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LRE: Examining Evidence-Based Strategies Used to Support Students with an Intellectual Disability in LifeSkills
No full textBackground: Future goals and postsecondary options can be limited for students with an Intellectual Disability (ID), especially when they are not provided a strong foundation in their elementary years. Students with ID are often served in LifeSkills, a specialized classroom setting that serves students who need an instructional setting focusing on prerequisite skills such as basic life skills and language development through their Individualized Education Plan (IEP). As students master their IEP goals, the appropriateness of students’ placement in the LifeSkills setting should be reviewed as an IEP team to determine if they can move to the general education setting to work on future IEP goals. Purpose: IEP teams should discuss and determine the student’s appropriate placement by reviewing their needs, abilities, and data from their evaluation. IEP goals should be wellwritten with criteria and skills that support them moving to a less restrictive environment and prepare them for the general education curriculum and expectations. This study aims to analyze the academic goals of elementary students identified with ID served in LifeSkills to determine if they are working toward a more inclusive setting. Method: This study will use de-identified archival data of K-5 th students with ID served in LifeSkills at four target elementary schools. The data will be disaggregated by educational environment, reports (from the district’s proprietary special education management system), primary condition, and annual reading, language arts, and math goals and objectives for the 2023-2024 school year. The researcher will analyze the data to identify the number of students according to the target schools by educational environment and compare them to the educational environment of the national, state and local/district numbers. Further, the data will be analyzed to identify the frequency and proportion of the levels of prompts and learning that are included and determine the connectivity of the goals from one year to the next and its ability to support student progress. Results: This study showed me that the rates or number of students with an Intellectual Disability is higher at some of the target campuses when compared to the state level. Data showed that many of the levels of prompts and levels of learning were included in reading language arts and math goals, except for the prompt of independence and the level of learning of generalization. According to the data, there was a level of connectivity of the reading language arts and math goals from one year to the next. Conclusion: In conclusion for students with an Intellectual Disability and are LifeSkills we should focus on students’ IEP goals to support them with spending more time in the general education setting. When looking at their goals, we should make sure that the range of prompts are included in the goals: verbal, visual, physical, tactile, modeling, and independent. Similarly, we should include the entire range of levels of learning when writing goals: identify, produce, fluency, maintain, generalize. By including the entire range of prompts and levels of learning we are ensuring that students are supported when learning a new concept but move through the stages so the level of support decreases so the student can perform target skills independently and in different setting and under different conditions. Being able to perform target skills independently and being able to generalize skills will prepare them for life post high school and into adulthood. Keywords: Intellectual Disability, LifeSkills, Individualized Education Plan, Instructional Arrangement Code/Educational Environment, Inclusion, Mainstream, Least Restrictive Environment (LRE), Placement
Nephroprotective Strategies to Attenuate Antibiotic-Associated Acute Kidney Injury
No full textInfections caused by multidrug-resistant (MDR) bacteria are increasing, presenting a major threat to global public health. Resistance to first-line antibiotics, such as carbapenems, is widespread and has rendered them ineffective treatment options. Glycopeptides, aminoglycosides, and polymyxins are three classes of antibiotics that have generally maintained in vitro activity against drug resistant bacteria. Unfortunately, the clinical usage of these antibiotics is hindered by dose-limiting nephrotoxicity. Attenuation of nephrotoxicity could allow the optimal clinical use of these antibiotics to treat difficult infections and provide timely solutions to the MDR bacteria crisis. The mechanism of antibiotic nephrotoxicity is well understood to involve accumulation in the kidney, subsequent oxidative stress, and renal cell death. Despite this, effective therapeutic strategies to interfere in antibiotic nephrotoxicity and reduce renal injury are less well understood.
Therefore, this project aimed to investigate various nephroprotective strategies (e.g., megalin inhibition, mitochondrial protection, antioxidant) and identify renal protective adjuvant compounds. To accomplish this, robust assays for the quantification of antibiotics and renal protectants were developed, in vitro studies were used to provide insights into renal cell protection, and in vivo studies were used to evaluate the renal protective efficacy/safety of the promising adjuvant, zileuton.
For the quantification assays, liquid chromatography tandem mass spectrometry (LCMS) assays were developed and validated for vancomycin and zileuton. These assays were used to assess renal cell accumulation of vancomycin and evaluate the serum/renal tissue pharmacokinetics of vancomycin/zileuton in rats. Renal cell protection against antibiotic nephrotoxicity was evaluated in vitro using imaging, molecular biology, and biochemical approaches. The efficacy of the most promising renal protective compound, zileuton, was assessed using a clinically relevant animal model. Clinical relevancy was established with pharmacokinetic studies demonstrating comparable antibiotic systemic exposure in animals and humans. Zileuton was found to reduce antibiotic-associated nephrotoxicity and was formulated for parenteral administration using a ternary cosolvent system. Zileuton stability, multidose safety, and pharmacokinetics were evaluated using this formulation.
