dataesr/scientific-paragraphs-categorization huggingface dataset:
wilkinsoni  (Kerdelhue et al. , 2009) , the longhorn beetle Monochamus galloprovincialis and its sister species M. sutor  (Haran, Koutroumpa, Magnoux, Roques, & Roux, 2015)  or the green lacewing Chrysopa perla / C. formosa  (Bozsik, 1992). Such taxonomic resolution is highly important when dealing with arthropod pests or arthropod species involved in biological pest control. In particular cases  (Mayer & von Helversen, 2001) , knowledge on geographic distribution may help deciphering the most likely taxa in presence (e. g. Eptesicus serotinus and Eptesicus nilssonii, or Myotis myotis and Myotis blythii that cannot be distinguished whatever the mitochondrial marker used). We have also evidenced some limitations with regard to public sequence databases. Two arthropod species included in the mock communities could not be identified as they were not included in BOLD and GENBANK databases.
We have also emphasized some errors that may have consequences for further identification (Fig. S1 ). For example, one sequence of Pipistrellus pipistrellus included in BOLD is mis-assigned to Pipistrellus kuhlii (GENBANK Accession JX008080 / BOLD Sequence ID: SKBPA621-11. COI-5P). This mistake made it impossible to distinguish both species using BOLD Systems tool. Although recently reported  (Shen, Chen, & Murphy, 2013) , it has not yet been cleaned. It seems that such errors are quite frequent in GENBANK  (Shen et al. , 2013) , and unfortunately BOLD database does not appear to be spared. Completeness and reliability of public databases are therefore still a main pitfall in metabarcoding studies.
We reported the presence of snails (Cepaea hortensis and Cepaea nemoralis) Altogether, our dataset enabled to reveal the presence in bat diets of 61 pest species (Table  S5 ) among which some are important for agricultural management (e. g. the cotton bollworm Helicoverpa armigera, the spotted-wing drosophila Drosophila suzukii and the pine processionary Thaumetopoea pityocampa) or veterinary and Public Health issues (e. g. the malaria vector Anopheles claviger, the biting house fly Stomoxys calcitrans, the common house mosquito Culex pipiens). Our results therefore confirmed the possibility to use our metabarcoding approach as an indirect tool for "chirosurveillance" without any a priori with regard to the pests that need to be surveyed  (Maslo et al. , 2017). This study also illustrated the capacity of our approach to reveal variation in diet richness and composition.
Acknowledgements First of all, we are indebted to all of our collaborators in the naturalist associations who helped us to gather and generate this comprehensive bat sampling:  Matthieu Dorfiac  ( Charente Nature ),  Anthony Le Guen  ( Deux-Sèvres Nature Environnement ),  Virginie Barret  and  Philippe Jourde  ( LPO France ),  Sébastien Roué  ( Groupe Chiroptères Aquitaine ) and all the numerous volunteers that contributed to field work. We thank  Jean-Claude Streito ,  Guenaelle Genson ,  Antoine Foucard ,  Laure Benoit ,  Carole Kerdelhué  and  Laure Sauné  for the insect DNA provided to constitute the mock communities. Many thanks to  Emese Meglecz ,  Vincent Dubut  and  Emmanuel Corse  for the constructive discussions and sharing about the metabarcoding analyses. We thank  Eric Petit  and two anonymous reviewers for their constructive comments and suggestions on a previous version of the manuscript.
Data used in this work were partly produced through the genotyping and sequencing facilities of  ISEM (Institut des Sciences de l'Evolution-Montpellier ) and  LabEx Centre Méditerranéen Environnement Biodiversité. Analyses were performed on the CBGP HPC computational platform thanks to  Alexandre Dehne-Garcia. This work was funded by the  LabEx ECOFECT  and the internal funding from the  CBGP laboratory. Orianne Tournayre PhD  is funded by the  LabEx CeMEB. Part of this work (JBP, DP) was performed within the framework of the  LabEx ECOFECT  ( ANR-11-LABX-0048 ) of  Université de Lyon  and the  Poitou-Charentes Nature Feder  project « Grand Rhinolophe et trame verte bocagère : étude des facteurs environnementaux influant sur la dynamique des populations » (ML, JBP, DP).
