Effect of eucalyptus (Eucalyptus citriodora) fresh or residue leaves on methane emission in vitro

Rumen fermentation and methane emission for eucalyptus (Eucalyptus citriodora) fresh leaves (FL) or residue leaves (RL), after essential oil extraction from eucalyptus leaves in comparison with alfalfa (Medicago sativa) hay, were investigated in vitro. Eucalyptus FL and RL were obtained from the Distillery Trees Barras Company, Torrinha City, Sao Paulo, Brazil. The semi-automatic system of gas production was used to measure gas production, methane emission and rumen fermentation after 24 h incubation in vitro. The results showed that the crude protein (CP) contents were 76.4, 78.1 and 181.9 g kg(-1) DM for eucalyptus FL, RL and alfalfa hay, respectively. The neutral-detergent fibre (NDF) and acid-detergent fibre (ADF) were significantly lower in eucalyptus FL and RL than alfalfa hay. The Eucalyptus fresh and residue leaves were rich in total phenols (TP) and total tannins (TT) but had negligible content of condensed tannins (CT). There was significant reduction in cumulative gas production about 54 and 51% with eucalyptus FL and RL, respectively, compared with alfalfa hay. The methane emission (mL/g DM) was reduced (P<0.05) by 53 and 57% with eucalyptus FL and RL, respectively, but the reduction was 21 and 16% when expressed on truly digested organic matter basis. There were a decline (P<0.05) in true dry and organic matter degradation in vitro in eucalyptus FL and RL compared with alfalfa hay substrate. The partitioning factor values were higher (P<0.05) in eucalyptus FL and RL than alfalfa hay. There was no significant difference observed between eucalyptus FL, RL and alfalfa hay in protozoa count. It is concluded that the eucalyptus leaves have potential effect to mitigate CH4 production in vitro, which may be attributed to a decrease in fermentable substrate rather than to a direct effect on methanogenesis.

Soliton dynamics at an interface between a uniform medium and a nonlinear optical lattice

We study trapping and propagation of a matter-wave soliton through the interface between uniform medium and a nonlinear optical lattice. Different regimes for transmission of a broad and a narrow solitons are investigated. Reflections and transmissions of solitons are predicted as a function of the lattice phase. The existence of a threshold in the amplitude of the nonlinear optical lattice, separating the transmission and reflection regimes, is verified. The localized nonlinear surface state, corresponding to the soliton trapped by the interface, is found. Variational approach predictions are confirmed by numerical simulations for the original Gross-Pitaevskii equation with nonlinear periodic potentials.

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.

TESTING STATISTICAL HYPOTHESIS ON RANDOM TREES AND APPLICATIONS TO THE PROTEIN CLASSIFICATION PROBLEM

Efficient automatic protein classification is of central importance in genomic annotation. As an independent way to check the reliability of the classification, we propose a statistical approach to test if two sets of protein domain sequences coming from two families of the Pfam database are significantly different. We model protein sequences as realizations of Variable Length Markov Chains (VLMC) and we use the context trees as a signature of each protein family. Our approach is based on a Kolmogorov-Smirnov-type goodness-of-fit test proposed by Balding et at. [Limit theorems for sequences of random trees (2008), DOI: 10.1007/s11749-008-0092-z]. The test statistic is a supremum over the space of trees of a function of the two samples; its computation grows, in principle, exponentially fast with the maximal number of nodes of the potential trees. We show how to transform this problem into a max-flow over a related graph which can be solved using a Ford-Fulkerson algorithm in polynomial time on that number. We apply the test to 10 randomly chosen protein domain families from the seed of Pfam-A database (high quality, manually curated families). The test shows that the distributions of context trees coming from different families are significantly different. We emphasize that this is a novel mathematical approach to validate the automatic clustering of sequences in any context. We also study the performance of the test via simulations on Galton-Watson related processes.

