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Colloquia Tuesdays

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 Colloquia link in the Event Calendar in your Student Portal to join the event.

Spring 2023 Colloquia Dates:

  • Jan 10, 17, 24, 31

  • Feb 7, 14, 21, 28

  • Mar 7, 14, 21, 28

  • Apr 4, 11, 18, 25

  • May 2, 9, 16, 23, 30

 

Watch it again! Watch prior Colloquia on the ASDRP YouTube Channel.

Weekly - Every Tuesday
7:00 - 8:30 PM (Pacific Time)


Join the Colloquia
Tuesday, January 10, 2023

Department of Chemistry, Biochemistry, and Physics

Benchtop NMR Enabled Synthetic Studies of Natural Products and Small Molecule Inhibitors Towards Novel Antiproliferative and Antiviral Agents

The first project that Sarah worked on was on berberine, a bioactive isoquinoline alkaloid small molecule isolated from a plant whose therapeutic use in treating human disease dates back several centuries in ancient southeast Asia. A few years ago, we and others reported could act as a photosensitizer to excite ground state triplet oxygen into excited state triplet oxygen, thereby acting as photosensitizer for light-induced biological activity, and Sarah has published on this extensively [Photochemical analog (Sun, et al. JEI 2021); Initial antibacterial SAR (Sun, et al. JEI 2020)]. Specifically, Sarah led our first efforts on non-canonical uses of benchtop NMR spectroscopy to use benchtop NMR to quantify 1O2 by trapping it with a cyclic 1,3-diene to form [2.2.2]bicyclo endoperoxides, and we now have two publications on this, along with an application note co-developed with Nanalysis [App Note, Interview Video]. In parallel, Sarah has also grown a great deal of expertise in using computer modeling for understanding reactive intermediates and small molecule drug candidates (Link to Sarah's Ted Talk here), first in our use of DFT, TD-DFT, and MD in our computational SAR of berberine analogs as DNA-Gquad stabilizing agents (Sun/Ashok, et. al., JEI 2020), later in our SARS-CoV-2 Mpro inhibitors project (Sun, et al., J. Res. HS 2020). When we transitioned the project to work on carmofur, a small molecule originally developed for colorectal cancer but later repurposed for SARS-CoV-2, Sarah was involved in a high throughput analog screen of novel carmofur analogs against wild type and mutant variants of SARS-CoV-2 (Luk, et al., manuscript accepted, 2022). Late in 2022, Sarah was part of a team that worked on our group’s flagship paper of the year, using 19F NMR spectroscopy for monitoring (Chen, et al. ChemRXiv 2022), specifically for tracking the reactive intermediates present in complex multicomponent reactions. This project was shared at STEM Week at Los Altos High School (Link to talk: https://www.youtube.com/watch?v=vIJ-C1tVUbA) and is now under peer review for publication! Currently, Sarah works on several projects in the interface of chemical synthesis, chemical biology, catalysis, and small molecule drug discovery, including our development of difluorocyclopropanation catalyst strategies as well as using stereo- and regio-controlled inverse demand Diels Alder cycloadditions for construction of the tricyclic core of forskolin, a bioactive diterpenoid with therapeutic value in aging research.
 

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Researchers: Sarah Su, Los Altos High School '23

Advisor: Njoo, Organic Chemistry

Keywords: Benchtop NMR | Berberine | Biginelli Cyclocondensation | Natural Products | Medicinal Chemistry | Antiviral Agents

Tuesday, January 10, 2023

​Department of Biological, Human, and Life Sciences

DNBWM-CT: Using Multidimensionality and Engagement to Increase Working Memory
 

N-back tasks are a form of cognitive training requiring patients to remember and recall information previously shown to them. In previous studies, cognitive patients completed N-back tasks while undergoing fMRI, and  areas associated with working memory, such as the prefrontal cortex, fronto parietal network, and salience network, activated during this task. Working memory involves the use of attention to manipulate and store short term memory. There has been a scientifically proven correlation between N-back training and increase of working memory. Furthermore, the dopaminergic system, located in the midbrain, consist of the Mesolimbic, Mesocortical, Nigrostriatal, and Tuberoinfundibular pathways. Within this system contains dopamine, a neurotransmitter and hormone produced during blissful and pleasantful experiences. There is a scientific correlation between increase of dopamine and increase of productivity during cognitive tasks. Nevertheless, cognitive research patients often complain that cognitive tasks are boring and mundane. In this study, we aim to measure the effect of multidimensionality and gamification on cognitive research tasks.

