dataesr/scientific-paragraphs-categorization huggingface dataset:
Seeds were surface-sterilized by treatments with 70% EtOH and 5% hypochloryte-1% SDS, washed and stratified at 4°C for 2 days to obtain homogeneous germination. Seedlings were grown at 23°C or 29°C on solid half-strength Murashige and Skoog (MS/2) medium (Duchefa), under long day conditions (16h light, 95µE. m -2. sec -  /8h dark). For phenotypic characterization of hypocotyl elongation, seedlings were grown for 3h at 23°C and transferred to 29°C or maintained at 23°C for 4 days. Hypocotyl lengths were measured using the ImageJ software. For RNAsequencing, chromatin immunoprecipitation and methylated DNA immunoprecipitation assays performed in Arabidopsis stable lines, seedlings were grown for 4 days at 23°C and transferred to 29°C or maintained at 23°C for 6h. For chromatin isolation by RNA purification and DNA-RNA Immunoprecipitation, seedlings were grown for 11 days at 23°C.
, 2012). Processed reads were aligned using STAR with the following arguments: --alignIntronMin 20 --alignIntronMax 3000 --outSAMtype BAM SortedByCoordinate --alignIntronMax 3000 --outSAMtype BAM SortedByCoordinate --alignIntronMax 3000 --outSAMtype BAM SortedByCoordinate. Read overlapping exons per genes were counted using the featureCounts of the subreads package using the GTF annotation files from the Araport11 project  (Cheng et al. , 2017). Significance of differential gene expression was estimated using DEseq2  (Love et al. , 2014)  and the FDR correction of the p-value was used during pairwise comparison between genotypes. A gene was declared differentially expressed if its adjusted p-value (FDR) was < 0. 01. Heatmap was generated using log2 transforfed fold change values compared WT 23°C and computed with the pheatmap R  (Kolde, 2019).
Rothbart (Van Andel Research Institute) for the pGE-HIS-MBP UHRF1 clone;  T. Akiyma  ( University of Tokio ) for the pBluescript:UPAT clone. This project was financially supported by grants from  Agencia Nacional de Promoción Científica y Tecnológica (PICT) ,  Universidad Nacional del Litoral (CAI+D)  and  Fima Leloir Award (Argentina) ;  International Associated Lab NOCOSYM (CNRS-CONICET) ; the  Centre National de la Recherche Scientifique (MOMENTUM program , to LQ); IPS2 benefits from the support of  Saclay Plant Sciences-SPS  ( ANR-17-EUR-0007 ). LL, DG and FA are members of  CONICET. CFF, MFL, MM, and LF are fellows of the same institution.
Results Reduced aldehyde detoxification characterizes glandular tissues in the esophagus. We first asked whether a cell-autonomous mechanism could contribute to exhaled biomarkers, so we began by searching for thematic differences in metabolic gene expression between EAC and relevant control tissue in archived transcriptomic datasets. Compared to EAC, squamous mucosa for healthy controls (SqN) was defined by a strong aldehyde oxidation theme (Fig. 1a, b  and Supplementary Data 3), suggesting reduced aldehyde defense in EAC tissue. Ingenuity Pathway Analysis identified an analogous leading phenotype (Supplementary Fig. 1a ). The EAC precursor Barrett's metaplasia also had differences in redox gene expression (Supplementary Data 3), in keeping with a previous report of disengaged glutathione metabolism  11.
A meta-analysis of the public datasets at a single gene level identified ALDH1A3, -3A1, -3A2, -4A1, and -9A1 as consistently under-expressed in EAC compared to normal esophageal squamous mucosa (Supplementary Fig. 1b , ALDH3B2 is a pseudogene). We validated these observations experimentally using quantitative polymerase chain reaction (qPCR) on paired endoscopic biopsies from 67 patients (Fig. 1c ). These data complement those from epidemiological and genomic studies which link deactivating ALDH variants to esophageal malignancies  10, 13. Importantly, these five aldehyde dehydrogenase isoenzymes show specificity to products of lipid peroxidation, a major source of endogenous aldehydes  14. Further assessments suggested that ALDH3A2 was expressed in BAR but not EAC, whereas other ALDH genes were underexpressed in both BAR and EAC (Supplementary Fig.
