dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS T. J. F. , G. L. V. and M. J. W. acknowledge support from the  GSFC Sellers Exoplanet Environments Collaboration (SEEC) , which is funded in part by the  NASA Planetary Science Divisions Internal Scientist Funding Model. D. E. S. , I. A. B. , J. M. and N. J. M. acknowledge use of the Monsoon system, a collaborative facility supplied under the  Joint Weather and Climate Research Programme , a strategic partnership between the Met Office and the  Natural Environment Research Council. We acknowledge support of the  Met Office Academic Partnership secondment program. This work was partly supported by a  Science and Technology Facilities Council  Consolidated Grant ( ST/R000395/1 ),  UKRI   Future Leaders Fellowship  ( MR/T040866/1 ), and the  Leverhulme Trust  ( RPG-2020-82 ). This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie Grant  Agreement No.
832738/ESCAPE. M. T. thanks the  Gruber Foundation  for its generous support to this research. M. T. was granted access to the  High-Performance Computing (HPC) resources of Centre Informatique National de l'Enseignement Supérieur (CINES)  under the allocations №  A0020101167  and  A0040110391  made by  Grand Équipement National de Calcul Intensif (GENCI). This work has been carried out within the framework of the  National Centre of Competence in Research PlanetS  supported by the  Swiss National Science Foundation. M. T. acknowledges the financial support of the SNSF. M. T. and F. F. thank the  LMD Generic Global Climate team  for the teamwork development and improvement of the model. J. H. M. acknowledges funding from the  NASA   Habitable Worlds program  under award  80NSSC20K0230. The authors acknowledge the help of  Andrew Ackerman  to set up the cloud diagnostics in ROCKE-3D.
6  (Durand et al. 2016a ) and used for scaffolding the 10x assembly with 3DDNA v. 180922  (Dudchenko et al. 2017). Resulting assemblies were reviewed with Juicebox v1. 11. 08  (Durand et al. 2016b ). In addition to minor corrections to the initial assembly, two chromosome-sized scaffolds were merged. The assembly was finalized with the script "run-asm-pipeline-post-review. sh" from 3DDNA v. 180922  (Dudchenko et al. 2017).
Because Lepidoptera are holocentric and lack defined centromeres, a cutoff value of 150 kb was set instead, so that any longer invariable region was considered as missing data and discarded for the demographic inference. The demographic trajectories were inferred for the last 5 × 10 6 generations. To test the accuracy of the inferences, the demographic analyses were performed again using a randomly selected set of six distinguished individuals. The most recent estimates of N e , which are key for scaling the population recombination rate with pyrho (Spence and Song 2019), were used to assess if the choice of distinguished individuals significantly changed the output.
Permutation test to assess TE density in the coldspots and hotspots We ran 10,000 permutation tests in R v4. 2. 1 (R Core Team 2021) with windows reflecting the count and average length of recombination hotspots and coldspots to obtain neutral distributions of TE abundances. We then compared these distributions with the empirical data and assessed the significance of our findings with two-sided tests. Gene Set Enrichment Analysis (GSEA) for the genes present in the coldspot and hotspot regions We assessed potential enrichment of functional categories in the hotspot and coldspot regions using topGO v2. 44. 0 (https ://bioconductor. org/packages/release/bioc/html/topGO. html) in R v4. 2. 1 (R Core Team 2021). We used the annotated gene set with Gene Ontology (GO) terms associated to molecular function and Fisher's exact test to test for significance, with the default algorithm ("weight01") to account for the hierarchical structure of the GO terms  (Alexa et al.
Acknowledgments This work was supported by a research grant from the  Swedish Research Council  ( Vetenskapsrådet Grant  ID#:  2019-04791 ) to N. B. The authors acknowledge support from the  National Genomics Infrastructure in Stockholm  funded by  Science for Life Laboratory , the  Knut and Alice Wallenberg Foundation  and the  Swedish Research Council , and  SNIC/Uppsala Multidisciplinary Center for Advanced Computational Science  for assistance with massively parallel sequencing and access to the UPPMAX computational infrastructure. This work was also supported by  NBIS/SciLifeLab  long-term bioinformatics support (WABI). R. V. was supported by Grant No. PID2019-107078GB-I00 , funded by  Ministerio de Ciencia e Innovación-Agencia Estatal de Investigación MCIN /AEI/ 10. 13039/501100011033.
