Identification, expression, and DNA sequence of the GDP-mannose biosynthesis genes encoded by the O7 rfb gene cluster of strain VW187 (Escherichia coli O7:K1)

The O7-specific lipopolysaccharide (LPS) in strains of Escherichia coli consists of a repeating unit made of galactose, mannose, rhamnose, 4-acetamido-2,6-dideoxyglucose, and N-acetylglucosamine. We have recently cloned and characterized genetically the O7-specific LPS biosynthesis region (rfbEcO7) of the E. coli O7:K1 strain VW187 (C. L. Marolda, J. Welsh, L. Dafoe, and M. A. Valvano, J. Bacteriol. 172:3590-3599, 1990). In this study, we localized the gnd gene encoding gluconate-6-phosphate dehydrogenase at one end of the rfbEcO7 gene cluster and sequenced that end of the cluster. Three open reading frames (ORF) encoding polypeptides of 275, 464, and 453 amino acids were identified upstream of gndEcO7, all transcribed toward the gnd gene. ORF275 had 45% similarity at the protein level with ORF16.5, which occupies a similar position in the Salmonella enterica LT2 rfb region, and presumably encodes a nucleotide sugar transferase. The polypeptides encoded by ORFs 464 and 453 were expressed under the control of the ptac promoter and visualized in Coomassie blue-stained sodium dodecyl sulfate-polyacrylamide gels and by maxicell analysis. ORF464 expressed GDP-mannose pyrophosphorylase and ORF453 encoded a phosphomannomutase, the enzymes for the biosynthesis pathway of GDP-mannose, one of the nucleotide sugar precursors for the formation of the O7 repeating unit. They were designated rfbMEcO7 and rfbKEcO7, respectively. The RfbMEcO7 polypeptide was homologous to the corresponding protein in S. enterica LT2, XanB of Xanthomonas campestris, and AlgA of Pseudomonas aeruginosa, all GDP-mannose pyrophosphorylases. RfbKEcO7 was very similar to CpsG of S. enterica LT2, an enzyme presumably involved in the biosynthesis of the capsular polysaccharide colanic acid, but quite different from the corresponding RfbK protein of S. enterica LT2.

Algorithms for Hidden Markov Models With Imprecisely Specified Parameters

Hidden Markov models (HMMs) are widely used models for sequential data. As with other probabilistic graphical models, they require the specification of precise probability values, which can be too restrictive for some domains, especially when data are scarce or costly to acquire. We present a generalized version of HMMs, whose quantification can be done by sets of, instead of single, probability distributions. Our models have the ability to suspend judgment when there is not enough statistical evidence, and can serve as a sensitivity analysis tool for standard non-stationary HMMs. Efficient inference algorithms are developed to address standard HMM usage such as the computation of likelihoods and most probable explanations. Experiments with real data show that the use of imprecise probabilities leads to more reliable inferences without compromising efficiency.

A Continuous Time Markov Chain Based Sequential Analytical Approach for Composite Power System Reliability Assessment

This paper proposes a continuous time Markov chain (CTMC) based sequential analytical approach for composite generation and transmission systems reliability assessment. The basic idea is to construct a CTMC model for the composite system. Based on this model, sequential analyses are performed. Various kinds of reliability indices can be obtained, including expectation, variance, frequency, duration and probability distribution. In order to reduce the dimension of the state space, traditional CTMC modeling approach is modified by merging all high order contingencies into a single state, which can be calculated by Monte Carlo simulation (MCS). Then a state mergence technique is developed to integrate all normal states to further reduce the dimension of the CTMC model. Moreover, a time discretization method is presented for the CTMC model calculation. Case studies are performed on the RBTS and a modified IEEE 300-bus test system. The results indicate that sequential reliability assessment can be performed by the proposed approach. Comparing with the traditional sequential Monte Carlo simulation method, the proposed method is more efficient, especially in small scale or very reliable power systems.

Hidden Markov Models With Set-Valued Parameters

Hidden Markov models (HMMs) are widely used probabilistic models of sequential data. As with other probabilistic models, they require the specification of local conditional probability distributions, whose assessment can be too difficult and error-prone, especially when data are scarce or costly to acquire. The imprecise HMM (iHMM) generalizes HMMs by allowing the quantification to be done by sets of, instead of single, probability distributions. iHMMs have the ability to suspend judgment when there is not enough statistical evidence, and can serve as a sensitivity analysis tool for standard non-stationary HMMs. In this paper, we consider iHMMs under the strong independence interpretation, for which we develop efficient inference algorithms to address standard HMM usage such as the computation of likelihoods and most probable explanations, as well as performing filtering and predictive inference. Experiments with real data show that iHMMs produce more reliable inferences without compromising the computational efficiency.

