Rainfall erosivity for the State of Rio de Janeiro estimated by artificial neural network

The Artificial Neural Networks (ANNs) are mathematical models method capable of estimating non-linear response plans. The advantage of these models is to present different responses of the statistical models. Thus, the objective of this study was to develop and to test ANNs for estimating rainfall erosivity index (EI30) as a function of the geographical location for the state of Rio de Janeiro, Brazil and generating a thematic visualization map. The characteristics of latitude, longitude e altitude using ANNs were acceptable to estimating EI30 and allowing visualization of the space variability of EI30. Thus, ANN is a potential option for the estimate of climatic variables in substitution to the traditional methods of interpolation.

Covalidation of Integral Transforms and Method of Lines in Nonlinear Convection-Diffusion with Mathematica

The Mathematica system (version 4.0) is employed in the solution of nonlinear difusion and convection-difusion problems, formulated as transient one-dimensional partial diferential equations with potential dependent equation coefficients. The Generalized Integral Transform Technique (GITT) is first implemented for the hybrid numerical-analytical solution of such classes of problems, through the symbolic integral transformation and elimination of the space variable, followed by the utilization of the built-in Mathematica function NDSolve for handling the resulting transformed ODE system. This approach ofers an error-controlled final numerical solution, through the simultaneous control of local errors in this reliable ODE's solver and of the proposed eigenfunction expansion truncation order. For covalidation purposes, the same built-in function NDSolve is employed in the direct solution of these partial diferential equations, as made possible by the algorithms implemented in Mathematica (versions 3.0 and up), based on application of the method of lines. Various numerical experiments are performed and relative merits of each approach are critically pointed out.

Radiography, computed tomography and magnetic resonance imaging at 0.5 Tesla of mechanically inducedosteoarthritis in rabbit knees

In the present experimental study we assessed induced osteoarthritis data in rabbits, compared three diagnostic methods, i.e., radiography (XR), computed tomography (CT) and magnetic resonance imaging (MRI), and correlated the imaging findings with those obtained by macroscopic evaluation. Ten young female rabbits of the Norfolk breed were used. Seven rabbits had the right knee immobilized in extension for a period of 12 weeks (immobilized group), and three others did not have a limb immobilized and were maintained under the same conditions (control group). Alterations observed by XR, CT and MRI after the period of immobilization were osteophytes, osteochondral lesions, increase and decrease of joint space, all of them present both in the immobilized and non-immobilized contralateral limbs. However, a significantly higher score was obtained for the immobilized limbs (XT: P = 0.016, CT: P = 0.031, MRI: P = 0.0156). All imaging methods were able to detect osteoarthritis changes after the 12 weeks of immobilization. Macroscopic evaluation identified increased thickening of joint capsule, proliferative and connective tissue in the femoropatellar joint, and irregularities of articular cartilage, especially in immobilized knees. The differences among XR, CT and MRI were not statistically significant for the immobilized knees. However, MRI using a 0.5 Tesla scanner was statistically different from CT and XR for the non-immobilized contralateral knees. We conclude that the three methods detected osteoarthritis lesions in rabbit knees, but MRI was less sensitive than XR and CT in detecting lesions compatible with initial osteoarthritis. Since none of the techniques revealed all the lesions, it is important to use all methods to establish an accurate diagnosis.

Antagonic effect of the inhibition of inducible nitric oxide on the mortality of mice acutely infected with Escherichia coli and Bacteroides fragilis

Sepsis, the leading cause of death in intensive care units, is associated with overproduction of nitric oxide (NO) due to inducible NO synthase (iNOS), responsible for some of the pathologic changes. Aminoguanidine (AG) is a selective iNOS inhibitor with reported inconsistent actions in sepsis. To investigate the influence of iNOS, we studied models of acute bacterial sepsis using acute challenges with aerobic (Escherichia coli) and anaerobic (Bacteroides fragilis) bacteria in the presence of AG. Six-week-old, 23 g, male and female BALB/c and C57Bl/6j mice, in equal proportions, were inoculated (ip) with bacteria in groups of 4 animals for each dose and each experiment in the absence or presence of AG (50 mg/kg, ip, starting 24 h before challenge and daily until day 6) and serum nitrate was measured by chemiluminescence. Both types of bacteria were lethal to mice, with an LD50 of 6 nephelometric units (U) for E. coli and 8 U for B. fragilis. Nitrate production peaked on the second day after E. coli inoculation with 8 and 6 U (P < 0.05), but was absent after non-lethal lower doses. After challenge with B. fragilis this early peak occurred at all tested doses after 24 h, including non-lethal ones (P < 0.05). AG-treated mice challenged with E. coli presented higher survival (P < 0.05) and increased LD50. AG-treated mice challenged with B. fragilis had lower LD50 and higher mortality. Control AG-treated animals presented no toxic effects. The opposite effect of iNOS blockade by AG in these models could be explained by restriction of oxygen for immune cells or an efficient action of NO in anaerobic localized infections. The antagonic role of NO production observed in our bacterial models could explain the reported discrepancy of NO action in sepsis.