Lets congratulate Princess Mycia Cox, fellow nanoscale PhD student, for defending her Ph.D. dissertation, entitled: "Design and fabrication of low loss and low index optical metamaterials" last week. She completed the work under the supervision of Dr. Michael Fiddy (Electrical and Computer Engineering).
"Meta-materials" is an highly active area of research at UNC Charlotte. The researchers are specially interested in making materials which have their index of refraction close to zero or negative. Mycia's talk was focused on making and studying these type of low index and preferably low loss materials -particularly at optical frequencies. Its also important that we make these materials through low cost processes in order to make them in large enough quantities. Given these considerations, nanomaterials especially metallic or semiconductor nanocrystals fit the criteria .In mycia's case ,she fabricated and studied aluminium doped zinc-oxide nanoparticles. Her work was also supplemented by extensive simulations and modeling. A large number of students including fellow nano students and...Read More
Department of Biostatistics & Bioinformatics,Duke University
“USING HIGH-THROUGHPUT DATA TO DERIVE NEW MODELS OF PROTEIN-DNA BINDING SPECIFICITY”
Transcription factors (TFs) regulate gene expression by binding to specific, short DNA sites in the promoters or enhancers of the genes they regulate. Most eukaryotic TFs are members of large protein families that share a common DNA binding domain and thus have similar binding specificities. However, paralogous TFs (i.e., members of the same family) typically perform different regulatory functions in vivo by binding to different sets of genomic sites. Current models for DNA binding specificity, such as position weight matrices (PWMs), cannot typically distinguish among the DNA sites bound by paralogous TFs. This significantly restricts our ability to computationally predict genomic targets for individual members of TF families. I will describe new regression-based models for TF-DNA binding specificity, that are able to capture differences among closely-related TFs even when their PWMs are virtually identical. We train our models using high-throughput in vitro data from custom protein binding microarray (PBM) experiments. Our PBMs are specifically designed to cover a large number of potential TF-DNA binding...Read More