Estimating survival in continuous space from mark-dead-recovery data - Towards a continuous version of the multinomial dead recovery model.

Understanding spatially varying survival is crucial for understanding the ecology and evolution of migratory animals, which may ultimately help to conserve such species. We develop an approach to estimate an annual survival probability function varying continuously in geographic space, if the recovery probability is constant over space. This estimate is based on a density function over continuous geographic space and the discrete age at death obtained from dead recovery data.

Estimation in the multinomial reencounter model - Where do migrating animals go and how do they survive in their destination area?

Spatial variation in survival has individual fitness consequences and influences population dynamics. Which space animals use during the annual cycle determines how they are affected by this spatial variability. Therefore, knowing spatial patterns of survival and space use is crucial to understand demography of migrating animals.

A Fitted Sparse-Group Lasso for Genome-Based Evaluations.

In life sciences, high-throughput techniques typically lead to high-dimensional data and often the number of covariates is much larger than the number of observations. This inherently comes with multicollinearity challenging a statistical analysis in a linear regression framework. Penalization methods such as the lasso, ridge regression, the group lasso, and convex combinations thereof, which introduce additional conditions on regression variables, have proven themselves effective.

Detection of ssp. in Cultures From Fecal and Tissue Samples Using VOC Analysis and Machine Learning Tools.

Analysis of volatile organic compounds (VOCs) is a novel approach to accelerate bacterial culture diagnostics of subsp. (MAP). In the present study, cultures of fecal and tissue samples from MAP-infected and non-suspect dairy cattle and goats were explored to elucidate the effects of sample matrix and of animal species on VOC emissions during bacterial cultivation and to identify early markers for bacterial growth.

Seagull: lasso, group lasso and sparse-group lasso regularization for linear regression models via proximal gradient descent.

Background: Statistical analyses of biological problems in life sciences often lead to high-dimensional linear models. To solve the corresponding system of equations, penalization approaches are often the methods of choice. They are especially useful in case of multicollinearity, which appears if the number of explanatory variables exceeds the number of observations or for some biological reason.

Non-invasive and label-free 3D-visualization shows in vivo oligomerization of the staphylococcal alkaline shock protein 23 (Asp23).

Fluorescence-tags, commonly used to visualize the spatial distribution of proteins within cells, can influence the localization of the tagged proteins by affecting their stability, interaction with other proteins or the induction of oligomerization artifacts. To circumvent these obstacles, a protocol was developed to generate 50 nm thick serial sections suitable for immunogold labeling and subsequent reconstruction of the spatial distribution of immuno-labeled native proteins within individual bacterial cells. Applying this method, we show a cellular distribution of the staphylococcal alkaline shock protein 23 (Asp23), which is compatible with filament formation, a property of Asp23 that we also demonstrate in vitro.

Substrate specificity of thioredoxins and glutaredoxins - towards a functional classification.

The spatio-temporal reduction and oxidation of protein thiols is an essential mechanism in signal transduction in all kingdoms of life. Thioredoxin (Trx) family proteins efficiently catalyze thiol-disulfide exchange reactions and the proteins are widely recognized for their importance in the operation of thiol switches. Trx family proteins have a broad and at the same time very distinct substrate specificity - a prerequisite for redox switching.

An approximate Bayesian significance test for genomic evaluations.

Genomic information can be used to study the genetic architecture of some trait. Not only the size of the genetic effect captured by molecular markers and their position on the genome but also the mode of inheritance, which might be additive or dominant, and the presence of interactions are interesting parameters. When searching for interacting loci, estimating the effect size and determining the significant marker pairs increases the computational burden in terms of speed and memory allocation dramatically.

The Rosenzweig-MacArthur system via reduction of an individual based model.

The Rosenzweig-MacArthur system is a particular case of the Gause model, which is widely used to describe predator-prey systems. In the classical derivation, the interaction terms in the differential equation are essentially derived from considering handling time vs. search time, and moreover there exist derivations in the literature which are based on quasi-steady state assumptions.

New Gromov-Inspired Metrics on Phylogenetic Tree Space.

We present a new class of metrics for unrooted phylogenetic X-trees inspired by the Gromov-Hausdorff distance for (compact) metric spaces. These metrics can be efficiently computed by linear or quadratic programming. They are robust under NNI operations, too.

Strategies for the identification of disease-related patterns of volatile organic compounds: prediction of paratuberculosis in an animal model using random forests.

Modern statistical methods which were developed for pattern recognition are increasingly being used for data analysis in studies on emissions of volatile organic compounds (VOCs). With the detection of disease-related VOC profiles, novel non-invasive diagnostic tools could be developed for clinical applications. However, it is important to bear in mind that not all statistical methods are equally suitable for the investigation of VOC profiles.

Revealing complex function, process and pathway interactions with high-throughput expression and biological annotation data.