). https://datadryad. org/resource/doi:10. 5061/dryad. kv02g/8Dryad). DNA extractions were normalized to 5ng/µL using Qubit fluorimeter quantification (Invitrogen). First, each normalized DNA was amplified and sequenced independently. Second, normalized DNA extractions were pooled in equal proportion to build the two mock communities MC1 and MC2. These latter were amplified and sequenced (see details in Table Arthropod DNA was extracted individually using the DNeasy Blood & Tissue kit (Qiagen). Sanger sequences were available for the cytochrome oxydase I (COI) gene of each individual (see the alignment file of the 19 insect species included in the mock communities deposited in Dryad:.
Prey sequencing was marginally influenced by storage duration, with a marked decrease being visible after three months storage at room temperature. Mock communities deposited in Dryad: https://datadryad. org/resource/doi:10. 5061/dryad. kv02g/8Dryad). They represented 79% of the reads for these individual PCRs, confirming the high quality of the MiSeq sequencing. BOLD identification tool enabled to identify specifically 14 of the 19 COI minibarcode sequences. Forficula lesnei (MC1) and Acorypha sp. (MC2) were not identified using the public databases BOLD and GENBANK because they were not community analysesThe 19 arthropod DNA extracts from the mock communities were first amplified and sequenced independently in the MiSeq run. The MiSeq sequences produced similar identifications than Sanger sequences when considering the 19 most abundant MiSeq variants (Table1).
Up to now, less than half of COI sequences deposited in BOLD Systems are made public (Species Level Barcode Records in January 2017: 2,697,359 Sequences/175,125 Species; Public Record Barcode Database: 1,018,338 Sequences/85,514 Species). Because we could not identify all variants using the web application of BOLD Systems, we decided to analyse the 1318 most abundant variants of the whole dataset (including bat samples and mock communities). They were represented by more than 100 reads what was equivalent to 99. 1% of all remaining reads. Further analyses revealed that a majority of the remaining 1318 rare sequences (less than 100 reads) could not be assigned clearly to any taxa. They are mainly chimeric sequences or pseudogenes that had not been removed during the filtering process (see the data for the mock communities in the abundance table after filtering deposited in Dryad : https://datadryad.
Background: Malaria transmission is driven by multiple factors, including complex and multifaceted connections between malaria transmission, socioeconomic conditions, climate and interventions. Forecasting models should account for all significant drivers of malaria incidence although it is first necessary to understand the relationship between malaria burden and the various determinants of risk to inform the development of forecasting models. In this study, the associations between malaria risk, environmental factors, and interventions were evaluated through a systematic review. Methods: Five electronic databases (CAB Abstracts, EMBASE, Global Health, MEDLINE and ProQuest Dissertations & Theses) were searched for studies that included both the effects of the environment and interventions on malaria within the same statistical model.
Eligibility criteria The searches were not restricted by year or country but by language, as only English and French literature was selected. Only original research studies with quantitative analysis were considered, thereby excluding reviews, short communications, letters, posters, and conference abstracts. Studies were included if the analysis took into account both the effects of environmental factors and malaria control interventions within the same statistical model. Environment was considered as a generic term covering both climatic and meteorological concepts and includes meteorological data and remote sensing data captured from satellites. The intervention term encompassed a wide range of measures related to malaria control including vector control (e. g. , bed nets, spraying, larval control), medical treatment for malaria, health services (accessibility and research), health practices and knowledge, community health workers, health education and promotion, health policy, and surveillance.
Data extraction From each selected study, the following information were abstracted: references, country, population setting, outcome, environmental data, time-frame of observed data, interventions, analytical approach, results and limitations. Extracted data were entered into an Excel table. To evaluate how well the interventions were described in the studies, the 12-item checklist of the TIDieR (template for intervention description and replication)  [37]  was applied: name of intervention, why, what (materials), what (procedure), who provided, how, where, when and who much, tailoring, modifications, how well (planned), and how well (actual). All study authors were contacted for further information.
