136879 research outputs found
Sort by
Customer Visitation Pattern: A Robust Heterogenous Network Model of Human Mobility
No full textStudying human mobility across time and space has significant implications for urban planning, business management, and disaster studies. However, previous research in this area has identified several gaps, including a lack of detailed understanding of the connection between location characteristics and visit frequencies, a lack of suitable spatial-temporal network models, and limitations in applying existing models to various scenarios. To address these gaps, this doctoral dissertation research aims to provide a general framework from a network perspective to model customer visit patterns, particularly before and after natural disasters. This research will develop a Geospatial Artificial Intelligence (GeoAI) based framework to derive the visitation pattern from heterogeneous data sources, such as mobile phone trajectories, online reviews, and official census data. The proposed research will address the following research questions: How to quantitatively delineate customer visitation patterns based on mobility data and deep learning? What is the typical visitation pattern, and how does it change after a natural disaster? What would the visitation pattern be if the business changed some strategies? The proposed research will model heterogenous data in a network and use deep learning methods to validate and derive knowledge from it. This knowledge can guide business management and support spatial decision-making. Overall, this research will contribute to the development of a network perspective framework for modeling customer visit patterns, which can be applied to various scenarios and guide urban planning and business management decisions
Ira Greenbaum field notebook: GK3501-GK4000.pdf
No full textBound book, each page corresponds to a karyotype slide data.Data pages for GK3501-GK4000 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
Role of Steroid Hormones and Their Neuroactive Metabolites in Pavlovian Fear Conditioning
No full textIn the United States, one in every three individuals will experience an anxiety disorder in their lifetime. Importantly, there are significant sex differences in the incidence of these disorders. Anxiety disorders are almost twice as prevalent in females than in males, particularly post-traumatic stress disorder (PTSD). This significant sex difference indicates a possible role for gonadal hormones in the development of these disorders. Indeed, progesterone, estrogen, and testosterone have been found to modulate fear and anxiety in clinical populations and in rodent research models. A better understanding of how hormones contribute to the development and maintenance of anxiety disorders is vital to the development of targeted, effective treatments. Here, we use a Pavlovian fear conditioning model in which rats are trained to associate a context and cue with an aversive footshock, which leads to conditioned responding in the absence of the footshock. Using this model, we explore the role of progesterone (PROG), its metabolite allopregnanolone (ALLO), and the testosterone metabolite 3a-androstanediol (3a-diol) on the acquisition, expression, and extinction of conditioned fear. Ovariectomized females were treated systemically with PROG whereas intact males received either ALLO or 3a-diol infusions into the bed nucleus of the stria terminalis (BNST), a sexually dimorphic brain structure shown to modulate anxiety and context-dependent fear. Our laboratory has previously shown that intra-BNST ALLO in male rats and estrous cycle phase in cycling female rats can confer state dependence to contextual fear such that retrieval of the fear memory is optimal when animals are tested in the same hormonal state experienced during conditioning. The results here demonstrate that although acute changes in systemic PROG of ovariectomized females can causes changes in conditioned fear expression, they do not mirror the state-dependent regulation observed in cycling females. In male rats, intra-BNST 3a-diol confers state dependence to contextual, but not cued, fear such that animals trained and tested in different hormonal states exhibit a state-dependent generalization decrement. Finally, we show that in male rats, intra-BNST ALLO can modulate extinction learning but not in a state-dependent manner. The work presented here further demonstrates that hormones and their neuroactive metabolites can influence different aspects of acquisition, expression, and extinction of conditioned fear and supports the consideration of interoceptive cues associated with hormonal state when developing treatments and interventions for anxiety- and trauma-related disorders
Exploiting Plant-Based Protein Functionalities Through Tannin-Mediated Structural Modification and Application as Texturized Vegetable Protein (TVP��) and Edible Film
No full textThe surging global demand for alternative diverse and sustainable sources has been driven by an increasing global population and a growing number of health-conscious consumers. The current trend has caused a significant focus on pulse proteins as a potential alternative, primarily because of their exceptional nutritional profile and the reduced consumer concerns surrounding allergens, and negative perception. As a result, research initiatives are now underway to improve the functionality and value of pulse proteins, with a specific goal of enhancing their role as alternatives meat source as texturized vegetable proteins (TVP��) and edible films. The major obstacle for pulse proteins is their limited effectiveness when compared to conventional sources. Pulse proteins face limited competitiveness due to high solubility and low cross-linking propensity in comparison to traditional meat sources. This stems from their high solubility and a low propensity for cross-linking, which affects their ability to imitate the texture and structure of the meat. To address these areas, this research primarily revolves around the utilization of polymeric polyphenols, specifically proanthocyanidins (PA), to modify and enhance the structure and rheological properties of pulse proteins. The fundamental premise of this approach is to harness the abundant biofunctional polyphenols found in tannins to fundamentally alter the characteristics of pulse proteins. This alteration aims to improve key attributes such as solubility, water-binding capacity, and texture. The ultimate objective is to make pulse proteins highly functional and competitive as meat substitutes and edible films, thereby meeting the demands of health-conscious consumers while addressing concerns about the sustainability of food production systems.
