Exercise Performance and Dynamic Hyperinflation in Lymphangioleiomyomatosis

Rationale: Lymphangioleiomyomatosis (LAM) is characterized by exercise performance impairment. Although airflow limitation is common, no previous studies have evaluated the prevalence and impact of dynamic hyperinflation (DH) in LAM. Objectives: To investigate the dynamic responses during maximal exercise and the prevalence, predictors, and repercussions of DH in LAM. Methods: Forty-two patients with LAM performed symptom-limited incremental cycle exercise and pulmonary functions tests (PFTs) and were compared with 10 age-matched healthy women. Dyspnea intensity, inspiratory capacity, oxygen saturation, and cardiac, metabolic, and respiratory variables were assessed during exercise. Patients with LAM also performed a 6-minute walk test (6MWT). Measurements and Main Results: Patients with LAM had higher baseline dyspnea, poorer quality of life, obstructive pattern, air trapping, and reduced diffusing capacity of carbon monoxide in PFTs. Although they had the same level of regular physical activity, their maximal exercise performance was reduced and was associated with ventilatory limitation, greater desaturation, and dyspnea. The prevalence of DH was high in LAM (55%), even in patients with mild spirometric abnormalities, and was correlated with airflow obstruction, air trapping, and diffusing capacity of carbon monoxide. Compared with the non-DH subgroup, the patients who developed DH had a ventilatory limitation contributing to exercise cessation on cycling and higher desaturation and dyspnea intensity during the 6MWT. Conclusions: Ventilatory limitation and gas exchange impairment are important causes of exercise limitation in LAM. DH is frequent in LAM, even in patients with mild spirometric abnormalities. DH was associated with the severity of disease, higher dyspnea, and lower oxygen saturation. In the 6MWT, desaturation and dyspnea were greater in patients with DH.

Tailored SERS substrates obtained with cathodic arc plasma ion implantation of gold nanoparticles into a polymer matrix

This manuscript reports on the fabrication of plasmonic substrates using cathodic arc plasma ion implantation, in addition to their performance as SERS substrates. The technique allows for the incorporation of a wide layer of metallic nanoparticles into a polymer matrix, such as PMMA. The ability to pattern different structures using the PMMA matrix is one of the main advantages of the fabrication method. This opens up new possibilities for obtaining tailored substrates with enhanced performance for SERS and other surface-enhanced spectroscopies, as well as for exploring the basic physics of patterned metal nanostructures. The architecture of the SERS-active substrate was varied using three adsorption strategies for incorporating a laser dye (rhodamine): alongside the nanoparticles into the polymer matrix, during the polymer cure and within nanoholes lithographed on the polymer. As a proof-of-concept, we obtained the SERS spectra of rhodamine for the three types of substrates. The hypothesis of incorporation of rhodamine molecules into the polymer matrix during the cathodic arc plasma ion implantation was supported by FDTD (Finite-Difference Time-Domain) simulations. In the case of arrays of nanoholes, rhodamine molecules could be adsorbed directly on the gold surface, then yielding a well-resolved SERS spectrum for a small amount of analyte owing to the short-range interactions and the large longitudinal field component inside the nanoholes. The results shown here demonstrate that the approach based on ion implantation can be adapted to produce reproducible tailored substrates for SERS and other surface-enhanced spectroscopies.

A model of road effect using line integrals and a test of the performance of two new road indices using the distribution of small mammals in an Atlantic Forest landscape

Effects of roads on wildlife and its habitat have been measured using metrics, such as the nearest road distance, road density, and effective mesh size. In this work we introduce two new indices: (1) Integral Road Effect (IRE), which measured the sum effects of points in a road at a fixed point in the forest; and (2) Average Value of the Infinitesimal Road Effect (AVIRE), which measured the average of the effects of roads at this point. IRE is formally defined as the line integral of a special function (the infinitesimal road effect) along the curves that model the roads, whereas AVIRE is the quotient of IRE by the length of the roads. Combining tools of ArcGIS software with a numerical algorithm, we calculated these and other road and habitat cover indices in a sample of points in a human-modified landscape in the Brazilian Atlantic Forest, where data on the abundance of two groups of small mammals (forest specialists and habitat generalists) were collected in the field. We then compared through the Akaike Information Criterion (AIC) a set of candidate regression models to explain the variation in small mammal abundance, including models with our two new road indices (AVIRE and IRE) or models with other road effect indices (nearest road distance, mesh size, and road density), and reference models (containing only habitat indices, or only the intercept without the effect of any variable). Compared to other road effect indices, AVIRE showed the best performance to explain abundance of forest specialist species, whereas the nearest road distance obtained the best performance to generalist species. AVIRE and habitat together were included in the best model for both small mammal groups, that is, higher abundance of specialist and generalist small mammals occurred where there is lower average road effect (less AVIRE) and more habitat. Moreover, AVIRE was not significantly correlated with habitat cover of specialists and generalists differing from the other road effect indices, except mesh size, which allows for separating the effect of roads from the effect of habitat on small mammal communities. We suggest that the proposed indices and GIS procedures could also be useful to describe other spatial ecological phenomena, such as edge effect in habitat fragments. (C) 2012 Elsevier B.V. All rights reserved.

