The differential roles of D-Pax2 variants in regulating Drosophila eye and bristle development

The ability to appropriately interact with the environment is crucial to an organism’s survival. The establishment of functional sensory systems, such as the bristles and eyes in Drosophila, is a critical event during the development of the organism. The transcription factor D Pax2 is involved in the differentiation of the shaft and glial cells in the developing bristle (Kavaler et al., Dev, 126:2261-2272, 1999) and of the cone and primary pigment cells in the developing eye (Fu and Noll, Genes Dev, 11:389-405, 1997). How D-Pax2 contributes to distinct differentiative pathways in different cell types is not known. Recent work by Anna Czechowski and Katherine Harmon (personal communication) identified a mutation in the D-Pax2 gene that introduced a stop codon at the end of exon 9, effectively truncating the protein. This mutation affects bristle, but not eye, development. We thus suspected regions after exon 9 are required for D-Pax2 function only in the bristles and may also be associated with alternative splicing of the D Pax2 transcript. We plan to assess the role of the carboxy terminal region of the protein by establishing transgenic lines bearing rescue constructs of D-Pax2 with either the complete coding sequence or with deletions of specific exons. To date, we have generated the first rescue construct bearing the complete coding region of the gene driven by a 3 KB upstream regulatory region of D-Pax2 and are currently generating transgenic fly lines with this construct.

Machine learning and small robot navigation

Developing successful navigation and mapping strategies is an essential part of autonomous robot research. However, hardware limitations often make for inaccurate systems. This project serves to investigate efficient alternatives to mapping an environment, by first creating a mobile robot, and then applying machine learning to the robot and controlling systems to increase the robustness of the robot system. My mapping system consists of a semi-autonomous robot drone in communication with a stationary Linux computer system. There are learning systems running on both the robot and the more powerful Linux system. The first stage of this project was devoted to designing and building an inexpensive robot. Utilizing my prior experience from independent studies in robotics, I designed a small mobile robot that was well suited for simple navigation and mapping research. When the major components of the robot base were designed, I began to implement my design. This involved physically constructing the base of the robot, as well as researching and acquiring components such as sensors. Implementing the more complex sensors became a time-consuming task, involving much research and assistance from a variety of sources. A concurrent stage of the project involved researching and experimenting with different types of machine learning systems. I finally settled on using neural networks as the machine learning system to incorporate into my project. Neural nets can be thought of as a structure of interconnected nodes, through which information filters. The type of neural net that I chose to use is a type that requires a known set of data that serves to train the net to produce the desired output. Neural nets are particularly well suited for use with robotic systems as they can handle cases that lie at the extreme edges of the training set, such as may be produced by "noisy" sensor data. Through experimenting with available neural net code, I became familiar with the code and its function, and modified it to be more generic and reusable for multiple applications of neural nets.