Intervention content Based upon the TIDieR checklist, comprehensive intervention reporting was extremely poor for ten of the eleven studies. Details on implementation, procedure, the mobilized staff and agents, monitoring of interventions were not provided or were but with minimal information. Only one study  [46]  provided details regarding the period of implementation, amount of insecticide sprayed, and information regarding policy changes about intervention coverage and suppliers. TIDieR tables (Additional file 4) were completed based on external searches as publications information provided in the articles was insufficient. The percentages of studies with data according to TIDieR items are presented in Fig. 2. Details of interventions are available on Additional file 3.
e. node D is necessarily older than node R). c) Sequence divergence (here measured in units of expected number of nucleotide substitutions along a strict molecular clock time tree, see supplementary materials) for 36 mammals 2 is correlated (Pearson's R 2 =0. 664, p<10 -2 ) with age estimates based on the fossil record (ages corresponding to the time of divergence in million years). d) A similar relationship can be seen for gene transfer based relative ages by plotting the sequence divergence (measured similar to part c) against the relative age of ancestral nodes for 40 cyanobacterial genomes (Spearman's rank correlation rho=0. 741, p<10 -6 ) inferred by the MaxTiC (maximal time consistency) algorithm  26. We examined genome-scale datasets consisting of homologous gene families from complete genomes in Cyanobacteria (40 genomes  27  ), Archaea (60 genomes  28  ) and Fungi (60 genomes  29  ).
664; p = 0. 0025 and Spearman's ρ = 0. 83; p = 0. 0056). We observed a strong correlation between time estimates from MaxTiC and molecular clocks in all our datasets (p<10 -3 -Fig S14  -S16 ). This suggests that LGT indeed carries information on the relative age of nodes in all three domains of life. However, it is not conclusive because part of the correlation trivially results from the fact that parent nodes are necessarily both older and more distant to extant sequences than their direct descendants  31. To control for this effect, we compared the relative time orders of speciation events inferred from transfers to dates obtained using molecular clocks in the absence of calibrations. We used Phylobayes 32 on a concatenate of nearly universal gene family alignments to sample chronograms (i.
e. , dated trees) under four different uncalibrated molecular clock models  33  (the strict molecular clock, the autocorrelated lognormal, the uncorrelated gamma and the white-noise model). As a control for the shape of the tree, we measured the random expectation by sampling chronograms from the prior on divergence times but keeping the species phylogeny fixed (without any sequence information). To compare the dating information from transfers to the information conveyed by fossils, we used the same uncalibrated approach on the same mammalian dataset as above  2, 34  and derived relative node age constraints from fossil calibrations (see supplementary text). For the prokaryotic and fungi datasets, we derived relative node age constraints from the maximal consistent subsets of transfers obtained using MaxTiC  26.
While in mammals approximately half of the chronograms proposed by the lognormal model agree with 100% of relative constraints, for other datasets no model reaches 80% agreement. This means that some relative constraints derived from LGT consistently disagree with uncalibrated molecular clock estimates. These disagreements are difficult to interpret because both molecular clocks and our transferbased inferences may be subject to error; simulations suggest that spurious gene transfer inferences do occur with ALE, albeit at a low rate (Chauve et al. 35  , Fig. S23 ). Nonetheless this low error rate on simulations • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • -0.
S20-S22 ). We then counted the number of times a constraint was observed. In all datasets, a large majority of constraints were highly supported (found in at least 95% of the replicates) and among these, a significant number (between 20% and 32%) consistently disagreed with molecular clock estimates (see Table  S2 ). These strongly-supported transfer-based constraints that disagree with the clocks could result from the inability of uncalibrated molecular clock estimates to recover the correct timing of speciations in groups with large variations in the substitution rate over time. Specifically, LGTs provide strong support for the relatively recent emergence of the Prochlorococcus -Synechococcus clade in Cyanobacteria (blue clade in Fig. 4a ), irrespective of uncertainty in the root of Cyanobacteria (see Supp.
LGT-based constraints were sampled per data set (a: Cyanobacteria, b: Archaea, c: Fungi). The black line corresponds to the consensus chronogram. Red shading represents the spread of node orders within the sample: nodes are in bright red if there is little or no uncertainty on their order according to LGT, in a light red smear if there is high uncertainty on their order. Clades discussed in the text are labelled and shaded. Supplementary Figures  S1, S2  and S3 provide the same consensus chronograms with species names at the tips.
