Since 1975, the prevalence of obesity has nearly tripled around the world. In 2016, 39% of adults, or 1.9 billion people, were considered overweight, and 13% of adults, or 650 million people, were considered obese. Furthermore, Cardiovascular disease remains to be the leading cause of death for adults in the United States, with 655,000 people dying from related conditions and consequences each year. Including fiber in one’s dietary regimen has been shown to greatly improve health outcomes in regards to these two areas of health. However, not much literature is available on the effects of corn-based fiber, especially detailing the individual components of the grain itself. The purpose of this preliminary study was to test the differences in influence on both LDL-cholesterol and triglycerides between treatments based on whole-grain corn flour, refined corn flour, and 50% refined corn flour + 50% corn bran derived from whole grain cornmeal (excellent fiber) in healthy overweight (BMI ≥ 25.0 kg/m2) adults (ages 18 - 70) with high LDL cholesterol (LDL ≥ 120mg/dL). 20 participants, ages 18 - 64 (10 males, 10 females) were involved. Data was derived from blood draws taken before and after each of the three treatments as well as before and after each treatment’s wash out periods. A general linear model was used to assess the effect of corn products on circulating concentrations of LDL-cholesterol and triglycerides. From the model, it was found that the whole-grain corn flour and the 50% refined corn flour + 50% corn bran drive from whole grain cornmeal treatments produced a higher, similar benefit in reductions in LDL-cholesterol. However, the whole grain flour, refined flour, and bran-based fiber treatments did not influence the triglyceride levels of the participants throughout this study. Further research is needed to elucidate the effects of these fiber items on cardiometabolic disease markers in the long-term as well as with a larger sample size.

The research presented in this Honors Thesis provides development in machine learning models which predict future states of a system with unknown dynamics, based on observations of the system. Two case studies are presented for (1) a non-conservative pendulum and (2) a differential game dictating a two-car uncontrolled intersection scenario. In the paper we investigate how learning architectures can be manipulated for problem specific geometry. The result of this research provides that these problem specific models are valuable for accurate learning and predicting the dynamics of physics systems.<br/><br/>In order to properly model the physics of a real pendulum, modifications were made to a prior architecture which was sufficient in modeling an ideal pendulum. The necessary modifications to the previous network [13] were problem specific and not transferrable to all other non-conservative physics scenarios. The modified architecture successfully models real pendulum dynamics. This case study provides a basis for future research in augmenting the symplectic gradient of a Hamiltonian energy function to provide a generalized, non-conservative physics model.<br/><br/>A problem specific architecture was also utilized to create an accurate model for the two-car intersection case. The Costate Network proved to be an improvement from the previously used Value Network [17]. Note that this comparison is applied lightly due to slight implementation differences. The development of the Costate Network provides a basis for using characteristics to decompose functions and create a simplified learning problem.<br/><br/>This paper is successful in creating new opportunities to develop physics models, in which the sample cases should be used as a guide for modeling other real and pseudo physics. Although the focused models in this paper are not generalizable, it is important to note that these cases provide direction for future research.

High-entropy alloys possessing mechanical, chemical, and electrical properties that far exceed those of conventional alloys have the potential to make a significant impact on many areas of engineering. Identifying element combinations and configurations to form these alloys, however, is a difficult, time-consuming, computationally intensive task. Machine learning has revolutionized many different fields due to its ability to generalize well to different problems and produce computationally efficient, accurate predictions regarding the system of interest. In this thesis, we demonstrate the effectiveness of machine learning models applied to toy cases representative of simplified physics that are relevant to high-entropy alloy simulation. We show these models are effective at learning nonlinear dynamics for single and multi-particle cases and that more work is needed to accurately represent complex cases in which the system dynamics are chaotic. This thesis serves as a demonstration of the potential benefits of machine learning applied to high-entropy alloy simulations to generate fast, accurate predictions of nonlinear dynamics.

Robots are often used in long-duration scenarios, such as on the surface of Mars,where they may need to adapt to environmental changes. Typically, robots have been built specifically for single tasks, such as moving boxes in a warehouse or surveying construction sites. However, there is a modern trend away from human hand-engineering and toward robot learning. To this end, the ideal robot is not engineered,but automatically designed for a specific task. This thesis focuses on robots which learn path-planning algorithms for specific environments. Learning is accomplished via genetic programming. Path-planners are represented as Python code, which is optimized via Pareto evolution. These planners are encouraged to explore curiously and efficiently. This research asks the questions: “How can robots exhibit life-long learning where they adapt to changing environments in a robust way?”, and “How can robots learn to be curious?”.

Colorimetric assays are an important tool in point-of-care testing that offers several advantages to traditional testing methods such as rapid response times and inexpensive costs. A factor that currently limits the portability and accessibility of these assays are methods that can objectively determine the results of these assays. Current solutions consist of creating a test reader that standardizes the conditions the strip is under before being measured in some way. However, this increases the cost and decreases the portability of these assays. The focus of this study is to create a machine learning algorithm that can objectively determine results of colorimetric assays under varying conditions. To ensure the flexibility of a model to several types of colorimetric assays, three models were trained on the same convolutional neural network with different datasets. The images these models are trained on consist of positive and negative images of ETG, fentanyl, and HPV Antibodies test strips taken under different lighting and background conditions. A fourth model is trained on an image set composed of all three strip types. The results from these models show it is able to predict positive and negative results to a high level of accuracy.

The field of biomedical research relies on the knowledge of binding interactions between various proteins of interest to create novel molecular targets for therapeutic purposes. While many of these interactions remain a mystery, knowledge of these properties and interactions could have significant medical applications in terms of understanding cell signaling and immunological defenses. Furthermore, there is evidence that machine learning and peptide microarrays can be used to make reliable predictions of where proteins could interact with each other without the definitive knowledge of the interactions. In this case, a neural network was used to predict the unknown binding interactions of TNFR2 onto LT-ɑ and TRAF2, and PD-L1 onto CD80, based off of the binding data from a sampling of protein-peptide interactions on a microarray. The accuracy and reliability of these predictions would rely on future research to confirm the interactions of these proteins, but the knowledge from these methods and predictions could have a future impact with regards to rational and structure-based drug design.

The various health benefits of vinegar ingestion have been studied extensively in the<br/>literature. Moreover, emerging research suggests vinegar may also have an effect on mental<br/>health. Beneficial effects of certain diets on mood have been reported, however, the mechanisms<br/>are unknown. The current study aimed to determine if vinegar ingestion positively affects mood<br/>state in healthy young adults. This was a randomized, single blinded controlled trial consisting of<br/>25 subjects. Participants were randomly assigned to either the vinegar group (consumed 2<br/>tablespoons of liquid vinegar diluted in one cup water twice daily with meals) or the control<br/>group (consumed one vinegar pill daily with a meal), and the intervention lasted 4 weeks.<br/>Subjects completed mood questionnaires pre- and post-intervention. Results showed a significant<br/>improvement in CES-D and POMS-Depression scores for the vinegar group compared to the<br/>control. This study suggests that vinegar ingestion may improve depressive symptoms in healthy<br/>young adults.