Journal Articles, Multidisciplinary Digital Publishing Institute (MDPI)

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In-Field Diadegma insulare (Cresson) (Hymenoptera: Ichneumonidae) Parasitism Rates of Plutella xylostella (L.) (Lepidoptera: Plutellidae) in Virginia Cole Crops
Tomlinson, Taylore A.; Del Pozo-Valdivia, Alejandro I.; Kuhar, Thomas P. (MDPI, 2026-03-03)
The diamondback moth, Plutella xylostella (L.), is a significant pest of brassica crops that is found across the globe. Due to the development of insecticide resistance, control tactics have shifted focus towards integrating pest management techniques such as biological control. Diadegma insulare (C.), Oomyzus sokolowskii (K.), and Microplites plutellae (M.) are parasitoids of P. xylostella found in the Eastern United States. From 2022 to 2025, we surveyed P. xylostella larvae and pupae in locations across Virginia to assess the current rates of parasitism in brassica fields. Specimens were brought to the laboratory and reared to assess parasitoid emergence rates. Only D. insulare specimens were found during the study. Adult P. xylostella, larvae and pupae, adult D. insulare, D. insulare pupae, unknown parasitoids, and unknown deaths were recorded and used to calculate the rates of parasitism at each location. We concluded that the parasitism rate varied by location and year, which was expected due to regional conditions and seasonality. Rates averaged between 30.1 and 65% by year, with the lowest individual rate being 15% in 2025 and the highest at 100% in 2022. This suggests that D. insulare is actively present in Virginia and could be a successful biological control agent when paired with other integrated pest management techniques to reduce P. xylostella populations.
The Role of EDA in Developing Robust Machine Learning Models for Lithology and Penetration Rate Prediction from MWD Data
Addy, Jesse; Anafo, Ishmael; Westman, Erik (MDPI, 2026-03-04)
Measure-While-Drilling (MWD) data provide real-time insight into subsurface conditions and drilling performance, yet their complexity and operational noise often hinder reliable modeling. This study demonstrates the role of Exploratory Data Analysis (EDA) in developing robust machine learning (ML) models for lithology classification and penetration rate (PR) prediction in mining operations. A structured EDA workflow—comprising data integrity assessment, feature distribution analysis, correlation mapping, and depth-wise parameter profiling—was implemented to identify redundant attributes, isolate non-productive intervals, and enhance dataset consistency. Through EDA-informed normalization and feature selection, data consistency and model performance were significantly improved. Machine learning algorithms, including Decision Tree, Random Forest, and Multi-Layer Perceptron, were trained on the refined dataset. The Random Forest Classifier achieved 98.45% accuracy in lithology prediction, while the Random Forest Regressor produced the most accurate PR estimation (R² = 0.83, RMSE = 0.52). These results highlight EDA as a critical foundation for constructing physics-informed, data-driven models that enhance predictive reliability and operational efficiency in mining environments.
Deep-Neural-Network-Aided Genetic Association Testing in Samples with Related Individuals
Wu, Xiaowei (MDPI, 2026-03-04)
Genome-wide association studies (GWAS) have successfully identified thousands of genetic loci associated with complex traits and diseases, providing critical insights into genetic architecture, biological pathways, and disease mechanisms. With the advance of machine learning, the analytical scope of GWAS can be substantially expanded by enabling joint modeling, nonlinear effects, and integrative analysis. However, deep learning approaches remain underutilized in augmenting traditional GWAS frameworks, particularly in the presence of cryptic relatedness among sampled individuals. In this paper, we propose a deep neural network (DNN)-based machine learning method to assist genetic association testing in samples with related individuals. By approximating the phenotype–genotype relationships in classical association tests and combining approximations across multiple tests, the proposed method aims to improve predictive performance in the identification of associated variants. Simulation studies demonstrate that our approach effectively complements conventional statistical methods and generally achieves increased power for detecting genetic associations. We further apply the method to data from the Framingham Heart Study, illustrating how DNN-based machine learning can facilitate the identification of genome-wide SNPs associated with average systolic blood pressure.
