Host preference of the bean weevil Zabrotes subfasciatus

It is largely known that the range of an insect diet is mostly determined by oviposition behavior, mainly in species with endophytic larvae such as Zabrotes subfasciatus. However, the proximate factors determining host choice and the subsequent steps leading to the expansion or reduction of the host number and occasional host shifts are largely unknown. We analyzed various factors determining host preference of Z. subfasciatus through the evaluation of: (i) oviposition preference of a wild population of Z subfasciatus on the usual host (bean) and unusual hosts (lentil, chickpea and soy), and the performance of the offspring; (ii) artificial selection for increasing preference for hosts initially less frequently chosen; (iii) comparison of oviposition behavior between two different populations (reared for similar to 30 generations in beans or chickpeas, respectively); (iv) oviposition timing on usual and unusual hosts; and (v) identification of preference hierarchies. We found that when using unusual hosts, there is no correlation between performance and preference and that the preference hierarchy changes only slightly when the population passes through several generations on the less frequently accepted host. We also found a positive response to artificial selection for increasing oviposition on the less preferred host; however, when the host-choice experiment involved two varieties of the usual host, the response was faster than when the choice involved usual and unusual hosts. Finally, beetles reared on an unusual host (chickpea) for 26 generations showed similar good fitness on both usual and unusual hosts, indicating that the use of a new host does not necessarily result in the loss of performance on the original host. Nevertheless, this population showed lower fitness on the usual host than that of the original population, suggesting an underlying partial trade-off phenomenon which may contribute to a broadening of diet of this insect species.

Accelerating autonomous learning by using heuristic selection of actions

This paper investigates how to make improved action selection for online policy learning in robotic scenarios using reinforcement learning (RL) algorithms. Since finding control policies using any RL algorithm can be very time consuming, we propose to combine RL algorithms with heuristic functions for selecting promising actions during the learning process. With this aim, we investigate the use of heuristics for increasing the rate of convergence of RL algorithms and contribute with a new learning algorithm, Heuristically Accelerated Q-learning (HAQL), which incorporates heuristics for action selection to the Q-Learning algorithm. Experimental results on robot navigation show that the use of even very simple heuristic functions results in significant performance enhancement of the learning rate.

Sites of Defoliation by Atta sexdens rubropilosa (Hymenoptera, Formicidae) in Artificial Plants

This study aimed to achieve a better understanding about the foraging behavior of leaf-cutter ant (Atta sexdens rubropilosa Forel) workers with respect to defoliation sites in plants. To accomplish that, artificial plants 70 cm in height were prepared and divided into four levels (heights), having natural plant leaves attached to them. Evaluations during the bioassays included the number of leaves dropped by the ants, as well as the percentage of plant mass removed. In all replicates, it became evident that the most exploited plant site is the apical region, which significantly differed from other plant levels.

Partitions selection strategy for set of clustering solutions

Clustering is a difficult task: there is no single cluster definition and the data can have more than one underlying structure. Pareto-based multi-objective genetic algorithms (e.g., MOCK Multi-Objective Clustering with automatic K-determination and MOCLE-Multi-Objective Clustering Ensemble) were proposed to tackle these problems. However, the output of such algorithms can often contains a high number of partitions, becoming difficult for an expert to manually analyze all of them. In order to deal with this problem, we present two selection strategies, which are based on the corrected Rand, to choose a subset of solutions. To test them, they are applied to the set of solutions produced by MOCK and MOCLE in the context of several datasets. The study was also extended to select a reduced set of partitions from the initial population of MOCLE. These analysis show that both versions of selection strategy proposed are very effective. They can significantly reduce the number of solutions and, at the same time, keep the quality and the diversity of the partitions in the original set of solutions. (C) 2010 Elsevier B.V. All rights reserved.

Chaotic phase synchronization and desynchronization in an oscillator network for object selection

Object selection refers to the mechanism of extracting objects of interest while ignoring other objects and background in a given visual scene. It is a fundamental issue for many computer vision and image analysis techniques and it is still a challenging task to artificial Visual systems. Chaotic phase synchronization takes place in cases involving almost identical dynamical systems and it means that the phase difference between the systems is kept bounded over the time, while their amplitudes remain chaotic and may be uncorrelated. Instead of complete synchronization, phase synchronization is believed to be a mechanism for neural integration in brain. In this paper, an object selection model is proposed. Oscillators in the network representing the salient object in a given scene are phase synchronized, while no phase synchronization occurs for background objects. In this way, the salient object can be extracted. In this model, a shift mechanism is also introduced to change attention from one object to another. Computer simulations show that the model produces some results similar to those observed in natural vision systems.

