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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 "Join the Colloquia" link to add the event to your calendar.

Spring 2022 Colloquia Dates:

  • January 11, 18, 25

  • February 1, 8, 15, 22

  • March 1, 8, 15 Guest Speaker Dr. Lucas, 22, 29

  • April 5 Guest Speaker Dr. Valentine, 12 (cancelled), 19, 26 (Advisor Open House)

  • May 3, 10, 17, 24

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, May 17, 2022

Department of Computer Science & Engineering

Predicting crime in the United States.

The ability to predict the crime rate of a city is useful for adjusting policing, giving insight to prospective citizens of that city, and more. We built a prediction model using random forest classification to predict the crime rate of a city as low, medium, or high, based on various data on the city. We built our dataset with many sources including the US Census. We then trained our model with 20% of the data collected. Finally, we analyzed and interpreted the results of our model. In the future we hope to gather more data, balance classes, and try out other algorithms for our model such as XGBoost and neural networks.

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Researchers: Aamood G., Mission San Jose High School ' 25; Harshaan C, Bellarmine College Prep '24; Andrew Z, Monta Vista High School '24


Advisor: Subramaniam, Data Science






Keywords: Crime | Crime Prediction | Data Science | Model | Random Forest Classifier

Tuesday, May 17, 2022

Department of Computer Science & Engineering

Identifying the Association of Political Affiliation and Sentiment on the Russia-Ukraine Conflict.

The Russia-Ukraine conflict has been one of the greatest threats to global peace since the Second World War, but one factor that makes it unique is its occurrence in an era of technological literacy and widespread social media usage. In fact, millions of tweets have already been posted in the last few months regarding the destructive situation, providing data scientists a wealth of information about users' sentiments and opinions. While other researchers have already performed sentiment analysis on Twitter users' tweets, our group aimed to also determine how a person's political affiliation relates to their opinion/s on the conflict and how the opinions of right-leaning and left-leaning groups have changed over time. By utilizing natural language processing and machine learning, we have been successfully able to correlate sentiment and political affiliation over the span of seven days. This presentation gives insight into our methods and future research to improve our models and results.

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Researchers: Pareekshith K., Mission San Jose High School '23; Siddh S., Leland High School '23; Yue C., Piedmont High School '22


Advisor: McMahan, Quantum Computing, Computer Engineering





Keywords: Russia and Ukraine | Political Affiliation | Sentiment Analysis | Natural Language Processing | Machine Learning | Logistic Regression | Data Analysis | Big Data

Tuesday, May 10, 2022

Department of Computer Science & Engineering

Restricted Hartree-Fock approximations of the Schrödinger Equation for multielectron atoms from He-Xe through STO-nG basis sets.

Various self-consistent field methods have been formulated to approximate the Schrödinger equation for multi-electron atoms, which employ recursive approximations to converge at a stable value for the energy of atomic or molecular orbitals. Through the use of methods such as Hartree-Fock and post-Hartree-Fock that include the effects of interelectron Coulombic repulsion and electron correlation energy in approximating electronic wavefunctions, a highly accurate calculation of atomic energy of multielectron elements can be obtained. Here, the calculation of atomic energy for elements from helium to xenon using the restricted Hartree-Fock method will be discussed. Multiple aspects of the accuracy, precision, and efficiency of RHF calculations using the PySCF Python library will be evaluated, including the nature, necessity, and scope of basis sets, the differences between Slater-type and Gaussian-type basis sets, and STO-nG Gaussian-type basis sets. Calculations of atomic energies using STO-nG basis sets will be presented, along with a comparison between their results and experimentally determined atomic energies, an analysis of exponential and trigonometric regressions modelled to approximate the STO-nG RHF results, and a discussion on the reliability of these models. The presentation will also survey the challenges faced in this project, future goals for the research group in optimizing the runtime of the calculations and their applications, and exploring other SCF methods and basis sets for atomic energy approximations.

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Researchers: Vaibhav V. The Quarry Lane School '23


Advisor: McMahan, Quantum Computing, Computer Engineering




Keywords: Self-consistent Field |Restricted Hartree-Fock | Atomic Energy | STO-nG | Quantum Physics

Tuesday, May 3, 2022

Department of Computer Science & Engineering

Hybrid Quantum-Classical Generative Adversarial Network for Generating Synthetic, Chemically Stable Molecules.

