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.

Structural refinement, optical and microwave dielectric properties of BaZrO3

In this work, barium zirconate (BaZrO3) ceramics synthesized by solid state reaction method and sintered at 1670 degrees C for 4 h were characterized by X-ray diffraction (XRD), Rietveld refinement, and Fourier transform infrared (FT-IR) spectroscopy. XRD patterns, Rietveld refinement data and FT-IR spectra which confirmed that BaZrO3 ceramics have a perovskite-type cubic structure. Optical properties were investigated by ultraviolet-visible (UV-vis) absorption and photoluminescence (PL) measurements. UV-vis absorption spectra suggested an indirect allowed transition with the existence of intermediary energy levels within the band gap. Intense visible green PL emission was observed in BaZrO3 ceramics upon excitation with a 350 nm wavelength. This behavior is due to a majority of deep defects within the band gap caused by symmetry breaking in octahedral [ZrO6] clusters in the lattice. The microwave dielectric constant and quality factor were measured using the method proposed by Hakki-Coleman. The dielectric resonator antenna (DRA) was investigated experimentally and numerically using a monopole antenna through an infinite ground plane and Ansoft's high frequency structure simulator software, respectively. The required resonance frequency and bandwidth of DRA were investigated by adjusting the dimension of the same material. (C) 2011 Elsevier Ltd and Techna Group S.r.l. All rights reserved.

Structural Refinement and Photoluminescence Properties of MnWO4 Nanorods Obtained by Microwave-Hydrothermal Synthesis

Manganese tungstate (MnWO4) nanorods were prepared at room temperature by the co-precipitation method and synthesized after processing in a microwave-hydrothermal (MH) system at 140 degrees C for 6-96 min. These nanorods were structurally characterized by X-ray diffraction (XRD), Rietveld refinements and Fourier transform (FT)-Raman spectroscopy. The growth direction, shape and average size distribution of nanorods were observed by means of transmission electron microscopy (TEM) and high resolution TEM (HR-TEM). The optical properties of the nanorods were investigated by ultraviolet visible (UV-vis) absorption and photoluminescence (PL) measurements. XRD patterns, Rietveld refinement data and FT-Raman spectroscopy indicate that the MnWO4 precipitate is not a single phase structure while the nanorods synthesized by MH processing have a wolframite-type monoclinic structure without deleterious phases. FT-Raman spectra exhibited the presence of 17 Raman-active modes from 50 to 1,000 cm(-1). TEM and HR-TEM micrographs indicated that the nanorods are aggregated due to surface energy by Van der Waals forces and grow along the [100] direction. UV-vis absorption measurements confirmed non-linear values for the optical band gap (from 3.2 to 2.72 eV), which increased as the MH processing time increased. The structural characterizations indicated that the presence of defects in the MnWO4 precipitate promotes a significant contribution to maximum PL emission, while MnWO4 nanorods obtained by MH processing decrease the PL emission due to the reduction of defects in the lattice.

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.

Structural refinement, growth process, photoluminescence and photocatalytic properties of (Ba1-xPr2x/3)WO4 crystals synthesized by the coprecipitation method

Barium praseodymium tungstate (Ba1-xPr2x/3)WO4 crystals with (x = 0, 0.01, and 0.02) were prepared by the coprecipitation method. These crystals were structurally characterized by X-ray diffraction (XRD), Rietveld refinements, Fourier-transform Raman (FT-Raman) and Fourier-transform infrared (FT-IR) spectroscopies. The shape and size of these crystals were observed by field emission scanning electron microcopy (FE-SEM). Their optical properties were investigated by ultraviolet visible (UV-vis) absorption and photoluminescence (PL) measurements. Moreover, we have studied the photocatalytic (PC) activity of crystals for degradation of rhodamine B (RhB) dye. XRD patterns, Rietveld refinements data, FT-Raman and FT-IR spectroscopies indicate that all crystals exhibit a tetragonal structure without deleterious phases. FT-Raman spectra exhibited 13 Raman-active modes in a range from 50 to 1000 cm(-1), while FT-IR spectra have 8 infrared active modes in a range from 200 to 1050 cm(-1). FE-SEM images showed different shapes (bonbon-, spindle-, rice-and flake-like) as well as a reduction in the crystal size with an increase in Pr3+ ions. A possible growth process was proposed for these crystals. UV-vis absorption measurements revealed a decrease in optical band gap values with an increase of Pr3+ into the matrix. An intense green PL emission was noted for (Ba1-xPr2x/3)WO4 crystals (x = 0), while crystals with (x = 0.01 and 0.02) produced a reduction in the wide band PL emission and the narrow band PL emission which is related to f-f transitions from Pr3+ ions. High photocatalytic efficiency was verified for the bonbon-like BaWO4 crystals as a catalyst in the degradation of the RhB dye after 25 min under UV-light. Finally, we discuss possible mechanisms for PL and PC properties of these crystals.

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.

QuMinS: fast and scalable querying, mining and summarizing multi-modal databases

Given a large image set, in which very few images have labels, how to guess labels for the remaining majority? How to spot images that need brand new labels different from the predefined ones? How to summarize these data to route the user’s attention to what really matters? Here we answer all these questions. Specifically, we propose QuMinS, a fast, scalable solution to two problems: (i) Low-labor labeling (LLL) – given an image set, very few images have labels, find the most appropriate labels for the rest; and (ii) Mining and attention routing – in the same setting, find clusters, the top-'N IND.O' outlier images, and the 'N IND.R' images that best represent the data. Experiments on satellite images spanning up to 2.25 GB show that, contrasting to the state-of-the-art labeling techniques, QuMinS scales linearly on the data size, being up to 40 times faster than top competitors (GCap), still achieving better or equal accuracy, it spots images that potentially require unpredicted labels, and it works even with tiny initial label sets, i.e., nearly five examples. We also report a case study of our method’s practical usage to show that QuMinS is a viable tool for automatic coffee crop detection from remote sensing images.