bluuebunny/crossref_metadata_2025_split huggingface dataset:
Durability of SARS-CoV-2 IgG Antibodies: Insights from a Longitudinal Study, Puerto Rico
Authors: Madewell Zachary J. , Graff Nathan , Lopez Velma K. , Rodriguez Dania M. , Wong Joshua M. , Maniatis Panagiotis , Medina Freddy A. , Muñoz Jorge L. , Briggs-Hagen Melissa , Adams Laura E. , Rivera-Amill Vanessa , Paz-Bailey Gabriela , Major Chelsea G. 
Published: August 2024

AbstractUnderstanding the dynamics of antibody responses following vaccination and SARS-CoV-2 infection is important for informing effective vaccination strategies and other public health interventions. This study investigates SARS-CoV-2 antibody dynamics in a Puerto Rican cohort, analyzing how IgG levels vary by vaccination status and previous infection. We assess waning immunity and the distribution of hybrid immunity with the aim to inform public health strategies and vaccination programs in Puerto Rico and similar settings. We conducted a prospective, longitudinal cohort study to identify SARS-CoV-2 infections and related outcomes in Ponce, Puerto Rico, from June 2020–August 2022. Participants provided self-collected nasal swabs every week and serum every six months for RT-PCR and IgG testing, respectively. IgG reactivity against nucleocapsid (N) antigens, which generally indicate previous infection, and spike (S1) and receptor-binding domain (RBD) antigens, which indicate history of either infection or vaccination, was assessed using the Luminex Corporation xMAP® SARS-CoV-2 Multi-Antigen IgG Assay. Prior infection was defined by positive RT-PCRs, categorized by the predominant circulating SARS-CoV-2 variant at the event time. Demographic information, medical history, and COVID-19 vaccination history were collected through standardized questionnaires. Of 882 participants included in our analysis, 34.0% experienced at least one SARS-CoV-2 infection, with most (78.7%) occurring during the Omicron wave (December 2021 onwards). SARS-CoV-2 antibody prevalence increased over time, reaching 98.4% by the final serum collection, 67.0% attributable to vaccination alone, 1.6% from infection alone, and 31.4% from both. Regardless of prior infection status, RBD and S1 IgG levels gradually declined following two vaccine doses. A third dose boosted these antibody levels and showed a slower decline over time. N-antibody levels peaked during the Omicron surge and waned over time. Vaccination in individuals with prior SARS-CoV-2 infection elicited the highest and most durable antibody responses. N or S1 seropositivity was associated with lower odds of a subsequent positive PCR test during the Omicron period, with N antibodies showing a stronger association. By elucidating the differential decay of RBD and S1 antibodies following vaccination and the complexities of N-antibody response following infection, this study in a Puerto Rican cohort strengthens the foundation for developing targeted interventions and public health strategies.
Telehealth Utilization and Patient Experiences: The Role of Social Determinants of Health Among Individuals with Hypertension and Diabetes
Authors: Chen Haoxin , Simmons Will , Hashish Malak Abu , Ye Jiancheng 
Published: August 2024

ABSTRACTObjectiveTo evaluate the utilization patterns, effectiveness, and patient satisfaction of telehealth services among individuals with hypertension and/or diabetes, and to investigate the influence of social determinants of health (SDOH) on telehealth access and utilization in this population.MethodsWe conducted a cross-sectional analysis using data from the 2022 Health Information National Trends Survey (HINTS 6) by the National Cancer Institute. The study sample included 3,009 respondents with self-reported diabetes, hypertension, or both conditions. Telehealth usage was assessed through 14 survey questions, and participant characteristics were analyzed using sociodemographic, baseline health, and SDOH data.ResultsOf the 6,252 HINTS 6 survey respondents, 3,009 met the inclusion criteria. Significant sociodemographic differences were observed across the diabetes and/or hypertension groups. No significant differences were found in telehealth usage among the groups, with 43.9% of respondents utilizing telehealth in the past year. Common reasons for telehealth use included provider recommendation, convenience, and infection avoidance. Social determinants of health, such as food insecurity and transportation issues, were more prevalent among individuals with both conditions, though no significant differences in telehealth experiences were noted across groups.ConclusionTelehealth shows potential for managing chronic conditions like hypertension and diabetes, demonstrating substantial adoption and universal accessibility. However, disparities influenced by SDOH highlight the need for targeted interventions to ensure equitable access. Addressing privacy concerns, leveraging healthcare providers’ recommendations, and tackling SDOH barriers are crucial for fostering wider telehealth adoption and improving outcomes. Future research should focus on the long-term impacts of telehealth and further investigate SDOH factors to develop tailored interventions that enhance engagement and equitable access across diverse patient populations.
Unraveling the Neural Landscape of Mental Disorders using Double Functional Independent Primitives (dFIPs)
Authors: Soleimani Najme , Iraji Armin , Pearlson Godfrey , Preda Adrian , Calhoun Vince D. 
Published: August 2024