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Researchers: Aidan G., Germantown Academy, Fort Washington, PA '25; Ying C., Castro Valley High School '23, Akshat W., Lynbrook High School ' 25

Advisor: Jahanikia, Life Sciences, Neuroimaging, Psychology & Bioinformatics

Keywords: Game Design | Working Memory | Multidimensional Cognitive Enhancement

Tuesday, December 6, 2022

​Department of Biological, Human, and Life Sciences

In-silico characterization of potential serum response factor (SRF) inhibitors in colorectal cancer (HCT116)

SRF (Serum Response Factor) is a transcription factor that is activated by growth factor stimulation and mitosis, leading to the expression of genes that influence growth and the cytoskeleton. Additionally, HOPX, which is associated with reduced cell proliferation and tumor suppression, inhibits the binding of SRF to DNA. Additionally, SRF in gastric cancer is associated with an aggressive phenotype and a poor outcome due to the downregulation of E-cadherin which promotes the epithelial-mesenchymal transition. Furthermore, in colorectal cancer, SRF is overexpressed in metastatic tissues, leading to increased cell motility and invasiveness. Based on this, we decided to look for potential SRF inhibitors. We are currently working with chemical similarity algorithms and clustering techniques, like Tanimoto similarity and UMAP, to determine SRF inhibitor candidates based on limited existing inhibitors. Those candidates will then be docked to the target using Autodock Vina. Molecules with high binding affinities will be tested for drug-induced liver injury (DILI) and toxicity in cells (DeepCDR). We anticipate that these drugs will eventually be tested in-vitro on colorectal cancer cell models.

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Researchers: Aksithi E., Notre Dame San Jose High School '24; Ojasvi M., Evergreen Valley High School '24

Advisor: Cunha, Bioinformatics and Cancer Biology

Keywords: Bioinformatics | Serum Response Factor | Colorectal Cancer | Computational Drug Screening

Tuesday, November 29, 2022

​Department of Biological, Human, and Life Sciences

Modeling the impact of SARS-Cov-2 on sleep quality through the analysis of mental, behavioral, and physical states of adults before and after COVID-19 vaccination

The COVID-19 pandemic has impacted nearly every corner of the globe within the past three years socially, physically, and mentally. In response to the outbreak, many pharmaceutical companies released vaccines in hopes of combatting the virus and protecting humans against it. Our study aims to discover if receiving the vaccine had any effect on sleep quality in adults. We hypothesized that after receiving the vaccine, sleep quality in individuals would improve as their stress levels regarding contracting the virus would supposedly go down.

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Researchers: Ananya R., Irving High School '24; Anika M., Basis Independent of Silicon Valley '24; Avi U., Dougherty Valley High School '23; Destiny P., Dougherty Valley High School '23; Devan M., Mountain View High School '23; Heejee Y., Amador Valley High School '23; Shreya A., Amador Valley High School '23

Advisor: Jahanikia, Life Sciences, Neuroimaging, Psychology & Bioinformatics

Keywords: Covid-19 | Sleep Study | Vaccines

Tuesday, November 22, 2022

​Department of Biological, Human, and Life Sciences

The in-silico and in-vitro characterization of epigenetic drugs (BET Pathway Targets) on a colorectal cancer cell line

Bromodomain and extra-terminal domain (BET) proteins have been linked to increases in oncogene expression and tumor progression in a wide array of cancers (Shorstova et al., 2021). Previous research on BET proteins has demonstrated that BET inhibitors (BETi) and other drugs in combination can moderately reduce cancer cell proliferation in colorectal cancer (Wu et al., 2022). Limited treatments exist for colorectal cancer due to its malignant nature and existing treatments are often costly or ineffective (Centers for Disease Control and Prevention, 2021). Our research centers around determining potential BETi in colorectal cancer through in-silico research and testing identified drug candidates in an in-vitro setting. While previous research has been conducted on BETi, few studies examine the effects of BETi in colorectal cancer. So far, we have created a list of one hundred possible BETi drugs that we will continue to narrow down. We are also working on identifying additional targets in HCT116 cells that are related to the BET protein pathway to expand our research. Once the targets have been identified, the drugs will be ordered/synthesized and tested on HCT116 colorectal cancer cells. They will be tested through MTT Assays (Freimoser et al., 1999), Western Blot (Mahmood et al., 2012 ) and the prize(Kralik et al., 2016) and the ones with the most BETi properties as well as the least harmful side effects will be selected. With the increase in BETi, we hope to increase cancer treatment for patients.

