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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.

Fall 2021 Semester Colloquia Dates:

  • September 14, 21, 28

  • October 5, 12, 19, 26

  • November 2, 9, 16 (Special Guest speaker), 23, 30

  • December 7, 14

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, December 7, 2021
Tuesday, November 30, 2021
Tuesday, November 16, 2021 Guest Speaker
Tuesday, November 2, 2021
Nov 2
Oct 26
Tuesday, October 26, 2021
Tuesday, November 2, 2021

Department of Chemistry, Biochemistry, and Physics

Rational Design, Semisynthesis, and Quantitative Biological Mechanistic Studies of Andrographolide Derivatives

Andrographolide is a natural product extracted from the plant Andrographis paniculata, and has been shown to be incredibly versatile in biological activity. Andrographolide demonstrates anticancer, antiviral, antioxidant, and more properties, while also being relatively nontoxic. The compound has an ɑ, β unsaturated lactone, which serves as a Michael acceptor, irreversibly alkylating its protein targets. Numerous studies have been reported attempting to create novel analogs of andrographolide in order to increase efficacy, but the complexity of the compound proves to be an issue in development. We aim to build on current approaches to improving andrographolide as a drug, as well as experimenting with chemoselective modification of both the A/B trans-decalin core, which we postulate is important for target binding, and the C-ring butenolide warhead.

Samyukta Athreya.jpg

Researcher:  Samyukta A., American High School


Advisor: Njoo, Synthetic Organic & Medicinal Chemistry

Keywords: Organic Synthesis | Natural Products | Chemical Biology

Department of Computer Science and Engineering

Predict Next Word: Text Generation Using LSMT in a Professional Biomedical Context

The use of machine intelligence to facilitate daily tasks has increased substantially, such as suggestions of what may be written next in text messages, emails, and more. However, sometimes the scope of the context may be too broad and the prediction fails to generate an appropriate term. The main goal of this project is to focus on the professional biomedical context and use bidirectional long short-term memory (bi-LSTM) for text prediction. To achieve this, the model is trained with Wikipedia documents related to this field, which narrows down the ranges of vocabularies fed into it, thus increasing the accuracy and specificity of the output. When given a starter string, our language model, using machine learning algorithms, is successful in predicting the next few words in a sentence with correct usage of biomedical terminology. Extending our research further can allow for the suggestions of nomenclatures in other professional contexts as well.

Irene Wang.JPG

Researcher:  Irene W. , Basis Independent Silicon Valley

Advisor: Subramaniam, Data Science & Analytics

Keywords: Computer Science | Machine Learning | AI, Bidirectional LSTM | Natural Language Processing | Language Models | Text Prediction

Department of Biological, Human & Life Sciences

Aggregation of Computer-Based Cognitive-Training/Personalized Brain-Care/Music-Therapy Interventions into the CognoTrain App

Methods of CBCT/CBCR (Computer-based Cognitive Training/Rehabilitation or Brain-Care) and music therapy have shown effectiveness as a means of positive intervention for geriatric groups of Alzheimer’s dementia patients, but their development and testing occurred independent of one another. CognoTrain will function as an aggregate of these therapies to provide a holistic means of rehabilitation. Implemented CBCT measures in the app such as a mnemonic address reminder system have proven to minimize symptoms such as topographical disorientation. Alongside CBCT will be music therapy, whose interpretation as Karaoke has improved psychomotor speed in the target demographic. The combined power of these techniques would produce an unprecedented level of amelioration, introducing the possibility of a better life for more than 50 million dementia patients worldwide.

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Researcher:  Shashank S. , Acton-Boxborough Regional High School (Massachusetts)

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

Keywords:  Computer-Based Cognitive Training | Dementia | Neurodegenerative Diseases | Computational Neuroscience | Computer Science | App Development

Department of Biological, Human & Life Sciences

MCSMI Assessment: A novel psychological assessment Measuring the Creativity of Social Media Influencers

Creative assessments are psychological assessments that measure the creativity of an individual in different categories such as art, dance, music, visual arts, literature, etc. Examples of frequently used creativity assessments are the CAQ, ICAA, TTCT, and BICF. A category that all existing creative assessments are lacking is in social media, more specifically, creative influencers. In this study, we are looking at various existing creativity assessments and creating a subset of assessment questions for creative influencers to assess the assess the creativity of these individuals.

