dataesr/scientific-paragraphs-categorization huggingface dataset:
It is also important to highlight that there could be some analytical bias related to the combination of different datasets using different mutation calling approaches. However, the signal associated with the AID-related mutations was similar throughout the studies and the pipelines, and results of the different included studies are public and well standardized, limiting in part this mutation call bias. We propose a model in which AID ICN has higher probabilities of being recognized by T-cells, triggering selective expression CXCL13, previously found to be a marker of antigen reactive CD8 T-cells, for recruitment of CXCR5 + T and B cells  18. These recruited cells, subsequently exhausted by the continuous expression of inhibitory immune-checkpoint molecules, can be reinvigorated after ICI treatment.
Subject details The total cohort at bulk level consisted of 50,631 tumor samples representing more than 80 cancer types. TCGA information consisted of: mutational data in Mutation Annotation Format (MAF) included 9264 cancer patients and 741 normal samples; RNA-seq data; immune data, allele-specific integer copy numbers, and previously predicted neoepitopes. The PCAWG data (ICGC) included 2775 cancer patients along with 35 different cancer types with whole-genome sequencing (WGS) information (SNV and CNV) from which 1522 had the expression data available. Composite mutations data included 31,353 cancer patients from the MSKCC comprising 41 tumor types by the MSK-IMPACT assay (sizes depending on the date of sequencing comprising 341, 410, and 468 cancer-associated targeted genes) downloaded from CBioPortal for the general maf or their github repository (https:// github.
com/taylor-lab/composite-mutations/tree/master/data) for the clinical, mutational burden classification, mutational signatures, composite mutation annotation, phasing information, and molecular timing  39. 33, 63. Riaz et al. melanoma cohort consisted of 68 patients treated with Nivolumab (anti-PD-1) from which 35 had previously progressed on Ipilimumab (anti-CTLA-4) treatment from which data was obtained prior treatment (pre) or 4 weeks after initiation of Nivo (on). Data consisted of whole-exome sequencing (WES), neoepitopes (n pre = 68; n on = 41), and RNA-seq (n pre = 45; n on = 41)  34. The canine cohort consisted of a total of 187 samples representing 3 tumor types including glioma (n = 81), osteosarcoma (n = 35), and melanoma (n = 1)  [64] [65] [66].
AID motifs distribution across the genome The AID motifs, i. e. WRCY motifs W = adenine or thymine, R = purine, C = cytosine, Y = pyrimidine, that is AACC, AGCC, AACT, AGCT, TACC, TGCC, TACT, and TGCT. We used an R script to find these patterns in the GRCh38 genome using the Biostrings package v2. 60. 0. In addition, we also calculated the number of these AID motifs in 20 kb binned windows throughout the genome using bedtools, adjusting by the chromosome size.
c-AID mutations genomic distribution The inter-mutational distance, i. e. , the distance between each somatic substitution and the substitution immediately prior, was calculated per tumor type by combining the MAF data of all samples within that tumor type using the R package "karyoplo-teR"  67. Mutation density was calculated within 1 megabase window and added to rainfall plots in Supplementary Figs. 4 5 6 7 8. Furthermore, within each tumor type, mutation density per chromosome was calculated for each sample to then compare densities within chromosomes. Attributing mutations to mutagenic processes Mutations previously tagged as not AID were subjected to signature attribution to 46 (we excluded signatures SBS: 27, 39, 43, 45-60 since they are attributed to sequencing artifacts) of the 78 COSMIC mutational signatures (v3.
2) using Palimpsest package with default parameters  6, 8, 68. To avoid over-fitting, signatures not contributing with at least one mutation within 50% of the samples per tumor type (Median-SBS n TumorX < 1) were removed and mutations were refitted using the remaining signatures. Furthermore, signatures proportions per sample were re-calculated adding the number of previously identified AID mutations to the signature data. Cosine similarity scores of comparing our tetranucleotide-based c-AID mutations to the 78 COSMIC signatures, were computed using the 'compare_results' and 'deconvolution_compare' functions from the Palimpsest package. Signatures were not calculated from the MSKCC-Composites and ICI cohorts because they were already available or not used.
