dataesr/scientific-paragraphs-categorization huggingface dataset:
Citation Statement and Classification Pipeline TEI XML data is parsed in Python using the BeautifulSoup library and further segmented into sentences using a combination of spaCy  (Honnibal, Montani et al. , 2018)  and Natural Language Toolkit's Punkt Sentence Tokenizer  (Bird, Klein, & Loper, 2009). These sentence segmentation candidates are then postprocessed with custom rules to better fit scientific texts, existing text structures, and inline markups. For instance, a sentence split is forbidden inside a reference callout, around common abbreviations not supported by the general-purpose sentence segmenters, or if it is conflicting with a list item, paragraph, or section break. The implementation of the classifier is realized by a component we have named Veracity, which provides a custom set of deep learning classifiers built on top of the open source DeLFT library  (Lopez, 2020d).
Veracity is written in Python and employs Keras and TensorFlow for text classification. It runs on a single server with an NVIDIA GP102 (GeForce GTX 1080 Ti) graphics card with 3,584 CUDA cores. This single machine is capable of classifying all citation statements as they are processed. Veracity retrieves batches of text from the scite database that have yet to be classified, processes them, and updates the database with the results. When deploying classification models in production, we balance the precision/recall so that all the classes have a precision higher than 80%. For this purpose, we use the holdout data set to adjust the class weights at the prediction level. After evaluation, we can exploit all available labeled data to maximize the quality, and the holdout set captures a real-world distribution adapted to this final tuning.
User Interface The resulting classified citations are stored and made available on the scite platform. Data from scite can be accessed in a number of ways (downloads of citations to a particular paper; the scite API, etc. ). However, users will most commonly access scite through its web interface. Scite provides a number of core features, detailed below. The scite report page (Figure  1 ) displays summary information about a given paper. All citations in the scite database to the paper are displayed, and users can filter results by classification (supporting, mentioning, contrasting), paper section (e. g. , Introduction, Results), and the type of citing article (e. g. , preprint, book, etc. ). Users can also search for text within citation statements and surrounding citation context. For example, if a user wishes to examine how an article has been cited with respect to a given concept (e.
This is an important feature of scite that allows researchers to interact with the automated citation types, correcting classifications that might otherwise be difficult for a machine to classify. It also opens the possibility for authors and readers to add more nuance to citation typing by allowing them to annotate snippets. To improve the utility and usability of the smart citation data, scite offers a wide variety of tools common to other citation platforms, such as Scopus and Web of Science and other information retrieval software. These include literature searching functionality for researchers to find supported and contrasted research, visualizations to see research in context, reference checking for automatically evaluating references with scite's data on an uploaded manuscript and more.
Implications for Scholarly Publishers There are a number of implications for scholarly publishers. At a very basic level, this is evident in the features that scite provides that are of particular use to publishers. For example, the scite Reference Check parses the reference list of an uploaded document and produces a report indicating how items in the list have been cited, flagging those that have been retracted or have otherwise been the subject of editorial concern. This type of screening can help publishers and editors ensure that articles appearing in their journals do not inadvertently cite discredited works. Evidence in scite's own database indicates that this would solve a seemingly significant problem, as in 2019 alone nearly 6,000 published papers cited works that had been retracted prior to 2019.
Limitations At present, the biggest limitation for researchers using scite is the size of the database. At the time of this writing, scite has ingested over 880 million separate citation statements from over 25 million scholarly publications. However, there are over 70 million scientific publications in existence  (Ware & Mabe, 2015) ; scite is constantly ingesting new papers from established sources and signing new licensing agreements with publishers, so this limitation should abate over time. However, given that the ingestion pipeline fails to identify approximately 30% of citation statements/references in PDF files (~5% in XML), the platform will necessarily contain fewer references than services such as Google Scholar and Web of Science, which do not rely on ingesting the full text of papers.
