dataesr/scientific-paragraphs-categorization huggingface dataset:
Image Data Analyses For image analysis, the freeware ImageJ v1. 47b and the LOCI plug-in were downloaded from the National Institutes of Health websites (http://rsb. info. nih. gov/ij and http://loci. wisc. edu/software/bio-formats, respectively). To perform automated counting of caspase-3 + cells through time, images were binarized and further processed using the erosion, dilatation, and watershed ImageJ tools. For the single-CTL assay, microwells were selected on the basis of the presence of a single calcein-stained CTL all along the 12-hr movie by visual inspection. Killing scores were measured as mean of caspase-3 + cells over 2-hr time intervals. well as Sophie Allart and Astrid Canivet from the CPTP microscopy platform.
ACKNOWLEDGMENTS We wish to thank  Fatima-Ezzahra L'Faqihi-Olive ,  Valerie Duplan-Eche ,  Delphine Lestrade , and  Manon Farce  ´from the CPTP cytometry platform, as We are grateful to  Eric Espinosa  for helpful discussion and suggestions. We thank  Nicolas Fazilleau  and  Corine Perals  for CTL clone characterization and discussion. We also thank  Daniel Dunia  for discussion. This work was supported by the Brazilian post-doctoral programs from  CAPES  ( 6486/10-0 ) and  CNPq  ( 237654/2012-1 ). Z. V. is supported by  PAPES V and FAPERJ. W. Y. received funding support from the  Damon Runyon Cancer Research Foundation  (postdoctoral fellowship) and the  NIH  (grant  1K08DK093709-01 ). This work also benefited from funding from the  Fondation ARC pour la Recherche sur le Cancer  (grants  EML2012090493  and  SL220100601347 ), the  Institut National du Cancer  (Grant INCa  2012-054 ), and the  Agence Nationale de la Recherche (Laboratoire d'Excellence Toulouse Cancer).
However, even if the most severe mutations were studied in vitro, mild mutations or variants of unknown (clinical) significance (VUCs or VUS) were barely analysed. Thus, only one third of CFTR variations are functionally classified. Finally, in CF, as in other genetic diseases, new molecular diagnosis techniques such as Next Generation Sequencing (NGS) allow the analysis of the whole CFTR locus (including deep intronic regions) but increase the number of VUCS reported on patients. Therefore, geneticists and clinicians have to combine CFTR epidemiological databases, functional in vitro/in vivo analysis and exhaustive clinical data to perform pertinent genotype/ phenotype correlations in patients in order to provide appropriate genetic counselling to families.
Bioinformatics software to analyse NGS data Then, generated data are analysed by bioinformatics tools. Quality criteria such as confidence or coverage are guarantees for optimal sensitivity and specificity of a sequencing run. Bioinformatics analyses are frequently performed by using software developed by sequencers' companies. The three steps include (1) base calling and quality score computation, (2) assembly and alignment and (3) variant calling and annotation. Laboratories developed their own 'inhouse' pipeline mainly to apply filters allowing an easy focus on causing-mutations. Others use free web resources to realign files including BWA (http://bio-bwa. sourceforge. net) or to perform a new variant calling such as Samtools and GATK  [21] [22] [23]. Others serve as Viewer such as Integrative Genomics Viewer (http://www.
broadinstitute. org/igv/). Galaxy website is an integrative platform offering the possibility to view and process files generated by NGS. Many databases, useful for variant annotation and sometimes inserted in others programs or pipelines, are publicly available including the 1000 Genomes Project [24] and the dbSNP database  [25]  (further described in part 3. 1. 1). Many tracks are currently studied (i. e. improvement of bioinformatics tools, comparison of NGS approaches between CF laboratories, development of databases including newly detected NGS variants, publication of guidelines and definition of a diagnosis report model) to make CF molecular diagnosis by NGS suitable for different clinical and familial cases.
Functional characterization of CFTR sequence variations To provide appropriate diagnosis and prognosis to CF patients and also appropriate genetic counselling to families, the impact of variants identified by the techniques detailed above has to be functionally characterized. Some variants such as frequent mutations (found in more than 1% of CF mutated alleles), nonsense or frameshift mutations are readily classified as pathogenic mutations. However, the frequent identification of rare sequence alterations of unknown pathogenicity (VUCS, VUS) substantially complicates test interpretation. Moreover, their number will increase with the diffusion of NGS technologies. To facilitate classification of these variants, CF laboratories have to combine several tools like central mutation databases or CFTR locus specific databases, in silico prediction tools and ex vivo/in vivo functional analyses  [38, 39].
