Stress for college students is nothing new and as more kids go to college the number of cases are on the rise. This issue is apparent at colleges across the nation including Arizona State University. StreetWise aims to help students prevent or appropriately deal with stress through interactive lessons teaching students life skills, social skills, and emotional intelligence.<br/>In order to prove the value of our service, StreetWise conducted a survey that asked students about their habits, thoughts on stress, and their future. Students from Arizona State University were surveyed with questions on respondent background, employment, number one stressor, preferred learning method, and topics that students were interested in learning. We found that students’ number one stressor was school but was interested in learning skills that would prepare them for their future after graduation. We used the results to make final decisions so that StreetWise could offer lessons that students would get the most value out of. This led to us conducting a second survey which included mock ups of the website, examples of interactive lesson plans, and an overview of the app. Students from the first survey were surveyed in addition to new respondents. This survey was intended for us to ensure that our service would maintain its value to students with the aesthetic and interface that we envisioned.

Stress for college students is nothing new and as more kids go to college the number of cases are on the rise. This issue is apparent at colleges across the nation including Arizona State University. StreetWise aims to help students prevent or appropriately deal with stress through interactive lessons teaching students life skills, social skills, and emotional intelligence.<br/>In order to prove the value of our service, StreetWise conducted a survey that asked students about their habits, thoughts on stress, and their future. Students from Arizona State University were surveyed with questions on respondent background, employment, number one stressor, preferred learning method, and topics that students were interested in learning. We found that students’ number one stressor was school but was interested in learning skills that would prepare them for their future after graduation. We used the results to make final decisions so that StreetWise could offer lessons that students would get the most value out of. This led to us conducting a second survey which included mock ups of the website, examples of interactive lesson plans, and an overview of the app. Students from the first survey were surveyed in addition to new respondents. This survey was intended for us to ensure that our service would maintain its value to students with the aesthetic and interface that we envisioned.

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.

Alzheimer’s disease (AD) is a common neurodegenerative disorder affecting approximately 10% of people aged 65 and up and 30-50% over 85. In pathological AD representations, a way to recognize early onset AD is the increased levels of pro-NGF in BFCNs that come from the downregulation of NGF with age. Pro-NGF has a higher affinity for p75NTR, which binds and participates in the pro-NGF-p75NTR-sortilin complex sequentially cleaved by α- and γ-secretase. Pro-NGF triggers apoptosis through the cleavage of the intracellular membrane by γ-secretase. Since γ-secretase physically cleaves off the intramembrane portion that promotes TNF- and Fas-dependent apoptotic signaling pathways, it has a crucial role in AD and must be better understood. This research aims to understand better and visualize γ-secretase and its actions, specifically with its interactions with the substrate p75NTR in the RIP process. To analyze γ-secretase function, the proteins must be produced and analyzed through the protein expression protocol. During protein production, DNA, cell concentrations, and optical density measurements were difficult to produce due to the incompetency of e. coli cells (DH5α), contamination of the Sf9 insect cell culture, and decreased viability of aged insect cells. We identified the problems and improved the conditions for future project development.