dataesr/scientific-paragraphs-categorization huggingface dataset:
DATA AVAILABILITY STATEMENT The WIOD data can be downloaded freely from the website (http://wiod. org/database/wiots16). The STRI database can be downloaded from the OECD website (https://qdd. oecd. org/subject. aspx?Subject=063bee63-475f-427c-8b50-c19bffa7392d). The other trade costs determinants come from the CEPII GeoDist database (http://www. cepii. fr/CEPII/fr/bdd_modele/presentation. asp? id=6). The information on the trade agreement comes from the dynamic gravity dataset (https:// www. usitc. gov/data/gravity/dgd. htm). Replication files can be accessed here: https://u. pcloud. link/publink/show?code=kZql1fXZOO1EWbvAS445ig6ehhOWebR8tAQV.
GWL time series were extracted from a database consisting of relatively undisturbed GWL time series regarding anthropogenic influence (water abstraction by either continuous or periodic pumping) over metropolitan France. Based on this dataset, our approach consisted in exploring the effect of GWL low-frequency components on threshold exceedance and non-exceedance by successively filtering out low-frequency components of GWL signals using maximum overlap discrete wavelet transform (MODWT). Multi-annual (∼ 7-year) and decadal (∼ 17-year) variabilities were found to be the predominant LFVs in GWL signals, in accordance with previous studies in the northern France area. By filtering out these components (either independently or jointly), it is possible to (i) examine the proportion of high-level (HL) and low-level (LL) occurrences generated by these variabilities and (ii) estimate the contribution of each of these variabilities in explaining the occurrence of major historical events associated with wellrecognized societal impacts.
Second, we propose determining on four well-known historical events the contribution of multi-annual and decadal variabilities in the amplitude of threshold exceedance (ATE) and identify what parameters may control this contribution (Sect. 6). In other words, we estimate the percentage contribution of each low-frequency component to the emergence of the historical event. 2 Effective precipitation and groundwater data For this study, we used 78 boreholes in the Paris Basin (northern France), with GWL time series being little or not affected by pumping (Fig. 1 ). The study area was restricted to the Paris Basin in order to carry out the analysis over a relatively long period. Boreholes were selected from a BRGM database and were required to be undisturbed from human activities  (Baulon et al.
, 2020). We selected the boreholes by following the three steps below: i. a selection of boreholes with time series satisfying criteria of duration, minimum amount of data per month, and maximum length of gaps; ii. the cross-referencing of preselected boreholes with other BRGM databases on known anthropogenic influences; iii. numerous visualizations of time series with the hydrogeologists responsible for piezometric networks, in order to retain only non-influenced boreholes. Time series of boreholes in this database were initially gathered in the ADES database that contains all groundwater data (quantity and quality) across continental France (https: //ades. eaufrance. fr/, last access: 1 April 2020). The criteria satisfied for selecting GWL time series for the present study were as follows: -The length of time series must be higher than or at least equal to 44 years.
-The minimum amount of data within a month is set to one monthly datum before the measurement automation and three data after the measurement automation. -The length of consecutive gaps must be < 3 years for time series starting after 1950 and < 10 years for time series starting before 1950. This allows time series in the new database to preserve LFV in the data. Several gaps in the time series can be allowed if these criteria are respected and if the number of gaps and their lengths are small. Before data analysis, a visual check of the GWL time series served to remove or correct erroneous data. The analysis of the influence of LFV on HLs and LLs was conducted over the 1976-2019 period, providing the best compromise between the spatial distribution of boreholes and time series length. In this work, all time series had a monthly time step to consider all possible GWL variation types from most reactive to most inertial ones.
, 2003 Cornish et al. , , 2006)). While not necessarily essential for signal or image processing or numerical compression, this property is fundamental for physical interpretation of the wavelet details in multiresolution analysis and has already been used for that purpose in several studies such as  Percival and Mofjeld (1997) ,  Massei et al. (2017), and Pérez Ciria et al. (2019). The dominant frequency associated with each MODWT wavelet detail was calculated by Fourier transform of each wavelet detail (Fig. 2 ). The MODWT also provides the amount of variance (or energy) explained by each wavelet detail and frequency level. The energy percentage of a given wavelet detail expresses the relative importance of this variability in the total signal variability. As a result, the energy distribution between wavelet details for each borehole in the database can be extracted and mapped.
