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%。     

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.     

Information theoretic clustering

Clustering is an important topic in pattern recognition. Since only the structure of the data dictates the grouping (unsupervised learning), information theory is an obvious criteria to establish the clustering rule. The paper describes a novel valley seeking clustering algorithm using an information theoretic measure to estimate the cost of partitioning the data set. The information theoretic criteria developed here evolved from a Renyi entropy estimator (A. Renyi, 1960) that was proposed recently and has been successfully applied to other machine learning applications (J.C. Principe et al., 2000). An improved version of the k-change algorithm is used in optimization because of the stepwise nature of the cost function and existence of local minima. Even when applied to nonlinearly separable data, the new algorithm performs well, and was able to find nonlinear boundaries between clusters. The algorithm is also applied to the segmentation of magnetic resonance imaging data (MRI) with very promising results     

An error-entropy minimization algorithm for supervised training ofnonlinear adaptive systems

The paper investigates error-entropy-minimization in adaptive systems training. We prove the equivalence between minimization of error's Renyi (1970) entropy of order α and minimization of a Csiszar (1981) distance measure between the densities of desired and system outputs. A nonparametric estimator for Renyi's entropy is presented, and it is shown that the global minimum of this estimator is the same as the actual entropy. The performance of the error-entropy-minimization criterion is compared with mean-square-error-minimization in the short-term prediction of a chaotic time series and in nonlinear system identification     

Functional connectivity dynamics among cortical neurons: a dependence analysis

This paper quantifies and comparatively validates functional connectivity between neurons by measuring the statistical dependence between their firing rates. Based on statistical analysis of the pairwise functional connectivity, we estimate, exclusively from neural data, the neural assembly functional connectivity given a behavior task, which provides a quantifiable representation of the dynamic nature during the behavioral task. Because of the time scale of behavior (100-1000 ms), a statistical method that yields robust estimators for this small sample size is desirable. In this work, the temporal resolutions of four estimators of functional connectivity are compared on both simulated data and real neural ensemble recordings. The comparison highlights how the properties and assumptions of statistical-based and phase-based metrics affect the interpretation of connectivity. Simulation results show that mean square contingency (MSC) and mutual information (MI) create more robust quantification of functional connectivity under identical conditions than cross correlation (CC) and phase synchronization (PhS) when the sample size is 1 s. The results of the simulated analysis are extended to real neuronal recordings to assess the functional connectivity in monkey's cortex corresponding to three movement states in a food reaching task and construct the assembly graph given a movement state and the activation degree of a state-related assembly over time using the statistical test exclusively from neural data dependencies. The activation degree of a given state-related assembly reaches the peak repeatedly when the specific movement states occur, which also reveals the network of interactions among the neurons are key for the operation of a specific behavior.     

An analysis of the gamma memory in dynamic neural networks

Presents a vector space framework to study short-term memory filters in dynamic neural networks. The authors define parameters to quantify the function of feedforward and recursive linear memory filters. They show, using vector spaces, what is the optimization problem solved by the PEs of the first hidden layer of the single input focused network architecture. Due to the special properties of the gamma bases, recursion brings an extra parameter lambda (the time constant of the leaky integrator) that displaces the memory manifold towards the desired signal when the mean square error is minimized. In contrast, for the feedforward memory filter the angle between the desired signal and the memory manifold is fixed for a given memory order. The adaptation of the feedback parameter can be done using gradient descent, but the optimization is nonconvex.     

Making sense of a complex world [chaotic events modeling]

Addresses the identification of nonlinear systems from output time series, which we have called dynamic modeling. We start by providing the mathematical basis for dynamic modeling and show that it is equivalent to a multivariate nonlinear prediction problem in the reconstructed space. We address the importance of dynamic reconstruction for dynamic modeling. Recognizing that dynamic reconstruction is an ill-defined inverse problem, we describe a regularized radial basis function network for solving the dynamic reconstruction problem. Prior knowledge in the form of smoothness of the mapping is imposed on the solution via regularization. We also show that, in time-series analysis, some form of regularization can be accomplished by using the structure of the time series instead of imposing a smoothness constraint on the cost function. We develop a methodology based on iterated prediction to train the network weights with an error derived through trajectory learning. This method provides a robust performance because during learning the weights are constrained to follow a trajectory. The dynamic invariants estimated from the generated time series are similar to the ones estimated from the original time series, which means that the properties of the attractor have been captured by the neural network. We finally raise the question that generalized delay operators may have advantages in dynamic reconstruction, primarily in cases where the time series is corrupted by noise. We show how to set the recursive parameter of the gamma operator to attenuate noise and preserve the dynamics     

Target discrimination in synthetic aperture radar using artificial neural networks

This paper addresses target discrimination in synthetic aperture radar (SAR) imagery using linear and nonlinear adaptive networks. Neural networks are extensively used for pattern classification but here the goal is discrimination. We show that the two applications require different cost functions. We start by analyzing with a pattern recognition perspective the two-parameter constant false alarm rate (CFAR) detector which is widely utilized as a target detector in SAR. Then we generalize its principle to construct the quadratic gamma discriminator (QGD), a nonparametrically trained classifier based on local image intensity. The linear processing element of the QCD is further extended with nonlinearities yielding a multilayer perceptron (MLP) which we call the NL-QGD (nonlinear QGD). MLPs are normally trained based on the L(2) norm. We experimentally show that the L(2) norm is not recommended to train MLPs for discriminating targets in SAR. Inspired by the Neyman-Pearson criterion, we create a cost function based on a mixed norm to weight the false alarms and the missed detections differently. Mixed norms can easily be incorporated into the backpropagation algorithm, and lead to better performance. Several other norms (L(8), cross-entropy) are applied to train the NL-QGD and all outperformed the L(2) norm when validated by receiver operating characteristics (ROC) curves. The data sets are constructed from TABILS 24 ISAR targets embedded in 7 km(2) of SAR imagery (MIT/LL mission 90).     

Returns on investment in public health: effect of public health expenditures on infant health, 1983-1990

In this paper a number of methodological issues relating to research on the relationship between the menopause, mood and hormone replacement therapy (HRT) are discussed. These issues relate to problems of design and statistical analyses, problems which have prevented the reaching of definite conclusions regarding the relationship between menopause, mood and hormones. These problems are discussed under three main headings, namely, the assessment of menopausal status, statistical modelling and the design and analyses of clinical trials. Problems relating to concepts and measurement of dependent variables are the subject matter of the papers that follow. Within the three main headings more specific issues are detailed. The paper concludes with a list of recommendations on how research in this important area might be further advanced.     

Sleep spindle characteristics as a function of age

The characteristics of sleep spindles as a function of age were studied with an automated analysis system. Five age groups (group 0, 3-5 years; I, 13 years; II, 25-34 years; III, 42-53 years; and IV, 67-79 years) were analyzed. Significant differences in spindle frequency were found between groups 0-I and II-III-IV. The frequency increased with increasing age. No age differences were found in spindle duration. Spindle amplitude reached a peak in the group I subjects, then decreased with increasing age.