Symbolic dynamics of a chaotic neuron model

Chaotic neurons change their internal state according to a bimodal map, and when they communicate with other neurons their internal state is transformed into one of two separate outputs, firing or resting. We address and investigate the topological entropy of the two-valued output of the chaotic neuron from two different viewpoints: the dependence upon the parameters of the neurons, and the relationship to their threshold. From the former viewpoint, we clarify the mechanism that changes the shift space corresponding to the time series of the neuronal output. From the latter viewpoint, we examine the effect of small fluctuations on the threshold of the chaotic neuron. This work was presented in part at the 7th International Symposium on Artificial Life and Robotics, Oita, Japan, January 16–18, 2002     

Computer-aided method for quantification of cartilage thickness and volume changes using MRI: validation study using a synthetic model

The primary objective of this study was to develop a computer-aided method for the quantification of three-dimensional (3-D) cartilage changes over time in knees with osteoarthritis (OA). We introduced a local coordinate system (LCS) for the femoral and tibial cartilage boundaries that provides a standardized representation of cartilage geometry, thickness, and volume. The LCS can be registered in different data sets from the same patient so that results can be directly compared. Cartilage boundaries are segmented from 3-D magnetic resonance (MR) slices with a semi-automated method and transformed into offset-maps, defined by the LCS. Volumes and thickness are computed from these offset-maps. Further anatomical labeling allows focal volumes to be evaluated in predefined subregions. The accuracy of the automated behavior of the method was assessed, without any human intervention, using realistic, synthetic 3-D MR images of a human knee. The error in thickness evaluation is lower than 0.12 mm for the tibia and femur. Cartilage volumes in anatomical subregions show a coefficient of variation ranging from 0.11% to 0.32%. This method improves noninvasive 3-D analysis of cartilage thickness and volume and is well suited for in vivo follow-up clinical studies of OA knees.     

The local spatial autocorrelation and the kernel method for identifying black zones. A comparative approach

This article aims to determine the location and the length of road sections characterized by a concentration of accidents (black zones). Two methods are compared: one based on a local decomposition of a global autocorrelation index, the other on kernel estimation. After explanation, both methods are applied and compared in terms of operational results, respective advantages and shortcomings, as well as underlying conceptual elements. The operationality of both methods is illustrated by an application to one Belgian road.     

Improving Escherichia coli alkaline phosphatase efficacy by additional mutations inside and outside the catalytic pocket

We describe a strategy that allowed us to confer on a bacterial (E. coli) alkaline phosphatase (AP) the high catalytic activity of the mammalian enzyme while maintaining its high thermostability. First, we identified mutations, at positions other than those occupied by essential catalytic residues, which inactivate the bacterial enzyme without destroying its overall conformation. We transferred concomitantly into the bacterial enzyme four residues of the mammalian enzyme, two being in the catalytic pocket and two being outside. Second, the gene encoding the inactive mutant was submitted to random mutagenesis. Enzyme activity was restored upon the single mutation D330N, at a position that is 12 A away from the center of the catalytic pocket. Third, this mutation was combined with other mutations previously reported to increase AP activity slightly in the presence of magnesium. As a result, at pH 10.0 the phosphatase activity of both mutants D330N/D153H and D330N/D153G was 17-fold higher than that of the wild-type AP. Strikingly, although the two individual mutations D153H and D153G destabilize the enzyme, the double mutant D330N/D153G remained highly stable (T(m)=87 degrees C). Moreover, when combining the phosphatase and transferase activities, the catalytic activity of the mutant D330N/D153G increased 40-fold (k(cat)=3200 s-1) relative to that of the wild-type enzyme (k(cat)=80 s-1). Due to the simultaneous increase in K(m), the resulting k(cat)/K(m) value was only increased by a factor of two. Therefore, a single mutation occurring outside a catalytic pocket can dramatically control not only the activity of an enzyme, but also its thermostability. Preliminary crystallographic data of a covalent D330N/D153G enzyme-phosphate complex show that the phosphate group has significantly moved away from the catalytic pocket, relative to its position in the structure of another mutant previously reported.     

Library design using BCUT chemistry-space descriptors and multiple four-point pharmacophore fingerprints: simultaneous optimization and structure-based diversity

New applications of fingerprints of multiple potential 4-point three-dimensional (3D) pharmacophores in combinatorial library design and virtual screening are presented. Preliminary results demonstrating the feasibility of a simulated annealing process for combinatorial reagent selection that concurrently optimizes product diversity in BCUT chemistry space and in terms of unique 4-point pharmacophores are discussed, and the advantage of using a customized chemistry-space derived for the library design is demonstrated. In addition, an extension to the multiple pharmacophore method for structure-based design that uses the shape of the target site as an additional constraint is presented. This development enables the docking process to be quantified in terms of the number and identities of the pharmacophoric hypotheses that can be matched by a compound or a library of compounds. The design of an example combinatorial library based on the Ugi condensation reaction and a serine protease active site is described.     

