18525 research outputs found
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RTI² in Action: A K-12 Analysis of Implementation, Variation, and Resource Use in Rutherford County Schools
No full textLeadership Policy and Organizations Department capstone projectK-12 education continues to evolve to ensure equity and access for all students. Response to Intervention and Instruction (RTI²) is a data-driven framework designed to deliver individualized academic interventions, monitor student progress, and adjust instruction as needed to facilitate growth. This explanatory-sequential mixed-methods study examines RTI² implementation in Rutherford County Schools (RCS), focusing on intervention staffing, curriculum, scheduling, and variations across schools. Key research questions explore the structure of Tier II and Tier III interventions, the impact of implementation variation on student progression, and the effects of resource investments, particularly the allocation of two interventionists per school. Findings indicate that interventionists play a critical role in student growth, and staffing models, financial resource allocation, and school-level variations significantly influence RTI² effectiveness. While RCS follows a standardized framework, differences in scheduling, data-driven decision-making, and instructional adjustments impact student outcomes. RCS has demonstrated notable student growth, earning a five-out-of-five rating in 2024 from the Tennessee Department of Education. This study underscores the importance of consistent, data-informed intervention practices and resource optimization in improving student achievement.Peabody College of Education and Human DevelopmentDepartment of Leadership Policy and Organization
Parent-Adolescent Communication: An Actor-Partner Interdependence Model within a Family Group Cognitive-Behavioral Preventive Intervention with Families of Depressed Parents
No full textPositive communication between parents and adolescents is important for healthy psychosocial development. Extant literature examining the links between communication and psychopathology is limited by cross-sectional designs, inconsistent definitions of key constructs, and self-report assessments of parent-adolescent communication. By contrast, behavioral observation offers an objective, systematic method for assessing communication. The current study examined cross-sectional, longitudinal, and bidirectional associations between observed communication and psychopathology among a high-risk sample of youth and parents with a history of depression in the context of a cognitive behavioral preventive intervention. Participants included 180 adolescents (M = 11.46 years) and one of their parents enrolled in a randomized controlled trial. Communication composite scores for parents and adolescents were created with codes from The Iowa Family Interaction Rating Scales at baseline and six-months post intervention. Youths’ anxiety and depression symptoms were assessed with the Youth Self-Report, and parents’ anxiety and depression symptoms were assessed with the Beck Depression and Beck Anxiety Inventory, respectively. Actor-Partner Interdependence Model (APIM) data analysis was implemented utilizing an R analysis program, “APIM_SEM.” APIM models evidenced a significant parent actor effect, such that better parent communication was associated with lower parent anxiety/depression symptoms at baseline ( = -0.21, p = .02). There was also a significant parent partner effect, such that greater parent communication was associated with fewer adolescent anxiety/depression symptoms ( = -0.23, p = .01) at baseline. Results from a longitudinal APIM model did not find any significant actor or partner effects between communication and anxiety/depression symptoms (ps > .05). There were significant within-person actor effects, whereby greater symptoms and better communication at baseline predicted greater symptoms and better communication at 6-month follow-up for both parents ( = 0.55, p = <.001) and adolescents ( = 0.58, p = <.001). Finally, there was a significant adolescent partner effect, such that greater adolescent symptoms at baseline were associated with fewer parent symptoms at 6-months ( = -0.18, p = .02). Results suggest significant cross-sectional dyadic associations between parent communication and parent and adolescent anxiety/depression symptoms. Additional research is needed to understand the unexpected association between greater adolescent symptoms and fewer parent symptoms
Enhanced Brain Graph Construction in Neuroimaging: A Data-Centric AI Approach
