dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments AD was supported by  Fundação para a Ciência e a Tecnologia ,  Portugal  ( SFRH/BPD/78075/2011  and  EXPL/BBB-BEP/1356/2013 ). FP has been supported by the  National Grid Infrastructure NGI_GRNET, HellasGRID , as part of the  EGI. IFB  acknowledges funding from the  "National Infrastructures in Biology and Health" call of the French "Investments for the Future" initiative. The  WeNMR  project has been funded by a European FP7 e- Infrastructure grant , contract no. 261572. AF was supported by a grant from  Labex CEBA  ( Centre d'études de la Biodiversité Amazonienne ) from  ANR. MC is supported by  UK's BBSRC  core funding. CSC was supported by  Academy of Finland  grant No. 273655  for ELIXIR Finland. The  EGI-InSPIRE project (Integrated Sustainable Pan-European Infrastructure for Researchers in Europe)  is co-funded by the  European Commission  (contract number:  RI-261323 ).
That said, we could verify that mutations within the NFKBIE gene were somatic and not germline variants in all cases with available material (Tables  S3  and S7 ). For the remaining NF-B mutations, we cannot formally exclude the possibility that they are rare germline variants (despite extensive filtering against various SNP databases) and hence decided to focus on NFKBIE, which, importantly, was also the most frequently mutated NF-B gene.
5%) recalls the significantly higher frequency of SF3B1 (A) Schematic representation of the human IB protein with its key functional domains. Color-coded symbols depict NFKBIE alterations with a variant allelic frequency >10% detected in the discovery and validation CLL cohorts using targeted deep sequencing. All missense mutations were predicted to be damaging by the prediction software Polyphen-2. (B) NFKBIE mutation frequencies as determined by HaloPlex or GeneScan analysis. The total number of tested cases included in each category is indicated above each bar. Significant differences in NFKBIE mutation frequencies between IGHVunmutated CLL and selected poor-prognostic stereotyped subsets are indicated; a borderline significant trend was also seen when comparing U-CLL with #6 (P = 0. 06).
Genes that showed at least 50% difference in both c1 and c2 compared with either Mock or UT were selected. The list of genes is provided in Table  S12. (G) Dose-response curves to the BTK inhibitor ibrutinib in primary CLL samples with WT NFKBIE (n = 4) and deleted NFKBIE (n = 4). Data were normalized against ibrutinib-naive CLL cells in culture. In NFKBIE WT cases, borderline significance was observed when comparing IgM-stimulated versus unstimulated cells (P = 0. 07 at 0. 1 µM concentration, P = 0. 06 at 1 µM concentration, and P = 0. 05 at 2. 5 µM concentration). Error bars indicate standard error. DNA Analyzer (Applied Biosystems) and analyzed with Peak Scanner Software v1. 0 (Applied Biosystems). Western blots, co-IP assays, and cell fractions. Primary CLL cells were washed in PBS and lysed for 10 min in ice-cold RIPA buffer supplemented with phosphatase/protease inhibitors (Roche).
1% Tween-20) with 5% wt/vol BSA, washed (3× 15 min) in TBS-T (0. 1% Tween-20), and finally rinsed 5 min in TBS buffer only. All incubation steps were performed at room temperature. Membranes were scanned and imaged using the Odyssey CLx Infrared Imaging System (LI-COR Biosciences). Protein bands were quantified using ImageJ software (National Institutes of Health). Stimulation of BcR and CD40 signaling pathways. Two million viable primary CLL cells per milliliter were grown in RPMI medium supplemented with 10% FBS (Gibco), 1% PEST (Gibco), and 1% l-glutamate (Gibco), supplemented with 25% vol/vol serum-free supernatant from the T cell hybridoma cell line MP6  (Rosén et al. , 1986) , as a source of thioredoxin  (Söderberg et al. , 1999; Nilsson et al. , 2000). For stimulation of the BcR, the modified RPMI medium was supplemented with 3 µg/ml AffiniPure F(ab)2 fragment rabbit anti-human IgM (Jackson ImmunoResearch Laboratories, Inc.
For the validation cohort, we took advantage of the automated HaloPlex protocol (http:// www. chem. agilent. com/Library/usermanuals/Public/G9900-90020. pdf) using a Bravo Automated Liquid Handling Platform (Agilent Technologies). The libraries were subsequently sequenced using a HiSeq 2000 sequencing system (Illumina). Targeted sequencing data analysis. Illumina adapters were trimmed using Cutadapt, and the reads were aligned to the human genome reference hg19/ NCBI GRCh37 using the MOSAIK alignment tool version 2. 2  (Martin, 2011). SAMTools was used for file format conversion and sorting. Using a modified version of GATK-lite, the aligned reads were mapped to their corresponding HaloPlex fragment. Variants were detected using an in-house, purpose-built variant caller (SNPmania) and annotated using ANNOVAR  (Wang et al.
