Effect of 655-nm Low-Level Laser Therapy on Exercise-Induced Skeletal Muscle Fatigue in Humans

Objective: To investigate if development of skeletal muscle fatigue during repeated voluntary biceps contractions could be attenuated by low-level laser therapy (LLLT). Background Data: Previous animal studies have indicated that LLLT can reduce oxidative stress and delay the onset of skeletal muscle fatigue. Materials and Methods: Twelve male professional volleyball players were entered into a randomized double-blind placebo-controlled trial, for two sessions (on day 1 and day 8) at a 1-wk interval, with both groups performing as many voluntary biceps contractions as possible, with a load of 75% of the maximal voluntary contraction force (MVC). At the second session on day 8, the groups were either given LLLT (655 nm) of 5 J at an energy density of 500 J/cm(2) administered at each of four points along the middle of the biceps muscle belly, or placebo LLLT in the same manner immediately before the exercise session. The number of muscle contractions with 75% of MVC was counted by a blinded observer and blood lactate concentration was measured. Results: Compared to the first session (on day 1), the mean number of repetitions increased significantly by 8.5 repetitions (+/- 1.9) in the active LLLT group at the second session (on day 8), while in the placebo LLLT group the increase was only 2.7 repetitions (+/- 2.9) (p = 0.0001). At the second session, blood lactate levels increased from a pre-exercise mean of 2.4 mmol/L (+/- 0.5 mmol/L), to 3.6 mmol/L (+/- 0.5 mmol/L) in the placebo group, and to 3.8 mmol/L (+/- 0.4 mmol/L) in the active LLLT group after exercise, but this difference between groups was not statistically significant. Conclusion: We conclude that LLLT appears to delay the onset of muscle fatigue and exhaustion by a local mechanism in spite of increased blood lactate levels.

Particle competition for complex network community detection

In many real situations, randomness is considered to be uncertainty or even confusion which impedes human beings from making a correct decision. Here we study the combined role of randomness and determinism in particle dynamics for complex network community detection. In the proposed model, particles walk in the network and compete with each other in such a way that each of them tries to possess as many nodes as possible. Moreover, we introduce a rule to adjust the level of randomness of particle walking in the network, and we have found that a portion of randomness can largely improve the community detection rate. Computer simulations show that the model has good community detection performance and at the same time presents low computational complexity. (C) 2008 American Institute of Physics.

Generalized connectivity between any two nodes in a complex network

This article focuses on the identification of the number of paths with different lengths between pairs of nodes in complex networks and how these paths can be used for characterization of topological properties of theoretical and real-world complex networks. This analysis revealed that the number of paths can provide a better discrimination of network models than traditional network measurements. In addition, the analysis of real-world networks suggests that the long-range connectivity tends to be limited in these networks and may be strongly related to network growth and organization.

Entropy production in irreversible systems described by a Fokker-Planck equation

We analyze the irreversibility and the entropy production in nonequilibrium interacting particle systems described by a Fokker-Planck equation by the use of a suitable master equation representation. The irreversible character is provided either by nonconservative forces or by the contact with heat baths at distinct temperatures. The expression for the entropy production is deduced from a general definition, which is related to the probability of a trajectory in phase space and its time reversal, that makes no reference a priori to the dissipated power. Our formalism is applied to calculate the heat conductance in a simple system consisting of two Brownian particles each one in contact to a heat reservoir. We show also the connection between the definition of entropy production rate and the Jarzynski equality.

Finding Quasi-Optimal Network Topologies for Information Transmission in Active Networks

This work clarifies the relation between network circuit (topology) and behaviour (information transmission and synchronization) in active networks, e.g. neural networks. As an application, we show how one can find network topologies that are able to transmit a large amount of information, possess a large number of communication channels, and are robust under large variations of the network coupling configuration. This theoretical approach is general and does not depend on the particular dynamic of the elements forming the network, since the network topology can be determined by finding a Laplacian matrix (the matrix that describes the connections and the coupling strengths among the elements) whose eigenvalues satisfy some special conditions. To illustrate our ideas and theoretical approaches, we use neural networks of electrically connected chaotic Hindmarsh-Rose neurons.

