Microbial control of diamondback moth, Plutella xylostella L. (Lepidoptera:Yponomeutidae) using bacteria (Xenorhabdus nematophila) and its metabolites from the entomopathogenic nematode Steinernema carpocapsae

Cells and cell-free solutions of the culture filtrate of the bacterial symbiont, Xenorhabdus nematophila taken from the entomopathogenic nematode Steinernema carpocapsae in aqueous broth suspensions were lethal to larvae of the diamondback moth Plutella xylostella. Their application on leaves of Chinese cabbage indicated that the cells can penetrate into the insects in the absence of the nematode vector. Cell-free solutions containing metabolites were also proved as effective as bacterial cells suspension. The application of aqueous suspensions of cells of X. nematophila or solutions containing its toxic metabolites to the leaves represents a possible new strategy for controlling insect pests on foliage.

Pathogenicity of the bacterium Xenorhabdus nematophila isolated from the entomopathogenic nematode Steinernema carpocapsae and its secretions against Galleria mellonella larvae

The entomopathogenic bacterium, Xenorhabdus nematophila was isolated from the hemolymph of Galleria mellonella infected with Steinernema carpocapsae. The bacterial cells and its metabolic secretions have been found lethal to the Galleria larvae. Toxic secretion in broth caused 95% mortality within 4 d of application whereas the bacterial cells caused 93% mortality after 6 d. When filter and sand substrates were compared, the later one was observed as appropriate. Similarly, bacterial cells and secretion in broth were more effective at 14% moisture and 25 °C temperature treatments. Maximum insect mortality (100%) was observed when bacterial concentration of 4×106 cells/ml was used. Similarly, maximum bacterial cells in broth (95%) were penetrated into the insect body within 2 h of their application. However, when stored bacterial toxic secretion was applied to the insects its efficacy declined. On the other hand, when the same toxic secretion was dried and then dissolved either in broth or water was proved to be effective. The present study showed that the bacterium, X. nematophila or its toxic secretion can be used as an important component of integrated pest management against Galleria.

Inhibition of vascular smooth muscle cell proliferation by non-steroidal anti-inflammatory drugs

Abnormal vascular smooth muscle cell (VSMC) proliferation is known to play an important role in the pathogenesis of atherosclerosis, restenosis and instent stenosis. Recent studies suggest that salicylates, in addition to inhibiting cyclooxygenase activity, exert an antiproliferative effect on VSMC growth both in vitro and in vivo. However, whether all non-steroidal anti-inflammatory drugs (NSAID) exert similar antiproliferative effects on VSMCs, and do so via a common mechanism of action, remains unknown. In the present study, we demonstrated that the NSAIDs, aspirin, ibuprofen and sulindac induced a dose-dependent inhibition of proliferation in rat A10 VSMCs (IC50 = 1666 mumol/L, 937 mumol/L and 520 mumol/L, respectively). These drugs did not show significant cytotoxic effects as determined by LDH release assay, even at the highest concentrations tested (aspirin, 5000 mumol/L; ibuprofen, 2500 mumol/L; and sulindac, 1000 mumol/L). Flow cytometric analyses showed that a 48 h exposure of A10 VSMCs to ibuprofen (1000 mumol/L) and sulindac (750 mumol/L) led to a significant G1 arrest (from 68.7 +/- 2.0% of cells in G1 to 76.6 +/- 2.2% and 75.8 +/- 2.2%, respectively, p < 0.05). In contrast, aspirin (2500 mumol/L) failed to induce a significant G1 arrest (68.1 +/- 5.2%). Clearer evidence of a G1 block was obtained by treatment of cells with the mitotic inhibitor, nocodazole (40 ng/ml), for the final 24 h of the experiment. Under these conditions, aspirin still failed to induce a G1 arrest (from 25.9 +/- 10.9% of cells in G1 to 19.6 +/- 2.3%) whereas ibuprofen and sulindac led to a significant accumulation of cells in G1(51.8% +/- 17.2% and 54.1% +/- 10.6%, respectively, p < 0.05). These results indicate that ibuprofen and sulindac inhibit VSMC proliferation by arresting the cell cycle in the G1 phase whereas the effect of aspirin appears to be independent of any special phase of the cell cycle. Irrespective of mechanism, our results suggest that NSAIDs might be of benefit to the treatment of vascular proliferative disorders.

Particle swarm optimization aided orthogonal forward regression for unified data modeling

We propose a unified data modeling approach that is equally applicable to supervised regression and classification applications, as well as to unsupervised probability density function estimation. A particle swarm optimization (PSO) aided orthogonal forward regression (OFR) algorithm based on leave-one-out (LOO) criteria is developed to construct parsimonious radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines the center vector and diagonal covariance matrix of one RBF node by minimizing the LOO statistics. For regression applications, the LOO criterion is chosen to be the LOO mean square error, while the LOO misclassification rate is adopted in two-class classification applications. By adopting the Parzen window estimate as the desired response, the unsupervised density estimation problem is transformed into a constrained regression problem. This PSO aided OFR algorithm for tunable-node RBF networks is capable of constructing very parsimonious RBF models that generalize well, and our analysis and experimental results demonstrate that the algorithm is computationally even simpler than the efficient regularization assisted orthogonal least square algorithm based on LOO criteria for selecting fixed-node RBF models. Another significant advantage of the proposed learning procedure is that it does not have learning hyperparameters that have to be tuned using costly cross validation. The effectiveness of the proposed PSO aided OFR construction procedure is illustrated using several examples taken from regression and classification, as well as density estimation applications.