Track Transition Performance: A Sensor-Centric Literature Review and Optical Sensing Advances
Gharizadehvarnosefaderani, Mahsa; Rabbi, Md. Fazle; Mishra, Debakanta (MDPI, 2026-03-04)
The structural and geotechnical characteristics of railroad tracks change abruptly at transition zones. At these locations, a change from ‘rigid’ to ‘flexible’ track conditions or the opposite leads to amplified dynamic responses, large deformations, accelerated track deterioration, and increased maintenance expenses. Researchers have conducted numerous field and numerical studies into track transitions’ behavior; however, their investigations are often limited by point-based and short-term measurements and assumptions that overlook critical mechanisms in track transitions. This review presents current sensor-centric knowledge achieved by integrating insights from field instrumentations and numerical modellings of transition zones. The objective is to expose the overlooked behavioral aspects of track transitions and identify the limitations of conventional monitoring systems. To address these gaps, this review introduces optical fiber sensors (OFSs) as an emerging technology for track condition monitoring. Focusing on recent OFS applications, this study demonstrates how OFSs can improve the quantity and quality of field data through spatial continuity, multiplexing, and higher sensitivity, thus marking a significant practical improvement. This review also outlines OFS-based monitoring challenges, such as sensor durability, measurement quality, temperature-strain cross-sensitivity, and lack of a standardized data interpretation framework. Altogether, this work’s novelty is in connecting transition zone behavior, monitoring limitations, and the inherent potential of OFS systems.
Emerging Power Electronics Technologies for Electric Vehicles: Intelligent Architectures and Sustainable Energy Conversion
Duan, Chen; Zhu, Liyan; Chen, Jianfei (MDPI, 2026-03-05)
Power electronics plays a pivotal role in enabling the widespread adoption of electric vehicles (EVs), serving as the backbone for energy conversion, control, and management across propulsion systems, auxiliary loads, charging infrastructures, and hybrid energy architectures [...]
Residential Mobility, Housing Instability, Adverse Childhood Experiences, and the Moderating Role of Neighborhood Contexts
Yoo, Jaeyong; Fisher, Satya; Kim, Jaehwan (MDPI, 2026-03-06)
Housing instability, particularly frequent residential moves, has been associated with poor developmental outcomes, yet its relationship with adverse childhood experiences (ACEs) remains insufficiently understood at the national level. This study addresses this gap by investigating how frequent moves shape children’s exposure to ACEs, and whether community and household contexts influence these effects. Using the 2020–2021 National Survey of Children’s Health data, we ask two questions: (1) Do children who experience frequent moves face greater risk of ACEs? and (2) Do neighborhood and metropolitan contexts mitigate or exacerbate this association? Our contribution is twofold. First, we examine both directions of the relationship: how ACEs predict frequent moves and how frequent moves increase ACE exposure. Second, we incorporate contextual moderators, including supportive neighborhoods, safety, amenities, and urban residence, to provide a more nuanced account of how environments shape resilience or vulnerability. Using logistic and negative binomial regression models, we find that all ACEs significantly predict frequent moves, with parental divorce/separation showing the largest effect. Economic hardship is also a strong predictor of frequent residential mobility, and while food or cash assistance is associated with higher mobility, it moderates the hardship-mobility association. Supportive neighborhoods are associated with lower odds of moving. In turn, frequent moves more than double children’s risk of ACEs. Supportive and safe neighborhoods provide protective benefits, while detracting elements exacerbate adversity. We conclude that reducing frequent moves and strengthening neighborhood supports are critical strategies for mitigating childhood adversity.
A Comparative Study of Machine Learning and Traditional Techniques for Grade Prediction and Grade-Tonnage Evaluation in a Small VMS Deposit
Bağ, Cemile Dilara; Frieman, Ben M.; Westman, Erik (MDPI, 2026-03-07)
Estimating grades in small-volume, high-grade volcanogenic massive sulfide (VMS) deposits can be difficult due to sharp changes in mineralization and limited data coverage around high-grade zones. This study compares ensemble machine learning models with interpolation and geostatistical methods to compare gold estimation and grade-tonnage results. Random Forest and Gradient Boosting were trained using drillhole composites and evaluated against Inverse Distance Weighting (IDW), Simple Kriging (SK), and Ordinary Kriging (OK). The trained models were applied across the block model to generate continuous grade predictions and support grade-tonnage calculations at multiple cutoff grades. The ensemble models showed lower RMSE and higher R² values and captured grade patterns more efficiently than traditional methods. Grade-tonnage comparison indicated that IDW generated the highest contained gold equivalent at low cutoff grades, while OK and Gradient Boosting produced more consistent and geologically reasonable estimates. Overall, the results show that machine learning methods can complement traditional estimation techniques when combined with geological domain control and appropriate model tuning.
