Department of Computer Science and Engineering

 

Supplementary Cementitious Materials

 

SCMs can react with the CH to produce more CSH. This attribute makes SCMs pozzolans. With some SCM substitution, the same amount of cement can now lead to greater CSH yield Improved compressive strength, lower carbon footprint, and improved longevity “An inorganic material that contributes to the properties of cementitious mixtures through hydraulic or pozzolanic activity or both”. Definition provided by American Society for Testing and Materials Hydraulic activity: hardening and setting of cement after chemically reacting with water. Pozzolanic activity: the chemical reaction between calcium hydroxide (Ca(OH)2), silica (SiO2), and aluminum oxide (Al2O3) Examples: fly ash, iron slag, silica fume. Find an equivalence point between CO2 emissions and the MPa of the concrete. Derived by changing the amount of binder used to reduce the amount of OPC. The data is used to create a standard equation which can be used for every type of cement.

Department of Computer Science and Engineering

 

A Novel System for Dynamic Hand Gesture Recognition Using Multiple Deep Learning Architectures

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Hand gesture recognition has various applications such as video games, telesurgery techniques, and sign language translation. Here, we explore and work towards a novel system for ASL recognition. The system utilizes multiple deep learning architectures to address the challenges of hand segmentation, local and global feature representations, and gesture sequence modeling. The proposed system is evaluated on a challenging, wide dataset, achieving superior performance compared to state-of-the-art approaches. The results highlight the effectiveness of the system in recognizing dynamic hand gestures in an uncontrolled environment.

Department of Computer Science and Engineering

 

Coincidence-Filtering-Based CNNs for Effective EEG-Based Health Monitoring

 

Electroencephalogram (EEG) obtained through wearable devices has emerged as a powerful tool for human health monitoring. While traditional methods using artificial features have shown promise in EEG-based recognition, recent studies have started exploring the application of deep learning techniques in this field. This article introduces a coincidence-filtering-based method that bridges the gap between artificial-feature-based approaches and convolutional neural networks (CNNs). By simulating the information extraction pattern of artificial-feature-based methods, a novel, simple, and effective CNN structure for EEG-based classification is designed. The proposed network is evaluated through experiments on emotion recognition and fatigue driving detection tasks, demonstrating prominent average accuracy. With its versatility, this framework holds the potential to find broader applications in health monitoring domains.

Department of Computer Science and Engineering

 

A Hybrid Quantum-Classical Graph Generative Adversarial Network for Generating Chemically Stable Molecules

 

Present-day drug discovery methods cost billions of dollars and usually take five to ten years. People have been researching and implementing various computational approaches to search for molecules from the chemical space, which can be on the order of 1060 molecules. One approach involves deep generative models, artificial intelligence models that learn from nonlinear data by modeling the probability distribution of chemical structures and creating similar data points from the trends it identifies. These generative models can extract salient features that characterize the molecules. However, they often suffer from increased time and memory complexity. Aiming for an even faster runtime and greater robustness when analyzing high-dimensional data, we built upon our previous Hybrid Quantum-Classical Generative Adversarial Network (QGAN) and remodeled it to specifically generate molecular graphs (QNetGAN) instead of molecular coordinates. We hypothesized that adapting the internal architecture to operate on molecular graphs would enforce bonding between atoms and create connected molecules, instead of the scattered atoms from before. To test this, we synthesized several sample molecules, achieving an appreciable 33% success rate. To verify the validity of these generated molecules, Lipinski’s Rule of Five and the Octet Rule were used. Though the generated molecules’ geometries were not optimized, they were much more structurally valid than those of QGAN. These results are particularly promising as they demonstrate the future potential of our QNetGAN and a path towards more efficient drug development, which would accelerate the development of medicines and reduce costs for the whole R&D process.

