dataesr/scientific-paragraphs-categorization huggingface dataset:
DNA extraction and sequencing Fungal isolates were grown on cellophane/PDA for two to four days at 27 °C. DNA was extracted as in  [22]. ITS sequences were PCR amplified with the following universal primers: ITS1 (5'-TCC GTA GGT GAA CCT GCG G-3') and ITS4 (5'-TCC TCC GCT TAT TGA TAT GC-3'). Amplification and direct sequencing of fungal ribosomal RNA genes was as in  [23]. Sequences (Additional file 1: Table  S1 ) were blasted against NCBI GenBank for identification purposes  [24].
Isolation and identification of fungi from cadavers In total, from 17 different dead animals, 130 fungal germinations were isolated and purified on PDA plates. The precise identity of the fungal species isolated from dead arthropods was determined by sequencing PCRamplified Internal Transcribed Spacers (ITSs) and comparing the results to the GenBank database. The list of insects collected and the corresponding fungi is reported in Table  1. Obtained ITS sequences have been deposited in the GenBank database (accession numbers KX394525-KX394566). In a first step, fungi were clustered according to the morphology of their mycelium. Two morphologies were overrepresented and present on several cadavers. The decision was made to sequence one isolate per cadaver for the overrepresented fungi. The genus Cladosporium represented 46 isolates and was found on 12 cadavers (Additional file 1: Table  S1 ).
Ultrasound Power Doppler Analysis Ultrasound imaging was performed using an ultrasound Biomicroscope (VEVO770; Visualsonics, Toronto, ON, Canada, http:// visualsonics. com) using either a 40 MHz or 55 MHz central frequency probe (RMV-704 or RMV-708, respectively). Vascularization was assessed using either the power Doppler mode or pulsed wave Doppler mode. Tridimensional acquisitions were recorded using an integrated 3D step motor. Data were analyzed and reconstructed in three dimensions using the VEVO770 software. The parameter analyzed was the vascular volume fraction (in percentages).
PET/CT Data Acquisition Mice were anesthetized (isoflurane/oxygen, 2. 5% for induction at 0. 8-1. 5 l/minute and 1. 5% at 400-800 ml/minute thereafter). Eight to 10 MBq of 18 F-FDG (n =3)or 18 F-K5 (n = 6) were injected in thejugular vein. The mice were kept in standby for 1 hour after injection, and a 20-minute static PET acquisition was performed. The mice were imaged using an Inveon micro-PET/CT scanner (Siemens Healthcare, Erlangen, Germany, http://www. siemens. com). CT imaging was performed before PET acquisition for anatomical localization.
PET/CT Data Analysis PET/CT images were analyzed by drawing volumes of interest (VOI; approximately 4 mm 3 ) on the implanted tissue construct area visualized on consecutive sections. For comparison, all values of radioactivity concentrations were normalized by the injected dose and expressed as a percentage of the injected dose per volume of tissue (% ID×cm -3 ) and as the mean standardized uptake value inside each VOI (ratio of tissue radioactivity concentration at one time point, over injected activity per body weight extrapolated to the same time point).
Micro-X-Ray-CT Analysis For angiogenesis exploration of the in-tissue constructs, the mice were anesthetized, and an intracardiac (left ventricle) injection of a contrast agent (barium chloride) was performed before scanning using an x-ray micro-CT device (Quantum FX Caliper; Life Sciences, Perkin Elmer, Waltham, MA, http://www. perkinelmer. com) hosted by the IDV Platform (EA2496; IDV Solutions, Montrouge, France, http://www. idvsolutions. com). The x-ray source was set at 90 kV and 160 mA. Tridimensional images were acquired with an isotropic voxel size of 20 mm. Full 3D highresolution raw data were obtained by rotating both the x-ray source and the flat panel detector 360°around the sample (scanning time, 3 minutes). Tridimensional renderings were subsequently extracted from Digital Imaging and Communications in Medicine data frames using the open-source OsiriX imaging software, version 3.
7. 1 (distributed under LGPL license; Dr A. Rosset, Geneva, Switzerland). Quantification of the vascular density inside each tissue construct was realized using CTan Analyzer software, release 1. 13. 5. 1 (SkyScan [now Bruker microCT], Kontich, Belgium, http://www. bruker-microct. com). A global VOI was drawn by interpolating two-dimensional regions of interest on consecutive sections to isolate tissue constructs. The obtained interpolated VOI included only 3D collagen matrices and the invading vessels into which the lumens were counter-contrasted using barium chloride. The brightness of each voxel inside the VOI was summed and distributed along a histogram. The VOI was finally segmented interactively to select barium chloride-contrasted vessels according to the threshold (a brighter range of brightness) and to eliminate background noise (a darker range of brightness).
