Chaotic activity during iron-induced `epileptiform'' discharge inrat hippocampal slices

Low-dimensional chaotic dynamics have been suggested in the rat hippocampal slice during iron-induced epileptiform activity. The dimensionality of this chaotic activity has been found to be similar in slices bathed in the same ionic extracellular medium. Some slices also displayed a drop in dimensionality prior to the onset of seizure-like activity. We suggest that techniques of nonlinear dynamical analysis are a useful reverse-engineering tool for studying the in vitro brain slice. We further conclude that neuronal circuits capable of displaying chaotic activity could exist at the level of the in vitro brain slice.     

Quantification of Spice Mixture Compositions by Electronic Nose: Part I. Experimental Design and Data Analysis Using Neural Networks

ABSTRACT: A quantitative procedure was developed to predict the composition of ternary ground spice mixtures using an electronic nose. Basil, cinnamon, and garlic were mixed in different compositions and presented to an e-nose. Nineteen training mixtures were used to build predictive models. Model performance was tested using 5 other mixtures. Three neural network structures—multilayer perceptron (MLP), MLP using principal component analysis as a preprocessor (PCA-MLP), and the time-delay neural network (TDNN)—were used for predictive model building. All 3 neural network models predicted the testing mixtures' compositions with a mean square error (MSE) equal or less than 0.0051 (in a fraction domain where sum of fractions = 1). The TDNN provided the smallest MSE.     

Super-resolution of images based on local correlations

An adaptive two-step paradigm for the super-resolution of optical images is developed in this paper. The procedure locally projects image samples onto a family of kernels that are learned from image data. First, an unsupervised feature extraction is performed on local neighborhood information from a training image. These features are then used to cluster the neighborhoods into disjoint sets for which an optimal mapping relating homologous neighborhoods across scales can be learned in a supervised manner. A super-resolved image is obtained through the convolution of a low-resolution test image with the established family of kernels. Results demonstrate the effectiveness of the approach.     

Unsuspected carcinoma of the gall bladder: case report of trocar-site metastasis following laparoscopic cholecystectomy

Cancer of the gall bladder is a rare malignant neoplasm with an unfavourable prognosis. Laparoscopic surgery has brought about the emergence of possible neoplastic diffusion along trocar tracts in cases where unrecognized carcinoma of the gall bladder is present. The authors present a case of neoplastic abdominal diffusion discovered 4 months after laparoscopic cholecystectomy in which histologic examination of the surgical specimen revealed the presence of unrecognized carcinoma of the gall bladder.     

Incremental backpropagation learning networks

How to learn new knowledge without forgetting old knowledge is a key issue in designing an incremental-learning neural network. In this paper, we present a new incremental learning method for pattern recognition, called the "incremental backpropagation learning network", which employs bounded weight modification and structural adaptation learning rules and applies initial knowledge to constrain the learning process. The viability of this approach is demonstrated for classification problems including the iris and the promoter domains.     

重组大肠杆菌全细胞催化合成4-羟基异亮氨酸

4-羟基异亮氨酸(4-HIL)是一种很有前景的药物,它具有促进胰岛素分泌、改善外周组织对胰岛素的抵抗性和调节血脂异常等作用,而L-异亮氨酸羟化酶(IDO)常用于4-HIL的生产。首先,克隆了苏云金芽孢杆菌来源的L-异亮氨酸羟化酶,实现了其在E. coli BL21(DE3)中的异源表达。其次,通过同源建模和蛋白质结构分析,本着将与底物氨基酸侧链结合的氨基酸残基从亲水性或长链疏水性结构突变成丙氨酸Ala的原则,对I156位点进行了定点突变,以增大底物结合口袋,扩宽底物通道进而提高4-HIL的产量。最后,对野生酶及突变酶的酶学性质、突变酶的羟基化反应体系进行研究,在最优催化条件下,分批补料转化底物进行4-HIL的生产。酶学性质结果显示,野生酶及突变酶I156A的最适温度均为25℃,最适pH均为7.0;突变酶I156A比酶活比野生酶提高了1.9倍,L-ILe转化率提高了28%。羟基化反应体系的最优转化条件为:20 mmol/L L-ILe,20 mmol/L α-酮戊二酸,8 mmol/L Fe~(2+),30 mmol/L抗坏血酸和HEPES(50 mmol/L,pH 7.0)缓冲液。在最优转化条件下,重组菌E. coli BL21/pET28a-ido~(I156A)进行分批补料转化底物,时间间隔为4 h,32 h后得到77.3 mmol/L 4-HIL,底物最高转化率98.35%。     

On the stabilization parameter in the subgrid scale approximation of scalar convection–diffusion–reaction equations on distorted meshes

In this paper we revisit the definition of the stabilization parameter in the finite element approximation of the convection–diffusion–reaction equation. The starting point is the decomposition of the unknown into its finite element component and a subgrid scale that needs to be approximated. In order to incorporate the distortion of the mesh into this approximation, we transform the equation for the subgrid scale within each element to the shape-regular reference domain. The expression for the subgrid scale arises from an approximate Fourier analysis and the identification of the wave number direction where instabilities are most likely to occur. The final outcome is an expression for the stabilization parameter that accounts for anisotropy and the dominance of either convection or reaction terms in the equation.     

Experimental upper bound for the performance of convolutive source separation methods

An important problem in the field of blind source separation (BSS) of real convolutive mixtures is the determination of the role of the demixing filter structure and the criterion/optimization method in limiting separation performance. This issue requires the knowledge of the optimal performance for a given structure, which is unknown for real mixtures. Herein, the authors introduce an experimental upper bound on the separation performance for a class of convolutive blind source separation structures, which can be used to approximate the optimal performance. As opposed to a theoretical upper bound, the experimental upper bound produces an estimate of the optimal separating parameters for each dataset in addition to specifying an upper bound on separation performance. Estimation of the upper bound involves the application of a supervised learning method to the set of observations found by recording the sources one at a time. Using the upper bound, it is demonstrated that structures other than the finite-impulse-response (FIR) structure should be considered for real (convolutive) mixtures, there is still much room for improvement in current convolutive BSS algorithms, and the separation performance of these algorithms is not necessarily limited by local minima.     

Error whitening criterion for adaptive filtering: theory and algorithms

Mean squared error (MSE) has been the dominant criterion in adaptive filter theory. A major drawback of the MSE criterion in linear filter adaptation is the parameter bias in the Wiener solution when the input data are contaminated with noise. We propose and analyze a new augmented MSE criterion called the Error Whitening Criterion (EWC). EWC is able to eliminate this bias when the noise is white. We will determine the analytical solution of the EWC, discuss some interesting properties, and develop stochastic gradient and other fast algorithms to calculate the EWC solution in an online fashion. The stochastic algorithms are locally computable and have structures and complexities similar to their MSE-based counterparts (LMS and NLMS). Convergence of the stochastic gradient algorithm is established with mild assumptions, and upper bounds on the step sizes are deduced for guaranteed convergence. We will briefly discuss an RLS-like Recursive Error Whitening (REW) algorithm and a minor components analysis (MCA) based EWC-total least squares (TLS) algorithm and further draw parallels between the REW algorithm and the Instrumental Variables (IV) method for system identification. Finally, we will demonstrate the noise-rejection capability of the EWC by comparing the performance with MSE criterion and TLS.