Robust and optimal adjustment of Power System Stabilizers through Linear Matrix Inequalities

This work presents the application of Linear Matrix Inequalities to the robust and optimal adjustment of Power System Stabilizers with pre-defined structure. Results of some tests show that gain and zeros adjustments are sufficient to guarantee robust stability and performance with respect to various operating points. Making use of the flexible structure of LMI's, we propose an algorithm that minimizes the norm of the controllers gain matrix while it guarantees the damping factor specified for the closed loop system, always using a controller with flexible structure. The technique used here is the pole placement, whose objective is to place the poles of the closed loop system in a specific region of the complex plane. Results of tests with a nine-machine system are presented and discussed, in order to validate the algorithm proposed. (C) 2012 Elsevier Ltd. All rights reserved.

Performance of an electronic tongue during monitoring 2-rnethylisoborneol and geosmin in water samples

2-Methylisoborneol (MIB) and geosmin (GSM) are sub products from algae decomposition and, depending on their concentration, can be toxic: otherwise, they give unpleasant taste and odor to water. For water treatment companies it is important to constantly monitor their presence in the distributed water and avoid further costumer complaints. Lower-cost and easy-to-read instrumentation would be very promising in this regard. In this study, we evaluate the potentiality of an electronic tongue (ET) system based on non-specific polymeric sensors and impedance measurements in monitoring MIB and GSM in water samples. Principal component analysis (PCA) applied to the generated data matrix indicated that this ET was capable to perform with remarkable reproducibility the discrimination of these two contaminants in either distilled or tap water, in concentrations as low as 25 ng L-1. Nonetheless, this analysis methodology was rather qualitative and laborious, and the outputs it provided were greatly subjective. Also, data analysis based on PCA severely restricts automation of the measuring system or its use by non-specialized operators. To circumvent these drawbacks, a fuzzy controller was designed to quantitatively perform sample classification while providing outputs in simpler data charts. For instance, the ET along with the referred fuzzy controller performed with a 100% hit rate the quantification of MIB and GSM samples in distilled and tap water. The hit rate could be read directly from the plot. The lower cost of these polymeric sensors allied to the especial features of the fuzzy controller (easiness on programming and numerical outputs) provided initial requirements for developing an automated ET system to monitor odorant species in water production and distribution. (C) 2012 Elsevier B.V. All rights reserved.

A Single Statistic for Monitoring the Covariance Matrix of Bivariate Processes

In this article, we present a new control chart for monitoring the covariance matrix in a bivariate process. In this method, n observations of the two variables were considered as if they came from a single variable (as a sample of 2n observations), and a sample variance was calculated. This statistic was used to build a new control chart specifically as a VMIX chart. The performance of the new control chart was compared with its main competitors: the generalized sampled variance chart, the likelihood ratio test, Nagao's test, probability integral transformation (v(t)), and the recently proposed VMAX chart. Among these statistics, only the VMAX chart was competitive with the VMIX chart. For shifts in both variances, the VMIX chart outperformed VMAX; however, VMAX showed better performance for large shifts (higher than 10%) in one variance.

Matrix-vector multiplication and triangular linear solver using GPGPU for symmetric positive definite matrices derived from elliptic equations

The modern GPUs are well suited for intensive computational tasks and massive parallel computation. Sparse matrix multiplication and linear triangular solver are the most important and heavily used kernels in scientific computation, and several challenges in developing a high performance kernel with the two modules is investigated. The main interest it to solve linear systems derived from the elliptic equations with triangular elements. The resulting linear system has a symmetric positive definite matrix. The sparse matrix is stored in the compressed sparse row (CSR) format. It is proposed a CUDA algorithm to execute the matrix vector multiplication using directly the CSR format. A dependence tree algorithm is used to determine which variables the linear triangular solver can determine in parallel. To increase the number of the parallel threads, a coloring graph algorithm is implemented to reorder the mesh numbering in a pre-processing phase. The proposed method is compared with parallel and serial available libraries. The results show that the proposed method improves the computation cost of the matrix vector multiplication. The pre-processing associated with the triangular solver needs to be executed just once in the proposed method. The conjugate gradient method was implemented and showed similar convergence rate for all the compared methods. The proposed method showed significant smaller execution time.