Faculty Research Mentors and Projects

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Summer Research Program 2025

Biology || Chemistry || Computer Science and Software Engineering || Mathematics || Urban Coast Institute

Biology

Project: Using Environmental DNA to Measure Marine Biodiversity

Faculty Mentor: Jason E. Adolf, Ph.D.

Biodiversity is an important ecological measurement used to assess marine communities. Traditionally this is done by catching, identifying and counting organisms, whether ‘micro’ or ‘macro’. While capture surveys are still important, genomics-based technologies are being developed that allow biodiversity of a wide range of organisms to be determined by sequencing DNA left behind in the environment where they live, so called environmental DNA or ‘eDNA’. Projects this summer will focus on using eDNA to determine fish biodiversity in regional waters related to a shoreline renovation project and offshore wind development. Specifically, students will use eDNA to characterize fish populations in the waters off the shoreline renovation project at Gansevoort Peninsula located off lower Manhattan. This is a project in collaboration with the environmental consulting firm, AKRF, and is part of a larger Hudson River Park River Project Gansevoort Habitat Enhancement Monitoring Plan. Work will also include analyses of eDNA collected as part of a NJDEP-funded offshore wind monitoring program. Both projects will involve a combination of field sampling, laboratory analyses, as well as bioinformatic and ecological analyses on the computer, and preparation of results for professional presentation. 

  • Sample at least one day the waters off Gansevoort peninsula for eDNA and hydrographic properties
  • Process sample in the lab to prepare for sequencing
  • Analyze sequence data to determine fish community composition

Project: Surface-Atmosphere Microbiome Interactions in Carnivorous Pitcher Plants

Faculty Mentor: Kevin Dillon, Ph.D.

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. These insects are metabolized by microbes inside the plant. These microbes are derived from the surrounding environment, the trapped insects, and the atmosphere. This summer, the project will be investigating the role of the atmosphere in “seeding” these pitcher plants with microbes. This project will involve both field work and lab experiments. Besides general environmental and botanical measures, the pitcher plant and atmospheric microbiome will be characterized using classical microbiological techniques and DNA sequencing techniques.

Goals and Objectives

The goal is to characterize pitcher plant microbiomes in the NJ Pine Barrens and assess if the atmosphere influences it. Work that was started in Summer 2024 will be concluded as well.

Research Objectives
  • Conduct pitcher plant censusing and plant form assessments
  • Conduct invertebrate surveys at each field location
  • 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
  • Implement a metagenomic method for analyzing microbiomes
  • Evaluate nutrient conditions across multiple sampling locations and correlate those to plant investments in trapping mechanisms

Project: Antimicrobial and Anti-Cancer Activity of Essential Oils

Faculty Mentors: Dottie Lobo, Ph.D., and James Mack, Ed.D

One project to be addressed is the influence of specific essential oils on cell signaling pathways and the stress response of normal and cancerous cells grown in culture. With the rise of antibiotic resistance, there has been increased emphasis on alternative, natural products that may be used in medicine. Previous research in this laboratory has indicated that cypress essential oil treatment leads to decreased proliferation and apoptosis in a variety of human cell lines. This summer, we would like to continue to study the effects of cypress essential oils on the regulation of the JNK signaling pathway in cancer cell lines, and to compare all results to normal cell lines. A second project may be done to evaluate the role of hypoxia in stress signaling. Additionally, there has been work performed to characterize the anti-bacterial role of essential oils at Monmouth, We will also address the effects of specific essential oils (Cassia, Oregano, Cinnamon, Cypress, Madagascar Vanilla, Citrus Bloom, Sensitive Skin, Siberian Fur, etc.) and Methylglyoxal on the growth of multidrug resistant bacteria including: Acinobacter baumanni, Enterobacter cloacae, and Klebsiella pneumonia.

