bluuebunny/crossref_metadata_2025_split huggingface dataset:
Community factors and excess mortality in the COVID-19 pandemic in England, Italy and Sweden
Authors: Parkes Brandon , Stafoggia Massimo , Fecht Daniela , Davies Bethan , Bonander Carl , de’ Donato Francesca , Michelozzi Paola , Piel Frédéric B. , Strömberg Ulf , Blangiardo Marta 
Published: April 2022

AbstractBackgroundAnalyses of COVID-19 suggest specific risk factors make communities more or less vulnerable to pandemic related deaths within countries. What is unclear is whether the characteristics affecting vulnerability of small communities within countries produce similar patterns of excess mortality across countries with different demographics and public health responses to the pandemic. Our aim is to quantify community-level variations in excess mortality within England, Italy and Sweden and identify how such spatial variability was driven by community-level characteristics.MethodsWe applied a two-stage Bayesian model to quantify inequalities in excess mortality in people aged 40 years and older at the community level in England, Italy and Sweden during the first year of the pandemic (March 2020–February 2021). We used community characteristics measuring deprivation, air pollution, living conditions, population density and movement of people as covariates to quantify their associations with excess mortality.ResultsWe found just under half of communities in England (48.1%) and Italy (45.8%) had an excess mortality of over 300 per 100,000 males over the age of 40, while for Sweden that covered 23.1% of communities. We showed that deprivation is a strong predictor of excess mortality across the three countries, and communities with high levels of overcrowding were associated with higher excess mortality in England and Sweden.ConclusionThese results highlight some international similarities in factors affecting mortality that will help policy makers target public health measures to increase resilience to the mortality impacts of this and future pandemics.
Incorporating Mass Vaccination into Compartment Models for Infectious Diseases
Authors: Ledder Glenn 
Published: April 2022

AbstractThe standard way of incorporating mass vaccination into a compartment model for an infectious disease is as a spontaneous transition process that applies to the entire susceptible class. The large degree of COVID-19 vaccine refusal, hesitancy, and ineligibility, and initial limitations of supply and distribution require reconsideration of this standard treatment. In this paper, we address these issues for models on endemic and epidemic time scales. On an endemic time scale, we partition the susceptible class into prevaccinated and unprotected subclasses and show that vaccine refusal/hesitancy/ineligibility has a significant impact on endemic behavior, particularly for diseases where immunity is short-lived. On an epidemic time scale, we develop a supply-limited Holling type 3 vaccination model and show that it is an excellent fit to vaccination data. We also extend the Holling model to a COVID-19 scenario in which the population is divided into two risk classes, with the highrisk class being prioritized for vaccination. For both cases with and without stratification by risk, we see significant differences in epidemiological outcomes between the Holling vaccination model and naive models. Finally, we use the new model to explore implications for public health policies in future pandemics.
Heterogeneity of Provider Preferences for HIV Care Coordination Program Features: Latent Class Analysis of a Discrete Choice Experiment
Authors: Fong Chunki , Conte Madellena , Zimba Rebecca , Carmona Jennifer , Gambone Gina , Baim-Lance Abigail , Robertson McKaylee , Irvine Mary , Nash Denis 
Published: April 2022

AbstractBackgroundThe PROMISE study was launched in 2018 to assess revisions to an HIV care coordination program (CCP) designed to address gaps in care and treatment engagement among people living with HIV in New York City (NYC). We report on the heterogeneity of provider preferences regarding a revised CCP elicited from a discrete choice experiment (DCE).MethodsFrom January to March 2020, 152 CCP providers in NYC completed a DCE with 4 program attributes: 1) help with adherence to antiretroviral therapy, 2) help with primary care appointments, 3) help with issues other than primary care, and 4) program visit location. Each attribute had 3-4 levels. Latent class analysis (LCA) was used to detect subgroups with differing attribute importance and part-worth utility patterns. Choice simulation was used to estimate providers’ endorsement of eight hypothetical CCPs.ResultsLCA identified three subgroups. The two larger subgroups (n = 133) endorsed more intensive attribute levels, particularly clients receiving directly observed therapy, and home visits. The remaining smaller subgroup (n = 19) endorsed clients receiving medication reminders and meeting with clients at the program. Simulation showed that intensive medical case management programs had the highest degree of endorsement (62%).ConclusionWhile our results indicate high endorsement among providers for intensive CCP features, overall, they also suggest the need for flexible service delivery options to meet the needs of the clients that these programs serve. Additional information sharing across and within agencies may be warranted to improve the fidelity with which the CCP is implemented.
Systems analysis of metabolic responses to a mixed meal test in an obese cohort reveals links between tissue metabolism and the gut microbiota
Authors: Li Peishun , Ji Boyang , Lappa Dimitra , Meijnikman Abraham S , Olsson Lisa M. , Aydin Ömrüm , Bruin Sjoerd C. , van de Laar Arnold , Tremaroli Valentina , Luo Hao , Geng Jun , Krautkramer Kimberly A. , Lundqvist Annika , Herrema Hilde , Groen Albert K. , Gerdes Victor E.A. , Schwartz Thue W. , Bäckhed Fredrik , Nieuwdorp Max , Olofsson Louise E. , Nielsen Jens 
Published: April 2022