Data access All raw data generated in this study have been submitted to the European Nucleotide Archive (ENA; https://www. ebi. ac. uk/ena/ browser/home) under accession number PRJEB56690. The VCF files for each chromosome that were used for the demographic inferences and recombination rate estimations have been submitted to Dryad (doi: https://doi. org/10. 5061/dryad. qrfj6q5m7). Dovetail Omni-C raw data  (Höök et al. 2023)  for the reference genome used here is available at ENA under accession number PRJEB58697. In-house developed scripts and pipelines are available as Supplemental Code and on GitHub (https://github. com/EBC-butterflygenomics-team/Leptidea-sinapis-Recombination-Rate).
Due to inaccessibility, we carried out a remotely-based analysis of El Chiral and Patacocha volcanic centers. Furthermore, combining field data and morphometric parameters we propose the eruption style and classification of each volcano in this monogenetic field, contributing to the understanding of monogenetic volcanism in southern Peru. The Yura Monogenetic Field, located along the border of a tectonic basin, where the small monogenetic volcanoes were emplaced over ignimbrite deposits and near the Chachani large volcanic cluster and Misti stratovolcano, constitutes a very interesting example of monogenetic activity occurring in a temporal and spatial proximity to polygenetic volcanism with larger explosive and effusive eruptions. Reconstructing the history of volcanic activity around urban areas is fundamental for constraining the hazard scenarios and risk assessment.
The geological maps of the Yura Monogenetic Field were digitized using ArcMap ® 10. 1 (https://desktop. arcgis. com) at 1:25000 scale, the same scale used by INGEMMET (Instituto Geológico Minero y Metalúrgico) for geological maps of active volcanoes in Peru. The coordinate system of all maps presented herein are UTM WGS84, Zone 19S. Polygons delimiting the volcanoes were used to calculate their surface areas in ArcMap. In some areas, highresolution digital elevation models (UTM-WGS84 DEM, 60-cm spatial resolution) were obtained by oblique photogrammetry with a Phantom-4PRO drone. The methodology of photogrammetry was similar to that described by  Diefenbach et al. (2012)  and  James et al. (2020). We used AgiSoft Photoscan ® software (https://www. agisoft. com) for photogrammetric analysis.
Patacocha This eruptive center (~0. 45 km 2 ) shows an irregularly shaped and complex edifice formed by two coalescing cones and three Frontiers in Earth Science frontiersin. org 13 craters. The northwestern cone is capped by two coalescing craters (craters II and III), whereas the southeastern cone (crater I) has only one crater (Figure  8 ). Using Google Earth images and GIS software, we estimated the morphological parameters of the two cones: 1) Wco of 420 m, Wcr of 220 m, Hco of 24 m, and average flank slope of 14 °for the northwestern cone. The smaller crater of 80-m diameter cuts the first larger crater. 2) Wco of 380 m, Wcr of 135 m, Hco of 40 m, and the average flank slope is 18 °for the southeastern cone. The material expelled by this volcano covers deposits of the Tacaza Group, and apparently closed the channel of a stream, causing the damming of water and the formation a lagoon called Patacocha (highland lagoon in Quechua).
Acknowledgments We thank the editor,  S. Guzman , reviewers  K. Németh  and  Dario Pedrazzi  for their comments and constructive suggestions made on the early version of the manuscript, which helped to improve its content. This work stems from the research studies developed by  Instituto Geológico Minero y Metalúrgico (INGEMMET)  through the project " GA-17D: Estudio de peligros del Complejo Volcánico Chachani ". The work was carried out within the framework of  UNESCO -IGCP 692 Project: Geoheritage for Geohazard Resilience Project. Fieldwork of BV  was supported by the  Agence Nationale de la Recherche of the French government  through the  program Investissements d'Avenir. We thank the staff of the analytical chemistry and sample preparation units of the INGEMMET laboratories. T. Flaherty is also acknowledged for the English improvement of a previous version of the text.