Robust Estimation and Inference for Semiparametric and Nonparametric Regression Models
Mahmoud, Hamdy F. F.; Ali, Ahmed AbdelWahab A.; Mohamed, Wael Mahmoud A. (MDPI, 2026-03-11)
Parametric regression methods are efficient when correctly specified but are sensitive to model misspecification and outliers. Nonparametric regression offers greater flexibility at the cost of reduced interpretability and susceptibility to the curse of dimensionality. Semiparametric models provide a compromise between these approaches by combining structural interpretability with functional flexibility. A key limitation of many classical semiparametric and nonparametric methods, however, is their lack of robustness to heavy-tailed errors and contaminated data. In this paper, we develop robust kernel, spline, and single-index regression estimators based on robust loss functions. To facilitate inference, we propose bootstrap-based procedures that remain valid in settings where classical assumptions may be violated. Through extensive simulation studies under normal, heavy-tailed, and contaminated error distributions, we demonstrate that the proposed robust methods achieve comparable performance to classical approaches in clean settings while providing substantial gains in stability and inferential reliability under contamination. Unlike existing works that study these robust estimators in isolation, the proposed approach provides a unified framework that integrates robust kernel regression, robust spline regression, and robust single-index modeling with a coherent bootstrap-based inference procedure. Application to Boston housing data further illustrates the practical usefulness of the proposed methodology.
Impact of Medicaid Enrollment Timing on Tumor Stage at Diagnosis and Survival in Breast, Colorectal, and Lung Cancer
Benavidez, Gabriel A.; Self, Stella; Alberg, Anthony J.; Probst, Janice; Eberth, Jan M. (MDPI, 2026-03-11)
Background: Medicaid-insured patients experience higher rates of late-stage cancer diagnosis and worse survival than non-Medicaid patients. The impact of Medicaid enrollment timing on cancer outcomes is less clear. This study examines the association between Medicaid enrollment and timing with tumor stage and cancer-specific survival for breast, colorectal, and lung cancers. Methods: We analyzed SEER-Medicaid linked data for 276,755 breast, 104,784 colorectal, and 101,058 lung cancer patients < 65 years of age. Patients were categorized as non-Medicaid enrollees, pre-diagnosis enrollees (≥12 months before), or post-diagnosis enrollees (≤12 months after). Multivariable logistic regression estimated odds ratios of late-stage diagnosis, and cause-specific Cox proportional hazards models were used to assess cancer-specific survival, adjusting for demographic and socioeconomic factors. Results: Compared to non-Medicaid enrollees, post-diagnosis enrollees had the highest odds of late-stage diagnosis (breast cancer: OR: 3.41; colorectal cancer: OR: 3.78; lung cancer: OR: 1.87). Pre-diagnosis enrollees also had increased odds, but the association was weaker than post-diagnosis enrollees. Cancer-specific mortality was higher for both pre- and post-diagnosis enrollees compared to non-Medicaid enrollees for each cancer examined across tumor stage at diagnosis. Among Medicaid enrollees, those enrolled post-diagnosis had higher cancer-specific mortality than those enrolled pre-diagnosis for localized-stage colorectal (HR: 1.82) and lung cancer (HR: 1.30). In contrast, those enrolled post-diagnosis had lower mortality than those enrolled pre diagnosis for distant-stage breast cancer (HR: 0.91). Conclusions: Compared with cancer patients not insured by Medicaid, post-diagnosis Medicaid enrollment was associated with a greater likelihood of late-stage cancer and worse cancer-specific survival across each cancer type examined. Future research is warranted to examine the role of Medicaid enrollment timing in cancer care to better understand its impact on cancer outcomes.
