dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We sincerely thank  Takashi Irie ,  Kyoko Imai ,  Shigeki Matsubara ,  Taku Sakazume ,  Toshihide Agemura , and the  Hitachi team in Japan (Hitachi High-Tech Corporation ,  Toranomon Hills Business Tower , 1-17-1 Toranomon, Minato-ku, Tokyo  105-6409 ,  Japan ) for the collaborative study conducted together with  IHU Méditerranée Infection , and for the installation of a TM4000 Plus microscope at the  IHU Méditerranée Infection facility.
FUNDING This study was supported by the  Institut Hospitalo-Universitaire (IHU) Méditerranée -Infection  and the  French National Research Agency  under the  Investissements d'avenir programme reference   ANR-10-IAHU-03. In addition, this work conducted at IHU Méditerranée Infection was funded by a grant from  Hitachi High-Tech Corporation. Funding sources had no role in the design and conduct of the study, the collection, management, analysis, and interpretation of the data, nor in the preparation, review, or approval of the manuscript.
bovis WGS database for tracing sources of infection, including the potential role of cross-border importation and of wild fauna, as reported in other countries such as the UK  [22, 23] , and the cross-transmission in farms. Further comparisons between human  [15]  and animal isolates would also help to trace sources of zoonotic M. bovis tuberculosis in patients from a medical prevention perspective. Given such a small number of isolates, this analysis could be very skewed, and a larger number of studied isolates may provide similarities with other locations. This indicates that continued research in this area is required. Despite this limitation, we did find similarities with European nations, which is consistent with cattle being imported into Algeria from Europe and the historical links between these regions.
Statistical Analysis The data collected were analyzed using SPSS (Statistical Package for Social Sciences, version 23. 0) and Microsoft Office Excel software programs. As a criterion, reliability difference indicators used the profile p < 0. 05. Statistical analyses were performed using Chisquare tests to compare the presence of risk factors between suspected cases of tuberculosis (Table  1 ) and to compare the proportions of M. bovis genotypes, among other genotypes in different geographic regions/countries. We delineated four groups of data: Group 1, all countries in the 3364-isolates database; Group 2, all African countries from the database; Group 3, all American countries; and Group 4, all European countries.
Acknowledgments: The authors thank staff and veterinarians of the five slaughterhouses in Algeria for their support and help with our work. The authors acknowledge the help of  Zolikha Ali Mahine ,  Wahiba Akkouche ,  Yanis Dissi ,  Amir Affoune ,  Fatima Aissaoui ,  Kenza Medini  and  Abderrahmane Benkouar  for their participation and collection of samples. We would also like to offer our thanks to  Sofiane Tahrikt  for his help in preparing and sending samples from Blida to IHU in Marseille.
The moth innate immune system, mediated by hemocytes, can fight against a large spectrum of pathogens via phagocytosis, melanization, and secretion of antimicrobial peptides  [16]. Other intriguing points are its fast and high reproductive rate at low cost and the easy maintenance of its larvae in laboratory without the need for expensive equipment  [17, 18]. In comparison with other invertebrate models, G. mellonella can survive within a wide temperature range (18°C to 37°C)  [3, 5] , an essential point to mimic mammals physiology and facilitate the study of human pathogens. Furthermore, the genome of G. mellonella was entirely sequenced in 2018  [19] , which makes it easy to have well-defined populations of larvae, and perhaps allows to create a biobank with database as with other invertebrate models, Flybase and WormBase  [12].
Questionnaire Survey A standardized questionnaire was designed to collect information about each participating Syrian refugee including gender, age and city of origin as well as clinical data regarding the presence of any digestive symptoms (diarrhea, abdominal pain, vomiting, bloating and constipation). A participant was considered symptomatic if at least one of the five selected digestive disorders described above was present. All data collected from each subject remained confidential and were fully anonymized through the encryption of the identity of individuals.
isolates. Positive (DNA from Blastocystis sp. ST7 axenic culture) and negative (DNA replaced by water) qPCR controls were included with each batch of samples analyzed. The qPCR product from each positive sample was purified and directly sequenced on both strands (Genoscreen, Lille, France). For a proportion of samples, sequence chromatograms analysis revealed the presence of double traces, suggesting mixed infections by at least two different Blastocystis STs that were not determined. The SSU rDNA sequences obtained in this study from samples presenting single infection were deposited in GenBank under accession numbers MW168447 to MW168610. Obtained sequences were compared with all Blastocystis sp. homologous sequences of known STs available from the National Centre for Biotechnology Information (NCBI) using the nucleotide Basic Local Alignment Search Tool (BLAST) program.
