dataesr/scientific-paragraphs-categorization huggingface dataset:
The number of light positions was based on optimal quality over computational time parameters suggested as in  [32]. Figure  2  shows the variations in the number of light sources used and the selection of uniform and non-uniform (Ring) distribution of light structures for the data acquisition. For the selection of acquisition parameters and data collection, a purposemade user interface was used  [21]. The reconstruction coefficients PTM and HSH were exported following the Eq. 1 and 3 written in the proprietary software MATLAB ®. For tracking changes over time, the same lp and same scene were applied at each time interval.
Creating discriminant analysis models and prediction The multiclass discriminant model was created  [37]  in MATLAB ® , where each class (B) generates data (A) using the multivariate normal distribution. The model assumes that each observation (a) has a Gaussian mixture distribution and the same covariance matrix for each class with varying mean. Under this assumption, the model computes each class's mean and covariance parameters. The computation of the sample mean ( µ ) for the data  [38]  of each class n is shown in Eq. 5. Let us consider, X is an M-by-N class membership matrix, then X mn = 1, if observation m is from class n and X mn = 0, if observation m is not from class n. The estimate of the class mean for data is The sample covariance ( σ ) is calculated by subtracting the sample mean of each class from the observations of that class and taking the empirical covariance matrix of the result (Eq.
Segmentation and visualisation The method was developed in MATLAB ® using the Statistics and Machine Learning Toolbox and multivariate  (6) σ = M m=1 N n=1 X mn (a m -μn )(a m -μn ) T M -N (7 P(a|n) = (8) ((2π) k |σ n |) 1 2 exp(- 1 2 (a -µ n )σ -1 n (a -µ n ) T ) (9) P(n|a) = P(a|n)P(n) P(n) (10) C(b|n) = 0, if b = n 1, if b � = n 1 normal density analysis. We served the selected pixels and the corresponding class to create a discriminant model and then applied it to the sample data to predict the class for each pixel for the entire surface. After predicting the classes for all the pixels, the results are set back to the surface for its visualisation. Result visualisation was possible through a user-friendly colourmap generated for each identified category of surface appearance assigned to a specific colour (see result section for examples).
Evaluation of the proposed method To validate the accuracy and robustness of the proposed method, the method was introduced to the invariance parameters of the acquisition device. Then, the trained data for the Dominoes was applied to respective RTI data sets collected with varying parameters, such as different light positions/structure and scale, as well as surface rotation (on Domino No. 4). The calculated results of the segmentation method are shown in Fig. 12. The necessity of normalising the reconstruction coefficients proved effective in claiming the method's robustness. The percentage of the presence of white areas on the surface was calculated to assess the method's accuracy to the invariance parameters. Standard image processing can be applied because the surface information is easily segmentable due to the high variance between the predominant surface colours.
Results on cultural heritage objects Following the proof of concept, a series of more challenging surfaces were examined to evaluate the feasibility of applying the method in a generalized way. The main goal was to evaluate the possibility of documenting the condition of CH objects by classifying the main visible characteristics. In addition, following condition documentation, the potentiality of monitoring surface changes (i. e. , alteration over time, conservation-restoration treatments) was also evaluated for classifying and quantifying the degree of change (based on the segmented data). The The proposed segmentation is evaluated using both PTM and HSH reconstruction coefficient per pixel on the trained data for creating the discriminant model that is then applied to sample data. Evaluation of the resulting segmentation is based on visual examination by a conservation-restoration expert.
Finally, the proposed methodology is built based on open-source algorithms for light reconstruction of RTI image stack and is not specific to the data acquisition. Therefore, it can apply to any system with access to raw RTI data. The supervision process of annotating the pixels is performed using "LabelMe Image Annotation Tool", which is an open source plugin. Therefore, even though built on MATLAB ® , the script can be available to end-users. Future directions extended to use the DMD coefficients method, which is known to perform more accurately, especially in the case of specular surfaces. Furthermore, incorporating multispectral RTI data can lead to better colour segmentation. It can also be considered to expand the use of the method to other application fields of the RTI technique.
