dataesr/scientific-paragraphs-categorization huggingface dataset:
Schistosoma miracidium recoveries from urine and stool of schoolchildren In order to get comparable data from the same period (December 2019) on schistosomiasis in humans, urine samples from 50 primary schoolchildren volunteers of Kessounou village (11 girls and 16 boys in the 4th grade; and 9 girls and 14 boys in the 5th grade) were collected individually and then mixed and passed through a 45-μm pore size sieve. The retained eggs were placed in a beaker containing well water for miracidial hatching. The stools from 46 of the primary schoolchildren were collected individually then mixed in a 0. 9% NaCl saline solution. The mixture was passed through a series of sieves (425-, 180-, 106-, and 45-μm pore size, successively) and washed through with another 0. 9% NaCl saline solution using a pressure pump.
Partial COI mtDNA phylogenetic analysis DNA multiple sequence alignments were performed using Muscle program  (Edgar 2004 ) implemented in MEGA 7. 0 software  (Kumar et al. 2016) , and then refined using Gblocks 0. 91b  (Castresana 2000; Dereeper et al. 2008 Dereeper et al. , 2010)). The probabilistic model of sequence evolution (Nei and  Kumar 2000)  and the gamma distribution (G) to approximate rate heterogeneity among haplotypes was applied using MEGA 7. 0 software. The best model showing the lowest BIC score (Bayesian information criterion) was HKY + G (0. 16) (Hasegawa-Kishino-Yano with the gamma distribution). A phylogenetic tree using the maximum likelihood method was constructed using this model of molecular evolution under MEGA 7. 0 software. Robustness of internal nodes was systematically assessed using bootstrapping procedures (1000 replicates).
rattus individual. From the 23 sequences of schistosomes retrieved fromchildren's urine, we found 7 haplotypes (KU1_Hs_Hap6; KU3_Hs_Hap7; KU4_Hs_Hap8; KU13_Hs_Hap9; KU5_Hs_Hap10; KU10_Hs_Hap11; KU11_Hs_Hap12) all different from those extracted from rodents. From the 24 sequences of schistosomes extracted from children stool, we found 8 haplotypes (KU1_Hs_Hap6; KU3_Hs_Hap7; KU4_Hs_Hap8; KU13_Hs_Hap9; KU10_Hs_Hap11, KS5_Hs_Hap13, KS10_Hs_Hap14), among which five were shared with some haplotypes found in the urine, and one haplotype (K14M1_Mn_Hap1) shared with the schistosome haplotypes found in M. natalensis. Sequence data were deposited in the NCBI GenBank database under the accession numbers from MW022134 to MW022147.
COI mtDNA phylogeny (884 bp) The 14 haplotypes obtained from the COI mtDNA sequences (MW022134 toMW022147) were compared to 18 nucleotide sequences of Schistosoma with terminal-spined eggs  (Rollinson and Southgate 1987)  available in GenBank: 8 sequences of S. haematobium, from Gambia (JQ397349), Mali (AY157209), Egypt (JQ397368), Toho-Todougba_Benin (KT354661) , Melen_Gabon (KT354660) , Kenya (JQ397378), South Africa (JQ397397), and Madagascar (JQ397399); 3 sequences of S. bovis, from Senegal (AJ519521), Kenya (FJ897160), and Tanzania (AY157212); 4 sequences of S. bovis x S. haematobium, from Corsica, France (KT354656, KT354657, KT354658), and Sô-Tchanhoué, Benin (KT354662); 1 sequence of S. guineensis Pagès, Jourdane, Southgate & Tchuem Tchenté, 2003 from Sao Tome and Principe (AJ519517); 1 sequence of S.
ITS rDNA (946 bp) One hundred and sixty-one 18S, ITS1, 5. 8S, and ITS2 sequences were analyzed. The first 22 bp of our ITS rDNA sequences belonged to the 18S gene and no variability was found between any of our samples. We thus no longer gave this gene further consideration. Eleven profiles were found with the ITS1, 5. 8S, and ITS2 sequences, with 1 to 3 profiles per rodent, only 1 profile for all the miracidia, only one profile for all the cercariae, and 6 and 7 profiles for urine and stool of schoolchildren, respectively (Table  1 ). Sequence data were deposited in the NCBI GenBank database under the accession numbers from MW027648 to MW027658. Table  3  shows that the 11 profiles differ by their ITS1 gene (458 bp: from position 23 to position 480), with 5 genetic variants at four positions (TGAT, TGAY, TAAT, TART, and YAAT); by their 5.
