dataesr/scientific-paragraphs-categorization huggingface dataset:
Data on early life and adult body size Genetic variants associated with early life and adult body size were identified from a recent GWAS of 453,169 participants of European descent from the UK Biobank  [15]. For early life body size, participants were asked at baseline: "When you were 10 years old, compared to average would you describe yourself as thinner, plumper, or about average?". For adult body size, body mass index (BMI) was derived using height (centimeters) and weight (kilograms) measured at baseline. To improve comparability between the different body size traits, BMI in adults was converted into a categorical variable with three groups like early life body size. A linear regression model was applied, assuming similar effects of a given single nucleotide polymorphism (SNP) on moving from the lowest to the middle and from the middle to the highest category of the body size variables.
Univariable MR analysis to estimate the total effect of early and adult body size on colorectal cancer A two-sample MR approach using summary data and the fixed-effect inverse-variance weighted method was implemented. Where Cochran's Q statistics identified heterogeneity across the individual SNPs in the early life and adult body size instruments, random-effect inversevariance weighted analyses were conducted  [22] [23] [24]. Univariable MR analyses for men and women combined were conducted to estimate the effect of both early life and adult body size independently on colorectal cancer risk. Analyses according to sex and tumor anatomical subsite were also conducted. Heterogeneity of associations according to sex and colorectal anatomical subsites was assessed by calculating the χ 2 statistic  [25].
LD clumping between early life and adult body size SNPs in the multivariable MR analyses was done using the ieugwasr R package (based on linkage disequilibrium R 2 = 0. 001) and the plots were created using the forestplot R package  [33, 34]. Summary statistics were harmonized using the harmonise_data function within the TwoSampleMR R package. All GWAS were assumed to be coded on the forward strand. The list of SNPs included in the multivariable MR analyses is given in Additional file 1: Table  S5. Reporting guidelines for MR studies were followed (MR STROBE checklist outlined in Additional file 1: Table  S6 )  [35, 36].
A recent MR study examined the associations between childhood obesity and cancer risk without taking into account adult body size, using a genetic instrument of 15 SNPs from a GWAS of 47,541 children from the Early Growth Genetics (EGG) consortium  [40]. Effect estimates from this study did not support a positive relationship between childhood BMI and overall colorectal cancer (OR, 1. 11; 95% CI, 0. 93-1. 32) [40]. Results from our analyses that crucially adjusted for adult body size also reflect little evidence of a positive relationship between early life body size and later life colorectal cancer risk. An additional MR study following a similar approach but using a different dataset for colorectal cancer also did not find any positive relationship between childhood BMI and overall colorectal cancer after adjusting for adult body size  [20].
We obtained summary stats for colorectal cancer from the Huyghe et al. GWAS excluding UK biobank participants; thus, independent datasets for the exposure and outcome phenotypes were used, avoiding potential bias due to sample overlap  [42]. Finally, the large cancer sample size allowed us to conduct a more detailed analysis by sex and cancer sub-site. A limitation of our study is that the early life body size phenotype was based on a self-reported questionnaire data than on direct measurements, so we cannot exclude potential recall bias in the measurements. However, the early life body size SNPs were successfully validated in three independent studies with direct measures of childhood BMI, demonstrating their validity as instruments for early life body size  [15, 16, 19]. In the ALSPAC cohort, a genetic score based on the SNPs early life body size was found to be a stronger predictor of childhood BMI compared with the score based on the adult variants (area under the curve [AUC]: 0.
In a context of accelerated soil erosion and sediment supply to water bodies, sediment fingerprinting techniques have received an increasing interest in the last two decades. The selection of tracers is a particularly critical step for the subsequent accurate prediction of sediment source contributions. To select tracers, the most conventional approach is the so-called threestep method, although, more recently, the consensus method has also been proposed as an alternative. The outputs of these two approaches were compared in terms of identification of conservative properties, tracer selection, contribution modelling tendency and performance on a single dataset. As for the tree-step method, several range test criteria were compared, along with the impact of the discriminant function analysis (DFA).
The dataset was composed of tracing properties analysed in soil (through the consideration of three potential sources; n = 56) and sediment core samples (n = 32). Soil and sediment samples were sieved to 63 µm and analysed for organic matter, elemental geochemistry and diffuse visible spectrometry. Virtual mixtures (n = 138) with known source proportions were generated in order to assess model accuracy of each tracer selection method. The Bayesian un-mixing model MixSIAR was used to predict source contributions on virtual mixtures and actual sediments. The different methods tested in the current research can be distributed into three groups according to their more or less restrictive identification of conservative properties, which were found to be associated with different sediment source contribution tendencies.
