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Molecular Characterization and Geographic Incidence of Two Pestiviruses Infecting the Corn Leafhopper (Dalbulus maidis) in the United States
No full textThe corn leafhopper Dalbulus maidis (DeLong & Wolcott) (Hemiptera: Cicadellidae) is a major pest of corn in the Americas, transmitting pathogens that cause corn stunt disease. Using highthroughput sequencing and PCR-based assays, we identified and characterized two novel large genome pestiviruses (LGPs), chicharrita del maiz pestivirus 1 (ChMPV1) and 2 (ChMPV2), from D. maidis populations collected in Texas, Oklahoma, Missouri, and Iowa. Genome analyses revealed long polyproteins with conserved Pestiviridae domains, and virus incidence varied geographically (0–43%). Phylogenetic analyses placed both viruses within the newly proposed family Pestiviridae. These findings expand the insect virome and provide tools for future studies on insect virus–vector–plant pathogen interactions.This is a manuscript of an article published as Osse de Souza, J., Kalita, H., Casey, C. et al. Molecular characterization and geographic incidence of two pestiviruses infecting the corn leafhopper (Dalbulus maidis) in the United States. Arch Virol 171, 57 (2026). https://doi.org/10.1007/s00705-025-06515-yFunding for this research was provided by the College of Agriculture and Life Sciences (CALS) at Iowa State University
FiCOPS: Hardware/Software Co-Design of FPGA Computational Framework for Mass Spectrometry-Based Peptide Database Search
No full textImproving the speed and efficiency of database search algorithms that deduce peptides from mass spectrometry (MS) data has been an active area of research for more than three decades. The significance of the need for faster database search methods has rapidly increased due to the growing interest in studying non-model organisms, meta-proteomics, and proteogenomic data, which are notorious for their enormous search space. Poor scalability of serial algorithms with the growing size of the database and increasing parameters of post-translational modifications is a widely recognized problem. While high-performance computing techniques can be used on supercomputing machines, the need for real-time, on-the-instrument solutions necessitates the development of an efficient sytem-on-chip that optimizes design constraints such as cost, performance, and power of the system. To show case that such a system can work, we present an FPGA-based computational framework called FiCOPS to accelerate database search using a hardware/software co-design methodology. First, we theoretically analyze the database-search algorithm (closed-search) to reveal opportunities for parallelism and uncover computational bottlenecks. We then design an FPGA-based architectural template to exploit parallelism inherent in the search workload. We also formulate an analytical performance model for the architecture template to perform rapid design space exploration and find a near-optimal accelerator configuration. Finally, we implement our design on the Intel Stratix 10 FPGA platform and evaluate it using real-world datasets. Our experiments demonstrate that FiCOPS achieves 3.5 × speed-up over existing CPU solutions and 3× and 5× reduction in power consumption compared to existing CPU and GPU solutions.This is a preprint from Kumar, Sumesh, Joseph Zambreno, Ashfaq Khokhar, Shoaib Akram, and Fahad Saeed. "FiCOPS: Hardware/Software Co-Design of FPGA Computational Framework for Mass Spectrometry-Based Peptide Database Search." bioRxiv (2026): 2026-02. doi: https://doi.org/10.64898/2026.02.15.70601
Healthful Patterns of Ageing and Longevity
No full textThe chapter attempts to unfold the secret of longevity through evidence-based discussion of “healthy longevity” and how to achieve it. The concept of “healthy longevity” is presented to encompass not just the extension of life, but also the quality of life in old age. A series of studies focusing on centenarians from diverse geographical locations, including the United States and Japan, are summarized to highlight the combined effect of distal and proximal factors on longevity. Factors such as personality, social support, physical, functional and emotional health and health behaviors are discussed as facilitators of healthy ageing. The chapter reveals that “secret” to living longer lies in a combination of genetics, personality, good health behaviors and social support.This accepted book chapter is published as Martin, P., Lee, G. (2026). Healthful Patterns of Ageing and Longevity. In: Kapadia, S., Bhangaokar, R. (eds) A Handbook of Human Development: Perspectives From India. Springer, Singapore. https://doi.org/10.1007/978-981-95-4037-2_
Who minds their pleas and queues? Quick and slow misdemeanor pleas pose similar risk of incarceration
No full textMillions of people in the United States are convicted of misdemeanor crimes annually, yet we know little about the processes generating these convictions. The current research used 87,248 misdemeanor cases from an urban Florida county to examine predictors and punishments for quick plea dispositions—pleas entered at defendants’ first court appearance. Results indicate that the 47% of defendants who pleaded quickly were more likely to receive a non-carceral sentence and less likely to receive time-served only than those who pleaded later, but the probability of further jail time was essentially equal. In contrast, defendants convicted at trial faced a nearly threefold increase in the likelihood of jail time. These findings suggest that the process may be sufficiently onerous to induce quick pleas in misdemeanor cases, even when direct sentence benefits are unclear. The prevalence of quick pleas also indicates that the “bargaining” in plea bargaining could be overstated for misdemeanors.This accepted article is published as Dunlea, R. R., & Wilford, M. M. (2026). Who minds their pleas and queues? Quick and slow misdemeanor pleas pose similar risk of incarceration. Criminal Justice and Behavior, 0(0). https://doi.org/10.1177/00938548251415014