Current drug discovery processes can cost billions of dollars and usually take five to ten years. People have been researching and implementing various computational approaches to search for molecules and compounds from the chemical space, which can be on the order of 10^60. One solution involves deep generative models, which learn from nonlinear data by modeling the probability distribution of chemical structures. These generative models can extract salient features which characterize the molecules. However, they often suffer from increased time complexity. Aiming for faster runtime and greater robustness when dealing with high-dimensional data, we implemented a Hybrid Quantum-Classical Generative Adversarial Network (QGAN) to generate chemically stable molecules. There are two parts to the QGAN: a generator that creates molecules based on the probability distributions of likely combinations and a discriminator that classifies real molecules from generated molecules. We hypothesized that a quantum generator would be more impactful because we could use superposition to analyze more possibilities than a classical generator. Also, we implemented a classical discriminator because it performs a simple classification task, which does not need quantum computing speedups. Although this hybrid approach forces us to work with floating-point numbers in our quantum circuit, we avoided this issue by implementing a Quantum Analog-to-Digital Converter. Our Pytorch-Pennylane implementation of the QGAN generated seven chemically stable molecules out of 300, a 2.3% success rate. Although it is still a work in progress, our QGAN has shown the path towards more efficient drug development.

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Researchers: Diptanshu S., BASIS Independent Silicon Valley '22 | Max C., Sir Winston Churchill Secondary School '24 | Adelina C., Archbishop Mitty High School '24


Advisor: McMahan, Quantum Computing, Computer Engineering




Keywords: Quantum Computing | Quantum Machine Learning | Generative Adversarial Network | Generator | Discriminator | Quantum Analog-to-Digital Converter

Tuesday, April 26, 2022

All Departments - Advisor Virtual Open House - Join using Colloquia link

Come learn from all of our advisors about the various projects and research laboratories as you consider other options this summer or are new to ASDRP.

You'll get the opportunity to hear from


  • Michael Amadi, Biotechnology and Synthetic biology

  • Omar Amer, Civil Engineering & Environmental Engineering

  • Andrew Benson, Marine Science

  • Harman Brah, Computational Biochemistry, Biophysics, & Structural Biology

  • Raymond Chen, Bioanalytical chemistry

  • Clinton Cunha, Bioinformatics and Cancer Biology

  • Robert Downing, Data Science, Machine Learning, Astronomy (Dept Chair)

  • Sahar Jahanikia, Life Sciences, Neuroimaging, Psychology & Bioinformatics (Dept Chair)

  • Prabhjeet Kaur, Microbiology & Environmental Genetics

  • Joseph Laurienzo, Computer Science, Physical & Biological Systems Simulation   

  • Larry McMahan, Quantum Computing, Computer Engineering

  • Phil Mui, Computer Science, Machine Learning

  • Edward Njoo, Organic & Medicinal Chemistry, Chemical Biology, Catalysis (Dept Chair)

  • Nilai Patel, Inorganic & Electrochemistry, Materials Science

  • Gayathri Renganathan, Biochemistry & Medicinal Chemistry

  • Neelima Sangenei, Materials Science

  • Suresh Subramanian, Data Science and Computer-Guided Statistical Modeling

  • Soumya Suresh, Environmental Studies, Conservation & Ecology   

  • Akira Yamamoto, Biomaterials Engineering, Materials Science

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Researchers: All


Advisor: All



Keywords: ASDRP Research | New Admits | Summer 2022

Tuesday, April 19, 2022

Department of Computer Science & Engineering

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. Through the information we have collected so far, 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..

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Researchers: Akarsh O., Basis Silicon Valley HS '23; Nathan J., John F. Kennedy HS '23; Krishna C., Mission San Jose HS '24


Advisor: McMahan, Quantum Computing and Computer Science



Keywords: Artificial Intelligence | Jets | Heavy Ion Physics | Quantum Computing | Particle Physics | Quantum Mechanics | Denoising

Tuesday, April 5, 2022

GUEST SPEAKER - Kristin M. Valentine, Ph.D.

"Exploring infection by different SAR-CoV-2 viral variants"

The SARS-CoV-2 infections causing the coronavirus disease 2019 (COVID-19) started a pandemic nearly three years ago. During SARS-CoV-2 circulation around the world it has continued to adapt resulting in a series of viral variants. Understanding the differences in how each viral variant can cause infections is imperative in our continued vaccination development. We evaluated three SARS-CoV-2 viral variants in the humanized ACE2 mouse model. All three viral variants are capable of infecting the upper and lower airway of mice. However, lethal disease in these mice may largely be driven by brain infections. 100% of mice infected with the Beta variant develop a brain whereas only 50% of mice infected with the delta and 0% of mice infected with Omicron develop a brain infection. Further, the infection severity in these mice is associated with differential T cell activation. Thus, while each viral variant can result in productive airway infection there is variability in the severity of infection.   


