined transcriptomic and secretomic models in four cultivation conditions at two time points.
Term-mining and enrichment analysis The GO pipeline was used to count the frequency of gene annotations per nodes in the integrated omics models generated from the SHIN pipeline. It was performed using the individual annotation data sets CAZy database  [7, 8] , The Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and EuKaryotic Orthologous Groups (KOG)  [33] [34] [35]. The appearance of terms was counted using tm package  [42]. The genes with the same annotation appearing more than twice in a single node were selected for enrichment analysis. P values of enriched gene annotations per node were calculated using a function phyper in R stats package for the hypergenometric test  [43]. The following parameters were considered to estimate the overrepresentation (enrichment) of biological terms in nodes; 1) the number of specific terms of interest in nodes; 2) the number of all terms present in the nodes; 3) the number of specific terms of interest in the genome; 4) the number of all terms present in the genome excluding the specific terms of interest.
Acknowledgments We would like to express our gratitude to the  Mesocentre at Aix-Marseille University  for the computing facilities, the  INRA MIGALE bioinformatics platform  (http://migale. jouy. inra. fr)for providing computational resources,  Pedro Coutinho  from  Glycogenomics group at AFMB, Polytech Marseille, France  for assisting RNA-sequencing.
Cases were genotyped using the Illumina Oncoarray (Illumina Inc. ). Controls which were also genotyped using the oncoarray comprised: Published studies: We used GWAS data generated on two non-overlapping case-control series of Northern European ancestry, which have been the subject of previous analyses that are summarised in Supplementary Table  1. Briefly: (1) The UK-GWAS was based on 622 cases ascertained through the Royal Marsden Hospital National Health Service Trust Family History study during 2004-2008  7  , and 5,677 controls from the UK Wellcome Trust Case Control Consortium 2 (WTCCC2)  62. (2) The German-GWAS comprised 1,001 cases ascertained by the German Hodgkin Study Group during 1998-2007  8  , and 1,226 controls from the Heinz Nixdorf Recall (HNR) study. GWAS and meta-analysis.
Standard quality control measures were applied to each of the three GWAS (Supplementary Tables 2, 3 and 4)  7, 8, 63. Specifically, individuals with a low call rate (< 95%) as well as all individuals evaluated to be of non-European ancestry (using the HapMap version III CEU, JPT/CHB and YRI populations as a reference, Supplementary Fig. 1 ) were excluded. For apparent first-degree relative pairs, we excluded the control from a case-control pair or the individual with the lower call rate. SNPs with a call rate < 95% were excluded as were those with a MAF < 0. 01 or displaying deviation from Hardy-Weinberg equilibrium (HWE) (i. e. , P < 10 -6 , Supplementary Table  4 ). GWAS data were imputed to >10 million SNP with IMPUTE2 v2. 3 64 software, using a merged reference panel consisting of data from 1000 Genomes Project (phase 1 integrated release 3, March 2012)  11  and UK10K (ALSPAC, EGAS00001000090/ EGAD00001000195 and TwinsUK EGAS00001000108/EGAS00001000194 studies)  12.
HLA imputation was conducted using SNP2HLA and the Type I Diabetes Genetics Consortium reference panel of 5,225 individuals of European descent  32. The number of variants in the HLA imputation recovered with an information measure of > 0. 80 were 8,436 (94% of total variants), 8506 (95% of total variants) and 8599 (96% of total variants) in the UK-GWAS, German-GWAS and UK-NSHLG-GWAS data sets, respectively. Imputation was conducted separately for each study, and in each, the data were pruned to a common set of SNPs between cases and controls prior to imputation. Poorly imputed SNPs defined by an information measure <0. 80 were excluded. Tests of association between SNPs and cHL were performed using logistic regression under an additive genetic model in SNPTESTv2. 5  65. The adequacy of the case-control matching was evaluated using Q-Q plots of test statistics (Supplementary Fig.