, 2016a). The Hayama lake catchment is mainly underlied by non-alkaline mafic volcanic rocks (42%), granite (31%) granodiorite (17%) and sedimentary rocks (7%) (supplementary materials S1). Main soil groups, according to the Comprehensive Soil Classification System of Japan  (Obara et al. , 2011, 2015, and  equivalent soil types according to the World Reference Base for Soil Resources (WRB) were provided) are: Brown Forest soils (37%; WRB: Cambisols/Stagnosols), Allophanic Andosols (36%; Silandic Andosols), Cambic Red-Yellow soils (9%; WRB: Cambisols) and Lithosols (9%; WRB: Leptosols) (Supplementary Materials S2) (data from NARO (2011)). All maps and geographical processing were performed using the QGIS software  (QGIS Development Team, 2022).
Discriminant Function Analysis. A discriminant function analysis (DFA) stepwise selection is carried out to identify a subset of predictors among the identified tracers  (Collins and Walling, 2002; Collins et al. , 2010). A forward stepwise variable selection based on Wilk's Lambda criterion was performed using the function greedy. wilks from the klaR package  (Weihs et al. , 2005). The function first initiates a model with the variable that discriminates groups. Then, the model is extended by including other variables based on Wilk's Lambda criterion: the one variable that minimises the Wilk's Lambda is only included if the model's p-value remains statistically significant after its inclusion. Wilk's Lambda statistic tends to zero when the variability between groups is higher than the variability within each group, then this criterion maximizes the difference between groups.
Un-mixing model An un-mixing model was run on actual sediment samples and virtual mixtures using the tracers selected by the three-step method (TSM) without and with DFA, and the consensus method (CM). To do so, the widely used Bayesian un-mixing model, MixSIAR, was employed using the R package MixSIAR  (Stock et al. , 2020, ver. 3. 1. 12)  with JAGS  (Stock et al. , 2022, ver. 4. 3. 1)  (Collins et al. , 2020; Evrard et al. , 2022; Lizaga et al. , 2020a; Batista et al. , 2022). As source and mixture (i. e. sediment samples or virtual mixtures) tracer data are assumed to be normally-distributed in MiXSIAR, tracers were log-transformed to enforce a higher degree of normality prior to modelling  (Laceby et al. , 2021b). The model was run with a "long" Markov Chain Monte Carlo sampling algorithm (i.
Although this is not a bias, it does not mean that the model outputs are devoid of errors. The RMSE is a measure of the accuracy and allows to calculate prediction errors of different models for a particular dataset. RMSE is always positive and its ideal value is zero, which indicates a perfect fit to the data. As RMSE depends on the squared error, it is sensitive to outliers. The r 2 describes how linear the prediction is. The NSE indicates the magnitude of variance explained by the model, i. e. how well the predictions match with the observations. A negative RMSE indicates than then mean of the measured values provides a better predictor than the model. The joint use of r 2 and NSE allows a better appreciation of the distribution shape of predictions and thus facilitates the understanding of the nature of model prediction errors.
The CRPS evaluates both the accuracy and sharpness (i. e. precision) of a distribution of predicted continuous values from a probabilistic model for each sample  (Matheson and Winkler, 1976). The CRPS is minimised when the observed value corresponds to a high probability value in the distribution of model outputs. The formulae and the full description of this score are available in  Jordan et al. (2017)  and  Laio and Tamea (2007). The calculation of the CRPS was performed using the crps_sample function from the scoringRules package (Jordan All data analyses were performed using R (R Core Team, 2022, ver. 4. 2. 2) within RStudio  (RStudio Team, 2022 ). An R package (fingR) implementing the approach followed in this study was developed and is freely available (Chalaux-Clergue and Bizeul, 2023).
Conclusions In this study, we compared two source sediment fingerprinting tracer selection methods, the most conventional three-step method and the consensus method, on their tracer selections, and their source contribution predictions for the same dataset. Conservativity tests of both methods were compared, along with several three-steps method range test criteria and the consensus method conservativeness index. The different conservativity tests resulted in a more or less restrictive identification of conservative properties. On the one hand, the minimum-maximum, minimum-maximum ± 10% and whiskers range test criteria were not restrictive enough and selected non-conservative properties. On the other hand, mean and median criteria and the conservativeness index were too restrictive and selected too few properties, which can lead to limitations when modelling source contributions in target samples.