This gave rise to 16,223 regions (median 626 bp) from which only G skewed regions (8059; associated with R-loops formation during transcription) were used to find overlaps with the genomic position of each mutation and to label them as "in R-loops" or "out R-loops". Transcription correlations with c-AID-related mutations were performed by the Spearman correlation method. For each tumor type, we correlated the number of c-AID-related mutations occurring in gene i (AID_Muts gi ) to the expression of the same gene i (Exp gi ). The AID_Muts gi were considered associated with the transcription process in a specific tumor type if the adjusted p-value was inferior to 0. 05 and the Spearman Rho value superior to 0. Functional enrichment analysis of the associated genes was performed using the DAVID database  71.
These filters left 2143 patients that were considered for Fig. 4a  and b  and Supplementary Fig. 15. To account for immunogenicity based on the prediction of neopeptide TCR recognition and neopeptide HLA binding, PRIME software was run with default parameters. Neoepitopes were classified as "Immunogenic" if having a PRIME %rank score (the fraction of random 700,000 8-to 14-mers that would have a score higher than the peptide provided in input) lower or equal to 0. 5% for the corresponding HLA haplotype of the patient where the neopeptide occurred, or as "Non-Immunogenic" otherwise  35. For simplicity some cosmic signatures were grouped as: MMR (SBS6, SBS15, SBS20, SBS21, SBS26, and SBS44); Smokingassociated (SBS4, SBS18, SBS24, and SBS29); POLE (SBS10a, SBS10b, and SBS14), and APOBEC (SBS2 and SBS13).
This data combined with the clonality of the mutation giving rise to a specific neopeptide was used to classify them as immunogenic clonal neoepitope (ICN); samples were further classified as "Presence" if having at least one ICN due to a specific mutational signature or "Absence" otherwise. TCGA RNA counts were retrieved from GEO GSE62944 and normalized using the variance-stabilizing transformation (VST) function from DESeq2  76. For the advanced melanoma anti-PD-1 treated cohort 34 mutational signatures, clonality, immunogenicity, and RNA counts normalization was assessed as described above. Samples were classified as having high ICN AID load if their load was superior to the cohort's median. Furthermore, differential gene expression analysis (Ipi-Naive samples only) was executed with DESeq2 comparing high ICN AID load versus low within pre-therapy samples only (Supplementary Fig.
16 ) or comparing pre-therapy to on-therapy adjusting for AID ICN load groups (high or low) to identify expression changes on-therapy (design = ~CIN_AID_bin-ary+PreOn). For each comparison, DEGs (adjusted p-value <0. 2) were used as input for hierarchical clustering (Euclidean distance followed by complete-linkage agglomeration algorithm) to obtain gene clusters used for enrichment analysis (Fig. 4d ). GSEA analysis was performed using the GO database through R package clusterProfiler  77  or DAVID database  71  applying Bonferroni correction (q-value <0. 05).
Analysis of single-cell RNA sequencing data We downloaded the expression matrix of the raw count, transcript per million (TPM), or Fragments Per Kilobase of transcript per Million mapped reads (FPKM) from Jerby-Arnon et al. (2018)  44. We collected sample information such as the patient ID, tissue origin, treatment condition, and response groups. For processing all the collected datasets, including quality control, batch effect removal, cell clustering, differential expression analysis, cell-type annotation, malignant cell classification, and gene set enrichment analysis that internally uses the Seurat package  79. The raw count, TPM or FPKM table was used as input for the standardized workflow. The quality of cells was determined by two metrics: the number of total counts (UMI) per cell (library size) and the number of detected genes per cell.
Overall survival analysis to ICI was assessed using logrank Kaplan-Meier curves and univariate/multivariate Cox proportional hazards regression modeling. We have assessed several Cox proportional models for every study (i. e. , analyzing the deciles, from 10th to 90th, of the fraction of c-AID mutations in every included study, unadjusted, using the median of the fraction of AID mutations, and also these models were adjusted by TMB ≥ 10mut/Mb). To combine the different survival models, we used a random-effects model with the meta v4. 18-1 package 81 , using log hazard ratio and standard errors of each model per study. The inverse variance method was used for pooling. The randomeffects estimate was based on the DerSimonian-Laird method  82. The meta-analysis results were represented in a forestplot using the forestplot function of the ggforestplot v0.
