dataesr/scientific-paragraphs-categorization huggingface dataset:
The mean accuracy of all three classifiers ranged from 67. 6% for the Naïve Bayes classifier to 73-74. 7% for the other two classifiers (Fig. 1 ). This performance was not impressive, in part because of the very small number of attributes, but nevertheless was considerably better than one could get by assigning outcomes by chance (50% accuracy). A closer look at the data set shows that, while the two classes have very similar characteristics (Table  2 ), the age distributions have subtle differences, particularly at their ends (Fig. 2 ). A calculation of 5-year survival probability from the dataset (Table  3 ) shows that the 5-year survival of the older women was lower than for younger patients, although the differences were small and perhaps not statistically significant (Table  3 ). By contrast, the number of tumor nodes among the 5-year survivors at time of surgery was slightly higher than among the decedents (2.
Results Quantifying changes in effective population size and dating with MSVAR. We investigated whether the genetic data were consistent with a very recent introduction of E. maximus into Borneo 300 years ago, followed by a large population expansion, as assumed on the basis of historical records. We conducted MSVAR analyses on a data set constructed by resampling 35 individuals randomly, first in the Bornean elephant sample as a whole, and then within each sampled population separately. We found that the genetic data exhibited a signal consistent with a population decline whether we assumed a linear or an exponential model for population size change (Fig. 1 ). Population-level results are presented in the supporting materials. The median value from pooled posterior distributions of log 10 (N 0 /N 1 ) was -1.
Moreover, additional analyses showed that when we allow for very recent events in our priors, the posterior distributions always support the ancient event (the AC model with extended priors, see supporting materials). Parameter estimates obtained for simulations exploring prior boundaries on the time of bottleneck were in general highly congruent. Support for the selected model (AC) was high in all analyses performed and was robust to different prior assumptions. Cross-validation based on simulated pseudo-observed datasets, however, revealed only moderate identifiability of the models when all five models were examined at once. This could indicate the limited power of the summary statistics and/or that the models differ in their specificity -e. g. some models encompass a much larger demographic parameter space than others.
Methods Sample information. We used microsatellite data analysed in Goossens et al. 10. This data set is derived from 224 individuals from across the range of the Bornean elephant genotyped at 18 microsatellite markers, with the addition of 630 bp of mtDNA region sequences from a subset of individuals (n = 60). Microsatellite data were available from four different locations in Sabah: (i) Lower Kinabatangan, (ii) North Kinabatangan range (Deramakot), (iii) multiple sites in the Central Forest (Ulu Segama-Malua, Gunung Rara, Kalabakan, Kuamut, Maliau Basin Conservation Area), and (iv) Tabin Wildlife Reserve as described in Goossens et al. 10.
Data analyses. MSVAR. i) Tests for changes in effective population size. We investigated the demographic history of Bornean elephants, examining population size changes while accounting for population structure. We tested whether a signature of population decline is found in Bornean elephant populations and dated the occurrence of any such event. We used two different sampling schemes to investigate changes in effective size of Bornean elephant populations: (i) examining each sampling site, and (ii) examining a pooled (n = 35) sample set  20. Our analyses of population size changes were carried out using two different but complementary approaches (see Beaumont  41  , and Storz and Beaumont  39  ) as in  37, 38. Both methods identify a single major change (increase or decrease), and quantify and date changes in effective population size.
These are full-likelihood Bayesian methods that utilize the information from the full allelic distribution in a coalescent framework. We used the full data set with 18 microsatellite loci for both methods. Further details on the MSVAR analyses are provided in the supporting materials. For each dataset, we performed two independent runs with wide uninformative priors and a 15 year generation length  42. ii) Recent versus older population size changes To separate recent from ancient factors, we further analysed the posterior distributions obtained for the time T since the population change (in generations). Additionally, we assessed the relative probability of recent versus ancient events by determining whether the data favoured events that were older or more recent than T = 53. 3 generations.
