dataesr/scientific-paragraphs-categorization huggingface dataset:
Single cell analysis Breast cancer SMART-Seq2 data was downloaded from the NCBI Gene Expression Omnibus database (accession number : GSE75688). SRA files were converted to FASTQ files with SRA Toolkit (version 2. 3. 2-4). RNA AU: The funding information provided has been checked against the Open Funder Registry and we failed to find a match. Please check and resupply the funding details. genome sequences (hg19) using the 2-pass mode of STAR (version 2. 5. 3. a)  41 (parameters: quantMode GeneCounts, twopassMode Basic, alignSJDBoverhangMin 1, bamRemoveDuplicatesType UniqueIdentical, winAnchor MultimapNmax 1000, outFilterMultimapNmax 1000, outFilter ScoreMinOverLread 0. 33, outFilterMatchNminOverLread 0. 33, outFilterMismatchNoverLmax 0. 04, outMultimapperOrder Random, sjdbOverhang 99). Raw count tables were obtained with Subread tool FeatureCounts (version 1.
5. 1)  42 and formatted as a matrix with a python script (version 3. 6. 2). Then, ENSEMBL IDs in the matrix were changed to gene symbols using biotools online converter (https://www. biotools. fr/human/ensembl_sym bol_converter). Metadata from the authors and the raw matrix were used to create a SingleCellExperiment (version 1. 4. 1) R object. Quality control, normalization and filtering steps were performed in R environment (version 3. 5. 2). From the initial 563 cells profiles, 36 were removed because of missing information about their cell type in the metadata. Cells were filtered by their library size and their number of genes (respectively 7 and 2 cells were removed) using 3*Median Absolute Deviation (MAD) method from Scater (version 1. 10. 1). 43 Counts were normalized using deconvolution strategy, which divide the counts for each cell by the size factor for that cell.
This was performed using Scater (version 1. 10. 1) which perform the normalization and Scran (version 1. 10. 2)  44  which compute size factors. Only genes with at least 1 count by cell were kept considering their size factors. Finally, a PCA on quality control metrics was performed to remove outlier cells profiles that have substantially different quality control metrics from the others (8 removed) using Scater (version 1. 10. 1). Expression scaling, Principal Component Analysis and data visualization were done using R packages Seurat 3. 0  45  on the 510 remaining single-cell profiles.
Acknowledgments We particularly thank  Sophie Viel  ( INSERM U932 ),  Philemon Sirven  ( Clinical Immunology Laboratory ) and  Anne Vincent-Salomon  ( Center of Clinical Investigations CICBT507 IGR/Curie and Department of Pathology ) for providing clinical samples and associated clinical data and for organizing the logistics of these samples. We thank to the  Pathex  and  Flow Cytometry  platforms from  Institute Curie. Thanks to all the participating patients.
Funding Research funded by  Agencia Nacional de Promoción Científica y Tecnológica , Grant/Award Number:  PICT 2015/2558 ;  H2020   Marie Skłodowska-Curie Actions  [No. 799882 ];  SIRIC [Site de Recherche Intégrée sur le Cancer, INCa-DGOS -Inserm_12554];  Labex DCBIOL [Agence Nationale de la Recherche ,  ANR-10-IDEX-0001-02 PSL] and [ANR-11-LABX0043 ].
2  -volcano plot; i. e. , 1960 up-regulated in cluster 1 compared to cluster 2; 1031 up-regulated in cluster 2 compared to cluster 1) that met an average read count threshold >5, FDR adjusted p-value < 0. 05 and a fold change of 1. 3 (or its inverse). The top five canonical pathways, physiologic functions, and diseases and biological functions significantly over-represented by this gene set, as identified by the Ingenuity Pathway Analysis (IPA) software, are reported in Table  2. Cluster 1. The top five canonical pathways listed in Table  2  involve the cellular and humoral immune responses, cytokine signaling, pathogen-influenced signaling and disease-specific pathways. The vast majority of DETs populating each of these pathways are significantly up-regulated in cluster 1 (i. e. the fast responder samples).
