SOCRAT: a Dynamic Web Toolbox for Interactive Data Processing, Analysis and Visualization.

Many systems for exploratory and visual data analytics require platform-dependent software installation, coding skills, and analytical expertise. The rapid advances in data-acquisition, web-based information, and communication and computation technologies promoted the explosive growth of online services and tools implementing novel solutions for interactive data exploration and visualization. However, web-based solutions for visual analytics remain scattered and relatively problem-specific. This leads to per-case re-implementations of common components, system architectures, and user interfaces, rather than focusing on innovation and building sophisticated applications for visual analytics. In this paper, we present the Statistics Online Computational Resource Analytical Toolbox (SOCRAT), a dynamic, flexible, and extensible web-based visual analytics framework. The SOCRAT platform is designed and implemented using multi-level modularity and declarative specifications. This enables easy integration of a number of components for data management, analysis, and visualization. SOCRAT benefits from the diverse landscape of existing in-browser solutions by combining them with flexible template modules into a unique, powerful, and feature-rich visual analytics toolbox. The platform integrates a number of independently developed tools for data import, display, storage, interactive visualization, statistical analysis, and machine learning. Various use cases demonstrate the unique features of SOCRAT for visual and statistical analysis of heterogeneous types of data.

Individual differences in honey bee behavior enabled by plasticity in brain gene regulatory networks.

Understanding the regulatory architecture of phenotypic variation is a fundamental goal in biology, but connections between gene regulatory network (GRN) activity and individual differences in behavior are poorly understood. We characterized the molecular basis of behavioral plasticity in queenless honey bee (Apis mellifera) colonies, where individuals engage in both reproductive and non-reproductive behaviors. Using high-throughput behavioral tracking, we discovered these colonies contain a continuum of phenotypes, with some individuals specialized for either egg-laying or foraging and 'generalists' that perform both. Brain gene expression and chromatin accessibility profiles were correlated with behavioral variation, with generalists intermediate in behavior and molecular profiles. Models of brain GRNs constructed for individuals revealed that transcription factor (TF) activity was highly predictive of behavior, and behavior-associated regulatory regions had more TF motifs. These results provide new insights into the important role played by brain GRN plasticity in the regulation of behavior, with implications for social evolution.

3D Shape Modeling for Cell Nuclear Morphological Analysis and Classification.

Quantitative analysis of morphological changes in a cell nucleus is important for the understanding of nuclear architecture and its relationship with pathological conditions such as cancer. However, dimensionality of imaging data, together with a great variability of nuclear shapes, presents challenges for 3D morphological analysis. Thus, there is a compelling need for robust 3D nuclear morphometric techniques to carry out population-wide analysis. We propose a new approach that combines modeling, analysis, and interpretation of morphometric characteristics of cell nuclei and nucleoli in 3D. We used robust surface reconstruction that allows accurate approximation of 3D object boundary. Then, we computed geometric morphological measures characterizing the form of cell nuclei and nucleoli. Using these features, we compared over 450 nuclei with about 1,000 nucleoli of epithelial and mesenchymal prostate cancer cells, as well as 1,000 nuclei with over 2,000 nucleoli from serum-starved and proliferating fibroblast cells. Classification of sets of 9 and 15 cells achieved accuracy of 95.4% and 98%, respectively, for prostate cancer cells, and 95% and 98% for fibroblast cells. To our knowledge, this is the first attempt to combine these methods for 3D nuclear shape modeling and morphometry into a highly parallel pipeline workflow for morphometric analysis of thousands of nuclei and nucleoli in 3D.

Publisher Correction: 3D Shape Modeling for Cell Nuclear Morphological Analysis and Classification.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

SOCR data dashboard: an integrated big data archive mashing medicare, labor, census and econometric information.

INTRODUCTION: Intuitive formulation of informative and computationally-efficient queries on big and complex datasets present a number of challenges. As data collection is increasingly streamlined and ubiquitous, data exploration, discovery and analytics get considerably harder. Exploratory querying of heterogeneous and multi-source information is both difficult and necessary to advance our knowledge about the world around us. RESEARCH DESIGN: We developed a mechanism to integrate dispersed multi-source data and service the mashed information via human and machine interfaces in a secure, scalable manner. This process facilitates the exploration of subtle associations between variables, population strata, or clusters of data elements, which may be opaque to standard independent inspection of the individual sources. This a new platform includes a device agnostic tool (Dashboard webapp, http://socr.umich.edu/HTML5/Dashboard/) for graphical querying, navigating and exploring the multivariate associations in complex heterogeneous datasets. RESULTS: The paper illustrates this core functionality and serviceoriented infrastructure using healthcare data (e.g., US data from the 2010 Census, Demographic and Economic surveys, Bureau of Labor Statistics, and Center for Medicare Services) as well as Parkinson's Disease neuroimaging data. Both the back-end data archive and the front-end dashboard interfaces are continuously expanded to include additional data elements and new ways to customize the human and machine interactions. CONCLUSIONS: A client-side data import utility allows for easy and intuitive integration of user-supplied datasets. This completely open-science framework may be used for exploratory analytics, confirmatory analyses, meta-analyses, and education and training purposes in a wide variety of fields.

