62970 research outputs found
Sort by
Two essays in Trade Policy Analysis of Differentiated Products
No full textThe inward-oriented protectionist policies of leading economies can cause instabilities in supply-demand dynamics, lead to or exacerbate price volatility in the global market, and hence have a rippling effect throughout the world economy. The current study, organized into two separate essays, primarily quantifies the impact of restrictive policies in the global market, specifically examining the effect of India’s non-basmati rice export restriction policy and the Middle East’s import restriction policies on Indian cardamom. We employ the Spatial Equilibrium Model (SEM) with product differentiation to examine the impact of these restrictive policies on prices, supply, demand, and bilateral trade flows of the commodities.
SEM for the world rice trade, comprising non-basmati and basmati rice, quantifies the impact of India’s non-basmati rice export restriction policy in the global rice market. The study found that the export restriction policy affects the global rice supply, leading to a steep increase in non-basmati rice prices worldwide, as India is the world’s largest rice exporter. Consequently, import-dependent rice consumers in Africa and Asia endure lower consumption because of higher prices. However, this higher rice prices benefit other rice-exporting countries, such as Cambodia, Pakistan, Myanmar, Thailand, and Vietnam, which capitalize on market conditions by selling more rice in the world market, even though their consumers are affected by the higher prices. The trade reallocations created by the policy result in a net welfare loss of approximately 7.4 billion dollars in the global non-basmati rice market, representing inefficiencies in production and consumption across the regions. The marginal increase in the welfare of the basmati rice market suggests a limited substitution of basmati rice for non-basmati rice among consumers worldwide.
In the second essay, we examined the import restriction policies of Middle Eastern countries (Saudi Arabia, the United Arab Emirates, and Kuwait) on Indian cardamom and their repercussions in the global cardamom market. Since Guatemala and India produce 97 percent of the cardamom due to their suitable agro-climatic conditions, coupled with intense competition between the two countries based on product differentiation, results in a duopoly market structure in the global cardamom market. We employ SEM to model the product differentiation and duopoly market structure of the world cardamom market. The study revealed that this import restriction policy results in a sharp price increase of 41 percent in these Middle Eastern countries, leading to a substantial loss of consumer surplus. Overall, the import restriction policy generates a net welfare loss of $65.26 million in the global cardamom market, with the three Middle Eastern countries and India bearing the majority of the welfare loss worldwide. However, Guatemala reallocates its cardamom production by diverting sales from other countries to these Middle Eastern economies and benefits by capturing India’s market share in these countries
Aerolysin and cholesterol dependent cytolysins reveal distinct membrane protection and repair strategies in Leishmania major
No full textLeishmania major, the causative agent of leishmaniasis, relies heavily on its plasma membrane for survival and virulence. Antileishmanial drugs such as Amphotericin B exploit this vulnerability by targeting ergosterol to disrupt membrane integrity. While previous work from our group demonstrated that Leishmanial sphingolipids confer protection against cholesterol-dependent cytolysins (CDCs), the roles of other membrane components and the parasite’s repair mechanisms remain poorly defined. Notably, L. major encodes only a limited subset of ESCRT-III-associated repair proteins—Alix, Chmp4b, and Chmp3—with uncharacterized functions. Here, we hypothesized that L. major utilizes glycosylphosphatidylinositol (GPI)-anchored molecules to defend against pore-forming toxins and ESCRT III orthologs to repair via microvesicle shedding. Using mutants deficient in specific phospholipids, sterols, and GPI-anchored molecules—alongside fluorescently tagged promastigotes—we evaluated responses to CDCs and aerolysin via flow cytometry-based cytotoxicity assays, western blotting, and ultracentrifugation. We found that lipophosphoglycan (LPG) and GP63 are critical for protection against CDCs; however, GP63 paradoxically enhanced susceptibility to aerolysin. Intriguingly, we identified a non-canonical membrane repair pathway in L. major that is independent of Ca2+ but requires Cl- ions and hydrogen peroxide. A functional mCherry-tagged LmAlix protein was not released in microvesicles, suggesting its role occurs upstream in the repair cascade. These findings position L. major as a valuable model for studying non-canonical membrane repair mechanisms and identify LPG, GP63, and LmAlix as potential therapeutic targets
Effect of Cotton Gin Waste Biochar Amendment on Soil Physical Properties and Cotton Responses under Deficit Subsurface Drip Irrigation