First, we clustered the cells based on the expression of mRNAs and lncRNAs separately. We then used a multiomic version of hSBM  (18)  to perform an integrative clustering that combines the information coming from the two RNA families treated as disjoint omic layers. We show that this strategy improves cell classification compared to clustering obtained by investigating mRNAs and lncRNAs separately but also compared to clustering obtained on the expression measures of mRNAs and lncRNAs joined in a single matrix. Then, we investigated the gene content associated with cell clusters through functional enrichment analysis. To do this, we have developed a procedure to assign the most specific topics to each cluster and query them through gene set functional annotation databases (see Methods) With the topic modeling approach, we have identified some lncRNAs associated with breast cancer cell subpopulations already well known in the literature, such as MALAT1 and NEAT1, and highlighted some others that may be clinically relevant.
Clustering of drug-sensitive and resistant breast cancer cells using hSBM To explore intra-tumor transcriptional heterogeneity in breast cancer, we used a dataset of scRNA-seq profiles of patient-derived xenograft (PDX) models of luminal and triple negative breast cancer with acquired drug resistance  (17). After scRNA-seq data pre-processing (see Methods), we obtained two expression matrices containing respectively 1959 mRNAs and 1136 lncRNAs profiled in 3723 cells from drug-sensitive and resistant tumors. We translated each expression matrix into a weighted bipartite network where nodes represent genes (mRNAs or lncRNAs) and cells. Genes are linked to the cells with weighted edges based on their level of expression. We applied the hSBM algorithm to calculate the best partition separately for the two bipartite networks.
Functional enrichment analysis of topics We wanted to assess whether the gene content associated with the breast cancer cell clusters according to tri-partite SBM was enriched for specific biological functions or lncRNA sets associated with upstream and downstream target regulators. To do this, we developed a procedure to select the most specific topics to each cluster, based on the probability matrix of association of a given topic to a given cell (see Methods). We tested through hypergeometric statistics the enrichment of topics for the gene functional annotations of the MsigDB Molecular Signature Database  (19)  and for the lncRNA sets associated with upstream regulators and downstream targets, based on the lncSEA database  (20). Results of topic enrichment analysis are provided in Figure  4.
scRNA-seq dataset pre-processing Single cell expression count matrices were obtained from the NCBI GEO repository (GSE117309). This dataset includes scRNA-seq profiles from 3723 cells of patient-derived xenograft breast cancer models. Cells were collected from four cancer models: a luminal untreated drug-sensitive tumor, a luminal breast tumor with acquired resistance to Tamoxifen, a triple negative tumor initially responsive to Capecitabine and a triple negative tumor with acquired resistance to Capecitabine. Protein-coding mRNAs and lncRNAs raw expression matrices were obtained based on Ensemble grch37 gene annotations, after removing mithocondrial and ribosomal genes. Data were analyzed with 1. 8. 2. Library size normalization was applied and highly variable mRNAs and lncRNAs were selected according to the Scanpy procedure, setting different thresholds for the two RNA families, thus selecting 1959 and 1136 features respectively.
hSBM algorithm We used the original hSBM clustering algorithm available at https://github. com/martingerlach/hSBM_Topicmodel/tree/develop for handling gene expression data. The algorithm maximises the probability that the model  describe the data. Formally it maximises the quantity (|) = (|)(). In our context,  is the gene expression matrix and the model  is composed of the levels of the hierarchy, each with its own partitions of genes and cells. hSBM minimises the description length of the model, defined as Σ = -ln (|). The most important hyperparameter in the hSBM implementation of (  14 ) is the number of initializations, i. e. the number of different starting points from which the algorithm begins to estimate the posterior probability (|). We decided to run hSBM with seven initializations for each experiment because we found that seven initializations best balanced the time cost and the ability to reach the minimum description length (see Supp.