The equation for the normalisation of data set D is shown in Eq. 4 with an arbitrary interval of [a b, where b > a] leaving the shape of the distribution unchanged. The coefficients are then rescaled, by stretching or squeezing the points along a number line of [ -1 , 1], to normalize the distances between the D min and D max values for the calculated coefficients. where, D is normalized dataset of D , D min = min|(D )| and D max = max|(D)|.
From an engineering perspective, this work addresses the following question. Are we able to numerically model tributary-junction alluvial fan connectivity changes during a debris flow event? To answer this question we used the FLO-2D commercial software application due to its reasonable computational cost and its broad applicability for hazard assessment projects. In March 2015, an extreme rainstorm event occurred over a large area of the Chilean Atacama Desert  (Bozkurt et al. , 2016; Wilcox et al. , 2016; Jordan et al. , 2019; Cabré et al. , 2020b) , hereinafter called the 25M event. This study benefits from a detailed sedimentological field characterization of the debris flow deposits on the Crucecita Alta fan for the 25M event  (Cabré et al. , 2020a). The Crucecita Alta catch-   2020a ) is based on the characteristic storm signature of the 25M event, which resulted in characteristic debris flow surges.
We used these data to differentiate two main sediment transport modes and assigned them to different sediment transport models for each surge. We developed a workflow that includes the calibration of the flow's rheological parameters and a Python routine to concatenate different numerical models for the different debris flow surges. We used the geomorphic changes that occurred in Crucecita Alta to test the suitability of our workflow in the reproduction of the 25M event on this alluvial fan. Inhabitants of the Huasco Valley tend to dwell in the alluvial fans because of their gentle slopes and because alluvial fans are safe places during river floods. However, highmagnitude debris flow events directly impact these populated areas. To reduce the impacts of debris flows, mitigation works in such environments are divided into sediment retention (pools, barriers, etc.
Study area and data Tributary-junction alluvial fans in the Huasco Valley, located between 1500 and 3000 m a. s. l. (above sea level), supply sediments to the main river. They are so abundant that they can reach densities of 1. 9 fans per kilometer. During the 25M event, rainfall gauges in the valley recorded precipitation ranging from 20 to 76 mm in 3 d, with a maximum in-tensity of 16 mm h -1. The high elevation of the snow line in March 2015 (3200 m a. s. l. ), 400 m higher than its average altitude  (Lagos and Jara, 2017) , exposed areas usually covered by snow, thus increasing run-off  (Wilcox et al. , 2016; Jordan et al. , 2019). Many of the tributary catchments in the Huasco Valley were activated during the 25M event causing casualties and great economic losses  (Izquierdo et al. , 2021).
1 and including sediment transport. For this purpose, we developed a Python routine to concatenate the four surges into one model. Each surge is modeled in a sequence in which the results of the previous surge modify the topography for the next surge. The workflow steps are presented in Fig. 4. In the first step of this workflow, "subdivision into surges" (Fig. 4 ), we set the number of surges i = 1. N and the rheology model of each one, i. e. , non-Newtonian (viscous debris flow) or Newtonian (inertial debris flow). For example, facies F1 and F2 have high fine sediment concentration, present evidence of a laminar flow, and show negligible erohttps://doi. org/10. 5194/nhess-22-377-2022 Nat. Hazards Earth Syst. Sci. , 22, 377-393, 2022 sion. Therefore, surges 1 and 2 are assigned as viscous debris flows.
The sediment rating curve function C V (Q, t) is presented in  Zegers et al. (2020). Porosity p is set equal to 0. 3 for facies F1 and F2  (Nicolleti and Sorriso-Valvo, 1991). Finally, h d is added to the original topography at each cell by the Python routine, which updates the model for the next surge. h d = h f C mean V 1 -p C mean V = T t=0 Q(t)C V (Q, t)dt T t=0 Q(t)dt (11) For the sediment transport model branch of the workflow (Fig. 4 ), on the other hand, the model is directly run since no parameters need to be calibrated. The sediment transport model only needs the parameters D 50 and S CC (Eq. 10) of the substrate. In Crucecita Alta alluvial fan, D 50 = 7. 8 mm and S CC = 1. 79  (Cabré et al. , 2020a). We assumed that the sediment transport equation and the grain size distribution of the substrate do not change between surges.
