dataesr/scientific-paragraphs-categorization huggingface dataset:
0 assembly, gene models were significantly improved and most manually curated genes in this dataset were found to be of good quality. However this assembly still contains errors, such as incorrect ordering or orientation of contigs within a scaffold (a known problem of Hi-C-scaffolded genomes) and tandem repetitions due to unresolved polymorphic repeats (M. Paris, unpublished observations). These problems have not been quantified genomewide. EST data and transcriptomes helped to reveal that a large fraction of mRNAs in Parhyale are subject to trans-splicing  (Douris et al. 2010) , whereby a shared leader sequence is spliced to the 5' end of diverse mRNAs (including mRNAs encoding the transcription factors Ultrabithorax, Twist and Mef2). The leader sequence comes from snRNAs that resemble spliceosomal snRNAs.
Acknowledgements: We thank the  Parhyale community  for sharing information and resources, and for allowing us to refer to unpublished information cited in this article. We acknowledge  Vincent Moncorgé  for the image shown in figure 1. Parhyale research in the Patel lab has been supported by the  HHMI  and  NSF  grant  IOS-1257379. Parhyale research in the Averof lab is supported by the  European Research Council of the E. U. , grant  ERC-2015-AdG 694918.
Participants Eighty-two participants ranging from 18 to 39 years old (55 female; mean age = 23. 39, SD = 4. 83) volunteered for this experiment. The data were collected online, and the experiment was made available for one month and a half. Participants were either recruited through announcements spread by the French Information Network for Cognitive Sciences (RISC) or through various social media platforms. Each participant indicated being a French native speaker.
Materials and design Sixty abstract (e. g. , rich, end, myth, south) and 60 concrete (e. g. , nail, razor, coat, wall) French target words were chosen from a recently developed French norming database  (Bonin et al. , 2018). 4 We selected target words from the extremes of the concreteabstract continuum while respecting the constraints imposed by the other variables we controlled for. This led to concreteness being best expressed as a binary variable. Using the French Lexicon Project database  (Ferrand et al. , 2010) , our targets were controlled for length, word frequency, orthographic neighborhood density, context availability, valence and arousal. Target and flanker frequencies are expressed in Zipf values (see van Heuven, Mandera,  Keuleers, & Brysbaert, 2014) ; word length in number of letters; neighborhood density (the number of single-letter substitution neighbors); and the remaining variables were measured using a 5-point Likert scale.
Results Means of reaction times (RTs) and error rates per condition are shown in Fig. 2. The data were analyzed using linear mixed effect models fitted with the (g)lmer functions from the lme4 package  (Bates, Mächler, Bolker, & Walker, 2015)  in RStudio version 3. 6. 1 statistical computing environment. Items and participants were entered as crossed random effects, and where the model structure allowed it, by-item and by-participant random intercepts were included  (Baayen, Davidson, & Bates, 2008; Barr, Levy, Scheepers, & Tily, 2013). We report b-values, standard errors (SEs) and t-or z-values (for RTs and error rates respectively), with those beyond |1. 96| deemed as significant (b-values and SEs were multiplied by a fixed factor to increase interpretability). Only data from trails with word targets were included in these analyses.
Control experiment with pseudoword and nonword flankers In order to be sure that the effects of flanker presence found in the main experiment were not being driven by uncontrolled relations between target and flanker words such as target-flanker congruency (see e. g. , Snell, Meeter, & Grainger, 2017, Snell, Vitu, & Grainger, 2017, for an   5 In order to fully control for semantic overlap across target-flanker pairs, we ran a separate rating study. Fifty participants were asked to rate how semantically similar the word pairs were on a scale from 1 to 5. We also included 80 highly semantically-related word pairs from the  Lakhzoum, Izaute, and Ferrand (2020)  database in order to induce a full use of the rating scale. Results showed that ratings between target-flanker pairs were overall very low (M = 1.
Species and taxonomy richness Species richness is the number of species recorded for a specific group of organisms during a given period. To calculate the species richness of the riparian forests of the Togolese Mountains, a general floristic list was established by compiling the inventories of the 198 floristic surveys, completed by the species not identified in the 198 surveys but observed in the riparian forests during the field surveys. However, only the species recorded in the plots were used in the statistical analyses. The inventory of the accepted species was based on those reported by  Aubréville (1959 ), Brunel et al. (1984 ), and Hutchinson et al. (1954 -1972). To avoid duplication, the accepted species were listed alphabetically. The synonyms for each species were checked using the African plant database.
