dataesr/scientific-paragraphs-categorization huggingface dataset:
NGS data analysis Reads were processed by adaptor trimming (Illumina Pipeline Casava v1. 8. 2 or Cutadapt for RNA-seq), filtered for low quality reads (Trim Galore v0. 2. 8) and subjected to quality control (FastQC). Reads were aligned to the Mus musculus genome (assembly NCBI37/mm9) and results were displayed on the UCSC Genome Browser (ChIPseq and global and allele-specific RNA-seq tracks have normalized genome coverage as reads per million; WGBS and RRBS tracks displayed methylation levels of symmetric CpGs covered by at least five reads). ChIP-seq reads were aligned using the Bowtie v0. 12. 7 with uniquely mapping option  (20)  and duplicate reads were removed. Peaks were defined using the MACS algorithm  (21)  implemented in SeqMonk v0. 32. 1 (Simon Andrews, Babraham Institute, UK) with 1.
0e-5 P-values cut-off; to reduce false positives, we considered only ZFP57 peaks overlapping KAP1 binding sites. Hierarchical cluster analysis was performed using package heatmap. 2 of the R v3. 0. 1 gplots library (Gregory R. Warnes). WGBS/RRBS-seq reads were aligned using Bismark v0. 7. 9  (22)  and CpG methylation calls were extracted using the Bismark methylation extractor v0. 7. 9. The methylation level of a DNA region was defined as the average methylation level of all its symmetric CpGs (at least 2 CpGs covered by at least five reads). Hypomethylated DNA regions in Zfp57 -/compared to WT ESCs were identified by searching for sequences with common symmetric CpGs (at least 5 CpGs covered by at least 5 reads that were less than 1 kb apart) with a decrease in methylation of >25%. H3K4me1 and H3K4me3 ChIP-seq peaks regions data sets for mouse ESC and differentiated tissues were downloaded from the UCSC mm9 annotation database (GEO GSE29184, 23).
The number of ICR BS and non-ICR BS (E14 ESC) overlapping with H3K4me1 and H3K4me3 peaks were calculated by using the BEDTools suite v2. 14. 3-1  (24). The significance of such co-bindings was assessed using permutations. An analogous number of random genomic locations of both ICR BS and non-ICR BS were generated preserving region sizes, chromosome distributions and not allowing overlaps among them. The process was repeated 1000 times in order to estimate the P-value (Python custom script and BEDTools suite v2. 14. 3-1). RNA-seq reads were aligned using the TopHat v2. 0. 8b  (25). Aligned RNA-seq reads were analysed using Seq-Monk v0. 32. 1; for gene expression, the RNA-seq quantification pipeline was used with default parameters returning log2-transformed counts of reads falling within the exons of each gene (Ensembl Database modified by adding uc012fxx.
1, uc007wb0. 1, uc009hep. 2 and uc007iem. 1 genes from UCSC Genes) corrected for the total number of sequences in the dataset. The differentially expressed genes between WT and knockout samples were identified using the intensity difference method (without multiple testing correction and with 0. 05 P-values cut-off) on the genes with a minimum of 10 reads. To reduce the false positives, we considered only the genes that were concordantly deregulated in both knockout cell lines. The distance of the deregulated genes from the ZFP57 binding sites was calculated as the shortest distance between the ZFP57 ChIP-seq peaks and the gene coordinates. The distance between overlapping peaks and genes was set to 0. ZFP57 and KAP1 ChIP-seq datasets on JB1 hybrid mouse ESC were downloaded from GEO (GSE74757).
The allele-specific mapping of ChIP-seq and RNA-seq data in hybrid JB1 mouse ESC was performed as previously described  (13). Allele-specific ZFP57/KAP1 binding sites were identified as described by Anvar et al. (13) , with range of bi-allelicity Y = j¯± 2 and considering only the ZFP57/KAP1 JB1 peaks (monoallelic = x JB1 ∈ Y JB1 ). Allele-specific expression was calculated as for global gene expression (see above), but considering only allele-specific reads without log 2 transformation (values were rounded to the nearest integer number). We considered only the genes with at least five SNPs in the exons and at least 10 reads in one allele of either the WT or the knockout. To find the differences in the JF1/B6 allelic expression ratio, we applied the Fisher-exact test with Benjamini-Hochberg multiple-testing correction (P-value < 0.
