bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evidence for admixture and rapid evolution during glacial climate change in an alpine specialist
Authors: Weng Yi-Ming , Kavanaugh David H. , Ralph Peter L. , Patterson Gilia , Schoville Sean D. 
Published: September 2023

AbstractThe pace of current climate change is expected to be problematic for alpine flora and fauna, as their adaptive capacity may be limited by small population size. Yet despite substantial genetic drift following post-glacial recolonization of alpine habitats, alpine species are notable for their success in surviving highly heterogeneous environments. Population genomic analyses demonstrating how alpine species have adapted to novel environments with limited genetic diversity remain rare, yet are important in understanding the potential for species to respond to contemporary climate change. In this study, we explored the evolutionary history of alpine ground beetles in theNebria ingenscomplex, including the demographic and adaptive changes that followed the last glacier retreat. Using whole genome data from hundreds of beetles, to test alternative models of evolutionary divergence in the species complex, we found evidence that theNebria ingenscomplex has been formed by past admixture of lineages responding to glacial cycles. Recolonization of alpine sites involved a distributional range shift to higher elevation, which was accompanied by a reduction in suitable habitat and the emergence of complex spatial genetic structure. We also used genome-wide association and genotype-environment association methods to look for genetic pathways involved in adaptation to heterogeneous new environments during this range shift. The identified genes were enriched for functions broadly associated with abiotic stress responses, with strong evidence for adaptation to hypoxia-related pathways. The results demonstrate that despite rapid environmental changes, alpine beetles in theN. ingenscomplex have shown rapid physiological evolution.
starTracer: An Accelerated Approach for Precise Marker Gene Identification in Single-Cell RNA-Seq Analysis
Authors: Zhang Feiyang , Huang Kaixin , Chen Ruixi , Zhao Qiongyi , Liu Zechen , Ma Wenhao , Hou Shengqun , Wang Dan Ohtan , Wei Wei , Li Xiang 
Published: September 2023

AbstractWe introduce starTracer, a novel R package designed to enhance the specificity and efficiency of marker gene identification in single-cell RNA-seq data analysis. The package consists of two primary functional modules: “searchMarker” and “filterMarker”. The “searchMarker” module, operating as an independent pipeline, exhibits superior flexibility by accepting a variety of input file types. Its primary output is a marker gene matrix, where genes are sorted by their potential to function as cluster-specific markers, with those exhibiting the greatest potential positioned at the top of the matrix for each respective cluster. In contrast, the “filterMarker” module is designed as a complementary pipeline to the Seurat “FindAllMarkers” function, providing a more accurate marker gene list for each cluster in conjunction with Seurat results. Benchmark analyses demonstrate that starTracer not only achieves excellent specificity in identifying marker genes compared to Seurat but also significantly surpasses it in processing speed. Impressively, the speed improvement ranges by 1~2 orders of magnitude compared to Seurat, as observed across three independent datasets. It is worth noting that starTracer exhibits increasing speed improvement with larger data volumes. It also excels in identifying markers in smaller clusters. Furthermore, the “filterMarker” reordering process considerably enhances Seurat’s marker matrix specificity. These advantages solidify starTracer as an invaluable tool for researchers working with single-cell RNA-seq data, merging robust accuracy with exceptional speed.
Radiomic-Based Prediction of Lesion-Specific Systemic Treatment Response in Metastatic Disease
Authors: Geady Caryn , Abbas-Aghababazadeh Farnoosh , Kohan Andres , Schuetze Scott , Shultz David , Haibe-Kains Benjamin 
Published: September 2023