2 ). The inflation factor λ was based on the 90% least-significant SNP  66. Where appropriate, principal components, generated using common SNPs, were included in the analysis to limit the effects of cryptic population stratification that otherwise might cause inflation of test statistics. Eigenvectors for the GWAS data sets were inferred using smartpca (part of EIGENSOFT) by merging cases and controls with Phase III HapMap samples. LD metrics were calculated in vcftools v0. 1. 12b  67 , using UK10K merged 1000 Genomes Project data and plotted using visPIG  68. Replication studies and technical validation. The eight SNPs in the most promising loci (Table  1 ; Supplementary Table  6 ), were taken forward for de novo replication in an additional 1,284 cases from the NSHLG and 2,504 controls from the UK replication 1 series (Supplementary Table  1 ).
78. For each risk locus, a region of strong LD (defined as r 2 > 0. 8 and D′ > 0. 8) was determined, and these SNPs were termed the associated variant set (AVS). TF ChIP-seq uniform peak data were obtained from ENCODE for the GM12878 cell line, and included data for 82 TFs. For each of these marks, the overlap of the SNP in the AVS and the binding sites was determined to produce a mapping tally. A null distribution was produced by randomly selecting SNP with the same LD structure (generated from 1000 Genomes Project and UK10K data) as the risk associated SNP, and the null mapping tally calculated. This process was repeated 10,000 times, and approximate P-values were calculated as the proportion of permutations where the null mapping tally was greater or equal to the AVS mapping tally. An enrichment score was calculated by the tallies to the median of the null distribution.
Thus the enrichment score is the number of standard deviations of the AVS mapping tally from the mean of the null distribution tallies. Promoter capture Hi-C data. To map risk SNPs to interactions involving promoter contacts and identify genes involved in cHL susceptibility, we analysed promoter capture Hi-C data on the LCL cell line GM12878 as a model B-cell  26. Reads from technical replicates (E-MTAB-2323) were combined before processing with HiCUP  79. Significant interactions (i. e. , score ≥ 5) on two biological replicates were determined using CHiCAGO  80. Co-heritability of Hodgkin lymphoma with autoimmune disease. We utilised LD regression to estimate genetic correlation between individual autoimmune diseases and cHL, NSHL and MCHL  35. Summary statistics for published studies of coeliac disease  41  , systemic lupus erythematosus  37  , primary biliary cirrhosis  39  , rheumatoid arthritis  36  , MS  38  and UC  40  were downloaded from the ImmunoBase website (http://www.
Genotype data that support the findings of this study have been deposited in the European Genome-phenome Archive (EGA) under accession codes EGAD00000000022 and EGAD00000000024. Sequencing data, which forms the reference panel for imputation, have been deposited in the European Genome-phenome Archive (EGA) under accession codes EGAS00001000090, EGAD00001000195, EGAS00001000108. Transcriptional profiling data from the MuTHER consortium that support the findings of this work have been deposited in the European Bioinformatics Institute (Part of the European Molecular Biology Laboratory, EMBL-EBI) under accession code E-TABM-1140. Transcriptional profiling data from the Genotype-Tissue Expression (GTEx) project, that support the findings of this work are available here: https://www. gtexportal.
org/ Transcription factor binding data that support the findings of this work are available here: http://genome. ucsc. edu/ENCODE/downloads. html. Promoter capture Hi-C data in GM12878 cells that support the findings of this work have been deposited in the European Bioinformatics Institute (Part of the European Molecular Biology Laboratory, EMBL-EBI) under accession code E-MTAB-2323. The remaining data contained within the paper and supplementary files are available from the author upon request.