Improving statistical models for count data
No full textStatistics is the science of collecting, analyzing, and interpreting data. Data can be continuous or discrete. Count data is one example of discrete data, often seen in the manufacturing industry, the pharmaceutical industry, and various other places. Analyzing count data and interpreting the results correctly is essential to statistics. This dissertation describes improvements in analyzing complex count data found in compelling datasets.
After a brief introductory chapter, the second chapter explores the BAAD dataset, consisting of information about different terrorist organizations and the number of fatalities they caused between 1998-2005. The fatality counts are very overdispersed, with many organizations causing zero deaths in this period. We discuss dataset complexities and propose mixture of hurdle negative binomial regressions to identify the critical factors affecting the number of fatalities.
The third chapter analyzes count data extracted from High Throughput Sequencing (HTS). These counts are also overdispersed, leading to broad reliance on the negative binomial distribution. We model the biological process of HTS using a novel branching process and delineate conditions where the branching process is more useful than the negative binomial model. Notably, the branching process model involves relevant and important biological interpretation parameters.
The count data from both applications are overdispersed, but not all datasets are sufficiently overdispersed to require sophisticated count models. Many current publications simply assume overdispersion or incorrectly compare the Poisson and negative binomial distributions using a simple likelihood ratio test with one degree of freedom. In our last chapter, we develop a likelihood ratio test to compare the Poisson and negative binomial distributions, with and without regressors. We demonstrate inflated false rejection of the Poisson model when using the incorrect test
Highly-filled systems: Understanding interfaces and interactions between parts of a whole
No full textThis dissertation examines the combination of materials and offers insight into the ways in which these materials interact with one another. While native materials can often be modified to improve their functionalities and become property-advantaged, here we have studied the potential of combining two completely different materials to create unified composites and suspensions. Initial research endeavors focused on the ability to utilize two counter-expanding materials together, yielding potential for lower coefficients of thermal expansion in the formed composite. Results obtained with zinc cyanide-epoxy composites indicate that the interfaces between the filler and matrix remain intact even after undergoing thermal cyclic wear. This line of research was continued by studying other negative thermal expansion materials, specifically metal-organic frameworks, which allowed for tunability through chemical bonding chemistry. With the addition of a chemical bond, we demonstrated further reinforcement of interface strength, as well as improved overall composite strength. Building on the promising results of metal-organic frameworks, research continued by combining carbon nanotubes and metal-organic frameworks to enhance thermal conduction. Finally, we focused on rheological arrangements in suspensions of epoxidized methyl soyate and multi-walled carbon nanotubes in order to gain an understanding of the non-linear regime in Newtonian fluids with elongated particles. Through this compilation of works, we provide insight into the complexities of multi-component systems and outline methods for understanding the interactions of parts, utilizing various techniques with an emphasis on microscopy
Project-based language learning in the Middle East and North Africa: An AI-assisted systematic review
No full textProject-based language learning (PBLL) is unarguably popular worldwide. However, how commonly is this approach implemented in the Middle East and North Africa (MENA)? To find out, a systematic review of research on MENA’s PBLL practices was conducted to shed light on the region. Using a qualitative analysis approach with the assistance of generative artificial Intelligence (GenAI), this article reviews PBLL empirical research from 1980 to 2024 in MENA. Four themes emerged: PBLL implementation and effectiveness, PBLL form and function, PBLL technological integration, and challenges implementing PBLL. PBLL, which slowly began to increase in popularity in the early 2000s in MENA, was found to enhance language teaching, learning, student engagement, and critical thinking across the MENA countries, despite varying degrees of challenges. The findings stressed the role of teacher professional development and the availability of technological resources for successful PBLL implementation. The study concludes with recommendations for PBLL in MENA.This accepted article is published as Garib, A., Beckett, G., Beck, J., Bordbarjavidi, F., Project-based language learning in the Middle East and North Africa: An AI-assisted systematic review. Italian Journal of Educational Technology. February 2026, Special Issue. https://doi.org/10.17471/2499-4324/149
Generalizable low-latency accelerated dynamic MRI
No full textMagnetic Resonance Imaging (MRI) provides exceptional soft-tissue contrast but suffers from long acquisition times, which lead to patient discomfort and motion artifacts. While undersampling k-space data can reduce scan times, it introduces severe aliasing artifacts that compromise diagnostic quality.
