Skull diversity and evolution in miniaturized amphibians, genus Brachycephalus (Anura: Brachycephalidae).

Miniaturized amphibians of the genus Brachycephalus are phenotypically diverse. The species of Brachycephalus have bufoniform or leptodactyliform Baupläne and any of three skeletal states: nonhyperossified, hyperossified without dorsal shield, and hyperossified with dorsal shield. We integrate high-resolution microcomputed tomography, geometric morphometrics, and an estimate of molecular phylogenetic relationships to investigate skull diversity in shape and size-shape space in selected species of Brachycephalus. Skull diversity amongst species of Brachycephalus can be partitioned into shape and size-shape space according to the four conditions of skeletal states-Baupläne, namely, nonhyperossified leptodactyliform, nonhyperossified bufoniform, hyperossified bufoniform without dorsal shield, and hyperossified bufoniform with dorsal shield. Skull diversity in shape and size-shape space in nonhyperossified leptodactyliform species of Brachycephalus is markedly larger, when compared to skull diversity in species of the three other conditions of skeletal states-Baupläne. Variation in skull shape scales with size across Brachycephalus and, therefore, can be explained by allometry. Skull diversity, Baupläne, and skeletal states covary to a large extent with monophyletic lineages of Brachycephalus, as revealed by a mitochondrial DNA species tree. Nonhyperossified bufoniform species and hyperossified bufoniform species with or without dorsal shield are monophyletic lineages, as inferred from a mitochondrial DNA species tree. Nonhyperossified leptodactyliform species of Brachycephalus do not share, however, a most recent common ancestor. The nonhyperossified leptodactyliform species of Brachycephalus, due to their marked skull diversity and lack of monophyly, emerge as evolutionarily complex. Therefore, further sampling of the nonhyperossified leptodactyliform condition of skeletal states-Baupläne will be necessary to further understand the evolutionary history of Brachycephalus.

Os anfíbios miniaturizados do gênero Brachycephalus são fenotipicamente diversos. As espécies de Brachycephalus têm o plano corporal bufoniforme ou leptodactyliforme e três estados esqueléticos: não-hiperossificado, hiperossificado sem placa dorsal e hiperossificado com placa dorsal. Neste trabalho nós integramos tomografia micro-computadorizada de alta resolução, morfometria geométrica e uma estimativa de relações filogenéticas moleculares para investigar diversidade craniana nos espaços de forma e tamanho-forma em determinadas espécies de Brachycephalus. A diversidade craniana entre espécies de Brachycephalus pode ser dividida no espaço de forma e tamanho-forma segundo as quatro condições de plano corporal-esqueleto, a saber, leptodactiliforme não-hiperossificado, bufoniforme não-hiperossificado, bufoniforme hiperossificado sem placa dorsal e bufoniforme hiperossificado com placa dorsal. A diversidade craniana nos espaços de forma e tamanho-forma nas espécies de Brachycephalus leptodactiliformes não-hiperossificadas é pronunciadamente maior quando comparada àquela das espécies nas outras trcs condições de plano corporal-esqueleto. A variação na forma craniana aumenta com o tamanho craniano em Brachycephalus e, portanto, pode ser explicada por alometria. Diversidade craniana, plano corporal e estados esqueléticos covariam consideravelmente com as linhagens monofiléticas de Brachycephalus, como estimado pela filogenia mitocondrial. As espécies de Brachycephalus leptodactiliformes não-hiperossificadas e bufoniformes hiperossificadas com ou sem placa dorsal são linhagens monofiléticas, como estimado pela filogenia mitocondrial. As espécies leptodactiliformes não-hiperossificadas não compartilham, todavia, um ancestral comum mais recente. As espécies de Brachycephalus leptodactiliformes não-hiperossificadas devido a sua pronunciada diversidade e não-monofilia emergem claramente como entidades evolutivamente complexas. Por conseguinte, a amostragem adicional de populações leptodactiliformes não-hiperossificadas será necessária para uma melhor compreensão da história evolutiva do gênero Brachycephalus.

Using association rule mining to jointly detect clinical features and differentially expressed genes related to chronic inflammatory diseases.

OBJECTIVE: It is increasingly common to find patients affected by a combination of type 2 diabetes mellitus (T2DM), dyslipidemia (DLP) and periodontitis (PD), which are chronic inflammatory diseases. More studies able to capture unknown relationships among these diseases will contribute to raise biological and clinical evidence. The aim of this study was to apply association rule mining (ARM) to discover whether there are consistent patterns of clinical features (CFs) and differentially expressed genes (DEGs) relevant to these diseases. We intend to reinforce the evidence of the T2DM-DLP-PD-interplay and demonstrate the ARM ability to provide new insights into multivariate pattern discovery. METHODS: We utilized 29 clinical glycemic, lipid and periodontal parameters from 143 patients divided into five groups based upon diabetic, dyslipidemic and periodontal conditions (including a healthy-control group). At least 5 patients from each group were selected to assess the transcriptome by microarray. ARM was utilized to assess relevant association rules considering: (i) only CFs; and (ii) CFs+DEGs, such that the identified DEGs, specific to each group of patients, were submitted to gene expression validation by quantitative polymerase chain reaction (qPCR). RESULTS: We obtained 78 CF-rules and 161 CF+DEG-rules. Based on their clinical significance, Periodontists and Geneticist experts selected 11 CF-rules, and 5 CF+DEG-rules. From the five DEGs prospected by the rules, four of them were validated by qPCR as significantly different from the control group; and two of them validated the previous microarray findings. CONCLUSIONS: ARM was a powerful data analysis technique to identify multivariate patterns involving clinical and molecular profiles of patients affected by specific pathological panels. ARM proved to be an effective mining approach to analyze gene expression with the advantage of including patient's CFs. A combination of CFs and DEGs might be employed in modeling the patient's chance to develop complex diseases, such as those studied here.

