BIOMED Master's Thesis Defense

Title:
The Role of DIPs and Dprs in Synaptic Connection Specificity
 
Speaker:
Pratishtha Guckhool, Master's Candidate
School of Biomedical Engineering, Science and Health Systems
Drexel University

Advisor:
Catherine von Reyn, PhD
Assistant Professor
School of Biomedical Engineering, Science and Health Systems
Drexel University
 
Abstract:
The precise wiring patterns between neurons are crucial for the proper formation and maintenance of neuronal circuits. Synaptic adhesion molecules are thought to play an important role in the regulation of synaptic connection specificity. The immunoglobulin-like subfamily of synaptic adhesion molecules, the 21-member DIPs and 11-member Dprs have been hypothesized to represent the two families of neuronal recognition proteins in the Drosophila melanogaster model organism. The protein expression previously characterized in the outer layers of the optic lobe has shown that some of the Dprs are broadly expressed in the lamina neurons and their cognate DIPs are expressed in their synaptic target medulla neurons. Here, we investigate the differential gene expression patterns of DIPs and Dprs within the optic glomeruli of the three Lobular Columnar cell types: LC4, LPLC1, and LPLC2. Using a publicly available RNA-seq dataset, we develop and compare multiple analyses pipelines to predict the variation in gene expression levels of each DIP and Dpr across cell types within the visual system and their expression state in a specific cell population. Dpr1/DIP-eta were identified as being more highly expressed in LPLC2, and Dpr9/DIP-beta and Dpr12/DIP-delta in LPLC1.

To validate these predictions, we investigate the protein expression patterns of the identified DIPs and Dprs. Endogenous labeling of the DIPs and Dprs shows that the differential gene expression patterns match the protein expression patterns for DIP-eta while the Dprs were not differentially expressed across the optic glomeruli of the three LC cell types. We then sought to identify the DIPs and Dprs that may be involved in synaptic partner recognition during development in the three LC cell types. A differential gene expression analysis identified Dpr1 that is differentially expressed between LC4 and LPLC1 and Dpr13, Dpr20 and DIP-eta between LPLC2 and LPLC1. This work provides a better understanding of the differential expression of the DIPs and Dprs within cell types in the visual system and their potential role in synaptic partner recognition.

BIOMED Master's Thesis Defense

Title:
Investigating the Performance of Sensor-driven Biometrics in the Assessment of Cognitive Workload
 
Speaker:
Emma Katherine MacNeil, Master's Candidate
School of Biomedical Engineering, Science and Health Systems
Drexel University

Advisor:
Kurtulus Izzetoglu, PhD
Associate Professor
School of Biomedical Engineering, Science and Health Systems
Drexel University
 
Abstract:
Cognitive workload changes in the assessment of human performance have long been monitored through the use of subjective self-reports, physiological, or neuro-physiological measures. Wearable sensors allow us to monitor direct physiological changes associated with cognitive workload in real time. This thesis focuses on utilizing multiple physiological and neurological measures: functional near-infrared spectroscopy (fNIRS), eye-tracking, electrodermal activity, heart rate, and respiratory rate; in order to assess cognitive workload changes during different simulation-based tasks designed within the context of Federal Law Enforcement Training. The primary objective is to use multi-modal sensors and a machine learning (ML) approach as an objective assessment method and to investigate which set of biometrics serves as the best predictor of cognitive workload.

To achieve this objective, several biometrics, including prefrontal cortex (PFC) oxygenation changes, heart rate, and respiratory rate, were collected in both high and low workload conditions in simulated use of force (UOF) scenarios. These biometrics were then used to train logistic regression, feature mapping, SVM and LDA classifiers. F1-score and accuracy measures were used to evaluate the performance of these classifiers. Oxygenation changes in the left anterior medial PFC region and the combined biomarkers including oxygenation changes in the PFC and heart rate revealed the highest performing feature sets across all the classifiers. Future work should include the use of non-linear classification models and study of additional biomarkers to enhance training outcomes and performance.

The School of Biomedical Engineering, Science and Health Systems is pleased to announce its 4th Annual Immune Modulation & Engineering Symposium (IMES).