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Researchers: Madison D., Dougherty Valley High School '24; Sofia P., Notre Dame High School '24; Sanjana S., California High School '24

Advisor: Cunha, Bioinformatics and Cancer Biology

Keywords: Bioinformatics | Cancer Biology | Cellular and Molecular Medicine

Tuesday, November 8, 2022

​Department of Computer Science & Engineering

Exploring Game Theory Algorithms to Minimize Traffic Collision and Congestion of Autonomous Vehicles in 4-Way Intersections

Road congestion is a significant problem with transportation because it increases air pollution and travel time. Congestion also increases the likelihood of accidents and fatalities. Our research aims to mitigate these scenarios by reducing the time vehicles spend at four-way intersections. In order to efficiently traverse through intersections, incoming vehicles use a non-cooperative strategy to minimize the time to cross the intersection. Game Theory principles allow vehicles to more efficiently cross four-way intersections, reducing congestion and its associated problems. The decision-making vehicle needs to take the optimal path through the intersection. Many factors, such as speed, and distance between vehicles, need to be considered to dictate this path through an intersection. Our predefined game theory equations use these factors to guide the decision-making vehicle through its responses to the actions of a vehicle using a predetermined pathing system. During this research, we plan to construct two autonomous vehicles, one of them using a non-cooperative decision-making strategy to cross an intersection. We will test the algorithm by showing the improvement between using modern driving practices and using our team’s algorithm. The measure of improvement will be the amount of time saved using our team’s algorithm. In future research, we plan to experiment with three or more autonomous vehicles and various real-world obstacles including pedestrians, cyclists, traffic cones, and more.

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Researchers: Monish M., Foothill High School '23; Ajtih B., Westwood High School '24; Atharv D., Basis Independent Fremont '25; Evan Z., The Harker School '26

Advisor: McMahan, Computer Science & Quantum Computing

Keywords: Autonomous Vehicles | Game Theory | NEAT | Collisions | 4-way intersections

Tuesday, November 8, 2022

​Department of Biological, Human, and Life Sciences

CovidFatigue: Characterization and Severity Assessment of COVID-19 After-Effects

In our study, we are surveying the long-term effects of COVID-19. These effects are referred to as brain-fog and are characterized by fatigue and difficulty concentrating. To study the physiological and cognitive symptoms of brain-fog, we are designing a survey on Gorilla that will ask people who have been infected with COVID-19 to answer a series of questions about their recovery from COVID-19 as well as to complete certain cognitive tasks. Data from this questionnaire will allow us to learn about the frequency of certain symptoms of brain-fog and will help us determine how long these symptoms typically last. Collecting this data also enables us to look for external factors that affect recovery, such as vaccination status and age. We have developed a scoring scale for this questionnaire and have tested it with a sample group.

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Researchers: Rohan V., Amador Valley High School '23, Shashank S., Acton-Boxborough Regional High School '23

Advisor: Jahanikia, Life Sciences, Neuroimaging, Psychology & Bioinformatics

Keywords: Covid-19 | Brain Fog | Cognitive Symptoms | Physiological Symptoms | Recovery

Tuesday, November 1, 2022

​Department of Biological, Human, and Life Sciences

Cognitive Dissonance and COVID-19 in Adolescents

Cognitive dissonance theory is a theory stating that when there exists a discrepancy between someone’s external actions (behavior) and their internal values (attitude), this discrepancy will cause dissonance. To justify this dissonance, cognitive rationalization occurs. Our group is studying the effects of COVID-19 on cognitive dissonance in adolescents. We have developed a questionnaire to measure the dissonance teens hold between their attitudes about the pandemic and their behaviors during it. Constant streams of new, conflicting, and complicated information make young people increasingly likely to experiences dissonances between their actions and their beliefs. We analyzed our data using R to reveal trends and correlations between demographic information, the scores of individual groups, and overall average scores.