Department of Computer Science and Engineering

 

A Meta-analysis on Post Quantum Cryptographic Primitives

 

With the advent of quantum computing, classical cryptographic algorithms that were previously considered secure, such as RSA, AES and Elliptic Curve Cryptography (ECC), are now vulnerable to attacks by quantum computers. These attacks are made possible by the use of quantum algorithms such as Shor's algorithm and Grover's algorithm. As a result, there is an urgent need to develop new cryptographic algorithms that are resistant to these quantum attacks, known as post-quantum cryptography. In this research paper, we investigate the four new quantum-resistant cryptographic primitives recommended by the National Institute of Standards and Technology (NIST) in 2022: CRYSTALS-Kyber, CRYSTALS-Dilithium, FALCON, and SPHINCS+. The algorithms we investigate are designed to be secure against quantum computers, which are expected to become a reality in the near future and pose a significant threat to the security of currently used classical algorithms. We evaluate the security and performance of these algorithms by considering various parameters such as key size, encryption/decryption time, and implementation complexity. We also compare these algorithms with each other as well as with existing quantum-resistant algorithms to provide a comprehensive understanding of their relative strengths and weaknesses. Additionally, we discuss the potential applications and future directions for research in quantum-resistant cryptography.

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.

Department of Biological, Human, and Life Sciences

 

ADHD AI Assist: Exploring the Feasibility of Artificial Intelligence-Based Interventions for Managing Symptoms of Attention-Deficit/Hyperactivity Disorder

 

Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder characterized by symptoms of inattention, hyperactivity, and impulsivity that can significantly impair academic, social, and occupational functioning. Traditional interventions for ADHD often rely on student-teacher-parent interactions to manage symptoms, but the implementation and effectiveness of these interventions can vary greatly. In recent years, there has been increasing interest in exploring the use of Artificial Intelligence (AI)-based interventions for managing symptoms of ADHD. This presentation aims to explore the efficacy and feasibility of AI-based interventions for managing symptoms of ADHD, with a focus on the importance of student-teacher-parent interactions in promoting positive outcomes. By examining the current research and identifying potential areas for future investigation, this presentation seeks to provide a comprehensive overview of the potential benefits and limitations of AI-based interventions for managing symptoms of ADHD, and to highlight the importance of collaboration between students, teachers, and parents in supporting individuals with ADHD.

Department of Biological, Human, and Life Sciences

 

Effect of Andrographolide and its Analogs on Colon Carcinoma Cells

 

The plant-based, labdane diterpenoid andrographolide, found in the plant Andrographis paniculata, is selectively cytotoxic to cancer cells. Solid, metastatic tumors often present difficulties in cancer treatments. Surgical removal may not remove the entirety of the tumor and immunotherapy often does not reach the core of the tumor. Andrographolide has many therapeutic applications such as anti-inflammatory, anti-platelet aggregating, immunoregulatory, antibacterial, antiangiogenic, antineoplastic, and cancer metastasis-blocking properties. It selectively induces apoptosis in cancer cells due to the enhanced expression of TRAIL molecules. It inhibits the expression of MMP-9, ICAM-1, c-Myc, and VEGF genes regulating proliferation, angiogenesis, and migration. It induces replication arrest in the G0, G1, and G2/M stages in mitosis. Additionally, it suppresses activity on the NF-kB pathway genes EGFR, cyclin D1, and survivin. The enhanced expression of Bax, caspase-3, and caspase-9 and down-regulation of Hcl-2 also causes apoptosis. Our current progress in researching the effectiveness of andrographolide in inhibiting cancer growth and migration includes performing MTT assays on HCT-116 colorectal carcinoma cells with 2 drugs: andrographolide and its analog acetonide at varied concentrations. We have yet to provide more concrete data on the effects of andrographolide and acetonide on inhibiting cancer growth to provide a definitive conclusion.

Department of Biological, Human, and Life Sciences

 

Assessing the efficacy of podophyllotoxin derivatives against colorectal carcinoma cells

 

The antimitotic drug podophyllotoxin (PDOX) and its derivatives have previously shown antimetastatic properties with a limited range of applications approved for clinical use due to factors such as systemic toxicity and low bioavailability. PDOX, derived from the podophyllum peltatum plant, causes cell cycle arrest through inhibiting microtubule formation; mechanisms of action for its derivatives are currently unknown. Unsubstituted PDOX prodrug (unsub), dimethoxy PDOX prodrug, and PDOX prodrug with alkyne were assessed for their cytotoxicity specifically against HCT-116 colorectal carcinoma cells through MTT assays. Time dependent graphs of cell viability over time for both unsub and PDOX with alkyne show reduced cell viability percentages for all concentration treatments compared to the untreated cells, with cell viability percentages decreasing as concentrations increase. Dosage dependent graphs for all drugs overall show the sharpest decrease in cell viability between the lowest drug concentrations across all times. Experiment results suggest some efficacy of the derivatives in reducing HCT-116 cell viability, with unsubstituted PDOX prodrug the most potent of the three drugs.

