Received signal strength in large-scale wireless relay sensor network: a stochastic ray approach

The authors consider a point percolation lattice representation of a large-scale wireless relay sensor network (WRSN) deployed in a cluttered environment. Each relay sensor corresponds to a grid point in the random lattice and the signal sent by the source is modelled as an ensemble of photons that spread in the space, which may 'hit' other sensors and are 'scattered' around. At each hit, the relay node forwards the received signal to its nearest neighbour through direction-selective relaying. The authors first derive the distribution that a relay path reaches a prescribed location after undergoing certain number of hops. Subsequently, a closed-form expression of the average received signal strength (RSS) at the destination can be computed as the summation of all signal echoes' energy. Finally, the effect of the anomalous diffusion exponent ß on the mean RSS in a WRSN is studied, for which it is found that the RSS scaling exponent e is given by (3ß-1)/ß. The results would provide useful insight into the design and deployment of large-scale WRSNs in future. © 2011 The Institution of Engineering and Technology.

Approximating the Set of Local Minima in Partial RNA Folding Landscapes

Motivation: We study a stochastic method for approximating the set of local minima in partial RNA folding landscapes associated with a bounded-distance neighbourhood of folding conformations. The conformations are limited to RNA secondary structures without pseudoknots. The method aims at exploring partial energy landscapes pL induced by folding simulations and their underlying neighbourhood relations. It combines an approximation of the number of local optima devised by Garnier and Kallel (2002) with a run-time estimation for identifying sets of local optima established by Reeves and Eremeev (2004).

Results: The method is tested on nine sequences of length between 50 nt and 400 nt, which allows us to compare the results with data generated by RNAsubopt and subsequent barrier tree calculations. On the nine sequences, the method captures on average 92% of local minima with settings designed for a target of 95%. The run-time of the heuristic can be estimated by O(n2D?ln?), where n is the sequence length, ? is the number of local minima in the partial landscape pL under consideration and D is the maximum number of steepest descent steps in attraction basins associated with pL.

Complete Genome Sequences of Three Propionibacterium acnes Isolates from the Type IA(2) Cluster

Propionibacterium acnes is an anaerobic Gram-positive bacterium that has been linked to a wide range of opportunistic human infections and conditions, most notably acne vulgaris (I. Kurokawa et al., Exp. Dermatol. 18:821-832, 2009). We now present the whole-genome sequences of three P. acnes strains from the type IA(2) cluster which were recovered from ophthalmic infections (A. McDowell et al., Microbiology 157:1990-2003, 2011).

Reliability Assessment of 3-D Space Frame Structures Applying Stochastic Finite Element Analysis

Throughout design development of satellite structure, stress engineer is usually challenged with randomness in applied loads and material properties. To overcome such problem, a risk-based design is applied which estimates satellite structure probability of failure under static and thermal loads. Determining probability of failure can help to update initially applied factors of safety that were used during structure preliminary design phase. These factors of safety are related to the satellite mission objective. Sensitivity-based analysis is to be implemented in the context of finite element analysis (probabilistic finite element method or stochastic finite element method (SFEM)) to determine the probability of failure for satellite structure or one of its components.

A spatio-temporal 2D-models framework for human pose recovery in monocular sequences

This paper addresses the pose recovery problem of a particular articulated object: the human body. In this model-based approach, the 2D-shape is associated to the corresponding stick figure allowing the joint segmentation and pose recovery of the subject observed in the scene. The main disadvantage of 2D-models is their restriction to the viewpoint. To cope with this limitation, local spatio-temporal 2D-models corresponding to many views of the same sequences are trained, concatenated and sorted in a global framework. Temporal and spatial constraints are then considered to build the probabilistic transition matrix (PTM) that gives a frame to frame estimation of the most probable local models to use during the fitting procedure, thus limiting the feature space. This approach takes advantage of 3D information avoiding the use of a complex 3D human model. The experiments carried out on both indoor and outdoor sequences have demonstrated the ability of this approach to adequately segment pedestrians and estimate their poses independently of the direction of motion during the sequence. (c) 2008 Elsevier Ltd. All rights reserved.

Detecting sequence dependent transcriptional pauses from RNA and protein number time series

Background: Evidence suggests that in prokaryotes sequence-dependent transcriptional pauses a?ect the dynamics of transcription and translation, as well as of small genetic circuits. So far, a few pause-prone sequences have been identi?ed from in vitro measurements of transcription elongation kinetics.

Results: Using a stochastic model of gene expression at the nucleotide and codon levels with realistic parameter values, we investigate three di?erent but related questions and present statistical methods for their analysis. First, we show that information from in vivo RNA and protein temporal numbers is su?cient to discriminate between models with and without a pause site in their coding sequence. Second, we demonstrate that it is possible to separate a large variety of models from each other with pauses of various durations and locations in the template by means of a hierarchical clustering and a random forest classi?er. Third, we introduce an approximate likelihood function that allows to estimate the location of a pause site.

Conclusions: This method can aid in detecting unknown pause-prone sequences from temporal measurements of RNA and protein numbers at a genome-wide scale and thus elucidate possible roles that these sequences play in the dynamics of genetic networks and phenotype.

REMA: A computer-based mapping tool for analysis of restriction sites in multiple DNA sequences

REMA is an interactive web-based program which predicts endonuclease cut sites in DNA sequences. It analyses Multiple sequences simultaneously and predicts the number and size of fragments as well as provides restriction maps. The users can select single or paired combinations of all commercially available enzymes. Additionally, REMA permits prediction of multiple sequence terminal fragment sizes and suggests suitable restriction enzymes for maximally discriminatory results. REMA is an easy to use, web based program which will have a wide application in molecular biology research. Availability: REMA is written in Perl and is freely available for non-commercial use. Detailed information on installation can be obtained from Jan Szubert (jan.szubert@gmail.com) and the web based application is accessible on the internet at the URL http://www.macaulay.ac.uk/rema. Contact: b.singh@macaulay.ac.uk. (C) 2007 Elsevier B.V. All rights reserved.

Regularized Online Mixture of Gaussians for Background Subtraction

Mixture of Gaussians (MoG) modelling [13] is a popular approach to background subtraction in video sequences. Although the algorithm shows good empirical performance, it lacks theoretical justification. In this paper, we give a justification for it from an online stochastic expectation maximization (EM) viewpoint and extend it to a general framework of regularized online classification EM for MoG with guaranteed convergence. By choosing a special regularization function, l1 norm, we derived a new set of updating equations for l1 regularized online MoG. It is shown empirically that l1 regularized online MoG converge faster than the original online MoG .