Stochastic competitive learning in complex networks

Competitive learning is an important machine learning approach which is widely employed in artificial neural networks. In this paper, we present a rigorous definition of a new type of competitive learning scheme realized on large-scale networks. The model consists of several particles walking within the network and competing with each other to occupy as many nodes as possible, while attempting to reject intruder particles. The particle's walking rule is composed of a stochastic combination of random and preferential movements. The model has been applied to solve community detection and data clustering problems. Computer simulations reveal that the proposed technique presents high precision of community and cluster detections, as well as low computational complexity. Moreover, we have developed an efficient method for estimating the most likely number of clusters by using an evaluator index that monitors the information generated by the competition process itself. We hope this paper will provide an alternative way to the study of competitive learning.

Echinococcus granulosus Antigen B Structure: Subunit Composition and Oligomeric States

Background: Antigen B (AgB) is the major protein secreted by the Echinococcus granulosus metacestode and is involved in key host-parasite interactions during infection. The full comprehension of AgB functions depends on the elucidation of several structural aspects that remain unknown, such as its subunit composition and oligomeric states. Methodology/Principal Findings: The subunit composition of E. granulosus AgB oligomers from individual bovine and human cysts was assessed by mass spectrometry associated with electrophoretic analysis. AgB8/1, AgB8/2, AgB8/3 and AgB8/4 subunits were identified in all samples analyzed, and an AgB8/2 variant (AgB8/2v8) was found in one bovine sample. The exponentially modified protein abundance index (emPAI) was used to estimate the relative abundance of the AgB subunits, revealing that AgB8/1 subunit was relatively overrepresented in all samples. The abundance of AgB8/3 subunit varied between bovine and human cysts. The oligomeric states formed by E. granulosus AgB and recombinant subunits available, rAgB8/1, rAgB8/2 and rAgB8/3, were characterized by native PAGE, light scattering and microscopy. Recombinant subunits showed markedly distinct oligomerization behaviors, forming oligomers with a maximum size relation of rAgB8/3 >rAgB8/2>rAgB8/1. Moreover, the oligomeric states formed by rAgB8/3 subunit were more similar to those observed for AgB purified from hydatid fluid. Pressure-induced dissociation experiments demonstrated that the molecular assemblies formed by the more aggregative subunits, rAgB8/2 and rAgB8/3, also display higher structural stability. Conclusions/Significance: For the first time, AgB subunit composition was analyzed in samples from single hydatid cysts, revealing qualitative and quantitative differences between samples. We showed that AgB oligomers are formed by different subunits, which have distinct abundances and oligomerization properties. Overall, our findings have significantly contributed to increase the current knowledge on AgB expression and structure, highlighting issues that may help to understand the parasite adaptive response during chronic infection.

A multi-objective adaptive immune algorithm for multi-application NoC mapping

Current SoC design trends are characterized by the integration of larger amount of IPs targeting a wide range of application fields. Such multi-application systems are constrained by a set of requirements. In such scenario network-on-chips (NoC) are becoming more important as the on-chip communication structure. Designing an optimal NoC for satisfying the requirements of each individual application requires the specification of a large set of configuration parameters leading to a wide solution space. It has been shown that IP mapping is one of the most critical parameters in NoC design, strongly influencing the SoC performance. IP mapping has been solved for single application systems using single and multi-objective optimization algorithms. In this paper we propose the use of a multi-objective adaptive immune algorithm (M(2)AIA), an evolutionary approach to solve the multi-application NoC mapping problem. Latency and power consumption were adopted as the target multi-objective functions. To compare the efficiency of our approach, our results are compared with those of the genetic and branch and bound multi-objective mapping algorithms. We tested 11 well-known benchmarks, including random and real applications, and combines up to 8 applications at the same SoC. The experimental results showed that the M(2)AIA decreases in average the power consumption and the latency 27.3 and 42.1 % compared to the branch and bound approach and 29.3 and 36.1 % over the genetic approach.

SAG2A protein from Toxoplasma gondii interacts with both innate and adaptive immune compartments of infected hosts

Abstract Background Toxoplasma gondii is an intracellular parasite that causes relevant clinical disease in humans and animals. Several studies have been performed in order to understand the interactions between proteins of the parasite and host cells. SAG2A is a 22 kDa protein that is mainly found in the surface of tachyzoites. In the present work, our aim was to correlate the predicted three-dimensional structure of this protein with the immune system of infected hosts. Methods To accomplish our goals, we performed in silico analysis of the amino acid sequence of SAG2A, correlating the predictions with in vitro stimulation of antigen presenting cells and serological assays. Results Structure modeling predicts that SAG2A protein possesses an unfolded C-terminal end, which varies its conformation within distinct strain types of T. gondii. This structure within the protein shelters a known B-cell immunodominant epitope, which presents low identity with its closest phyllogenetically related protein, an orthologue predicted in Neospora caninum. In agreement with the in silico observations, sera of known T. gondii infected mice and goats recognized recombinant SAG2A, whereas no serological cross-reactivity was observed with samples from N. caninum animals. Additionally, the C-terminal end of the protein was able to down-modulate pro-inflammatory responses of activated macrophages and dendritic cells. Conclusions Altogether, we demonstrate herein that recombinant SAG2A protein from T. gondii is immunologically relevant in the host-parasite interface and may be targeted in therapeutic and diagnostic procedures designed against the infection.