dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  Chao-Hsi Chang ,  Cai-Dian Lü ,  Xing-Gang Wu , and  Fu-Sheng Yu  for the discussions on the production and decays of double-heavy-flavor baryons. We express our gratitude to our colleagues in the CERN accelerator departments for the excellent performance of the LHC. We thank the technical and administrative staff at the LHCb institutes. We acknowledge support from  CERN  and from the national agencies:  CAPES ,  CNPq ,  FAPERJ  and  FINEP (Brazil) ;  MOST  and  NSFC (China) ;  CNRS/IN2P3  (France);  BMBF ,  DFG  and  MPG (Germany) ;  INFN (Italy) ;  NWO (Netherlands) ;  MNiSW  and  NCN (Poland) ;  MEN/IFA (Romania) ;  MSHE (Russia) ;  MinECo (Spain) ;  SNSF  and  SER (Switzerland) ;  NASU (Ukraine) ;  STFC  ( United Kingdom );  DOE NP  and  NSF (USA). We acknowledge the computing resources that are provided by  CERN ,  IN2P3 (France) ,  KIT  and  DESY (Germany) ,  INFN (Italy) ,  SURF (Netherlands) ,  PIC (Spain) ,  GridPP  ( United Kingdom ),  RRCKI and Yandex LLC (Russia) ,  CSCS (Switzerland) ,  IFIN-HH (Romania) ,  CBPF (Brazil) ,  PL-GRID (Poland)  and  OSC (USA).
OTUs (Operational Taxonomic Units) or groups of similar sequences analogous to species, were formed using the Swarm algorithm  49  ; two clustering iterations are performed -the first to denoise the data, and the second using the first iteration as seed sequences for grouping. Chimera detection utilized VSEARCH  50  with de novo UCHIME  51  , in conjunction with cross-validation; that is, a sequence was considered chimeric and removed only if it was flagged as chimeric in all samples in which it appeared. Following the above steps, taxonomic assignment was conducted using blastn+ and the SILVA 16S database version 132  52  : the best alignment hits were retained for each seed sequence, to a limit of 500, and were used to assign a consensus taxonomic affiliation. Blastn+ results for seed sequences, including best hits, % similarities, e-values, etc.
Chimera detection was performed again on the seed sequences of the selected OTUs using USEARCH v11. 0. 667 (high-confidence mode) with the SILVA 16S database as a reference database; OTUs with seed sequences identified as chimeras using this method were also removed from the analysis. Seed sequences of the OTUs presented here were submitted to GenBank (accession numbers MN594311 -MN594465, see Supplementary Table  S12  for additional details).
Second, we used the iNEXT function of package iNext  57, 58  to extrapolate these curves to the largest sample size at the component and meta-component scales, 37 and 165 animals respectively. That is, we estimated the number of unique OTUs we would have found had we sampled 37 animals in each sampling session at each location, and 165 animals in each session when sites are pooled. The extrapolation performed by iNEXT requires an asymptotic estimate of species richness (the number of species or OTUs detected with an infinite sample size)  57  , we used the Chao2 richness estimator as it uses incidence data and has been found to be relatively precise and minimally biased compared to other estimators  59. We chose to extrapolate curves only to the largest sample size because such extrapolations are expected to be unbiased only at sample sizes two to three times larger than the observed data  60.
We used 100 bootstrap iterations to generate 95% confidence intervals. OTU persistence. To assess the persistence of OTUs within the host meta-population over the course of the outbreak, we selected the OTUs found in animals in spring 2018, and performed co-occurrence analysis using the whole data set (not just occurrence data from spring 2018). We used the cooccur function in package cooccur  61  to measure the associations between the isolated OTUs (i. e. are some pairs of OTUs found co-infecting an animal more often than we would expect if these OTUs are distributed randomly in the host population).
Acknowledgements This article is dedicated to the memory of  Jean-François Cosson , recently deceased. The high-throughput sequencing results 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  Centre de Biologie pour la Gestion des Populations HPC  computational platform thanks to  Alexandre Dehne-Garcia , and on the supercomputer facilities of the  Mésocentre de calcul de Franche-Comté. This work was carried out within the framework of the  Zone Atelier Arc Jurassien  (http://zaaj. univ-fcomte. fr). We are grateful to the  genotoul bioinformatics platform Toulouse Midi-Pyrenees and Sigenae group  for providing help and/or computing and/or storage resources.