8 and 0. 2, respectively (Fig. 4a ). Percentiles 0. 2 and 0. 8 were determined for all boreholes of the whole dataset over the 1976-2019 period. Such percentiles were selected because they are the optimal thresholds to have a correct and sufficient number of HLs and LLs on a 44-year period, particularly for time series with inertial GWL variation type. Once these HLs or LLs have been identified, for each studied time series, details corresponding to multiannual variability, decadal variability, and both variabilities were successively subtracted from the original signal. From these filtered time series, we evaluated if the subtraction of one given component affected HL peaks or LL peaks as initially identified in the original data. We were more par- ticularly interested by the threshold exceedance of HL and LL peaks and their characterization as extreme levels.
In addition, such a stochastic nature of LFV does not enable any deter-ministic prediction for future hydrogeological extremes on mid-and long-term horizons (i. e. , several years or longer). For mid-and long-term hydrogeological extreme prediction purposes, identifying large-scale ocean-atmosphere drivers leading to such variabilities in GWL remains fundamental. Finally, future research should investigate to what extent potential changes in the amplitude of internal climate modes driving LFV could impact hydrogeological extremes. Data availability. The groundwater level data used for this analysis can be obtained from https://ades. eaufrance. fr/ (ADES, 2020). For the database of relatively undisturbed GWL time series regarding water abstraction, please contact the corresponding author. The Safran precipitation dataset can be obtained from https: //donneespubliques.
Acknowledgements. This work was partially supported by the  Geo-ERA  project  TACTIC , funded by the  European Union 's  Horizon 2020 Research and Innovation programme  under grant agreement number  731166. We would also like to thank the  Agence de l'Eau Seine Normandie, PIREN Seine, BRGM , and  Région Normandie  for their financial support. Finally, we would like to thank  Sandra Lanini  for the calculation of effective precipitation and the two anonymous referees who helped improve the paper. Financial support. This research has been supported by the  Horizon 2020 (GeoERA , grant no. 731166 ).
FACS Analysis The cells were trypsinized, counted, and then resuspended in 1 ml PBS containing 1% BSA. Data acquisition was performed with the BD FACSymphony (BD Biosciences ® ) and BD FACSDiva 8. 5 analysis software. The Cerulean protein was excited at 405 nm and detected with a 525/50 filter. The Venus protein was excited at 488 nm and detected with a 530/30 filter, and the Cherry protein was excited at 561 nm and detected with a 610/20 filter. The cells were initially sorted according to their size (forward scatter, FSC) and their complexity (side scatter, SSC). The U251-RGB cell population was selected, and doublets were excluded. Using three different strategies, namely, a green strategy allows for the visualization in a dot plot containing the blue, red, and magenta cells; a red strategy allows for the visualization in a dot plot containing the blue, green, and cyan cells; and a blue strategy allows for the visualization of red, green, and yellow cells.
Transcriptomic and Single-Cell Analysis Transcriptomic analyses are based on previously published results  (Rabé et al. , 2020). Gene expression analysis in single cells was measured using the C1 Single-Cell Auto Prep System coupled with the real-time PCR reader BioMark HD (Fluidigm, CA, USA) according to Fluidigm recommendations in untreated cells and in cells treated with 50 µM temozolomide for 4, 9, 12, and 16 days. Single-Cell Preamp IFC of 10-17 μM and 17-25 µM was used, respectively, for U251-S (untreated), U251-R (resistant), and U251-TR (persister population: day 4, day 9, and day 12). The results were analyzed using the Singular Analysis Toolset Software provided by Fluidigm.
Quite strikingly, all these genes encode for proteins with extracellular functions and have implications in wound healing and tissue remodeling. Of note, this signature was also partially found in other glioma cell lines in response to temozolomide, and TCGA glioblastoma database analysis for KLK5, FAT2, CHI3L1, and HB-EGF expression showed that alteration of these genes impacts Kaplan-Meier estimate of overall survival and disease/ progression-free  (Rabé et al. , 2020). Single-cell analysis by the Fluidigm C1/HD BioMark quantitative PCR technique  (Rabé et al. , 2020)  showed that two of the markers (HB-EGF and CHI3L1) were present in all cells and that their expression was increased during the persister state (Figure  3A ). On the other hand, FAT2 and KLK5 were only expressed after day 4 of temozolomide treatment and more importantly, in different cells until day 12 and day 16 (Figure  3A ).
