dataesr/scientific-paragraphs-categorization huggingface dataset:
Using formula (2) we can compare our prediction for the total number of somatic mutations found when sequencing the cancer type of a patient of age a with the actual number of mutations observed via the bulk sequencing of that individual cancer, using the public genomic TCGA database. The comparison is provided in Fig. 1a  for 10 cancer types. We obtain a strongly significant correlation between our predictions and the actual observed values (Spearman's rho = 0. 56, P < 2. 2 × 10 -16 ). In Fig. 1a , the placement of the points with respect to the identity line y = x is naturally very sensitive to the choice of the expected number of mutations across the genome per cell division, which we set equal to 3, a typical value found in the literature (resulting in μ = 0. 03 on the exome, see ref. 5).
This rough estimation for the increase of the mutation rate is based on: 1) ref. 18 for smoking, and 2) the average ratio of 3. 6 obtained in ref. 12 between the yearly number of mutations in sunexposed versus sun-shielded skin, an underestimation of the increase due to UV light exposure since UV-induced mutations are also prominent in sun-shielded sites  19. The inclusion of even just these two environmental factors yields an improvement in the fitting of our predictions to the observed values, with a correlation going from 0. 26 in Fig. 1b  to 0. 67 in Fig. 1c. We provide a summary of the parameters estimates used in our model, as well as the quantitative results obtained from it in Table  1. We computed for each cancer type the expected number of mutations in healthy individuals versus cancer patients in the absence of E and H factors thanks to formulas (1) and (2), where the expected value is calculated at the median age of the patients in the TCGA database.
Observed number of mutations We downloaded somatic exomic mutational data from the TCGA Bioportal (https://portal. gdc. cancer. gov) and whole-genome sequencing data from the ICGC database (https://dcc. icgc. org/releases/ PCAWG). Only ICGC datasets that were not present in the TCGA database were used. The total number of mutations across the exome was inferred by adding all the point mutations (divided by 100 in the case of whole-genome sequencing). This number then provided the input for the analysis presented in Figs. 1 2 , Table  1 , and Supplementary Fig. S2-S3 , for patients for which the age was specified. Source data including the patient ID are provided with this paper.
Parameter estimates and number of somatic mutations (predicted, observed, and in healthy individuals)For 12 cancer types and associated tissue (see Table2for the meaning of the abbreviations), (a) parameter estimates obtained from the literature (columns 1-4): D0 number of divisions during the tissue development phase, b and b n ð Þ yearly number of divisions in a healthy and in cancer tissue, ρ proportion of the population that gets cancer by age 80, (b) data (columns 5 and 9): a m median age of the TCGA database cancer patients, η o interquartile interval of the actual observed number of somatic mutations found in the tissue of cancer patients aged am -5 to am + 5, (c) values computed from our model (columns 6-8):.
Data availability The results published here are based on data generated by The Cancer Genome Atlas Research Network (https://www. cancer. gov/tcga) and the International Cancer Genome Consortium (https://dcc. icgc. org). There are no restrictions on access to these data, which are all publicly available. The data generated for this study ("expected number of mutations" in Figs. 1 2 , Table  1 , and Supplementary Figs. S2-S3 ) are obtained by the direct application of formula  (2). Source data are provided with this paper.
Methods Ethical approval. The inter-individual contact data used in this work was collected previously during the i-Bird study. The i-Bird study obtained all authorizations in accordance with French regulations regarding medical research and information processing. All French IRB-equivalent agencies accorded the i-Bird program official approval (CPP 08061; Afssaps 2008-A01284-51; CCTIRS 08. 533; CNIL AT/YPA/SV/ SN/GDP/AR091118 N°909036). Signed consent by patients and staff was not required according to the French Ethics Committee to which the project was submitted. Simulating SARS-CoV-2 outbreaks in the long-term care hospital setting. We simulated SARS-CoV-2 outbreaks using CTCmodeler, a previously developed stochastic, individual-based transmission model in the LTCF setting  36, 40. Using high-resolution close-proximity interaction data from a 170-bed rehabilitation hospital in northern France, this model simulates (i) detailed inter-individual contacts among patients and staff, (ii) transmission of SARS-CoV-2 along simulated contact networks, and (iii) clinical progression of COVID-19 among infected individuals.
