EVOLUTIONARY AESTHETICS AND SEXUAL SELECTION IN THE EVOLUTION OF ROCK ART AESTHETICS

This theoretical proposal applies evolutionary aesthetic, animal signalling and sexual selection to understand our artistic cognition, especially rock art aesthetics. Iconographic motifs, universally found in rock art, indicate which set of pre-artistic aesthetic psychological bias has been co-opted to catch the viewer`s attention. The co-evolutionary process of sexual selection could have shaped the design features of both rock art images and their aesthetic cognition by conferring mutual benefits on both producers, via manipulation, and receivers, via information extraction. We show some strategic techniques identified in rock art and art that indicate the occurrence of this co-evolution between producers and receivers.

Determination of trace elements in bovine semen samples by inductively coupled plasma mass spectrometry and data mining techniques for identification of bovine class

The reproductive performance of cattle may be influenced by several factors, but mineral imbalances are crucial in terms of direct effects on reproduction. Several studies have shown that elements such as calcium, copper, iron, magnesium, selenium, and zinc are essential for reproduction and can prevent oxidative stress. However, toxic elements such as lead, nickel, and arsenic can have adverse effects on reproduction. In this paper, we applied a simple and fast method of multi-element analysis to bovine semen samples from Zebu and European classes used in reproduction programs and artificial insemination. Samples were analyzed by inductively coupled plasma spectrometry (ICP-MS) using aqueous medium calibration and the samples were diluted in a proportion of 1:50 in a solution containing 0.01% (vol/vol) Triton X-100 and 0.5% (vol/vol) nitric acid. Rhodium, iridium, and yttrium were used as the internal standards for ICP-MS analysis. To develop a reliable method of tracing the class of bovine semen, we used data mining techniques that make it possible to classify unknown samples after checking the differentiation of known-class samples. Based on the determination of 15 elements in 41 samples of bovine semen, 3 machine-learning tools for classification were applied to determine cattle class. Our results demonstrate the potential of support vector machine (SVM), multilayer perceptron (MLP), and random forest (RF) chemometric tools to identify cattle class. Moreover, the selection tools made it possible to reduce the number of chemical elements needed from 15 to just 8.

Multi-element determination in Brazilian honey samples by inductively coupled plasma mass spectrometry and estimation of geographic origin with data mining techniques

Multi-element analysis of honey samples was carried out with the aim of developing a reliable method of tracing the origin of honey. Forty-two chemical elements were determined (Al, Cu, Pb, Zn, Mn, Cd, Tl, Co, Ni, Rb, Ba, Be, Bi, U, V, Fe, Pt, Pd, Te, Hf, Mo, Sn, Sb, P, La, Mg, I, Sm, Tb, Dy, Sd, Th, Pr, Nd, Tm, Yb, Lu, Gd, Ho, Er, Ce, Cr) by inductively coupled plasma mass spectrometry (ICP-MS). Then, three machine learning tools for classification and two for attribute selection were applied in order to prove that it is possible to use data mining tools to find the region where honey originated. Our results clearly demonstrate the potential of Support Vector Machine (SVM), Multilayer Perceptron (MLP) and Random Forest (RF) chemometric tools for honey origin identification. Moreover, the selection tools allowed a reduction from 42 trace element concentrations to only 5. (C) 2012 Elsevier Ltd. All rights reserved.

Combining meta-learning and search techniques to select parameters for support vector machines

Support Vector Machines (SVMs) have achieved very good performance on different learning problems. However, the success of SVMs depends on the adequate choice of the values of a number of parameters (e.g., the kernel and regularization parameters). In the current work, we propose the combination of meta-learning and search algorithms to deal with the problem of SVM parameter selection. In this combination, given a new problem to be solved, meta-learning is employed to recommend SVM parameter values based on parameter configurations that have been successfully adopted in previous similar problems. The parameter values returned by meta-learning are then used as initial search points by a search technique, which will further explore the parameter space. In this proposal, we envisioned that the initial solutions provided by meta-learning are located in good regions of the search space (i.e. they are closer to optimum solutions). Hence, the search algorithm would need to evaluate a lower number of candidate solutions when looking for an adequate solution. In this work, we investigate the combination of meta-learning with two search algorithms: Particle Swarm Optimization and Tabu Search. The implemented hybrid algorithms were used to select the values of two SVM parameters in the regression domain. These combinations were compared with the use of the search algorithms without meta-learning. The experimental results on a set of 40 regression problems showed that, on average, the proposed hybrid methods obtained lower error rates when compared to their components applied in isolation.