0001, Supplementary Fig. 4a ). Metformin is a biguanide with several nucleophilic moieties capable of addition reactions with reactive carbonyls  22  , and has been shown to reduce aldehyde-mediated genotoxicity in hematological malignancies  23. A re-analysis of a previously published breath dataset  6  revealed that diabetics with EAC who were taking metformin had several significantly reduced alkanals compared to those not taking metformin (Supplementary Fig. 4b ). Initial experiments revealed that 48-h pretreatment with pharmacologically relevant concentrations of metformin could prevent stabilization of phosphorylated γ-H2AX in a panel of esophageal cell lines, following a 5-h exposure to pathologically relevant concentrations of formaldehyde or acetaldehyde (Fig. 3c, d ). Further functional assessments of the DNA damage response indicated that metformin caused a dose-dependent suppression of dsDNA-mediated ATM/Chk2 signaling in EAC and Barrett's cell models (Fig.
5c, d ). Thus, in EAC in vivo and in vitro, reduced ALDH3A1 and -3A2 activity is associated with increased decanal. In The Cancer Genome Atlas (TCGA) data, reduced ALDH3A2 expression in EAC was associated with significantly poorer survival (Supplementary Fig. 6a ), a pattern we verified in the OCCAMS immunophenotyping cohort (n = 360, log-rank P = 0. 0001, Fig. 5e ). ALDH3A2 expression loss was also associated with transformation to invasive disease, and later Tstage (Fig. 1e -f and Supplementary Data 4). On multivariable regression, low ALDH3A2 expression independently predicted death (odds ratio 1. 64, 95% CI 1. 13-2. 39, P = 0. 01, Supplementary Data 6). Similarly, analysis of a previous breath dataset  6  linked high exhaled decanal to later T-stage and significantly poorer survival (Fig.
6c ). However, in transcriptomic data, ALDH3A2 expression was most highly correlated with telomeric neighbors (Supplementary Fig. 6d ), and an analysis of ALDH3A2 expression in The Cancer Cell Line Encyclopedia dataset showed a positive correlation with copy number (Supplementary Fig. 6e ). Hypothesizing that ALDH3A2 copy influences ALDH3A2 expression in EAC, we first checked for focal or regional copy change at 17p11 by applying GISTIC  25  to TCGA single-nucleotide polymorphism array data. This suggested that 17p frequently undergoes whole-arm shallow deletion, affecting both the ALDH3A1/2 and TP53 loci (Fig. 5h ). Chromosome 17p loss-ofheterozygosity is a common event in transformation to EAC and conveys poorer prognosis  26  , and TP53 is the only recurrently deactivated gene in EAC (>80% cases)  27, 28.
Importantly, in two public datasets, comparing copy number to expression revealed a positive correlation for ALDH3A2 but not ALDH3A1 (Supplementary Fig. 5f-k ). To confirm this experimentally, we sought to compare ALDH3A2 copy number with expression in the OCCAMS immunophenotyping cohort (n = 360), using four-color fluorescence in situ hybridization (FISH), confocal image acquisition in three dimensions, and automated image analysis (Fig. 5i ). This uses cell morphology to restrict analysis, and thus minimizes signal contamination from undissectable normal populations (e. g. , fibroblasts, endothelia, immune cells). Tumors with reduced ALDH3A2 immunostaining had a significantly reduced ALDH3A2 copy ratio, although ALDH3A1 copy number had no effect on ALDH3A1 expression (Fig. 5j ). We then tested the hypothesis that 17p arm deletion involves both TP53 and ALDH3A2 loci by comparing corresponding FISH signal ratios.