Other outcomes evaluated to assess the performance of testing and screening interventions were true-positive rate (TPR), true-negative rate (TNR), negative predictive value (NPV) and positive predictive value (PPV). Statistics. All surveillance outcomes are reported as means across n = 10,000 simulations (100 outbreaks × 100 surveillance runs) and were calculated in R software v3. 6. 0. 95% confidence intervals were calculated using bootstrap resampling with 100 replicates and normal approximation (R package boot). the EMAE-MESuRS Working Group on Nosocomial SARS-CoV-2 Modelling Audrey Duval 1,2,4,8 , Niels Hendrickx 1 , Kévin Jean  3, 6, 7  , Sofía Jijón  3, 6  , Ajmal Oodally 1,2,3 , Lulla Opatowski 1,2 , George Shirreff 1,2,3 , David R. M. Smith.
Acknowledgements The work was supported directly by internal resources from the  French National Institute for Health and Medical Research (Inserm) , the  Institut Pasteur , the  Conservatoire National des Arts et Métiers , and the  University of Versailles-Saint-Quentin-en-Yvelines/University of Paris-Saclay. This study received funding from the  French Government 's " Investissement d'Avenir" program ,  Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases"  (Grant  ANR-10-LABX-62-IBEID ), the  MODCOV  project from the  Fondation de France  (Grant  106059 ) as part of the alliance framework "Tous unis contre le virus", the  Université Paris-Saclay  (AAP Covid-19 2020) and the  French government through its National Research Agency  project  SPHINX-17-CE36-0008-01.
Epidemiological Surveillance Data We obtained weekly dengue surveillance reports from the French Public Health Agency. These reports are based on data collected by general practitioners from the French Sentinel Network. They also provide the official start and end dates of each dengue outbreak and the weekly PSAGE stage during the outbreak. These reports are not continuously published but only if the dengue risk level is above stage 1 (ie, the baseline stage). We used the PSAGE stage described in each report to create the PSAGE ordinal variable with 4 levels. Indeed, although the PSAGE program has 5 levels, stage 5 ("back to normal") was used only 5 times in the last 15 years, and experts prefer to use stage 1 ("sporadic transmission") after stage 4 ("dengue outbreak"). Moreover, when stage 5 was used, it was for 1 week, except once in 2021, when it lasted 2 weeks.
Clinical and Laboratory Data We obtained weekly aggregated data from Martinique University Hospital: (1) inpatient data (age and diagnoses associated with dengue disease or dengue symptoms),  (2)   We selected these diagnoses with the help of infectious disease physicians. All reimbursement claims data were obtained from the Martinique University Hospital, where the only infectious disease department for the whole island is located. The relevant ICD-10 codes are listed in Multimedia Appendix 1. We normalized all administrative data as follows: where x is the weekly number of hospitalizations, consultations, or emergency department visits and min and are the minimum and maximum values observed in the data set, respectively. For laboratory data, we used the DENV positivity rate determined by RT-PCR.
Entomological Data We used data from the CEDRE surveillance database, such as the weekly number of entomological interventions and where they were carried out. Entomological interventions were defined as all vector control interventions and measures taken by CEDRE: information and education of the households, physical vector control (ie, eliminating mosquito sites, such as old containers filled with water), and chemical vector control with insecticides  [26]. This agency manages entomological surveillance and vector control in Martinique and collects data on each intervention.
