Water exchange between the subglacial Lake Vostok and the overlying ice sheet

It has now been known for several years that a 200-km-long lake, called Lake Vostok, lies beneath the ice sheet on which sits Vostok Station in Antarctica. The conditions at the base of the ice sheet above this subglacial lake can provide information about the environment within the lake, including the likelihood that it supports life. Here we present an analysis of the ice-sheet structure from airborne 60-MHz radar studies, which indicates that distinct zones of basal ice loss and accretion occur at the ice-water interface. Subglacial melting and net ice loss occur in the north of the lake and across its 200-km-long western margin, whereas about 150 m of ice is gained by subglacial freezing in the south. This indicates that significant quantities of water are exchanged between the base of the ice sheet and the lake waters, which will enrich the lake with gas hydrates, cause sediment deposition and encourage circulation of the lake water.     

A map of human genome sequence variation containing 1.42 million single nucleotide polymorphisms

We describe a map of 1.42 million single nucleotide polymorphisms (SNPs) distributed throughout the human genome, providing an average density on available sequence of one SNP every 1.9 kilobases. These SNPs were primarily discovered by two projects: The SNP Consortium and the analysis of clone overlaps by the International Human Genome Sequencing Consortium. The map integrates all publicly available SNPs with described genes and other genomic features. We estimate that 60,000 SNPs fall within exon (coding and untranslated regions), and 85% of exons are within 5 kb of the nearest SNP. Nucleotide diversity varies greatly across the genome, in a manner broadly consistent with a standard population genetic model of human history. This high-density SNP map provides a public resource for defining haplotype variation across the genome, and should help to identify biomedically important genes for diagnosis and therapy.     

Helicobacter exploits integrin for type IV secretion and kinase activation

Integrins are important mammalian receptors involved in normal cellular functions as well as pathogenesis of chronic inflammation and cancer. We propose that integrins are exploited by the gastric pathogen and type-1 carcinogen Helicobacter pylori for injection of the bacterial oncoprotein cytotoxin-associated gene A (CagA) into gastric epithelial cells. Virulent H. pylori express a type-IV secretion pilus that injects CagA into the host cell; CagA then becomes tyrosine-phosphorylated by Src family kinases. However, the identity of the host cell receptor involved in this process has remained unknown. Here we show that the H. pylori CagL protein is a specialized adhesin that is targeted to the pilus surface, where it binds to and activates integrin alpha5beta1 receptor on gastric epithelial cells through an arginine-glycine-aspartate motif. This interaction triggers CagA delivery into target cells as well as activation of focal adhesion kinase and Src. Our findings provide insights into the role of integrins in H.-pylori-induced pathogenesis. CagL may be exploited as a new molecular tool for our further understanding of integrin signalling.     

A new approach to forecasting intermittent demand based on the mixed zero-truncated Poisson model

This paper proposes a new approach to forecasting intermittent demand by considering the effects of external factors. We classify intermittent demand data into two parts—zero value and nonzero value—and fit nonzero values into a mixed zero-truncated Poisson model. All the parameters in this model are obtained by an EM algorithm, which regards external factors as independent variables of a logistic regression model and log-linear regression model. We then calculate the probability of occurrence of zero value at each period and predict demand occurrence by comparing it with critical value. When demand occurs, we use the weighted average of the mixed zero-truncated Poisson model as predicted nonzero demands, which are combined with predicted demand occurrences to form the final forecasting demand series. Two performance measures are developed to assess the forecasting methods. By presenting a case study of electric power material from the State Grid Shanghai Electric Power Company in China, we show that our approach provides greater accuracy in forecasting than the Poisson model, the hurdle shifted Poisson model, the hurdle Poisson model, and Croston's method.     

An unusual phase transition to a second liquid vortex phase in the superconductor YBa2Cu3O7

A magnetic field penetrates a superconductor through an array of 'vortices', each of which carries one quantum of flux that is surrounded by a circulating supercurrent. In this vortex state, the resistivity is determined by the dynamical properties of the vortex 'matter'. For the high-temperature copper oxide superconductors (see ref.1 for a theoretical review), the vortex phase can be a 'solid', in which the vortices are pinned, but the solid can 'melt' into a 'liquid' phase, in which their mobility gives rise to a finite resistance. (This melting phenomenon is also believed to occur in conventional superconductors, but in an experimentally inaccessible part of the phase diagram.) For the case of YBa2Cu3O7, there are indications of the existence of a critical point, at which the character of the melting changes. But neither the thermodynamic nature of the melting, nor the phase diagram in the vicinity of the critical point, has been well established. Here we report measurements of specific heat and magnetization that determine the phase diagram in this material to 26 T, well above the critical point. Our results reveal the presence of a reversible second-order transition above the critical point. An unusual feature of this transition-namely, that the high-temperature phase is the less symmetric in the sense of the Landau theory-is in accord with theoretical predictions of a transition to a second vortex-liquid phase.     

Advances in simulated modeling of vibration systems based on computational intelligence

Computational intelligence is the computational simulation of the bio-intelligence, which includes artificial neural networks, fuzzy systems and evolutionary computations. This article summarizes the state of the art in the field of simulated modeling of vibration systems using methods of computational intelligence, based on some relevant subjects and the authors' own research work. First, contributions to the applications of computational intelligence to the identification of nonlinear characteristics of packaging are reviewed. Subsequently, applications of the newly developed training algorithms for feedforward neural networks to the identification of restoring forces in multi-degree-of-freedom nonlinear systems are discussed. Finally, the neural-network-based method of model reduction for the dynamic simulation of microelectromechanical systems (MEMS) using generalized Hebbian algorithm (GHA) and robust GHA is outlined. The prospects of the simulated modeling of vibration systems using techniques of computational intelligence are also indicated.