Statistical Analysis Numerical variables are expressed as the mean 6 SEM, unless otherwise indicated. The statistic analyses were performed using XLStat, version 7. 5. 3, software (Addinsoft, New York, NY, http:// www. xlstat. com). The normality of the distribution was tested with the Shapiro-Wilk test, and the homogeneity of variance was tested with the Fisher F test. When the distribution of the data for each group respected the normality law and the variance of the groups was homogeneous, the t test for paired measures was used. When the distribution of at least one of the groups did not follow the normality law, comparisons between groups were performed with the nonparametric Kruskal-Wallis test, followed by two-by-two comparisons performed with the Mann-Whitney U test. The critical p value for statistical significance was p ,.
HGF and VEGF are both potent stimulators of angiogenesis  [22, 41]. Considering the limited Figure  3. Capillary formation induced by SHED according to culture conditions. Endothelial cells (ECs) seeded on Cytodex beads were embedded in fibrin gels that were topped with fibroblasts and cultivated in ECGM2 depleted for VEGF or containing 5 ng/ml VEGF for 5 days (A); cultivated in fibroblast-conditioned medium containing no VEGF or 5 ng/ml VEGF (B) (in both cases, media were supplemented with increasing amounts of HGF [1. 5 or 10 ng/ml]); topped with SHED and cultivated in DMEM or in ECGM2 depleted for VEGF, EGF, and FGF-2, and supplemented with the indicated doses of EGF or FGF-2 (C); or topped with SHED and cultivated in ECGM2-DV containing the following inhibitors of the HGF or VEGF pathway: anti-HGF or control IgG, crizotininb, PF04217903, soluble VEGF receptor 1, or sunitinib (D).
Capillary length per bead was measured from spinning disc acquisitions using ImageJ. Mean 6 SD. p, p ,. 05; pp, p ,. 01; ppp, p ,. 001. Abbreviations: aHGF, anti-hepatocyte growth factor; Ct, control; Ct Ig, control IgG; CZ, crizotininb; ECGM2, endothelial cell growth medium 2; ECGM2-DV, ECGM2 depleted for VEGF; EGF, epidermal growth factor; FGF, fibroblast growth factor-2; HGF, hepatocyte growth factor; PF, PF04217903; SHED, dental pulp stem cells derived from deciduous teeth; SR1, soluble VEGF receptor 1; SU, sunitinib; VEGF, vascular endothelial growth factor. sensitivity of antibody arrays, we further quantified their secretion using ELISA. Fivefold higher amounts of HGF were detected in SHED secretion medium compared with fibroblasts. Although hypoxia dramatically inhibited HGF secretion by SHED in DMEM, it only slightly decreased it in ECGM2 (Fig.
3C ), through activation of HGF and VEGF pathways, using specific inhibitors (Fig. 3D ). Cytokine traps (anti-HGF and soluble VEGF-R1) or inhibitors of receptor tyrosine kinase (crizotinib and PF04217903 for cMet; sunitinib for VEGF-R2) both affected SHED-induced capillary formation. Although inhibition of the HGF pathway was only partially efficient, VEGF inhibition totally abolished capillary formation. These results thus confirmed that VEGF is required for capillary formation and that HGF acts as a coinducer to further enhance the VEGF-mediated effect. In order to determine whether hypoxic or FGF-2 priming, or the combination of both, had an effect on SHED differentiation, the expression of a series of cell surface markers associated with the mesenchymal stem cell (MSC) phenotype was investigated using flow cytometry.
Several reconstructions in different orientations (longitudinal and cross-sections) were performed to assess the vessels' penetration inside the matrices. Reconstructed micro-CT data vessels (red) inside and near the tissue constructs were performed showing the vessels penetrating the matrices in the group with 24-hour hypoxic priming, in contrast to the control group (supplemental online Movie 3). p, p ,. 05. Abbreviations: CT, computed tomography; FDG, 18 F-fluorodeoxyglucose; metab. , metabolism; PET, positron emission tomography; SUV, standard uptake value; VOI, volume of interest. by hypoxia. HGF secretion by BMSCs and ASCs has been previously reported  [42, 43]. In the case of ASCs, both regulation of HGF secretion by culture conditions and impact on vascularization were reported  [44] [45] [46].
