EdD, University of Cincinnati, 2006, Education : Urban Educational Leadership

The primary purpose of this research study was to investigate parents', teachers', and students' perception of performance projects being used as an additional or alternative form of assessing student achievement. Projects in this study were tied to learning goals, and were representative of curricular thematic units. Examples of projects would be student-generated videos, poems, songs, raps, commercials, game shows, skits, brochures, dioramas, posters, or written reports, to name a few. Projects provided students an equitable opportunity to express knowledge, and demonstrate major understandings in multiple ways. All projects were guided by rubrics, as well as teacher expectations and approval. The secondary purpose was to view paper/pencil, high-stakes standardized tests, in addition to projects, through the lens of Brain-Compatible Learning theory. Perceptions of projects as a tool of assessment were being investigated because of their inherent correlation to the core tenets of BCL theory, which involves how the brain receives, stores, and retrieves information for optimal learning. High-stakes tests were being viewed through the lens of BCL theory because of their punitive characteristics, which seem antagonistic to producing an educational environment that is conducive to optimal learning and academic achievement. Two school sites were chosen for this study. One site was located in an affluent, suburban neighborhood, and the other was located in an economically deprived, urban neighborhood. This research study, while modest, is authentic and unique in that it provides scientific findings as reported in the literature, that support the perceptions of the parents, teachers, and students that participated.

Committee: Dr. Mary Pitman (Advisor) Subjects:

Doctor of Philosophy, The Ohio State University, 2020, Biomedical Sciences

There are trillions of cells, which make up hundreds of different cell types, found in the human body. These cells make up not only tissues but dictate the functions of those tissues. In diseased tissues, cell types can have a profound impact on the outcome of a patient. For these reasons, having a comprehensive understanding of cell types is important. In the past 10 years, single cell RNA sequencing has profoundly impacted our understanding of known and previously unknown cell types. Along with the numerous single cell datasets, a multitude of bulk expression datasets, multi-omic datasets, and curated information also exist. All of these data sources must be leveraged together to most improve our understanding of human tissues and diseases at the single cell level. We developed methodologies, frameworks, and algorithms that leverage multiple diverse datasets simultaneously to better understand single cell RNA sequencing data and as a result tissue heterogeneity as a whole.

Committee: Yan Zhang (Advisor); Kun Huang (Advisor); Jeffrey Parvin (Committee Member); Christopher Bartlett (Committee Member) Subjects: Bioinformatics; Biomedical Research

Master of Arts, University of Toledo, 2014, College of Languages, Literature, and Social Sciences

Recently, there has been an increasing interest in incorporating one of the innovative technologies, the iPad, into the learning-teaching process to enhance students' academic success in different educational contexts. However, there are a number of factors that may influence the students' choice whether or not to use the iPad. Therefore, assessing the students' behavioral intentions towards using the iPad is necessary. Accordingly, this paper examines students' behavioral intentions towards using the iPad in their language learning courses through utilizing the Technology Acceptance Model of Davis (1989). Twenty five undergraduate student participants completed the iPad-usage questionnaire to measure their perceived usefulness (PU), perceived ease of use (PEOU), attitude towards usage (ATU), and behavioral intention to use the iPad (BIU) in their integrated language learning courses (reading, writing, listening, and speaking). The results of the study indicated that students' perceived usefulness and perceived ease of use of the iPad positively predicted the students' attitudes towards using the iPad and their behavioral intentions to use it in their language classes and other contexts.

Committee: Douglas Coleman PhD (Committee Chair); An Chung Cheng PhD (Committee Member); Gaby Semaan PhD (Committee Member) Subjects: Adult Education; Behavioral Psychology; Cognitive Psychology; Educational Technology; English As A Second Language; Experiments; Modern Language

Master of Arts, The Ohio State University, 1963, Graduate School

Committee: Not Provided (Other) Subjects:

PhD, University of Cincinnati, 2024, Medicine: Biomedical Informatics

The advent of sequencing technologies has revolutionized our understanding of disease. Researchers can now investigate the complex processes involved in the multi-layered transcription of genetic content, which regulates cell activity, homeostasis, and ultimately the organism's health. A disease can be conceived as a deviation from a homeostatic state, leading to cascading negative effects. A disease state, or more generally a disrupting factor (sometimes called a "perturbagen"), can be characterized by how it impacts the organism. This information constitutes its "signature", such as a list of differentially expressed genes or vectors of abundance of proteins or lipids. Significant efforts have focused on gathering these signatures into connectivity maps (CMAPs), which allow the identification of related disrupting factors based on the similarity of their signatures. CMAPs can overcome some limitations of traditional enrichment analysis. However, challenges remain. The integrative analysis of multi-domain data, as opposed to concurrent or sequential analysis, is still a challenge. The complexity of multi-omics analysis, involving retrieving datasets, annotations, and applying analytical pipelines, requires advanced programming skills, which can be a barrier for researchers without dedicated resources. Additionally, analysis pipelines need to scale up as assays become clinically available and more data is generated. To address these challenges, we developed machine learning tools to predict health outcomes, ranging from sepsis to dementia. Our goal is to build knowledge and expertise about integrative and extensible analytical pipelines for clinical, transcriptomics, and proteomics data. Specifically, we developed a statistical and machine learning model to classify patients by phenotype and predict mortality risk. We analyzed a prospective cohort of sepsis patients, selected predictive features, built and validated models, and then refined a robust model u (open full item for complete abstract)

Committee: Jaroslaw Meller Ph.D. (Committee Chair); Michal Kouril Ph.D. (Committee Member); Robert Smith M.D. Ph.D. (Committee Member); Faheem Guirgis Ph.D M.A B.A. (Committee Member); Michael Wagner Ph.D. (Committee Member) Subjects: Bioinformatics

Master of Science in Electrical Engineering, University of Dayton, 2024, Electrical Engineering

This thesis presents a novel method to control an industrial robotic arm using multiple sensors. This system consists of a hybrid brain activity and vision sensors that convey a human being's intention and visual perception. We fuse and analyze the data from those sensors using a machine learning-based approach to automatically guide the manipulator to a designated location. We believe that this Brain–Machine–Interface (BMI) can greatly alleviate the burdensome traditional method used to program a robot (greatly aids the end-user). We experiment with different modular configurations for the brain activity information, i.e., parallelized models and what we refer to as a global model for fusing the information. We explore various machine learning and pattern recognition techniques as well as existing feature selection methods. Our experimental results show that the subject can control the robot to a destination of interest using a machine—robot–interface. We attain accuracy in the order of 99.6% when it comes to the desired motion and 99.8% for the case of deducing the desired characteristic (color) of the targeted object. These results outperform any similar existing approaches that we have researched. Moreover, in comparison to those similar operational systems, we present a unique modular configuration for brain activity interpretation and object detection mechanism that yields an overall system that is highly computationally efficient. Although, in this work, we implemented and demoed our approach using a simple pick and place demo, our work presents the basic structure underlying a system that can be efficiently used to benefit people with restricted ability to function physically (tetraplegic patients), and allowing them to perform complex and robotics related duties in an industrial setting.

Committee: Temesguen Messay-Kebede (Advisor); Barath Narayanan (Committee Member); Russell Hardie (Committee Member) Subjects: Computer Engineering; Computer Science; Electrical Engineering; Engineering; Remote Sensing; Robotics

MS, University of Cincinnati, 2024, Engineering and Applied Science: Mechanical Engineering

This study proposes a novel approach to intracranial pressure (ICP) analysis. ICP is the pressure exerted by fluids and tissue inside of the brain and reveals crucial insights regarding a patient's physiological state after undergoing traumatic brain injury (TBI). ICP waveform morphological analysis can provide clinicians with information regarding a patient's health and facilitate proactive intervention and inhibit the development of secondary pathologies such as cerebral edema (swelling), ischemia (lack of blood to the brain), vascular injuries, neurological dysfunction, and cognitive/behavioral changes. By integrating arterial blood pressure (ABP) and electrocardiogram (ECG) data from patients who have undergone TBI this method aims to enhance the analysis of not just ICP waveform morphology but cross-signal morphological features. The proposed methodology was evaluated on ten patients and their respective ICP, ABP, and ECG data; it involved three key steps: 1) multimodal signal pre-processing alongside manual labeling of ICP waveform morphologies to train a base support vector machine (SVM) morphological classifier. 2) The use of semi-supervised learning leveraging a subject matter expert (SME) to further train the SVM ICP waveform morphological classifier and augment its training data set on all ten patients to assign incoming pre-processed ICP waveforms with a morphology label. A SME used the posterior probability of the SVM machine learning model to aid the algorithm in adapting to new and unseen ICP waveform morphologies that were not present in the initial manually labeled SVM training data set. 3) The utilization of dynamic time warping barycenter averaging (DBA) to produce representative averages (centroids) of ICP waveforms present in the SVM training data set and derivative dynamic time warping (DDTW)-driven subpeak identification to map subpeaks from DBA generated centroid templates with SME assigned ground truth subpeak(s) to incoming SVM clas (open full item for complete abstract)