AbstractMental illnesses extract a high personal and societal cost, and thus explorations of the links between mental illness and functional connectivity in the brain are critical. Investigating major mental illnesses, believed to arise from disruptions in sophisticated neural connections, allows us to comprehend how these neural network disruptions may be linked to altered cognition, emotional regulation, and social interactions. Although neuroimaging has opened new avenues to explore neural alterations linked to mental illnesses, the field still requires precise and sensitive methodologies to inspect these neural substrates of various psychological disorders. In this study, we employ a hierarchical methodology to derive double functionally independent primitives (dFIPs) from resting state functional magnetic resonance neuroimaging data (rs-fMRI). These dFIPs encapsulate canonical overlapping patterns of functional network connectivity (FNC) within the brain. Our investigation focuses on the examination of how combinations of these dFIPs relate to different mental disorder diagnoses. The central aim is to unravel the complex patterns of FNC that correspond to the diverse manifestations of mental illnesses. To achieve this objective, we used a large brain imaging dataset from multiple sites, comprising 5805 total individuals diagnosed with schizophrenia (SCZ), autism spectrum disorder (ASD), bipolar disorder (BPD), major depressive disorder (MDD), and controls. The key revelations of our study unveil distinct patterns associated with each mental disorder through the combination of dFIPs. Notably, certain individual dFIPs exhibit disorder-specific characteristics, while others demonstrate commonalities across disorders. This approach offers a novel, data-driven synthesis of intricate neuroimaging data, thereby illuminating the functional changes intertwined with various mental illnesses. Our results show distinct signatures associated with psychiatric disorders, revealing unique connectivity patterns such as heightened cerebellar connectivity in SCZ and sensory domain hyperconnectivity in ASD, both contrasted with reduced cerebellar-subcortical connectivity. Utilizing the dFIP concept, we pinpoint specific functional connections that differentiate healthy controls from individuals with mental illness, underscoring its utility in identifying neurobiological markers. In summary, our findings delineate how dFIPs serve as unique fingerprints for different mental disorders.
Intra-subgenome regulation induces unbalanced expression and function among bread wheat homoeologs
Authors: Wang Xiaoming , Liu Yuxiu , Zhao Peng , Hou Wenyang , Cheng Mingzhu , Shi Xue , Simmonds James , Borrill Philippa , Ji Wanquan , Xu Shengbao 
Published: August 2024

AbstractThe differential expression of homoeologous genes confers expression plasticity and facilitates the adaptation and domestication of major polyploid crops. However, how this homoeolog expression bias (HEB) is regulated remains elusive. Here, transcriptome analysis of 406 wheat (Triticum aestivum; AABBDD) accessions revealed great variation in HEB among accessions. We identified 14,727 QTLs regulating HEB (hebQTLs), indicating that HEB is genetically regulated and can be predicted using genotyping data. The hebQTLs only regulate the expression of homoeologs in the same subgenome and downregulate their expression to result in HEB, suggesting that intra-subgenomic rather than inter-subgenomic interactions induce HEB. Furthermore, non-hebQTL-regulated homoeologs have stronger biological functions, are under higher selection pressure and exhibit lower genetic diversity than hebQTL-regulated homoeologs and compensate for the downregulated expressions of hebQTL-regulated homoeologs. Our findings reveal how homoeolog expression is coordinated at the genetic level and provide an avenue for leveraging HEB to improve polyploid crops.
An Evolutionary Statistics Toolkit for Simplified Sequence Analysis on Web with Client-Side Processing
Authors: Karagöl Alper , Karagöl Taner 
Published: August 2024