STs were identified by determining the exact match or closest similarity against all known Blastocystis sp. STs  [14, 26]. Moreover, the sequences of Blastocystis sp. isolates belonging to the same ST (ST1, ST2 or ST3) were aligned with each other using the BioEdit v7. 0. 1 package (Date of release 06/10/2019; http://www. mbio. ncsu. edu/BioEdit/bioedit. html) to determine intra-ST diversity and identify so-called genotypes referring to genetically distinct strains within the same ST as described in recent surveys  [9, 16]. Subsequently, ST1, ST2 and ST3 sequences from isolates previously identified in the local population of North Lebanon  [4, 16, 35, 36]  were extracted from databases and compared to genotypes reported herein from the cohort of Syrian refugees.
Distribution of Blastocystis sp. STs and Genotypes The qPCR products of the 195 samples positive for Blastocystis sp. in the present study were all sequenced on both strands. For 31 of these samples (15. 9%), sequence chromatograms with multiple signals were obtained, suggesting mixed infections by different STs. The remaining 164 isolates corresponded to single infections by one ST, exhibiting 100% sequence identity with the reference sequences of known STs as for instance isolate 26 sequence for ST1 (GenBank accession number MT645808), isolate 515 sequence for ST2 (MN836830), isolate 31 sequence for ST3 (MT645813) and isolate HC104 for ST10 (MF573945). The most frequent ST identified in the entire cohort of Syrian refugees was ST3 (54. 3%; 89/164), followed by ST1 (26. 2%; 43/164), ST2 (18.
958, CI: 1. 227-15. 172, p = 0. 03) To clarify the circulation of Blastocystis sp. between refugees and the host population, the 277 sequences of isolates previously identified in Lebanese individuals living in the region of North Lebanon and belonging to ST1 (68 isolates), ST2 (74 isolates), and ST3 (135) (Table  2 ) were extracted from databases and aligned with those obtained in the present study. Briefly, among the 68 Lebanese ST1 isolates, 22 of them (32. 4%) showed identical sequences with Syrian ST1-1 (10), ST1-2 (2), ST1-3 (5) or ST1-5 (5) genotypes. Syrian genotype ST1-4 was not found in the Lebanese population. Regarding the 74 Lebanese ST2 isolates, 19 of them (25. 7%) exhibited 100% sequence identity with Syrian ST2-1 (12), ST2-2 (6) or ST2-3 (1) genotype. Strikingly, only 3. 7% of the Lebanese ST3 isolates (5/135) belonged to ST3-2 (1) and ST3-3 (4) Syrian genotypes.
Background Secukinumab has demonstrated sustained long-term efficacy with a favourable safety profile in various psoriatic disease manifestations in adults. Objectives Here, the efficacy and safety of two secukinumab dosing regimens [low dose (LD) and high dose (HD)] in paediatric patients with severe chronic plaque psoriasis over one year are reported. Methods In this multicentre, double-blind study (NCT02471144), patients aged 6 to <18 years with severe chronic plaque psoriasis were stratified and randomized by weight (<25 kg, 25 to <50 kg, ≥50 kg) and age (6 to <12 years, 12 to <18 years) to receive low-dose (LD: 75/75/150 mg) or high-dose (HD: 75/150/300 mg) subcutaneous secukinumab or placebo or etanercept 0. 8 mg/kg (up to a max of 50 mg). Results Overall, 162 patients were randomized to receive secukinumab LD (n = 40) or HD (n = 40), etanercept (n = 41) or placebo (n = 41).
Enrolment for the study (ClinicalTrials. gov: NCT02471144) occurred between 29 Sep, 2015 and 24 Aug, 2018. At the time of publication of this report, the study is ongoing. The institutional review board of each participating centre approved the study protocol. The trial was conducted in accordance with the Declaration of Helsinki and Good Clinical Practice (GCP) and in compliance with all federal, local, or regional requirements.
Statistical methods The following analysis sets were used for data analysis: • Randomized set: all randomized patients. • Full analysis set (FAS): all patients from randomized set who had been assigned study treatment. • Safety set: all patients who took at least one dose of study treatment during the treatment period. The analysis of co-primary and key secondary variables was based on FAS and was performed using the logistic regression analysis with non-responder imputations (NRI). Week 12 analysis was performed by NRI with extended analysis visit window. Summary statistics were provided for number of patients achieving CDLQI score of 0 or 1, secukinumab groups were compared for controls by Fisher's exact test. For CDLQI, missing values were replaced by last observation carried forward (LOCF) approach.
