dataesr/scientific-paragraphs-categorization huggingface dataset:
Identification of tumor-and stroma-specific subtypes in patients with PDAC A total of 69 treatment-na€ ve PDAC tumors were chosen for RNA isolation and histologic analysis. The RNA quality and cancer content from each tumor sample was carefully assessed, yielding 28 samples suitable for LCM (Fig. 1A ). Cancer cells and adjacent (<280 mm) stromal cells were collected separately (Fig. 1B ). Prior to RNA isolation, images of the cancers and adjacent stroma captured by LCM underwent pathologic review and samples of questionable purity were discarded. Ultimately, RNA from 19 cancer samples and their matched adjacent stroma samples were successfully sequenced (Supplementary Table  S2 ). Unsupervised analysis of these 38 total samples using paired data spontaneously classified the cancer and stroma samples into two separated branches (Fig.
The robustness of this 111-cancer gene prognostic classifier was examined in an independent validation set of 573 patients. The   (20). The significance status of the given proportion test is indicated above each bar ( Ã , P < 0. 05; N. S, P > 0. 05). D, Stack bar plot showing Moffitt stroma groups proportion across the three NMF stroma groups within 677 PDAC from public datasets informed on Moffitt molecular subtypes (normal and activated stroma in orange and red, respectively). Above each bar is the significance status of the given proportion test ( ÃÃÃ , P < 1E-03; N. S, P > 0. 05). E, Kaplan-Meier DFS curves corresponding to the three NMF stromal groups in 573 PDAC from public datasets. classifier sorted the patients into two subgroups, STS (n ¼ 323; 56%) and LTS (n ¼ 250; 44%), with 2-year DFS of 30% (95% CI: 25-36) and 42% (95% CI: 36-50), respectively (P ¼ 3.
B, Heatmap of GSVA relative enrichment score of the top 15 gene sets associated with each cancer cell group. Each row represents a gene, and each column represents a sample. Colored bars above and to the left of the heatmap indicate the attribution of cancer cell groups. C, Graph of MCA of the four cancer NMF groups with the four molecular subtype classifiers in public datasets. D, Kaplan-Meier DFS curves corresponding to the four NMF cancer groups identified in 573 PDAC samples from public datasets.
B, Heatmap of GSVA relative enrichment score of the top 20 gene sets associated with each stromal group. Each row represents a gene, and each column represents a sample. Colored bars above and to the left of the heatmap indicate the attribution of stromal groups. C, Stack bar plot showing LCM stroma group proportions across previously defined CAF subtypes (20). The significance status of the given proportion test is indicated above each bar ( Ã , P < 0. 05; N. S, P > 0. 05). D, Stack bar plot showing Moffitt stroma groups proportion across the three NMF stroma groups within 677 PDAC from public datasets informed on Moffitt molecular subtypes (normal and activated stroma in orange and red, respectively). Above each bar is the significance status of the given proportion test ( ÃÃÃ , P < 1E-03; N.
Acknowledgments A. S. Liss is supported by the  NIH  Grant  R03CA241971  (to A. S. Liss) and the  National Institute of Standards and Technology (NIST)  Grant  60NANB20D166  (to A. S. Liss). D. T. Ting ,  V. Deshpande ,  A. Neyaz , and  A. S. Kulkarni  receive sponsored research support from  ACD-Biotechne. D. T. Ting  is supported by the  NIH  Grant  R01CA235412  (to D. T. Ting), the  NSF   PHY-1549535 , an  SU2C-NSF-Lustgarten Foundation Pancreatic Cancer Convergence Research Team  grant, and a  Stand Up To Cancer-Lustgarten Foundation Pancreatic Cancer Interception Translational Cancer.
Screening of Patients We collected the data from 162 patients treated at the Oscar Lambret Center (Lille, France), retrospectively. We included all the cancer patients treated with prostate SBRT using a dedicated Cyberknife ® VSI or Cyberknife ® M6 between January 1, 2011 and March 1, 2020. The patients were divided into two different population groups, with the first group comprising patients with an indication for SBRT as a treatment for localized prostate disease who had never received local treatment, and the second group comprising patients treated with SBRT for an intraprostatic recurrence after the first radiation of the external beam radiation therapy (EBRT) type or brachytherapy. Hormone therapy was administered before or during irradiation. Prostate biopsy was systematic before treatment initiation in both groups.
