dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The authors want to thank  Wencheng Ge  and  Jie Zhou  ( Guilin University of Electronic Technology ) for their help building the three-dimensional models,  Dr. Jennifer Leichliter  for the English review and comments in the manuscript,  Dr. Thomas A. Pu  ¨schel and  Dr. Soledad de Esteban  for his comments in the manuscript and the colleagues at  Steinmann Institut Bonn  as well as  Dr. Jan-Hendrik Buhk  at  Universita ¨tsklinikum Eppendorf  for CT scanning of specimens. We want to thank  YXLON International GmbH  for the CT-scan of Dicerorhinus sumatrensis. The authors acknowledge the  Museum fu ¨r Naturkunde Berlin  for specimen loan and  A. C. Sharp  for the original files of the scanned  G. Camelopardalis  and C. dromedarius. L. glama and T. terrestris are hosted in DigiMorph.
Funding: J. F. is supported by the postdoc grant " Beatriu de Pinos "  2014 -BP-A 00048  from the  Generalitat de Catalunya , and by the  Spanish Ministerio de Economı ´a, Industria y Competitividad  and the  European Regional Development Fund of the European Union (MINECO/FEDER EU , project  CGL2017-82654-P ) and by the Catalonian Research Group 2017 SGR 86. D. E. W. was funded by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  ( ERC CoG  grant agreement no. 681450  to  T. Tu  ¨tken). J. M-N. was supported by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ,  KA 1525/9-2 ). This research is publication no. 93 of the  DFG Research Unit 771 'Function  and performance enhancement in the mammalian dentition-phylogenetic and ontogenetic impact on the masticatory apparatus'.
FESEM The cell structure of the biofoams was analyzed by scanning electron microscopy of the fractured surface of the composites. The microphotographs were taken with a Carl Zeiss Ultra Plus field-emission scanning electron microscope (FESEM, Oberkochen, Germany) equipped with an energy dispersive X-ray spectrometer (EDXS). Prior to FESEM analysis, samples were Au-coated. Calculations of pore size distributions were examined from a survey of over 30 cells and using ImageJ software from Broken Symmetry software.
Compression Test The compression properties of biofoams in direction perpendicular to the panel surface were performed using a universal testing machine model 3365 (Instron, Norwood, MA, USA) equipped with a 1 KN loading cell and controlled by Bluehill Lite software developed by Instron. The dimension of the foam specimen was 10 mm × 10 mm × 10 mm and the results were calculated using a compression test according to the ISO 844 standard  [30]. A crosshead speed of 2 mm/min was used. The compressive strength at 10% and 25% deformation of its original thickness were recorded. The number of tested specimens for the mechanical properties was five for the average calculations.
In all biofoams, the principal cell structure was closed with certain broken cells. In RPU0, the cells were predominantly circular with a homogeneous size distribution and diameters ranging from 175 to 265 µm and the addition of CF in RPUF modified the cell wall nucleation towards pentagonal and hexagonal shapes, which distortion increased progressively and widening the cell size distribution of the foams with increasing filler concentration. The shape of the cells varied with CF addition, affecting uniformity of the cellular structure and increasing the number of damaged open cells. According to a previous study, this fact was probably due to the high viscosity of the premixes and the large size of the ground CF that modify notably the growth process of the cells  [32]. The effect of the CF content in mean cell size of the RPUF was determined from FESEM images using imageJ software and summarized in Table  3.
Statistical analysis Baseline and follow-up characteristics were described as median (interquartile range) for continuous variables and numbers (percentages) for categorical variables. The Wilcoxon signedrank test was used to compare distributions of continuous variables between the intervention group and the control group. For categorical variables, chi-squared tests or Fisher's exact tests were performed. A P-value <0. 05 was considered statistically significant. Statistics were determined with SAS version 9. 4 software (SAS Institute, Cary, NC, USA). Missing data were dealt with as follows: complete case analysis if <5%, multiple imputations by equation chained if between 5% and 30% and variable excluded if >30%. In case of multiple imputation by equation chained, data were imputed using an imputation model repeated 10 times.
