Biochemical changes in cerebrospinal fluid associated with the neurotoxic action of HIV-1]

Different intravascular ultrasound (IVUS) systems vary in their image presentation. The purpose of this study was to compare four IVUS systems in vitro to determine the accuracy of tissue characterization of atherosclerotic plaque compared with histology. Ninety-eight plaque segments from 23 formalin-fixed human iliac arteries were imaged in saline at room temperature with four different IVUS systems. To assess the accuracy of IVUS in describing plaque, three types of analysis were performed: (1) the ability to identify the presence and extent of lumen or plaque boundary; (2) sensitivity, specificity, and interobserver variability of IVUS in qualitatively identifying plaque components compared with histology; and (3) quantification of calcification. The synthetic aperture device had a lower sensitivity in identifying lumen and plaque boundaries (87%, 38% respectively) compared with other machines (96%-100%, 95%-100%). All three mechanically rotating systems had fair to good sensitivities for identifying calcification (57%-73%) or lipid filled areas (50%-83%). The sensitivity of discriminating fibrous tissue from fatty areas was low (39%-52%). The synthetic aperture system had a significantly lower sensitivity for identifying all three tissue types (4%-21%). There was significant interobserver variability (kappa value = 0.47-0.68) as well as machine to machine variability (kappa value = 0.52) for tissue characterization. Calcified areas were underestimated by System 1 (p < .05) and System 4 (p < .01) because of weaker echo reflections or poor image quality. There are significant differences in image representation among these four IVUS systems in the diagnosis of tissue components of complex atherosclerotic plaque. These variabilities should be considered when interpreting studies performed with different machines.     

Partial oxidation of methane towards hydrogen production over a promising class of catalysts: Rh supported on Ti-modified MgO

The partial oxidation of methane over the supported Rh (0.8 wt.%) catalysts was investigated. Two kinds of supports were used, MgO and Ti-modified MgO (prepared by grafting technique). Among the Ti-modified MgO supports, two different compounds were used as source of Ti: inorganic (chloride) and organic (alkoxide). The catalytic performance of Rh-supported catalysts depends on the support and varies in the sequence: Ti-MgO/I > Ti-MgO/O > MgO. Ti-containing catalysts exhibited higher activity and selectivity compared to MgO, which is especially noticeable at low temperature. Possible explanations for the phenomena observed were proposed on the basis of characterization results.     

Burned area forecasting using past burned area records and Southern Oscillation Index for tropical Africa (1981-1999)

An 18-year time series of monthly NOAA-AVHRR Pathfinder Land burned area was analyzed for the region of tropical Africa, from July 1981 to June 1999. The transition period between NOAA-11 and NOAA-14 platforms from July 1993 to June 1995 was not included due to missing and outlier data. Stability of the time series was addressed for the input variables in the burned area algorithm, reflectance and temperature channels.A Seasonal AutoRegressive Integrated Moving-Average (SARIMA) model was developed for forecasting potential burned area. The SARIMA model identified an autoregressive regular term with 1-month lag and an autoregressive 12-month seasonal term with one season (12 months) component. A cross-correlation between Southern Oscillation Index (SOI) and burned area was statistically significant predictor variable in a time series with 20-month lag. Results show that the SARIMA model with this predictor improved both, fitting and forecasting, residual variance, by 4.1% and 5.6%, respectively, thereby, demonstrating potential relationship between SOI and burned area for the study region. Forecasting was estimated by considering only the first 16 years of the monthly burned area in the time series, from July 1981 to June 1997. The prediction for the following 24 months (from July 1997 to June 1999) was within the 95% confidence level indicating that the forecast was a valid characterization of the modeled process.     

Incremental wrapper-based gene selection from microarray data for cancer classification

Gene expression microarray is a rapidly maturing technology that provides the opportunity to assay the expression levels of thousands or tens of thousands of genes in a single experiment. We present a new heuristic to select relevant gene subsets in order to further use them for the classification task. Our method is based on the statistical significance of adding a gene from a ranked-list to the final subset. The efficiency and effectiveness of our technique is demonstrated through extensive comparisons with other representative heuristics. Our approach shows an excellent performance, not only at identifying relevant genes, but also with respect to the computational cost.     

The minimum dosage of praziquantel in the treatment of Taenia saginata, 1986-1993

Patients who have a unicornuate uterus with a noncommunicating rudimentary horn that contains an endometrial cavity are at risk for endometriosis and obstetric complications. As in this case, resection of the rudimentary horn can be performed laparoscopically without increased risk to the patient and with some potential benefit.     