Altogether, these data demonstrated that VEGF and HGF overcome the stabilization of ECs induced by these antiangiogenic cytokines and trigger capillary growth. We thus identified HGF as a new major regulator of SHED angiogenic effects. Capillary formation induced by SHED was strongly increased by hypoxia, as recently described for DPSCs  [56, 57]. In addition to such proangiogenic effects, we have demonstrated that FGF-2 and/or hypoxia priming notably enhanced the expression of stemness markers by SHED. Such priming increased the percentage of Stro-1-positive cells, a cell membrane marker related to the CGRP immunohistochemistry was then performed to investigate the sensitive innervation within the matrices. Positive immunolabeling was observed in both types of tissue constructs, especially around the neoformed vessels.
Several reconstructions in different orientations (longitudinal and cross-sections) were performed to assess the vessels' penetration inside the matrices. Reconstructed micro-CT data vessels (red) inside and near the tissue constructs were performed showing the vessels penetrating the matrices in the group with 24-hour hypoxic priming, in contrast to the control group (supplemental online Movie 3). p, p ,. 05. Abbreviations: CT, computed tomography; FDG, 18 F-fluorodeoxyglucose; metab. , metabolism; PET, positron emission tomography; SUV, standard uptake value; VOI, volume of interest.
ACKNOWLEDGMENTS This work was supported by grants from the  University Paris Descartes ,  Fondation de la Recherche Médicale  for  EA2496  (Grant  DBS20131128438 ), and  Plateforme d'imagerie du Vivant Paris Descartes  (Grant  FRM DGE20111123012 ),  Fondation des Gueules Cass  ées for EA2496, and the  National French Agency for Research  (Grant  ANR PulpCell 2014-2017 ). This work has also received support under the program " Investissements d'Avenir " launched by the  French Government  and implemented by the  ANR , with the references  ANR-10-LABX-54 MEMOLIFEANR-11-IDEX-0001-02 PSL Research University. N. B. and S. B. were supported by the  Groupe de Reflexion sur la Recherche Cardiovasculaire and Labex Memo-Life , respectively. We thank  Philippe Mailly  and  Nicole Quenech'Du  from the  Imachem imaging platform at Collège de France  for image analysis,  Gilles Renault  ( Plateforme Imagerie du Vivant Université Paris Descartes, Institut Cochin, Paris, France ) for constructive comments regarding ultrasound power Doppler analysis, and  Professor Michel Vidal  ( Faculté de Pharmacie, Université Paris Descartes, France ) for helpful discussions regarding angiogenesis exploration by positron emission tomography/computed tomography scan.
We combine these data with previously published reports of roosting behavior by Amazonian bats, resulting in an aggregate dataset of >500 observations. Statistical analyses of this dataset, the first to quantify the similarity structure of roost-occupancy data for any bat fauna, provide the basis for a guild classification that can be used in future studies of Amazonian bat community structure. We discuss the application of  Root's (1967)  guild concept to our results, discuss the extent to which roost availability might determine bat community structure in a variety of natural and anthropogenic Amazonian landscapes, and suggest how evolutionary scenarios previously interpreted in terms of trophic adaptations might usefully be supplemented by taking roosting-guild membership into account.
Later, after the Matses had been trained to record data and collect specimens themselves, small teams of Indians were encouraged to work independently. All of our Matses employees recorded their roost observations in the Matses language, which is rich in zoological, botanical, and habitat terminology  (Fleck and Harder, 2000). Matses-authored roost observations (recorded in waterproof notebooks; see  Fleck et al. , 2002: fig. 2)  were translated by D. W. F. soon after voucher specimens were preserved and recorded in archival documents as described above. Over the course of the 1999 field season, D. W. F. had numerous opportunities to visit roosts described in Matses field notes and found the latter to provide consistently accurate descriptions. Specimens collected as vouchers for roost observations at Nuevo San Juan were subsequently identified at the American Museum of Natural History (AMNH) by N.
(2014) , who also described the species H. pattoni; (3) our use of binomina in Platyrrhinus follows  Velazco et al. (2010) , who recognized P. incarum as a distinct species from P. helleri and described additional new species; and (4) we use Micronycteris homezorum as the appropriate spelling for M. "homezi" following  Solari (2008). Additionally, we recognize that the bats currently known as Sturnira lilium represent a species complex that includes a distinct but still unnamed endemic Amazonian taxon  (Velazco and Patterson, 2013). Statistical analysis: To assess the extent to which Amazonian bats form distinct groups of species with similar roosting habits, we analyzed a dataset consisting of 34 species with five or more roost observations each. To estimate similarities in habitat use among pairs of species, we used  Pianka's (1973)  symmetric index of resource overlap between species j and k, O jk = O kj = (∑p ij p ik )(∑p ij 2 ∑p ik 2 ) -1/2 , where p ij is the proportion of roosts of species j belonging to roost category i, and the summations are over the previously defined roost categories (i = 1-9).
