1976 University of Michigan Men's Golf Team

Back Row: Bruce Patterson, Randy McClelland, Jim Marshall, Tim Van Tongeren, Mark Promack, Rod Pafford, Doug Davis, Frank Sims

Front Row: Dave Burnham, assistant coach Jim Lipe, Harvey Ely, coach Bill Newcomb, Kenneth Walchuck

1975 UM Track Team

Top Row: Pat Shaw, Tim Bell, Sam Duran, Mark Foster, Phil Stotz, Dave Heikkinen, Lynn Dobosy, Bob Maistros.

5th Row: Randy Foss, John McHugh, Mark Conner, Doug Hennigar, Mike McMahon, Tom Schmidt, John Risk, Dave Furst.

4th Row: Wally Barnowski, Mark Bohlke, Jay Anstaett, Jim Stokes, Jeff Swanson, Bill Schaeffer, James Grace, Bob Scheper.

3rd Row: Bill Donakowski, Andy Johnson, Abe Butler, Rob Lytle, Greg Meyer, Mike McGuire, st. mngr. Mike D'Agostino, trainer Len Paddock.

2nd Row: asst. coach Ron Warhurst, asst. coach Greg Syphax, Ed Kulka, Kevin Briggs, George Przygodski, Terry Hart, Steve Thiry, Quincy Evans, Jeff McLeod.

Front Row: Geoff LePlatte, Jesse Myers, Doug Gibbs, Bob Mills, head coach Jack Harvey, Jim Howe, Jim Simpson, Dave Williams, Jon Cross.

1996 Men's Gymnastics Team

Back Row: Jorge Jimenez, Anthony Wai, Edwin Ledgard, Randy D'Amura

Middle Row:: head coach Bob Darden, Tim Lauring, Justin Semion, Jason MacDonald, Brad Terris, Flavio Martins, Paul Bischoff, trainer Jenn Davis, asst. coach Tim O'Connell

Front Row: Kris Klinger, Bob Young, captain Chris Onuska

Not pictured: Jin Bin Im, Jason Taft, Kevin Matthews

1998 Men's Gymnastics Team

Back Row: Edwin Ledgard, Tim Lauring, Tim DeGraw

Middle Row: undergrad. Asst. coach Jason MacDonald, Adam Hattersley, Chris Peyton, Ethan Johnson, Tim Dehr, asst. coach Mike Bums, Jesse Coleman

Front Row: head coach Kurt Golder, Kevin Roulston, Kenny Keener, Randy D'Amura, LaLo Haro, Bryan Pascoe, Justin Toman, trainer Jen Nauman

Identification of MIMO Hammerstein systems using cardinal spline functions

A new approach to identify multivariable Hammerstein systems is proposed in this paper. By using cardinal cubic spline functions to model the static nonlinearities, the proposed method is effective in modelling processes with hard and/or coupled nonlinearities. With an appropriate transformation, the nonlinear models are parameterized such that the nonlinear identification problem is converted into a linear one. The persistently exciting condition for the transformed input is derived to ensure the estimates are consistent with the true system. A simulation study is performed to demonstrate the effectiveness of the proposed method compared with the existing approaches based on polynomials. (C) 2006 Elsevier Ltd. All rights reserved.

Designs for generalized linear models with several variables and model uncertainty

Standard factorial designs sometimes may be inadequate for experiments that aim to estimate a generalized linear model, for example, for describing a binary response in terms of several variables. A method is proposed for finding exact designs for such experiments that uses a criterion allowing for uncertainty in the link function, the linear predictor, or the model parameters, together with a design search. Designs are assessed and compared by simulation of the distribution of efficiencies relative to locally optimal designs over a space of possible models. Exact designs are investigated for two applications, and their advantages over factorial and central composite designs are demonstrated.

Stochastic models for regulatory networks of the genetic toggle switch

Bistability arises within a wide range of biological systems from the A phage switch in bacteria to cellular signal transduction pathways in mammalian cells. Changes in regulatory mechanisms may result in genetic switching in a bistable system. Recently, more and more experimental evidence in the form of bimodal population distributions indicates that noise plays a very important role in the switching of bistable systems. Although deterministic models have been used for studying the existence of bistability properties under various system conditions, these models cannot realize cell-to-cell fluctuations in genetic switching. However, there is a lag in the development of stochastic models for studying the impact of noise in bistable systems because of the lack of detailed knowledge of biochemical reactions, kinetic rates, and molecular numbers. in this work, we develop a previously undescribed general technique for developing quantitative stochastic models for large-scale genetic regulatory networks by introducing Poisson random variables into deterministic models described by ordinary differential equations. Two stochastic models have been proposed for the genetic toggle switch interfaced with either the SOS signaling pathway or a quorum-sensing signaling pathway, and we have successfully realized experimental results showing bimodal population distributions. Because the introduced stochastic models are based on widely used ordinary differential equation models, the success of this work suggests that this approach is a very promising one for studying noise in large-scale genetic regulatory networks.

Specific detection of enterovirus 71 directly from clinical specimens using real-time RT-PCR hybridization probe assay

Enterovirus 71 (EV71) is one of the main causative agents of hand, foot and mouth disease (HFMD) in young children. Infections caused by EV71 could lead to many complications, ranging from brainstem encephalitis to pulmonary oedema, resulting in high mortality. Thus, rapid detection of the virus is required to enable measures to be implemented in preventing widespread transmission. Based on primers and probes targeting at the VP1 region, a real-time reverse-transcriptase polymerase chain reaction (RT-PCR) hybridization probe assay was developed for specific detection of EV71 from clinical specimens. Quantitative analysis showed that the assay was able to detect as low as 5 EV71 viral copies and EV71 was detected from 46 of the 55 clinical specimens obtained from pediatric patients suffering from HFMD during the period from 2000 to 2003 in Singapore. This study showed that the single tube real-time RT-PCR assay developed in this study can be applied as a rapid and sensitive method for specific detection of EV71 directly from clinical specimens. (c) 2005 Elsevier Ltd. All rights reserved.