Data availability Meta-data for the data set analyzed here can be accessed through the dat@osu platform: Fossorial water vole (Arvicola terrestris) population cycles and their phase-associated microbial communities (https://dataosu. obsbesancon. fr/FR-180089013067312017-02-03_Fossorial-water-vole-Arvicola-terrestris. html). The raw sequences (fastq format), and raw and filtered tables of abundance for the eight sequencing runs are available in the Zenodo data repository (https://doi. org/10. 5281/zenodo. 3784637). Seed sequences of the OTUs included in the analysis can be found on GenBank, see Supplementary Table  S12  for accession numbers.
Processing of the Shadowgraphs The analysis of the shadowgraphs and the extraction of the radius of the cloud have been performed automatically thanks to a dedicated tool written in Python and making use of the image processing library Scikit-Image  [37]. Since the images of the top and front view differ, the processing method has been adapted to each view. In the top view images, the cloud of krypton has a circular shape. Consequently, the processing method consists of determining a circle which fits the contours of the cloud. The processing steps are illustrated in Figure  4. First, the background of the image is subtracted (Figure  4b ). The background image is calculated by taking the average of several dozens of pictures that are taken between the moment when the pin leaves the field of view and the moment when the container reaches its lowest Light source Camera lens Plane mirror position.
Study area and samples During YF outbreaks in Brazil (January 2017 to December 2018), we analyzed 771 liver samples from carcasses of free-living NHP from different areas of Minas Gerais state. Data regarding ten NHP carcasses previously screened for the YFV RNA  [7]  were included in data analysis, totalizing 781 individuals analyzed here. The NHP carcasses consisted of a convenient sampling, collected in the context of the Brazilian Yellow Fever Surveillance Program  [16] , of epizootics that occurred in 2017 and 2018. Since biological samples were obtained from carcasses, blood sampling was not possible, and liver samples were selected for YFV investigation, due to the hepatotropic of YFV in humans and NHP  [1, 4, 16, 17]. Minas Gerais is in Southeast Brazil (Fig  1 ), with 586,521. 121 km 2 and 21,040,662 inhabitants.
We determined the number of genomic copies per gram (gc/g) of the liver, and we estimated the genomic loads in all YFV-positive carcasses using linear regression and the observed Cqs. From 11 YFV-positive samples, a partial sequence of the envelope gene (primers 5-left and 6-right) was obtained  [5] , using the GoTaq Hot Start Colorless Master Mix (Promega, USA). The amplicons were purified and sequenced with the ABI3130 platform (Applied Biosystems, USA). The consensus sequences were generated (Geneious v. 9. 1. 6-Biomatters, NZL; https:// www. geneious. com/) and used for phylogenetic reconstruction, using the maximum likelihood method implemented in MEGA7 (https://www. megasoftware. net/). The final tree was edited and visualized in FigTree v. 1. 4. 4 (http://tree. bio. ed. ac. uk/software/figtree/).
In 2017 and 2018, the highest numbers of YFV-positive carcasses were from Metropolitan (42. 6%), Zona da Mata (51. 0%), and the South/Southwest (43. 9%) regions (S1 Table ). � 95% CI: 95% confidence interval. OR: odds ratio. Pos: yellow fever virus genome detected by RT-qPCR. 1 The investigation of the association between the positivity and NHP genera considered only data regarding Callithrix, Callicebus, and Alouatta (n = 743, excluding non-identified specimens and one specimen of Sapajus). 2,3 The investigation of the association between the positivity and 2 the period of sampling (epidemic or non-epidemic) and 3 the investigation of the association between the positivity and the environment (urban, urban-rural interface, and rural/sylvatic areas) included all specimens (n = 781). 4 The investigation of the association between the positivity and the environment together with NHP genera included part of specimens (n = 728, non-identified specimens; specimens of Sapajus; and specimens of Alouatta and Callicebus from urban and urban-rural interface were not included in the analyses).
001) and to Callicebus (p = 0. 0045). Finally, for one specimen of Sapajus nigritus, Cq of 30. 2 (estimate of 3. 10E +04 gc/g liver) was observed. When we analyzed median Cqs considering both NHP genera and environments, differences were observed (x 2 = 51. 58, p< 0. 0001) (Table  3 ). Rural Alouatta (median Cq = 14. 1), rural Callicebus (median Cq = 13. 9), and rural Callithrix (median Cq = 15. 0) presented lower median Cqs (higher estimated YFV genomic loads) compared to urban Callithrix (median Cq = 33. 0) (p� 0. 0014). No difference was observed in median Cqs when Alouatta, Callicebus, and Callithrix collected in rural areas were compared to each other (p> 0. 20). No difference in median Cqs was observed between urban-rural (median Cq = 33. 8) and urban Callithrix (p = 0. 62). Partial nucleotide sequences of the envelope gene of 11 samples were obtained (Genbank accession numbers: MN517211-MN517221) (S2 Table ), confirming wild-type YFV in NHP carcasses from urban and rural areas of different municipalities of Minas Gerais state.
