dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The authors thank  Joann Hettasch, PhD , of  Arbor Communications, Inc. ,  Ann Arbor , MI,  USA , for medical writing and editorial assistance. The study was supported by funding from  Forest Research Institute, Inc. , an affiliate of  Actavis Inc. ,  Jersey City, NJ, USA , and  Pierre Fabre Médicament, Boulogne, France. Medical writing and editorial assistance were funded by  Pierre Fabre Médicament, Boulogne, France. Forest Research Institute, Inc. , and Pierre Fabre Médicament were involved in developing the study design, data collection, data analysis and interpretation, and the decision to publish these results.
Spoligotyping Spoligotyping is a simple, rapid and cost effective method for simultaneous detection and typing of the M. tuberculosis complex. It is the method of choice for strains with less than five copies of the insertion sequence IS6110, like M. bovis strains, which usually contain only one or two IS6110 copies  [17] [18] [19]. By spoligotyping, M. tuberculosis isolates are characterized by the absence of spacers 33-36, while M. bovis usually lack spacers 39-43  [20]. Spoligotyping was performed on 45 isolates. Of them, all were defined as M. tuberculosis and no M. bovis was found. Among the 44 isolates (excluding the mixed infection isolate), 23 spoligopatterns were obtained. Three patterns corresponded to orphan strains that were unique in the SITVIT2 database, as opposed to 20 patterns from 41 patients that corresponded to shared-types (SITs), i.
e. an identical pattern shared by two or more patients worldwide (within this study, or matching another strain in the SITVIT2 database), as shown in Table  2. For each isolate, their binary/octal description, their lineages and SITs are summarized in Table  2. Four SITs (containing seven isolates) were newly created either within the present study or after a match with an orphan in the database. Nine patterns were in clusters, containing 30 isolates (2 to 6 isolates per cluster), amounting to an overall clustering rate of 68. 2 % (30/44). As shown in Table  2 , the most common spoligotypes found in this study were SIT48 (East African-Indian_ Somalia; EAI1_SOM) with six isolates; SIT1 (Beijing lineage) and SIT42 (Latin America Mediterranean; LAM 9) with five isolates each and SIT53 (T1) with four isolates.
Laboratory detection of bovine TB is a challenge, particularly in low income countries. Microscopy for mycobacteria on the FNA is the initial diagnostic procedure for lymphadenitis in Mozambique; although it does not differentiate between M. tuberculosis and M. bovis, it is Table  2  Shared Types and orphans description in the study, their percentage and lineage within the study *Newly created SITs, either within the present study or after a match with an orphan in the database **Orphans, unique in the SITVIT2 database considered a reliable TBLN diagnostic method, including in HIV positive individuals  [26] [27] [28] [29]. Molecular typing methods for M. tuberculosis complex detection on FNA specimen are costly and require technical expertise, therefore, are not implemented as a routine method in the country, making the detection of bovine TB difficult.
Spoligotyping Standard spoligotyping  [53]  was performed generally as described by Kamerbeek and colleagues. Spoligotyping results were analysed and dendograms created using the BioNumerics Software ver. 7. 5 (Applied Maths, Kortrijk, Belgium). Spoligotyping patterns were also compared with the ones existing in the international Spoligotyping database SITVIT2, which is an updated version of SITVITWEB  [54]. Shared international types (SITs) that were newly-created either within the present study or after a match with an orphan in the database, were assigned a new SIT number.
Mycobacterial Interspersed Repetitive Unit -Variable Number Tandem Repeat (MIRU-VNTR) analysis Standardized 24-loci MIRU-VNTR typing  [55]  was performed using the MIRU-VNTR typing kit (Genoscreen, Lille, France). The PCR-products were run with 1200 LIZ size standard (GeneScan, Applied Biosystems) on ABI3500 sequencers. Sizing of the PCR-fragments and assignments of MIRU-VNTR alleles were done with the GeneMapper software version 4. 1 (Applied Biosystems) according to the manufacturers' instructions. MIRU-VNTR were also compared with SITVIT2, and newly created 24loci MIRU-VNTR International Types (24-MITs) were assigned.