, 2014;  African plant database https://www. ville -ge. ch/musin fo/bd/cjb/ afric a/index. php?langu e=an). According to  Kindt and Coe (2005) , we recorded the number of species for several sites in most situations. We did not cover the whole area of interest. Accordingly, we do not know the species composition of the unsampled area. However, as species richness depends on sample size, we cannot expect to have recorded all species present in the study area. Richness estimators were therefore used to estimate the total number of species in the riparian forests surveyed. The approach used is as follows: based on the presence/ absence matrix (198 surveys × 828 species), species accumulation curves were drawn to determine whether the surveyed sites were sufficiently sampled. Next, first and second-order jackknife richness estimators (Jack1, Jack2) were used to estimate the total number of species in the surveyed riparian forest.
Species diversity and habitat heterogeneity analysis To assess the habitat diversity and heterogeneity level, gamma, alpha, beta diversity, and Shannon's diversity index were calculated. Gamma, alpha, and beta diversity were used as basic diversity indices  (Whittaker, 1972). Gamma diversity, the diversity at the landscape scale, is calculated as the total number of species or total richness across plots. Alpha diversity is calculated as the average species richness per plot. Beta diversity is a measure of data heterogeneity or habitat diversity  (Mccune et al. , 2002). The classical metrics were used to determine beta diversity: βw = (Sc/S) - 1, where βw is beta diversity, Sc is the number of species in the composite sample (the number of species in the dataset), and S is the average species richness in the sample units.
If βw = 0, then all sample units have all species. Dataset heterogeneity increases with increased βw. As a rule, in this context, βw < 1 is rather low and βw > 5 can be considered high. The maximum value of βw is Sc - 1, which is obtained when the sampling units do not share any species. The Shannon's diversity index, derived from information theory, considers the number of species present in survey i (n i ) and the relative cover (R ij ) of the different species j in survey i  (Shannon & Weaver, 1949). This index quantifies the heterogeneity of the biodiversity of a study area and thus indicates changes over time. The index decreases (≈0) when the number of species is low and only few species are dominant. H′: Shannon biodiversity index; i: a species in the study area; S: species richness; p i : proportion of a species i in relation to the total number of species (S) in the study environment, which is calculated as follows: where is the number of individuals for species i and N is the total number of individuals; log2: logarithm to base 2.
| Endemism analysis The sub-Saharan tropical rainforests were defined by  White (1979 White ( , 1983) )  as a phytogeographic domain comprising about 8000 plant species, of which 80% are endemic. These forests have been subdivided into sub-centers of endemism  (Hardy et al. , 2013; Linder et al. , 2012; Poorter et al. , 2004). In this study, the analysis of endemism was based on the chorological subdivisions reported by  White (1979 White ( , 1983) )   (Adjossou, 2009; Adou et al. , 2005; Aké Assi, 1984; Akpagana, 1989; Aubréville, 1959; Berhaut, 1967 Berhaut, -1988;; Brunel et al. , 1984; Guelly, 1994; Hawthorne & Jongkind, 2006; Hutchinson et al. , 1954 Hutchinson et al. , -1972;; Kokou, 1998; Lebrun & Stork, 1991 -1992; Natta, 2003; Poorter et al. , 2004) , and the African Plant Distribution Database (https://www.
| Representativeness of rainforest species and forest type analysis The relative proportion of rainforest species in the riparian forests of the Togolese Mountains was assessed according to the procedure reported by  Meave and Kellman (1994) , who considered the species of not only the rainforest block but also the xeric environments surrounding the rainforest zone. In this study, these xeric species include the GC-SZ regional or forest-savanna transition species. To ensure that the species inventoried in the forests of the Togolese mountain riparian areas were from moist forests, they were compared with the list of moist forest species of West Africa  (Holmgren et al. , 2004). For ambiguous species, they were compared with the African plant database. Forest types in vegetation can be identified based on the occurrence of typical tree species  (Langenberger et al.
