Researchers: Aditi Venkatraman, Shalini Singh, and Aashika Duvoor
Advisor: Nardeen Mikhail
There is a current rise in the invasive fungal infections due to the increase in immunosuppressive therapies such as chemotherapy and organ transplantation. Despite the constant progress in medical practices, invasive fungal infections are becoming more common with the increase of resistant strains to current antifungal drugs. Moreover, the hypersensitivity and toxicity to the drugs, because of their improper and excessive application, represent some of the major problems of the present use of synthetic antimicrobials. Therefore, new classes of antimicrobials of plant origin are recommended for the treatment of certain diseases transmitted by organisms, one such class being essential oils. Essential oils are natural compounds extracted from plants, well-known for their antiseptic and medicinal properties. Eugenol is an essential oil extracted from clove buds, cinnamon bark, and tulsi leaves and has been shown to posses strong antimicrobial activities when tested against opportunistic pathogens. Some derivatives synthesized from eugenol have been found to possess significant biological activities, as well. Therefore, by using these analogs, there is more attention being paid to the drug discovery world. The aim of this research is to compare the antifungal effect of eugenol with its analogs on the opportunistic human pathogen Alternaria alternata, which is responsible for many human diseases, including asthma, onychomycosis and rhinosinusitis. The results from the experiments indicate that fluorobenzoyl chloride eugenol is the most efficient in inhibiting fungal growth, with a percent inhibition of 30.8%. Our research could act as a guide for future in-vivo studies of clinical use of eugenol analogs in treating invasive fungal diseases in humans. Further research must be conducted to determine its minimum inhibitory concentration and clinical applications of the essential oil, which can be done through cytoxicity tests, the sorbitol protection assay, and ergosterol effect assay.
Coronavirus disease (COVID-19) has rapidly spread all over the world, beginning in early 2020, creating a global pandemic. As of now there have been over 102.52 million confirmed cases along with 2.21 million deaths worldwide. There have been efforts through the use of technology to look at the severity of COVID-19. Our objective is to create an algorithm that will predict the severity of COIVID-19 for an individual based on their demographic data such as race, age, gender, and location. Using international, national and local datasets, we organized the data into demographic categories. Our algorithm then inputs this data and works around the principle of probability. While compiling that data we also saw trends within the three demographics. Specifically, around the age thirty, cases were higher compared to other age ranges. Our algorithm uses such trends to develop a risk assessment and create a model based on the data collected, which can be used to prioritize and prepare for patients that may be in critical danger, providing a chance for us to preemptively address the situation early.
E-commerce and online shopping is a booming industry, especially during the COVID-19 pandemic. Online shopping allows customers to browse and purchase products from home using just a phone or laptop. Customers often never end up seeing their product in real life before the purchase, and are instead dependent on photos and descriptions uploaded by the seller. Thus comes the need for customer reviews: an evaluation of a product by other buyers which can influence other shoppers’ decisions to make the purchase. However, reviews can be tainted to provide a fake or unrealistic depiction of a product. Sellers can pay people or robots to leave fake reviews under competitors or their own stores to increase/decrease sales turnout. Such reviews can be harmful to the buyer or other sellers, often ending up with an unhappy customer. Using supervised datasets consisting of real and fake reviews, we can train a variety of machine learning and deep learning models to recognize attributes differentiating between the two types of reviews. Big e-commerce platforms such as Amazon, Yelp, and Tripadvisor are all common targets of fake reviews, and the implementation of fake review detection could create a more assuring shopping experience for customers. In this paper, we analyze and break down customer review data and attempt to build models that form conclusions from it.