No full textThe construction of brain graphs from functional Magnetic Resonance Imaging (fMRI) data plays a crucial role in leveraging Graph Neural Networks (GNNs) for neuroimaging analysis. However, existing approaches are often constrained by limitations in signal selection, suboptimal topology extraction, and the omission of dynamic functional interactions. To address these limitations, a Data-Centric AI framework is introduced to systematically explore a design space for brain graph construction, optimizing high-amplitude signal retention, topological representations, and graph featurization. Specifically, a high-amplitude signal retention strategy is developed to preserve only high-amplitude blood-oxygen-level-dependent (BOLD) signal fluctuations, retaining 30% of the original data while enhancing co-activation patterns. Furthermore, alternative correlation metrics and a unified graph topology are explored to more effectively capture robust functional connectivity. For advanced graph featurization, lagged correlation is integrated to incorporate dynamic properties, capturing temporal delays between brain region activations. Additionally, edge features are introduced to encode multi-scale and multi-metric functional connectivity, enriching the structural expressiveness of the graphs. These innovations enable brain graphs to more comprehensively reflect the evolving and complex interactions between brain regions. Extensive experiments are conducted on the HCP1200 and ABIDE datasets, demonstrating that the proposed strategies generally improve downstream classification performance across multiple GNNs and ROI settings. Building on the proposed strategies, we systematically explore the data-centric design space to optimize brain graph representations, demonstrating consistent and significant improvements in classification accuracy and representation quality compared with the baseline. Our methods offer a more robust and generalizable foundation for advancing neuroimaging research with GNNs across datasets and tasks
Essays on Big Data and High-Dimensional Models: Machine Learning Approaches in Econometrics
No full textThe increasing availability of high-dimensional and complex economic data presents both opportunities and challenges for econometric analysis. This dissertation, Essays on Big Data and High-Dimensional Models: Machine Learning Approaches in Econometrics, explores novel statistical and computational techniques for high-dimensional inference in threshold regression models and efficient data processing under multiway clustering. The three chapters collectively contribute to the advancement of econometric theory and practice by addressing critical methodological challenges in big data econometrics.
The first chapter develops a uniform inference theory for high-dimensional slope parameters in threshold regression models, allowing for either cross-sectional or time series data. It establishes oracle inequalities for Lasso estimators and introduces a debiased Lasso estimator for threshold models. The results allow researchers to perform uniform inference without specifying whether the model is a linear or threshold regression. The empirical applications to economic growth and the effect of military news shocks on U.S. government spending demonstrate the practical importance of this method in economic analysis.
The second chapter focuses on inference for the threshold parameter in high-dimensional threshold regression models. It derives the asymptotic distribution of the threshold parameter under different specifications, including kink, diminishing jump, and fixed jump models. The chapter rigorously proves the continuity of these asymptotic distributions and validates the use of subsampling for inference. The methods are applied to empirical settings where capturing nonlinearities and regime shifts is essential for understanding economic dynamics and informing policy decisions.
The third chapter proposes a novel method of algorithmic subsampling (data sketching) for multiway cluster-dependent data. It develops a new uniform weak law of large numbers and a central limit theorem for multiway algorithmic subsample means, demonstrating that algorithmic subsampling ensures robustness against potential degeneracy, and even non-Gaussian degeneracy, of the asymptotic distribution under multiway clustering at the cost of efficiency and power loss due to algorithmic subsampling. An empirical application using scanner data from Dominick’s Finer Foods illustrates the method’s effectiveness in demand estimation for differentiated product markets.
Collectively, this dissertation advances the intersection of big data econometrics and machine learning by developing theoretically rigorous methods for inference in high-dimensional threshold models and efficient data processing under multiway clustering. The findings have broad applications in economic policy and market analysis where large datasets and complex structural relationships are prevalent
Bi-exactness of Negatively Curved Groups
No full textBi-exactness is an analytic property of groups introduced by Ozawa. This
property provided a fundamentally new way to prove primeness of group von Neumann algebras and has been intensively studied from operator algebraic perspective.
On the other hand, bi-exactness is not well understood from group theoretic perspective. Indeed, not many examples of bi-exact groups are found. Also, bi exactness is not preserved under some basic group theoretic constructions.
