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.

A list of mathematics seminars by subject and other seminars at BGSU is available  here.

If you have comments or material for the calendar, send e-mail to Anita Serda,

If you wish to be placed on the e-mail distribution list, send e-mail to Craig Zirbel,

Previous calendars are available individually or in one single file for searching.

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