These traditional citations can later be augmented to include citation contexts as we gain access to full text. Another limitation is related to the classification of citations. First, as noted previously, the Veracity software does not perfectly classify citations. This can partly be explained by the fact that language in the (biomedical) sciences is little standardized (unlike law, where shepardizing is a standing term describing the "process of using a citator to discover the history of a case or statute to determine whether it is still good law"; see  Lehman & Phelps, 2005). However, the accuracy of the classifier will likely increase over time as technology improves and the training data set increases in size. Second, the ontology currently employed by scite (supporting, mentioning, and contrasting) necessarily misses some nuance regarding how references are cited in scientific papers.
DATA AVAILABILITY Code used in the ingestion of manuscripts is available at https://github. com/kermitt2/grobid, https://github. com/kermitt2/biblio-glutton, and https://github. com/kermitt2/Pub2TEI. The classification of citation statements is performed by a modified version of DeLFT (https://github. com/kermitt2/delft). The training data used by the scite classifier is proprietary and not publicly available. The 880+ million citation statements are available at scite. ai but cannot be shared in full due to licensing arrangements made with publishers.
Data sources We included industrialised countries in our analysis if: • Their total population in 2020 was more than 100,000. We excluded countries (e. g. , Liechtenstein) with data but with smaller populations because, in many weeks, the number of deaths would be small or zero. This would, in turn, lead to either large uncertainty that would make it hard to differentiate between those places with and without an effect or unstable estimates because the model is fitted to many weeks with zero deaths. • We could access up-to-date weekly data on all-cause mortality divided by age group and sex that extended through February 2021. • The time series of data went back at least to the beginning of 2016 so that model parameters could be reliably estimated. For countries with longer time series, we used data starting in 2010.
The sources of population and mortality data are provided in Extended Data Table  2. We calculated weekly population through interpolation of yearly population, consistent with the approach taken by national statistical offices for intra-annual population calculation  38. Population for 2020 and 2021, where not available, was obtained through linear extrapolation from the last five years. We obtained data on temperature from ERA5  39  , which uses data from global in situ and satellite measurements to generate a worldwide meteorological dataset, with full space and time coverage over our analysis period. We used gridded temperature estimates measured four times daily at a resolution of 30.
The components α 0 , α holiday(week) , β ⋅ week, 𝜃𝜃 week , ε week and ζ week (𝑖𝑖) (for autoregressive order 𝑖𝑖 = 1, 2, 4 or 8) appear in the expression for log(death rate week ) in all models. The remaining components appear in some models only. Extended Data Table  5  shows the terms included in each of the 16 models in the ensemble. We used data on weekly deaths from the start of time series through mid-February 2020 to estimate the parameters of each model, which were then used to predict death rates for the subsequent 52 weeks as estimates of the counterfactual death rates if the pandemic had not occurred. For the projection period, we used recorded temperature so that our projections take into consideration actual temperature in 2020-2021. This choice of training and prediction periods assumes that the number of deaths that are directly or indirectly related to the Covid-19 pandemic was negligible through mid-February 2020 in these countries  1  , and separates the training data from subsequent weeks when impacts may have appeared.
All models were fitted using integrated nested Laplace approximation (INLA)  54  , implemented in the R-INLA software (version 20. 03). We used a model averaging approach to combine the predictions from the 16 models in the ensemble  55, 56. Specifically, we took 2,000 draws from the posterior distribution of age-specific deaths under each of the 16 models, and pooled the 32,000 draws to obtain the posterior distribution of deaths if the Covid-19 pandemic had not taken place. This approach generates a distribution of estimates that has equal samples from each model in the ensemble, and hence incorporates both the uncertainty of estimates from each model and the uncertainty in the choice of model. The reported credible intervals represent the 2. 5 th and 97. 5 th percentiles of the resultant posterior distribution of the draws from the entire ensemble.
