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Faculty Research Mentors and Projects

Summer Research Program 2024

Biology || Chemistry and Physics || Computer Science and Software Engineering


Pedram Daneshgar, Ph.D., and Kevin Dillon, Ph.D.

Project: Ecology of Carnivorous Plant Communities in the New Jersey Pine Barrens

The New Jersey Pine Barrens is a unique ecosystem, the likes of which exist nowhere else in the world. Within this ecosystem, there are highly evolved plant species such as pitcher plants that use carnivory (trapping insects) to attain nutrients. In pitcher plants, micro-communities form whose composition is likely influenced by the local environment and invertebrate community. With this work we will be conducting a comparative study examining the composition of pitcher plant communities (microbial and invertebrates) across the region and then assess the environmental factors that influence them. This project will be both field and lab based with environmental and botanical measures done in the field, as well as the characterization of the pitcher plant microbiome using classical and molecular microbiological techniques.

The goal is to characterize pitcher plant associated communities in the NJ Pine Barrens and identify the physical, chemical, and biological factors affecting community composition. Specific research objectives include:

  • Conduct pitcher plant censusing and plant form assessments
  • Conduct invertebrate surveys at each field location
  • Collect pitcher plant fluid and cultivate and identify bacterial and fungal isolates
  • Collect atmospheric samples and culture potential pitcher plant colonizers
  • Investigate the overlap in the microbial community of pitcher plants and the atmosphere to show that aeolian-dispersed microbes affect communities
  • Compare the bacterial and fungal populations in pitcher plants and atmospheric samples through marker-gene surveys
  • Evaluate nutrient conditions across multiple sampling locations and correlate those to plant investments in trapping mechanisms

Sean Sterrett, Ph.D.

Project: Spatial Ecology of a Suburban Box Turtle Population

Reptiles and amphibians are diverse groups of vertebrates undergoing global declines due to human activity. Urbanization and suburbanization are types of development that create challenging threats for these animals to persist (i.e., habitat loss, increasing levels of disease, collection for pet trade). This project aims to continue exploring ecological research and conservation for reptiles in New Jersey and represents an opportunity to how populations persist alongside high human density populations. Eastern box turtle is a wide ranging and mostly terrestrial species that has declined due to various human-related threats; habitat loss and conversion, disease and poaching. Since 2022, we have been working to understand the movement ecology and habitat needs of this species, using radiotelemetry (i.e., attaching radiotransmitters to turtles in order to track their movement, behavior, etc.), in a suburban island environment, which is surrounded by housing developments, high traffic roads and other infrastructure. This project will include several elements: radiotelemetry, habitat data collected from used and random locations and an experiment examining a new approach to radiotelemetry (i.e., Apple AirTags). In 2024, we will work to finalize the tracking and habitat use data collection, and will continue radiotracking this species, but will be adding an emphasis on habitat selection and thermal ecology.

The goal of this work is to better understand Eastern box turtle movement and habitat use by employing a novel method of tracking individuals.  Specific objectives include:

  • Continue tracking 23 current box turtles (1-2 times per week) and continue a systematic habitat selection study on these individuals using a use/availability model.
  • Conduct a formal analysis of movement (i.e., various home range estimation approaches) and habitat use and availability data to be included in a manuscript.
  • Design and conduct an experiment evaluating the use of Apple AirTags for tracking box turtles with a comparison to traditional radiotelemetry.

Chemistry and Physics

Davis Jose, Ph.D.

Project: A Biophysical Approach to Understand Flavonoid Degradation by Various Gut Bacterial Enzymes

Flavonoids are a large group of polyphenolic compounds found in fruits, vegetables, and in beverages like tea and wine that have anticancer, anti-microbial, anti-inflammatory, anti-oxidant, anti-osteoporotic, and anti-allergic actions. This project aims to understand the mechanisms by which gut bacterial enzymes convert flavonoids into secondary metabolites with health benefits. Gut microbiota modulates the biological activities of flavonoids because some bacteria can biotransform flavonoids such as quercetin into secondary metabolites with more potent biological effects. The primary site of flavonoid biotransformation is the large intestine, where the action of gut microbial enzymes converts flavonoids into different metabolites through deglycosylation, ring fission, dehydroxylation, and demethylation. However, biochemical properties and the substrate range of flavonoid biotransforming bacterial enzymes and their distribution in gut microbial species are poorly understood. The bacterial enzyme quercetin 2,3-dioxygenase identified in Bacillus subtilis is known to degrade quercetin to metabolites such as 3,4,6-trihydroxybenzoicacid, 3,4-dihydroxyphenylaceticacid (DOPAC), etc. which are with potent biological activities. The bio transforming action of quercetin 2,3-dioxygenase is believed to be broad range, and it is known to complex with several other flavonoids. Our short-term goal is to understand how dietary metal ions and sequence variation alter the binding and mechanism of enzymatic action of quercetin 2, 3-dioxygenase on different classes of flavonoids in vitro usinga combination of biochemical, spectroscopic, and biophysical methods. In the future, this will help us design and develop microbiota-targeted nutraceuticals to treat lifestyle-related diseases.

The goals and objectives of this project include:

  • Determine the binding mechanism and factors affecting the structure and stability of Quercetin 2,3-dioxygenase-Quercetin complex. We will use quercetin 2, 3-dioxygenase from B. subtilis with known crystal structure as the reference for the characterization of the enzyme-substrate complex using complementary biochemical and spectroscopic methods in vitro at physiological conditions.
  • Collect data for a conference presentation and possibly for a publication too. The proposed project will focus on understanding the details of interaction of Quercetin 2,3-dioxygenase-Quercetin. Students will use the three major spectroscopic instruments in E 220 (UV-Visible, Circular Dichroism and Fluorometer) to collect the data.

