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Impact of Facial Divergence on Post-Treatment Settling in Patients Retained With Clear-Overlay Retainers
No full textVertical growth patterns are well-recognized by the orthodontic community as being impactful on function, form, and subsequent treatment. The hyperdivergent phenotype is characterized by smaller masticatory muscles, lower bite forces, and thinner cortical bone compared to hypodivergent subjects. As occlusion depends on dentition, muscle function, and periodontium, facial divergence plays a role in establishing occlusion. Just as orthodontics aims to improve a patient���s occlusion, a goal of orthodontics is to retain the occlusion after debonding but to allow for the vertical movement of teeth into better intercuspation, known as settling. With the determinants of occlusion being influenced by factors affected by facial divergence, there is the possibility that different facial divergence patterns have different capacities to experience settling.
The purpose of the study is to investigate and compare the changes in areas of contact and near contact in hypodivergent and hyperdivergent patients retained with a clear overlay retainer.
This study is a retrospective cohort study with patients recruited from a single private practice. 7 hyperdivergent and 11 hypodivergent patients were identified. All patients were retained with upper and lower clear overlay retainers and a lower bonded retainer. Initial lateral cephalograms were used to categorize patients as hyperdivergent or hypodivergent. Intraoral scans from debond and first retention check appointments were compared. Changes in areas of contact (AC) and areas of contact and near contact (ACNC) were used to characterize settling.
Hyperdivergent patients had a statistically significantly higher number of AC as compared to hypodivergent patients at the time of debond (T0) but not at first retention check (T1). There was no statistical difference between AC or ACNC at T0 or T2 between the two groups. There were no statistical differences detected within each group for either AC or ACNC when doing pairwise comparisons.
Without adequate power, this study instead serves to justify the benefits of repeating this study with a larger sample size. It indicates the potential for different facial divergence patterns to respond differently with the same retention protocol which could therefore impact the settling that can be achieved
The Development of Texneut and Spectroscopy of 10Li Using Isobaric Analog States
No full textFor at least two decades there has been uncertainty in the description of the low-level structure of 10Li, including the J �� and energy of the ground state. The properties of the system are crucial for the understanding of the evolution of nuclear structure out beyond the drip line. The 9Li + n dynamics also play an important role in the 11Li system. 11Li has been experimentally shown to be a di-neutron halo nucleus whose structure, in part, is determined by the interaction of the 9Li + n system. Reliable experimental results on 10Li are also important to benchmark predictions of contemporary nuclear structure models.
Various experiments and calculations have been conducted that target the low-lying structure of 10Li. Many of the calculations, including ab initio, were performed. Past experiments have used different reactions to populate the 10Li nucleus. Yet, no clear understanding on the J �� of 10Li has been achieved. We propose a new way to study 10Li.
With the TexAT time-projection chamber, we measured the excitation function for 9Li + p elastic scattering populating T = 2 isobaric analog states (IAS) in 10Be. The presence of the T = 2 IAS is expected in 10Be just above the 9Li+p threshold. These resonances lead to enhancements in the elastic scattering cross section at the resonance energies. Comparing the experimental data for 9Li + p elastic scattering to R-matrix calculations we hoped to assess the spin-parity assignment for the T = 2 states in 10Be, and infer the low-lying level spin-parity of 10Li
Furthermore, we have developed a neutron detector array, TexNeut. The spectroscopy of fast neutrons opens up a wide range of experiments that can be performed with rare isotope beams (RIBs) at Texas A&M University. These experiments complement those performed using charged particles. TexNeut is comprised of small modular detector bars which fit compactly together to form a thick array. TexNeut has been characterized and shown to give excellent n/�� pulse shape discrimination (PSD), fast timing, and gives a discrete position spectrum with a resolution fixed to 2 �� 2 �� 2 cm3 . The commissioning experiment of TexNeut is a measurement of 9Li(p, n) 9Be reaction to study the same IASs in 10Be as previously mentioned. The development of the detector modules, construction of the array, and detector commissioning will be discussed in this work
John Bickham field notebook: AK25501-AK26000.pdf
No full textBound book, each page corresponds to a karyotype slide data.Data pages for AK26001-AK26500 corresponding to unique identifiers of specimens/samples examined for biological research. Specimens are primarily housed at Texas A&M University; Biodiverstiy Research and Teaching Collection
Evidence for Metabolism of Creatine by the Conceptus, Placenta, and Uterus for Production of ATP During Conceptus Development in Pigs