Comparison with other estimates Financial Times and The Economist's excess deaths tracker report the number of excess deaths for various countries based on comparisons of deaths in 2020 and 2021 with 2015-2019 averages. This approach does not account for general trends in mortality nor for factors like temperature that affect mortality and vary from year to year. The Economist has also recently published a set of excess deaths estimates using data from the Human Mortality Database and the World Mortality Dataset, and an ensemble of gradient boosted decision trees. Countries with small, medium and large number of excess deaths are largely consistent between our analysis and these sources. There are nonetheless some differences. For example, we estimated ~621,000 excess deaths for the US, compared to ~549,000 by Financial Times and ~600,000 by The Economist (for comparison, US CDC estimated 646,000 excess deaths).
For example, in the northern hemisphere, the first and last three months of 2020 were on average warmer than the average of the past five years but weeks 13-40 were on average slightly cooler. demo_r_mwk_05 ). Deaths with unknown age (0. 03% of all deaths) were distributed across age groups proportional to the overall distribution of deaths for each year and month. 4  https://ec. europa. eu/eurostat/data/database (table demo_pjangroup)  5  https://www150. statcan. gc. ca/t1/tbl1/en/tv. action?pid=1310076801. Death counts rounded to a neighbouring multiple of 5. There were no data for Yukon from 2017 to 2021 (before 2017, there were <10 deaths per week in Yukon). 6  https://www150. statcan. gc. ca/t1/tbl1/en/tv. action?pid=1710000501 7 https://deis. minsal. cl/#datosabiertos. Deaths with unknown age and/or sex (0.
02% of all deaths) were distributed across age groups and sexes proportional to the overall distribution of deaths for each year and month. 8  Data for the constituent nations in the UK are provided separately by NISRA for Northern Ireland, NRS for Scotland and ONS for England and Wales. These datasets use different reporting week definitions and could therefore not be combined into a single time series for the UK. Extended Data Table  3. Results of the external predictive validity (out-of-sample validation) of the estimated no-pandemic counterfactual weekly deaths from the Bayesian model ensemble. Each number represents the total error over the validation period, averaged across countries. * Australia, Chile, Cyprus, Germany, Greece, Romania and USA were not used for validation analysis because they had shorter time series.
Acknowledgements The development of methodology for estimating the impact of pandemic as an extreme event was supported by the  Pathways to Equitable Healthy Cities  grant from the  Wellcome Trust  [ 209376/Z/17/Z ]. Work on the US mortality data was partially supported by a grant from the  US Environmental Protection Agency (EPA) , as part of the  Center for Clean Air Climate Solution  (assistance agreement no. R835873 ). This article has not been formally reviewed by the EPA. The views expressed in this document are solely those of the authors and do not necessarily reflect those of the EPA. The EPA does not endorse any products or commercial services mentioned in this publication. Work on the UK mortality data was partially supported by the  British Heart Foundation  ( Centre of Research Excellence  grant  RE/18/4/34215 ).
Recent advances in genome sequencing and imputation technologies provide an exciting opportunity to comprehensively study the contribution of genetic variants to complex phenotypes. However, our ability to translate genetic discoveries into mechanistic insights remains limited at this point. In this paper, we propose an efficient knockoff-based method, GhostKnockoff, for genome-wide association studies (GWAS) that leads to improved power and ability to prioritize putative causal variants relative to conventional GWAS approaches. The method requires only Z-scores from conventional GWAS and hence can be easily applied to enhance existing and future studies. The method can also be applied to meta-analysis of multiple GWAS allowing for arbitrary sample overlap. We demonstrate its performance using empirical simulations and two applications: (1) analysis of 1,403 binary phenotypes from the UK Biobank data in 408,961 samples of European ancestry, and (2) a meta-analysis for Alzheimer's disease (AD) comprising nine overlapping large-scale GWAS, whole-exome and whole-genome sequencing studies.