Approximate Bayesian Computation (ABC) analysis. (i) Microsatellite coalescent simulations. To infer the introduction history of elephants into Borneo, we used fastsimcoal  43  to generate simulated microsatellite data under different demographic scenarios and arlsumstat  44  to calculate summary statistics, coupled together in ABCtoolbox  45. We defined and parameterized a number of potential colonisation models that constituted alternative hypotheses of demographic-and hence colonization, history -that have been proposed for the species. We kept demographic scenarios general and relatively simple and tested seven (5 simple and 2 complex) models in total (graphically depicted in Fig. 2 , details in the Supporting materials). We set the number of loci and elephant sample sizes to those of the actual full dataset (224 individuals genotyped at 18 loci 10 ), and microsatellite diversity was generated under the generalized stepwise mutational model (GSM) with some proportion of multistep mutations.
In total, one million  (10 6  ) simulations were run for each model, and the 10,000 (1%) simulations with the smallest Euclidean distance were retained for ABC model choice testing and parameter estimation. Model choice and parameter estimation. We compared the seven demographic models, which differed in the order of introduction and on whether founder events occurred. Following the selection of the datasets with the smallest Euclidean distance (d) to the observed summary statistics, we estimated the model fit and compared competing historical demographic models using two different methods. First, we used ABCsampler and ABCestimator from ABCtoolbox to calculate the marginal density for each of the retained 10,000 simulations and calculate a p value as the fraction of these that have a lower marginal density.
The 'abc' R package  48  also includes a cross-validation tool for model selection, the accuracy of ABC estimates and also calculates the misclassification probabilities when performing model selection. For that, we randomly selected 1,000 datasets from the simulation output for each of the demographic model and used the function cv4postpr to assign a model to each of these datasets. Finally, we recorded the number of times that the true model was correctly identified (details on cross-validation is given in the Supporting materials). Parameter inference was performed using a local linear regression  15  with a correction for heteroscedasticity  51. We report the mean, median, mode and 95% highest posterior density (HPD) interval as an estimate of that population parameter. (ii) Mitochondrial DNA (mtDNA) simulations In a previous study  10  , we observed no mtDNA variability and a single mitochondrial haplotype in all Bornean elephant samples (n = 60) analysed from Sabah.
Acknowledgements This work was supported by  Portuguese Science Foundation ("Fundação para a Ciência e a Tecnologia" (FCT) ) individual fellowship (ref. SFRH/BPD/64837/2009 ) to RS and  FCT  project (ref. PTDC/BIA-BDE/71299/2006 ) to LC. LC was also supported by the  "Laboratoire d'Excellence (LABEX) " entitled  TULIP  ( ANR -10-LABX-41 ) and the " Laboratoire International Associé "  BEEG-B (Bioinformatics, Ecology, Evolution, Genomics and Behaviour ). BG and MWB were supported by a grant from the  Darwin Initiative for the Survival of Species  (Grant no. 09/016 ,  DEFRA, UK). BG was supported by grants from  US Fish and Wildlife Service Asian Elephant Conservation Fund, Elephant Family and Columbus Zoo. RH was supported by a  Danish Research Council (DFF-FNU)  grant and a  Villum Foundation Young Investigator  grant.
Patients were selected by availability of appropriate tumour material and consent. Patient characteristics are summarized in Table  1. Six tumours were classified as gd HSTL and four as ab HSTL; in one case the subtype could not be unambiguously determined. We included MACS-sorted CD8, CD4 (TCR ab) and TCR gd T cells from buffy coats of four healthy blood donors (males, mean age 37 yrs) as controls. The study was approved by the institutional review board of the Christian-Albrecht-University (ID: D405/10). Nine cases were obtained from the Tenomic database, which is approved by the ethical committee CPP-Ile-de-France-IX-08-009. The genomic data of these patients (ID 3-11) were available from a previously published exomesequencing study. 8 In two patients (ID 1, 2) SETD2 was analysed by means of a custom Ion AmpliSeq DNA panel (see supplement).