Acknowledgment. We are grateful to the maintainers of StarExec for letting us use their service. Ahmed Bhayat and  Giles Reger  guided us through details of Vampire 4. 5. Ahmed Bhayat ,  Michael Färber ,  Mathias Fleury ,  Predrag Janičić ,  Mark Summerfield , and the anonymous reviewers suggested content, textual, and typesetting improvements. We thank them all. Vukmirović, Bentkamp, and Blanchette's research has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No. 713999 ,  Matryoshka). Blanchette  and  Nummelin 's research has received funding from the  Netherlands Organization for Scientific Research (NWO)  under the  Vidi program  (project No. 016. Vidi. 189. 037 , Lean Forward ) and the  Incidental Financial Support scheme.
In addition, stairshaped branches (e. g. clade 1c) indicated probable patient-to-patient transmission. Although the pulsed-field gel electrophoresis pattern of BES isolates was the same (2), WGS analysis showed two hidden parallel outbreaks simultaneously occurring in the same hospital (Fig. 1B ), as already observed for other Gram-negative pathogens  (17). The dating of emergence of the clone ST395 at the hospital required the calculation of the clock rate of our ST395 population, that was estimated to be 3. 96 × 10 -6 mutations per site per year by Bayesian analysis (95% highest posterior density, 3. 08 × 10 -6 -4. 96 × 10 -6 ), which is higher than that estimated for the P. aeruginosa species  (18). With the addition of an ST395 outgroup isolate retrieved in our hospital, but not belonging to BES, to the database (Table  S1 ), Bayesian analysis dated the putative common ancestor that linked the ST395 isolates from our hospital to 1990 (95% confidence interval [CI] = 1987-1993), seven years before its first isolation from a patient in May 1997.
Limitations and strengths. This study had several limitations. Although we selected randomly distributed isolates throughout the period of the outbreak, the selection and inclusion criteria were not systematic and dependent on the availability of the isolates. Thus, the data presented here may be affected by a sampling bias that could have obscured epidemiological and evolutionary signals. This retrospective study did not allow water sampling at the time of emergence of the clone in the hospital (ca. 1978) that could have showed water contamination with BES and V. vermiformis. However, others have showed that V. vermiformis is endemic in hospital plumbing systems  (52). Here, we made use of a rare and valuable resource (i. e. longitudinally collected isolates from a variety of infection types in a single hospital from a period of more than a decade).
STs were determined in silico using the PubMLST database (https://pubmlst. org/) and the homemade pipeline pyMLST (https://github. com/bvalot/pyMLST). The BES pangenome was built by combining the BES-1 circular genome with nonredundant contigs from the other BES isolates using the Ragout tool  (54). For this purpose, the assemblies of each BES isolate were aligned against the BES-1 genome using Mummer  (55). Genes of the BES pangenome were detected using Prodigal and then annotated with BLAST  (56, 57). Resistance and virulence genes were sought using ResFinder and the Virulence Factor DataBase, respectively  (58, 59). The reads from all isolates were then aligned against the BES pangenome using BWA and the alignments used for further analysis (https://github. com/lh3/bwa). The presence or absence of each gene was tested using FeatureCounts  (60).
SNPs were identified using freebayes with a minimum coverage of 10 reads per isolate and a minimum quality of 30 (https://github. com/ekg/freebayes). Non-synonymous SNPs and SNPs in promoter regions (RBS and promoter sequences at -10 and -35) were further sorted. SNPs and gained or lost gene blocks were clustered in relation to the phylogenetic tree with MixOmics  (61). IS insertion events were detected using panISa software  (62). The boundaries of each potential IS were sought in the BES-1 genome to exclude chromosomal rearrangement before searching for them in the BES genomes to assess the length of the potential ISs. The presence of the potential IS was confirmed by PCR and sequencing with specific IS1 and IS2 primers (Table  S2 ).
Phylogeny and Most Recent Common Ancestor (MRCA) calculation. The 265 core genes of the BES isolates that harbored at least one SNP were concatenated for phylogenetic analysis, producing a 397,062-bp alignment with 285 SNPs. MrBayes software was used to build the maximum clade credibility tree  (63). The isolation dates of the BES and outgroup isolates were used as calibration nodes to calculate the age of the MRCA.
