MOTIVATION: Abrupt reduction/resumption of thermal fluctuations of a force probe has been used to identify association/dissociation events of protein-ligand bonds. We show that off-rate of molecular dissociation can be estimated by the analysis of the bond lifetime, while the on-rate of molecular association can be estimated by the analysis of the waiting time between two neighboring bond events. However, the analysis relies heavily on subjective judgments and is time-consuming. To automate the process of mapping out bond events from thermal fluctuation data, we develop a hidden Markov model (HMM)-based method. RESULTS: The HMM method represents the bond state by a hidden variable with two values: bound and unbound. The bond association/dissociation is visualized and pinpointed. We apply the method to analyze a key receptor-ligand interaction in the early stage of hemostasis and thrombosis: the von Willebrand factor (VWF) binding to platelet glycoprotein Ibα (GPIbα). The numbers of bond lifetime and waiting time events estimated by the HMM are much more than those estimated by a descriptive statistical method from the same set of raw data. The kinetic parameters estimated by the HMM are in excellent agreement with those by a descriptive statistical analysis, but have much smaller errors for both wild-type and two mutant VWF-A1 domains. Thus, the computerized analysis allows us to speed up the analysis and improve the quality of estimates of receptor-ligand binding kinetics.

Robust parameter design (RPD) has been widely used as a cost-effective tool in quality control to reduce variability, in which the controllable factors are set to minimize the variability of response variables due to noise factors, assuming their distributions are known. It is essentially an offline tool without considering that some noise factors can be measured online. Recently, the concept of design of experiment (DOE)-based automatic process control (APC) has been proposed for online process control based on regression models obtained from DOE and with consideration of the online measurement of noise factors. The existing literature investigates the DOE-based APC with assumption that both regression models and the online noise measurement are precisely known, which limits the applicability of the technique. This paper develops the DOE-based APC scheme that considers both the observation and the modeling uncertainties. The controller is implemented under two APC strategies, i.e., cautious control strategy and certainty equivalence control strategy. The comparison among online APC and robust design approaches demonstrates that automatic controller with consideration of both uncertainties can achieve better process performance than conventional design, and is more stable than normal DOE-based APC controllers. The proposed approach is illustrated using an industrial process.

Proposes, implements, and evaluates a class of nonstationary-state hidden Markov models (HMMs) having each state associated with a distinct polynomial regression function of time plus white Gaussian noise. The model represents the transitional acoustic trajectories of speech in a parametric manner, and includes the standard stationary-state HMM as a special, degenerated case. The authors develop an efficient dynamic programming technique which includes the state sojourn time as an optimization variable, in conjunction with a state-dependent orthogonal polynomial regression method, for estimating the model parameters. Experiments on fitting models to speech data and on limited-vocabulary speech recognition demonstrate consistent superiority of these nonstationary-state HMMs over the traditional stationary-state HMMs.>