Measurements of Electronic Band Structure in CeCoGe₃ by Angle-Resolved Photoemission Spectroscopy
Prater, Robert; Chen, Mingkun; Staab, Matthew; Sreedhar, Sudheer; Byland, Journey; Shen, Zihao; Savrasov, Sergey Y.; Taufour, Valentin; Ivanov, Vsevolod; Vishik, Inna (MDPI, 2026-02-25)
In this paper, we present a comprehensive study of the electronic structure of CeCoGe₃ throughout the entire Brillouin zone in the non-magnetic regime using angle-resolved photoemission spectroscopy (ARPES). The electronic structure agrees in large part with first principles calculations, including predicted topological nodal lines. Two new features in the band structure are also observed, namely a surface state and folded bands, the latter of which is argued to originate from a unit cell reconstruction.
Neural Encoding Strategies for Neuromorphic Computing
Liu, Michael; Zheng, Honghao; Yi, Yang (MDPI, 2026-03-14)
Neuromorphic computing seeks to mimic structure and function of biological neural systems to enable energy-efficient, adaptive information processing. A critical component of this paradigm is neural encoding—the translation of analog or digital input data into spike-based representations suitable for spiking neural networks (SNNs). This paper provides a comprehensive overview of major neural encoding schemes used in neuromorphic systems, including rate and temporal encoding, as well as latency, interspike interval, phase, and multiplexed encoding. The purpose of this paper is to explore the use of encoding techniques for deep learning applications. We discussed the underlying principles of spike encoding approaches, their biological inspiration, computational efficiency, power consumption, integrated circuit design and implementation, and suitability for various neuromorphic applications. We also presented our research on a hardware-and-software co-design platform for different encoding schemes and demonstrated their performance. By comparing their strengths, limitations, and implementation challenges, we aim to provide insights that will guide the development of more efficient and application-specific neuromorphic systems. We also performed an encoder performance analysis via Python 3.12 simulations to compare classification accuracies across these spike encoders on three popular image and video datasets. The performance of neural encoders working with both deep neural networks (DNNs) and SNNs is analyzed. Our performance data is largely consistent with the benchmark data on image classification from other papers, while limited performance data on the University of Central Florida’s 101 (UCF-101) video dataset were found in comparable studies on spike encoders. Based on our encoder performance data, the Interspike Interval (ISI) encoder performs well across all three datasets, preserving continuous, detailed spike timing and richer temporal information for standard classification tasks. Further, for image classification, multiplexing encoders outperform other spike encoders as they simplify timing patterns by enforcing phase locking and improve stability and robustness to noise. Within the SNN testbenches, the ISI-Phase encoder achieved the highest accuracy on the Modified National Institute of Standards and Technology (MNIST) dataset, surpassing the Time-To-First Spike (TTFS) encoder by 1.9%. On the Canadian Institute For Advanced Research (CIFAR-10) dataset, the ISI encoder achieved the highest accuracy. This ISI encoder had 22.7% higher accuracy than the TTFS encoder on the CIFAR-10 dataset. The ISI encoder performed best on the UCF-101 dataset, achieving 12.7% better performance than the TTFS encoder.
CherryZZZ: A Protocol for a Randomized, Double-Blind, Placebo-Controlled, Cross-Over Pilot Study Testing Tart Cherry Juice in Older Adults with Self-Reported Insomnia
VanderMark, Esther; Baniassadi, Amir; Wolfe, Alex; Cladis, Dennis P.; Dufour, Alyssa B.; Millar, Courtney L. (MDPI, 2026-03-14)
Introduction: Two small, preliminary pilot studies report that 2 weeks of daily tart cherry juice consumption (half of the dose in the morning, half of the dose at night) may increase sleep quantity (assessed via a sleep diary or 1 night of polysomnography) in older adults with insomnia. A study of longer duration, with doses closer to bedtime, and daily objective monitoring of sleep via a wearable device may potentiate the observed impact of tart cherry juice intake on sleep. With the proposed changes to the study protocol, it is paramount to evaluate the study’s feasibility. Methods: The current study is a single-site, randomized, double-blind, cross-over pilot study in 20 older adults with self-reported insomnia. Eligible individuals will be randomly assigned to consume 16 oz. of tart cherry juice/day or placebo juice for 4 weeks each, separated by a 3-week washout period. Information on study feasibility, including recruitment rate, retention rate, safety, compliance, and study practicality, will be collected, as well as pre- and post-arm evaluations of sleep quantity/quality and biomarkers related to melatonin, cortisol, serotonin, and inflammation. Discussion: Identification of a dietary intervention that improves sleep quantity and quality may serve as a novel and feasible approach for older adults who suffer from insomnia. If successful, such a strategy would help mitigate the plethora of health consequences associated with poor sleep.
