dataesr/scientific-paragraphs-categorization huggingface dataset:
Study cohorts TP53 p. R337H paediatric ACC cohorts. A cohort of children diagnosed with ACC with the R337H germline mutation was used in this study. We divided this cohort into a discovery cohort (BRZ1, N = 26) and a validation cohort (BRZ2, N = 16) based on when the DNAs were received from the Pathology unit. The patients were treated in the Pequeno Príncipe Hospital, Brazil. This study was approved by the Ethics Committee from Pequeno Príncipe Hospital, and authorisation was obtained by signing a consent form. Clinical data that included the age at diagnosis for each patient, gender, and TP53 mutation status were available. Adult ACC cohort. A retrospective database for primarily sporadic ACC patients (N = 92) was analysed from the Cancer Genome Atlas. 27 These patients were diagnosed with adult ACC and bloodderived genotype data from the Affymetrix™ Genome-Wide Human SNP Array 6.
0 was available together with the relevant clinical information. Approval from the National Institute of Health (NIH) Data Access Committee was obtained (request number 34594-5). Somatic mutations of TP53 for this data set were retrieved from cBioPortal (http://www. cbioportal. org/) website (accessed December 2018). Genomic DNA preparation Genomic DNAs from the BRZ patients were extracted from peripheral blood using the ReliaPrep™ gDNA kit as per the manufacturer's recommendations (A2051, Promega).
SNP genotyping Genomic DNAs from the BRZ cohort were genotyped using the Infinium © LCG assay on the Human Omni 2. 5-8 v1. 3 BeadChip (Illumina platform), according to the manufacturer's instructions and at the High-Throughput Genomics at the Wellcome Trust Centre for Human Genetics, University of Oxford. Cancer GWAS and GWAS SNPs The GWAS Catalog was downloaded from the European Bioinformatics Institute (EMBL-EBI) website (https://www. ebi. ac. uk/gwas/) on February 2018. We found 35,705 GWAS lead SNPs in studies with individuals with European (EUR) ancestry. We then retrieved all closely linked SNPs (proxies) based on the 1000 Genomes phase 3 data acquired through the web server: rAggr (http://raggr. usc. edu), and only considered the proxies that meet the following criteria: population: EUR; minimum MAF (minor allele frequency): ≥0.
01; r 2 ≥ 0. 8; maximum distance: 500 kb; maximum # Mendel error: 1; Hardy-Weinberg equilibrium (HWE) p-value: 1e-6; and minimum genotyping rate of 95%. Cancer GWAS proxy SNPs and GWAS proxy SNPs were then pulled to intersect with the significant SNPs genotyped on the Infinium array as described below. SNPs on the Infinium genotyping array with MAF ≥ 0. 05 were selected based on dbSNP150 hg19 genome build. Briefly, snp150Common. txt were accessed in August 2018 from http:// hgdownload. soe. ucsc. edu/goldenPath/hg19/database/. Triallelic SNPs were removed. SNPs with MAF ≥ 0. 05 (8,209,080 SNPs) were then pulled to identify the number of SNPs genotyped on the Infinium array with MAF ≥ 0. 05 among cancer GWAS and GWAS SNP lists.
P-values were subsequently adjusted for multiple hypothesis correction (Bonferroni correction). mRNA expression levels and survival analyses RNA-seq data for the adult ACC cohort (illumine hiseq Level 3 RSEM normalised genes) were retrieved from http://firebrowse. org/?cohort=ACC accessed February 2019. Genes involved in the regulation of retinoid metabolism and those involved in retinoic acid receptor-mediated signalling were retrieved from http://software. broadinstitute. org/gsea/ msigdb/cards/GO_RETINOIC_ACID_METABOLIC_PROCESS. html (GO:0042573) and http://software. broadinstitute. org/gsea/ msigdb/cards/PID_RETINOIC_ACID_PATHWAY. html, accessed 18 July 2019. The cut-off point between high and low mRNA expression levels for each gene was computed based on the maximally selected rank statistics using the "survminer" package in R.
mRNA levels with values above the cut-off point were considered high and below were considered low. Survival data for the adult ACC were retrieved from Liu et al. ,  28  Table  S1  Tab TCGA-CDR, accessed May 2019. The progression-free interval (PFI) endpoint was analysed (PFI, the length of time during and after the treatment of a disease when a patient lives with the disease but it does not get worse). The patients in the adult ACC cohort were randomly separated into Discovery and Validation cohort pairs at 50:50 ratios 10 times using the "R Base" package. 29 e first performed Kaplan-Meier survival analyses to estimate the survival probability as a function of time in relation to mRNA expression levels for each transcript (patients with low versus high expression levels in tumours). The Log-Mantel and Haenszel survival test (a Log-rank test) was used to determine the statistical differences betwen the groups.
