Complex sequencing rules of birdsong can be explained by simple hidden Markov processes

Complex sequencing rules observed in birdsongs provide an opportunity to investigate the neural mechanism for generating complex sequential behaviors. To relate the findings from studying birdsongs to other sequential behaviors, it is crucial to characterize the statistical properties of the sequencing rules in birdsongs. However, the properties of the sequencing rules in birdsongs have not yet been fully addressed. In this study, we investigate the statistical propertiesof the complex birdsong of the Bengalese finch (Lonchura striata var. domestica). Based on manual-annotated syllable sequences, we first show that there are significant higher-order context dependencies in Bengalese finch songs, that is, which syllable appears next depends on more than one previous syllable. This property is shared with other complex sequential behaviors. We then analyze acoustic features of the song and show that higher-order context dependencies can be explained using first-order hidden state transition dynamics with redundant hidden states. This model corresponds to hidden Markov models (HMMs), well known statistical models with a large range of application for time series modeling. The song annotation with these models with first-order hidden state dynamics agreed well with manual annotation, the score was comparable to that of a second-order HMM, and surpassed the zeroth-order model (the Gaussian mixture model (GMM)), which does not use context information. Our results imply that the hierarchical representation with hidden state dynamics may underlie the neural implementation for generating complex sequences with higher-order dependencies

Long-term impact of four different strategies for delivering an on-line curriculum about herbs and other dietary supplements

BACKGROUND: Previous research has shown that internet education can lead to short-term improvements in clinicians' knowledge, confidence and communication practices. We wished to better understand the duration of these improvements and whether different curriculum delivery strategies differed in affecting these improvements. METHODS: As previously described, we conducted a randomized control trial comparing four different strategies for delivering an e-curriculum about herbs and other dietary supplements (HDS) to clinicians. The four strategies were delivering the curriculum by: a) email over 10 weeks; b) email within one week; c) web-site over 10 weeks; d) web-site within one week. Participants were surveyed at baseline, immediately after the course and 6–10 months after completing the course (long-term). Long-term outcomes focused on clinicians' knowledge, confidence and communication practices. RESULTS: Of the 780 clinicians who completed the course, 385 (49%) completed the long-term survey. Completers and non-completers of the long-term survey had similar demographics and professional characteristics at baseline. There were statistically significant improvements from baseline to long-term follow-up in knowledge, confidence and communication practices; these improvements did not differ by curriculum delivery strategy. Knowledge scores improved from 67.7 ± 10.3 at baseline to 78.8 ± 12.3 at long-term follow-up (P < 0.001). Confidence scores improved from 53.7 ± 17.8 at baseline to 66.9 ± 12.0 at long term follow-up (P < 0.001); communication scores improved from 2.6 ± 1.9 at baseline to 3.6 ± 2.1 (P < 0.001) at long-term follow-up. CONCLUSION: This e- curriculum led to significant and sustained improvements in clinicians' expertise about HDS regardless of the delivery strategy. Future studies should compare the impact of required vs. elective courses and self-reported vs. objective measures of behavior change

Measuring affective well-being at work using short-form scales : implications for affective structures and participant instructions

Measuring affective well-being in organizational studies has become increasingly widespread, given its association with key work-performance and other markers of organizational functioning. As such, researchers and policy-makers need to be confident that well-being measures are valid, reliable and robust. To reduce the burden on participants in applied settings, short-form measures of affective well-being are proving popular. However, these scales are seldom validated as standalone, comprehensive measures in their own right. In this article, we used a short-form measure of affective well-being with 10 items: the Daniels five-factor measure of affective well-being (D-FAW). In Study 1, across six applied sample groups (N = 2624), we found that the factor structure of the short-form D-FAW is robust when issued as a standalone measure, and that it should be scored differently depending on the participant instruction used. When participant instructions focus on now or today, then affect is best represented by five discrete emotion factors. When participant instructions focus on the past week, then affect is best represented by two or three mood-based factors. In Study 2 (N = 39), we found good construct convergent validity of short-form D-FAW with another widely used scale (PANAS). Implications for the measurement and structure of affect are discussed

TDP-43 Identified from a Genome Wide RNAi Screen for SOD1 Regulators

Amyotrophic Lateral Sclerosis (ALS) is a late-onset, progressive neurodegenerative disease affecting motor neurons in the brain stem and spinal cord leading to loss of voluntary muscular function and ultimately, death due to respiratory failure. A subset of ALS cases are familial and associated with mutations in superoxide dismutase 1 (SOD1) that destabilize the protein and predispose it to aggregation. In spite of the fact that sporadic and familial forms of ALS share many common patho-physiological features, the mechanistic relationship between SOD1-associated and sporadic forms of the disease if any, is not well understood. To better understand any molecular connections, a cell-based protein folding assay was employed to screen a whole genome RNAi library for genes that regulate levels of soluble SOD1. Statistically significant hits that modulate SOD1 levels, when analyzed by pathway analysis revealed a highly ranked network containing TAR DNA binging protein (TDP-43), a major component of aggregates characteristic of sporadic ALS. Biochemical experiments confirmed the action of TDP-43 on SOD1. These results highlight an unexpected relationship between TDP-43 and SOD1 which may have implications in disease pathogenesis