All safety evaluations were performed on the safety set and were based on data collected till Week 52 visit (or early termination visit) for individual subjects. In this study, an Interactive Response Technology (IRT) error led to additional dosing of some patients after the primary endpoint (Week 12) assessment. Since the error occurred after Week 12, primary endpoint analysis (as well as secondary endpoints at Week 12) were not affected. Additional summary analysis was performed for PASI and IGA response by (IRT dosing error and by bodyweight and dose group to evaluate potential impact of this incident on safety and efficacy beyond Week 12 (refer to Supporting information). The number of patients in the groups that were affected and not affected by the IRT error is small, for any of these differences to be considered as clinically relevant.
CGRP Entry and HIV-1 Transmission in Human Mucosal Tissue Explants Polarized penile fossa navicularis tissue explants were prepared as we previously described (  21 ), and exposed for 3 h to 500 nM or 5 mM biotinylated CGRP in 100 ml RPMI 1640 added to the apical side. After incubation, tissue penetration of biotinylated CGRP was examined by histochemistry of 4 mm paraffin sections as we described (  21 ), using HRP-coupled streptavidin (Vector), followed by the red 3-amino-9-ethylcarbazole (AEC) HRP substrate (Dako) and counterstaining with hematoxylin. Images were acquired with an Olympus BX63F microscope using MetaMorph (Molecular Devices) and analyzed with ImageJ software. For infection experiments, round (8 mm diameter) inner foreskin tissue pieces were placed in 24-well plates and incubated submerged for 24 h at 37°C with 1 ml complete RPMI medium, alone or supplemented with CGRP or SAX (1 mM, four explants per condition).
Pooled cells of each duplicate were surface stained as above using FITC-conjugated mouse-anti-human CD3 Ab (Pharmingen), fixed, permeabilized, and stained for 30 min at room temperature with 1:160 dilution of PE-conjugated mouseanti-human Ab to HIV-1 p24 and core antigens (Beckman Coulter). Fluorescent profiles were recorded using a Guava easyCyte and InCyte software.
Data and Statistical Analysis Data was analyzed using Prism software (GraphPad). Concentration-response curves were analyzed with the [log (agonist) vs. response (three parameters)] model for langrin upregulation and the [log(inhibitor) vs. normalized responsevariable slope] model for HIV-1 trans-infection inhibition. The -log molar concentrations of agonists generating 50% response represented potencies (i. e. , pEC 50 and pIC 50 ). Statistical significance was analyzed with the two-tailed Student's t-test.
FUNDING The study was funded by research grants from the  La SATT IDFinnov  (maturation project  n°005  to YG and MB), the  Agence Nationale de la Recherches sur le Sida et les Heṕatites virales (ANRS) | Maladies Infectieuses E ́mergentes  ( ECTZ159208  to YG), the  Fondation pour la Recherche Medicale  ( FRM EQU201903007830 ; to MB). The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication. JM, EC and ASe were supported by fellowships from the  ANRS.
PATIENTS AND METHODS Samples and data types. Two cohorts of individuals with 7 different systemic autoimmune diseases were recruited: a crosssectional cohort composed of 955 patients and 267 healthy controls, and an inception cohort of 113 patients followed up and sampled at the time of recruitment and at 6 and/or 14 months (Supplementary  Blood and serum samples were obtained from all patients. High-dimensional genome-wide genotype, transcriptome, and DNA methylome data, and proportions of relevant cell types were analyzed in whole blood. Low-dimensional information including selected serologic findings, such as the presence of autoantibodies, cytokines, chemokines, and inflammatory mediators, was obtained (Supplementary Tables  7 8 9 , available on the Arthritis & Rheumatology website at http://onlinelibrary.
Each time point in the inception cohort was assigned to the clusters by means of the SNF model trained with the cross-sectional cohort, and the results were summarized by patients. Details on the statistical analy sis can be found in the Supplementary Methods. Data and code availability. Data are hosted by ELIXIR Luxembourg  (20). Data are available upon request, and the access procedure is described on the data landing page (doi. org/10. 17881/th9v-xt85). Gene expression and DNA methylation data can be found online at http://bioin fo. genyo. es/ preci sesad sdata/. The R code used for clustering is available online at https://github. com/bartg 01/Neste dFCV. SNF.
