Evolution of Arabidopsis microRNA families through duplication events

Recently there has been a great interest in the identification of microRNAs and their targets as well as understanding the spatial and temporal regulation of microRNA genes. To understand how microRNA genes evolve, we looked at several rapidly evolving families in Arabidopsis thaliana, and found that they arose from a process of genome-wide duplication, tandem duplication, and segmental duplication followed by dispersal and diversification, similar to the processes that drive the evolution of protein gene families. Using multiple expression data sets to examine the transcription patterns of different members of the microRNA families, we find the sequence diversification of duplicated microRNA genes to be accompanied by a change in spatial and temporal expression patterns, suggesting that duplicated copies acquire new functionality as they evolve

A tool for subjective and interactive visual data exploration

We present SIDE, a tool for Subjective and Interactive Visual Data Exploration, which lets users explore high dimensional data via subjectively informative 2D data visualizations. Many existing visual analytics tools are either restricted to specific problems and domains or they aim to find visualizations that align with user’s belief about the data. In contrast, our generic tool computes data visualizations that are surprising given a user’s current understanding of the data. The user’s belief state is represented as a set of projection tiles. Hence, this user-awareness offers users an efficient way to interactively explore yet-unknown features of complex high dimensional datasets

Comprehensive data infrastructure for plant bioinformatics

The iPlant Collaborative is a 5-year, National Science Foundation-funded effort to develop cyberinfrastructure to address a series of grand challenges in plant science. The second of these grand challenges is the Genotype-to- Phenotype project, which seeks to provide tools, in the form of a web-based Discovery Environment, for understanding the developmental process from DNA to a full-grown plant. Addressing this challenge requires the integration of multiple data types that may be stored in multiple formats, with varying levels of standardization. Providing for reproducibility requires that detailed information documenting the experimental provenance of data, and the computational transformations applied to data once it is brought into the iPlant environment. Handling the large quantities of data involved in high-throughput sequencing and other experimental sources of bioinformatics data requires a robust infrastructure for storing and reusing large data objects. We describe the currently planned workflows to be developed for the Genotype-to-Phenotype discovery environment, the data types and formats that must be imported and manipulated within the environment, and we describe the data model that has been developed to express and exchange data within the Discovery Environment, along with the provenance model defined for capturing experimental source and digital transformation descriptions. Capabilities for interaction with reference databases are addressed, focusing not just on the ability to retrieve data from such data sources, but on the ability to use the iPlant Discovery Environment to further populate these important resources. Future activities and the challenges they will present to the data infrastructure of the iPlant Collaborative are also described. © 2010 IEEE

Developing a Data Dashboard Framework for Population Health Surveillance: Widening Access to Clinical Trial Findings

Background: Population surveillance sites generate many datasets relevant to disease surveillance. However, there is a risk that these data are underutilized because of the volumes of data gathered and the lack of means to quickly disseminate analysis. Data visualization offers a means to quickly disseminate, understand, and interpret datasets, facilitating evidence-driven decision making through increased access to information. Objectives: This paper describes the development and evaluation of a framework for data dashboard design, to visualize datasets produced at a demographic health surveillance site. The aim of this research was to produce a comprehensive, reusable, and scalable dashboard design framework to fit the unique requirements of the context. Methods: The framework was developed and implemented at a demographic surveillance platform at the Africa Health Research Institute, in KwaZulu-Natal, South Africa. This context represents an exemplar implementation for the use of data dashboards within a population health-monitoring setting. Before the full launch, an evaluation study was undertaken to assess the effectiveness of the dashboard framework as a data communication and decision-making tool. The evaluation included a quantitative task evaluation to assess usability and a qualitative questionnaire exploring the attitudes to the use of dashboards. Results: The evaluation participants were drawn from a diverse group of users working at the site (n=20), comprising of community members, nurses, scientific and operational staff. Evaluation demonstrated high usability for the dashboard across user groups, with scientific and operational staff having minimal issues in completing tasks. There were notable differences in the efficiency of task completion among user groups, indicating varying familiarity with data visualization. The majority of users felt that the dashboards provided a clear understanding of the datasets presented and had a positive attitude to their increased use. Conclusions: Overall, this exploratory study indicates the viability of the data dashboard framework in communicating data trends within population surveillance setting. The usability differences among the user groups discovered during the evaluation demonstrate the need for the user-led design of dashboards in this context, addressing heterogeneous computer and visualization literacy present among the diverse potential users present in such settings. The questionnaire highlighted the enthusiasm for increased access to datasets from all stakeholders highlighting the potential of dashboards in this context

Correlated physical and mental health summary scores for the SF-36 and SF-12 Health Survey, V.1

<p>Abstract</p> <p>Background</p> <p>The SF-36 and SF-12 summary scores were derived using an uncorrelated (orthogonal) factor solution. We estimate SF-36 and SF-12 summary scores using a correlated (oblique) physical and mental health factor model.</p> <p>Methods</p> <p>We administered the SF-36 to 7,093 patients who received medical care from an independent association of 48 physician groups in the western United States. Correlated physical health (PCS_c) and mental health (MCS_c) scores were constructed by multiplying each SF-36 scale z-score by its respective scoring coefficient from the obliquely rotated two factor solution. PCS_c-12 and MCS_c-12 scores were estimated using an approach similar to the one used to derive the original SF-12 summary scores.</p> <p>Results</p> <p>The estimated correlation between SF-36 PCS_cand MCS_cscores was 0.62. There were far fewer negative factor scoring coefficients for the oblique factor solution compared to the factor scoring coefficients produced by the standard orthogonal factor solution. Similar results were found for PCS_c-12, and MCS_c-12 summary scores.</p> <p>Conclusion</p> <p>Correlated physical and mental health summary scores for the SF-36 and SF-12 derived from an obliquely rotated factor solution should be used along with the uncorrelated summary scores. The new scoring algorithm can reduce inconsistent results between the SF-36 scale scores and physical and mental health summary scores reported in some prior studies.</p> <p>(Subscripts C = correlated and UC = uncorrelated)</p