Graphical models for inferring single molecule dynamics

<p>Abstract</p> <p>Background</p> <p>The recent explosion of experimental techniques in single molecule biophysics has generated a variety of novel time series data requiring equally novel computational tools for analysis and inference. This article describes in general terms how graphical modeling may be used to learn from biophysical time series data using the variational Bayesian expectation maximization algorithm (VBEM). The discussion is illustrated by the example of single-molecule fluorescence resonance energy transfer (smFRET)<it> versus</it> time data, where the smFRET time series is modeled as a hidden Markov model (HMM) with Gaussian observables. A detailed description of smFRET is provided as well.</p> <p>Results</p> <p>The VBEM algorithm returns the model’s evidence and an approximating posterior parameter distribution given the data. The former provides a metric for model selection via maximum evidence (ME), and the latter a description of the model’s parameters learned from the data. ME/VBEM provide several advantages over the more commonly used approach of maximum likelihood (ML) optimized by the expectation maximization (EM) algorithm, the most important being a natural form of model selection and a well-posed (non-divergent) optimization problem.</p> <p>Conclusions</p> <p>The results demonstrate the utility of graphical modeling for inference of dynamic processes in single molecule biophysics.</p

The Effect of Azithromycin on Ivermectin Pharmacokinetics—A Population Pharmacokinetic Model Analysis

This paper describes the use of a modeling and simulation approach to explore a reported pharmacokinetic interaction between two drugs (ivermectin and azithromycin), which along with albendazole, are being developed for combination use in neglected tropical diseases. This approach is complementary to more traditional pharmacokinetic and safety studies that need to be conducted to support combined use of different health interventions. A mathematical model of ivermectin pharmacokinetics was created and used to simulate multiple trials, and the probability of certain outcomes (very high peak blood ivermectin levels when given in combination) was determined. All simulated peak blood levels were within ranges known to be safe and well tolerated. Additional field studies are needed to confirm these findings

Genome sequence of an Australian kangaroo, Macropus eugenii, provides insight into the evolution of mammalian reproduction and development.

BACKGROUND: We present the genome sequence of the tammar wallaby, Macropus eugenii, which is a member of the kangaroo family and the first representative of the iconic hopping mammals that symbolize Australia to be sequenced. The tammar has many unusual biological characteristics, including the longest period of embryonic diapause of any mammal, extremely synchronized seasonal breeding and prolonged and sophisticated lactation within a well-defined pouch. Like other marsupials, it gives birth to highly altricial young, and has a small number of very large chromosomes, making it a valuable model for genomics, reproduction and development. RESULTS: The genome has been sequenced to 2 × coverage using Sanger sequencing, enhanced with additional next generation sequencing and the integration of extensive physical and linkage maps to build the genome assembly. We also sequenced the tammar transcriptome across many tissues and developmental time points. Our analyses of these data shed light on mammalian reproduction, development and genome evolution: there is innovation in reproductive and lactational genes, rapid evolution of germ cell genes, and incomplete, locus-specific X inactivation. We also observe novel retrotransposons and a highly rearranged major histocompatibility complex, with many class I genes located outside the complex. Novel microRNAs in the tammar HOX clusters uncover new potential mammalian HOX regulatory elements. CONCLUSIONS: Analyses of these resources enhance our understanding of marsupial gene evolution, identify marsupial-specific conserved non-coding elements and critical genes across a range of biological systems, including reproduction, development and immunity, and provide new insight into marsupial and mammalian biology and genome evolution

Constraint-based probabilistic learning of metabolic pathways from tomato volatiles

Clustering and correlation analysis techniques have become popular tools for the analysis of data produced by metabolomics experiments. The results obtained from these approaches provide an overview of the interactions between objects of interest. Often in these experiments, one is more interested in information about the nature of these relationships, e.g., cause-effect relationships, than in the actual strength of the interactions. Finding such relationships is of crucial importance as most biological processes can only be understood in this way. Bayesian networks allow representation of these cause-effect relationships among variables of interest in terms of whether and how they influence each other given that a third, possibly empty, group of variables is known. This technique also allows the incorporation of prior knowledge as established from the literature or from biologists. The representation as a directed graph of these relationship is highly intuitive and helps to understand these processes. This paper describes how constraint-based Bayesian networks can be applied to metabolomics data and can be used to uncover the important pathways which play a significant role in the ripening of fresh tomatoes. We also show here how this methods of reconstructing pathways is intuitive and performs better than classical techniques. Methods for learning Bayesian network models are powerful tools for the analysis of data of the magnitude as generated by metabolomics experiments. It allows one to model cause-effect relationships and helps in understanding the underlying processes

A Visual Data Mining Tool that Facilitates Reconstruction of Transcription Regulatory Networks

Background: Although the use of microarray technology has seen exponential growth, analysis of microarray data remains a challenge to many investigators. One difficulty lies in the interpretation of a list of differentially expressed genes, or in how to plan new experiments given that knowledge. Clustering methods can be used to identify groups of genes with similar expression patterns, and genes with unknown function can be provisionally annotated based on the concept of ‘‘guilt by association’’, where function is tentatively inferred from the known functions of genes with similar expression patterns. These methods frequently suffer from two limitations: (1) visualization usually only gives access to group membership, rather than specific information about nearest neighbors, and (2) the resolution or quality of the relationships are not easily inferred. Methodology/Principal Findings: We have addressed these issues by improving the precision of similarity detection over that of a single experiment and by creating a tool to visualize tractable association networks: we (1) performed metaanalysis computation of correlation coefficients for all gene pairs in a heterogeneous data set collected from 2,145 publicly available micorarray samples in mouse, (2) filtered the resulting distribution of over 130 million correlation coefficients to build new, more tractable distributions from the strongest correlations, and (3) designed and implemented a new Web based tool (StarNet