FUNDING Telethon-Italia  [ GGP15131  to A. R. ];  Associazione Italiana Ricerca sul Cancro  [ IG 2012 N. 12815  to A. R. ]; PON Ricerca e Competitività 2007-2013:  Codice Progetto  [ PON02 00677-PON02 00619 3470457  to G. G. ];  Progetto Bandiera MIUR-CNR Epigenomica  (to A. R. and G. G. );  EU-FP7-ITN INGENIUM [290123  to A. R. , G. G. , K. H. and R. F. ];  Marie Curie  fellowships from the  EU-FP7-ITN INGENIUM  (H. K. , A. S. and S. L. ); POR Campania [FSE 2007/2013 to V. R. and Z. A. ];  Reti di eccellenza tra Università -Centri di Ricerca -Imprese , progetto ' MODO -Model Organism '. Funding for open access charge:  Fondazione Telethon.
Survey of herd feeding management practices We have conducted various previous informal surveys from 2011 to 2013 in order to become familiar with the farming systems under study. Fifty-seven additional formal participant observations, including both semidirected interviews and measurements, were conducted with individual farmers (mainly male family heads) by means of snowball sampling  (Biernacki and Waldorf 1981). They were performed from August to November 2013, aiming to assess the feeding calendar of the flocks, and to better characterize ash tree uses and management practices. Each interview was performed directly on rangelands in the Berber language in order to facilitate information transmission, and lasted between two and three hours. Data were obtained on the number of trees owned, the number of branches cut per day in relation with the size of the flock, criteria of selection for individual trees to be exploited, rotation of cuts, knowledge of the biology of the tree and its interactions with livestock feeding, practices performed for safe cutting, and overall daily diet provided to livestock.
Data analysis Treatment of the participant survey was mainly qualitative, employing descriptive statistics to give a better understanding of the processes and practices associated with ash tree management; hence, considering proportions, or means and standard error. Data resulting from quantitative measurements were treated following GLM procedures, evaluated for normality distribution, and linear correlation coefficients were calculated after logarithmic transformation  (Snedecor and Cochran 1957) , and tested for significance. For the digestion experiment, data on nutrient components, intake, and digestibility were combined across days, and analysed by one-way analysis of variance (ANOVA), with animal species included as the main effect. Animals were fed individually; therefore, individual sheep and goats were considered as the experimental units.
Acknowledgements Research was carried out and partly funded within the collaborative structure:  Laboratoire Mixte International MEDITER (Terroirs méditerranéens ), and by the research programme  MED-INN-LOCAL (Local Innovations in Mediterranean Landscapes ) supported by the  French National Research Agency  ( ANR-12-TMED-0001 ). Special thanks is due to  Mohamed Izaghaghrine  and his family who welcomed us and allowed us the possibility to conduct digestion trials in his farm, and particularly to Hassan for his dexterity to climb on trees and solve the small day-to-day harassments. Our sincere thanks also go to  Saïd Oughzif  for his patience to translate in Berber our questions, and to  Peoples of the Commune of Aït M'Hamed  for having spent time with us. Many thanks to the staff of the  Animal Production Laboratory of the Hassan II Institute of Agronomy and Veterinary Medicine, Rabat , and of the  Phytochemistry Laboratory of Cadi Ayyad University, Marrakesh , for their assistance.
ATLAS detector and data sample The ATLAS detector is described in detail in Ref. [32]. It covers nearly the entire solid angle 1 around the interaction point and has an approximately cylindrical geometry. It consists of an inner tracking detector (ID) placed within a 2 T axial magnetic field surrounded by electromagnetic and hadronic calorimeters and followed by a muon spectrometer (MS) with a magnetic field provided by a system of superconducting toroids. The results presented in this article use the dataset collected in 2012 by ATLAS from the LHC pp collisions at √ s = 8 TeV, using a single-lepton (electron or muon) trigger  [33]  with a p T threshold of 24 GeV, or a single large-radius jet trigger with a p T threshold of 360 GeV. The integrated luminosity of this dataset after requiring data quality criteria to ensure that all detector components have been operational during data taking is 20.
