Detection and classification of neurotoxins using a novel short-term plasticity quantification method

A tissue-based biosensor is described for screening chemical compounds that rapidly affect the nervous system. The proposed sensor is an extension of a previous work on cultured hippocampal slices [Biosens. Bioelectron. 16 (2001) 491]. The detection of the chemical compounds is based on a novel quantification method of short-term plasticity (STP) of the CA1 system in acute hippocampal slices, using random electrical impulse sequences as inputs and population spike (PS) amplitudes as outputs. STP is quantified by the first and the second order kernels using a variant of the Volterra modeling approach. This approach is more specific and time-efficient than the conventional paired pulse and fixed frequency train methods [J. Neurosci. Methods 2 (2002) 111]. Describing the functional state of the biosensor, the kernels changed accordingly as chemical compounds were added. The second order kernel was decomposed into nine Laguerre functions. The corresponding Laguerre coefficients along with the first order kernel were used as features for classification purposes. The biosensor was tested using picrotoxin (100 μM), trimethylopropane phosphate (10 μM), tetraethylammonium (4 mM), valproate (5 mM), carbachol (5 mM), DAP5 (25 μM), CNQX (3 μM), and DNQX (0.15, 1.5, 3, 5 and 10 μM). Each chemical compound gave a different feature profile corresponding to its pharmacological class. The first order kernel and the Laguerre coefficients formed the input to an artificial neural network (ANN) comprised of a single layer of perceptrons. The ANN was able to classify each tested compound into its respective class.     

A Multi-Label Learning Based Kernel Automatic Recommendation Method for Support Vector Machine

Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance.     

A two-stage evolutionary approach for effective classification of hypersensitive DNA sequences

Hypersensitive (HS) sites in genomic sequences are reliable markers of DNA regulatory regions that control gene expression. Annotation of regulatory regions is important in understanding phenotypical differences among cells and diseases linked to pathologies in protein expression. Several computational techniques are devoted to mapping out regulatory regions in DNA by initially identifying HS sequences. Statistical learning techniques like Support Vector Machines (SVM), for instance, are employed to classify DNA sequences as HS or non-HS. This paper proposes a method to automate the basic steps in designing an SVM that improves the accuracy of such classification. The method proceeds in two stages and makes use of evolutionary algorithms. An evolutionary algorithm first designs optimal sequence motifs to associate explicit discriminating feature vectors with input DNA sequences. A second evolutionary algorithm then designs SVM kernel functions and parameters that optimally separate the HS and non-HS classes. Results show that this two-stage method significantly improves SVM classification accuracy. The method promises to be generally useful in automating the analysis of biological sequences, and we post its source code on our website.     

Evolutionary optimization of sequence kernels for detection of bacterial gene starts

Oligo kernels for biological sequence classification have a high discriminative power. A new parameterization for the K-mer oligo kernel is presented, where all oligomers of length K are weighted individually. The task specific choice of these parameters increases the classification performance and reveals information about discriminative features. For adapting the multiple kernel parameters based on cross-validation the covariance matrix adaptation evolution strategy is proposed. It is applied to optimize the trimer oligo kernels for the detection of bacterial gene starts. The resulting kernels lead to higher classification rates, and the adapted parameters reveal the importance of particular triplets for classification, for example of those occurring in the Shine-Dalgarno Sequence.     

Hazard function for cancer patients and cancer cell dynamics

The aim of the paper is to develop a procedure for an estimate of an analytical form of a hazard function for cancer patients. Although a deterministic approach based on cancer cell population dynamics yields the analytical expression, it depends on several parameters which should be estimated. On the other hand, a kernel estimate is an effective nonparametric method for estimating hazard functions. This method provides the pointwise estimate of the hazard function. Our procedure consists of two steps: in the first step we find the kernel estimate of the hazard function and in the second step the parameters in the deterministic model are obtained by the least squares method. A simulation study with different types of censorship is carried out and the developed procedure is applied to real data.     