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Researcher: Amirtha S. , Amador Valley High School

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

Keywords:  Computer-Based Cognitive Training | Social Media | Psychological Assessment

Cindy Hsin-Yu Lee, M.A. - Guest Speaker - Stanford University School of Medicine

Will present from her most recent published work: "Neuroanatomical Profile of Young Females with Fragile X Syndrome: A Voxel-Based Morphometry Analysis"

Fragile X syndrome is a genetic condition associated with alterations in brain and subsequent cognitive development. However, due to a milder phenotype relative to males, females with fragile X syndrome are underrepresented in research studies. In the current study, we investigate neuroanatomical differences in young females (age range: 6.03–16.32 years) with fragile X syndrome (N = 46) as compared to age-, sex-, and verbal abilities-matched participants (comparison group; N = 35). Between-group analyses of whole-brain and regional brain volumes were assessed using voxel-based morphometry. Results demonstrate significantly larger total gray and white matter volumes in girls with fragile X syndrome compared to a matched comparison group (Ps < 0.001). In addition, the fragile X group showed significantly larger gray matter volume in a bilateral parieto-occipital cluster and a right parieto-occipital cluster (Ps < 0.001). Conversely, the fragile X group showed significantly smaller gray matter volume in the bilateral gyrus rectus (P < 0.03). Associations between these regional brain volumes and key socio-emotional variables provide insight into gene–brain–behavior relationships underlying the fragile X syndrome phenotype in females. These findings represent the first characterization of a neuroanatomical phenotype in a large sample of girls with fragile X syndrome and expand our knowledge about potential neurodevelopmental mechanisms underlying cognitive–behavioral outcomes in this condition.


Researcher:  Cindy Hsin-Yu Lee, Senior Research Associate at the Center of Interdisciplinary Brain Sciences, Stanford University

Advisor: Guest Speaker

Keywords:  Fragile X | Voxel-Based Morphometry Analysis | Stanford University| Nueroanatomical | Cognitive Development

Department of Biological, Human & Life Sciences

Overview of cognitive dissonance theory with associated neuroimaging modalities

Cognitive dissonance theory is a theory stating that when one’s attitude conflicts with their behavior, a discomfort arises in the brain. When there exists a discrepancy between someone’s external actions and their internal values, in most people this discrepancy will cause cognitive dissonance, and, to justify this dissonance, cognitive rationalization. Cognitive dissonance has been studied and measured using neuroimaging modalities, namely EEG and fMRI. Certain event-related potentials, measured by EEG, and areas of the brain, detected by fMRI, are involved primarily in cognitive dissonance and cognitive rationalization. This information can be utilized to analyze cognitive dissonance and its applications in aspects of everyday life by contrasting a positive behavior with a negative/biased attitude, as opposed to the typical model of a negative behavior and a positive/morally 'just' attitude.

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Researcher:  Myra M., Horace Mann School

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

Keywords:  Psychology | Cognitive Dissonance Theory | Neuroimaging | fMRI | EEG | Psychological Assessment

Department of Biological, Human & Life Sciences

fMRIusic: Understanding the Brain Network Associated with Measuring Accuracy of the Perception of Musical Genres With and Without Audio

Functional Magnetic Resonance Imaging, known as fMRI, is a non-invasive neuroimaging technique. It utilizes BOLD signals to construct high resolution images of brain activity from subjects instructed to perform tasks or respond to stimuli within an MRI machine. By using neuroimaging tools and techniques, such as AFNI and Freesurfer, we will preprocess and analyze a fMRI dataset obtained from the Psychoinformatics Lab at the University of Magdeburg in Germany. The dataset comes from fMRI scans of 20 participants who were shown clips of the movie “Forrest Gump” with different genres of music featured in the movie, such as Country, Symphonic, and 50s Rock’n’Roll. The participants were asked to guess the genre of a piece of music with and without audio. The aim of our research is to identify the networks of the brain associated with guessing a music genre correctly without audio. In previous studies, researchers have gathered a large amount of data regarding the functions and growth of our auditory network. One key aspect of the network is its relation to music and the effects music has on the brain. Both playing and listening to music have been found to increase the plasticity and strength of the brain. These activities also trigger reactions that are only achievable through musical stimulus. We aim to understand and explain the role of the auditory network in genre association.

