Effect of low-level laser therapy (LLLT) on peripheral nerve regeneration using fibrin glue derived from snake venom

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Acute auditory stimulation with different styles of music influences cardiac autonomic regulation in men

No clear evidence is available in the literature regarding the acute effect of different styles of music on cardiac autonomic control. The present study aimed to evaluate the acute effects of classical baroque and heavy metal musical auditory stimulation on Heart Rate Variability (HRV) in healthy men. In this study, HRV was analyzed regarding time (SDNN, RMSSD, NN50, and pNN50) and frequency domain (LF, HF, and LF / HF) in 12 healthy men. HRV was recorded at seated rest for 10 minutes. Subsequently, the participants were exposed to classical baroque or heavy metal music for five minutes through an earphone at seated rest. After exposure to the first song, they remained at rest for five minutes and they were again exposed to classical baroque or heavy metal music. The music sequence was random for each individual. Standard statistical methods were used for calculation of means and standard deviations. Besides, ANOVA and Friedman test were used for parametric and non-parametric distributions, respectively. While listening to heavy metal music, SDNN was reduced compared to the baseline (P = 0.023). In addition, the LF index (ms(2) and nu) was reduced during exposure to both heavy metal and classical baroque musical auditory stimulation compared to the control condition (P = 0.010 and P = 0.048, respectively). However, the HF index (ms(2)) was reduced only during auditory stimulation with music heavy metal (P = 0.01). The LF/HF ratio on the other hand decreased during auditory stimulation with classical baroque music (P = 0.019). Acute auditory stimulation with the selected heavy metal musical auditory stimulation decreased the sympathetic and parasympathetic modulation on the heart, while exposure to a selected classical baroque music reduced sympathetic regulation on the heart.

Modelos lineares generalizados mistos e equações de estimação generalizadas para dados binário aplicados em anestesiologia veterinária

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Microhardness of luting agents used for fiber post cementation submitted to thermo cycling

Aims and Objectives: The aim of this study was to analyze the microhardness of three resin cements used in cementing glass fiber posts in bovine incisor. The microhardness was analyzed in cervical, middle and apical thirds before and after thermocycling process. Materials and Methods: Bovine teeth were instrumented and divided into 3 groups composed of 10 teeth each. Then, the teeth were sectioned and obturated and had their canals prepared at a depth of 12mm. Once proceeded the desobturation, the roots and glass fiber posts were prepared for adhesive cementation. After cementation, the microhardness reading was carried out. After initial reading, the samples were placed in a thermocycler and subjected to 2,000 cycles and a new microhardness reading. The data collected were subjected to analysis of variance (ANOVA) and Turkey’s test. Results: It was observed a statistical difference among the microhardness of resin cements. However, the statistical difference of microhardness before and after thermocycling appeared only in group U-200. Conclusion: Thermocycling reduced microhardness values in all cements evaluated in this study. The autopolymerizing cement Multilink presented the most stable microhardness mean values after thermocycling in the coronal, middle and apical thirds.

Qualidade dos processos mecanizados de semeadura e colheita na cultura da soja

Pós-graduação em Agronomia (Produção Vegetal) - FCAV

Recolhimento mecanizado do café em função do manejo do solo e da declividade do terreno

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Design e semântica: investigação de técnicas estatísticas para auxílio no projeto de produto

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Controle de qualidade no plantio convencional e mecanizado de cana-de-açúcar

Pós-graduação em Agronomia (Ciência do Solo) - FCAV

The use of artificial neural networks in analysing the nutritional ecology of Chrysomya megacephala (F.) (Diptera: Calliphoridae), compared with a statistical model

Artificial neural networks (ANNs) have been widely applied to the resolution of complex biological problems. An important feature of neural models is that their implementation is not precluded by the theoretical distribution shape of the data used. Frequently, the performance of ANNs over linear or non-linear regression-based statistical methods is deemed to be significantly superior if suitable sample sizes are provided, especially in multidimensional and non-linear processes. The current work was aimed at utilising three well-known neural network methods in order to evaluate whether these models would be able to provide more accurate outcomes in relation to a conventional regression method in pupal weight predictions of Chrysomya megacephala, a species of blowfly (Diptera: Calliphoridae), using larval density (i.e. the initial number of larvae), amount of available food and pupal size as input data. It was possible to notice that the neural networks yielded more accurate performances in comparison with the statistical model (multiple regression). Assessing the three types of networks utilised (Multi-layer Perceptron, Radial Basis Function and Generalised Regression Neural Network), no considerable differences between these models were detected. The superiority of these neural models over a classical statistical method represents an important fact, because more accurate models may clarify several intricate aspects concerning the nutritional ecology of blowflies.

Detecting an abnormality in a recovery boiler using dynamic multivariate data analysis with parallel coordinate plots

Data visualization techniques are powerful in the handling and analysis of multivariate systems. One such technique known as parallel coordinates was used to support the diagnosis of an event, detected by a neural network-based monitoring system, in a boiler at a Brazilian Kraft pulp mill. Its attractiveness is the possibility of the visualization of several variables simultaneously. The diagnostic procedure was carried out step-by-step going through exploratory, explanatory, confirmatory, and communicative goals. This tool allowed the visualization of the boiler dynamics in an easier way, compared to commonly used univariate trend plots. In addition it facilitated analysis of other aspects, namely relationships among process variables, distinct modes of operation and discrepant data. The whole analysis revealed firstly that the period involving the detected event was associated with a transition between two distinct normal modes of operation, and secondly the presence of unusual changes in process variables at this time.