Committee: Xiaodong Jia Ph.D. (Committee Chair); Brandon Foreman M.D. (Committee Member); Manish Kumar Ph.D. (Committee Member) Subjects: Mechanical Engineering

Master of Science, The Ohio State University, 2023, Computer Science and Engineering

Optimizing rehabilitation stays is crucial for quick and effective treatment of patients. Traumatic Brain Injury (TBI) treatment at rehabilitation facilities involves selecting different sets of therapeutic activities depending on the medical and functional status of the patients. Predicting how much functional improvement a patient can achieve or how much care assistance is reduced with a tailored set of activities can inform on the efficacy of the selected therapy activities. In this study, our objective is to predict such efficacy given a set of treatment activities and the status of the patients. The efficacy is quantified using treatment outcome measures, such as the length of stay (in days) at the rehabilitation facility, whether the patient is discharged to home, FIM cognitive and FIM motor scores at discharge and at 9-months post discharge. Advanced machine learning models, specifically gradient boosting tree model, performed consistently better than all other models, including classical linear regression models. Top ranked predictive features were identified for each of the six outcome variables. Level of effort shown during rehab, days to rehab admission from injury, age at rehab admission, and advanced mobility activities were the most frequently top ranked predictive features. The highest-ranking predictive feature differed across the specific outcome variable. Identifying patient, injury and rehabilitation treatment variables that are predictive of better outcomes will contribute to cost-effective care delivery and guide evidence-based clinical practice. Machine learning methods can contribute to these efforts. The most predictive features out of the gradient boosting model are then validated against domain knowledge for clinical accuracy.

Committee: Xia Ning (Advisor); John Paparrizos (Committee Member); Cynthia Beaulieu (Committee Member) Subjects: Artificial Intelligence; Bioinformatics; Computer Science; Rehabilitation

Doctor of Philosophy, University of Toledo, 2023, Physics

A major concern in radiation therapy has always been to deliver the prescribed dose to a tumor volume while keeping the surrounding organs at risk (OAR) safe. This is significantly important for cases of using Intensity Modulated Radiosurgery (IMRS) with a single isocenter to treat multiple brain lesions. In these cases, small and wide spread-out tumors in the brain are irradiated with larger doses while sparing OAR is critical but significantly more challenging. It is common to perform pre-treatment verification to make sure the accurate treatment dose is delivered. Numerous applications including predictive modeling of treatment outcomes in radiation oncology, treatment optimization, error detection, and prevention have been developed and are now accessible. Typically, patient-specific QA compares dose distribution generated by the treatment planning system (TPS) with the delivery of that patient's treatment plan to an array of detectors. In other words, patient-specific QA (pre-treatment verification) compares the dose distributions between measured and predicted. This process compounds numerous potential sources of error, including dose calculation, data transfer, linac performance, device setup, and dosimeter response among others. Consequently, the cumulation of errors may cause the results to fail. Several reports show that common pre-treatment verification measurements are insensitive to delivery errors mand unable to predict the acceptability of plan delivery. Therefore, by using machine learning, we plan to devise an algorithm to achieve a higher level of understanding and insight to improve each patient's treatment plan and safely deliver precise radiation to tumors while minimizing the radiation dose to the surrounding normal tissues. Another crucial task is the differentiation of Radiation Necrosis (RN) from recurrence tumors. RN is one of the common adverse effects resulting from irradiation to the brain, nevertheless, RN is hard to diagnose and m (open full item for complete abstract)

Committee: Aniruddha Ray Ph.D (Committee Chair) Subjects: Medical Imaging; Oncology; Radiation

