Ascertaining the importance of neurons to develop better brain-machine interfaces

In the design of brain-machine interface (BMI) algorithms, the activity of hundreds of chronically recorded neurons is used to reconstruct a variety of kinematic variables. A significant problem introduced with the use of neural ensemble inputs for model building is the explosion in the number of free parameters. Large models not only affect model generalization but also put a computational burden on computing an optimal solution especially when the goal is to implement the BMI in low-power, portable hardware. In this paper, three methods are presented to quantitatively rate the importance of neurons in neural to motor mapping, using single neuron correlation analysis, sensitivity analysis through a vector linear model, and a model-independent cellular directional tuning analysis for comparisons purpose. Although, the rankings are not identical, up to sixty percent of the top 10 ranking cells were in common. This set can then be used to determine a reduced-order model whose performance is similar to that of the ensemble. It is further shown that by pruning the initial ensemble neural input with the ranked importance of cells, a reduced sets of cells (between 40 and 80, depending upon the methods) can be found that exceed the BMI performance levels of the full ensemble.     

Adaptive epileptic seizure prediction system

Current epileptic seizure "prediction" algorithms are generally based on the knowledge of seizure occurring time and analyze the electroencephalogram (EEG) recordings retrospectively. It is then obvious that, although these analyses provide evidence of brain activity changes prior to epileptic seizures, they cannot be applied to develop implantable devices for diagnostic and therapeutic purposes. In this paper, we describe an adaptive procedure to prospectively analyze continuous, long-term EEG recordings when only the occurring time of the first seizure is known. The algorithm is based on the convergence and divergence of short-term maximum Lyapunov exponents (STLmax) among critical electrode sites selected adaptively. A warning of an impending seizure is then issued. Global optimization techniques are applied for selecting the critical groups of electrode sites. The adaptive seizure prediction algorithm (ASPA) was tested in continuous 0.76 to 5.84 days intracranial EEG recordings from a group of five patients with refractory temporal lobe epilepsy. A fixed parameter setting applied to all cases predicted 82% of seizures with a false prediction rate of 0.16/h. Seizure warnings occurred an average of 71.7 min before ictal onset. Similar results were produced by dividing the available EEG recordings into half training and testing portions. Optimizing the parameters for individual patients improved sensitivity (84% overall) and reduced false prediction rate (0.12/h overall). These results indicate that ASPA can be applied to implantable devices for diagnostic and therapeutic purposes.     

Learning from Examples with Information Theoretic Criteria

This paper discusses a framework for learning based on information theoretic criteria. A novel algorithm based on Renyi's quadratic entropy is used to train, directly from a data set, linear or nonlinear mappers for entropy maximization or minimization. We provide an intriguing analogy between the computation and an information potential measuring the interactions among the data samples. We also propose two approximations to the Kulback-Leibler divergence based on quadratic distances (Cauchy-Schwartz inequality and Euclidean distance). These distances can still be computed using the information potential. We test the newly proposed distances in blind source separation (unsupervised learning) and in feature extraction for classification (supervised learning). In blind source separation our algorithm is capable of separating instantaneously mixed sources, and for classification the performance of our classifier is comparable to the support vector machines (SVMs).     

The gamma-filter-a new class of adaptive IIR filters withrestricted feedback

Generalized feedforward filters, a class of adaptive filters that combines attractive properties of finite impulse response (FIR) filters with some of the power of infinite impulse response (IIR) filters, are described. A particular case, the gamma filter, generalizes Widrow's adaptive transversal filter (adaline) to an infinite impulse response filter. Yet, the stability condition for the gamma filter is trivial, and LMS adaptation is of the same computational complexity as the conventional transversal filter structure. Preliminary results indicate that the gamma filter is more efficient than the adaptive transversal filter. The authors extend the Wiener-Kopf equation to the gamma filter and develop some analysis tools     

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.     

From linear adaptive filtering to nonlinear information processing - The design and analysis of information processing systems

Recent advances in computing capabilities and the interest in new challenging signal processing problems that cannot be successfully solved using traditional techniques have sparked an interest in information-theoretic signal processing techniques. Adaptive nonlinear filters that process signals based on their information content have become a major focus of interest. The design and analysis of such nonlinear information processing systems is demonstrated in this paper. Theoretical background on necessary information theoretic concepts are provided, nonparametric sample estimators for these quantities are derived and discussed, the use of these estimators for various statistical signal processing problems have been illustrated. These include data density modeling, system identification, blind source separation, dimensionality reduction, image registration, and data clustering     

SAMICOS?A Sleep Analyzing Microcomputer System

A microcomputer-based sleep analyzing system capable of real-time waveform detection and storage is presented. A system design methodology necessary to build the real-time, multiobject, multichannel signal detection scheme is described with the crucial steps in algorithm development and implementation. The system performance is comparable to that of a hybrid, older version of a sleep analyzer which was extensively tested with good results.