Sources of data, data collection and validation The MDS Lancet Laboratory uses an electronic laboratory information system (LIS) where all the data related to the patient and samples are entered. Right from the collection of the sample, information is entered into the system and verified at each stage from transportation to processing of the samples. Results of the tests are reported onto the same LIS and the information becomes readily available for collection at any MDS Lancet branch. The LIS of the MDS Lancet Laboratories was used to extract data for the study period. It was extracted into MS Excel format and analyzed using STATA® (version 16. 0 Copyright 1985-2019 StataCorp LLC).
UTI in males is usually associated with a urological abnormality such as prostatic hyperplasia or posterior urethral valves which are usually associated with instrumentation and risk of infection  [38] [39]  [40]. Lastly, the highest risk of MDR was found in patients with the presence of antibiotics in the urine. This presupposes that most of these patients have difficult to treat infections and have probably been started on ineffective antibiotics or have misused antibiotics, all of which may have contributed to the development of resistance. A major strength of this study is the large number of isolates included from different regions of Ghana, and the study represents one of the largest datasets on AMR in uropathogens on the African continent as well as in Ghana. This data may give a better understanding of AMR in uropathogens in Ghana, although the preponderance of data from the Greater Accra and Ashanti regions may introduce some bias into data representativeness.
Mountain Daytime duration and meteorological variables. Daytime duration ( daytime ) from January 2013 to June 2021 at each sampling site was obtained from the corresponding latitude using geosphere package  33. Hourly meteorological data (2-m temperature and relative humidity) were recorded locally at each forest. Subsequently, daily mean, minimum, and maximum values of temperature ( T M , T N , and T X ; in °C) and relative humidity ( U M , U N , and U X ; in %) were derived from these hourly records. The meteorological seasons of the temperate area in northern hemisphere are defined as: ( Missing values found on these local daily-level variables were imputed by the random forest algorithm in mice package  34. External daily meteorological data, i. e. , daily average temperature and relative humidity, derived from neighbouring weather stations (Météo-France or INRAE), as well as month and year information, were used as auxiliary variables (Table  S2 ).
Land cover, topography, and bioclimatic variables. We obtained land cover, topography, and bioclimatic data from a 1-km radius buffer area around the center of each sampling site to capture habitat characteristics across all 10 transects. All the variables were handled and obtained by using QGIS version 3. 8. 0  35. The digital elevation model (DEM) data derived from the Shuttle Radar Topography Mission (SRTM) database  36  was used to describe the topographic features of sampling sites, which included the mean ( mean elv ) and standard deviation ( sd elv ) of the elevation (in m above sea level), the proportion of flat area ( p flat ; defined by the slope ≤ 2. 5%  37  ), the proportion of area facing north ( p north ), east ( p east ), west ( p west ), and south ( p south ), and the catchment area ( catchment ) as a proxy variable for moisture.
Bioclimatic variables for each site (historical average condi- tions during 1970-2000) were derived from the WorldClim database  38  , including the annual mean temperature ( BIO1 Temp ; in °C), the mean diurnal range ( BIO2 Diur ; in °C), the maximum temperature of the warmest month ( BIO5 maxTemp ; in °C), and the annual precipitation ( BIO12 Prec ; in mm). The land cover features of each sampling site were described using the CORINE Land Cover (CLC) 2018  39  , while the characteristics of forests were explained by the BD forêt version 2 data  40. The forest fragmentation was characterized by the percentage of forest-covering area ( p Forest ), the forest edge density ( ED Forest ; in m/km 2 ), and the number of forest patches ( n Forest ). While the diversities of the land cover types (level-1 and level-2 CLC) and the forest types were calcu- lated by using the Shannon's diversity index  41  ( H ) as H = S i=1 p i lnp i , where S is the total number of land cover/ forest types and p i is the proportion of land cover/forest type i within the 1-km radius buffer area.
