bluuebunny/crossref_metadata_2025_split huggingface dataset:
Yield of genetic association signals from genomes, exomes, and imputation in the UK biobank
Authors: Gaynor Sheila M. , Joseph Tyler , Bai Xiaodong , Krasheninina Olga , Boutkov Boris , Maxwell Evan , Balasubramanian Suganthi , Marcketta Anthony , Backman Joshua ,   Regeneron Genetics Center, Reid Jeffrey G. , Overton John D. , Lotta Luca A. , Marchini Jonathan , Salerno William J. , Baras Aris , Abecasis Goncalo R. , Thornton Timothy A. 
Published: September 2023

AbstractWhole genome sequencing (WGS), whole exome sequencing (WES), and array genotyping with imputation (IMP) are common strategies for assessing genetic variation and its association with medically relevant phenotypes. To date there has been no systematic empirical assessment of the yield of these approaches when applied to 100,000s of samples to enable discovery of complex trait genetic signals. Using data for 100 complex traits in 149,195 individuals in the UK Biobank, we systematically compare the relative yield of these strategies in genetic association studies. We find that WGS and WES combined with arrays and imputation (WES+IMP) have the largest association yield. While WGS results in a ∼5-fold increase in the total number of assayed variants over WES+IMP, the number of detected signals differed by only 1% for both single-variant and gene-based association analyses. Since WES+IMP typically results in savings of lab and computational time and resources expended per sample, we evaluate the potential benefits of applying WES+IMP to larger samples. When we extend our WES+IMP analyses to 468,169 UK Biobank individuals, we observe a ∼4-fold increase in association signals with the ∼3-fold increase in sample size. We conclude that prioritizing WES+IMP and large sample sizes, rather than current short-read WGS alternatives, will maximize the number of discoveries in genetic association studies.
Proteomic aging clock predicts mortality and risk of common age-related diseases in diverse populations
Authors: Argentieri M. Austin , Xiao Sihao , Bennett Derrick , Winchester Laura , Nevado-Holgado Alejo J. , Albukhari Ashwag , Yao Pang , Mazidi Mohsen , Lv Jun , Li Liming , Adams Cassandra J. , Clarke Robert , Amin Najaf , Chen Zhengming , van Duijn Cornelia M. 
Published: September 2023

AbstractCirculating plasma proteins play key roles in human health and could be used to measure biological aging to predict risk of mortality, disease, and multimorbidity beyond chronological age. We developed a proteomic age clock using 1,459 plasma proteins (Olink Explore) in two prospective biobanks in the UK (n=45,117) and China (n=2,026) and explored its utility to predict incident risk of 26 major age-related diseases and all-cause mortality. We identified 226 proteins that accurately predicted chronological age (Pearson r=0.92). Individuals in the top versus bottom deciles of accelerated proteomic aging differed by approximately 10 years of biological aging. In the UK population, accelerated proteomic aging was associated with 25 aging phenotypes (e.g., telomere length, IGF-1, creatinine, cystatin C, hand grip strength, cognitive function, frailty index), 18 chronic diseases (e.g., diseases of the heart, liver, kidneys, lungs; diabetes; neurodegeneration; cancers), multimorbidity, and all-cause mortality. In the smaller Chinese population, accelerated proteomic aging was associated with ischemic heart disease, stroke, and all-cause mortality. Our results demonstrate that plasma proteins are a reliable instrument for prediction of multiple common diseases in diverse populations and can be used as a robust biochemical aging signature to improve early detection and management of common diseases.
Conventional and unconventional T cell responses contribute to the prediction of clinical outcome and causative bacterial pathogen in sepsis patients
Authors: Burton Ross J. , Raffray Loïc , Moet Linda M. , Cuff Simone M. , White Daniel A. , Baker Sarah E. , Moser Bernhard , O’Donnell Valerie B. , Ghazal Peter , Morgan Matt P. , Artemiou Andreas , Eberl Matthias 
Published: September 2023