The overall goals of these projects are:

  1. To determine the effect of cypress essential oil on the JNK signaling pathway.
  2. To evaluate specific essential oils for antimicrobial activity.
  3. To investigate the ability of hypoxia to alter cell signaling.
  4. To appropriately train undergraduate students to conduct research in these areas, so that they will be able to continue the projects more independently in the Fall 2025 semester.

Project: Investigating the Neural and the Molecular Basis of Aggression Using the Fruit Fly Model

Faculty Mentor: Saheli Sengupta, Ph.D.

Aggression is an evolutionarily conserved behavior that animals use to acquire food, territory, and mating partners. The appropriate level of aggression is crucial, as both excessive and reduced aggression can endanger survival. While excessive aggression resulting in socially disruptive and/or violent behavior can be destructive and incur huge psychological and economic burdens on the society, very low aggression leading to reduced resource acquisition and territorial defense can similarly threaten survival. Understanding how the intensity of aggression is regulated is a key question in the field of neuroscience. Though research in the last decade has implicated certain genes and neuromodulatory systems in controlling aggressive behavior, the specifics of genetic mechanisms and circuit dynamics are still poorly understood. Delineating the neural mechanisms of aggression has proven challenging in mammalian models because of its complexity. However, the invertebrate model of Drosophila melanogaster (fruit fly) has emerged as an excellent model for aggression studies. Fruit flies display aggression using stereotypical motor patterns that are easily recognizable and highly quantifiable. Despite the morphological dissimilarities between the mammalian and fly brains, the two nervous systems share many similarities in genes, neuromodulatory systems, and principles of circuit organization. Therefore, results from the fly model can provide valuable insights for understanding human aggression. Using the fruit fly model, the aim of my research is to investigate the neurobiology of aggression regulation with the overarching goal of identifying candidate genes for therapy in treating aggression disorders.

Project I: Investigating Genetic Mechanisms Regulating Aggression

Aggression is influenced by multiple genetic factors, yet the specific genes and pathways involved remain poorly understood. This project will focus on identifying genes that influence aggression using the advanced genetic, molecular, and cell biological tools available in the Drosophila model. By leveraging gain-of-function and loss-of-function mutants and temporally regulated gene expression systems, we aim to uncover the genetic and neuronal mechanisms underlying aggression.

Approach
  • Conduct genetic screens to identify genes affecting aggression.
  • Validate candidate genes using gain-of-function and loss-of-function mutants.
  • Employ temporally controlled gene expression systems to distinguish developmental versus adult-specific roles.
  • Use RNAi knockdown in targeted neuronal groups to examine their contributions to aggression.
What Do We Hope to Learn?

This project aims to uncover specific genes and their associated neuronal circuits that influence aggression. Understanding these genetic pathways will not only provide insight into the molecular basis of aggression but also identify potential therapeutic targets for aggression disorders.

Why Is This Important?

Identifying the genetic underpinnings of aggression in Drosophila can reveal conserved molecular mechanisms applicable to higher organisms, including humans. This knowledge has the potential to inform therapies for aggression-related disorders.

Project II: Masculinizing and Feminizing Sexually Dimorphic Neurons in Fruit Fly Brain to Study Aggression

Aggression in Drosophila melanogaster exhibits distinct sex-specific motor patterns. Male flies display aggression through lunging, while females exhibit headbutting behaviors. These differences are driven by male- and female-specific sets of neurons in the fly brain, such as the sexually dimorphic mAL neurons. mAL neurons are GABAergic interneurons with structural and functional differences between sexes. Previous research has shown that activating mAL neurons increases aggression in both males and females. This project will explore how altering the sexual identity of mAL neurons affects aggression intensity and patterns.

Approach
  • Use genetic tools to masculinize and feminize mAL neurons in males and females.
  • Observe and quantify aggression intensity and patterns in same-sex and mixed-sex pairings for masculinized and feminized flies.
What Do We Hope to Learn?

This project aims to determine how the sexual identity of mAL neurons shapes aggression. By studying masculinized and feminized neurons.

Why Is This Important?

This research will provide insights into how sexually dimorphic neurons influence behavior. The results may shed light on the adaptability of neural circuits.