Acknowledgements. The collection and the analysis of the soil and sediment samples were funded by the  TOFU  ( ANR-11-JAPN-001 ) and the  AMORAD  ( ANR-11-RSNR-0002 ) projects, under the supervision of the  French National Research Agency (ANR ,  Agence Nationale de la Recherche). The support of  CEA (Commissariat à l'Energie Atomique et aux Energies Alternatives, France) ,  CNRS (Centre National de la Recherche Scientifique, France)  and  JSPS (Japan Society for the Promotion of Science)  through the funding of  PhD fellowships  ( H. Lepage ,  H. Jaegler ,  T. Chalaux-Clergue ) and collaboration projects (grant no. PRC CNRS   JSPS 2019-2020 , no. 10;  CNRS International Research  Project - IRP -MITATE Lab ) is also gratefully acknowledged. T. Chalaux-Clergue  obtained a  JSPS  grant to spend his second year of PhD at Kyoto Prefectural University (Oct.
Code and data availability. The dataset is available on line at  Chalaux-Clergue et al. (2022). The code for running models, summarizing and plotting results is available in the supplementary material. In order to facilitate the implementation of the presented framework, an R package: fingR (Chalaux-Clergue and Bizeul, 2023), with all the functions used in the current study, has been developed and is freely accessible.
Clinical data collection for GLIOTRAIN (GT) patient cohort The GT cohort comprises GBM tumors collected across four clinical centers: RCSI (Beaumont Hospital, Dublin, Ireland), ICM (Paris, France), EMC (Rotterdam, Netherlands), and LIH (Luxembourg, in collaboration with the Neurosurgical Department of the Centre Hospitalier de Luxembourg). All patient samples met the GLIOTRAIN biobank inclusion criteria  [10]  and were divided into Shortterm survivors (STS) (≤ 9 months), Intermediate-term survivors (ITS) (> 9 and < 36 months), and Long-term survivors (LTS) (≥ 36 months) based on their overall survival (OS) among the N = 133 included patients.
Reverse phase protein array (RPPA) analysis The entire GT cohort, consisting of N =133 samples, were analysed via RPPA  [11]. 7 samples were removed due to initial quality control fail. 72 antibodies were selected to quantify proteins associated with different signalling pathways  [12]. Microvigene software (v5. 1) was used to generate spot signal intensities and normalize the spots via protein loading  [13]. The data was divided into linear values by the correction factor to obtain the normalized value  [13]. Final protein count data was normalized using z-score technique. ConsensusClusterPlus package (v1. 48. 0) was implemented for clustering analysis with specific parameters; innerLinkage = "average", finalLinkage = "average" and distance= "spearman".
RNA-seq data and transcriptomics analysis The GT RNA-seq transcriptomics dataset was downloaded from the curated GT database (tranSMART)  [10]. Count data were normalized using variance stabilizing transformation (VST). DESeq2 (v3. 11) package was used to identify the differentially expressed genes (DGEs) from RNA-Seq data. Heatmaps were generated using 'Complex Heatmap' (v1. 0. 12) and Gene Ontology (GO), and Volcano plots generated using Bioconductor packages 'clusterProfiler' (v3. 11) and 'EnhancedVolcano  ' (v3. 11). GBM subtype classification [Classical (CL), Mesenchymal (Mes), Proneural (PL)] was performed using the 'gliovis' tool (http://gliovis. bioinfo. cnio. es/) as previously published  [10].
Master regulator analysis To obtain master regulators, the Genome Enhancer pipeline was employed (www. geneXplain. com) [14] [15] [16]. Significantly upregulated genes in STSs were analsyed using Composite Module Analyst (CMA)  [17]  to detect potential enhancers. To identify the important transcription factors (TFs) we: a. ranked TF motifs (PWMs) based on a Yes/No ratio of their frequency in promoter sequences. A set of promoter sequences of interested genes is called the Yes set, while the promoter sequences of unchanged genes under the same experimental condition are called the No set. Motifs with a high Yes/No ratio and statistically significant enrichment of occurrences in Yes sequences, as determined by the binomial p-value, were considered important. b. We computed a regulatory score that measures the TF's involvement in controlling genes that encode master regulators.
