Metabolite and transcriptome analysis of Campylobacter jejuni in vitro growth reveals a stationary-phase physiological switch.

Campylobacter jejuni is a prevalent cause of food-borne diarrhoeal illness in humans. Understanding of the physiological and metabolic capabilities of the organism is limited. We report a detailed analysis of the C. jejuni growth cycle in batch culture. Combined transcriptomic, phenotypic and metabolic analysis demonstrates a highly dynamic 'stationary phase', characterized by a peak in motility, numerous gene expression changes and substrate switching, despite transcript changes that indicate a metabolic downshift upon the onset of stationary phase. Video tracking of bacterial motility identifies peak activity during stationary phase. Amino acid analysis of culture supernatants shows a preferential order of amino acid utilization. Proton NMR (1H-NMR) highlights an acetate switch mechanism whereby bacteria change from acetate excretion to acetate uptake, most probably in response to depletion of other substrates. Acetate production requires pta (Cj0688) and ackA (Cj0689), although the acs homologue (Cj1537c) is not required. Insertion mutants in Cj0688 and Cj0689 maintain viability less well during the stationary and decline phases of the growth cycle than wild-type C. jejuni, suggesting that these genes, and the acetate pathway, are important for survival.

Fixed-lag blind equalization and sequence estimation in digital communications systems using sequential importance sampling

We present methods for fixed-lag smoothing using Sequential Importance sampling (SIS) on a discrete non-linear, non-Gaussian state space system with unknown parameters. Our particular application is in the field of digital communication systems. Each input data point is taken from a finite set of symbols. We represent transmission media as a fixed filter with a finite impulse response (FIR), hence a discrete state-space system is formed. Conventional Markov chain Monte Carlo (MCMC) techniques such as the Gibbs sampler are unsuitable for this task because they can only perform processing on a batch of data. Data arrives sequentially, so it would seem sensible to process it in this way. In addition, many communication systems are interactive, so there is a maximum level of latency that can be tolerated before a symbol is decoded. We will demonstrate this method by simulation and compare its performance to existing techniques.

ROLE OF CHLORINE AND ALKALI PROMOTERS IN ETHYLENE EPOXIDATION OVER SINGLE CRYSTAL SILVER SURFACES.

Experiments with N//2O were carried out with a view to obtaining additional information about the reactivity of oxygen surface species. On clean Ag, N//2O decomposition was found to be an activated process which led exclusively to the deposition of O(a) species. The presence of preadsorbed oxygen or subsurface oxygen served to enhance the deposition rate of O(a). Subsequent dosing with ethylene at 300 K of such an oxygen-populated surface followed by TPR examination showed it to be active for ethylene oxide formation. Control experiments established that adventitious decomposition of N//2O at the reactor walls or specimen supports followed by possible re-absorption of O//2(a) was an entirely negligible process. ) The oxidation activity of N//2O was also investigated at elevated pressures in the batch reactor.

Transparent electrode with a nanostructured coating.

Using single-walled nanotubes as an example, we fabricated transparent conductive coatings and demonstrated a new technique of centrifuge coating as a potential low-waste, solution-based batch process for the fabrication of nanostructured coatings. A theoretical model is developed to account for the sheet resistance exhibited by layered random-network coatings such as nanofilaments and graphene. The model equation is analytical and compact, and allows the correlation of very different scaling regimes reported in the literature to the underlying coating microstructure. Finally, we also show a refined experimental setup to systematically measure the curvature-dependent sheet resistance.

Sequential Monte Carlo computation of the score and observed information matrix in state-space models with application to parameter estimation

Sequential Monte Carlo (SMC) methods are popular computational tools for Bayesian inference in non-linear non-Gaussian state-space models. For this class of models, we propose SMC algorithms to compute the score vector and observed information matrix recursively in time. We propose two different SMC implementations, one with computational complexity $\mathcal{O}(N)$ and the other with complexity $\mathcal{O}(N^{2})$ where $N$ is the number of importance sampling draws. Although cheaper, the performance of the $\mathcal{O}(N)$ method degrades quickly in time as it inherently relies on the SMC approximation of a sequence of probability distributions whose dimension is increasing linearly with time. In particular, even under strong \textit{mixing} assumptions, the variance of the estimates computed with the $\mathcal{O}(N)$ method increases at least quadratically in time. The $\mathcal{O}(N^{2})$ is a non-standard SMC implementation that does not suffer from this rapid degrade. We then show how both methods can be used to perform batch and recursive parameter estimation.