Evaluating Chronic Sex-Specific Changes in Glutamatergic Signaling Markers Following Traumatic Brain Injury
Talty, Caiti-Erin; Wypyski, Madison S.; Murphy, Susan F.; VandeVord, Pamela J. (MDPI, 2026-03-14)
Traumatic brain injury (TBI) can lead to persistent adverse outcomes, including cognitive and emotional dysfunction, with recent estimates indicating that up to 50% of individuals with mild TBI experience long-term symptoms. Growing evidence suggests that biological sex influences TBI outcomes and recovery trajectories; however, the molecular underpinnings driving these sex-specific differences remain poorly understood. In this study, a preclinical TBI model was used to directly compare chronic glutamatergic alterations in adult male and female Sprague Dawley rats. To define frontocortical molecular signatures associated with sex-specific glutamatergic dysfunction, proteomic analyses were conducted. Proteomic data revealed dysregulation of key pathways, cellular processes, and molecular regulators involved in excitatory signaling and synaptic function in both sexes. Biomarker profiling identified a single common biomarker between males and females, along with multiple biomarkers unique to each sex. Furthermore, two key brain regions highly susceptible to TBI, the prefrontal cortex and hippocampal subregions, were examined for chronic alterations in key glutamatergic signaling proteins, including N-methyl-D-aspartate (NMDA) receptors and the excitatory synaptic marker postsynaptic density protein 95 (PSD95). Immunofluorescence analyses revealed both sex- and region-specific alterations in the expression of NMDA receptor subunits, as well as in PSD95. Notably, many of these changes were concentrated within the hippocampal subregions, suggesting long-term dysregulation of hippocampal glutamatergic circuitry following injury. Together, these findings indicate the emergence of chronic sex-specific pathophysiology in glutamate signaling after TBI and highlight the importance of incorporating sex as a biological variable in the development of precision medicine-based therapeutic strategies for TBI.
Predictive Models for Lamb Meat Cuts and Carcass Tissue Based on Ultrasonographic Images and Body Weight
Matos, Alexsander Toniazzo de; Fernandes, Tatiane; Hirata, Adriana Sathie Ozaki; Fuzikawa, Ingrid Harumi de Souza; Fernandes, Alexandre Rodrigo Mendes; Silva, Adrielly Lais Alves da; Santos, Rodrigo Andreo; Leonardo, Ariadne Patrícia; Santos, Aylpy Renan Dutra; Vargas Junior, Fernando Miranda de (MDPI, 2026-03-14)
Sheep farming length of stay in the feedlot directly influences system profitability, mainly due to the high cost of feed. Thus, the use of predictive models based on body measurements is an important tool to define the optimal slaughter point and the ideal feedlot period. Thus, the aim was to evaluate predictive models of meat cuts and tissue carcasses concerning weight at slaughter (WS), loin eye area (LEA), and subcutaneous fat thickness (SFT) obtained by ultrasound of the lumbar region of lambs. The WS and ultrasound measurements were obtained from a pre-slaughter collection of 45 lambs, divided into five groups, each weighing 15, 20, 25, 30, or 35 kg, with nine replications per group. Three regression models were evaluated: WS, LEA, and SFT (independent variables) and the cuts yield or tissue composition (dependent variable). Increasing WS resulted in greater carcass weight and commercial cuts. Above 15 kg body weight, bone weight showed little or no increase (allometric coefficient = 0.06), whereas muscle and fat tissues increased steadily, with allometric coefficients of 0.25 and 0.12, respectively. The commercial cuts showed a high and significant correlation with WS and LEA. The muscle and bone proportion of the leg had a significant (p < 0.10) correlation with SFT. For the weight of commercial cuts estimates, the inclusion of LEA and/or SFT with WS did not improve the coefficient of determination but made the predictions equivalent to the measured values. There were high determination coefficients when WS was only used to predict muscle, fat, and bone weight, but it was not efficient in predicting the muscle/fat and muscle/bone ratios and the percentage of tissues. The WS was the variable that best explained the weight and tissue content. The inclusion of LEA and/or SFT made little improvement to the predictive models.