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Researcher:  Julia W., The Nueva School

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

Keywords:  Neuroimaging | Auditory network | fMRI | Data analysis | Computational network analysis

Tuesday, November 16, 2021
Tuesday, October 26, 2021
Tuesday, November 2, 2021
Oct 19
Tuesday, October 19, 2021

Department of Biological, Human & Life Sciences

Understanding the Phases, Applications, and Exploring Real-Time Data from the Human Connectome Project (HCP)

The Human Connectome Project is a large-scale initiative involving teams of researchers at institutions around the world. The main goal of the project is to create a completed map of the human brain through the use of various MRI scanners and digital software. The map will serve as a baseline for future studies of brain connectivity during physical development, aging, and neurodevelopment, as well as aiding in the study and classification of neuropsychiatric and neurological disorders. The end goal of connectomics is to understand how brain areas are connected and contribute to human behavior, and how complicated systems are altered or exhibit different functions in individuals with neurological and psychiatric diseases. Once completed, the human connectome will provide valuable insights into what makes humans human, and what accounts for diversity in the behavior of healthy adults. This review includes an overview of the history of the HCP, a comparison between the brain network and the connectome, and a look into the C. elegans connectome, which is the first and only organism with a completed brain map. Our project aims at creating a comprehensive book/textbook to disseminate the knowledge of the Human Connectome Project, which includes studying all aspects of the project from its phases, to its costs, to its modalities, to its assessments, fMRI techniques, applications and implementations to psycho degenerative diseases.

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Researcher:  Gia O., Granada High School

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

Keywords:  HCP | Brain Network | Connectome | fMRI | C. elegans Connectome

Department of Chemistry, Biochemistry, and Physics

Optimizing conjugation and linker chemistry in the development of a novel photoreleasable antibody drug conjugate technology

The selectivity of anticancer molecules plays a vital role in their therapeutic potential. Over the past decade, antibody-drug conjugates (ADCs)—a novel class of immunotherapeutics—have gained traction due to their highly specific delivery of cytotoxic payloads to tumor sites. Several activation methods of ADCs have been designed and evaluated; however, photorelease has shown immense potential due to its increased bioorthogonality and ease of control. Through a new, simplified three-step synthetic route, we gained access to a complete photoreleasable ADC by combining our drug, photolabile group, and maleimide-conjugated antibody. The developmental process and key findings established through the synthesis of our ADC linker will be discussed. Moreover, ongoing research in our laboratory to improve the bioorthogonal photoactivation of such entities will be presented.

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Researcher:  Harsha R., Fremont Christian High School

Advisor:   Njoo, Organic & Medicinal Chemistry, Chemical Biology, Catalysis

Keywords:  Medicinal chemistry | Bioorthogonal chemistry | Antibody Drug Conjugate | LCMS methodology | Photorelease

Tuesday, October 5, 2021

Department of Chemistry, Biochemistry, and Physics

Synthesis of SARS-CoV-2 main protease and kinesin inhibitors through in silico screening with quantitative spectroscopy towards the optimization of antiviral and anticancer agents

Interactions between proteins are essential for the regulation of their functions in biological pathways, and the ability to modulate disease-relevant protein interactions through small-molecule inhibitors becomes a powerful approach to avert disease progression. Homology modeling and quantitative molecular spectroscopy are crucial in identifying binding affinities and providing mechanistic insight into reaction kinetics, enabling the efficient synthesis of these small molecules. Two of interest are carmofur, a 5-fluorouracil derivative and antineoplastic agent identified as a covalent inhibitor of the SARS-CoV-2 main protease (Mpro), and monastrol, a dihydropyrimidine privileged scaffold inhibiting kinesin Eg5 to induce apoptosis. However, with new mutations arising in Mpro as well as increasing interest in the pharmaceutical properties of fluorinated compounds, we seek to optimize the efficacy of both small molecules by designing their respective analogs. Here, we present the in silico evaluation and synthesis of carmofur and a library of carmofur analogs with aliphatic, amino acid, and aromatic fragments against mutations in Mpro, as well as the utilization of 19F NMR for the quantification and real-time monitoring of the synthetic pathways of carmofur and trifluorinated monastrol analogs.

Xina Wang_edited.jpg

Researcher:  Xina W., Amador Valley High School

Advisor:   Njoo, Synthetic Organic & Medicinal Chemistry

Keywords:  Synthetic Medicinal Chemistry | Computational Chemistry | Benchtop NMR | SARS-CoV-2 | Carmofur | Kinesin Eg5 | Monastrol

Department of Computer Science and Engineering

Voynich Manuscript paint connections to Persia

The Voynich Manuscript is a mysterious manuscript who's text has remained unknown for hundreds of years, despite the efforts of the worlds best cryptologists to decipher what is behind the mysterious glyphs. Discovering the origins of the Manuscript through any means no matter how small would prove to be immensely beneficial to helping to crack the code behind it. In this presentation, the background of the manuscript is explored as well as the possible origin of the manuscript which was discovered after character mapping the text to find similarities to Persian Characters as well as tracking down the origins of the constituents of the inks used in the manuscript.