Researchers: Nithya Ganti, Alisha Shah, Cameron Tran, Gia Oscherwitz, Isha Kale, Khushi Kethana, Raynard Khow, Sadhana Chari, Stacey Le, Yuanjun Cai
Advisor: Carly Truong
Schizophrenia is a chronic mental illness characterized by a distorted perception of reality. Causes are largely unknown (with the exception of possible genetic links) and, subsequently, the disorder has been very difficult to treat. In this study, we will be assessing the efficacy of a drug named clozapine to treat depression and avolition (the lack of motivation), which are some of the key symptoms of schizophrenia. Clozapine is a neuroactive benzodiazepine, which is a class of psychoactive drugs. We are assessing the efficacy of this drug in E1370 transgenic C. elegans that displays the characteristics of avolition and depression.
Parkinson’s disease (PD) is one of the most prevalent progressive neurodegenerative disorders in the world, affecting 1-2% of those over 65 years of age and 4-5% of those over 85 years of age. PD, like other neurodegenerative diseases such as dementia with Lewy bodies (DLB) and multiple system atrophy (MSA), has been linked to the aggregation of alpha-synuclein proteins. The misfolding of alpha-synuclein monomers caused by a mutation in the SNCA gene leads to insoluble aggregates of proteins. These insoluble oligomers are believed to be the cause of Parkinson’s disease. The goal of our research is to establish a Parkinson’s disease cell model in E. coli to further study the aggregation of alpha-synuclein and potential treatments in the future. Through the processes of plasmid isolation and curcumin synthesis, our hope is to create an effective model PD cell model. Future experiments would allow us to evaluate curcumin as a treatment for Parkinson’s disease.
Quantum Entanglement is a concept used to describe how the physical properties of one particle change as the function of another. Here, we are studying the movement of two or more particles based on their positions in space and time. To better understand entanglement dynamics, we will use two different approaches. Our first approach utilizes experiments to observe the variation in the wave function of two entangled photons for detecting X-entanglement, a hyperbolic geometric relationship between the spatial and temporal dimensions. Because current research has found this relationship in particles generated by Spontaneous Parametric Down Conversion experiments, we want to determine if it occurs between twin photons generated by the Four-Wave Mixing process. Our second approach consists of a mathematical analysis of data gathered from the Tpx3cam. We would use the time difference for the detection of entangled photons to determine the strength of entanglement. The second approach uses Quantum Field Theory, which focuses on the physical principles governing the fields that contain the particles. We hope to combine our findings from the first and second approaches to discover how spatial and temporal entanglement relate to each other.
Dani Research group focuses on research of smart microwave and millimeter wave sensor development for water detection in plants and animal tissues. Research is conducted by understanding electrical properties of natural elements such as plant, soil, etc along with effect of microwave radiation on plant and animal tissue cell dynamics.
In this project, our team used machine learning models in Python to see trends in healthy foods and unhealthy ones. We compiled a dataset, containing 17 different types of nutrients and labelled them as healthy or unhealthy. Then we ran machine learning algorithms to see if it could predict foods as healthy or unhealthy and assessed the trends it used to predict them such as the feature importance. We also analyzed trends within the data itself such as the correlation between 2 different nutrients.
Researchers: Khushi Yadav, Frances Jing, Simran Tawari, Tanvi Sri Sai Penugonda, Arnav Rao, Sripradha Manikantan
Advisor: Carly Truong
Alzheimer's disease (AD) is a neurodegenerative condition that affects over 50 million worldwide. Its main symptoms include loss of mobility and memory. While current treatments seek to relieve the symptoms, there is no known cause yet to stop the disease's progression. There is indication that multiple factors work to cause the disease, since some types (Early-Onset AD) are more impacted by genetics, while others (Late-Onset AD) worsen with age and other unknown factors. Amyloid beta aggregation has been observed to cause toxicity in neurons and is highly common in AD patients, and thus its role in the propagation of AD has been studied extensively. Here, we will discuss the observation of amyloid beta aggregation's effects on the muscle cells and neurons of C. elegans, taking in data from both our lab work and past studies. We predict a loss of short term associative memory (STAM) and long term associative memory (LTAM), as well as decreased mobility.