Methods Patients. Fresh human material and associated clinical metadata was collected and accessed under UK National Research Ethics Service Ref: 14/LO/0742, Imperial College Healthcare Tissuebank Review Board Approvals R14067, R14087, and R16018. Archived paraffin embedded tissue was accessed under 14/LO/0742, approval R14067, and through 10/H0305/1. Written informed consent was taken from all patients. Patients were identified via the North West London Oesophago-gastric (OG) Cancer Unit (St. Marys' Hospital, Paddington, London, UK) and associated endoscopy department. Tissue samples were collected at endoscopy using 3 mm biopsy forceps thoroughly cleaned in UPLC-grade water. Typically 4-6 biopsies were harvested at 2 cm intervals per tissue type, separated circumferentially, to capture clonal diversity.
All patients were starved for at least 6 h prior to sampling. Demographic details were compiled prospectively and listed in Supplementary Data 1. Cell lines and treatments. Supplementary Data 1 lists the materials used in the study. Commercial EAC cell lines were acquired from European Collection of Authenticated Cell Cultures (via Public Health England) or the American Type Culture Collection at the start of the study and cultured according to instructions. Primary keratinocyte cultures were established using a rho-kinase method developed for skin keratinocytes  16. Briefly, three endoscopic biopsies from patients with normal endoscopic appearances were pooled and minced in serum free Dulbecco's modified Eagle's media (DMEM; ThermoLife) containing gentamicin/amphotericin (Invitrogen). The homogenate was incubated in collagenase III (Sigma) for an hour at 37 °C, neutralized in DMEM containing 10% FBS and the antibiotics, and forced through a 100 μm mesh.
1%). RNA interference was achieved with oligofectamine (ThermoLife) according to the manufacturer's instructions and confirmed by immunoblotting. The total transfected RNA was kept constant across the conditions, supplementing with non-targeting RNA as needed. Five-day proliferation assays were in 96-well format using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay (ThermoLIfe). Reduced and oxidized glutathione levels were calculated using a fluorescence-based kit (Promega V6611). Cells were pre-treated with metformin (Sigma) for 48 h, including refreshed media after 24 h, and consistent metformin concentrations maintained for the aldehyde incubation. Concentrations for the formaldehyde and acetaldehyde challenge experiments were selected based on literature evaluation  43, 44  , and were within the measured concentrations from the tissue LC-MS data in the present study.
Alternating low and high calibration points were injected regularly to ensure consistent instrument response. Forty-two aldehydes and ketones were selected for study (as indicated in Supplementary Data 1). The rationale for analyte selection was any of: (i) known genotoxin, (ii) physiological metabolite, (iii) present in exhaled breath, (iv) a specific target for ALDH isoenzymes of interest. Analytical variation was controlled with six isotope-labeled standards (ISTDs)-C2:0-d 4 (Sigma, for short-chain alkanals), C6:0-d 12 (Sigma, for medium-chain alkanals), C16:0-d 5 (Santa Cruz, for fatty alkanals), HNE-d 3 (Cambridge Bioscience, for dienals), ONE-d 3 (Cambridge Bioscience, for dialdehydes), and MDA-d 2 (prepared by diluting 10 μL 1,1,3,3tetraethoxypropane-1,3-d 2 (Santa Cruz) in 10 mL of 0.