Dictyandra arborescens Welw. ex Hook. f. , Parkia filicoidea Welw. ex Oliv. , Pterocarpus mildbraedii Harms, Sterculia rhinopetala K. Schum. , Stephania dinklagei (Engl. ) Diels, and Trichilia megalantha Harms, would appear in the forests of southwest Côte d'Ivoire according to the African plant database but not Adou et al. , 2005 records. Considering these typically Guinean-Congolese species, the number of shared species would reach 60%. Almost all of these.
Statistical analyses. In this study, several hypothesis-driven statistical data analyses were implemented. To assess the normal distribution of participants' cortical thicknesses and NV7 scores, multiple Shapiro-Wilk test Participants were asked to identify which of the four 3D figures showed on the right corresponded to the 2D pattern on the left. Both the subtests were extracted from the NV7 battery  34. were performed. We normalized participants' cortical thicknesses by considering the whole-brain mean cortical thickness, thus considering all the HCP-MMP1 parcellations for both hemispheres, at the participant level (see Supplementary Results for non-normalized analyses). The first exploratory analysis comprised a forward stepwise regression model to identify possible predictors of the NV7 scores among the cortical thicknesses (CT) of both the left and right IPC areas (10 areas per hemisphere).
Acknowledgements This work was performed within the framework of the  LABEX CORTEX  ( ANR-11-LABX-0042 ) of the  Université de Lyon , within the program " Investissements d' Avenir"  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR). Funding for this paper was also provided by a grant from the  Région Auvergne Rhône-Alpes ,  France (Project NUMERICOG ).
Molecular Analysis The DNA Repair Laboratory received, from October 2018 to April 2019, saliva samples from 16 individuals (7 patients and 9 relatives) collected using an Oragene DNA self-collection kit (DNA Genotek Inc. , Ottawa, Canada). We extracted the genomic DNA as recommended by the protocols of the manufacturers. DNA quantity and quality were analyzed by Nanodrop-1000 (Wilmington, DE, USA) and 1% agarose gel electrophoresis. We used a customized DNA repair panel to prepare the DNA library for NGS from samples of the patients, as previously described  (Menck and Munford, 2014). The alignment and variant calling were performed at Surecall software v3. 5. 1. 46 (Agilent ® ), using the GRCh37/hg19 version of the reference human genome. Following the assertion criteria established by the DNA repair laboratory, we validated the mutations by Sanger sequencing and classified the variants according to the American College of Medical Genetics (ACMG) guidelines, using the VarSome variants search Engine  (Richards et al.
, 2015; Kopanos et al. , 2019). The DNA repair laboratory assertion criteria and the accessions for this submission (SCV001652706 -SCV001652707) can be found on the ClinVar database (ClinVar data access: https://www. ncbi. nlm. nih. gov/clinvar/submitters/500218/). We performed the segregation analysis of the mutation at the XPC (3p25. 1; NC_000003. 11; NM_004628. 4) and POLH (6p21. 1; NC_000006. 11; NM_006502. 2) genes by genotyping the relatives of the patient. Variants were reported at the nucleotide and protein levels as recommended by the Human Genome Variation Society (HGVS)  (den Dunnen et al. , 2016). The pedigrees were drawn following the Standardized Human Pedigree Nomenclature  (Bennett et al. , 1995).
The Genome Aggregation database (gnomAD, https:// gnomad. broadinstitute. org/) reported the p. Leu225Serfs*33 (rs772778835) variant in heterozygous state in Latin American (3 out of 34,592 alleles) and European (1 out of 113,736 alleles) populations with a total allele frequency of 0. 000008 and 0. 000016, respectively. Although the mutation was identified in a low frequency in heterozygosity in Latin American, it was not found in Brazilian databases of germline exonic variants (ABraOM/https://abraom. ib. usp. br/; SELAdb http://intranet. fm. usp. br/sela). One patient, out of seven, was homozygote for the splicing site mutation, c. 2251-1 G>C, in the XPC gene (patient XP07RN. br, Figure  4 ). This mutation was first described in individuals from the western Indian Ocean in Comoros, IVS 12-1 G>C, the "Comoros mutation"  (Cartault et al.