8S gene (155 bp: from position 481 to position 635), with 2 genetic variants at 1 position (C and Y); and by their ITS2 gene (311 bp: from position 636 to position 946), with 5 genetic variants at 6 positions (AATGAT, WATRAT, AATRAT, AGCGGC, and ARYGRY). Among these 11 profiles, four were found only in R. rattus (profiles 1 to 4: accession numbersMW027648 to MW027651), one profile corresponded to the typical S. bovis ITS rDNA (profile 1: accession number MW027648) and three profiles showed variants of the S. bovis typical profile with double peaks either in the ITS1 or in the ITS2 gene (profiles 2 to 4: accession numbers MW027649 to MW027651). Two profiles were found in both M. natalensis and schoolchildren (profiles 5 and 6: accession numbers MW027652 and MW027653), with profile 5 harboring both a typical S.
haematobium ITS1 gene together with a typical S. bovis ITS2 gene, and profile 6 harboring a new combination which included the ITS1 gene (TART, variant of S. haematobium) together with a typical S. bovis ITS2 gene. Profile 6 was observed in all the miracidia and all the cercariae collected from the Bg1, Bg2, and Bg3 snails experimentally infected with one miracidium extracted from naturally infected M. natalensis. The other five profiles were found only in schoolchildren (profiles 7 to 11: accession numbers MW027654 to MW027658) and showed variants of the S. haematobium typical profile, with double peaks in the chromatograms of at least one of the ITS1, 5. 8S, or ITS2 genes.
Acknowledgments Thanks to  Dr Gualbert Houéménou  ( Laboratoire de Recherche en Biologie Appliquée, Unité de Recherche sur les Invasions Biologiques; Ecole d'Abomey-Calavi, Université d'Abomey-Calavi, Benin ) for welcoming us into his Lab and providing us with excellent working conditions. We thank all the councilors ( Marius Codjo ,  Rémi Hounkanni ,  Nathael Houssou ,  Omer Kouton , and  Mathieu Zingbé ), the boatmen ( Boniface Ahissouvou , and  Moïse Hounvi-Edjê ), and local volunteers for providing us with all the facilities during our study, as well as the director of the  Kessounou primary public school (Pr Jean-Pierre Tokpo ) and his schoolchildren for their collaboration. A big thanks to  Jeanine Almany  for the linguistic revision of the paper.
Funding This research was funded by the  French Ministry of Foreign Affairs , the  International Joint Laboratory  through the  CONS-HELM  project, the  French Institute for Sustainable Development (IRD) , the  French National Agency for Research (ANR)  [grant  ANR-17-CE12-0005-01 ] CHRONOGET and CNRS. BS is a student fellow from the  SCAC (Service de Coopération et d'Action Culturelle) ,  French Embassy , in Benin.
2004), Δ from Searchinger et al. (2018) , > / from www. web-agri. fr (August 2020), and τ from www. ember-climate. org/carbon-price-viewer (September 2020). SOC «r¬ , -,/ , A•2 -, δ, σ, φ and rts were obtained from IPCC(2006). η was calculated by averaging the monetary values of the erosion prevention service in grasslands in comparable Western countries from the database of Van der Ploeg and de Groot (2010). Stoch was calculated from the CO2 chemical composition and the element masses obtained from www. chemicalelements. com (accessed May 2019). TableA. 1 summarizes the values of the parameters used in the model.
( 2004), Δ from Searchinger et al. (2018) , > / from www. web-agri. fr (August 2020), and τ from www. ember-climate. org/carbon-price-viewer (September 2020). SOC «r¬ , -,/ , A•2 -, δ, σ, φ and rts were obtained from IPCC(2006). η was calculated by averaging the monetary values of the erosion prevention service in grasslands in comparable Western countries from the database of Van der Ploeg and de Groot (2010). Stoch was calculated from the CO2 chemical composition and the element masses obtained from www. chemicalelements. com (accessed May 2019). TableA. 1 summarizes the values of the parameters used in the model.