No full textIntegrating the use of locally produced cotton gin waste (CGW) biochar (BC) soil amendment with deficit subsurface drip irrigation (SDI) strategies can be an effective approach to improve the growth and yield of cotton while conserving soil water and sustaining soil health, especially in the groundwater-dependent cotton producing areas of the Southern High Plains of Texas. However, the effects of the combined CGW-BC with deficit SDI strategies on the root zone hydrophysical regime and cotton responses under semi-arid conditions remain unclear. The complexities of the integrative effects of CGW-BC and deficit SDI on soil hydrophysical regimes and cotton responses in the soil-plant-atmosphere system, as well as the difficulties associated with field measurements of soil- and crop-based processes affecting root zone soil water dynamics, necessitate the application of process-based agricultural system models, such as the RZWQM2 (Root Zone Water Quality Model 2), to analyze these complex and interactive processes in cotton production systems. Therefore, the objectives of this study were twofold: (1) to evaluate the integrated effects of CGW- BC soil amendment and deficit SDI on soil hydrophysical and thermal properties and cotton responses under semi-arid conditions, and (2) to evaluate the applicability of the RZWQM2 in simulating the integrative effects of CGW-BC amendment and deficit SDI on soil hydrophysical regime and cotton responses in the soil-plant- atmosphere system. Using combinations of three deficit SDI levels [i.e., full irrigation or 100% (SDI100ET), 75% (SDI75ET), and 50% (SDI50ET) of the cotton evapotranspiration] and three rates of CGW-BC soil amendment [i.e., 0 t ha−1 or no biochar (BC0), 10 t ha−1 (BC10), and 20 t ha−1(BC20)] over two consecutive growing seasons suggested that integrating deficit SDI with CGW-BC amendment improved the hydrophysical properties of a semi-arid, sandy clay loam cotton field within the upper 20 cm soil depths, including soil water retention characteristics and soil water thresholds (i.e., water contents at saturation, field capacity, permanent wilting point, as well as plant available water), saturated hydraulic conductivity, and infiltration characteristics especially unsaturated hydraulic conductivity. These SDI and CGW-BC treatment combinations also provided desirable soil thermal regime within the upper 20 cm depths, i.e., desirable enhanced ranges of thermal conductivity, volumetric heat capacity, and thermal diffusivity that would most likely facilitate great energy movement, larger heat storage, and soil’s ability to adjust to any temperature changes rapidly. Temporal variations in plant water (i.e., stem water potential SWP) and root zone soil water (i.e., water contents, soil water potentials, and soil water depletion) under various SDI and CGW-BC treatment combinations, especially under SDI75ET and SDI50ET, suggested the enhanced soil water regime that optimized the cotton water requirements, sustaining the improved cotton growth, leaf area index, and lint yield. Notably, regardless of CGW-BC application rates [i.e., 10 t ha−1 (BC10) or 20 t ha−1 (BC20)], the improved hydrophysical properties and soil water availability under SDI75ET and SDI50ET treatments enhanced soil water and plant water status dynamics by optimizing root zone soil water depletion throughout a growing season as compared to the control SDI100ET. The RZWQM2, calibrated and validated using experimental data from two consecutive cotton growing seasons, effectively simulated the integrative effects of CGW-BC amendment and deficit SDI strategies on the soil hydrophysical regime under semi-arid conditions. RZWQM2 simulations were found to agree with measured water contents, soil water potentials, and soil temperatures at various depths, extending down to 80 cm, under different SDI and CGW-BC treatment combinations. Simulations revealed that regardless of CGW-BC application rates [i.e., 10 t ha−1 (BC10) or 20 t ha−1 (BC20)], the improved soil hydrophysical properties and soil water availability under deficit SDI75ET and SDI50ET resulted in the optimized soil water and plant water status dynamics to maintain enhanced actual ET and transpiration as compared to the control SDI100ET. Simulated soil water stress (i.e., ratio between daily actual ET and potential ET) agreed well with the measured plant- based stress indicator SWP, suggesting that the RZWQM2 could help understand and predict cotton water stress throughout a growing season. Overall, the simulation results of the root zone soil water dynamics under the strategies of deficit SDI and CGW-BC amendment in cotton provide support for using the RZWQM2 as a tool to address various agricultural management effects on improving soil hydrophysical and thermal regimes and enhancing crop growth and yield in semi-arid cotton production
Role of Nephrotoxicant - Induced Oxidative Stress in Acute Kidney Injury and Kidney Fibrosis
No full textThe kidney is an essential organ that filters metabolic waste from the body and maintains the balance of body fluids necessary for normal functioning. Kidney disease can affect individuals of any age, with an estimated 10% of the global population impacted by chronic kidney disease. Due to the kidney's filtration function, it is exposed to various xenobiotics, including nephrotoxins. Kidney epithelial cells, which play a significant role in filtration and active transport, are particularly vulnerable to exposure to nephrotoxins.