The diboson events are produced with the Herwig generator and CTEQ6L1 PDFs, except for the ℓνℓ ′ ℓ ′ channel which uses POWHEG  [54, 55]  interfaced to Pythia 6. The diboson production cross sections are normalized to nextto-leading-order predictions  [56]. Additional diboson samples for the ℓνq q channel are produced with the Sherpa generator. QCD multijet samples are simulated with Pythia 6, Herwig, and POWHEG interfaced to Pythia 6. Generated events are processed with the ATLAS detector simulation program  [57]  based on the GEANT4 package  [58]. Signal and background samples simulated or interfaced with Pythia use an ATLAS specific tune of Pythia  [59]. Effects from additional inelastic pp interactions (pile-up) occurring in the same and neighbouring bunch crossings are taken into account by overlaying minimum-bias events simulated by Pythia 8.
Conclusion A combination of individual searches in all-leptonic, semileptonic, and all-hadronic final states to search for new heavy bosons decaying to WW, WZ and ZZ is presented. The searches use 20. 3 fb -1 of 8 TeV pp collision data collected by the ATLAS detector at the LHC. Within the combined result, no significant excess over the background-only expectation in the invariant mass distribution of the diboson candidates is observed. Upper limits on the production cross section times branching ratio to dibosons at the 95% CL are evaluated within the context of an extended gauge model with a heavy W ′ boson and a bulk Randall-Sundrum model with a heavy spin-2 graviton. The combination significantly improves both the cross-section limits and the mass limits for EGM W ′ and bulk G * production over the most stringent limits of the individual analyses.
The absolute error tolerance for the approximated solutions was set to 10 -6. During each iteration of the CEM algorithm, the solving of the system of ODE is necessary for both estimation and maximization steps. The M-step involves non-linear regression through the maximization of the classification log-likelihood (Eq. 3). This was done with a Gauss-Newton algorithm that updates iteratively the parameter values without requiring the computation of the second derivatives of the parameters  [23]. Optimization during the M-step was performed by function nls  [24]  in R software that allows also constrained optimization. Threshold 10 -6 was used as convergence criterion for the Gauss-Newton algorithm. Simulations were performed to test the validity of the proposed method in estimating the parameters of a system of ODE in a classification context.
Adding the random effect can be implemented with Monolix, the a Type of residual hCG production: constant (r(t) = A), proportional to time (r(t) = A × t), or non-biological model standard software for mixed-effect modeling in a population context that uses a stochastic approximation of EM (SAEM) algorithm to simulate the unobserved individual parameters  [33]. The implementation of this approach needs further investigations. Here, we have chosen for the data a Gaussian distribution per group, though various other distributions could have been used. More research is needed to work with more flexible distributions according to the assumption on the distribution of the residuals  [34]. CART or random forest methods could be alternatives to the proposed classification method. For example, the regression trees approach has been extended to longitudinal data  [35].
A more reliable evaluation would have been obtained if histological proofs had been available for all women. In the present study, we did not consider the initial type of mole because, despite pathological and clinical differences, the management of women with complete or partial mole is the same  [7]. However, this distinction might be beneficial because these two types do not have the same risks of developing complications: 0. 5 to 9. 9 % vs. 20 %, respectively  [9]. Further investigations on a larger sample of sujects or on the complete-mole part of the dataset are needed. A polytomous logistic regression could be coupled with the classification model to include predictors of group membership, such as the type of mole. The present method may be applied to other clinical classification problems that involve longitudinal measurements and comply with compartment modeling.
RivCM is directly coupled to the dynamic vegetation and hydrology model LPJmL  (Bondeau et al. , 2007; Gerten et al. , 2004; Rost et al. , 2008; Sitch et al. , 2003). The riverine carbon model focuses on the export, transport and conversion of terrestrial fixed carbon. Carbon pools and fluxes for the most important transport and transformation processes are validated for current conditions based on observational data from the Amazon basin. To investigate potential future changes in the different carbon fluxes and pools, the model was forced by climate change scenarios that cover a large range of uncertainty in climate change projections for the Amazon basin. Based on these simulations, we identify areas most heavily affected by climate change. We estimated temporal changes in the different carbon fluxes and pools, as well as the carbon released to the atmosphere and exported to the ocean.
Size of monthly inundated area The inundated area at time t (InunArea t (km 2 )) was defined as the area covered by water, including river and floodplain. It is determined by the current monthly discharge (Mdis t (m 3 s -1 )) relative to the mean maximum discharge of the reference period 1971-2000 (RefMeanMaxMdis (m 3 s -1 )) produced under the climate forcing of CRU TS2. 1  (Österle et al. , 2003; Mitchell and Jones, 2005). The potentially floodable inundated area (MaxInunArea (km 2 )  (Langerwisch et al. , 2013)  (Eq. 8) was calculated using the fraction of the cell that is potentially floodable  (Langerwisch et al. , 2013)  multiplied by the cell area. The potentially floodable area was calculated by applying a modified Topographic Relative Moisture Index (based on  Parker, 1982)  to a digital elevation model provided by the WWF database HydroSHEDS (WWF HydroSHEDS, 2007) as described by  Langerwisch et al.