AbstractDespite sharing the same histologic classification, individual tumors in multi metastatic patients may present with different characteristics and varying sensitivities to anticancer therapies. In this study, we investigate the utility of radiomic biomarkers for prediction of lesion-specific treatment resistance in multi metastatic leiomyosarcoma patients. Using a dataset of n=202 lung metastases (LM) from n=80 patients with 1648 pre-treatment computed tomography (CT) radiomics features and LM progression determined from follow-up CT, we developed a radiomic model to predict the progression of each lesion. Repeat experiments assessed the relative predictive performance across LM volume groups. Lesion-specific radiomic models indicate up to a 4.5-fold increase in predictive capacity compared with a no-skill classifier, with an area under the precision-recall curve of 0.70 for the most precise model (FDR = 0.05). Precision varied by administered drug and LM volume. The effect of LM volume was controlled by removing radiomic features at a volume-correlation coefficient threshold of 0.20. Predicting lesion-specific responses using radiomic features represents a novel strategy by which to assess treatment response that acknowledges biological diversity within metastatic subclones, which could facilitate management strategies involving selective ablation of resistant clones in the setting of systemic therapy.HighlightsIntensity values in CT scans and their corresponding spatial distribution convey important information.A model to predict lesion-specific response to systemic treatment using image-derived features is proposed.Up to a 4.5-fold increase in predictive capacity compared to a no-skill classifier was obtained, with AUPRC of 0.70 for the most precise model (FDR = 0.05).Assessing treatment response on a lesion-level acknowledges biological diversity within metastatic subclones, which could facilitate management strategies involving selective ablation of resistant clones in the setting of systemic therapy.
What does it mean to use the mean? The impact of different data handling strategies on the proportion of children classified as meeting 24-hr movement guidelines and associations with overweight and obesity
Authors: Pfledderer Christopher D. , Burkart Sarah , Dugger Roddrick , Parker Hannah , von Klinggraeff Lauren , Okely Anthony D. , Weaver R. Glenn , Beets Michael W. 
Published: September 2023

AbstractBackgroundDespite the widespread endorsement of 24-hour movement guidelines (physical activity, sleep, screentime) for youth, no standardized processes for categorizing guideline achievement exists. The purpose of this study was to illustrate the impact of different data handling strategies on the proportion of children meeting 24-hour movement guidelines (24hrG) and associations with overweight and obesity.MethodsA subset of 524 children (ages 5-12yrs) with complete 24-hour behavior measures on at least 10 days was used to compare the impact of data handling strategies on estimates of meeting 24hrG. Physical activity and sleep were measured via accelerometry. Screentime was measured via parent self-report. Comparison of meeting 24hrG were made using 1) average of behaviors across all days (AVG-24hr), 2) classifying each day and evaluating the percentage meeting 24hrG from 10-100% of their measured days (DAYS-24hr), and 3) the average of a random sample of 4 days across 10 iterations (RAND-24hr). A second subset of children (N=475) with height and weight data was used to explore the influence of each data handling strategy on children meeting guidelines and the odds of overweight/obesity via logistic regression.ResultsClassification for AVG-24hr resulted in 14.7% of participants meeting 24hrG. Classification for DAYS-24hr resulted in 63.5% meeting 24hrG on 10% of measured days with <1% meeting 24hrG on 100% of days. Classification for RAND-24hr resulted in 15.9% of participants meeting 24hrG. Across 10 iterations, 63.6% of participants never met 24hrG regardless of the days sampled, 3.4% always met 24hrG, with the remaining 33.0% classified as meeting 24hrG for at least one of the 10 random iterations of days. Using AVG-24hr as a strategy, meeting all three guidelines associated with lower odds of having overweight obesity (OR=0.38, p<0.05). The RAND-24hr strategy produced a range of odds from 0.27 to 0.56. Using the criteria of needing to meet 24hrG on 100% of days, meeting all three guidelines associated with the lowest odds of having overweight and obesity as well (OR=0.04, p<0.05).ConclusionsVarying estimates of meeting the 24hrG and the odds of overweight and obesity results from different data handling strategies and days sampled.
A digital twin model incorporating generalized metabolic fluxes to predict chronic kidney disease in type 2 diabetes mellitus
Authors: Surian Naveenah Udaya , Batagov Arsen , Wu Andrew , Lai Wen Bin , Sun Yan , Bee Yong Mong , Dalan Rinkoo 
Published: September 2023

