Outer membrane vesicles can contribute to cellulose degradation in Teredinibacter turnerae, a cultivable intracellular endosymbiont of shipworms

Teredinibacter turnerae is a cultivable cellulolytic Gammaproeteobacterium (Cellvibrionaceae) that commonly occurs as an intracellular endosymbiont in the gills of wood-eating bivalves of the family Teredinidae (shipworms). The genome of T. turnerae encodes a broad range of enzymes that deconstruct cellulose, hemicellulose, and pectin and contribute to lignocellulose digestion in the shipworm gut. However, the mechanism by which symbiont-made enzymes are secreted by T. turnerae and subsequently transported to the site of lignocellulose digestion in the shipworm gut is incompletely understood. Here, we show that T. turnerae cultures grown on carboxymethyl cellulose (CMC) produce outer membrane vesicles (OMVs) that contain a variety of proteins identified by LC-MS/MS as carbohydrate-active enzymes with predicted activities against cellulose, hemicellulose, and pectin. Reducing sugar assays and zymography confirm that these OMVs retain cellulolytic activity, as evidenced by hydrolysis of CMC. Additionally, these OMVs were enriched with TonB-dependent receptors, which are essential to carbohydrate and iron acquisition by free-living bacteria. These observations suggest potential roles for OMVs in lignocellulose utilization by T. turnerae in the free-living state, in enzyme transport and host interaction during symbiotic association, and in commercial applications such as lignocellulosic biomass conversion.

A study of pattern recovery in recurrent correlation associative memories

In this paper, we analyze the recurrent correlation associative memory (RCAM) model of Chiueh and Goodman (1990, 1991). This is an associative memory in which stored binary memory patterns are recalled via an iterative update rule. The update of the individual pattern-bits is controlled by an excitation function, which takes as its argument the inner product between the stored memory patterns and the input patterns. Our contribution is to analyze the dynamics of pattern recall when the input patterns are corrupted by noise of a relatively unrestricted class. We show how to identify the excitation function which maximizes the separation (the Fisher discriminant) between the uncorrupted realization of the noisy input pattern and the remaining patterns residing in the memory. The excitation function which gives maximum separation is exponential when the input bit-errors follow a binomial distribution. We develop an expression for the expectation value of bit-error probability on the input pattern after one iteration. We show how to identify the excitation function which minimizes the bit-error probability. The relationship between the excitation functions which result from the two different approaches is examined for a binomial distribution of bit-errors. We develop a semiempirical approach to the modeling of the dynamics of the RCAM.

Graph edit distance from spectral seriation

This paper is concerned with computing graph edit distance. One of the criticisms that can be leveled at existing methods for computing graph edit distance is that they lack some of the formality and rigor of the computation of string edit distance. Hence, our aim is to convert graphs to string sequences so that string matching techniques can be used. To do this, we use a graph spectral seriation method to convert the adjacency matrix into a string or sequence order. We show how the serial ordering can be established using the leading eigenvector of the graph adjacency matrix. We pose the problem of graph-matching as a maximum a posteriori probability (MAP) alignment of the seriation sequences for pairs of graphs. This treatment leads to an expression in which the edit cost is the negative logarithm of the a posteriori sequence alignment probability. We compute the edit distance by finding the sequence of string edit operations which minimizes the cost of the path traversing the edit lattice. The edit costs are determined by the components of the leading eigenvectors of the adjacency matrix and by the edge densities of the graphs being matched. We demonstrate the utility of the edit distance on a number of graph clustering problems.

Empirical modelling of genetic algorithms

This paper addresses the problem of reliably setting genetic algorithm parameters for consistent labelling problems. Genetic algorithm parameters are notoriously difficult to determine. This paper proposes a robust empirical framework, based on the analysis of factorial experiments. The use of a graeco-latin square permits an initial study of a wide range of parameter settings. This is followed by fully crossed factorial experiments with narrower ranges, which allow detailed analysis by logistic regression. The empirical models derived can be used to determine optimal algorithm parameters and to shed light on interactions between the parameters and their relative importance. Refined models are produced, which are shown to be robust under extrapolation to up to triple the problem size.

An Energy Function and Continuous Edit Process for Graph Matching

The contributions of this article are twofold. First, we develop a new nonquadratic energy function for graph matching. The starting point is a recently reported mixture model that gauges relational consistency using a series of exponential functions of the Hamming distances between graph neighborhoods. We compute the effective neighborhood potentials associated with the mixture model by identifying the single probability function of zero Kullback divergence. This new energy function is simply a weighted sum of graph Hamming distances. The second contribution is to locate matches by graduated assignment. Rather than solving the mean-field saddle-point equations, which are intractable for our nonquadratic energy function, we apply the soft-assign ansatz to the derivatives of our energy function. Here we introduce a novel departure from the standard graduated assignment formulation of graph matching by allowing the connection strengths of the data graph to update themselves. The aim is to provide a means by which the structure of the data graph can be updated so as to rectify structural errors. The method is evaluated experimentally and is shown to outperform its quadratic counterpart.