Project: Drone and Invasion Ecology in Suburban Environments

Faculty Mentor: Sean Sterrett, Ph.D.

Wildlife populations are constantly under pressure from local, national and global declines due to human activity. Urbanization and suburbanization are types of development that create challenging stressors for animals to persist (i.e., habitat loss, increasing levels of disease, collection for pet trade). These two projects aim to focus on wildlife populations in suburban environments, but from contrasting viewpoints; using drones to study turtle populations and documenting the spread of a novel parthenogenetic invasive species in aquatic environments of New Jersey.

There is growing evidence that drones have the potential to improve sampling for aquatic and terrestrial wildlife. However, there remains a need to evaluate the efficacy of these novel methods relative to traditional sampling protocols. As part of an external grant on developing drone-based protocols for sampling Diamond-backed terrapin (DT) populations, we will take the opportunity to collect drone images of DT populations using both red-green-blue and thermal sensors with the intention of using that data to meet our objectives in this project. We will evaluate the ability to use thermal drones for identifying DT by comparing the ability of an independent observer to count terrapins in images taken by 2 types of drone sensors.

Human-mediated aquatic invasive species are clearly on the rise and are often found in greater diversity in suburban areas. Crayfish are one of these taxa with many invasive species that can outcompete native species for resources. Recently, the parthenogenetic marbled crayfish (Procambarus) was discovered in New Jersey (NJ). Because this is a breaking discovery for the NJ DEP Fish and Wildlife program, very basic ecological questions related to distribution, density and methods used to determine status are needed. We will approach this objective by using conventional trapping methods (used to sample for closely-related taxa) as well as environmental DNA methods (collaborator: Jason Adolf) to understand invaded populations.

Chemistry

Project: Probing the Effect of Flavonoid Metabolites on G-Quadruplex Structures Using Spectroscopic Approaches

Faculty Mentor: Davis Jose, Ph.D.

Flavonoids are a large group of polyphenolic compounds found in fruits, vegetables, and beverages like tea and wine that have anticancer, anti-microbial, anti-inflammatory, anti-oxidant, anti-osteoporotic, and anti-allergic actions. The interaction of flavonoids with DNA is important as many of them have anti-cancer and anti-inflammatory properties. There are few studies that suggest that the interaction might be caused by intercalation or groove binding. However, the mechanism of interaction of flavonoids with DNA is not well defined. In this project, our aim is to find the mechanism of the interaction of flavonoids on DNA G-quadruplexes (GQ). G-quadruplexes are non-canonical nucleic acid secondary structures that can inhibit the elevated telomerase activity that is common in most cancers. Spectroscopic methods and thermal denaturation properties are usually used to assess the global structure and thermal stability of GQs. Previously, we developed a method to study the local conformations of individual G4 layers in GQs that uses 6-methylisoxanthopterine (6MI), a Circular Dichroism (CD)-active fluorescent base analogue of guanine in synthetic GQs to explore the local conformational changes of individual G4 layers. We found that individual G4 layers, depending on their location on the GQ, have different accessibility to solvents and other molecules. Here, we aim to extend this fluorescent base analogue based approach to investigate the interaction of flavonoids with individual guanine tetrad layers of human telomeric G-quadruplexes.

Project: Structure/Function of Nucleic Acids: Aptamer, Riboswitch, and DNA Enzyme

Faculty Mentor: Jonathan Ouellet, Ph.D.

The lab is interested in synthetic biology, where the nucleic acids can specifically bind a small molecule to relate an action. Subprojects are established this summer to further the overall lab goals. From continuing the development of a glucose aptamer by SELEX, testing the efficiency of a riboswitch by fluorescence, cloning the insulin gene for bacterial expression to DNA-cleaving DNA-enzyme kinetic assays by fluorescence, the possibilities to learn lab skills about biochemistry and molecular biology are vast. These subprojects will eventually merge together to potentially create a new treatment (if not a cure) for Type I diabetes. The way it would work is that the RNA we developed binds selectively only glucose and then directly produce insulin. In the absence of glucose, the insulin production would also automatically stop, akin to the working of the pancreas.