For patients with end-stage renal disease, renal replacement therapies such as hemodialysis are often the only available options. However, these treatments can be invasive and costly, making them inaccessible to individuals across different socioeconomic backgrounds. Unfortunately, there are currently no approved therapeutics for reversing kidney injury or kidney fibrosis. Reactive oxygen species (ROS) are often observed during kidney injury caused by nephrotoxins, and several studies indicate that oxidative stress-induced molecular pathways contribute to the initiation and progression of fibrosis. The folic acid-induced kidney fibrosis mouse model is a well-established model for studying the mechanisms of kidney injury and fibrosis.
In this study, we investigate how exposure to nephrotoxins—specifically high doses of folic acid and arsenic—leads to oxidative stress, resulting in acute kidney injury and the long-term development of kidney fibrosis. We utilized C57BL/6 mice as our in vivo model and Caki-1 and HK-2 epithelial cell lines as our in vitro models to evaluate nephrotoxin-induced kidney injury and fibrosis. The generation of ROS in kidney epithelial cells due to arsenic exposure was measured using the DCF assay. The impact of ROS on DNA damage was assessed through RAPD PCR in kidney epithelial cells. We evaluated the effects of a high dose (125 mg/kg) of folic acid injection on mouse health and kidney function by measuring body weight, serum albumin levels, and creatinine levels. Kidney injury and fibrosis were assessed using histopathological examination and immunofluorescence analysis. We examined the molecular consequences of ROS by analyzing transcript and protein expression through qRT-PCR and Western blotting of RNA and protein isolated from both in vivo mouse kidneys and in vitro cell lines.
The results of this study revealed that nephrotoxins induce ROS production, leading to DNA damage and cytotoxicity in vitro, as well as reduced kidney function in the in vivo mouse model. This response was associated with changes in antioxidant levels, as well as alterations in genes and proteins related to DNA damage. Consequently, fibrogenic genes were activated through specific signaling pathways, including TGF-Beta/Smad, Notch, and Wnt/B-catenin signaling. The underlying molecular mechanisms for these alterations were linked to oxidative stress-induced changes in epigenetic regulatory mechanisms, such as histone modification and DNA methylation.