A network of integrate and fire neurons for visual selection

Biological systems have facility to capture salient object(s) in a given scene, but it is still a difficult task to be accomplished by artificial vision systems. In this paper a visual selection mechanism based on the integrate and fire neural network is proposed. The model not only can discriminate objects in a given visual scene, but also can deliver focus of attention to the salient object. Moreover, it processes a combination of relevant features of an input scene, such as intensity, color, orientation, and the contrast of them. In comparison to other visual selection approaches, this model presents several interesting features. It is able to capture attention of objects in complex forms, including those linearly nonseparable. Moreover, computer simulations show that the model produces results similar to those observed in natural vision systems.

U-curve: A branch-and-bound optimization algorithm for U-shaped cost functions on Boolean lattices applied to the feature selection problem

This paper presents the formulation of a combinatorial optimization problem with the following characteristics: (i) the search space is the power set of a finite set structured as a Boolean lattice; (ii) the cost function forms a U-shaped curve when applied to any lattice chain. This formulation applies for feature selection in the context of pattern recognition. The known approaches for this problem are branch-and-bound algorithms and heuristics that explore partially the search space. Branch-and-bound algorithms are equivalent to the full search, while heuristics are not. This paper presents a branch-and-bound algorithm that differs from the others known by exploring the lattice structure and the U-shaped chain curves of the search space. The main contribution of this paper is the architecture of this algorithm that is based on the representation and exploration of the search space by new lattice properties proven here. Several experiments, with well known public data, indicate the superiority of the proposed method to the sequential floating forward selection (SFFS), which is a popular heuristic that gives good results in very short computational time. In all experiments, the proposed method got better or equal results in similar or even smaller computational time. (C) 2009 Elsevier Ltd. All rights reserved.

Artificial Neural Networks and the Study of the Psychoactivity of Cannabinoid Compounds

Cannabinoid compounds have widely been employed because of its medicinal and psychotropic properties. These compounds are isolated from Cannabis sativa (or marijuana) and are used in several medical treatments, such as glaucoma, nausea associated to chemotherapy, pain and many other situations. More recently, its use as appetite stimulant has been indicated in patients with cachexia or AIDS. In this work, the influence of several molecular descriptors on the psychoactivity of 50 cannabinoid compounds is analyzed aiming one obtain a model able to predict the psychoactivity of new cannabinoids. For this purpose, initially, the selection of descriptors was carried out using the Fisher`s weight, the correlation matrix among the calculated variables and principal component analysis. From these analyses, the following descriptors have been considered more relevant: E(LUMO) (energy of the lowest unoccupied molecular orbital), Log P (logarithm of the partition coefficient), VC4 (volume of the substituent at the C4 position) and LP1 (Lovasz-Pelikan index, a molecular branching index). To follow, two neural network models were used to construct a more adequate model for classifying new cannabinoid compounds. The first model employed was multi-layer perceptrons, with algorithm back-propagation, and the second model used was the Kohonen network. The results obtained from both networks were compared and showed that both techniques presented a high percentage of correctness to discriminate psychoactive and psychoinactive compounds. However, the Kohonen network was superior to multi-layer perceptrons.

Abrasion wear resistance of different artificial teeth opposed to metal and composite antagonists

One of the most important properties of artificial teeth is the abrasion wear resistance, which is determinant in the maintenance of the rehabilitation's occlusal pattern. OBJECTIVES: This in vitro study aims to evaluate the abrasion wear resistance of 7 brands of artificial teeth opposed to two types of antagonists. MATERIAL AND METHODS: Seven groups were prepared with 12 specimens each (BIOLUX & BL, TRILUX & TR, BLUE DENT & BD, BIOCLER & BC, POSTARIS & PO, ORTHOSIT & OR, GNATHOSTAR & GN), opposed to metallic (M & nickel-chromium alloy), and to composite antagonists (C & Solidex indirect composite). A mechanical loading device was used (240 cycles/min, 4 Hz speed, 10 mm antagonist course). Initial and final contours of each specimen were registered with aid of a profile projector (20x magnification). The linear difference between the two profiles was measured and the registered values were subjected to ANOVA and Tukey's test. RESULTS: Regarding the antagonists, only OR (M = 10.45 ± 1.42 µm and C = 2.77 ± 0.69 µm) and BC (M = 6.70 ± 1.37 µm and C = 4.48 ± 0.80 µm) presented statistically significant differences (p < 0.05). Best results were obtained with PO (C = 2.33 ± 0.91 µm and M = 1.78 ± 0.42 µm), followed by BL (C = 3.70 ± 1.32 µm and M = 3.70 ± 0.61 µm), statistically similar for both antagonists (p>0.05). Greater result variance was obtained with OR, which presented the worse results opposed to Ni-Cr (10.45 ± 1.42 µm), and results similar to the best ones against composite (2.77 ± 0.69 µm). CONCLUSIONS: Within the limitations of this study, it may be concluded that the antagonist material is a factor of major importance to be considered in the choice of the artificial teeth to be used in the prosthesis.