Moreover, better understanding of the DNA enzyme could eventually lead to treatment of puppies when infected by a parvovirus. The DNA-enzyme is single-stranded and requires another complementary ssDNA to cut it in presence of zinc. A careful design of the DNA enzyme to target the parvovirus ssDNA may be a treatment for young infected dogs that are currently euthanatized.

For this summer, the goals are:

  • Currently at 27 generations of SELEX cycles, the goal is to bring to cycles at 35 to finally isolate the various RNA aptamers by cloning their DNAs into plasmids;
  • The cloning of the insulin gene for bacterial expression;
  • Growing bacteria with the riboswitch to test its activity by fluorescence. Eventually, this with be modified to allow selection of new riboswitch on petri dish without the need of expensive flow cytometry;
  • Optimize the slow-cooling experiment for the DNA-Enzyme. A better understanding of the DNA-enzyme should lead to a collaboration with a research veterinary school to further the application phase.

Computer Science and Software Engineering

Project: Formal Verification of Quantitative Properties Supporting Mutable Arrays

Faculty Mentor: Weihao Qu, Ph.D.

Software systems often need to handle sensitive data securely, maintain user privacy, and operate efficiently. One way to ensure these qualities is by analyzing how a program behaves when it processes different inputs or runs in different situations. This type of analysis, called relational reasoning, helps uncover important properties like whether a program protects sensitive information or performs tasks consistently. While tools exist for analyzing some programs, they often struggle to handle features like mutable arrays, which are widely used to store and manage data in practical applications. The project’s novelties are creating better tools to analyze programs that use arrays, making the process more precise and broadly applicable. By addressing key challenges in existing techniques, the research aims to bridge gaps in both theoretical understanding and practical implementation. 

  • Develop a program logic which is able to reason about programs with mutable arrays and implements the related automation tools such as type checker, proof systems. This project is related to the grant supported by NSF 2451384.
  • Design the syntax of a programming language that supports mutable array operations, design the semantics of the language and prove the theoretical soundness of the language. Design a logic to reason about general properties of such programs

Project: Predicting Patient Length of Stay in Hospitals Using Machine Learning

Faculty Mentor: Jiacun Wang, Ph.D.

The primary mission of hospitals is to meet the demand for care by efficiently moving patients through the care pathway while simultaneously improving patient satisfaction and health outcomes. One of the crucial determinants for hospitals to maintain resource efficiency and deliver quality treatment is the patient length of stay (LOS), as it directly impacts bed availability, staffing requirements, and overall operational costs. Reducing LOS without compromising patient care is a significant challenge that hospitals face.

The goal of this project is to develop a machine learning system to predict LOS at the admission phase of patients, using initial diagnosis and test results. By accurately predicting LOS, hospitals can better allocate resources, optimize patient flow, and improve overall patient care. The emphasis of this research is on feature engineering, one-hot encoding, and Synthetic Minority Over-sampling Technique (SMOTE) to handle imbalanced datasets. Feature engineering involves selecting and transforming relevant variables to enhance the predictive power of the model. One-hot encoding is used to convert categorical variables into a format that can be provided to machine learning algorithms. SMOTE is employed to address the issue of imbalanced datasets, which is common in real-world healthcare data. Both regression models and classification models, including deep learning and some most recently developed algorithms such as federated learning, will be investigated, implemented, and tested in this study. Regression models will predict the exact LOS, while classification models will categorize patients into different LOS ranges. Real-world datasets will be used for model training and validation to ensure the robustness and generalizability of the developed models. The results of the different models will be compared and discussed to identify the most effective approach for predicting LOS. Given that most real-world datasets are significantly imbalanced, the imbalanced nature of the dataset will be thoroughly discussed and addressed to improve model performance and reliability.

