Effects of Infrared-LED Illumination Applied During High-Intensity Treadmill Training in Postmenopausal Women

Background data: Technology and physical exercise can enhance physical performance during aging. Objective: The purpose of this study was to investigate the effects of infrared-light-emitting diode (LED) illumination (850 nm) applied during treadmill training. Materials and methods: Twenty postmenopausal women participated in this study. They were randomly divided into two groups. The LED group performed treadmill training associated with infrared-LED illumination (n = 10) and the control group performed only treadmill training (n = 10). The training was performed during 3 months, twice a week during 30 min at intensities between 85 and 90% of maximal heart rate. The irradiation parameters were 31 mW/cm(2), treatment time 30 min, 14,400 J of total energy and 55.8 J/cm(2) of fluence. Physiological, biomechanical, and body composition parameters were measured at the baseline and after 3 months. Results: Both groups improved the time of tolerance limit (Tlim) (p < 0.05) during submaximal constant-speed testing. The peak torque did not differ between groups. However, the results showed significantly higher values of power [from 56 +/- 10 to 73 +/- 8W (p = 0.002)] and total work [from 1,537 +/- 295 to 1,760 +/- 262 J (p = 0.006)] for the LED group when compared to the control group [power: from 58 +/- 14 to 60 +/- 15W (p >= 0.05) and total work: from 1,504 +/- 404 to 1,622 +/- 418 J (p >= 0.05)]. The fatigue significantly increased for the control group [from 51 +/- 6 to 58 +/- 5 % (p = 0.04)], but not for the LED group [from 60 +/- 10 to 60 +/- 4 % (p >= 0.05)]. No significant differences in body composition were observed for either group. Conclusions: Infrared-LED illumination associated with treadmill training can improve muscle power and delay leg fatigue in postmenopausal women.

Effective coordination concept applied for phase change (GeTe)(m)(Sb(2)Te(3))(n) compounds

In this work, we employed the effective coordination concept to study the local environments of the Ge, Sb, and Te atoms in the Ge(m)Sb(2n)Te(m+3n) compounds. From our calculations and analysis, we found an average effective coordination number (ECN) reduction of 1.59, 1.42, and 1.37, for the Ge, Sb, Te atoms in the phase transition from crystalline, ECN=5.55 (Ge), 5.73 (Sb), 4.37 (Te), to the amorphous phase, ECN=3.96 (Ge), 4.31 (Sb), 3.09 (Te), for the Ge(2)Sb(2)Te(5) composition. Similar changes are observed for other compositions. Thus, our results indicate that the coordination changes from the crystalline to amorphous phase are not large as previously assumed in the literature, i.e., from sixfold to fourfold for Ge, which can contribute to obtain a better understanding of the crystalline to amorphous phase transition. (C) 2011 American Institute of Physics. [doi:10.1063/1.3533422]

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.

Screening of Microorganisms Producing Cold-Active Oxidoreductases to Be Applied in Enantioselective Alcohol Oxidation. An Antarctic Survey

Several microorganisms were isolated from soil/sediment samples of Antarctic Peninsula. The enrichment technique using (RS)-.1-(phenyl) ethanol as a carbon source allowed us to isolate 232 psychrophile/psychrotroph microorganisms. We also evaluated the enzyme activity (oxidoreductases) for enantioselective oxidation reactions, by using derivatives of (RS)-.1-(phenyl) ethanol as substrates. Among the studied microorganisms, 15 psychrophile/psychrotroph strains contain oxidoreductases that catalyze the (S)-.enantiomer oxidation from racemic alcohols to their corresponding ketones. Among the identified microorganisms, Flavobacterium sp. and Arthrobacter sp. showed excellent enzymatic activity. These new bacteria strains were selected for optimization study, in which the (RS)-.1-(4-.methyl-.phenyl) ethanol oxidation was evaluated in several reaction conditions. From these studies, it was observed that Flavobacterium sp. has an excellent enzymatic activity at 10 degrees C and Arthrobacter sp. at 15 and 25 degrees C. We have also determined the growth curves of these bacteria, and both strains showed optimum growth at 25 degrees C, indicating that these bacteria are psychrotroph.