Master Regulator analysis reveals signalling proteins and upregulated TFs in STS samples We used a Master Regulator analysis to identify new targetable signaling pathways by examining 1577 DEGs and locating clusters of TF binding sites in upstream regulatory regions. We identified 263 TFs and enhancers targeted by them, and then used CMA to identify two modules containing 13 TFs regulating regions of our genes of interest  [17]. Using the TRANSPATH® database  [18] , we reconstructed the signaling network, identifying 25 distinct master regulators and 13 TFs in our upregulated gene network in STS samples in comparison to LTS samples (Figure  S7 ). These could be novel targets for inhibiting overactivated signaling pathways in GBM. Table  S2  shows the lists of identified master regulators and their associations with pathways, such as integrin signaling and cell cycle regulation.
Discussion The project aimed to discover new prognostic biomarkers and new therapeutic targets for GBM. To achieve this, we compared patients with favorable outcomes (LTS) to those with unfavorable outcomes (STS) using the internal GT cohort and RNA-seq transcriptomics and RPPA molecular datasets. The differences between the LTS and STS samples were analysed to identify potential biomarkers. RPPA analysis on the entire GT cohort identified 4 clusters of (phospho)-proteins in GBM, revealing significant signaling heterogeneity. Although no significant difference in patients' OS was observed, cluster 1 showed higher levels of BCLXL, BAX, PARP, PDK1, and FAK compared to the other clusters  [23]. FAK and BAX were identified as potential targets for GBM treatment, while samples in cluster 1 with higher BCLXL levels could be treated with BH3 mimetics, and those with elevated SMAC/DIABLO levels could benefit from Smac inhibitor therapy  [23] [24] [25].
genes was associated with better prognosis. The presence of cilia in LTS was verified through IHC. Previous work has identified defects in ciliogenesis in glioblastoma  [35] , and cilia have been identified on cells expressing Ki67 and cells associated with pseudopalisading necroses  [36]. Our study suggests that cilia may serve as a new prognostic biomarker and potential therapeutic target in GBM, and further validation and functional studies are required to explore their role. Further analysis found master regulators of upregulated genes in STS, which are related to important pro-oncogenic Fig. 5  (A) OS analysis for the median expression for the cilium genes mapped from GO analysis. 44 cilium genes were mapped, and their median expression were taken for the GT dataset. The pink line demonstrates lower level of expression from the median value and the blue line shows higher level of expression from the calculated median value.
org/10. 5281/zenodo. 7400922). In these former works, the exchanges of wave terms through the OASIS-MCT coupler only included significant wave height, mean or peak direction and frequency. The wave-induced terms were then computed using a monochromatic approximation following the implementation made with the WKB model. However, in cases where the sea state is more complex than a single close-to-monochromatic wave system (e. g. multi-modal or spread spectra), the wave-induced terms computed from the full spectrum (which are provided by spectral wave models) may be significantly different from their monochromatic approximation. This paper thus presents the implementation of the spectral wave-induced terms in CROCO and evaluate their added-value in a coastal simulation of the Bay of Somme (France).
Offshore BoS, the seabed is composed of a mixture of coarse to fine sands moulded with tidal sandbanks covered with very large sand dunes  (Ferret et al. , 2010). Within BoS, the seabed sediment displays a fining gradient from fine sands in the external part of the bay to muddy fine sands in some of its internal parts  (Michel et al. , 2017). Widespread dynamic morphological features are present, such as small to large dunes, tidal channels and sand bars  (Michel et al. , 2017). As most of estuaries and bays, BoS is concerned with an infilling sedimentary process in relation with marine sediment contribution and the erosion of the front submarine part of the bay sedimentary prism  (Verger, 2005; Michel et al. , 2017). In-situ data used for the validation step (wave characteristics, flow velocity magnitude and direction as well as water levels) were recorded off BoS over a submarine sand dune field located in the southwest region of the study area.