Thus, the genetic associations do not depend on the underlying clinical diagnosis (data not shown) but on the cluster the patients belong to. To expand our knowledge about the relationship between genetics and the differential features driving the clusters, expression quantitative trait locus (eQTL) and methylation QTL (mQTL) analyses were performed on defined DEGs and DMCs. Overall, 97,854 single-nucleotide polymorphisms (SNPs) were associated (FDR < 1 × 10 -5 ) with 1,208 DEGs, while 107,059 SNPs were associated with 1,289 DMCs. In summary, 35% of the DEGs were associated with ≥1 SNP, including cis and trans associations (36%, 30%, and 30% for the inflammatory, lymphoid, and interferon clusters, respectively). Regarding DMCs, 21% were associated with ≥1 SNP (20%, 13%, and 64% for the inflammatory, lymphoid, and interferon clusters, respectively).
ACKNOWLEDGMENTS The authors would like to particularly express their gratitude to the patients, nurses, and many others who helped directly or indirectly in the conduct of this study. This work is supported by  ELIXIR Luxembourg  via its data hosting service. for important intellectual content, and all authors approved the final version to be published. Dr. Alarcón-Riquelme  had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study conception and  design. Laigle, Beretta, Ballestar, McDonald, Pers, Alarcón-Riquelme. Acquisition of data. Makowska, Le Lann, Jamin ,  Rodríguez-Ubreva ,  García-Gómez ,  Buttgereit, Mueller, Lesche, Hernandez-Fuentes, Juarez, Rowley, White, Marañón, Gomes Anjos, Varela, Aguilar-Quesada, Garrancho ,  López-Berrio ,  Rodriguez Maresca ,  Navarro-Linares, Almeida, Azevedo, Brandão, Campar, Faria, Farinha, Marinho, Neves, Tavares, Vasconcelos, Trombetta, Montanelli, Vigone, Alvarez-Errico, Li, Thiagaran ,  Blanco Alonso ,  Corrales Martínez , Genre,  López Mejías ,  Gonzalez-Gay ,  Remuzgo ,  Ubilla Garcia ,  Cervera, Espinosa ,  Rodríguez-Pintó ,  De Langhe, Cremer, Lories, Belz, Hunzelmann, Baerlecken, Kniesch, Witte, Lehner, Stummvoll, Zauner, Aguirre-Zamorano, Barbarroja, Castro-Villegas, Collantes-Estevez, de Ramon ,  Díaz Quintero ,  Escudero-Contreras ,  Fernández Roldán ,  Jiménez Gómez ,  Jiménez Moleón ,  Lopez-Pedrera ,  Ortega-Castro ,  Ortego, Raya, Artusi, Gerosa, Meroni, Schioppo, De Groof, Ducreux, Lauwerys, Maudoux, Cornec, Devauchelle-Pensec, Jousse-Joulin, Jouve, Rouvière, Saraux, Simon, Alvarez, Chizzolini, Dufour, Wynar, Balog, Bocskai, Deák, Dulic, Kádár, Kovács, Cheng, Gerl, Hiepe, Khodadadi, Thiel, de Rinaldis, Rao, Benschop, Chamberlain, Dow, Ioannou, Marovac, Renaudineau, Borghi, Frostegård.
Pharmacokinetics analysis Pharmacokinetic (PK) parameters were calculated from individual plasma concentrations using the non-compartmental analysis function of WinNonlin Ò software (Phoenix 64 Build, version 6. 3). The non-compartmental approach was consistent with the extravascular (topical) or IV bolus route of administration. The peak drug plasma concentration C max and the time from dosing to the maximum concentration values T max were obtained directly from plasma concentration versus time data for each cat and then averaged for each topical treatment group. The terminal elimination phase was determined using at least the final three observed concentrations and not including C max. The first order rate constant, k z , associated with the terminal log-linear portion of the curve was estimated via linear regression of the log plasma concentration versus time curve.
The incidence of Leptospira on pig farms in Europe remains unknown. However, epidemiological information has been extracted from data recorded in laboratories offering MAT for diagnostic purposes. According to these data, between 19 % and 26 % of tested pigs were seropositive in Italy, Germany and France (6-9). However, the inclusion criteria (e. g. , the inclusion or not of pigs without clinical suspicion of infection) and the panel of serogroups included may vary among studies, leading to study variation in seroprevalence and limiting reliable comparisons of the results. In addition, the previous studies did not report how the MAT results, including the cross sero-reactivity results, were managed (6-9). According to previous studies, MAT result interpretation is subjective and should consider possible cross reactions  (10, 11).