Statistical analysis Statistical analysis was performed on Data Analysis and Statistical Software (STATA), version 13. Descriptive statistics was used for summarizing demographics data. Categorical variables were presented using frequencies and percentages. Bivariate analyses were performed for TB lymphadenitis versus HIV status, HIV status x lineage using chi-square test. Multinomial Logistic regression model were created with TB lymphadenitis as outcome and sex age and HIV status included as predictors. Interactions were tested and all were not statistically significant. Since no interactions were statistically significant, they are not presented.
Acknowledgements This study was funded by the  Swedish International Development Cooperation Agency / Department for Research Cooperation (Sida/SAREC)  through  Eduardo Mondlane University  and  Karolinska Institutet  Research and Training (KIRT) collaboration. We thank the staff of the  Pathology Service , at  Maputo Central Hospital , who assisted with the sample collection; the nurses who assisted in the data collection and the staff of the  National Tuberculosis Reference Laboratory , who assisted in sample processing and culture. We also thank  Mikael Mansjo  from the  Public Health Agency of Sweden , for his support on the molecular analysis.
5,] 0. 5-1][,]1-2] and >2 log 10 IU/mL. Friedman's test was used to compare HBsAg fluctuations between the three values. Correlation between viral load fluctuations was based on the Spearman coefficient. All variables associated with HBV DNA fluctuations > 0. 5 log 10 IU/mL in univariate analysis (p < 0. 25) were included in a backward stepwise logistic regression model. A P value of ≤0. 05 was considered to denote statistical significance. Statistical analyses were performed using STATA software version 12. 0 (Stata Corporation, College Station, TX).
Preparation of CB Units for Infusion Using an Automated Dilution and Washing Protocol The CB bags stored in liquid nitrogen were thawed by immersion in a preheated 37 C water bath. When thawed, the CB bags were weighed, samples were taken for laboratory analysis, and the bag was then connected to a kit designed for umbilical CB cell washing and processed using the Sepax S-100 (Biosafe) in a closed system. A stock solution of 7. 5% dextran-40 (molecular weight 40,000, Fresenius Kabi, Italy) and 5% human albumin (Grifols, Barcelona, Spain) was prepared and connected to the kit. The Sepax software "UCB-Washing" was used. CB units were automatically diluted 1:1 with the buffer and the product was mixed in the chamber and input bag in 5 minutes. Then, the chamber was filled with the washing solution.
Death without relapse was the competing event for relapse. Relapse or death without developing acute or chronic GVHD was the competing event for acute GVHD and cGVHD, respectively. Death without engraftment was the competing event for neutrophil and platelet engraftment. All statistical tests were conducted using the SPSS statistical software (SPSS version 15. 0, Chicago, IL). Cumulative incidence with competing risks was conducted in R software, version 2. 15. 2 (The CRAN project).
Materials and Methods: We performed integrative analysis of lncRNAs in EEC using The Cancer Genome Atlas (TCGA) molecular RNAseq profiles of 191 primary tumors for which genomic data were also available. We established lncRNA subgroup classification, correlated it with chromatin modifying gene expression, and described correlations between our lncRNA classification and clinico-genomic tumor features. Results: Using stringent criteria, we identified 1,931 expressed lncRNAs and predicted potential drivers through integrative analysis. Unsupervised clustering of lncRNA expression revealed three robust categories: basal-like, luminal-like and CTNNB1-enriched subgroups. Basal-like subgroup was enriched for aggressive tumors with higher pathological grade (p < 0. 0001), TNM stage (p = 0. 01), and somatic mutations in trithorax-group genes (MLL, MLL2 and MLL3); and it overexpressed polycomb genes EZH2 and CBX2.
For the first time, to our knowledge, we herein report the lncRNA subgroup classification of EEC through unsupervised clustering and integrative analysis of a large dataset of primary tumors. We intentionally focused exclusively in EEC and excluded serous-like EC, as EEC share similar pathological features and there is an urgent need to investigate the input of molecular classification of those tumors. We identified three lncRNAs subgroups that correlated with clinico-genomic tumor aberrations. Those include basal-like and luminal-like subgroups, which are reminiscent of breast cancer, as well as a third subgroup, namely CTNNB1-enriched, which is reminiscent of CTNNB1-enriched liver cancer. The application of lncRNA profiling can potentially be utilized as a tool for predicting clinical outcome and selecting targeted therapy.
