Almost Sure Invariance Principle for Continuous-Space Random Walk in Dynamic Random Environment

We consider a random walk on $\R^d$ in a polynomially mixing random environment that is refreshed at each time step. We use a martingale approach to give a necessary and sufficient condition for the almost-sure functional central limit theorem to hold.Comment: minor typos fixe

Strong invariance and noise-comparison principles for some parabolic stochastic PDEs

We consider a system of interacting diffusions on the integer lattice. By letting the mesh size go to zero and by using a suitable scaling, we show that the system converges (in a strong sense) to a solution of the stochastic heat equation on the real line. As a consequence, we obtain comparison inequalities for product moments of the stochastic heat equation with different nonlinearities.Comment: 26 page

Thermodynamic and kinetic insights into stop codon recognition by release factor 1.

Stop codon recognition is a crucial event during translation termination and is performed by class I release factors (RF1 and RF2 in bacterial cells). Recent crystal structures showed that stop codon recognition is achieved mainly through a network of hydrogen bonds and stacking interactions between the stop codon and conserved residues in domain II of RF1/RF2. Additionally, previous studies suggested that recognition of stop codons is coupled to proper positioning of RF1 on the ribosome, which is essential for triggering peptide release. In this study we mutated four conserved residues in Escherichia coli RF1 (Gln185, Arg186, Thr190, and Thr198) that are proposed to be critical for discriminating stop codons from sense codons. Our thermodynamic and kinetic analysis of these RF1 mutants showed that the mutations inhibited the binding of RF1 to the ribosome. However, the mutations in RF1 did not affect the rate of peptide release, showing that imperfect recognition of the stop codon does not affect the proper positioning of RF1 on the ribosome

Structural reliability prediction of a steel bridge element using dynamic object oriented Bayesian Network (DOOBN)

Different from conventional methods for structural reliability evaluation, such as, first/second-order reliability methods (FORM/SORM) or Monte Carlo simulation based on corresponding limit state functions, a novel approach based on dynamic objective oriented Bayesian network (DOOBN) for prediction of structural reliability of a steel bridge element has been proposed in this paper. The DOOBN approach can effectively model the deterioration processes of a steel bridge element and predict their structural reliability over time. This approach is also able to achieve Bayesian updating with observed information from measurements, monitoring and visual inspection. Moreover, the computational capacity embedded in the approach can be used to facilitate integrated management and maintenance optimization in a bridge system. A steel bridge girder is used to validate the proposed approach. The predicted results are compared with those evaluated by FORM method