RNA sequencing (RNAseq) was performed on an Illumina NextSeq500 NGS at the Cancer Genomics Shared Resource Facility, Wake Forest Health Sciences. Estimating gene expression and differential gene expression analysis. Raw sequences were examined for quality using FASTQC, and the alignment of reads was performed using STAR two pass alignment  46. Gene counts were determined using the program featureCounts  47  and DESeq2 48 was used to normalize gene counts and find differentially expressed genes while adjusting for age and sex. Differentially expressed transcripts (DETs) that met an average read count threshold >5, had a false discovery rate (FDR) adjusted p-value < 0. 05, and a fold change >1. 3 or <0. 77 (i. e. , 1/1. 3) were retained for further evaluation. To generate biologic context and hypotheses for the DET (P < 0.
Study design Participants will be randomly allocated to a three-armed randomized controlled clinical trial with three conditions: a) "face-to-face MBI + TAU", b) "Internet-delivered MBI + TAU", and c) "TAU alone". They will be assessed at pre-and post-treatment and 6-and 12month follow-ups. The study flowchart is shown in Fig. 1. The study is currently in progress in the data recruitment stage. This study will follow the CONSORT statement (Consolidated Standards of Reporting Trials, http://www. consort-statement. org) [32]  and the SPIRIT guidelines (Standard Protocol Items: Recommendations for Intervention Trials)  [33]. The study's trial registration number is ClinicalTrials. gov NCT03034343.
Ethics This study will be conducted according to the international standards of the Declaration of Helsinki and subsequent amendments. This trial will be performed in compliance with the study protocol and with good clinical practice guidelines with the main aim of protecting and preserving human rights  [34]. Data security will be Fig. 1  Flowchart of the study guaranteed, the participants included in the study will be protected by the Organic Law on Protection of Personal Data (15/1999 of December 13, LOPD) and all relevant EU legislation in this regard, and international privacy agreements will be observed and respected. The Internet platform will be accessed through a unique username + password combination. An advanced encryption standard will be used to protect the data (AES-256). The study has been approved by the Ethics Committee of Universitat Jaume I (Castellón, Spain) and the Ethics Committee of Aragón (Zaragoza, Spain).
If the patient is interested, s/he will have to come to the Arrabal Health Center to clarify any doubts about the study and sign an informed consent form. Then, the patient will undergo a clinical interview and fill out the paper-and-pencil screening survey describing his/her sociodemographic and clinical characteristics. This first data collection and screening will be performed by specially trained psychologists with the objective of assessing whether the participants meet the eligibility criteria to participate in the study. If the participant meets the inclusion criteria, an independent researcher will implement randomization by telephone to avoid predictability. Inclusion criteria will be as follows: a) older than 18 years; b) DSM-5 diagnosis of Major Depression or Dysthymia or mild or moderate depression, expressed as a score between 5 and 14 on the Patient Health Questionnaire (PHQ)  [35] ; c) depressive symptoms present for at least 2 months; d) having a computer and Internet at home; e) being able to read and understand the Spanish language; and f) willingness to participate in the study and sign the written informed consent form.
g. , adaptation) under and OLS (mean) regressions for a simulated dataset. The horizontal axis represents percentiles. The time-trend estimate from OLS is shown as a constant. Quantile regression estimates are obtained through the modified Barrodale-Roberts algorithm  (38). The mean of a hypothetical Gumbeldistributed stressor (GEV(µ, σ, ξ) with shape parameter ξ = 0) undergoes equal yearly shifts for 55 "years" (the time range of the data in Fig. 2 ). For each year, we generate i = 1,. 1,000 draws and the corresponding damage values Da i. We then consider the simple model Da i = α + βt i (where t i is the year of i) and fit quantile and OLS regressions. An alternative visualization is provided in SI Appendix, Fig. S1.
Rightward Skewing and Tail Fattening: Economic Impacts Are Mounting Turning from simulated to actual data, we find strong evidence of an accelerating rightward skewing and tail fattening of the damage distribution over time. We consider economic damages due to natural disasters from 1960 to 2015 as recorded in the Emergency Events Database  (39) , restricting attention to hazards potentially related to climate change (see Data and Code and SI Appendix, Data Treatment for details). Yearly distributions of damages from such disasters change markedly over time (Fig. 2A ). In fact, their right tails appear to change in a way qualitatively similar to the one we illustrated by simulations (Figs. 1 A , Inset and 2 A, Inset). Notably, there is almost no temporal trend in the centers (medians in Fig.