Acknowledgements In the  United Kingdom, Bloodwise  (LLR; 10021) provided principal funding for the study. Support from  Cancer Research UK  ( C1298/A8362  supported by the  Bobby Moore Fund)  and the  Lymphoma Research Trust  is also acknowledged. A. S. is supported by a clinical fellowship from  Cancer Research UK. For the UK-GWAS, sample and data acquisition were supported by  Breast Cancer Now , the  European Union  and the  Lymphoma Research Trust. The UK-GWAS made use of control genotyping data generated by the WTCCC. We acknowledge use of genotype data from the British 1958 Birth Cohort DNA collection, which was funded by the  Medical Research Council  Grant  G0000934  and the  Wellcome Trust  Grant  068545/Z/02. A full list of the investigators who contributed to the generation of the data is available from http://www.
We thank the  SEARCH  and EPIC teams, which were funded by a programme grant from  Cancer Research UK  ( C490/A10124 ) and supported by the  UK NIHR BRC  at the  University of Cambridge. We thank  Breast Cancer Now  and the  Institute of Cancer Research  ( ICR ) for support and funding of the UKBGS, and the study participants, study staff, and the doctors, nurses and other health-care providers and health information sources who have contributed to the study. We acknowledge NHS funding to the  Royal Marsden/ICR NIHR BRC. UKGPCS would like to thank  The Institute of Cancer Research and The Everyman Campaign  for funding support. The  UKGPCS acknowledges The Prostate Cancer Research Foundation ,  Prostate Action , The Orchid Cancer Appeal, The  National Cancer Research Network UK, The National Cancer Research Institute (NCRI) , the  NIHR  funding to the  NIHR Biomedical Research data managers  and consultants for their work in the UKGPCS study and urologists and other persons involved in the planning, and data collection of the CAPS study.
Genotyping of the OncoArray was funded by the  US National Institutes of Health (NIH)  ( U19   CA 148537 for ELucidating Loci Involved in Prostate cancer SuscEptibility (ELLIPSE)  project and  X01HG007492  to the  Center for Inherited Disease Research (CIDR)  under contract number  HHSN268201200008I ). Additional analytic support was provided by  NIH NCI   U01 CA188392  (PI: Schumacher). The PRACTICAL consortium was supported by  Cancer Research UK  Grants  C5047/ A7357 ,  C1287/A10118 ,  C1287/A16563 ,  C5047/A3354 ,  C5047/A10692 ,  C16913/A6135 ,  European Commission's Seventh Framework Programme  grant agreement no. 223175  ( HEALTH-F2-2009-223175 ), and The  National Institute of Health (NIH) Cancer Post-Cancer GWAS  initiative grant: No. 1 U19 CA 148537-01  (the  GAME-ON initiative).
At the  MRC University of Glasgow Centre for Virus Research , funding was provided by  Leukaemia Lymphoma Research  ( 12022 ). The  Scotland and Newcastle Epidemiological Study of Hodgkin Disease (SNEHD)  was funded by the  Kay Kendall Leukaemia Fund  and the  Young Adult Hodgkin Case-Control Study (YHCCS)  and the  Epidemiology and Cancer Statistics Group Lymphoma Case-Control Study (ELCCS)  were funded by  Bloodwise. German funding was provided by the  German Cancer Aid , the  Harald Huppert Foundations , The  German Federal Ministry of Education and Research  (eMed, Cliommics  01ZX1309B ), the  Multiple Myeloma Research Foundation , the  Heinz Nixdorf Foundation (Germany) , the  Ministerium für Innovation, Wissenschaft und Forschung des Landes Nordrhein-Westfalen  and the  Faculty of Medicine University Duisburg-Essen.
Flow cytometry Flow cytometry was applied to evaluate the expression of CD83 on B, T, or DCs from spleen, thymus, and lymph nodes. Cell suspensions were first incubated with CD16/32 mAb Fc block (BD Pharmingen, Heidelberg, Germany) for 5 min. Staining with fluorochrome-labeled specific antibodies was performed for 30 min at 4°C in the dark. Samples from spleen were additionally stained with Fixable Viability Dye eFluor 780 (eBioscience, San Diego, CA, USA) for 30 min and later washed with FACS buffer (1% BSA, 0. 1% NaN3, 0. 955% PBS) before Fc blocking. Data were acquired on FACSCanto (BD Bioscience) and analyzed by using FlowJo software (FlowJo, LLC, Ashland, TN, USA). Information about gating strategies can be provided upon request.