Dr. Valentine started her education as a pre-medical student at the University of California, Merced. During that time she discovered a love of research and immunology. After obtaining a Bachelor of Science in Biological Sciences with an emphasis in Human Biology, she continued her pursuit of an advanced degree and was accepted as a Ph.D. candidate at University of California, Merced in the laboratory of Dr. Katrina Hoyer. Together, they identified a new population of immune cells (CD8 T cells) capable of driving autoimmune disease like lupus. After completing her Ph.D. she decided to apply her knowledge in immunology to emerging infectious diseases. For her post-doctoral studies, Dr. Valentine joined the La Jolla Institute for Immunology. Her research focused on the study of the role of T cells in protecting against different SARS-CoV-2 viral variants. Most recently, she has accepted a position as a Scientist with Encoded Therapeutics in South San Francisco. Her research will be to evaluate treatments for pediatric brain disorders.

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Researchers: Guest Speaker, Kristin M. Valentine, Ph.D., Scientist with Encoded Therapeutics in South San Francisco






Tuesday, March 29, 2022

Department of Biological, Human, & Life Sciences

In Vitro Characterization of Human DNA Methyltransferase Inhibitors on HCT-116 Colorectal Cancer Cells.

Colorectal cancer (CRC), the development of cancer in the colon or rectum, is a common cancer which leads to death (American Cancer Society, 2019). Epigenetic therapy is a novel cancer treatment method displaying promising clinical results (Nepali, Liou, 2021). Epigenetic modifiers activate genes regulating the cell cycle and apoptosis by demethylating certain regions of DNA to inhibit the expression of cancer (Nepali, Liou, 2021). This project focuses on the use of new analogs of the drug N-Phthaloyl L Tryptophan (RG108), a non-nucleoside DNA Methyltransferase 1 (DNMT1) inhibitor able to reverse the effects of DNA methylation while reactivating tumor suppressor genes (Hagemann et al., 2011). Certain tumor suppressor genes were chosen based on their antiproliferative traits. These genes work to slow down cell cycle progression and induce apoptosis. Our interest in RG108 and its analogs was initiated by studies on effective inhibition of proliferation by nucleoside and non-nucleoside inhibitors (Brueckner et al., 2005). RG108 is not an FDA-approved treatment for colorectal cancer, so this work hopes to derive any meaningful insights by using RG108 as a control along with similar analogs to RG108 to potentially find novel or better inhibitors for DNMT1 (Medina-Franco et. al, 2015). The cell line used was the epithelial human HCT116 cell line. Current experimentation focuses on validating RG108’s role on HCT116 cells to ensure accurate technique. Cell proliferation assays and gene expression analysis will be used to test the efficacy of RG108 (Riss et al, 2016). In the future, analysis of the reactivation of tumor-suppressor genes when treated with RG108 will be implemented with qPCR (Assis et. al, 2018). Immunoblotting will serve a similar purpose, determining levels of tumor suppressor proteins involved in cell cycle checkpoint regulation or levels of proteins involved in the intrinsic and extrinsic apoptotic pathways after RG108 treatment (Jan et. al, 2019). Cytotoxicity of RG108 will be analyzed with the study of migration and colony formation with the clonogenic and cell migration assay (Ou et. al, 2018). Further analysis of cell viability will be done using flow cytometry, examining cell cycle progression in the context of G2/M checkpoint status (Ou et. al, 2018). Fluorescent microscopy will be used to visualize caspase activation (Zheng et. al, 2021) After conducting the validation experiments mentioned above, future work aims to isolate connections between treatment with novel RG108 analogs and reactivation of tumor suppressor genes and determining whether the analogs capability for immunomodulation would have increased efficacy when compared with RG108 and other FDA-approved drugs.

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Researchers: Ojasvi M., Evergreen Valley High School '24;  Hena P., American High School '24;  Alfiya R., Dublin High School '23;  Sreshta Y., American High School '24


Advisor: Cunha, Bioinformatics and Cancer Biology


Keywords: Colon Cancer | Biology | Methylation | RNA | Cancer Cell Lines | Epigenetics | Biochemistry

Tuesday, March 22, 2022

Department of Biological, Human, & Life Sciences

Using Dimensionality Reduction and Molecular Docking to Find Novel DNA Methyltransferase 1 Inhibitors for Colon Cancer.