The National Center for Biotechnology Information (NCBI) short genetic variations database dbSNP dbSNP  [25]  is the most comprehensive directory of single nucleotide variations. It catalogues short variations in nucleotide sequences from a wide range of organisms. Genetic Variations may be common, thus representing true polymorphisms, or they may be rare. Some of these rare human entries have additional information associated with them, including disease associations, genotype information and allele origin, as some variations are somatic rather than germ-line events. Genotypes and allele frequencies information for various populations from different studies, including data form the HapMap project, are also available.
Databases specifically collecting data from NGS projects 1000Genomes [24] aimed to find most genetic variants that have frequencies of at least 1% in samples from five populations: East Asian, South Asian, African, European and American ancestries. As in dbSNP, genotypes and allele frequencies information are available for a large number of variants  [40]. Exome Variant Server  [41]  is a database that collects data of the NHLBI GO Exome Sequencing Project (ESP). This project aimed to discover novel genes and mechanisms contributing to various disorders by sequencing the protein coding regions of the human genome (i. e. exome) using NGS technology. As the CFTR gene is widely studied, this tool would not be of added value compared to dbSNP and 1000 Genomes.
Locus Specific Databases (LSDBs) dedicated to CFTR LSDBs are now recognized as the best mode of collecting and curating lists of mutations related to human genetic diseases  [42]. They compile in a single bioinformatics tool disease-causing and non-disease-causing sequence variations identified by genetics laboratories in families with a history of a given Mendelian disease. The most sophisticated ones integrate clinical and biological data, information on the geographic and/or ethnic origin, frequency of variations in the general population, mutation hot spots and all useful information for diagnosis, prognosis and the evaluation of genotype/phenotype relationships  [43]. Here we choose to detail three LSDB dedicated to CFTR that provide complementary information for the interpretation and the characterization of variants identified in diagnostics practice.
Clinical and functional translation of CFTR database CFTR2 CFTR2  [45]  is a website designed to provide information about specific CF mutations to patients, researchers and the general public. For each mutation included in the database, it provides information about whether a given mutation causes cystic fibrosis when combined with another CF-causing mutation and clinical and biological information (sweat chloride, lung function, pancreatic status and pseudomonas infection rates) in patients carrying the mutation. A specific section for health practitioners and scientists provides more in-depth and research-related information. The goal of the CFTR2 project is to categorize all mutations seen in CF patients as diseasecausing (always resulting in CF when combined with another CF-causing mutation), neutral or mutation of varying clinical consequences (CF and CFTR-RD).
Mutations that have not been fully analysed are considered of unknown clinical significance. The major advantages of CFTR2 are (i) the collection of detailed clinical characteristics on large cohorts of individuals  [46]  that provide useful information related to a given genotype, and (ii) results of functional testing that are key arguments for their final interpretation  [47]. However, this database only collects clinical and genetic data of CF patients (from national registers) that can lead to a bias of phenotypic spectrum assessment of several mutations considered as CF-causing mutations while they were also reported in CFTR-RD patients in trans of other CF-causing mutations.
The French molecular database CFTR-France CFTR-France  [48]  has been developed since 2012 with the aim to collect, store and process any category of variants identified in the CFTR gene, thanks to the collaboration of nine French laboratories with high expertise in the molecular analyses of this gene. Its specificity is to compile and annotate any category of variations (disease-causing, non-disease-causing and variants of unknown clinical significance) that have been identified by collaborators in patients affected with CF or CFTR-RD, in foetuses with abnormal ultrasonography (e. g. echogenic bowel), newborns with pending or inconclusive diagnosis and asymptomatic individuals carrying at least one sequence variation on each CFTR gene (i. e. carrying two variations in trans). The database includes the main clinical data of these individuals, genetic information from familial segregation studies and various variant annotations (frequency in patients and controls populations, sequence homology, predicted or experimentally assessed functional impact, etc.
