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Now showing 1 - 10 of 936

Production of ryanodine receptor calcium release channel ATP-binding site mutants

(Georgia Institute of Technology, 2023-01-18) Cutter, Catarina Santos

Ryanodine receptors (RyRs) are a class of mammalian ion channels which are the primary efflux pathways for the release of Ca2+ from the sarcoplasmic reticulum. They play a critical role in muscle excitation-contraction coupling (ECC). Because it is the largest known ion channel, the mechanisms for its activation are not fully understood. ATP is a well characterized channel activator. However, its mechanism of activation has not been determined and the importance of ATP regulation of RyRs in vivo is not clear. In 2016, des George, et al. published a structure of RyR1 with ATP bound. The adenosine group of ATP is contained within a hydrophobic cleft while the triphosphate tail is extended and interacts with positively charged residues. The goal of this study was to identify residues important for ATP binding to the channel. Site-directed mutagenesis of the receptor was used to substitute specific residues in order to change their size and or charge. After transfection with recombinant DNA, HEK293 cells were harvested for isolation of microsomal membranes. Two of the largest hydrophobic residues of the cleft were replaced with alanine with the goal of drastically reducing or abolishing ATP binding to RyR1. The selected mutations F4960A and L4985A were expected to impair channel activation by both ATP and adenosine. After initial verification of wild type channel expression in HEK293 cells, later transfections with wild type and mutant RyR1 DNA failed to produce detectable amounts of protein. Low DNA transfection efficiency combined with the low yield of microsomal membrane likely contributed to the inability to detect channels in these preparations. Optimizing DNA transfections and scaling up the cell culture may increase the likelihood of successful protein production.
Intelligent Buffer Pool Prefetching

(Georgia Institute of Technology, 2023-01-18) Suresh, Sylesh Kyle

Buffer pools are essential for disk-based database management system (DBMS) performance as accessing memory on disk is orders of magnitude more expensive than accessing data in-memory. As such, one of the most important techniques for DBMS performance improvement is proper buffer pool management. Although much work has already gone into page replacement policies for buffer pools, relatively little attention has been paid to developing intelligent page prefetching strategies. Commonly used sequential prefetching strategies only handle sequential accesses but fail to predict more complex page reference patterns. More complex prediction techniques exist---particularly those that leverage the predictive power of deep learning. Although such models can achieve a high prediction accuracy, due to their size and complexity, they cannot deliver predictions in time for the corresponding pages to be prefetched. With the tension between timeliness and prediction accuracy in mind, in this work, we introduce a machine learning-based strategy capable of predicting useful pages to prefetch for complex memory access patterns with an inference latency low enough for its predictions to be delivered in time. When evaluated on a subset of the TPC-C benchmark, our strategy is capable of reducing execution time by up to 13% while a commonly-used sequential prefetching yields only a 6% reduction.
Forecasting Atlanta Gentrification with Transformers

(Georgia Institute of Technology, 2023-01-18) Sett, Gaurav

Gentrification is an impactful trend in American cities, yet our ability to measure and predict this process remains weak. This thesis explores the use of machine learning to predict gentrification in Atlanta, Georgia. We use a dataset of land parcels collected by Fulton County Tax Assessors and set out to forecast changes in local land value. Inspired by progress in natural language processing, we apply a machine learning model called a transformer to forecast gentrification. We find our model outperforms typical methods for time-series forecasting of gentrification, and we discuss the implications of our findings for future research.
Developing a Recommendation-Based Application to Help Endocrinologists Treat Type II Diabetes Mellitus