Network of recurrent events for the Olami-Feder-Christensen model

We numerically study the dynamics of a discrete spring-block model introduced by Olami, Feder, and Christensen (OFC) to mimic earthquakes and investigate to what extent this simple model is able to reproduce the observed spatiotemporal clustering of seismicity. Following a recently proposed method to characterize such clustering by networks of recurrent events [J. Davidsen, P. Grassberger, and M. Paczuski, Geophys. Res. Lett. 33, L11304 (2006)], we find that for synthetic catalogs generated by the OFC model these networks have many nontrivial statistical properties. This includes characteristic degree distributions, very similar to what has been observed for real seismicity. There are, however, also significant differences between the OFC model and earthquake catalogs, indicating that this simple model is insufficient to account for certain aspects of the spatiotemporal clustering of seismicity.

Automatic Network Fingerprinting through Single-Node Motifs

Complex networks have been characterised by their specific connectivity patterns (network motifs), but their building blocks can also be identified and described by node-motifs-a combination of local network features. One technique to identify single node-motifs has been presented by Costa et al. (L. D. F. Costa, F. A. Rodrigues, C. C. Hilgetag, and M. Kaiser, Europhys. Lett., 87, 1, 2009). Here, we first suggest improvements to the method including how its parameters can be determined automatically. Such automatic routines make high-throughput studies of many networks feasible. Second, the new routines are validated in different network-series. Third, we provide an example of how the method can be used to analyse network time-series. In conclusion, we provide a robust method for systematically discovering and classifying characteristic nodes of a network. In contrast to classical motif analysis, our approach can identify individual components (here: nodes) that are specific to a network. Such special nodes, as hubs before, might be found to play critical roles in real-world networks.

Knowledge acquisition by networks of interacting agents in the presence of observation errors

In this work we investigate knowledge acquisition as performed by multiple agents interacting as they infer, under the presence of observation errors, respective models of a complex system. We focus the specific case in which, at each time step, each agent takes into account its current observation as well as the average of the models of its neighbors. The agents are connected by a network of interaction of Erdos-Renyi or Barabasi-Albert type. First, we investigate situations in which one of the agents has a different probability of observation error (higher or lower). It is shown that the influence of this special agent over the quality of the models inferred by the rest of the network can be substantial, varying linearly with the respective degree of the agent with different estimation error. In case the degree of this agent is taken as a respective fitness parameter, the effect of the different estimation error is even more pronounced, becoming superlinear. To complement our analysis, we provide the analytical solution of the overall performance of the system. We also investigate the knowledge acquisition dynamic when the agents are grouped into communities. We verify that the inclusion of edges between agents (within a community) having higher probability of observation error promotes the loss of quality in the estimation of the agents in the other communities.

Social interaction as a heuristic for combinatorial optimization problems

We investigate the performance of a variant of Axelrod's model for dissemination of culture-the Adaptive Culture Heuristic (ACH)-on solving an NP-Complete optimization problem, namely, the classification of binary input patterns of size F by a Boolean Binary Perceptron. In this heuristic, N agents, characterized by binary strings of length F which represent possible solutions to the optimization problem, are fixed at the sites of a square lattice and interact with their nearest neighbors only. The interactions are such that the agents' strings (or cultures) become more similar to the low-cost strings of their neighbors resulting in the dissemination of these strings across the lattice. Eventually the dynamics freezes into a homogeneous absorbing configuration in which all agents exhibit identical solutions to the optimization problem. We find through extensive simulations that the probability of finding the optimal solution is a function of the reduced variable F/N(1/4) so that the number of agents must increase with the fourth power of the problem size, N proportional to F(4), to guarantee a fixed probability of success. In this case, we find that the relaxation time to reach an absorbing configuration scales with F(6) which can be interpreted as the overall computational cost of the ACH to find an optimal set of weights for a Boolean binary perceptron, given a fixed probability of success.

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.

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.

Fatigue in Brazilian cancer patients, caregivers, and nursing students: a psychometric validation study of the Piper Fatigue Scale-Revised

