in Wiley Online Library (wileyonlinelibrary.com).Abstract: APBS and PDB2PQR are widely utilized free software packages for biomolecular electrostatics calculations. Using the Opal toolkit, we have developed a Web services framework for these software packages that enables the use of APBS and PDB2PQR by users who do not have local access to the necessary amount of computational capabilities. This not only increases accessibility of the software to a wider range of scientists, educators, and students but also increases the availability of electrostatics calculations on portable computing platforms. Users can access this new functionality in two ways. First, an Opal-enabled version of APBS is provided in current distributions, available freely on the web. Second, we have extended the PDB2PQR web server to provide an interface for the setup, execution, and visualization of electrostatic potentials as calculated by APBS. This web interface also uses the Opal framework which ensures the scalability needed to support the large APBS user community. Both of these resources are available from the APBS/PDB2PQR website: http://www.poissonboltzmann.org/.q

SummaryAutomated microscopy to detect Mycobacterium tuberculosis in sputum smear slides would enable laboratories in countries with a high tuberculosis burden to cope efficiently with large numbers of smears. Focusing is a core component of automated microscopy, and successful autofocusing depends on selection of an appropriate focus algorithm for a specific task. We examined autofocusing algorithms for bright-field microscopy of Ziehl-Neelsen stained sputum smears. Six focus measures, defined in the spatial domain, were examined with respect to accuracy, execution time, range, full width at half maximum of the peak and the presence of local maxima. Curve fitting around an estimate of the focal plane was found to produce good results and is therefore an acceptable strategy to reduce the number of images captured for focusing and the processing time. Vollath's F 4 measure performed best for full z-stacks, with a mean difference of 0.27 µm between manually and automatically determined focal positions, whereas it is jointly ranked best with the Brenner gradient for curve fitting.

Ultrasound is a main noninvasive modality for the assessment of the heart function. Wall tracking from ultrasound data is, however, inherently difficult due to weak echoes, clutter, poor signal-to-noise ratio, and signal dropouts. To cope with these artifacts, pretrained shape models can be applied to constrain the tracking. However, existing methods for incorporating subspace shape constraints in myocardial border tracking use only partial information from the model distribution, and do not exploit spatially varying uncertainties from feature tracking. In this paper, we propose a complete fusion formulation in the information space for robust shape tracking, optimally resolving uncertainties from the system dynamics, heteroscedastic measurement noise, and subspace shape model. We also exploit information from the ground truth initialization where this is available. The new framework is applied for tracking of myocardial borders in very noisy echocardiography sequences. Numerous myocardium tracking experiments validate the theory and show the potential of very accurate wall motion measurements. The proposed framework outperforms the traditional shape-space-constrained tracking algorithm by a significant margin. Due to the optimal fusion of different sources of uncertainties, robust performance is observed even for the most challenging cases.

The hypoxic stress elicits a wide panel of temporal responses corresponding to different biological pathways. Early hypoxia signatures were shown to have a significant prognostic power. These data suggest that gene signatures identified from in vitro experiments could contribute to individualized medicine.

A novel image segmentation approach by exploring one-class support vector machine (SVM) has been developed for the extraction of brain tumor from magnetic resonance (MR) images. Based on one-class SVM, the proposed method has the ability of learning the nonlinear distribution of the image data without prior knowledge, via the automatic procedure of SVM parameters training and an implicit learning kernel. After the learning process, the segmentation task is performed. The proposed technique is applied to 24 clinical MR images of brain tumor for both visual and quantitative evaluations. Experimental results suggest that the proposed query-based approach provides an effective and promising method for brain tumor extraction from MR images with high accuracy.

Cardiovascular Disease (CVD) is the single largest killer in the world. Although, several CVD treatment guidelines have been developed to improve quality of care and reduce healthcare costs, for a number of reasons, adherence to these guidelines remains poor. Further, due to the extremely poor quality of data in medical patient records, most of today's healthcare IT systems cannot provide significant support to improve the quality of CVD care (particularly in chronic CVD situations which contribute to the majority of costs).We present REMIND, a Probabilistic framework for Reliable Extraction and Meaningful Inference from Nonstructured Data. REMIND integrates the structured and unstructured clinical data in patient records to automatically create high-quality structured clinical data. There are two principal factors that enable REMIND to overcome the barriers associated with inference from medical records. First, patient data is highly redundant -- exploiting this redundancy allows us to deal with the inherent errors in the data. Second, REMIND performs inference based on external medical domain knowledge to combine data from multiple sources and to enforce consistency between different medical conclusions drawn from the data -- via a probabilistic reasoning framework that overcomes the incomplete, inconsistent, and incorrect nature of data in medical patient records.This high-quality structuring allows existing patient records to be mined to support guideline compliance and to improve patient care. However, once REMIND is configured for an institution's data repository, many other important clinical applications are also enabled, including: quality assurance; therapy selection for individual patients; automated patient identification for clinical trials; data extraction for research studies; and to relate financial and clinical factors. REMIND provides value across the continuum of healthcare, ranging from small physician practice databases to the most complex hospital IT systems, from acute cardiac care to chronic CVD management, and to experimental research studies. REMIND is currently deployed across multiple disease areas over a total of 5,000,000 patients across the US.

Screening for tuberculosis (TB) in low-and middle-income countries is centered on the microscope. We present methods for the automated identification of Mycobacterium tuberculosis in images of Ziehl-Neelsen (ZN) stained sputum smears obtained using a bright-field microscope. We segment candidate bacillus objects using a combination of two-class pixel classifiers. The algorithm produces results that agree well with manual segmentations, as judged by the Hausdorff distance and the modified Williams index. The extraction of geometric-transformation-invariant features and optimization of the feature set by feature subset selection and Fisher transformation follow. Finally, different two-class object classifiers are compared. The sensitivity and specificity of all tested classifiers is above 95% for the identification of bacillus objects represented by Fisher-transformed features. Our results may be used to reduce technician involvement in screening for TB, and would be particularly useful in laboratories in countries with a high burden of TB, where, typically, ZN rather than auramine staining of sputum smears is the method of choice.

Rapid detection of disease biomarkers at the patient point-of-care is essential to timely and effective treatment. The research described herein focuses on the development of an electrochemical surface-enhanced Raman spectroscopy (EC-SERS) DNA aptasensor capable of direct detection of tuberculosis (TB) DNA. Specifically, a plausible DNA biomarker present in TB patient urine was chosen as the model target for detection. Cost-effective screen printed electrodes (SPEs) modified with silver nanoparticles (AgNP) were used as the aptasensor platform, onto which the aptamer specific for the target DNA was immobilized. Direct detection of the target DNA was demonstrated through the appearance of SERS peaks characteristic for adenine, present only in the target strand. Modulation of the applied potential allowed for a sizeable increase in the observed SERS response and the use of thiol back-filling prevented non-specific adsorption of non-target DNA. To our knowledge, this work represents the first EC-SERS study of an aptasensor for the direct, label-free detection of DNA hybridization. Such a technology paves the way for rapid detection of disease biomarkers at the patient point-of-care.