Tunable hydrodynamic chromatography of microparticles localized in short microchannels.

This paper describes a new way to perform hydrodynamic chromatography (HDC) for the size separation of particles based on a unique recirculating flow pattern. Pressure-driven (PF) and electro-osmotic flows (EOF) are opposed in narrow glass microchannels that expand at both ends. The resulting bidirectional flow turns into recirculating flow because of nonuniform microchannel dimensions. This hydrodynamic effect, combined with the electrokinetic migration of the particles themselves, results in a trapping phenomenon, which we have termed flow-induced electrokinetic trapping (FIET). In this paper, we exploit recirculating flow and FIET to perform a size-based separation of samples of microparticles trapped in a short separation channel using a HDC approach. Because these particles have the same charge (same zeta potential), they exhibit the same electrophoretic mobility, but they can be separated according to size in the recirculating flow. While trapped, particles have a net drift velocity toward the low-pressure end of the channel. When, because of a change in the externally applied PF or electric field, the sign of the net drift velocity reverses, particles can escape the separation channel in the direction of EOF. Larger particles exhibit a larger net drift velocity opposing EOF, so that the smaller particles escape the separation channel first. In the example presented here, a sample plug containing 2.33 and 2.82 microm polymer particles was introduced from the inlet into a 3-mm-long separation channel and trapped. Through tuning of the electric field with respect to the applied PF, the particles could be separated, with the advantage that larger particles remained trapped. The separation of particles with less than 500 nm differences in diameter was performed with an analytical resolution comparable to that of baseline separation in chromatography. When the sample was not trapped in the separation channel but located further downstream, separations could be carried out continuously rather than in batch. Smaller particles could successfully pass through the separation channel, and particles were separated by size. One of the main advantages of exploiting FIET for HDC is that this method can be applied in quite short (a few millimeters) channel geometries. This is in great contrast to examples published to date for the separation of nanoparticles in much longer micro- and nanochannels.

On-line learning of mutually orthogonal subspaces for face recognition by image sets.

We address the problem of face recognition by matching image sets. Each set of face images is represented by a subspace (or linear manifold) and recognition is carried out by subspace-to-subspace matching. In this paper, 1) a new discriminative method that maximises orthogonality between subspaces is proposed. The method improves the discrimination power of the subspace angle based face recognition method by maximizing the angles between different classes. 2) We propose a method for on-line updating the discriminative subspaces as a mechanism for continuously improving recognition accuracy. 3) A further enhancement called locally orthogonal subspace method is presented to maximise the orthogonality between competing classes. Experiments using 700 face image sets have shown that the proposed method outperforms relevant prior art and effectively boosts its accuracy by online learning. It is shown that the method for online learning delivers the same solution as the batch computation at far lower computational cost and the locally orthogonal method exhibits improved accuracy. We also demonstrate the merit of the proposed face recognition method on portal scenarios of multiple biometric grand challenge.

HVIC switch for high-frequency power converters

A high voltage integrated circuit (HVIC) switch designed as a building block for power converters operating up to 13.56 MHz from off-line voltages is presented. A CMOS-compatible, 500 V power device process is used to integrate control circuitry with a high-speed MOS gate driver and high voltage lateral power MOSFET. Fabrication of the HVIC switches has proceeded in two stages. The first batch of devices showed switching times of less than 5 ns for the power switch and good high frequency performance of a level-shifter for driving half bridge converters. In the second phase, a switch that monolithically integrates all the elements required to form a complete high-frequency converter has been designed.

Incremental Linear Discriminant Analysis Using Sufficient Spanning Sets and Its Applications

This paper presents an incremental learning solution for Linear Discriminant Analysis (LDA) and its applications to object recognition problems. We apply the sufficient spanning set approximation in three steps i.e. update for the total scatter matrix, between-class scatter matrix and the projected data matrix, which leads an online solution which closely agrees with the batch solution in accuracy while significantly reducing the computational complexity. The algorithm yields an efficient solution to incremental LDA even when the number of classes as well as the set size is large. The incremental LDA method has been also shown useful for semi-supervised online learning. Label propagation is done by integrating the incremental LDA into an EM framework. The method has been demonstrated in the task of merging large datasets which were collected during MPEG standardization for face image retrieval, face authentication using the BANCA dataset, and object categorisation using the Caltech101 dataset. © 2010 Springer Science+Business Media, LLC.