, 2010). Exonic variants were kept if they fulfilled the following criteria: (a) having a variant allele ratio of ≥0. 1, (b) not in dbSNP and/or 1,000 genomes unless annotated in the Cosmic database, and (c) supported by at least two amplicons unless the position was covered by a single amplicon only. Sanger sequencing and GeneScan analysis. Selected variants were sequenced using the BigDye Terminator v3. 1 Cycle Sequencing kit and an ABI 3730 DNA Analyzer (Life Technologies) using standard protocols. For GeneScan analysis, the following oligonucleotides were used for the analysis of the 4-bp deletion in NFKBIE: forward primer, 5-[Hex]CCTCAAAAGTGGGCTGAG-3; and reverse primer, 5-CAAGGAACCACAGGAGAAGG-3. Genomic DNA was amplified by hot-start PCR with Platinum-Taq DNA Polymerase (Invitrogen) and 60°C as annealing temperature.
Gene expression analysis. Gene expression was studied using Affymetrix GeneChip Human Gene 2. 0 ST Arrays (Affymetrix) and 250 ng total RNA according to standard protocols. Data were analyzed in R (The R Project for Statistical Computing) using packages from the Bioconductor project and normalized using the robust multi-array average method  (Irizarry et al. , 2003). To search for differentially expressed genes, an empirical Bayesmoderated Student's t test was applied using the "limma" package. The p-values were adjusted using the method of Benjamini and Hochberg to address potential problems with multiple testing  (Benjamini and Hochberg, 1995). Genes with an adjusted p-value <0. 05 were regarded as differentially expressed. Gene annotation enrichment analysis was performed using the DAVID Bioinformatics Resources.
Ibrutinib treatment and cell viability test. Unstimulated and IgMstimulated (10 µg/ml for 15 min) primary CLL cells from four NFKBIE WT patients and four NFKBIE-deleted patients were plated in quadruplicate wells at a density of 100,000 cells per well in 96-well plates followed by ibrutinib (PCI-32765; Selleckchem) treatment at 0, 0. 1, 1, 2. 5, and 10 µM concentration for 72 h. Alamar Blue (Life Technologies) was added, and cell viability was measured after 24 h using a VICTOR plate reader (PerkinElmer) and standard protocols. Data were normalized against ibrutinib naive matched controls.
Figure1. Recurrent aberrations within the NFKBIE gene. (A) Schematic representation of the human IB protein with its key functional domains. Color-coded symbols depict NFKBIE alterations with a variant allelic frequency >10% detected in the discovery and validation CLL cohorts using targeted deep sequencing. All missense mutations were predicted to be damaging by the prediction software Polyphen-2. (B) NFKBIE mutation frequencies as determined by HaloPlex or GeneScan analysis. The total number of tested cases included in each category is indicated above each bar. Significant differences in NFKBIE mutation frequencies between IGHVunmutated CLL and selected poor-prognostic stereotyped subsets are indicated; a borderline significant trend was also seen when comparing U-CLL with #6 (P = 0. 06).
Chiorazzi, and the European Community's Seventh Framework Program (FP7/2007-2013) under grant agreement no. 259796 (DiaTools), Associazione Italiana per la Ricerca sul Cancro (AIRC; Investigator Grant and Special Program Molecular Clinical Oncology-IG and 5 per mille #9965) and Ricerca Finalizzata 2010 (RF-2010-2318823)-Ministero della Salute, Roma; IGA MZ CR NT13493-4, CEITEC project CZ. 1. 05/1. 1. 00/02. 0068, and FP7-HEALTH, no. 306242 and MSMT, no. 7E13008. The authors declare no competing financial interests.
Chiorazzi, and the European Community's Seventh Framework Program (FP7/2007-2013) under grant agreement no. 259796 (DiaTools), Associazione Italiana per la Ricerca sul Cancro (AIRC; Investigator Grant and Special Program Molecular Clinical Oncology-IG and 5 per mille #9965) and Ricerca Finalizzata 2010 (RF-2010-2318823)-Ministero della Salute, Roma; IGA MZ CR NT13493-4, CEITEC project CZ. 1. 05/1. 1. 00/02. 0068, and FP7-HEALTH, no. 306242 and MSMT, no. 7E13008. The authors declare no competing financial interests. Author contributions: L. Mansouri, L. -A. Sutton, and V. Ljungström performed research, analyzed data, and wrote the paper. S. Bondza, L. Arngården, S. Bhoi, J. Larsson, D. Cortese, A. Kalushkova, R. Gunnarsson, E. Young, E. Falk-Sörqvist, P. Lönn, K. Plevova, A. F. Muggen, X. -J.