AbstractIndividuals with prediabetes and type 2 diabetes mellitus (T2DM) have poor ability to adapt to diet-triggered perturbations. We investigated global metabolic responses to a mixed meal test (MMT) in morbidly obese individuals with different diabetic status by performing plasma metabolomic profiling. Abnormal metabolism of carbohydrates, (branched-chain) amino acids, fatty acids and acylcholines in individuals with (pre)diabetes was observed. Moreover, differences in metabolic responses were associated with altered fecal metagenomics and transcriptomes of liver, jejunum and adipose tissues, which revealed a modified gut microbiome and multi-tissue metabolism in individuals having insulin resistance. Finally, using integrative machine learning models, we built a predictive model based on metabolomics data after 2h MMT, and identified possible new biomarkers for glycemic control including N−acetylaspartate and phenylalanine-derived metabolites that may be useful for diagnosis, intervention and prevention of T2DM.
Disease diagnostics using machine learning of immune receptors
Authors: Zaslavsky Maxim E. , Craig Erin , Michuda Jackson K. , Sehgal Nidhi , Ram-Mohan Nikhil , Lee Ji-Yeun , Nguyen Khoa D. , Hoh Ramona A. , Pham Tho D. , Röltgen Katharina , Lam Brandon , Parsons Ella S. , Macwana Susan R. , DeJager Wade , Drapeau Elizabeth M. , Roskin Krishna M. , Cunningham-Rundles Charlotte , Moody M. Anthony , Haynes Barton F. , Goldman Jason D. , Heath James R. , Nadeau Kari C. , Pinsky Benjamin A. , Blish Catherine A. , Hensley Scott E. , Jensen Kent , Meyer Everett , Balboni Imelda , Utz Paul J , Merrill Joan T. , Guthridge Joel M. , James Judith A. , Yang Samuel , Tibshirani Robert , Kundaje Anshul , Boyd Scott D. 
Published: April 2022

AbstractClinical diagnosis typically incorporates physical examination, patient history, and various laboratory tests and imaging studies, but makes limited use of the human system’s own record of antigen exposures encoded by receptors on B cells and T cells. We analyzed immune receptor datasets from 593 individuals to developMAchine Learning for Immunological Diagnosis (Mal-ID), an interpretive framework to screen for multiple illnesses simultaneously or precisely test for one condition. This approach detects specific infections, autoimmune disorders, vaccine responses, and disease severity differences. Human-interpretable features of the model recapitulate known immune responses to SARS-CoV-2, Influenza, and HIV, highlight antigen-specific receptors, and reveal distinct characteristics of Systemic Lupus Erythematosus and Type-1 Diabetes autoreactivity. This analysis framework has broad potential for scientific and clinical interpretation of human immune responses.
Decoding Surface Fingerprints for Protein-Ligand Interactions
Authors: Igashov Ilia , Jamasb Arian R. , Sadek Ahmed , Sverrisson Freyr , Schneuing Arne , Liò Pietro , Blundell Tom L. , Bronstein Michael , Correia Bruno 
Published: April 2022

AbstractSmall molecules have been the preferred modality for drug development and therapeutic interventions. This molecular format presents a number of advantages, e.g. long half-lives and cell permeability, making it possible to access a wide range of therapeutic targets. However, finding small molecules that engage “hard-to-drug” protein targets specifically and potently remains an arduous process, requiring experimental screening of extensive compound libraries to identify candidate leads. The search continues with further optimization of compound leads to meet the required potency and toxicity thresholds for clinical applications. Here, we propose a new computational workflow for high-throughput fragment-based screening and binding affinity prediction where we leverage the available protein-ligand complex structures using a state-of-the-art protein surface embedding framework (dMaSIF). We developed a tool capable of finding suitable ligands and fragments for a given protein pocket solely based on protein surface descriptors, that capture chemical and geometric features of the target pocket. The identified fragments can be further combined into novel ligands. Using the structural data, our ligand discovery pipeline learns the signatures of interactions between surface patches and small pharmacophores. On a query target pocket, the algorithm matches known target pockets and returns either potential ligands or identifies multiple ligand fragments in the binding site. Our binding affinity predictor is capable of predicting the affinity of a given protein-ligand pair, requiring only limited information about the ligand pose. This enables screening without the costly step of first docking candidate molecules. Our framework will facilitate the design of ligands based on the target’s surface information. It may significantly reduce the experimental screening load and ultimately reveal novel chemical compounds for targeting challenging proteins.
Enhanced recovery of single-cell RNA-sequencing reads for missing gene expression data
Authors: Pool Allan-Hermann , Poldsam Helen , Chen Sisi , Thomson Matt , Oka Yuki 
Published: April 2022