ACKNOWLEDGEMENTS We greatly thank all investigators, funders, and industry partners that supported the generation of the data within this study, as well as patients for their participation. Specifically, we thank  Eli Van Allen ,  Roel Verhaak , and  Timothy A Chan  for the academic datasets. The results published here are in whole or part based upon data generated by the  TCGA Research Network : https://www. cancer. gov/tcgaand the ICGC consortium: https://dcc. icgc. org. This work was in part supported by a grant from Investissements d'avenir and by the grant  INCa-DGOS-Inserm_12560 of the SiRIC CURAMUS , the program " investissements d'avenir "  ANR-10-IAIHU-06 ,  PRT-K/INC  a grant LOC-model reference 2017-1-RT-04,  BETPSY  project, overseen by the  French National Research Agency , as part of the second " Investissements d'Avenir" program  (Grant No.
ANR-18-RHUS-0012 ), RAM foundation, ARTC foundation, an unrestricted grant from  Bristol Myers Squibb (BMS) :  RDON06618  and  IDeATIon  project with an unrestricted grant from  MSD Avenir. D. R. was partially supported by a  Bicocca 2020 Starting Grant  and by a  Premio Giovani Talenti dell'Università degli Studi di Milano-Bicocca. The funders had no roles in study design, data collection, and analysis, decision to publish, or preparation of the manuscript.
org  (Riemann et al 2016) , and UNDINE of the Federal Office of Hydrology and the Federal Ministry for the Environment, Germany. Furthermore, the European Flood Awareness Systems (EFAS) and the Global Flood Awareness Systems (GloFAS) provide forecasts and temporal analyses. In addition, social media rises in importance (Casagrande et al 2015,  Jüpner 2016 , Douvinet et al 2017 , Jüpner et al 2018 , de Bruijn et al 2019). The European Drought Impact Report Inventory (EDII) and the European Drought Reference (EDR) database  (Stagge et al 2013 , Stahl et al 2016)  are used to comprehensively analyze textual material about the impact structures of droughts via preconceived categories. In this paper, we investigate, how text harvesting of online media and natural language processing (NLP) can be used to explore and evaluate extreme events such as the catastrophic flood of July 2021 in the Ahrtal Region (Germany).
Online news: harvesting, data mining, and NLP The focus of the analysis was put on publicly available German online articles collected using the news search engine "newsapi. org'. The whole processing was done in Python V3. 8. 5 and used the modules pandas, nltk  (Bird et al 2009) , spacy, scikit-learn  (Pedregosa et al 2011)  and HanoverTagger  (Wartena 2019 ) for data wrangling. The visualization was done using matplotlib (Hunter 2007), seaborn, networkx, and cartopy (Met Office 2015). The basis for the harvesting were defined keywords, which led to a total of more than 5,000 datasets containing the url, the title and a short description of the articles (Kahle 2021b ). These were sifted regarding their plausibility and duplicates were automatically removed. Thus, about 1500 of the articles were excluded from further processing and analysis.
For the most 20 strongest domains mining routines for the html structure were applied using Beautiful Soup (Richardson 2020) to gain a more detailed extraction of the content. The remaining 3474 articles finally sum up to 115748 sentences and 2157390 words (Kahle 2022). Significant shares are distributed among 20 different newspapers (figure  2 ), representing a broad political and content spectrum. The articles also include German-language titles from Switzerland and Austria. The shares remain fairly constant over time. In a first step, the appearance of the keywords in the various newspapers was quantified. These counted appearances can be grouped according to further criteria, e. g. the date of publication or the media internet domain. For further refinement, the text corpus of each article was broken down to sentences, tokenized, and lemmatized on word level.
Numerical weather and gauging data In addition to the text processing numerical data was analyzed. Daily and hourly time series were acquired from the national weather and federal hydrological services to evaluate the instrumental run-off, water level, and precipitation development during July 2021. GIS-based maps and time series analysis illustrate the spatiotemporal dimension and the correlation between the extreme precipitation and the flooding event from the 14th to the 15th of July. Furthermore, the data served as a cross-evaluation parameter. Precipitation values for July were acquired from the DWD open data portal for the reference period 1900-2021. The arithmetic mean value (m) and the standard deviation (SD) were calculated from all values. SD was subtracted from and added to the July monthly mean value (m-SD; m+SD) to create the range of the standard deviation for the reference period.
