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Unraveling the Complexity of Cdec Expression During Clostridioides difficile Sporulation: Insights into the Role of Sigma Factors and Non-Coding RNA on Cdec Expression
No full textGram-positive, obligatory anaerobic Clostridioides difficile (C. difficile) is a spore-forming, obligate anaerobic bacterium that causes diarrhea and other healthcare-associated illnesses globally. This study aimed to identify the sigma factor boxes in the promoter region of cdeC that affect the early and late stages of C. difficile sporulation. Therefore, we first identified the sigma factors' positions in the promoter region. Then, we constructed transcriptional fusions containing a different deletion of these sigma factors and introduced them into the C. difficile R20291CM210 WT strain. However, no detectable fluorescence was observed due to lack of sporulation in the C. difficile R20291CM210. Both increasing the time of incubation and increasing thiamphenicol concentrations failed to induce sporulation for R20291CM210. After that, we introduced all transcriptional fusions into the C. difficile R20291CM196 WT strain. We found out that only P���ncRNA-cdeC-mScarlett transcriptional fusion that contains the promoter region with a deletion of the ncRNA shows a modest fluorescence signal compared to other fusions. These results suggest that ncRNA located in the promoter region of cdeC may have an activity for cdeC expression during sporulation
DATA: Active surveillance of cats and dogs from households with human COVID-19 cases reveals over one quarter of pets infected with SARS-CoV-2 in 2020-2021 in Texas, United States
No full textImproving the Ability of Activity Recognition Systems to Detect Activities of Daily Living Performed In-the-Wild
No full textFailing to keep track of the performance of activities of daily living (ADLs) can lead to adverse health outcomes for people with health concerns. However, current recommended practices for keeping track are tedious and burdensome, making it easy for people to forget or stop managing their health. Using activity recognition systems to automatically detect and record ADL performance would address this issue, but most works in activity recognition focus on controlled or semi-naturalistic data in contrast to real world, in-the-wild data. As such, real world ADL recognition remains an open problem. Specifically, real world ADL recognition requires tackling several fundamental challenges for machine learning systems, and it is unclear if existing approaches would be robust to these challenges. We expect that semi-naturalistic data does not capture the diversity of all of the everyday activities such a system would encounter and that robust performance requires using in-the-wild data.
In this work, we focus on quantifying the challenges associated with in-the-wild settings and investigating the design of in-the-wild ADL recognition systems. To achieve these goals, we conduct a series of analyses and machine learning experiments on two ADL datasets, one semi-naturalistic and one in-the-wild. First, we measure the class imbalance, interpersonal variability, and pairwise class overlap to motivate the difficulty of recognizing in-the-wild data. Second, we demonstrate the importance of training on negative samples, showing that training on NULL data results in more robust models than using unknown class rejection. Third, we investigate the design of in-the-wild ADL recognition systems, exploring both classical and deep learning methods as well as models with varying levels of context of the user���s hands. In doing so, we develop a recognition system that can recognize several ADLs with high event-based recall and precision with only the context of the dominant hand. These efforts represent a thorough investigation of a challenging open problem in human activity recognition. The results and insights serve as a meaningful step forward toward making robust in-the-wild ADL recognition a reality in order to make it easier for people to manage their health
Biological Roles of Bone Morphogenetic Protein 1 (BMP1) in Periodontium
No full textThis thesis explores the pivotal role of Bone Morphogenetic Protein 1 (BMP1) in the development and maintenance of the periodontium, employing Bmp1 conditional knockout (cKO) mouse models. The research involved creating a unique lineage of mice with a targeted deletion of the Bmp1 gene in dental follicle cells during early embryonic development, specifically in the progenitor cells destined to differentiate into alveolar bone osteoblasts, cementoblasts, and periodontal ligament (PDL) fibroblasts, known as Osr2-Cre;Bmp1^flox/flox mice. This study provides a detailed comparative analysis of the alveolar bone, cementum, and PDL structures in these Bmp1 cKO mice against normal control mice through various analytical methods, including plain x-ray radiography, histological examination, and immunohistochemical (IHC) analysis. Our findings reveal significant disruptions in PDL collagen fiber organization and bone matrix development, leading to substantial alveolar bone loss in Bmp1 cKO mice at both 6 and 24 weeks of age. Histological and IHC analyses highlighted a reduction in PDL integrity, abnormal protein distributions, and significant decreases in Dentin Matrix Protein 1 (DMP1) levels, suggesting BMP1's essential role in collagen synthesis and the overall mineralization process. Notably, irregular distribution patterns of periostin and fibrillin, underscore the profound impact of BMP1 deletion on dental morphology and periodontal health. The study conclusively demonstrates that BMP1 is critical for the structural integrity and functional maintenance of the periodontium, influencing various proteins involved in the development and health of the periodontal ligament and alveolar bone. The observed defects in Bmp1 cKO mice highlight the importance of BMP1 in collagen network maintenance and alveolar bone formation, with significant implications for periodontal disease pathology and treatment strategies. This research underlines the complex interplay between BMP1 and other proteins in periodontal development, providing invaluable insights into the mechanisms underpinning periodontal health and disease
Essays on Belief Updating and Decision-Making
No full textMy dissertation consists of three chapters on belief updating and decision-making.
