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Environmental pressures shape regional patterns of genetic diversity and ancestry in cotton landraces
Get PDFUpland cotton has undergone extensive domestication and breeding, leading to substantial genetic improvement but also a pronounced narrowing of its genetic base. To better characterize and leverage the diversity preserved in traditional gene pools, we examined the population structure, phylogenetic relationships, and genomic signatures of selection in a globally sourced panel of cotton landraces and elite cultivars. STRUCTURE and neighbor-joining analyses based on whole-genome SNP genotyping identified four ancestral populations divided into nine major clusters. The landrace accessions formed deep, regionally coherent lineages characterized by high heterozygosity and an abundance of private alleles. Consistent with these patterns, Nei’s genetic distance and pairwise FST estimates revealed strong divergence between Mesoamerican and Central American landraces relative to modern breeding lines. Flowering time, a key adaptive trait, was strongly associated with genetic clusters, with photoperiod-sensitive genotypes primarily originating from highland and tropical regions. Genome-wide scans of Tajima’s D further differentiated landraces from cultivars, revealing signatures of balancing selection and ancestral polymorphism in the landraces, and selective sweeps in cultivated accessions. Notably, flowering-related genes on chromosomes D05 and A05 were located in regions exhibiting contrasting Tajima’s D values between the two gene pools. These findings demonstrate that cotton landraces have retained valuable genomic regions lost from modern cultivars through domestication and decades of intensive improvement. As such, they represent an important reservoir for enhancing resilience, adaptation, and fiber traits in modern cotton. Collectively, our results provide a high-resolution framework for targeted pre-breeding and conservation initiatives, underscoring the untapped potential of landraces in broadening the genetic base of cultivated G. hirsutum
Comparative study of continuously reinforced concrete pavement (CRCP) using GFRP and reinforcing bars: comprehensive review and numerical analysis
Get PDFGlass fiber-reinforced polymer (GFRP) bars are increasingly recognized as a promising alternative to conventional steel reinforcement in concrete structures due to their corrosion resistance, high tensile strength, and eco-friendly production. Their application in continuously reinforced concrete pavement (CRCP) systems offers an opportunity to leverage these advantages. However, challenges remain, including GFRP’s relatively low elastic modulus, thermal incompatibility with concrete, and manufacturing limitations. To address these issues, this study integrates a comprehensive literature review with three-dimensional finite element simulations. The review highlights GFRP’s superior durability against chloride-induced corrosion and its ability to reduce local stress concentrations. Nonetheless, concerns remain regarding its lower stiffness, which may result in increased deflection and crack width in flexural members. Numerical analyses further suggest that the reduced modulus of GFRP lowers effective stresses, thereby potentially mitigating horizontal cracking. Consequently, GFRP bars demonstrate considerable promise as a substitute for conventional steel in CRCP applications. This study provides insights into the structural feasibility and performance benefits of GFRP reinforcement in pavement systems, contributing to the development of more durable and corrosion-resistant designs.This research was supported by Basic Science Research
Program through the National Research Foundation of
Korea (NRF) funded by the Ministry of Education [RS-2025-
25426121]
The Role of Fatigue in the Relationship Between Sleep and Concentration Among Online College Students
Get PDFSleep deprivation is a common issue among college students, critically impairing their well-being and academic performance. This study specifically investigated the impact of sleep duration on concentration among online college students, a population with unique living situations and often irregular sleep patterns. Furthermore, it explored how this relationship is mediated by subjective physical and mental fatigue, providing a clearer understanding of the underlying process. An online survey assessed these variables using the Demographics and Background Questionnaire for sleep duration, the Student Mental Fatigue Survey (SMFS) for mental fatigue, and two subscales from the Checklist Individual Strength (CIS) for subjective physical fatigue and concentration. Path analysis revealed that both subjective physical and mental fatigue significantly and negatively predicted concentration, and that sleep duration positively influenced concentration indirectly by reducing both types of fatigue. The findings highlight the essential function of sleep in improving concentration. The results provide valuable guidance for developing targeted interventions to improve sleep quality and manage fatigue, which can directly promote mental and physical health, and academic success of this growing, often overlooked, online college student population
Capturing Change: A Proposal for Texas to Assume Long-Term Liability for Carbon Storage Facilities
Get PDFCapturing Change: A Proposal for Texas to Assume Long-Term Liability for Carbon Storage Facilities examines the legal and regulatory challenges associated with long-term liability for carbon capture and storage projects in Texas. It describes how uncertainty over post-closure responsibility affects investment, permitting, and public acceptance of carbon storage facilities. The discussion surveys existing state and federal approaches to long-term liability and stewardship of sequestration sites. It evaluates how a state-assumed liability framework could interact with environmental protection, financial assurance, and regulatory oversight mechanisms. The abstract concludes by outlining considerations for implementing a long-term liability regime that supports carbon management objectives while addressing risk and accountability
Can socioeconomic status moderate the effect of a conflictive family environment on brain structure and externalizing/internalizing behavior in children and adolescents?