We demonstrate its performance in empirical simulations and two applications: (1) analysis of 1,403 individual binary phenotypes from UK Biobank data on 408,961 samples with European ancestry; (2) meta-analysis study of Alzheimer's disease (AD) aggregating five genome-wide association studies, three whole-exome sequencing studies and one whole-genome sequencing study. These analyses demonstrate the superior performance of the proposed method compared to conventional GWAS, in both statistical power and ability to localize putative causal variants at each locus. Additional discoveries made by the proposed method are further supported by functional enrichment analyses and single-cell transcriptomic analyses. The method is computationally efficient and required only 0. 45 hours on ten central processing units (CPU) to analyze genome-wide summary statistics from the nine AD studies.
Phenome-wide analysis of UK Biobank data We applied GhostKnockoff separately to each of the 1,403 binary phenotypes from the UK Biobank data with 408,961 white British participants (European ancestry). In this analysis, GhostKnockoff reduces to a knockoff inference based on summary statistics from a single study. We aim to evaluate the performance of GhostKnockoff relative to a GWAS analysis. The Z-scores were calculated using SAIGE, a method that controls for case-control imbalance and sample relatedness  16. GhostKnockoff was applied to each phenotype separately to select associated genetic variants at FDR 0. 1. We used LD matrices estimated using the high coverage whole-genome sequencing data of the expanded 1000 Genomes Project  23. Due to the relatively small sample size of the 1000 Genomes Project (503 individuals of European ancestry) used to estimate the LD matrices, we restrict the analyses to common and low-frequency variants with minor allele frequency >1%.
1. More interesting, GhostKnockoff identifies less genetic variants within each locus (46% less variants on averages after accounting for LD, Figure  3B-3C ), even though FDR control is more liberal than the FWER control. These results are consistent with simulation results showing that GhostKnockoff is more powerful and can prioritize variants that are more likely to be causal. We additionally present the results stratified by phecode category (phecodes are grouped into different categories as in SAIGE) in Figure 3D-3E  16. Again, we observed that the proposed method consistently outperformed conventional GWAS across disease categories. To evaluate the functional effect of the identified variants, we performed functional enrichment analysis using 19 functional scores included in regBase  24  , including: CADD 25 , DANN   42.
We used this fixed effect model instead of a random effect model because the number of clusters is small relative to the total number of cells. Among the 55 proximal genes, 41 had expression measurements in the scRNAseq dataset, with 20/41 corresponding to new loci identified by GhostKnockoff. We considered pvalue threshold 0. 05 for suggestive signals and a more stringent Bonferroni correction 0. 05/41 = 0. 0012 for significant signals (more details on the analyses are available in the Methods section). We show the scRNAseq results (-log10(p-value) vs. log2 fold change) in Figure  5A , and their corresponding significance in GWAS studies in Table  1. Overall, we observed a consistently higher proportion of differentially expressed genes for the proximal genes compared to all other 23496 genes that are observed in the scRNAseq data (background genes), across the 14 cell types (Figure  5B ).
Discussion We have proposed GhostKnockoff to perform knockoff-based inference without generating any individuallevel knockoff variants. GhostKnockoff can be applied to commonly available summary statistics from conventional GWAS to improve the power to identify additional, potentially weaker, associations and to prioritize the causal variants at each locus. Additionally, GhostKnockoff can be applied to meta-analysis of possibly overlapping studies. In applications to phenome-wide analyses of UK Biobank data and a metaanalysis of Alzheimer's disease studies, we identified loci that were missed by conventional marginal association tests with improved precision. The additional discoveries are supported by functional enrichment analyses and single-cell transcriptomic analyses. These results demonstrate the improved performance of GhostKnockoff in distinguishing small effect loci that are potentially functional from noisy background genome.
53e to the integration of all available summary statistics from different studies, and the improved power of the proposed method compared to conventional GWAS due to FDR control, consistent with our findings in simulations and the UK Biobank analyses. Table1summarizes the 31 new loci. The 24 identified loci that were previously reported are shown in the SupplementaryThe results show that most of the new associations exhibit suggestive signals in individual studies, and most identified loci, including existing and new ones, have the same direction of effects across all studies, except very few loci where one dataset has an opposite direction of effect, although not significant, relative to other studies. Several new genes are worth mentioning. For example, LRPPRC (leucine-rich pentatricopeptide repeat motif containing protein) and APP (Amyloid beta precursor protein) have support for their possible involvement in AD from multiple studies51.
