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.

A survey of evolutionary algorithms for decision-tree induction

This paper presents a survey of evolutionary algorithms that are designed for decision-tree induction. In this context, most of the paper focuses on approaches that evolve decision trees as an alternate heuristics to the traditional top-down divide-and-conquer approach. Additionally, we present some alternative methods that make use of evolutionary algorithms to improve particular components of decision-tree classifiers. The paper's original contributions are the following. First, it provides an up-to-date overview that is fully focused on evolutionary algorithms and decision trees and does not concentrate on any specific evolutionary approach. Second, it provides a taxonomy, which addresses works that evolve decision trees and works that design decision-tree components by the use of evolutionary algorithms. Finally, a number of references are provided that describe applications of evolutionary algorithms for decision-tree induction in different domains. At the end of this paper, we address some important issues and open questions that can be the subject of future research.

A New Method of Current Switching for Linear Wide-Range Measurement Systems

This new and general method here called overflow current switching allows a fast, continuous, and smooth transition between scales in wide-range current measurement systems, like electrometers. This is achieved, using a hydraulic analogy, by diverting only the overflow current, such that no slow element is forced to change its state during the switching. As a result, this approach practically eliminates the long dead time in low-current (picoamperes) switching. Similar to a logarithmic scale, a composition of n adjacent linear scales, like a segmented ruler, measures the current. The use of a linear wide-range system based on this technique assures fast and continuous measurement in the entire range, without blind regions during transitions and still holding suitable accuracy for many applications. A full mathematical development of the method is given. Several computer realistic simulations demonstrated the viability of the technique.

Average control of Markov decision processes with Feller transition probabilities and general action spaces

This paper studies the average control problem of discrete-time Markov Decision Processes (MDPs for short) with general state space, Feller transition probabilities, and possibly non-compact control constraint sets A(x). Two hypotheses are considered: either the cost function c is strictly unbounded or the multifunctions A(r)(x) = {a is an element of A(x) : c(x, a) <= r} are upper-semicontinuous and compact-valued for each real r. For these two cases we provide new results for the existence of a solution to the average-cost optimality equality and inequality using the vanishing discount approach. We also study the convergence of the policy iteration approach under these conditions. It should be pointed out that we do not make any assumptions regarding the convergence and the continuity of the limit function generated by the sequence of relative difference of the alpha-discounted value functions and the Poisson equations as often encountered in the literature. (C) 2012 Elsevier Inc. All rights reserved.

Responses of transplanted native tree species to invasive alien grass removals in an abandoned cattle pasture in the Lacandon region, Mexico

We tested the early performance of 16 native early-, mid-, and late-successional tree species in response to four intensities of grass removal in an abandoned cattle pasture dominated by the introduced, invasive African grass, Cynodon plectostachyus, within the Lacandon rainforest region, southeast Mexico. The increase in grass removals significantly improved the performance of many species, especially of early-and mid-successional species, while performance of late-successional species was relatively poor and did not differ significantly among treatments. Good site preparation and at least one additional grass removal four months after seedling transplant were found to be essential; additional grass removals led to improved significantly performance of saplings in most cases. In order to evaluate the potential of transplanting tree seedlings successfully in abandoned tropical pastures, we developed a "planting risk index", combining field performance measurements and plantation cost estimations. Our results showed a great potential for establishing restoration plantings with many early-and mid-successional species. Although planting risk of late-successional species was considered high, certain species showed some possibilities of acclimation after 18 months and should be considered in future plantation arrangements in view of their long-term contributions to biodiversity maintenance and also to human welfare through delivery of ecosystem services. Conducting a planting risk analysis can help avoid failure of restoration strategies involving simultaneous planting of early-, mid-, and late-successional tree species. This in turn will improve cost-effectiveness of initial interventions in large-scale, long-term restoration programs.