Department of Biological, Human, and Life Sciences

 

Enhancing Cognition and Working Memory through a multi-dimensional dopaminergic N-Back Memory game

 

N-back tasks are a form of cognitive training that requires patients to recall information from a certain stimulus previously shown to them. Cognitive research patients often complain that these tasks are boring and mundane when involved in a study. In our study, we aim to use a Dual N-Back Working Memory (DNB-WM) task to enhance working memory. Through fMRI scanning, it has been proven that N-back tasks improve working memory, an executive function of the brain associated with the prefrontal cortex, frontoparietal network, and salience network. Moreover, it is hypothesized that if dopaminergic pathways, such as the Mesolimbic, Mesocortical, Nigrostriatal, and Tuberoinfundibular, are targeted, dopamine production will increase engagement and productivity during cognitive tests, therefore further increasing working memory.

 

Our study is designed to involve 32 participants; 16 for an experimental group and 16 for a control group. Patients will be given the list sorting test from the NIH toolbox cognition battery, which will act as a baseline for their working memory. Then, the experimental group will be given a multidimensional N-back game that reflects a ‘gamified’ task, while the control group will be given a task that reflects traditional 'mundane' tasks. Following a four-week training period, the list sorting test will be readministered to the participants to quantify the improvement in working memory. Additionally, a questionnaire will be administered to record and evaluate participant engagement levels during the training.

 

Based on the preliminary data analysis of three days of training, it was observed that the reaction times either remained constant or decreased, which suggests a moderate to high level of engagement among the participants. Additionally, the accuracy consistency throughout all genres indicated that the variability between genres was not a nuisance–furthermore, overall accuracy improved by the end of the training period.

 

Our preliminary data suggests a positive correlation between increasing engagement in N-back memory tasks and overall working memory capability. In conclusion, the implementation of multidimensionality to enhance engagement in our modern N-back task has resulted in improved working memory among adults. Therefore, it can be inferred that the use of multidimensionality is an effective strategy to boost engagement and enhance working memory.

Department of Chemistry, Biochemistry, and Physics

 

Understanding the mechanisms behind biochemical assays and their use in drug discovery campaigns, and methods development to quantify neurotransmitters in neural tissue samples

 

The ability to objectively assess cellular responses to chemical treatment is essential for drug development efforts to succeed, so biochemical assays are used to provide readouts regarding cell viability. We studied the MTT assay, which depends on the conversion of tetrazolium salt 5-diphenyl tetrazolium bromide to formizan, which offers an optical readout for the redox activity in living cells. We found that interference from medium elements that react with MTT has made it difficult to repeat data and standardize the assay. In this study, common reducing agents were screened to determine the degree of interference with cell-free MTT autoreduction.

 

Parkinson’s disease (PD) decreases the production of the neurotransmitter dopamine following its onset. Dopamine, in the mammalian brain, was found to be metabolized into homovanillic acid (HVA) and 3,4-dihydroxyphenylacetic acid (DOPAC). The ability to quantitatively measure dopamine and its metabolites will establish a highly sensitive biomarker basis for evaluating the progress and state of diseased brain tissues. We've been developing liquid chromatography mass spectrometry methods for high throughput metabolite screening taken directly from in-vivo tissue samples.
 

Department of Biological, Human, and Life Sciences

 

Cognitive Dissonance and Technology

 

Cognitive dissonance theory is a theory stating that when there is a discrepancy between someone’s external actions (behavior) and their internal values (attitude), this discrepancy will cause dissonance. The research will be focusing on the effects of different technologies, such as AI, smart home devices, and autonomous vehicle on cognitive dissonance. The study will be developing a questionnaire to measure the dissonance people hold between their attitudes and behaviors when utilizing these different technologies. The conflicting views of how technology can be beneficial or harmful towards an individual can create high or low cognitive dissonance. The study will utilize R to reveal trends of the different sets of data.