In cancerous states, DNA methyltransferase 1 (DNMT1), an enzyme that catalyzes the addition of methyl groups, adds excess methyl groups to the CpG islands in tumor suppressor genes, which results in the genes being silenced. DNMT1 inhibitors reverse this process and activate the genes again (Hu, Liu, Zeng, et al., 2021). There are many obstacles to finding novel inhibitors in the modern era, such as narrowing down potential inhibitors from the large chemical space, with the largest one being cost challenges to testing a large number of compounds (Wouters, McKee, Luyten, 2020). Furthermore, after a compound likely to have high efficiency has been identified, the materials to synthesize the compound could be inaccessible or dangerous to handle. Even if these obstacles are overcome and the drug is created, it might not work as efficiently as predicted. Instead of finding drugs the conventional way, we are taking a more computational approach to finding novel treatments to colon cancer with the processes of dimensionality reduction (Rhys, 2020) and molecular docking (Meng, et al., 2011). With UMAP (McInnes, et al., 2018; Corsello, et al., 2020), a dimensionality reduction statistical technique, we used the knowledge of pre-existing inhibitors, such as RG108, to cluster compounds with similar chemical properties together from a ChemBL (Mendez, et al., 2018) dataset representing the chemical space. Then using Avogadro (Hanwell, et al., 2012), Orca (Neese, et al., 2011), and AutoDock Vina (Trott, Olson, 2010), we will batch dock those similar compounds with AutoDock Vina. With the binding affinities that AutoDock Vina outputs, we can narrow down the list of possible drugs that will be effective against colon cancer. Finally, we will use DeepCDR to, in silico, determine different drugs’ efficiency on colon cancer cell lines based on the transcriptomic, genomic and epigenomic data of cell lines along with previous drug-cell line pair patterns (Liu et al, 2020). Our research allows for easier discoveries of protein inhibitors than the conventional method does by combining a variety of software tools. With this method, we can look through multiple compounds and spot structural and chemical connections between different drugs that are hard to see through the human eye and physical experiments.

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Researchers: Shikha K., Dougherty Valley High School

23; Ashley L., Mission San Jose High School '24;  Aksithi E., Notre Dame San Jose High School '24

Advisor: Cunha, Bioinformatics and Cancer Biology


Keywords: Bioinformatics | Molecular Docking | AutoDock Vina | DNA Methyltransferase 1 | Computational Analysis | UMAP | Dimensionality Reduction

Tuesday, March 22, 2022

Department of Biological, Human, & Life Sciences

Effects of Ocean Acidification on Barnacle Feeding Behavior and Predator Avoidance.

Oceans are a crucial part of the Earth’s anatomy; it gives life to marine organisms, allows for transportation for ships, acts as a storage area for inorganic material, and prevents extreme heating of the Earth by absorbing excess CO2 from the Earth’s atmosphere. This leads to a decreased pH level, which can harm marine life. Our experiment aims to understand how differing pH levels affects marine life. Specifically, our experiment is geared towards understanding the effects of how an increase in ocean acidification affects its ability to maintain a sustainable environment for marine creatures.

In our experiment, we placed two types of barnacles(balanus aquila-acorn and tetraclita rubescens-volcano) into saltwater tanks of different pHs. The control tank was at 8.1 pH, and the other two were at levels of 7.8 pH and 7.5 pH. Barnacles were fed zooplankton per data collection, and their feeding activity was measured by counting the amount of cirri extensions at 10 minute intervals leading up to 30 minutes. We also tested predator avoidance response for barnacles by using a sponge to replicate its predator's effects, and our previous finding that a light brush to the barnacle’s surface by a sponge caused it to retract still holds true. Previous data presented itself as statistically significant, but current findings show possible changes, and further study is needed to rectify a proper conclusion.

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Researcher: Anish J., Dougherty  Valley High School '24


Advisor: Benson, Marine Science


Keywords: Marine Biology | Ocean Acidification | Barnacles | Semibalanus Balanoides | Tetraclita Rubescens

Tuesday, March 15, 2022

GUEST SPEAKER - Donald D. Lucas, Ph.D.

Protecting Our Nation From Atmospheric Hazards Through Science and Technology.

From industrial accidents to wildfire plumes, hazardous materials that are lofted into the atmosphere can be transported far downwind and impact populated areas. To predict where the materials travel and when they reach certain locations, scientists create and run complex computer models driven by meteorological data and containing many important chemical and physical transformations. These models provide critical information used by emergency responders and decision makers during times of crisis or to plan for future events. This presentation provides an overview of the operational modeling capabilities of the National Atmospheric Release Advisory Center, which has been predicting atmospheric hazards for the Department of Energy for over forty years. I also highlight my recent research efforts to improve and accelerate atmospheric modeling using machine learning.