AbstractDroplet-based 3’ single-cell RNA-sequencing (scRNA-seq) methods have proved transformational in characterizing cellular diversity and generating valuable hypotheses throughout biology1,2. Here we outline a common problem with 3’ scRNA-seq datasets where genes that have been documented to be expressed with other methods, are either completely missing or are dramatically under-represented thereby compromising the discovery of cell types, states, and genetic mechanisms. We show that this problem stems from three main sources of sequencing read loss: (1) reads mapping immediately 3’ to known gene boundaries due to poor 3’ UTR annotation; (2) intronic reads stemming from unannotated exons or pre-mRNA; (3) discarded reads due to gene overlaps3. Each of these issues impacts the detection of thousands of genes even in well-characterized mouse and human genomes rendering downstream analysis either partially or fully blind to their expression. We outline a simple three-step solution to recover the missing gene expression data that entails compiling a hybrid pre-mRNA reference to retrieve intronic reads4, resolving gene collision derived read loss through removal of readthrough and premature start transcripts, and redefining 3’ gene boundaries to capture false intergenic reads. We demonstrate with mouse brain and human peripheral blood datasets that this approach dramatically increases the amount of sequencing data included in downstream analysis revealing 20 - 50% more genes per cell and incorporates 15-20% more sequencing reads than with standard solutions5. These improvements reveal previously missing biologically relevant cell types, states, and marker genes in the mouse brain and human blood profiling data. Finally, we provide scRNA-seq optimized transcriptomic references for human and mouse data as well as simple algorithmic implementation of these solutions that can be deployed to both thoroughly as well as poorly annotated genomes. Our results demonstrate that optimizing the sequencing read mapping step can significantly improve the analysis resolution as well as biological insight from scRNA-seq. Moreover, this approach warrants a fresh look at preceding analyses of this popular and scalable cellular profiling technology.
Widespread allele-specific topological domains in the human genome are not confined to imprinted gene clusters
Authors: Richer Stephen , Yuan Tian , Schoenfelder Stefan , Hurst Laurence , Murrell Adele , Pisignano Giuseppina 
Published: April 2022

AbstractBackgroundThere is widespread interest in the three-dimensional chromatin conformation of the genome and its impact on gene expression. However, these studies frequently do not consider parent-of-origin differences, such as genomic imprinting, which result in monoallelic expression. In addition, genome-wide allele-specific chromatin conformation associations have not been extensively explored. There are few accessible bioinformatic workflows for investigating allelic conformation differences and these require pre-phased haplotypes which are not widely available.ResultsWe developed a bioinformatic pipeline, “HiCFlow”, which performs haplotype assembly and visualisation of parental chromatin architecture. We benchmarked the pipeline using prototype haplotype phased Hi-C data from GM12878 cells at three disease associated imprinted gene clusters. Using RC-HiC (Region Capture Hi-C) and Hi-C data from further human cell lines (1-7HB2, IMR-90, and H1-hESCs) we were able to robustly identify the known stable allele-specific interactions at the H19/IGF2 locus. Other imprinted loci (DLK1 and SNRPN) were more variable and there was no “canonical imprinted 3D structure”, but we could detect allele-specific differences in A/B compartmentalisation. Genome-wide, when TADs were unbiasedly ranked according to their allele-specific contact frequencies, a set of “allele-specific TADs” (ASTADs) could be defined. These occurred in genomic regions of high sequence variation. In addition to imprinted genes, ASTADs were also enriched for allele-specific expressed (ASE) genes. We found loci that have not previously been identified as ASE such as the bitter taste receptors (TAS2Rs).ConclusionsThis study highlights the widespread differences in chromatin conformation between heterozygous loci and provides a new framework for understanding ASE.
Extensive protected area coverage and an updated global population estimate for the Endangered Madagascar Serpent-eagle identified from species-habitat associations using remote sensing data
Authors: Sutton Luke J. , Benjara Armand , Rene de Roland Lily-Arison , Thorstrom Russell , McClure Christopher J.W. 
Published: April 2022