ABSTRACTSepsis is characterised by a dysfunctional host response to infection culminating in life-threatening organ failure that requires complex patient management and rapid intervention. Timely diagnosis of the underlying cause of sepsis is crucial, and identifying those at risk of complications and death is imperative for triaging treatment and resource allocation. Here, we explored the potential of explainable machine learning models to predict mortality and causative pathogen in sepsis patients. By using a modelling pipeline employing multiple feature selection algorithms, we demonstrate the feasibility to identify integrative patterns from clinical parameters, plasma biomarkers and extensive phenotyping of blood immune cells. Whilst no single variable had sufficient predictive power, models that combined five and more features showed a macro area under the curve (AUC) of 0.85 to predict 90 day mortality after sepsis diagnosis, and a macro AUC of 0.86 to discriminate between Gram-positive and Gram-negative bacterial infections. Parameters associated with the cellular immune response contributed the most to models predictive of 90 day mortality, most notably, the proportion of T cells among PBMCs, together with expression of CXCR3 by CD4+T cells and CD25 by mucosal-associated invariant T (MAIT) cells. Frequencies of Vδ2+γδ T cells had the most profound impact on the prediction of Gram-negative infections, alongside other T cell-related variables and total neutrophil count. Overall, our findings highlight the added value of measuring the proportion and activation patterns of conventional and unconventional T cells in the blood of sepsis patients in combination with other immunological, biochemical and clinical parameters.
Neonatal outcomes after maternal biomarker-guided preterm birth intervention: the AVERT PRETERM trial
Authors: Hoffman Matthew K. , Kitto Carrie , Zhang Zugui , Shi Jing , Walker Michael G. , Shahbaba Babak , Ruhstaller Kelly 
Published: September 2023

AbstractThe AVERT PRETERM trial (NCT03151330) evaluated whether screening clinically low-risk pregnancies with a validated maternal blood biomarker test for spontaneous preterm birth (sPTB) risk, followed by preventive treatments for those screening positive, would improve neonatal outcomes compared to a clinically low-risk historical population that had received usual care. Prospective arm participants with singleton non-anomalous pregnancies and no PTB history were tested for sPTB risk at 191/7-206/7weeks’ gestation and followed through neonatal discharge. Screen-positive individuals (≥16% sPTB risk) were offered vaginal progesterone (200 mg) and aspirin (81 mg) daily, with twice-weekly nurse phone calls. Co-primary outcomes were neonatal morbidity and mortality, measured using a validated composite index (NMI), and neonatal hospital length of stay (NNLOS). Endpoints were assessed using survival analysis and logistic regression in a modified intent-to-treat population comprising screen-negative individuals and screen-positive individuals accepting treatment. Of 1460 eligible participants, 34.7% screened positive; of these, 56.4% accepted interventions and 43.6% declined. Compared to historical controls, prospective arm neonates whose mothers accepted treatment had lower NMI scores (odds ratio 0.81, 95% CI, 0.67-0.98, P=0.03) and an 18% reduction in severe morbidity. NNLOS was shorter (hazard ratio 0.73, 95% CI, 0.58-0.92,P=0.01), with a 21% mean stay decrease among neonates having the longest stays. Sensitivity analyses of the entire intent-to-treat population supported these findings. These results suggest that biomarker PTB risk stratification and preventive interventions can ameliorate PTB complications in singleton, often nulliparous, pregnancies historically deemed low risk.
Proteomic signaling of dual specificity phosphatase 4 (DUSP4) in Alzheimer’s disease
Authors: Wang Erming , Pan Allen L. , Bagchi Pritha , Ranjaraju Srikant , Seyfried Nicholas T. , Ehrlich Michelle E. , Salton Stephen R. , Zhang Bin 
Published: September 2023