Evaluating Environmental and Crop Factors Affecting Drone-Mounted GPR Performance in Agricultural Fields
Vahidi, Milad; Shafian, Sanaz (MDPI, 2026-03-16)
Drone-mounted ground-penetrating radar (GPR) systems offer new opportunities for integrating subsurface characterization into remote sensing workflows. However, the interaction between flight parameters, surface conditions, and vegetation characteristics remains poorly understood. This study investigates the impact of flight altitude, surface topography, crop presence, and canopy water content on the stability and interpretability of GPR signals collected using a drone. Field experiments were conducted under controlled conditions using agricultural plots with variable canopy cover and soil moisture regimes. Radargrams were processed to evaluate signal amplitude, reflection continuity, and attenuation patterns in relation to terrain slope and vegetation structure derived from co-registered RGB drone imagery. The results reveal that lower flight altitudes and smoother surfaces yield higher signal coherence and greater subsurface penetration, while increased canopy water content and biomass reduce signal strength and clarity. Integrating drone-based GPR observations with surface spectral and thermal data improved discrimination between soil and vegetation-induced signal distortions. The findings highlight the potential of drone–GPR systems as a complementary layer in a multi-sensor remote sensing framework for precision agriculture, environmental monitoring, and 3D soil mapping.
Estrogen, Epigenetics, and Cardiometabolic Health: Mechanisms and Therapeutic Strategies in Postmenopausal Women
Edwards, Ailene; Singh, Pranjal; Shah, Vyan; Chander, Vivek; Mishra, Sumita (MDPI, 2026-03-16)
The loss of estrogen following menopause is associated with a marked increase in cardiometabolic risk, accompanied by adverse changes in lipid metabolism, insulin sensitivity, vascular function, and systemic inflammatory tone. Emerging evidence suggests that estrogen signaling interacts with chromatin regulatory mechanisms, including DNA methylation, histone modifications, and chromatin remodeling, across multiple metabolic tissues. In this review, we examine current evidence linking estrogen receptor signaling to epigenetic modulation in cardiovascular, hepatic, adipose, vascular, and immune systems. We propose that epigenetic remodeling represents a plausible and testable mechanistic framework connecting estrogen depletion to cardiometabolic disease progression, while acknowledging that much of the mechanistic evidence derives from preclinical and in vitro systems and that direct longitudinal validation in human cardiovascular tissues remains limited. We further explore how this framework may contribute to understanding the “estrogen paradox” and the heterogeneous outcomes of hormone replacement therapy (HRT), particularly within the context of the timing hypothesis. Finally, we evaluate pharmacologic and lifestyle interventions, including structured exercise, dietary modulation, and cardiometabolic therapeutics, through the lens of potential epigenetic influence. Clarifying tissue-specific and immune-integrated chromatin responses to estrogen loss will be essential for advancing precision strategies aimed at improving cardiometabolic health in postmenopausal women.
Regional Variation in Mood Use in Spanish: A Comparison Among Three Spanish-Speaking Regions
Tort-Ranson, Silvia; Gudmestad, Aarnes (MDPI, 2026-03-20)
The current investigation, couched within variationist sociolinguistics, has the purpose of advancing knowledge of regional variation in mood use (the subjunctive and indicative contrast) in Spanish. Prior cross-dialectal research has reported that mood use in Spanish varies geographically. To contribute to the understanding of mood variation in Spanish, this study explored a range of sociolinguistic independent variables across three Spanish-speaking regions. The participant pool (N = 107) consisted of Spanish speakers residing in three metropolitan areas (Rosario, Argentina; Barcelona, Spain; and Seville, Spain). The analysis substantiated evidence of geographical variation in the frequency of use of verbal moods, the governors (e.g., preferir que ‘to prefer that’) that exhibited categorical and variable use, and the influence of time reference on mood use. These results provide additional insights into the presence of regional variation in mood use and reinforce the value of cross-dialectal analyses with the same type of data and mood-use contexts.
