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School of Science

Faculty Research Mentors and Projects

Summer Research Program 2021


BIOLOGY

Dr. Jason Adolf

Endowed Associate Professor of Marine Science

Email: jadolf@monmouth.edu

Project Title: Phytoplankton and Harmful Algal Blooms of New Jersey

Project Description:

Phytoplankton are an essential component of aquatic ecosystems, transforming sunlight and inorganic nutrients to food for numerous species at higher trophic levels. However, a handful of the ~25,000 species of phytoplankton can cause troubling ‘Harmful Algal Blooms’ (HABs) that affect human health, sicken or kill aquatic organisms and disrupt aquatic ecosystems. Marine, estuarine and freshwater systems have seen an increase in HABs in recent decades, related to climate change and eutrophication. This SRP project will build on PHABLab (Phytoplankton and Harmful Algal Bloom Lab) research that started in 2017 in Coastal Lakes associated with the Coastal Lakes Observing Network (https://www.monmouth.edu/clonet/) and the Hudson-Raritan Estuary (HRE) including the Shrewsbury and Navesink Rivers. Students will work as a team with their professor, but individual students will take lead responsibility for HAB research in different environments. Coastal Lakes projects will be done under a NJDEP / EPA-funded project focused on HAB monitoring with cutting edge qPCR, ELISA, and microscopic techniques. The HRE projects will be done as part of an Achelis & Bodman Foundation-funded project to evaluate the use of environmental DNA for monitoring waters between NY and NJ. These projects will provide students with hands on field and laboratory experience in a real-world field of marine and environmental science, improve our understanding of HAB formation, and will aid prediction and management of HAB events.

 

The objective of both projects is to describe the distribution and activity (e.g. toxins) of phytoplankton, with respect to dynamic environmental variability occurring over relevant spatio-temporal scales, in marine, estuarine and freshwater environments of NJ. In each project we will do weekly to bi-weekly sampling (by R/V HLS, 18’ foot boat or shoreline) of set stations. Field sampling will be followed-up with laboratory processing of water samples, including Chlorophyll a analysis (total phytoplankton biomass) using techniques employed by state and federal agencies; flow cytometric analysis of phytoplankton and heterotrophic bacteria (to see specific subsets of phytoplankton that may be HAB species; microscopic and genetic analysis of phytoplankton species composition, including production of photomicrographs of key species (for visual identification of larger HAB species); chemical analyses of HAB toxins and nutrients in the habitats studied. Collaboration with NJ DEP is an important part of the work we do.

Please Note: applicants for this project must currently be enrolled as undergraduate students at Monmouth University


Dr. Pedram Daneshgar

Associate Professor, Biology

Email: pdaneshg@monmouth.edu

Project Title: Ghost Forests – Exploring Sea Level Rise Impacts on Coastal Forests

Project Description:

Climate change induced sea level rise and storm related flooding events have had a dramatic effect on the coastal ecosystems of New Jersey. Salt water intrusion into coastal forests that are normally buffered by salt marsh ecosystems forests results in extensive tree die offs leaving behind what has been termed a “ghost forest,” a novel marsh hybrid ecosystem filled with dead trees. Saltwater inundation into normally freshwater-dominated ecosystems results in changes in forest community composition from plants to wildlife to the microbial community. The ecology of ghost forests has yet to be described by the scientific community. This summer we will explore ghost forest communities and compare their ecology to healthy coastal forests.

 

Our study objectives are to identify the plant and microbial species found in ghost forests and compare them to healthy coastal forests. We will then use this information to analyze how ecosystem function has changed.

 

We will also be preparing some education videos for the community and schools on how sea level rise impacts coastal forests.