, 2011) , and is also present in the ClinVar database  (ClinVar ID 190213). A further ancestry study indicated that these patients were derived from the Bantu group and had a founder effect on the Comoros island about 800 years ago  (Sarasin et al. , 2019a). At gnomAD and TOPMed databases, this splicing site mutation (rs754673606) was observed in heterozygosity with an allele frequency of 0. 00002 and 0. 00011, respectively, observed in the African/African-American and Latino/Admixed American population. This mutation has a high incidence in XP patients in Brazil: 15 cases out of 32  (Santiago et al. , 2020). The Bantu African people, mainly located on the Atlantic coast of Africa (Congo and Angola) and on the Indian Ocean coast (Mozambique), were one of the major groups (90%) brought to Brazil during the 500 years of the slave trade (Lemos  Cardoso and Farias Guerreiro, 2006).
ACKNOWLEDGMENTS We are especially grateful to the XP patients from Montanhas and to the ABRAXP, the  Brazilian XP support group , for their contribution to this research. We are grateful to  Veridiana Munford , who enables and coordinates the research at the  DNA Repair laboratory at ICB-USP. We also thank the  Core Facility for Scientific Research-University of São Paulo  ( CEFAP-USP/GENIAL ) for providing NGS and  Pedro L. F. Machini  for providing the map figure.
Deoxyribonucleic acid extraction, Illumina MiSeq sequencing, data processing, and analysis Total genomic deoxyribonucleic acid (DNA) from the individual samples of colonic contents was extracted using the Fast DNA SPIN extraction kits (MP Biomedicals, Santa Ana, CA, United States) according to the manufacturer's instructions. The DNA concentration of each sample was quantified using the NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, United States), and the resulting polymerase chain reaction (PCR) products were separated using agarose gel electrophoresis. The genes of all microbial 16S rRNA in the hypervariable regions of V3-V4 were amplified by PCR using a universal forward primer F (5 -ACTCCTACGGGAGGCAGCA-3 ) and a reverse primer R (5 -GGACTACHVGGGTWTCTAAT-3 ).
, Ltd. (Shanghai, China). The raw sequence data generated from 16S rRNA NovaSeq sequencing were demultiplexed and quality-filtered using quantitative insights into microbial ecology (QIIME2; version 2019. 4) with slight modification according to the official tutorials. 1 Sequences were quality filtered, denoised, merged, and chimera removed using the DADA2 plugin  (Callahan et al. , 2016). After quality control and filtering chimeras, nonsingleton amplicon sequence variants (ASVs) were aligned with mafft  (Katoh et al. , 2002)  and used to construct a phylogeny with fasttree2  (Price et al. , 2010). Taxonomy was assigned to ASVs in the Greengenes database using a classify-sklearn naïve Bayes taxonomy classifier in the featureclassifier plugin against the Greengenes 13_8 99% operational taxonomic units (OTUs) reference sequences  (Bokulich et al.
, 2018). The α-diversity indices, including rarefaction analysis, Chao1, Observed species, Shannon, Simpson, and Pielou's evenness, were estimated using the diversity plugin. Non-metric multidimensional scaling (NMDS) plots based on the Bray-Curtis distance metric were used to visualize differences in microbial community composition among the groups. Analysis of similarity (ANOSIM) for multivariate data was performed using the "vegan" package in R. 2 The partial least square discriminant analysis (PLS-DA) was also performed using the R package to visualize the differences in microbial community composition. The Kruskal-Wallis test was used to identify statistically different microbial taxa at phylum and genus levels among the four groups. The abundance of different microbiota and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were classified using the linear discriminant analysis 1 https://docs.
qiime2. org/2019. 4/tutorials/ 2 https://www. r-project. org/ (LDA) effect size algorithm if the logarithmic LDA values of bacteria exceeded 2. 0 using the online procedure of Galaxy. 3 The 16S rRNA sequencing data obtained in this study are deposited in the NCBI Sequence Read Archive (SRA) with under the accession number PRJNA825463 4.
Colonic metabolome and data analysis The colonic contents were put into the 2-mL EP tube with two steel balls and homogenized by the tissue grinder. The homogenized samples (100 mg) were vortexed for 30 s with 0. 6 mL methanol (including internal standard), then grinded, and centrifuged at 12,000 × g for 10 min at 4 • C to obtain the supernatant. The supernatant was filtered through 0. 22 µM membrane to obtain the prepared samples for liquid chromatography-tandem mass spectrometry (LC-MS). The details were as previously described  (Chen et al. , 2017). The quality control samples were obtained by mixing a small and equal volume of each experimental sample and injected at regular intervals to monitor the stability of the analysis. The LC-MS analysis was performed on Vanquish Ultrahighperformance LC System (Thermo Fisher Scientific, Waltham, MA, United States) coupled with an Orbitrap Q Exactive series mass spectrometer (Thermo Fisher Scientific).
