bluuebunny/crossref_metadata_2025_split huggingface dataset:
Discovering Novel Prognostic Biomarkers of Hepatocellular Carcinoma using eXplainable Artificial Intelligence
Authors: Gutierrez-Chakraborty Elizabeth , Chakraborty Debaditya , Das Debodipta , Bai Yidong 
Published: November 2023

AbstractHepatocellular carcinoma (HCC) remains a global health challenge with high mortality rates, largely due to late diagnosis and suboptimal efficacy of current therapies. With the imperative need for more reliable, non-invasive diagnostic tools and novel therapeutic strategies, this study focuses on the discovery and application of novel genetic biomarkers for HCC using explainable artificial intelligence (XAI). Despite advances in HCC research, current biomarkers like Alpha-fetoprotein (AFP) exhibit limitations in sensitivity and specificity, necessitating a shift towards more precise and reliable markers. This paper presents an innovative XAI framework to identify and validate key genetic biomarkers for HCC prognosis. Our methodology involved analyzing clinical and gene expression data to identify potential biomarkers with prognostic significance. The study utilized robust AI models validated against extensive gene expression datasets, demonstrating not only the predictive accuracy but also the clinical relevance of the identified biomarkers through explainable metrics. The findings highlight the importance of biomarkers such as TOP3B, SSBP3, and COX7A2L, which were consistently influential across multiple models, suggesting their role in improving the predictive accuracy for HCC prognosis beyond AFP. Notably, the study also emphasizes the relevance of these biomarkers to the Hispanic population, aligning with the larger goal of demographic-specific research. The application of XAI in biomarker discovery represents a significant advancement in HCC research, offering a more nuanced understanding of the disease and laying the groundwork for improved diagnostic and therapeutic strategies.
A Bayesian Approach to Correcting the Attenuation Bias of Regression Using Polygenic Risk Score
Authors: Zhou Geyu , Qie Xinyue , Zhao Hongyu 
Published: November 2023

AbstractPolygenic risk score (PRS) has become increasingly popular for predicting the value of complex traits. In many settings, PRS is used as a covariate in regression analysis to study the association between different phenotypes. However, measurement error in PRS causes attenuation bias in the estimation of regression coefficients. In this paper, we employ a Bayesian approach to accounting for the measurement error of PRS and correcting the attenuation bias in linear and logistic regression. Through simulation, we show that our approach is able to obtain approximately unbiased estimation of coefficients and credible intervals with correct coverage probability. We also empirically compare our Bayesian measurement error model to the conventional regression model by analyzing real traits in the UK Biobank. The results demonstrate the effectiveness of our approach as it significantly reduces the error in coefficient estimates.
Understanding epistatic networks in the B1 β-lactamases through coevolutionary statistical modeling and deep mutational scanning
Authors: Chen JZ , Bisardi M , Lee D , Cotogno S , Zamponi F , Weigt M , Tokuriki N 
Published: November 2023

AbstractOver the course of evolution, proteins families undergo sequence diversification via mutation accumulation, with extant homologs often sharing less than 25% sequence identity. The resulting diversity presents a complex view of sequence-structure-function relationships, as epistasis is prevalent, and deleterious mutations in one protein can be tolerated in homologous sequences through networks of intramolecular, compensatory interactions. Understanding these epistatic networks is crucial for understanding and predicting protein function, yet comprehensive analysis of such networks across protein families is limited. In this study, we combine computational and experimental approaches to examine epistatic networks in the class B1 metallo-β-lactamases, a diverse family of antibiotic-degrading enzymes. Using Direct Coupling Analysis, we assess global coevolutionary signatures across the B1 family. We also obtain detailed experimental data from deep mutational scanning on two distant B1 homologs, NDM-1 and VIM-2. There is good agreement between the two approaches, revealing both family-wide and homolog specific patterns that can be associated with 3D structure. However, specific interactions remain complex, and strong epistasis in evolutionarily entrenched residues are not easily compensated for by changes in nearby interactions.
Virus-induced gene silencing (VIGS) as a tool for functional genetic analysis in passion fruit plants
Authors: Wang Xiaoqing , Shen Wentao , Cui Hongguang , Dai Zhaoji 
Published: November 2023