Department of Chemistry, Biochemistry, and Physics

Synthesis of Novel Fluorinated Corticosteroid Analogs of Dexamethasone as Anti-inflammatory Agents

 

Dexamethasone is a highly potent and fluorinated corticosteroid, drugs derived from steroids, commonly used to treat diseases including various cancers, arthritis, and even skin conditions. It produces anti-inflammatory effects by binding to the glucocorticoid receptor (GR) in the nucleus, which inhibits the NF-κB signaling pathway involved in many immune responses. The purpose of this research is to improve dexemthasone’s stability and bioavailability in the body and its target selectivity to GR. Computational modeling and docking was first performed to calculate the relative binding affinities of existing analogs to GR and to determine potential advantageous structures that would contribute to more efficient drugs. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) will then be conducted to quantify mRNA to help identify specific genes and pathways involved in the inflammatory response. Protecting group strategies and various reaction mechanisms were used to synthesize the analogs, which will then be tested on cell culture via inflammation assays.

Department of Computer Science and Engineering

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Comparing Supervised, Unsupervised, and Deep Reinforcement Learning Algorithms to Determine Efficiency

 

According to recent data, there will be 320,000 AV vehicles shipped out by the end of 2023. In the limited amount of fully automated vehicles out right now, there have already been over 400 crashes because of software errors. Additionally, government reports state that 94 percent of car crashes are due to driver behavior or error. Considering this data, our research aims to compare and contrast the efficiency of Supervised, Unsupervised, and Deep Reinforcement learning algorithms. Supervised learning is when a dataset with a certain set of actions and results is provided to the algorithm. Unsupervised learning is when the algorithm needs to determine the right decisions to certain actions on its own based on an unlabeled dataset and its experience. Deep Reinforcement learning is a machine learning algorithm that uses an agent to explore the environment around it to decide the best course of action. The agent uses a set of neural networks that act as a human brain while running scenarios of all courses of actions with each possible choice which is similar to trial and error. Our algorithms’ efficiencies will be tested based on the time taken to reach a certain point, as well as the obstacles hit. We will compare those times and obstacles hit among the three different types of algorithms in order to find the most efficient and accurate algorithm.

Department of Biological, Human, and Life Sciences

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Investigating the Relationship Between COVID-19 Vaccination Status and Sleep Quality in Adults Analyzing the Impact of the COVID-19 Lockdown on Dietary Habits

 

In the past few years, the COVID-19 pandemic has dramatically altered the lifestyles of people around the world. Impacts ranged from social to physiological areas of life; these studies aim to determine how COVID-19 vaccination and lock down has altered sleep and dietary patterns, respectively. Implementing unique questionnaires, we plan to look at the changes in such patterns from before, during, and after the pandemic.

Department of Chemistry, Biochemistry, and Physics

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Reactivity-Informed Design of Self-Regulating Drug Molecules: A Novel Approach to Personalized Medicine

 

Optimization of a drug’s efficacy for a patient’s specific real-time condition is critical for safe and efficient dosages of drugs during treatment. Current efforts to create personalized medicine solutions have involved tailoring and adjusting dosages uniquely to each patient in real time, a process that is laborious, not scalable, and prone to human error. To address this issue, we have envisioned a new class of drug molecules utilizing a designed chemical logic to control drug activity in situ based on real-time sensing of cell states. This construct entails a negative feedback system in which the drug compound inversely responds to the level of the targeted healthy metabolic activity to increasingly neutralize the otherwise active drug — the more healthy metabolic activity is present, the more of the drug that is rendered inert. This system allows for an administered dose to be coded with adaptive efficacy, naturally auto-tuning to the state of the disease for that patient in real-time. We have synthesized and demonstrated the efficacy of a proof-of-concept molecule in vitro utilizing a simple, harsh alkylating agent as the active cytotoxic payload. The approach, experimental results, and potential future applications will be discussed.