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Researchers: Myra M., Horace Mann School '23

Advisor: Jahanikia, Life Sciences, Neuroimaging, Psychology & Bioinformatics

Keywords: Social Psychology | Cognitive Dissonance | COVID-19 | Risk-Taking Behavior

Tuesday, October 25, 2022

​Department of Biological, Human, and Life Sciences

NeuroTDA: Using Topological Data Analysis to Analyze Neurodegenerative Disease and Genomics

Neurodegenerative diseases often have underlying genetic causes that are difficult to pinpoint and analyze. However, topological algorithms have recently become popular for extracting information from large genomic datasets. Topological Data Analysis (TDA) is a field that utilizes persistent homology and computational geometry to provide insight into the underlying shape and structure of data. Applications to cancer research and disease analysis have demonstrated the value of using TDA to identify significant patterns in genetic markers. We aim to use TDA to analyze genomic data in an effort to better understand how neurodegenerative diseases manifest. Using TDA algorithms such as Kepler Mapper, we will analyze the structure of the specific areas of the genome that are associated with such diseases. Identifying indicators of neurodegenerative disease can revolutionize diagnosis and respective treatments.

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Researchers: Krishnaveni P., BASIS Independent Silicon Valley, '24, Deniz Yilmaz,  Palo Alto High School, '24, Aditya Dawar, Amador Valley High School, '24

Advisor: Jahanikia, Life Sciences, Neuroimaging, Psychology & Bioinformatics

Keywords: Neurodegenerative Disease | Topological Data Analysis | Genome Sequencing | Genetics | Data Science

Tuesday, October 18, 2022

fMRIusic:  Understanding Brain Networks Associated with Emotional Responses to Music

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Researchers: Jeonghyun A., BASIS Independent Silicon Valley '24 & Ayaan K., Lynbrook High School '25

 

Advisor: Jahanikia, Life Sciences, Neuroimaging, Psychology & Bioinformatics

Keywords:

Tuesday, October 11, 2022

60 Second Lectures

Fast paced Lectures featuring all of the ASDRP Faculty Advisors. Our researchers work under the tutelage of some of the worlds biggest brains. Catch a glimpse of the incredible minds at work with ASDRP!

We are very excited for our weekly Colloquia this evening. Tonight we will be holding our "60 Second Lectures". We've challenged each of our incredible advisors to share about their research projects in 60 seconds - that's right 60 seconds!

 

Vote for Your Favorite

We've gone one step further and are giving you, our researchers, the opportunity to vote on your favorite "60 Second Lecture". A google form will be sent after the lectures, we'll tally the votes and share the results!

 

Join in the fun and learn about what's going on at ASDRP in 60 seconds.

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Researcher: 

 

Advisor: All ASDRP Advisors

Keywords:

Tuesday, October 4, 2022

GUEST Speaker: Robert Downing, Chair of the ASDRP

Department of Computer Science & Engineering

Research Ain’t Purty or, How do we Keep Going in the Face of Adversity?" Research is a long series of failures converging asymptotically on Proof of our Hypothesis. Or not.

Prof. Downing has 40 years of industry research experience from 3COM and IBM and as a former professor. The Downing research group applies the tools of data mining and data science to astronomy and cryptography. Through applied computer science and data science, the group hopes to move towards deciphering the Voynich manuscript and also detecting near-earth objects using signal-to-noise detection mining of NASA datasets. He is also the director for the Astrophysics Research Institute at ASDRP. If you have not had a chance to meet Mr. Downing and speak with him about his areas of interest, check out his research group website.

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Researcher: 

 

Advisor: Downing, Computer Science

Keywords:

Tuesday, September 27, 2022

​Department of Engineering and Computer Science

Identifying and isolating collimated jets from heavy-ion collision open data through quantum optimization.

