Meta-analyses identify 13 loci associated with age at menopause and highlight DNA repair and immune pathways.

To newly identify loci for age at natural menopause, we carried out a meta-analysis of 22 genome-wide association studies (GWAS) in 38,968 women of European descent, with replication in up to 14,435 women. In addition to four known loci, we identified 13 loci newly associated with age at natural menopause (at P < 5 × 10(-8)). Candidate genes located at these newly associated loci include genes implicated in DNA repair (EXO1, HELQ, UIMC1, FAM175A, FANCI, TLK1, POLG and PRIM1) and immune function (IL11, NLRP11 and PRRC2A (also known as BAT2)). Gene-set enrichment pathway analyses using the full GWAS data set identified exoDNase, NF-κB signaling and mitochondrial dysfunction as biological processes related to timing of menopause.

Shout and Act: an Algorithm for Digital Objects Preservation Inspired from Rescue Robots

We adapt the Shout and Act algorithm to Digital Objects Preservation where agents explore file systems looking for digital objects to be preserved (victims). When they find something they “shout” so that agent mates can hear it. The louder the shout, the urgent or most important the finding is. Louder shouts can also refer to closeness. We perform several experiments to show that this system works very scalably, showing that heterogeneous teams of agents outperform homogeneous ones over a wide range of tasks complexity. The target at-risk documents are MS Office documents (including an RTF file) with Excel content or in Excel format. Thus, an interesting conclusion from the experiments is that fewer heterogeneous (varying skills) agents can equal the performance of many homogeneous (combined super-skilled) agents, implying significant performance increases with lower overall cost growth. Our results impact the design of Digital Objects Preservation teams: a properly designed combination of heterogeneous teams is cheaper and more scalable when confronted with uncertain maps of digital objects that need to be preserved. A cost pyramid is proposed for engineers to use for modeling the most effective agent combinations

Solving Leontief Input-Output Model with Trapezoidal Fuzzy Numbers and Gauss-Seidel Algorithm

In this work a fuzzy linear system is used to solve Leontief input-output model with fuzzy entries. For solving this model, we assume that the consumption matrix from di erent sectors of the economy and demand are known. These assumptions heavily depend on the information obtained from the industries. Hence uncertainties are involved in this information. The aim of this work is to model these uncertainties and to address them by fuzzy entries such as fuzzy numbers and LR-type fuzzy numbers (triangular and trapezoidal). Fuzzy linear system has been developed using fuzzy data and it is solved using Gauss-Seidel algorithm. Numerical examples show the e ciency of this algorithm. The famous example from Prof. Leontief, where he solved the production levels for U.S. economy in 1958, is also further analyzed.

Hierarchical Mixtures of Experts and the EM Algorithm

We present a tree-structured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models (GLIM's). Learning is treated as a maximum likelihood problem; in particular, we present an Expectation-Maximization (EM) algorithm for adjusting the parameters of the architecture. We also develop an on-line learning algorithm in which the parameters are updated incrementally. Comparative simulation results are presented in the robot dynamics domain.

Protochordate Zic genes define primitive somite compartments and highlight molecular changes underlying neural crest evolution

The vertebrate Zic gene family encodes C2H2 zinc finger transcription factors closely related to the Gli proteins. Zic genes are expressed in multiple areas of developing vertebrate embryos, including the dorsal neural tube where they act as potent neural crest inducers. Here we describe the characterization of a Zic ortholog from the amphioxus Branchiostoma floridae and further describe the expression of a Zic ortholog from the ascidian Ciona intestinalis. Molecular phylogenetic analysis and sequence comparisons suggest the gene duplications that formed the vertebrate Zic family were specific to the vertebrate lineage. In Ciona maternal CiZic/Ci-macho1 transcripts are localized during cleavage stages by asymmetric cell division, whereas zygotic expression by neural plate cells commences during neurulation. The amphioxus Zic ortholog AmphiZic is expressed in dorsal mesoderm and ectoderm during gastrulation, before being eliminated first from midline cells and then from all neurectoderm during neurulation. After neurulation, expression is reactivated in the dorsal neural tube and dorsolateral somite. Comparison of CiZic and AmphiZic expression with vertebrate Zic expression leads to two main conclusions. First, Zic expression allows us to define homologous compartments between vertebrate and amphioxus somites, showing primitive subdivision of vertebrate segmented mesoderm. Second, we show that neural Zic expression is a chordate synapomorphy, whereas the precise pattern of neural expression has evolved differently on the different chordate lineages. Based on these observations we suggest that a change in Zic regulation, specifically the evolution of a dorsal neural expression domain in vertebrate neurulae, was an important step in the evolution of the neural crest.