Both raster sets were merged to create the precipitation anomaly range for July 2021. The rivers Ahr, Rhine, Rur, Inde, Erft and the river Ahr's tributaries were extracted from the geoportal of the BfG (WasserBLIcK 2021). Using the ASTER Global Digital Elevation Map V3 (DEM) from the NASA  (NASA 2019 (NASA , 2021) )  earthdata open science portal and SAGA GIS (2021) the drainage basin was simulated based on the topographic features of the DEM. In a next step, the DEM was clipped to the extent of the drainage basin and reclassified using QGIS and a classification into 100 m ranges. From the DWD RADOLAN data portal, hourly precipitation values were acquired and accumulated for the period between the 12th to the 15th of July 2021. The July precipitation values within the drainage basin were accumulated for each topographical range and a time-series was plotted, which represents topography-dependent precipitation variability within the extent of the drainage basin of the river Ahr over the long-term observation period 1900-2021.
A comparison between the long-term precipitation records and the short-term totals for the period 12th of July-17th of July has been carried out based on hourly gridded precipitation data from the DWD RADOLAN dataset. The reprojected data has been clipped to the extent of the drainage basin and the summed-up total values for each hour were calculated. 2. 4. Historical sources and records of flood events along the river Ahr Historical data were used to enable long-term comparison. In the 19th century, a total of 17 severely damaging flood events were recorded in the river Ahr valley. Only four were related to snowmelt and ice drift in winter, most of them were triggered by heavy and continuous rainfall during summer  (Frick 1933 1955 , Seel 1983 , Börngen and Tetzlaff 2000 , Kohl 2007 ). For the 20th century,  Seel (1983)  listed 21 events, seven of which were summer floods.
In general, the integration of historical sources can help to improve modern flood risk management. Further insights into the social, mental, economic, and ecological implications can be gained through text analysis. The quantitative methods of NLP enable it to rapidly open-up large data volumes and to represent the diversity of aspects more objectively than using manual methods  (Assael et al 2022). Since large data collections covering hydrological extremes are available for Central Europe (e. g. , tambora. org, ORRION, or the Weikinn Collection, or digitized historical newspaper archives), there are multiple opportunities to explore them for an improved flood assessment.
Acknowledgments We are very grateful to three anonymous reviewers for their detailed and constructive suggestions and critics that helped to reshape the approach behind the article. We would further like to thank  Yvonne Henrichs  and  Michael Göller ,  Hydrologischer Dienst  der oberirdischen Gewässer,  Landesamt für Umwelt, Rheinland-Pfalz  who provided daily and hourly gauge data. M Ke  received funding from the  Masaryk University  (grant number  CZ. 02. 2. 69/0. 0/0. /18_053/0016952 ; Postdoc2MUNIm order number  21 0053 ). This publication is a contribution to the  Past Global Changes (PAGES)   Floods Working Group. In memory to its co-founder  Bruno Wilhelm.
Another study confirmed that 3D photography is strongly correlated with 3D computed tomography (CT)  [25]. With the emergence of artificial intelligence, several tools have been developed to improve OSA prediction  [27, 28]. Machine learning (ML) and deep learning models have been used to identify OSA patients based on 2D and 3D photographs  [28, 29]. Here, taking as reference the results obtained by PSG, the primary objective was to investigate the reliability of OSA diagnosis obtained by the 3D geometric morphometric analysis of maxillofacial scans, combined with ML analysis. The novelty of this study is that the dataset consists of the entire 3D surface, not only the frontal and profile images, or depth maps. Another originality is that a data-driven approach based on ML analysis can be performed in 10 min, economizing both time and resources.
Study design The EPISAS monocentric prospective study was conducted at the Grenoble Alpes University Hospital from 2018 to 2020. The study was registered on Clinicaltrials. gov (NCT03632382). Consecutive adult Caucasian men (age40) with suspected OSA, referred for gold standard PSG, were invited to participate. Exclusion criteria were previous maxillofacial interventions or dental malocclusion, body mass index (BMI) 35 kg/m 2 , or a thick beard (impeding maxillofacial characterization). All participants signed written informed consent.