Study design We did a crosssectional study, including a molecular epidemiology investigation, of a diphtheria outbreak across Yemen, in 2017-20. For epidemiological investi gations, several nationallevel data sources were used. Yemen is divided into 22 governorates, which are further divided into 333 districts. In this study, the majority of districts that provided epidemiological data were located within governorates in the central and northern regions of Yemen. In each district, a rapid response team of the surveillance department was mobilised through warning reports from health facilities. This team then both investigated the clinical cases and collected the samples for confirmation. Throat swabs were done for every tenth case if there was a cluster of more than ten cases. In.
Research in context Evidence before this study An outbreak of diphtheria has been ongoing in Yemen since October, 2017. We did a search of PubMed and Google on March 22, 2021, with no language restrictions, using the search terms "Yemen" and "diphtheria". This search identified that the epidemiology of the outbreak was described only up to mid-2018, and its microbiological and clinical features have not been reported. A search of PubMed for studies published from database inception up to March 22, 2021, using the term "diphtheria+outbreak+genomic", showed that only two outbreaks of toxigenic diphtheria have been investigated using genomic sequencing: an outbreak in South Africa in 2015, and a Belarus outbreak in 1996-2014. In both outbreaks, the coexistence of diverse sublineages of Corynebacterium diphtheriae and variable toxigenicity status were reported.
Implications of all the available evidence The combined epidemiological, clinical, and microbiological data from Yemen provide insight into one of the largest outbreaks of diphtheria in the past 20 years. Differences in the proportion of infections in children aged 0-4 years between the first and second period are consistent with some success from the mass immunisation catch-up programme. The evidence of genetic heterogeneity, antimicrobial susceptibility, and toxigenicity variation among C diphtheriae strains within a single large diphtheria outbreak shows the need for increased laboratory capacity and rapid testing, to improve diphtheria case management. Further investigation of both the local and regional spread of C diphtheriae will inform infection prevention strategies. parallel, the electronic diseases early warning system (eDEWS) questionnaire was filled by the eDEWS focal person in each health facility on a weekly basis.
98 of these stored samples were retrieved from storage (two tubes corresponding to selected samples had been lost) and were sent to Institut Pasteur (Paris, France) for complementary analyses that included re isolation on Tinsdale medium, tox gene and speciesspecific rpoB realtime PCR,  19   pp 4-5 ). Sublineages were defined as deep tree branches. For the combined phylogenetic analysis of the Yemen and DIFT045 isolates with global C diphtheriae genomes, we used all genomic sequence assemblies that were publicly available from the National Center for Biotechnology Information database on June 1, 2020.
Results Large numbers of probable cases of diphtheria were recorded in Yemen in 2017 (n=442), 2018 (n=2606), 2019 (n=2004), and 2020 (up to week 17: n=649). Altogether, 5701 cases, including 330 deaths, were reported up to April 26, 2020, corresponding to a case fatality rate of 5•8%. Between February, 2018, and November, 2019, we collected clinical data for 888 probable cases with throat samples referred to the NCPHL for biological confirmation (table), and genomic data for 42 positive cases corresponding to 43 isolates (figure  1 ). These 43 isolates were derived from 98 randomly selected samples, out of the 340 biologically confirmed cases. Some clinical data were missing (table ). We divided the epidemic into three time periods (figure  2A ) and qualitatively observed incidence valleys in June, 2018, and June, 2019.
In the third period For the Yemen nutrition smart survey see https://data. humdata. org/dataset/yemennutrition-smart-survey See Online for appendix 2 (June 4, 2019, to April 26, 2020), the recorded number of probable cases peaked in November, 2019, with more than 80 cases per week at that time. The diphtheria attack rates were 6•34 probable cases per 100 000 individuals in the first period and 6•24 probable cases per 100 000 in the second period (p=0•66). The outbreak started in October, 2017, in Ibb and affected almost all governorates of Yemen (figure  2 , appendix 2 pp 13-14, 22). The two first periods had distinct geographical distributions (p=0•0005). The percentage of the 888 patients for whom clinical data were available who had been vaccinated did not change between 2018 and 2019 (57% vs 59%, p=0•49).