In summary, our findings indicate that the activation of fibrogenic signaling is reversible by antioxidants, which restore epigenetic regulatory proteins, suggesting that oxidative stress plays a crucial role in fibrogenesis
Building Teacher Collective Self-Efficacy through Micro-Teaching Opportunities in Professional Learning Communities for Instructing Emergent Bilingual Students
No full textThis study utilized an insider action approach to the design-based school improvement model to examine how micro-teaching within the professional learning community (PLC) could increase general education teachers’ collective self-efficacy in implementing the English Language Proficiency Standards (ELPS) in a small rural elementary school experiencing unstable leadership. A significant opportunity gap persists for emergent bilingual students despite lawmakers’ efforts at legal and policy reform to ensure that all students receive a more equitable education. Many federal and state policies end up relying on implementation at the local level, which may be inconsistent at times due to high turnover in leadership positions. The study uses the constructivist theory of learning and Bandura’s theory of the development of self-efficacy to leverage existing PLC structures to build teachers' knowledge and competency in implementing the ELPS independent of administrative mandates. Data was collected from three fifth-grade teachers across multiple content areas and included a pre- and post-survey adapted from the Teacher's Sense of Efficacy Scale (TSES), participants' process reflections throughout the iterative cycles, and summative interviews to evaluate the effects that micro-teaching and collaboration within the PLC had on teacher self-efficacy in implementing the ELPS. The results showed a positive relation between the opportunity to collaborate and practice implementing the ELPS to increase teachers’ belief that they could successfully meet the needs of their emergent bilingual students. These results support that micro-teaching and collaboration in the PLC can be leveraged as a tool to increase teachers’ self-efficacy in better meeting the needs of their emergent bilingual students despite leadership challenges
Analysis of QLCS Three Ingredient Method Robustness using CM1
No full textQuasi-Linear Convective Systems (QLCSs) are convective storms that are much longer than they are wide, falling under a meso-α or meso-β scale. In addition to their propensity towards wide areas of severe straight-line winds, they also have become notorious to produce brief tornadoes. QLCS tornadoes are also very difficult to warn for. They are detected significantly less than supercell tornadoes and, when detected, have smaller lead times.
A very popular method in the United States for detecting tornadogenesis in QLCSs is called the Three-Ingredient Method (3IM). 3IM first emerged from Schaumann and Przybylinksi (2012), where they discovered that QLCS tornadogenesis is more likely to occur when three criteria are met. 3IM quickly became a well-known and widely adopted method to detect tornadogenesis and became especially popular among forecasters thanks to its simplicity. However, 3IM needed to be reanalyzed to be certain of its significance and since this initial paper was released multiple other papers have analyzed the capabilities of 3IM in detail. Despite this, some details on how 3IM works and where it doesn’t work are still unknown and need to be investigated further. My thesis will focus on testing this method using Cloud Model 1 (CM1), to analyze how robust each ingredient is, and why
Evaluating Course-Based CCMR Indicators: Which Pathways Signal True Readiness?
No full textThis study evaluates the predictive validity of Texas’s course-based College, Career, and Military Readiness (CCMR) indicators by examining how different pathways relate to postsecondary outcomes. While the state’s accountability system treats college prep and dual credit pathways as equivalent signals of readiness, this analysis shows that they yield starkly different results. Using student-level longitudinal data and school-by-cohort fixed effects, the study isolates students who earned CCMR exclusively through one of three course-based routes: College Prep, Math and English Dual Credit, or Nine Hours of Dual Credit. The findings show that students marked college ready via College Prep courses are significantly less likely to complete postsecondary credentials, transfer to four-year institutions, or avoid remedial coursework when compared to their dual credit peers. College Prep students are more than twice as likely to require remediation and up to 25% less likely to earn a bachelor’s degree within six years of high school graduation. These disparities call into question the inclusion and weighting of College Prep courses in the CCMR framework and highlight the need for policymakers to refine readiness metrics to better align with postsecondary success
Calibration of Microscopic Traffic Simulation Models for Proactive Safety Performance based on LiDAR Trajectory Data
No full textThe increasing complexity of urban transportation systems and the growing demand for reliable, high-fidelity traffic models have highlighted the limitations of traditional analytical tools in assessing traffic performance and safety. Conventional traffic modeling approaches, such as those presented in the Highway Capacity Manual, operate under steady-state assumptions and rely heavily on aggregate-level metrics. These macroscopic models often fail to capture the complex, dynamic, and stochastic interactions that characterize real-world traffic, particularly under congested or safety-critical conditions. Consequently, traffic engineers and researchers have increasingly turned to microscopic simulation models that allow for the representation of individual vehicles, driver behavior, and temporal variations. However, these models also face limitations, particularly in behavior realism, parameter calibration, and predictive safety accuracy. This research addresses these challenges by introducing two data-driven calibration frameworks that integrate high-resolution LiDAR trajectory data and driver behavior heterogeneity into widely used microsimulation models.