05 was considered significant. The effects of environmental variables (meteorological, land cover, topographical and bioclimatic variables) on the abundance of I. ricinus nymphs, were evaluated by a multivariate mixed-effects negative binomial regression, using the glmmTMB package  44. Recalled that the negative binomial distribution is a generalized form of the Poisson distribution with an additional shape parameter θ that allows the variance to be independent to the mean µ. The number of I. ricinus nymphs per 100 m 2 was considered the response variable, and the sampling site served as the random effects for intercepts as displayed in Eq. (  2 ): (1) Ñ * i,t = Ñi,t max( ∼ N i ) where N i denoted nymph counts per 100 m 2 of site i , µ i and θ represented mean of site i and the shape parameter of the negative binomial distribution, respectively.
Non-linearity and interaction effects of the fixed effects were also investigated. Multivariable models were explored by both forward and backward selection of significant variables decreasing the Akaike information criterion (AIC). Variations of the data explained by fixed effects were monitored by a pseudo-R 2 statistic using the MuMIn package  47. Residuals of the models were evaluated using the DHARMa package  48, 49. The performance of the best-fitted model was assessed by a resampling method. Briefly, 50% of the original dataset was randomly sampled to fit with the best model for 500 iterations, referred to as resampling models. The distributions of regression coefficients across 500 resampling models were compared against those of the model with the original dataset, called the complete model.
S6 ). The strength of the best-fitted model was assessed by resampling half of the data to fit with this model. The regression coefficients yielded from 500 resampling models were similar to those of the complete model with mostly high coverage probability (Fig. S7  and Table  S6 ). The best-fitted model (Model 5) suggested that phenology of I. ricinus nymphs non-linearly responded to meteorological factors, such as 1-month moving average temperature T 0:1 M , previous 3-to-6-month moving average temperature T  between the 1-month moving average temperature T 0:1 M (weather) and the annual average temperature BIO Temp (climate) explained that the nymph counts observed at different climates arrive at peaks at different 1-month moving average temperatures, where nymph counts in warmer climates peaked at lower 1-month moving average temperatures (Fig.
Acknowledgements Most tick observatories were supported by the  CC-EID and Climatick projects: "Adaptation of Agriculture and Forests to Climate Change " metaprogramme of the  French National Research Institute for Agriculture, Food and Environment (INRAE). The observatory of  Les Bordes  was installed and equipped by the  Centre Val-de-Loire region , the Loiret and the  French National Forest Office. We also would like to thank the  TEMPO French Network  (https:// tempo. pheno. fr/) for providing resources to support this study. The Les Bordes site belongs to the French national research infrastructure,  ANAEE-F  (http:// www. anaee-france. fr/ fr/), and is also included in the SOERE TEMPO (https:// tempo. pheno. fr/). The Les Bordes site is also part of the framework of the  ZAL (LTSER Zone Atelier Loire)  and the  GIS Coop network  (https:// www6.
inra. fr/ gisco op/), which is supported by the  French Ministry for Agriculture and Food. This study was partially funded by  EU  grant  874850 MOOD  and is catalogued as MOOD 040. The contents of this publication are the sole responsibility of the authors and do not necessarily reflect the views of the  European Commission. Finally, PW is grateful for a grant from the  Association Nationale de la Recherche et de la Technologie , bourse CIFRE (n°  2019/0393 ).
Whole exome sequencing WES analysis was run in NovaSeq 6000 (Illumina, San Diego, CA) in the New York Genome Center facilities. WES data was used for mutation calling, mutational signature analysis, and tumor mutational burden (TMB) evaluation. TMB was calculated based on protein-coding mutations assuming an average exome size of 30 Mb, in accordance with previously published studies  10. Mutations were called by comparing the tumor with its paired non-tumoral counterpart. Molecular variant calling was performed by Sema4 (Stamford, CT, USA), using the Tigris pipeline (v2. 0. 1), which carries out modified GATK4 (4. 0. 11. 0) best practices (https://software. broadinstitute. org/gatk/). In addition, WES data from a European (n=241)  8  , Korean (n=231)  13  , TCGA (n=363)  14  and Mongolian NCI (n=71)  12  HCC cohorts were used for mutation calling and TMB evaluation.