Hydrodynamic model CROCO (Coastal and Regional Ocean COmmunity model, https://doi. org/10. 5281/zenodo. 7415343) is a new oceanic modelling system built upon the ROMS-AGRIF model  (Shchepetkin and McWilliams, 2004; Penven et al. , 2006; Debreu et al. , 2012). It solves finite-difference approximations of the Reynolds-averaged Navier-Stokes Equations (RANS) on a horizontal free-surface Arakawa C grid and vertical stretched terrain-following coordinates with a split-explicit time stepping algorithm. CROCO has a flexible structure that allows to choose several numerical schemes and parameterizations. Here we use the model with the hydrostatic and Boussinesq approximations, the WENO5 scheme  (Acker et al. , 2016)  for horizontal and vertical advection, the generic length scale scheme for vertical mixing based on the k-𝜖 turbulent closure  (Jones and Launder, 1972; Umlauf and Burchard, 2005; Warner et al.
From these surface Stokes velocity components, the dispersion relationship (𝜎 2 = 𝑔𝑘 tanh(𝑘 𝐷)), and the expansion of the hyperbolic functions describing the vertical distribution of the wave field, it is possible to obtain the following formulation for the 3D Stokes velocity field through the water column: (𝑢 𝑠 (𝑧 𝑟 ), 𝑣 𝑠 (𝑧 𝑟 )) = (𝑢 𝑠𝑠 , 𝑣 𝑠𝑠 ) 1 2𝑘 (1 + 𝑒 -4𝑘𝐷 ) 1 𝑧 𝑢 𝑝 -𝑧 𝑙𝑜𝑤 𝑒 2𝑘 (𝑧 𝑢 𝑝 +ℎ-𝐷) -𝑒 -2𝑘 (𝑧 𝑢 𝑝 +ℎ+𝐷) -𝑒 2𝑘 (𝑧 𝑙𝑜𝑤 +ℎ-𝐷) -𝑒 -2𝑘 (𝑧 𝑙𝑜𝑤 +ℎ+𝐷) , (6) where 𝑧 𝑟 , 𝑧 𝑢 𝑝 and 𝑧 𝑙𝑜𝑤 represent respectively the vertical coordinate of the levels at RHO-points (located at the centre of the computational cells) and those of the surrounding PSI-points (located at the edge of the computational cells). The interested reader is referred to the CROCO manual (https://doi. org/10. 5281/zenodo.
04 -0. 7 s -1 ) and 24 directions leading to a directional resolution of 15 •. Bi-dimensional (frequency and direction) wave spectra from the HOMERE database  (Boudiere et al. , 2013)   PREVIMER atlas MANE focused on the east part of the English Channel, which includes 37 harmonic constituents  (Pineau-Guillou et al. , 2014). A cold start is imposed as initial condition and no stratification due to temperature and salinity gradients has been considered. Wind forcing as stresses at the free surface is provided by NCEP throughout hourly CFSR Reanalysis at 0. 312 • horizontal resolution  (Saha et al. , 2010; Center, 2014). No water and heat fluxes from the atmosphere are considered. Hydrodynamic motions were not computed for depths smaller than 1 m while wave-induced forcing terms were activated for depths greater than 2 m.
Acknowledgements. This study was funded by  SHOM (French Navy Hydrographic and Oceanographic Service)  in the framework of the  DEMLIT research project. Current data acquisition benefited from funding from  Région Haute-Normandie  and  SHOM  (reasearch contract relative to dune dynamics in a under-saturated environment). The numerical modelling was performed using computational resources from  CRIANN (Normandy, France ). Authors also thank the  CROCO development  team.
Code and data availability. The two exact versions of the CROCO model used to produce the results presented in this study are archived on Zenodo (https://doi. org/10. 5281/zenodo. 7415343), as are simulation configurations, input files, in-situ data used for assessing the simulations and codes to run the model and generate the figures of the paper (https://doi. org/10. 5281/zenodo. 8046629). The official repository of the WAVEWATCH-III code can be found at https://github. com/NOAA-EMC/WW3/releases/tag/6. 07.
MicroRNAs are small non-coding RNAs that control gene expression during development, physiology, and disease. Transcription is a key factor in microRNA abundance and tissue-specific expression. Many databases predict the location of microRNA transcription start sites and promoters. However, these candidate regions require functional validation. Here, dCas9 fused to transcriptional activators or repressors -CRISPR activation (CRISPRa) and inhibition (CRISPRi)-were targeted to the candidate promoters of two intronic microRNAs, mmu-miR-335 and hsa-miR-3662, including the promoters of their respective host genes Mest and HBS1L. We report that in mouse embryonic stem cells and brain organoids, miR-335 was downregulated upon CRISPRi of its host gene Mest. Reciprocally, CRISPRa of Mest promoter upregulated miR-335.