Haplotype distribution of five nuclear genes based on network genealogies and Bayesian inference indicates that Trypanosoma cruzi hybrid strains are polyphyletic

Chagas disease is still a major public health problem in Latin America. Its causative agent, Trypanosoma cruzi, can be typed into three major groups, T. cruzi I, T. cruzi II and hybrids. These groups each have specific genetic characteristics and epidemiological distributions. Several highly virulent strains are found in the hybrid group; their origin is still a matter of debate. The null hypothesis is that the hybrids are of polyphyletic origin, evolving independently from various hybridization events. The alternative hypothesis is that all extant hybrid strains originated from a single hybridization event. We sequenced both alleles of genes encoding EF-1 alpha, actin and SSU rDNA of 26 T. cruzi strains and DHFR-TS and TR of 12 strains. This information was used for network genealogy analysis and Bayesian phylogenies. We found T. cruzi I and T. cruzi II to be monophyletic and that all hybrids had different combinations of T. cruzi I and T. cruzi II haplotypes plus hybrid-specific haplotypes. Bootstrap values (networks) and posterior probabilities (Bayesian phylogenies) of clades supporting the monophyly of hybrids were far below the 95% confidence interval, indicating that the hybrid group is polyphyletic. We hypothesize that T. cruzi I and T. cruzi II are two different species and that the hybrids are extant representatives of independent events of genome hybridization, which sporadically have sufficient fitness to impact on the epidemiology of Chagas disease.

Neural network-based H(infinity) control for fully actuated and underactuated cooperative manipulators

This paper develops H(infinity) control designs based on neural networks for fully actuated and underactuated cooperative manipulators. The neural networks proposed in this paper only adapt the uncertain dynamics of the robot manipulators. They work as a complement of the nominal model. The H(infinity) performance index includes the position errors as well the squeeze force errors between the manipulator end-effectors and the object, which represents a complete disturbance rejection scenario. For the underactuated case, the squeeze force control problem is more difficult to solve due to the loss of some degrees of manipulator actuation. Results obtained from an actual cooperative manipulator, which is able to work as a fully actuated and an underactuated manipulator, are presented. (C) 2008 Elsevier Ltd. All rights reserved.

Minimizing total tardiness in a stochastic single machine scheduling problem using approximate dynamic programming

This paper addresses the non-preemptive single machine scheduling problem to minimize total tardiness. We are interested in the online version of this problem, where orders arrive at the system at random times. Jobs have to be scheduled without knowledge of what jobs will come afterwards. The processing times and the due dates become known when the order is placed. The order release date occurs only at the beginning of periodic intervals. A customized approximate dynamic programming method is introduced for this problem. The authors also present numerical experiments that assess the reliability of the new approach and show that it performs better than a myopic policy.

Scatter search for a real-life heterogeneous fleet vehicle routing problem with time windows and split deliveries in Brazil

In this paper, we consider a real-life heterogeneous fleet vehicle routing problem with time windows and split deliveries that occurs in a major Brazilian retail group. A single depot attends 519 stores of the group distributed in 11 Brazilian states. To find good solutions to this problem, we propose heuristics as initial solutions and a scatter search (SS) approach. Next, the produced solutions are compared with the routes actually covered by the company. Our results show that the total distribution cost can be reduced significantly when such methods are used. Experimental testing with benchmark instances is used to assess the merit of our proposed procedure. (C) 2008 Published by Elsevier B.V.

New simple and efficient heuristics for the uncapacitated single allocation hub location problem