AbstractWe present the Evolutionary Statistics Toolkit, a user-friendly web-based platform designed for specialized analysis of genetic sequences, which integrates multiple evolutionary statistics. The toolkit focuses on a selection of specialized tools, including Tajima’s D calculator with Site Frequency Spectrum (SFS), Shannon’s Entropy (H), alignment re-formatting, HGSV to FASTA conversion, pair-wise frequency analysis, FASTA to SEQRES, RNA 2D structure alignment, Kyte-Doolittle hydrophilicity plot tool and kurtosis coefficient calculator. Tajima’s D is calculated using the reference formula: D = (π - θW) / sqrt(VD), where π corresponds to the average number of differences, θWis Watterson’s estimator of θ, and VDis the variance of π - θW. Shannon’s Entropy is defined as H = -∑ pi* log2(pi), where piis the probability of occurrence of each unique character (nucleotide or amino acid) in the sequence. The toolkit facilitates streamlined workflows for early researchers in evolutionary biology, genomics, and related fields. With comparing with existing codes, we propose it also emerges as an educational interactive website for beginners in evolutionary statistics. The source code for each tool in the toolkit is available through GitHub links provided on the website. This open-source approach allows users to inspect the code, suggest improvements, or further adapt the tools for their specific usage and research needs. This article describes the functionalities, and validation of each tool within the platform, along with comparison with accessible existing statistical utilities. The toolkit is freely accessible on:https://www.alperkaragol.com/toolkit
Primed responses to damage signals mediate mycorrhiza-induced resistance in tomato plants
Authors: Minchev Zhivko , Garcia Juan M. , Pozo Estefania , Pozo Maria J. , Gamir Jordi 
Published: August 2024

AbstractArbuscular mycorrhizal fungi establish mutualistic associations with the roots of most vascular plants, enhancing plant immunity and activating mycorrhiza-induced resistance (MIR). In this study, we hypothesised that differential recognition of endogenous damage signals contributes to MIR in tomato plants. To test the hypothesis, we compared responses in mycorrhizal and non-mycorrhizal tomato plants after applying the cell-wall derived damage signal oligogalacturonides (OGs). We analysed the proteomic and metabolomic profiles, and the expression of marker genes related to plant defense, and the effects on plant resistance to the necrotrophic pathogenBotrytis cinerea. Our results show that mycorrhizal plants are more sensitive to these damage signals, as they respond to lower doses and exhibit stronger responses at the protein and metabolic level compared to non-mycorrhizal plants. Mycorrhizal plants showed primed accumulation of defense proteins, receptor kinases, flavonoids, and activation of the jasmonic acid and ethylene signaling pathways in response to OGs. Expression levels of the wall-associated kinase 1 (slWAK1) gene, coding for an OG receptor kinase in tomato, are elevated in mycorrhizal plants, and MIR againstB. cinereais abolished in awak1mutant. Together, these results provide the first indication that self-damage recognition is essential to induce MIR againstB. cinerea.HighlightMycorrhizal tomato plants exhibit enhanced sensitivity to damage signals, leading to primed defense responses and induced resistance to fungal pathogens.
Modeling impairment of ionic regulation with extended Adaptive Exponential integrate-and-fire models
Authors: Depannemaecker Damien , Tesler Federico , Desroches Mathieu , Jirsa Viktor , Destexhe Alain 
Published: August 2024

AbstractTo model the dynamics of neuron membrane excitability many models can be considered, from the most biophysically detailed to the highest level of phenomenological description. Recent works at the single neuron level have shown the importance of taking into account the evolution of slow variables such as ionic concentration. A reduction of such a model to models of the integrate-and-fire family is interesting to then go to large network models. In this paper, we introduce a way to consider the impairment of ionic regulation by adding a third, slow, variable to the adaptive Exponential integrate-and-fire model (AdEx). We then implement and simulate a network including this model. We find that this network was able to generate normal and epileptic discharges. This model should be useful for the design of network simulations of normal and pathological states.
Single-cell differential expression analysis between conditions within nested settings
Authors: Hafner Leon , Sturm Gregor , List Markus 
Published: August 2024

