dataesr/scientific-paragraphs-categorization huggingface dataset:
These DNA products were purified, and only those having adapter molecules on both ends were enriched with PCR by using a Cocktail Primer that anneals specifically to the adapters. The obtained libraries were validated by Bioanalyzer (Agilent DNA 1000; Agilent Technologies) and pooled and sequenced using the Illumina HiSeq 2000 sequencing system. The global quality of each library was checked using the FastQC software (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/). To reduce any PCR amplification bias, duplicated reads were removed before the duplicated reads before the mapping based on a 100% sequence identity between reads. Then, five nucleotides were trimmed at both 5′ and 3′ ends of all remnant reads using FASTX-Toolkit v0. 0. 14 (http://hannonlab. cshl. edu/fastx_toolkit/ index.
html). Reads were mapped against the mouse mm10 genome (Version GRCm38. p3) using the TopHat2 software v2. 0. 10 17 with default parameters. Only uniquely mapped reads were kept and were counted on genes' exons using the HTSeq-count software v0. 6. 1p1  18 with "union" mode using the Mus musculus Ensembl annotation (release 79). Only coding and expressed genes were considered. "R" package DESeq2 v1. 8. 0  19  was used for statistical analyses to determine differential gene expression levels (cutoff: adjusted P-value ≤0. 1). Samples' homogeneity was checked realizing a principal component analysis with "R" package FactoMineR, following which, one mutant mouse was not considered for further analysis. To identify mouse and human orthologs, we used the BioMart tool, provided by Ensembl, Heidelberg, Germany.
9 14] 20  Hawkins et al  12  described 2083 deregulated genes in endometriotic lesions; among this set of genes, 997 genes are underexpressed and 1086 genes are overexpressed. Several of these genes were also identified in the three other studies, which we have considered,  13, 14, 20  providing a shared database to identify potential therapeutic targets. Comparison of Dlx5/6-mouse mutant uterus RNAseq dataset with the human datasets permits identifying 30 genes that present a common deregulation in the mouse and in endometriotic lesions: 13 downregulated and 17 upregulated genes (Table  2 ). If indeed the downregulation of Dlx5/6 is an early event in endometriosis progression, this set of 30 genes could well constitute a more focused molecular signature of endometriosis implants (Figure  2 ).
Acknowledgments We thank  Dr Elisabeth Da Maia ,  Department of Pathology, Pitié Salpêtrière Hospital , for her kind help in endometrial and endometriosis section analysis. This research was partially supported by the  EU Consortium HUMAN  ( EU-FP7-HEALTH-602757 ) to GL and by  CNRS  and MNHN general support to UMR7221. This study was supported in part by the  French Centre National de la Recherche Scientifique , the  French National Museum of Natural History , and the  EU Consortium HUMAN  ( EU-FP7-HEALTH-602757 ).
Leptospirosis affects 1 million and kills 60,000 people annually, but it remains poorly documented in Africa. We aim to describe the large West African Conurbation Corridor where the omnipresence of slums, water and close animal/human interactions may result in high leptospiral risk. Though scarce, data from this region point towards the wide circulation of pathogenic leptospires in the urban environment as well as in humans. However, because of the absence of reliable surveillance systems together with lack of awareness, the absence of reference laboratory and/or a high number of infected people showing only mild manifestations, it is likely that the burden is much higher. We believe raising awareness of leptospirosis may have a positive impact on many vulnerable African city dwellers, as the disease is a preventable and treatable.
Non-Steroidal Anti-inflammatory Drugs Since in SpA inflammation leads to stiffness and loss of mobility, anti-inflammatory drugs are expected, through both the increased mobility related to pain relief and the increased activity, to have an effect on BMD. In a prospective analysis of the early SpA DESIR cohort of 265 patients (54% male, mean age 34. 4 years) who had BMD measurements at baseline and at 2 years, use of non-steroidal anti-inflammatory drugs (NSAIDs) had protective effects on hip bone loss in patients (OR = 0. 09, 95% CI = 0. 02-0. 50) (26). This effect has also been observed for VFs: in a primary care-based nested case-control study, including patients with SpA, the risk of any clinical fracture was decreased in patients taking NSAIDs (OR = 0. 65, 95% CI = 0. 50-0. 84) (25). However, conflicting results have been also reported, with an excess risk of any clinical fracture in patients with SpA, and even a higher risk in patients using NSAIDs, probably because these patients had a more severe disease and thus, a higher utilization of NSAIDs, since these latter data are issued from claim databases  (27).
