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- Status: Published
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 goal of this project was to design and create a genetic construct that would allow for <br/>tumor growth to be induced in the center of the wing imaginal disc of Drosophila larvae, the <br/>R85E08 domain, using a heat shock. The resulting transgene would be combined with other <br/>transgenes in a single fly that would allow for simultaneous expression of the oncogene and, in <br/>the surrounding cells, other genes of interest. This system would help establish Drosophila as a <br/>more versatile and reliable model organism for cancer research. Furthermore, pilot studies were <br/>performed, using elements of the final proposed system, to determine if tumor growth is possible <br/>in the center of the disc, which oncogene produces the best results, and if oncogene expression <br/>induced later in development causes tumor growth. Three different candidate genes were <br/>investigated: RasV12, PvrACT, and Avli.
Hispanic youth have the highest risk for obesity, making this population a key priority for early childhood interventions to prevent the development of adult obesity and its consequences. Involving parents in these interventions is essential to support positive long-term physical activity and nutrition habits. Interventions in the past have engaged parents by providing information about nutrition and fruit and vegetable intake through written materials or text such as newsletters and text messages. The Sustainability via Active Garden Education (SAGE) intervention used gardening and interactive activities to teach preschool children ages 3-5 about healthy eating and physical activity. It aimed to increase physical activity and fruit and vegetable intake in preschool children as well as improve related parenting practices. The intervention utilized newsletters to engage parents by promoting opportunities to increase physical activity and fruit and vegetable intake for their children at home. The newsletters also encouraged parents to discuss what was learned during the SAGE lessons with their children. The purpose of this paper is to describe the content of the newsletters and determine the parent perception of the newsletters through parent survey responses. This can help inform future childhood obesity interventions and parent engagement.