3D time series analysis of cell shape using Laplacian approaches

Background: Fundamental cellular processes such as cell movement, division or food uptake critically depend on cells being able to change shape. Fast acquisition of three-dimensional image time series has now become possible, but we lack efficient tools for analysing shape deformations in order to understand the real three-dimensional nature of shape changes. Results: We present a framework for 3D+time cell shape analysis. The main contribution is three-fold: First, we develop a fast, automatic random walker method for cell segmentation. Second, a novel topology fixing method is proposed to fix segmented binary volumes without spherical topology. Third, we show that algorithms used for each individual step of the analysis pipeline (cell segmentation, topology fixing, spherical parameterization, and shape representation) are closely related to the Laplacian operator. The framework is applied to the shape analysis of neutrophil cells. Conclusions: The method we propose for cell segmentation is faster than the traditional random walker method or the level set method, and performs better on 3D time-series of neutrophil cells, which are comparatively noisy as stacks have to be acquired fast enough to account for cell motion. Our method for topology fixing outperforms the tools provided by SPHARM-MAT and SPHARM-PDM in terms of their successful fixing rates. The different tasks in the presented pipeline for 3D+time shape analysis of cells can be solved using Laplacian approaches, opening the possibility of eventually combining individual steps in order to speed up computations

The month of July: an early experience with pandemic influenza A (H1N1) in adults with cystic fibrosis

<p>Abstract</p> <p>Background</p> <p>Pandemic Influenza A (H1N1) 2009 is a novel viral infection that emerged in March 2009. This is the first report addressing the clinical course of patients with cystic fibrosis (CF) and H1N1 infection.</p> <p>Methods</p> <p>All patients with an influenza-like illness (ILI) attending our adult centre during July 2009 were identified. Baseline respiratory function, nutritional status, approach to management and short-term clinical course were recorded.</p> <p>Results</p> <p>Most patients experienced a mild course and were able to be managed with antiviral agents as an outpatient. Robust infection control policies were implemented to limit transmission of H1N1 infection within our CF centre. Patients with severe lung disease, poor baseline nutritional reserve and presenting with more than 48 hours of ILI experienced a more severe course. Prompt antiviral therapy within the first 48 hours of illness may have been important in improving outcomes.</p> <p>Conclusions</p> <p>This observational study demonstrates that most adults with CF with H1N1 infection had mild clinical courses and recovered rapidly.</p

Quantitative utilization of prior biological knowledge in the Bayesian network modeling of gene expression data

<p>Abstract</p> <p>Background</p> <p>Bayesian Network (BN) is a powerful approach to reconstructing genetic regulatory networks from gene expression data. However, expression data by itself suffers from high noise and lack of power. Incorporating prior biological knowledge can improve the performance. As each type of prior knowledge on its own may be incomplete or limited by quality issues, integrating multiple sources of prior knowledge to utilize their consensus is desirable.</p> <p>Results</p> <p>We introduce a new method to incorporate the quantitative information from multiple sources of prior knowledge. It first uses the Naïve Bayesian classifier to assess the likelihood of functional linkage between gene pairs based on prior knowledge. In this study we included cocitation in PubMed and schematic similarity in Gene Ontology annotation. A candidate network edge reservoir is then created in which the copy number of each edge is proportional to the estimated likelihood of linkage between the two corresponding genes. In network simulation the Markov Chain Monte Carlo sampling algorithm is adopted, and samples from this reservoir at each iteration to generate new candidate networks. We evaluated the new algorithm using both simulated and real gene expression data including that from a yeast cell cycle and a mouse pancreas development/growth study. Incorporating prior knowledge led to a ~2 fold increase in the number of known transcription regulations recovered, without significant change in false positive rate. In contrast, without the prior knowledge BN modeling is not always better than a random selection, demonstrating the necessity in network modeling to supplement the gene expression data with additional information.</p> <p>Conclusion</p> <p>our new development provides a statistical means to utilize the quantitative information in prior biological knowledge in the BN modeling of gene expression data, which significantly improves the performance.</p

Accuracy of genomic breeding values in multi-breed dairy cattle populations

<p>Abstract</p> <p>Background</p> <p>Two key findings from genomic selection experiments are 1) the reference population used must be very large to subsequently predict accurate genomic estimated breeding values (GEBV), and 2) prediction equations derived in one breed do not predict accurate GEBV when applied to other breeds. Both findings are a problem for breeds where the number of individuals in the reference population is limited. A multi-breed reference population is a potential solution, and here we investigate the accuracies of GEBV in Holstein dairy cattle and Jersey dairy cattle when the reference population is single breed or multi-breed. The accuracies were obtained both as a function of elements of the inverse coefficient matrix and from the realised accuracies of GEBV.</p> <p>Methods</p> <p>Best linear unbiased prediction with a multi-breed genomic relationship matrix (GBLUP) and two Bayesian methods (BAYESA and BAYES_SSVS) which estimate individual SNP effects were used to predict GEBV for 400 and 77 young Holstein and Jersey bulls respectively, from a reference population of 781 and 287 Holstein and Jersey bulls, respectively. Genotypes of 39,048 SNP markers were used. Phenotypes in the reference population were de-regressed breeding values for production traits. For the GBLUP method, expected accuracies calculated from the diagonal of the inverse of coefficient matrix were compared to realised accuracies.</p> <p>Results</p> <p>When GBLUP was used, expected accuracies from a function of elements of the inverse coefficient matrix agreed reasonably well with realised accuracies calculated from the correlation between GEBV and EBV in single breed populations, but not in multi-breed populations. When the Bayesian methods were used, realised accuracies of GEBV were up to 13% higher when the multi-breed reference population was used than when a pure breed reference was used. However no consistent increase in accuracy across traits was obtained.</p> <p>Conclusion</p> <p>Predicting genomic breeding values using a genomic relationship matrix is an attractive approach to implement genomic selection as expected accuracies of GEBV can be readily derived. However in multi-breed populations, Bayesian approaches give higher accuracies for some traits. Finally, multi-breed reference populations will be a valuable resource to fine map QTL.</p