Department of Biological, Human, and Life Sciences

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CovidVacMap: A Global Network Analysis of COVID-19 Vaccine Distribution to Predict Breakthrough Cases Among the Vaccinated Population

 

The ongoing COVID-19 pandemic, also known as the coronavirus pandemic, is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Multiple vaccines have been developed, underwent clinical trials, and are being distributed along with many boosters and doses. While these vaccines are currently being distributed the virus still spreads as we have not reached herd immunity. As the virus spreads new variants like enter. This study aims to map the vaccination candidate connectome using network analysis with the Barabási-Albert model. This network science will provide scale-free networks that accurately model the powerful distribution of SARS-CoV-2 vaccines on a global scale. From there, the study will use R & Java Script as well as Gephi to visually model these networks and predict the next pandemic among vaccinated candidates on a global scale.

Department of Computer Science & Engineering

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Using Image Classification of Google Street View Images of College Campuses to Predict College Retention Rates and Discover Environmental Factors that Affect College Retention Rates

 

Education is essential to every part of society and understanding why a student decides to give up their education is important. No previous research has been conducted looking at the physical environment in which students spend their educational lives and if that is in any way connected to whether a learning institution can keep students engaged and prevent them from leaving. Using Google Street View and image classification, our research aims to find factors that affect a college's retention rate. The results of this study can be used to profile schools based on their built environment's effect on learning and to provide insights into how learning institutions can adapt their built environments into one that is more suitable and beneficial to student satisfaction.

Department of Biological, Human, and Life Sciences

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Exploring Cognitive and Physical symptoms of Long Covid

 

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 Jotform 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, which we have tested with two sets of generated sample data. We are currently collecting and scoring data from pilot participants, implementing their feedback on all aspects of our data collection process.

Department of Chemistry, Biochemistry, and Physics

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Enzymatic and Photocaged Strategies for the Selective Delivery and Release of Podophyllotoxin

 

Podophyllotoxin is a cytotoxic small molecule with valuable potential as a potent antitumor agent due to its ability to inhibit cell division by halting tubulin polymerization. However, as the absence of absolute specificity to cancer cells causes damage in non-cancerous tissues and results in adverse side effects, the applications of podophyllotoxin in the clinic are limited despite being FDA approved and commercially available. To mitigate these challenges, we introduce a chemically modified, inactive version of podophyllotoxin that can enter active cytotoxic form upon photochemical release, which has shown immense potential due to its bioorthogonality and ease of control. Furthermore, we synthesized six esters of podophyllotoxin as previous studies with the Taft Equation have demonstrated that adding esters onto C-4 can potentially regulate the rate of esterase cleavage within different cells.

Department of Computer Science & Engineering

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Measuring the Diversity of Ideas in Colleges Over Time Using Word Vectors

 

Diversity is widely celebrated as a positive sign of progress in society, and many colleges prioritize it in their admissions and on campus-initiative. However, the question remains as to whether this emphasis on diversity has led to an increase in the diversity of ideas being discussed. In this study, we analyzed student opinion writings in college newspapers over the past decade using word vectors and topic modeling to measure the range of ideas. We then compared this data with the trend in the frequency of diversity-related words mentioned in the same newspapers. Our findings reveal that while explicit mentions of diversity have increased across colleges over the past decade, the diversity of ideas - as measured by the size of the covered embedding space or the topics discussed - has actually decreased in most colleges during this period. This indicates that while colleges are encouraging more discourse about diversity, the actual space of ideas being discussed in school newspaper opinion sections has been shrinking. Furthermore, we investigated the political polarity of college newspapers and the bipartisan trend over time using an API from The Bipartisan Press. Our analysis indicates that many college newspapers are becoming increasingly liberal over time as their bias indicators are decreasing.

Department of Biological, Human, and Life Sciences

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Effects of Meditation Revealed by EEG

 

The popularity in the research of the impact of meditation on brain activity has spiked over the past few years. Even so, analysis on the effects of mediation through electroencephalography (EEG) has been done for decades but its impacts are still uncertain. This is due to how various meditation practices affect brain activity differently, shown evident in our dataset. The dataset included data from four blocks of meditation: two thinking blocks, one breathing block, and one tradition specific meditation block. After sorting through 98 subjects, and running it through our software titled EEGLab, the data had to be pre-processed to make accurate conclusions. With continuing data analysis through EEGLab, we hope to find the direct effects of meditation and how it may benefit cognition, perception, and emotional processing.

