A study on orthogonality sampling.

The goal of this paper is to study and further develop the orthogonality sampling or stationary waves algorithm for the detection of the location and shape of objects from the far field pattern of scattered waves in electromagnetics or acoustics. Orthogonality sampling can be seen as a special beam forming algorithm with some links to the point source method and to the linear sampling method. The basic idea of orthogonality sampling is to sample the space under consideration by calculating scalar products of the measured far field pattern , with a test function for all y in a subset Q of the space , m = 2, 3. The way in which this is carried out is important to extract the information which the scattered fields contain. The theoretical foundation of orthogonality sampling is only partly resolved, and the goal of this work is to initiate further research by numerical demonstration of the high potential of the approach. We implement the method for a two-dimensional setting for the Helmholtz equation, which represents electromagnetic scattering when the setup is independent of the third coordinate. We show reconstructions of the location and shape of objects from measurements of the scattered field for one or several directions of incidence and one or many frequencies or wave numbers, respectively. In particular, we visualize the indicator function both with the Dirichlet and Neumann boundary condition and for complicated inhomogeneous media.

Multiple routes to mammalian diversity

The radiation of the mammals provides a 165-million-year test case for evolutionary theories of how species occupy and then fill ecological niches. It is widely assumed that species often diverge rapidly early in their evolution, and that this is followed by a longer, drawn-out period of slower evolutionary fine-tuning as natural selection fits organisms into an increasingly occupied niche space1,2. But recent studies have hinted that the process may not be so simple3–5. Here we apply statistical methods that automatically detect temporal shifts in the rate of evolution through time to a comprehensive mammalian phylogeny6 and data set7 of body sizes of 3,185 extant species. Unexpectedly, the majority of mammal species, including two of the most speciose orders (Rodentia and Chiroptera), have no history of substantial and sustained increases in the rates of evolution. Instead, a subset of the mammals has experienced an explosive increase (between 10- and 52-fold) in the rate of evolution along the single branch leading to the common ancestor of their monophyletic group (for example Chiroptera), followed by a quick return to lower or background levels. The remaining species are a taxonomically diverse assemblage showing a significant, sustained increase or decrease in their rates of evolution. These results necessarily decouple morphological diversification from speciation and suggest that the processes that give rise to the morphological diversity of a class of animals are far more free to vary than previously considered. Niches do not seem to fill up, and diversity seems to arise whenever, wherever and at whatever rate it is advantageous.

Distributed hoeffding trees for pocket data mining

Collaborative mining of distributed data streams in a mobile computing environment is referred to as Pocket Data Mining PDM. Hoeffding trees techniques have been experimentally and analytically validated for data stream classification. In this paper, we have proposed, developed and evaluated the adoption of distributed Hoeffding trees for classifying streaming data in PDM applications. We have identified a realistic scenario in which different users equipped with smart mobile devices run a local Hoeffding tree classifier on a subset of the attributes. Thus, we have investigated the mining of vertically partitioned datasets with possible overlap of attributes, which is the more likely case. Our experimental results have validated the efficiency of our proposed model achieving promising accuracy for real deployment.

Protease-activated receptor 2 sensitizes TRPV1 by protein kinase Cepsilon- and A-dependent mechanisms in rats and mice

Proteases that are released during inflammation and injury cleave protease-activated receptor 2 (PAR2) on primary afferent neurons to cause neurogenic inflammation and hyperalgesia. PAR2-induced thermal hyperalgesia depends on sensitization of transient receptor potential vanilloid receptor 1 (TRPV1), which is gated by capsaicin, protons and noxious heat. However, the signalling mechanisms by which PAR2 sensitizes TRPV1 are not fully characterized. Using immunofluorescence and confocal microscopy, we observed that PAR2 was colocalized with protein kinase (PK) Cepsilon and PKA in a subset of dorsal root ganglia neurons in rats, and that PAR2 agonists promoted translocation of PKCepsilon and PKA catalytic subunits from the cytosol to the plasma membrane of cultured neurons and HEK 293 cells. Subcellular fractionation and Western blotting confirmed this redistribution of kinases, which is indicative of activation. Although PAR2 couples to phospholipase Cbeta, leading to stimulation of PKC, we also observed that PAR2 agonists increased cAMP generation in neurons and HEK 293 cells, which would activate PKA. PAR2 agonists enhanced capsaicin-stimulated increases in [Ca2+]i and whole-cell currents in HEK 293 cells, indicating TRPV1 sensitization. The combined intraplantar injection of non-algesic doses of PAR2 agonist and capsaicin decreased the latency of paw withdrawal to radiant heat in mice, indicative of thermal hyperalgesia. Antagonists of PKCepsilon and PKA prevented sensitization of TRPV1 Ca2+ signals and currents in HEK 293 cells, and suppressed thermal hyperalgesia in mice. Thus, PAR2 activates PKCepsilon and PKA in sensory neurons, and thereby sensitizes TRPV1 to cause thermal hyperalgesia. These mechanisms may underlie inflammatory pain, where multiple proteases are generated and released.