No full textDuring gestation in pigs, most embryonic mortality occurs during two gestational time points with high energy demands due to extensive cell proliferation and migration. Between Days 14 and 25, free-floating conceptuses (embryo/fetus and associated placental membranes) elongate and attach to the uterus. Between Days 50 and 70, the uterine-placental interface undergoes extensive folding and develops mature areolae to maximize support for development of the fetuses. We hypothesize that insufficient energy in the form of ATP in conceptuses and uterine tissue may contribute to conceptus loss in pigs. Creatine, an organic compound commonly stored in the muscle as phosphocreatine, can regenerate ATP through the creatine (Cr)-creatine kinase (CK)- phosphocreatine (PCr) pathway. However, the expression of factors involved in creatine metabolism has not been examined in conceptus and uterine tissues throughout gestation in pigs. In the present study, we performed real-time qPCR to quantify expression of mRNAs for enzymes and the creatine transporter involved in the creatine metabolic pathway in conceptus and uterine tissues from Days 10, 12, 15, 18, 20, 24, 30, 40, 60, and 90 of gestation. Results of qPCR analyses revealed increases in expression of AGAT, GAMT, CKM, CKB, and SLC6A8 mRNAs in conceptuses on Day 15, and a further increase in AGAT mRNA in the chorioallantois on Day 90 of gestation. Immunofluorescence staining of the uterine-placental interface from Days 15, 16, 20, and 25 corroborated qPCR results, with the expression of GAMT, CKM, and CKB proteins appearing to increase in conceptus Tr cells on Day 15. The presence of GAMT, CKB, and CKM proteins were confirmed with Western blot analyses. Levels of endometrial AGAT and CKM mRNAs increased on Day 15, CKB mRNA increased again on Day 30, and AGAT, GAMT, and SLC6A8 mRNAs increased significantly on Days 40, 60, and 90. Furthermore, HPLC analyses confirmed the presence of Cr and PCr metabolites in uterine luminal fluid, allantoic fluid, and amniotic fluid with significant increases in the uterine fluid and allantoic fluid on Days 11 and 40, respectively. Collectively, results of this study indicate that the Cr-CK-PCr pathway could establish sufficient energy stores to support cell proliferation and migration required for conceptus elongation, implantation, and remodeling of the uterine-placental interface during gestation in pigs
Investigation of Co-resident Attacks in Serverless Cloud Environment
No full textIn the rapidly evolving landscape of cloud computing, serverless environments have gained prominence for their scalability and efficiency. However, the security of such environments re-mains a critical concern given how ubiquitous their implementation is in both academic and industrial settings. This study delves into the realm of co-resident attacks within serverless cloud con-texts, aiming to exploit the weak isolation within software infrastructure implementation. Specifically, this research investigates the potential flaws in the Function as a Service (FaaS) framework and sheds light on the vulnerabilities that arise when multiple tenants share the same underlying hardware. By identifying these vulnerabilities, this research aims to improve the security of serverless cloud domain and explores avenues to protect against co-resident attacks. This work specifically investigates the feasibility of cache covert channel attacks within serverless environments deployed on commercial cloud platforms. In this study, the open-source Apache OpenWhisk function-as-a-service (FaaS) is deployed on top of a Kubernetes (K8s) cluster. The cluster is pro-visioned and managed using Google Kubernetes Engine (GKE) on the Google Cloud Platform (GCP) to emulate a real-world scenario. With this, the potential of malicious actors establishing covert communication channel across co-resident functions is investigated. The research is conducted in a shared host environment within GCP, emphasizing on virtual CPU (vCPU) resource sharing. Dedicated hosts are intentionally avoided to simulate real-world cloud deployment scenarios. Our findings indicate that commercial-grade clouds like GCP inherently provide a degree of security obfuscation, making covert channel establishment more challenging. While attacks may be more feasible on dedicated single-tenant machines, the shared nature of commercial cloud environments adds a layer of complexity. Serverless frameworks, while introducing new abstractions, ultimately rely on the same underlying infrastructure; therefore, they introduce additional noise rather than fundamentally altering the attack surface. While effective defense mechanisms can be implemented, they invariably introduce performance overheads
Planning Under Uncertainty with Unreliable Robotic Actuators
No full textWe focus on a critical aspect of autonomous robotics: the challenge of decision-making under the uncertainty of inevitable actuator degradation. Through the lens of physically embodied decision-makers, this research explores the complexity in modeling and planning for robotic actuator deterioration and failure. By an analogy to biological aging, we explore the necessity for agents to anticipate and plan for their own senescence, thus embracing their finite lifespan to maximize their utility. This shift towards acknowledging and planning for actuator frailty is particularly crucial for robotic explorers on interplanetary and interstellar missions, where autonomous, resilient decision-making is paramount.
