dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We thank  I. Salazar-Ciudad  and  S. F. Gilbert  for advice and the members of the  Center of Excellence in Experimental and Computational Developmental Biology Research  for discussions. We thank  A. Viherä  for technical assistance. We thank  P. Auvinen ,  L. Paulin  and  P. Laamanen  at  DNA Sequencing and Genomics Laboratory  for bulk RNA sequencing. We thank  J. Lahtela  at  FIMM Single Cell Analytics  for single cell RNA sequencing. Financial support was provided by the  Academy of Finland ,  Jane and Aatos Erkko Foundation ,  John Templeton Foundation , and  Sigrid Jusélius Foundation.
Acknowledgements We acknowledge the  MRC Methodology Grant   G0800596. L. P. acknowledges the  EPSRC  (grant  EP/J002437/1 ) and the  Wellcome Trust and Royal Society  (grant  202562/Z/16/Z ); S. C. acknowledges funding from the  NIGMS MIDAS initiative , the  French Government 's  Investissement d'Avenir program ,  Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases " (grant number  ANR-10-LABX-62-IBEID ), the  AXA Research Fund and Grand Prix Robert Debré ; and  N. M. F. acknowledges Centre  funding from the  MRC  and  DFID, Health Protection Research Unit  funding from  NIHR , Institute funding from  Community Jameel  and grant funding from the  Bill and Melinda Gates Foundation. We also thank  Niel Hens  for support with the analysis of the POLYMOD data, the members of the  Zeeman Institute at the University of Warwick  for useful discussions, and the anonymous reviewers for their insightful and constructive comments.
Data availability Household data for Great Britain (England, Wales and Scotland) are available from 2001 UK census data, table number C0844 61 ; source: Office for National Statistics licensed under the Open Government Licence v. 1. 0. The table can be downloaded from https:// doi. org/10. 5281/zenodo. 3629873 or https://github. com/lorenzo-pellis/model-mapping, or can be requested directly from the Office for National Statistics. UK social contact data are obtained from the POLYMOD study 25 (raw data available from the authors upon request). Household data for Sierra Leone are available from the 2008 Sierra Leone Demographic and Health Survey 63 and household data for South Africa from the 1998 South Africa Demographic and Health Survey 64. Both can be requested from the Demographic and Health Surveys Program 65 (free registration).
Code availability Numerical codes, output files and figures have been archived at the time of publication with https://doi. org/10. 5281/zenodo. 3629873. Potential improvements or corrections will be made available at https://github. com/lorenzo-pellis/model-mapping. The main code to perform the model mapping is written in MATLAB and has been tested on both R2016a on Mac OSX El Capitan and R2019a on Microsoft Windows 7 Professional (Service Pack 1). The individual-based stochastic simulation is written in C++ and source codes are freely available.
MATERIALS AND METHODS Bacterial strains and construction of inoculated consortia. We used Ralstonia solanacearum strain QL-Rs1115 (GenBank accession GU390462) tagged with the pYC12-mCherry plasmid (13) as a model pathogen in our study. The inoculated consortia comprised of five closely related Ralstonia strains (Ralstonia mannitolilytica QL-A2, Ralstonia mannitolilytica QL-A3, Ralstonia pickettii QL-A6, Ralstonia taiwanensis QL-117, and Ralstonia pickettii QL-140) that have previously been shown to inhibit pathogen growth solely via resource competition without detectable toxin production  (13). For the experiments, one colony of each strain, recovered from -80°C 20% glycerol stocks, was selected and grown in nutrient broth (NB, glucose 10. 0 g liter Ϫ1 , tryptone 5. 0 g liter Ϫ1 , beef extract 3.
ACKNOWLEDGMENTS Financial support. This research was supported by the  National Key Research and Development Program of China  ( 2018YFD1000800 ),  National Natural Science Foundation of China  ( 41807045  to  Tianjie Yang ;  31972504  to  Yangchun Xu ;  41922053  to  Zhong Wei ),  Natural Science Foundation of Jiangsu Province  ( BK20170085  to  Zhong Wei ;  BK20180527  to  Tianjie Yang ), the  Fundamental Research Funds for the Central Universities  ( KY2201719 ;  KJYQ202002 ;  KJQN201745 ;  KJQN201922 ;  KYXK202010 ). Ville-Petri Friman  is supported by  Royal Society Research Grants  ( RSG\R1\180213  and  CHL\R1\180031 ) at the  University of York. Alexandre Jousset is supported by the  Nederlandse Organisatie voor Wetenschappelijk Onderzoek  ( ALW. 870. 15. 050 ) and the  Koninklijke Nederlandse Akademie van Wetenschappen  ( 530-5CDP18 ).