SummaryDUSP4 is a member of the DUSP (Dual-Specificity Phosphatase) subfamily that is selective to the mitogen-activated protein kinases (MAPK) and has been implicated in a range of biological processes and functions in Alzheimer’s disease (AD). In this study, we utilized stereotactic delivery of adeno-associated virus (AAV)-DUSP4 to overexpress DUSP4 in the dorsal hippocampus of 5×FAD and wildtype (WT) mice, then used mass spectrometry (MS)-based proteomics along with label-free quantification to profile the proteome and phosphoproteome in the hippocampus. We identified patterns of protein expression and phosphorylation that are modulated in 5×FAD mice and examined the sex-specific impact of DUSP4 overexpression on the 5×FAD proteome/phosphoproteome. In 5×FAD mice, a substantial number of proteins were up– or down-regulated in both male and female mice in comparison to age and sex-matched WT mice, many of which are involved in AD-related biological processes, such as the activated immune response or suppression of synaptic activities. Upon DUSP4 overexpression, significantly regulated proteins were found in pathways that were suppressed, such as the immune response, in male 5×FAD mice. In contrast, such a shift was absent in female mice. For the phosphoproteome, we detected an array of phosphorylation sites that are regulated in 5×FAD compared to WT, and are modulated by DUSP4 overexpression in each sex. Interestingly, the changes in 5×FAD– and DUSP4-associated phosphorylation occurred in opposite directions. Strikingly, both the 5×FAD– and DUSP4-associated phosphorylation changes were found for the most part in neurons, and play key roles in neuronal processes and synaptic function. Site-centric pathway analysis revealed that both the 5×FAD– and DUSP4-associated phosphorylation sites were enriched for a number of kinase sets in female, but only a limited number of sets of kinases in male mice. Taken together, our results suggest that male and female 5×FAD mice respond to DUSP4 overexpression via shared and sex-specific molecular mechanisms, which might underly similar reductions in amyloid pathology in both sexes, while learning deficits were reduced in only females with DUSP4 overexpression. Finally, we validated our findings with the sex-specific AD-associated proteomes in human cohorts and further developed DUSP4-centric proteomic network models and signaling maps for each sex.
Tissue-Specific Dependence of Th1 Cells on the Amino Acid Transporter SLC38A1 in Inflammation
Authors: Sugiura Ayaka , Beier Katherine L. , Chi Channing , Heintzman Darren R. , Ye Xiang , Wolf Melissa M. , Patterson Andrew R. , Cephus Jacqueline-Yvonne , Hong Hanna S. , Lyssiotis Costas A. , Newcomb Dawn C. , Rathmell Jeffrey C. 
Published: September 2023

SUMMARYAmino acid (AA) uptake is essential for T cell metabolism and function, but how tissue sites and inflammation affect CD4+T cell subset requirements for specific AA remains uncertain. Here we tested CD4+T cell AA demands within vitroand multiplein vivoCRISPR screens and identify subset- and tissue-specific dependencies on the AA transporter SLC38A1 (SNAT1). While dispensable for T cell persistence and expansion over timein vitroandin vivolung inflammation, SLC38A1 was critical for Th1 but not Th17 cell-driven Experimental Autoimmune Encephalomyelitis (EAE) and contributed to Th1 cell-driven inflammatory bowel disease. SLC38A1 deficiency reduced mTORC1 signaling and glycolytic activity in Th1 cells, in part by reducing intracellular glutamine and disrupting hexosamine biosynthesis and redox regulation. Similarly, pharmacological inhibition of SLC38 transporters delayed EAE but did not affect lung inflammation. Subset- and tissue-specific dependencies of CD4+T cells on AA transporters may guide selective immunotherapies.HIGHLIGHTST cells dynamically regulate glutamine amino acid transporters when activatedSLC38A1 supports Th1 cell mTORC1 and proliferation by redox and hexosamine pathwaysTargeting SLC38A1 does not affect lung inflammation but delays IBD and EAENutrient transporter needs of T cell subsets vary based on disease and tissue site
In vivoselection in non-human primates identifies superior AAV capsids for on-target CSF delivery to spinal cord
Authors: Hanlon Killian S. , Cheng Ming , De La Cruz Demitri , Patel Nikita , Santoscoy Miguel C. , Gong Yi , Ng Carrie , Nguyen Diane M. , Nammour Josette , Clark Sean W. , Kozarsky Karen , Maguire Casey A. 
Published: September 2023