Use of blanks to determine in vitro net gas and methane production when using rumen fermentation modifiers

Blanks (flasks without substrate containing only inoculum and medium) are used in vitro to correct for gas. CH(4) and residual organic matter (OM) fermented in inoculum. However inclusion of rumen fermentation modifiers may affect fermentation of OM in the substrate and inoculum. Thus, data correction using blanks that lack additives may result in inaccurate adjustment for background fermentation. Our objective was to evaluate impacts of using blanks containing additive (i.e., specific blanks) or blanks without additive on estimation of in vitro net gas and CH(4) production. We used the semi-automatic in vitro gas production technique including monensin sodium at 2.08 mg/l of buffered rumen fluid (Experiment 1) or carvacrol, eugenol and 1,8-cineol at 667 mg/l (Experiment 2) in flasks with substrate and in blank flasks. At 16h of incubation, monensin reduced (P <= 0.02) total gas production in flasks containing substrate (162.0 ml versus 146.3 ml) and in blanks (84.4 ml versus 79.2 ml). Total methane production was also decreased (P <= 0.05) by adding monensin to flasks containing substrate (15.7 ml versus 11.9 ml) as well as in blanks (6.4 ml versus 5.0 ml). Inclusion of carvacrol or eugenol reduced (P <= 0.05) total gas and CH(4) production in flasks with substrate and in blanks, but in a more pronounced manner than monensin. For these three additives, correction for blank without additive resulted in lower net gas and CH(4) production than correction for a treatment specific blank. For instance, correcting carvacrol data using a blank without the additive resulted in negative net gas and CH(4) production (-6.5 and -1.5 ml. respectively). These biologically impossible results occurred because total gas and CH(4) production in blanks without carvacrol (46.1 and 2.1 ml, respectively) were higher than in flasks containing substrate plus carvacrol (39.7 and 0.6 ml, respectively). Results demonstrated that inclusion of rumen additives affected fermentation of OM in the substrate and the inoculum. Thus, correction of gas and CH(4) production using blanks without additives resulted in overestimation of these variables. Blanks containing the additive of interest should be included when rumen fermentation modifiers are evaluated in vitro. This paper is part of the special issue entitled: Greenhouse Gases in Animal Agriculture Finding a Balance between Food and Emissions, Guest Edited by T.A. McAllister, Section Guest Editors: K.A. Beauchemin, X. Hao, S. McGinn and Editor for Animal Feed Science and Technology, P.H. Robinson. (C) 2011 Elsevier B.V. All rights reserved.

Kernel machines for epilepsy diagnosis via EEG signal classification: A comparative study

Objective: We carry out a systematic assessment on a suite of kernel-based learning machines while coping with the task of epilepsy diagnosis through automatic electroencephalogram (EEG) signal classification. Methods and materials: The kernel machines investigated include the standard support vector machine (SVM), the least squares SVM, the Lagrangian SVM, the smooth SVM, the proximal SVM, and the relevance vector machine. An extensive series of experiments was conducted on publicly available data, whose clinical EEG recordings were obtained from five normal subjects and five epileptic patients. The performance levels delivered by the different kernel machines are contrasted in terms of the criteria of predictive accuracy, sensitivity to the kernel function/parameter value, and sensitivity to the type of features extracted from the signal. For this purpose, 26 values for the kernel parameter (radius) of two well-known kernel functions (namely. Gaussian and exponential radial basis functions) were considered as well as 21 types of features extracted from the EEG signal, including statistical values derived from the discrete wavelet transform, Lyapunov exponents, and combinations thereof. Results: We first quantitatively assess the impact of the choice of the wavelet basis on the quality of the features extracted. Four wavelet basis functions were considered in this study. Then, we provide the average accuracy (i.e., cross-validation error) values delivered by 252 kernel machine configurations; in particular, 40%/35% of the best-calibrated models of the standard and least squares SVMs reached 100% accuracy rate for the two kernel functions considered. Moreover, we show the sensitivity profiles exhibited by a large sample of the configurations whereby one can visually inspect their levels of sensitiveness to the type of feature and to the kernel function/parameter value. Conclusions: Overall, the results evidence that all kernel machines are competitive in terms of accuracy, with the standard and least squares SVMs prevailing more consistently. Moreover, the choice of the kernel function and parameter value as well as the choice of the feature extractor are critical decisions to be taken, albeit the choice of the wavelet family seems not to be so relevant. Also, the statistical values calculated over the Lyapunov exponents were good sources of signal representation, but not as informative as their wavelet counterparts. Finally, a typical sensitivity profile has emerged among all types of machines, involving some regions of stability separated by zones of sharp variation, with some kernel parameter values clearly associated with better accuracy rates (zones of optimality). (C) 2011 Elsevier B.V. All rights reserved.