Get PDFOBJECTIVE: This study examined the effects of socioeconomic status as a possible moderator of the effects of family conflict on externalizing/internalizing behavior and hippocampal and amygdala volume.
METHODS: A longitudinal complete-case analysis of 714 children and adolescents (mean age: 11.2 years; 46.2% female) was conducted using data from the Brazilian High-Risk Cohort Study for Psychiatric Disorders in Childhood. At baseline, parents/guardians completed the Family Environment Scale and a socioeconomic status scale. Three years after baseline assessment, the same participants underwent brain magnetic resonance imaging, and the Child Behavior Checklist was administered. Automated segmentation of the amygdala and hippocampus was performed in FreeSurfer 5.1.
RESULTS: Although family conflict at baseline predicted externalizing/internalizing behavior at follow-up, we found no evidence that family conflict and socioeconomic status affected brain structure or that family conflict had a moderating effect on psychopathology and brain outcomes conditioned on socioeconomic status.
CONCLUSION: These results are consistent with emerging evidence that family conflict is a risk factor for externalizing/internalizing behavior in youth. These findings warrant further attention, focusing on prevention and intervention efforts and social policy development.This study was mainly funded by Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP; process 15/50469-0) and in part by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES; Finance Code 001).
AZ has received funding from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior/Conselho Nacional de Desenvolvimento Científico e Tecnológico and the National Institute of Mental Health /National Institutes of Health outside the scope of this work
Contrasting Depth Dependencies of Plant Root Presence and Mass Across Biomes Underscore Prolific Root-Regolith Interactions
Get PDFRoot distributions are typically based on root mass per soil volume. This plant-focused approach masks the biogeochemical influence of fine roots, which weigh little. We assert that centimeter-scale root presence-absence data from soil profiles provide a more soil-focused approach for probing depth distributions of root-regolith interfaces, where microsite-scale processes drive whole-ecosystem functioning. In 75 soil pits across the continental USA, Puerto Rico, and the Alps, we quantified fine and coarse root presence as deep as 2 m. In 70 of these pits we estimated root mass and created standardized metrics of both data sets to compare their depth distributions. We addressed whether: (a) depth distributions of root presence-absence data differ from root mass data, thus implying different degrees of root-regolith interactions with depth; and (b) if root presence or any depth-dependent differences between these data sets vary predictably with environmental conditions. Presence of fine roots exhibited diverse depth-dependent patterns; root mass generally declined with depth. In B and C horizons, standardized root presence was greater than standardized root mass; random forest analyses suggest these discrepancies are greater in B horizons with increasing mean annual precipitation and in C horizons with increasing mean annual temperature. Our work suggests that deep in the subsurface, biogeochemical and reactive transport processes result from more numerous root-regolith interfaces than mass data suggest. We present a new paradigm for discerning patterns in depth distributions of root-regolith interfaces across multiple biomes and land uses that promotes understanding of the roles of those interfaces in driving key critical zone processes.National Science Foundation. Grant Numbers: 2121639, 2012633, 2121694, 2121621, 2121760, 2121652, 2121595, 2121659, 2012878, 20122403, 2129402
National Institute of Food and Agriculture. Grant Number: 2021-67019-34340
European Union Italian National Biodiversity Future Center National Recovery and Resilience Plan, NexGenerationEU. Grant Number: CN0000003
Quantification of Total Free Radicals in Drosophila Using a Fluorescence-Based Biochemical Assay
No full textFree radicals, including reactive oxygen species (ROS) and reactive nitrogen species (RNS), induce oxidative stress. This stress plays crucial roles in cellular signaling, stress response, and disease progression, making the quantification of free radicals essential for understanding oxidative stress mechanisms. Here, we present a high-throughput fluorescence-based protocol for measuring the presence of total free radicals, including ROS and RNS, in the whole adult Drosophila melanogaster (fruit fly). The protocol involves homogenizing whole adult flies in PBS and treating only the supernatant of the lysate with dichlorodihydrofluorescein-DiOxyQ (DCFH-DiOxyQ), which then converts into a fluorescent molecule, dichlorofluorescein (DCF), upon reacting with free radicals. The level of fluorescence is directly proportional to the amount of free radicals present in the sample. This protocol offers simplicity, scalability, and adaptability, making it ideal for studying oxidative stress in the model organism Drosophila and its different tissues under different dietary regimes, environmental stresses, genetic mutations, or pharmacological treatments. It is to be noted that the protocol uses a kit from Abcam, which has been used to measure free radicals in mice, rats, human blood, and cell lines. It can also be applied to biofluids, culture supernatants, and cell lysates, making it suitable for a wide range of sample types beyond whole organisms or tissues. However, due to our research focus and expertise, here we describe a detailed protocol to measure free radicals responsible for inducing oxidative stress only in fruit flies
AI-Driven Approaches for Real-Time Beat-by-Beat ECG Signal Classification and Cardiac Health Monitoring in Smart Healthcare Systems
No full textCardiovascular disease, which refers to a range of diseases that affect the heart, including vascular diseases such as coronary artery disease, arrhythmia, myocardial disease and various forms of heart disease, is a leading cause of death worldwide. Smart Health (sHealth) in future can transform the utilization of biosignals including Electrocardiogram (ECG) for monitoring and managing cardiac patients. The ECG classification system uses technologies such as artificial intelligence (AI) to automatically detect and classify complex cardiac events with high accuracy. With the operability of wearable devices and IoT in the healthcare field, continuous real-time ECG monitoring has become more feasible. If the heart rhythm is abnormal, point-of-care ECG monitoring can immediately generate alert and notify the individual or medical professional.