Current compressed sensing methods require extensive parameter tuning, lose spatial information through matrix vectorization, and often fail in dynamic imaging. Deep learning approaches, while effective in certain applications, require application-specific training and have high computational demands, making them unsuitable for real-time use. Importantly, both compressed sensing and deep learning methods are not designed with low-latency reconstruction in mind—an essential requirement for MRI-guided procedures. Existing low-latency techniques, on the other hand, fail to provide high-quality reconstructions when significant motion is present in the underlying MRI sequence.
This dissertation presents a set of novel algorithmic solutions to address these challenges by enabling both high-quality and low-latency MRI reconstruction from undersampled data. The proposed methods are validated across cardiac, speech, and abdominal imaging, demonstrating significant improvements in reconstruction quality and computational efficiency across different sampling schemes and acceleration factors. Furthermore, the developed algorithms eliminate the need for parameter tuning and consistently provide robust performance across diverse MRI applications.
Overall, this work enables practical, low-latency dynamic MRI reconstruction—reducing scan times, improving diagnostic confidence, and opening the door to new low-latency MRI-guided procedures, thereby facilitating broader clinical adoption of accelerated MRI techniques
A qualitative study of farm stressors and safety practices among Iowa farmers
No full textIt has been acknowledged that agriculture is a stressful profession. Mental health conditions like depression and anxiety can arise because of ongoing stress. Agriculture has also been recognized as a dangerous profession, but all farm deaths are preventable, and addressing existing safety procedures could reduce deaths or injuries in farm activities. In addition, many children live or work on farms and face potential hazards around the clock, with at least one out of every five farm injury victims being a child. This situation causes stress among farmers. However, more research is needed to understand farm stressors, safety practices, and the influence of children living on the farm. To fill this gap, this study aims to identify the most stressful farm events or activities and to understand the experiences of grandparents and parents with farm stressors, safety practices, and having children on the farm. Previous studies demonstrated that safety procedures differ between generations (Freeman et al., 1998). Safety procedures help to avoid or reduce fatalities. For example, those who most influence the choice of farm safety procedures for children will give an idea of who should receive safety training. A survey with 61 life activities or events was sent to Iowa farmers, with responses from 11 grandparents and 15 parents, who rated them from 1 (less stressful) to 100 (more stressful). Later, interviews were conducted to ask more specific questions regarding farm stressors, safety procedures, and their perspectives. Similar to previous studies, the survey results found that family losses were the most stressful experiences: the loss of a child, followed by money stresses. This was true across genders and generations. In contrast, the interviews showed that the weather and the lack of money were the most stressful experiences mentioned. Regarding safety practices, the interviews revealed that the most noted among participants were training, the usage of Personal Protective Equipment (PPE), and Collective Protective Equipment (CPE). Having children on the farm was not stressful, but adults needed to watch them constantly. Some participants mentioned that it is more stressful to encourage children to finish their farm tasks. These results can help understand and find the best method to reduce the chance of injury, can help specialists better understand farm issues, and could give some advice on how to manage stress
Effectiveness of vehicle-infrastructure communication: A temporal and facility-based analysis with pilot data
No full textVehicle-to-Infrastructure (V2I) speed advisories represent a promising connected vehicle technology for improving roadway safety and traffic operations. However, existing evaluations have relied on aggregate performance metrics that obscure critical variations in effectiveness across operational contexts. This research addresses a fundamental gap in connected vehicle literature: understanding not merely whether V2I advisories influence driver behavior, but how, when, and under what conditions these systems succeed or fail.