Fertilizers applied to certified organic tomato culture

The tomato culture demands large quantities of mineral nutrients, which are supplied by synthetic fertilizers in the conventional cultivation system. In the organic cultivation system only alternative fertilizers are allowed by the certifiers and accepted as safe for humans and environment. The chemical composition of rice bran, oyster flour, cattle manure and ground charcoal, as well as soils and tomato fruits were evaluated by instrumental neutron activation analysis (INAA). The potential contribution of organic fertilizers to the enrichment of chemical elements in soil and their transfer to fruits was investigated using concentration ratios for fertilizer and soil samples, and also for soil and tomato. Results evidenced that these alternative fertilizers could be taken as important sources of Br, Ca, Ce, K, Na and Zn for the organic tomato culture.

Chemical characterization of agricultural supplies applied to organic tomato cultivation

The agricultural supplies used in the organic system to control pests and diseases as well as to fertilize soil are claimed to be beneficial to plants and innocuous to human health and to the environment. The chemical composition of six agricultural supplies commonly used in the organic tomato culture, was evaluated by instrumental neutron activation analysis (INAA). Results were compared to the maximum limits established by the Environment Control Agency of the Sao Paulo State (CETESB) and the Guidelines for Organic Quality Standard of Instituto Biodinamico (IBD). Concentrations above reference values were found for Co, Cr and Zn in compost, Cr and Zn in cattle manure and Zn in rice bran.

Kallikrein-Kinin System Mediated Inflammation in Alzheimer`s Disease In Vivo

The Kallikrein-Kinin System (KKS) has been associated to inflammatory and immunogenic responses in the peripheral and central nervous system by the activation of two receptors, namely B1 receptor and B2 receptor. The B1 receptor is absent or under-expressed in physiological conditions, being up-regulated during tissue injury or in the presence of cytokines. The B2 receptor is constitutive and mediates most of the biological effects of kinins. Some authors suggest a link between the KKS and the neuroinflammation in Alzheimer`s disease (AD). We have recently described an increase in bradykinin (BK) in the cerebrospinal fluid and in densities of B1 and B2 receptors in brain areas related to memory, after chronic infusion of amyloid-beta (A beta) peptide in rats, which was accompanied by memory disruption and neuronal loss. Mice lacking B1 or B2 receptors presented reduced cognitive deficits related to the learning process, after acute intracerebroventricular (i.c.v). administration of A. Nevertheless, our group showed an early disruption of cognitive function by i.c.v. chronic infusion of A beta after a learned task, in the knock-out B2 mice. This suggests a neuroprotective role for B2 receptors. In knock-out B1 mice the memory disruption was absent, implying the participation of this receptor in neurodegenerative processes. The acute or chronic infusion of A beta can lead to different responses of the brain tissue. In this way, the proper involvement of KKS on neuroinflammation in AD probably depends on the amount of A beta injected. Though, BK applied to neurons can exert inflammatory effects, whereas in glial cells, BK can have a potential protective role for neurons, by inhibiting proinflammatory cytokines. This review discusses this duality concerning the KKS and neuroinflammation in AD in vivo.

Constant-random practice and the adaptive process in motor learning with varying amounts of constant practice

The adaptive process in motor learning was examined in terms of effects of varying amounts of constant practice performed before random practice. Participants pressed five response keys sequentially, the last one coincident with the lighting of a final visual stimulus provided by a complex coincident timing apparatus. Different visual stimulus speeds were used during the random practice. 33 children (M age=11.6 yr.) were randomly assigned to one of three experimental groups: constant-random, constant-random 33%, and constant-random 66%. The constant-random group practiced constantly until they reached a criterion of performance stabilization three consecutive trials within 50 msec. of error. The other two groups had additional constant practice of 33 and 66%, respectively, of the number of trials needed to achieve the stabilization criterion. All three groups performed 36 trials under random practice; in the adaptation phase, they practiced at a different visual stimulus speed adopted in the stabilization phase. Global performance measures were absolute, constant, and variable errors, and movement pattern was analyzed by relative timing and overall movement time. There was no group difference in relation to global performance measures and overall movement time. However, differences between the groups were observed on movement pattern, since constant-random 66% group changed its relative timing performance in the adaptation phase.