Acknowledgements. This research was supported by the  Advanced Mining Technology Center (AMTC) , the  Department of Civil Engineering  from  Universidad de Chile  and the  National Agency of Research and Development (ANID). The authors thank the  Public Works Ministry (DOH-MOP)  and  "Centro de Investigación, Desarrollo e Innovación de Estructuras y Materiales" (IDIEM)  for the lidar topographical data. We thank  Tomás Gómez  and  Miguel Lagos  for contributing with the streamflow data used in this study. We also thank  Eric Barefoot  and an anonymous reviewer for their thoughtful comments and suggestions for improving this article. Financial support. This research has been supported by the  National Agency of Research and Development ANID/PIA  project  AFB180004. Alex Garcés  and  Gerardo Zegers  were financially supported by the  ANID  doctoral grants  21191593  and  72200390 , respectively.
2C ). To explore the biological pathways associated with the chromatin profiles that could distinguish Luminal from Basal bladder cancers, we ranked genes based on the MCA outputs for Dim 2 and performed Gene Set Enrichment Analysis (GSEA  (26, 27) , Fig. 2D ). As expected, for the basal cluster, we found that active chromatin (E2) was strongly enriched at genes involved in decreased Luminal differentiation, while repressive chromatin (E4) was strongly depleted for these genes (Fig. 2D ). Interestingly, genes involved in increased tumour aggressiveness, stemness, extracellular matrix, epithelialmesenchymal transition and invasion were also enriched in active chromatin and depleted for repressive chromatin in the basal cluster (Fig. 2D ). Taking an alternative approach, we derived Basal and Luminal gene signatures from an independent publicly available scRNA-seq dataset (GSM4307111) and compared these genes with the chromatin states associated with the basal and non-basal clusters identified in Fig.
3D ). We then validated the subgroup-specific SEassociated genes identified in our samples in a larger dataset, leveraging the gene expression profiles of the TCGA-BLCA MIBC cohort across molecular subtypes (n=406)  (32, 33). Hierarchical clustering of TCGA samples using the genes associated to the most differentially regulated SEs in our consensus repertoire recovered the molecular classification (Fig. S3D , Table  S3 ). Strikingly, differential analysis between Basal and Luminal SEs revealed that Luminalspecific SEs were attributed to known transcriptional drivers of the luminal phenotype, namely GATA3, PPARg, FOXA1  (14, 22). Luminal-gained SEs were associated with "Signalling by TGF-beta family members", notably due to SEs annotated close to negative regulators of TGFbeta signalling such as the E3 Ubiquitin ligase SMURF1 or SMAD6 (Fig.
Super-enhancers regulate a network of candidate master transcription factors for bladder cancer subgroups SEs often regulate the expression of master TFs, forming autoregulatory loops and correlated networks  (35, 36). Having established the SE landscape in bladder cancer, we next sought to determine which master regulators control the subtype-specific transcriptional programs. To this end, we overlaid the genomic coordinates of subgroup-specific peaks inside SEs with publicly available ChIP-seq datasets  (37, 38). Our analysis revealed that Luminal-specific SEs were significantly enriched in several TF binding sites (Fig. 4A , Table  S5 ), including known regulators of Luminal subtypes FOXA1, GATA3, and ESR1  (3, 14, 32). Basal-specific SEs were enriched in binding sites of a different set of regulators, including components of the AP-1 complex (FOSL1, FOSL2, JUND, JUNB), as well as SMAD2/3, NFkB, and STAT3.
ChIP-seq data analysis and integration Peak detection was performed using MACS2 (model-based analysis for ChIP-seq v2. 1. 0. 20140616) software under settings where input samples were used as a negative control. We used a default cutoff and -B option for broad peaks. To identify enhancer regions in each tumour we used ROSE (Ranked Ordering of Super-Enhancers) algorithm  (18, 30) , with the following parameters: 12. 5kb stitching distance, exclusion of promoter regions 2500 bp around TSS. For each sample, stitched enhancer regions are normalized, ranked and plotted. The regions above the inflexion point are considered superenhancers by the algorithm. However, the number of called super-enhancers was notably lower in cases with a known amplified gene. The very high H3K27ac signal in the amplified region likely created a bias in the plot of ranked enhancers.