Upon reaching the Amazon Basin, it started to broaden and to subside, due to convective mixing and entrainment of the BB aerosol into the boundary layer. Satellite observations show that the transatlantic transport of pollution layers is a frequently occurring process, seasonally peaking in August/September. By analyzing the aircraft observations together with the long-term data from the Amazon Tall Tower Observatory (ATTO), we found that the transatlantic transport of African BB smoke layers has a strong impact on the northern and central Amazonian aerosol population during the BBinfluenced season (July to December). In fact, the early BB season (July to September) in this part of the Amazon appears to be dominated by African smoke, whereas the later BB season (October to December) appears to be dominated by South American fires.
Airborne measurements of aerosol, trace gas, and meteorology during ACRIDICON-CHUVA Navigation and basic meteorological data (e. g. , air pressure, temperature, humidity, and water vapor mixing ratio) were obtained from the BAsic HALO Measurement And Sensor System (BAHAMAS) at 1 s time resolution. BAHAMAS acquires data from air flow and thermodynamic sensors as well as from the aircraft avionics and a high-precision inertial reference system to derive basic meteorological parameters like pressure, temperature, and the 3-D wind vector, B. A. Holanda et al. :  Influx of African biomass burning aerosol during the Amazonian dry season as well as aircraft position and attitude. Water vapor mixing ratio and further derived humidity parameters are measured by SHARC (Sophisticated Hygrometer for Atmospheric Re-searCh) based on direct absorption measurement by a tunable diode laser (TDL) system.
GIS data products and analysis The analysis of geographic information system (GIS) data sets was conducted with the QGIS software package (Las Palmas version 2. 18. 2, QGIS development team). The GIS data sets were handled using the coordinate reference of the World Geodetic System from 1984 (WGS84   Olson et al. (2001). For further details, we refer the reader to  Pöhlker et al. (2019).
Note that Ascension Island is located in the main BT path of the transatlantic pollution transport and therefore a well-located observational site en route (Fig. 7a  1 ). The influence of South American BB for the ATTO observations is represented by a BT data product, CF BT , based on pixel-wise accumulation of fire intensities along individual ATTO BTs over a multi-year period. Generally, the Amazonian atmosphere is strongly influenced by BB aerosols during the dry season and to a somewhat lower extent during the flanking transition periods, causing significant increases in scattering and absorption coefficients  (Rizzo et al. , 2013; Saturno et al. , 2018b). In Fig. 12 , the long-lasting BB influence can be seen by means of the broad seasonal maxima in M BC e , N CCN,0. 5 , and c CO. The BB-impacted part of the year in the Amazon, including dry season and transition periods, will be called hereafter the BB season.
This process clearly merits future modeling investigations to assess its effects on regional radiative forcing as well as cloud properties and lifecycle. Data availability. The data of the key results presented here have been deposited in associated data files for use in follow-up studies. The HALO aircraft data (named "HALO. dat") and the ATTO data (named "ATTO. dat") used in this study are available in NASA Ames format under https://doi. org/10. 17617/3. 3r (Holanda et al. , 2020a). Additional aircraft data from the ACRIDICON-CHUVA campaign can be found on the HALO database under https://halo-db. pa. op. dlr. de/ (DLR, 2020). Additional ATTO data can be found in the ATTO data portal under https://www. attodata. org/ (ATTO, 2020). The time series with cumulative fire count along backward trajectories (see Sect.
2. 6) are available in NASA Ames format (named "H1000_Cer_Rain. dat") under https://doi. org/10. 17617/3. 3q (Holanda et al. , 2020b). For data requests beyond the available data, please refer to the corresponding authors. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-20-4757-2020-supplement. Author contributions. BAH conducted most of the data analysis and wrote the paper. CP, MOA, and UP supervised the work. MAF, QW, TD, JWK, CK, NM, HP, JS, CS, DW, and MW contributed specific parts of the data analysis. MLP, MD, TK, DF, LATM, DS, HS, JS, CS, BW, AW, MW, SB, HZ, MZ, CP, UP, and MOA conducted the measurements during the ACRIDICON-CHUVA campaign. BAH, MLP, JSa MS, FD, OOK, JB, MAF, FGM, JVL, and CP conducted the measurements at the ATTO site.