Central to our approach is the introduction of Fallible Actuator Markov Decision Processes (FA-MDPs), an extension of the traditional MDP framework that incorporates actuator reliability into planning. This allows for the anticipation of failures, enabling strategic actuator usage and rapid adaptation post-failure. Our methodology leverages the inherent structure of FA-MDPs to decompose the problem into manageable sub-problems which increases solver efficiency. Furthermore, we explore the concept of actuator dominance and introduce virtual actuators to model k-shot and degrading actuators, thereby extending our failure model and improving planning performance.
The contributions of this thesis include: (1) A novel framework for incorporating actuator reliability into planning, enabling proactive planning for actuator failures. (2) An improved solution methodology for FA-MDPs through problem decomposition and a value function lattice, demonstrating superior performance over naive solvers. (3) An analysis of actuator relationships to further enhance planning performance and address actuator degradation. This work represents a step towards the development of long-lived autonomous robots capable of navigating the uncertainties of dynamic environments and their own inevitable deterioration
Methods for Large-Scale Inference with Application to Genomics Data
No full textGenomic data are subject to various sources of confounding, such as demographic variables, biological heterogeneity, and batch effects. To identify genomic features associated with a variable of interest in the presence of confounders, the traditional approach involves fitting a confounder-adjusted regression model to each genomic feature, followed by multiplicity correction. The first project shows that the traditional approach is sub-optimal and proposes a new two-dimensional false discovery rate control framework (2dFDR+) that provides significant power improvement over the conventional method and applies to a wide range of settings. 2dFDR+ uses marginal independence test statistics as auxiliary information to filter out less promising features, and FDR control is performed based on conditional independence test statistics in the remaining features. 2dFDR+ provides (asymptotically) valid inference from samples in settings where the conditional distribution of the genomic variables given the covariate of interest and the confounders is arbitrary and completely unknown. In genome-wide epigenetic studies, exposures (e.g., Single Nucleotide Polymorphisms) affect outcomes (e.g., gene expression) through intermediate variables such as DNA methylation. Mediation analysis offers a way to study these intermediate variables and identify the presence or absence of causal mediation effects. Testing for mediation effects leads to a composite null hypothesis. Existing methods like Sobel���s test or the Max-P test are often underpowered because 1) statistical inference is often conducted based on distributions determined under a subset of the null, and 2) they are not designed to shoulder the multiple testing burden. To tackle these issues, we introduce a technique called MLFDR (Mediation Analysis using Local False Discovery Rates) for high dimensional mediation analysis, which uses the local false discovery rates based on the coefficients of the structural equation model specifying the mediation relationship to construct a rejection region. We have shown theoretically as well as through simulation studies that in the high-dimensional setting, the new method of identifying the mediating variables controls the false discovery rate asymptotically and performs better with respect to power than several existing methods
An Examination of Texas Virtual Teachers' Understanding of Self-Efficacy and Perception of Students' Self-Efficacious Behaviors in a Digital Learning Environment
No full textSelf-efficacy, or one���s ability to achieve the desired result on a task, is a key factor in student
success, particularly in virtual learning contexts. Self-efficacy has been linked to students���
persistence, levels of effort, and goal-setting, as well as their self-regulatory behaviors, such as
time management. The purpose of this study was to explore what virtual middle school teachers
know about self-efficacy, what misconceptions they have, and how teachers believe virtual
students exhibit behaviors of persistence, effort, and goal-setting in asynchronous digital learning
environments. A case study of four teachers from a virtual school in Texas was conducted.