), allowing the analysis of genotype/phenotype relationships. Thus, CFTR-France, by collecting all phenotypes, reflects the phenotypic spectrum of a large number of mutations. It also reports mutations in complex alleles with association frequencies (related to all individuals recorded in the database), and gives the up-to-date HGVS nomenclature of mutations. Data collected in CFTR-France are provided by level 2 (specialised) and reference laboratories, so that patients analysed only by level 1 laboratories (searching for the most common mutations) are not included in the database. Note: Access to CFTR-France is currently restricted to collaborators. A public access program is in progress for the medical and scientific community and for patients and families.
also called 'CFTR1' is an open access database dedicated to the collection of sequence variations in the CFTR gene for the international CF genetics research community. It was initiated by the Cystic Fibrosis Genetic Analysis Consortium (CFGAC) in 1989 and is maintained by the Cystic Fibrosis Centre at the Hospital for Sick Children in Toronto. CFMDB allows the direct submission of new variants by laboratories, by filling out an on-line standardized form with the possibility to detail pheno-.
Acknowledgements We acknowledge all members of the technical team for CF molecular diagnosis:  Jean-Pierre Altieri ,  Fanny Verneau  and  Lise Larrieu. We also thank the coordination team of the CFTR-France genetic database:  Corinne Bareil  and  Corinne Thèze  and all the collaborating members of this project. We are also grateful to the  French Network of laboratories specialized in molecular diagnosis of CF (GenMucoFrance). Finally, we thank the  French Association against CF (Vaincre la Mucoviscidose)  for its constant support.
0; QUAL<25. 0; QD<4. 0; DP<5; DP<2000; GQ<15) GATK Combine Variant identified Threshold 30% of reads Heterozygous: Read support: 30-65% Homozygous >70% Study objectif Databases Sequencing protocol Regions analyzed Target enrichment In silico analysis Performance Initial DNA quantity Diagnostic rate per sample Proof of concept of CFTR assay by NGS Variant frequency > 1% dbSNP Exome Variant Server CFMDB All CFTR exons and 20bp of all intron/splice sites (10 343bp) CFTR2 Custom AmpliSeq panel (Life technologies) SIFT Mutation Taster 20ng ND False positive c. 2052del Resequencing mutations Validation technique 1000 Genomes project dbSNP Exome Variant Server CFMDB All CFTR locus (208kb → 181kb after masking repetitive DNA elements) CFTR2 Hybrid capture: Custom NimbleGen SeqCap EZ Annovar SIFT, PolyPhen2, PhyloP, Mutation Taster, phastCons UCSC Genome Browser 98.
also called 'CFTR1' is an open access database dedicated to the collection of sequence variations in the CFTR gene for the international CF genetics research community. It was initiated by the Cystic Fibrosis Genetic Analysis Consortium (CFGAC) in 1989 and is maintained by the Cystic Fibrosis Centre at the Hospital for Sick Children in Toronto. CFMDB allows the direct submission of new variants by laboratories, by filling out an on-line standardized form with the possibility to detail pheno- typic data, genotype (i. e. other variants identified in patient) or epidemiological data. The key point of this database is to collect the largest number of CFTR sequence variations identified in patients, relatives and partners. On the other side, because the submission procedure applies only to the initial report of each variant, CFMDB does not provide frequency data, available with the two other databases described below.
A. Data sets and benchmarking methods Three hyperspectral data sets have been used in these experiments. University of Pavia: The data set has been acquired by the ROSIS sensor during a flight campaign over Pavia, northern Italy. 103 spectral channels were recorded from 430 to 860 nm. 9 classes have been defined for a total of 42,776 referenced pixels. Kennedy Space Center: The data set has been acquired by the AVIRIS sensor during a flight campaign over the Kennedy Space Center, Florida USA. 224 spectral channels were recorded from 400 to 2500 nm. Because of water absorption the final data set contains 176 spectral bands. 13 classes have been defined for a total of 4,561 referenced pixels. Heves: The data set has been acquired by the AISA Eagle sensor during a flight campaign over Heves, Hungary.