Testing of the multi-application small light water reactor (MASLWR) passive safety systems

Experimental thermal hydraulic research has been conducted at Oregon State University for the purpose of assessing the performance of a new reactor design concept, the multi-application small light water reactor (MASLWR). The MASLWR is a pressurized light water reactor design with a net output of 35 MWe that uses natural circulation in both normal and transient operation. Due to its small size, portability and modularity, the MASLWR design is well suited to help fill the potential need for grid appropriate reactor designs for smaller electricity grids as may be found in developing or remote regions. The purpose of the OSU MASLWR test facility is to assess the operation of the MASLWR under normal full operating pressure and full temperature conditions and to assess the passive safety systems under transient conditions. The data generated by the testing program will be used to assess computer code calculations and to provide a better understanding of the thermal-hydraulic phenomena in the design of the MASLWR NSSS. During this testing program, four tests were conducted at the OSU MASLWR test facility. These tests included one design basis accident and one beyond design basis accident. During the performance of these tests, plant operations to include start up, normal operation and shut down evolutions were demonstrated successfully.     

Distributivity and conditional distributivity of a uninorm and a continuous t-conorm

The open problem recalled by Klement in the Linz2000 closing session, related to distributivity and conditional distributivity of a uninorm and a continuous t-conorm, is solved for the most usual known classes of uninorms. From the obtained results, it is deduced that distributivity and conditional distributivity are equivalent for these cases. It is remarkable that solutions appear involving not only strict t-conorms but also ordinal sums of the maximum with a strict t-conorm. Conversely, the distributivity of a t-conorm over a uninorm is also studied leading only to already known solutions. Moreover, the dual case of distributivity and conditional distributivity involving uninorms and continuous t-norms is also solved, proving again the equivalence of both kinds of distributivities.     

Enantiomer analysis of chiral lactones in foods by on-line coupled reversed-phase liquid chromatography-gas chromatography

A new application is proposed for the on-line coupling of reversed-phase liquid chromatography to gas chromatography (RPLC-GC) that allows the GC chirospecific analysis of gamma-lactones in fruits and commercially available fruit-containing products. The use of a programmed temperature vaporizer as an interface with the system makes the transfer of large volume fractions (i.e., 2520 microL) of aqueous eluents from LC to GC possible (speed of sample transfer, 1800 microL/min). Relative standard deviations obtained for the investigated lactones under the experimental conditions vary from 7 to 14%. The described system enlarges the LC-GC application field and overcomes the limitations reported thus far concerning the use of typical normal-phase eluents (i.e., the transfer of rather small volume fractions at low speeds of sample introduction).     

Evolutionary Generalized Radial Basis Function neural networks for improving prediction accuracy in gene classification using feature selection

Radial Basis Function Neural Networks (RBFNNs) have been successfully employed in several function approximation and pattern recognition problems. The use of different RBFs in RBFNN has been reported in the literature and here the study centres on the use of the Generalized Radial Basis Function Neural Networks (GRBFNNs). An interesting property of the GRBF is that it can continuously and smoothly reproduce different RBFs by changing a real parameter τ. In addition, the mixed use of different RBF shapes in only one RBFNN is allowed. Generalized Radial Basis Function (GRBF) is based on Generalized Gaussian Distribution (GGD), which adds a shape parameter, τ, to standard Gaussian Distribution. Moreover, this paper describes a hybrid approach, Hybrid Algorithm (HA), which combines evolutionary and gradient-based learning methods to estimate the architecture, weights and node topology of GRBFNN classifiers. The feasibility and benefits of the approach are demonstrated by means of six gene microarray classification problems taken from bioinformatic and biomedical domains. Three filters were applied: Fast Correlation-Based Filter (FCBF), Best Incremental Ranked Subset (BIRS), and Best Agglomerative Ranked Subset (BARS); this was done in order to identify salient expression genes from among the thousands of genes in microarray data that can directly contribute to determining the class membership of each pattern. After different gene subsets were obtained, the proposed methodology was performed using the selected gene subsets as new input variables. The results confirm that the GRBFNN classifier leads to a promising improvement in accuracy.     

On the sizing of a solar thermal electricity plant for multiple objectives using evolutionary optimization

Design, implementation and operation of solar thermal electricity plants are no more an academic task, rather they have become a necessity. In this paper, we work with power industries to formulate a multi-objective optimization model and attempt to solve the resulting problem using classical as well as evolutionary optimization techniques. On a set of four objectives having complex trade-offs, our proposed procedure first finds a set of trade-off solutions showing the entire range of optimal solutions. Thereafter, the evolutionary optimization procedure is combined with a multiple criterion decision making (MCDM) approach to focus on preferred regions of the trade-off frontier. Obtained solutions are compared with a classical generating method. Eventually, a decision-maker is involved in the process and a single preferred solution is obtained in a systematic manner. Starting with generating a wide spectrum of trade-off solutions to have a global understanding of feasible solutions, then concentrating on specific preferred regions for having a more detailed understanding of preferred solutions, and then zeroing on a single preferred solution with the help of a decision-maker demonstrates the use of multi-objective optimization and decision making methodologies in practice. As a by-product, useful properties among decision variables that are common to the obtained solutions are gathered as vital knowledge for the problem. The procedures used in this paper are ready to be used to other similar real-world problem solving tasks.