AbstractSystemic administration of adeno-associated virus (AAV) vectors for spinal cord gene therapy has challenges including toxicity at high doses and pre-existing immunity that reduces efficacy. Intrathecal delivery of AAV vectors into the cerebral spinal fluid (CSF) can avoid many of the issues of systemic delivery, although achieving broad distribution of the vector and transgene expression throughout the spinal cord is challenging and vector entry to the periphery occurs, sometimes initiating hepatotoxicity. Here we performed two rounds ofin vivobiopanning in non-human primates (NHPs) with an AAV9 peptide display library injected intrathecally and performed insert sequencing on DNA isolated from either whole tissue (conventional selection), isolated nuclei, or nuclei from transgene-expressing cells. A subsequent barcoded pool of candidates and AAV9 was compared at the DNA (biodistribution) and RNA (expression) level in spinal cord and liver of intrathecally injected NHPs. Most of the candidates displayed enhanced biodistribution compared to AAV9 at all levels of spinal cord ranging from 2 to 265-fold. Nuclear isolation or expression-based selection yielded 4 of 7 candidate capsids with enhanced transgene expression in spinal cord (up to 2.4-fold), while no capsid obtained by conventional selection achieved that level. Furthermore, several capsids displayed lower biodistribution to the liver of up to 1,250-fold, compared to AAV9, providing a remarkable on target/off target biodistribution ratio. These capsids may have potential for gene therapy programs directed at the spinal cord and the selection method described here should be useful in clinically relevant large animal models.
Genomic analysis ofCoccomyxa viridis, a common low-abundance alga associated with lichen symbioses
Authors: Tagirdzhanova Gulnara , Scharnagl Klara , Yan Xia , Talbot Nicholas J. 
Published: September 2023

AbstractLichen symbiosis is centered around a relationship between a fungus and a photosynthetic microbe, usually a green alga. In addition to their main photosynthetic partner (the photobiont), lichen symbioses can contain additional algae present in low abundance. The biology of these algae and the way they interact with the rest of lichen symbionts remains largely unknown. Here we present the first genome sequence of a non-photobiont lichen-associated alga.Coccomyxa viridiswas unexpectedly found in 12% of publicly available lichen metagenomes. With few exceptions, members of theCoccomyxa viridisclade occur in lichens as non-photobionts, potentially growing in thalli endophytically. The 45.7 Mbp genome ofC. viridiswas assembled into 18 near chromosome-level contigs, making it one of the most contiguous genomic assemblies for any lichen-associated algae. Comparing theC. viridisgenome to its close relatives revealed the presence of traits associated with the lichen lifestyle. The genome ofC. viridisprovides a new resource for exploring the evolution of the lichen symbiosis, and how symbiotic lifestyles shaped evolution in green algae.
Self-supervised deep learning uncovers the semantic landscape of drug-induced latent mitochondrial phenotypes
Authors: Natekar Parth , Wang Zichen , Arora Mehul , Hakozaki Hiroyuki , Schöneberg Johannes 
Published: September 2023

SUMMARYImaging-based high-content screening aims to identify substances that modulate cellular phenotypes. Traditional approaches screen compounds for their ability to shift disease phenotypes toward healthy phenotypes, but these end point-based screens lack an atlas-like mapping between phenotype and cell state that covers the full spectrum of possible phenotypic responses. In this study, we present MitoSpace: a novel mitochondrial phenotypic atlas that leverages self-supervised deep learning to create a semantically meaningful latent space from images without relying on any data labels for training. Our approach employs a dataset of ∼100,000 microscopy images of Cal27 and HeLa cells treated with 25 drugs affecting mitochondria, but can be generalized to any cell type, cell organelle, or drug library with no changes to the methodology. We demonstrate how MitoSpace enhances our understanding of the range of mitochondrial phenotypes induced by pharmacological interventions. We find that i) self-supervised learning can automatically uncover the semantic landscape of drug induced latent mitochondrial phenotypes and can map individual cells to the correct functional area of the drug they are treated with, ii) the traditional classification of mitochondrial morphology along a fragmented to fused axis is more complex than previously thought, with additional axes being identified, and iii) latent spaces trained in a self-supervised manner are superior to those trained with supervised models, and generalize to other cell types and drug conditions without explicit training on those cell types or drug conditions. Future applications of MitoSpace include creating mitochondrial biomarkers for drug discovery and determining the effects of unknown drugs and diseases for diagnostic purposes.
Impact of village saving groups on adoption intensity of sustainable agricultural practices among smallholder farmers in Northern region, Ghana
Authors: Alhassan Abdul-Karim , Boateng Vivian Fiatusey , Danso-Abbeam Gideon 
Published: September 2023