Teacher-Identified Needs-Driven Professional Development in Rural Education: Designing for Engineering and Interdisciplinary Integration
Glisson, Hannah; Grohs, Jacob R.; Bilow, Felicity; Schilling, Malle (MDPI, 2026-03-21)
Rural educators face persistent structural barriers to accessing professional development that supports instructional change, particularly in disciplines such as engineering that require specialized knowledge and resources. This study examines a needs-driven professional development initiative designed to support rural K–12 educators in integrating engineering concepts through a school–university partnership in Southwest Virginia. Using a mixed-methods needs assessment consisting of a regional survey and in-depth interviews with teachers and administrators, we identified key challenges related to professional development access, relevance, and sustainability. These findings informed the design of a two-day professional development workshop grounded in place-based education and teacher pedagogical choice. Results highlight educators’ preferences for contextually relevant, hands-on learning experiences and the importance of ongoing support and professional community-building. While situated in a rural region, the findings have broader implications for professional development policy and practice across diverse educational settings. By explicitly examining how needs assessment findings were translated into professional development design decisions, this study contributes practice-based evidence for creating more equitable and context-responsive professional learning models.
Whole-Genome Sequencing Reveals Breed-Specific SNPs, Indels, and Signatures of Selection in Royal White and White Dorper Sheep
Liao, Mingsi; Kravitz, Amanda; Haak, David C.; Sriranganathan, Nammalwar; Cockrum, Rebecca R. (MDPI, 2026-03-05)
Whole-genome sequencing (WGS) is a powerful tool for uncovering genome-wide variation, identifying selection signatures, and guiding genetic improvement in livestock. Royal White (RW) and White Dorper (WD) sheep are economically important meat-type hair breeds in the U.S., yet their genomic architecture remains poorly characterized. In this study, WGS was performed on 20 ewes (n = 11 RW, n = 9 WD) to identify and annotate SNPs and small insertions and deletions (indels). Functional annotation, gene enrichment, population structure, and selective sweep analysis were also performed. Selective sweep analysis was conducted by integrating the fixation index (F_ST), nucleotide diversity (π), and Tajima’s D to identify candidate regions under putative recent positive selection. A total of 21,957,139 SNPs and 2,866,600 indels were identified in RW sheep, whereas 18,641,789 SNPs and 2,397,368 indels were identified in WD sheep. In RW sheep, candidate genes under selection were associated with health and parasite resistance (NRXN1, HERC6, TGFB2) and growth traits (JADE2). In WD sheep, selective sweep regions included genes linked to immune response and parasite resistance (TRIM14), body weight (PLXDC2), and reproduction (STPG3). These findings were supported by sheep-specific quantitative trait loci (QTL) annotations and previously reported SNP–trait associations. This study provides the first WGS-based genomic comparison between RW and WD sheep, establishing a foundation for future genetic improvement, including targeted selection for enhanced immune function, disease resistance, and other economically important traits in these breeds.
A Decade of Evidence on Broiler Chicken Dead-on-Arrival Rates and Risk Factors: A Scoping Review
Vitek, Samantha; Jacobs, Leonie (MDPI, 2026-03-05)
The preslaughter phase for broiler chickens is distressing and can result in death prior to slaughter. The severity of this animal welfare concern warrants the exploration of the rates and risk factors. The aim of this scoping review was to synthesize current knowledge on rates and associated farm, flock, and preslaughter risk factors for dead-on-arrivals (DOA). Peer-reviewed experimental or observational studies were included that were written in English, published between January 2014 and December 2024, and that reported broiler chicken DOA with rates or associated risk factors in Google Scholar and ScienceDirect. A total of 344 articles were identified, and 24 articles met the eligibility criteria. Mean DOA rates ranged from 0 to 0.85%. In total, nine on-farm or flock-level and 11 preslaughter risk factors were identified, which could be categorized under four major causes of DOA: poor health, distress, thermal stress, and trauma. The risk factors most commonly identified were journey duration and distance, season, ambient temperature, lairage duration, and body weight. The findings highlight multiple opportunities to reduce DOA, including greater consideration of flock characteristics in preslaughter decision making, growing flocks that are at reduced risk of DOA, improvements in catching and loading practices, and better alignment of preslaughter management with environmental conditions.

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