It is not associated with a conference. Research Infrastructures (A-LIFE (grant no. 640458)). This study was also supported by EU Project HAIC under FP7-AAT-2012-3. 5. 1-1 and by the German Science Foundation within DFG SPP 1294 HALO by contract nos. VO1504/4-1, JU 3059/1-1 and SCHN1138/1-2. The article processing charges for this open-access publication were covered by the Max Planck Society. Review statement. This paper was edited by Gilberto Fisch and reviewed by two anonymous referees.
Acknowledgements. We recognize the  Max Planck Society , the  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, Brazil) , and the  Max Planck Graduate Center with the Johannes Gutenberg University Mainz (MPGC)  for the financial support. We thank the entire ACRIDICON-CHUVA team for the fruitful scientific cooperation. Special thanks goes to the HALO pilots,  Steffen Gemsa ,  Michael Grossrubatscher , and  Stefan Grillenbeck. We thank  Volker Dreiling , the  Sensor and Data Team of DLR Flight Experiments , and the  HALO team of the DLR  for their cooperation. We acknowledge the generous support of the ACRIDICON-CHUVA campaign by the  Max Planck Society , the  German Aerospace Center (DLR) ,  FAPESP (São Paulo Research Foundation ), and the  German Science Foundation (Deutsche Forschungsgemeinschaft, DFG)  within the  DFG Priority Program  "Atmospheric and Earth System Research with the  Research Aircraft HALO (High Altitude and Long Range Research Aircraft)".
Clear-cell renal cell carcinomas (ccRCC) can be divided into four transcriptomic subtypes, two of which have a favorable and two an unfavorable prognosis. To assess mechanisms driving these subtypes, we investigated their miRNA expression patterns. miRNAs are master regulators of mRNAs, that are widely deregulated in cancer. Unsupervised clustering in our dataset (n = 128) and The Cancer Genome Atlas (TCGA) validation set identified two distinct miRNA clusters that overlapped with the transcriptomic subtypes, underscoring the validity of these subtypes on a multi-omics level and suggesting a driving role for miRNAs. Discriminatory miRNAs for the favorable subtypes repressed epithelial-to-mesenchymal transition, based on gene set enrichment analysis and target-mRNA expression levels. Strikingly, throughout the entire dataset, miRNAs associated with favorable subtypes were also associated with longer overall survival after diagnosis, and miRNAs associated with unfavorable subtypes with shorter overall survival (Pearson r = -0.
Results We included 128 patients for miRNome and clinical analysis, and determined the molecular subtype in 95 cases where fresh frozen tissue was available. Forty-five tumors had a favorable ccrcc2_3 subtype (39 ccrcc2, 6 ccrcc3), 50 had an unfavorable ccrcc1_4 subtype (24 ccrcc1, 26 ccrcc4). Of the 16 paired normal and tumor samples, the molecular subtype determined for 11 cases. Patient characteristics are summarized in Table  1. miRNA-seq data have been deposited in the ArrayExpress database at EMBL-EBI (www. ebi. ac. uk/arrayexpress) under accession number E-MTAB-9380. In the TCGA validation set, combined miRNA and mRNA expression data with ccrcc1 to -4 molecular subtype were available for 107 samples.
Exploratory unsupervised clustering reveals two miRNA clusters that overlap with molecular subtypes We evaluated miRNA expression patterns across all samples in an unbiased way, using principal component analysis and unsupervised clustering. These analyses revealed a marked differentiation of tumoral vs normal kidney samples (Fig 1A  -1C ). As shown in the heatmap, the tumor cluster further separated into two clusters that mainly overlapped with the molecular subtypes. Unsupervised clustering using only the tumor samples, separated the samples into one cluster that consisted for 81% of the unfavorable ccrcc1_4 tumors, and a second cluster that consisted for 62% of the favorable ccrcc2_3 tumors (p = 0. 0005). These findings were validated on the TCGA dataset, where we identified two miRNA clusters with 66% and 91% overlap with favorable and unfavorable molecular subtypes respectively (p = 4.