Support vector machines for detecting age-related changes in running kinematics

Age-related changes in running kinematics have been reported in the literature using classical inferential statistics. However, this approach has been hampered by the increased number of biomechanical gait variables reported and subsequently the lack of differences presented in these studies. Data mining techniques have been applied in recent biomedical studies to solve this problem using a more general approach. In the present work, we re-analyzed lower extremity running kinematic data of 17 young and 17 elderly male runners using the Support Vector Machine (SVM) classification approach. In total, 31 kinematic variables were extracted to train the classification algorithm and test the generalized performance. The results revealed different accuracy rates across three different kernel methods adopted in the classifier, with the linear kernel performing the best. A subsequent forward feature selection algorithm demonstrated that with only six features, the linear kernel SVM achieved 100% classification performance rate, showing that these features provided powerful combined information to distinguish age groups. The results of the present work demonstrate potential in applying this approach to improve knowledge about the age-related differences in running gait biomechanics and encourages the use of the SVM in other clinical contexts. (C) 2010 Elsevier Ltd. All rights reserved.

Exercise training improves muscle vasodilatation in individuals with T786C polymorphism of endothelial nitric oxide synthase gene

Negrão M.V, Alves CR, Alves G.B, Pereira A.C, Dias R.G, Laterza M.C, Mota G.F, Oliveira E.M, Bassaneze V, Krieger J.E, Negrão C.E, Rondon M.U.P. Exercise training improves muscle vasodilatation in individuals with T786C polymorphism of endothelial nitric oxide synthase gene. Physiol Genomics 42A: 71-77, 2010. First published July 6, 2010; doi:10.1152/physiolgenomics.00145.2009.-Allele T at promoter region of the eNOS gene has been associated with an increase in coronary disease mortality, suggesting that this allele increases susceptibility for endothelial dysfunction. In contrast, exercise training improves endothelial function. Thus, we hypothesized that: 1) Muscle vasodilatation during exercise is attenuated in individuals homozygous for allele T, and 2) Exercise training improves muscle vasodilatation in response to exercise for TT genotype individuals. From 133 preselected healthy individuals genotyped for the T786C polymorphism, 72 participated in the study: TT (n = 37; age 27 +/- 1 yr) and CT + CC (n = 35; age 26 +/- 1 yr). Forearm blood flow (venous occlusion plethysmography) and blood pressure (oscillometric automatic cuff) were evaluated at rest and during 30% handgrip exercise. Exercise training consisted of three sessions per week for 18 wk, with intensity between anaerobic threshold and respiratory compensation point. Resting forearm vascular conductance (FVC, P = 0.17) and mean blood pressure (P = 0.70) were similar between groups. However, FVC responses during handgrip exercise were significantly lower in TT individuals compared with CT + CC individuals (0.39 +/- 0.12 vs. 1.08 +/- 0.27 units, P = 0.01). Exercise training significantly increased peak VO(2) in both groups, but resting FVC remained unchanged. This intervention significantly increased FVC response to handgrip exercise in TT individuals (P = 0.03), but not in CT + CC individuals (P = 0.49), leading to an equivalent FVC response between TT and CT + CC individuals (1.05 +/- 0.18 vs. 1.59 +/- 0.27 units, P = 0.27). In conclusion, exercise training improves muscle vasodilatation in response to exercise in TT genotype individuals, demonstrating that genetic variants influence the effects of interventions such as exercise training.

Information Filtering and Array Algorithms for Discrete-Time Markovian Jump Linear Systems

This technical note develops information filter and array algorithms for a linear minimum mean square error estimator of discrete-time Markovian jump linear systems. A numerical example for a two-mode Markovian jump linear system, to show the advantage of using array algorithms to filter this class of systems, is provided.

A safety assessment methodology applied to CNS/ATM-based air traffic control system

In the last decades, the air traffic system has been changing to adapt itself to new social demands, mainly the safe growth of worldwide traffic capacity. Those changes are ruled by the Communication, Navigation, Surveillance/Air Traffic Management (CNS/ATM) paradigm, based on digital communication technologies (mainly satellites) as a way of improving communication, surveillance, navigation and air traffic management services. However, CNS/ATM poses new challenges and needs, mainly related to the safety assessment process. In face of these new challenges, and considering the main characteristics of the CNS/ATM, a methodology is proposed at this work by combining ""absolute"" and ""relative"" safety assessment methods adopted by the International Civil Aviation Organization (ICAO) in ICAO Doc.9689 [14], using Fluid Stochastic Petri Nets (FSPN) as the modeling formalism, and compares the safety metrics estimated from the simulation of both the proposed (in analysis) and the legacy system models. To demonstrate its usefulness, the proposed methodology was applied to the ""Automatic Dependent Surveillance-Broadcasting"" (ADS-B) based air traffic control system. As conclusions, the proposed methodology assured to assess CNS/ATM system safety properties, in which FSPN formalism provides important modeling capabilities, and discrete event simulation allowing the estimation of the desired safety metric. (C) 2011 Elsevier Ltd. All rights reserved.