Materials and Methods Data and Code. We consider monetary damages and casualties for disasters occurred between 1960 and 2015, as recorded in the Emergency Events Database  (39). This data were provided by the Center for Research on the Epidemiology of Disasters (Universit é Catholique de Louvain) under an agreement of non-third-party disclosure. We restricted attention to 10,901 disasters belonging to the 6 categories most directly associated with climate change: floods, extreme temperatures, droughts, storms, wildfires, and landslides. We geolocalized these disasters with Google Maps API, using the location information originally present in the Emergency Events Database. K öppen-Geiger climate zones are obtained through spatial matching with ref. 49  (resolution 5 • × 5 • ) and, in 371 point-to-cell mismatches, with ref.
50  (resolution 1 • × 1 • ). GDP and population (at the country level) are obtained from the Penn World Table dataset  (51)  and income classes (also at the country level) from the taxonomy by the World Bank. We express damages in current Purchasing Power Parity (PPP) US$; since GDP is expressed in current PPP US$, too, this also controls for inflation. We remove some events during data preprocessing, resulting in a sample size of 9,495 for our regression analyses. See SI Appendix, Data Treatment for details. Code for our analyses is available at https://github. com/mcoronese/ extreme-disasters. Regression Models. Let i be an event occurring at time t i in location i. Let Da i and De i be its damages and deaths, and c( i ) and k( i ) the country and geolocalization cell where it occurred. Finally, let x i,t i , i be a generic vector of covariates measured on the event itself, and/or at its time/location.
ACKNOWLEDGMENTS. We thank  Federico Tamagni ,  Irene Monasterolo ,  James Rising ,  Giulio Bottazzi ,  Matteo Sostero ,  Daniele Giachini , and the participants in several conferences and workshops for their useful comments. This work was supported by the  European Union   Horizon 2020 Research and Innovation Program  under Grant  822781  (GROWINPRO) and partially by the  Penn State Center for Climate Risk Management.
Clinical data and genetic profiles obtained from 15 of these JMML patients that were studied using xenotransplantation models are shown Table  1. Pulmonary involvement was determined as clinical signs of respiratory distress ± documented leukemia infiltration. Blast crisis was defined as the presence of ≥20% blasts assessed by cytomorphological examination of the BM. A second RASactivating mutation was observed in 6/15 patients including duplication of the oncogenic mutation due to acquired uniparental disomy (aUPD) in two cases. BM of healthy age-matched children (n = 19) was obtained from intrafamilial BM transplantation donors. The collection and use of these samples were approved by the Institutional Review Board of "Hôpitaux Universitaires Paris Nord Val-de-Seine," Paris 7 University, AP-HP), (IRB: 00006477), in accordance with the Helsinki declaration.
Depositing dataset Whole genome sequencing data were available via ArrayExpress database: http://www. ebi. ac. uk/arrayexpress, Accession number E-MTAB-6461 (patients) and E-MTAB-6467 (xenograft samples). For SNP/CGH array: Accession numbers E-MTAB-3729 (SNP array) and E-MTAB-6468 (CGH + SNP array).
Acknowledgements We thank pediatric oncologists from the  "Société Française de lutte contre les cancers et leucémies de l'enfant et de l'adolescent" (SFCE)  for sending us patients samples. We thank the  Center for Biological Resources (CRB-cancer)  ( BB-0033-00076 ) of the Robert Debré hospital. We thank  Nazia Roodur  for expert technical assistance and  Jocelyne Vivent  for clinical research assistance. We thank the technical team of the  service de Biologie Cellulaire, hospital Saint-Louis, Paris , for excellent technical help in the management of myeloid progenitor assays. We thank staff in the core facilities at the  Francis Crick Institute (Biological Research facility, Flow Cytometry and Experimental Histopathology)  for their valuable help. We also thank  S. Rasika  for careful english editing of the manuscript.