Selection of Top 10 Quality Indicators for Nutrition Therapy

Background: The identification of useful quality indicators for nutrition therapy (QINTs) is of great interest and a challenge. This study attempted to identify the 10 QINTs that best suit the practice of quality control in nutrition therapy (NT) by evaluating the opinion of experts in NT with the use of psychometric techniques and statistical tools. Methods: Thirty-six QINTs available for clinical application in Brazil were assessed in 2 distinct phases. In phase 1, 26 nutrition experts ranked QINTs by scoring 4 attributes (utility, simplicity, objectivity, low cost) to assess each QINT on a 5-point Likert scale. The top 10 QINTs were identified from the 10 best scores obtained, and the reliability of expert opinion for each indicator was assessed by Cronbach's alpha. In phase 2, experts provided feedback regarding the selected top 10 QINTs by answering 2 closed questions. Results: The top 10 QINTs, in descending order, are the frequency of nutrition screening of hospitalized patients, diarrhea, involuntary withdrawal of enteral feeding tubes, feeding tube obstruction, fasting longer than 24 hours, glycemic dysfunction, estimated energy expenditure and protein needs, central venous catheter infection, compliance of NT indication, and frequency of application of subjective global assessment. Opinions were consistent among the interviewed experts. During feedback, 96% of experts were satisfied with the top 10 QINTs, and 100% had considered them in accordance with their previous opinion. Conclusion: The top 10 QINTs were identified according to their usefulness in clinical practice by obtaining adequate agreement and representativeness of opinion of nutrition experts. (Nutr Clin Pract. 2012;27:261-267)

Utility Harmonic Impedance Measurement Based on Data Selection

Determination of the utility harmonic impedance based on measurements is a significant task for utility power-quality improvement and management. Compared to those well-established, accurate invasive methods, the noninvasive methods are more desirable since they work with natural variations of the loads connected to the point of common coupling (PCC), so that no intentional disturbance is needed. However, the accuracy of these methods has to be improved. In this context, this paper first points out that the critical problem of the noninvasive methods is how to select the measurements that can be used with confidence for utility harmonic impedance calculation. Then, this paper presents a new measurement technique which is based on the complex data-based least-square regression, combined with two techniques of data selection. Simulation and field test results show that the proposed noninvasive method is practical and robust so that it can be used with confidence to determine the utility harmonic impedances.

New Insights on Nontechnical Losses Characterization Through Evolutionary-Based Feature Selection

Although nontechnical losses automatic identification has been massively studied, the problem of selecting the most representative features in order to boost the identification accuracy and to characterize possible illegal consumers has not attracted much attention in this context. In this paper, we focus on this problem by reviewing three evolutionary-based techniques for feature selection, and we also introduce one of them in this context. The results demonstrated that selecting the most representative features can improve a lot of the classification accuracy of possible frauds in datasets composed by industrial and commercial profiles.

Clinical strategies for esthetic excellence in anterior tooth restorations: understanding color and composite resin selection

Direct composite resin restorations have become a viable alternative for patients that require anterior restorative procedures to be integrated to the other teeth that compose the smile, especially for presenting satisfactory esthetic results and minimum wear of the dental structure. Technological evolution along with a better understanding of the behavior of dental tissues to light incidence has allowed the development of new composite resins with better mechanical and optical properties, making possible a more artistic approach for anterior restorations. The combination of the increasing demand of patients for esthetics and the capacity to preserve the dental structure resulted in the development of different incremental techniques for restoring fractured anterior teeth in a natural way. In order to achieve esthetic excellence, dentists should understand and apply artistic and scientific principles when choosing color of restorative materials, as well as during the insertion of the composite resin. The discussion of these strategies will be divided into two papers. In this paper, the criteria for color and material selection to obtain a natural reproduction of the lost dental structures and an imperceptible restoration will be addressed.

A feature selection approach for identification of signature genes from SAGE data

Abstract Background One goal of gene expression profiling is to identify signature genes that robustly distinguish different types or grades of tumors. Several tumor classifiers based on expression profiling have been proposed using microarray technique. Due to important differences in the probabilistic models of microarray and SAGE technologies, it is important to develop suitable techniques to select specific genes from SAGE measurements. Results A new framework to select specific genes that distinguish different biological states based on the analysis of SAGE data is proposed. The new framework applies the bolstered error for the identification of strong genes that separate the biological states in a feature space defined by the gene expression of a training set. Credibility intervals defined from a probabilistic model of SAGE measurements are used to identify the genes that distinguish the different states with more reliability among all gene groups selected by the strong genes method. A score taking into account the credibility and the bolstered error values in order to rank the groups of considered genes is proposed. Results obtained using SAGE data from gliomas are presented, thus corroborating the introduced methodology. Conclusion The model representing counting data, such as SAGE, provides additional statistical information that allows a more robust analysis. The additional statistical information provided by the probabilistic model is incorporated in the methodology described in the paper. The introduced method is suitable to identify signature genes that lead to a good separation of the biological states using SAGE and may be adapted for other counting methods such as Massive Parallel Signature Sequencing (MPSS) or the recent Sequencing-By-Synthesis (SBS) technique. Some of such genes identified by the proposed method may be useful to generate classifiers.