Monte Carlo simulation of properties of monolayers and metal islands adsorbed on metallic (111) surfaces

To obtain the surface stress changes due to the adsorption of metal monolayers onto metallic surfaces, a new model derived from thermodynamic considerations is presented. Such a model is based on continuum Monte Carlo simulations with embedded atom method potentials in the canonical ensemble, and it is extended to consider the behavior on different islands adsorbed onto (111) substrate surfaces. Homoepitaxial and heteroepitaxial systems are studied. Pseudomorphic growth is not observed for small metal islands with considerable positive misfit with the substrate. Instead, the islands become compressed upon increase of their size. A simple model is proposed to interpolate between the misfits of atoms in small islands and the pseudomorphic behavior of the monolayer.

Power-law behaviour, heterogeneity, and trend chasing

Long-range dependence in volatility is one of the most prominent examples in financial market research involving universal power laws. Its characterization has recently spurred attempts to provide some explanations of the underlying mechanism. This paper contributes to this recent line of research by analyzing a simple market fraction asset pricing model with two types of traders---fundamentalists who trade on the price deviation from estimated fundamental value and trend followers whose conditional mean and variance of the trend are updated through a geometric learning process. Our analysis shows that agent heterogeneity, risk-adjusted trend chasing through the geometric learning process, and the interplay of noisy fundamental and demand processes and the underlying deterministic dynamics can be the source of power-law distributed fluctuations. In particular, the noisy demand plays an important role in the generation of insignificant autocorrelations (ACs) on returns, while the significant decaying AC patterns of the absolute returns and squared returns are more influenced by the noisy fundamental process. A statistical analysis based on Monte Carlo simulations is conducted to characterize the decay rate. Realistic estimates of the power-law decay indices and the (FI)GARCH parameters are presented.

A molecular model for drug binding to tandem repeats of telomeric G-quadruplexes.

The extreme 3'-ends of human telomeres consist of 150–250 nucleotides of single-stranded DNA sequence together with associated proteins. Small-molecule ligands can compete with these proteins and induce a conformational change in the DNA to a four-stranded quadruplex arrangement, which is also no longer a substrate for the telomerase enzyme. The modified telomere ends provide signals to the DNA-damage-response system and trigger senescence and apoptosis. Experimental structural data are available on such quadruplex complexes comprising up to four telomeric DNA repeats, but not on longer systems that are more directly relevant to the single-stranded overhang in human cells. The present paper reports on a molecular modelling study that uses Molecular Dynamics simulation methods to build dimer and tetramer quadruplex repeats. These incorporate ligand-binding sites and are models for overhang–ligand complexes.

Probabilistic Inference in Credal Networks: New Complexity Results

Credal networks are graph-based statistical models whose parameters take values in a set, instead of being sharply specified as in traditional statistical models (e.g., Bayesian networks). The computational complexity of inferences on such models depends on the irrelevance/independence concept adopted. In this paper, we study inferential complexity under the concepts of epistemic irrelevance and strong independence. We show that inferences under strong independence are NP-hard even in trees with binary variables except for a single ternary one. We prove that under epistemic irrelevance the polynomial-time complexity of inferences in credal trees is not likely to extend to more general models (e.g., singly connected topologies). These results clearly distinguish networks that admit efficient inferences and those where inferences are most likely hard, and settle several open questions regarding their computational complexity. We show that these results remain valid even if we disallow the use of zero probabilities. We also show that the computation of bounds on the probability of the future state in a hidden Markov model is the same whether we assume epistemic irrelevance or strong independence, and we prove an analogous result for inference in Naive Bayes structures. These inferential equivalences are important for practitioners, as hidden Markov models and Naive Bayes networks are used in real applications of imprecise probability.

The Coxian Phase-type distribution with a Hidden Node

Healthcare providers are under increased pressure to ensure that the quality
of care delivered to patients are off the highest standard. Modelling quality of
care is difficult due to the many ways of defining it. This paper introduces a potential
model which could be used to take quality of care into account when modelling
length of stay. The Coxian phase-type distribution is used to model length of stay
and quality of care incorporated into this using a Hidden Markov model. This model
is then applied to