The role of the interaction network in the emergence of diversity of behavior.

How can systems in which individuals' inner workings are very similar to each other, as neural networks or ant colonies, produce so many qualitatively different behaviors, giving rise to roles and specialization? In this work, we bring new perspectives to this question by focusing on the underlying network that defines how individuals in these systems interact. We applied a genetic algorithm to optimize rules and connections of cellular automata in order to solve the density classification task, a classical problem used to study emergent behaviors in decentralized computational systems. The networks used were all generated by the introduction of shortcuts in an originally regular topology, following the small-world model. Even though all cells follow the exact same rules, we observed the existence of different classes of cells' behaviors in the best cellular automata found-most cells were responsible for memory and others for integration of information. Through the analysis of structural measures and patterns of connections (motifs) in successful cellular automata, we observed that the distribution of shortcuts between distant regions and the speed in which a cell can gather information from different parts of the system seem to be the main factors for the specialization we observed, demonstrating how heterogeneity in a network can create heterogeneity of behavior.

An extended echo state network using Volterra filtering and principal component analysis.

Echo state networks (ESNs) can be interpreted as promoting an encouraging compromise between two seemingly conflicting objectives: (i) simplicity of the resulting mathematical model and (ii) capability to express a wide range of nonlinear dynamics. By imposing fixed weights to the recurrent connections, the echo state approach avoids the well-known difficulties faced by recurrent neural network training strategies, but still preserves, to a certain extent, the potential of the underlying structure due to the existence of feedback loops within the dynamical reservoir. Moreover, the overall training process is relatively simple, as it amounts essentially to adapting the readout, which usually corresponds to a linear combiner. However, the linear nature of the output layer may limit the capability of exploring the available information, since higher-order statistics of the signals are not taken into account. In this work, we present a novel architecture for an ESN in which the linear combiner is replaced by a Volterra filter structure. Additionally, the principal component analysis technique is used to reduce the number of effective signals transmitted to the output layer. This idea not only improves the processing capability of the network, but also preserves the simplicity of the training process. The proposed architecture is then analyzed in the context of a set of representative information extraction problems, more specifically supervised and unsupervised channel equalization, and blind separation of convolutive mixtures. The obtained results, when compared to those produced by already proposed ESN versions, highlight the benefits brought by the novel network proposal and characterize it as a promising tool to deal with challenging signal processing tasks.

Recent advances in gene expression data clustering: a case study with comparative results.

Several advanced techniques have been proposed for data clustering and many of them have been applied to gene expression data, with partial success. The high dimensionality and the multitude of admissible perspectives for data analysis of gene expression require additional computational resources, such as hierarchical structures and dynamic allocation of resources. We present an immune-inspired hierarchical clustering device, called hierarchical artificial immune network (HaiNet), especially devoted to the analysis of gene expression data. This technique was applied to a newly generated data set, involving maize plants exposed to different aluminum concentrations. The performance of the algorithm was compared with that of a self-organizing map, which is commonly adopted to deal with gene expression data sets. More consistent and informative results were obtained with HaiNet.

A multi-neighbor-joining approach for phylogenetic tree reconstruction and visualization.

The computationally challenging problem of reconstructing the phylogeny of a set of contemporary data, such as DNA sequences or morphological attributes, was treated by an extended version of the neighbor-joining (NJ) algorithm. The original NJ algorithm provides a single-tree topology, after a cascade of greedy pairing decisions that tries to simultaneously optimize the minimum evolution and the least squares criteria. Given that some sub-trees are more stable than others, and that the minimum evolution tree may not be achieved by the original NJ algorithm, we propose a multi-neighbor-joining (MNJ) algorithm capable of performing multiple pairing decisions at each level of the tree reconstruction, keeping various partial solutions along the recursive execution of the NJ algorithm. The main advantages of the new reconstruction procedure are: 1) as is the case for the original NJ algorithm, the MNJ algorithm is still a low-cost reconstruction method; 2) a further investigation of the alternative topologies may reveal stable and unstable sub-trees; 3) the chance of achieving the minimum evolution tree is greater; 4) tree topologies with very similar performances will be simultaneously presented at the output. When there are multiple unrooted tree topologies to be compared, a visualization tool is also proposed, using a radial layout to uniformly distribute the branches with the help of well-known metaheuristics used in computer science.

Delayed density dependence at the immature stage in insects and the dynamic behavior of nonlinear difference equations

A growing body of evidence indicates that the conditions experienced by immatures in insects, in particular crowding, have a lasting consequence for the population dynamics o adults. In this case, as first demonstrated by Prout (1), the dynamic characteristics of populations sampled at the adult stage may not be derived. We examine the dynamic properties of the model proposed by Prout to take into account the delayed effect of two life-history traits, survival and fecundity, occurring at the immature stage. Two parameters are present in the model> Beta, which describes the rate of change in survival and fecundity with respect to increasing density of immatures, and alpha which combines maximum survival and fecundity. The latter parameter is found to determine the dynamic behavior of Prount'a model, and this model is shown to tbe a reparametrization of the classical discrete logistic equation. In the interval 1 < alpha < e2 there is one fixed point, at alpha = e2 there is period doubling bifuraction, and due to the appearance of period three Prout's model shows chaotic behavior. The theoretical results are briefly discussed in the light of data on the equilibrium dynamics of Drosophila and blowflies.