7e-9) (S1 Fig). The heterogeneity of miRNA expression was higher in the unfavorable ccrcc1_4 subtypes. We also compared 11 tumor samples with their matched normal kidney (7 ccrcc2_3, 4 ccrcc1_4). The miRNAs that were differentially expressed with normal kidney, largely overlapped between ccrcc2_3 and ccrcc1_4 tumors (Fig  1D ). However, the extent to which they were differentially expressed between normal kidney and tumors, differed between ccrcc2_3 and ccrcc1_4. widely associated with relevant pathways in cancer biology (Table  3 ). These findings were validated on the TCGA data: the subtype-specific expression of miRNAs, as quantified by fold change, was strongly correlated between our dataset and the TCGA with a Pearson coefficient of r = 0. 59 (p < 2. 2e-16) (Fig  2B ). In order to identify functional miRNA-mRNA interactions of subtype-specific miRNAs, we correlated these miRNAs with their predicted mRNA targets using the TCGA dataset (full transcriptome data were available for TCGA, but not for our own dataset).
MiRNA expression is correlated with both overall survival and molecular subtype To assess the translational relevance of our findings we tested the correlation of miRNA expression with overall survival (OS) using Cox regression. Our hypothesis was that the limited subset of miRNAs with marked subtype-specific expression would also show a prognostic value. However, strikingly, OS analysis revealed a much more remarkable association: Table  2. List of miRNAs that exhibited subtype-specific expression (FDR < 0. 05) and that were either significantly associated with OS (Leuven dataset) or suppressed an mRNA target that was downregulated with a fold change of < 0. 5 compared to the other molecular subtype (TCGA dataset). miRNAs with higher expression in ccrcc1_4 subtypes were systematically associated with shorter OS, and those with higher expression in ccrcc2_3 with longer OS.
Discussion In this study, we analyzed miRNomes of ccRCC to assess the link between miRNA expression and transcriptomic molecular subtypes, mRNA expression and clinical outcomes. The ccrcc1 to -4 molecular subtypes showed clearly different miRNA expression patterns, both in our dataset and the TCGA validation set. First, unsupervised clustering of miRNAs revealed two clusters that overlapped with the favorable (ccrcc2_3) and unfavorable (ccrcc1_4) subtypes. This is in line with earlier findings from TCGA, where four miRNA clusters were found, of which only two overlapped with molecular subtypes  [4]. Secondly, a supervised analysis revealed several miRNAs that were discriminatory between the subtypes. Moreover, miR-NAs that were expressed higher in unfavorable subtypes were systematically associated with poor OS, and miRNAs that were expressed higher in favorable subtypes with short OS.
This held true in the entire dataset, and not only for those miRNAs with significant subtype-specific expression  (Fig 4, S1 Table). These findings underscore the robustness of these subtypes. A striking finding, which was not expected at study set-up, was the strong correlation between subtype-specific miRNA expression and OS, throughout the entire dataset. Put otherwise, there was a shift in global miRNA expression between less vs more aggressive tumors. This is in contrast to published literature, which typically describes small subsets of miRNAs that are significantly associated with prognosis in some studies but not in others. Our study reveals that aggressive tumors are not set apart by only a few distinctive miRNAs. Rather, these tumors display an entirely different miRNome expression, regardless of single miRNAs reaching the significance threshold for OS.
Importanly, this is the case in both advanced and earlier stage disease, as findings were similar in our dataset (stage IV ccRCC) and the TCGA (mostly stage I-III ccRCC). Many of the most discriminatory miRNAs for both molecular subtype and OS are well described oncomiRs or tumor suppressors in ccRCC or other tumor types. Previously, two miRNA signatures have been published that discriminated localized from metastatic ccRCC  [5, 9]. According to these signatures, miRs 10b, 139-5p, 30a-3p, 30c-5p, 139-5p and 144-5p were discriminative for localized disease, and miRs 130b and 199b-5p for metastatic tumors. Our series validated the prognostic value of all these miRs, with the former all being associated with ccrcc2_3 and long OS, and the latter with ccrcc1_4 and short OS. As we only used biological material from metastatic tumors we could not validate the capability of these signatures to predict the risk of relapse.
Conclusion The ccrcc1 to -4 transcriptomic molecular subtypes in ccRCC have fundamentally different miRNA expression profiles that underlie their mRNA expression, which underscores the robustness of these subtypes on a multi-omics scale. There was a very strong association between subtype-specific miRNA expression (favorable vs unfavorable) and outcome (long vs short OS), throughout the entire dataset. This reflects a general shift in miRNA expression from less to more aggressive tumors. Many of the discriminative miRNAs repressed mRNA targets that are involved in tumor epithelial-to-mesenchymal transition.