The scRNA-seq data of the Hippocampus region were divided into training, validation, and test datasets based on the cohort samples, with 5 AD (23,525 cells) and 4 healthy controls(23,186 cells)in the training dataset, 1 AD (7,454 ells) and 1 healthy control (6,948 cells) in the validation dataset, and 3 AD (15,707 cells) and 3 healthy controls (18,366 cells) in the test dataset. For each group of proximal genes (all 41 genes; 21 known genes; 20 new genes), we trained a nonlinear model using AdaBoost with a decision tree classifier and a linear model using logistic regression. We present the receiver operating characteristic (ROC) curves and area under the curve in Figure5E. We observed that the identified new genes have comparable classification accuracy as the previously reported genes for both linear (0.
Data Availability The manuscript used summary statistics from existing studies from the UK Biobank available at https://pheweb. org/UKB-SAIGE/. The summary statistics from each GWAS for Alzheimer's disease can be found at https://www. niagads. org/datasets. The single cell RNASeq data for the candidate genes is available at https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE163577.
The most mature thymocytes, SP4 and SP8, were enriched in immune response and apoptosis reflecting the positive and negative selection processes. In contrast, genes associated with sharp H3K4me3 peaks were associated with unrelated (e. g. viral process) or metabolic (tRNA processing) terms, consistent with previous results  (Chen et al. 2015). Next, we compared the presence of broad H3K4me3 domains between the thymic subpopulations and observed this to be highly dynamic throughout T cell differentiation (Fig. 1C ). We next assessed the expression of the broad H3K4me3 domain-associated genes using our previously published RNA-seq dataset  (Cieslak et al. 2020 ) (Fig. 1D ). We observed that Cold Spring Harbor Laboratory Press on June 24, 2022 -Published by genome. cshlp. org Downloaded from average gene expression was higher in the thymic subpopulation where the broad H3K4me3 domain was present over the gene-body, suggesting that the dynamic of broad domains correlates with changes in gene expression.
Broad H3K4me3 domains are associated with preferentially essential genes Previous studies have suggested that broad H3K4me3 domains in a given cell type preferentially mark genes that are essential for the identity and function of that cell type  (Benayoun et al. 2014; Chen et al. 2015). To directly assess the functional relevance of broad H3K4me3 domain-associated genes, we analyzed the DepMap dataset, a large-scale loss-offunction CRISPR-Cas9-based screen for essential genes, which was performed in 808 human cancer cell lines, including the T-ALL Jurkat cell line  (Behan et al. 2019)  (https://depmap. org). We assessed the relative enrichment of broad H3K4me3 domain-versus sharp-associated genes in the Jurkat cell line that were found to be essential in the majority of the cell lines (common essential), essential in Jurkat (all), or preferentially essential in Jurkat (Fig.
The expression of broad H3K4me3 domain-associated genes is preferentially deregulated in leukemia To gain more insight into the regulation of broad H3K4me3 domain-associated genes between normal and leukemic cells, we analyzed the changes in gene expression between normal thymocytes and T-ALL samples for both coding genes and LncRNAs (Fig. 4 ). In the case of T-ALL cell lines, the comparison was performed with total thymus using a consistent RNAseq dataset  (Atak et al. 2013 )(Fig. 4A ). Primary T-ALLs were compared with the averaged thymocyte subpopulations using, depending on the availability, either RNA-seq (Fig. 4A ) or gene body enrichment in H3K36me3 (Fig. 4B ), a histone modification directly correlated with gene expression  (Barski et al. 2007 ). We selected broad H3K4me3 domain-associated genes in the T-ALL samples, as well as, a set of genes with equal expression levels in T-ALL samples, but associated with sharp H3K4me3 profiles around the TSS.