Inteligência artificial aplicada à Zootecnia

Os sistemas biológicos são surpreendentemente flexíveis pra processar informação proveniente do mundo real. Alguns organismos biológicos possuem uma unidade central de processamento denominada de cérebro. O cérebro humano consiste de 10(11) neurônios e realiza processamento inteligente de forma exata e subjetiva. A Inteligência Artificial (IA) tenta trazer para o mundo da computação digital a heurística dos sistemas biológicos de várias maneiras, mas, ainda resta muito para que isso seja concretizado. No entanto, algumas técnicas como Redes neurais artificiais e lógica fuzzy tem mostrado efetivas para resolver problemas complexos usando a heurística dos sistemas biológicos. Recentemente o numero de aplicação dos métodos da IA em sistemas zootécnicos tem aumentado significativamente. O objetivo deste artigo é explicar os princípios básicos da resolução de problemas usando heurística e demonstrar como a IA pode ser aplicada para construir um sistema especialista para resolver problemas na área de zootecnia.

Epilithic community development on artificial reefs deployed along a cross-shelf environmental gradient off Paraná state, southern Brazil

Concrete modules were deployed on the bottom of the 11, 18 and 30 meters isobaths along a cross-shelf hydrographic gradient off Paraná State, Southern Brazil, with the purpose of studying the colonization of sessile epilithic macroinvertebrates on artificial surfaces. After one year of submersion a total of 63 species of epilithic organisms were identified, dominated by Ostrea puelchana, Chthamalus bisinuatus, Balanus cf spongicola, Astrangia cf rathbuni, Didemnum spp, poryphers and bryozoans. Diversity index and percent cover at reef stations placed at 11, 18 and 30 meters isobaths were respectively 2.28 and 66.7%, 2.79 and 96.6% and 1.66 and 77.4%. Differences of general community structure among the three assemblages were not clearly related to the general environmental conditions at the bottom layers near the reef stations. Turbidity and larval abundance are discussed as important factors affecting colonization processes. Results indicate that depths between 15-20 meters are more suitable for the implementation of large scale artificial reef systems in the inner shelf off Paraná and, possibly, throughout the inner shelves off southern Brazil with similar hydrographic conditions.

Pitfalls of artificial grouping and stratification of scientific journals based on their Impact Factor: a case study in Brazilian Zoology

The present contribution explores the impact of the QUALIS metric system for academic evaluation implemented by CAPES (Coordination for the Development of Personnel in Higher Education) upon Brazilian Zoological research. The QUALIS system is based on the grouping and ranking of scientific journals according to their Impact Factor (IF). We examined two main points implied by this system, namely: 1) its reliability as a guideline for authors; 2) if Zoology possesses the same publication profile as Botany and Oceanography, three fields of knowledge grouped by CAPES under the subarea "BOZ" for purposes of evaluation. Additionally, we tested CAPES' recent suggestion that the area of Ecology would represent a fourth field of research compatible with the former three. Our results indicate that this system of classification is inappropriate as a guideline for publication improvement, with approximately one third of the journals changing their strata between years. We also demonstrate that the citation profile of Zoology is distinct from those of Botany and Oceanography. Finally, we show that Ecology shows an IF that is significantly different from those of Botany, Oceanography, and Zoology, and that grouping these fields together would be particularly detrimental to Zoology. We conclude that the use of only one parameter of analysis for the stratification of journals, i.e., the Impact Factor calculated for a comparatively small number of journals, fails to evaluate with accuracy the pattern of publication present in Zoology, Botany, and Oceanography. While such simplified procedure might appeals to our sense of objectivity, it dismisses any real attempt to evaluate with clarity the merit embedded in at least three very distinct aspects of scientific practice, namely: productivity, quality, and specificity.