AbstractOne of the biggest complication in diabetes patients is chronic kidney disease (CKD), making it a tremendous burden on the country’s public healthcare system. We have developed HealthVector Diabetes (HVD), a digital twin model that leverages generalized metabolic fluxes (GMF) to proficiently predict the onset of CKD and facilitate its early detection. Our HVD GMF model utilized commonly available clinical and physiological biomarkers as inputs for identification and prediction of CKD. We employed four diverse multi-ethnic cohorts (n=7072): one Singaporean cohort (EVAS, n=289) and one North American cohort (NHANES, n=1044) for baseline CKD identification, and two multi-center Singaporean cohorts (CDMD, n=2119 and SDR, n=3627) for 3-year CKD prediction. We developed one identification model and two prediction models (with complete or incomplete parameters). The identification model demonstrated strong performance with an AUC ranging from 0.80 to 0.82. For prediction, with incomplete parameters, we achieved an AUC of 0.75, while the complete parameter model achieved an improved AUC of 0.86. Our model also effectively stratifies patients into low, moderate, and high-risk categories, with the high-risk category having the highest proportion (53.3-62.9%) of patients with CKD. Our method also reveals metabolic health profile differences among patient subgroups at baseline, indicating that patient subgroups who develop future CKD exhibit more deteriorated profiles compared to future non-CKD patients. Furthermore, we also show that GMF-based clustering reveals distinct metabolic profile differences that act as drivers for CKD progression, and the distance between different patient clusters can be used to map patient health trajectories. Our HVD GMF digital twin model has the ability to identify patients with baseline CKD and predict future CKD within a 3-year time frame. Furthermore, our approach enables risk stratification, sub-grouping and clustering based on metabolic health profiles, positioning our model as a valuable clinical application tool for healthcare practitioners.
Impact of lesion location and functional parameters on vision-related quality of life in geographic atrophy secondary to AMD
Authors: Künzel Sandrine H. , Broadbent Eliza , Möller Philipp T , Lindner Moritz , Goerdt Lukas , Czauderna Joanna , Schmitz-Valckenberg Steffen , Holz Frank G , Pfau Maximilian , Fleckenstein Monika 
Published: September 2023

AbstractBackground/AimsThe primary objective was to determine how structural and functional parameters influence the vision-related quality of life (VRQoL) in patients with geographic atrophy (GA) secondary to age-related macular degeneration (AMD).MethodsThis prospective, non-interventional, natural-history ‘Directional Spread in Geographic-Atrophy’ study was conducted at the University Eye Hospital in Bonn, enrolling 82 patients with bilateral GA. Parameters such as GA location (assessed by the Early Treatment Diabetic Retinopathy Study grid), best-corrected visual acuity (BCVA), low-luminance visual acuity (LLVA), reading acuity, and speed were examined. The association between these parameters and VRQoL, as gauged using the National Eye Institute Visual Function Questionnaire 25 (NEI VFQ-25), was analyzed through least absolute shrinkage and selection operator with linear mixed-effects models.ResultsThe average total GA area observed was 2.9 ± 1.2 mm2(better eye) and 3.1 ± 1.3 mm2(worse eye). The VRQoL scores for distance and near activities were most associated with the inner lower and inner left subfields of the better eye. For foveal-sparing patients, the LLVA of the better eye was the predominant determinate impacting all VRQoL scales.ConclusionGA location, specifically the inner lower and inner left subfields of the better eye, has a notable effect on VRQoL in GA patients. LLVA stands out as especially vital in foveal-sparing patients, underscoring the importance for clinicians to incorporate considerations of GA location and functional parameters into their risk-benefit assessments for emerging treatments.Key messengesWhat is already known on this topicDespite recent therapeutic advancements, the relationship between GA markers and VRQoL remains unclear.What this study addsThis research reveals the distinct value of specific structural and functional GA markers, notably the inner left and inner lower subfields, and LLVA, in relation to VRQoL in GA patients.How this study might affect research, practice, or policyUnderstanding these structural and functional markers will guide clinicians in personalized treatment decisions and encourage further research into more tailored therapeutic interventions.
Distinct explanations underlie gene-environment interactions in the UK Biobank
Authors: Durvasula Arun , Price Alkes L. 
Published: September 2023