AbstractPassion fruit (Passiflora edulis) is grown perennially in sub-tropical and tropical areas. Its fruits contain multiple vitamins and antioxidants, and thus are consumed increasingly in drinks and foods. However, the functions and regulations of genes that are engaged in the biosynthesis of the health-promoting compounds in passion fruits remain largely unknown. Its whole genome sequence has just been published recently. Virus-induced gene silencing (VIGS) is a reverse genetics tool for analyzing gene function. Here, we engineered telosma mosaic virus (TelMV), a potyvirus infecting passion fruit, into a VIGS vector by inserting the Gateway-compatible recombination sites. The newly constructed TelMV-VIGS virus successfully expressed foreign protein and induced systemic infection in bothNicotiana benthamianaandP. edulisplants. Intriguingly, TelMV-VIGS vector containing different fragments of green fluorescent protein (GFP) gene induced systemic gene silencing on the GFP-transgenicN. benthamianaplants (16c). When thephytoene desaturase(PDS) gene, an endogenous gene in passion fruit, was engineered into the vector, it triggered the silencing ofPePDS, as evidenced by the reduced mRNA levels and photobleached phenotype. We reported the first development of VIGS vector in passion fruit, as the first step in our endeavor of discovering horticulturally important genes for improving passion fruit production and quality.
Association between late-life air pollution exposure and medial temporal lobe atrophy in older women
Authors: Wang Xinhui , Salminen Lauren E. , Petkus Andrew J. , Driscoll Ira , Millstein Joshua , Beavers Daniel P. , Espeland Mark A. , Erus Guray , Braskie Meredith N. , Thompson Paul M. , Gatz Margaret , Chui Helena C. , Resnick Susan M , Kaufman Joel D. , Rapp Stephen R. , Shumaker Sally , Brown Mark , Younan Diana , Chen Jiu-Chiuan 
Published: November 2023

AbstractBackgroundAmbient air pollution exposures increase risk for Alzheimer’s disease (AD) and related dementias, possibly due to structural changes in the medial temporal lobe (MTL). However, existing MRI studies examining exposure effects on the MTL were cross-sectional and focused on the hippocampus, yielding mixed results.MethodTo determine whether air pollution exposures were associated with MTL atrophy over time, we conducted a longitudinal study including 653 cognitively unimpaired community-dwelling older women from the Women’s Health Initiative Memory Study with two MRI brain scans (MRI-1: 2005-6; MRI-2: 2009-10; Mageat MRI-1=77.3±3.5years). Using regionalized universal kriging models, exposures at residential locations were estimated as 3-year annual averages of fine particulate matter (PM2.5) and nitrogen dioxide (NO2) prior to MRI-1. Bilateral gray matter volumes of the hippocampus, amygdala, parahippocampal gyrus (PHG), and entorhinal cortex (ERC) were summed to operationalize the MTL. We used linear regressions to estimate exposure effects on 5-year volume changes in the MTL and its subregions, adjusting for intracranial volume, sociodemographic, lifestyle, and clinical characteristics.ResultsOn average, MTL volume decreased by 0.53±1.00cm3over 5 years. For each interquartile increase of PM2.5(3.26µg/m3) and NO2(6.77ppb), adjusted MTL volume had greater shrinkage by 0.32cm3(95%CI=[-0.43,-0.21]) and 0.12cm3(95%CI=[-0.22,-0.01]), respectively. The exposure effects did not differ byAPOEε4 genotype, sociodemographic, and cardiovascular risk factors, and remained among women with low-level PM2.5exposure. Greater PHG atrophy was associated with higher PM2.5(b=-0.24, 95%CI=[-0.29,-0.19]) and NO2exposures (b=-0.09, 95%CI=[-0.14,-0.04]). Higher exposure to PM2.5but not NO2was also associated with greater ERC atrophy. Exposures were not associated with amygdala or hippocampal atrophy.ConclusionIn summary, higher late-life PM2.5and NO2exposures were associated with greater MTL atrophy over time in cognitively unimpaired older women. The PHG and ERC - the MTL cortical subregions where AD neuropathologies likely begin, may be preferentially vulnerable to air pollution neurotoxicity.HighlightsFirst longitudinal study on air pollution and medial temporal lobe (MTL) volume.Late-life PM2.5and NO2associated with MTL atrophy over time in older women.Heterogeneous adverse effects were observed across different subregions of the MTL.Results not differ byAPOEgenotype, age, education, or cardiovascular risk factors.Adverse effects remained at low-level exposure compliant with regulatory standards.
Neuronal-specific methylome and hydroxymethylome analysis reveal replicated and novel loci associated with alcohol use disorder
Authors: Andrade-Brito Diego E. , Núñez-Ríos Diana L. , Martínez-Magaña José Jaime , Nagamatsu Sheila T. , Rompala Gregory , Zillich Lea , Witt Stephanie H. , Clark Shaunna L. , Latig Maria C. ,   Traumatic Stress Brain Research Group,   PGC SUD Epigenetics Working Group, Montalvo-Ortiz Janitza L. 
Published: November 2023