Multibranched SBM algorithm The analytical framework for adding multiple layers of information to a hSBM inference problem was recently introduced for text analysis  (48). In the context of scRNA-seq data analysis, the algorithm was adapted to handle a tri-partite network where nodes represent mRNAs, lncRNAs and cells. mRNAs and lncRNAs were connected to cells according to their expression level, therefore there is no edge between the mRNAs and lncRNAs nodes. The statistical inference procedure, as well as the definition of topics and probability distributions, leading to the tri-partite network clustering is an extension of the hSBM algorithm. The n-partite SBM, introduced in (18) extends the hSBM algorithm described in  (14). Its implementation is available on GitHub at https://github. com/BioPhys-Turin/nsbm.
The same definitions apply to P(lncRNA | lncRNA-topic) and P(lncRNA-topic | cell). Finally, let us highlight that looking at the probability of accepting a move ( → |) it is clear the advantage of using multibranched SBM on multi-omics problems: when moving a cell from block r to block s the algorithm samples a neighbour of the cell and it labels its group as t. At this point the model only considers the links between the cell and the nodes in group  , so the normalisations of other branches are irrelevant. In other words, at each move cells are clustered looking at a single omics and this allows to naturally perform inference on multiomics datasets in which each omic has a different normalisation.
Functional enrichment of topics The gene content of topics was tested through hypergeometric statistics for functional enrichment using annotations from the MsigDB Molecular Signature Database  (19)  and the lncSEA database  (20). We tested all topics assigned to clusters, excluding those not associated with any clusters. After correcting hypergeometric test p-values for multiple testing using Benjamini-Hochberg, we associated each topic to the most enriched functional gene set. When a gene set is associated with multiple topics, the one with the lowest p-value was retained.
DISCUSSION The topic modeling algorithms proposed here have proved to be a valid approach for unsupervised clustering of lncRNA expression at the single cell level, which allows to compensate for the sparsity of this type of data. The multibranch SBM enables the classification of breast cancer cell populations, showing that mRNAs and lncRNAs datasets provide complementary information about the heterogeneity of the tumors under study, when they are treated as disjoint omics layers. Differently from state-of-art clustering methods used in single cell data analysis, such as Leiden algorithm and graph-based algorithms  (57, 58) , multibranched SBM not only provides an integrative clustering of cells based on both mRNAs and lncRNAs expression, but also output the probability distribution of the association of these features to clusters, that is their relative importance in driving cell classification.
Clusters of cells and the sets of associated features (topics) are simultaneously identified with stochastic block modeling procedures. Another great advantage of the algorithm is that it automatically detects the optimal number of clusters and hierarchically clusters both the features and cells. This avoids researchers from fixing the number of expected clusters, which is difficult in real datasets. In addition, we show that clustering performed with multibranched SBM is not affected by batch effect, a common problem affecting the analysis of Large-scale single-cell transcriptomic datasets (Supp. Figure  2 ). In our study of breast cancer PDX models, the first hierarchical level separated the luminal and basal cells, while the next level showed a finer heterogeneity of the cells present in the.
National authorities publish COVID-19 death counts, which are extensively re-circulated and compared; but data are generally poorly sourced and documented. academics and stakeholders need tools to assess data quality and to track data-related discrepancies for comparability over time or across countries. "The Demography of COVID-19 Deaths" database aims at bridging this gap. It provides COVID-19 death counts along with associated documentation, which includes the exact data sources and points out issues of quality and coverage of the data. The database -launched in April 2020 and continuously updated -contains daily cumulative death counts attributable to COVID-19 broken down by sex and age, place and date of occurrence of the death. Data and metadata undergo quality control checks prior to online release.