The Orban-Hu model has been programmed with Matlab© and validated with results presented in their paper  [9]. The solution procedure is summarized by the following set of equations: 2 -2 2 2 - 0 0 0 0 0 1 1 0 0 1 - - Δ Δ Δ Δ = 0 Δ 0 Δ where Δ is the increment in internal pressure, the current corner radius, a parameter linked to the current corner thickness and the current slope to the hardening curve, and two "constants" which depend on the friction coefficient, the current internal pressure, the current slope to the hardening curve and the increase in length of the contact surface in the wall area. The resolution of this set of equations permits to calculate the increase of the corner radius Δ , contact length in the wall Δ , the strain in the corner Δ and the friction force Δ.
Finite element model Fig. 2  shows the finite element half-model for tube hydroforming in a square die. The simulations are run with LS-DYNA/Explicit© software  [12]. The geometry is meshed with 51448 nodes and 51164 Belytschko-Tsay shell elements. The element size is based on the smaller detail in the model, meaning the die corner radius r=5mm. The die is supposed to be a rigid material. The tube material, a copper, is considered elastic-plastic and isotropic; in the plastic range, it follows the Swift law: ( ) = 263. 63(0. 0043 + ̅ ). The Swift parameters have been identified from experimental hardening curve obtained with the tube bulging test  [13]. The tube is clamped at its two ends. Finally, an internal pressure is applied on the inner surface of the tube; it varies from 15 to 28MPa, based on experimental measurements, in a virtual period of 0.
Conclusion The paper has proposed the study of friction effect and thinning during tube hydroforming in a square die with the analytical model developed by Orban and Hu, numerical simulations with LS-DYNA/Explicit software and experiments. Comparisons between results obtained with these three approaches have been done. The tendencies are well described by the two numerical approaches but it is difficult to find similar results. Amongst the different parameters that can be post-processed, the minimal thickness seems to be the best for the evaluation of the friction coefficient. The difficulty to find similar results is due on the different assumptions done for the two numerical approaches: the Orban-Hu model considers that a slice of the shaped tube is representative of the tube hydroforming in a square die and does not take account what happen along the tube length.
Method 2. 1. Participants. The initial sample included 391 individuals referred consecutively for assessment and outpatient treatment. All were recruited exclusively in one specialized unit, which is part of the public health system (viz. , Pathological Gambling Unit of the Psychiatry Department at the University Hospital of Bellvitge, in Barcelona, Spain). The diagnosis of gambling disorder was confirmed by administration of the Structured Clinical Interview for DSM-IV Axis I disorders  [26]. Of this initial sample, 37 participants were excluded due to missing data in the main variables considered by the present study. The final sample of 354 individuals was predominantly male (89. 5%), and a majority had only primary or no formal education (58. 6%). Over half the participants were in employment (57.
Procedure. In accordance with the manualized assessment protocol and treatment model of the unit  [45] , participation in the study involved two visits. On the first visit, participants took part in a semistructured, face-to-face interview in order to further explore their gambling behavior, psychopathology, and personality traits. Sociodemographic data (e. g. , education, occupation, and marital status) and required additional clinical information was also collected during this visit. On the second visit, participants completed all the remaining instruments mentioned above. The two interviews were conducted within a time-frame of one week by a clinical psychologist and a psychiatrist.
Acknowledgments Partial financial support was received from the  Ministerio de Economía y Competitividad  ( PSI2011-28349 ),  AGAUR  ( 2009SGR1554 ), and  PSI2014-56303-REDT: PROMOSAM. CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn)  and  CIBER Salud Mental (CIBERsam)  are both initiatives of the ISCIII. This work forms part of the Ph. D. thesis of Neus Aymamí at the  Universitat Autònoma de Barcelona (School of Psychology ).
C. Distributed directory I2P uses a distributed directory to store its network metadata that is composed of leasesets and routerinfos. The database is known as the netDB and is a Kademlia-based  [7]  distributed hash table, composed of floodfill nodes. Floodfill nodes are normal I2P routers with high bandwidth rates. This means that the netDB is not formed by the entire network, as in KAD, but only by a subset of all I2P routers. The netDB works as any Kademlia-based distributed hash table when storing and retrieving data. Figure  2  presents a simplification of this procedure, where a node N stores data with a particular key value. The first step is to retrieve those nodes close to the key value, known as replica set, in this case nodes B, C, D and E (considering a replica set of four nodes, as an example).
