Sequence diversity and evolutionary dynamics of the dimorphic antigen merozoite surface protein-6 and other Msp genes of Plasmodium falciparum

Immune evasion by Plasmodium falciparum is favored by extensive allelic diversity of surface antigens. Some of them, most notably the vaccine-candidate merozoite surface protein (MSP)-1, exhibit a poorly understood pattern of allelic dimorphism, in which all observed alleles group into two highly diverged allelic families with few or no inter-family recombinants. Here we describe contrasting levels and patterns of sequence diversity in genes encoding three MSP-1-associated surface antigens of P. falciparum, ranging from an ancient allelic dimorphism in the Msp-6 gene to a near lack of allelic divergence in Msp-9 to a more classical multi-allele polymorphism in Msp-7 Other members of the Msp-7 gene family exhibit very little polymorphism in non-repetitive regions. A comparison of P. falciparum Msp-6 sequences to an orthologous sequence from P. reichenowi provided evidence for distinct evolutionary histories of the 5` and 3` segments of the dimorphic region in PfMsp-6, consistent with one dimorphic lineage having arisen from recombination between now-extinct ancestral alleles. In addition. we uncovered two surprising patterns of evolution in repetitive sequence. Firsts in Msp-6, large deletions are associated with (nearly) identical sequence motifs at their borders. Second, a comparison of PfMsp-9 with the P. reichenowi ortholog indicated retention of a significant inter-unit diversity within an 18-base pair repeat within the coding region of P. falciparum, but homogenization in P. reichenowi. (C) 2009 Elsevier B.V. All rights reserved.

Evolutionary history of trypanosomes from South American caiman (Caiman yacare) and African crocodiles inferred by phylogenetic analyses using SSU rDNA and gGAPDH genes

In this study, using a combined data set of SSU rDNA and gGAPDH gene sequences, we provide phylogenetic evidence that supports Clustering of crocodilian trypanosomes from the Brazilian Caiman yacare (Alligatoridae) and Trypanosoma grayi, a species that Circulates between African crocodiles (Crocodilydae) and tsetse flies. In a survey of trypanosomes in Caiman yacare from the Brazilian Pantanal, the prevalence of trypanosome infection was 35% as determined by microhaematocrit and haemoculture, and 9 cultures were obtained. The morphology of trypomastigotes from caiman blood and tissue imprints was compared with those described for other crocodilian trypanosomes. Differences in morphology and growth behaviour of caiman trypanosomes were corroborated by molecular polymorphism that revealed 2 genotypes. Eight isolates were ascribed to genotype Cay01 and 1 to genotype Cay02. Phylogenetic inferences based on concatenated SSU rDNA and gGAPDII sequences showed that caiman isolates are closely related to T. grayi, constituting a well-supported monophyletic assemblage (clade T. grayi). Divergence time estimates based on clade composition, and biogeographical and geological events were used to discuss the relationships between the evolutionary histories of crocodilian trypanosomes and their hosts.

Efficiency issues of evolutionary k-means

One of the top ten most influential data mining algorithms, k-means, is known for being simple and scalable. However, it is sensitive to initialization of prototypes and requires that the number of clusters be specified in advance. This paper shows that evolutionary techniques conceived to guide the application of k-means can be more computationally efficient than systematic (i.e., repetitive) approaches that try to get around the above-mentioned drawbacks by repeatedly running the algorithm from different configurations for the number of clusters and initial positions of prototypes. To do so, a modified version of a (k-means based) fast evolutionary algorithm for clustering is employed. Theoretical complexity analyses for the systematic and evolutionary algorithms under interest are provided. Computational experiments and statistical analyses of the results are presented for artificial and text mining data sets. (C) 2010 Elsevier B.V. All rights reserved.

Evolutionary fuzzy clustering of relational data

This paper is concerned with the computational efficiency of fuzzy clustering algorithms when the data set to be clustered is described by a proximity matrix only (relational data) and the number of clusters must be automatically estimated from such data. A fuzzy variant of an evolutionary algorithm for relational clustering is derived and compared against two systematic (pseudo-exhaustive) approaches that can also be used to automatically estimate the number of fuzzy clusters in relational data. An extensive collection of experiments involving 18 artificial and two real data sets is reported and analyzed. (C) 2011 Elsevier B.V. All rights reserved.

On the efficiency of evolutionary fuzzy clustering

This paper tackles the problem of showing that evolutionary algorithms for fuzzy clustering can be more efficient than systematic (i.e. repetitive) approaches when the number of clusters in a data set is unknown. To do so, a fuzzy version of an Evolutionary Algorithm for Clustering (EAC) is introduced. A fuzzy cluster validity criterion and a fuzzy local search algorithm are used instead of their hard counterparts employed by EAC. Theoretical complexity analyses for both the systematic and evolutionary algorithms under interest are provided. Examples with computational experiments and statistical analyses are also presented.

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.

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.

Crop rotation scheduling with adjacency constraints

In this article we propose a 0-1 optimization model to determine a crop rotation schedule for each plot in a cropping area. The rotations have the same duration in all the plots and the crops are selected to maximize plot occupation. The crops may have different production times and planting dates. The problem includes planting constraints for adjacent plots and also for sequences of crops in the rotations. Moreover, cultivating crops for green manuring and fallow periods are scheduled into each plot. As the model has, in general, a great number of constraints and variables, we propose a heuristics based on column generation. To evaluate the performance of the model and the method, computational experiments using real-world data were performed. The solutions obtained indicate that the method generates good results.