Background & Summary The COVID-19 pandemic represents an unprecedented health crisis. The importance of timely, high-quality, publicly accessible COVID-19 related data has been essential in order to monitor this impact. National authorities started collecting data, often through their health surveillance systems or by establishing ad hoc data collection systems after the start of the epidemic. Statistics on the number of COVID-19 cases, hospitalizations, intensive care admissions, and death counts, among other figures, became quickly available, often broken down at different geographical levels and for specific population characteristics. These data have been extensively recirculated, collated, commented on, and compared; however, they are usually poorly sourced and documented. Although there are other international databases of deaths related to COVID-19, some of which cover many countries (e.
g. those compiled by the World Health Organization 1 , Our World in Data 2 , and John Hopkins University  3  ), they all have limitations, mostly related to the quality and degree of detail in the metadata and lack of user-friendly ways to download time-series data. Because data on deaths attributable to COVID-19 are mainly imperfect statistics, the lack of documentation limits accurate comparisons. "The Demography of COVID-19 Deaths" database  4  focuses on collecting death counts attributable to COVID-19, providing details on the exact definition of COVID-19 death used in each country and the processing of the data. The database specifically aims to provide tools for assessing data coverage and comparability, over time and across countries that can be used by the research community and others (policymakers, journalists, etc.
) for accurate trend analysis. Related documentation is collected from official statistics bureaus and epidemiological surveillance agencies, and published in the database with the following information: 1. Identify the exact source of data and its main features, such as the type of data collection system (e. g. health surveillance system, vital statistics, hospital records, etc. ), collection and publication patterns, and quality control protocols. 2. Describe the conditions for reporting deaths from COVID-19 and assess the degree of completeness of the statistical information, as well as the changes over time in each country (e. g. whether the reported deaths only include those occurring in hospital settings or also those occurring in nursing homes or private residences). 3. Illustrate possible sources of misinterpretation, by drawing attention to the reference date for which deaths are reported and their possible reporting delays.
Depending on the country, deaths may be reported at the time of occurrence, at the time of registration, at the time the information is entered into the statistical system, or at the time when the figures are officially published. 4. Specify the criteria considered in the attribution of a death to COVID-19 and its confirmation mechanisms. The criteria may vary among countries or between one data source and another within the same country. Some data sources only include confirmed cases, e. g. on the basis of biological tests or clinical diagnosis, while others also include suspected cases based on symptoms or proximity to a known case. The database provides cumulative death counts by sex and age. This demographic information is an important determinant of COVID-19: older people are more vulnerable to the infection and its most severe forms, and differences between men and women have also been well-documented  5.
As such, variations in population structure are expected to affect the number of deaths and must be taken into account in international comparisons of COVID-19 mortality. The database also provides the most up-to-date population counts in each country. Information on the place and date of occurrence of the deaths was added where available. Data collection started at the beginning of the pandemic. The database was launched on 2 April 2020. As of mid-December 2021, it covers 21 countries: Austria, Belgium, Canada, England and Wales, Denmark, France, Germany, Italy, Japan, the Netherlands, Norway, Portugal, the Republic of Korea, the Republic of Moldova, Romania, Scotland, Spain, Sweden, Switzerland, Ukraine, and the United States of America. An online survey on the database use revealed visits from researchers, students and journalists, with more than 660,000 views up to mid-December 2021.
Methods The operational team of the "The Demography of COVID-19 Deaths" has selected the 21 countries in the database based on the availability of metadata and of periodic data publications. Each member of the database operational team collects death counts and the related documentation for up to two countries. These country specialists monitor data availability daily and collect all the information relating to COVID-19 mortality (statistical data and documentation) made available by national statistics offices and health institutions since the beginning of the pandemic. This implies that for each country, data are added retrospectively according to their date of reference (when possible). Country specialists prepare country data files in pre-established standard formats in Excel spreadsheets and update the country-specific documentation.
For some countries data files can be formatted in statistical software because they are provided in. csv,. txt or. xlsx. However, for other countries this is not possible because the data comes in. pdf or in screen shots of online dashboard or images. For the latter countries, data must be copied "by hand" in the standard Excel spreadsheet. Data for each country included in the database are accompanied by explanatory notes providing detailed explanations about data heterogeneity. These notes are structured in five sections and describe: 1. The data source(s), the specific institution and official website(s); 2. Data coverage (i. e. whether the data include only hospital deaths, all deaths, or deaths which occurred elsewhere, and, if available, a description of the cause-of-death certification process); 3.