Effect of bandwidth knowledge of results on the learning of a grip force control task

An experiment was conducted to investigate the persistence of the effect of ""bandwidth knowledge of results (KR)"" manipulated during the learning phase of performing a manual force-control task. The experiment consisted of two phases, an acquisition phase with the goal of maintaining 60% maximum force in 30 trials, and a second phase with the objective of maintaining 40% of maximum force in 20 further trials. There were four bandwidths of KR: when performance error exceeded 5, 10, or 15% of the target, and a control group (0% bandwidth). Analysis showed that 5, 10, and 15% bandwidth led to better performance than 0% bandwidth KR at the beginning of the second phase and persisted during the extended trials.

Optimization of Heat Treatment Profiles Applied to Nanometric-Scale Nb(3)Sn Wires With Cu-Sn Artificial Pinning Centers

Nb(3)Sn is one of the most used superconducting materials for applications in high magnetic fields. The improvement of the critical current densities (J(c)) is important, and must be analyzed together with the optimization of the flux pinning acting in the material. For Nb(3)Sn, it is known that the grain boundaries are the most effective pinning centers. However, the introduction of artificial pinning centers (APCs) with different superconducting properties has been proved to be beneficial for J(c). As these APCs are normally in the nanometric-scale, the conventional heat treatment profiles used for Nb(3)Sn wires cannot be directly applied, leading to excessive grain growth and/or increase of the APCs cross sections. In this work, the heat treatment profiles for Nb(3)Sn superconductor wires with Cu(Sn) artificial pinning centers in nanometric-scale were analyzed in an attempt to improve J(c) . It is described a methodology to optimize the heat treatment profiles in respect to diffusion, reaction and formation of the superconducting phases. Microstructural, transport and magnetic characterization were performed in an attempt to find the pinning mechanisms acting in the samples. It was concluded that the maximum current densities were found when normal phases (due to the introduction of the APCs) are acting as main pinning centers in the global behavior of the Nb(3)Sn superconducting wire.

Modeling associations between genetic markers using Bayesian networks

Motivation: Understanding the patterns of association between polymorphisms at different loci in a population ( linkage disequilibrium, LD) is of fundamental importance in various genetic studies. Many coefficients were proposed for measuring the degree of LD, but they provide only a static view of the current LD structure. Generative models (GMs) were proposed to go beyond these measures, giving not only a description of the actual LD structure but also a tool to help understanding the process that generated such structure. GMs based in coalescent theory have been the most appealing because they link LD to evolutionary factors. Nevertheless, the inference and parameter estimation of such models is still computationally challenging. Results: We present a more practical method to build GM that describe LD. The method is based on learning weighted Bayesian network structures from haplotype data, extracting equivalence structure classes and using them to model LD. The results obtained in public data from the HapMap database showed that the method is a promising tool for modeling LD. The associations represented by the learned models are correlated with the traditional measure of LD D`. The method was able to represent LD blocks found by standard tools. The granularity of the association blocks and the readability of the models can be controlled in the method. The results suggest that the causality information gained by our method can be useful to tell about the conservability of the genetic markers and to guide the selection of subset of representative markers.