Dr. Keith Dunton

Assistant Professor, Biology

Email: kdunton@monmouth.edu

Project Title: Conservation and Demographics of New Jersey Coastal Sharks and Sturgeon

Project Description:

Worldwide, species with k-selected life history traits (long live, late maturing) are of great conservation need due to the drastic declines in populations from various anthropogenic threats. The coast of New Jersey has been shown to be a migratory corridor for many of these fish species including sturgeons and coastal sharks. By collecting information on their population demographics (size, age, sex, species) and spatial/temporal habitat uses along the New Jersey Coast, we can gain a greater understanding of their population ecology, which is essential for both the conservation and management issues. Through the use of acoustic telemetry we can answer many of these questions. This years, SRP will primarily focus on 1) understanding the population demographics and survival of shark species captured in the lad-based recreational fishery and 2) examining the spatial and temporal habitat use of the endangered Atlantic sturgeon in Raritan and Sandy Hook Bay. Specifically:

Sharks and Rays – We will work directly with this recreational community to continue to collect information of population demographics as well as tag sharks with conventional and acoustic tags to monitor movements after release. This project builds on previous years collaborations with the recreational community. Understanding the population demographics of specific shark species as well as migratory pathways along the coast of New Jersey can be used to create better management and conservation efforts for the shark fishery.


Dr. Martin Hicks

Assistant Professor, Biology

Email: mhicks@monmouth.edu

Project Title: Rna Therapeutics for the Treatment of Brain Tumors and Covid-19

Project Description:

During the summer 2021, The Hicks Lab will be focused on the development and testing of Gene Therapy Vectors that encode novel Rna therapeutics for the treatment of Glioblastoma Multiforme (Gbm) and Covid-19. We have developed Rna therapeutic platforms that may be directed against Oncogenic Rna Transcripts in cancers as well as critical sequences of the Sars-Cov-2 Genome.

Gbm Project:
Gbm is the most common malignant primary brain tumor in adults. Individuals diagnosed with Gbm have a short life expectancy of 12-14 months (Hicks Et Al., 2016, Hicks Et Al., 2015). Thus, a therapeutic strategy that can reach the Cns tumor microenvironment and reduce tumor proliferation would offer a novel and potentially effective therapy. Our approach is administration of Gene Therapy Vectors that encode Rna therapeutics to the tumor microenvironment. The proliferation of Gbm is often coupled to the overexpression of Tyrosine Kinase Receptors (Tkrs). Tkrs are cell-surface receptors that activate a cell signaling cascade that leads to cell growth, migration and tumor vasculature. As these receptors are often upregulated in Gbm, our Rna therapy modulates their expression inhibiting their translation thereby reducing tumor growth, migration and vasculature in Gbm. We are creating a profile of the Rna transcriptome and structurome of Tkrs in our Gbm tumors using minion nanopore sequencing to optimize our Rna therapeutic approach.

Covid-19 Project: Emerging viral diseases have increased in recent decades. In December 2019, an epidemic with lower respiratory infections emerged in Wuhan, China. The disease, Covid-19 was found to be caused by a novel Coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (Sars-Cov-2). As of January 07, 2021, WHO has confirmed 88,005,213 global cases and 1,897,568 deaths worldwide, 365,174 in the USA. Fortunately, a vaccine has recently been approved, yet there are no therapeutics for infected individuals. The genome and structure of Sars-Cov-2 is known. Three proteins are anchored in the viral envelope, Spike (S), Envelope (E), and Membrane (M), Which is linked to the Nucleocapsid (N) protein connecting to the viral Rna genome. Our lab has developed an innovative therapy that delivers multiple therapeutic Rnas to simultaneously block the expression of these distinct viral proteins. We have stably transfected the S And N genes into our tissue culture model and will be testing the efficacy of the Anti-Covid Microrna to knockdown the expression of the viral proteins, S And N. In addition, we are examining the secondary structure of our Rna Therapy using shape-map to optimize Rna therapeutics.


Dr. James P. Mack

Professor, Biology

Email: mack@monmouth.edu

Project Title: Effects of Specific Essential Oils and Methylglyoxal

on the Growth and Proliferation of Specific Multidrug Resistant Bacteria

Project Description:

Previous research has indicated that cinnamon bark and cassia essential oils have growth-inhibitory effects on Enterobacter cloacae and Pseudomonas aeruginosa. This summer, we would like to explore the potential for these essential oils to inhibit the growth of two different strains of bacteria. Here at Monmouth, there has been work performed to characterize the anti-bacterial role of essential oils, but we have the ability to expand this work to determine its synergistic effects. The minimal inhibitory concentration (MIC) of the top essential oils will be measured. This work may lead to further evaluation of synergic testing of essential oil treatment.