ACKNOWLEDGEMENTS The authors sincerely thank  Nancy Kentish-Barnes  ( Reanimation Medicale, Hopital Saint Louis -AP-HP, Paris, France ) for her help in preparing the article. The members of the  Epo-ACR-02 Study Group include Alain Cariou (Paris, France) ,  Nicolas Deye (Paris, France) ,  Benoı ˆt Vivien (Paris, France) ,  Olivier Richard  ( Versailles, France ),  Nicolas Pichon  ( Limoges, France), Angele Bourg (Limoges, France), Loı ¨c Huet (Creteil, France), Clement Buleon (Caen, France ),  Jerome Frey (Nancy, France) ,  Pierre Asfar  ( Angers, France), Stephane Legriel (Versailles, France ),  Sophie Narcisse  ( Orleans, France ),  Armelle Mathonnet  ( Orleans, France), Aurelie Cravoisy (Nancy, France), Pierre-Francois Dequin (Tours, France), Eric Wiel (Lille, France ),  Keyvan Razazi  ( Creteil, France), Cedric Daubin (Caen, France ),  Antoine Kimmoun  ( Nancy, France), Lionel Lamhaut (Paris, France ),  Jean-Sebastien Marx (Paris, France) ,  Didier Payen de la Garanderie (Paris, France) ,  Patrick Ecollan (Paris, France), Alain Combes (Paris, France) ,  Christian Spaulding (Paris, France) ,  Florence Barat (Paris, France) ,  Myriam Ben Boutieb (Paris, France), Joe ¨l Coste (Paris, France), Jean-Daniel Chiche (Paris, France), Frederic Pene (Paris, France) ,  Jean-Paul Mira  ( Paris, France ),  Jean-Marc Treluyer  ( Paris, France ),  Olivier Hermine  ( Paris, France ) and  Pierre Carli (Paris, France ).
A link to an online questionnaire was sent to zoo and wildlife veterinarians through the Species Survival Plan Programme and European Endangered Species Programme coordinators and via the 'Wildlife VetNet' Google group forum. The questionnaire consisted of 50 questions relating to the veterinarians' country of residence and experience, the medicine combinations used, standard monitoring procedures, capture-related complications and mortalities experienced in this species under different capture conditions. In addition, necropsy data from the national wildlife disease database of the National Zoological Gardens of South Africa were examined for cases where anaesthetic death was listed as the cause of death in cheetahs. A total of 75 veterinarians completed the survey, with 38 from African countries and a combined total of 37 from Europe, the United States (US) and Asia.
Questions 24-50 asked about the objective of the immobilisation, the complications and the mortalities experienced with this species. Microsoft Excel was used to create a database and to enter the data from the survey. In addition, as few necropsy reports were available for the cheetahs' mortalities reported in response to the questionnaire, the wildlife disease database of the National Zoological Gardens of South Africa (NZG) was examined for cases where anaesthetic death was listed as the cause of death in cheetahs (n = 13) and the lesions listed in the necropsy reports were recorded. Univariate analyses were conducted for each potential predictor using a general linear model. As it was impossible to gather anaesthetic records for each capture event, stepwise regressions were conducted to determine the bestfit model for predicting outcomes.
Reported mortalities We received data through the survey for 30 capture-related cheetah mortalities from 18 veterinarians. The records included three juveniles (< 1 year), five subadults (< 2 years) and 22 adults, corresponding to 23 male cheetahs and 7 female cheetahs. The majority of deaths were reported in individuals captured for translocation purposes (17/30) followed by cheetahs immobilised for health examinations (6/30), other procedures (4/30) and the fitting of telemetric collars (3/30). The majority (43. 3%; n = 13/30) were immobilised in an enclosure or boma or from a vehicle (26. 67%; n = 8/30), while relatively few were immobilised in a transport crate (20%; n = 6/30) or from a helicopter (10%; n = 3/30). A large proportion of the cheetahs that died were subjectively judged by the surveyed veterinarians to be stressed at the time of capture (20/30; 66.
3%) between 30 minutes and 1 h after immobilisation and only 2/30 (7%) within the first 30 min of immobilisation. Necropsy reports of 13 cheetahs (seven female cheetahs, six male cheetahs) were collected from the NZG wildlife disease database. In those cases, a complete set of tissues was not submitted for examination and autolysis of some individuals precluded data evaluation in detail. Body condition, where noted, was described as good (n = 2) or excellent (n = 3). In 10 cases, variable combinations of tissue congestion, pulmonary oedema, renal tubular necrosis, periacinar hepatocellular degeneration or necrosis were present. Terminal aspiration of stomach contents was seen in six cases and tracheal haemorrhage, alveolar atelectasis or haemorrhage in three cases. Suspected bronchospasm was seen in two cases and laryngeal oedema in one case.