  • Investigate and develop a machine learning system to predict LOS at the admission phase of patients, using initial diagnosis and test results.
  • Find adequate datasets to validate the machine learning model.
  • Present research findings in a paper.

Mathematics

Faculty Mentor: Joe Coyle, Ph.D.

Compartmental modeling is frequently used as a model technique when the variables of interest can be grouped into distinct categories or compartments. This is typically the case, for example, when simulating the spread and behavior of infectious diseases.  More realistic models often result in a differential system that is coupled in a nonlinear way. Numerical techniques are often the only way to approximate the true solutions. The manner in which these numerical techniques accommodate the nonlinear nature of the problem varies. 

The method of weighted residuals is a particularly attractive technique in these types of problems. This method essentially builds solutions as finite sums of test functions with the goal to efficiently determine the coefficient value for each of the test functions. The technique offers flexibility in several ways including the choice of test functions. In fact, the properties of the test functions can directly impact the convergence rate and overall efficiency in computing solutions.

The goals of the project include analysis of the technique based and test functions and the impact of this choice on techniques employed to solve nonlinear equations such as Newton’s method or even Broyden’s method. Peripherally, the project involves some programing in either Matlab or Python as part of the implementation which could be especially important where mathematical analysis proves to be challenging.

Project: Orbital Detection Using Multiresolution Analysis

Faculty Mentor: Torrey Gallagher, Ph.D.

In 1846, astronomers Urbain Le Verrier and John Couch Adams noticed small deviations in Uranus’ theoretical orbit and deduced that there must be another massive body in the solar system causing these deviations. Using only the deviations in Uranus’ orbit, they worked backwards to calculate where this mystery body must be. Using only these calculations, Johann Gottfried Galle knew where to point his telescope and found Neptune.

The goal of this project is to put ourselves into the following situation: imagine you are on a planet that orbits around a star, and you calculate your theoretical orbit only to find that it deviates from your actual observed orbit. There must be a mystery planet in your system, so what can we determine about it using only the deviations in your planet’s orbit? To try and answer this question, we will use Fourier transform techniques to derive frequency information from time series, and we will use techniques which preserve both time and frequency resolution (known as multiresolution or wavelet analysis). This project will incorporate elements of calculus/differential equations, orbital mechanics, and coding to generate numerical experiments, but only knowledge of calculus is required to get your foot in the door.

Using only the frequency domain, we would like to determine what, if anything can be rigorously deduced about the mystery planet. In particular, there are three unknowns which (primarily) govern the mystery planet’s orbit: its mass, its velocity, and its distance from the star. Using only frequency resolution, can we determine the orbital frequency (i.e. how long a “year” is) for the mystery planet? Can we build a catalog of such determinations that can distinguish between situations in which the orbital frequency of the mystery planet is fixed, but vary the orbital properties of our planet?

The ultimate goal is to know precisely when and where to look in the sky to see the mystery planet. Since we need to know when to look, we necessarily need to include some amount of time resolution in the analysis. This can be accomplished using a variety of wavelet transforms (e.g. the Haar or Daubechies wavelets). In this way, if we can determine precisely how often the mystery planet perturbs our orbit, then time resolution can tell us when those perturbations occur and we will have a clearer sense of when to look for the mystery planet.

Project: Heronian Shapes

Faculty Mentor: Susan H. Marshall, Ph.D.

A Heronian triangle is a triangle with integer side-lengths and integer area. These shapes have been studied by mathematicians over the centuries, using tools from both number theory and geometry. We can generalize the idea of a Heronian triangle to other shapes by insisting various associated quantities are integers. For example, a Heronian quadrilateral is a quadrilateral with integer side-lengths and integer area, whereas a Heronian tetrahedron is a tetrahedron with integer side-lengths, integer face areas, and integer volume. The goal of this project is to take known results about Heronian triangles and extend them to other Heronian shapes. 

