Resting metabolic rates of two orbweb spiders: A first approach to evolutionary success of ecribellate spiders

Spiders are considered conservative with regard to their resting metabolic rate, presenting the same allometric relation with body mass as the majority of land-arthropods. Nevertheless, web-building is thought to have a great impact on the energetic metabolism, and any modification that affects this complex behavior is expected to have an impact over the daily energetic budget. We analyzed the possibility of the presence of the cribellum having an effect on the allometric relation between resting metabolic rate and body mass for an ecribellate species (Zosis geniculata) and a cribellate one (Metazygia rogenhoferi), and employed a model selection approach to test if these species had the same allometric relationship as other land-arthropods. Our results show that M. rogenhoferi has a higher resting metabolic rate, while Z. geniculata fitted the allometric prediction for land arthropods. This indicates that the absence of the cribellum is associated with a higher resting metabolic rate, thus explaining the higher promptness to activity found for the ecribellate species. If our result proves to be a general rule among spiders, the radiation of Araneoidea could be connected to a more energy-consuming life style. Thus, we briefly outline an alternative model of diversification of Araneoidea that accounts for this possibility. (C) 2011 Elsevier Ltd. All rights reserved.

A hybrid approach to learn with imbalanced classes using evolutionary algorithms

There is an increasing interest in the application of Evolutionary Algorithms (EAs) to induce classification rules. This hybrid approach can benefit areas where classical methods for rule induction have not been very successful. One example is the induction of classification rules in imbalanced domains. Imbalanced data occur when one or more classes heavily outnumber other classes. Frequently, classical machine learning (ML) classifiers are not able to learn in the presence of imbalanced data sets, inducing classification models that always predict the most numerous classes. In this work, we propose a novel hybrid approach to deal with this problem. We create several balanced data sets with all minority class cases and a random sample of majority class cases. These balanced data sets are fed to classical ML systems that produce rule sets. The rule sets are combined creating a pool of rules and an EA is used to build a classifier from this pool of rules. This hybrid approach has some advantages over undersampling, since it reduces the amount of discarded information, and some advantages over oversampling, since it avoids overfitting. The proposed approach was experimentally analysed and the experimental results show an improvement in the classification performance measured as the area under the receiver operating characteristics (ROC) curve.

An approach to chemotaxonomy to the fatty acid content of some Malvaceae species

Malvales is an order of flowering plants with a controversial circumscription. The relationships between taxa, particularly Malvaceae, Bombacaceae, Sterculiaceae, and Tiliaceae, are not well delineated. Several studies have reported the fatty acid compositions of Malvaceae plants but not for taxonomic purposes. In the present study, the fatty acid composition of oilseeds from seven species belonging to the Malvaceae family was determined by capillary gas chromatography/mass spectrometry (GC/MS), and the quantitative distribution of fatty acids was analyzed by a cluster analysis With Euclidean Distance and UPGMA. The oil content in the seeds was very low (8.3-11.8%). The profile of fatty acids showed that there were two distinct groups: species rich in palmitic acid (Herissantia tiubae, Sidastrum paniculatum and Sida rhombifolia) and species rich in linoleic acid (other Sida species). The fatty acid profiles found for Sida species are consistent with other reported data. Although our data support a distinction between Sida and Sidastrum, more species should be analyzed to evaluate the real taxonomic value of differences in fatty acid content for distinguishing Malvaceae. (C) 2010 Elsevier Ltd. All rights reserved.

Phylogeny of the Neotropical genus Acestrorhynchus (Ostariophysi: Characiformes) based on nuclear and mitochondrial gene sequences and morphology: A total evidence approach

Acestrorhynchus is the sole genus of the family Acestrorhynchidae which includes 14 species currently recognized as valid. Species of Acestrorhynchus comprise small-to-medium sized piscivorous fishes and have been traditionally grouped on the basis of well-defined color patterns. A recent phylogeny, based on morphological characters, could not resolve the phylogenetic affinities of A. heterolepis and the relationships among the species of the clade formed by A. abbreviatus, A. altus, A. falcatus, A. lacustris, and A. pantaneiro. The simultaneous analysis of two mitochondrial genes (16S and ATP synthase subunits 6 and 8) and one nuclear intron (S7) was able to resolve the latter clade, but the position of A. heterolepis remained unresolved. The combination of the molecular and morphological data sets in a total evidence analysis resulted in a well-resolved hypothesis regarding the phylogenetic relationships of Acestrorhynchus species. (C) 2009 Elsevier Inc. All rights reserved.