Acknowledgments We thank  Marilou Bourgeon  for her technical support and  Catherine Llanes  and  Thilo Köhler  for helpful discussions. We also thank  Dr Beryl Oppenheim  for the gift of a ST395 isolate from the  Queen Elizabeth Hospital, Birmingham, UK. Computations were performed at the  Mésocentre de Calculs de Franche-comté (Besançon, France ). X. B. , B. V. , and D. H. designed the research; M. P. , P. J. , A. M. , H. P. , and B. V. performed the research; B. V. contributed new reagents/analytical tools; M. P. , P. J. , E. D. , B. V. , and D. H. analyzed the data; and M. P. , P. J. , X. B. , B. V. , and D. H. wrote the paper. This work was supported by a grant from the  Région de Franche-Comté  and from the  University Hospital of Besançon, France  ( API 3A -Promes 2013 ) to M. P. , the  French "Ministère de l'Enseignement Supérieur et de la Recherche  to P.
0 and SSP5-8. 5). We have limited this analysis to two variables and simple descriptive statistics of their behavior. The ScenarioMIP design together with the presence of complementary experiments in several other MIPs and of the richness of the archived data  (Jukes et al. , 2020)  from the ESM simulations is going to provide the basis for many more in-depth analyses of the physical system behavior. This will be further supported by a subset of CMIP6 models that are running CMIP5 RCPs, thus enabling a rigorous separation of the sources of variation between the two generations of experiments. Importantly, the ScenarioMIP effort aims at supporting integrated analyses of Earth and human systems' responses to future changes. These studies will integrate socioeconomic changes described by SSPs with climate system changes characterized by ESM outcomes to assess risks and possible mitigation and adaptation response options.
6, SSP2-4. 5 and SSP5-8. 5 for CMIP6 models, RCP2. 6, RCP4. 5 and RCP8. 5 for CMIP5 models. NA values indicate that the corresponding ensemble metric (mean, lower or upper bound of the confidence interval) does not reach the corresponding warming level by 2100. The numbers on the bottom row of each cell indicate the number of models that reach that warming level. The largest ensemble available under all three scenarios considered is used in both cases, with 36 CMIP6 models and 29 CMIP5 models. Code and data availability. CMIP5 (see Table  A2 ) and CMIP6 (see Table  A1 ) model output is available through the Earth System Grid Foundation (ESGF) and can be directly used within the ESM-ValTool (e. g. , https://esgf-data. dkrz. de/projects/esgf-dkrz/, last access: 7 January 2021)  (ESGF, 2021).
The corresponding recipe that can be used to reproduce the figures of this paper will be included in ESMValTool v2. 0  (Righi et al. , 2020; Eyring et al. , 2020; Lauer et al. , 2020; Weigel et al. , 2020)  as soon as the paper is published. ESMValTool (v2. 1) is released under the Apache License, VER-SION 2. 0. The latest release of ESMValTool v2. 1 is publicly available on Zenodo at https://doi. org/10. 5281/zenodo. 4300499 (Andela et al. , 2020b). The source code of the ESMValCore package, which is installed as a dependency of ESMValTool v2. 1, is also publicly available on Zenodo at https://doi. org/10. 5281/zenodo. 4525749 (Andela et al. , 2020a). ESMValTool and ESMValCore are developed on the GitHub repositories available at https://github. com/ESMValGroup (last access: 7 January 2021)  (ESMValGroup, 2021).
Acknowledgements. We acknowledge the  World Climate Research Programme (WCRP) , which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP. We thank the climate modeling groups (listed in Tables A1 andA2) for producing and making available their model output, the  Earth System Grid Federation (ESGF)  for archiving the data and providing access and the multiple funding agencies who support CMIP,  ESGF  and the individual modeling centers' efforts. Aurore Voldoire  and  Roland Séférian  are grateful for the support of the team in charge of the CNRM-CM climate model. Supercomputing time was provided by the Météo-France/DSI supercomputing center. The computational resources of the  Deutsches Klimarechenzentrum (DKRZ, Hamburg, Germany ) that allowed the analysis of this study with the ESMValTool are kindly acknowledged.
Financial support. Claudia Tebaldi  was supported by the  Energy Exascale Earth System Model  ( E3SM )  project, funded by  US Department of Energy, Office of Science, Office of Biological and Environmental Research. The  Pacific Northwest National Laboratory  is operated by  Battelle for the US Department of Energy  under contract no. DE-AC05-76RLO1830. This work has been also supported by the  European Union 's  Horizon 2020 Framework Programme for Research and Innovation  " Coordinated Research in Earth Systems and Climate: Experiments, kNowledge, Dissemination and Outreach (CRESCENDO) " project under grant agreement no. 641816  and the  EVal4CMIP  project funded by the  Helmholtz Society. Work at LLNL was performed under the auspices of the  US Department of Energy  by  Lawrence Livermore National Laboratory  under contract no.