In this research, we investigated the effect of PA on pulse protein rheology and film properties and assessed the mechanisms behind these interactions. The main focus of this research was to develop texturized pulse proteins (TXVP) utilizing a twin-screw extruder using pea proteins (PP), lentil proteins (LP), faba bean proteins (FP) and conduct post testing with these texturized proteins extrudates. Commercial pea protein (77.4% protein), lentil protein (81.9% protein), and faba bean protein (80.9 % protein) were prepared for production of texturized vegetable proteins and soy proteins (SP, 66.5% protein) was used as a control.
Polymeric PA from sorghum (mean degree of polymerization, mDP 19.5) dramatically strengthened pulse protein, e.g., at 2.5 mg/g flour. The network of the texturized proteins from the two pulses (pea and faba) also exhibited increased hardness and springiness, which are indications of the protein crosslinking and holding together better with increasing levels of PA. Lentil proteins did not texturize and that of soy was not showing consistent properties. Polymeric surface hydrophobicity of protein (pea and faba) was reduced by PA (69 ��� 75% vs control).
To evaluate the opposite behavior of lentil protein during extrusion (did not texturized because of its high-water holding capacity), lentil protein and pea protein were utilized for making edible films. Polymeric PA increased lentil protein film strength (e.g., at 2.5 mg/g protein, force to extend was 2.3X greater than control without reduced extensibility). Thus, PA may improve lentil film flexibility and structural integrity. Overall evidence indicates PA complexed with pulse protein by hydrophobic interaction and hydrogen bonding. In conclusion, the research outlined in this study holds the promise of transforming pulse proteins into versatile and sustainable alternatives to traditional meat sources and edible films. By enhancing their functionality and competitiveness, this work seeks to contribute to the global effort to promote sustainable food production and meet the evolving dietary preferences of a growing population
Using Stimuli-Responsive Material for the Design and Fabrication of Artificial Muscles
No full textStimuli-responsive materials that change shape (i.e., elongate, contract, and/or twist) when exposed to an appropriate stimulus are promising candidates to replace traditional machines in biomedical devices. This dissertation explores the innovative use of stimuli-responsive materials in addressing the challenges of treating stress urinary incontinence (SUI), a condition that affects nearly 50% of women during their lifetime. Current treatments for SUI are associated with complications leading to undesirable outcomes such as postoperative voiding dysfunction. The research, divided into four key chapters, focuses on the development and application of artificial muscle devices based on two distinct stimuli-responsive materials ��� Liquid Crystal Elastomers (LCEs) and Magnetoactive Elastomers (MAEs) for the potential treatment of SUI.