For metric MDS, the cophenetic correlation and stress are perfectly negatively correlated, so only the cophenetic correlation was calculated. To estimate the minimum number of dimensions needed to adequately reproduce the original distance matrix, we varied N from 1 to 6 and evaluated the corresponding cophenetic correlations. To estimate the number and compositions of groups of species in the MDS coordinate space, we used a "k nearest-neighbor" (kNN) classification procedure  (Strauss, 2001). We modified the nearest-neighbor algorithm described by  Knorr et al. (2000)  to identify and omit outliers from the recognized groups based on k rather than all nearest neighbors. All statistical analyses were performed using Matlab v7. 10 (Strauss, 2012).
RESULTS We recorded 169 occupied roosts sheltering 41 species of bats at Nuevo San Juan from 1998 to 1999 (appendix 2). The commonest types of roosts encountered at this locality were cavities in standing trees (22% of all recorded roosts), foliage (20%), cavities in fallen trees (19%), the undersides of fallen trees (14%), the exposed surfaces of standing trees (7%), animal burrows (7%), and undercut earth banks (6%). Additionally, a few roosts were found in termite nests and in "other" (difficult to categorize) situations. Most roosts were found at ground level or within a few meters of the ground-an inevitable consequence of ground-based visual searching-but some trees were climbed and others were felled to search for roosts in subcanopy woodpecker holes. Unfortunately, these data are too few to infer the preferred roost types of more than a handful of species.
We recorded only one roost each for nine species, only two roosts each for six more species, and only three roosts each for another six species. In effect, over 60% of the species for which roosting data are available from this locality are represented by fewer than five observations. However, many commonly observed species (with ≥5 roost records) clearly preferred just one type of roost: under fallen trees (e. g. , Cormura brevirostris), inside cavities in fallen trees (Furipterus horrens, Micronycteris microtis), inside cavities in standing trees (Carollia perspicillata, Molossus rufus, Phyllostomus elongatus, Phyllostomus hastatus), exposed on standing trees (Saccopteryx leptura), and in foliage (Artibeus anderseni, Mesophylla macconnelli, Thyroptera tricolor). Such results suggest that modal patterns of roost use could be determined for many more bats if it were possible to accumulate enough observations.
We therefore combined our roost-occupancy data from Nuevo San Juan with previously published observations to obtain a total of 585 roost observations for 83 species of bats from 39 research sites across the length and breadth of Amazonia (table 1, fig. 1 ). Visual inspection of this larger dataset supports some inferences about modal roosting behavior inferred from the Nuevo San Juan results and suggests that additional species also have strong predilections for particular kinds of roosts: in foliage (e. g. , Artibeus gnomus, A. planirostris, Rhinophylla pumilio, Uroderma bilobatum), in cavities in standing trees (Noctilio albiventris, Gardnerycteris crenulatum, Micronycteris hirsuta), in cavities in fallen trees (Hsunycteris thomasi, Micronycteris microtis), exposed on standing trees (Rhynchonycteris naso), under fallen trees (Choeroniscus minor), inside arboreal termite nests (Lophostoma silvicolum), and beneath undercut earth banks (Carollia castanea).
ACKNOWLEDGMENTS As always, we are grateful to our Matses friends and colleagues-especially  Joaquín Rojas René ,  Fernando Shoque Uaquí Bëso , and  Antonio Jiménez Tajur -who hosted our visits to  Nuevo San Juan  and participated in many aspects of the fieldwork described here. Our text was improved by critical comments provided by  Liliana Dávalos ,  Louise Emmons , and  Brock Fenton , none of whom should be held responsible for any remaining errors in our data, analyses, or interpretation.
Sampling analysis To assess the environmental impact on the MHC class II DRB diversity, the data were analyzed according to the location of the capture sites and the type of environment at these sites. For D. rotundus and M. molossus, each capture site corresponded to one environment type. D. rotundus sampling sites corresponded to two different roots (Cave F (CF) and Cave M (CM)), considered as PF sites, and one foraging area (Saut Athanase (SA)), an EH site (Fig. 1 , Additional file 1: Table  S1 ). M. molossus individuals were captured in one foraging area (Cacao (CC)), an AA site, and two different roosts located at the EH site with different disturbance levels: Paracou (PA) with a high level of disturbance and the SA site with a low level of disturbance (Fig. 1 , Additional file 1: Table  S1 ).