Doctor of Philosophy, Case Western Reserve University, 0, Molecular Medicine

Novel therapeutic regimens which spur the endogenous immune system to kill cancer cells, such as stereotactic radiosurgery (SRS) and immune checkpoint inhibition (ICI), are heterogeneously effective. Understanding causal factors of response is vital to guide risk assessment and treatment decisions. In this thesis, I evaluate the ability of three methods to prognosticate survival for brain metastases patients following SRS and ICI treatment. These include the clinically utilized response assessment in neuro-oncology for brain metastases (RANO-BM) protocol, as well as investigational computational methods such as radiomic feature analysis and convolutional neural network (CNN) image analysis. I find that easing the 10mm RANO-BM diameter threshold for measurable disease allows new lesions to be discovered as proof of progression in ICI-treated metastases. Further, I find that the trajectory of RANO-BM diameter can be more instructive for risk prediction than the ratio-change and that neither volume nor number of metastases, nor RANO-BM diameter can significantly predict survival until a year after treatment. Reproducing common radiomic methodology flaws observed in the published literature, I demonstrate that inconsistent partitioning, or the improper division of radiomic feature data into Training, Validation, Test, and External test sets, can provide a 1.4x performance boost to reported accuracy (AUROC) for predictive models. Additionally, I highlight how spurious correlations with biological variables can overstate the importance of radiomic features. Leveraging the conclusions from my radiomic reproduction study, I assess the ability of radiomic features and convolutional neural networks (CNNs) to predict overall survival in the largest ICI-treated brain metastases cohort assembled to date, comprising 175 patients from three institutions in two countries. I find that neither radiomic features nor any architecture of the survival AI model MetsSurv is capable of p (open full item for complete abstract)

Committee: Jacob Scott (Advisor); Brian Rubin (Committee Chair); Elizabeth Gerstner (Committee Member); Anant Madabhushi (Committee Member); Jayashree Kalpathy-Cramer (Advisor); Nathan Pennell (Committee Member) Subjects: Artificial Intelligence; Computer Science; Immunology; Medical Imaging; Molecular Biology; Neurology; Oncology; Radiology

Doctor of Philosophy (PhD), Wright State University, 2021, Engineering PhD

Measuring cerebral blood flow (CBF) is a crucial element in monitoring a vast variety of human brain disorders. Current imaging modalities used for measuring CBF has various limitations that restricts its usefulness especially in the neuroscience intensive care unit (NSICU). Here, the use of Time-gated DCS (TG-DCS) which has significant advantages compared to its predecessor, CW-DCS, was proposed as the solution. However, this technology is still in its infancy and its clinical capability has yet to be established. To show the feasibility of deploying TG-DCS in NSICU settings, the time-domain analytical model for TG-DCS was expanded for multi-layered cases. Next, CW-DCS was validated in humans and in NSICU settings on patients suffering from Traumatic Brain Injury (TBI). A prototype 1064nm TG-DCS system was built and validated on several in-vivo experiments. Finally, the feasibility of the system was shown by deploying it in NSICU settings for measuring CBF in TBI patients. Lastly, deep learning was used to show the feasibility of obtaining real-time results.

Committee: Ulas Sunar Ph.D. (Advisor); Sherif Elbasiouny Ph.D. (Committee Member); Robert Lober M.D., Ph.D. (Committee Member); Brandon Foreman M.D. (Committee Member); Jonathan Lovell Ph.D. (Committee Member) Subjects: Artificial Intelligence; Biomedical Engineering; Biomedical Research; Biophysics; Medical Imaging; Neurosciences; Optics

MS, University of Cincinnati, 2020, Engineering and Applied Science: Computer Science

We propose a novel signal processing algorithm to detect spreading depolarizations using ECoG (Electrocorticography) signals. The spreading depolarizations are considered to be the significant pathomechanism responsible for lesion development or a secondary injury. The development of a detection algorithm for Cortical Spreading Depolarizations (CSD) will help in providing targeted therapeutics and advancing the treatment for Traumatic Brain Injury(TBI), which is the major cause of mortality and disability in adults.

Committee: Anca Ralescu Ph.D. (Committee Chair); Kenneth Berman Ph.D. (Committee Member); Jed Hartings Ph.D. (Committee Member) Subjects: Computer Science

Master of Sciences, Case Western Reserve University, 2020, Biomedical Engineering

Genomic characterization of Medulloblastoma (MB), an aggressive pediatric tumor, has recently identified 4 distinct molecular subgroups: Sonic Hedgehog (SHH) , Wingless (WNT) , Group 3, Group 4 each exhibiting different clinical behavior. The molecular sub-types have unique risk-profiles and outcomes, and patients could potentially benefit from sub-group specific treatments. However, the transition of these molecular MB sub-types into clinical practice has been limited due to challenges in availability of molecular profiling in most clinics. The hypotheses we sought to examine in this preliminary study was whether computer extracted deformation features of medulloblastomas from T1-weighted MRI could independently (1) distinguish between molecularly determined subgroups, and (2) distinguish between high risk and low risk MB patient populations based on their overall survival. Our feasibility results suggest that the subtle tissue deformation features in the brain around tumor region on routine MRI may potentially serve as surrogate markers to non-invasively characterize molecular sub-types, as well as to predict survival risk in pediatric MB patients.