C. Tebaldi et al. :  Climate model projections from ScenarioMIP of CMIP6 Table A1. Modeling centers and their model(s) contributing to CMIP6 ScenarioMIP. The citations are included in the main bibliography. DOIs refer to the data available through the Earth System Grid Federation. The last columns details the experiments to which the model(s) contributed. Institution Model(s) Model reference(s) and dataset DOIs Experiments Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (Germany) AWI Semmler et al. (2020) https://doi. org/10. 22033/esgf/cmip6. 376 https://doi. org/10. 22033/esgf/cmip6. 359 historical, ssp126, ssp245, ssp370, ssp585 Beijing Climate Center (China) BCC-CSM2-MR Wu et al. (2019); Xin et al. (2019) https://doi. org/10. 22033/ESGF/CMIP6. 1732 historical, ssp126, ssp245, ssp370, ssp585 Canadian Centre for Climate Modelling and Analysis (Canada) CanESM5-CanOE; CanESM5 Swart et al.
It should be noted that the capsules used for administration to rats were of a size suitable for this type of experiment. Cmax and the total area under curves (AUC t ) were determined for each group. The data presented in Figure  8  show the potential value of the developed formulation as Cmax (302. 4 ng•mL -1 ) and AUC t (497 ng•mL -1 •h) are, respectively, about 38 and 15 higher than when using Capsugel PCcaps ® containing the same amount of EGCG (Cmax = 7. 9 ng•mL -1 ; AUC t = 33. 8 ng•mL -1 •h). Based on the two last results where EGCG is detected in the plasma, the half-life of EGCG provided by the aqueous formulation and by the capsules is of 2. 0 h and 2. 8 h, respectively. considering the fact that the aqueous solubility of drugs is often predictive of their bioavailability  [33]. Finally, the observed soaring of bioavailability may be the consequence of an increase in EGCG in the gastrointestinal tract, as tea polyphenols are known to inhibit the activity of the efflux protein P-GP  [34].
Acknowledgments: The authors thank Audrey Solgadi and Bastien Prost (Université Paris-Saclay, Inserm, CNRS, Ingénierie et Plateformes au Service de l'Innovation Thérapeutique, 92296, Châtenay-Malabry, France) as well as Katia Manerlax (Université Paris-Saclay, Matériaux et santé, 92296, Châtenay-Malabry, France) and Jennifer Avril (Department of Pharmacy, Henri Mondor Hospital, AP-HP, Créteil, France) for their highly appreciated contributions to this work and their advice.
Therefore, this study aimed to explain the possible relations between weather variables and air pollution on four health outcomes known to be weather and/or environment sensitive  (Godsmark et al. , 2019). We used wavelet transform (WT) analysis to establish possible associations between temperature, rainfall, PM and NO2 (air pollutants for which data were available) on hospital admission symptoms relating to asthma, pneumonia, malaria, and gastrointestinal ailments obtained from admission records at two provincial hospitals in Limpopo province, South Africa. Wavelets have been used as a data analysis tool across several scientific disciplines providing important new insights into the underlying characteristics of the signals involved  (Addison, 2018). The property of WT procedure -in that it does not assume stationarity of records  (Lancaster et al.
, 2018)  -renders its applicability to research of a range of natural or human-made complex systems outputs. In this paper, we calculated wavelet power spectra (WTS), a wavelet energy density functions relatable  (Perrieret al. , 1995)  to Fourier power spectra (PwS) to investigate global (i. e. , over entire time of recording) and local (i. e. , at a specific point in time) temporal behaviour of individual time series in our dataset. We furthermore used cross-wavelet transforms (CWT) comparable to cross-correlation functions  (Addison, 2002)  and cross-correlation coefficients  (Frick et al. , 2001)  to discern associations between admission records in our dataset with meteorological and air quality changes, by monitoring local temporal regions of their coincidental energy  (Torrence and Compo, 1998; Addison, 2018).