The biological relationships both between and within the functions, processes and pathways that operate within complex biological systems are only poorly characterized, making the interpretation of large scale gene expression datasets extremely challenging. Here, we present an approach that integrates gene expression and biological annotation data to identify and describe the interactions between biological functions, processes and pathways that govern a phenotype of interest. The product is a global, interconnected network, not of genes but of functions, processes and pathways, that represents the biological relationships within the system.

Costs of life - Dynamics of the protein inventory of Staphylococcus aureus during anaerobiosis.

Absolute protein quantification was applied to follow the dynamics of the cytoplasmic proteome of Staphylococcus aureus in response to long-term oxygen starvation. For 1,168 proteins, the majority of all expressed proteins, molecule numbers per cell have been determined to monitor the cellular investments in single branches of bacterial life for the first time. In the presence of glucose the anaerobic protein pattern is characterized by increased amounts of glycolytic and fermentative enzymes such as Eno, GapA1, Ldh1, and PflB.

An efficient level set method for simultaneous intensity inhomogeneity correction and segmentation of MR images.

Intensity inhomogeneity (bias field) is a common artefact in magnetic resonance (MR) images, which hinders successful automatic segmentation. In this work, a novel algorithm for simultaneous segmentation and bias field correction is presented. The proposed energy functional allows for explicit regularization of the bias field term, making the model more flexible, which is crucial in presence of strong inhomogeneities.

Associations of circulating plasma microRNAs with age, body mass index and sex in a population-based study.

Background: Non-cellular blood circulating microRNAs (plasma miRNAs) represent a promising source for the development of prognostic and diagnostic tools owing to their minimally invasive sampling, high stability, and simple quantification by standard techniques such as RT-qPCR. So far, the majority of association studies involving plasma miRNAs were disease-specific case-control analyses. In contrast, in the present study, plasma miRNAs were analysed in a sample of 372 individuals from a population-based cohort study, the Study of Health in Pomerania (SHIP).

Microarray-based identification of human antibodies against Staphylococcus aureus antigens.

Purpose: The mortality rate of patients with Staphylococcus aureus infections is alarming and urgently demands new strategies to attenuate the course of these infections or to detect them at earlier stages.

Experimental Design: To study the adaptive immune response to S. aureus antigens in healthy human volunteers, a protein microarray containing 44 S.

A level set based framework for quantitative evaluation of breast tissue density from MRI data.

Breast density is a risk factor associated with the development of breast cancer. Usually, breast density is assessed on two dimensional (2D) mammograms using the American College of Radiology (ACR) classification. Magnetic resonance imaging (MRI) is a non-radiation based examination method, which offers a three dimensional (3D) alternative to classical 2D mammograms.

CellFateScout - a bioinformatics tool for elucidating small molecule signaling pathways that drive cells in a specific direction.

Background: Small molecule effects can be represented by active signaling pathways within functional networks. Identifying these can help to design new strategies to utilize known small molecules, e.g.

Identifying genes relevant to specific biological conditions in time course microarray experiments.

Microarrays have been useful in understanding various biological processes by allowing the simultaneous study of the expression of thousands of genes. However, the analysis of microarray data is a challenging task. One of the key problems in microarray analysis is the classification of unknown expression profiles.

An algebraic analysis of the two state Markov model on tripod trees.

Methods of phylogenetic inference use more and more complex models to generate trees from data. However, even simple models and their implications are not fully understood. Here, we investigate the two-state Markov model on a tripod tree, inferring conditions under which a given set of observations gives rise to such a model.

Prior shape level set segmentation on multistep generated probability maps of MR datasets for fully automatic kidney parenchyma volumetry.

Fully automatic 3-D segmentation techniques for clinical applications or epidemiological studies have proven to be a very challenging task in the domain of medical image analysis. 3-D organ segmentation on magnetic resonance (MR) datasets requires a well-designed segmentation strategy due to imaging artifacts, partial volume effects, and similar tissue properties of adjacent tissues. We developed a 3-D segmentation framework for fully automatic kidney parenchyma volumetry that uses Bayesian concepts for probability map generation.

On a class of deterministic population models with stochastic foundation.

Generalising a site-based stochastic model due to Royama, Solé et al. and Sumpter et al., we investigate competition in a single species with discrete, non-overlapping generations.

Autocatalytic genetic networks modeled by piecewise-deterministic Markov processes.

In the present work we propose an alternative approach to model autocatalytic networks, called piecewise-deterministic Markov processes. These were originally introduced by Davis in 1984. Such a model allows for random transitions between the active and inactive state of a gene, whereas subsequent transcription and translation processes are modeled in a deterministic manner.

Hybrid modeling of noise reduction by a negatively autoregulated system.

We analyze the reduction of intrinsic noise caused by transition of a promoter between its active and inactive state in a negatively regulated genetic network, i.e., transcription of the gene is inhibited by its own gene product.

Transcription, intercellular variability and correlated random walk.

We develop a simple model for the random distribution of a gene product. It is assumed that the only source of variance is due to switching transcription on and off by a random process. Under the condition that the transition rates between on and off are constant we find that the amount of mRNA follows a scaled Beta distribution.