The first chapter, coauthored with Marco Castillo, aims to understand how to mitigate negative economic consequences that can arise when individuals have biased beliefs about their surroundings. Specifically, we design two task assignment rules in teams based on a theoretical framework of Heidhues et al. (2018), and investigate their causal impact on task allocative efficiency. Using a series of laboratory experiments, we find that equally biased beliefs about team members��� productivities can lead to different efficiency outcomes depending on task assignment rules. The study highlights the importance of institutional designs that ensure economically desirable outcomes even in the presence of biased beliefs.
The second chapter, coauthored with Andy Cao, Marco Castillo, and Ragan Petrie, examines how non-pecuniary costs incurred during college impact college major choices. We conduct a randomized controlled trial to provide truthful information on major-specific teaching quality and inclusive climate to college freshmen and sophomores at Texas A&M University. We find significant effects of teaching and climate information, especially for choosing among business and economics majors. We also find that information has affected the college major choices of women more than it has for men. Our study shows that non-pecuniary aspects of human capital accumulation experienced during college can have an impact on the allocation of talent across fields.
The third chapter, co-authored with Hyundam Je, presents novel evidence that the timing of informativeness affects the level of motivated reasoning. Building upon the primacy effect, our framework predicts that receiving a signal before learning about its informativeness can exacerbate the extent of motivated reasoning compared to receiving it afterward. Our online experiment finds supporting evidence for this prediction. When participants learn about informativeness after receiving a signal, there is a stronger tendency for them to interpret the signal in a way that reinforces their preferred beliefs. These findings suggest the practical importance of structuring information as a strategy to mitigate motivated reasoning in information transmission
Effect of Overnight Storage Conditions on Dimensional Stability of Conventional Injection Molded, 3D Printed and Milled Complete Dentures
No full textStatement of problem: In modern prosthodontics the fabrication method of complete dentures has become more diverse than ever with the introduction of 3D printing and milling manufacturing. Despite the variety of manufacturing methods developed, the storage recommendations have remained virtually unchanged and untested for new manufacturing methods. Water storage has traditionally been advised at nighttime to prevent denture base distortion through the loss of moisture in the acrylic resin matrix. With the advent of CAD/CAM 3D printed denture bases and Milled denture bases from solid pucks of preprocessed acrylic the necessity to store dentures in liquid when not in use to the authors knowledge has remained untested.
Purpose: The aim of this study was to evaluate whether there is any typical deformation pattern of Injection molded, 3D printed and milled complete dentures stored in different overnight storage conditions for 14 days.
Materials & Methods: One edentulous maxillary typodont model was used as reference for fabrication of n=60 samples of identical digitally designed denture bases. Each denture base was approximately 2 mm thick and n= 20 samples were fabricated from each respective material; Ivobase injection molded pink hybrid denture base resin, Sprintray denture base EU 3D printed resin and Avadent Milled denture bases PMMA Puck LIGHT ��� Universal Shoulder. n=10 samples from each material group were randomly assigned to either a wet or dry storage group. All denture bases were submerged in 37 ��C Water bath for 12 hrs. and alternated for 14 days between their respective storage conditions and heated water bath simulating the oral cavity. Samples were scanned at 3-time intervals time ���0��� (T0), on the 7th day (T1), on the 14th day (T2). Scans were measured for deviations from the reference scan. Statistical analysis performed using Kruskal-Wallis with Bonferroni corrections (����=0.05) for between group differences and Related Samples Friedmans Two-way analysis of variance by ranks for within group differences.