A neural network enhanced generalized minimum variance self-tuning proportional, integral and derivative control algorithm for complex dynamic systems

A neural network enhanced proportional, integral and derivative (PID) controller is presented that combines the attributes of neural network learning with a generalized minimum-variance self-tuning control (STC) strategy. The neuro PID controller is structured with plant model identification and PID parameter tuning. The plants to be controlled are approximated by an equivalent model composed of a simple linear submodel to approximate plant dynamics around operating points, plus an error agent to accommodate the errors induced by linear submodel inaccuracy due to non-linearities and other complexities. A generalized recursive least-squares algorithm is used to identify the linear submodel, and a layered neural network is used to detect the error agent in which the weights are updated on the basis of the error between the plant output and the output from the linear submodel. The procedure for controller design is based on the equivalent model, and therefore the error agent is naturally functioned within the control law. In this way the controller can deal not only with a wide range of linear dynamic plants but also with those complex plants characterized by severe non-linearity, uncertainties and non-minimum phase behaviours. Two simulation studies are provided to demonstrate the effectiveness of the controller design procedure.

Near real-time flood detection in urban and rural areas using high resolution Synthetic Aperture Radar images

A near real-time flood detection algorithm giving a synoptic overview of the extent of flooding in both urban and rural areas, and capable of working during night-time and day-time even if cloud was present, could be a useful tool for operational flood relief management. The paper describes an automatic algorithm using high resolution Synthetic Aperture Radar (SAR) satellite data that builds on existing approaches, including the use of image segmentation techniques prior to object classification to cope with the very large number of pixels in these scenes. Flood detection in urban areas is guided by the flood extent derived in adjacent rural areas. The algorithm assumes that high resolution topographic height data are available for at least the urban areas of the scene, in order that a SAR simulator may be used to estimate areas of radar shadow and layover. The algorithm proved capable of detecting flooding in rural areas using TerraSAR-X with good accuracy, and in urban areas with reasonable accuracy. The accuracy was reduced in urban areas partly because of TerraSAR-X’s restricted visibility of the ground surface due to radar shadow and layover.

Near real-time flood detection in urban and rural areas using high resolution Synthetic Aperture Radar images

A near real-time flood detection algorithm giving a synoptic overview of the extent of flooding in both urban and rural areas, and capable of working during night-time and day-time even if cloud was present, could be a useful tool for operational flood relief management. The paper describes an automatic algorithm using high resolution Synthetic Aperture Radar (SAR) satellite data that builds on existing approaches, including the use of image segmentation techniques prior to object classification to cope with the very large number of pixels in these scenes. Flood detection in urban areas is guided by the flood extent derived in adjacent rural areas. The algorithm assumes that high resolution topographic height data are available for at least the urban areas of the scene, in order that a SAR simulator may be used to estimate areas of radar shadow and layover. The algorithm proved capable of detecting flooding in rural areas using TerraSAR-X with good accuracy, classifying 89% of flooded pixels correctly, with an associated false positive rate of 6%. Of the urban water pixels visible to TerraSAR-X, 75% were correctly detected, with a false positive rate of 24%. If all urban water pixels were considered, including those in shadow and layover regions, these figures fell to 57% and 18% respectively.

Proteomic analyses of a Listeria monocytogenes mutant lacking σB identify new components of the σB regulon and highlight a role for σB in the utilization of glycerol

In Listeria monocytogenes the alternative sigma factor σB plays important roles in both virulence and stress tolerance. In this study a proteomic approach was used to define components of the σB regulon in L. monocytogenes 10403S (serotype 1/2a). Using two-dimensional gel electrophoresis and the recently developed isobaric tags for relative and absolute quantitation technique, the protein expression profiles of the wild type and an isogenic ΔsigB deletion strain were compared. Overall, this study identified 38 proteins whose expression was σB dependent; 17 of these proteins were found to require the presence of σB for full expression, while 21 were expressed at a higher level in the ΔsigB mutant background. The data obtained with the two proteomic approaches showed limited overlap (four proteins were identified by both methods), a finding that highlights the complementarity of the two technologies. Overall, the proteomic data reaffirmed a role for σB in the general stress response and highlighted a probable role for σB in metabolism, especially in the utilization of alternative carbon sources. Proteomic and physiological data revealed the involvement of σB in glycerol metabolism. Five newly identified members of the σB regulon were shown to be under direct regulation of σB using reverse transcription-PCR (RT-PCR), while random amplification of cDNA ends-PCR was used to map four σB-dependent promoters upstream from lmo0796, lmo1830, lmo2391, and lmo2695. Using RT-PCR analysis of known and newly identified σB-dependent genes, as well as proteomic analyses, σB was shown to play a major role in the stationary phase of growth in complex media.