1AbstractAlcohol use disorder (AUD) is a complex condition associated with adverse health consequences that affect millions of individuals worldwide. Epigenetic modifications, including DNA methylation (5mC), have been associated with AUD and other alcohol-related traits. Epigenome-wide association studies (EWAS) have identified differentially methylated genes associated with AUD in human peripheral and brain tissue. More recently, epigenetic studies of AUD have also evaluated DNA hydroxymethylation (5hmC) in the human brain. However, most of the epigenetic work in postmortem brain tissue has examined bulk tissue. In this study, we investigated neuronal-specific 5mC and 5hmC alterations at CpG sites associated with AUD in the human orbitofrontal cortex (OFC). Neuronal nuclei from the OFC were evaluated in 34 human postmortem brain samples (10 AUD, 24 non-AUD). Reduced representation oxidative bisulfite sequencing was used to assess 5mC and 5hmC at the genome-wide level. Differential 5mC and 5hmC were evaluated using the methylKit R package and significance was set at false discovery rate <0.05 and differential methylation >2. Functional enrichment analyses were performed and replication was evaluated replication in an independent dataset that assessed 5mC and 5hmC of AUD in bulk cortical tissue. We identified 417 5mC and 363 5hmC genome-wide significant differential CpG sites associated with AUD, with 59% in gene promoters. We also identified genes previously implicated in alcohol consumption, such asSYK,CHRM2,DNMT3A, andGATA4, for 5mC andGATA4, andGAD1,GATA4,DLX1for 5hmC. Replication was observed for 28 CpG sites from a previous AUD 5mC and 5hmC study, includingFOXP1. Lastly, GWAS enrichment analysis showed an association with AUD for differential 5mC genes. This study reveals neuronal-specific methylome and hydroxymethylome dysregulation associated with AUD. We replicated previous findings and identified novel associations with AUD for both 5mC and 5hmC marks within the OFC. Our findings provide new insights into the epigenomic dysregulation of AUD in the human brain.
Hepatitis E in Kathmandu Valley: Insights from a Representative Longitudinal Serosurvey
Authors: Katuwal Nishan , Thapa Melina , Shrestha Sony , Vaidya Krista , Bogoch Isaac I , Andrews Jason , Shrestha Rajeev , Tamrakar Dipesh , Aiemjoy Kristen 
Published: November 2023

ABSTRACTHepatitis-E virus (HEV), an etiologic agent of acute inflammatory liver disease, is a significant cause of morbidity and mortality in South Asia. HEV is considered endemic in Nepal; but data on population-level infection transmission is sparse. We conducted a representative longitudinal serologic study between February 2019 and April 2021 in urban and peri-urban areas of central Nepal to characterize community-level HEV transmission. Individuals were followed up to four times, during which capillary blood samples were collected on dried blood spots and tested for anti-HEV immunoglobulin-G antibodies. Analyzing 2513 dried blood samples from 923 participants aged 0-25 years, we found a seroprevalence of 4.8% and a seroincidence rate of 10.9 per 1000 person-years. Notably, young adults, including women of childbearing age, faced the highest incidence of infection. Geospatial analysis identified potential HEV clusters in Kavre and Kathmandu districts, emphasizing the need for targeted interventions. Water source played a crucial role in HEV transmission, with individuals consuming surface water facing the highest risk of seroconversion. Our findings underscore the endemic nature of HEV in Nepal, emphasizing the importance of safe water practices and potential vaccination strategies for high-risk groups.
The Development, Optimization, and Validation of Four Different Machine Learning Algorithms to Identify Ventilator Dyssynchrony
Authors: Sottile Peter D , Smith Bradford , Moss Marc , Albers David J 
Published: November 2023