Climate data sets For model evaluation, climate forcing data from a homogenized and extended CRU TS2. 1 global climate dataset  (Österle et al. , 2003; Mitchell and Jones, 2005)  were used. Annual atmospheric CO 2 concentrations were prescribed as given by  Keeling and Whorf (2003). For the assessment of climate change impacts, three Special Report on Emissions Scenarios (SRES) scenarios (A1B, A2, B1)  (Nakićenović et al. , 2000)  were applied. Five General Circulation Models (GCMs)  (Jupp et al. , 2010;   see also  Randall et al. , 2007) , namely, MIUB_ECHO_G, MPI_ECHAM5, MRI_CGCM2_3_2a, NCAR_CCSM3_0 and UKMO_HADCM3, were chosen to cover a wide range of uncertainty within climatic projections with respect to precipitation patterns and temperature. The GCMs used the SRES scenarios to calculate future climate.
Modelling protocol and simulation experiments We performed LPJmL simulations with potential natural vegetation only, i. e. without land use, on a 0. 5 • × 0. 5 • spatial resolution. To obtain equilibria for vegetation distribution, carbon and water pools in LPJmL, all transient LPJmL runs were preceded by a 1000-year spinup during which the preindustrial CO 2 level of 280 ppm and the climate of the years 1901-1930 were repeated. All transient runs of the coupled model LPJmL-RivCM were preceded by a 90-year spinup during which the climate and CO 2 levels of 1901-1930 were repeated to obtain equilibria for riverine carbon pools. Transient climate simulations are then performed for current climate  and future climate. The data sets used are described in Sect. 2. 2. To identify how relevant the amount of riverine outgassed carbon for the basin-wide carbon budget in a changing climate is, we compared the output of coupled terrestrialriverine modelling runs with purely terrestrial or purely riverine modelling settings.
To equalize a 10-fold increase (10 +1 ) and a reduction to 1/10 (10 -1 ), the quotient was log-transformed (log 10 ). E CC n = log 10 2099 n=2070 C n 2000 n=1971 C n (29) To show the model uncertainty that arises from differences in climate model projections, an indicator of the agreement between simulation results was calculated; it indicates a common significant increase or decrease in three, four or all five climate models. In addition, the significance (p value < 0. 05) of the difference between reference and future was assessed by a Wilcoxon rank-sum test  (Bauer, 1972). This test can be used for datasets that are not normally distributed and is therefore applicable to these data with high intra-annual fluctuations. Additionally to the analysis of spatial patterns, an analysis of changes in mean carbon pools over time was conducted.
Validation of the simulation results To assess the potential changes in the riverine carbon pools and fluxes, it is essential that we trust the model's ability to reproduce observed patterns and fluxes. For this, the first step is to validate river discharge before assessing carbon pools and fluxes. This has been done in  Langerwisch et al. (2013)  for 44 gauging stations in the Amazon basin and shows that the observed discharge patterns can be reproduced. Here, we validated the carbon concentration and export fluxes with published data (Table  4 ). If possible, we compared the simulated with observed values from the same period of time. We only included spatial data for which we could find the exact location (latitude/longitude). The validation of exported data shows that the outgassed carbon (export to the atmosphere) is underestimated by the model on a small scale by more than 90 % and on the basinwide scale by 70 %.
Acknowledgements. We thank the  "Pakt für Forschung der Leibniz-Gemeinschaft " for funding the  TRACES  project for  F. Langerwisch. A. Rammig  was funded by  FP7 AMAZALERT (ProjectID 282664 ) and  Helmholtz Alliance  " Remote Sensing and Earth System Dynamics ". We also thank  Susanne Rolinski  and  Dieter Gerten  for discussing the hydrological aspects. We thank  Alice Boit  for fruitful comments on the manuscript. Additionally, we thank our LPJmL and ECOSTAB colleagues at  PIK  for fruitful comments on the design of the study and the manuscript. We also thank the anonymous reviewers whose comments and suggestions greatly improved the manuscript.