AbstractThe role of gene-environment (GxE) interaction in disease and complex trait architectures is widely hypothesized, but currently unknown. Here, we apply three statistical approaches to quantify and distinguish three different types of GxE interaction for a given trait and E variable. First, we detect locus-specific GxE interaction by testing for genetic correlation (rg) < 1 across E bins. Second, we detect genome-wide effects of the E variable on genetic variance by leveraging polygenic risk scores (PRS) to test for significant PRSxE in a regression of phenotypes on PRS, E, and PRSxE, together with differences in SNP-heritability across E bins. Third, we detect genome-wide proportional amplification of genetic and environmental effects as a function of the E variable by testing for significant PRSxE with no differences in SNP-heritability across E bins. Simulations show that these approaches achieve high sensitivity and specificity in distinguishing these three GxE scenarios. We applied our framework to 33 UK Biobank traits (25 quantitative traits and 8 diseases; averageN=325K) and 10 E variables spanning lifestyle, diet, and other environmental exposures. First, we identified 19 trait-E pairs withrgsignificantly < 1 (FDR<5%) (averagerg=0.95); for example, white blood cell count hadrg=0.95 (s.e. 0.01) between smokers and non-smokers. Second, we identified 28 trait-E pairs with significant PRSxE and significant SNP-heritability differences across E bins; for example, BMI had a significant PRSxE for physical activity (P=4.6e-5) with 5% larger SNP-heritability in the largest versus smallest quintiles of physical activity (P=7e-4). Third, we identified 15 trait-E pairs with significant PRSxE with no SNP-heritability differences across E bins; for example, waist-hip ratio adjusted for BMI had a significant PRSxE effect for time spent watching television (P=5e-3) with no SNP-heritability differences. Across the three scenarios, 8 of the trait-E pairs involved disease traits, whose interpretation is complicated by scale effects. Analyses using biological sex as the E variable produced additional significant findings in each of the three scenarios. Overall, we infer a significant contribution of GxE and GxSex effects to complex trait and disease variance.
Breathing zone pollutant levels are associated with asthma exacerbations in high-risk children
Authors: Moore Camille M. , Thornburg Jonathan , Secor Elizabeth A. , Hamlington Katharine L. , Schiltz Allison M. , Freeman Kristy L. , Everman Jamie L. , Fingerlin Tasha E. , Liu Andrew H. , Seibold Max A. 
Published: September 2023

ABSTRACTBackgroundIndoor and outdoor air pollution levels are associated with poor asthma outcomes in children. However, few studies have evaluated whether breathing zone pollutant levels associate with asthma outcomes.ObjectiveDetermine breathing zone exposure levels of NO2, O3, total PM10and PM10constituents among children with exacerbation-prone asthma, and examine correspondence with in-home and community measurements and associations with outcomes.MethodsWe assessed children’s personal breathing zone exposures using wearable monitors. Personal exposures were compared to in-home and community measurements and tested for association with lung function, asthma control, and asthma exacerbations.Results81 children completed 219 monitoring sessions. Correlations between personal and community levels of PM10, NO2, and O3were poor, whereas personal PM10and NO2levels correlated with in-home measurements. However, in-home monitoring underdetected brown carbon (Personal:79%, Home:36.8%) and ETS (Personal:83.7%, Home:4.1%) personal exposures, and detected black carbon in participants without these personal exposures (Personal: 26.5%, Home: 96%). Personal exposures were not associated with lung function or asthma control. Children experiencing an asthma exacerbation within 60 days of personal exposure monitoring had 1.98, 2.21 and 2.04 times higher brown carbon (p<0.001), ETS (p=0.007), and endotoxin (p=0.012), respectively. These outcomes were not associated with community or in-home exposure levels.ConclusionsMonitoring pollutant levels in the breathing zone is essential to understand how exposures influence asthma outcomes, as agreement between personal and in-home monitors is limited. Inhaled exposure to PM10constituents modifies asthma exacerbation risk, suggesting efforts to limit these exposures among high-risk children may decrease their asthma burden.CLINICAL IMPLICATIONSIn-home and community monitoring of environmental pollutants may underestimate personal exposures. Levels of inhaled exposure to PM10constituents appear to strongly influence asthma exacerbation risk. Therefore, efforts should be made to mitigate these exposures.CAPSULE SUMMARYLeveraging wearable, breathing-zone monitors, we show exposures to inhaled pollutants are poorly proxied by in-home and community monitors, among children with exacerbation-prone asthma. Inhaled exposure to multiple PM10constituents is associated with asthma exacerbation risk.
Cortical lesions uniquely predict motor disability accrual and form rarely in the absence of new white matter lesions in multiple sclerosis
Authors: Beck Erin S , Mullins W Andrew , Silva Jonadab dos Santos , Filippini Stefano , Parvathaneni Prasanna , Maranzano Josefina , Morrison Mark , Suto Daniel J , Donnay Corinne , Dieckhaus Henry , Luciano Nicholas J , Sharma Kanika , Gaitán María Ines , Liu Jiaen , de Zwart Jacco A , van Gelderen Peter , Cortese Irene , Narayanan Sridar , Duyn Jeff H , Nair Govind , Sati Pascal , Reich Daniel S 
Published: September 2023