Donald D. Lucas, Ph.D., received a B.S. in Chemistry in 1996 and Ph.D. in Atmospheric Chemistry in 2003, both from MIT. He conducted postdoctoral research in atmospheric nano-particle formation at the Research Institute for Global Change and Japan Agency for Marine-Earth Science and Technology in Yokohama, Japan between 2004 and 2006. Thereafter, he joined the Department of Atmospheric Sciences at Texas A&M University as an assistant professor and then moved to private industry, where he developed automated machine learning algorithms for a start-up company.


Dr. Lucas is currently an Atmospheric Scientist in the National Atmospheric Release Advisory Center at the Lawrence Livermore National Laboratory. As the principal investigator for multiple projects, his research focuses on applications of statistical methods, machine learning, inverse modeling, and uncertainty quantification to atmospheric, radiological, and climatological systems. In recognition of his research, he has won numerous laboratory awards and was co-recipient of the 2019 Mitchell Prize from the American Statistical Association. He previously served as the secretary and treasurer for the Physics of Climate group in the American Physical Society and currently serves as a topical editor for the journals Atmosphere and SN Applied Sciences.

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Researcher: Guest Speaker, Donald D. Lucas, Ph.D.


Advisor: Lawrence Livermore National Laboratory


Keywords: Atmospheric Release | Meteorological Data | Computer Modeling | Hazardous Materials | Emergency Responders

Tuesday, March 8, 2022

Department of Biological, Human, & Life Sciences

Investigating the ecological impacts of invasive mud snail species, ilyanassa obsoleta, on native San Francisco Bay mudflat inhabitants.

Ilyanassa obsoleta, native to the Atlantic coast, is a foreign yet abundant mollusk in the San Francisco Bay mudflats. It is a well-established invasive species noted for displacing the native mud snail, Cerithidea californica (Race, 1982). The objective of this study was to analyze the impact of I. obsoleta’s presence on the biodiversity, species abundance, and size distribution of native mudflat species. Using random sampling and biodiversity hotspot methods, our team collected samples from four different locations: Berkeley Marina (North and South), Point Emery, and Crab Cove. We processed samples based on specimen wet weights and sizes and compared data from sites with a presence to sites with an absence of I. obsoleta. With biostatistical analysis, we found an overall decrease in biodiversity and size distribution (only between Hemigrapsus oregenensus and Mytilus trossulus) in sites abundant in I. obsoleta, however, conducting an unpaired t-Test (p=0.05) concludes that there is no statistically significant difference. We used Whittaker plots among others to access species abundance and found an overall higher species abundance and species richness in sites absent in I. obsoleta. We plan to extend our project by acquiring more field data, especially in sites observed to have an abundance of I. obsoleta, and diversifying our means of processing and analysis.

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Researcher: Nitya S., Alsion Montessori High School '24


Advisor: Benson, Marine Science & Ecosystems


Keywords: Marine Biology | Ilyanassa Obsoleta | Mudsnails | Invasive Species | San Francisco Bay | Mudflats | Biodiversity

Tuesday, March 8, 2022

Department of Chemistry, Biochemistry, and Physics

Combinatorial chemical synthesis of bioactive scaffolds: diversity-generating strategies enables rapid analog library preparation and mechanistic insight in medicinal chemistry.

Nowadays, scientists can rapidly screen chemical libraries of structural analogs from a singular optimized protocol for their bioactivity, thus allowing for more cost and time-efficient drug discovery. In designing analogs of synthetic compounds, we can not only compare the potency of novel therapeutics, but also gain insight into the role of various substituents in determining complex reaction mechanisms. For instance, the mechanism of the Biginelli cyclocondensation reaction, whose product has been shown to be a promising anticancer and antiviral agent, has been debated for over a century. Through the synthesis of novel trifluorinated 2,4-dihydropyrimidines, we utilize aryl substitution to diversify our products in efforts to gain mechanistic insight. Additionally, we discuss the synthesis of rivastigmine, a modular mimic of the naturally occurring neuroactive alkaloid physostigmine, and its analogs to compare the potency of such neuroactive small molecules in treating neurological disorders. We further utilize nuclear magnetic resonance spectroscopy and computer modeling to probe reaction kinetics and provide structural insight into the biochemical activity of our compounds.

