Opinion dynamics of learning agents: does seeking consensus lead to disagreement?

We study opinion dynamics in a population of interacting adaptive agents voting on a set of issues represented by vectors. We consider agents who can classify issues into one of two categories and can arrive at their opinions using an adaptive algorithm. Adaptation comes from learning and the information for the learning process comes from interacting with other neighboring agents and trying to change the internal state in order to concur with their opinions. The change in the internal state is driven by the information contained in the issue and in the opinion of the other agent. We present results in a simple yet rich context where each agent uses a Boolean perceptron to state their opinion. If the update occurs with information asynchronously exchanged among pairs of agents, then the typical case, if the number of issues is kept small, is the evolution into a society torn by the emergence of factions with extreme opposite beliefs. This occurs even when seeking consensus with agents with opposite opinions. If the number of issues is large, the dynamics becomes trapped, the society does not evolve into factions and a distribution of moderate opinions is observed. The synchronous case is technically simpler and is studied by formulating the problem in terms of differential equations that describe the evolution of order parameters that measure the consensus between pairs of agents. We show that for a large number of issues and unidirectional information flow, global consensus is a fixed point; however, the approach to this consensus is glassy for large societies.

In Vitro and In Vivo Antiplasmodial Activities of Risedronate and Its Interference with Protein Prenylation in Plasmodium falciparum

The increasing resistance of malarial parasites to almost all available drugs calls for the identification of new compounds and the detection of novel targets. Here, we establish the antimalarial activities of risedronate, one of the most potent bisphosphonates clinically used to treat bone resorption diseases, against blood stages of Plasmodium falciparum (50% inhibitory concentration [IC(50)] of 20.3 +/- 1.0 mu M). We also suggest a mechanism of action for risedronate against the intraerythrocytic stage of P. falciparum and show that protein prenylation seems to be modulated directly by this drug. Risedronate inhibits the transfer of the farnesyl pyrophosphate group to parasite proteins, an effect not observed for the transfer of geranylgeranyl pyrophosphate. Our in vivo experiments further demonstrate that risedronate leads to an 88.9% inhibition of the rodent parasite Plasmodium berghei in mice on the seventh day of treatment; however, risedronate treatment did not result in a general increase of survival rates.

Violacein Extracted from Chromobacterium violaceum Inhibits Plasmodium Growth In Vitro and In Vivo

Violacein is a violet pigment extracted from the gram-negative bacterium Chromobacterium violaceum. It presents bactericidal, tumoricidal, trypanocidal, and antileishmanial activities. We show that micromolar concentrations efficiently killed chloroquine-sensitive and -resistant Plasmodium falciparum strains in vitro; inhibited parasitemia in vivo, even after parasite establishment; and protected Plasmodium chabaudi chabaudi-infected mice from a lethal challenge.

In Vitro Activity of the Antifungal Plant Defensin RsAFP2 against Candida Isolates and Its In Vivo Efficacy in Prophylactic Murine Models of Candidiasis

We show that RsAFP2, a plant defensin that interacts with fungal glucosylceramides, is active against Candida albicans, inhibits to a lesser extent other Candida species, and is nontoxic to mammalian cells. Moreover, glucosylceramide levels in Candida species correlate with RsAFP2 sensitivity. We found RsAFP2 prophylactically effective against murine candidiasis.

Design of novel antituberculosis compounds using graph-theoretical and substructural approaches

The increasing resistance of Mycobacterium tuberculosis to the existing drugs has alarmed the worldwide scientific community. In an attempt to overcome this problem, two models for the design and prediction of new antituberculosis agents were obtained. The first used a mixed approach, containing descriptors based on fragments and the topological substructural molecular design approach (TOPS-MODE) descriptors. The other model used a combination of two-dimensional (2D) and three-dimensional (3D) descriptors. A data set of 167 compounds with great structural variability, 72 of them antituberculosis agents and 95 compounds belonging to other pharmaceutical categories, was analyzed. The first model showed sensitivity, specificity, and accuracy values above 80% and the second one showed values higher than 75% for these statistical indices. Subsequently, 12 structures of imidazoles not included in this study were designed, taking into account the two models. In both cases accuracy was 100%, showing that the methodology in silico developed by us is promising for the rational design of antituberculosis drugs.