Cell lysis and sample deproteinisation Immediately after purification or cell culture, 250,000 T cells were aliquoted and lysed with the buffer supplied in the L-Lactate assay kit (ref ab65330, Abcam, Cambridge, UK). Efficacy of this cell lysis protocol was verified on two consecutive experiments using Trypan blue exclusion test (data not shown). To avoid further modifications of lactate concentration in the samples due to the potential presence of enzymes, we decided to deproteinate the obtained cell lysates or cell culture supernatants. We used a chemical process, according to manufacturer's instructions. The whole process took place on ice and in a cold 4 °C centrifuge. Perchloric acid was added to a final concentration of 1 M, resulting in protein denaturation. After centrifugation, supernatant was added with potassium hydroxide to normalize sample pH.
Data are also presented as Tukey boxplots. Bottom and top of the box represent the first and third quartiles, respectively. The horizontal bar in the box represents the median value. Lower and higher extremities of the whiskers respectively represent the lowest datum still within 1. 5 inter-quartile range (IQR) of the lower quartile, and the highest datum still within 1. 5 IQR of the upper quartile. Difference between NS and PHA groups was assessed with Wilcoxon test Fig. 4  Minimal cell number for lactate measurement in healthy volunteers' T lymphocytes. T lymphocytes were purified from four healthy donors' blood. Cells were cultured in complete RPMI medium at a concentration of one million cells/mL and were stimulated with 4 μg/mL PHA (black circles) or left unstimulated (NS, open circles).
Lactate concentration in peripheral T lymphocytes Immediately after T lymphocyte purification process, intracellular lactate concentration was measured in 250,000 T cells from septic shock patients and healthy donors. We observed that intra-T lymphocyte lactate concentration was not different in T cells from septic shock patients compared with healthy donors (Fig. 5 ). Unfortunately, due to the low number of T cells that could be purified from septic patients' blood, we were unable to measure intra-lymphocyte pyruvate concentration in parallel. Interestingly, we observed that intra-lymphocyte lactate concentration was higher in day 28 non-survivors patients than in healthy volunteers, whereas it did not differ between survivors and controls (data not shown). This should not be regarded as a Fig.
These include mAbs that block CTLA-4 or PD-1 checkpoints  [1]. Indeed, KIR and CTLA-4 or KIR and PD-1 regulate nonredundant inhibitory pathways and can be expressed by distinct immune effectors (mainly CD8+ T cells for PD-1 and NK cells for KIR2D). Furthermore, blockade of CTLA-4 or PD-1 on T cells can induce the release of cytokines, including IL-2, that enhance NK cell function, whereas blockade of KIR can result in the secretion of IFN-γ that may boost both myeloid-and T cell-mediated antitumor responses  [22]. Therefore, combination treatment with checkpoint blockers that stimulate T cells and NK cells may promote NK-T-cell cross-talks, leading to more robust responses in some patients who fail to respond to treatment with single agents. Encouraging preliminary safety and efficacy data from phase 1 or phase 2 trials of the combination of lirilumab with nivolumab and/or ipilimumab (NCT01714739/NCT01750580) have been recently presented  [23, 24].
Through enhancement of NK cell activity, KIR2D blockade may also improve antibody-dependent cellular cytotoxicity. Therefore, lirilumab may act synergistically with other mAbs; such synergism has been demonstrated between lirilumab and the anti-CD20 mAb in an animal model  [25]. In addition, hypomethylating drugs (5-azacytidine or decitabine) that are currently approved as treatments for AML and MDS have shown immunomodulatory effects, including enhanced production of Th1-type chemokines  [26]  and up-regulation of NKG2D ligand (MICA/B, ULBP) expression  [27, 28]. Lirilumab in combination with 5-azacytidine is currently being assessed as a treatment for relapsed/refractory AML (NCT02399917) and MDS (NCT02599649). Finally, a combination of lirilumab with immunomodulatory drugs might also be considered.