In this study, we aimed to establish a computational approach to identify and classify FOXP3 + T cells into subpopulations, addressing the immunological and clinical significance of the method, and thereby to revisit the fundamental subclassification of FOXP3 + T cells. To establish the method, we used a data set of PBMCs from melanoma patients and healthy controls (HCs), which previously identified that total FOXP3 + T cells increased in melanoma patients, and that FOXP3 low naive Tregs and FOXP3 low non-Tregs increased as the stage progressed  (8). Furthermore, in this study, we have newly obtained a flow cytometric data set of FOXP3 + T cells from melanoma patients and HCs (designated as the second data set), to address the efficiency of the proposed method. Thus, we first show the clinical and immunological significance of a data-oriented clustering approach to the subclassification of the FOXP3 + T cells.
Patient samples All PBMC data sets were obtained from patients with malignant melanoma who were treated in the Department of Dermatology, Kyoto University Hospital. The first data set analyzed 23 individuals by FACSCalibur (BD Biosciences)  (8). The second data set analyzed 19 individuals by LSR Fortessa (BD Biosciences). The patients' characteristics in the second data set are summarized in Table  I. We also obtained data from age-and sex-matched HCs (first data set, n = 28; second data set, n = 15). This study was approved by the Medical Ethics Committee of Kyoto University and was conducted in accordance with the principles of the Declaration of Helsinki. All participants provided written informed consent.
Flow cytometric analysis PBMCs were isolated with Ficoll-Isopaque (Lymphoprep; Axis-Shield, Oslo, Norway) gradient centrifugation. All cells were freshly stained with the following mAbs and analyzed promptly as previously described (9): FITC-conjugated anti-CD45RO (UCHL1; BD Biosciences); PEconjugated anti-CD25 (M-A251; BD Biosciences); PerCP-Cy5. 5-conjugated anti-CD4 (SK3; BD Biosciences); and biotinylated anti-FOXP3 (236A/E7; eBioscience, San Diego, CA) and allophycocyanin-streptavidin (BD Biosciences). The second data set (Table  I ) was obtained by LSR Fortessa (BD Biosciences), using the following settings for voltage: FSC-A (319), SSC-A (335), FL1-A (CD45RO, 582), FL2-A (CD25, 478), FL3-A (CD4, 742), and FL4-A (FOXP3, 676). FlowJo (Tree Star) was used for manual gating.
Data-oriented clustering (automatic gating) of FOXP3 + T cell subpopulations We aimed to establish an automated clustering method for identifying FOXP3 + T cells and classifying them into three subpopulations, and thereby to revisit the immunological significance of the FOXP3 + T cell classification. Importantly, there is no major controversy regarding the identification of the total FOXP3 + CD4 + T cells, but there are multiple ways to classify FOXP3 + T cells, which we aimed to address in this study. Thus, we used the fol-lowing approach to identify the FOXP3 + T cell subpopulations: 1) to identify FOXP3 + CD4 + T cells; and 2) to classify FOXP3 + CD4 + T cells into three subpopulations without using the manual gating strategy. We used the flow cytometric data set in our previous report, which was obtained by a FACSCalibur (8) (the first data set), to establish a clustering method.
Study design and data collection Caregivers were evaluated upon enrolment and were reevaluated at 1 and 3 months. General characteristics related to the caregiver and the patient were collected upon inclusion. Self-administered survey materials were handed out and completed by the caregivers during the 3 evaluation times. These materials included the CarGO-QoL questionnaire, the generic 36 Item Short Form (SF-36) questionnaire, and two visual analogic scales (VAS) to assess QoL and burden on a scale from 0 to 10. At the 1-and 3-month evaluations, caregivers were asked closed-ended questions about important (negative and/ or positive) changes in their own lives since enrolment and about their perceptions of changes in the patient's health (improved/stable/deteriorated).
In accordance with the US Food and Drug Administration and the European Medicines Agency which encourages the use of QoL assessments, the CarGOQoL may be more appropriate than generic questionnaires due to its better ability to discern QoL differences in cancer caregivers. This measure could be used to monitor response to any intervention. An examination of responsiveness requires longitudinal data collection and is, therefore, rarely used in studies reporting the validation of QoL questionnaires. Finally, environmental barriers have been described  [28]  to explain why QoL measures have not been routinely implemented in clinical practice and clinical research. A great asset of a QoL questionnaire is its acceptability. The low rate of missing data and the short time of completion ensure the future use of this measure and make the CarGOQoL fully compatible with clinical practice.