AbstractBackground and objectivesCortical lesions (CL) are common in multiple sclerosis (MS) and associate with disability and progressive disease. We asked whether CL continue to form in people with stable white matter lesions (WML) and whether the association of CL with worsening disability relates to pre-existing or new CL.MethodsA cohort of adults with MS were evaluated annually with 7 tesla (T) brain magnetic resonance imaging (MRI) and 3T brain and spine MRI for 2 years, and clinical assessments for 3 years. CL were identified on 7T images at each timepoint. WML and brain tissue segmentation were performed using 3T images at baseline and year 2.Results59 adults with MS had ≥1 7T follow-up visit (mean follow-up time 2±0.5 years). 9 had “active” relapsing-remitting MS (RRMS), defined as new WML in the year prior to enrollment. Of the remaining 50, 33 had “stable” RRMS, 14 secondary progressive MS (SPMS), and 3 primary progressive MS. 16 total new CL formed in the active RRMS group (median 1, range 0-10), 7 in the stable RRMS group (median 0, range 0-5), and 4 in the progressive MS group (median 0, range 0-1) (p=0.006, stable RR vs PMS p=0.88). New CL were not associated with greater change in any individual disability measure or in a composite measure of disability worsening (worsening Expanded Disability Status Scale or 9-hole peg test or 25-foot timed walk). Baseline CL volume was higher in people with worsening disability (median 1010μl, range 13-9888 vs median 267μl, range 0-3539, p=0.001, adjusted for age and sex) and in individuals with RRMS who subsequently transitioned to SPMS (median 2183μl, range 270-9888 vs median 321μl, range 0-6392 in those who remained RRMS, p=0.01, adjusted for age and sex). Baseline WML volume was not associated with worsening disability or transition from RRMS to SPMS.DiscussionCL formation is rare in people with stable WML, even in those with worsening disability. CL but not WML burden predicts future worsening of disability, suggesting that the relationship between CL and disability progression is related to long-term effects of lesions that form in the earlier stages of disease, rather than to ongoing lesion formation.
Role Of The C-C Motif Chemokine Ligand 5 (CCL5) And Its Receptor, C-C Motif Chemokine Receptor 5 (CCR5) In The Genesis Of Aldosterone-induced Hypertension, Vascular Dysfunction, And End-organ Damage
Authors: Costa Rafael M. , Cerqueira Débora M. , Bruder-Nascimento Ariane , Alves Juliano V. , Awata Wanessa A.C. , Singh Shubhnita , Kufner Alexander , Cifuentes-Pagano Eugenia , Pagano Patrick J. , Ho Jacqueline , Bruder-Nascimento Thiago 
Published: September 2023

AbstractBackgroundAldosterone, a mineralocorticoid steroid hormone, has been described to initiate cardiovascular diseases by triggering exacerbated sterile vascular inflammation. The functions of C-C Motif Chemokine Ligand 5 (CCL5) and its receptor, C-C Motif Chemokine Receptor 5 (CCR5), are well known in infectious diseases, but their roles in the genesis of aldosterone-induced vascular injury and hypertension are unknown.MethodsWe analyzed the vascular profile, blood pressure, and renal damage in wild-type (CCR5+/+) and CCR5 knockout (CCR5−/−) mice treated with aldosterone (600 µg/kg/day for 14 days) while receiving 1% saline to drink.ResultsHere, we show that CCR5 plays a central role in aldosterone-induced vascular injury, hypertension, and renal damage. Long-term infusion of aldosterone in CCR5+/+mice resulted in exaggerated CCL5 circulating levels and vascular CCR5 expression. Aldosterone treatment also triggered vascular injury, characterized by endothelial dysfunction and inflammation, hypertension, and renal damage. Mice lacking CCR5 were protected from aldosterone-induced vascular damage, hypertension, and renal injury. Mechanistically, we demonstrated that CCL5 increased NADPH oxidase 1 (Nox1) expression, reactive oxygen species (ROS) formation, NFκB activation, and inflammation and reduced nitric oxide production in isolated endothelial cells. These effects were abolished by antagonizing CCR5 with Maraviroc. Finally, aortae incubated with CCL5 displayed severe endothelial dysfunction, which is prevented by blocking Nox1, NFκB, or with Maraviroc treatment.ConclusionsOur data demonstrate that CCL5/CCR5, through activation of NFkB and Nox1, is critically involved in aldosterone-induced vascular and renal damage and hypertension. Our data place CCL5 and CCR5 as potential targets for therapeutic interventions in conditions with aldosterone excess.