Adrienne Ferguson ASDRP

Researcher: Adrienne F., Prospect High School '23


Advisor: Njoo, Organic Chemistry

Keywords: Medicinal Chemistry | Chemical Neuroscience | Synthetic Organic Chemistry

Tuesday, March 1, 2022

Department of Biological, Human, & Life Sciences

Investigating the efficiency of the saltwater mussel Mytilus californianus’s ability to filter microplastics from aquatic ecosystems

The increase of microplastic pollution, otherwise known as plastics smaller than five millimeters, actively contributes to the threats faced by life in marine ecosystems. Microplastic pollution has the ability to adversely affect aquatic and human life by manipulating organisms’ functions. However, novel research has demonstrated that as filter feeders who have been shown to intake and retain microplastics, mussels may have the ability to minimize such pollution. Therefore, we explored the potential that mussels may have in filtering and retaining microplastics and worked towards developing a method to extract, purify, clean, and quantify microplastics from mussels, as there currently is not a standardized method to do so. Given the rising trends of microplastic pollution, different-sized mussels collected from a local tidepool were exposed to varying levels of microplastics over a period of time. After exposure, the retained microplastics from the mussels were isolated and quantified using a mussel digestion process and vacuum filtration. In the future, we plan to analyze this digestion using a dissecting microscope, ultraviolet light, centrifuge technology, and FTIR spectroscopy. All microplastics used in the experiment were created using a power sander, including 1 PETE, 2 HDPE, 3 PVC, 4 LDPE, and 5 PP plastics. Elementary data suggests that mussels may be able to efficiently filter microplastics, illuminating the role mussels play in microplastic pollution. While we are still pursuing results, these findings may prove to be insightful for addressing an environmental issue presently affecting aquatic ecosystems and we hope to be able to implement our findings in a real water system.

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Researcher: Advay C., American High School '24


Advisor: Benson, Marine Science & Ecosystems


Keywords: Microplastics I Filter Feeding I Aquatic Ecosystems I Filtration I Digestion I FTIR Spectometry

Tuesday, March 1, 2022

Department of Computer Science & Engineering

Self-consistent field approximations of the Schrödinger Equation for multielectron atoms from He-La to calculate atomic energy

Various self-consistent field methods have been formulated to approximate the Schrödinger equation for multi-electron atoms, which employ recursive approximations to converge at a stable value for the energy of atomic or molecular orbitals. Through the usage of methods such as Hartree-Fock and post-Hartree-Fock methods that include the effects of interelectron Coulombic repulsion and electron correlation energy in calculating electronic wavefunctions, a highly accurate calculation of the atomic energy of multielectron elements can be obtained. Here, multiple aspects of the efficiency and accuracy of self-consistent field methods in calculating atomic energy will be discussed, including the differences in SCF method, the differences between Gaussian-type orbital basis sets as computed by the PySCF module, and comparisons to previous data computed with Slater-type orbitals. The presentation will also survey the challenges in arriving at these, including restricting SCF mechanisms, standardizing basis sets, and identifying errors in the Slater-type orbital calculations.

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Researchers: Vaibhav V., The Quarry Lane School '23; Tanvi G., Irvington High School '23


Advisor: McMahan, Quantum Computing, Computer Engineering


Keywords: Self-Consistent Field | Hartree-Fock | Atomic Energy | Physical Chemistry | Quantum Physics

Tuesday, February 22, 2022

Department of Chemistry, Biochemistry, and Physics

Synthesis of novel fluorinated non-nucleoside reverse transcriptase inhibitors (NNRTIs) analogs toward HIV/AIDs treatment

As one of the greatest global health issues, human immunodeficiency virus (HIV) and the subsequent infection, acquired immunodeficiency syndrome (AIDS), affect more than 37 million people globally. HIV impairs the cells in the immune system, rendering a person unable to fight other diseases and infections, and when a person’s CD4+ T-cells fall below 200 cells/mm3 of blood, they are considered to have progressed to AIDS. Since there is currently no cure — only treatment — for the virus, in many third world countries, where treatment is not readily available, the virus has caused severe economic and social dilemmas. To combat this virus, one approved treatment is through the use of NNRTIs or non-nucleoside reverse transcriptase inhibitors. These inhibitors bind allosterically to the reverse transcriptase enzyme that HIV uses to replicate its genetic material, disabling the enzyme’s ability and terminating the viral insertion into the host cell. Here, we investigated two fluorinated NNRTIs, rilpivirine and efavirenz, to respectively, design a library of analogs that were each screened in a high throughput virtual screening. Furthermore, we have used 19F NMR spectroscopy to monitor the synthesis of rilpivirine through palladium-catalyzed Buchwald Hartwig cross couplings and efavirenz through metal-catalyzed asymmetric alkynylations of trifluoromethyl ketones.