Fast heuristics for the Steiner tree problem with revenues, budget and hop constraints

This article describes and compares three heuristics for a variant of the Steiner tree problem with revenues, which includes budget and hop constraints. First, a greedy method which obtains good approximations in short computational times is proposed. This initial solution is then improved by means of a destroy-and-repair method or a tabu search algorithm. Computational results compare the three methods in terms of accuracy and speed. (C) 2007 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.

Observational constraints on the dark energy and dark matter mutual coupling

We examine different phenomenological interaction models for Dark Energy and Dark Matter by performing statistical joint analysis with observational data arising from the 182 Gold type la supernova samples, the shift parameter of the Cosmic Microwave Background given by the three-year Wilkinson Microwave Anisotropy Probe observations, the baryon acoustic oscillation measurement from the Sloan Digital Sky Survey and age estimates of 35 galaxies. Including the time-dependent observable, we add sensitivity of measurement and give complementary results for the fitting. The compatibility among three different data sets seem to imply that the coupling between dark energy and dark matter is a small positive value, which satisfies the requirement to solve the coincidence problem and the second law of thermodynamics, being compatible with previous estimates. (c) 2008 Elsevier B.V. All rights reserved.

Observational constraints on holographic tachyonic dark energy in interaction with dark matter

We discuss an interacting tachyonic dark energy model in the context of the holographic principle. The potential of the holographic tachyon field in interaction with dark matter is constructed. The model results are compared with CMB shift parameter, baryonic acoustic oscilations, lookback time and the Constitution supernovae sample. The coupling constant of the model is compatible with zero, but dark energy is not given by a cosmological constant.

Electroweak precision constraints on the Lee-Wick standard model

We perform an analysis of the electroweak precision observables in the Lee-Wick Standard Model. The most stringent restrictions come from the S and T parameters that receive important tree level and one loop contributions. In general the model predicts a large positive S and a negative T. To reproduce the electroweak data, if all the Lee-Wick masses are of the same order, the Lee-Wick scale is of order 5 TeV. We show that it is possible to find some regions in the parameter space with a fermionic state as light as 2.4-3.5 TeV, at the price of rising all the other masses to be larger than 5-8 TeV. To obtain a light Higgs with such heavy resonances a fine-tuning of order a few per cent, at least, is needed. We also propose a simple extension of the model including a fourth generation of Standard Model fermions with their Lee-Wick partners. We show that in this case it is possible to pass the electroweak constraints with Lee-Wick fermionic masses of order 0.4-1.5 TeV and Lee-Wick gauge masses of order 3 TeV.

On the effects of geographical constraints on task execution in complex

In the present work, the effects of spatial constraints on the efficiency of task execution in systems underlain by geographical complex networks are investigated, where the probability of connection decreases with the distance between the nodes. The investigation considers several configurations of the parameters defining the network connectivity, and the Barabasi-Albert network model is also considered for comparisons. The results show that the effect of connectivity is significant only for shorter tasks, the locality of connection simplied by the spatial constraints reduces efficiency, and the addition of edges can improve the efficiency of the execution, although with increasing locality of the connections the improvement is small.

Evaluation of petrogenetic models for intermediate and silicic plutonic rocks from the Sierra de Valle Fertil-La Huerta, Argentina: Petrologic constraints on the origin of igneous rocks in the Ordovician Famatinian-Puna paleoarc

The whole Valle Fertil-La Huerta section appears as a calc-alkaline plutonic suite typical of a destructive plate margin. New Sr and Nd isotopic whole-rock data and published whole-rock geochemistry suggest that the less-evolved intermediate (dioritic) rocks can be derived by magmatic differentiation, mainly by hornblende + plagioclase +/- Fe-Ti oxide fractional crystallization, from mafic (gabbroic) igneous precursors. Closed-system differentiation, however, cannot produce the typical intermediate (tonalitic) and silicic (granodioritic) plutonic rocks, which requires a preponderant contribution of crustal components. Intermediate and silicic plutonic rocks from Valle Fertil-La Huerta section have formed in a plate subduction setting where the thermal and material input of mantle-derived magmas promoted fusion of fertile metasedimentary rocks and favored mixing of gabbroic or dioritic magmas with crustal granitic melts. Magma mixing is observable in the field and evident in variations of chemical elemental parameters and isotopic ratios, revealing that hybridization coupled with fractionation of magmas took place in the crust. Consideration of the whole-rock geochemical and isotopic data in the context of the Famatinian-Puna magmatic belt as a whole demonstrates that the petrologic model postulated for the Sierra Valle Fertil-La Huerta section has the potential to explain the generation of plutonic and volcanic rocks across the Early Ordovician paleoarc from central and northwestern Argentina. As the petrologic model does not require the intervention of old Precambrian continental crust, the nature of the basement on which thick accretionary turbiditic sequences were deposited remains a puzzling aspect. Discussion in this paper provides insights into the nature of magmatic source rocks and mechanisms of magma generation in Cordilleran-type volcano-plutonic arcs of destructive plate margins. (C) 2010 Elsevier Ltd. All rights reserved.