Patients and samples We selected primary ccRCC samples from the Leuven University Hospitals ccRCC tumor bank, which prospectively collects tissue and detailed clinical data from metastatic ccRCC patients after informed consent. Patients were eligible if archived tissue of the primary tumor was available, provided they had not received systemic therapy prior to tissue retrieval. Clearcell histology was confirmed by an expert uro-pathologist. As a reference, we included paired normal kidney samples from 16 patients as well (healthy tissue block from the nephrectomy specimen removed during ccRCC surgery). The protocol for miRNome analysis has been described previously  [6]. Briefly, total RNA was extracted from formalin-fixed paraffinembedded (FFPE) tissue using the Recover All Total Nucleic Acid Isolation kit for FFPE (Ambion).
Libraries were constructed with the NEBNext Multiplex Small RNA Library Prep Set for Illumina (New England Biolabs E7300) and sequenced for 50 bases in a single-read format (Genome Analyzer Ilx, Illumina). In cases where fresh frozen primary tumor tissue was available, this was used to determine ccrcc1 to -4 transcriptomic molecular subtype, using the established 35-gene classifier algorithm as described previously  [10]. The TCGA ccRCC cohort was used as a validation set. In TCGA samples for which combined miRNome and transcriptome data derived from fresh frozen tissue were available, we determined molecular subtype using the classification algorithm.
Small RNAseq preprocessing Raw reads were trimmed using Trim Galore! (Babraham Bioinformatics) and aligned against the miRbase mature miRNA sequences using Bowtie1 (Johns Hopkins University), resulting in raw read counts for 2589 miRNAs  [29]. For the clustering and differential expression results, raw count data was analyzed according to the DESeq2 workflow (Bioconductor) after filtering out miRNA with a normalized count smaller than 5 in more than 50 samples  [30]. For the correlation with overall survival (OS) since diagnosis of ccRCC, counts were normalized using the upperquartile method as implemented in the R package edgeR after removing lowly expressed miRNAs (zero counts per million reads in more than 100 samples)  [31].
Unsupervised clustering To assess the presence of miRNA clusters and their overlap with previously determined mRNA molecular subtypes, we performed unsupervised clustering. For this, a principal component analysis was run on variance stabilized counts obtained from DESeq2 (version 1. 25. 16). In addition, the samples were clustered hierarchically using the 50 most variable miR-NAs using correlation as a distance metric as implemented in the R package ComplexHeatmap (version 2. 1. 1, Bioconductor)  [32]. To further investigate the miRNA based clustering, variance stabilized counts of all expressed miRNAs were normalized and tumor samples were partitioned into two clusters according to the 'partitioning around medoids' algorithm as implemented in the R package cluster (version 2. 1. 0). A Chi-Square test was performed to assess the association of the favorable and unfavorable prognosis mRNA subtypes with the two miRNA clusters.
Functional characterization For miRNAs with subtype-specific expression (defined by log2 (fold change) � 0. 5 or � -0. 5 and false discovery rate (FDR) < 0. 05), Ingenuity Pathway Analysis (IPA, Qiagen) was used to collect the predicted downstream mRNA targets. Moreover, this data was integrated with the subtype-specific expression of mRNA targets in the TCGA dataset to detect putative miRNA-mRNA functional interactions (microRNA Target Filter module, IPA). Kyoto Encyclopedia of Genes and Genomes (KEGG) gene set enrichment analysis on the differentially expressed miRNAs was performed using the R package RBiomirGS (version 0. 2. 12)  [33]. Briefly, the miRNA differential expression list is used to search multiple databases for miRNA-mRNA interactions, resulting in a target mRNA list. For each miRNA, the fold change and p-value from the miRNA differential expression list are used to calculate an expression score.
Correlation with clinical outcome We performed univariate Cox regression to correlate individual miRNAs with OS since diagnosis and progression free survival on first-line sunitinib or pazopanib. We used a Mann-Whitney U test to analyze expression differences between patients with partial response versus progressive disease at first evaluation. P-values were corrected for multiple testing using the Benjamini and Hochberg method, and are reported as FDR  [34]. In another effort to confirm the validity of these results, we also performed Cox regression for other outcome parameters (OS since metastasis and time to metastasis). Lastly, as internal validation, we repeated all analyses after randomly dividing our patient population into two cohorts. In a selection of miRNAs with the strongest association with both subtype and OS, we performed multivariate Cox regressions with International Metastatic ccRCC Database Consortium (IMDC) risk groups and molecular subtype  [35].