Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data

Background: This paper addresses the prediction of the free energy of binding of a drug candidate with enzyme InhA associated with Mycobacterium tuberculosis. This problem is found within rational drug design, where interactions between drug candidates and target proteins are verified through molecular docking simulations. In this application, it is important not only to correctly predict the free energy of binding, but also to provide a comprehensible model that could be validated by a domain specialist. Decision-tree induction algorithms have been successfully used in drug-design related applications, specially considering that decision trees are simple to understand, interpret, and validate. There are several decision-tree induction algorithms available for general-use, but each one has a bias that makes it more suitable for a particular data distribution. In this article, we propose and investigate the automatic design of decision-tree induction algorithms tailored to particular drug-enzyme binding data sets. We investigate the performance of our new method for evaluating binding conformations of different drug candidates to InhA, and we analyze our findings with respect to decision tree accuracy, comprehensibility, and biological relevance. Results: The empirical analysis indicates that our method is capable of automatically generating decision-tree induction algorithms that significantly outperform the traditional C4.5 algorithm with respect to both accuracy and comprehensibility. In addition, we provide the biological interpretation of the rules generated by our approach, reinforcing the importance of comprehensible predictive models in this particular bioinformatics application. Conclusions: We conclude that automatically designing a decision-tree algorithm tailored to molecular docking data is a promising alternative for the prediction of the free energy from the binding of a drug candidate with a flexible-receptor.

Automatic aspect discrimination in data clustering

The attributes describing a data set may often be arranged in meaningful subsets, each of which corresponds to a different aspect of the data. An unsupervised algorithm (SCAD) that simultaneously performs fuzzy clustering and aspects weighting was proposed in the literature. However, SCAD may fail and halt given certain conditions. To fix this problem, its steps are modified and then reordered to reduce the number of parameters required to be set by the user. In this paper we prove that each step of the resulting algorithm, named ASCAD, globally minimizes its cost-function with respect to the argument being optimized. The asymptotic analysis of ASCAD leads to a time complexity which is the same as that of fuzzy c-means. A hard version of the algorithm and a novel validity criterion that considers aspect weights in order to estimate the number of clusters are also described. The proposed method is assessed over several artificial and real data sets.

Experimental analysis of reinforced concrete block masonry spandrels using pre-fabricated planar trussed bars

Masonry spandrels together with shear walls are structural components of a masonry building subjected to lateral loads. Shear walls are the main components of this structural system, even if masonry spandrels are the elements that ensure the connection of shear wall panels and the distribution of stresses through the masonry piers. The use of prefabricated truss type bars in the transversal and longitudinal directions is usually considered a challenge, even if the simplicity of the applications suggested here alleviate some of the possible difficulties. This paper focus on the experimental behavior of masonry spandrels reinforced with prefabricated trusses, considering different possibilities for the arrangement of reinforcement and blocks. Reinforced spandrels with three and two hollow cell concrete blocks and with different reinforcement ratios have been built and tested using a four and three point loading test configuration. Horizontal bed joint reinforcement increased the capacity of deformation as well as the ultimate load, leading to ductile responses. Vertical reinforcement increased the shear strength of the masonry spandrels and its distribution play a central role on the shear behavior. (C) 2011 Elsevier Ltd. All rights reserved.

Delineation of vertebral area on the coronal plane using three-dimensional ultrasonography advanced volume contrast imaging (VCI) Omni view: intrarater reliability and agreement using standard mouse, high definition mouse, and pen-tablet

Objective: To assess the fetal lumbosacral spine by three-dimensional (3D) ultrasonography using volume contrast imaging (VCI) omni view method and compare reproducibility and agreement between three different measurement techniques: standard mouse, high definition mouse and pen-tablet. Methods: A comparative and prospective study with 40 pregnant women between 20 and 34+6 weeks was realized. 3D volume datasets of the fetal spine were acquired using a convex transabdominal transducer. Starting scan plane was the coronal section of fetal lumbosacral spine by VCI-C function. Omni view manual trace was selected and a parallel plane of fetal spine was drawn including interest region. Intraclass correlation coefficient (ICC) was used for reproducibility analysis. The relative difference between three used techniques was compared by chi-square test and Fischer test. Results: Pen-tablet showed better reliability (ICC = 0.987). In the relative proportion of differences, this was significantly higher for the pen-tablet (82.14%; p < 0.01). In paired comparison, the relative difference was significantly greater for the pen-tablet (p < 0.01). Conclusion: The pen-tablet showed to be the most reproductive and concordant method in the measurement of body vertebral area of fetal lumbosacral spine by 3D ultrasonography using the VCI.

Development of a high sensitive automatic setup for screening of microcystins in surface waters by employing a LED-based photometric detector

In this manuscript, an automatic setup for screening of microcystins in surface waters by employing photometric detection is described. Microcystins are toxins delivered by cyanobacteria within an aquatic environment, which have been considered strongly poisonous for humans. For that reason, the World Health Organization (WHO) has proposed a provisional guideline value for drinking water of 1 mu g L-1. In this work, we developed an automated equipment setup, which allows the screening of water for concentration of microcystins below 0.1 mu g V. The photometric method was based on the enzyme-linked immunosorbent assay (ELISA) and the analytical signal was monitored at 458 nm using a homemade LED-based photometer. The proposed system was employed for the detection of microcystins in rivers and lakes waters. Accuracy was assessed by processing samples using a reference method and applying the paired t-test between results. No significant difference at the 95% confidence level was observed. Other useful features including a linear response ranging from 0.05 up to 2.00 mu g L-1 (R-2 =0.999) and a detection limit of 0.03 mu g L-1 microcystins were achieved. (C) 2011 Elsevier B.V. All rights reserved.

Automatic lumen segmentation in IVOCT images using binary morphological reconstruction

Abstract Background Atherosclerosis causes millions of deaths, annually yielding billions in expenses round the world. Intravascular Optical Coherence Tomography (IVOCT) is a medical imaging modality, which displays high resolution images of coronary cross-section. Nonetheless, quantitative information can only be obtained with segmentation; consequently, more adequate diagnostics, therapies and interventions can be provided. Since it is a relatively new modality, many different segmentation methods, available in the literature for other modalities, could be successfully applied to IVOCT images, improving accuracies and uses. Method An automatic lumen segmentation approach, based on Wavelet Transform and Mathematical Morphology, is presented. The methodology is divided into three main parts. First, the preprocessing stage attenuates and enhances undesirable and important information, respectively. Second, in the feature extraction block, wavelet is associated with an adapted version of Otsu threshold; hence, tissue information is discriminated and binarized. Finally, binary morphological reconstruction improves the binary information and constructs the binary lumen object. Results The evaluation was carried out by segmenting 290 challenging images from human and pig coronaries, and rabbit iliac arteries; the outcomes were compared with the gold standards made by experts. The resultant accuracy was obtained: True Positive (%) = 99.29 ± 2.96, False Positive (%) = 3.69 ± 2.88, False Negative (%) = 0.71 ± 2.96, Max False Positive Distance (mm) = 0.1 ± 0.07, Max False Negative Distance (mm) = 0.06 ± 0.1. Conclusions In conclusion, by segmenting a number of IVOCT images with various features, the proposed technique showed to be robust and more accurate than published studies; in addition, the method is completely automatic, providing a new tool for IVOCT segmentation.