For all patients, exons of the genes STAT3 and STAT5B containing hot-spot mutations were amplified from genomic DNA by polymerase chain reaction and Sanger sequenced, in addition to the exome data (Online Supplementary Table  S1 , Online Supplementary Methods). For microarray-based DNA methylation profiling, bisulfite conversion was performed using the EZ-DNA Methylation Kit ® (Zymo Research Corporation, Irvine, CA, USA) followed by DNA methylation analysis with the Infinium HumanMethylation450 BeadChip (Illumina, San Diego, CA, USA). The dataset is available from Gene Expression Omnibus (GEO accession: GSE110081). The analysed CpGs were mapped to the chromatin states determined in CD4 naïve and CD8 naïve cells by the BLUEPRINT/IHEC Project (see Online Supplementary Methods). These T-cell populations were selected as they are the T-cell subsets with available chromatin segmentations in BLUEPRINT most closely related the maturation stage of HSTL cells.
Immunohistochemistry was performed on deparaffinised tissue sections from five reactive (normal) spleen samples and on ten of the current series of eleven HSTL patients (six on full sections, four on tissue micro-array sections). For details of methods and data analyses see Supplemental Method file. We analysed the DNA methylation of HSTL tumours from 11 patients (Table  1 ) in comparison to 12 samples of non-neoplastic T-cells i. e. , sorted CD4+ab, CD8+ab and gd T-cells from each of four healthy donors. After quality control, all 23 hybridizations and a total of 450,959 CpG loci were included in the analyses. Unsupervised analysis of all cases and controls based on the 5,376 CpG loci with the highest variance (σ/σmax>= 0. 5) clearly segregated the dataset into HSTL and benign T-cells (Figure  1A ).
Precision Nutrition and Cancer Research Program. IMDEA Food Institute. Madrid, Spain; 11  Duke Center for Genomics and Computational Biology, Duke University, Durham ,  NC, U S A; 12 Barcelona Supercomputing Centre (BSC) ,  Barcelona Spain ; 13  ICREA, Pg. Lluis Companys 23, Barcelona, Spain  and  14 Ernest  and  Helen Scott Haematological Research Institute, University of Leicester , UK Funding: this work was funded by the  European Union 's  Seventh Framework Program  through the  Blueprint Consortium  (grant agreement  282510 ). MJSDs work was supported by the  Leicester Experimental Cancer Medicine Centre  ( C325/A15575   Cancer Research UK/UK Department of Health ). PG's work was supported by a grant from the  Fondation pour la Recherche Médicale , grant number "DEQ20160334875". RS' work was supported by the  DFG  in the framework of the SFB1074.
Image and statistical analyses All embryos were analyzed visually with LSM510 or Zen software (Zeiss), step-by-step through the confocal z stacks and with the help of 3D reconstructions using AMIRA software. Except for the 1-cell stage embryos, which displayed a peculiar nuclear organization, we analyzed all the preimplantation embryos using the semi-automated image processing and analytical tools described below. Three-dimensional images of nuclei acquired with the LSM510 software and saved as lsm files were processed using the ITK library  (Yoo et al. 2002)  and its Python interface  (Lehmann et al. 2006). Nuclear volumes were segmented for both CENP and Rsat images. Rsat spots were segmented in Rsat images. The HP1ß signal was smoothed before thresholding using several standard filters (median, Gaussian, opening/closing, gray hole filling).
005; Fig. 3e ) and showed that there were no significant differences between the distributions of Rsat I and II signals, although the latter were more central than those of Rsat I (p values > 0. 1; data not shown). Statistical analysis (linear model) of Rsat signal distribution during development indicated a slight tendency to move towards the periphery at the 4-cell stage (p value = 0. 016). The Rsat sequences then return to the center of the nuclei at the 8-cell stage (p value = 0. 033). We then determined whether the spots were polarized in the nuclei by comparing the distances between the center of gravity of the nucleus (called centroid) and that of the Rsat spots. We evaluated whether the distribution of this distance differed significantly from a theoretical distribution and calculated a p value (Fig.
Acknowledgments We would like to thank  Prof. Qi Zhou  and  Dr. Cai-Xia Yang  from the  Institute of Zoology (IOZ) ,  Chinese Academy of Sciences (CAS) , for the fruitful discussions at the start of this project. We also thank  N. Daniel  for the technical assistance with rabbit embryo collection and  Claire Boulesteix  for the laboratory facilities, as well as the MIMA2 platform and particularly  Pierre Adenot  and  Renaud Fleurot  for the access to confocal and apotome microscope facilities.