An unstructured CVFEM and moving interface algorithm for non-Newtonian Hele-Shaw flows in injection molding

Purpose - The purpose of this paper is to develop a novel unstructured simulation approach for injection molding processes described by the Hele-Shaw model. Design/methodology/approach - The scheme involves dual dynamic meshes with active and inactive cells determined from an initial background pointset. The quasi-static pressure solution in each timestep for this evolving unstructured mesh system is approximated using a control volume finite element method formulation coupled to a corresponding modified volume of fluid method. The flow is considered to be isothermal and non-Newtonian. Findings - Supporting numerical tests and performance studies for polystyrene described by Carreau, Cross, Ellis and Power-law fluid models are conducted. Results for the present method are shown to be comparable to those from other methods for both Newtonian fluid and polystyrene fluid injected in different mold geometries. Research limitations/implications - With respect to the methodology, the background pointset infers a mesh that is dynamically reconstructed here, and there are a number of efficiency issues and improvements that would be relevant to industrial applications. For instance, one can use the pointset to construct special bases and invoke a so-called ""meshless"" scheme using the basis. This would require some interesting strategies to deal with the dynamic point enrichment of the moving front that could benefit from the present front treatment strategy. There are also issues related to mass conservation and fill-time errors that might be addressed by introducing suitable projections. The general question of ""rate of convergence"" of these schemes requires analysis. Numerical results here suggest first-order accuracy and are consistent with the approximations made, but theoretical results are not available yet for these methods. Originality/value - This novel unstructured simulation approach involves dual meshes with active and inactive cells determined from an initial background pointset: local active dual patches are constructed ""on-the-fly"" for each ""active point"" to form a dynamic virtual mesh of active elements that evolves with the moving interface.

FAST, PARALLEL AND SECURE CRYPTOGRAPHY ALGORITHM USING LORENZ`S ATTRACTOR

A novel cryptography method based on the Lorenz`s attractor chaotic system is presented. The proposed algorithm is secure and fast, making it practical for general use. We introduce the chaotic operation mode, which provides an interaction among the password, message and a chaotic system. It ensures that the algorithm yields a secure codification, even if the nature of the chaotic system is known. The algorithm has been implemented in two versions: one sequential and slow and the other, parallel and fast. Our algorithm assures the integrity of the ciphertext (we know if it has been altered, which is not assured by traditional algorithms) and consequently its authenticity. Numerical experiments are presented, discussed and show the behavior of the method in terms of security and performance. The fast version of the algorithm has a performance comparable to AES, a popular cryptography program used commercially nowadays, but it is more secure, which makes it immediately suitable for general purpose cryptography applications. An internet page has been set up, which enables the readers to test the algorithm and also to try to break into the cipher.

U-curve: A branch-and-bound optimization algorithm for U-shaped cost functions on Boolean lattices applied to the feature selection problem

This paper presents the formulation of a combinatorial optimization problem with the following characteristics: (i) the search space is the power set of a finite set structured as a Boolean lattice; (ii) the cost function forms a U-shaped curve when applied to any lattice chain. This formulation applies for feature selection in the context of pattern recognition. The known approaches for this problem are branch-and-bound algorithms and heuristics that explore partially the search space. Branch-and-bound algorithms are equivalent to the full search, while heuristics are not. This paper presents a branch-and-bound algorithm that differs from the others known by exploring the lattice structure and the U-shaped chain curves of the search space. The main contribution of this paper is the architecture of this algorithm that is based on the representation and exploration of the search space by new lattice properties proven here. Several experiments, with well known public data, indicate the superiority of the proposed method to the sequential floating forward selection (SFFS), which is a popular heuristic that gives good results in very short computational time. In all experiments, the proposed method got better or equal results in similar or even smaller computational time. (C) 2009 Elsevier Ltd. All rights reserved.

Efficient robust optimization based on polynomial chaos and tabu search algorithm

The study of robust design methodologies and techniques has become a new topical area in design optimizations in nearly all engineering and applied science disciplines in the last 10 years due to inevitable and unavoidable imprecision or uncertainty which is existed in real word design problems. To develop a fast optimizer for robust designs, a methodology based on polynomial chaos and tabu search algorithm is proposed. In the methodology, the polynomial chaos is employed as a stochastic response surface model of the objective function to efficiently evaluate the robust performance parameter while a mechanism to assign expected fitness only to promising solutions is introduced in tabu search algorithm to minimize the requirement for determining robust metrics of intermediate solutions. The proposed methodology is applied to the robust design of a practical inverse problem with satisfactory results.