STAR Scholars Abstract

STAR 2019: The Misregulation of Tip60 in the Tau Drosophila Alzheimer’s Disease Model

Alzheimer’s Disease (AD) is a prevalent neurodegenerative disorder. A common feature of AD in the brain is neurofibrillary tangles, which block axonal transport of neurons due to hyperphosphorylation of the Tau protein. Through the examination of the Tau fly line, a Drosophila AD model, it is possible to see if there are cognitive defects resulting from decreased levels of the histone acetyltransferase Tip60. Histone acetylation is an important epigenetic regulatory mechanism, so the misregulation of Tip60 is thought to play a role in AD. This project investigated whether the Tau fly model exhibits a neurodegenerative phenotype and whether this phenotype is due to misregulation of Tip60 and its target genes. Behavioral assays including learning and memory, gustatory, and olfactory were conducted to test the cognitive defects of the Tau model. qPCR was used to determine expression levels of Tip60 and its target genes. Our results show that the Tau model has decreased cognitive abilities in comparison to the wild type, and this is likely due to misregulation of Tip60. Further work should investigate if overexpression of Tip60 can rescue the disease phenotype in the Tau model.

Reshma Brown
STAR Scholars Program Abstract
August 14th 2019

The Knockdown of Rok and PDGFRB and its Effect on Early Cognition in Drosophila

Traumatic Brain Injuries (TBIs) are caused by violent jolts of the brain within the skull from falling or being struck by objects and can disrupt normal brain function. Recent studies show that TBIs increase the risk of developing early-onset Alzheimer’s disease (AD) most prevalently in military veterans that experience head trauma. To understand the long-term effects of TBIs, we examined the cognitive abilities of fruit flies that express TBI related genes Rok and PDGFRB. The human homologs of these genes are known to cause cell death and glial scaring after a TBI. In addition, the expression of Rok and a mutation in PDGFRB may be implicated in AD onset. Using genes identified in both RNA sequencing data from a well-characterized AD model fly and RNA sequencing data from human TBI patients, we investigated if the knockdown of these genes played a role in early cognition through behavioral assays. Here, we observed that the knockdown of Rok and PDGFRB are not standalone factors that lead to decline in early cognition.

Unlike traditional robots of the past, with harsh and broken movements, soft robots are modelled after living organisms, manufactured out of flexible materials, and controlled in unique ways. The goal of this research is to develop a minimally invasive micro-robotic device that would move within brain tissue without damaging it. Such devices can be inserted into the brain to treat Parkinson disease or monitor epileptic events.

The principle that guides this research is dielectrophoretic force. This force is experienced by polarizable micro-particles suspended in a polarizable medium when immersed in a non-uniform electric field. Previously, it has been used to control and trap particles and cells for the purpose of separation and micro-analysis. Dielectrophoretic force can also be used to create wave-like movements of micro-particles between two soft polymer sheets which will generate its worm-like propulsion.

In this research project, electric fields and dielectrophoretic forces are modeled for a device design based on interlaced electrodes. The models are then employed to design a flexible circuit board consisting of an appropriate micro-electrode array that will be sealed with a polymer film filled with fluid and microparticles.

Additive Manufacturing (AM) is a rapidly emerging class of techniques that involve synthesizing parts layer-by-layer. The creation of highly complex geometries with high dimensional precision and good surface integrity, in contrast to traditional manufacturing techniques which limit design freedom, enables AM processes to be desirable choices in industrial applications. Nonetheless, in order to attain desirable metal products, relationships between manufacturing methods, observed microstructures, and resulting mechanical behaviors must be contrasted. A material’s microstructure has a crucial impact on its mechanical properties due to the phase distribution and grains in the material itself. Furthermore, variations in metal additive manufacturing processes induce alterations in the microstructure of materials, thereby affecting the overall mechanical behaviors of their structures. Therein, this study focuses on the correlations between the microstructure and the tensile properties of an aluminum alloy (AlSi10Mg) fabricated through AM as compared to literature. Furthermore, the impact of heat treatment on the tensile behavior of AlSi10Mg is investigated with an emphasis on microstructure evolution.

Summer 2019

In primitively eusocial Mischocyttarus pallidipectus paper wasp colonies, separation of members into reproductive castes is characteristic. Unlike many insects which are divided into morphologically and functionally distinct castes, M. pallidipectus castes are monomorphic: females on the nest are identical at emergence but will differentiate into castes by social dominance. The July 2019 field season in Monteverde, Costa Rica was devoted to testing the hypothesis that ovary size is correlated positively with dominance and connecting this to differences in individual chemical cues. In the field, I worked alongside a small team of researchers to locate wasp nests and observe individual wasp behavior for four hours each day over two days to identify dominance interactions and determine the social hierarchy of females. Following observations, the wasps were collected, and females were dissected. Female ovaries were photographed and measured in order to examine the behavioral data against the size and development of their ovaries. The gasters of each wasp were swabbed for their cuticular hydrocarbons which were then run on chromatography columns, allowing us to evaluate whether there is a difference in the chemical identity of females with and without developed ovaries. 