2 mL, and that 1 ng/mL was well above the limit of detection for all targets. The manually checked integrations were exported to R to reformat the matrix (RStudio version 1. 1. 456, RStudio, Inc. , code provided in Supplementary Data 7) and complete the processing as above. All data processing was blinded. Aldehyde quantification using proton transfer reaction time-of-flight mass spectrometry. Measurements were conducted employing a commercial PTR-MS instrument (PTR-TOF 1000; Ionicon Analytik GmbH, Innsbruck, Austria). Drift tube conditions were temperature 110 °C, pressure 2. 30 mbar, and voltage 350 V, resulting in an E/N of 84 Td (1 Townsend = 10-17 V cm 2 ). Optimal measurement conditions were chosen based on a validated experimental workflow  47. Sample inlet flow was set to 40 sccm. During the described experiments, a series of daily quality checks were conducted on the PTR-TOF-MS: we assessed the amounts of spurious ions with the two ionization modes used (H 3 O + and NO + ), and we measured accuracy on a benzene certified standard and fragmentation on butanal and butyric acid.
Mass resolution was checked daily and optimized whenever needed. Tissues samples (10-20 mg) were thawed in glass 20 ml headspace vials with Teflon insert screw-caps (ThermoLife) left to equilibrate at 25 °C for 30 min. The headspace was sampled for 10 s at 40 mL/min with isobaric volume replacement with cylinder air. All data analysis was carried out using PTR-MS Viewer 3. 2. 2. 2 (Iconicon), following the manufacturer's instructions. Accurate mass peaks reaching a signal-tonoise ratio >10:1 were integrated and normalized to the total ion emission. This procedure accounted for sample-to-sample variation in mass and surface area. Aldehyde-nucleotide adduct quantification by ultra-performance liquid chromatography tandem mass spectrometry. Two aldehyde adducts of purine nucleotide were selected for quantification: 1-N 6 -etheno-2′-deoxyadenosine (edA), and α-methyl-γ-hydroxy-1,N 2 -propano-2′-deoxyguanosine (CrodG).
For tissue samples, five endoscopic biopsies from SqN, SqT, or EAC collected and pooled. Leukocytes were extracted from the buffy coat of 10 mL blood collected from EAC patients. DNA clean-up was with the Gentra Puregene kit (Qiagen). Three isotopelabeled internal standards were used at a concentration of 5 ng/mL: deoxyadenosine-N  13  5 (Cambridge Isotopes), 1-N 6 -etheno-2′-deoxyadenosine-N 13 5 (produced by reacting chloroacetadlehyde with deoxyadenosine-N 13 5 )  48  , and αmethyl-γ-hydroxy-1,N 2 -propano-2′-deoxyguanosine-13 C,  15  N 2 (Toronto Research Chemicals). UPLC-MS/MS and data processing were as previously described  19. Bioinformatics. Two datasets, GSE26886 and GSE13898 were found on Arrar-yExpress and Gene Expression Omnibus reporting esophageal squamous mucosa and EAC transcriptomes and were included in geneset enrichment analysis (Broad Institute, MIT, Cambridge, MA, USA)  49.
The default settings and the Gene Ontology geneset set (version 4. 0) were selected for the analysis. GSE26886 used laser capture microdissection for tissue isolation and was thus selected for presentation in Fig. 2. To identify candidate genes in the aldehyde oxidoreductase geneset, a univariate analysis of the datasets was conducting by comparing groups with a Student's t-test, using a Bonferroni significance threshold of P = 10 -6. GSE26886 was also selected for Ingenuity Pathway Analysis (Core Analysis, Qiagen) using standard settings. Legacy data from The Cancer Genome Atlas (available at https://portal. gdc. cancer. gov/projects/TCGA-ESCA) and the International Cancer Genome Consortium ICGC (available at https://ega-archive. org/studies/EGAS00001000725) were analyzed for ALDH3A2 regulatory hypotheses (last date accessed: 1 June 2019).
TCGA-ESCA data are a mixed of EAC and squamous cell cancers, and so EAC cases (n = 87) were selected in these analyses, and a subset of these had survival data (n = 57). ICGC data from OCCAMS were limited to 85 cases that had a predicted normal-cell contamination <50%. For each case germline SNPs and indels were called using the GATK (3. 2-2) HaplotypeCaller, and reads supporting each allele at germline heterozygous positions were counted for both the tumor and matched normal. Copy number alterations along with ploidy and purity estimates were then derived from these read counts using ASCAT (2. 3), and compared to matched RNA-seq expression data. mRNA quantification. MIQE guidelines were adhered to for all polymerase chain reaction (PCR) experiments  50   was determined from GSE26886 expression distributions, taking α = 0.