A general framework of nonparametric feature selection in high-dimensional data

Nonparametric feature selection for high-dimensional data is an important and challenging problem in the fields of statistics and machine learning. Most of the existing methods for feature selection focus on parametric or additive models which may suffer from model misspecification. In this paper, we propose a new framework to perform nonparametric feature selection for both regression and classification problems. Under this framework, we learn prediction functions through empirical risk minimization over a reproducing kernel Hilbert space. The space is generated by a novel tensor product kernel, which depends on a set of parameters that determines the importance of the features. Computationally, we minimize the empirical risk with a penalty to estimate the prediction and kernel parameters simultaneously. The solution can be obtained by iteratively solving convex optimization problems. We study the theoretical property of the kernel feature space and prove the oracle selection property and Fisher consistency of our proposed method. Finally, we demonstrate the superior performance of our approach compared to existing methods via extensive simulation studies and applications to two real studies.     

高密度基因芯片探针布局方法

为了提高基因芯片制备质量和检测的准确性,提出两种基因芯片布局方法,一是分子印章凸点优化布局方法,另一种是基于探针杂交解链温度的梯度场布局方法。利用上述两种方法对所设计的高密度基因芯片进行控针布局实验,结果表明,第一种方法能够使制备基因芯片的分子印章上凸点均匀分布,解决误压印问题,从而提高基因芯片的制备质量;而第二种方法能够使基因芯片上的探针按照杂交解链温度有序地组织起来,从而提高基因芯片对碱基错配的辨别力。     

Multivariate adaptive embedding (MAE) for the identification of bacterial pathogens in the field

In this work, we demonstrate a new classification machine based on multivariate adaptive embedding (MAE) that is capable of a robust identification of potential bacterial biological warfare agents (BWA). By employing Raman spectroscopy, this method proves to be reliable in application, easy to use and while retaining spectral quality, it is much faster than the often used support vector machines (SVM) and other supervised multivariate statistical classification machines. The multivariate adaptive embedding multi‐species classification ability was developed in order to serve as a real‐time detection method for biological threat detection and pathogen identification. A mean classification accuracy of 99.25±0.45% could be achieved with a representative set of biological warfare agents and simulant bacteria as a first approach for a user‐friendly and fieldable classification application for first responders and researchers.     

Stiphodon niraikanaiensis,a new species of sicydiine goby from Okinawa Island (Gobiidae: Sicydiinae)

A new goby species, Stiphodon niraikanaiensis, is described on the basis of three specimens (two females and one male) collected from a freshwater stream in Okinawa Island, Japan. This species can be distinguished from its congeners by nine soft rays in the second dorsal fin, 16 rays in the pectoral fin, a pointed first dorsal fin in male, the premaxilla with 46–50 tricuspid teeth in 27–36 mm SL; no white patch behind the pectoral-fin base in male, the nape and posterior half of the occipital region covered by cycloid scales, broad black band along the distal margin of the second dorsal fin in male, 11 or 12 dusky transverse bars laterally on the trunk and tail of female intersecting with the mid-lateral longitudinal band, several conspicuous black spots on each spine and soft ray on the first and second dorsal fins of female, the anal fin of female lacking remarkable marking, and the pectoral-fin rays with 2–5 and 1–4 black spots, respectively, for male and female. The new species is known only from the type locality.     

An adaptive bivariate kernel smoothing method for determining instars of Austrosimulium tillyardianum (Diptera: Simuliidae) larvae

1. In insects, instar determination is generally based on the frequency distribution of sclerotised body part measurements. Commonly used univariate methods, such as histograms and univariate kernel smoothing, are not sufficient to reflect the distribution of the measurements, because development of sclerotised body parts is multidimensional. 2. This study used an adaptive bivariate kernel smoothing method, based on 10 pairs of separating variables, to differentiate instars of Austrosimulium tillyardianum (Diptera: Simuliidae) larvae in two‐dimensional space. A variable bandwidth matrix was used and separation lines between instars were defined. Using the Crosby growth ratio, Brooks' rule and the new standard recently proposed, larvae were separated into nine instars. It was found that, using the bivariate kernel smoothing method, the clustering accuracy and determination of separation lines as instar class limits were higher than those associated with the univariate kernel smoothing method. With the exceptions of the paired separating variables, head capsule length and antennal segment 3 length (AS3L), the mean probabilities of correct classifications was > 85%. The pair of separating variables that yielded the greatest classification accuracy comprised mandible length (ML) and AS3L, which had mean probabilities of 0.8984. The clustering accuracy was higher for early‐ and late‐instar larvae, but lower for instars 6 and 7. The adaptive bivariate kernel smoothing method was better than univariate methods for instar determination, especially in the detection of divisions between instars and identification of a larval instar.     