Hospital admission data Handwritten, non-digitised hospital admission records for 1 January 2002 to 31 December 2017 were collected from two large public hospitals -Nkhensani Hospital and Maphutha L. Malatjie Hospital, located in Mopani District Municipality in Limpopo province (Figure  1 ). Research ethics clearance to obtain the data for analyses was granted by the South African Medical Research Council (EC005-3/2014). Hospital records were scanned using an SV 600 overhead snap scanner, pages were saved as soft copies (i. e. , PDF files) and later printed for double data entry into an electronic database using EpiData  (Christiansen and Lauritsen,. 2010). Each hospital admission record included patient's date of birth, patient's age, date of admission and reason for admission. The medical records included those from the children, female, and male wards only.
Gastrointestinal illnesses (GE) including diarrhoea, pneumonia-related diagnosis (hereafter called pneumonia), malaria and asthma cases were extracted from the hospital admission records database using the criteria and terms provided by South African medical doctors. Abdominal distention was not included in GE as it could be associated with a variety of medical conditions other than GE. Data were unavailable in 2006 (for reasons unknown, e. g. , missing hospital admission books) for both hospitals as well as at one hospital for weeks 1-23 in 2002 and weeks 1-40 in 2007. Entries for the day of admissions were sparse for the period before 2011. Therefore, we chose the time period from the beginning of 2011 to the end of 2017 for the statistical analysis in this study, to avoid bias to the statistical function estimation that comes from artificial jumps due to the amount of missing data  (Rust et al.
Temperature and rainfall data Daily temperature data from 2011 to 2017 were obtained from the South African Weather Service. The closest operational automatic weather station was about 50 km from the area in which the two public hospitals were located. Rainfall estimate data (RFE) were obtained from RFE version 2. 0 (v2. 0) implemented by the National Oceanic and Atmospheric Administration's (NOAA) Climate Prediction Centre  (CPC, 2001). RFE v2. 0 obtains the final daily rainfall estimation by combining all satellite data using the maximum likelihood estimation method, thereafter, Global Telecommunication System (GTS) station data are used to remove bias. The daily data are then summed up to produce decadal (10-day) totals in mm for each month at a resolution of 8 km x 8 km.
Air pollution reanalysis data Air pollution data were taken from the Copernicus Atmosphere Monitoring Service (CAMS) global reanalysis dataset. The CAMS consists of 3-dimensional atmospheric composition data, including aerosols and chemical species. It is assimilated with the European Centre for Medium range Weather Forecast's (ECMWF) Integrated Forecast System (IFS). The CAMS reanalysis data consist of 25 pressure levels with a spatial resolution of approximately 80 km at 12-hour intervals  (Inness et al. , 2019; ECMWF, 2020). The CAMS reanalysis dataset uses observations including O3, NO2, CO, methane (CH4) and CO2 from various satellites for input into the reanalysis data. The validation of the CAMS reanalysis data has shown that seasonal cycles of air quality parameters compare well to the observed surface data  (Christophe et al.
LCWTS give local covariance of different time series at each moment in time and scale and thus provide local insight into the similarity of LWTS of the two records (Aguiar-Conraria and Soares, 2014). In analogy to the global wavelet spectra, a global cross-correlation wavelet spectrum can be defined as an integral or a sum of LCWTS over time or space. Assessments of wavelet cross-correlations are usually followed by assessments of phase differences between the two records and 0. We did not calculate phase differences in this study because we assumed that the public health outcomes under investigation here tend to follow meteorological or environmental variations  (Godsmark et al. , 2019; Raymond et al. , 2020). To obtain statistically significant results and avoid effects of records' finite sizes on WTS statistics, we calculated WTSs between the time scales of = 1 and = /5 (in our dataset, with time series length of = 2557 data points (given in days), this limits investigations to approximately 17 months).
We used this scale range for visualization of our results. In drawing conclusions, however, we limited ourselves to a more rigorous, maximum meaningful scale of 5 = /10 (Koscielny-  Bunde et al. , 2006) , which in this paper corresponds to approximately 8. 5 months. To assess the significance of our results we used tests of significance for detection of cycles in WTS and CWTS functions in this study; we used the technique explained in  (Torrence and Compo, 1998)  against the analysed signals as LTP noise backgrounds. We provide illustration of the results of this significance testing on the wavelet spectrum of the 'all admissions' record from our dataset (please see explanation in Results below) in Supplementary material to this paper and explain our choice of the background noise. For critical assessment of some of the results related to significance testing for WT please also see (Aguiar-Conraria and Soares, 2014) and (Rodríguez-Murillo and Filella, 2020).