Associação da medroxiprogesterona ao protocolo Ovsynch para inseminação artificial em tempo fixo de búfalas cíclicas (Bubalus bubalis) criadas na Amazônia Oriental

O presente estudo visou avaliar os efeitos da associação da medroxiprogesterona (análogo sintético da progesterona) ao protocolo Ovsynch sobre o crescimento folicular, a ovulação e a taxa de concepção de búfalas criadas na Amazônia Oriental (Tracuateua-PA). Vinte e sete fêmeas adultas (G1 n=14 e G2 n=13), cíclicas, sem bezerro ao pé e com ECC 3,5 foram submetidas a Ovsynch. Os animais do G2 receberam 60 mg de medroxiprogesterona entre D0 e D7 (D0=início do tratamento). A ultra-sonografia ovariana foi realizada nos D 0, 7, 9 e 10. O contingente de folículos pequenos diferiu no D7 (G1: 4,57±0,60 versus G2: 6,54±0,67; P=0,05). Tempo e tratamento influenciaram o diâmetro folicular no D7. O crescimento do folículo dominante entre D7 e D9 foi maior nos animais tratados (G1: 2,05±0,49 mm/dia versus 3,48±0,41 mm/dia; P<0,05). Mais animais do G1 ovularam precocemente (35,71% versus 30,77%), porém isso não afetou as taxas de concepção (G1: 50,00% e G2: 30,77%; P>0,05). Os achados sugerem que a medroxiprogesterona (1) aumenta recrutamento folicular e retarda o crescimento dos folículos com diâmetro maior que 5,0 mm entre D0 e D7; (2) sua retirada incrementa em 1,7 vezes o crescimento folicular do D7 ao D9; (3) pode contribuir para a ovulação de folículos maiores e, em tese, para maior formação de tecido luteínico; (4) não promove ovulação precoce após o Ovsynch; (5) não eleva as taxas de concepção após sincronização de fêmeas cíclicas e com bom escore corporal, devendo ser avaliada para uso em fêmeas acíclicas ou com ECC mais baixo.

Evaluation of follicular lymphoid depletion in the Bursa of Fabricius: an alternative methodology using digital image analysis and artificial neural networks

Fifty Bursa of Fabricius (BF) were examined by conventional optical microscopy and digital images were acquired and processed using Matlab® 6.5 software. The Artificial Neuronal Network (ANN) was generated using Neuroshell® Classifier software and the optical and digital data were compared. The ANN was able to make a comparable classification of digital and optical scores. The use of ANN was able to classify correctly the majority of the follicles, reaching sensibility and specificity of 89% and 96%, respectively. When the follicles were scored and grouped in a binary fashion the sensibility increased to 90% and obtained the maximum value for the specificity of 92%. These results demonstrate that the use of digital image analysis and ANN is a useful tool for the pathological classification of the BF lymphoid depletion. In addition it provides objective results that allow measuring the dimension of the error in the diagnosis and classification therefore making comparison between databases feasible.

Classification of breast masses using a committee machine of artificial neural networks

This work proposes a new approach using a committee machine of artificial neural networks to classify masses found in mammograms as benign or malignant. Three shape factors, three edge-sharpness measures, and 14 texture measures are used for the classification of 20 regions of interest (ROIs) related to malignant tumors and 37 ROIs related to benign masses. A group of multilayer perceptrons (MLPs) is employed as a committee machine of neural network classifiers. The classification results are reached by combining the responses of the individual classifiers. Experiments involving changes in the learning algorithm of the committee machine are conducted. The classification accuracy is evaluated using the area A. under the receiver operating characteristics (ROC) curve. The A, result for the committee machine is compared with the A, results obtained using MLPs and single-layer perceptrons (SLPs), as well as a linear discriminant analysis (LDA) classifier Tests are carried out using the student's t-distribution. The committee machine classifier outperforms the MLP SLP, and LDA classifiers in the following cases: with the shape measure of spiculation index, the A, values of the four methods are, in order 0.93, 0.84, 0.75, and 0.76; and with the edge-sharpness measure of acutance, the values are 0.79, 0.70, 0.69, and 0.74. Although the features with which improvement is obtained with the committee machines are not the same as those that provided the maximal value of A(z) (A(z) = 0.99 with some shape features, with or without the committee machine), they correspond to features that are not critically dependent on the accuracy of the boundaries of the masses, which is an important result. (c) 2008 SPIE and IS&T.