ObjectiveInvasive mechanical ventilation can worsen lung injury. Ventilator dyssynchrony (VD) may propagate ventilator-induced lung injury (VILI) and is challenging to detect and systematically monitor because each patient takes approximately 25,000 breaths a day yet some types of VD are rare, accounting for less than 1% of all breaths. Therefore, we sought to develop and validate accurate machine learning (ML) algorithms to detect multiple types of VD by leveraging esophageal pressure waveform data to quantify patient effort with airway pressure, flow, and volume data generated during mechanical ventilation, building a computational pipeline to facilitate the study of VD.Materials and MethodsWe collected ventilator waveform and esophageal pressure data from 30 patients admitted to the ICU. Esophageal pressure allows the measurement of transpulmonary pressure and patient effort. Waveform data were cleaned, features considered essential to VD detection were calculated, and a set of 10,000 breaths were manually labeled. Four ML algorithms were trained to classify each type of VD: logistic regression, support vector classification, random forest, and XGBoost.ResultsWe trained ML models to detect different families and seven types of VD with high sensitivity (>90% and >80%, respectively). Three types of VD remained difficult for ML to classify because of their rarity and lack of sample size. XGBoost classified breaths with increased specificity compared to other ML algorithms.DiscussionWe developed ML models to detect multiple types of VD accurately. The ability to accurately detect multiple VD types addresses one of the significant limitations in understanding the role of VD in affecting patient outcomes.ConclusionML models identify multiple types of VD by utilizing esophageal pressure data and airway pressure, flow, and volume waveforms. The development of such computational pipelines will facilitate the identification of VD in a scalable fashion, allowing for the systematic study of VD and its impact on patient outcomes.
Age and type of task-based impact of mental fatigue on balance: Systematic review and meta-analysis
Authors: Salihu Abubakar Tijjani , Hill Keith D. , Jaberzadeh Shapour 
Published: November 2023

AbstractThe role of cognition in balance control suggests that mental fatigue may negatively affect balance. However, cognitive involvement in balance control varies with the type or difficulty of the balance task and age. Steady-state balance tasks, such as quiet standing, are well learned tasks executed automatically through reflex activities controlled by the brainstem and spinal cord. In contrast, novel, and challenging balance tasks, such as proactively controlling balance while walking over rugged terrain or reacting to unexpected external perturbations, may require cognitive processing. Furthermore, individuals with pre-existing balance impairments due to aging or pathology may rely on cognitive processes to control balance in most circumstances. This systematic review and meta-analysis investigated the effect of mental fatigue on different types of balance control tasks in young and older adults. A literature search was conducted in seven electronic databases and 12 studies met eligibility criteria. The results indicated that mental fatigue had a negative impact on both proactive (under increased cognitive load) and reactive balance in young adults. In older adults, mental fatigue affected steady-state and proactive balance. Therefore, mentally fatigued older individuals may be at increased risk of a loss of balance during steady-state balance task compared to their younger counterparts.
Urban-use pesticides in stormwater ponds and their accumulation in biofilms
Authors: Izma Gab , Raby Melanie , Prosser Ryan , Rooney Rebecca 
Published: November 2023