B. Cost of increasing netDB's user-base The current design of the I2P network states that in order for a node to become a floodfill peer, i. e. become part of the netDB, it needs to have a certain bandwidth available and a certain uptime. Currently, a node needs to have more than 128 KBps of available bandwidth and a minimum uptime of 200 minutes in order to become a floodfill node. It becomes logical that a node needs more than three hours of uptime since the size of the replica set is formed by only three nodes and considering unstable nodes will lead to data loss. We argue that this analysis has become irrelevant for two reasons: nowadays available bandwidth and the minimal memory consumption a node needs when having a floodfill status are negligible. Bandwidth analysis: On the one hand, according to I2P's designers 5 the bandwidth a floodfill node needs (F BW) to maintain its floodfill status and to answer remote queries can be computed as shown in Equation  3 , where N is the 5 https://geti2p.
Memory consumption analysis: The extra memory consumption a floodfill node needs to maintain netDB's data is also very small. We monitored our local floodfill node to determine the required storage based on the number of routerinfos and leasesets kept. After 48 hours, where the floodfill is well integrated into the netDB, it keeps a total average of 35 leasesets and 1966 routerinfos. We computed an average leaseset size of 1068 bytes and an average routerinfo size of 849 bytes, where give us a total consumption of 35 * 1068 bytes + 1966 * 849 bytes = 1. 62 MB. We consider that this cost is completely affordable as well. Therefore, a floodfill node will need approximately an extra 0. 38 MBps of bandwidth and 1. 62 MB of memory, which is, for nowadays computer configurations, almost negligible.
Statistical Model Checking with PLASMA The algorithms we present here are implemented in our SMC platform PLASMA (Platform for Learning and Advanced Statistical Model checking Algorithms  [5] ). PLASMA is modular, allowing new modelling languages, logics and algorithms to be plugged-in and take advantage of its graphical user interface, its integrated development environment and its ability to correctly divide simulations on parallel computational architectures. We introduce here the basic features of SMC with PLASMA that are relevant to what follows. SMC algorithms work by constructing an automaton to accept only traces that satisfy a specified property. The state space of the system is not constructed explicitly -states are generated on the fly during simulation -hence SMC is efficient for large, possibly infinite state, systems.
Results We considered 5, 039 reconciled gene trees and 50, 389 extant gene adjacencies, forming 6, 074 DeCo instances, with genes taken from 36 extant mammalian genomes from the Ensembl database in 2012. In  [2] , this data was analyzed with DeCo, using the cost scheme (1, 1), that computed a single parsimonious adjacency forest per instance. These adjacency forests defined 96, 482 ancestral adjacencies (adjacencies between two pre-speciation genes from the same ancestral species), covering 112, 188 ancestral genes. We first considered all 6, 074 instances, and computed for each signature the robustness of the parsimonious signature obtained with the cost scheme (1, 1). Interestingly, we observe (Fig. 3(A) ) that for more than half of the instances, the parsimonious signature is robust to a change of cost scheme, as the associated cone is the complete first quadrant of the real plane.
albopictus genome size-ranging from 0. 62 to 1. 66 pg-has been suggested  (Rao and Rai 1987; Kumar and Rai 1990) , supporting the hypothesis of a significant amount of TE activity, with more copies present in some populations than in others  (McLain et al. 1987; Black et al. 1988 ). However, no study is currently aimed at finding and quantifying TEs in a comprehensive manner in this species. Several bioinformatic solutions now enable the de novo assembly of TE sequences directly from NGS genomic data sets without the need for a reference genome. These methods assume that reads belonging to TEs or other repetitive DNAs are overrepresented among the sequenced reads. Current pipelines such as RepARK  (Koch et al. 2014)  and TEdna  (Zytnicki et al. 2014 ) use whole NGS genomic data sets or only the unassembled reads left after a genome assembly.
Here, we present a new pipeline, dnaPipeTE (De Novo Assembly and Annotation Pipeline for Transposable Elements), that combines previous methods by allowing fast and accurate assembly of repeat sequences from a small genomic sample with dedicated NGS tools and by performing quantification and annotation of TEs and repeats for comparative analysis. The cornerstone of dnaPipeTE is the use of Trinity  (Grabherr et al. 2011 )-originally designed for RNAseq data assembly-to assemble repeats from lowcoverage genomic data sets, which produce complete repeat sequences and enable the recovery of alternative consensuses within one TE family. Our pipeline also performs an automatic annotation of repeats using RM and the Repbase database  (Jurka et al. 2005)  and produces different data and figures for the quantification of repeats.