Hub-and-spoke networks are widely studied in the area of location theory. They arise in several contexts, including passenger airlines, postal and parcel delivery, and computer and telecommunication networks. Hub location problems usually involve three simultaneous decisions to be made: the optimal number of hub nodes, their locations and the allocation of the non-hub nodes to the hubs. In the uncapacitated single allocation hub location problem (USAHLP) hub nodes have no capacity constraints and non-hub nodes must be assigned to only one hub. In this paper, we propose three variants of a simple and efficient multi-start tabu search heuristic as well as a two-stage integrated tabu search heuristic to solve this problem. With multi-start heuristics, several different initial solutions are constructed and then improved by tabu search, while in the two-stage integrated heuristic tabu search is applied to improve both the locational and allocational part of the problem. Computational experiments using typical benchmark problems (Civil Aeronautics Board (CAB) and Australian Post (AP) data sets) as well as new and modified instances show that our approaches consistently return the optimal or best-known results in very short CPU times, thus allowing the possibility of efficiently solving larger instances of the USAHLP than those found in the literature. We also report the integer optimal solutions for all 80 CAB data set instances and the 12 AP instances up to 100 nodes, as well as for the corresponding new generated AP instances with reduced fixed costs. Published by Elsevier Ltd.

The single machine earliness and tardiness scheduling problem: lower bounds and a branch-and-bound algorithm

This paper addresses the single machine scheduling problem with a common due date aiming to minimize earliness and tardiness penalties. Due to its complexity, most of the previous studies in the literature deal with this problem using heuristics and metaheuristics approaches. With the intention of contributing to the study of this problem, a branch-and-bound algorithm is proposed. Lower bounds and pruning rules that exploit properties of the problem are introduced. The proposed approach is examined through a computational comparative study with 280 problems involving different due date scenarios. In addition, the values of optimal solutions for small problems from a known benchmark are provided.

The Effect of Framework Design on Fracture Resistance of Metal-Ceramic Implant-Supported Single Crowns

This study evaluated the effect of framework design on the fracture resistance of metal-ceramic implant-supported crowns. Screw-retained molar crowns with a screw access hole composed of metal or porcelain were compared to a cement-retained crown (control). For each group (n = 10), five crowns were subjected to dynamic loading (1,200,000 x 100 N x 2 Hz at 37 degrees C). Afterward, all specimens were loaded to failure using a universal testing machine. Significant differences could be established between the cement-and screw-retained groups (P <= .05), but no difference was found between the screw-retained groups and the specimens subjected to dynamic loading. Occlusal discontinuity of screw-retained crowns affects their resistance, and the metallic support on the screw access hole did not reinforce the crowns. Int J Prosthodont 2010;23:350-352.

Border figure detection using a phase oscillator network with dynamical coupling

Oscillator networks have been developed in order to perform specific tasks related to image processing. Here we analytically investigate the existence of synchronism in a pair of phase oscillators that are short-range dynamically coupled. Then, we use these analytical results to design a network able of detecting border of black-and-white figures. Each unit composing this network is a pair of such phase oscillators and is assigned to a pixel in the image. The couplings among the units forming the network are also dynamical. Border detection emerges from the network activity.

Hypotheses, rationale, design, and methods for prognostic evaluation in type 2 diabetic patients with angiographically normal coronary arteries. The MASS IV-DM Trial

Background: The MASS IV-DM Trial is a large project from a single institution, the Heart Institute (InCor), University of Sao Paulo Medical School, Brazil to study ventricular function and coronary arteries in patients with type 2 diabetes mellitus. Methods/Design: The study will enroll 600 patients with type 2 diabetes who have angiographically normal ventricular function and coronary arteries. The goal of the MASS IV-DM Trial is to achieve a long-term evaluation of the development of coronary atherosclerosis by using angiograms and coronary-artery calcium scan by electron-beam computed tomography at baseline and after 5 years of follow-up. In addition, the incidence of major cardiovascular events, the dysfunction of various organs involved in this disease, particularly microalbuminuria and renal function, will be analyzed through clinical evaluation. In addition, an effort will be made to investigate in depth the presence of major cardiovascular risk factors, especially the biochemical profile, metabolic syndrome inflammatory activity, oxidative stress, endothelial function, prothrombotic factors, and profibrinolytic and platelet activity. An evaluation will be made of the polymorphism as a determinant of disease and its possible role in the genesis of micro- and macrovascular damage. Discussion: The MASS IV-DM trial is designed to include diabetic patients with clinically suspected myocardial ischemia in whom conventional angiography shows angiographically normal coronary arteries. The result of extensive investigation including angiographic follow-up by several methods, vascular reactivity, pro-thrombotic mechanisms, genetic and biochemical studies may facilitate the understanding of so-called micro- and macrovascular disease of DM.