Biological invasions and the acoustic niche: the effect of bullfrog calls on the acoustic signals of white-banded tree frogs

Invasive species are known to affect native species in a variety of ways, but the effect of acoustic invaders has not been examined previously. We simulated an invasion of the acoustic niche by exposing calling native male white-banded tree frogs (Hypsiboas albomarginatus) to recorded invasive American bullfrog (Lithobates catesbeianus) calls. In response, tree frogs immediately shifted calls to significantly higher frequencies. In the post-stimulus period, they continued to use higher frequencies while also decreasing signal duration. Acoustic signals are the primary basis of mate selection in many anurans, suggesting that such changes could negatively affect the reproductive success of native species. The effects of bullfrog vocalizations on acoustic communities are expected to be especially severe due to their broad frequency band, which masks the calls of multiple species simultaneously.

A Novel Method of Evaluating Ureteropelvic Junction Obstruction: Dynamic Near Infrared Fluorescence Imaging Compared to Standard Modalities to Assess Urinary Obstruction in a Swine Model

Purpose: Dynamic near infrared fluorescence imaging of the urinary tract provides a promising way to diagnose ureteropelvic junction obstruction. Initial studies demonstrated the ability to visualize urine flow and peristalsis in great detail. We analyzed the efficacy of near infrared imaging in evaluating ureteropelvic junction obstruction, renal involvement and the anatomical detail provided compared to conventional imaging modalities. Materials and Methods: Ten swine underwent partial or complete unilateral ureteral obstruction. Groups were survived for the short or the long term. Imaging was performed with mercaptoacetyltriglycine diuretic renogram, magnetic resonance urogram, excretory urogram, ultrasound and near infrared imaging. Scoring systems for ureteropelvic junction obstruction were developed for magnetic resonance urogram and near infrared imaging. Physicians and medical students graded ureteropelvic junction obstruction based on magnetic resonance urogram and near infrared imaging results. Results: Markers of vascular and urinary dynamics were quantitatively consistent among control renal units. The same markers were abnormal in obstructed renal units with significantly different times of renal phase peak, start of pelvic phase and start of renal uptake. Such parameters were consistent with those obtained with mercaptoacetyltriglycine diuretic renography. Near infrared imaging provided live imaging of urinary flow, which was helpful in identifying the area of obstruction for surgical planning. Physicians and medical students categorized the degree of obstruction appropriately for fluorescence imaging and magnetic resonance urogram. Conclusions: Near infrared imaging offers a feasible way to obtain live, dynamic images of urine flow and ureteral peristalsis. Qualitative and quantitative parameters were comparable to those of conventional imaging. Findings support fluorescence imaging as an accurate, easy to use method of diagnosing ureteropelvic junction obstruction.

Diurnal changes in storage carbohydrate metabolism in cotyledons of the tropical tree Hymenaea courbaril L. (Leguminosae)

(Diurnal changes in storage carbohydrate metabolism in cotyledons of the tropical tree Hymenaea courbaril L. (Leguminosae)). The cotyledons of Hymenaea courbaril store large amounts of xyloglucan, a cell wall polysaccharide that is believed to serve as storage for the period of seedling establishment. During storage mobilisation, xyloglucan seems to be degraded by a continuous process that starts right after radicle protrusion and follows up to the establishment of photosynthesis. Here we show evidence that events related to the hydrolases activities and production (alpha-xylosidase, beta-galactosidase, beta-glucosidase and xyloglucan endo-beta-transglucosilase) as well as auxin, showed changes that follow the diurnal cycle. The period of higher hydrolases activities was between 6pm and 6am, which is out of phase with photosynthesis. Among the enzymes, alpha-xilosidase seems to be more important than beta-glucosidase and beta-galactosidase in the xyloglucan disassembling mechanism. Likewise, the sugars related with sucrose metabolism followed the rhythm of the hydrolases, but starch levels were shown to be practically constant. A high level of auxin was observed during the night, what is compatible with the hypothesis that this hormone would be one of the regulators of the whole process. The probable biological meaning of the existence of such a complex control mechanism during storage mobilisation is likely to be related to a remarkably high level of efficiency of carbon usage by the growing seedling of Hymenaea courbaril, allowing the establishment of very vigorous seedlings in the tropical forest.