Evaluation of Adaptive Genetic Algorithm to Environmental Modeling of Peponapis and Cucurbita

This paper presents a free software tool that supports the next-generation Mobile Communications, through the automatic generation of models of components and electronic devices based on neural networks. This tool enables the creation, training, validation and simulation of the model directly from measurements made on devices of interest, using an interface totally oriented to non-experts in neural models. The resulting model can be exported automatically to a traditional circuit simulator to test different scenarios.

Comparison Between Voltage Control Structures of Synchronous Machines

This paper compares the behaviour of two different control structures of automatic voltage regulators of synchronous machines equipped with static excitation systems. These systems have a fully controlled thyristor bridge that supplies DC current to the rotor winding. The rectifier bridge is fed by the stator terminals through a step-down transformer. The first control structure, named ""Direct Control"", has a single proportional-integral (PI) regulator that compares stator voltage setpoint with measured voltage and acts directly on the thyristor bridge`s firing angle. This control structure is usually employed in commercial excitation systems for hydrogenerators. The second structure, named ""Cascade Control"", was inspired on control loops of commercial DC motor drives. Such drives employ two PIs in a cascade arrangement, the external PI deals with the motor speed while the internal one regulates the armature current. In the adaptation proposed, the external PI compares setpoint with the actual stator voltage and produces the setpoint to the internal PI-loop which controls the field current.

Post-processing and visualization techniques in 2D boundary element analysis

Most post-processors for boundary element (BE) analysis use an auxiliary domain mesh to display domain results, working against the profitable modelling process of a pure boundary discretization. This paper introduces a novel visualization technique which preserves the basic properties of the boundary element methods. The proposed algorithm does not require any domain discretization and is based on the direct and automatic identification of isolines. Another critical aspect of the visualization of domain results in BE analysis is the effort required to evaluate results in interior points. In order to tackle this issue, the present article also provides a comparison between the performance of two different BE formulations (conventional and hybrid). In addition, this paper presents an overview of the most common post-processing and visualization techniques in BE analysis, such as the classical algorithms of scan line and the interpolation over a domain discretization. The results presented herein show that the proposed algorithm offers a very high performance compared with other visualization procedures.

Optimal conditions for the wetting balance test

Wetting balance tests of copper sheets submerged in tin solder baths were carried out in a completely automatic wetting balance. Wetting curves were examined for three different values of sheet thickness and four different solder bath temperatures. Most of the wetting curves showed a distorted shape relative to that of a standard curve, preventing calculation of important wetting parameters, such as the wetting rate and the wetting force. The wetting tests showed that the distortion increased for a thicker sheet thickness and a lower solder bath temperature, being the result of solder bath solidification around the submerged sheet substrate. (C) 2008 Elsevier B.V. All rights reserved.

A Separation Principle for the Continuous-Time LQ-Problem With Markovian Jump Parameters

In this paper, we devise a separation principle for the finite horizon quadratic optimal control problem of continuous-time Markovian jump linear systems driven by a Wiener process and with partial observations. We assume that the output variable and the jump parameters are available to the controller. It is desired to design a dynamic Markovian jump controller such that the closed loop system minimizes the quadratic functional cost of the system over a finite horizon period of time. As in the case with no jumps, we show that an optimal controller can be obtained from two coupled Riccati differential equations, one associated to the optimal control problem when the state variable is available, and the other one associated to the optimal filtering problem. This is a separation principle for the finite horizon quadratic optimal control problem for continuous-time Markovian jump linear systems. For the case in which the matrices are all time-invariant we analyze the asymptotic behavior of the solution of the derived interconnected Riccati differential equations to the solution of the associated set of coupled algebraic Riccati equations as well as the mean square stabilizing property of this limiting solution. When there is only one mode of operation our results coincide with the traditional ones for the LQG control of continuous-time linear systems.