A study of online and blockwise updating of the EM algorithm for Gaussian mixtures.

A variant of the EM algorithm for the estimation of multivariate Gaussian mixtures, which allows for online as well as blockwise updating of sequentially obtained parameter estimates, is investigated. Several dierent update schemes are considered and compared, and the benets of articially performing EM in batches, even though all data are available, are discussed

Bayesian shape modelling of cross-sectional geological data.

Shape information is of great importance in many applications. For example, the oil-bearing capacity of sand bodies, the subterranean remnants of ancient rivers, is related to their cross-sectional shapes. The analysis of these shapes is therefore of some interest, but current classifications are simplistic and ad hoc. In this paper, we describe the first steps towards a coherent statistical analysis of these shapes by deriving the integrated likelihood for data shapes given class parameters. The result is of interest beyond this particular application

Bivariate estimation of distribution algorithms for protein structure prediction.

A real-valued bivariate ‘Estimation of Distribution Algorithm’ specific for the ab initio and full-atom Protein Structure Prediction problem is proposed. It is known that this is a multidimensional and multimodal problem. In order to deal with the multimodality and the correlation of dihedral angles φ and ψ, we developed approaches based on Kernel Density Estimation and Finite Gaussian Mixtures. Simulation results have shown that both techniques are promising when applied to that problem

A study of online and blockwise updating of the EM algorithm for Gaussian mixtures

A variant of the EM algorithm for the estimation of multivariate Gaussian mixtures, which allows for online as well as blockwise updating of sequentially obtained parameter estimates, is investigated. Several dierent update schemes are considered and compared, and the benets of articially performing EM in batches, even though all data are available, are discussed

Bivariate Estimation of Distribution Algorithms for Protein Structure Prediction

A real-valued bivariate ‘Estimation of Distribution Algorithm’ specific for the ab initio and full-atom Protein Structure Prediction problem is proposed. It is known that this is a multidimensional and multimodal problem. In order to deal with the multimodality and the correlation of dihedral angles φ and ψ, we developed approaches based on Kernel Density Estimation and Finite Gaussian Mixtures. Simulation results have shown that both techniques are promising when applied to that problem

Bayesian shape modelling of cross-sectional geological data

Shape information is of great importance in many applications. For example, the oil-bearing capacity of sand bodies, the subterranean remnants of ancient rivers, is related to their cross-sectional shapes. The analysis of these shapes is therefore of some interest, but current classifications are simplistic and ad hoc. In this paper, we describe the first steps towards a coherent statistical analysis of these shapes by deriving the integrated likelihood for data shapes given class parameters. The result is of interest beyond this particular application

RNAseq analysis of Aspergillus fumigatus in blood reveals a just wait and see resting stage behavior

Background: Invasive aspergillosis is started after germination of Aspergillus fumigatus conidia that are inhaled by susceptible individuals. Fungal hyphae can grow in the lung through the epithelial tissue and disseminate hematogenously to invade into other organs. Low fungaemia indicates that fungal elements do not reside in the bloodstream for long. Results: We analyzed whether blood represents a hostile environment to which the physiology of A. fumigatus has to adapt. An in vitro model of A. fumigatus infection was established by incubating mycelium in blood. Our model allowed to discern the changes of the gene expression profile of A. fumigatus at various stages of the infection. The majority of described virulence factors that are connected to pulmonary infections appeared not to be activated during the blood phase. Three active processes were identified that presumably help the fungus to survive the blood environment in an advanced phase of the infection: iron homeostasis, secondary metabolism, and the formation of detoxifying enzymes. Conclusions: We propose that A. fumigatus is hardly able to propagate in blood. After an early stage of sensing the environment, virtually all uptake mechanisms and energy-consuming metabolic pathways are shut-down. The fungus appears to adapt by trans-differentiation into a resting mycelial stage. This might reflect the harsh conditions in blood where A. fumigatus cannot take up sufficient nutrients to establish self-defense mechanisms combined with significant growth

Senate Executive Committee Minutes January 25, 2011

Minutes for the meeting of the Senate Executive Committee on January 25, 2011