Node-Depth Encoding and Multiobjective Evolutionary Algorithm Applied to Large-Scale Distribution System Reconfiguration

The power loss reduction in distribution systems (DSs) is a nonlinear and multiobjective problem. Service restoration in DSs is even computationally hard since it additionally requires a solution in real-time. Both DS problems are computationally complex. For large-scale networks, the usual problem formulation has thousands of constraint equations. The node-depth encoding (NDE) enables a modeling of DSs problems that eliminates several constraint equations from the usual formulation, making the problem solution simpler. On the other hand, a multiobjective evolutionary algorithm (EA) based on subpopulation tables adequately models several objectives and constraints, enabling a better exploration of the search space. The combination of the multiobjective EA with NDE (MEAN) results in the proposed approach for solving DSs problems for large-scale networks. Simulation results have shown the MEAN is able to find adequate restoration plans for a real DS with 3860 buses and 632 switches in a running time of 0.68 s. Moreover, the MEAN has shown a sublinear running time in function of the system size. Tests with networks ranging from 632 to 5166 switches indicate that the MEAN can find network configurations corresponding to a power loss reduction of 27.64% for very large networks requiring relatively low running time.

An approach of optimal sensitivity applied in the tertiary loop of the automatic generation control

This paper proposes an approach of optimal sensitivity applied in the tertiary loop of the automatic generation control. The approach is based on the theorem of non-linear perturbation. From an optimal operation point obtained by an optimal power flow a new optimal operation point is directly determined after a perturbation, i.e., without the necessity of an iterative process. This new optimal operation point satisfies the constraints of the problem for small perturbation in the loads. The participation factors and the voltage set point of the automatic voltage regulators (AVR) of the generators are determined by the technique of optimal sensitivity, considering the effects of the active power losses minimization and the network constraints. The participation factors and voltage set point of the generators are supplied directly to a computational program of dynamic simulation of the automatic generation control, named by power sensitivity mode. Test results are presented to show the good performance of this approach. (C) 2008 Elsevier B.V. All rights reserved.

Data Mining Applied to Harmonic Current Sources Identification in Residential Consumers

This work proposes a method based on both preprocessing and data mining with the objective of identify harmonic current sources in residential consumers. In addition, this methodology can also be applied to identify linear and nonlinear loads. It should be emphasized that the entire database was obtained through laboratory essays, i.e., real data were acquired from residential loads. Thus, the residential system created in laboratory was fed by a configurable power source and in its output were placed the loads and the power quality analyzers (all measurements were stored in a microcomputer). So, the data were submitted to pre-processing, which was based on attribute selection techniques in order to minimize the complexity in identifying the loads. A newer database was generated maintaining only the attributes selected, thus, Artificial Neural Networks were trained to realized the identification of loads. In order to validate the methodology proposed, the loads were fed both under ideal conditions (without harmonics), but also by harmonic voltages within limits pre-established. These limits are in accordance with IEEE Std. 519-1992 and PRODIST (procedures to delivery energy employed by Brazilian`s utilities). The results obtained seek to validate the methodology proposed and furnish a method that can serve as alternative to conventional methods.

Film thickness measurement techniques applied to micro-scale two-phase flow systems

Recently semi-empirical models to estimate flow boiling heat transfer coefficient, saturated CHF and pressure drop in micro-scale channels have been proposed. Most of the models were developed based on elongated bubbles and annular flows in the view of the fact that these flow patterns are predominant in smaller channels. In these models, the liquid film thickness plays an important role and such a fact emphasizes that the accurate measurement of the liquid film thickness is a key point to validate them. On the other hand, several techniques have been successfully applied to measure liquid film thicknesses during condensation and evaporation under macro-scale conditions. However, although this subject has been targeted by several leading laboratories around the world, it seems that there is no conclusive result describing a successful technique capable of measuring dynamic liquid film thickness during evaporation inside micro-scale round channels. This work presents a comprehensive literature review of the methods used to measure liquid film thickness in macro- and micro-scale systems. The methods are described and the main difficulties related to their use in micro-scale systems are identified. Based on this discussion, the most promising methods to measure dynamic liquid film thickness in micro-scale channels are identified. (C) 2009 Elsevier Inc. All rights reserved.