Learning transcriptional regulatory networks from high throughput gene expression data using continuous three-way mutual information

<p>Abstract</p> <p>Background</p> <p>Probability based statistical learning methods such as mutual information and Bayesian networks have emerged as a major category of tools for reverse engineering mechanistic relationships from quantitative biological data. In this work we introduce a new statistical learning strategy, MI3 that addresses three common issues in previous methods simultaneously: (1) handling of continuous variables, (2) detection of more complex three-way relationships and (3) better differentiation of causal versus confounding relationships. With these improvements, we provide a more realistic representation of the underlying biological system.</p> <p>Results</p> <p>We test the MI3 algorithm using both synthetic and experimental data. In the synthetic data experiment, MI3 achieved an absolute sensitivity/precision of 0.77/0.83 and a relative sensitivity/precision both of 0.99. In addition, MI3 significantly outperformed the control methods, including Bayesian networks, classical two-way mutual information and a discrete version of MI3. We then used MI3 and control methods to infer a regulatory network centered at the MYC transcription factor from a published microarray dataset. Models selected by MI3 were numerically and biologically distinct from those selected by control methods. Unlike control methods, MI3 effectively differentiated true causal models from confounding models. MI3 recovered major MYC cofactors, and revealed major mechanisms involved in MYC dependent transcriptional regulation, which are strongly supported by literature. The MI3 network showed that limited sets of regulatory mechanisms are employed repeatedly to control the expression of large number of genes.</p> <p>Conclusion</p> <p>Overall, our work demonstrates that MI3 outperforms the frequently used control methods, and provides a powerful method for inferring mechanistic relationships underlying biological and other complex systems. The MI3 method is implemented in R in the "mi3" package, available under the GNU GPL from <url>http://sysbio.engin.umich.edu/~luow/downloads.php</url> and from the R package archive CRAN.</p

Evaluation of two interaction techniques for visualization of dynamic graphs

Several techniques for visualization of dynamic graphs are based on different spatial arrangements of a temporal sequence of node-link diagrams. Many studies in the literature have investigated the importance of maintaining the user's mental map across this temporal sequence, but usually each layout is considered as a static graph drawing and the effect of user interaction is disregarded. We conducted a task-based controlled experiment to assess the effectiveness of two basic interaction techniques: the adjustment of the layout stability and the highlighting of adjacent nodes and edges. We found that generally both interaction techniques increase accuracy, sometimes at the cost of longer completion times, and that the highlighting outclasses the stability adjustment for many tasks except the most complex ones.Comment: Appears in the Proceedings of the 24th International Symposium on Graph Drawing and Network Visualization (GD 2016

Polarization sensitive spectroscopy of charged Quantum Dots

We present an experimental and theoretical study of the polarized photoluminescence spectrum of single semiconductor quantum dots in various charge states. We compare our high resolution polarization sensitive spectral measurements with a new many-carrier theoretical model, which was developed for this purpose. The model considers both the isotropic and anisotropic exchange interactions between all participating electron-hole pairs. With this addition, we calculate both the energies and polarizations of all optical transitions between collective, quantum dot confined charge carrier states. We succeed in identifying most of the measured spectral lines. In particular, the lines resulting from singly-, doubly- and triply- negatively charged excitons and biexcitons. We demonstrate that lines emanating from evenly charged states are linearly polarized. Their polarization direction does not necessarily coincide with the traditional crystallographic direction. It depends on the shells of the single carriers, which participate in the recombination process.Comment: 11 pages, 9 figures. Revised versio

Electronic Chart of the Future: The Hampton Roads Project

ECDIS is evolving from a two-dimensional static display of chart-related data to a decision support system capable of providing real-time or forecast information. While there may not be consensus on how this will occur, it is clear that to do this, ENC data and the shipboard display environment must incorporate both depth and time in an intuitively understandable way. Currently, we have the ability to conduct high-density hydrographic surveys capable of producing ENCs with decimeter contour intervals or depth areas. Yet, our existing systems and specifications do not provide for a full utilization of this capability. Ideally, a mariner should be able to benefit from detailed hydrographic data, coupled with both forecast and real-time water levels, and presented in a variety of perspectives. With this information mariners will be able to plan and carry out transits with the benefit of precisely determined and easily perceived underkeel, overhead, and lateral clearances. This paper describes a Hampton Roads Demonstration Project to investigate the challenges and opportunities of developing the “Electronic Chart of the Future.” In particular, a three-phase demonstration project is being planned: 1. Compile test datasets from existing and new hydrographic surveys using advanced data processing and compilation procedures developed at the University of New Hampshire’s Center for Coastal and Ocean Mapping/Joint Hydrographic Center (CCOM/JHC); 2. Investigate innovative approaches being developed at the CCOM/JHC to produce an interactive time- and tide-aware navigation display, and to evaluate such a display on commercial and/or government vessels; 3. Integrate real-time/forecast water depth information and port information services transmitted via an AIS communications broadcast