Expressed long non-coding RNAs in endometrioid endometrial carcinomas are located in the vicinity of genes involved in histone H4 acetylation In order to describe lncRNAs important in the development of EEC, we performed genomic analysis of lncRNAs using TCGA molecular RNAseq profiles of 191 primary tumors for which copy-number alterations and exome-sequencing data were available (Figure  1 ). According to GENCODE gene annotation v15, which represents the largest manually curated catalog of human lncRNA, the genome comprises 13,159 lncRNAs (i. e. , lincRNA) categorized in seven categories as compared to 19,595 coding genes. To identify relevant lncRNAs, we first filtered the dataset to remove lncRNAs with low expression, defined as having an RPKM (reads per kilobase of transcript per million reads mapped) value <1 in at least 90% of the tumor samples.
Integrative analysis of lncRNA classification identifies basal-like, luminal-like and CTNNB1enriched subgroups We then performed integrative analysis in order to identify molecularly distinct tumor EEC subgroups associated with specific clinico-biological features. Unsupervised hierarchical clustering revealed three lncRNA subgroups that highly correlated with the mRNA transcriptomic classification (p < 0. 0001 by Chi square test, and the Adjusted RAND Index is 0. 4654), suggesting a crosstalk between lncRNA and mRNA (Figure  2 ). Uterine serous endometrial carcinoma was previously shown to share genomic features with basal-like breast carcinomas  [6] ; therefore, we investigated whether our EEC lncRNA subgroup classification resembles the molecular classification of breast cancer. To answer this question, we performed supervised clustering using the PAM50 intrinsic subtype classification in a large dataset of breast tumors from TCGA, as previously reported  [13].
05), as reported by Creighton et al. [14]. To investigate pathways differentially regulated between cluster C2 and the other clusters, we performed GSEA and discovered that cluster C2 was exclusively enriched for signatures of hepatocellular carcinomas that harbor CTNNB1 mutations in the two following datasets: BOYAULT_LIVER_CANCER_SUBCLASS_ G6_UP (p < 10 -10 , FDR < 10 -6 ) (Figure  S1 ) and CHIANG_ LIVER_CANCER_SUBCLASS_CTNNB1_UP (p < 10 -10 , FDR = 0. 004) (Figure  S2 ). We then analyzed the frequency of CTNNB1 somatic mutations in the three EEC subgroups and confirmed enrichment for CTNNB1 mutations in the C2 cluster. Overall, 69. 3% (n = 43/62) of EEC samples in the C2 cluster harbored CTNNB1 mutations as compared to 24. 7% (n = 19/77) and 15. 4% (n = 8/52) in the basal-like and luminal-like subgroups, respectively (p = 3.
5 × 10 -10 ) (Figure  4A ). Using TCGA liver lncRNA dataset, we discovered a high correlation between EEC cluster 2 and the CTNNB1-mutated subclass of liver tumors (p = 3. 1 × 10 -27 ) (Figure  4B ). We thus termed the C2 cluster as the CTNNB1-enriched subgroup. As a result, our lncRNA classification of EEC is composed of 3 molecular subgroups: (1) basal-like, which is similar to the basal-like subtype of breast cancer, (2) CTNNB1-enriched, which is similar to CTNNB1mutated subtypes of hepatocellular carcinomas, and finally (3) luminal-like, which is similar to the luminallike subtype of breast cancer.
To establish the lncRNA subgroup classification of EEC, we mined the dataset of TCGA to define the portrait of expressed lncRNA in this disease, which to our knowledge has not been described. Indeed, only a handful of lncRNAs has been reported so far as being expressed in EEC, such as HOTAIR and CASC2  [10, 25]. Overall, we identified almost 2,000 expressed lncRNAs in EEC, representing 14. 7% of GENECODE lncRNAs. Importantly, we discovered an enrichment of lncRNA located in the vicinity of genes involved in histone H4 acetylation. This is the case for EP300, which has been recently described to be mutated in 8% of serous endometrial carcinomas  [26]. We thus conclude that in addition to the high rate of mutations in chromatin-remodeling genes (~36. 5%) observed in serous endometrial carcinomas, deregulation of lncRNAs located in cis-regulatory elements (CREs) may also affect the expression of nearby genes involved in histone acetylation.