The first framework enhances the Wiedemann 99 model in PTV VISSIM by calibrating it with 3D high-resolution trajectory data obtained from Velodyne VLP-32 LiDAR units deployed at six signalized intersections in Lubbock, Texas. A robust data processing pipeline was implemented to extract high-fidelity vehicle trajectories. Sensitivity analysis identified four key parameters, which were calibrated using a multi-objective Directed Brute Force optimization strategy targeting volume, speed, headway, and lane–usage accuracy. From the calibrated simulations, surrogate safety metrics such as Modified Time-to-Collision (MTTC), Stopping Distance Index (SDI), and Crash Severity Index (CSI) were computed using FHWA’s SSAM tool. These were synthesized into a Rear-End Conflict Index (RECI), which showed strong statistical correlation with actual crash frequencies (R² up to 0.65) in a ten-year crash dataset using Negative Binomial regression models. This study establishes a scalable approach for proactive traffic safety evaluation based on high-resolution data sensing and advanced simulation techniques.
The second study addresses the oversimplified assumption of homogeneous driver behavior by classifying drivers into aggressive, normal, and cautious types using LiDAR-derived vehicle kinematic data. A tailored calibration strategy for the Wiedemann 74 model was developed, assigning behavior-specific parameters and introducing a hierarchical error metric that weighted calibration objectives based on real-world behavior occurrence. Calibrated using a Random Search Optimization algorithm, the behavior-aware model outperformed conventional models by an average of 14.5% decrease in error score across the 3 study days. It better captured acceleration-deceleration patterns and headway variability, making it more effective for safety-critical assessments. Collectively, these two studies present a unified calibration paradigm that combines sensor precision with behavioral realism, offering a significant advancement in traffic modeling capabilities for both research and applied urban traffic management
Exact Subdomain and Embedded Interface Polynomial Integration In Finite Elements With Conic Cuts and Modeling and classifying Circulating Tumor Cells in Microfluidic Devices
No full text1. In finite element methods, integrating discontinuous functions remains a significant challenge, especially when the discontinuity lies within an element’s interior. The most prevalent approach, adaptive subdomain integration, incurs high computational costs due to grid refinement. Recent approaches, such as equivalent polynomial techniques, replace the discontinuous function with a polynomial, enabling integration over the entire domain, rather than integrating over complex subdomains. Although it circumvents some of the challenges of discontinuous function integration, the equivalent polynomial tactic introduces its own set of problems. In particular, either adaptivity is required to capture the discontinuity, or error is introduced when the regularization of the discontinuous function is implemented. Loftin’s (2022) work eliminates both of these issues. His method provided exact polynomial integration for line cuts. Our current work extends the exact polynomial integration for more complex conic cuts (parabolas and hyperbolas). The use of conic cuts enhances accuracy in the integration process without significantly increasing computational complexity. Our algebraic expressions for the exact evaluation of subdomain integrals are adapted to these curved interfaces, offering improved results for complex geometries that extend beyond line approximations. Our method achieves a balance between precision and efficiency, making it well-suited for applications that require accurate solutions. Detailed algorithms for practical implementation across standard 2D finite element shapes are provided, demonstrating applicability to complex geometries. This work significantly improves the robustness of numerical simulations in engineering and applied mathematics.
2. Circulating tumor cells (CTCs) are rare cancer cells that break away from the tumor and travel in the bloodstream. Microfluidic devices (MDs) present a novel method for detecting circulating tumor cells (CTCs), enhancing the process through targeted techniques and visual inspection. However, current approaches often yield heterogeneous CTC populations, necessitating additional processing for comprehensive analysis and phenotype identification. These procedures are often expensive, time-consuming, and need to be performed by skilled technicians. In this study, we investigate the potential of a cost-effective and efficient hyperuniform micropost MD approach for CTC classification. Our approach combines mathematical modeling of fluid-structure interactions in a simulated microfluidic channel with machine learning techniques. Specifically, we developed a cell-based modeling framework to assess CTC dynamics in erythrocyte-laden plasma flow, generating a large dataset of CTC trajectories that account for two distinct CTC phenotypes. Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) were then employed to analyze the dataset and classify these phenotypes. The results demonstrate the potential effectiveness of the hyperuniform micropost MD design and analysis approach in distinguishing between different CTC phenotypes based on cell trajectory, offering a promising avenue for early cancer detection