Assessment of pharmacokinetics Pharmacokinetic assessments were performed by ELISA on serum samples collected at baseline and at various time points after lirilumab administration. The population pharmacokinetic analysis was performed by modeling lirilumab concentration data using nonlinear mixed-effect modeling with Phoenix ® WinNonLin6. 4, NLME™ 1. 3 software. Any lirilumab concentration below the lower limit of quantitation (LLOQ) was not considered in the analysis. A preliminary base population pharmacokinetic model was developed and evaluated for lirilumab in patients from this study to derive major pharmacokinetic parameters that describe lirilumab pharmacokinetics, including: central volume of distribution, clearance, peripheral volume of distribution, inter-compartmental clearance, and the Michaelis-Menten parameters of the saturable clearance pathway, V max and K m.
Environmental data The environmental variables tested in our study include rainfall, flooded area, elevation, and human and livestock population densities. Flooding is an important driver of leptospirosis, but no data is readily available. The flooding variable was calculated based on the modified normalized difference water index (MNDWI). Other variables were collected from national or international databases. All variables were aggregated at the district level to match the spatial resolution of the epidemiological data. The amount of rainfall was obtained from near Real-time TRMM (Tropical Rainfall Measuring Mission) multi-satellite precipitation analysis (TMPA-RT), which is produced at the National Aeronautics and Space Administration, Goddard Earth Sciences Data and Information Services Center (NASA GES DISC)  [20].
The daily accumulated precipitation product is generated from the Near Real-Time Precipitation 3-hourly 1 day TMPA at a spatial resolution of 0. 25 degree × 0. 25 degree Version 7 (TRMM 3B42RT Daily)  [21, 22]. In this study, given the homogeneity of rainfall at the district level, we only extracted the TRMM data at the centroid of each district. To identify flooded areas, we used the data from the Moderate Resolution Imaging Spectroradiometer (MODIS) of the Terra satellite (Surface Reflectance 8-Day L3 Global 500 m SIN Grid V005 (MOD09A1)). In each image pixel, the data provides an estimation of the surface spectral reflectance measured at ground level in the absence of atmospheric scattering or absorption. The band 4 (green) and band 7 (infrared) were used to calculate the modified normalized difference water index (MNDWI)  [18, 23] , which allows an estimate of the water presence in each pixel.
Within all districts, each pixel was classified as flooded if the MNDWI value was more than or equal to zero. This threshold of zero for MNDWI is in the range of optimal thresholds calibrated in previous studies  [23] [24] [25]. Permanent water bodies such as rivers and lakes were masked out using QGIS version 2. 8. 3  [26]. Then, the number of flooded pixels were counted to calculate the percentage of flooded land in each district. Elevation can be associated with slopes and increased movement of surface water  [27] , but slope data was not available at a national scale in Thailand. Elevation data was derived from the NASA Shuttle Radar Topographic Mission (SRTM) 90 m Digital Elevation Data, which provides elevation data for the entire world (http:// srtm. csi. cgiar. org/index. asp). The average elevation at the district level was used in the model.
Human population data was obtained from the World-Pop database, which presents the number of people per hectare (http://www. worldpop. org. uk) (Additional file 2: Figure  S5 ). Human population density was included in the model because it could be associated with different agricultural practices in areas with different levels of economic development. The animal population density of livestock species (buffalo, cattle, goat, pigs and sheep) were obtained from the Information and Communication Technology Center (ICT), Department of Livestock Development of Thailand at the district level (http:// ict. dld. go. th) (Additional file 2: Figure  S5 ). Goats, pigs and sheep were included because they may also contribute to the circulation of leptospirosis in cattle and buffaloes. Seroprevalences of other livestock were shown in Thailand from January to August 2001 in a previous study  [28].
Statistical analysis To investigate the association between the risk factors listed in the previous paragraph (explanatory variables with a fixed effect) and leptospirosis infection (the response variable), we first study univariate linear regressions. Using a generalized linear mixed model (GLMM) with a logit link since the response variable had a binomial distribution. We used R software  [29]  with the package lme4  [30]. Since all individual urine samples were not independent because they were collected during common sampling occasions, we used the sampling occasion index as a random effect variable. Each sampling occasion was identified by a date, a year and a district geocode. The best multivariable model was selected using a stepwise backward approach based on the Akaike Information Criterion (AIC).
The Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) plot was used to estimate the model performance. We also used cross-validation to measure the performance of the best model. Data was randomly split into training (2/ 3 of data) and test (1/3 of data) sets. Training data is used to produce the prediction model, while the test data is used to test the model performance. Given the size of our dataset, we chose to keep 2/3 of the data in the training set to optimize model performance. We performed repeated cross-validations 1000 times to estimate the mean and standard deviation of the cross-validated AUC (cvAUC) of the best model. The best model was used to predict leptospirosis infection risk in 2012 and 2016 for three periods (mid-January, mid-May and mid-September) which represents the middle of the dry season, the beginning of the rainy season and the end of the rainy season, respectively for central and northern Thailand.
8861) but a lower and quite variable cross-validated AUC (mean cvAUC = 0. 6427, sd = 0. 0827, Fig. 4 ). This difference between AUC and cvAUC, and the variability of the cvAUC may be explained by the relatively small size of our dataset at the district level leading to a small validation dataset (71 districts for the training dataset and only 36 for the validation dataset). Furthermore, given this size limit, some validation datasets may include a different proportion of southern districts than their matching training datasets. The difference of flooding patterns between southern Thailand and the rest of the country may then further explain the lower cvAUC. Training the model on a larger dataset and having an independent large dataset to validate it would help build a more robust model. The presence of pathogenic leptospires in livestock was tested with LAMP  [4, 10] , which allows a simple and rapid diagnosis of leptospirosis with high accuracy.
Acknowledgments This research was supported by the  Thailand Research Fund  and the  Office of the Higher Education Commission  under Grant no. MRG6180051. We wish to thank the staff of the provincial Department of Livestock Development and the  Regional Veterinary Research and Development Center, and National Institute of Animal Health , the  Department of Livestock Development, Ministry of Agriculture and Cooperatives of Thailand  for providing data on animal leptospirosis. We thank also  Naresuan University and the Ministries of Foreign Affairs, France (MAEDI) ,  Higher Education and Research, France (MESR)  for their support. We thank  Charin Modchang  for a helpful discussion. We thank  Chuthamas Warapathirunmas ,  Kanokkorn Suksabai  and  Patch Thongthaisong  for help in extracting environmental data.
PaTIenTs and MeTHOds study design We performed a cross-sectional analysis of baseline findings from the prospective longitudinal DESIR cohort (#NCT01648907) of 708 patients with recent-onset IBP (<3 years) and a strong suspicion of axSpA. These patients were recruited in 25 rheumatology centres in France between December 2007 and April 2010. 16 17 atient age at baseline ranged from 18 to 50 years. The IBP affected the thoracic or lumbar spine or the buttock area and fulfilled either Calin criteria (4/5) or Berlin criteria (2/4). The rheumatologist rated the manifestations ≥5 on a 10-point scale where 0 indicated no suspicion of SpA and 10 a very strong suspicion of SpA. A detailed description of the study centres, cohort organisation, protocol and case-report form is available online http://www.
data collection The following characteristics were collected at baseline: sociodemographic data, history of the disease, main rheumatic symptoms (past or current enthesitis, dactylitis, arthralgia, arthritis) and extrarheumatic symptoms (past or current uveitis, psoriasis, inflammatory bowel disease (IBD)), environmental factors (tobacco or alcohol consumption, yes/no), biological parameters (C reactive protein (CRP) level, presence/absence of HLA-B27), treatments (use of non-steroidal anti-inflammatory drugs (NSAIDs), responsiveness to NSAIDs, ASAS NSAID score, use of disease-modifying antirheumatic drugs (DMARDs), use of analgesics), disease activity and function scores (patient global assessment, Bath Ankylosing Spondylitis Disease Activity Index (BASDAI), Bath Ankylosing Spondylitis Functional Index (BASFI), ASDAS-crp), physical findings (enthesitis index, synovitis index) and spondyloarthritis classification criteria (Amor's criteria, European Spondyloarthropathy Study Group (ESSG) criteria, ASAS criteria for axSpA).
Baseline imaging data obtained using a standardised procedure were also collected: pelvic and spinal radiographs, MRI of the SIJs and spine and PDUS of predefined entheses. 16 wer doppler ultrasonography (Pdus) procedure and Pdusdefined enthesitis PDUS of the entheses was performed in 14 out of the 25 centres of the cohort. These 14 centres recruited 402 patients into DESIR, who were evaluated for the actual research question. PDUS was performed by a rheumatologist or radiologist expert in PDUS blinded to clinical data according to a standardised procedure. Rheumatologists were specifically trained to PDUS of the entheses by the person in charge of the PDUS protocol of the DESIR cohort (MADA). In addition, all rheumatologists had access to a set of images showing different degrees of enthesis inflammation as comparator.