Closed-loop stable model predictive control of integrating systems with dead time

Model predictive control (MPC) applications in the process industry usually deal with process systems that show time delays (dead times) between the system inputs and outputs. Also, in many industrial applications of MPC, integrating outputs resulting from liquid level control or recycle streams need to be considered as controlled outputs. Conventional MPC packages can be applied to time-delay systems but stability of the closed loop system will depend on the tuning parameters of the controller and cannot be guaranteed even in the nominal case. In this work, a state space model based on the analytical step response model is extended to the case of integrating time systems with time delays. This model is applied to the development of two versions of a nominally stable MPC, which is designed to the practical scenario in which one has targets for some of the inputs and/or outputs that may be unreachable and zone control (or interval tracking) for the remaining outputs. The controller is tested through simulation of a multivariable industrial reactor system. (C) 2012 Elsevier Ltd. All rights reserved.

OPTIMIZATION AND CONTROL OF A CONTINUOUS POLYMERIZATION REACTOR

This work studies the optimization and control of a styrene polymerization reactor. The proposed strategy deals with the case where, because of market conditions and equipment deterioration, the optimal operating point of the continuous reactor is modified significantly along the operation time and the control system has to search for this optimum point, besides keeping the reactor system stable at any possible point. The approach considered here consists of three layers: the Real Time Optimization (RTO), the Model Predictive Control (MPC) and a Target Calculation (TC) that coordinates the communication between the two other layers and guarantees the stability of the whole structure. The proposed algorithm is simulated with the phenomenological model of a styrene polymerization reactor, which has been widely used as a benchmark for process control. The complete optimization structure for the styrene process including disturbances rejection is developed. The simulation results show the robustness of the proposed strategy and the capability to deal with disturbances while the economic objective is optimized.

A Bayesian destructive weighted Poisson cure rate model and an application to a cutaneous melanoma data

In this article, we propose a new Bayesian flexible cure rate survival model, which generalises the stochastic model of Klebanov et al. [Klebanov LB, Rachev ST and Yakovlev AY. A stochastic-model of radiation carcinogenesis - latent time distributions and their properties. Math Biosci 1993; 113: 51-75], and has much in common with the destructive model formulated by Rodrigues et al. [Rodrigues J, de Castro M, Balakrishnan N and Cancho VG. Destructive weighted Poisson cure rate models. Technical Report, Universidade Federal de Sao Carlos, Sao Carlos-SP. Brazil, 2009 (accepted in Lifetime Data Analysis)]. In our approach, the accumulated number of lesions or altered cells follows a compound weighted Poisson distribution. This model is more flexible than the promotion time cure model in terms of dispersion. Moreover, it possesses an interesting and realistic interpretation of the biological mechanism of the occurrence of the event of interest as it includes a destructive process of tumour cells after an initial treatment or the capacity of an individual exposed to irradiation to repair altered cells that results in cancer induction. In other words, what is recorded is only the damaged portion of the original number of altered cells not eliminated by the treatment or repaired by the repair system of an individual. Markov Chain Monte Carlo (MCMC) methods are then used to develop Bayesian inference for the proposed model. Also, some discussions on the model selection and an illustration with a cutaneous melanoma data set analysed by Rodrigues et al. [Rodrigues J, de Castro M, Balakrishnan N and Cancho VG. Destructive weighted Poisson cure rate models. Technical Report, Universidade Federal de Sao Carlos, Sao Carlos-SP. Brazil, 2009 (accepted in Lifetime Data Analysis)] are presented.

Modeling gene expression regulatory networks with the sparse vector autoregressive model

Abstract Background To understand the molecular mechanisms underlying important biological processes, a detailed description of the gene products networks involved is required. In order to define and understand such molecular networks, some statistical methods are proposed in the literature to estimate gene regulatory networks from time-series microarray data. However, several problems still need to be overcome. Firstly, information flow need to be inferred, in addition to the correlation between genes. Secondly, we usually try to identify large networks from a large number of genes (parameters) originating from a smaller number of microarray experiments (samples). Due to this situation, which is rather frequent in Bioinformatics, it is difficult to perform statistical tests using methods that model large gene-gene networks. In addition, most of the models are based on dimension reduction using clustering techniques, therefore, the resulting network is not a gene-gene network but a module-module network. Here, we present the Sparse Vector Autoregressive model as a solution to these problems. Results We have applied the Sparse Vector Autoregressive model to estimate gene regulatory networks based on gene expression profiles obtained from time-series microarray experiments. Through extensive simulations, by applying the SVAR method to artificial regulatory networks, we show that SVAR can infer true positive edges even under conditions in which the number of samples is smaller than the number of genes. Moreover, it is possible to control for false positives, a significant advantage when compared to other methods described in the literature, which are based on ranks or score functions. By applying SVAR to actual HeLa cell cycle gene expression data, we were able to identify well known transcription factor targets. Conclusion The proposed SVAR method is able to model gene regulatory networks in frequent situations in which the number of samples is lower than the number of genes, making it possible to naturally infer partial Granger causalities without any a priori information. In addition, we present a statistical test to control the false discovery rate, which was not previously possible using other gene regulatory network models.

Optimization and control of a continuous polymerization reactor

This work studies the optimization and control of a styrene polymerization reactor. The proposed strategy deals with the case where, because of market conditions and equipment deterioration, the optimal operating point of the continuous reactor is modified significantly along the operation time and the control system has to search for this optimum point, besides keeping the reactor system stable at any possible point. The approach considered here consists of three layers: the Real Time Optimization (RTO), the Model Predictive Control (MPC) and a Target Calculation (TC) that coordinates the communication between the two other layers and guarantees the stability of the whole structure. The proposed algorithm is simulated with the phenomenological model of a styrene polymerization reactor, which has been widely used as a benchmark for process control. The complete optimization structure for the styrene process including disturbances rejection is developed. The simulation results show the robustness of the proposed strategy and the capability to deal with disturbances while the economic objective is optimized.