Additional research is needed to fully understand the mechanism(s) of renal protection associated with zileuton. This would be an important finding to provide more effective and safe therapeutic options for combatting the MDR bacteria crisis
Examining Prenatal and Early Childhood Influences of Maternal Stress and Mental Health on Young Children’s Brain Maturation and Neurocognition Related to Executive Functioning
No full textPrenatal maternal stress has been linked to abnormal development of cognitive, psychological, and behavioral systems in offspring; however, the underlying neural mechanisms accounting for disparities among affected children are poorly understood. Understanding cognitive outcomes and neural substrates that subserve emerging self-regulation and executive function (EF) during early childhood is paramount to support cognitive development and school readiness. This study investigates the effects of pre- and post-natal exposures on early childhood behavioral and neural development to identify key developmental epochs for targeted intervention. This project examines the impact of prenatal Hurricane Harvey exposure on EF and white matter tract connectivity presumed to underlie EF, such as the frontoparietal network (FPN), in young children aged 4-7. We found that prenatal Hurricane Harvey exposure was associated with lower child working memory performance on several cognitive tasks, but not with inhibitory control. We also found associations between prenatal Hurricane Harvey exposure and white matter alterations in several tracts—a positive association in the inferior longitudinal fasciculus and a portion of the cingulum bundle and a negative association with superior longitudinal fasciculus I. As prior work also shows that postnatal maternal psychological well-being contributes to early childhood outcomes, we tested main and moderating effects of current maternal mental health (i.e., depression and anxiety symptoms) on the association between prenatal Harvey exposure and child EF and FPN measurements. We found that maternal mental health moderates the impact of prenatal Harvey exposure on child working memory and white matter integrity in several tracts, namely a portion of the cingulum bundle and superior longitudinal fasciculus I. The results from this study emphasize the importance of examining both prenatal and postnatal factors on child neurodevelopmental outcomes
Disproportionate Graduation Rates of Students in the Foster Care System: The Influence of Teacher Preparedness
No full textBackground: Research indicates that students with a history of foster care face a high risk of academic challenges while in public school and persistently rank as having one of the significantly lowest graduation rates across local, state, and national levels. Purpose: This study explores the influence teachers’ experience while teaching students in the foster care system has on student graduation achievement. This study seeks to answer the following three questions: 1) What is the current trend of graduation rates for students in foster care at the national, state, and local levels? 2.) What are teachers’ perceptions of their professional preparedness when instructing students in foster care? 3) What training, initiatives, or programs are offered to a local educational agency to equip teachers who teach students in the foster care system? Method: This mixed-method study will focus on one comprehensive high school and the experiences of a randomly selected group of twelve teachers from the campus who have experience teaching students in the foster care system. Qualitative data with a narrative analysis will be used to explore the experiences of this group of teachers to understand their perception of their professional preparedness to teach students in the foster care system. Quantitative data collected through the Texas Education Agency (TEA) will be analyzed using a descriptive and casual-comparative analysis to explore the relationship between graduation and dropout rates and teacher preparedness to teach students in the foster care system. Results: The data collected through this study found three key themes in participant responses: (a) academic challenges, (b) preparedness and training, and (c) resources and support. These themes not only highlight the complex needs of students in foster care but identify areas of support needed for teacher readiness to serve these students effectively. Conclusion: National percentages show a disproportionate graduation rate for students in the foster care system. Teacher participants in this study, which represent a wide range of experience, identify the need for increased awareness, support, and professional development to help these students achieve academic success. Without such opportunities, public schools are not preparing teachers to understand the unique challenges students in the foster care system face and how these challenges affect foster youth’s academic success
Anthropomorphic Model for Medical Image Quality Assessment
No full textThis dissertation explores advancements in task-based image quality assessment through the development and evaluation of an anthropomorphic visual search model observer. The model incorporates a novel threshold mechanism inspired by human visual system principles, particularly emphasizing the selective processing of high-salience features. This mechanism aims to enhance discrimination performance by filtering out irrelevant variability and noise, effectively improving the efficiency and accuracy of image analysis. The proposed model builds upon foundational visual search frameworks and employs a two-stage approach: candidate selection and decision-making. Thresholding during the candidate selection stage dynamically refines regions of interest, while stage-specific feature usage in the decision-making stage optimizes diagnostic accuracy. These innovations allow the model to align more closely with human visual behaviors, offering robust predictions of observer performance and practical applicability to real-world diagnostic imaging. Extensive experiments were conducted to validate the model, including simulations with Gabor features, feature selection strategies, and thresholding studies across single and multi-feature scenarios. Results demonstrate that thresholding not only improves observer performance but also reduces training resource requirements, enabling effective model training with fewer images. Furthermore, comparisons with human observer performance highlight the model’s ability to replicate critical aspects of human decision-making in visual search tasks. The findings of this research contribute to the advancement of model observers for medical image quality assessment, providing an innovative framework for optimizing imaging systems and diagnostic tasks