In Chapter I, the dissertation commences with a comprehensive introduction to stimuli-responsive materials, elucidating their pivotal role in the design and fabrication of artificial muscles. Emphasizing the versatility of two materials (LCEs and MAEs), the chapter provides a foundation for their application in the subsequent chapters. Chapter II delves into the pathophysiology of SUI, providing a thorough overview of the condition, including its causes, prevalence, and impact. This section establishes the contextual framework for the subsequent exploration of the subsequent development of LCE and MAE-based devices for urethral support. We also provide relevant information that must be considered when designing in vitro models of the urinary tract and selecting appropriate animal models to evaluate devices. Chapter III focuses on the design, fabrication, and in vitro and in vivo evaluation of a dynamic urethral support device based on LCEs. In Chapter IV, I extend the exploration to MAEs and investigate their integration into a dynamic urethral support device. MAE-based devices were fabricated, characterized, and then evaluated using a simple in vitro urinary system simulating the effects of stress or cough.
Collectively, this dissertation contributes to the interdisciplinary field of biomedical engineering by integrating stimuli-responsive materials with innovative solutions for SUI. Investigating the potential of LCEs and MAEs in providing adaptive and customizable support, this dissertation presents a novel approach to addressing SUI through advanced materials. The findings presented herein pave the way for further advancements in the design and fabrication of artificial muscles, offering hope for improved therapeutic interventions for complex healthcare challenges
Immunomodulatory Roles of Renal Lymphatic Endothelial Cells in Kidney Injury
No full textAcute Kidney Injury (AKI) is defined as a sudden decrease in renal filtration and is found to be associated with increased risk of developing chronic renal conditions such as Chronic Kidney Disease (CKD) and End-Stage Renal Disease (ESRD). While the mechanisms of the AKI-to-CKD transition remain unclear, AKI associated injury promotes an environment of persistent renal inflammation and fibrotic remodeling which may drive irreversible loss of nephrons and other renal cell types. Inflammation associated lymphangiogenesis (IAL) maintains tissue homeostasis through the uptake of proteins, fluids, macromolecules, and immune cells. IAL occurs upon AKI insult and is driven by Vascular endothelial growth factor D (VEGF-D) and VEGF-C. Recent work has demonstrated lymphatics and their components, lymphatic endothelial cells (LECs), have immunomodulatory roles such as antigen presentation, major histocompatibility complex (MHC) expression, and chemokine receptors. This dissertation investigates how renal lymphangigogenesis and LEC genetic adaptations in various models of kidney injury alter the adaptive immune cells in the kidney and the outcomes of renal function post-injury. Increased renal lymphatic density prior to the onset of kidney injury in a mouse model of kidney specific inducible overexpression of VEGF-D (KidVD) demonstrated an altered CD4:CD8 T cell ratio, decreased fibrotic remodeling, and improved functional recovery. Single cell RNA sequencing (scRNA seq) revealed that after AKI isolated renal LECs have T cell related immunomodulatory roles. To address the direct effect of LEC- T cell interactions, a murine genetic model with a LEC specific deletion of the Sphingosine-1- Phosphate (S1P) transporter Spinster 2 (SPNS2), key for S1P chemokine gradients to direct tissue immune cell egress, revealed altered renal immune cell subtypes and differential responses to injury challenge. Additionally, mice with disrupted LEC-S1P signaling demonstrate increased renal immunoglobulin deposition and, with nephrotoxic serum challenge, increased B cell activation and B effector subtype presence. This dissertation strengthens the connection between lymphangiogenesis and immunomodulation in kidney injury through the alteration of the adaptive immune response and demonstrates lymphatic targeted therapeutics may provide a novel renal targeted treatment option to prevent inflammatory progression to CKD
Views of Rural Community Members on Building or Rebuilding Public Trust in Healthcare
No full textHow, why, and what determines public trust in health entities is a critical element that is yet to be well documented particularly among rural residents. Identifying the perceptions of rural community members on trust in health entities is considered a fundamental strategy for improving health outcomes. To investigate the views of rural community members on trust in health entities, the purpose of this study was to assess and conceptualize a framework of building and rebuilding public trust in health entities among rural communities in the U.S.
Two different research strategies were utilized to complete this dissertation. First, a mixed-methods approach including a review process with systematic elements was performed to locate and evaluate the literature followed by a narrative approach to complete a scoping review. Second, a qualitative research study was used to explore the views of rural community members on trust in health entities using three different interview formats (intercept, dyadic, and focus groups).