We will address the effects of specific essential oils (Arborvitae, Cassia, Cinnamon Bark, Clove, Cumin, Cypress, Oregano, and Thyme) and Methylglyoxal on the growth of multidrug resistant bacteria including: Enterobacter cloacae and Pseudomonas aeruginosa.

 

The goals of this work are to determine the effect of essential oils and methylglyoxal on the growth of specific multidrug resistant bacteria.


Dr. Megan Phifer-Rixey

Assistant Professor, Biology

Email: mphiferr@monmouth.edu

Project Title: Evolutionary Genetics in the Wild

Project Description:

Genetic tools can provide insight into wild populations—everything from species’ ranges and distributions to specific adaptations to local environments. This summer, my lab will use genetic tools to investigate two distinct research areas 1) environmental adaptation in wild house mice (Mus musculus domesticus) and 2) local marine and estuarine community composition. While these two systems are very different, they are united by common research methods, spanning molecular genetics, bioinformatics, and population genetics, and by a common research goal—using an evolutionary perspective to better understand wild populations of ecologically important species. The house mouse is one of the most widely distributed mammals and one of the most widely used genetic model organisms. Nevertheless, relatively little is known about genetic variation in natural populations. Recently, house mice have expanded their range in association with humans establishing populations in a variety of novel habitats, providing an exceptional opportunity to study the genetic basis of rapid evolutionary change. This summer, we will collect and analyze data to evaluate the effects of diet on aspects of body size on strains that originate from different climates. Data will range from morphology to patterns of gene expression. We will also collect and analyze data to evaluate differences among populations in maternal investment with respect to litter size, pup size, and maternal care. To better understand local marine and estuarine communities, my lab will also be part of a collaborative project using eDNA (environmental DNA) to survey the lower Hudson-Raritan estuary. We will use bioinformatics to analyze eDNA from local waterways, focusing on the Lower Hudson-Raritan Estuary. These data will be combined with trawl data, benthic surveys, and physical and chemical data to help characterize a commercially and ecologically important system.


Dr. Sean Sterrett

Assistant Professor, Biology

Email: ssterret@monmouth.edu

Project Title: Reptile and amphibian surveys in urbanized ecosystems

Project Description:

Reptiles and amphibians are diverse groups of vertebrates that are experiencing global declines due to anthropogenic effects. These taxa exist in urban and suburban areas, yet there are threats that these human dominated areas create that challenge their persistence (i.e. habitat loss, increasing levels of disease, warming, pollutants, etc.). This projects aims to explore ecological research and conservation opportunities for reptiles and amphibians in New Jersey and represents an opportunity to study if and how reptiles and amphibian populations persist alongside high human density populations using several specific study systems. The coastal lakes of Monmouth County, New Jersey represent suburban habitats that have been used for a variety of recreational activities, including fishing, boating and swimming. Coastal lakes are also among the only remaining habitats for wildlife species that are able to persist in highly developed, urban areas. First, we will continue sampling freshwater turtles that inhabit coastal lakes and manmade canals in New Jersey. For this study, we will use baited hoop traps and capture mark recapture to collect data to estimate population such as density and annual survival. Amphibians are another wildlife group that may use coastal lakes. Most frogs are seasonal breeders which are very challenging to observe because they breed quickly during rainy nights. We will deploy “frog loggers”, which are automated acoustic monitoring systems to sample for frogs in five coastal lakes. We will then listen to samples to identify frog use across a subset of coastal lakes. Third, we will be designing and completing an experiment to understand the influence of hovering height (e.g., 15-60 m), environmental conditions (e.g., sunny, cloudy) or terrapin depth (0-1 m) on the detection of 3D-printed diamondback terrapins (Malaclemys terrapin) by unmanned aerial systems (e.g., drones). This drone research is attempting to demonstrate proof of concept for wildlife managers to estimate population parameters using drones. Our lab has already demonstrated that drones can identify and count individuals terrapins (including the use of machine learning); however evaluation of detection is an important step for validating this sampling method.