Google RSV We used data from Google Trends  [27] , which provides real-time and archived information on Google queries from 2004 onward. These queries are normalized by Google as RSV by dividing the total search volume for a query in a geographic location by the total number of queries in that region at a given point in time  [28]. We used this tool to retrieve information on the search interest for keywords associated with dengue during our selected time frame (January 2007 to February 2021). However, we could not retrieve weekly data for the Martinique region, especially for the first years of the study period, because there were not always enough RSV (as indicated by the Google error message "Sorry, not enough search volume to show graphs"). Therefore, we based our methodology to retrieve Google Trends data on previously published methodology frameworks, indicating that Google Trends data should be retrieved for exactly the same period as the other data under study and as a single data set rather than as individual queries for each year  [29].
Data Processing Clinical and laboratory data were already aggregated into a structured database and did not require data processing. Similarly, data from Google queries are normalized by Google as RSV. Conversely, most of the information in the CEDRE database was unstructured and required processing. Indeed, the CEDRE database is a comprehensive database with some structured data (eg, the date of an entomological intervention), but the details associated with entomological interventions (ie, the type of insecticide used or the number of old containers removed) were in free text; we needed this information to count the number of weekly interventions. Therefore, we used rule-based natural language processing methods (ie, part-of-speech tags) to process the data and extract relevant information for our study.
In Martinique, the early signs identified in this study could be used to set up more hospital beds (including in the intensive care unit), increase staffing, particularly in emergency services and infectious diseases department, and increase the blood bank stock levels for patients with severe dengue who may need blood transfusions. Moreover, stakeholders could use them to justify requests for reinforcements from other territories (for Martinique, mostly from mainland France), medical equipment, and hospital staff. In addition, they could be used to notify earlier the Pan-American Health Organization, which is the Regional Office for the Americas of the World Health Organization  [33] , and help other islands to better prepare for an incoming outbreak. Previous studies have already investigated the role of entomological data  [34] , inpatient data  [35] , and internet data streams  [36]  in dengue management, but few have assessed all these data sources simultaneously.
Finally, dengue hospitalizations and the symptoms associated with severe dengue cases (thrombocytopenia and liver dysfunction symptoms) should be closely monitored in inpatients, especially in children, because they tend to precede the DENV RT-PCR positive rate increase by several weeks. Our study also highlighted homogenous time lags across different data sources, despite their heterogeneity. This further demonstrates the importance of considering them globally rather than individually, although some of these correlations were low or moderate. For instance, an increase in hospitalized patients with liver dysfunction symptoms could prompt physicians to pay closer attention to the dengue hospitalization rate because both precede the increase in DENV RT-PCR by 5 weeks and 3 weeks, respectively.
Conclusions Our study shows that real-world data are valuable data sources for dengue surveillance in Martinique. Several heterogeneous data sources are relevant, from clinical data to vector control data and Google Trends data. Their increase precedes the increase in dengue cases by several weeks, and therefore, they can help to improve traditional surveillance systems to provide an early response to dengue outbreaks. By improving the integration of many different sources, we might better respond to dengue outbreaks in endemic regions, as well as to other types of vector-borne diseases such as Zika and chikungunya.
S. M. has been funded by the  Russian Science Foundation  project (project No. 20-11-20119 ). X. Z. is supported by  NNSF of China  (Nos. 11731009 ,  12131019 ) and the  DFG  through the  CRC 1283 "Taming uncertainty  and profiting from randomness and low regularity in analysis, stochastics and their applications".
Acknowledgements Funding: We acknowledge financial support from the  Investissement d'Avenir program , the  Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases program  (grant  ANR-10-LABX-62-IBEID ),  HAS ,  Santé Publique France , the  EMERGEN  project ( ANRS0151 ), the  INCEPTION  project ( PIA/ANR-16-CONV-0005 ), the  European Union 's  Horizon 2020 research and innovation program  under grant  101003589  (RECOVER) and 874735 (VEO), AXA and Groupama.