Using naturalistic driving data from the Tampa Connected Vehicle Pilot (January-March 2021), this study conducted the first comprehensive vehicle-level analysis of context-dependent V2I effectiveness. Through spatiotemporal matching of 56 weekdays of Basic Safety Message (BSM) trajectories with Traveler Information Message (TIM) alerts, individual vehicle episodes were constructed with defined pre-alert, during-alert, and post-alert phases. Two-way analysis of variance (ANOVA) and logistic regression were employed to test Road × Time-of-Day interaction effects on speed change, deceleration intensity, reaction timing, and compliance outcomes across two expressway facilities: the Lee Roy Selmon Expressway mainline and the Reversible Express Lanes.
Results demonstrate that V2I effectiveness is fundamentally context-dependent, varying by orders of magnitude based on facility characteristics and temporal traffic patterns. The Reversible Express Lanes during AM Peak (6:00-10:00 AM) achieved maximum effectiveness with a mean speed reduction of 36.66 mph (Hedges' g = -3.09, p < 0.001) and 22.72% compliance, characterized by delayed but aggressive braking (-1.46 m/s² mean maximum deceleration at 247 m from alert activation) attributed to the combined effects of advisory messaging and prevailing traffic conditions including westbound congestion approaching downtown Tampa and queuing at the East Twiggs Street intersection. In stark contrast, the same facility during PM Peak (4:00-8:00 PM) exhibited paradoxical acceleration (+24.61 mph, g = +2.64) and near-total compliance failure (0.67%), resulting from directional operational transitions to free-flow eastbound traffic. The Lee Roy Selmon Expressway demonstrated minimal responsiveness across all periods (-2.14 to +1.15 mph, |g| ≤ 0.23), reflecting fundamental resistance to advisory messaging on high-speed free-flow facilities where baseline speeds approach posted limits.
Statistically significant Road × Time-of-Day interactions were observed for all outcomes: speed change, braking intensity (p < 0.001), reaction distance and compliance. Despite 14.6-fold difference in speed reduction magnitude between facilities (19.32 mph vs. 1.32 mph), both achieved statistically equivalent overall compliance rates (~27%, χ² = 0.66, p = 0.42), revealing that compliance is ceiling-constrained by baseline speed distributions and posted limits rather than determined by speed reduction magnitude alone.
These findings challenge fundamental assumptions underlying V2I deployment strategies. The results demonstrate that (1) effectiveness cannot be characterized by universal aggregate metrics but must be understood as conditional, context-dependent patterns; (2) advisory systems exhibit fundamental authority limits—information alone cannot override traffic flow physics or operational constraints; (3) facility characteristics and temporal patterns dominate effectiveness more than message design or communication protocols; and (4) speed reduction is not a valid proxy for success when divorced from baseline conditions and regulatory thresholds.
Practical implications include the need for context-aware deployment frameworks prioritizing high-effectiveness scenarios (congested moderate-speed facilities during peak periods), time-adaptive alert protocols that suspend advisories during directional transitions or acceleration regimes, infrastructure investment reallocation based on demonstrated effectiveness patterns, and recognition that advisory systems alone are insufficient for achieving high compliance without complementary enforcement or variable speed limit strategies. The study also provides actionable feedback for pilot operators: increased Roadside Unit (RSU) density to address urban non-line-of-sight conditions, comprehensive RSU performance monitoring, and resource reallocation from persistently low-effectiveness contexts to proven high-performers.
This research establishes that context is not noise but the fundamental organizing principle of V2I effectiveness. Future connected vehicle deployments must embrace this complexity through intelligent, adaptive strategies rather than uniform coverage, measuring effectiveness conditionally rather than aggregately. The study provides both empirical evidence and methodological frameworks for this paradigm shift in V2I system design, deployment, and evaluation, contributing to the advancement of context-aware connected infrastructure strategies for safer and more efficient transportation systems