Acknowledgments This work was supported financially by institutional funding from the  NCPHL (Sana'a, Yemen)  and  Institut Pasteur. The French National Reference Center for Corynebacteria of the diphtheriae complex received support from  Institut Pasteur and Public Health France (Santé publique France, Saint Maurice, France). This work was also supported financially by the  French Government Investissement d'Avenir Programme Laboratoire d'Excellence, Integrative Biology of Emerging Infectious Diseases  ( ANR10LABX62IBEID ). We thank  FrançoisXavier Weill  ( Institut Pasteur, Paris, France ) for helpful discussions, and  Isabelle Cailleau  ( Institut Pasteur) , the  Médecins Sans Frontières team in Djibouti  and  Stephane Crisan  ( Epicentre, Paris, France ) for logistical support. The NCPHL also acknowledges  WHO  for providing some reagents used in this study.
Results Three-hundred and fifteen respondents provided data from a total of six different countries (Spain = 119, Portugal = 113, Italy = 38, France = 38, Belgium = 6, and Luxembourg = 1). One-hundred and thirty-one respondents (41. 6%) were excluded because they had not diagnosed hypoAC within twelve months prior to data collection. Of the remaining 184, 30 respondents had never prescribed DOCP (Figure  1 ). This became a secondary exclusion criterion. Prior to dissemination, the survey was reviewed by six veterinarians and one epidemiologist. Surveys were accessible from 1 November 2019 through 31 May 2020. Participation was voluntary and no incentive was offered. No identifiers were attached to the survey; thus, all responses were anonymous. To be included in the study, participants were required to certify that they consented to the authors' use of information.
Results Three-hundred and fifteen respondents provided data from a total of six different countries (Spain = 119, Portugal = 113, Italy = 38, France = 38, Belgium = 6, and Luxembourg = 1). One-hundred and thirty-one respondents (41. 6%) were excluded because they had not diagnosed hypoAC within twelve months prior to data collection. Of the remaining 184, 30 respondents had never prescribed DOCP (Figure  1 ). This became a secondary exclusion criterion. Considering the case load over the last 12 months, 95. 7% (176/184) of respondents diagnosed 1 to 5 cases of hypoAC, 3. 3% (6/184) diagnosed 5 to 10 cases, and 1% (2/184) diagnosed more than 10 cases. Considering the case load over the last 12 months, 95. 7% (176/184) of respondents diagnosed 1 to 5 cases of hypoAC, 3. 3% (6/184) diagnosed 5 to 10 cases, and 1% (2/184) diagnosed more than 10 cases.
Automata for Automatic Data Collection Each pen had two connected feeders for recording feed consumption, a connected drinker for collecting water consumption and a connected weighing pan for recording the live weights of piglets. The data records are individual because each piglet was equipped with an RFID (Radio Frequency Identification) chip positioned in the ear, communicating by radio frequency with the automata. The chips were installed on the weaning day with a specific plier, during the distribution in the six pens. Thus, when an animal was detected by an automaton, its individual number was immediately identified by an RFID antenna and the information generated by the automaton was automatically assigned to it (water or feed consumption or weight, depending on the automaton concerned).
Low Frequency RFID technology was able to detect an animal at a distance of 20 cm and only one piglet at the same time, which guaranteed the recorded information. When the animal left the detection field of the antenna, this information was sent to a collection box which compiled and aggregated it into a single piece of information before transmitting it to a central computer. Finally, the computer was responsible for time stamping and then archiving all the compiled information transmitted to it to create a daily flat file database (csv). In this daily flat file, a line was produced that corresponded to an event that occurred on one of the automata. The connected feeder consisted of a trough equipped with a probe capable of detecting the presence of dry feed, a radio frequency detection antenna (RFID) and a gear motor connected to a worm screw  (Rousseliere et al.
Each time a piglet ear tag was detected by the RFID antenna, the computer located in the office time stamped the visit and recorded the amount of feed consumed. To ensure the consistency of the data collected by the machine, the feed was weighed every day by the animal handlers and the accuracy of the distribution was checked every week. The connected drinker consisted of a bowl drinker, adjusted at 1 l/min (± 1ml), connected to an individual water meter and an RFID antenna to identify piglets  (Rousselière et al. , 2016). The flow rate of the drinkers was checked at the start and at the middle of the test to always guarantee a flow rate of 1 l/min. Each drinker was associated with a weighing pan suspended using two strain gauges, at the front and at the rear of the automaton  (Marcon et al.