Practical implantation Morphometric data were prepared with the R v3. 3 programming language (https://www. r-project. org/), using mainly the Morpho  [48]  and geomorph packages  [49]. The ML phase was carried out with a homemade snippet programmed under Python 3. 7 (https://www. python. org/), using the scikit-learn (https:// scikit-learn. org) and XGBoost (https://xgboost. readthedocs. io/) libraries. Mesh cleaning was performed using the free Meshlab software (https://www. meshlab. net/).
Algorithm testing Further tests were carried out with the first 2 to 5 PCs, by applying 13 different supervised algorithms, varying from simple to more advanced ML techniques (Table  2 ). Given the cost of dimensionality with LDA and the higher complexity of most of the other ML algorithms tested, a parsimonious strategy concerning the number of feature inputs was adopted. Further experiments therefore took into account only 5 PCs at most. Whatever the model and the number of PCs retained, the auROC values were within the 0. 62e0. 70 range (i. e. always clearly better than 0. 5, corresponding to random classification). In more detail, the ANN and RF algorithms probably suffer from a lack of data to be fully effective. The most efficient were LDA, Adaboost, extra trees classifier, XGBoost, and LR, with from 3 to 5 PCs.
OSA prediction from morphometric data, including questionnaires and anthropometric data Using simple descriptive statistics (Student's t-test or Mann-Whitney U test, depending on the nature of the variables), ten anthropometric variables and symptoms were identified as significantly discriminating for OSA: hip, neck, and waist circumferences, age, BMI, Mallampati class, hypertension (HTA), witnessed apnea, and sleepiness while driving. These features were therefore processed together with the probability of belonging to the OSA risk group, provided by the LR model previously built from morphometric data alone. An XGBoost algorithm was applied at this step since it accepts both categorical and numerical variables as feature inputs. The inclusion of the above-mentioned variables in the model slightly boosted performance scores: the auROC reached 0.
Overall evaluation Several studies have previously assessed the performance of screening tools and scales, including the Epworth sleepiness scale (ESS), and the Berlin and NoSAS questionnaires [51e53]. Although these methods are widely used nowadays for OSA identification, their low specificity leads to a high burden of subsequent negative PSG [53e55]. The accuracy of questionnaires depends on population characteristics  [56, 57]. In a recent meta-analysis evaluating the Berlin questionnaire in different settings (n ¼ 8222), pooled specificity varied from 33% to 47%  [51] , a range consistent with our findings. Interestingly, the present study demonstrates that 3D geometric morphometrics, combined with an appropriate ML algorithm, exhibits a predictive performance for OSA diagnosis similar to that obtained from questionnaires and data routinely collected in sleep centers.
The impact of established models Special attention should be paid to the learning curve of the model established from shape data combined with anthropometric data and questionnaire responses (Fig. 9 ), which depicts both training and cross-validation scores (together with their 95% confidence interval) as a function of the size of the training dataset. Once 75 to 90 patients have been included during the learning phase, the training scores are high, with an auROC of about 0. 95, whereas cross-validation scores are much lower, around 0. 68. This result is not surprising, as the model overfits this relatively small learning dataset, while instances are lacking to produce high  validation scores and appropriate generalization capabilities for unseen instances. As expected, as the training set increases in size, the training score decreases (the model starts to underfit the dataset), while the validation score increases.
the overall geometry observable in Fig. 8 ). With a larger cohort, three further gains become possible. Substantial improvement should be observed in predictive performance, as suggested by the training curves. More PCs capturing finer details of maxillofacial shape can be included and their influence comprehensively evaluated. As the OSA/non-OSA groups are probably not linearly separable, more complex algorithms (such as artificial neural networks or random forests) are potentially better adapted, as greater quantities of data become available. Clinicaltrials. gov:NCT03632382.
Funding This work was supported by the  French National Research Agency  in the framework of the " Investissements d'avenir" program  ( ANR-15-IDEX-02 ) and the "e-health and integrated care and trajectories medicine and MIAI artificial intelligence" Chairs of excellence, from the  Grenoble Alpes University Foundation. This work was also supported by  MIAI @ Grenoble Alpes  ( ANR-19-P3IA-0003 ), and  SATT Linksium.