Bone Marrow Concentrate and Bovine Bone Mineral for Sinus Floor Augmentation: A Controlled, Randomized, Single-Blinded Clinical and Histological Trial-Per-Protocol Analysis

Purpose: The purpose of this work was to evaluate the potential of substituting autogenous bone (AB) by bone marrow aspirate concentrate (BMAC). Both AB and BMAC were tested in combination with a bovine bone mineral (BBM) for their ability of new bone formation (NBF) in a multicentric, randomized, controlled, clinical and histological noninferiority trial. Materials and Methods: Forty-five severely atrophied maxillary sinus from 26 patients were evaluated in a partial cross-over design. As test arm, 34 sinus of 25 patients were augmented with BBM and BMAC containing mesenchymal stem cells. Eleven control sinus from 11 patients were augmented with a mixture of 70% BBM and 30% AB. Biopsies were obtained after a 3-4-month healing period at time of implant placement and histomorphometrically analyzed for NBF. Results: NBF was 14.3%+/- 1.8% for the control and nonsignificantly lower (12.6%+/- 1.7%) for the test (90% confidence interval: -4.6 to 1.2). Values for BBM (31.3%+/- 2.7%) were significantly higher for the test compared with control (19.3%+/- 2.5%) (p < 0.0001). Nonmineralized tissue was lower by 3.3% in the test compared with control (57.6%; p = 0.137). Conclusions: NBF after 3-4 months is equivalent in sinus, augmented with BMAC and BBM or a mixture of AB and BBM. This technique could be an alternative for using autografts to stimulate bone formation.

Musical genres: beating to the rhythms of different drums

Online music databases have increased significantly as a consequence of the rapid growth of the Internet and digital audio, requiring the development of faster and more efficient tools for music content analysis. Musical genres are widely used to organize music collections. In this paper, the problem of automatic single and multi-label music genre classification is addressed by exploring rhythm-based features obtained from a respective complex network representation. A Markov model is built in order to analyse the temporal sequence of rhythmic notation events. Feature analysis is performed by using two multi-variate statistical approaches: principal components analysis (unsupervised) and linear discriminant analysis (supervised). Similarly, two classifiers are applied in order to identify the category of rhythms: parametric Bayesian classifier under the Gaussian hypothesis (supervised) and agglomerative hierarchical clustering (unsupervised). Qualitative results obtained by using the kappa coefficient and the obtained clusters corroborated the effectiveness of the proposed method.

Design of 1D and 2D molecule-based magnets with the ligand 4,5-dimethyl-1,2-phenylenebis(oxamato)

Three new bimetallic oxamato-based magnets with the proligand 4,5-dimethyl-1,2-phenylenebis-(oxamato) (dmopba) were synthesized using water or dimethylsulfoxide (DMSO) as solvents. Single crystal X-ray diffraction provided structures for two of them: [MnCu(dmopba)(H(2)O)(3)]n center dot 4nH(2)O (1) and [MnCu(dmopba)(DMSO)(3)](n center dot)nDMSO (2). The crystalline structures for both 1 and 2 consist of linearly ordered oxamato-bridged Mn(II)Cu(II) bimetallic chains. The magnetic characterization revealed a typical behaviour of ferrimagnetic chains for 1 and 2. Least-squares fits of the experimental magnetic data performed in the 300-20 K temperature range led to J(MnCu) = -27.9 cm(-1), g(Cu) = 2.09 and g(Mn) = 1.98 for 1 and J(MnCu) = -30.5 cm(-1), g(Cu) = 2.09 and g(Mn) = 2.02 for 2 (H = -J(MnCu)Sigma S(Mn, i)(S(Cu, i) + S(Cu, i-1))). The two-dimensional ferrimagnetic system [Me(4)N](2n){Co(2)[Cu(dmopba)](3)}center dot 4nDMSO center dot nH(2)O (3) was prepared by reaction of Co(II) ions and an excess of [Cu(dmopba)](2-) in DMSO. The study of the temperature dependence of the magnetic susceptibility as well as the temperature and field dependences of the magnetization revealed a cluster glass-like behaviour for 3.