It contains 252 bands ranging from 395 to 975 nm. 16 classes have been defined for a total of 360,953 referenced pixels. For each data set, 50 training pixels per class were randomly selected and the remaining referenced pixels were used for validation. 20 repetitions were done for which a new training set have been generated randomly. The range of each spectral variable has been stretched between 0 and 1. Reported results are the average Kappa coefficient and the average processing time in seconds (including selection of hyperparameters, training process and prediction process). In order to test the statistical significance of the observed differences, a Wilcoxon rank-sum test has been computed between each pair of methods. For comparison, SVM and RF classifiers have been tested using the Scikit-learn Python package  [23].
B. Discussion Results are reported in Table  II. In terms of accuracy, one of the proposed parsimonious models performs the best for each data set. SVM performs the best for two data sets and KDC performs the best for one data set. In particular, for Heves data set, (n)pGP 1 provides the best results. For University of Pavia and Kennedy Spectral Center data sets, SVM provides the best results but the differences with (n)pGP 1 are not statistically significant. RF usually provides lower accuracy. However, RF is the fastest algorithm, by far. KDC performs the worst in terms of processing time, because each class involves a n × n kernel matrix. Parsimonious models perform on average as fast as SVM and are much faster than KDC. For the proposed models, best results are obtained by (n)pPG 1 , which are the least parsimonious models.
Phylogenetic and Sequence Analysis The previously described CpomOR19 amino acid sequence  (Bengtsson et al. , 2012)  was used as a query in BLASTp search on the NCBI database (http://www. ncbi. nlm. nih. gov/BLAST/Blast. cgi). Among hits, putative ORs belonged to lepidopteran species only (C. pomonella, S. littoralis, Bombyx mori, Heliothis virescens, Helicoverpa armigera, Helicoverpa assulta, Manduca sexta, and Danaus plexippus). Sequences of the putative ORs retrieved were aligned with MAFFT, using the FFT-NS-2 algorithm with default parameters. A maximum likelihood tree was constructed with MEGA6 using the JTT+F algorithm with a bootstrap consensus inferred from 1000 replicates and Poisson correction of distances  (Tamura et al. , 2013). The membrane topologies and transmembrane domains of CpomOR19 and SlitOR19 were predicted with five different prediction models-TMHMM (https://www.
cbs. dtu. dk/services/ TMHMM/), METSAM-SVM (http://bioinf. cs. ucl. ac. uk/ psipred/), TOPCONS (http://topcons. cbr. su. se/), RHYTHM (http://proteinformatics. charite. de/rhythm/), and TMPRED (http://www. ch. embnet. org/software/TMPRED_form. html). From these, we selected the model that best fitted the OR characteristic structure (seven-transmembrane domains and extracellular C-terminus) and illustrated it with Protter  (Omasits et al. , 2014).
The reference electrode was manually inserted through the eye. The signal from the olfactory sensory neurons (OSNs) was amplified 10 times with a probe (INR-02, Syntech, Hilversum, the Netherlands), digitally converted through an IDAC-4-USB (Syntech) interface, visualized and analyzed with the software Autospike v. 3. 4 (Syntech). During the recording sessions, a constant flow of 0. 65 m/s of charcoal-filtered and humidified air was delivered through a glass tube with the outlet 15 mm apart from the antenna. The panel of odorant stimuli was presented to the insect by blowing air through pipettes inserted in a lateral hole of the glass tube delivering the constant charcoal-filtered humidified air. The air puff was controlled with a stimulus controller (Syntech SFC-1/b) and consisted of a flow of 2.
SSR responses were quantified by counting the number of spikes for 500 ms starting from the onset of the response (as determined by the earliest response for the recording session), subtracting the number of spikes during 500 ms before response. Five whole-panel screenings for ligands of CpomOR19 were performed, screenings of the panel of structurally related compounds were done five times for CpomOR19 and SlitOR19. For dose response experiments, eight replicates were carried out at each dose for each receptor. Responses of CpomOR19 and SlitOR19 to the panel of structurally related compounds and dose response experiments  were compared with Two-way ANOVA with repeated measures, followed by LSD post-hoc test. All statistical analyses were performed using SPSS Version 19. 0 (IBM Corp. , Armonk, NY, USA).