Physics-Informed Decision Tree
No full textPhysics-informed neural networks (PINNs) have become a well-known machine learning (ML) model in the physics domain where partial differential equations (PDEs) are commonly used. PINN can efficiently learn the underlying physics pattern and perform accurate predictions. However, due to the black-box nature of the artificial neural networks (ANNs), it is lack of interpretability. Furthermore, calculating the derivatives for solving PDEs makes the model computationally expensive. To address these limitations of PINN, we introduce a physics-informed decision tree (PIDT). This novel approach aims to leverage the advantages of the decision trees, such as interpretability and cost-effective computation, while still having physics-integrated architecture. The introduced model’s accuracy and efficiency are illustrated and compared with the traditional PINN and traditional decision tree on Burgers' equation and heat equation. Experiment results show that PIDT has significantly faster training time than the traditional PINN while maintaining a sufficiently similar accuracy. It has comparable training time with the traditional decision tree and ANN, but PIDT has considerably better model accuracy. The comparison of the models demonstrates a promising result that PIDT can accurately perform and accelerate training speed, which can be essential in real-world problems
A Unified Diffusion Based Representation Learning Framework for Hyperspectral Image Analysis
No full textHyperspectral imaging is a promising remote sensing modality for robust land-cover mapping - however, analysis of such imagery is often challenging Land-cover mapping with remote sensing modalities such as hyperspectral passive optical imagery is challenged by high spectral dimensionality, low spatial resolution, and limited pixel-wise annotations, making supervised classification difficult. Diffusion models exhibit strong generative capabilities and effectively preserve spatial structure, making them well-suited for feature extraction in low-resolution hyperspectral imagery with degraded textures. We validate their effectiveness in GeoDiffNet-F, which leverages pseudo-RGB representations and a diffusion model pre-trained on natural im- ages (ImageNet) without domain adaptation to extract transferable low-level spatial fea- tures. Combined with per-pixel spectral reflectance, these features significantly improve classification performance and outperform existing baselines, highlighting the strength of diffusion-based spatial feature extraction in hyperspectral land-cover mapping. While GeoDiffNet-F demonstrates the utility of low-level features, the full potential of diffusion models lies in their ability to generate hierarchical, tree-like representations through multi-step denoising—progressing from global structures to fine details. Fully leveraging this capacity requires adaptation to the target domain. A central challenge is catastrophic forgetting, which can degrade generalization from large-scale pretraining, especially under limited data. To address this, we propose a parameter-efficient domain- adaptive pre-training strategy for unsupervised representation learning, which updates only adaptive normalization layers (e.g., FiLM-like affine modulation). This enables the model to extract modality-aware features and adapt rapidly while preserving general spatial priors. Building on these insights, we introduce UniDiff-MM, a unified diffusion-based frame- work that addresses the dual challenges of domain adaptation and multimodal fusion in hy- perspectral imagery. UniDiff-MM combines two key innovations: (1) the above parameter- efficient adaptation strategy, and (2) modality-aware conditioning, where the diffusion pro- cess is conditioned on distinct spectral views—pseudo-RGB for spatial and PCA-reduced bands for spectral content. This enables a single shared diffusion model to adapt to mul- tiple domain-specific representations. It preserves modality-specific features while aligning them through shared weights, projecting each view into a shared representation space. In practice, UniDiff-MM can generate modality-specific outputs when conditioned accordingly, demonstrating effective domain adaptation and consistent structure across modalities. We validate UniDiff-MM on hyperspectral pixel-wise classification tasks, where it achieves state-of-the-art performance and demonstrates its effectiveness for robust, multimodal domain-adaptive representation learning
Ready When You Are: Strategies to Account for Sensorimotor Deconditioning during Early Mars Surface Missions