When a quark or a gluon ejects out of a heavy-ion particle collision, it pulls hadrons and other particles out of the vacuum and becomes a "cone" comprised of high-energy particles called jets. Jets are crucial event-shaped observable objects that are used in high-energy particle and heavy-ion physics. To determine the properties of the collision, namely of the original quark, jets and their products have to go under a technique called jet reconstruction (Salam, Gavin P., "Towards jetography"). Though physicists have found several ways to reconstruct the properties of quarks in a heavy-ion collision using the end products of jet creation and separate jets from other collision data utilizing another concrete event-shaped observable called thrust (V. D. Barger, R. J. N. Philips, "Collider Physics"), our topic aims to not only classify jets and non-jets through analyzing CERN's heavy-ion collision data from CMS but also utilize quantum annealing (a faster and comprehensive method to optimize machine learning algorithms which have been newly introduced to the realm of denoising data (J. Avron, "Quantum advantage and noise reduction in distributed quantum computing")) to isolate jet "clouds" from CERN's CMS data. We have developed a comprehensive method to develop this novel method to identify and isolate jets, which will allow us to not only determine the applications of modified deep learning in jet reconstruction but also the applications of quantum computing in general particle physics. To elaboriate further, we developed a hybrid quantum-classical approach to classify jets from data collected from high-energy heavy ion collisions, which has proved to be quite effective so far.

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Researcher: Akarsh O., BASIS Independent Silicon Valley '23

 

Advisor: McMahan, Computer Science and Quantum Computing

Keywords: Particle Physics | Quantum Physics | Artificial Intelligence | Quantum Computing | Jets | Denoising

Tuesday, September 27, 2022

Department of Chemistry, Biochemistry, and Physics

Mechanistic insights into the design and synthesis of natural product analogs and modular mimics for anticancer and neurodegenerative therapeutics.

The study of natural products offers an excellent strategy toward identifying novel biological probes for a number of diseases. Historically, natural products have played an important role in the development of pharmaceutical drugs for a number of diseases including cancer and infection. Here, we overview the importance of natural product synthesis and the synthesis of analogs of multiple compounds. The research in our group focused on the synthetic optimization of rivastigmine and its analogs, utilizing computer modeling and biological assays to determine the most favorable analog for inhibition of acetylcholinesterase (AChE). Additionally, our group has done significant synthetic efforts in analogs of andrographolide, an Nf-kB inhibitor and active anticancer therapeutic..

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Researcher: Harrison X., Dougherty Valley High School '23

 

Advisor: Njoo, Organic Chemistry

Keywords: Organic Synthesis | Natural Product Chemistry | Medicinal Chemistry | Chemical Biology

Tuesday, September 20, 2022

GUEST Speaker: Edward Njoo, Chair of the ASDRP

Department of Chemistry, Biochemistry, and Physics

From the oceans and trees, to the laboratory, and back again: a history and philosophy of chemical synthesis and the close interrelationships between organic chemistry and biomedical innovation.

Since Friedrich Wohler's original chemical synthesis of urea in 1828, chemists have been involved in replicating the production of chemical structures from nature, synthetically in a laboratory. From this initial discovery came the advent of natural product synthesis, and combined with advances in catalyst development and synthetic methodology, our ability to construct complex natural products found in nature has greatly improved in its sophistication, efficiency, and scalability. Today, chemical science has progressed from one of merely making molecules that nature has made, to designing our own chemical structures with new and novel functions - some inspired by or derived from the natural realm, and others contrived out of de novo design, and certainly these advances have revolutionized downstream targets in medicines that treat or cure human disease or materials with newfound properties and unique utility. Along these lines, we discuss the impact of methodology in expanding the modern chemist's toolbox for constructing chemical bonds. Additionally, the modern chemist is faced with a number of considerations for developing an economical synthetic route, not only from a monetary cost perspective but also from the perspectives of atom economy and step economy. On a broader level, though, the chemist is faced with decisions of which chemical structures are the most important to make among the billions of possibilities in chemical space. To this end, we discuss a philosophy of designing both synthetic targets and synthetic routes, and the different orientations that one might adopt in creating chemical structures and synthetic routes motivated by function, by diversity, by bio-inspired design, or some combination of the aforementioned. We finish with a prospectus on the future of synthetic organic chemistry and its enabling impact on medicine, materials, and more, and how research in our laboratory at ASDRP has found its way into real-world impact in medicinal and process chemistry. 

 

Disclaimer: This presentation contains unpublished intellectual property (IP) from the Chemistry department at ASDRP and its collaborators. 

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Researcher: 

 

Advisor: Njoo, Organic Chemistry

Keywords:

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