However, we observed a strong negative correlation for detection of YFV in urban marmosets and higher median Cqs compared to Callicebus and Alouatta, suggesting lower genomic viral loads in those urban marmosets. Previous studies showed similar data when Callithrix presented higher Cqs (lower viral loads) than Alouatta during the recent YFV epizootics in Brazil  [33]. On the other hand, we did not observe a difference among rural NHP (Callithrix, Alouatta, and Callicebus), which presented median estimates of genomic viral loads of 10E+6 times higher than Callithrix from urban and urban-rural transition areas. When only marmosets were evaluated, we detected a higher positivity in rural areas and a decreasing gradient towards urban areas, consistent with the sylvatic pattern of YF. In that way, the lower overall positivity for YFV could result from the lower exposure to YFV-infected sylvatic vectors in urban areas.
This work was supported by different agencies. BPD was supported by  Fundacão de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG) , under Grant  APQ-01574-17 , and  Secretaria de Estado de Saude de Minas Gerais (SES-MG) / Secretaria de Estado de Planejamento Conclusions/Significance de Minas Gerais (SEPLAG-MG)/Instituto Rene Ŕachou/Fundacão Oswaldo Cruz (IRR/FIOCRUZ)  under Grant Yellow fever. GST was supported by  Conselho Nacional de Desenvolvimento Cientı ´fico e Tecnologico (CNPq)  under grant  440593/2016-6  and  Decit/SCTIE/MoH  under grant  440593/2016-6. APP was supported by  FAPEMIG  and  Coodernacão de Aperfeicoamento de Pessoal de Nı ´vel Superior (CAPES). BdT was supported by the  RESERVOIRS program  supported by  European (ERDF/FEDER)  funds and assistance from  CollectiviteTerritoriale de la Guyane and Direction Regionale pour la Recherche et la Technologie , and the  MicroBIOME project granted by the Laboratoire d'Excellence CEBA "Investissement d'Avenir"  under Grant  ANR-10-LABX-2501.
PAA was supported by  FAPEMIG  under grant  APQ-01989-18. MLN was supported by  Fundacão de Amparo à Pesquisa do estado de São Paulo (FAPESP)  under grant  13/21719-3. LS and IMR were supported by  Coordenacão de Aperfeicoamento de Pessoal de Nı ´vel Superior (CAPES) / Brazil under graduate scholarship grant  ( 0001 ) and NIOS was supported by  Conselho Nacional de Desenvolvimento Cientı ´fico e Tecnologico (CNPq)  under graduate scholarship grant, VEM was supported by  Pro-Reitoria de Graduacão-Universidade Federal de Minas Gerais (UFMG)  under graduate scholarship grant. MAS, TAC, and GFGO were supported by  CNPq FAPEMIG , and  CAPES  undergraduate scholarship grants. BPD, APP, GST, EGK, and MLN are  CNPq Research Fellows. CNPq: http://www. cnpq. br/CAPES: https://www. capes. gov. br/FAPEMIG: https://fapemig.
Experimental design The aim of this study was to investigate the role of endogenous ATRA signaling by using an in vivo genetic approach of deletion of Aldh1a1, Aldh1a2, and Aldh1a3 genes (i. e. , mice lacking all sources of ATRA in vivo) to challenge the admitted concept of ATRA being the MIS. The number of samples was determined on the basis of experimental approach, availability, and feasibility required to obtain definitive results. No data were excluded from the analyses. The numbers of replicates are specified in Materials and Methods. The researchers were not blinded during data collection or analysis.
In situ hybridization Samples were dissected and fixed overnight with 4% (w/v) paraformaldehyde and then processed for paraffin embedding. Microtome sections of 7-m thickness were processed for in situ hybridization. Stra8 digoxigenin-labeled riboprobe was synthesized, and in situ hybridization analyses were performed as described in  (29). Imaging was performed on an MZ9. 5 microscope (Leica) coupled with a DHC490 camera (Leica) and Leica application suite V3. 3. 0 software and processed with Adobe Photoshop. For each genotype, n = 3 to 5 embryos.