Materials and Methods dnaPipeTE: A Pipeline to Assemble, Annotate, and Quantify Repetitive Sequences from Small Unassembled NGS Data Sets dnaPipeTE is a fully automated pipeline designed to assemble and quantify repeats from genomic NGS reads. It is freely available for download at https://lbbe. univ-lyon1. fr/-dnaPipeTE-. html (under the GPLv3). Figure  1  shows the main steps in the dnaPipeTE pipeline. Our pipeline takes as input a FASTQ  (Cock et al. 2010 ) file containing quality filtered short reads. dnaPipeTE then performs uniform samplings of the reads to produce low coverage data sets used during analysis. The samples must represent less than 1Â coverage to avoid the assembly of nonrepeated genome content; using -Overview of the dnaPipeTE pipeline. First, genomic reads in FASTQ format are sampled.
S1 , Supplementary Material online, for examples with 0. 1Â and 0. 25Â). dnaPipeTE requires at least three samples of the original genomic data set: Two for the assembly step and an independent third used for the quantification steps. Our pipeline is currently designed to use only single-end reads because training analyses showed that using paired-end reads could produce chimeras during repeat assembly (data not shown). We developed dnaPipeTE using 100-bp reads, which are currently the most frequently generated NGS data sets, but our implementation would work with any read size.
S2 , Supplementary Material online). In the latest versions of Trinity (!r20140717), contigs are built from "clusters" that correspond to units of the de Bruijn graph made during the assembly. These clusters are divided into genes and finally "isoforms" that represent the alternative transcripts of a gene in RNAseq studies. Applied to low-coverage DNA data, one gene ideally represents one repeat family, in which isoforms are structural variant copies belonging to one family (copies with insertions or deletions for example) or to closely related families. An isoform present in Trinity. fasta output following all iterations of the Trinity program is referred to as a "dnaPipeTE contig. " During the assembly step in dnaPipeTE, Trinity (version r20140717) was used with default parameters for single-end reads, with the exception of the minimum coverage to join k-mer contigs set to 1 to retain contigs from low copy repeats (Haas B, personal communication).
Contig Annotation with RepeatMasker After the assembly step, dnaPipeTE contigs are annotated using RM, for which a built-in or custom repeat library can be specified. Following the 80-80-80 rule proposed by  Wicker et al. (2007) , contigs with 80% query coverage on 80% of subjects (databases) were stored as "full-length," and queries with 80% hits on fewer than 80% of subjects were stored as "partial" (fig. 2 ). Of the other contigs annotated by RM, only the order information (according to  Wicker et al. 2007.
Repeat Quantification For quantifying the repeats, BLASTN software  (Altschul et al. 1990 ) was found to perform better than classic short-read aligners such as Bowtie2  (Langmead and Salzberg 2012). Indeed, the divergence between a dnaPipeTE contig-that is, a consensus sequence for a repeat family-and its reads belonging to different copies can be higher than the divergence between a gene or a transcript and its reads, and requires a more sensitive approach. During the "BLAST 1" step (fig. 1 ), reads from the "BLAST" sample are matched against all the dnaPipeTE contigs to estimate the genome proportion of each assembled repeat. However, we cannot quantify the unassembled repeats during this step. Thus, to obtain an overall estimation of repeat content, the BLAST sample is first matched against a database composed of the annotated contigs of dnaPipeTE and the repeat library in order to recover reads associated with misassembled or missing repeats ("BLASTN 2," fig.
Efficiency of dnaPipeTE Prior to A. albopictus genome analysis, we tested the efficiency of dnaPipeTE on well-annotated genomes that varied in size and TE content. We used available Illumina reads from the species Drosophila melanogaster (Diptera: Drosophilidae), Anopheles gambiae (Diptera: Culicidae), Caenorhabditis elegans (Rhabditida: Rhabditidae), Ciona intestinalis (Enterogona: Cionidae), Gasterosteus aculeatus (Gasterosteiformes: Gasterosteidae), and A. aegypti-the closest fully sequenced species to A. albopictus. We also tested the behavior of dnaPipeTE on older repeatomes, such as that of the human genome (Homo sapiens), in which copies of one TE family are highly divergent. All data management information and references are given in supplementary table S1, Supplementary Material online.