Please Note: applicants for this project must currently be enrolled as undergraduate students at Monmouth University


CHEMISTRY AND PHYSICS

Dr. Davis Jose

Assistant Professor, Chemistry and Physics

E-Mail: djose@monmouth.edu

Project Title: Understanding the Conformational Polymorphism of DNA

Using Fluorescent Base Analogues

Project Description:

The deoxyribonucleic acid (DNA), the genetic information carrier of living organisms, is highly malleable and exhibits conformational polymorphism. Apart from the classic Watson-Crick B-form double helix, it can adopt other structures such as A-form, Z-form, G-quadruplexes, i-motif, etc. This polymorphism exhibited by DNA is crucial in the execution of many important biological processes. It is known that the A-form DNA is involved in DNA packaging and the G-quadruplex structures found at the guanine-rich regions of DNA and RNA plays important role in cancer and aging. The two fundamental forces responsible for the unique conformation of DNA are base stacking and hydrogen bonding. Therefore, a conformational transition must involve disruption and/or reordering of these forces within the molecule. The mechanism of interconversions between different conformers of DNA is not yet determined experimentally. The goal of this project is to experimentally determine the mechanism and pathway of different conformational transitions exhibited by DNA double helices using DNA frameworks with site-specifically placed fluorescent base analogues and cyanine dyes.


Dr. Ilyong Jung

Assistant Professor, Chemistry and Physics

E-mail: ijung@monmouth.edu

Project Title:

Investigation of Swimming Microorganisms

Using Magnetic Tweezers and High Speed Camera Analysis

Project Description:

1) The bacterial flagellar motor (BFM) in Escherichia coli (E. coli), a nano-sized rotary machine 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 relationship between the motor speed and the number of stators. Our preliminary results indicate that single ion can generate torque for a discrete angular step for the rotation of the bacterial flagellar motors. We will also investigate the torque generating mechanism of the BFM in E. coli using innovative instrumentation, Magnetic Tweezers (MT).

 

2) Paramecium is a unicellular microorganism 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 magnetic field, electric field, temperature, light, and chemical gradients. However, 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 response to gravity, called gravi-kinesis, under varying viscosity.

The overarching objectives for these two studies are;

1-A) Finding the minimum number of protons to drive the BFM

1-B) Resurrecting E. coli to estimate the number of protons per motor revolution

2-A) Designing swimming chambers using galvano-taxis

2-B) Investigating gravi-kinesis of swimming Paramecia under varying viscosity

COMPUTER SOFTWARE AND SOFTWARE ENGINEERING

Dr. Joseph Chung

UNIX Administrator, Teacher, Computer Science and Software Engineering

E-mail: jchung@monmouth.edu

Project Title: Neural Network-Enabled Image Recognition With FPGAs

Project Description:

This research will explore the topic of neural network-enabled image recognition using field programmable gate arrays (FPGA). For real-time image recognition in the field using neural networks, FPGAs are seen as an increasingly attractive option because of their low latency, portability and power efficiency. FPGAs are repeatedly programmable integrated circuit devices whose logic for transforming their inputs into their outputs can be changed as needed. The algorithmic transformation of the FPGA’s inputs to outputs happens at hardware speeds without the performance hurdles faced by an algorithm running on traditionally layered computing system architectures. The learning curve of hardware design that is required to program FPGAs has become less of a hurdle in recent years due to “high-level synthesis” toolkits like LeFlow, which can be used by data scientists to deploy Python and Tensorflow-trained neural networks to FPGAs. In this project, neural networks will be trained using Python and Tensorflow. Using LeFlow, the networks will be deployed on low-cost FPGA hardware and tested using a variety of digital input peripherals to obtain metrics including image recognition performance, accuracy and power consumption. These metrics will enable comparisons to similar neural networks that are deployed using on-premise computers with graphics processing units and cloud-based image recognition services. Through this project, students and faculty will be able to gain deeper understanding of machine learning algorithms, increase their foundational understanding of computing hardware, and advance the accessibility of FPGAs as a machine learning platform for data scientists who are not trained in hardware design.