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Researchers: Shelly Li, Mission San Jose High School '23


Advisor: Njoo, Organic Chemistry


Keywords: Organic Chemistry Snthesis | Medicinal Chemistry | Computational Biochemistry

Tuesday, February 22, 2022

Department of Computer Science & Engineering

Predicting the Instance of Breast Cancer within Patients using a Convolutional Neural Network

Diseases have been rapidly evolving and have proven to be devastating. With the evolution of diseases across the world, it is important to provide rapid diagnosis for such diseases. Specifically, breast cancer is a widespread issue that affects millions of people across the world. It kills tens of thousands of people across the world, with early detection being critical for heightened chance of survival. Although there are many ways for breast cancer to be detected, many are detected late. Indeed, there is a median diagnosis delay of 7.5 months. Thus, it is crucial to provide doctors and medical professionals with the ability to effectively diagnose breast cancer promptly. In this paper, we present a novel way to detect breast cancer within patients. Our method uses medical imaging of breast cancer data and classifies them as either benign or malignant using a convolutional neural network (CNN). Specifically, we utilize 200 studies from Duke University’s Breast Cancer Screening - Digital Breast Tomosynthesis data, where 140 of them are from the normal group and 60 of them are from the cancerous group. To classify the images, we use a convolutional neural network with 5 layers. Our approach can provide much higher accuracy compared to past approaches. Our accuracy is 100 percent, which is a 23.1 percent increase in accuracy than previous approaches that had an accuracy of 76.9 percent.

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Researchers: Aradhya Kapoor, Milpitas High School '23; Karthik Sabhanayakam, Basis Independent Silicon Valley '23

Advisor: McMahan, Quantum Computing, Computer Engineering

Keywords: Breast Cancer | Convolutional Neural Network | Machine Learning | Deep Learning | Image Classification

Tuesday, February 15, 2022

Department of Chemistry, Biochemistry, and Physics

Efforts Toward the Synthesis of Macrocyclic Natural Products and Assessments of their Biological Activity 

In the development of novel therapeutic molecules, macrocyclic compounds have been found to effectively combine the pharmacokinetic properties of small molecules with the high binding selectivity of large biomolecules. Many natural products, chemical compounds produced by living organisms, employ these macrocyclic structures that confer high biological potency. As such, natural products are an area of interest among chemists as potent synthetic targets in drug discovery. Here, we present the syntheses of the cyclic tripeptide Psychrophilin E, novel derivatives of the labdane diterpenoid Andrographolide, and members of the Avermectin family of antihelminthic macrocyclic lactones. Our synthetic efforts are furthermore coupled by the use of computer modeling to determine the structure-activity relationships of these compounds.

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Researcher: Andrew Chen, Amador Valley High School '23

Advisor: Njoo, Organic Chemistry

Keywords: Natural Product Total Synthesis | Chemical Biology

Tuesday, February 15, 2022

Department of Biological, Human, & Life Sciences

The Effect of COVID-19’s Impact on Human Activity on non-Protected and Protected Rocky Intertidal Tide Pools in San Mateo County

After watching the profound impacts the COVID-19 pandemic had on human activity – a reduction in large indoor events and an uptick in outdoor activities – our group sought to investigate the impact of the change in human activity on the Northern California rocky intertidal  ecosystem. Our research centered around the question: How has the impact that COVID-19 has had on the amount of human interaction with rocky intertidal zones affected their ecosystem health, as well as species biodiversity and abundance, when compared between protected and non-protected marine areas? During the COVID-19 pandemic, people began to look to outdoor recreational activities to stay within COVID-19 restrictions. One of these activities was visiting non-protected tide pools such as Maverick’s Beach, which stayed open throughout the pandemic. Park rangers noted seeing a dramatic uptick in the number of visitors to the tide pools and the amount of species being taken. On the other hand, protected locations such as Fitzgerald Marine Reserve, shut down for a six month period during the peak of the pandemic. During this time, the tide pools were completely undisturbed as no one could enter the reserve or remove any species. 

Due to humans being able to take species from non-protected sites including but not limited to: moon snails, shore crabs, rock crabs, limpets, turban snails, sea urchins, mussels, oysters, hermit crabs, and even octopuses, the ecosystems of the non-protected tidepools have been severely affected. For example, because of predator species (ex: sea urchins are a predator to algaes and plants, and they are now being taken) being removed by humans, many species of seagrass and other organisms have grown dramatically due to the uncompetitive environment. In addition, overfishing is largely unregulated and reduces the population count of organisms belonging to a particular species. To support our hypothesis and the reality of the issue, we first went to Maverick’s Beach, a non-protected area. There, we took quadrat data using random sampling and transect lines. After collecting data from Maverick’s Beach through multiple trups, we obtained permits from the Fitzgerald Marine Reserve and began data collection for a protected area


Researcher: Arohi Chirputkar, Irvington High '22 & Rowan Campbell, California Crosspoint Academy '23