The purpose of this dissertation is to study bi-exactness from group theoretic perspective by discovering a new permanence property of bi-exactness and a new class of bi-exact groups
Synthetic Morphogenesis to Instruct Early Brain and Central Nervous System Development
No full textStudying brain and central nervous system (CNS) formation remains challenging due to the complexity of morphogenetic signaling during early embryonic development. During embryogenesis, Sonic Hedgehog (SHH) from the notochord induces floor plate formation, which subsequently patterns the neural tube along the dorsal-ventral axis. Human pluripotent stem cells (hPSCs) offer a platform to model these processes in vitro, typically using small molecules and recombinant proteins. However, conventional two-dimensional (2D) and three-dimensional (3D) differentiation protocols fail to recapitulate intrinsic, cell-driven signaling centers that regulate development in vivo. Synthetic morphogenesis aims to address these limitations by engineering multicellular systems to drive tissue formation and differentiation. While many synthetic morphogenetic strategies rely on externally controlled drug- or light-inducible systems, they still lack endogenous cell-cell signaling cues. Here, we investigate synthetic Notch (synNotch) as an alternative platform for cell-guided differentiation. However, synNotch has not yet been applied in hPSC:hPSC juxtacrine signaling. We first demonstrate that the widely used synNotch ligand, green fluorescent protein (GFP), presented on a platelet-derived growth factor receptor-β transmembrane domain, fails to induce robust synNotch activation in hPSC co-cultures. We identify an optimized ligand-presenting chassis, Epithelial Cadherin (E-Cadherin), which enables efficient receptor activation, driving mCherry reporter expression in sender-receiver co-cultures, as confirmed by flow cytometry. Using this optimized system, we establish a 2D synNotch-driven differentiation strategy in which receiver hPSCs express SHH upon ligand recognition, resulting in the emergence of floor plate-like cells. Single-cell RNA sequencing confirms the presence of floor plate markers in these cultures, and these cells exhibit comparable marker expression to a conventional 2D differentiation strategy using bulk recombinant SHH protein. To extend this approach into a 3D model, we integrate synNotch into brain organoids, polarizing SHH-expressing receiver hPSCs and surrounding them with ligand-presenting senders. Immunofluorescence analysis reveals markers of floor plate, ventral, and dorsal neural tube identity, indicating spatially organized patterning. This work represents the first demonstration of synNotch-mediated hPSC:hPSC juxtacrine signaling in both 2D and 3D, expanding synthetic morphogenesis strategies for engineering cell-cell communication, directing hPSC differentiation, and modeling developmental processes
Workload-Aware Power and Thermal Optimization for NVIDIA Jetson Processors using Advantage Weighted Regression
No full textAchieving power efficiency in embedded systems like NVIDIA Jetson processors requires balancing resource utilization, thermal management, and energy efficiency across workloads. A study proposes
offline reinforcement learning (RL) for efficient system resource management without hardware interaction. By supporting CPU-intensive, GPU-intensive, memory-intensive, and hybrid workloads, energy
efficiency and thermal stability improve. Advantage Weighted Regression, an offline reinforcement learning method, simplifies policy optimization. High-advantage actions are weighted more, guiding resource
and power efficiency decisions. We optimize power adjustments and prevent overheating with a reward
function to maximize system resource use. The method improves decision-making to mitigate noisy
data-induced power changes in embedded systems due to inaccurate sensor readings and variable resource utilization. We compared the AWR-based policy to a static baseline method that cycles between
increasing and decreasing load 50% of the time and randomly selects actions the rest of the time. In mixed
workloads, the AWR-based policy reduces power consumption by 6.63%, CPU and GPU temperatures
by 9.61% and 10.05%, swap memory usage by 20.67%, RAM utilization by 11.63%, and CPU utilization
by 7.53%. CPU usage rises by 20.89% and RAM usage by 4.72%, respectively, during CPU-intensive
tasks. Power consumption, GPU temperature, and swap memory usage in GPU-intensive tasks decrease
by 17.14%, 17.35%, and 36.37%. Memory-intensive tasks reduce power consumption 17.6%, CPU temperature 6.07%, swap memory usage 52.06%, and RAM utilization 9.66%. Offline reinforcement learning
with workload optimization outperforms non-adaptive power management at controlling temperature,
using system resources, and saving power. This study recommends intelligent power management for
energy-constrained embedded systems
Temporary Ambiguity in the Learning Context
