Week of September 12, 2011 - September 19, 2011 Monday, September 12, 2011 1:30 PM Advisory Committee Meeting 400 MSC 1:30 PM Math 1150 Instructor Meeting 459 MSC Tuesday, September 13, 2011 11:30 AM Calculator Workshop (Grant) 459 MSC 12:45 PM Math 1150 Instructor Meeting 459 MSC 1:30 PM FMC Meeting 400 MSC Wednesday, September 14, 2011 10:30 AM R Seminar 400 MSC Jim Albert, BGSU I'll talk about using R to create special plots where one divides the graphics window into panes and uses the wide selection of parameters to create special effects. I'll talk about adding mathematical expressions to a graph. I'll discuss saving graphical output and introduce several advanced systems, lattice and ggplot2, for creating graphs. 10:30 AM Math 1210 Instructor Meeting 459 MSC 11:30 AM Statistics Seminar 459 MSC Peng Wang, BGSU Model Selection for Correlation Structure for Correlated Data with Large Cluster Size Model selection of correlation structure for non-normal correlated data is very challenging because of high dimensional correlation parameters involved and the complexity of the likelihood function for non-normal correlated data. However, identifying the correct correlation structure can improve estimation efficiency and the testing power for correlated data. We propose to approximate the inverse of the empirical correlation matrix using a linear combination of candidate basis matrices, and select the correlation structure by identifying non-zero coefficients of basis matrices. This is carried out by minimizing penalized estimating functions, balancing the complexity and informativeness of modeling for the correlation matrix. The new approach does not require estimating each entry of the correlation matrix, nor specification of the likelihood function, and thus can effectively handle non-normal correlated data. Asymptotic theory on model selection consistency and oracle properties are established in the framework of varying cluster size of correlated data, where the derivation of the asymptotic results is quite challenging. Our numerical studies indicate that even when the cluster size is very large, the correlation structure can be identified effectively for both normal responses and binary responses. 2:30 PM Putnam Team Meeting 459 MSC Thursday, September 15, 2011 10:30 AM Math 1210 Instructor Meeting 459 MSC 12:30 PM Algebra & Geometry Seminar 459 MSC Elmas Irmak Mapping Class Groups I will talk about the mapping class groups of orientable and nonorientable surfaces and their relation to automorphism groups of several complexes on these surfaces. 7:30 PM - 10:30 PM Math 1220 Common Exam 459 MSC Friday, September 16, 2011 3:30 PM Refreshments served prior to the colloquium 3:45 PM COLLOQUIUM 459 MSC Craig L. Zirbel, BGSU Predicting the 3D structure of RNA hairpins and internal loops from sequence alone All living organisms have DNA to store their genetic information and use RNA to copy and transmit this information within each cell. But RNA molecules have other important roles as well, which they perform by folding back on themselves to form helices, hairpins, junctions, and internal loops before assembling into a specific 3D structure. The helices are known to be made up of GC and AU Watson-Crick basepairs (very similar to the GC and AT basepairs in DNA), but the hairpins and internal loops have other basepairs which come in 11 additional families. All 12 families of RNA basepairs have characteristic sequence variability, which we see when we compare the same RNA molecule across many species. In this talk, we show that the basepair structure of hairpin and internal loops leads to specific rules for sequence variability for the loop as a whole. We build probabilistic models for this sequence variability using stochastic context-free grammars (SCFG) and Markov random fields (MRF). There are roughly 100 distinct hairpins and 100 distinct internal loops. We show that we can infer the correct hairpin or internal loop from the sequence alone with high accuracy. This will be of great use to biologists studying new RNAs which are known only by their sequences.
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