Genes that were significant with p < 0. 05 were tested in the paired Validation cohort. In the Validation cohort the analyses were undertaken in the exact same manner except for the final adjustment of the p-values for multiple hypothesis testing, which was performed using the Bonferroni method. Adjusted p < 0. 05 were considered significant. These analyses were performed in each of the 10 Discovery/ Validation pairs and those genes whose transcript levels associated with PFI six times or more were considered hits. Gene expression data from two independent paediatric ACC cohorts were used in this study: the Children's Oncology Group (COG, n = 34, accession number; GSE76019) and International Pediatric Adrenocortical Tumor Registry group (IPACTR, n = 19, accession number; GSE76021). The data from the COG and the IPACTR were accessed via the Gene Expression Omnibus (GEO) in June 2019.
For these cohorts, the progression-free survival (PFS) endpoint was considered (PFS, the time elapsed between protocol enrolment and the date of disease progression, death, or most recent follow-up). 30 mRNA expression data was available as a normalised log2 signal. The data were generated on two different array platforms: IPACTR on the GLP96 platform, COG on the GPL13158 platform. In order to maximise consistency in the analyses, we considered only probes with the same Transcript ID design. The cut-off point between high and low mRNA expression levels, the statistical difference in survival, and the approach used to validate the significant genes were performed as described above. All analyses were performed on R (version 3. 5. 1) using the following packages "data. table", "dplyr", "reshape2", "tidyr", "plyr", "survminer" and "survival" in addition to base R.
For all patients, genotyping information was available from blood DNA (Affymetrix SNP 6. 0 Array), while somatic mutation data from the tumours was available for 91 patients (Supplementary Table  2 ). Out of the seven SNPs that significantly associated with differential age-of-onset of paediatric ACC, six SNPs could be studied in the primarily adult ACC cohort, as three were successfully genotyped with the Affymetrix array, and three SNPs had successfully genotyped proxies. One of these six SNPs, rs971074, significantly associated with differential age-of-onset (adj. p = 0. 00114, Kruskal-Wallis test). rs971074 (C/T, chr4:100341861, Genome Reference Consortium Human Build 37 (GRCh37)/hg19) is a synonymous SNP located in exon 6 of the alcohol dehydrogenase 7 gene (ADH7) gene (Fig. 1b-d ). The TP53 mutation carriers with rs971074 minor allele (CT) were diagnosed at a median age of 0.
00038; 32 years later than TT patients, p = 0. 00414, Mann-Whitney U test). Altogether, these data show that the rs971074 SNP associates with differential ACC age-of-onset in both TP53 mutation carriers and non-carriers alike. However, in ACC patients with no inherited TP53 mutations, the TP53 gene is known to be somatically mutated (19. 6%, cBioPortal). In the adult cohort, 18 patients (19. 6%) had known pathogenic TP53 mutations in their tumours,  and 2 had the R337H germline mutation. When we restrict our analyses to those 71 patients without either inherited or somatic TP53 mutations, and excluding the patient with no somatic mutation data, similar allelic differences in the age-of-onset were found (p = 0. 00075, Kruskal-Wallis test, Table  2  and Supplementary Fig. 1 ), wherein TT patients were diagnosed at a median age of 26 years compared to a median age of 46.
In the adult ACC cohort, we processed RNA-seq data derived from tumours. We were able to detect significant RNA levels of 48 of the 50 genes. Next, we randomly assigned the patients into Discovery and Validation groups at a 50:50 ratio. We repeated these analyses ten times. For each Discovery group, we first separated the patients into two groups based on the expression levels of a given RA pathway gene using a Rank statistic (high and low expressing groups). We then explored potential differences in PFI between these two groups using a Log-rank test. In the patients of the Discovery cohorts, we identified an average of 28 transcripts (ranging from 20 to 33) that were associated with PFI with a raw p < 0. 05. Next, we sought validation of the associations identified in the Discovery groups, in the paired Validation groups.