Results: The Group 2 denture bases fabricated from the Ivobase and placed in the dry storage conditions demonstrated greater dimensional change with statistically significant differences in RMS value compared to the Group 1 Ivobase wet storage conditions (p< 0.003). In addition, the Group 3 & 4 Sprintray (wet/dry) demonstrated lower RMS values (p<0.007 & p<0.015) and better stability over time compared to the Group 2 Ivobase dry storage at time points T0-T2. The Avadent milled dentures demonstrated greater RMS value and distortion than the Group 1 Ivobase wet (p<.001), Group 3 Sprint ray wet (p<.001) and Group 4 Sprintray dry (p<.001). There was no significant difference in RMS value between the dry storage and wet storage of the Avadent denture bases.
Conclusions: Statistically greater dimensional changes were found in the dry storage group of Ivobase denture bases compared to the wet denture bases. Sprintray 3D printed denture bases exhibited no statistically significant dimensional changes in 14 days when stored in wet or dry conditions. Avadent milled denture bases exhibited no statistically significant dimensional changes in 14 days when stored in wet or dry conditions, but experienced greater dimensional change compared to Ivobase wet and Sprintray wet/dry storage conditions. All groups regardless of timepoint or storage conditions experience dimensional changes less than <1mm
Uncertainty-Aware Data-Driven Approaches for Modelling Sparse Agricultural Datasets to Sustain a Society
No full textThe objective of my dissertation is to tackle the issue of sparse datasets in case of agricultural domain to design data-driven approaches that can be used to make Decision Support Systems (DSS) for optimal growth of plants, thereby reducing the cost of labor as well as improving the overall food security and environmental sustainability. In order to achieve this, my research is structured into three primary components. Firstly, it focuses on utilizing Machine Learning (ML) models and data-driven approaches to optimize nutrients in hydroponic and aquaponic environments, enhancing the growth of fish and plants within a unified system using two distinct methodologies. This addresses the inherent sparsity in agricultural datasets. Secondly, the thesis delves into the development of forecasting models for in-season prediction of canopy features in cotton crops. This predictive capability enables timely management decisions to maximize crop yield. The third objective involves the creation of data-driven approaches to model growth stages and nutrient uptake of soybeans cultivated in hydroponic environments, spanning from seeding to maturation
John Bickham field notebook: Mamm_AK1-AK100.pdf
No full textEach page/AK number corresponds to a karyotype slide data and/or unique specimen.Data pages for Mamm_AK1-AK100 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
R.M. Pitts field not book: Pitts_7354-12755.pdf
No full textEach page/AK number corresponds to a karyotype slide data and/or unique specimen.Data pages for Pitts_7354-12755 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
In Tlilli, in Tlapalli ���The Black Ink, The Red Ink���: Indigenous Experiences in Society and Healthcare from an Indigenous Philosophy
No full textThis dissertation consists of three articles that will address aspects of the Indigenous social and medical experience within United States society. Elevating Indigenous epistemologies and criticisms of standard epistemological traditions in concert with an Indigenous focused and crafted research method informs the overall methodology and analysis of the project. This focuses the paper to be an explicitly Indigenous focused research project created by an Indigenous person utilizing Indigenous millennia knowledge.
Indigenous peoples interact with healthcare systems just as much as any other social or ethnic group in the U.S., however they are unique since the U.S. government is obligated by treaty to provide healthcare for Indigenous communities through the Indian Health Service (IHS). Indigenous people are the only racialized ethnic group in the U.S. to be guaranteed such healthcare which creates a complex system where a colonial government and bureaucracy are directly responsible for some of the most important, sensitive, and life-threatening aspects of a racialized group���s lives. The first section will provide a discussion of the white racial framing and anti-Indigenous subframing of Indigenous people in the U.S. The second section will provide a brief tee-up for the third section by providing an overview and discussion of Indigenous history in the U.S., the scholarly defined eras of Indigenous history, and a comparison of Indigenous and Western bioethics. The final section will address how the history of anti-Indigenous framing has impacted the Indian Health Service and the federal government���s shocking mismanagement of the IHS, however it will also highlight how Indigenous nations are taking back their healthcare and fulfilling hard-fought principles of sovereignty