For example, Yui proved in 2001 that every Heronian triangle is a lattice triangle. This means that we can place any Heronian triangle in the xy-plane so that each vertex has integer coordinates. This was extended to Heronian tetrahedra by Marshall and Perlis in 2013. Previous Monmouth students addressed the research question “Is every Heronian quadrilateral a lattice quadrilateral?” and the question was answered affirmatively for Brahmagupta quadrilaterals by Marshall and Tortorelli in 2024. Progress was also made on special types of quadrilaterals such as parallelograms, trapezoids, and kites. This summer, we’ll continue to explore this question for quadrilaterals and polygons with more vertices. Other opportunities for research include determining the number of distinct integer placements of a Heronian shape and extending classification results to various types of Heronian shapes.

The overall goals of my summer research project are to extend known results for Heronian triangles to other Heronian shapes and to provide students of mathematics with a research experience. My own scholarship focuses on three-dimensional shapes, whereas my student projects focus on two-dimensional shapes such as quadrilaterals. The objects of study have particular relevance for future secondary teachers of mathematics, as they provide convenient examples for computational purposes in the classroom.

  • Complete the subcases from previous student work on parallelograms, trapezoids, and kites, and explore new subcases such as orthodiagonal quadrilaterals.
  • Generalize results to polygons with more than 4 vertices.
  • Determine the number of integer placements of Heronian shapes known to have at least one integer placement.
  • Extend known classification results to new Heronian shapes.

Urban Coast Institute

Project: Development of a Nature-based Solution (NbS) for Community and Ecosystem Resilience Along the Chesapeake Bay, Maryland

Faculty Mentor: Tom Herrington, Ph.D.

Coastal communities are being impacted by climate change driven variations in natural hazards such as increased precipitation and droughts, riverine flooding, and sea level rise that is amplifying high tide and storm surge flooding, wave attack and shoreline erosion, threatening coastal property and infrastructure. Resilience, the ability to withstand and rapidly recover from natural disasters, is often approached from the perspective of adapting the built environment to withstand the increasing impact of climate change driven natural hazards. There is a growing body of evidence, however, that indicates coastal ecosystems can, and often do, provide coastal protection and resilience to coastal communities. Nature-based Solutions (NbS), such as living shorelines and land adaptation, have been found to enhance the resilience of natural ecosystems and coastal communities to climate change. The ability of NbS to provide resilience is dependent on our ability to design coastal systems that reduce climate change driven damage and ensure the health and integrity of the coastal ecosystem both now and in the future.

The design of successful NbS projects requires multi-disciplinary teams of experts, including landscape architects, engineers, marine scientists, biologists, ecologists, regulators, land managers, and coastal community members. This summer research project will partner a team of two to three Monmouth University School of Science students with a team of engineering students from Stevens Institute of Technology. The combined team will collaboratively develop data-driven design solutions and innovative strategies to make our coastal environments more resilient in the face of climate change. This project is part of the Coastal and Estuarine Research Federation’s (CERF) 2025 Coastal Design Competition. Finalist will be able to attend the 2025 CERF Conference in Richmond, VA, Nov. 9–13, 2025 to present their designs to be judged by experts and community members.

Methodology and Outcomes

An interdisciplinary team of Monmouth science students and Stevens coastal engineering students will be formed to co-design a NbS to mitigate climate change threats to a coastal community in Maryland. The project team will collaboratively develop data-driven NbS designs and innovative strategies that work with and for the priorities of a coastal community in Maryland. Over the course of the summer, project team members will visit Stevens’ Coastal Engineering Research Lab and Monmouth’s Oyster Aquaculture Facility and Experimental Oyster Reef site to better understand the integration of structural and ecosystem components in NbS design. Project team members will work MU Aquaculture Facility faculty mentors to obtain supporting materials, detailed site information, attend webinars, and participate in community engagement opportunities, facilitated by CERF. A NbS design that prioritizes community goals and objectives and provides community and ecosystem resilience to climate change impacts will be delivered at the end of the project and submitted to the CERF 2025 Coastal Design Competition.