Funding This project was funding by the  EU   FP6  ("Clonet^MRTN-CT- 2006-035468 ),  EU   FP7  (BPluriSys, ^HEALTH-2007-B-223485 ), the  REVIVE Labex  ( Investissement d'Avenir ,  ANR-10-LABX-73 ), and the  French Embassy in Beijing (China)  which supports our Sino-French collaboration, the  BLabiocem^network.
Production and application to soils of manure excreta from livestock farming significantly perturb the global nutrient balance and result in significant greenhouse gas emissions that warm the earth's climate. Despite much attention paid to synthetic nitrogen (N) fertilizer and manure N applications to croplands, spatially explicit, continuous time-series datasets of manure and fertilizer N inputs on pastures and rangelands are lacking. We developed three global gridded datasets at a resolution of 0. 5 • × 0. 5 • for the period 1860-2016 (i. e. , annual manure N deposition (by grazing animals) rate, synthetic N fertilizer and N manure application rates), by combining annual and 5 arcmin spatial data on pastures and rangelands with country-level statistics on livestock manure, mineral and chemical fertilizers, and land use information for cropland and permanent meadows and pastures.
Based on the new data products, we estimated that total N inputs, the sum of manure N deposition, manure N application and fertilizer N application to pastures and rangelands, increased globally from 15 to 101 Tg N yr -1 during 1860-2016. In particular during the period 2000-2016, livestock manure N deposition accounted for 83 % of the total N inputs, whereas manure and fertilizer N application accounted 9 % and 8 %, respectively. At the regional scale, hotspots of manure N deposition remained largely similar during the period 1860-2016 (i. e. , southern Asia, Africa and South America); however, hotspots of manure and fertilizer N application shifted from Europe to southern Asia in the early 21st century. The new three global datasets contribute to the filling of the previous data gaps of global and regional N inputs in pastures and rangelands, improving the abilities of ecosystem and earth system models to investigate the global impacts of N enrichment due to agriculture, in terms of associated greenhouse gas emissions and environmental sustainability issues.
Increased meat and dairy product consumption worldwide was a major driver behind the documented increase in cattle herds globally  (FAOSTAT, 2018) , and thus a major cause in the observed atmospheric increase of N 2 O and CH 4 over the past several decades  (Bai et al. , 2018; Bouwman et al. , 2013; Dangal et al. , 2017; Tubiello, 2018). While the availability of national-level statistics is a fundamental component of our knowledge base, environmental problems related to nitrogen pollution or emissions are best tackled at the local scale and often require finer, geospatial information, for example to assess proximity to water bodies and thus pollution risks. In particular, a number of studies have focused on downscaling existing national information to develop geospatially explicit regional and global datasets of nitrogen fertilizer and livestock manure production and use, to better understand their feedback on the climate system.
Several datasets of N fertilizer use were used in this study, in particular the FAOSTAT annual, countryspecific statistics on mineral and chemical fertilizers and livestock manure over the period  1961 -2016 (FAOSTAT, 2018)) , as well as specific geospatially downscaled products (e. g. ,  Bouwman et al. , 2005; Lu and Tian, 2017; Mueller et al. , 2012; Nishina et al. , 2017; Potter et al. , 2010; Sheldrick et al. , 2002). Further, global manure production datasets were developed in different studies to achieve various research goals  (Bouwman et al. , 2009 (Bouwman et al. , , 2013;; Holland et al. , 2005; Potter et al. , 2010; Zhang et al. , 2017). Although datasets of manure application in croplands are increasingly becoming available, there is considerable uncertainty in the estimation of total manure application and their spatial distribution across different studies  (Gerber et al.