Committee: Pallavi Tiwari PhD (Advisor); Anant Madabhushi PhD (Committee Member); David Wilson PhD (Committee Member); Benita Tamrazi MD (Committee Member) Subjects: Biomedical Engineering

Doctor of Philosophy (PhD), Ohio University, 2019, Electrical Engineering & Computer Science (Engineering and Technology)

Medical image segmentation is the procedure to delineate anatomical structures and other regions of interest in various image modalities. While crucial and often a prerequisite step for other analysis tasks, accurate automatic segmentation is difficult to obtain, especially for three dimensional (3D) data. Recently, deep learning techniques have revolutionized many domains of artificial intelligence (AI) including image search, speech recognition and 2D/3D natural image/video segmentation. When it comes to 3D image segmentation, however, the majority of deep learning solutions either treat 3D volumes as stacked 2D slices, overlooking the adjacent information between slices, or directly perform 3D convolutional operations with isotropic kernels that are inconsistent with the anisotropic dimensions in 3D medical data. Neural networks based on 3D convolutions tend to be computationally costly, as well as require much more training data to account for the increased number of parameters that need to be tuned. The scarcity of annotated data in medical imaging also adds up the difficulty. To remedy the aforementioned drawbacks of existing solutions, we propose two works for 3D volume segmentation in this dissertation. The first work is multi-view ensemble convolutional neural network (CNN) framework in which multiple decision maps generated along different 2D views are integrated. The second work is a novel end-to-end deep learning architecture that combines CNN and Recurrent Neural Network (RNN) to better leverage the dimensional anisotropism in 3D medical data. Our model is designed with the aim to take advantage of CNNs remarkable power in capturing multi-scale 2D features, while rely on multi-view ensemble learning or inter-slice sequential learning to ensure certain level of output consistency through inter-slice contextual constraints. Experiments conducted on hippocampus magnetic resonance imaging (MRI) data for both work demonstrate that the multi-view soluti (open full item for complete abstract)

Committee: Jundong Liu (Advisor) Subjects: Computer Science

Master of Science, The Ohio State University, 2018, Biomedical Engineering

Approximately 285,000 people are living with a Spinal Cord Injury (SCI) in the United States alone and there are about 17,500 additional cases each year. Over half of these SCI cases result in tetraplegia, which impairs quality of life and requires the need for self-care assistance. Individuals with tetraplegia identify restoration of hand function as a critical, unmet need to regain their independence and improve quality of life. Brain-Computer Interface (BCI)-controlled Functional Electrical Stimulation (FES) technology addresses this need by reconnecting the brain with paralyzed limbs to restore function. There are multiple groups working to develop BCIs for SCI applications and incredible progress has been accomplished. However, there is still a substantial amount of research and development required to optimize the technology in order for people with tetraplegia to integrate the neurorehabilitation devices into their daily lives. The work presented in this thesis aims to (I) translate BCI- FES technology from research devices to clinical neuroprosthetics, (II) enhance decoder performance through optimal selection of neurally separable hand functions, and (III) improve neurorehabilitation BCI-FES systems through integration of error-based feedback. Three studies were conducted with a tetraplegic participant using an intracortically-controlled, transcutaneous FES system designed for motor recovery to address each aim. We demonstrate that (I) our BCI-FES system can enable seven functional, skilled hand grasps that can generate adequate force to manipulate everyday objects with high-precision and naturalist speed, (II) stable representations of different hand movements can form in a very small area of the motor cortex and discriminability between these neural representations can affect decoder performance, and (III) information regarding mismatches between motor intention and muscle activation in a tetraplegic participant using a BCI-FES is expressed through single (open full item for complete abstract)

Committee: Marcia Bockbrader MD, PhD (Advisor); Thomas Hund PhD (Committee Member) Subjects: Biomedical Engineering; Neurosciences

Doctor of Philosophy, Case Western Reserve University, 2017, Biomedical Engineering