MATERIALS AND METHODS The cancer genome atlas (tcga) data endometrioid endometrial carcinoma (EEC) RNA-Seq data (BAM files) and their related clinical data were obtained from the Cancer Genomics Hub (CGHub, https://cghub. ucsc. edu/) and TCGA Data Portal (https://tcga-data. nci. nih. gov/tcga/). The list of cases is provided in Supplementary Table  11. The pairedend FASTQ files for each sample were extracted from BAM files using bam2fastq (http://www. hudsonalpha. org/gsl/information/software/bam2fastq). Both copy number alterations and somatic mutations were downloaded from The Broad GDAC Firehose http:// gdac. broadinstitute. org/.
Mapping/alignment The raw paired-end (PE) reads in FASTQ format were aligned to the human reference genome, GRCh37/hg19, using MOSAIK alignment software  [31]. MOSAIK works with PE reads from Illumina HiSeq and uses both a hashing scheme and the Smith-Waterman algorithm to produce gapped optimal alignments and to map exon junctionspanning reads with a local alignment option for RNA-seq. The resulting alignments were then saved as a standard BAM file. We then counted the mapped reads in genomic features such as genes (mRNAs and lncRNAs) annotated in GENCODE15 to generate the raw counts for each gene using the HTSeq-count script distributed with the HTSeq package. We chose the "union" mode of HTSeq to mask the overlapping regions between mRNA and lncRNA to overcome the issue of non-strand-specific RNA sequencing KIT (TruSeq) in TCGA data.
Consensus clustering by lncRNAs To assess the stability of the discovered clusters, we performed consensus clustering. Using the resampled data, we conducted 500 runs of hierarchical clustering. For each run, 80% of the samples and 80% of the lncRNAs were randomly chosen. The distance measurement was set as the Pearson correlation, and linkage function was set as "Ward". Based on the 500 runs, a consensus was obtained by taking the average over the connectivity matrices of every perturbed dataset. Then we carried out hierarchical clustering with the consensus matrix as a similarity matrix, with "Euclidean" as the distance measurement and "Ward" as the linkage function. We also calculated the Bayesian information criterion to detect the number of clusters.
ACKNOWLEDGMENTS AND FUNDING This work was supported in part by the  U. S. National Institutes of Health , the  National Center for Research Resources  through grant  UL1TR000371  (X. S. ), and the  National Cancer Institute  through the  CCSG P30 CA016672 , and the  Leukemia SPORE   P50 CA100632  (X. S. ), and the  Uterine SPORE   P50 CA098258  (K. H. L. , R. B. , and Y. Y. ), also by the  Michael & Susan Dell Foundation  (X. S. ), and  La Fondation AVEC  (G. G. M. ). The authors acknowledge the  MDACC High Performance Research Computing Center  for computational resources. None of the authors has conflicts.
Docking studies For each docked compound a preliminary calculation on its protonation state at pH 7. 4 was carried out using standard tools of the ChemAxon Package (http://www. chemaxon. com/) and the majority microspecies protonated on nitrogen (corresponding to among 98% for morphine as well as for etorphine) at this pH was used for docking studies. The crystallographic coordinates of hDOR used for docking studies were obtained from X-ray structure of the naltrindole/deltaopioid receptor complex (PDB ID 4N6H, a structure refined to 1. 8 Å with an R factor of 17. 3%)  [20]. Docking of morphine and etorphine into hDOR was carried out by means of the GOLD program with the default parameters  [21]. This program applies a genetic algorithm to explore conformational spaces and ligand binding modes.
Acknowledgements This work was supported by the ministère de l'enseignement supérieur et de la recherche. Drs. B Aguila  and  E. Camacho  were recipients of the  Regional Council of Lower-Normandy  and the  ANRT (Association Régionale Recherche Technologie) , respectively with the financial support of the  Elitech group. We thank  Professors Cotecchia  ( University of Lausanne, Switzerland ),  Benmerah  ( Institut Cochin, France), Bunnett (University of California, San Francisco ),  Gurevich (Vanderbilt University medical center, USA) ,  Bouvier  ( Université de Montréal, Canada) and Laporte (McGill University, QC, Canada ) for kindly providing plasmids and the anti-arrestins 2 and 3 antibody. The authors would like to thank  Dr Nicolas Marie  ( Université Paris Descartes, France ) for helpful discussions and critical reading of the manuscript.