No full textDefined as ambiguity that resolves itself within the sentence, temporary ambiguity is a key area of research in language processing. This study investigates how temporary ambiguity influences information acquisition and recall, focusing on how ambiguity impacts the creation of memory errors during learning. Participants learned about objects based on sentences that either contained or did not contain temporary ambiguity, then completed a cued recall test and a two-alternative forced-choice (2AFC) test. Results from the recall test demonstrate significantly effects on recall accuracy. The findings have implications for understanding the cognitive mechanisms of language comprehension and memory
Investigating Innate Immune Signaling in Models of TET2 Deficient Hematopoiesis
No full textLoss of function mutations in the gene Tet methylcytosine dioxygenase 2 (TET2) are common in clonal hematopoiesis (CH) and myeloid malignancies such as myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML). Loss of TET2 function leads to hypermethylation of DNA and biases hematopoietic stem and progenitor cells (HSPCs) towards the myeloid lineage and enhances stem cell self-renewal. TET2 deficiency also exacerbates the response of hematopoietic cells to inflammatory stimulation in a cell-type specific manner, with LPS driving expansion of Tet2KO HSPCs and causing more differentiated macrophages to produce higher levels of inflammatory cytokines such as IL-1and IL-6. While the effects of TET2 loss on stem cell self-renewal and inflammation are well characterized, data on the role of TET2 in driving cell-type specific cytokine responses across the hematopoietic hierarchy are lacking. Here, I investigate cell-type specific effects of Tet2KO on inflammatory cytokine exposure using a wide range of modalities. In Chapter 2, I show with mass cytometry and flow cytometry that Tet2KO myeloid progenitors and monocytes have enhanced STAT1 phosphorylation in response to IFN and elevated STAT1 pathway output that can be targeted with the JAK/STAT inhibitor, ruxolitinib. In Chapter 3, RNA-seq showed that Tet2KO myeloid progenitor cells respond to IL-1 by upregulating transcription factors involved in proliferation/differentiation as well as inflammatory cytokines, while Mature Tet2KO cells produce copious amounts of cytokine. Both effects were ablated by inhibiting NFB signaling. In Chapter 4, I used PROseq to profile nascent transcription in response to IFNin Tet2KO HSPCs. Here, Tet2KO led to upregulation of several enhancers involved in inflammation and differentiation. Analysis of gene body transcription revealed Tet2KO HSPCs express lower levels of inflammation linked molecules and myeloid driving transcription factors, identifying a potential mechanism for the enhanced self-renewal capacity of Tet2KO HSPCs. Future work will focus on probing both the perturbed enhancer expression of dysregulated gene body transcription to better understand the effects of Tet2KO on HSPC self-renewal
Warmer Temperature and Aging Interact to Shape how Mosquitoes Survive and Respond to Infection
No full textInsect physiology is shaped by factors inherent to the environment, such as temperature, and factors inherent to the insect, such as age. As poikilotherms and ectotherms, the body temperature of most insects is predicated by the temperature of the environment. Global temperatures are warming, which is quickening insect development, raising metabolism, and altering the response to infection. Moreover, as insects age, they senesce, causing their body to deteriorate and immune function to weaken. An organism’s age is measured by time alive (chronological age) and by how well their body functions (physiological age). In homeotherms, these processes are linked. In poikilotherms, these processes may become unlinked by changes in temperature. Using the African malaria mosquito, Anopheles gambiae, I evaluated whether warmer temperature alters the progression of senescence. I hypothesized that mosquitoes age faster physiologically when the temperature is warmer, which may have implications for their competency as disease vectors. To test this, I investigated the effects of warmer temperature and aging in tandem and assessed changes in body size and composition, survival, and immune function. I demonstrate that warmer temperature and aging interact to affect many facets of mosquito physiology. Both the aging-dependent decrease in protein content and survival occur earlier in life when the temperature is warmer. Moreover, the aging-dependent increase in infection intensity occurs earlier in life when the temperature is warmer, indicating that a mosquito’s general ability to control a systemic infection declines at a faster rate when the temperature is warmer. I further show that the strength of the cellular immune response declines with warmer temperature and aging, and that this aging-dependent decline quickens when the temperature is warmer. Thus, warmer temperature accelerates senescence, decoupling chronological and physiological age in mosquitoes. Because most insects are poikilothermic ectotherms, the interactive effects between temperature and aging should be considered when constructing models that predict the efficacy of insects as pollinators, agricultural pests, and disease vectors in our warming world