Image fusion based on median filters and SOFM neural networks: a three-step scheme

This paper presents a new image data fusion scheme by combining median filtering with self-organizing feature map (SOFM) neural networks. The scheme consists of three steps: (1) pre-processing of the images, where weighted median filtering removes part of the noise components corrupting the image, (2) pixel clustering for each image using self-organizing feature map neural networks, and (3) fusion of the images obtained in Step (2), which suppresses the residual noise components and thus further improves the image quality. It proves that such a three-step combination offers an impressive effectiveness and performance improvement, which is confirmed by simulations involving three image sensors (each of which has a different noise structure).

An improved digital deconvolution equation

This paper first points out the important fact that the rectangle formulas of continuous convolution discretization, which was widely used in conventional digital deconvolution algorithms, can result in zero-time error. Then, an improved digital deconvolution equation is suggested which is equivalent to the trapezoid formulas of continuous convolution discretization and can overcome the disadvantage of conventional equation satisfactorily. Finally, a simulation in computer is given, thus confirming the theoretical result.

The Prony fitting method for digital deconvolution problem

The Prony fitting theory is applied in this paper to solve the deconvolution problem. There are two cases in deconvolution in which unstable solution is easy to appear. They are: (1)the frequency band of known kernel is more narraw than that of the unknown kernel; (2) there exists noise. These two cases are studied thoroughly and the effectiveness of Prony fitting method is showed. Finally, this method is simulated in computer. The simulation results are compared with those obtained by using FFT method directly.

The application of Prony’s method to time-domain waveform data modeling in the presence of noise

In this paper,the Prony's method is applied to the time-domain waveform data modelling in the presence of noise.The following three problems encountered in this work are studied:(1)determination of the order of waveform;(2)de-termination of numbers of multiple roots;(3)determination of the residues.The methods of solving these problems are given and simulated on the computer.Finally,an output pulse of model PG-10N signal generator and the distorted waveform obtained by transmitting the pulse above mentioned through a piece of coaxial cable are modelled,and satisfactory results are obtained.So the effectiveness of Prony's method in waveform data modelling in the presence of noise is confirmed.

Deconvolution method for removing the distortion from time jitter in equivalent-time waveform samplers

A sampling oscilloscope is one of the main units in automatic pulse measurement system (APMS). The time jitter in waveform samplers is an important error source that affect the precision of data acquisition. In this paper, this kind of error is greatly reduced by using the deconvolution method. First, the probability density function (PDF) of time jitter distribution is determined by the statistical approach, then, this PDF is used as convolution kern to deconvolve with the acquired waveform data with additional averaging, and the result is the waveform data in which the effect of time jitter has been removed, and the measurement precision of APMS is greatly improved. In addition, some computer simulations are given which prove the success of the method given in this paper.

Grey-box radial basis function modelling

A fundamental principle in data modelling is to incorporate available a priori information regarding the underlying data generating mechanism into the modelling process. We adopt this principle and consider grey-box radial basis function (RBF) modelling capable of incorporating prior knowledge. Specifically, we show how to explicitly incorporate the two types of prior knowledge: (i) the underlying data generating mechanism exhibits known symmetric property, and (ii) the underlying process obeys a set of given boundary value constraints. The class of efficient orthogonal least squares regression algorithms can readily be applied without any modification to construct parsimonious grey-box RBF models with enhanced generalisation capability.

Modelling and control of Hammerstein system using B-spline approximation and the inverse of De Boor algorithm

In this article a simple and effective controller design is introduced for the Hammerstein systems that are identified based on observational input/output data. The nonlinear static function in the Hammerstein system is modelled using a B-spline neural network. The controller is composed by computing the inverse of the B-spline approximated nonlinear static function, and a linear pole assignment controller. The contribution of this article is the inverse of De Boor algorithm that computes the inverse efficiently. Mathematical analysis is provided to prove the convergence of the proposed algorithm. Numerical examples are utilised to demonstrate the efficacy of the proposed approach.

Using zero-norm constraint for sparse probability density function estimation

A new sparse kernel probability density function (pdf) estimator based on zero-norm constraint is constructed using the classical Parzen window (PW) estimate as the target function. The so-called zero-norm of the parameters is used in order to achieve enhanced model sparsity, and it is suggested to minimize an approximate function of the zero-norm. It is shown that under certain condition, the kernel weights of the proposed pdf estimator based on the zero-norm approximation can be updated using the multiplicative nonnegative quadratic programming algorithm. Numerical examples are employed to demonstrate the efficacy of the proposed approach.

Modeling of complex-valued Wiener systems using B-spline neural network

In this brief, a new complex-valued B-spline neural network is introduced in order to model the complex-valued Wiener system using observational input/output data. The complex-valued nonlinear static function in the Wiener system is represented using the tensor product from two univariate B-spline neural networks, using the real and imaginary parts of the system input. Following the use of a simple least squares parameter initialization scheme, the Gauss-Newton algorithm is applied for the parameter estimation, which incorporates the De Boor algorithm, including both the B-spline curve and the first-order derivatives recursion. Numerical examples, including a nonlinear high-power amplifier model in communication systems, are used to demonstrate the efficacy of the proposed approaches.