Cells expressing proCPA6 with a FLAG tag (WT CPA6-FLAG) or with a mixture of FLAG and HA tagged WT CPA6 showed no significant difference in activity. WT CPA6-HA co-expressed with WT CPA6-FLAG was used to normalize data points to 100%. CPA6-FLAG co-expressed with inactive mutants (Asn271Ser or Glu398Gln) or pcDNA 3. 1 empty vector showed no significant difference from CPA6-FLAG co-expressed with Arg36His CPA6-HA, with means ranging from ~55-65% (Fig 5A ). ECM was collected and analyzed by SDS-PAGE and probed with an anti-HA antibody. WT, Asn271Ser or Glu398Gln CPA6 were present in the ECM at approximately equal levels, while CPA6 resulting from Arg36His-proCPA6 was present at approximately 50% the levels of WT CPA6 (Fig  5B ). Because the levels of ECM CPA6 activity were not reduced below the 50% value, the mutants do not appear to have dominant negative effects on WT CPA6.
Previous examinations of CPA6 in the ECM have focused on steady state levels at equilibrium. To examine the rate at which CPA6 is deposited into the ECM and degraded from the ECM, cells expressing proCPA6 were incubated with 35 S methionine and cysteine for 20 minutes (pulse) to allow incorporation of the labeled amino acids, followed by 1-24 hours incubation (chase) with media containing unlabeled methionine and cysteine. ECM samples were collected at the end of the indicated chase time and analyzed by SDS-PAGE. CPA6 represents the most prevalent 35 S-labeled protein in the SDS extracts of ECM from HEK293T cells transfected with CPA6 plasmid (Fig 6 ), although other methods of ECM extraction did not show CPA6 to be a major band (data not shown). Blank vector alone showed only faint bands between 37 and 50 kDa, and no band at 37 kDa where mature CPA6 is detected when cells are transfected with CPA6-expressing plasmid (Fig  6 ).
For species with as-yet uncharacterized centromeres, our predictions will serve as the basis for targeted experimental validation and could be used to create new plasmid tools in these yeasts. Our results suggest that Centurion has great potential to identify the centromere locations of many yeasts for which standard techniques have failed to date. Furthermore, we demonstrate that Centurion works well even with very limited sequencing depth Hi-C libraries generated from pooled samples, making it a practical as well as powerful tool to use on single microorganisms and metagenomic mixtures. Centurion is freely available as open source software at http://cbio. ensmp. fr/centurion.
Single organism Hi-C data We use Hi-C data gathered in two previous studies: an asynchronous budding yeast (S. cerevisiae) sample (  16 ) and three different stages of the human malaria parasite P. falciparum  (26). For the budding yeast Hi-C data, we download and use the files HindIII + MspI (intra and inter) from http://noble. gs. washington. edu/proj/yeastarchitecture/sup. html. For the three stages of P. falciparum, we download and use the Hi-C raw contact counts at 10 kb resolution from GEO archive (Accession codes: GSM1215592, GSM1215593, GSM1215594).
The Marie-Nelly et al. method often works well for identifying centromeres using Hi-C libraries with very high sequencing depth; however, when Hi-C sequencing depth is limited or when loci other than centromeres strongly cluster, the first step of the procedure, called 'pre-localization', sometimes fails to identify the correct fixed size window in which the centromeres reside. We hypothesized that the joint centromere calling by Centurion, which leverages data from all chromosomes at once, might alleviate this instability. To test this hypothesis, we applied the Marie-Nelly et al. method to the same four datasets (one S. cerevisiae and three P. falciparum) described above. As shown in Supplementary Figure  S4 , in each of these four datasets Centurion identifies centromeres with better accuracy than the Marie-Nelly et al.
Resolution, sequencing depth and prediction accuracy To assess the stability of our predictions, we simulated 500 bootstrapped datasets of S. cerevisiae and of each stage of P. falciparum with an expected total number of reads equal to the contact count matrices. These bootstrapped samples were obtained by drawing a contact count for each pair of loci i and j from a Poisson distribution of intensity c ij. We then ran the optimization process on the bootstrapped datasets, starting with initial values randomly placed within 40 kb of the centromere calls from our optimization in Supplementary Tables S1-S4. Our results show that the optimization is very stable (average variance of 25 bp for ring, 6 bp for schizont and 12 bp for trophozoite), suggesting that the stochastic sampling of the sequencing procedure does not significantly affect centromere predictions.