The raw data files were converted into mzXML format by Proteowizard software (v3. 0. 8789) and then processed by the XCMS software 5 for peaks identification, filtration, and alignment. The metabolites were identified by comparison with the internal library using the mass-to-charge ratio (m/z), retention time, and chromatographic data. The internal standard was used for data QC (reproducibility), and metabolic features in which the relative standard deviation (RSD) of QC > 30% was discarded. The metabolite annotation was performed with the Compound Discoverer program and referenced to the mzCloud database, 6 as well as MetDNA, BioDeepDB, and MoNA. 7  Principal components analysis (PCA) using an unsupervised method was applied to obtain an overview of the metabolic data, general clustering, and trends.
Statistical analysis One-way ANOVA (SPSS 25. 0; IBM Inc. , Chicago, IL, United States) and Tukey post-hoc test were used to analyze the data of colonic metabolites and visualized using GraphPad Prism version 8. 0 (GraphPad Software, San Diego, CA, United States). The level of significance was set at P-value < 0. 05. The analysis of the correlation between metabolites and microbiota abundance at the genus level was determined with the Spearman's rank correlation test using the R package (see text footnote 2).
As shown in Figure  12B , at 95 days of age, the positive correlation (P < 0. 05) included between 1-aminocyclopropanecarboxylic acid with Sphaerochaeta; (R)-3-hydroxybutyric acid with Clostridium, Streptococcus, 02d06, Allobaculum, and Sarcina; D-glucuronic acid with Clostridium, Streptococcus, 02d06, Allobaculum, and Sarcina; maleic acid with Treponema; (+)-7-isojasmonic acid with Lachnospira; The box plots of different metabolites at 125 days of age. Asterisk represents P < 0. 05. 125C, sow fed with basal diet; 125SA, sow fed with antibiotic; 125SP, sow fed with probiotics; 125SS, sow fed with synbiotics. The bubble chart of enrichment analysis for metabolic pathways in colonic contents of offspring pigs at 65 (A), 95 (B), and 125 (C) days of age. The manipulated metabolic pathways are based on the analysis of different metabolites in colonic contents of offspring pigs among different groups following the KEGG pathway database.
Acknowledgments We thank the staff and postgraduate students of  Hunan Provincial Key Laboratory of Animal Nutritional Physiology and Metabolic Process  for collecting samples, and technicians from the  Key Laboratory of Agro-ecological Processes in Subtropical Region  for providing technical assistance. We acknowledge  Suzhou Bionovogene  for metabolome measurement service. frontiersin. orgZhu et al. 10. 3389/fmicb. 2022. 934890.
Background: African animal trypanosomosis (AAT), transmitted by tsetse flies, is arguably the main disease constraint to integrated crop-livestock agriculture in sub-Saharan Africa, and African heads of state and governments adopted a resolution to rid the continent of this scourge. In order to sustainably reduce or eliminate the burden of AAT, a progressive and evidence-based approach is needed, which must hinge on harmonized, spatially explicit information on the occurrence of AAT and its vectors. Methods: A digital repository was assembled, containing tsetse and AAT data collected in Burkina Faso between 1990 and 2019. Data were collected either in the framework of control activities or for research purposes. Data were systematically verified, harmonized, georeferenced and integrated into a database (PostgreSQL).
A total of 2. 6 million doses of trypanocidal drugs are used every year to control the disease  [9, 10] , and 9 out of the 13 regions of the country are affected by tsetse flies. More than 60% of the country's cattle population is raised in zones with high trypanosomosis risk  [11, 12]. In particular, many zones where grazing potential for cattle is high and human activities are limited, also provide suitable habitats for tsetse flies  [12]. In 2000, the Government of Burkina Faso, along with other African countries in the Pan-African Tsetse and Trypanosomosis Eradication Campaign (PATTEC), embarked on an initiative led by the African Union  [13]. At the country level, a project in the PATTEC framework was implemented between 2006 and 2013  [14]. The project generated a vast amount of data, while other activities and projects implemented by other stakeholders also collected field data on the occurrence of AAT and its vectors in Burkina Faso.