Acknowledgements We would like to thank all the veterinarians and biologists who took the time to complete the survey. We also thank  Lars Versteege  and  Adrienne Crosier , the  Species Survival Plan Programmes  and European Endangered Species Programme coordinators,  Alexandre Caron  ( CIRAD ) who assisted with the diffusion of the survey and  David Chavernac ,  Johan Michaux ,  François Roger ,  Jean-Charles Sicard  and  Vladimir Grosbois (CIRAD)  without whom this project would not have been feasible.
Metagenomic sequencing of AMD microbiome Metagenomic analysis of water interface and sediment samples revealed that the microbial community consisted of Archaea (67%) and Bacteria (33%) (Fig. 2 , Additional file 1: Figure  S3 ). Among the archaea, Euryarchaeota accounted for 64%, 'Ca. Micrarchaeota' for 3% and 'Ca. Parvarchaeota' for 0. 2%, both belonging to the candidate superphylum 'DPANN' ('Diaphetotrites-Parvarchaeota-Aenigmarchaeota-Nanohaloarchaeota--Nanoarchaeota'), with the rest of the archaea contributing 0. 5% of total prokaryotic reads. Fig. 2  Taxonomic classification of microbial community from metagenomic DNA sequencing data. Taxonomic classification was obtained using 1 GraftM, classifying short reads belonging to marker genes from metagenomic datasets. Graphical representation was performed under R programing environment using basic R packages.
https://github. com/geronimp/graftM (This program does not have a publication yet, but they suggest to cite their github page) Thermoplasmata (64% from all prokaryotes) were the most abundant within euryarchaea, the majority of the former was represented by Thermoplasmatales (62%), specifically by unclassified Thermoplasmatales (58% of total sequencing reads) and Cuniculiplasmataceae (4%). Unclassified Thermoplasmatales were affiliated mainly with 'E-plasma' clonal variant. A better understanding the physiology and in situ function of this group could be achieved after its successful cultivation. Phylogenetic positioning of archaeal sequences deduced by V3-V4 amplicon sequencing and metagenome shotgun sequencing is presented in Fig. 3. The indigenous Thermoplasmatales archaea showed high SSU rRNA gene sequence identities with counterparts from other acidic environments: Iron Mountain  [8] , Tongling AMD samples (see Accession Numbers in Fig.
In some cases, the affiliation of these sequences to the order Thermoplasmatales has been erroneously considered. Our analysis pointed at a compact co-clustering of TMEG-related sequences from a wide (See figure on previous page. ) Fig. 3  16S rRNA gene sequence-based phylogenetic reconstruction of Thermoplasmatales clones from Parys Mt samples obtained in the course of this work. V3-V4 hypervariable region amplicon sequences and sequences extracted from metagenome contigs are highlighted by green. Number of amplified sequence variants, corresponding to each branch indicated in brackets. Reference sequences (brown) were taken from the Silva SSU 132 Ref NR 99 database (see the methods section). 'Plus' sign denotes presence of corresponding sequences in 16S V3-V4 amplicon, environmental and enrichment metagenomes Fig.
5 (Qiagen, Germany) while overlapping reads were merged with the SeqPrep tool (https://github. com/jstjohn/SeqPrep/). Metagenomic assembly was performed using SPAdes (v. 3. 10) without a read error correction step  [50]. The resulting contigs were used only for the extraction of sequences of phylogenetic markers. Community composition analysis of environmental metagenome (sample PM. Mona) was performed using raw sequencing reads, graftM package ver 0. 11. 1  [51]  provided rpsB reference database, extended with sequences of Cuniculiplasma divulgatum PM4 (GCA_900090055. 1), Thermoplasmatales archaea ' A-plasma' (GCA_00044 7225. 1), 'E-plasma' (GCA_000496135. 1), 'I-plasma' (G CA_001856825. 1), 'Candidatus Methanoplasma termitum' (GCA_000800805. 1), 'Ca. Micrarchaeum' sp. AZ1 (GCA_001896515.
1), 'Ca. Mancarchaeum acidiphilum' (GCA_002214165. 1), 'Ca. Parvarchaeum acidiphilum' ARMAN-4 (GCA_002412065. 1), 'Ca. Parvarchaeum acidophilus' ARMAN-5 (GCA_002412085. 1) as well as rpsB sequences from metagenomic bins related to TMEG-group and 'Ca. Micrarchaeota'. Due to the distinct phylogenetic position of members of Parys Mt extremely acidophilic microbial community, and therefore the high possibility of wrong taxonomic affiliation, the reference database was extended by rpsB sequences, identified in our shotgun metagenomic assembly. All resulting sequencing were aligned and used to build an HMM model for the search step of graftM. Sequences obtained from less diverse communities of enrichment cultures were analysed by CLARK k-mer-based sequence classification system  [52]. In the first step, 16S and 23S were extracted using barrnap tool (https://github.