Department of Biological, Human, and Life Sciences

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Decoding Inner Speech with Brain-Computer Interfaces: A Study in EEG Analysis and Machine Learning

 

Brain-Computer Interfaces (BCI) detect brain signals and translate them into commands which are carried out by other devices. For people affected by neuromuscular disorders, BCIs can greatly improve their quality of life by restoring lost function. These neuromuscular disorders often impede an individual’s ability to communicate, thus presenting a need for BCIs that can interpret inner speech. Electroencephalography (EEG) is a standard noninvasive neuroimaging technique measuring electrophysiological responses in the brain produced by synced neurons. Recent improvements in machine learning have led to advances in detecting brain patterns present in EEG data, allowing more promising and reliable BCIs. In this project, we utilize a dataset consisting of EEG data of inner speech commands from 10 subjects. Through analysis of the data using MATLAB and the application of machine learning, we aim to develop a model that can accurately interpret inner speech.

Department of Biological, Human, and Life Sciences

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fMRIusic: Preprocessing for Analysis of Emotional Responses to Music

 

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. Extracting meaning from fMRI data requires choosing an appropriate raw dataset and performing numerous preprocessing steps to reduce noise and artifacts. This summer, the dataset accompanying the paper “Dynamic intersubject neural synchronization reflects affective responses to sad music” (Sachs et al., 2020), publicly available at openneuro.org, was chosen for analysis. The dataset contains scans of the brain activity of 40 participants listening to music that evokes either sadness or happiness. The data was preprocessed through a fMRIprep pipeline, as well as using ICA-Aroma. Currently, this project works to analyze the preprocessed outputs, through C-PAC, another neuroimaging tool, to create a processing pipeline for this study’s purposes. The resulting time-series outputs of C-PAC will be implemented in a machine learning algorithm for data analysis that will help provide insight into how stimulus-driven changes in activity and connectivity in the brain correlate to emotional enjoyment and intensity.

Department of Chemistry, Biochemistry, and Physics

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Reactivity-guided Design, Spectroscopic Monitoring, and Scalable Syntheses of Novel Antibody-Drug Conjugates

Antibody-drug conjugates (ADCs) are an emerging class of cancer therapeutics that involve three main components: a monoclonal antibody, a selectively-degradable linker, and a cytotoxic payload. ADCs are advantageous compared to traditional therapeutics as they can selectively deliver cytotoxic payloads to cancer cells with minimal effects on healthy cells. Our group's work focuses on the development and synthesis of novel antibody-drug conjugates that use a variety of cytotoxic payloads and enzyme-labile linkers in an effort to selectively treat human cancer cells. Additionally, we present the use of fluorophores to monitor the bioconjugation process through UV-Visible spectroscopy.

Department of Chemistry, Biochemistry, and Physics

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

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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.
 

​Department of Biological, Human, and Life Sciences

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

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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.

​Department of Biological, Human, and Life Sciences

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

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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.

​Department of Biological, Human, and Life Sciences

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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

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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.

​Department of Biological, Human, and Life Sciences

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

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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.

​Department of Computer Science & Engineering

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

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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.

​Department of Biological, Human, and Life Sciences

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

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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.

​Department of Biological, Human, and Life Sciences

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Cognitive Dissonance and COVID-19 in Adolescents

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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.

​Department of Biological, Human, and Life Sciences

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

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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.

Department of Biological, Human, and Life Sciences

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fMRIusic:  Understanding Brain Networks Associated with Emotional Responses to Music

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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.

60 Second Lectures

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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!

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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.

GUEST Speaker: Robert Downing, Chair of the ASDRP

Department of Computer Science & Engineering

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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.

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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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​Department of Engineering and Computer Science

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

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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.

Department of Chemistry, Biochemistry, and Physics

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

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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..

GUEST Speaker: Edward Njoo, Chair of the ASDRP

Department of Chemistry, Biochemistry, and Physics

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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.

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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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