PurposeAccess to formal financial services is one of the main obstacles to the adoption of agricultural technologies such as Sustainable Agricultural Practices (SAPs). In order to increase financial inclusion and lessen farmers' liquidity restrictions, Village Savings and Loans Associations (VSLA) are being promoted in rural farming communities. However, there extent to which VSLA contributes to the acceleration of agricultural practices, such as SAP, remains little explored in existing literature. The objective of this study was to quantitatively assess the impact of VSLA on the intensity of adoption of SAPs.Design/methodology/approachThis study used cross-sectional data from 376 farming households in the East Gonja district of Ghana. An Endogenous Poisson Treatment Regression (EPTR) was applied to correct for self-selection bias that might emanate from both observed and unobserved differences in household characteristics.FindingsThe empirical results indicated that farmers' engagement in non-farm economic activities, ownership of land and size of agricultural land under cultivation positively and significantly influence the intensity of SAPs adoption. Moreover, participation in VSLA improves the adoption of SAPs, and that VSLA-participants adopted about three more SAPs than they would have if they did not participate in VSLA.Practical implicationsThis study re-affirmed the significance of VSLA in rural farming communities and recommend that it should be promoted as an alternative to formal financial services to enhance financial inclusiveness, and consequently boost the uptake of SAPs.Originality/valueIn the search of literature, this study is the first to estimate the impact of VSLA on adoption of SAPs. The use of EPTR helps to bring out the true treatment effects of VSLA on SAPs.
Adivasi women temporary workers in tea gardens and the Covid-19 pandemic
Authors: Barhoi Poonam , Dayal Surbhi 
Published: September 2023

PurposeThe tea plantation industry is characterized by the large-scale deployment of cheap women laborers and gender-blind practices that make the social positions of women workers vulnerable. This paper considers women temporary workers in tea gardens to study the exacerbated impact of Covid-19 on their lives. The impact of the pandemic on marginal tea garden women laborers has not received enough attention from researchers; hence, the authors have studied the gendered implications of the pandemic on Adivasi temporary women workers in tea gardens in India. “Adivasi” is an umbrella term to refer to all indigenous tribes in India.Design/methodology/approachThe authors conducted a qualitative study with 26 in-depth interviews with women temporary workers who identify themselves as Adivasis. For the discussion, the authors have mainly borrowed from intersectionality and subalternity literature.FindingsThe analysis explored the intersectional experiences of the women temporary workers (1) as members of Tea Tribes who are compelled to continue working at tea gardens as wage laborers, (2) job insecurities at work due to their temporary worker status, (3) disadvantages faced by women workers for their gender identity and (4) the gendered impact of the pandemic on their lives.Originality/valueThis study has explored the gendered impact of the Covid-19 pandemic on the lives of temporary women workers who belong to ethnic minority groups in the global south. The exploitation of labor rights in the tea industry during the pandemic has not been discussed enough by researchers earlier.
Grey clustering and grey ranking of bank branches based on grey efficiency
Authors: Karimi Tooraj , Ahmadian Mohamad 
Published: September 2023

PurposeCompetition in the banking sector is more complex than in the past, and survival has become more difficult than before. The purpose of this paper is to propose a grey methodology for evaluating, clustering and ranking the performance of bank branches with imprecise and uncertain data in order to determine the relative status of each branch.Design/methodology/approachIn this study, the two-stage data envelopment analysis model with grey data is applied to assess the efficiency of bank branches in terms of operations. The result of grey two-stage data envelopment analysis model is a grey number as efficiency value of each branch. In the following, the branches are classified into three grey categories of performance by grey clustering method, and the complete grey ranking of branches are performed using “minimax regret-based approach” and “whitening value rating”.FindingsThe results show that after grey clustering of 22 branches based on grey efficiency value obtained from the grey two-stage DEA model, 6 branches are assigned to “excellent” class, 4 branches to “good” class and 12 branches to “poor” class. Moreover, the results of MRA and whitening value rating models are integrated, and a complete ranking of 22 branches are presented.Practical implicationsGrey clustering of branches based on grey efficiency value can facilitate planning and policy-making for branches so that there is no need to plan separately for each branch. The grey ranking helps the branches find their current position compared to other branches, and the results can be a dashboard to find the best practices for benchmarking.Originality/valueCompared with traditional DEA methods which use deterministic data and consider decision-making units as black boxes, in this research, a grey two-stage DEA model is proposed to evaluate the efficiency of bank branches. Furthermore, grey clustering and grey ranking of efficiency values are used as a novel solution for improving the accuracy of grey two-stage DEA results.