STAR 2019

The Secret Life of Plants: Stable Isotope (CNS) Variability in Flora Throughout Barnegat Bay, NJ

Barnegat Bay (BB), New Jersey has long faced extensive nutrient pollution leading to eutrophic conditions and a degradation of water quality. This directly affects both the health and function of the surrounding ecosystems. In this study, I assessed the spatial variability and flow of nutrients through analysis of stable isotope (δ15N, δ13C, δ34S) values of marsh and aquatic plants, such as Spartina alterniflora, Spartina patens, Phragmites, and Ruppia; soil; macroalgae, particularly Ulva; and suspended particulate matter collected from 9 sites ranging from north to south in BB. Stable nitrogen (N), carbon (C), and sulfur (S) isotopes act as tracers of nutrient sources and movement as well as microbial activity. For example, stable N isotopes reflect the sources of N from which they originate such as fertilizer runoff, atmospheric deposition, N fixation, and upwelling. As land use differs from north to south over BB the isotopic signatures are expected to vary due to a potential difference in sources. This data is useful in that the identification of spatial patterns of these stable isotopes is indicative of spatial patterns in nutrient sources that can be used to aid and guide remediation efforts throughout BB.

STAR Scholar 2019

In-situ Monitoring for FDM Printers with Acoustic Emission

Fused deposition modeling (FDM) is the mainstream additive manufacturing process available for consumers. This process deposits sequential layers of polymer in layered profiles to achieve complex geometries with a single machining setup. With this robust manufacturing process comes disadvantages that include poor reliability and inconsistent part production. To address these drawbacks, acoustic emission (AE) technology was employed to monitor a 3D printer’s states during the manufacturing process. Specifically, this project focused on developing an effective closed-loop system that provides sensory feedback to monitor FDM machine states. To achieve this goal, AE data collected in real-time during printing was first processed offline using a machine learning method capable of attributing sets of data to a given machine state. A database of AE data was then developed for critical states on a given 3D printer. The concept was then validated in real-time by programming a controller to receive AE data and running the machine learning algorithm to diagnose changing states of the printer. Future steps for this project include the integration of the identified states with the actual printing process to enable higher-quality printing.

STAR Scholars 2017

Methods of Electrode Fabrication

Processing conditions of battery slurries into electrodes are known to affect final battery performance. However, there is a lack of fundamental understanding of how to control processing conditions to achieve better batteries. Previous work has focused on the formulation step of the battery manufacturing process. This study concentrates on two of the slurry processing steps, namely coating and drying, and their effect on film quality and electrode performance. We use rheological measurements to determine the starting slurry microstructure as well as determine the effect of flow on microstructure evolution. Well characterized slurries were subjected to a series of shear rates and drying temperatures to examine the effect of flow and drying rate on final electrode performance. The data suggests that there is a complex relationship between surface roughness and shear rate/temperature. While more data is needed to make concrete conclusions, the preliminary data presented here shows for the first time quantitative relationships between processing conditions and battery performance. 

Title: HAT Tip60 restores learning and memory defects in ALS Drosophila model

Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative disease that attacks motor neurons in the brain and spinal cord, leading to paralysis, locomotive defects, and cognitive impairments. Epigenetic studies have discovered a handful of genes linked to ALS. More importantly, such studies have shown that decreased histone acetyltransferase (HAT) activity, increased histone deacetylase (HDAC) activity, or overexpression of critical genes lead to the onset of ALS. Tip60, an important HAT greatly studied in Alzheimer’s disease (AD) in our lab (The Elefant Lab), has been shown to have potential neuroprotective roles in learning, memory, locomotion, synaptic plasticity, and axonal transport and outgrowth. Here, we investigated if increasing Tip60 HAT levels rescues cognitive impairments, as seen in the AD model, in Drosophila ALS model that overexpresses Vap-33-1, a VAPB protein homolog found in humans that is associated with ALS. We hypothesized that increasing Tip60 HAT levels will rescue ALS-related cognitive impairments in the Drosophila third instar larvae. To test this hypothesis, we carried out a single odor paradigm for olfactory associative learning using sucrose as the gustatory reinforcer and Linalool as the associative odor. The same paradigm was used to study short-term and long-term memory after the larvae completed the olfactory associative learning in order to investigate for any memory impairments. We show that ALS third instar larvae exhibited defects in learning and short-term memory. Importantly, these impairments were partially rescued by increasing Tip60 HAT levels. Together, our findings suggest a neuroprotective role for Tip60 in restoring short-term memory defects seen in ALS Drosophila larvae. 

Young stellar clusters are thought to form in small groups which merge into a final cluster. One method to computationally test this theory is using numeric quantities such as the fractal dimension or minimum separation of members to measure the "clumpiness" of star clusters of different ages. In this work, I analyze the structure of simulated systems by calculating the fractal dimension and the Q parameter of these clusters during formation and early evolution in their natal gas clouds. I then attempt to correlate changes in these quantities to structural changes in the young clusters.

The New Jersey Pine Barrens is a globally rare ecosystem where the biota is well adapted to acidic, nutrient poor soil and water, and frequent wildfires. The Pine Barrens support ~14 species of anurans (i.e frogs and toads). Because anurans face a worldwide decline as a result of habitat loss and deforestation, it is important to understand the impact of water quality disturbance on anuran assemblages. I compared the difference in local species richness along a wetland disturbance gradient at the Warren Grove Gunnery Range to elucidate the effects of landscape change on anuran community structure. Anuran presence/absence, fire frequency, and water quality data were collected from ponds (n=23) from March – July 2016. Anuran community composition was correlated among environmental variables to determine the strength of relationships. Understanding the relationship between anuran species composition and abiotic parameters associated with land-use change will be imperative for the long-term conservation of these bio-indicator species.