com/tseemann/barrnap). All sequences were then aligned using blastn algorithm against refseq genomes, wgs and HTGS NCBI databases. The nearest genomic contigs were used as references for the creation of CLARK database. In the second step, preliminary metagenomic contigs, assembled with SPAdes, were classified with CLARK against NCBI-derived reference database (kmer = 32 was used for better reliability). After that step, CLARK reference database was complemented by reliably classified contigs. Finally, resulting database was used for the final classification of shotgun metagenomic reads (kmer = 22 was used for better reliability).
Libraries were sequenced with MiSeq™ Personal Sequencing System technology of Illumina Inc. (San Diego, CA, USA) using paired-end 250-bp reads. Demultiplexing was performed as described previously  [53]. After demultiplexing, all reads were subjected to stringent quality filtering, and parts of reads, corresponding to 16S rRNA primers were removed using CLC Genomics Workbench 10. 0 (Qiagen, Germany). At the end, 18-22 thousand of reads were used for each analysed sample. The resulting demultiplexed paired sequenced files for each sample were used as an input for the DADA2 high-resolution 16S amplicon analysis package  [56]  according to the standard pipeline published on DADA2 web page (https://benjjneb. github. io/dada2/index. html). As a result, 384 individual amplified sequenced variants (ASVs) were produced, while only 123 ASVs were represented by 10 or more reads in all samples analysed.
Taxonomic classification of resulting ASVs was performed by Bayesian classification algorithm  [57]  built in DADA2 package against the Silva132 database  [58]. Visualisation of data and calculation of alpha diversity metrics was performed using the R phyloseq package  [59]. 16S rRNA phylogenetic analysis SSU rRNA gene sequence phylogenetic analysis for TMEG group was performed as described previously  [9]. Briefly, a set of 16S rRNA nucleotide sequences assigned in the literature as related to TMEG-group were selected and aligned using Muscle algorithm  [60]. Phylogenetic analysis was performed in MEGA6 Program  [61]  using the Maximum Likelihood method with 1000 repetitions bootstraps  [62]. The general time-reversible model (GTR, G + I, four categories)  [63]  was used for inferring the evolutionary distances.
The trees are drawn to scale, with branch lengths measured in the number of substitutions per site with corrections, associated with the model. All positions with less than 80% site coverage were eliminated. There were a total of 82 nucleotide sequences and 747 positions, involved in the calculations. The tree was displayed in packages ape  [64]  and phangorn  [65]  in the R programing environment. Phylogenetic analysis for SSU rRNA genes sequences from barcode libraries and that extracted from the metagenomic sequences were performed using ARB-parsimony tool  [66]. Briefly, all 16S rRNA gene sequences of V3-V4 fragments as well as that extracted from all four metagenomes (PM. Mona, EP, ES and EF) were added to the Silva SSU 132 Ref NR 99 arb file (including an alignment and a tree, https://www.
Acknowledgements The work of OVG, PNG and SVT was supported by the  Royal Society   UK-Russia Exchange Grant  # IE 160224  ( RFBR 16-54-10072   КО  project). OVG and PNG acknowledge  ERA   Net IB2  Project MetaCat through  UK Biotechnology and Biological Sciences Research Council (BBSRC)  Grant  BB/M029085/1  and the support of the  Centre of Environmental Biotechnology Project  funded by the  European Regional Development Fund (ERDF)  through the  Welsh Government. RB and PNG acknowledge the support of the Supercomputing Wales project, which is part-funded by the  European Regional Development Fund (ERDF)  via the  Welsh Government. The work of AAK and IVK was supported by the  Russian Science Foundation  grant #  18-44-04024. MF acknowledges grants  PCIN-2014-107  (within  ERA   NET IB2  grant  ERA-IB-14-030-MetaCat ),  PCIN-2017-078  (within the  Marine Biotechnology ERA-NET (ERA-MBT ) funded under the  European Commission's Seventh Framework Programme ,  2013-2017 , Grant agreement  604814 ),  BIO2014-54494-R  and  BIO2017-85522-R  from the  Spanish Ministry of Economy and Competitiveness.