Immunological analyses Samples were fixed overnight with 4% (w/v) paraformaldehyde and then processed either for paraffin embedding or directly for wholemount immunostaining. Microtome sections of 5-m thickness were processed for immunostaining. Immunofluorescence analyses were performed as described  (29). The following dilutions of primary antibodies were used: ALDH1A1 (1:50; catalog no. 52492, Abcam), ALDH1A2 (1:500; catalog no. HPA010022, Sigma-Aldrich), CDH1 (1:100; catalog no. 610182, BD Transduction Laboratories), DAZL (1:200; catalog no. GTX89448, GeneTex), DDX4 (1:200; catalog no. 13840, Abcam), GATA4 (1:200; catalog no. 1237, Santa Cruz Biotechnology), GFP (1:750; catalog no. TP401, Torrey Pines Bio Labs), POU5F1 (1:250; catalog no. 611202, BD Transduction Laboratories), phospho-H2AX (1:300; catalog no.
16193, Millipore), SOX2 (1:200; catalog no. 97959, Abcam), SYCP3 (1:200; catalog no. 15093, Abcam), TRA98 (1:150; catalog no. 82527, Abcam), and FUT4 (SSEA1) (1:200; catalog no. 21702, Santa Cruz Biotechnology). Slides were counterstained with 4′,6-diamidino-2-phenylindole (DAPI) diluted in the mounting medium at 10 g/ml (Vectashield, Vector Laboratories) to detect nuclei. Imaging was performed with a motorized Axio Imager Z1 microscope (Zeiss) coupled with an AxioCam MRm camera (Zeiss), and images were processed with AxioVision LE and ImageJ. ImageJ software was used for quantification of proliferating germ cells versus total germ cells. Whole-organ imaging was performed on an LSM 780 NLO inverted Axio Observer Z1 confocal microscope (Carl Zeiss Microscopy GmbH, Jena, Germany) using a Plan Apo 10× dry NA (numerical aperture) 0.
Immunodetection and quantification of proliferating germ cells Paraffin sections from each genotype were processed for immunohistological experiments with POU5F1, DDX4, or GATA4 antibody. Then, proliferation analysis was performed on the same sections by BrdU labeling, and detection was performed using an appropriate kit (catalog no. 11 296 736 001, Roche). Total germ cells and proliferating cells were quantified on the entire section using ImageJ software. For each picture, the number of BrdU-positive germ cells (proliferating) and the number of either OCT4-or DDX4-positive cells (total) were counted. Then, the percentage of BrdU-positive versus OCT4-or DDX4-positive germ cells was determined. For each genotype (n = 4 to 7; 15 pictures per genotype), the mean and mean + SEM of these percentages were calculated and reported on a graph after statistical analysis (for details, see the "Statistical analysis" section).
Evaluation method Recently, area attribute has been used for the classification of small woody features  (Merciol et al. , 2019). In this study, we will use a similar data, the high resolution Podgorica image, plus the Reykjavik dataset to compare the different attributes with the FPs. The evaluation on the Reykjavik data is based on the common approach used in the literature: a FP is computed on the data, the feature profiles of the training pixels are used to train a random forest and, then, the classification is performed on the entire ground truth. Regarding the classification of the small woody features, here is our evaluation process: • We start by checking the consistency of the classification samples. The set of reference values is separated: (A) for training and (B) for prediction.
For the area filtering, we considered the same set of threshold values used in the computation of FPs in  (Pham et al. , 2017a)  for the Pavia data, and the threshold values used in  (Merciol et al. , 2019)  for the Podgorica data: λa,Rey = {25, 100, 500, 1000, 5000, 10000, 20000, 50000, 100000, 150000} λ a,P od = {1000, 2500, 5000, 7500} For the perimeter attribute, we adopted the same threshold values used for area. Then, for the scale invariant attributes (moment of inertia, standard deviation and compactness), we considered the following set of thresholds commonly used in the literature: λi = λs = λc = {0. 2, 0. 3, 0. 4, 0. 5} The random forests trained on the Reykjavik and Podgorica data are composed of 100 and 64 trees, respectively. The experiments were performed on a machine with Intel Xeon Gold 6136 CPU, 3.
This outcome has already been discussed in  (Pham et al. , 2018a)  with respect to the attributes area, moment of inertia and standard deviation. Here, we show that this is also the case for rectangularity, perimeter and compactness. For example, the FP computed using the compactness attribute both for filtering and for projection outperforms the compactness AP by 7. 7%, 13. 62% and 10. 85% in terms of OA, AA and κ, respectively. Regarding our evaluation on the Podgorica data, our two baselines are the classification based only on the spectral data (composed of four spectral bands), and on the spectral information concatenated to the NDVI channel and to the Sobel gradient of each band. For each method, we show in Table  3  the precision and recall with respect to the SWF class. While that including the NDVI and Sobel bands provides little improvement in recall, using APs and FPs improves the recall by up to 4.