AbstractStormwater ponds frequently receive urban runoff, increasing the likelihood of pesticide contamination. Biofilms growing in surface waters of these ponds are known to accumulate a range of aquatic contaminants, paradoxically providing both water purification services and potentially posing a threat to urban wildlife. Thus, sampling biofilms in stormwater ponds may be a critical and biologically relevant tool for characterizing pesticide contamination and toxicity in urban environments. Here, we aimed to investigate pesticide occurrences at 21 stormwater ponds in Brampton, ON, one of Canada’s fastest growing municipalities, and quantify their accumulation in biofilm. Over nine weeks, we collected time-integrated composite water and biofilm samples for analysis of ∼500 current-use and legacy pesticides. Thirty-two pesticide compounds were detected across both matrices, with 2,4-D, MCPA, MCPP, azoxystrobin, bentazon, triclopyr, and diuron having near-ubiquitous occurrences. Several compounds not typically monitored in pesticide suites (e.g., melamine and nicotine) were also detected, but only in biofilms. Overall, 56% of analytes detected in biofilms were not found in water samples, indicating traditional pesticide monitoring practices fail to capture all exposure routes, as even when pesticides are below detection levels in water, organisms may still be exposed via dietary pathways. Calculated bioconcentration factors ranged from 4.2 – 1275 and were not predicted by standard pesticide physicochemical properties. Monitoring biofilms provides a sensitive and comprehensive supplement to water sampling for pesticide quantification in urban areas, and identifying pesticide occurrences in stormwater could improve source-tracking efforts in the future. Further research is needed to understand the mechanisms driving pesticide accumulation, to investigate toxicity risks associated with pesticide-contaminated biofilm, and to evaluate whether pesticide accumulation in stormwater pond biofilms represents a route through which contaminants are mobilized into the surrounding terrestrial and downstream aquatic environments.
Visual to default network pathways: A double dissociation between semantic and spatial cognition
Authors: Gonzalez Alam Tirso RJ , Krieger-Redwood Katya , Varga Dominika , Gao Zhiyao , Horner Aidan , Hartley Tom , Thiebaut de Schotten Michel , Sliwinska Magdalena W , Pitcher David , Margulies Daniel S. , Smallwood Jonathan , Jefferies Elizabeth 
Published: November 2023

AbstractProcessing pathways between sensory and default mode network (DMN) regions support recognition, navigation, and memory but their organisation is not well understood. We show that functional subdivisions of visual cortex and DMN sit at opposing ends of parallel streams of information processing that support visually-mediated semantic and spatial cognition, providing convergent evidence from univariate and multivariate task responses, intrinsic functional and structural connectivity. Participants learned virtual environments consisting of buildings populated with objects, drawn from either a single semantic category or multiple categories. Later, they made semantic and spatial context decisions about these objects and buildings during functional magnetic resonance imaging. A lateral ventral occipital to frontotemporal DMN pathway was primarily engaged by semantic judgements, while a medial visual to medial temporal DMN pathway supported spatial context judgements. These pathways had distinctive locations in functional connectivity space: the semantic pathway was both further from unimodal systems and more balanced between visual and auditory-motor regions compared with the spatial pathway. When semantic and spatial context information could be integrated (in buildings containing objects from a single category), regions at the intersection of these pathways responded, suggesting that parallel processing streams interact at multiple levels of the cortical hierarchy to produce coherent memory-guided cognition.
Bi-parental graph strategy to represent and analyze hybrid plant genomes
Authors: Kong Qianqian , Jiang Yi , Wang Zhiheng , Wang Zijie , Liu Yuting , Gan Yuanxian , Liu Han , Gao Xiang , Yang Xuerong , Song Xinyuan , Liu Hongjun , Shi Junpeng 
Published: November 2023

AbstractHybrid plants are universally existed in wild and often exhibit greater performance of complex traits compared with their parents and other selfing plants. This phenomenon, known as heterosis, has been extensively applied in plant breeding for decades. However, the process of decoding hybrid plant genomes has seriously lagged due to the challenges in their genome assembling and the lack of proper methods to further represent and analyze them. Here we report the assembly and analysis of two hybrids: an intraspecific hybrid between two maize inbred lines and an interspecific hybrid between maize and its wild relative teosinte, based on the combination of PacBio High Fidelity (HiFi) sequencing and chromatin conformation capture sequencing data. The haplotypic assemblies are well-phased at chromosomal scale, successfully resolving the complex loci with extensive parental structural variations (SVs). By integrating into a bi-parental genome graph, the haplotypic assemblies can facilitate downstream short-reads based SV calling and allele-specific gene expression analysis, demonstrating outstanding advantages over one single linear genome. Our work provides an entire workflow which hopefully can promote the deciphering of the large numbers of hybrid plant genomes, especially those whose parents are unknown or unavailable and help to understand genome evolution and heterosis.