Ilyong Jung, Ph.D.

Project: Investigation of Cell Motility Under Varying Viscosity and External Load

Motile flagella and cilia of swimming microorganisms at low Reynolds number have been under scrutiny due to their multi-functional roles such as sensing extracellular signals, nutrient uptake, and exerting propulsive force and torque for their locomotion.

Paramecium is a unicellular protozoan covered by thousands of cilia. It is commonly studied in biology as representative of the ciliates due to its being widespread in nature and its relatively large size. Moreover, it shows clear quantifiable responses to environmental stimuli such as gravity, viscosity, magnetic field, electric field, temperature, light, and chemical gradients. Of particular interest has been its response to gravity, called Gravikinesis, under varying viscosity that play important roles in cell life. In spite of its importance and many studies of responses to those environmental stimulations, some crucial properties such as ciliary motor characteristics have not been clearly elucidated. This project will investigate a detailed ciliary behavior of swimming paramecia and their gravity sensing under varying viscosity.

The bacterial flagellar motor (BFM) in Escherichia coli (E. coli), a tiny rotary engine (~ 40 nm) that powers microorganisms, is one of the most complex and the largest biological motors. Its components such as a rotor, stators, a flexible hook, and filaments consist of ~ 25 different proteins. In particular, the complex of rotor and stators constitutes a torque generating unit. In the model bacterium E. coli, for example, the rotor is connected to a hook and surrounded by approximately 11 stators, and the estimated maximum torque when fully loaded is ~ 1260 pN·nm. However, much remains to be investigated, in particular, the torque generating mechanism of the BFM. In this study, we will investigate the torque generating mechanism of the BFM in E. coli using innovative instrumentation, Magnetic Tweezers (MT).

The overarching objectives for these studies are to expand our understanding of motile flagella and cilia of swimming microorganisms using state-of-the-art techniques.

  • 1-a) Locomotion of swimming Paramecia under varying viscosity
  • 1-b) Studying gravity sensing ability of swimming Paramecia
  • 2-a) Study of single-proton torque generation of E. coli

Jonathan Ouellet, Ph.D.

Project: Structure/Function of Nucleic Acids: Aptamer Development for a Type I Diabetes Cure

A growing field in synthetic biology is the development of RNA molecules that bind tightly and selectivity to a specific ligand. Those RNA aptamers can be coupled to a gene regulation system called riboswitch. For example, a riboswitch could be developed to turn-ON gene expression strictly in presence of the ligand, while turn-OFF without.

The laboratory has been working extensively on the development of an RNA aptamer binding glucose. One can then think that in presence of glucose, the gene of insulin is expressed as the effective insulin protein, while it’s not produced without the presence of glucose. It’s with this future aim at curing type I diabetes that his project was designed.

This is an on-going project lead by many students. The glucose aptamer has currently been optimized for 27 SELEX cycles. The core objectives of this summer’s project are to:

  1. Enhance the selection process with lower magnesium concentrations to finally isolate the various RNA aptamers by cloning their DNAs into plasmids.
  2. Assay the isolated RNA aptamers for their glucose function.
  3. Begin the cloning of the proinsulin gene.

Computer Science and Software Engineering

Jiacun Wang, Ph.D.

Project: Reinforcement Learning-Based Delivery Path Optimization for Hospitals

Reinforcement learning (RL) is a branch of machine learning that facilitates autonomous agents’ interaction with their environments. This is done by “teaching” an agent efficient decision-making through iterative processes of exploration and trial-and-error. This project will apply RL within the healthcare industry, where the objective is to construct a model capable of efficient navigation and task execution in a simulated hospital environment. The purpose of this is to optimize the pickup and delivery of essential supplies and medications to rooms within hospital settings. A virtual hospital floor will be created, housing an agent programmed to discover the quickest routes to designated destinations while avoiding obstacles and completing tasks. A Python program will be developed that employs Q-learning to create a state-transition system, simulating potential agent movements. With each movement, the agent will gain a numerical reward. Throughout training, it will explore, calculate, and store Q-values, which estimate both immediate and long-term action value. This learning process will guide the agent to choose actions with the highest cumulative reward, ensuring efficient task completion. Such an innovation has the potential to enhance the efficiency of hospital operations, contributing to improved patient well-being.

The goal of the project is to construct a model capable of efficient navigation and task execution in a simulated hospital environment. The purpose of this is to optimize the pickup and delivery of essential supplies and medications to rooms within hospital settings.  Specific objectives of this project include:

  1. Establish an RL environment (states, actions, rewards) for the path optimization problem.
  2. Develop an RL program that will learn and produce optimal path.

Weihao Qu, Ph.D.

Project: Lip Synchronization on Animation

Lip-synchronizing a talking face in a video to match a target speech has its wide application in real life, for instance, when a famous movie in Italian is imported to the United States, the audience would like to see the characters in the movie to speak English, that is to say, their lip movements in the movie match the English pronunciation instead of Italian pronunciation. With the development of machine learning, the AI tool wav2lip uses deep learning techniques to perform lip-synchronization of characters in movies or TV series according to the target speech. Japanese animation is popular in the United States and adapting the tool wav2lio to match animation character lip motion to English pronunciation will significantly help the import of Japanese animations into an English-based countries.

The overall goal of this project is to exploit the possibility of lip-synchronization of talking faces in animation-based videos using AI techniques, which is one of the research interests of applications of the deep learning techniques and real life.