Observations of Health Status In order to collect a maximum of data on the animal's health status, observations of the piglets were made by animal keepers every day and by specialized technicians three times a week. All these observations were made according to a unique standardized grid and the observers were trained by a veterinary to limit the subjectivity of the data. Between animal keepers and technicians, the main difference was the time spent for the observations, technicians checked all the piglets one by one and spent at least 20 min at each pen. During each observation, the health status of each piglet was registered on a sheet previously printed with the ID of piglets in this pen. There was also the possibility for the observer to write other kind of useful information about the animal (dead, infirmary.
Database Creation At each end of batch, the files with feed and water consumption and the weight of all the piglets were automatically imported into a database developed with Access software. The data of health scoring status were also imported into the database after entry via a dedicated form developed in VBA, with different alert messages and buttons blocked to signal any missing information in order to reduce entry errors. For data analysis, all the information collected on the fifteen batches was exported to be manipulated and analyzed using Python language, via the Jupyter interface. The data were aggregated daily for each piglet and data cleaning was carried out: periods, pens, and animals with biased (for example a defective water meter) or incomplete (automata or computer errors, power cut) data on water, feed or weight were deleted.
Selection of Animals Included in the Data Analysis After cleaning the database, 15 batches representing 53,550 lines (one line represents one animal for 1 day) were used for the analysis. Of the 1,530 piglets registered in the database, ∼9% (138 pigs representing 4,830 lines in the database) showed no symptoms of all the clinical scores (not only diarrhea) during the 5 weeks of the trial, they represented the "Healthy" group of animals. To constitute the group of "Sick" animals, we used 2,568 lines of pigs rated D1 and 1,686 lines of pigs rated D2. Pigs having exhibited clinical signs other than diarrhea (cough, lameness, wounds, etc. ) were not included in this study. Furthermore, to compare healthy and sick animals, data corresponding to the first day of diarrhea and the days before were used.
Creating Variables for Analysis The water and feed consumption were directly connected to the live weight of the animals, and so were systematically expressed as a percentage of the live weight of the pigs (ml of water consumed/g of live weight and g of feed/g of live weight). This made it possible to compare the results of animals with several weights at a given age. To represent the piglet's behavioral changes over time and according to its digestive health for analysis via machine learning, the following variables were created: the accumulation, since the start of the test, of water consumption related to weight; the cumulation of feed consumption in relation to weight and the 2and 3-day moving averages of water and feed consumption. The moving averages made it possible to obtain a result considering the previous days, which indicated the dynamics of water and feed consumption of the piglet and not only to use data from a given day.
Data Analysis, Statistical Tests, and Machine Learning First, the water and feed intakes of healthy animals were studied to see if a healthy animal showed stable results over time, which was necessary to detect deviant behavior in sick animals. The results of the three weight categories were visualized by box plot and compared individually with a Tukey test. Analyses were performed with data from days 2 to 36, grouped by week from Friday to Friday. Day 1, which corresponded to the Thursday of arrival in post-weaning, was not considered in these analyses because the new environment strongly affected the behavior of the piglets. Then, the intra-individual variability of water and feed consumption in healthy animals was evaluated by calculating the coefficient of variation (ratio between the standard deviation and the mean) of each pig week by week.
On days 2, 3, and 4, the health observations were reduced due to the weekend. The start of data analysis therefore began on the Monday following weaning, the fifth day of presence in the room, with reliable health data. Due to our work on early disease detection, several days were excluded from the comparison between healthy and sick data, because of zero piglets with the first diarrhea symptoms on these days. Therefore, days 31, 32, 34, and 36 do not appear on the following graphs. Finally, a machine learning approach using scikit-learn package on Python language via Jupyter software was adopted using seven different methods (logistic regression, linear discriminant analysis, k nearest neighbor classifier, decision tree classifier, Gaussian naive Bayes classification, support vector machines and random forest).
These methods were used with the default parameters on this first comparison, on future studies we can work on the fine-tuning parameters. The objective was to classify the piglets in two groups: healthy or sick. To do this, the performance of each method was compared according to its sensitivity and specificity. Sensitivity is the method's ability to detect sick animals and to avoid false negatives. Its calculation is Sensitivity = a/a + c where a = number of true positive and c = number of false negative. Specificity is the method's ability to detect healthy animals and to avoid false positives. Its calculation is Specificity = d/b + d where d = number of true negative and b = number of false positive. For these analyses, we created two databases: training database, with the piglets of 10 batches randomly drawn, and test database with the five batches remaining.