Evolutionary status of Entamoeba

In addition to its medical importance as parasitic pathogen, Entamoeba has aroused people's interest in its evolutionary status for a long time. Lacking mitochondrion and other intracellular organelles common to typical eukaryotes, Entamoeba and several other amitochondrial protozoans have been recognized as ancient pre-mitochondriate eukaryotes and named "archezoa", the most primitive extant eukaryotes. It was suggested that they might be living fossils that remained in a primitive stage of evolution before acquisition of organelles, lying close to the transition between prokaryotes and eukaryotes. However, recent studies revealed that Entamoeba contained an organelle, "crypton" or "mitosome", which was regarded as specialized or reductive mitochondrion. Relative molecular phylogenetic analyses also indicated the existence or the probable existence of mitochondrion in Entamoeba. Our phylogenetic analysis based on DNA topoisomerase II strongly suggested its divergence after some mitchondriate eukaryotes. Here, all these recent researches are reviewed and the evolutionary status of Entamoeba is discussed.

Explosion pressure prediction via polynomial mathematical correlation based on advanced CFD Modelling

Computational Fluid Dynamics CFD can be used as a powerful tool supporting engineers throughout the steps of the design. The combination of CFD with response surface methodology can play an important role in such cases. During the conceptual engineering design phase, a quick response is always a matter of urgency. During this phase even a sketch of the geometrical model is rare. Therefore, the utilisation of typical response surface developed for congested and confined environment rather than CFD can be an important tool to help the decision making process, when the geometrical model is not available, provided that similarities can be considered when taking into account the characteristic of the geometry in which the response surface was developed. The present work investigates how three different types of response surfaces behave when predicting overpressure in accidental scenarios based on CFD input. First order, partial second order and complete second order polynomial expressions are investigated. The predicted results are compared with CFD findings for a classical offshore experiment conducted by British Gas on behalf of Mobil and good agreement is observed for higher order response surfaces. The higher order response surface calculations are also compared with CFD calculations for a typical offshore module and good agreement is also observed. © 2011 Elsevier Ltd.

SuperJunction IGBTS: An evolutionary step of silicon power devices with high impact potential

15 years ago the vertical SuperJunction (SJ) concept conceived for SJ power MOSFETs was the last, major breakthrough in the field of silicon power devices. Today, the SuperJunction MOSFET technologies have reached a mature stage characterized by gradual performance improvements. SuperJunction Insulated Gate Bipolar Transistors (SJ IGBTs) could interrupt this stagnation holding promise to revitalize voltage classes from 600 up to 1200 V. Such SJ IGBTs surpass by a very significant margin their SJ MOSFET counterparts both in terms of power handling capability, on-state and turn-off losses, all at the same time. On the higher end of the voltage class, SJ IGBTs would top the performance of 1.2 kV IGBTs by a similar margin. © 2012 IEEE.

Structure Discovery in Nonparametric Regression through Compositional Kernel Search

Despite its importance, choosing the structural form of the kernel in nonparametric regression remains a black art. We define a space of kernel structures which are built compositionally by adding and multiplying a small number of base kernels. We present a method for searching over this space of structures which mirrors the scientific discovery process. The learned structures can often decompose functions into interpretable components and enable long-range extrapolation on time-series datasets. Our structure search method outperforms many widely used kernels and kernel combination methods on a variety of prediction tasks.

Robust network reconstruction in polynomial time

This paper presents an efficient algorithm for robust network reconstruction of Linear Time-Invariant (LTI) systems in the presence of noise, estimation errors and unmodelled nonlinearities. The method here builds on previous work [1] on robust reconstruction to provide a practical implementation with polynomial computational complexity. Following the same experimental protocol, the algorithm obtains a set of structurally-related candidate solutions spanning every level of sparsity. We prove the existence of a magnitude bound on the noise, which if satisfied, guarantees that one of these structures is the correct solution. A problem-specific model-selection procedure then selects a single solution from this set and provides a measure of confidence in that solution. Extensive simulations quantify the expected performance for different levels of noise and show that significantly more noise can be tolerated in comparison to the original method. © 2012 IEEE.

Convex relaxation of mixture regression with efficient algorithms

We develop a convex relaxation of maximum a posteriori estimation of a mixture of regression models. Although our relaxation involves a semidefinite matrix variable, we reformulate the problem to eliminate the need for general semidefinite programming. In particular, we provide two reformulations that admit fast algorithms. The first is a max-min spectral reformulation exploiting quasi-Newton descent. The second is a min-min reformulation consisting of fast alternating steps of closed-form updates. We evaluate the methods against Expectation-Maximization in a real problem of motion segmentation from video data.