(Georgia Institute of Technology, 2023-01-18) Mithaiwala, Aamir

Diabetes Mellitus type II is a disease characterized by abnormally high levels of glucose in the bloodstream (hyperglycemia) due to decreased insulin secretion, insulin resistance, or both. It affects approximately 425 million adults worldwide and is the 7th most common chronic condition according to the CDC (Figure 1).[1] Patients with this disease typically have increased urination, increased thirst, and fatigue and can even be vulnerable to many types of infections. Patients with type II diabetes see diabetes specialists and endocrinologists to effectively treat their disease. Currently, however, there is a massive shortage of endocrinologists in the United States due to a growing demand of chronic diseases such as diabetes and osteoporosis.[2] In one study, the majority of endocrinologists surveyed believed the process of treating diabetes is difficult for these four reasons: the shortage of physicians, constantly evolving diabetes research, rapidly changing medication guidelines, and the rate at which medications are being added to the market.[3] Another major problem in the diabetes community is the risk of potentially inappropriate medications (PIMs), which are defined as prescribing medications that have a greater risk of potentially severe adverse effects. 74% of elderly patients with type II diabetes are prescribed at least one PIM when hospitalized.[4] The studies conducted by Healy et al. and Sharma et al. reveal that the process of treating type II diabetes is difficult because of 3 main reasons: The shortage of endocrinologists, rapidly evolving medication recommendations by diabetes associations, and the health risk to elderly diabetic patients due to PIMs. There is a growing need for technology that assists endocrinologists in prescribing medication based on factors that adjust to the evolving recommendations by the American Diabetes Association and uses patient biomarkers along with other factors to recommend appropriate medications for patients.
Usability Study of ImageScape Integration in the Prolonged Exposure Collective Sensing System (PECSS)

(Georgia Institute of Technology, 2023-01-18) Zou, Chunhao

We studied the usability and usefulness of the Prolonged Exposure Collective Sensing System (PECSS) for clinicians conducting prolonged exposure (PE) therapy with a special focus on one functional component of PECSS called ImageScape, which allowed patients to share photos of their in-vivo tasks with their clinicians. The usability study returned positive results for the PECSS system as a whole, but yielded mixed results for ImageScape in particular, suggesting that further research into the utility of ImageScape is needed. We begin by introducing relevant psychological backgrounds such as PTSD and prolonged exposure therapy. Then, we include an in-depth description of the PECSS ImageScape software design and implementation. This is followed by the design of a usability evaluation protocol for the clinician, which attempts to measure the importance of ImageScape both subjectively and objectively, quantitatively and qualitatively. Next, we provide the results of the usability study with one of the clinicians from our partner university. We will discuss the implications of our study, compare how our system with other existing systems for PTSD treatment, address the limits of our study, and discuss the next steps and future studies in the design and implementation of PECSS. Finally, We conclude our study with a summarization of our contributions.
Efficient Calculation of Frame Level Complex Predicates in Video Analytics

(Georgia Institute of Technology, 2023-01-18) Sengupta, Aubhro

The field of video analytics focuses on extracting useful information from video. Lets consider a scenario in which we have a large amount of video from a traffic camera at a certain busy intersection and we are looking for a black sedan. State of the art object detectors such as FasterRCNN [3] utilize computationally expensive methods like convolutional neural networks that analyze a frame of video and estimate the number of the object of interest and the locations of every instance of that object in the frame. The most basic approach to solving this problem would simply be to execute the object detector on all frames of the video and collect the frames which contain at least one black sedan to return to the user. However, this approach is impractical on longer videos as CNNs are computationally expensive and thus too slow. Instead the number of frames evaluated by the object detector must be limited. This field focuses on developing strategies for doing so, such as sampling, filtering, proxy models, and clustering.
Predicting Metabolic Cost Using Cumulative Muscle Activation Per unit Distance

(Georgia Institute of Technology, 2023-01-18) Carter, Jacob M.