In many ecological studies, counts or biomass of interacting species are collected from several sites. Such data are often very sparse, high-dimensional and include highly correlated responses, and the main aim of the statistical analysis is to understand relationships among such multiple, correlated responses  Niku et al. (2019) ,  Niku et al. (2021). In Environmental & socio-economic studies, some aquatic species are useful as indicators to study ecosystem health and habitat quality. Amphibian species have been considered as useful ecological indicators. Soltysiak et al. (2016)  use a Machine-learning methods in the classication of water bodies. The later use Amphibian species as indicators of environmental contamination. In ecosystemic approach, better understanding interaction between species and environment helps to monitoring sheries in the context of climate change.
Study design and data sources We performed a multicentric retrospective study using the EDS-COVID database, developed upon the Assistance Publique -Hôptiaux de Paris (AP-HP) clinical data warehouse (CDW), regrouping data from 39 different sites in the greater-Paris area  [28]. We used four types of data in this study: (i) medico-administrative data, including diagnosis codes recorded using the International Classification of Diseases, 10th edition (ICD-10), (ii) laboratory results from admission, (iii) physiological measurements (e. g. , blood pressure) at admission and (iv) all medical text reports associated with inpatient stays.
Drug exposure and clinical endpoint definition We extracted the patient's drug exposure status from the clinical data warehouse corresponding to the first 48 hours after the patient was admitted for COVID-19. A code from the anatomical therapeutic chemical (ATC) classification  [29]  was assigned to patients with at least one corresponding drug regardless of the dose. We restricted the analysis to ATC codes level 5 that were present for a minimum of 100 patients. In the following sections, we use the term drugs to refer to these codes. The outcome was defined as all-cause 28-day mortality, with patients discharged alive before 28 days assumed to be alive at 28 days.
Missing data management strategy Missing data management followed a two-step strategy targeting 2 different missing data mechanisms. In the first step, we excluded patients with a number of observations lower than the 2nd percentile for drugs, laboratory tests, or physiological measurements. A comparison of baseline characteristics between patients included in the analysis and patients excluded for missing data was performed to detect a possible selection bias. In a second step, we performed multiple imputation with chained equations (MICE)  [38]. MICE was performed using 5 imputed datasets and the predictive mean matching strategy was chosen, using all adjustment covariates (i. e. , not including drugs). The adjustment set selection was adapted to the setting of multiple imputed datasets by selecting variables that appeared in at least half of the imputed datasets.
The main strength of our study lies in its external validity: it used data collected across 16 different hospitals of the greater Paris area, and included a large number of patients with COVID-19. These characteristics make it likely to capture the variability of populations and disease management in real-life settings. Similarly, we addressed treatment specific indication biases in a fully data-driven fashion, which we validated empirically on drugs of reference. This methodology based on a hypothesis-free exploration of COVID-19-related EHRs is easily exportable to other settings. Population trimming based on stabilized PS allowed to restrict the analysis to comparable patients, which cannot be done by a simple outcome oriented regression adjustment. Finally, it allows to generate a measure of covariates balancing, which turns out to be a critical diagnostic for studying a large array of drug-outcome associations.
Figures Treatment of reference for validating data-driven adjustment sets selection Association between 28-days mortality and early exposure to treatment was measured as odds-ratio for 3 treatments of reference: (1) dexamethasone with expected beneficial effect on 28-days mortality, and (2) treatments with expected null effect, with high prevalence (Paracetamol) or low prevalence (Phloroglucinol). We compare two pretreatment covariate sets: 'Known PF' using prognostic factors from the literature (blue) and 'Data-driven' for a model selection procedure based on adaptive LASSO (green) targeting confusion factors. Odds-ratio were computed by logistic regression on the overall dataset (red), matched or weighted and trimmed subpopulations based on propensity scores.
Use of DTMs Based on previous studies  [17, 18] , the selection of a DTM with appropriate resolution is a key issue. Today global databases are available in a moderately high resolution (e. g. , ~10-30 m), and the local/regional sources can even exceed this. Although our basic goal was to use DTMs with the best possible resolution, we also aimed to create a processing algorithm that could be used for as many different databases as possible. That is why we worked with DTMs of various resolutions, but the lowest resolution was below 15 m.