Cancer is not a bounded, self-organized system. Most malignant tumors have heterogeneous growth, leading to disorderly proliferation well beyond the surgical margins. In fact, the impact of certain tumors is observed not just within the visible tumor, but also in the immediate peritumoral, as well as in seemingly normal-appearing adjacent field. Visual inspection is often not a reliable instrument in cancer diagnosis, providing only qualitative analysis of an image, thereby missing subtle disease signatures. These, and other imaging limitations can lead to unnecessary surgical interventions. Computerized image analysis has shown promise in comprehending disease heterogeneity through quantification and detection of sub-visual patterns. In this work, we present novel radiomic tools to identify subtle radiologic cues (radiomic descriptors) and address clinical challenges in cancer diagnosis, prognosis, and treatment-evaluation. The developed tools and techniques are modality- and domain-agnostic. They can be applied in a pan-cancer setting to mine information from radiographic images and discover associations with underlying molecular (radio-genomics) or histological (radio-pathomics) characteristics to provide a holistic characterization of disease. We have demonstrated their efficacy in addressing problems in prognosis and treatment management of brain tumors. The challenges we target specifically include (1) inability to estimate survival at a pre-treatment stage and (2) inability to avoid highly-invasive surgeries in patients with radiation-induced treatment changes that mimic tumor recurrence. Underlying heterogeneity is linked to poor prognosis and tumor recurrence. Cellular level differences associated with the distinct physiological pathways might also manifest at the radiographic (i.e. MRI) length scale. We present two radiomic descriptors, Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) and radiographic-Deformation and Textural Heterogenei (open full item for complete abstract)

Committee: Anant Madabhushi (Advisor); Pallavi Tiwari (Committee Chair); David Wilson (Committee Member); Lisa Rogers (Committee Member); Charles Lanzieri (Committee Member) Subjects: Biomedical Engineering; Biomedical Research

Doctor of Philosophy, The Ohio State University, 1986, Graduate School

Committee: Not Provided (Other) Subjects: Education

Doctor of Philosophy, The Ohio State University, 1979, Graduate School

Committee: Not Provided (Other) Subjects: Education

Doctor of Philosophy, The Ohio State University, 1976, Graduate School

Committee: Not Provided (Other) Subjects: Biology

Doctor of Philosophy (PhD), Ohio University, 2015, Molecular and Cellular Biology (Arts and Sciences)

The Growth hormone (GH)/ insulin-like growth factor-1 (IGF-1) axis is the primary regulator of mammalian growth and has significant influence in modulating cellular metabolism. The presence of GH, GH receptor (GHR) and IGF-1 in different regions of the mammalian brain suggests functional significance of GH-induced intracellular signaling on development and function of the central nervous system, similar to peripheral tissues. The physiological significance of GH action is manifested in laboratory mouse models of altered GH signaling. For example, bovine (b) GH transgenic (bGH) mice represent the human condition of acromegaly (GH hyperactivity) and are giant, lean, insulin-resistant and short-lived. In contrast, transgenic GHR antagonist mice (GHA) are dwarf, due to decreased GH/IGF-1 signaling, and have normal insulin signaling and lifespan. To understand the mechanism of action of GH in the brain, we assessed spatial learning and memory in bGH, GHA and their respective control littermates in a Barnes Maze (BM). Quantitative estimation of gene expression related to GH/IGF-1/insulin signaling and cognitive processing pathways were performed in four different brain regions (cortex, hippocampus, cerebellum and hypothalamus) by quantitative polymerase chain reaction (qPCR) technique, in bGH, GHA and their respective WT control mice. To analyze functional significance of GH action in the brain, we compared protein expression in the brain of bGH vs. WT and GHA vs. WT mice by Western blotting. A significant effect of altered GH signaling on learning and memory was noted in both bGH and GHA mice, compared to their respective controls. In the BM study, bGH mice demonstrated significantly poorer learning and retention of short- and long-term memory compared to the WT mice, suggesting a negative influence of excess GH action on cognition and memory. In contrast, GHA mice showed better learning and long-term retention of spatial memory compared to their control littermates. (open full item for complete abstract)

Committee: John Kopchick Ph.D. (Advisor); Robert Colvin Ph.D. (Committee Member); Calvin James Ph.D. (Committee Member); Darlene Berryman Ph.D. (Committee Member) Subjects: Behavioral Sciences; Biology; Biomedical Research; Molecular Biology; Neurobiology; Neurosciences