Group comparisons Of the 708 patients of the DESIR cohort, 402 patients underwent PDUS. When comparing patients with and without PDUS evaluation, after Bonferroni's correction, the only statistically significant difference was a higher prevalence of radiographic sacroiliitis in the group with PDUS evaluation (31. 7% vs 20. 5%, p=0. 001) (other data not shown). Of those 402 patients who underwent PDUS, 58 (14. 4%) had enthesitis defined as any vascularisation (power Doppler signal) at the bony insertion of the tendon, within 2 mm from the bony cortex, at one or  The sensitivity and specificity of PDUS-defined enthesitis were calculated on the 402 patients who underwent PDUS, using classification as axial spondyloarthritis according to the ASAS criteria as the reference standard (n=288 patients met these criteria).
Thus, PDUS of multiple entheseal sites may assist in the classification of axSpA in patients who have highly suggestive manifestations of SpA but fail to meet axSpA ASAS criteria. Further research, including longitudinal studies of PDUS+ patients, are required for understanding the potential value of this evaluation in the classification of IBP highly suggestive of axSpA. acknowledgements the DeSir cohort was sponsored by the non-profit public funding agency Département de la recherche clinique et du Développement, assistance Publique-Hôpitaux de Paris. this study was conducted under the umbrella of the French Society of rheumatology and inSerM (institut national de la Santé et de la recherche Médicale). the database is managed by the department of epidemiology and biostatistics (Professor Paul landais, D.
Parasite characterization Initial species characterization was carried out by several methods including: Internal Transcribed Spacer-1 (ITS-1) PCR followed by restriction fragment length polymorphism (RFLP)  (Schonian et al. , 2003) , cpbE/F -PCR  (Zackay et al. , 2013) , k26 -PCR  (Zackay et al. , 2013)  and/or multilocus enzyme electrophoresis (MLEE)  (Dereure et al. , 2003; El-Safi et al. , 2002; Pratlong et al. , 2001). The ITS-1 PCR product of one strain, SD9, which gave an atypical RFLP pattern (Fig. S1 , panel A), was purified using Wizard SV Gel and PCR Clean-Up System (Pro-mega®) and cloned into pGEM-T Easy Vector System (Promega®). Colonies (n = 4) were picked and the inserts were sequenced. In addition, the PCR product of the parental strain was sequenced. These sequences (Fig. S1 , panel B), and the sequence previously deposited in GenBank (Accession No.
, 1985)  with Gamma-distributed rates (5 categories, +G = 1. 6955) plus Invariant Sites (+I = 0. 1725) was determined using the "Find best DNA/protein model (ML) function" in MEGA 7. 0. 2 (Kumar et al. , 2016 ). An essentially identical tree was obtained using the Tamura-Nei model +G and + I  (Tamura and Nei, 1993). Nearest-Neighbor-Interchange (NNI) was used as the ML Heuristic Method. Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the Maximum Composite Likelihood (MCL) approach, and then selecting the topology with superior log likelihood value  (-4468. 83 ). The analysis involved 35 nucleotide sequences. All positions containing gaps and missing data were eliminated. There was a total of 287 positions in the final dataset.
Fragment and data analysis Labeled selective AFLP PCR fragments were detected by capillary gel electrophoresis (CGE). PCR products (1 μl) were combined with 0. 04 μl of Genescan 500 LIZ Size Standard (Applied Biosystems®) and HiDi formamide (10 μl, Applied Biosystems®). CGE was carried out on an ABI PRISM™ 3730xl DNA Analyzer (50 cm POP 7 polymer capillary and a G5 filter). The raw data were collected with Peak Scanner Software version 1. 0. (Applied Biosystems®) and analyzed using the following programs: optiFLP  (Arthofer et al. , 2011) , tinyFLP  (Arthofer, 2010) , tinyCAT  (Arthofer, 2010)  and SplitsTree 4  (Huson and Bryant, 2006). The AFLP scoring parameters were optimized using optiFLP  (Arthofer et al. , 2011). Nine different parameters were examined and individually set for each primer pair in order to identify genuine bands and eliminate background noise.