Acknowledgements G. J. Sz. received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  under grant agreement No. 714774. This project was supported by the  French Agence Nationale de la Recherche (ANR)  through grant no. ANR-10-BINF-01-01  'Ancestrome'. Computations were made using the Curie supercomputer thanks to  PRACE  project  2013081661  and the computing facilities of the  CC LBBE/PRABI. T. A. W. is supported by a  Royal Society University Research Fellowship. We thank  N. Lartillot ,  T. Warnow ,  M. Paris ,  I. Derényi  and  J. Miguel Blanca Postigo  for useful discussions, comments on the manuscript and further computing resources.
Statistical Analysis. The statistical analysis was conducted using Statistica (StatSoft). Paired t-tests were performed to evaluate the difference in pH and composition between saliva and tongue film. Pearson correlation was calculated between pH in saliva and pH in film. The correlation between taste sensitivity scores and biological data was evaluated using Spearman correlation tests. The choice of a nonparametric test in that case is justified by the presence of censored data (scores = 0 or 6) in the sensory dataset. A score of 0 indicates that the subject would perceive the tastant only above the highest concentration presented, while a score of 6 indicates that the subject could perceive the tastant even below the lowest concentration presented.
Each cecum was recovered in a sterile bag, respecting the conditions of asepsis and hygiene. The bags were stored in isothermal containers with ice and transported as quickly as possible to the Department of Microbiology of the AZM Center of Tripoli. The lower end of each of the 5 ceca collected from the same batch were cut using a pair of sterile tweezers and the contents of the 5 ceca were recovered into a sterile cup and pooled for further processing (within 24 h) of what was considered a single sample. Regarding slaughterhouse A, 120 samples were thus collected from 30 different farms (Additional file 1). For each of these farms, 4 samples were obtained throughout 2016 (one sample per season) in order to evaluate a possible seasonal effect on the prevalence of the parasite. In order to obtain epidemiological data from chickens in different geographical areas of Lebanon, 90 samples from 38 farms and 13 samples from 6 farms were obtained during summer, autumn and winter 2016 in slaughterhouses B and C (Additional file 1), respectively, for an overall total of 223 chicken samples screened in this survey.
This sample was thus reanalyzed by non-qPCR using the same primer pair as for qPCR. Non-qPCR amplification, as well as purification and cloning of the non-qPCR product, were performed as described previously  [20]. Purified non-qPCR product cloned in the T-vector, pCR 2. 1-TOPO (Invitrogen, Carlsbad, USA) was amplified in Escherichia coli One Shot TOP10 competent cells and minipreparations of plasmid DNA were done using the NucleoSpin Plasmid kit (Macherey-Nagel, Düren, Germany). Five positive clones containing inserts of the expected size were selected arbitrarily and sequenced on both strands. The SSU rDNA gene sequences obtained in this study were deposited in GenBank under accession numbers MG905462-MG905588. The sequences obtained were compared with all Blastocystis sp. homologous sequences available from the National Centre for Biotechnology Information (NCBI) using the nucleotide Basic Local Alignment Search Tool (BLAST) program.
The STs were identified by determining the exact match or closest similarity against all known mammalian and avian Blastocystis sp. STs according to the last classification of the parasite  [17]. Subsequently, the sequences of Blastocystis sp. isolates identified in the present study as belonging to ST6 were aligned with each other using the BioEdit v. 7. 0. 1 package (http://www. mbio. ncsu. edu/ BioEdit/bioedit. html), and then with all those of the same ST obtained from animal and human isolates in previous studies and available in the databases at the time of the study (Additional file 4).
Statistical analysis Statistical analysis was performed using Stata software, version 13 (StataCorp, College Station, TX, USA). All tests were two-sided with a Type I error set at 0. 05. Continuous data were expressed as mean ± standard-deviation or as median with interquartile range (IQR) according to statistical distribution and categorical parameters as frequencies and associated percentages. Comparisons concerning quantitative data were performed by Student's t-test or the Mann-Whitney test when the assumptions of the t-test were not met. Normality was studied with the Shapiro-Wilk test and homoscedasticity with the Fisher-Snedecor test. Concerning categorical data, Chi-square or Fisher's exact tests were performed. These analyses were complemented, when appropriate, by random-effects models useful to take between-and within-farm variability into account (as a random effect).