However, because we were chie y interested in the distribution of the Leptospira serogroups at a large geographical scale, any evidence of past infections was relevant to describing the distribution of the serogroups circulating in pigs. In addition, serogroups Australis and Icterohaemorrhagiae were predominant each year from 2007 Loading [MathJax]/jax/output/CommonHTML/jax. js to 2017 in our dataset, which suggests that misclassifying past or current infections would have limited effects on our results. Most of the samples came from Brittany, which is more intensive pig farming area than other regions in France. However, the serogroup distribution was similar among regions. The samples we used originated from swine herds with a clinical suspicion of leptospirosis; thus, we can conclude that throughout France, herds with reproductive disorders that are exposed to Leptospira are most likely exposed to the Australis and Icterohaemorrhagiae serogroups.
Literature Review and Data Consolidation Using a previously established protocol (see  [46, 47] ), we performed a comprehensive literature review. We searched the Thomson Reuters ISI Web of ScienceTM database, the Scopus database, and Google Scholar for relevant publications spanning 1994-2019. Specific terms were used in the topic field, which included the title, abstract, keywords and keywords plus (i. e. , words that frequently appear in the titles of articles cited within a publication). The terms included: "sea turtle", "green turtle", "Chelonia mydas", "cancer", "fibropapilloma", "fibropapillomatosis", and "neoplasia". To locate potential articles missed by the initial search, we checked the reference and citation lists of relevant papers based on the predefined keywords. We also screened and included conference proceedings and reports in French, English, and Spanish  [48].
Ultraviolet Light Ultraviolet light (UV), especially UVB, is a well-documented cancer risk factor causing damage to DNA and increasing its mutation rate, including in aquatic ecosystems  [80, 81]. In at least one publication, UV was suspected to contribute to the emergence of tumors in ChHV5-infected sea turtles  [82] ; however, this hypothesis was not tested at a global scale. To quantify exposure to UV, annual minimum, maximum, and mean exposure data, along with seasonal data (all expressed in J•m -2 •day -1 ), were obtained from the global UV-B radiation dataset for macroecological studies (glUV). This dataset included a series of climate surfaces containing information on various aspects of the global distribution of ultraviolet-B radiation  [80].
We obtained global daily sea surface temperature data (SST, expressed as degrees Celsius) from the Optimum Interpolation Sea Surface Temperature (OISST) v2. 1 website (https://www. ncdc. noaa. gov/oisst/data-access accessed on 25 July 2021). The OISST dataset extends from September 1981 to the present at a resolution of 1 /4 •  [96]. The daily SST dataset was assimilated to compute the following metrics on an annual time scale: minimum, maximum, mean, median, and standard deviation. To determine a threshold for a potential effect of temperature, we also computed the number of days in each year with an SST greater than a given threshold, from 20 • C to 30 • C (using 1 • C steps).
Data Consolidation and Spatial Scale Selection Most published studies on green turtles were conducted at the spatial scale of a bay (up to ~80 km coastline, e. g. , Moreton Bay in Australia  [76] ), island (up to ~85 km coastline, e. g. , in La Martinique, France  [66] ) or along a relatively long stretch of coastline (up to ~60 km coastline, e. g. , in Brazil  [70] ). Thus, to facilitate comparison, we computed the potential risk factors at a consistent scale, by calculating the metric of interest (min, max, mean, median, and standard deviation, etc. ) of the values within two circles of 1 /2 • and 1 • diameter around the coordinates of each study site, encompassing all study site scales. The environmental values associated to each prevalence measurement were computed for a time period starting one year before the start of a study to its end (over the 1994-2019 period).
e. , it was used as a null model to compare the effect of other risk factors). These models were fitted using maximum likelihood (ML). The model with the lowest AIC and highest AIC Weight was retained. The optimal model was then refitted using the restricted maximum likelihood method (REML) to generate predictions and interpret its coefficients  [102]. To visualize the effect of a risk factor on fibropapillomatosis prevalence, we computed marginal effects, which involve varying one risk factor at a time, while keeping all other factors constant (see  [107] ). The computation of marginal effects allows potential trends in the dataset to be visually represented. Since the computation of p-values is not reliable for mixed-effect models (see  [108, 109] ), we reported the 95% confidence intervals (95% CI) of odds ratios (OR).