Every week, some of our senior researchers in each department at ASDRP give public seminar 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. Simply click the Google Calendar link to add the event to your calendar.
Department of Biological, Human & Life Sciences
Tuesdays @ 8:00 - 9:00 PM PST
Tuesday, November 10, 2020
Study the effect of eugenol and its analogs on the opportunistic human pathogen, Alternaria alternata.
There is a current rise in the invasive fungal infections due to the increase in immunosuppressive therapies such as chemotherapy, transplantation, etc. Despite the constant progress in medical practices, invasive fungal infections are increasing with the increase of the resistant strains to the current antifungal drugs. Moreover, the hypersensitivity and toxicity to the drugs, because of their improper and excessive application, represent some of the major problems of the presently excessive use of synthetic antimicrobials. Therefore, new classes of antimicrobials of plant origin are recommended for the treatment of certain diseases transmitted by organisms. Essential oils are natural compounds extracted from plants, well-known for their antiseptic and medicinal properties. Eugenol is an example of these oils. The derivatives synthesized from eugenol have been found to possess huge antimicrobial activities. Thus, using these analogs is paying more attention in the drug discovery world. The aim of this research is to compare the anti fungal effect of eugenol with its analogs on the opportunistic human pathogen, Alternaria alternata,which is responsible for some human diseases such as asthma, and rhino-sinusitis. Our experiments would help us to suggest the best analogs that suppress the fungal growth to be used as anti fungal drugs.
Aditi V, Prospect High School
Mikhail Research Group
Department of Chemistry, Biochemistry & Physics
Fridays @ 8:00 - 9:00 PM PST
Friday, November 13, 2020
Synthesis, Characterization, and Evaluation of alpha-hederin loaded PLGA nanoparticles as an Anticancer Therapy.
Alpha-hederin is an established natural anti-cancer product, and although it proves potent in in-vitro studies, its poor bioavailability limits its use as a clinical therapeutic. One possible solution is to load the drug into nanoparticles, which have been shown to successfully improve the bioavailability of most drugs. Despite this, it is necessary to confirm the activity of nanoparticles against the free drug to confirm the potency of the formulation. Furthermore, few methods exist to quantify alpha-hederin colorimetrically due to the instability of the molecule and the lack of available reactive groups. As a result, the mechanism and kinetics of previous methods must be evaluated. The prospects of different types of nanoparticles, and the key factors affecting their use as clinical drugs will be discussed. In addition, the progress made in our laboratory in creating nanoparticle formulations to improve the bioavailability of this drug and current and novel detection methods of alpha-hederin will be presented.
Rohit S., Lynbrook High School
Renganathan Research Group
Department of Computer Science & Engineering
Wednesdays @ 8:00 - 9:00 PM PST
Wednesday, November 11, 2020
Building a Heuristic Genetic Algorithm for Stock Market Prediction.
Building algorithms to predict future stock market values has been a focus of research for many years because of the monetary benefits and ease of mind it provides to investors. Our research focuses on mimicking investor sentiment to make predictions based on 20 years of historic data and indicators investors would focus on. Compared to Artificial Neural Networks (ANN) or Support Vector Machines (SVM), Genetic Algorithms incorporate a heuristic element to the prediction. Genetic Algorithms are based on Charles Darwin’s theory of evolution, where the fittest individuals of each generation are explored iteratively. By utilizing Twitter’s API, we analyze relevant tweets to provide a sentiment score to correspond with different emotions, ultimately, making decisions similar to investors.
Jagannath P., American High School
McMahan Research Group