Advisor: Benson, Marine Biology

Keywords: Tide Pool Invertebrates, Ecology, COVID-19, San Mateo Shoreline

Tuesday, February 1, 2022

Department of Chemistry, Biochemistry, and Physics /

Department of Computer Science & Engineering

Programming our way through Drug Discovery - Informatics and Automation Based Approaches to Linearizing Process Efficiency

Drug discovery, synthesis, and screening have long been one of the most time-consuming and expensive efforts in the medical industry with costs ranging from $600 million to $1.4 billion and discovery to market times costing roughly 10 to 15 years. However, with recent developments in hardware and software, these processes can be accelerated leveraging the increase in available compute power such as GPUs and TPUs and complex deep learning algorithms. By automating these tasks, we can not only save massive amounts of time and money but also increase the safety of workers, as dangerous reactions would be handled autonomously. The pipeline we propose begins with a generative approach for the discovery of small molecules that are able to bind to our target protein. These compounds are then given to our automated synthesis platform to autonomously synthesize and optimize our desired compound. Lastly, we propose a quantitative approach to grading the efficacy of synthesized compounds allowing for a completely independent drug development workflow.

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Researcher: Nathaniel  T., Homestead High School '23


Advisor: Njoo, Organic Chemistry; Downing, Computer and Data Science

Keywords: Machine Learning | Chemical Biology | Robotics | Drug Discovery

Tuesday, January 18, 2022

Department of Chemistry, Biochemistry, and Physics /

Department of Computer Science & Engineering

"Modular mimics of neuroactive alkaloids - design, synthesis, and cholinesterase inhibitory activity of novel rivastigmine analogs, therapeutic leads for neurodegenerative disease"

For centuries, neuroactive alkaloids isolated from naturally occurring phytochemical sources have been crucial in the identification and optimization of small molecules with potency in treating neurological disorders. While some of these compounds have gone on to clinical use themselves, others have inspired the development of synthetic analogs, which might possess greater potency or better pharmacological features than the natural product itself. One such naturally occurring alkaloid, physostigmine, which is found in the calabar bean plant Physostigma venenosum, has been demonstrated to be a potent cholinesterase inhibitor. However, some of physostigmine's characteristics limits its therapeutic potential, prompting the development of its synthetic counterpart, rivastigmine. Previous reports of the synthesis of rivastigmine involved the usage of excess sodium hydride. While it has shown to give the desired carbamate in high yields, sodium hydride is pyrophoric and difficult to handle due to its sensitivity to air and moisture. Here, we present the synthetic optimization of rivastigmine and its analogs, avoiding the use of sodium hydride, and their cholinesterase inhibitory activity. Moreover, we utilize computer modeling to provide structural insight into the biochemical behavior of these compounds. 

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Researcher: Erika Y., Amador Valley High School '23


Advisor: Njoo, Organic Chemistry

Keywords: Synthetic Organic Chemistry | Medicinal Chemistry | Chemical Biology | Computational Biochemistry | Chemical Neuroscience

Tuesday, January 11, 2022

Department of Chemistry, Biochemistry, and Physics /

Department of Computer Science & Engineering

A Stability Study of Torula Yeast RNA With Applications in Drug Delivery

In being an unstable, typically single-stranded molecule, RNA plays a variety of roles within the genome. Through its unique properties of binding and condensation and applications in gene interference and alteration, the nucleic acid has been considered to be a molecule with great clinical potential. In our research, we are investigating RNA stability and condensation with goals of improving clinical drug delivery via cytoplasmic transport, specifically in how its capabilities for compaction can be leveraged as a delivery vehicle. RNase, or ribonuclease, a group of enzymes responsible for RNA degradation, is present in an abundance of environments and is the primary method of RNA degradation in our experiments, in the form of fetal bovine serum. These degradation assays serve to test the potential of calcium phosphate as a viable protective nanoparticle in which our susceptible RNA sample is enveloped. We bound our RNA to calcium phosphate to determine its degradation curve using RNase fetal bovine serum, which simulates in vivo environments with intense ribonuclease presence. We compared the degradation curves of RNA alone to RNA with calcium phosphate to determine its potential as a nucleic acid compaction agent. Depending on the results we receive, we plan to utilize the calcium phosphate nanoparticle to facilitate RNA compaction in order to encapsulate the nucleic acid within delivery vehicles, such as liposomes or lipid nanoparticles.

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Researchers: Harry W., San Mateo High School; Anika K., Notre Dame High School; Nathan C., Lynbrook High School; Paree M., St. Francis High School


Advisor: Yamamoto, Biomaterials Engineering, Materials Science

Keywords: Biochemistry | RNA Condensation | Drug Delivery | mRNA Delivery | Calcium Phosphate | Nucleic Acid Degradation | RNA Assaying | Nucleic Acid Stability