AbstractKnowledge gaps regarding distribution, habitat associations, and population size for rare and threatened range-restricted taxa leads to uncertainty in directing conservation action. Quantifying range metrics and species-habitat associations using Species Distribution Models (SDMs) with remote sensing habitat data can overcome these setbacks by establishing baseline estimates for biological parameters critical for conservation assessments. Area of habitat (AOH) is a new range metric developed by the International Union for the Conservation of Nature (IUCN) Red List. AOH seeks to quantify inferred habitat within a species’ range to inform extinction risk assessments. Here, we use SDMs correlating occurrences with remote-sensing covariates, to calculate a first estimate of AOH for the Endangered Madagascar Serpent-eagle (Eutriorchis astur), and then update additional IUCN range metrics and the current global population estimate. From these baselines we then conduct a gap analysis assessing protected area coverage. Our continuous SDM had robust predictive performance (Continuous Boyce Index = 0.835) and when reclassified to a binary model estimated an AOH = 30,121 km2, 13 % less than the current IUCN range map. We estimate a global population of 533 mature individuals derived from the Madagascar Serpent-eagle AOH metric, which was within the current IUCN population estimates. The current protected area network covered 95 % of AOH, with the binary model identifying three key habitat areas as new protected area designations to fully protect Madagascar Serpent-eagle habitat. Our results demonstrate that correlating presence-only occurrences with remote sensing habitat covariates can fill knowledge gaps useful for informing conservation action. Applying this spatial information to conservation planning would ensure almost full protected area coverage for this endangered raptor. For tropical forest habitat specialists, we recommend that potential predictors derived from remote sensing, such as vegetation indices and biophysical measures are considered as covariates, along with other variables including climate and topography.
Fine-grained topographic organization within somatosensory cortex during resting-state and emotional face-matching task and its association with ASD traits
Authors: Isakoglou Christina , Haak Koen V. , Wolfers Thomas , Floris Dorothea L. , Llera Alberto , Oldehinkel Marianne , Forde Natalie J. , Oakley Bethany F. M. , Tillmann Julian , Holt Rosemary J. , Moessnang Carolin , Loth Eva , Bourgeron Thomas , Baron-Cohen Simon , Charman Tony , Banaschewski Tobias , Murphy Declan G. M. , Buitelaar Jan K. , Marquand Andre F. , Beckmann Christian F. ,   the EU-AIMS LEAP Group
Published: April 2022

AbstractBACKGROUNDSensory atypicalities are particularly common in autism spectrum disorders (ASD). Nevertheless, our knowledge about the divergence of the underlying somatosensory region and its association with ASD phenotype features is limited.METHODSWe applied a data-driven approach to map the fine-grained variations in functional connectivity of the primary somatosensory cortex (S1) to the rest of the brain in 240 autistic and 164 neurotypical individuals from the EU-AIMS LEAP dataset, aged between 7 and 30. We estimated the S1 connection topography (‘connectopy’) during rest and during the emotional face-matching (Hariri) task, an established measure of emotion reactivity, and accessed its association with a set of clinical and behavioral variables.RESULTSWe demonstrated that the S1 connectopy is organized along a dorsoventral axis, mapping onto the somatotopic organization of S1. We found that its spatial characteristics were linked to the individuals’ adaptive functioning skills, as measured by the Vineland Adaptive Behavior Scales, across the whole sample. Higher functional differentiation characterized the S1 connectopies of individuals with higher daily life adaptive skills. Notably, we detected significant differences between rest and the Hariri task in the S1 connectopies, as well as their projection maps onto the rest of the brain suggesting a task-modulating effect on S1 due to emotion processing.CONCLUSIONSVariation of daily life adaptive skills appears to be reflected in the brain’s mesoscale neural circuitry, as shown by the S1 connectivity profile, which is also differentially modulated during rest and emotional processing.
Bimodal regulation of axonal transport by the GDNF-RET signalling axis in healthy and diseased motor neurons
Authors: Rhymes Elena R , Tosolini Andrew P , Fellows Alexander D , Mahy William , McDonald Neil , Schiavo Giampietro 
Published: April 2022

AbstractDeficits in axonal transport are one of the earliest pathological outcomes in several models of amyotrophic lateral sclerosis (ALS), including SOD1G93A mice. Evidence suggests that rescuing these deficits prevents disease progression, stops denervation, and extends survival. Kinase inhibitors have been previously identified as transport enhancers, and are being investigated as potential therapies for ALS. For example, inhibitors of p38 mitogen-activated protein kinase and insulin growth factor receptor 1 have been shown to rescue axonal transport deficits in vivo in symptomatic SOD1G93A mice. In this work, we investigated the impact of RET, the tyrosine kinase receptor for glial cell-line-derived neurotrophic factor (GDNF), as a modifier of axonal transport. We identified fundamental interplay between RET signalling and axonal transport in both wild type and SOD1G93A motor neurons in vitro. We demonstrated that blockade of RET signalling using pharmacological inhibitors and genetic knockdown enhances signalling endosome transport in wild type motor neurons and uncovered a divergence in the response of primary motor neurons to GDNF compared with cell lines. Finally, we demonstrated that inhibition of the GDNF-RET signalling axis rescues in vivo transport deficits in early symptomatic SOD1G93A mice, promoting RET as a potential therapeutic target in the treatment of ALS.