Pectoral sound generation in the blue catfish Ictalurus furcatus

Catfishes produce pectoral stridulatory sounds by “jerk” movements that rub ridges on the dorsal process against the cleithrum. We recorded sound synchronized with high-speed video to investigate the hypothesis that blue catfish Ictalurus furcatus produce sounds by a slip–stick mechanism, previously described only in invertebrates. Blue catfish produce a variably paced series of sound pulses during abduction sweeps (pulsers) although some individuals (sliders) form longer duration sound units (slides) interspersed with pulses. Typical pulser sounds are evoked by short 1–2 ms movements with a rotation of 2°–3°. Jerks excite sounds that increase in amplitude after motion stops, suggesting constructive interference, which decays before the next jerk. Longer contact of the ridges produces a more steady-state sound in slides. Pulse pattern during stridulation is determined by pauses without movement: the spine moves during about 14 % of the abduction sweep in pulsers (~45 % in sliders) although movement appears continuous to the human eye. Spine rotation parameters do not predict pulse amplitude, but amplitude correlates with pause duration suggesting that force between the dorsal process and cleithrum increases with longer pauses. Sound production, stimulated by a series of rapid movements that set the pectoral girdle into resonance, is caused by a slip–stick mechanism

Modeling and visualizing uncertainty in gene expression clusters using Dirichlet process mixtures

Although the use of clustering methods has rapidly become one of the standard computational approaches in the literature of microarray gene expression data, little attention has been paid to uncertainty in the results obtained. Dirichlet process mixture (DPM) models provide a nonparametric Bayesian alternative to the bootstrap approach to modeling uncertainty in gene expression clustering. Most previously published applications of Bayesian model-based clustering methods have been to short time series data. In this paper, we present a case study of the application of nonparametric Bayesian clustering methods to the clustering of high-dimensional nontime series gene expression data using full Gaussian covariances. We use the probability that two genes belong to the same cluster in a DPM model as a measure of the similarity of these gene expression profiles. Conversely, this probability can be used to define a dissimilarity measure, which, for the purposes of visualization, can be input to one of the standard linkage algorithms used for hierarchical clustering. Biologically plausible results are obtained from the Rosetta compendium of expression profiles which extend previously published cluster analyses of this data

General Health Status and Its Related Factors in the Nurses Working in the Educational Hospitals of Shiraz University of Medical Sciences, Shiraz, Iran, 2011

Background: Nursing is an extremely stressful profession. Nurses are confronted with a variety of personal, communicational and organizational stresses, which affect their health. Objectives: The current study aimed to assess general health level and its related factors in employed nurses in educational hospitals affiliated to Shiraz University of medical sciences. Materials and Methods: This cross-sectional study was performed on 126 nurses and practical nurses who work in educational hospitals affiliated to Shiraz University of medical sciences selected by multi stage sampling method. Data collecting tools included demographic characteristics and General Health Questionaire-28 (GHQ-28). Descriptive statistics are presented and Chi-square test, t-test, and analysis of variance were used to analyze the data. Results: Of 126 subjects 75 (59.5%) cases were suspects of mental disorders. Also 12.7% had physical disorders, 15.9% had anxiety and sleep disorders, 8.7% had social dysfunction and 6.3% had depression. Average score of mental health was 28.4 .In this study mental health was significantly associated with job satisfaction and economic satisfaction (P < 0.05). Conclusions: With regard to significant relation between mental health, job satisfaction, and economic satisfaction, a system for educating and stress reduction counseling should be established to help nurses effectively coping with stress. Also, improving the work environment, increasing staff, increasing salary, and decreasing working hours may reduce the nurses' exposure to stressful risk factors

Discovering transcriptional modules by Bayesian data integration

Motivation: We present a method for directly inferring transcriptional modules (TMs) by integrating gene expression and transcription factor binding (ChIP-chip) data. Our model extends a hierarchical Dirichlet process mixture model to allow data fusion on a gene-by-gene basis. This encodes the intuition that co-expression and co-regulation are not necessarily equivalent and hence we do not expect all genes to group similarly in both datasets. In particular, it allows us to identify the subset of genes that share the same structure of transcriptional modules in both datasets. Results: We find that by working on a gene-by-gene basis, our model is able to extract clusters with greater functional coherence than existing methods. By combining gene expression and transcription factor binding (ChIP-chip) data in this way, we are better able to determine the groups of genes that are most likely to represent underlying TMs

Accelerating Bayesian hierarchical clustering of time series data with a randomised algorithm

We live in an era of abundant data. This has necessitated the development of new and innovative statistical algorithms to get the most from experimental data. For example, faster algorithms make practical the analysis of larger genomic data sets, allowing us to extend the utility of cutting-edge statistical methods. We present a randomised algorithm that accelerates the clustering of time series data using the Bayesian Hierarchical Clustering (BHC) statistical method. BHC is a general method for clustering any discretely sampled time series data. In this paper we focus on a particular application to microarray gene expression data. We define and analyse the randomised algorithm, before presenting results on both synthetic and real biological data sets. We show that the randomised algorithm leads to substantial gains in speed with minimal loss in clustering quality. The randomised time series BHC algorithm is available as part of the R package BHC, which is available for download from Bioconductor (version 2.10 and above) via http://bioconductor.org/packages/2.10/bioc/html/BHC.html. We have also made available a set of R scripts which can be used to reproduce the analyses carried out in this paper. These are available from the following URL. https://sites.google.com/site/randomisedbhc/

A Geometric Variational Approach to Bayesian Inference

We propose a novel Riemannian geometric framework for variational inference in Bayesian models based on the nonparametric Fisher-Rao metric on the manifold of probability density functions. Under the square-root density representation, the manifold can be identified with the positive orthant of the unit hypersphere in L2, and the Fisher-Rao metric reduces to the standard L2 metric. Exploiting such a Riemannian structure, we formulate the task of approximating the posterior distribution as a variational problem on the hypersphere based on the alpha-divergence. This provides a tighter lower bound on the marginal distribution when compared to, and a corresponding upper bound unavailable with, approaches based on the Kullback-Leibler divergence. We propose a novel gradient-based algorithm for the variational problem based on Frechet derivative operators motivated by the geometry of the Hilbert sphere, and examine its properties. Through simulations and real-data applications, we demonstrate the utility of the proposed geometric framework and algorithm on several Bayesian models

Bayesian correlated clustering to integrate multiple datasets

Motivation: The integration of multiple datasets remains a key challenge in systems biology and genomic medicine. Modern high-throughput technologies generate a broad array of different data types, providing distinct – but often complementary – information. We present a Bayesian method for the unsupervised integrative modelling of multiple datasets, which we refer to as MDI (Multiple Dataset Integration). MDI can integrate information from a wide range of different datasets and data types simultaneously (including the ability to model time series data explicitly using Gaussian processes). Each dataset is modelled using a Dirichlet-multinomial allocation (DMA) mixture model, with dependencies between these models captured via parameters that describe the agreement among the datasets. Results: Using a set of 6 artificially constructed time series datasets, we show that MDI is able to integrate a significant number of datasets simultaneously, and that it successfully captures the underlying structural similarity between the datasets. We also analyse a variety of real S. cerevisiae datasets. In the 2-dataset case, we show that MDI’s performance is comparable to the present state of the art. We then move beyond the capabilities of current approaches and integrate gene expression, ChIP-chip and protein-protein interaction data, to identify a set of protein complexes for which genes are co-regulated during the cell cycle. Comparisons to other unsupervised data integration techniques – as well as to non-integrative approaches – demonstrate that MDI is very competitive, while also providing information that would be difficult or impossible to extract using other methods