The objective of this study was to validate the Piper Fatigue Scale-Revised (PFS-R) for use in Brazilian culture. Translation of the PFS-R into Portuguese and validity and reliability tests were performed. Convenience samples in Brazil we as follows: 584 cancer patients (mean age 57 +/- 13 years; 51.3% female); 184 caregivers (mean age 50 +/- 12.7 years; 65.8% female); and 189 undergraduate nursing students (mean age 21.6 +/- 2.8 years; 96.2% female); Instruments used were as follows: Brazilian PFS, Beck Depression Inventory (BDI), and Karnofsky Performance Scale (KPS). The 22 items of the Brazilian PFS loaded well (factor loading > 0.35) on three dimensions identified by factor analysis (behavioral, affective, and sensorial-psychological). These dimensions explained 65% of the variance. Internal consistency reliability was very good (Cronbach`s alpha ranged from 0.841 to 0.943 for the total scale and its dimensions). Cancer patients and their caregivers completed the Brazilian PFS twice for test-retest reliability and results showed good stability (Pearson`s r a parts per thousand yenaEuro parts per thousand 0,60, p < 0,001). Correlations among the Brazilian PFS and other scales were significant, in hypothesized directions, and mostly moderate contributing to divergent (Brazilian PFS x KPS) and convergent validity (Brazilian PFS x BDI). Mild, moderate, and severe fatigue in patients were reported by 73 (12.5%), 167 (28.6%), and 83 (14.2%), respectively. Surprisingly, students had the highest mean total fatigue scores; no significant differences were observed between patients and caregivers showing poor discriminant validity. While the Brazilian PFS is a reliable and valid instrument to measure fatigue in Brazilian cancer patients, further work is needed to evaluate the discriminant validity of the scale in Brazil.

Manipulation of rest period length induces different causes of fatigue in vertical jumping

The aim of this study was to directly compare the causes of fatigue after a short- and a long-rest interval between consecutive stretch-shortening cycle exercises. Eleven healthy males jumped with different resting period lengths (short = 6.1 +/- 1 s, long = 8.6 +/- 0.9 s), performing countermovement jumps at 95% of their maximal jump height until they were unable to sustain the target height. After short- and long-rest, the maximal voluntary isometric contraction knee extension torque decreased (-7%; p = 0.04), comparing to values obtained before exercise protocols. No change was seen from pre- to post-exercise, for either short- or long-rest, in biceps femoris coactivation (-1%; p = 0.95), peak-to-peak amplitude (1%; p = 0.95) and duration (-8%; p = 0.92) of the compound muscle action potential of the vastus lateralis. Evoked peak twitch torque reduced after both exercise protocols (short = -26%, long = -32%; p = 0.003) indicating peripheral fatigue. However, central fatigue occurred only after short-rest evidenced by a reduction in voluntary activation of the quadriceps muscle (-14%; p = 0.013) measured using the interpolated twitch technique. In conclusion, after Stretch-shortening cycle exercise using short rest period length, the cause of fatigue was central and peripheral, while after using long rest period length, the cause of fatigue was peripheral.

Effect of eight weeks of strength training on fatigue resistance in men and women

The purpose of the present study was to compare the effects of eight weeks of strength training on fatigue resistance in men and women. Thirty-three men and twenty-three women performed eight weeks of strength training in three weekly sessions. Subjects performed four sets using 80% of 1-RM tests on bench press, squat and arm curl. Fatigue index (FI) was used for analysis of decline in motor performance along the sets. The sum of the number of repetitions accomplished in the four sets in each exercise was used to indicate the fatigue resistance. Anova or Ancova two-way ( time x gender) was employed for statistical analysis ( P < 0.05). Eight weeks of strength training increased significantly 1-RM strength, fatigue resistance and total number of repetitions in both genders. FI decreased significantly in both genders after training ( men = 50% vs. women = Time x gender interaction was observed in the total number of repetitions in squat ( P = 0.04) and arm curl exercises, regarding gains to women ( P = 0.01). In conclusion, eight weeks of ST improved strength, FR, FI and total number of repetitions performed. However, women obtained greater adaptations than men.

Sugarcane bagasse cellulose/HDPE composites obtained by extrusion

Natural fibers used in this study were both pre-treated and modified residues from sugarcane bagasse. Polymer of high density polyethylene (HDPE) was employed as matrix in to composites, which were prodUced by mixing high density polyethylene with cellulose (10%) and Cell/ZrO(2)center dot nH(2)O (10%), using an extruder and hydraulic press. Tensile tests showed that the Cell/ZrO(2)center dot nH(2)O (10%)/HDPE composites present better tensile strength than cellulose (10%)/HDPE composites. Cellulose agglomerations were responsible for poor adhesion between fiber and matrix in cellulose (10%)/HDPE composites. HDPF/natural fibers composites showed also lower tensile strength in comparison to the polymer. The increase in Young`s modulus is associated to fibers reinforcement. SEM analysis showed that the cellulose fibers insertion in the matrix Caused all increase of defects, which were reduced When modified cellulose fibers were Used. (C) 2008 Elsevier Ltd. All rights reserved.