Chaîne des Puys Among the four areas, on average, the Chaîne des Puys area has the best resolution LIDAR terrain model courtesy of CRAIG (Centre Regional de Informational Geographique  [50]. The central part of the chain is accessible in 0. 5 m resolution while the whole Clermont Auvergne Metropolitan area is covered with a 5 m LIDAR set  [51]. The dataset can be downloaded from CRAIG.
Conclusions Based on the observations and calculations of this study, we can conclude as follows: 1. Whereas the individual behaviour of the slope patterns of scoria cones may vary and old and young cones may show similar morphometric values, the distributions of the average slope angles of the cone age groups follow a trend. According to the statistical tests, the distribution of the slope angles of the older groups is significantly different from the younger ones, with slope angles tending to decrease towards the older groups. 2. Applying our innovative idea, to merge the age groups of all four volcanic areas into a combined dataset and treating the age groups independently from the specific area, the distributions ordered by the increasing mid-period age show a similar trend with decreasing median values, except for Sierra Chichinautzin.
Acknowledgments: F. V. was supported by project no. TKP2021-NVA-29 , which has been implemented with the support provided by the  Ministry of Innovation and Technology of Hungary  from the  National Research, Development, and Innovation Fund , financed under the  TKP2021 -NVA funding scheme. This work is partly carried out as a contribution to the  UNESCO International Geosciences Programme project 692  ' Geoheritage for Resilience ' (www. geopoderes. com, accessed on 28 November 2022). Gustavo Vivó Vázquez  is thanked for his initial work on the Lidar dataset of the Sierra Chichinautzin. M. N. -G. received funding from the project:  UNAM   DGAPA PAPIIT IN103421.
The Chengjiang biota (Yunnan Province, China) is a treasure trove of soft-bodied animal fossils from the earliest stages of the Cambrian explosion. The mechanisms contributing to its unique preservation, known as the Burgess Shale-type preservation, are well understood. However, little is known about the preservation differences between various animal groups within this biota. This study compares tissue-occurrence data of 11 major animal groups in the Chengjiang biota using a probabilistic methodology. The fossil-based data from this study is compared to previous decay experiments. This shows that all groups are not equally preserved with some higher taxa more likely to preserve soft tissues than others. These differences in fossil preservation between taxa can be explained by the interaction of biological and environmental characteristics.
INTRODUCTION The early Cambrian Chengjiang biota (Epoch 2, Stage 3, Yunnan Province, China) records extraordinary evidence of early animal life on Earth. This fossil deposit shows exceptional preservation of soft-bodied animal macrofossils for both overall morphology and detailed anatomy, yielding a level of evolutionary and ecological data that is pivotal in our understanding of the very earliest history of animal life. Listed as a UNESCO World Heritage Site since 2012, the Chengjiang biota (https://whc. unesco. org/en/list/1388/) is particularly important because it is among the oldest animal fossil biotas  (Zhu et al. , 2019). Moreover, it captures an ecologically diverse assemblage, that colonized a deltaic environment, during the unfolding Cambrian Radiation  (Ma et al. , 2012; Ma et al.
MATERIALS & METHODS The eumetazoans in the Chengjiang biota are herein divided into 11 major groups: Annelida, Euarthropoda, Brachiopoda, Chordata, Cnidaria, Ctenophora, Hyolitha, Lobopodia, Priapulida, Radiodonta, and Vetulicolia. These groups represent, for the most part, well agreed upon animal phyla. Though no classification scheme is ever without detractors and controversy (e. g. ,  Daley & Antcliffe, 2019). These divisions represent well the state of play in the field of Cambrian palaeontology. Hyoliths are considered as a separate group from Brachiopoda because the phylogenetic position of Hyolitha is debated  (Liu et al. , 2020a; Liu et al. , 2020b). Euarthropoda, Lobopodia, Chordata, and Priapulida were chosen because they are well represented groups in terms of diversity and abundance and to allow direct comparisons with their respective data from modern decay experiments later on in the discussion.