Many studies are currently being conducted in order to optimize the functions of exoskeletons in a way that generates the greatest benefit for the user. However, this research is hampered by the traditional methodology of measuring metabolic cost, indirect calorimetry. This study proposes an alternative method, based not on the overall gas exchange of the body, but rather the relative activity of the relevant muscle groups in a method known as Cummulative Muscle Activation Per unit Distance(CMAPD) analysis.
Identifying the potential effect of zolmitriptan on the 1b pathway of Golgi tendon organs in regulating intermuscular inhibition in the extremities to find a link in the mechanism of spasticity

(Georgia Institute of Technology, 2023-01-18) Davis, Adam Eugene

The deep dorsal horn (DDH) of the spinal cord is a major integration center for receiving a variety of neural projections from the brainstem as well as a variety of afferent inputs from muscle spindles and Golgi tendon organs (GTOs) in the muscles. Following spinal cord injury (SCI) to the DDH, an overall loss of serotonergic input from the brainstem is observed, for which there is evidence to suggest that this may play a role in inhibiting the activity of bursting interneurons in the DDH, possibly leading to uncontrolled motoneuron activity, hyperreflexia. GTOs primarily supply the force feedback network (FBB), which also receives supraspinal input through the DDH, likely also affected by its loss in SCI. The purpose of this current study is to investigate if FBB function changes, with or without SCI, after the administration of a specific serotonin reuptake inhibitor (SSRI), zolmitriptan, which inhibits the activity of the bursting interneurons. FBB function was determined primarily as inhibitory signals from the flexor hallucis longus (FHL) onto the gastrocnemius (GAS), the muscle tensions compared after being stretched individually and pairwise, with some data from rectus femoris (RF) onto GAS. The autogenic stretch reflex was analyzed only in GAS. Animals with an intact spinal cord (n=1) and with a lateral hemisection (n=2) were used to compare the changes in reflexes following zolmitriptan administration. Data was variable across the subjects with no clear effect on the autogenic stretch reflex in GAS. The more stable lateral hemisection subject revealed that zolmitriptan largely and consistently increased inhibition from FHL onto GAS from a miniscule baseline, suggesting connectivity between the GTO circuit and the bursting interneurons of the DDH. Notably in the intact spinal cord animal, there was an immediate and complete correction of oscillations in the baseline tension of all muscles after drug administration, treating a symptom of hyperreflexia. These results suggest a connection between the two systems or a more significant role of this particular serotonin receptor on GTO circuit and the DDH. More studies may provide a deeper understanding of this network and these findings.
Fighting on Facebook: Political Conversations Between Strong and Weak Ties

(Georgia Institute of Technology, 2023-01-18) Bernstein, Lily

Most Facebook users have observed a heated disagreement on the site, or been involved in such a disagreement themselves. Why do we get into these disagreements and who are we fighting with? Are we learning anything from the disagreements we have online? In this research, we interviewed eighteen regular Facebook users about their experiences with conflict on the site. We found that people get into disagreements online because their \textit{expectations} about how the people in their network will react were violated. Conflict was often a result not of disagreement, but of breach of expectations. When conflict ensued, participants sometimes dissolved their relationship with their argument counterpart, and sometimes did not. We review strategies used for relationship maintenance. Weak-tie relationships were more susceptible to being dissolved, and strong-tie relationships were often salvaged by "agreeing to disagree" or ceasing political discussion. Participants report sometimes learning from these hard conversations, but not often. We follow-up with design recommendations for social media platforms that could help mitigate disagreements or give people access to tools that help them have productive, hard conversations.
Oracle Guided Image Synthesis with Relative Queries

(Georgia Institute of Technology, 2023-01-18) Helbling, Alec Fisher

Isolating and controlling specific features in the outputs of generative models in a user-friendly way is a difficult and open-ended problem. We develop techniques that allow a user to generate an image they are envisioning in their head by answering a sequence of relative queries of the form "do you prefer image a or image b?" Our framework consists of a Conditional VAE that uses the collected relative queries to partition the latent space into preference-relevant features and non-preference-relevant features. We then use the user's responses to relative queries to determine the preference-relevant features that correspond to their envisioned output image. Additionally, we develop techniques for modeling the uncertainty in images' predicted preference-relevant features, allowing our framework to generalize to scenarios in which the relative query training set contains noise.