Mutational signature analysis Somatic SNVs in exome region (defined by coding exons in Ensembl GRCh37 built) at allelic frequency cutoff of 0. 05 and with gnomAD population frequency or ethnic-specific frequency ≤ 0. 5% were selected. Tumor samples with total SNV count ≥ 50 (after the aforementioned filtering) were used in downstream mutational signature analyses, resulting in a total of 254 samples (148 from the Mongolian cohort plus 106 from the Western cohort). The remaining samples (n=9) were considered negative for the signatures. All signature fitting and de novo signature extraction analyses were performed using exome region SNVs and trinucleotide frequencies normalized viaexome2genome` approach in the deconstructSigs R package  15.
RNA sequencing RNA-seq data was processed by the RAPiD pipeline at the Mount Sinai Genomics Core Facility. Briefly, Fastq files were aligned using STAR (v 2. 7. 0f)  16  to hg19 with gencode annotation v19. QoRTs (v1. 3. 6)  17  was used for QC and obtaining raw counts. Batch correction was performed using RUVSeq  18. Empirical method (RUVg) with 10. 000 low expressed genes, and normalization for subsequent analysis was performed using VST method from DESeq2  19. Unsupervised clustering analysis of the whole cohort (n=224) was performed using the Nonnegative matrix factorization (NMFc) module from GenePattern and Euclidean distance and Ward's agglomerative procedure. Clustering of the Mongolian (n=118) and NCI Mongolian cohort  12  (n=70) was also performed using NMFc. Gene expression characterization was performed using Nearest Template Prediction (NTP), Gene Set Enrichment Analysis (GSEA), and single sample GSEA (ssGSEA) modules from GenePattern.
Statistical analysis Statistical analyses were performed using either SPSS software package (version 24. 0; SPSS Inc, Chicago, IL, USA) or scipy (v 1. 2. 1) and matplotlib (v3. 0. 3) modules from Python (v3. 7. 3). Differences between qualitative variables were assessed with the Fisher exact test and corrected for multiple comparisons using false discovery rate. Differences between quantitative variables were analyzed with a two-sided non-parametric test (Mann-Whitney or Kruskal-Wallis, were appropriate), and adjustments for multiple comparison analysis were performed using Dunn's test.
This particular co-infection profile was associated with two molecular subclasses only identified in Mongolian patients (MGL2 and MGL3), thus pointing towards a potential role of HDV in the oncogenic process, despite it is not currently considered a carcinogenic agent  7. At the genomic standpoint, Mongolian HCCs had a high rate of protein-coding mutations, which almost doubled that in the Western in-house cohort (121 vs 70 mutations per tumor) and publicly available datasets  8, [12] [13] [14]. This suggests the presence of intrinsic and/or extrinsic factors promoting mutagenesis in Mongolian HCC. Notably, while several HCC driver genes presented similar mutation rates in the Mongolian cohort compared to the Western tumors and previous studies 8,  13, 14  (e. g. CTNNB1 and ARID1A), others such as APOB, and the KMT2 gene family were significantly more mutated in Mongolian HCC (e.
fellowship IFI18/00006) of the Instituto de Salud Carlos III (ISCIII), co-funded by the European Social Fund (ESF). AV is supported by the US Department of Defense grant (CA150272P3) and the Tisch Cancer Institute (Cancer Center grant P30 CA196521). SPP is supported by the ISCIII through the Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016 and 2017-2020 co-funded by the European Regional Development Fund (ERDF) (PI16/00111 and PI19/00036). JZR's team is supported by Inserm, Labex OncoImmunology Investissement d'Avenir and is "Equipe labellisée par la Ligue Nationale Contre le Cancer». XF is supported by the ISCIII through the Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016 and 2017-2020 co-funded by the ERDF (PI18/00079), by the Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement (grant 2017_SGR_1753) and by CERCA Programme/Generalitat de Catalunya.
Data Sharing Total RNA-seq data and the original WES data from the Mongolia and Western cohorts have been deposited at the European Genome-Phenome Archive (EGA), which is hosted by the European Bioinformatics Institute (EBI) and the Centre for Genomic Regulation (CRG), under the study accession code EGAS00001005364. HBV and HDV genotypes are deposited in Genebank (accession IDs MZ984226 -MZ984323 and OM981174 -OM981226, respectively). The remaining data are available in the Article, Supplementary Information, or available from the authors upon reasonable request". Additional detailed information is provided in the Supplementary Materials and Methods.