AbstractDifferential expression analysis provides insights into fundamental biological processes and with the advent of single-cell transcriptomics, gene expression can now be studied at the level of individual cells. Many analyses treat cells as samples and assume statistical independence. As cells are pseudoreplicates, this assumption does not hold, leading to reduced robustness, reproducibility, and an inflated type 1 error rate.In this study, we investigate various methods for differential expression analysis on single-cell data, conduct extensive benchmarking and give recommendations for method choice. The tested methods include DESeq2, MAST, DREAM, scVI, the Permutation Test and distinct. We additionally adapt Hierarchical Bootstrapping to differential expression analysis on single-cell data and include it in our benchmark.We found that differential expression analysis methods designed specifically for single-cell data do not offer performance advantages over conventional pseudobulk methods such as DESeq2 when applied to individual data sets. In addition, they mostly require significantly longer run times. For atlas-level analysis, permutation-based methods excel in performance but show poor runtime, suggesting to use DREAM as a compromise between quality and runtime. Overall, our study offers the community a valuable benchmark of methods across diverse scenarios and offers guidelines on method selection.
Machine learning enables high-throughput, low-replicate screening for novel anti-seizure targets and compounds using combined movement and calcium fluorescence in larval zebrafish
Authors: McGraw Christopher Michael , Poduri Annapurna 
Published: August 2024

AbstractIdentifying new, more efficacious anti-seizure medications (ASMs) is challenging, partly due to limitations in animal-based assays. Zebrafish (Danio rerio) can serve as a model of chemical and genetic seizures, but methods for detecting seizure-like activity in zebrafish, though powerful, have been hampered by low sensitivity (locomotor/behavioral assays) or low-throughput (tectal electrophysiology or calcium fluorescence microscopy). To address these issues, we developed a novel approach to assay seizure-like activity using combined locomotor and calcium fluorescence features, measured simultaneously from unrestrained larval zebrafish using a 96-well fluorescent plate reader. Using custom software to track fish movement and changes in fluorescence (deltaF/F0) from high-speed time-series (12.6Hz), we trained logistic classifiers using elastic net regression to distinguish seizure-like activity from non-seizure related changes based on event-specific and subject-specific features in response to the GABAAR antagonist, pentylenetetrazole (PTZ). We demonstrate that a classifier trained on combined movement and fluorescence data achieves high accuracy (“PTZ M+F”; area-under-curve receiver-operator characteristic (AUC-ROC): 0.98; F1 score: 0.912) and out-performs classifiers trained on movement (“PTZ M”; AUC-ROC: 0.9, F1: 0.9) or fluorescence features alone (“PTZ F”; AUC-ROC 0.96; F1: 0.87). The rate of classified seizure-like events increases as a dose-response to PTZ (serial dose escalation, 0, 2.5mM, 15mM) and is strongly suppressed by ASM treatment (valproic acid, VPA; tiagabine, TGB). At high-dose PTZ, we show that VPA reduces seizure-like activity defined by either “PTZ M+F” or “PTZ M” classifiers. Meanwhile, TGB selectively reduces events defined by the “PTZ M+F” classifier, paralleling previous reports that TGB reduces electrographic but not locomotor seizures and highlighting the potential for our approach to combine features of previously orthogonal assays. Using ASM benchmark data, we employ bootstrap simulation to calculate the expected statistical power of our method as a function of sample size. We demonstrate that anti-seizure responses (robust strictly standardized mean difference, RSSMD, versus control) with magnitudes similar to those associated with VPA or TGB can be reliably detected (true positive rate (TPR) > 90%) with as few as N=4 biological replicates per group, while maintaining a 5% false positive rate. In a prospective test screen with 3-6 replicates per group and on-plate controls, the anti-seizure effect of 4 out of 5 tested ASMs (CBZ, LEV, LZP, TGB) was detected. In summary, we demonstrate a simple high-throughput approach to whole organism anti-seizure phenotyping combining two previously reported metrics to facilitate screens for novel anti-seizure interventions in zebrafish.
Brain magnetic resonance imaging software to support dementia diagnosis in routine clinical practice: a barrier to adoption study in the National Health Service (NHS) England
Authors: Griffanti Ludovica , Serres Florence , Cini Laura , Walsh Jessica , Hanayik Taylor , Pervaiz Usama , Smith Stephen , Johansen-Berg Heidi , Rose James , Bajre Mamta 
Published: August 2024

