Every week, senior researchers in each department at ASDRP give public seminars presenting the current state of the field and disseminating how their research at ASDRP fits into the broader context of the frontiers of modern science and engineering. Colloquia are public events, and anyone can join. Click on the "Join the Colloquia" link to add the event to your calendar.
Department of Biological, Human and Life Sciences
Tuesdays @ 8:00-9:00 PM PST
Tuesday, March 9, 2021
The Effect of Aβ Aggregation on Associative Memory Formation in Transgenic
Researcher: Simran T., American High School
Advisor: Truong, Molecular & Cell Biology
Alzheimer's disease (AD) is a neurodegenerative condition that affects over 50 million worldwide. Its main symptoms include loss of mobility and memory. While current treatments seek to relieve the symptoms, there is no known cause yet to stop the disease's progression. There is indication that multiple factors work to cause the disease, since some types (Early-Onset AD) are more impacted by genetics, while others (Late-Onset AD) worsen with age and other unknown factors. Amyloid beta aggregation has been observed to cause toxicity in neurons and is highly common in AD patients, and thus its role in the propagation of AD has been studied extensively. Here, the observations of amyloid beta aggregation's effects on the muscle cells and neurons of C. Elegans will be discussed, taking in data from both our lab work and past studies. We predict that amyloid beta aggregation will cause a loss of short term associative memory (STAM) and long term associative memory (LTAM), as well as decreased mobility. We will be also testing the effects of curcumin on Alzheimer’s Disease on C. Elegans.
Keywords: Alzheimer's Disease, Amyloid Beta Aggregation, Curcumin, Biology
Department of Chemistry, Biochemistry, and Physics
Fridays @ 8:00-9:00 PM PST
Friday, March 12, 2021
Dual-approach towards anticancer drug discovery: de novo design, chemical synthesis, and in vitro applications of monastrol and berberine-derived analogs
Researcher: Emma L., Amador Valley High School
Advisor: Njoo, Organic, Medicinal Chemistry
With cancer being one of the leading causes of death worldwide - accounting for almost 10 million deaths in 2020 alone - antiproliferative compounds have captured the interests of many within the pharmaceutical industry. Such compounds derive their oncotherapeutic abilities predominantly from two different approaches: the targeting of the proteinaceous material or genetic material of the cancerous cell. Monastrol, for example, is a dihydropyrimidine privileged scaffold that has been previously reported as a small molecule cell-permeable inhibitor of the human motor protein kinesin Eg5, which is needed for the assembly of mitotic spindle fibers, inevitably inducing apoptosis within cancerous cells through the primary approach. Moreover, in order to explore the benefit of fluorination within anti-cancer and antiproliferative drugs, specifically monastrol, a study of benchtop NMR-assisted small molecule fluorination will be presented. Disparately, berberine, an isoquinoline alkaloid natural product, utilizes the complementary approach of stabilizing G-quadruplexes or inducing photo-oxidative damage within the cells which inevitably leads to DNA damage accumulation and inadequate replication of such DNA. Herein, an in-depth analysis of the two different approaches towards antiproliferative mechanisms will be covered via the analysis of the syntheses of monastrol and berberine-derived analogs, quantified using benchtop NMR.
Keywords: Monastrol, Dihydropyrimidine, Kinesin Eg5, NMR-assisted small molecule fluorination, Berberine, Natural product, G-quadruplexes, Photo-oxidative damage
Department of Computer Science and Engineering
Wednesdays @ 8:00-9:00 PM PST
Wednesday, March 10, 2021
Sentiment Analysis of 2020 Presidential Campaign Speeches
Researcher: Darshan G., Aragon High School
Advisor: Johnson, Data Science, Environmental Studies
Sorted speeches from Trump and Biden's 2020 Presidential Campaigns into categories based on subject, calculated subjectivity and objectivity across each topic, looked at distributions and comparing the two. We used a merge sort algorithm to quickly score each speech, looking at keywords after stripping filler words from each speech in order to categorize the speeches. We determined whether a candidate is positive, negative, or neutral about a certain topic, and whether the candidate speaks in facts or opinions.
Keywords: Natural Language Processing, Data Science, 2020 Election, Statistics, Python