statistical analysis Normality was assessed by D' Agostino and Pearson omnibus normality test. MFI-data, serum sCD83 ELISA, and PCR of stimulated lymphocytes were analyzed using Kruskal-Wallis test with Dunn's posttest. Hormones stimulations experiments as well as lymphocyte depletion test was analyzed by ANOVA and Dunnett's test. In vitro secretion of sCD83 by ELISA in stimulated and nonstimulated np and 18 dpp isolated splenocytes was analyzed using two-way-ANOVA with Bonferroni posttests. Further data were analyzed by ANOVA with Tuckey's posttest. Significant differences between groups were indicated with asterisks as follows: *p < 0. 05; **p < 0. 01; and ***p < 0. 001.
The injection time was noted at the zero-time point of the refolding kinetics. A series of imino 1 H HADSOFAST spectra were recorded during the RNA refolding reaction with a time resolution of 10 s per experiment. Prior to Fourier transformation, the recorded data were combined using the appropriate Hadamard transformation to separate the NMR spectra of different species as explained in the text. Kinetic traces were then obtained for individual imino 1 H sites by extracting the signal intensities for individual peaks along the kinetic dimension. Subsequent data fitting was achieved using home-written Python scripts. The imino 1 H frequencies of the two RNA hairpins and the hetero-duplex were sequentially assigned with the help of 2D BEST-optimized 1 H-1 H NOESY spectra recorded with a mixing time of 200 ms.
The overall correlation time, related to the size of the particle, was probed by means of a TRACT experiment  (29)  measuring the relaxation rate difference of the two doublet components of a 1 H-coupled 15 N in a series of 1D experiments. Fast local motions were probed by a heteronuclear NOE (HETNOE) experiment using a standard Bruker library sequence. Finally, exchange contributions to the transverse relaxation of 15 N (CspA) and 13 C (RNA) were probed by CPMG-type relaxation-dispersion experiments  (30, 31)  using a relaxation delay of 60 ms  (40 ms)  for CspA (ACB), and CPMG frequencies ranging from 50 to 1000 Hz. Global data fitting to a 2-site exchange model was performed with the program CHEMEX  (32). Solvent hydrogen exchange rates of imino 1 H in CB and ACB RNA hairpins were measured using a 1D version of the CLEANEX-PM pulse sequence  (33).
The measured kinetic traces of RNA duplex formation in the presence of the different proteins/peptides are Figure  8. Normalized real-time NMR kinetics measured for RNA duplex formation in the presence of (A) CspA-WT (black), CspA-F12I (green), CspA-F18I (red), F-F-F peptide (purple), and (B) PV core (black), triple-K (green) and poly-K (red). In addition, the folding kinetics in the absence of any protein or peptide is shown in gray in both graphs. The displayed data points have been obtained by averaging over all individual traces from well-resolved imino 1 H of the RNA hetero-duplex, and the lines represent the results of a data fit to a bi-exponential model. plotted in Figure  8A , and the results of data fitting to a biexponential model are given in Table  1. Interestingly, both CspA mutants as well as the FFF peptide show some RNA chaperone activity, although reduced with respect to WT CspA.
FUNDING CEA; CNRS;  University Grenoble Alpes ;  French National Research Agency  [ ANR-11-IS07-001 ];  Austrian Science Fund  [ I844 ,  P26550 ,  P28725 ]. We acknowledge access to the high-field NMR and in-vitro expression platforms of the Grenoble Instruct Centre (ISBG:  UMS 3518 CNRS-CEA-UJF-EMBL ) with support from  FRISBI  [ ANR-10-INSB-05-02 ] and  GRAL  [ ANR-10-LABX-49-01 ] within the  Grenoble Partnership for Structural Biology  (PSB). Funding for open access charge: CNRS.