0, P = 0. 37, summer vs autumn and winter). Of the 13 samples remaining at slaughterhouse C, 8 were identified as positive for Blastocystis sp. (61. 5%) by qPCR (Table  1 ). Moreover, the parasite was identified in 5 of the 6 corresponding farms (83. 3%) sampled in at least one season (Additional file 1). By combining the data obtained for the three slaughterhouses, the prevalence of Blastocystis sp. reached 31. 8% (71/223) for chicken samples and 64. 9% for farms (48/74) for which at least one seasonal sample was positive. Human cohorts and prevalence of Blastocystis sp. Single stool samples were collected from a total of 100 individuals divided into two groups. The first group included 50 individuals (40 male, 10 female) all working at slaughterhouse A, either in the slaughtering (45 subjects) or in the poultry delivery area (4 subjects) (Additional file 2).
Distribution of Blastocystis sp. STs in animal and human populations The qPCR assay used in the present study targets a partial sequence of the Blastocystis sp. SSU rDNA gene. Without the primers, the DNA fragments sequenced in our study were 277 to 299 bp in size, depending on ST. All the partial SSU rDNA gene sequences obtained from animal or human samples showed 99 to 100% identity with homologous sequences available in databases allowing the direct subtyping of the corresponding isolates. Among the 43 positive chicken samples identified at slaughterhouse A, all corresponded to single infections by either ST6 (n = 38, 88. 4%) or ST7 (n = 5, 11. 6%) (Table  1 ). At slaughterhouse B, the Blastocystis sp. isolates identified also belonged to ST6 (n = 14, 70. 0%) and ST7 (n = 6, 30. 0%) (Table  1 ).
Similarly, all 8 animal isolates obtained at slaughterhouse C were identified as belonging to ST6 (n = 3, 32. 5%) and ST7 (n = 5, 62. 5%) (Table  1 ). Although the number of chicken samples analyzed in slaughterhouse C was limited, a growing increase in the prevalence of ST7 was observed from the north to the south of the country according to the location of the slaughterhouses along with a decrease of ST6. Despite these geographical variations, ST6 was largely predominant (55/71, 77. 5%) in poultry compared to ST7 (16/71, 22. 5%) after combining data from the 3 slaughterhouses. Among the 27 patients identified as sequence-positive for Blastocystis sp. by qPCR in the human group enrolled at Hamidi Medical Center in Tripoli, 26 presented single infections by the parasite and the latter subject, a mixed infection with 2 STs according to the sequence trace.
0 vs 3. 6%, P = 0. 05). In addition, ST6 was only present in the group of slaughterhouse A employees in two asymptomatic individuals while it was absent in the group of hospitalized patients without contact with poultry. All the 57 partial SSU rDNA gene sequences obtained in the present survey that were representative of ST6 were aligned with each other and with the 37 homologous sequences available in databases from animal and human isolates identified worldwide (Additional file 4). The length of the 94 ST6 sequences included in this alignment ranged from 274 to 280 bp and these sequences showed 96. 7 to 100% identity between them. In this alignment, the sequence of the Malaysian isolate LWA-9 from chicken (GenBank: KX234595) was selected as the reference sequence because it was the only one showing an insertion of 3 nucleotides at positions 121, 122 and 123.
ST6 isolates. In this reduced alignment (see complete alignment in Additional file 4), the sequence of a single representative isolate of each identified genotype is shown with the exception of genotypes 7 and 11 shown in shaded boxes for which all corresponding isolates are included. Only the 27 variable positions identified in the compared domain of the gene are included in this alignment. Their positions with respect to the reference sequence (LWA-9 Chicken Malaysia Accession number KX234595) are indicated above it. Nucleotides identical to those of the reference sequence are represented by dashes, and gaps are represented by asterisks. The genotypes are indicated on the right of the alignment followed, in parentheses, by the total number of isolates then the number of Lebanese isolates identified in our study for the corresponding genotype belonged to either ST6 or ST7, with a large predominance of ST6 (77.