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Researcher:  Rahul P., Irvington High School

Advisor:   Downing, Computer Science

Keywords:  Voynich Manuscript | Character Mapping | Ink composition | History

October 5
Sept 28

Department of Biological and Life Sciences

The Effects of Ocean Acidification on Barnacle Feeding and Predator Avoidance Behaviors

As more carbon dioxide is pumped into the atmosphere, the acidity of the ocean will be drastically altered, affecting marine life. The purpose of this study is to determine how decreasing pH levels affect barnacle feeding habits, as well as predator detection and avoidance behaviors. This experiment was carried out on both Balanus aquila and Tetraclita rubescens (also known as acorn barnacles and volcano barnacles) by placing a few of each type into saltwater tanks with differing pHs. The control tank, at a pH of 8.1, mirrored the current ocean’s pH level, while the other two tanks, at a pH of 7.8 and 7.5, were meant to replicate the projected acidity of the ocean in the future. Barnacles were fed zooplankton every three days, and the amount they ate was measured by how often they extended their cirri in one minute, immediately after being fed, then after 10, 20, and 30 minutes. We also tested for predator avoidance behaviors by touching the barnacles with a sponge to mimic the feeling of a major predator, sea stars. We noticed that whenever the sponge touches the barnacles, the barnacles close up as a means of protection. Further study is needed to quantify how predator avoidance behaviors change with decreased pH levels. From preliminary analysis and observations, we believe that the barnacles in decreased pH levels are feeding more than those in the control tank. As the acidity of the ocean increases, our preliminary data indicates that barnacles’ feeding rates may increase. We believe this is a result of their metabolic needs increasing; however,this is something that requires further study.

Harshini Vakkalagadda.jpg

Researcher:  Harshini V., American High School

Advisor:  Benson, Marine Biology

Keywords:  Acorn Barnacles | Volcano Barnacles | CO2 Levels | Marine Biology | Ocean Acidification | Global Warming | Defensive Behaviors | Feeding Habits

Department of Computer Science and Engineering

Solving the Schrödinger Equation for the Ground State Energy of Various Atoms Through the Hartree-Fock Method in Python

While the Schrödinger equation can be solved exactly for the single-electron atom, hydrogen, there is currently no method for obtaining the ground state energy level of a multi-electron system without approximation. This paper compares the accuracy of the Hartree-Fock method with various other methods when solving the ground state energy of various atoms, including lithium, boron, carbon, nitrogen, and sodium. The first part of our research consists of running a Python script to obtain the ground state energy and the runtimes of each iteration using the Hartree-Fock method. Comparing our results with other methods such as finite element, Hylleraas, and Monte Carlo, we then analyze the degree of differences and its respective errors. We find that our code gives accurate results for lithium, boron, carbon, and nitrogen when compared to two other outside references using the same method. However, only the sodium atom had a significant amount of inaccuracy due to the addition of a 3s orbital when jumping from nitrogen to sodium. This paper gives an overview of the current methods in approximating the Schrödinger equation for multi-electron systems and provides some insight into understanding the nature of the quantum mechanical world of many-electron systems.

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Researcher:  Archith I., Mission San Jose High School

Advisor:  McMahan, Quantum Computing

Keywords:  Quantum Physics | Python | Quantum Mechanics | Schrodinger's Equation

Tuesday, September 28, 2021

Tuesday, September 21, 2021

Sept 21

Department of Chemistry, Biochemistry, and Physics

GQD Semiconductor Band Gap

With the rapidly growing global energy demand, hydrogen gas (H2) has emerged as a promising candidate for alternative energy. However, the primary production method of H2 (electrolysis) is expensive and requires lots of energy. Photocatalysis is a process used to liberate H2 from water with light. It involves a semiconductor (photocatalyst) whose photoexcited electrons reduce water. Such photocatalysts need to have bandgaps greater than the reduction potential of oxygen (1.23 eV), but are also as low as possible to maximize the range of wavelengths of light which can cause electron excitation. Graphene quantum dots (GQDs) are one semiconductor that can be used as photocatalysts. This study compared the bandgaps of different doped and co-doped GQDs to systematically lower the bandgap of GQDs. We predicted that co-doped GQDs would contain lower and more bandgaps due to the introduction of nonbonding molecular orbitals. GQDs were synthesized using pyrene-based methods (plain pyrene GQDs, B-GQDs, S-GQDs, P-GQDs) and citric acid-based methods (plain citric acid GQDs, N-GQDs, SN-GQD, SP-GQD, and a novel BN-GQD). Bandgaps of doped GQDs, determined from UV-Visible spectroscopy, were compared to each other. GQDs were also characterized using Fourier-Transform Infrared Spectroscopy (FTIR) to identify functional groups. Bandgap analysis revealed that in general, single-atom-doped GQDs (N, B, S, P) had greater bandgaps than the respective controls (plain pyrene or citric acid), but the addition of a second dopant (SN, SP, BN) added a second peak on the UV-Vis graph corresponding to a second, and generally lower, bandgap. Interestingly, BN-GQDs, which were synthesized with a novel procedure, had three bandgaps, indicating the introduction of new nonbonding molecular orbitals. In particularly, the third bandgap of BN-GQDs (1.62 eV) was lower than any other GQD bandgap noted in the literature. Thus, this study presents a novel synthesis method for BN-GQDs with a characteristically low bandgap.