Classification and feature selection algorithms for multi-class CGH data

Recurrent chromosomal alterations provide cytological and molecular positions for the diagnosis and prognosis of cancer. Comparative genomic hybridization (CGH) has been useful in understanding these alterations in cancerous cells. CGH datasets consist of samples that are represented by large dimensional arrays of intervals. Each sample consists of long runs of intervals with losses and gains. In this article, we develop novel SVM-based methods for classification and feature selection of CGH data. For classification, we developed a novel similarity kernel that is shown to be more effective than the standard linear kernel used in SVM. For feature selection, we propose a novel method based on the new kernel that iteratively selects features that provides the maximum benefit for classification. We compared our methods against the best wrapper-based and filter-based approaches that have been used for feature selection of large dimensional biological data. Our results on datasets generated from the Progenetix database, suggests that our methods are considerably superior to existing methods. AVAILABILITY: All software developed in this article can be downloaded from http://plaza.ufl.edu/junliu/feature.tar.gz.     

Systematic benchmarking of microarray data classification: assessing the role of non-linearity and dimensionality reduction

MOTIVATION: Microarrays are capable of determining the expression levels of thousands of genes simultaneously. In combination with classification methods, this technology can be useful to support clinical management decisions for individual patients, e.g. in oncology. The aim of this paper is to systematically benchmark the role of non-linear versus linear techniques and dimensionality reduction methods. RESULTS: A systematic benchmarking study is performed by comparing linear versions of standard classification and dimensionality reduction techniques with their non-linear versions based on non-linear kernel functions with a radial basis function (RBF) kernel. A total of 9 binary cancer classification problems, derived from 7 publicly available microarray datasets, and 20 randomizations of each problem are examined. CONCLUSIONS: Three main conclusions can be formulated based on the performances on independent test sets. (1) When performing classification with least squares support vector machines (LS-SVMs) (without dimensionality reduction), RBF kernels can be used without risking too much overfitting. The results obtained with well-tuned RBF kernels are never worse and sometimes even statistically significantly better compared to results obtained with a linear kernel in terms of test set receiver operating characteristic and test set accuracy performances. (2) Even for classification with linear classifiers like LS-SVM with linear kernel, using regularization is very important. (3) When performing kernel principal component analysis (kernel PCA) before classification, using an RBF kernel for kernel PCA tends to result in overfitting, especially when using supervised feature selection. It has been observed that an optimal selection of a large number of features is often an indication for overfitting. Kernel PCA with linear kernel gives better results.     

基于支持向量机的皮肤自体荧光光谱分类方法在糖尿病筛查中的应用

将63例II型糖尿病患者以及140例正常人皮肤的自体荧光光谱分为训练集和测试集两类,针对常用的四种核函数,运用交叉验证、网格寻优法计算最优分类参数,然后结合训练集建模并对测试集分类,结果显示使用径向基核函数时分类效果相对最佳。在此基础上,构建了一种基于线性核函数与径向基核函数的混合核函数,该核函数对人体皮肤自体荧光光谱的分类效果较之于径向基核函数更优,其分类正确率为82．61％,敏感性为69．57％,特异性为95．65％。研究结果表明支持向量机可用于人体皮肤自体荧光光谱的分类,有助于提高糖尿病筛查的正确率。     

The Spectro-Temporal Receptive Field

The Spectro-Temporal Receptive Field (STRF) of an auditory neuron has been introduced experimentally on the base of the average spectrotemporal structure of the acoustic stimuli which precede the occurrence of action potentials (Aertsen et al., 1980, 1981). In the present paper the STRF is considered in the general framework of nonlinear system theory, especially in the form of the Volterra integral representation. The STRF is proposed to be formally identified with a linear functional of the second order Volterra kernel. The experimental determination of the STRF leads to a formulation in terms of the Wiener expansion where the kernels can be identified by evaluation of the system's input-output correlations. For a Gaussian stimulus ensemble and a nonlinear system with no even order contributions of order higher than two, it is shown that the second order cross correlation of stimulus and response, normalized with respect to the spectral contents of the stimulus ensemble, leads to the stimulus-invariant spectrotemporal receptive field. The investigation of stimulus-invariance of the STRF for more general nonlinear systems and for stimulus ensembles which can be generated by nonlinear transformations of Gaussian noise involve the evaluation of higher order stimulus-response correlation functions.     