Three major themes emerged based on both the scoping review and the qualitative research study. The themes included sources of information, trust barriers, and rebuilding trust which further resulted in other subthemes within each of them. The most common sources of information identified by both studies were medical professionals. Trust barriers according to the scoping review were specific to the health outcome and therefore the response identified varied from what was recorded in the qualitative studies. The qualitative study identified low expectations as the most dominant barrier of all others listed. Likewise, strategies identified for rebuilding trust included the inclusion of leadership engagement strategies, increasing awareness through information, and availability of services as the most presented.
In conclusion, the findings of the dissertation noted that strategic and multidimensional measures are required that are sustainable and long-term in order to increase public trust in health entities among rural communities in the U.S
Superconductivity Near a Quantum-Critical Point: Analysis of the Discrete ��-Model at Finite Temperatures
No full textNear a Quantum-Critical Point (QCP) in a metal, strong Fermion-Fermion interaction mediated by a soft collective Boson gives rise to two competing tendencies, non-Fermi Liquid behaviour and Superconductivity which differs from the standard BCS theory. In this thesis, we consider a class of models, known as ����� Model, in which the effective interaction potential takes the form V (���) ��� 1/|���| �� . We introduce some numerical and semi-analytical techniques to analyze the behaviour near QCP. Based on the mapping between Eliashberg theory and the classical spin chain, we get a discrete Hamiltonian in terms of the angles made by individual spins. 2 different numerical approaches are introduced to solve the infinite number of coupled non-linear equations resulting from minimizing the Hamiltonian, which will yield the topologically distinct minima and saddle points. We find that the minimum energy state is always a superconducting state with winding number n = 0. Other superconducting states were also found, they represent topologically different pairings with different winding numbers. These states represent the saddle points of the free energy functional. We focus our analysis mainly on the case 1 < �� < 2 at finite temperatures
Connecting the Dots: Improving Information Extraction by Modeling the Non-Sequential Dependencies of Entity Mentions
No full textInformation Extraction (IE) aims at automatically extracting structured information from unstructured and semi-structured documents. It is an important and challenging topic in Natural Language Processing (NLP) that plays a critical role in downstream applications such as Question Answering and Summarizing. It includes many sub-tasks like Named Entity Recognition (NER), Relation Extraction, Event Extraction, Table Annotations, etc.
Previous works on IE mainly focus on extracting knowledge from a document sentence by sentence and the text encoding models (e.g., RNNs, CNNs, and Transformers) regard the text as a linear sequence from the left to the right or from the right to the left. However, we humans do not always understand a document by sequentially reading it. We tend to connect the concepts across the whole document and then form structural knowledge in our brains. Motivated by the intuition, this work proposes to introduce the non-sequential connections within a document, and uses the relations among the entity mentions as the surrogates for the non-sequential conceptual connections to further improve the performance of the IE systems.
Firstly, I propose to connect the related entity mentions in a document and enforce information flow among them for consistent entity type predictions. Specifically, the work connects both the local dependency relations and global coreference relations for the entity mentions to build better entity mention representations. Experimental results show that applying Graph Neural Networks (GNNs) on the connections can improve the NER performance over strong baselines on two domain-specific datasets.
Secondly, I propose to connect the conceptually related regions in a document and encourage semantic interactions within and among regions. In particular, it builds the connections among the candidate role fillers (i.e., entity mentions from an event mention) for the event extraction task, and characterizes the connections based on different regional affiliations. Then edge-aware GNNs are applied to update the representations of the candidates for false positive filtering. Empirical results show that the proposed method can yield new state-of-the-art performance on two document-level event extraction datasets in two different languages.
Lastly, I propose to connect the entity mentions from semi-structured tables and incorporate the table structures into the table element representations, benefiting table annotation (knowledge extraction from tables) tasks. Specifically, the system will first build hyper-graphs for the cell values coming from the same row or column and then apply the hyper-graph Neural Networks to learn better table representations. The evaluation and analysis show the effectiveness of the structure-aware table representations in improving the table annotation tasks