Availability of data and materials Shotgun metagenomic reads were submitted to NCBI SRA archive under run accession number SRR7268657. Reads resulted from sequencing of genomic material of enrichment cultures were submitted to SRA archive under accession numbers SRR7303206-SRR7303208. The 50 most abundant ASVs from 16S metabarcoding experiments were submitted to GenBank under accession numbers MH574490-MH574538.
g. ,  30, 31  ). We succeeded in establishing an elaborate procedure of caesarian section  32  that allowed the pups to develop with minimal neurotoxic side effects and provided us with placental tissue from the same individuals. Due to an upbringing with foster dams, the animals developed into adulthood and were tested for behavioral phenotypes as adults. This allowed us to measure in the same animal the baseline DNA methylation pattern in placenta and behavioral phenotypes later in life. To our knowledge, the present study is the first one linking epigenetic features of fetal placental tissue directly to the behavior of the same animals in adulthood. Our data suggest broad and physiologically pertinent changes in DNA methylation in placenta that correlate with anxiety-like behavior in adulthood in response to Nr3c1 deficiency.
Spearman rho coefficient was used for correlating number of failures and escape latency in the learned helplessness paradigm. Moderation effects were tested using Hayes Process macro for SPSS  38. A MANOVA with subsequent univariate two-way ANOVAs and t-tests were performed for the analysis of global methylation levels. For DNA methylation analysis and annotation, the R package "methylkit" was used  39  , correcting for multiple testing using Benjamini-Hochberg false discovery rate (FDR). A non-parametric χ 2 -test was calculated to detect differences between all 19 foster dams in maternal care behavior. The hypergeometric test as well as the binomial test was applied to test significant overlaps. Adjusted pvalue ≤ 0. 05 was set as threshold for statistical significance except for genome wide DNA methylation data which was set at 0.
Acknowledgements This work was supported by an  Era-Net Neuron  grant to M. S. , P. G. , and M. D. and MOP-42411 from the  Canadian Institute of Health Research  to M. S. E. L. was supported by The Richard and Edith Strauss Postdoctoral Fellowship in Medicine,  McGill University. The authors thank  Carsten Pilger, PhD , for his excellent technical advice. The authors are grateful to  Madeleine Saitta  for outstanding animal caretaking.
Conflict of interest The authors declare that they have no conflict of interest. Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Supplementary Information accompanies this paper at (https://doi. org/ 10. 1038/s41398-018-0348-7). Raw data are archived in the Gene Expression Omnibus under the following accession numbers: GSE123188 and GSE123460.
Background: Although they are declining worldwide, neurotropic parasitic diseases are still common in developing and emerging countries. The aim of this study was to estimate the pooled prevalence and pooled association measures of comorbidities between mental disorders (anxiety, depression, bipolar disorder, and schizophrenia) and neurotropic parasitic diseases (malaria, cysticercosis, toxoplasmosis, human African trypanosomiasis, Chagas disease, and human toxocariasis) in developing and emerging countries. Methods: As the first meta-analysis on this topic, this study was performed in accordance with PRISMA guidelines. The protocol was registered in PROSPERO (N°CRD42017056521). The Medline, Embase, Lilacs, and Institute of Epidemiology and Tropical Neurology databases were used to search for articles without any restriction in language or date.
The protocol for this meta-analysis was recorded in PROSPERO (N°CRD42017056521) and accessible via the following link: http://www. crd. york. ac. uk/PROS-PERO. This meta-analysis follows the recommended methodology for the meta-analysis of observational studies  [36]  and was performed in accordance with Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines  [37]. The search for articles was conducted through four databases: Medline, Embase, Lilacs, and IENT (database of the Institute of Epidemiology and Tropical Neurology of the University of Limoges in France: http://www-ient. unilim. fr/) by LOD, the principal investigator, from February to May 2017 without linguistic or date restrictions. The same research equation built on Medline below and used for the previous meta-analysis  [35]  was used in the other databases to search for articles in each of the 139 countries studied: "("Depressive Disorder"[Mesh] OR "Depression" [Mesh] OR "Anxiety Disorders"[Mesh] OR "Anxiety" [Mesh] OR "Bipolar Disorder"[Mesh] OR "Schizophrenia"[Mesh]) AND ("Diabetes metillus"[Mesh] OR "Obesity"[Mesh] OR "Neoplasms"[Mesh] OR "Cardiovascular Diseases"[Mesh] OR "Pulmonary Disease, Chronic Obstructive"[Mesh] OR "Malaria"[Mesh] OR "Cysticercosis"[Mesh] OR "Toxoplasmosis"[Mesh] OR "Toxocariasis"[Mesh] OR "Trypanosomiasis"[Mesh] OR "Chagas Disease"[Mesh]) AND ("Name of a country"[Mesh]).