We have explored the role of feature engineering in optimizing heartbeat classification models. We utilized a large set of ECG features, then narrowed it down to the features that have the most impact on model performance through feature ranking technique. Machine learning (ML) and Deep learning (DL) models such as Random Forest and Support Vector Machine (SVM) achieved significant accuracy by leveraging this technique. We further explored the combination of signal-specific and signal-independent features to enhance the models, achieving peak performance through fine-tuned configurations.
We further examined the effects of intra- and inter-patient analysis in ECG beat-by-beat classification by using DL models and selecting features through variation analysis and principal component analysis (PCA). Our research highlights the complexity and necessity of customizing healthcare models based on individual patient characteristics and collective patient data sets. The approach supports personalized patient care by optimizing prediction accuracy and adapting models to individual and grouped patient data.
Finally, we proposed a novel hybrid transformer neural network model designed for efficient and accurate ECG heartbeat classification. This study evaluates the performance and energy consumption of various models, exploring the superiority of hybrid transformer models in terms of accuracy and computational resource management. This model can be used for real time cardiac patient monitoring and can be integrated into sHealth systems to enhance patient care
Lean Dust Combustion for Varied Surface Energy Aluminum Particles: Analyzing Dispersion and Light Emission
No full textThe lean combustion of micron-sized aluminum (Al) dust post ballistic impact was studied bi-spectrally in the visible (VIS) and near-infrared (NIR) using high-speed imaging. Powders were delivered loosely via a novel sabot design. Two identically sized Al powders were studied, one was untreated (UN), the other processed using a thermal treatment and called super-quenched (SQ). The SQ Al powder is known to have reduced surface energy compared to UN Al powder, which is induced by the annealing-quenching treatment. A field emission fraction is introduced as a metric that characterizes the light emission dispersion of burning powder and relates to combustibility. In the case of SQ Al, visible light emission from dispersed powder decays slower compared to UN Al. The peculiarities in powder dispersion were attributed to different combustion regimes and further confirmed by X-ray diffraction (XRD) analysis of post-burn products, which demonstrated different residue phase compositions
The Role of Social Media and Sense of Belonging in Influencing Enrollment Intentions Among Potential Non-traditional Students: A Moderated Mediation Analysis
No full textThis study explores the relationship between social media usage, sense of belonging, and enrollment intentions among non-traditional college students. While prior research has focused on financial constraints, family responsibilities, and academic preparation as barriers to enrollment, this study introduces a psychological perspective by examining the role of social and emotional factors. Specifically, it investigates whether searching for information about universities on social media leads to a stronger intention to enroll, with a sense of belonging serving as a key mediator and moderator. A moderated mediation model was used to assess how belonging influences the connection between information-seeking behavior and enrollment decisions and whether this relationship holds when accounting for financial concerns and family obligations. The findings reveal that, although information-seeking behavior alone does not significantly impact enrollment intentions, the introduction of a sense of belonging as a moderator enhances this effect. Higher levels of belonging strengthen the relationship between social media usage and enrollment intentions, suggesting that non-traditional students prioritize emotional connection to an institution over logistical factors when making enrollment decisions. These results offer important implications for higher education institutions, emphasizing the need for targeted strategies that foster a sense of belonging for non-traditional students to improve recruitment and retention