Assessment of the performances of multilayer perceptron neural networks in comparison with recurrent neural networks and two statistical methods for diagnosing coronary artery disease

Abstract: We aimed to examine the diagnostic performances of multilayer perceptron neural networks (MLPNNs) for predicting coronary artery disease and to compare them with different types of artificial neural network methods, namely recurrent neural networks (RNNs) and two statistical methods (quadratic discriminant analysis (QDA) and logistic regression (LR)). MLPNNs were trained with backpropagation, quick propagation, delta-bar-delta and extended delta-bar-delta algorithms as classifiers; the RNN was trained with the Levenberg–Marquardt algorithm; LR and QDA were used for predicting coronary artery disease. Coronary artery disease was classified with accuracy rates varying from 79.9% to 83.9% by MLPNNs. Even though MLPNNs achieved higher accuracy rates than the statistical methods, LR (73.2%) and QDA (58.4%), their performances were lower compared to the RNN (84.7%). Among the four different types of training algorithms that trained MLPNNs, quick propagation achieved the highest accuracy rate; however, it was lower than the RNN trained with the Levenberg–Marquardt algorithm. RNNs, which demonstrated 84.7% accuracy and 86.5% positive predictive rates, may be a helpful tool in medical decision making for diagnosis of coronary artery disease.     

Modular analysis and design of log-domain circuits based on LIN↔ELIN transformations

We systematized and developed some procedures for the modular design of externally-linear internally nonlinear (ELIN) circuits resulting in a general LIN↔ELIN transformation procedure. This one was also extended to analysis of these types of circuits. The procedure is exemplified on log-domain circuits. In the design one starts with the linear block diagram (LIN) described by transfer functions and one substitutes directly each linear building block by a corresponding nonlinear one. The parameters of each nonlinear component depend on the given parameters of its linear correspondent. Input F ⁻¹ and output F blocks are added. In the analysis one identifies the nonlinear basic circuit components and each of them is substituted by its corresponding linear building block. Input and output F ⁻¹-F cells are removed. The ideal transfer function can be calculated on the linear block diagram now. The LIN↔ELIN transformations make a direct connection between equivalent linear and ELIN circuits, simplify their design and analysis procedures and permit the development of CAD procedures.     

Prediction of reserve requirements in generation planning

An effective load-carrying capability prediction model is developed which preserves a linear structure of the generation planning problem. By this means, optimum investment plans can be found which link economic decisions to measures of system reliability such as the loss of load probability index. An example is described in full which compares investment decision and reliability analyses for this capability prediction method with corresponding results from the fixed reserve margin approach commonly used.     

Quantification of two decades of shallow-water coral reef habitat decline in the Florida Keys National Marine Sanctuary using Landsat data (1984–2002)

The loss of coral reef habitats has been witnessed at a global scale including in the Florida Keys and the Caribbean. In addition to field surveys that can be spatially limited, remote sensing can provide a synoptic view of the changes occurring on coral reef habitats. Here, we utilize an 18-year time series of Landsat 5/TM and 7/ETM+ images to assess changes in eight coral reef sites in the Florida Keys National Marine Sanctuary, namely Carysfort Reef, Grecian Rocks, Molasses Reef, Conch Reef, Sombrero Reef, Looe Key Reef, Western Sambo and Sand Key Reef. Twenty-eight Landsat images (1984–2002) were used, with imagery gathered every 2 years during spring, and every 6 years during fall. The image dataset was georectified, calibrated to remote sensing reflectance and corrected for atmospheric and water-column effects. A Mahalanobis distance classification was trained for four habitat classes (‘coral’, ‘sand’, ‘bare hardbottom’ and ‘covered hardbottom’) using in situ ground-truthing data collected in 2003–2004 and using the spectral statistics from a 2002 image. The red band was considered useful only for benthic habitats in depths less than 6 m. Overall mean coral habitat loss for all sites classified by Landsat was 61% (3.4%/year), from a percentage habitat cover of 19% (1984) down to 7.6% (2002). The classification results for the eight different sites were critically reviewed. A detailed pixel by pixel examination of the spatial patterns across time suggests that the results range from ecologically plausible to unreliable due to spatial inconsistencies and/or improbable ecological successions. In situ monitoring data acquired by the Coral Reef Evaluation and Monitoring Project (CREMP) for the eight reef sites between 1996 and 2002 showed a loss in coral cover of 52% (8.7%/year), whereas the Landsat-derived coral habitat areas decreased by 37% (6.2%/year). A direct trend comparison between the entire CREMP percent coral cover data set (1996–2004) and the entire Landsat-derived coral habitat areas showed no significant difference between the two time series (ANCOVA; F-test, p = 0.303, n = 32), despite the different scales of measurements.     

Genetic algorithms optimized fuzzy logic control for the maximum power point tracking in photovoltaic system

This paper presents an intelligent control method for the maximum power point tracking (MPPT) of a photovoltaic system under variable temperature and irradiance conditions. First, for the purpose of comparison and because of its proven and good performances, the perturbation and observation (P&O) technique is briefly introduced. A fuzzy logic controller based MPPT (FLC) is then proposed which has shown better performances compared to the P&O MPPT based approach. The proposed FLC has been also improved using genetic algorithms (GA) for optimisation. Different development stages are presented and the optimized fuzzy logic MPPT controller (OFLC) is then simulated and evaluated, which has shown better performances.