Feelings of women regarding end-of-life decision making after ultrasound diagnosis of a lethal fetal malformation

Objective: this study investigated the feelings of women regarding end-of-life decision making after ultrasound diagnosis of a lethal fetal malformation. The aim of this study was to present the decision making process of women that chose for pregnancy termination and to present selected speeches of women about their feelings. Design: open psychological interviews conducted by a psychologist immediately after the diagnosis of fetal malformation by ultrasound. Analysis of the results was performed through a content analysis technique. Setting: the study was carried out at a public university hospital in Brazil. Participants: 249 pregnant women who had received the diagnosis of a severe lethal fetal malformation. Findings: fetal anencephaly was the most frequent anomaly detected in 135 cases (54.3%). Termination of pregnancy was decided by 172 (69.1%) patients and legally authorised by the judiciary (66%). The reason for asking for termination was to reduce suffering in all of them. In the 77 women who chose not to terminate pregnancy (30.9%), the reasons were related to feelings of guilt (74%). Key conclusions: the results support the importance of psychological counselling for couples when lethal fetal malformation is diagnosed. The act of reviewing moral and cultural values and elements of the unconscious provides assurance in the decision-making process and mitigates the risk of emotional trauma and guilt that can continue long after the pregnancy is terminated. (C) 2011 Elsevier Ltd. All rights reserved.

CT-guided percutaneous drilling is a safe and reliable method of treating osteoid osteomas

Computed tomography (CT)-guided percutaneous drilling is an alternative for osteoid osteoma treatment. This study aims to evaluate the remodeling of the drill orifice. The success rate and complications were also recorded and compared with other treatment methods.

Hydric restriction as vigor assessment method of soybean seeds

The objective of this study was to investigate the possibility of using hydric restriction as a method for evaluating vigor of soybean seeds. The soybean seeds, cultivar BRS 245RR, represented by four different seed lots, were characterized by germination and vigor. For the treatment of hydric restriction and temperature, the combination of substrate water potential and temperature were the following: deionized water (0.0 MPa); polyethylene glycol (PEG 6000) aqueous solution (-0.1, -0.3 and -0.5 MPa); and four temperatures (20 ºC, 25 ºC, 30 ºC, and 35 ºC), respectively. A completely randomized experimental design was used, with four replications per treatment, and the ANOVA was performed individually for each combination of temperature and water potential of substrate. According to results obtained, the test of hydric restriction has the same efficiency of the accelerated aging test in estimating vigor of soybean seeds, cv. BRS 245RR, when water potentials of -0.1 MPa or -0.3 MPa at a temperature of 25 ºC, or -0.3 MPa at a temperature of 30 ºC are used.

Hierarchical multi-label classification using local neural networks

Hierarchical multi-label classification is a complex classification task where the classes involved in the problem are hierarchically structured and each example may simultaneously belong to more than one class in each hierarchical level. In this paper, we extend our previous works, where we investigated a new local-based classification method that incrementally trains a multi-layer perceptron for each level of the classification hierarchy. Predictions made by a neural network in a given level are used as inputs to the neural network responsible for the prediction in the next level. We compare the proposed method with one state-of-the-art decision-tree induction method and two decision-tree induction methods, using several hierarchical multi-label classification datasets. We perform a thorough experimental analysis, showing that our method obtains competitive results to a robust global method regarding both precision and recall evaluation measures.

A new method for decision making in multi-objective optimization problems

Many engineering sectors are challenged by multi-objective optimization problems. Even if the idea behind these problems is simple and well established, the implementation of any procedure to solve them is not a trivial task. The use of evolutionary algorithms to find candidate solutions is widespread. Usually they supply a discrete picture of the non-dominated solutions, a Pareto set. Although it is very interesting to know the non-dominated solutions, an additional criterion is needed to select one solution to be deployed. To better support the design process, this paper presents a new method of solving non-linear multi-objective optimization problems by adding a control function that will guide the optimization process over the Pareto set that does not need to be found explicitly. The proposed methodology differs from the classical methods that combine the objective functions in a single scale, and is based on a unique run of non-linear single-objective optimizers.