This work was supported by the  Ligue contre le cancer (LCC)-Ile-de-France ,  INCa  and  Canceropole Ile-de-France , and the  SFCE and "Enfants et santé " as well as by  The Francis Crick Institute , which receives its core funding from  Cancer Research UK  ( FC001045 ), The  UK Medical Research Council  ( FC001045 ) and the  Wellcome Trust  ( FC001045 ). KRP was recipient of the Kay Kendall leukemia fund. MS and JO are the recipients respectively of a PhD and a Master scholarship funded by ITMO cancer.
37  Details on other current resources can also be found via the British Universities Film and Video Council. 38  In Germany, the archival situation is very different for GDR and FRG sources. As the East German state and its broadcasters no longer exist, the sources are now part of historical archives and are more easily accessible than the West German ones. The most comprehensive collection of GDR material is held at the Deutsches Rundfunkarchiv in Potsdam-Babelsberg. Here about 60 per cent of broadcast programming is kept and organized in a database accessible on site. In addition, for health-related material, the collection of Deutsches Hygiene Museum in Dresden offers a variety of documentaries and educational fi lms that were aired on television. For West Germany, the situation is much more complicated.
Typical of each production is a short opening sequence with classical music on the soundtrack before the studio-based lecture begins (Fig. B , top left, the rostrum camerawork is from a two-dimensional work followed by an image relevant to the theme of the programme which gives it a distinct identity). There are short fi nal credits at the end ( The camera zooms in and out on the visual aids, in a restricted studio setting. Although the tone of the lecture is relatively formal, Cranston is a confi dent lecturer and has a clear spoken voice (with a Scottish accent), which lends him a certain degree of charisma and contributes towards ranking him somewhat highly in terms of being 'telegenic'. Whilst being videoed, Cranston is restless and uses the swivel and rocking mechanism of his offi ce chair; it is not hard to imagine him delivering this lecture by pacing in front of his students, but speech and movement isn't compatible with studio production -poise in front of the cameras is something which comes with experience and both behavioural ticks and gestures are magnifi ed on television.
36  After the publication of his 1955 book, The Doctor, His Patient, and the Illness, Balint's approach to the doctor-patient relationship, which explicitly made allowance for the effect of the doctor's behaviour on that of the patient, became infl uential throughout the sixties and beyond. Equally, it would be natural to expect the journalistically minded producers of Horizon to be drawn to the implications of Ivan Illich's critique of modern medicine, Medical Nemesis (1975). In either case, we might expect at least a richer rendering of the patient role and experience than in the programmes we have described here. As it is, a search of the BBC's online database of programmes yields no immediate candidates. 37  But, to make such explorations is as much a matter of the historical sociology of medicine as it is a matter of the kind of close study of medical media that we have undertaken here.
This research is based on a corpus of TV segments drawn from the national audiovisual archive institute (Institut National de l'Audiovisuel, INA), the repository of all French television audiovisual archives. We selected TV items that directly referred to or spoke of heart transplants. 8  This corpus was complemented by an inventory of articles on heart transplantation in two newspapers: the daily newspaper of record Le Monde (LM) and the regional daily Sud Ouest (SO) over the 1966 to 1974 period. This data is drawn from the Europresse database. 9  The early days of heart transplantation were marked by two peaks of media coverage (Figures  1  and 2 ). The fi rst took place in 1968-1969, as the fi rst transplants were performed in France and abroad. (1966-1974) in the regional daily paper Sud-Ouest and the national Le Monde.
As proxies to the in situ rainfall measurement, satellite or radar-based precipitation products have been widely used to derive the IDF curves in case of data scarcity. Ombadi et al. (2018)  developed a methodological framework to adjust the bias and transform the satellite areal rainfall to point rainfall, and applied the products to develop IDF curves in ungauged regions. Marra et al. (2017)  compared IDF curves from radar and satellite estimates over the eastern Mediterranean and quantified the uncertainty related to their limited record on varying climates. Awadallah and Awadallah (2012)  used Tropical Rainfall Measuring Mission (TRMM) satellite data to derive the relations between maximum sub-daily, daily and monthly rainfall and combined the relations with the coarse rainfall measurements to develop IDF curves for a scarce region in Africa.