No full textIf humans are to reach Mars in the near future, there will necessarily be an early period consisting of small-scale missions to demonstrate capabilities and inform future exploration. Early Mars surface missions will face limited resources and busy manifests, attempting to accomplish a lot in a short period of time. This workload density is concentrated during the surface segment of the mission, particularly in short-stay architectures which feature surface missions of approximately thirty days. Such missions are expected to depend heavily on physically demanding extravehicular activities to accomplish objectives. Furthermore, crew spending several months exposed to microgravity during transit will face sensorimotor deconditioning upon landing on Mars. This creates functional impairments that closely align with the kinds of tasks expected of a crew on the surface. Although rapid recovery takes place in a matter of hours, the time constant for full return to baseline functionality is on the order of weeks, comparable to the entire duration of a short-stay mission. On Earth, aggressive physical therapy is needed to ensure astronauts can return to duty following six-month expeditions to the International Space Station. Sensorimotor deconditioning therefore poses a key risk to completion of mission objectives. To ensure the success of early Mars missions, it is imperative that mission planners integrate mitigation strategies across a range of disciplines, including crew management and scheduling, habitat architecture, and health countermeasures. This paper proposes the treatment of these issues as a unified architecture to support crew health and performance through flexibility. The paper analyzes key parameters of Mars mission design which drive characteristics of post-landing sensorimotor deconditioning and crew workload. Intersections between these risk drivers are identified and their interactions studied in depth. A literature review of spaceflight sensorimotor deconditioning and associated countermeasures is presented. Conditioning and evaluation strategies used in terrestrial applications that could be adapted for Mars missions are identified and discussed. Habitat architecture design considerations to support recovery and accommodate functional impairments are presented. Systems supporting crew autonomy, including self-scheduling and decision-making capabilities, are also reviewed. Finally, guiding principles are identified that synthesize information in each of these areas, constituting a framework that can be used to steer design of future Mars missions. Research opportunities to fill relevant knowledge gaps are identified as forward work to inform future iterations on this approach
Detection and Segmentation of Glial Cells Using Deep Learning Models
No full textInitially thought to serve only as structural support, glial cells are now recognized as essential for maintaining brain homeostasis and mitigating neurodegenerative diseases. Over the past decade, research has further established their role in neural metabolism and synaptogenesis, with various subtypes performing specialized functions in the central nervous system (CNS). Among these, astrocytes regulate synaptic transmission and pruning, ensuring proper cognitive function. This process generates neural waste, such as discarded synapses, which are subsequently cleared by microglia. Additionally, microglial cells play a crucial role in responding to brain tissue injury and neuroinflammation. Given the significance of glial cells, numerous methods have been developed for their detection and segmentation. However, their high heterogeneity - stemming from frequent morphological remodeling in response to external stimuli or injury — poses major challenges for automated quantitative analysis. The goal of this work is to develop an object detection pipeline based on YOLOv8 to detect astrocytes and microglial cells in fluorescent images with higher accuracy than current state-of-the-art methods. We demonstrate the efficacy of our approach through extensive numerical experiments, including datasets with dense cell populations. Our results show that our model is highly accurate, robust, and generalizes well to other glial subfamilies. Unlike previous YOLO models, we find that YOLOv8 requires minimal fine-tuning of hyperparameters and the fitness function. Furthermore, given the morphological similarity between astrocytes and microglia, we show that a network pre-trained on astrocytes significantly improves detection accuracy and training efficiency on a microglia dataset. Lastly, we develop a YOLO-based segmentation model for astrocytes, achieving results that are often superior to U-Net and other well-established methods. Since our model effectively captures fine cellular details that other approaches miss, we anticipate that this method will greatly enhance the study of glial cell morphology
Evaluation of Drug Release from Polymeric Nanoparticles in Gastrointestinal Media
No full textPolymeric nanoparticles (NPs) are widely used as nanocarriers for active ingredient delivery due to their biocompatibility and biodegradability. For oral drug delivery, various biochemical environments are encountered in the gastrointestinal (GI) tract that could potentially induce different drug release behaviors. Polymeric NP formulations are difficult to detect and quantify in these media. Hence, it is important to establish a reliable and reproducible characterization method to evaluate their behavior in more realistic scenarios. This dissertation applies multi-detector asymmetric flow field-flow fractionation (AF4) to evaluate active ingredient release from poly (lactic-co-glycolic acid) (PLGA) NPs. In the first study, a relatively hydrophilic fluorescent antibiotic, enrofloxacin was released from PLGA NPs in three media: simulated gastric fluids (SGF), simulated saliva, and phosphate buffered saline (PBS). The change in PLGA state from glassy to rubbery near the glass transition temperature (Tg) is first necessary for any release to occur. When the temperature is near Tg, changes in pH from neutral to acidic induce accelerated enrofloxacin release because of the change in enrofloxacin charge state and a reduction in drug-polymer binding energy, resulting in faster release of the entrapped drug by radial diffusion. In the second study, AF4 methods were applied to evaluate release of a relatively hydrophobic fluorescent active ingredient, coumarin 6, from PLGA NPs in four media (simulated saliva, SGF, simulated intestinal fluids (SIF), and PBS), with or without their biomacromolecules. No significant difference in drug release rates was observed in the four media with different pH. Only bovine serum albumin in PBS and bile micelles (comprised of taurocholate-lecithin) in SIF accelerated the coumarin 6 release from the PLGA NP surface, whereas pepsin in SGF and amylase in simulated saliva did not