, 2016; Herrero et al. , 2013; Liu et al. , 2010; Zhang et al. , 2017). Although previous studies have provided spatially explicit datasets of N inputs in the form of mineral or chemical and manure N in cropland systems, the spatially explicit datasets on N inputs in grassland systems are still missing  (Chang et al. , 2016; Lassaletta et al. , 2014; Stehfest and Bouwman, 2006). By grassland systems we mean the FAO livestock land use definition, i. e. , land used as permanent meadows and pastures  (FAOSTAT, 2018). The same may also be referred to in the literature as "pastures and rangelands". We note that "grassland" is in fact a land cover definition. In order to avoid the confusion often made in the literature between land cover and land use terminology, we will adopt FAO land use terminology of permanent meadows and pastures, to which the various national regional and global land use statistics cited in this work refer.
Furthermore, using results from the HYDE 3. 2 dataset  (Klein Goldewijk et al. , 2017) , we may split the FAO land use category into "pastures" and "rangelands", to highlight differences between managed intensive and unmanaged extensive systems, as needed. To enhance our understanding of the role of livestock in the global GHG balance and nutrient budgets (e. g. , ammonia emissions, nitrate leaching), global biogeochemistry models require spatially explicit estimates of N inputs. In this study, we developed datasets for major sources of N inputs in agriculture (i. e. , manure and fertilizer application and manure deposition on permanent meadows and pastures), using the recently published FAOSTAT statistics on manure N use in agriculture  (FAOSTAT, 2018). The latter are estimates based on IPCC Tier 1 methodology; i.
e. , they rely on default coefficients prescribing, among other variables, N excretion rates by animal type and region, as well as regional compositions of manure management systems  (FAOSTAT, 2018). Through combining the land use dataset HYDE 3. 2, FAO-STAT fertilizer N statistics and gridded manure production data in  Zhang et al. (2017) , we developed three annual global datasets at a spatial resolution of 0. 5 • × 0. 5 • , as follows: (1) manure N application rates to pastures (1860-2016); (2) synthetic N fertilizer application rates to pastures ; and (3) manure deposition rates by grazing livestock to rangelands and pastures. We quantified regional variations in N inputs, identified hotspots of N inputs from different N sources from livestock and discussed their uncertainty. These datasets are developed for global model simulation studies in model intercomparison projects (e.
, 2000). For mineral and chemical fertilizers, we further split the FAO definition using HYDE 3. 2, into pastures and rangelands, the former representing land use areas managed to support high stocking densities of grass production for hay and silage, whereas the latter represents unmanaged and grazed at low stocking densities. Although FAOSTAT land use statics cover in principle these two subcategories of land use, data coverage needed is insufficient for the consistent global mapping needed herein. The spatial distribution map of pastures and rangelands provided by HYDE are nonetheless based on and normalized to FAOSTAT land use statistics, complemented by additional information  (Klein Goldewijk et al. , 2017). To investigate N inputs from livestock at a regional level, the global landmass was disaggregated into seven regions: North America, South America, Africa, Europe, southern Asia (i.
Global synthetic fertilizer N application on pastures We obtained national-level datasets of "Agricultural use of mineral or chemical fertilizers" from the FAOSTAT (2018) "Fertilizers by Nutrient" domain, over the time series 1961-2016. The FAOSTAT statistics of agricultural use include use for both agriculture and forestry, as well as use in aquaculture. Furthermore, agricultural use includes both cropland and permanent meadows and pastures. We assumed that the use of fertilizers for forestry and aquaculture was zero, as well as fertilizer applications on rangelands. Subsequently, we estimated N application rates to pastures by using the ratio of pasture to cropland N use total published by  Lassaletta et al. (2014). We finally spatialized the pasture N data using HYDE 3. 2, obtaining gridded maps of synthetic fertilizer N application rates on pastures in each grid cell area, over the period 1961-2016 (Fig.
Global manure N application to pastures We obtained country-level datasets of "manure applied to soils" from the FAOSTAT (2018) "Livestock Manure" domain for the period  1961 -2016 (FAO, 2018)). Following IPCC guidelines, the data in this domain do not consider N leaching during treatment  (FAOSTAT, 2018). Furthermore, the FAOSTAT data do not separate manure application to cropland and pastures, and data of manure N application rates to pastures are currently not available. We therefore assumed that manure N application rates in pastures and croplands were the same, considering that the overall uncertainty in the input manure N data would not justify further assumptions at this stage of knowledge. Improved FAO statistics on both use and application rates will be used when available to improve this current work.