The latter are therefore selected in the present study and require only 2 datasets in the study case, i. e. daily rainfall observation and publicly available GSMaP satellite rainfall. Several stochastic rainfall disaggregation models have been developed, such as (1) the random cascade models based on scale-invariance theory  (Gupta and Waymire 1993; Carsteanu and Foufoula-Georgiou 1996; Molnar and Burlando 2005) , and (2) the Bartlett-Lewis/Neyman-Scott rectangular pulses models based on point process theory  (Onof and Wheater 1993; Khaliq and Cunnane 1996; Koutsoyiannis and Mamassis 2001; Gyasi-Agyei and Mahbub 2007). Studies have been conducted to assess the performance of different disaggregation methods towards their suitability and effectiveness  (Ferraris et al. 2003; Serinaldi 2010; Licznar et al.
2011). A general conclusion as for the best model, however, cannot be drawn as rainfall properties significantly differ for different climatic regions with different generating mechanisms  (Sharma and Mehrotra 2010). In view of its wide applicability in various climatic conditions, Bartlett-Lewis rectangular pulses (BLRP) model, in particular HyetosR, is selected in this study as the rainfall disaggregation tool. HyetosR is an R package developed to disaggregate daily rainfall into hourly time series, which combines a rainfall simulation model based on the BLRP process with a proportional adjusting procedure to rescale the hourly totals to the required daily values  (Koutsoyiannis and Onof 2000, 2001; Kossieris et al. 2012). Hourly rainfall observations are not necessary as a direct input for HyetosR; however, sub-daily rainfall statistics is still required for the parameter estimation of the BLRP model.
Step 1 Calculate rainfall statistics Reanalysis version of GSMaP hourly rainfall (GSMaP_ MVK Ver. 5. 222) is downloaded from the database of JAXA (ftp://rainm ap:amech i-zu@hokus ai. eorc. jaxa. jp/). Rainfall statistics is calculated upon extracting the rainfall time series at the GSMaP grid cell nearest to the rainfall measurement station. The statistics includes mean E G , variance var G , auto-covariance acov G and probability of dry P G , where the subscript G indicates the rainfall sta- tistics calculated from the GSMaP rainfall. Step 2 Estimate BLRP model parameters The Bartlett-Lewis rectangular pulses (BLRP) model is essentially a random-parameter rainfall generator based upon a Poisson cluster process  (Rodriguez-Iturbe et al. 1987 , 1988). This paper uses the most enriched version of the BLRP model with seven parameters, i.
Mapping with indexes of institutional quality The two corollaries above summarize the main implications of the model as to the dependence of the equilibrium level of institutional quality on the current account. Before moving on to the empirical analysis, we establish here the connection between our theoretical variable for institutional quality and the empirical indexes of that quality that we will be using. Our empirical indexes come four sources, namely the World Governance Indicators (WGI), the Heritage Foundation and the International Country Risk Guide and the Fraser Institute, all of which are gathered in the Quality of Governance Dataset  (Teorell et al. , 2018). We first provide a brief description of the indexes we use, and then turn to the mapping with the model. The Rule of Law index (RL) measures the citizens' confidence in, and respect for, the country's laws and regulations.
This index notably aggregates information about contract enforcement, property rights, the police and the courts, as well as crime. The Government Effectiveness index (GE) assesses the quality of public services and of the civil service (e. g. independence from political pressures), as well as the quality of policy formulation and the credibility of the government in implementing them. Finally, the Control of Corruption index determines whether public policies favor private interests (via different forms of corruption or capture by elites or private interests). The WGI indexes are constructed by aggregating data from multiple sources, private or public. In particular, they gather data from both "experts" (multilateral development agencies or nongovernmental organizations) and surveys of domestic individuals and firms.
To deal with this potential limitations, we also look at other measures of the quality of institutions that we take from the QoG database. 11 In particular, we consider the following alternative indexes. First, we look at the Heritage Foundation's measure of Property Rights and Economic Freedom. As described by  Teorell et al. (2018) : "This factor scores the degree to which a country's laws protect private property rights and the degree to which its government enforces those laws. " Second, we take the International Country Risk Guide's Indicator of Quality of Government. Interestingly, this indicator mixes information on "Corruption", "Law and Order" and Institutional quality in the model and in the data Let us now explain how we can map these different measures of institutional quality with the level of institutional quality in the model or with the technology that the government may use ex ante to limit the prevalence of extractive projects.