Evolutionary history of Dengue virus type 4: Insights into genotype phylodynamics

Dengue virus type 4 (DENV-4) circulates in tropical and subtropical countries from Asia and the Americas. Despite the importance of dengue virus distribution, little is known about the worldwide viral spread. Following a Bayesian phylogenetic approach we inferred the evolutionary history of 310 isolates sampled from 37 countries during the time period 1956-2008 and the spreading dynamics for genotypes I and II. The region (tropical rainforest biome) comprised by Malaysia-Thailand was the most likely ancestral area from which the serotype has originated and spread. Interestingly, cross-correlation analysis on demographic time series with the Asian sequences showed a statistically significant negative correlation that could be suggestive of competition among genotypes within the same serotype. (C) 2011 Elsevier B.V. All rights reserved.

Evolutionary tuning of SVM parameter values in multiclass problems

Support vector machines (SVMs) were originally formulated for the solution of binary classification problems. In multiclass problems, a decomposition approach is often employed, in which the multiclass problem is divided into multiple binary subproblems, whose results are combined. Generally, the performance of SVM classifiers is affected by the selection of values for their parameters. This paper investigates the use of genetic algorithms (GAs) to tune the parameters of the binary SVMs in common multiclass decompositions. The developed GA may search for a set of parameter values common to all binary classifiers or for differentiated values for each binary classifier. (C) 2008 Elsevier B.V. All rights reserved.

Evolutionary model trees for handling continuous classes in machine learning

Model trees are a particular case of decision trees employed to solve regression problems. They have the advantage of presenting an interpretable output, helping the end-user to get more confidence in the prediction and providing the basis for the end-user to have new insight about the data, confirming or rejecting hypotheses previously formed. Moreover, model trees present an acceptable level of predictive performance in comparison to most techniques used for solving regression problems. Since generating the optimal model tree is an NP-Complete problem, traditional model tree induction algorithms make use of a greedy top-down divide-and-conquer strategy, which may not converge to the global optimal solution. In this paper, we propose a novel algorithm based on the use of the evolutionary algorithms paradigm as an alternate heuristic to generate model trees in order to improve the convergence to globally near-optimal solutions. We call our new approach evolutionary model tree induction (E-Motion). We test its predictive performance using public UCI data sets, and we compare the results to traditional greedy regression/model trees induction algorithms, as well as to other evolutionary approaches. Results show that our method presents a good trade-off between predictive performance and model comprehensibility, which may be crucial in many machine learning applications. (C) 2010 Elsevier Inc. All rights reserved.

A game theoretical approach to the evolution of structured communication codes

Structured meaning-signal mappings, i.e., mappings that preserve neighborhood relationships by associating similar signals with similar meanings, are advantageous in an environment where signals are corrupted by noise and sub-optimal meaning inferences are rewarded as well. The evolution of these mappings, however, cannot be explained within a traditional language evolutionary game scenario in which individuals meet randomly because the evolutionary dynamics is trapped in local maxima that do not reflect the structure of the meaning and signal spaces. Here we use a simple game theoretical model to show analytically that when individuals adopting the same communication code meet more frequently than individuals using different codes-a result of the spatial organization of the population-then advantageous linguistic innovations can spread and take over the population. In addition, we report results of simulations in which an individual can communicate only with its K nearest neighbors and show that the probability that the lineage of a mutant that uses a more efficient communication code becomes fixed decreases exponentially with increasing K. These findings support the mother tongue hypothesis that human language evolved as a communication system used among kin, especially between mothers and offspring.

A quasispecies approach to the evolution of sexual replication in unicellular organisms

This study develops a simplified model describing the evolutionary dynamics of a population composed of obligate sexually and asexually reproducing, unicellular organisms. The model assumes that the organisms have diploid genomes consisting of two chromosomes, and that the sexual organisms replicate by first dividing into haploid intermediates, which then combine with other haploids, followed by the normal mitotic division of the resulting diploid into two new daughter cells. We assume that the fitness landscape of the diploids is analogous to the single-fitness-peak approach often used in single-chromosome studies. That is, we assume a master chromosome that becomes defective with just one point mutation. The diploid fitness then depends on whether the genome has zero, one, or two copies of the master chromosome. We also assume that only pairs of haploids with a master chromosome are capable of combining so as to produce sexual diploid cells, and that this process is described by second-order kinetics. We find that, in a range of intermediate values of the replication fidelity, sexually reproducing cells can outcompete asexual ones, provided the initial abundance of sexual cells is above some threshold value. The range of values where sexual reproduction outcompetes asexual reproduction increases with decreasing replication rate and increasing population density. We critically evaluate a common approach, based on a group selection perspective, used to study the competition between populations and show its flaws in addressing the evolution of sex problem.