UV-vis spectra were measured on a Jasco V-670 spectrometer, solution phase spectra of 1,1,1,3,3,3-hexafluoropropane-2-ol (HFIP) solutions (5 mg/mL) were obtained using Hellma quartz cuvettes (1 mm path length), and spectra of thin films were measured on samples prepared onto a quartz substrate by spin-coating (4000 rpm, 1 min) using 100 μL of a stock solution (2 mg/mL). Mass Spectrometry. High-resolution mass spectrometry was carried out on either a Waters Q-ToF Ultima for ESI or on a Waters QTOF Xevo G2-S for APPI. Single Crystal X-Ray Measurements. Bragg-intensities were obtained from a single crystal of T2a at 200 K using a Rigaku SuperNova dual system diffractometer (CuKα radiation, λ = 1. 54184 A� ) equipped with an Atlas S2 CCD detector. The dataset was reduced and corrected for absorption using CrysAlis Pro.
54 A� ) at the MATEIS laboratory of the Institut National des Sciences Appliquées de Lyon, France. The data were recorded in transmission with a beam size of 500 μm × 500 μm and a 2D CCD detector (Princeton Instruments) with specimen-detector distances of 343 mm and 42. 1 mm and exposure times of 180 and 1200 s for SAXS and WAXD respectively, corrected for the thickness and the absorption of the specimens, and integrated azimuthally. A broad low-angle peak observed in the SAXS patterns was assumed to arise from approximately periodic stacking of high aspect ratio crystalline lamellae, giving direct access to the lamellar long period, Lp. The lamellar thickness, Lc, was estimated from the corresponding correlation function following Strobl and Schneider. 28 The volume fraction of the crystalline phase was then estimated from the ratio Lc / Lp.
Differential scanning calorimetry (DSC) was carried out with a TA Instruments Q100 calorimeter at a scanning rate of 10°C/min under nitrogen flow (50 mL/min). Specimens of about 3-5 mg were first heated to a temperature 30 °C above their melting point and then cooled to 0 °C at a cooling rate of -10 °C/min in order to erase the effects of their thermal history. The data given for the thermal transitions were obtained from the second heating and first cooling scans. Melting (Tm) and crystallization (Tc) temperatures were defined as the temperatures corresponding to the maxima of the respective enthalpy peaks, while Tg was defined as the half-height of the heat capacity step associated with the glass transition. The melting enthalpies ΔHm were estimated by integrating the area under the melting peaks.
Africa contains more human genetic variation than any other continent, but the majority of the population-scale analyses of the African peoples have focused on just two of the four major linguistic groups, the Niger-Congo and Afro-Asiatic, leaving the Nilo-Saharan and Khoisan populations under-represented. In order to assess genetic variation and signatures of selection within a Nilo-Saharan population and between the Nilo-Saharan and Niger-Congo and Afro-Asiatic, we sequenced 50 genomes from the Nilo-Saharan Lugbara population of North-West Uganda and 250 genomes from 6 previously unsequenced Niger-Congo populations. We compared these data to data from a further 16 Eurasian and African populations including the Gumuz, another putative Nilo-Saharan population from Ethiopia. Of the 21 million variants identified in the Nilo-Saharan population, 3.
4, 6, 9 he Nilo-Saharan family comprises 206 languages spoken by 34 million people (1996 estimate) and is divided into approximately 12 subgroups. 10, 11 This family is particularly problematic for linguists because there is only weak evidence for establishing the relationships between the subgroups and some authors treat Nilo-Saharan as a collection of isolated language groups rather than a single family. 11 Some smaller Nilo-Saharan groups (Gumuz, Koman, Kadu, Chabu) have been excluded from the Nilo-Saharan family by some authors or treated as early branching distantly related groups by others. 10, 12 Genetic data can be used to show how linguistic groups map onto genetically defined human populations. 4 ] [15] [16]  Here we have sequenced the genomes of 50 individuals from the Nilo-Saharan Lugbara population of Northwestern Uganda.
The Gumuz is the only other Nilo-Saharan population to be sequenced at this scale and the linguistic evidence for its inclusion in the Nilo-Saharan family is debated. 10, 12 For comparison we also sequenced the genomes of 250 individuals from 6 new Niger-Congo populations from Guinea, Cote d'Ivoire, Cameroon, Democratic Republic of Congo, Zambia, and Uganda and also included published data from 13 additional African populations from the 1000 Genomes and African Genome Variation Projects. 2, 17 We show that the Lugbara are genetically distinct from all Niger-Congo and Afro-Asiatic populations and from the Gumuz. 2, 5 Through this level of sequencing, we have been able to use the major methods for identification of loci under selection, iHS and xpEHH, which require at least 15 genomes to achieve 80% power.