Electro-thermal modelling for plasmonic structures in the TLM Method

This paper presents a coupled electromagnetic-thermal model for modelling temperature evolution in nano-size plasmonic heat sources. Both electromagnetic and thermal models are based on the Transmission Line Modelling (TLM) method and are coupled through a nonlinear and dispersive plasma material model. The stability and accuracy of the coupled EM-thermal model is analysed in the context of a nano-tip plasmonic heat source example

Techniques for accurate protein identification in shotgun proteomic studies of human, mouse, bovine, and chicken lenses

Analysis of shotgun proteomics datasets requires techniques to distinguish correct peptide identifications from incorrect identifications, such as linear discriminant functions and target/decoy protein databases. We report an efficient, flexible proteomic analysis workflow pipeline that implements these techniques to control both peptide and protein false discovery rates. We demonstrate its performance by analyzing two-dimensional liquid chromatography separations of lens proteins from human, mouse, bovine, and chicken lenses. We compared the use of International Protein Index databases to UniProt databases and no-enzyme SEQUEST searches to tryptic searches. Sequences present in the International Protein Index databases allowed detection of several novel crystallins. An alternate start codon isoform of βA4 was found in human lens. The minor crystallin γN was detected for the first time in bovine and chicken lenses. Chicken γS was identified and is the first member of the γ-crystallin family observed in avian lenses

Phenotypic Signatures Arising from Unbalanced Bacterial Growth

Fluctuations in the growth rate of a bacterial culture during unbalanced growth are generally considered undesirable in quantitative studies of bacterial physiology. Under well-controlled experimental conditions, however, these fluctuations are not random but instead reflect the interplay between intra-cellular networks underlying bacterial growth and the growth environment. Therefore, these fluctuations could be considered quantitative phenotypes of the bacteria under a specific growth condition. Here, we present a method to identify “phenotypic signatures” by time-frequency analysis of unbalanced growth curves measured with high temporal resolution. The signatures are then applied to differentiate amongst different bacterial strains or the same strain under different growth conditions, and to identify the essential architecture of the gene network underlying the observed growth dynamics. Our method has implications for both basic understanding of bacterial physiology and for the classification of bacterial strains

Autistic behavior in boys with fragile X syndrome: social approach and HPA-axis dysfunction

The primary goal of this study was to examine environmental and neuroendocrine factors that convey increased risk for elevated autistic behavior in boys with Fragile X syndrome (FXS). This study involves three related analyses: (1) examination of multiple dimensions of social approach behaviors and how they vary over time, (2) investigation of mean levels and modulation of salivary cortisol levels in response to social interaction, and (3) examination of the relationship of social approach and autistic behaviors to salivary cortisol. Poor social approach and elevated baseline and regulation cortisol are discernible traits that distinguish boys with FXS and ASD from boys with FXS only and from typically developing boys. In addition, blunted cortisol change is associated with increased severity of autistic behaviors only within the FXS and ASD group. Boys with FXS and ASD have distinct behavioral and neuroendocrine profiles that differentiate them from those with FXS alone and typically developing boys

D-β-Hydroxybutyrate Is Protective in Mouse Models of Huntington's Disease

Abnormalities in mitochondrial function and epigenetic regulation are thought to be instrumental in Huntington's disease (HD), a fatal genetic disorder caused by an expanded polyglutamine track in the protein huntingtin. Given the lack of effective therapies for HD, we sought to assess the neuroprotective properties of the mitochondrial energizing ketone body, D-β-hydroxybutyrate (DβHB), in the 3-nitropropionic acid (3-NP) toxic and the R6/2 genetic model of HD. In mice treated with 3-NP, a complex II inhibitor, infusion of DβHB attenuates motor deficits, striatal lesions, and microgliosis in this model of toxin induced-striatal neurodegeneration. In transgenic R6/2 mice, infusion of DβHB extends life span, attenuates motor deficits, and prevents striatal histone deacetylation. In PC12 cells with inducible expression of mutant huntingtin protein, we further demonstrate that DβHB prevents histone deacetylation via a mechanism independent of its mitochondrial effects and independent of histone deacetylase inhibition. These pre-clinical findings suggest that by simultaneously targeting the mitochondrial and the epigenetic abnormalities associated with mutant huntingtin, DβHB may be a valuable therapeutic agent for HD