To correct for this bias, we set a threshold to the top 1000 ranked enhancers to select candidate super-enhancer regions. Heatmaps and PCA of ChIP-seq signal were performed using Diffbind R package (version 2. 16. 0) or Easeq  (54). For super-enhancers analysis, the top 1000 SE regions of either tumours or cell lines were merged for a consensus using Diffbind. Then, H3K27ac signal was calculated in the consensus peak for each sample. Differential analysis between molecular subtypes was performed with Diffbind and DESeq2 default parameters using both IP and input bam files, and a file containing the consensus super enhancer regions evaluated for differential analysis as input. Regions with an pval <0. 05 were considered differentially bound. Genomic annotation and pathway enrichment analyses were performed using ChIPseeker, clusterProfiler and GREAT  (28).
Genomic annotation of the SE regions and cis-regulatory genes SE activity and gene expression was jointly analysed to determine the cis-regulatory between the SE and genes on proximity. In brief, the overlap / proximal genes corresponding to each SE were annotated using GREAT/ROSE tools, as the candidate proximal genes regulated by the SE  (30, 55). The spearman correlation coefficients between SE activity (H3K27ac read counts, log2RPKM normalized) and the expression of the candidate genes (RLE normalized) were calculated in the tumours. The gene whose expression showing the highest correlation with the activity of the corresponding SE was determined as the gene most likely regulated by the SE. The SE-gene relationships within the top 1% were also given, not limited to the proximal genes. The number of germline single nucleotide polymorphisms (SNPs) within a given SE as well as their association with bladder cancer (median -log10 p-value) was provided based on the UK biobank GWAS summary statistics (Neale lab Round 2, ukb-d-C67, extracted from the MRC IEU OpenGWAS database)  (56).
The germline SNPs falling within the SEs and with published GWAS-level association with BCa or with -log10P. value > 5 in GWAS summary statistics (PhenoScanner v2 database) were provided as GWAS SNPs within the SEs (57). For the genes most likely regulated by a given SE, we provided their median CERES dependency score of all and urothelial cancer cells from the Cancer Dependency Map database  (45) , as well as the pvalue for difference between the two. We checked if any bias compared to the background in mutation type (missense, non-sense, synonymous, etc. ) for the protein coding genes by Chisquare test. We checked if they were within the list of established cancer genes, including the COSMIC Cancer Gene Census and Network of Cancer Genes 6  (58, 59).
Chromatin state analysis and correlation with expression ChromHMM was used to identify chromatin states. The genome was analyzed at 1000 bp intervals and the tool was used to learn models from the 3 histone marks ChIP-seq reads files and corresponding Input controls. A model of 6 states was selected and applied on all samples. The 6 states identified were then given functional annotation based on histone marks enrichment and ENCODE published chromatin states. We checked the genome-wide association between gene expression and chromatin states of the TSS, in both tumour and cell line samples. In each tumour / cell line, we classified the genes according to the chromatin states of the TSS. For genes with multiple TSS, the chromatin states showing frequency dominance was considered. We then calculated the median expression of the genes by their TSS categories in each sample, and assessed the distribution of the median expression by chromatin state across all tumour and cell line samples.
Dimension reduction and functional ontology analysis We performed dimension reduction and visualization taking directly the categorical format of the above described selected features using multiple correspondence analysis (MCA). To explore the biological significance of the regions that contributed to the dimension that distinguishes the nonbasal and basal clusters, the chromosome segments' loading estimates to the Dim 2 were extracted from the MCA outputs and regions with a p-value < 0. 05 for the loading estimate were included (n =  12, 198). Genes mapped to Dim 2 contributing regions were pre-ranked by loading estimates for gene-set enrichment analysis (GSEA) by which we identified multiple biological gene sets / ontologies associated with Dim 2. The gene sets collections were retrieved from the Broad Institute Molecular Signature Database, spanning the H (hall mark gene sets), C2 (curated gene sets, eg.