Electroconvulsive therapy in depressive illness that has not responded to drug treatment.

BACKGROUND: Antidepressant medication resistance is the commonest indication for electroconvulsive therapy (ECT) in Scotland. Evidence from the USA suggests that clinical response is reduced for medication resistant patients. The aim of the present study was to establish if the American results were generalisable to routine clinical practice in Edinburgh. METHOD: Fifty eligible depressed patients consecutively referred for a new course of bilateral ECT at the Royal Edinburgh Hospital were prospectively assessed as part of the National Audit of ECT in Scotland. The patients were categorised into those who had received adequate drug treatment pre-ECT (and could therefore be classed as medication resistant) and those who had not, using five operational definitions. The clinical response of the ECT was then compared between groups, using the Montgomery-Asberg Rating Scale for Depression (MADRS) and Clinical Global Impression Scale (CGI). RESULTS: Patients defined as medication resistant had an identical response to patients who were not defined as medication resistant (in both groups 60% met the predetermined criterion for clinical response), and this was consistent across the five operational definitions. CONCLUSIONS: The hypothesis that medication resistance is associated with a reduced probability of clinical response to ECT was not supported. LIMITATIONS: Some patients who were inadequately drug treated might have proven eventually to be medication resistant, which would have obscured a potential difference in clinical response. It is not known how generalisable the results are to clinical practice in the rest of the UK.

The role of positron emission tomography in the evaluation of the N-positive neck.

BACKGROUND: A major prognostic indicator in patients with squamous cell carcinoma of the upper aerodigestive tract is the presence or absence of cervical metastasis. Nodal involvement at different levels affects treatment. Thus identification of the degree of nodal involvement is important. Evaluation of the neck by conventional imaging modalities (computed tomography or magnetic resonance imaging) is not completely accurate. Positron emission tomography (PET) scanning as a dynamic functional assessment may allow detection of multiple metastatic nodes at different levels. PURPOSE: We sought to compare the effectiveness of PET with pathologic examination for: presence, location, and number of cervical metastases in the clinically N-positive neck. SETTING: Tertiary care academic facility. Materials and methods From 1994 to 1997, 15 patients with clinically N-positive necks who had preoperative PET scans underwent 23 neck dissections. PET scans were correlated with the pathologic findings of the neck dissections in determining the ability to correctly identify the number and level(s) of nodal disease. RESULTS: When determining identification of the level of disease, PET demonstrated sensitivity of 81%; specificity, 99%; positive predictive value, 97%; negative predictive value, 90%; and accuracy, 92%. When evaluating the ability to correctly predict neck stage, PET demonstrated sensitivity of 86%, positive predictive value of 100%, and accuracy of 80% compared with clinical examination with sensitivity of 53% and accuracy of 53%. CONCLUSION: PET accurately identified disease in the N-positive neck. Its ability to identify multiple level disease may allow it to help predict the selectivity of neck dissection in the therapeutic protocol.

What do psychiatrists mean by medication resistance as an indication for electroconvulsive therapy?

OBJECTIVES: Resistance to antidepressant medication is the commonest stated indication for electroconvulsive therapy (ECT) by psychiatrists, but what this means in practice has not been described. Our aims were to survey what antidepressant drug treatment had been prescribed to patients before they underwent ECT and to what extent this treatment would satisfy operational criteria for adequate antidepressant drug treatment used in ECT research. METHODS: The survey was conducted prospectively among 37 depressed patients referred for a new course of bilateral ECT and where antidepressant medication resistant was identified as the indication for ECT. RESULTS: Only half the sample had been prescribed more than one antidepressant drug, and only 38% had been prescribed any augmentation compound. Although the majority of patients were resistant to medication when rated by the older operational criteria, only half the sample met contemporary criteria for medication resistance. CONCLUSIONS: The findings suggest a lack of agreement between clinicians and researchers in what constitutes medication resistance as a possible indication for ECT. It will take some time to establish if the revised UK Royal College of Psychiatrists ECT Handbook will encourage more intensive medical treatment of depressive illness before the use of ECT.