Reverse engineering in many-body quantum physics: Correspondence between many-body systems and effective single-particle equations

The mapping, exact or approximate, of a many-body problem onto an effective single-body problem is one of the most widely used conceptual and computational tools of physics. Here, we propose and investigate the inverse map of effective approximate single-particle equations onto the corresponding many-particle system. This approach allows us to understand which interacting system a given single-particle approximation is actually describing, and how far this is from the original physical many-body system. We illustrate the resulting reverse engineering process by means of the Kohn-Sham equations of density-functional theory. In this application, our procedure sheds light on the nonlocality of the density-potential mapping of density-functional theory, and on the self-interaction error inherent in approximate density functionals.

Gene Expression Complex Networks: Synthesis, Identification, and Analysis

Thanks to recent advances in molecular biology, allied to an ever increasing amount of experimental data, the functional state of thousands of genes can now be extracted simultaneously by using methods such as cDNA microarrays and RNA-Seq. Particularly important related investigations are the modeling and identification of gene regulatory networks from expression data sets. Such a knowledge is fundamental for many applications, such as disease treatment, therapeutic intervention strategies and drugs design, as well as for planning high-throughput new experiments. Methods have been developed for gene networks modeling and identification from expression profiles. However, an important open problem regards how to validate such approaches and its results. This work presents an objective approach for validation of gene network modeling and identification which comprises the following three main aspects: (1) Artificial Gene Networks (AGNs) model generation through theoretical models of complex networks, which is used to simulate temporal expression data; (2) a computational method for gene network identification from the simulated data, which is founded on a feature selection approach where a target gene is fixed and the expression profile is observed for all other genes in order to identify a relevant subset of predictors; and (3) validation of the identified AGN-based network through comparison with the original network. The proposed framework allows several types of AGNs to be generated and used in order to simulate temporal expression data. The results of the network identification method can then be compared to the original network in order to estimate its properties and accuracy. Some of the most important theoretical models of complex networks have been assessed: the uniformly-random Erdos-Renyi (ER), the small-world Watts-Strogatz (WS), the scale-free Barabasi-Albert (BA), and geographical networks (GG). The experimental results indicate that the inference method was sensitive to average degree k variation, decreasing its network recovery rate with the increase of k. The signal size was important for the inference method to get better accuracy in the network identification rate, presenting very good results with small expression profiles. However, the adopted inference method was not sensible to recognize distinct structures of interaction among genes, presenting a similar behavior when applied to different network topologies. In summary, the proposed framework, though simple, was adequate for the validation of the inferred networks by identifying some properties of the evaluated method, which can be extended to other inference methods.

Simultaneous optimization by neuro-genetic approach for analysis of plant materials by laser induced breakdown spectroscopy

A simultaneous optimization strategy based on a neuro-genetic approach is proposed for selection of laser induced breakdown spectroscopy operational conditions for the simultaneous determination of macronutrients (Ca, Mg and P), micro-nutrients (B, Cu, Fe, Mn and Zn), Al and Si in plant samples. A laser induced breakdown spectroscopy system equipped with a 10 Hz Q-switched Nd:YAG laser (12 ns, 532 nm, 140 mJ) and an Echelle spectrometer with intensified coupled-charge device was used. Integration time gate, delay time, amplification gain and number of pulses were optimized. Pellets of spinach leaves (NIST 1570a) were employed as laboratory samples. In order to find a model that could correlate laser induced breakdown spectroscopy operational conditions with compromised high peak areas of all elements simultaneously, a Bayesian Regularized Artificial Neural Network approach was employed. Subsequently, a genetic algorithm was applied to find optimal conditions for the neural network model, in an approach called neuro-genetic, A single laser induced breakdown spectroscopy working condition that maximizes peak areas of all elements simultaneously, was obtained with the following optimized parameters: 9.0 mu s integration time gate, 1.1 mu s delay time, 225 (a.u.) amplification gain and 30 accumulated laser pulses. The proposed approach is a useful and a suitable tool for the optimization process of such a complex analytical problem. (C) 2009 Elsevier B.V. All rights reserved.