AbstractWith the rise in numbers of people living with dementia and new disease modifying therapies entering the market, there is increasing need for brain magnetic resonance imaging (MRI) for diagnosis and safety monitoring. The number of scans that need reporting is expected to rapidly grow. Clinical radiology reports are currently largely qualitative and variable in structure and content. By contrast, research software typically uses automated methods to extract quantitative metrics from brain scans.To better understand the unmet clinical need for brain reporting software for dementia we conducted a barrier to adoption study using the Lean Assessment Process (LAP)methodology. We first assessed the role of brain imaging in the diagnostic pathway for people with suspected dementia in the NHS in England. We then explored the views of (neuro)radiologists, neurologists and psychiatrists on the potential benefits and level of acceptance of software to support brain MRI analysis, using the FMRIB software library (FSL) as a technology exemplar.The main perceived utilities of the proposed software were: increased diagnostic confidence; support for delivery of disease modifying therapies; and the possibility to compare individual results with population norms. In addition to assessment of global atrophy, hippocampal atrophy and white matter hyperintensities, additional user requirements included assessment of microbleeds, segmentation of multiple brain structures, clear information about the control population used for reference, and possibility to compare multiple scans. The main barriers to adoption related to the limited availability of 3T MRI scanners in the UK, integration into the clinical workflow, and the need to demonstrate cost-effectiveness. These findings will guide future technical development, clinical validation, and health economic evaluation.
The breath volatilome is shaped by the gut microbiota
Authors: Hernandez-Leyva Ariel J. , Berna Amalia Z. , Liu Yang , Rosen Anne L. , Lint Michael A. , Whiteside Samantha A. , Jaeger Natalia , McDonough Ryan T. , Joardar Nikhilesh , Santiago-Borges Jesús , Tomera Christopher P. , Luo Wentai , Odom John Audrey R. , Kau Andrew L. 
Published: August 2024

ABSTRACTThe gut microbiota is widely implicated in host health and disease, inspiring translational efforts to implement our growing body of knowledge in clinical settings. However, the need to characterize gut microbiota by its genomic content limits the feasibility of rapid, point-of-care diagnostics. The microbiota produces a diverse array of xenobiotic metabolites that disseminate into tissues, including volatile organic compounds (VOCs) that may be excreted in breath. We hypothesize that breath contains gut microbe-derived VOCs that inform the composition and metabolic state of the microbiota. To explore this idea, we compared the breath volatilome and fecal gut microbiomes of 27 healthy children and found that breath VOC composition is correlated with gut microbiomes. To experimentally interrogate this finding, we devised a method for capturing exhaled breath from gnotobiotic mice. Breath volatiles are then profiled by gas-chromatography mass-spectrometry (GC-MS). Using this novel methodology, we found that the murine breath profile is markedly shaped by the composition of the gut microbiota. We also find that VOCs produced by gut microbes in pure culture can be identifiedin vivoin the breath of mice monocolonized with the same bacteria. Altogether, our studies identify microbe-derived VOCs excreted in breath and support a mechanism by which gut bacterial metabolism directly contributes to the mammalian breath VOC profiles.
Social and environmental predictors of gut microbiome age in wild baboons
Authors: Dasari Mauna R. , Roche Kimberly E. , Jansen David , Anderson Jordan , Alberts Susan C. , Tung Jenny , Gilbert Jack A. , Blekhman Ran , Mukherjee Sayan , Archie Elizabeth A. 
Published: August 2024

AbstractMammalian gut microbiomes are highly dynamic communities that shape and are shaped by host aging, including age-related changes to host immunity, metabolism, and behavior. As such, gut microbial composition may provide valuable information on host biological age. Here we test this idea by creating a microbiome-based age predictor using 13,563 gut microbial profiles from 479 wild baboons collected over 14 years. The resulting “microbiome clock” predicts host chronological age. Deviations from the clock’s predictions are linked to some demographic and socio-environmental factors that predict baboon health and survival: animals who appear old-for-age tend to be male, sampled in the dry season (for females), and have high social status (both sexes). However, an individual’s “microbiome age” does not predict the attainment of developmental milestones or lifespan. Hence, in our host population, gut microbiome age largely reflects current, as opposed to past, social and environmental conditions, and does not predict the pace of host development or host mortality risk. We add to a growing understanding of how age is reflected in different host phenotypes and what forces modify biological age in primates.