05 and β = 0. 8, which returned 5 (ALDH4A1) to 67 (ALDH1A1) paired samples. Endoscopic tissue biopsies were flank cryosectioned and microdissected if necessary to achieve a cell purity >90%. Homogenization was in Trizol (ThermoLIfe) using three-step homogenization (manual pestle grinding for 2 min, ceramic bead-beating for 3 min, and then Qiashredder (Qiagen)). RNA was fractionated and then purified (RNAeasy, Qiagen). Reverse transcription was with Superscript III kit (Thermo-LIfe), and quantitative PCR using PowerSybr PCR master mix (ThermoLife). Primers were designed in Primer3 (see Supplementary Data 6). The MIQEsuggested panel of 10 reference genes was assayed in a panel of 10 random samples and HPRT1 was found to be the most stable. All data processing was blinded. Immunohistochemistry.
Antibodies were as for immunoblotting and details are provided in the Supplementary Data 1. Sections were imaged (NanoZoomer 2. 0HT, Hamamatsu). Scoring was by two pathologists who were blinded to the metadata. For whole-mount sections SqT, BAR, and EAC regions were scored in five random high-powered areas according 0-3 on a basis of staining intensity and prevalence (0 = no staining or <50% mild staining; 1 ≥ 50% mild staining, no moderate staining; 2 = any moderate staining, <50% strong staining; 3 ≥ 50% strong staining)  51  with a single average score per tissue type per patient used for comparative analysis. For correlation to metadata, the immunoscore was dichotomized, with a cut-off of >1 being positive. For tissue microarrays, replicate cores for each patient were provided in quintuplet (Imperial array) or triplicate (OCCAMS and POEM arrays, see "Acknowledgements").
Masks were generated using the DAPI image, selecting nuclei on size and roundness to filter out non-malignant cells, improving cellularity to >99% (average of 20 images, manual counting). The average counts per mask were calculated for each probe, and typically 300-500 masks were counted per field. Target probes count (ALDH3A1/2 and TP53) were presented as a function of the control probe count (centromere 17). Quantification and statistical analysis. Calculations for bench experiments were conducted in Metaboanalyst 4. 0 (https://www. metaboanalyst. ca/), Prism (version 7. 0), SPSS (version 26) or R. For principal component analysis, measured aldehyde concentrations were adjusted by weight, and then mean-centered and log-transformed to normalize the data and give each feature equal weight in the model.
Acknowledgements This research was supported by the  National Institute for Health Research (NIHR) London In-Vitro Diagnostic, NIHR-Biomedical Research Centre , the  Imperial College Charity , and the  Rosetrees and Stoneygate Trusts. The  Chromosome Dynamics Core  is supported by the  Wellcome Trust  (award  203141 ). The views expressed are those of the authors and not necessarily those of the  Wellcome Trust , the  NHS , the  NIHR  or the  Department of Health. The laboratory of R. C. F. is funded by a  Core Programme Grant  from the  Medical Research Council  ( RG84369 ). Whole-genome sequencing was funded by a program grant from  Cancer Research UK  ( RG81771/84119 ). The authors thank collaboration members for access to genomic data, tissue microarrays, and associated metadata. The following OCCAMS members and centers contributed:  P.