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Researcher:  Masroor U., Irvington High School

Advisor:  Sangeneni, Materials Science

Keywords:  Band Gap | GQD | Semiconductor | Photocatalysts

Tuesday, September 14, 2021

Sept 14

Department of Chemistry, Biochemistry, and Physics

Rational design and development of macrocyclic lactones with in-vivo motility studies on nematode models alongside the synthesis of rivastigmine and its analogs

Avermectins are a class of compounds extracted from the fermentation broth of a soil bacterium that possess antiparasitic properties. Often used in veterinary practice, avermectins are most commonly utilized in the treatment of parasitic roundworm infections. Doramectin, an avermectin, is an FDA approved drug used in the treatment of gastrointestinal parasites and is able to regulate the activity of the chloride ion channel in the nervous system of nematodes, an important biological aspect of the parasite. Synthetic and computational efforts allow us to design and develop two classes of compounds, derived from doramectin, and test their biological activity using in-vivo studies. Likewise, the design and development of neurodegenerative diseases employs similar techniques. Alzheimer’s Disease (AD) is a progressive disease that begins with mild memory loss and escalates to being unable to complete simple tasks. The symptoms of AD have been associated with a deficiency in acetylcholine as a result of loss of cholinergic neurons in the brain. Rivastigmine, one of the most common drugs used for symptomatic treatment, is able to to lessen the disease’s progression by enhancing cholinergic function. The synthesis and computational modeling of rivastigmine and its analogs, allow us to study changes in efficacy through in-vivo studies.

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Researcher:  Shloka R., Amador Valley High School

Advisor:  Njoo, Medicinal & Synthetic Organic Chemistry

Keywords:  Synthetic Organic chemistry | Computational Biochemistry | Neurodegenerative Diseases | Antiparasitic Natural Products | Macrocyclic Compounds

Department of Computer Science and Engineering

Quantum Convolutional Neural Networks via Variational Quantum Circuits for Efficient Image Classification

Quantum Machine Learning (QML) is a multidisciplinary field involving machine learning algorithms and quantum computing concepts, and it focuses on the construction of the quantum model representation and optimization of machine learning. It has the potential to outperform classical machine learning algorithms due to the significant computational speedup on parallel data. Because machine learning relies heavily on probability, the quantum computing environment is beneficial since it can significantly reduce resources that a machine learning (ML) model needs when learning from high dimensional data. To test this quantum advantage in the Noisy Intermediate Scale Quantum (NISQ) era, we implemented a Quantum Convolutional Neural Network (QCNN) for image recognition using Tensorflow Quantum. We hypothesized that our QCNN would have a higher accuracy and greater efficiency in training and runtime than a classical Convolution Neural Network (CNN). Our QCNN architecture parallels a classical CNN structure except that the bulk of the work is done in the quantum domain. It consists of a classical-to-quantum image data encoder, a cluster state quantum circuit, series of Quantum Convolutional and Quantum Pooling Layers via Variational Quantum Circuits (VQCs) and trainable parameters, and a measurement layer leading to the output. The data encoding/decoding and the cost function optimization are performed on a classical computer, while a quantum computer simply calculates the probabilities of the states in the VQCs. Based on probabilities generated by the quantum computer and optimization algorithm executed on a classical computer, the rotation parameters in the VQCs are updated after each back-propagation cycle.

IMG_3663 - Diptanshu Sikdar.jpg

Researcher:  Diptanshu S., BASIS Indepdendent Silicon Valley

Advisor:  McMahan, Quantum Computing

Keywords:  Quantum Machine Learning | Noisy Intermediate Scale Quantum (NISQ) | Convolutional Neural Networks | Cluster State Quantum Circuit | Variational Quantum Circuits (VQC) | Adaptive Moment Estimation (ADAM) | Convolutional Layer | Pooling Layer

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