pathways), C3 (regulatory target gene sets), C5 (ontology gene sets, eg. Gene Ontology), C6 (oncogenic signature gene sets), and C8 (cell type signature gene sets) categories, using the msigdbr R package  (27, 60). As complementary exploration, we in the meantime performed dimensional reduction to the numeric transformed data of the selected features, using MDS. Similar to what was observed in MCA, the Dim 2 represents the dimension that distinguishes the basal versus non-basal samples, and Dim 1 separates cell lines from tumours, suggesting potential batch effect and/or in vitro culture-specific effect. We then adjusted for these latent effects to obtain a refined clustering (basically on Dim 2), using the MNN algorithm implemented in the fastMNN function of the batchelor R Bioconductor package  (61).
Assignment of MIBC and NMIBC subtypes Gene expression data of the most tumour cases was previously generated and published  (9, 22). We assigned consensus classes using the previously generated gene expression data using ConsensusMIBC (v1. 1. 0) R package (3) (Table  S1 ). Given potential intra-tumour molecular heterogeneity, we also to verify the subtype in our ChIP-seq sampled tumour area using RNAseq from the same powder using the same ConsensusMIBC (v1. 1. 0) R package. NMIBC samples (n=2) were classified using classifyNMIBC R Package  (7).
Regulons The regulatory network was reverse engineered by ARACNe-AP (41) from human urothelial cancer tissue datasets profiled by RNA-seq from TCGA. ARACNe was run with 100 bootstrap iterations using all probe-clusters mapping to a set of 1,740 transcription factors. Parameters used were standard parameters, with Mutual Information p-value threshold of 10 -8. The VIPER (Virtual Inference of Protein-activity by Enriched Regulon analysis) (42) (R package viper 1. 24), using the regulatory network obtained from ARACNE on urothelial cancer, and we computed the enrichment of each regulon on the gene expression signature using different implementations of the analytic Rank-based Enrichment Analysis algorithm.
General bioinformatics, statistical analyses and public data Plots and statistical analyses were performed in R software version 3. 6. 1, using ggpubr package. Wilcoxon and Kruskal-Wallis tests were used to test the association between continuous and categorical variables, for 2 categories or >2 categories, respectively. P-values <0. 05 were considered statistically significant. Pairwise correlation of gene expression was calculated using Pearson coefficient and plotted using complexHeatmap R package. All gene expression heatmaps show mean-centered log2-transformed normalized counts of each represented gene. TCGA-BLCA MIBC RNA-seq data were downloaded from TCGA data portal using TCGAbiolinks package (R), raw counts were normalized to account for different library size and the variance was stabilized with VST function in the DESeq2 R-package  (62).
TCGA-BLCA samples (n=404) were classified using the consensus system using consensusMIBC R package. CCLE urinary tract cell line gene expression were downloaded from the DepMap portal (https://depmap. org/portal/download/). For consensus classification of CCLE bladder cancer cell lines, we adapted this classification considering only genes expressed by both tumours and cell lines. Ten cell lines were classified as Ba/Sq, all other were grouped as non-Ba/Sq. MGHU3 RNA-seq bulk data were download from GEO (accession number, GSE171129).
Public scRNA-seq and Basal/Luminal signature We downloaded the log2 TPM normalized gene expression of single cells from a Ba/Sq subtype MIBC tumour from the GEO database (accession number, GSM4307111). Initial quality control excluded genes expressed in less than 3 cells and cells with less than 200 genes. The top 2000 variable genes were used as features for subsequent PCA and the first 9 principal components were used for cell clustering and visualization by uniform manifold approximation and projection (UMAP) embedding. The marker genes of the luminal and basal tumour cells were calculated with Wilcoxon test based approach. The single cell RNA-seq data analyses were performed using the Seurat v4 package with default parameters unless otherwise specified. Given the single-cell derived luminal and basal tumour cell signature was based on single-cell sequencing of primary in vivo tumour sample, and the FOXA1 knock-out perturbation signature is likely limited to the genes regulated by FOXA1 in an in vitro setting, it is important to adopt the cell subtype signatures to refine to the marker genes regulated by FOXA1, as a FOXA1-depdent luminal-basal plasticity signature which could be then used for further analyses involving in vitro transcriptomes.