Data analysis Sequences were edited using GENEIOUS R6  [52]. DNA sequences were aligned using the MAFFT alignment tool  [53]  included in the software. Sequences were confirmed as MHC class II DRB and mtDNA D-loop by homology analysis using the NCBI BLAST search  [54]. A DRB clone sequence was regarded as valid if the following criteria were met: (1) incidence in at least two independent PCRs either from the same individual or different ones, or amplification by two different primer pairs, and (2) identified by at least three identical clones  [55]. Haplotypes were reconstructed for each bat species using the DNASP 5  [56]. As proposed by  [57] , the nomenclature of MHC alleles for non-human species was used: to each allele a prefix (the first two letters of the genus and the species names) was given, with the serial number attached as follows: Cape-DRB for C.
Sequence diversity Allele frequencies (F), proportion of segregating sites (S), nucleotide diversity (π), observed (H O ) and expected (H E ) heterozygosities, and gene diversity (H D ) were estimated using ARLEQUIN 3. 5  [58]. Allelic richness (R), with a correction for sample size using a rarefaction method and inbreeding coefficients (F IS ) were calculated using FSTAT 2. 9. [59]. The mean number of differences between nucleotide and amino acid alleles were counted using MEGA 6. 06  [60]. Analyses were performed per species for the entire dataset, and for each environment subgroup separately.
Gene conversion and recombination events Evidence of genetic recombination or gene conversion events between DRB sequences was assessed using GENE-CONV 1. 81a  [61]. This program detects lengthy patterns shared between sequences despite the existence of a pronounced polymorphism. P-values were determined from both global and pairwise permutation tests, with 10,000 replicates. No mismatches between fragments were accepted, and global p-values were corrected for multiple comparisons (gscale = 0). Recombination breakpoints were identified using the GARD and SBP algorithms  [62]  implemented in the HyPhy package  [63] , available in the standard Datamonkey analysis library (http://www. data monkey. org/, Delport et al. [64] ).
7 were executed  [69]. The first one (M7) allows codons to evolve either neutrally (ω = 1) or under purifying selection (ω < 1), while the second one (M8) considers a class of sites under positive selection (ω > 1). These models were compared with a likelihood ratio test (LTR; 2Δl = 2-l 1 -l 0 ) with two degrees of freedom (α = 0. 05)  [70]. As previously described, each analysis was run twice, with starting ω values of 0. 5 and 1. 5, to ensure convergence  [68]. The F3 × 4 model of codon frequencies was assumed for all analyses. The amino acids positively selected were identified using the Bayes empirical Bayes approach (BEB), as recommended by Yang  [71] , with the cutoff posterior probability set at 90 %. Secondly, five ML methods proposed in the HyPhy package were used. For all analyses, the best fitting nucleotide substitution model was assessed with the automatic model selection tool available on the server.
MHC class II DRB phylogeny Phylogenetic relationships and trans-species polymorphism between DRB alleles were inferred using a Bayesian inference approach implemented in MrBayes 3  [78]. The HLA-DRB1 sequence (accession number: NG_029921) was used as outgroup. The GTR + G + I model was selected as the best-fitted model of nucleotide substitution using jModelTest 2  [79, 80]  under corrected Akaike information criteria (AICc). The program was run 10 × 10 7 generations, with a sampling frequency of 500 and a 25 % burn-in. Validation of the inference was assessed based on the standard deviation of split frequencies, less than the expected threshold value of 0. 01.
One hundred and ten sequences were validated for 19 C. perspicillata, corresponding to 23 Cape-DRB alleles (previously reported Cape-DRB*05, GenBank accession number: JQ388834, and Cape-DRB*16-37; GenBank accession numbers: KU896612-KU896633; Additional file 1: Table  S3 ). Two hundred and thirty-three sequences were validated for 35 D. rotundus, corresponding to 30 Dero-DRB alleles (Dero-DRB*01-30; GenBank accession numbers: KU896562-KU896591; Additional file 1: Table  S4 ). One hundred and forty-seven sequences were validated for 28 M. molossus, corresponding to 20 Momo-DRB alleles (Momo-DRB*01-20; GenBank accession numbers: KU896592-KU896611; Additional file 1: Table  S5 ). All amino acid sequences were unique. A maximum of three transcribed alleles per individual was detected in one C.