Through qualitative semi-structured interviews and review of lessons, thematic analysis revealed
that virtual teachers had nearly equal amounts of knowledge and misconceptions about self-efficacy. Teachers were able to correctly link self-efficacy to motivation and self-regulation, and they used methods to increase student self-efficacy consistent with the literature in their teaching practice. Teachers had misconceptions about what causes digital learners to develop self-efficacy and around the relationship between self-efficacy and other concepts. Finally, virtual teachers determined that their virtual students do demonstrate behaviors of persistence and effort but did not autonomously exhibit the behavior of goal-setting. This study provides recommendations for increased professional learning through instructional materials to increase teachers��� knowledge of self-efficacy, and therefore inform their practice
Multi-Cycle Dynamic Compaction of At-Speed Tests for Reduction in Test Data Volume, Test Application Time, and Power Supply Noise
No full textThe semiconductor industry has made great strides over the last few decades. Chip makers taking advantage of Moore���s law have pushed the limits of physics to shrink feature sizes on Silicon (Si) wafers. Delay test is an essential structural manufacturing test used to determine the maximal frequency at which the chip can run without incurring any functional failures. Small delay defects that were previously benign now manifest as delay faults due to the reduced timing margins. Another challenge is achieving better delay correlation with functional test, which is dominated by power supply noise (PSN). Differences in PSN between functional and structural tests can lead to differences in chip operating frequencies of 30% or more. Pseudo functional test (PFT), based on a multicycle clocking scheme, has better PSN correlation with functional test compared with traditional two-cycle at-speed test. This research focuses on the development of new test. methods for small delay defects, within the limits of affordable test generation cost, pattern count and power supply noise.
First, this work proposes a new Dynamic Compaction algorithm to generate compacted test sets for K Longest paths per gate (KLPG) in scan based sequential circuits. The algorithm uses a greedy approach to compact paths with non-conflicting assignments together over multiple at-speed cycles during test generation. Reductions in pattern counts of as much as 67% are observed with the greedy approach.
Second, compression is introduced to the test flow and the ATPG engine to observe the combined effect of compression and compaction. Multi-cycle at-speed test has around 60% reduction in pattern count over single-cycle at-speed test with similar compression rates. Higher compression rates are required for single cycle test to achieve similar test data volume and test application time which indicates more DFT effort required for single cycle at-speed tests. The compression framework is built inside the CodGen ATPG to make this process more seamless.
Third, a simulation-based test relaxation algorithm is designed for CodGen ATPG as the patterns generated by the final justification SAT Engine assigns all the bits in the pattern. This algorithm implemented inside CodSim is able to produce similar don���t care bit density compared to the previous justification algorithms inside CodGen such as FAN and PODEM. Power supply noise (PSN) estimation using weighted switching activity (WSA) is then done for these partially specified patterns (with random or adjacent fill) to compare the single and multi-cycle test power. The multi-cycle test has very similar power profile and even lower in some cases to that of single-cycle test even though the patterns are more compacted.
Finally, CodGen ATPG is improved to handle larger industrial designs by updating the SAT engine MiniSat that runs all binary justifications during the ATPG process. It is replaced with CaDiCaL a much modern SAT engine which has been able to provide significant speedup to the test generation process
AI and Machine Learning Using Wearables for Diabetes Care
No full textThe emergence of Internet of things (IoT) devices and technological advances have revolutionized healthcare, particularly the advent of AI-based wearables have enabled frequent monitoring of glucose levels in patients. This is critical for an incurable disease like diabetes which can only be managed through frequent monitoring of glucose values. Despite the progress made, making AI-based solutions more patient-centric remains a challenge. To address this, we present methods for efficient application of AI/ML solutions for improving diabetes care with an emphasis on patient needs.
This dissertation has two primary objectives: (1) To build robust machine learning models to improve diabetes care, and (2) develop alternatives to current glucose monitoring technologies to enhance the accessibility and reduce the intrusiveness. To achieve the Objective 1, we focus on developing machine learning algorithms for prediction of impending hypoglycemia risk in patients. A feature-based machine-learning model is built based on previous CGM values that gives real-time predictions for hypoglycemia risk, enabling patients to take intervening actions. We subsequently work on improving the quality of hypoglycemia predictive alerts in a real-world setting by focusing on predictive alerts that are based on sustained hypoglycemia. This drastically reduces instances of false alerts, a major deterrent for technology adoption among patients.
Machine learning models with robust performance rely on large corpus of data for training. However, healthcare data is sensitive, and its accessibility is restricted with many regulations in place. To this, we develop FedGlu, a machine learning model trained in a federated learning framework that simultaneously addresses the dual challenge of data availability and model performance. FedGlu also incorporates a customized loss function that improves the model���s predictive capabilities in the glycemic excursion regions.
CGM devices are valuable, but accessibility is limited as they are expensive and also available based only on prescriptions. In addition, they are invasive which can be painful for patients. In Objective 2, as an alternative to CGM devices, we extend the use of IoT based noninvasive wearables for glucose monitoring. For this, we evaluate hyperglycemia detection along with hypoglycemia detecting using ECG and accelerometer signals that are collected noninvasively. We comprehensively evaluate the proposed algorithms on people with and without diabetes and demonstrate the efficacy of the proposed approach. In closing, a summary of the contributions and directions of future work are presented