In the Validation groups, the raw-p-values for each association were adjusted for multiple hypothesis testing and significance was considered with an adjusted p < 0. 05 (Bonferroni adjustment, Supplementary Table  5 ). Interestingly, expression levels of 5 different genes consistently associated with PFI in minimally 6 different Discovery/Validation cohort pairs (Fig. 4b ). One of the genes (BCO2) is involved in RA metabolism, while the other 4 genes (RARB, SCPEP1, CDK1 and PRKCA) are involved in RA signalling (Fig. 4c ). These data implicate a role for RA pathway genes in ACC progression. To further test this hypothesis, we next explored similar associations in paediatric ACC. We utilised two independent paediatric ACC cohorts with microarray gene expression data associated with outcome information (IPACTR and COG).
30 We investigated the expression data from histologically confirmed ACC paediatric patients (19 from the IPACTR and 34 from the COG). 30 We analysed these cohorts in a similar manner as with the adult ACC cohort, and thus randomly assigned the IPACTR as the Discovery cohort and the COG as the Validation cohort, and explored potential associations of gene expression for the 50 RA pathway genes with PFS using a Log-rank test. Specifically, as the cohorts utilised different arrays, we restricted our analyses to shared probes to maximise our ability to draw comparisons. Out of the 50 RA pathway genes, we were able to identify 79 shared probes for 43 RA pathway genes. Using these data and the abovedescribed analytical pipeline, we determined that the expression levels of 4 different transcripts from three different genes associated with PFS in both cohorts: Discovery cohort: raw p < 0.
e. the samples do not have to be chemically treated or sectioned. Wide-angle X-ray scattering (WAXS) reveals the structural information from the atomistic level up to the nm-level. Using WAXS, the amount of crystalline cellulose among the total weight of the sample, the width of cellulose crystallites and their orientation can be determined for the wood samples. Small-angle X-ray scattering (SAXS) gives information on the nanoscale (2-100 nm) structure of the sample: using SAXS, the size, and shape of the scattering particles and their close-range order can be analyzed. In this study, the SAXS patterns, measured from the wet wood samples, were analyzed using an analytical model (WoodSAS), recently developed for the analysis of small-angle scattering data from wood samples, in which cellulose microfibrils were modeled as infinite long cylinders with a paracrystalline hexagonal close-range order  (Penttilä et al.
X-ray scattering experiments at ESRF SAXS and WAXS experiments were conducted at the D2AM beamline of the European Synchrotron Radiation Facility (ESRF) using an X-ray energy of 16 keV with two XPAD hybrid-pixel detectors, separately for SAXS and WAXS. The measurements were conducted using perpendicular transmission geometry. Wet samples of Rosewood, Mahogany, Ebony, Maple, Alder, Ash and Msasa were sealed in Kapton tape and measured using both SAXS with two different detector distances (corresponding to low-q and high-q SAXS ranges) and WAXS. The WAXS data was used to compute the average crystallite width and moisture content (MC) in these samples. The other samples were measured as wet only in the high-q SAXS range. Besides, all the samples were measured dry in the high-q SAXS range. The low-q SAXS region (0.
1994 ). The WoodSAS model can be downloaded as a plug-in model in SasView, and both the software and the model are freely distributed. The analytical analysis of SAXS patterns is based on the assumption of two phases (i. e. two different electron densities): (1) crystalline cellulose and (2) water background including water-infused lignin-hemicellulose matrix  (Jakob et al. 1996; Leppänen et al. 2009). Wet wood can thus be modeled as a two-component system consisting of crystalline cellulose microfibrils in a water-hemicellulose-lignin matrix. The cellulose microfibrils were modeled as infinitely long cylinders with a Gaussian size distribution and having a hexagonal close-range ordering with a paracrystalline distortion. The model can be used to obtain the average diameter of the cylinders with Gaussian variation in addition to the average distances between the cylinders with the variation included in the paracrystal model  (Penttilä et al.