Professor Gil Eckert

Specialist Professor, Computer Science and Software Engineering

E-mail: geckert@monmouth.edu

Project Title: Analyze a University’s Assessment System for Alignment with Curriculum

Using Data Mining, Natural Language Processing and Artificial Intelligence

Project Description:

All universities have assessment systems that attempt to measure student performance versus a variety of outcomes. These systems use a multitude of documents to determine how the system should be built. Documents like curriculum charts, course catalogs, and syllabi all serve to inform how the assessment system should be built. For the most accurate accounting of student performance, these documents are supposed to coincide with intended department goals and objectives that contribute to outcomes. But how well do such documents which are really statements of what we want to do, align with what is actually being taught and measured in the classroom?

 

This project will mine several sources of information to construct curriculum, teaching and assessment maps to that will be used to analyze how closely course information aligns with the curriculum, how closely teaching aligns with the course information and the curriculum and how closely the they are all aligned with the assessment system.

 

Documents and web pages will be inspected using Natural Language Processing. Artificial Intelligence techniques will then be applied to create the most accurate data maps possible. The data maps will be used to measure the likelihood that a particular course, assessment, goal or objective is doing what it was intended to do as part of the assessment system.

 

Objectives for this project:

  • Students will learn how to find, access, and mine data
  • Students will learn how to use Natural Language Processing to organize and classify information
  • Students will learn how to use a programming language (probably Python) to automate processes
  • Students will learn how to present data in a visually meaningful way and document the methods used to accomplish that (probably using Jupyter notebook)
  • Students will learn how to work on a project team
  • Students will learn how to use a code repository like Github

Professor Katie Gatto

Specialist Professor, Computer Science and Software Engineering

E-mail: kgatto@monmouth.edu

Project Title: AI Scheduling: Creating a System to Optimized Matching System

Project Description:

This project will engage in the creation of a machine learning platform. This platform will be designed to build and test a smart scheduling system. This system will create an optimization of the matching between availability and needs students and available resources. Some possible factors will include: course section days/time, lab assistants availability, and tutor availability, based on the demonstrated needs and wants of stakeholders.


Dr. Jiacun Wang

Professor, Computer Science and Software Engineering

E-mail: jwang@monmouth.edu

Project Title: Design a Monmouth University Enquiry Chatbot with A NAO Robot

Project Description:

A chatbot is an application that is used to perform real-time conversation with human agents. It acts like a knowledgeable human and can answer questions that it has been trained. Chatbots are used as dialog systems for various purposes including customer service, request routing, or information gathering. A NAO humanoid robot can function as a chatbot and “talk” with people verbally. With this project students will explore the speech recognition capability of NAO robots and develop a NAO-based chatbot that can answer questions that a potential student may have about Monmouth University and the CSSE department. It should support three types of questions: (1) Quick facts about Monmouth University, such as history, enrollment, schools, degree programs, athletics, etc. (2) Information of each CSSE program, such as major courses, pre-requisites, admission requirements, scholarship, graduate assistantship, etc. (3) Information of CSSE faculty members, including name, research area, courses taught, office number, etc.


MATHEMATICS

Dr. Susan Marshall

Associate Professor, Mathematics

E-mail: smarshal@monmouth.edu

Project Title: Heronian Shapes

Project Description:

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. One research question we’ll address this summer is whether or not every Heronian quadrilateral can be placed in the xy-plane with integer coordinates. 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 expected outcomes of this project are (1) new examples and algorithms related to Heronian shapes and integer placements; and (2) new theorems and/or counterexamples related to Heronian shapes and integer placements. Partial results for subclasses of Heronian shapes (such as cyclic or orthodiagonal quadrilaterals) will be valued.

Students will have the opportunity to present their results at regional and national mathematics conferences.

To apply to the projects above, click here