Using Praat software some prosodic parameters were measured, such as pausing and reading rate (number and duration of the pauses and utterances), pitch and intensity changes and duration in declarative, exclamatory, and interrogative sentences and also errors and duration in words by frequency and stress. We compared the results of both groups with ANOVAs. The results showed that children with less reading comprehension made more inappropriate pauses and also intersentential pauses before comma than the other group and made more mistakes in content words; significant differences were also found in the final declination of pitch in declarative sentences and in the F0 range in interrogative ones. These results confirm that reading comprehension problems in children are related to a lack in the development of a good reading fluency.
These scales are very useful in the educational field, but they have some subjectivity. Today, thanks to programs like Praat  (Boersma and Weenink, 2015) , it is possible to measure the components of prosody by analyzing the acoustic wave. This is an objective measure of the prosodic features. This software is a tool for phonetic analysis of speech to analyze prosodic aspects such as frequency, intensity or duration. The aim of this study was to determine which aspects of reading fluency are related to understanding, that is we are interested in the mechanic aspects of reading that could be related to reading comprehension. In this way, several prosodic features, such as pitch, intensity, pauses, duration of syllables and utterances, were collected using Praat software. Besides, words with different lexical frequency and stress were included.
M (SD) M (SD) M (SD) M (SD) Oral comprehension accuracy (out of eight) 5. 9 (0. 9) 6 (0. 7) 6. 6 (0. 7) 6. 8 (0. 4) Written comprehension accuracy (out of eight) 6. 1 (0. 7) 3 (1. 1) 6. 9 (0. 7) 4. 8 (0. 9) ), half with low (M lexical frequency = 5. 9) and half with high lexical frequency (M lexical frequency = 143. 9). The lexical frequency was obtained from the database of  Martínez and García (2004) , who acquired their frequency from a sample of children's books. The text was presented on a piece of paper (Times New Roman, 12 point font, double spaced) and the participants had to read it aloud individually in a quiet room. The reading was recorded by an H4n voice recorder and an Ht2-P Audix headset dynamic microphone. Audio recordings were processed offline using Praat software.
General Assessments From the. wav files recorded we collected several prosodic parameters using Praat software. First, we analyzed some characteristics of the whole text, and then we extracted six sentences, two declarative, two exclamatory and two interrogative sentences, in order to evaluate different parameters. Finally, we selected eight low frequency words, half of them repeated twice in the text, and eight words with different stresses (on the penultimate and on the antepenultimate syllable) and frequency (high and low). From the whole text we considered the number of reading mistakes in the content and function words, and the number and duration of intersentential pauses (before commas and full stops) and inappropriate pauses (pauses made in not corresponding places). Also the total pause duration and the total pronunciation time (reading time between pauses) were collected.
RESULTS We compared the results of the different parameters of both groups and grades with ANOVAs using SPSS software. Therefore, we used the results of the measurements described above as dependent variables and the grade (third vs. sixth) and group (poor vs. good comprehension) as the independent variables. We named "Poor comprehension group" as the children with better oral than reading comprehension, and "Good comprehension group" as the children with similar oral and reading comprehension. We made ANOVAs with each dependent variable in order to check which the significant effects were. Only those significant are presented here to facilitate the understanding of the results.
Sentences Analyses We discarded for the analysis all the sentences read incorrectly, which was usually regressions in the reading, around 9%. We  analyzed with SPSS software the results from the Praat analysis comparing the two groups and grades using ANOVAs. We found a significant group effect in the fundamental frequency of syllables in declarative sentences [F(1,34) = 4. 6, p = 0. 038]. In the frequency range of interrogative sentences the interaction between group and grade was also significant [F(1,35) = 6. 9, p = 0. 012]. We made post hoc comparisons using the Tukey test, showing that the significant differences were between the two groups of third grade (p = 0. 048). See Figures  1  and 2  as examples of the effects found in these analyses and Table  4  with the means and SD.
Words Analyses Firstly, we analyzed the low frequency words repeated and nonrepeated, discarding those misread. SPSS software was used to conduct ANOVAs for comparing groups and grades. No significant effect by groups was found (p > 0. 05). Secondly, an ANOVA was performed with the words with different stress and frequency. There was a significant interaction between the number of errors in the words with different lexical frequency by group [F(1,36) = 6. 4, p = 0. 016]. Also an interaction of the mean time for reading high frequency words stressed on the penultimate syllable and group, and grade was found [F(1,36) = 5. 7, p = 0. 022]. Post hoc comparisons using the Tukey HSD test indicated that the mean reading time of children with poor comprehension from third grade was significantly different from the same group of sixth grade (p < 0.