2019). In addition to these terms, the WoodSAS model includes a Gaussian function centered at q = 0 Å -1 , which can describe the scattering from features other than the crystalline cellulose microfibrils, such as nano-sized pores. From all the WAXS data (measured both at ESRF and UH), the 200-reflection was analyzed to determine the crystallite width of the cellulose microfibrils. A Gaussian curve fitting method, which took into account the amorphous (isotropic) background of the cell wall components and the water background (only in the case of the wet samples), was used to calculate the FWHM from the cellulose 200 peak and the Scherrer equation was used to compute the average widths of the crystallites (as presented in  Leppänen et al. 2011). The instrumental broadening was estimated to be 0.
RESULTS AND DISCUSSION All the WAXS and SAXS results are given in Table  2. For the samples with an MC value, the crystallite width has been analyzed from the WAXS data measured from wet samples at ESRF and as for the rest of the samples, the crystallite width has been computed from the WAXS data measured at UH. For Rosewood samples, the value of crystallite width measured at UH has also been presented in brackets due to major divergence from the ESRF value.
ACKNOWLEDGEMENTS K. S. and M. V. acknowledge the  University of Helsinki  (3-year research grant). Samples were obtained with funding from  Kone Foundation. P. A. also thanks Kone Foundation  for funding and Finnish luthiers  Kauko Liikanen  and  Kari Nieminen ;  Atte Jussila  from  OP-Puu ; and  Dr. Tuula Jyske  for assistance in acquiring samples during the project. P. P. thanks  Emil Aaltonen Foundation  and  Academy of Finland  (grant no. 315768 ) for funding. The SAXS and the wet-state WAXS experiments were performed on the French CRG beamline D2am at the  ESRF  (experiment  02-01-885 ), and  Dr. Isabelle Morfin  from  CNRS/University of Grenoble Alpes, LIPhy , is thanked for providing assistance in using the beamline. The WOS detector at D2AM was funded by the  French National Research Agency (ANR)  under the " Investissements d'avenir" program  (grant no.
Local biodiversity trends over time are likely to be decoupled from global trends, as local processes may compensate or counteract global change. We analyze 161 long-term biological time series (15-91 years) collected across Europe, using a comprehensive dataset comprising ~6,200 marine, freshwater and terrestrial taxa. We test whether (i) local long-term biodiversity trends are consistent among biogeoregions, realms and taxonomic groups, and (ii) changes in biodiversity correlate with regional climate and local conditions. Our results reveal that local trends of abundance, richness and diversity differ among biogeoregions, realms and taxonomic groups, demonstrating that biodiversity changes at local scale are often complex and cannot be easily generalized. However, we find increases in richness and abundance with increasing temperature and naturalness as well as a clear spatial pattern in changes in community composition (i.
Discussion Our results demonstrate considerable heterogeneity in the extent and direction of change in biodiversity metrics in recent decades between biogeoregions, realms and taxonomic groups in Europe. We identified increases in taxon richness in Northern and Eastern Europe. Records in these regions are primarily represented by terrestrial and aquatic invertebrate datasets. These observed patterns are in line with recent modelled predictions of future responses to global changes  25  and are likely due to climatechange induced poleward range shifts across taxa  [27] [28] [29] [30]. We also detected declines in species abundances for terrestrial invertebrates and in the Atlantic biogeoregion (from which most of the terrestrial invertebrate time series in our dataset are derived). Our results, based on data with larger spatial, temporal and taxonomic coverage, and finer temporal resolution, corroborate recent reports of worldwide declines of local terrestrial insect communities  [31] [32] [33].
Thus, temporal changes in taxonomic composition, i. e. , turnover, are likely to be more sensitive than simply taxonomic richness (i. e. , alpha-diversity) and abundance in responses to global change  39, 40. Accordingly, we show that temporal changes in taxon turnover are more pertinent across biogeoregions than the other three studied biodiversity metrics. The observed increases in temporal taxon turnover are spatially structured across Europe, involving mostly biogeoregions of Northern and Eastern Europe. Such changes in community composition can reflect non-equilibrium dynamics (including time-lags and transient phenomena 41 ) with, for example, climate change  42  , pollution or the introduction and spread of alien species  43. Moreover, over the past 30-40 years (i. e. the period covered by most time series in our dataset) positive impacts of environmental regulation, e.