Data availability Legacy microarray expression studies re-analyzed here (in Fig. 1  and Supplementary Fig. 1 ) are available at https://www. ncbi. nlm. nih. gov/gds, under the accession codes: GSE26886, GSE13898, GSE39491) and GSE34619. Data from The Cancer Genome Atlas (used in Fig. 5  and Supplementary Fig. 6 ) is available at https://portal. gdc. cancer. gov/ projects/TCGA-ESCA. Data from the International Cancer Genome Consortium (used in Supplementary Fig. 6 ) is available at https://ega-archive. org/studies/ EGAS00001000725. Data from The Cancer Cell Line Encyclopedia (used in Supplementary Fig. 6 ) is available at https://portals. broadinstitute. org/ccle. Source data are provided with this paper.
A satisfactory performance of hydrological models under historical climate conditions is considered a prerequisite step in any hydrological climate change impact study. Despite the significant interest in global hydrological modeling, few systematic evaluations of global hydrological models (gHMs) at the catchment scale have been carried out. This study investigates the performance of 4 gHMs driven by 4 global observation-based meteorological inputs at simulating weekly discharges over 198 large-sized North American catchments for the 1971-2010 period. The 16 discharge simulations serve as the basis for evaluating gHM accuracy at the catchment scale within the second phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a). The simulated discharges by the four gHMs are compared against observed and simulated weekly discharge values by two regional hydrological models (rHMs) driven by a global meteorological dataset for the same period.
Introduction Climate change impact research is currently moving onto the provision of climate projection services by impact models for use in developing adaptation strategies in various environmental sectors  [1]. In the water sector, many worldwide initiatives have emerged from organizations and research centers, with the production and dissemination of information on projected climate change impacts on water resources through specific hydrological indicators. Such information is usually designed to cater to the needs of water and energy domain practitioners and is intended for use (and is sometimes used) in operational decision-making. Data suppliers and modelers thus have the responsibility of providing reliable and accurate information on the impacts of climate change on water as local adaptation measures stem from that.
In its second phase (phase 2a), the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP; https://www. isimip. org/about/ accessed on 1 November 2021) provides simulated discharges from several gHMs globally from which simulations for individual large-scale river basins can be extracted. The gHMs in the ISMIP2a are driven by multiple observation-based meteorological datasets. A systematic assessment of the gHMs' performance, along with the uncertainty associated with the choice of the driving meteorological inputs, is of great importance since it provides the basis for the following impact studies. Some studies consider multiple gHMs (e. g. ,  [11, 12, 21, 27, 28] ) with several forcing inputs (e. g. ,  [6, 29, 30] ), but with a macro (i. e. , continental to global scale) or regional (e.
However, the use of small-sized catchments is a limitation regarding the spatial resolution of the gHMs (0. 5-degree grid cells), while a weak sample of catchments precludes a spatially detailed assessment of the gHM's performance. Based on a multi-model approach composed of four gHMs (DBH, H08, LPJml, and PCR-GLOBWB) and two rHMs (GR4J and HMETS) driven by multiple forcing meteorological datasets over 198 large-sized NA catchments for the 1971-2010 period, this study aims at contributing to the ISIMIP2a topic for operational use purposes by: (1) assessing the gHMs' performance in terms of simulating seasonal flow dynamics; (2) comparing the gHMs' performance with that of the rHMs; and (3) based on (  1 ) and (  2 ), exploring the influence of the global driving datasets and catchment characteristics on gHM performance.
Study Area The catchments are selected from the HYSETS large-scale database (https://osf. io/ rpc3w/ accessed on 1 November 2021), which is comprised of 14,425 catchments in the NA region. The HYSETS database includes a wide array of hydrometeorological data over the 1950-2018 period: (1) daily precipitation, minimum and maximum temperature products from seven data sources; (2) hydrometric gauging station discharge time series from one data source per country (Canada, contiguous U. S. , Mexico); (3) SNODAS and ERA5-Land snow water equivalent; and (5) catchment properties from PAVICS-Hydro (area, elevation slope, land use, soil properties, and other physiographic information). See  [38]  for additional information regarding the HYSETS database. For this work, given the requirement for the gHM-gridded discharge values, largesized catchments with a drainage area of more than 10,000 km 2 were selected.