Automatic Annotation of Spatial Expression Patterns via Sparse Bayesian Factor Models

Advances in reporters for gene expression have made it possible to document and quantify expression patterns in 2D–4D. In contrast to microarrays, which provide data for many genes but averaged and/or at low resolution, images reveal the high spatial dynamics of gene expression. Developing computational methods to compare, annotate, and model gene expression based on images is imperative, considering that available data are rapidly increasing. We have developed a sparse Bayesian factor analysis model in which the observed expression diversity of among a large set of high-dimensional images is modeled by a small number of hidden common factors. We apply this approach on embryonic expression patterns from a Drosophila RNA in situ image database, and show that the automatically inferred factors provide for a meaningful decomposition and represent common co-regulation or biological functions. The low-dimensional set of factor mixing weights is further used as features by a classifier to annotate expression patterns with functional categories. On human-curated annotations, our sparse approach reaches similar or better classification of expression patterns at different developmental stages, when compared to other automatic image annotation methods using thousands of hard-to-interpret features. Our study therefore outlines a general framework for large microscopy data sets, in which both the generative model itself, as well as its application for analysis tasks such as automated annotation, can provide insight into biological questions

Analysis of an oscillatory Painlevé-Klein apparatus with a nonholonomic constraint

In dynamics, both the concepts of rigid body and Coulomb’s law of friction are well established, although it is known at least since Painlevé’s time that they may lead to irregularities and contradictions, such as loss of uniqueness or existence of the solution of the equations of motion. The problem is still of very actual interest, since it can be of practical significance also for the industrially used rigid body codes. One of the simplest mechanical systems in which these difficulties can be well described is the Painlevé–Klein apparatus. As most other systems discussed in this context in the literature, this is a holonomic system. In the present note, we briefly examine a nonholonomic oscillatory system which is an extension of the classical Painlevé–Klein apparatus and we study its dynamics with respect to the Painlevé paradox. Both the borders of paradoxical regions and their reachability are addressed

Construction of Lyapunov functions for the estimation of basins of attraction

Technical systems are often modeled through systems of differential equations in which the parameters and initial conditions are subject to uncertainties. Usually, special solutions of the differential equations like equilibrium positions and periodic orbits are of importance and frequently the corresponding equations are only set up with the intent to describe the behavior in the vicinity of a limit cycle or an equilibrium position. For the validity of the analysis it must therefore be assumed that the initial conditions lie indeed in the basins of attraction of the corresponding attractors. In order to estimate basins of attraction, Lyapunov functions can be used. However, there are no systematic approaches available for the construction of Lyapunov functions with the goal to achieve a good approximation of the basin of attraction. The present paper suggests a method for defining appropriate Lyapunov functions using insight from center manifold theory. With this approach, not only variations in the initial conditions, but also in the parameters can be studied. The results are used to calculate the likelihood for the system to reach a certain attractor assuming different random distributions for the initial conditions

Metagenomic Next-Generation Sequencing Reveal Presence of a Novel Ungulate Bocaparvovirus in Alpacas

Viruses belonging to the genus Bocaparvovirus (BoV) are a genetically diverse group of DNA viruses known to cause respiratory, enteric, and neurological diseases in animals, including humans. An intestinal sample from an alpaca (Vicugna pacos) herd with reoccurring diarrhea and respiratory disease was submitted for next-generation sequencing, revealing the presence of a BoV strain. The alpaca BoV strain (AlBoV) had a 58.58% whole genome nucleotide percent identity to a camel BoV from Dubai, belonging to a tentative ungulate BoV 8 species (UBoV8). Recombination events were lacking with other UBoV strains. The AlBoV genome was comprised of the NS1, NP1, and VP1 proteins. The NS1 protein had the highest amino acid percent identity range (57.89–67.85%) to the members of UBoV8, which was below the 85% cut-off set by the International Committee on Taxonomy of Viruses. The low NS1 amino acid identity suggests that AlBoV is a tentative new species. The whole genome, NS1, NP1, and VP1 phylogenetic trees illustrated distinct branching of AlBoV, sharing a common ancestor with UBoV8. Walker loop and Phospholipase A2 (PLA2) motifs that are vital for virus infectivity were identified in NS1 and VP1 proteins, respectively. Our study reports a novel BoV strain in an alpaca intestinal sample and highlights the need for additional BoV research