Our dataset is, to the best of our knowledge, the most comprehensive in terms of the spatial and temporal extent and taxonomic and realm representation of high temporal resolution long-term biodiversity monitoring records in Europe. Most sites included in this study are part of the global ILTER network and the vast majority are characterized by low anthropogenic pressure, which may have led to an underestimation of the true scale of biodiversity changes at continental scale. Perhaps most importantly, most studied sites are shielded from direct effects of changes in land-use and loss of habitat, e. g. , conversion to intensive agriculture and urbanization. To overcome such sources of bias, long-term monitoring programs should include a larger representation of more intensively used (e. g. agricultural) areas and incorporate sites vulnerable to significant anthropogenic perturbations  51.
Methods Data compilation. We circulated a call for biodiversity data within the ILTER network and additional partners to fill in geographical gaps. The criteria for data selection were: (1) each time series covers at least 15 years, (  2 ) with preferably at least ten survey events during that time, (3) sampling occurred at the same site (no space-for-time substitution), and (4) survey method, seasonal and taxonomic resolution were consistent throughout the whole study period for each time series. The final dataset included 161 time series from 89 ILTER sites and 26 additional long-term monitoring sites from other networks. The time span of the studied time series ranged between 15 and 91 years (median: 20), so that one time series started in 1921 while the others started between the 1980 and 2003 (median: 1994).
The end years range between 2005 and 2018 (median: 2015; Supplementary Fig. 2 ). Biodiversity data. Biodiversity data were expressed as abundance or biomass of surveyed taxa at each survey occasion, in some instances as percent coverage (e. g. for some time series of benthic algae and plants). Survey methods and season varied among time series, but were kept constant within each time series throughout the entire study period. In most cases, surveys were carried out once a year, but some time series had gaps or more frequent survey intervals (e. g. weekly resolution for phytoplankton, zooplankton and moths from the Finnish and Czech sites). In the latter case, we filtered the data in the time series to select only the months/seasons that were consistently surveyed throughout the whole study period, and we pooled the data (as sums or averages) within each year.
Taxonomic resolution was kept constant for each time series throughout the entire study period, generally at the species or genus level, with a few exceptions (i. e. a few macroinvertebrates groups were identified at family resolution). For each year within each time series, we computed four biodiversity metrics: total number of organisms or biomass (hereby referred to as abundance), taxonomic richness (i. e. number of taxa), Simpson´s diversity and temporal taxon turnover. The latter was computed as the proportion of taxa gained and lost between two subsequent years relative to the total number of taxa observed, using Eq. (  1 ), as implemented in the R package codyn  63. Number of taxa gained þ number of taxa lost total number of taxa : We classified the time series into nine biogeoregions, three realms and eight taxonomic groups (Fig.
1 ). Abiotic variables. For each study site we extracted the daily mean temperature and daily total precipitation data from the gridded observational dataset for precipitation, temperature and sea level pressure in Europe (spatial resolution: 0. 25 degrees  64  ), and computed the mean annual temperature and total annual precipitation. For aquatic ecosystems we used in situ water temperature measured at the surface and calculated the mean temperature across the yearly monitoring periods. We gathered information on local anthropogenic pressures in a standardized questionnaire (Supplementary Methods). The questionnaire asked the data providers to assess the impact (from 1 = no to 4 = strong impact) of a series of pressures at the site (e. g. urbanization, sources of pollution, agriculture, etc.
; for a full list see Supplementary Methods) and indicate whether impacts were constant or changed throughout the study period. Data providers were also asked to estimate the overall environmental quality of the site (i. e. naturalness; from 1 = low to 5 = high) and to state whether it was constant or changed throughout the study period. We used this information to define the quality-class of the sites, based on the overall assessment. The majority of sites (n = 67) scored 5 (i. e. high naturalness), 42 sites scored 4, 37 sites scored 3 and 15 sites scored 2. Data analysis. We used a two-step procedure that allowed us to combine very heterogeneous original datasets (see paragraph Biodiversity data). First, we analyzed each time series separately to quantify time-series-specific biodiversity trends.