Induction and deduction in morphology

Professor de Wilde's scientific contributions can be divided into two separate areas based on the applied methods: induction and deduction. The first method is used in the investigation of the finger ridge pattern and the second in that of the branching of bloodvessels. It is shown that with induction alternative classifications and explanations remain always possible and that the ultimate choice of the classification depends on supposed biological meanings or practical applicability. In the case of the finger ridge patterns the latter criterion is decisive. With the deductive method a sufficient logical explanation within the boundary conditions can be reached. Alternatives can be presented if the boundary conditions are changed or the functional parameters have to be chosen differently, which is necessary when direct observation (induction) "shows" that other functions are involved. A number of advantages and problematic points in both methods are analysed and their relationship and function in morphology are demonstrated in the mentioned research topics.     

Eviostachya hoegii Stockmans在中国五通组的首次发现

首次描述了Eviostachya hoegii Stockmans的营养部分,通过大量标本的观察,修订了前人有关其生殖部分和解剖部分的描述,并对其生殖部分进行了复原.同意Emberger(1968)的观点,将其归于Eviostachyrales中.     

Visual Evoked Cortical Potential (VECP) Elicited by Sinusoidal Gratings Controlled by Pseudo-Random Stimulation

The contributions of contrast detection mechanisms to the visual cortical evoked potential (VECP) have been investigated studying the contrast-response and spatial frequency-response functions. Previously, the use of m-sequences for stimulus control has been almost restricted to multifocal electrophysiology stimulation and, in some aspects, it substantially differs from conventional VECPs. Single stimulation with spatial contrast temporally controlled by m-sequences has not been extensively tested or compared to multifocal techniques. Our purpose was to evaluate the influence of spatial frequency and contrast of sinusoidal gratings on the VECP elicited by pseudo-random stimulation. Nine normal subjects were stimulated by achromatic sinusoidal gratings driven by pseudo random binary m-sequence at seven spatial frequencies (0.4–10 cpd) and three stimulus sizes (4°, 8°, and 16° of visual angle). At 8° subtence, six contrast levels were used (3.12–99%). The first order kernel (K1) did not provide a consistent measurable signal across spatial frequencies and contrasts that were tested–signal was very small or absent–while the second order kernel first (K2.1) and second (K2.2) slices exhibited reliable responses for the stimulus range. The main differences between results obtained with the K2.1 and K2.2 were in the contrast gain as measured in the amplitude versus contrast and amplitude versus spatial frequency functions. The results indicated that K2.1 was dominated by M-pathway, but for some stimulus condition some P-pathway contribution could be found, while the second slice reflected the P-pathway contribution. The present work extended previous findings of the visual pathways contribution to VECP elicited by pseudorandom stimulation for a wider range of spatial frequencies.     

Serial extraction of endosperm drillings (SEED)—A method for detecting transgenes and proteins in single viable maize kernels

We have developed a method for detecting a transgene and its protein product in maize endosperm that allows the kernel to be germinated after analysis. This technique could be highly useful for several monocots and dicots. Our method involves first sampling the endosperm with a hand-held rotary grinder so that the embryo is preserved and capable of germination. This tissue is then serially extracted, first with SDS-PAGE sample buffer to extract proteins, then with an aqueous buffer to extract DNA. The product of the transgene can be detected in the first extract by SDS-PAGE with visualization by total protein staining or immuno-blot detection. The second extract can be purified and used as template DNA in PCR reactions to detect the transgene. This method is particularly useful for screening transgenic kernels in breeding experiments and testing for gene silencing in kernels.