Process standardization for optimal virus recovery and removal of substrate DNA and bovine serum proteins in Vero cell-derived rabies vaccine

Purification of a rabies vaccine by a single zonal centrifugation run was replaced by two runs with optimal standardization of the sucrose density gradient. As a result, significant reductions in the levels of substrate DNA and bovine serum protein in the Vero cell-derived human rabies vaccine were achieved. Following many trials, for the first run, loading of the 3.2-l capacity K-3 rotor with 1800 ml of 60% sucrose solution and 1400 ml of vaccine PBS buffer solution gave a satisfactory linear gradient. However, after the first run, the substrate DNA and bovine serum contents exceeded the required levels. After protamine sulphate and Tween-80 treatment of the concentrated inactivated material, a second run using the same procedure as in the first run was tried. However, these purification procedures resulted in low virus recovery. To achieve optimal virus recovery, and removal of substrate DNA and bovine serum protein, the peak fractions from the first run as indicated by the haemagglutination, sucrose concentration, and optical density values were pooled and the sucrose concentration of the pooled fractions was increased to 60%. A second (flotation) run was then carried out. Using this method, the virus recovery rate was more than 95% that of the first run, and the levels of cellular DNA and bovine serum protein were well within the acceptable limits of less than 100 pg/dose and one part per million, respectively. The substrate DNA was quantified by both radioactive labeling and non-radioactive biotin labeling methods. For the quantification of calf serum protein, a counter-immunoelectrophoresis method was developed and effectively applied. A potency assay was performed using the National Institutes of Health (NIH) and well-standardized in vitro single radial immuno diffusion (SRD) methods. Finally, an immunogenicity study was conducted with human volunteers and the results were confirmed by a rapid fluorescent focus inhibition test (RFFIT).     

Load Balancing Parallel Explicit State Model Checking

This paper first identifies some of the key concerns about the techniques and algorithms developed for parallel model checking; specifically, the inherent problem with load balancing and large queue sizes resultant in a static partition algorithm. This paper then presents a load balancing algorithm to improve the run time performance in distributed model checking, reduce maximum queue size, and reduce the number of states expanded before error discovery. The load balancing algorithm is based on generalized dimension exchange (GDE). This paper presents an empirical analysis of the GDE based load balancing algorithm on three different supercomputing architectures—distributed memory clusters, Networks of Workstations (NOW) and shared memory machines. The analysis shows increased speedup, lower maximum queue sizes and fewer total states explored before error discovery on each of the architectures. Finally, this paper presents a study of the communication overhead incurred by using the load balancing algorithm, which although significant, does not offset performance gains.     

An analysis for AlGaN/GaN modulation doped field effect transistor using accurate velocity-field dependence for high power microwave frequency applications

An analytical model of Al_0.15Ga_0.85N/GaN modulation doped field effect transistor (MODFET), which uses an accurate velocity field relationship and incorporates the dominant effect of piezoelectric polarization induced charge at the AlGaN/GaN interface is presented. The effect of traps has also been taken into account. The calculated DC characteristics are in excellent agreement with the measured data. The model is extended to predict the microwave performance of the device. High current levels (>500 mA/mm), large transconductance (160.83 mS/mm) and a high cutoff frequency (9.6 GHz) have been achieved analytically and are in close agreement with the experimental data.     

A new grounded lamination gate (GLG) for diminished fringe-capacitance effects in high-/spl kappa/ gate-dielectric MOSFETs

A grounded lamination gate (GLG) structure for high-/spl kappa/ gate-dielectric MOSFETs is proposed, with grounded metal plates in the spacer oxide region. Two-dimensional device simulations performed on the new structure demonstrate a significant improvement with respect to the threshold voltage roll-off with increasing gate-dielectric constant (due to parasitic internal fringe capacitance), keeping the equivalent oxide thickness same. A simple fabrication procedure for the GLG MOSFET is also presented.     

Personal recognition using hand shape and texture.

This paper proposes a new bimodal biometric system using feature-level fusion of hand shape and palm texture. The proposed combination is of significance since both the palmprint and hand-shape images are proposed to be extracted from the single hand image acquired from a digital camera. Several new hand-shape features that can be used to represent the hand shape and improve the performance are investigated. The new approach for palmprint recognition using discrete cosine transform coefficients, which can be directly obtained from the camera hardware, is demonstrated. None of the prior work on hand-shape or palmprint recognition has given any attention on the critical issue of feature selection. Our experimental results demonstrate that while majority of palmprint or hand-shape features are useful in predicting the subjects identity, only a small subset of these features are necessary in practice for building an accurate model for identification. The comparison and combination of proposed features is evaluated on the diverse classification schemes; naive Bayes (normal, estimated, multinomial), decision trees (C4.5, LMT), k-NN, SVM, and FFN. Although more work remains to be done, our results to date indicate that the combination of selected hand-shape and palmprint features constitutes a promising addition to the biometrics-based personal recognition systems.     

Wave Interaction with a Flexible Porous Breakwater in a Two-Layer Fluid

An analysis has been carried out to study the performance of a flexible porous plate breakwater in a two-layer fluid where each fluid is assumed to be of finite depth and the breakwater is extended over the entire water depth. The problem is analyzed in two dimensions with the assumption of small amplitude wave theory and plate response. The effects of both surface and internal waves are taken into account in the present study. The associated mixed boundary value problem is reduced to a linear system of equations by utilizing a more general orthogonal relation along with least squares approximation method. The reflection and transmission coefficients for the surface and internal modes, wave load, and breakwater response are computed for various physical parameters of interest to analyze the efficiency of the flexible porous plate as a breakwater in the two-layer fluid.     

Support-vector-based fuzzy neural network for pattern classification

Fuzzy neural networks (FNNs) for pattern classification usually use the backpropagation or C-cluster type learning algorithms to learn the parameters of the fuzzy rules and membership functions from the training data. However, such kinds of learning algorithms usually cannot minimize the empirical risk (training error) and expected risk (testing error) simultaneously, and thus cannot reach a good classification performance in the testing phase. To tackle this drawback, a support-vector-based fuzzy neural network (SVFNN) is proposed for pattern classification in this paper. The SVFNN combines the superior classification power of support vector machine (SVM) in high dimensional data spaces and the efficient human-like reasoning of FNN in handling uncertainty information. A learning algorithm consisting of three learning phases is developed to construct the SVFNN and train its parameters. In the first phase, the fuzzy rules and membership functions are automatically determined by the clustering principle. In the second phase, the parameters of FNN are calculated by the SVM with the proposed adaptive fuzzy kernel function. In the third phase, the relevant fuzzy rules are selected by the proposed reducing fuzzy rule method. To investigate the effectiveness of the proposed SVFNN classification, it is applied to the Iris, Vehicle, Dna, Satimage, Ijcnn1 datasets from the UCI Repository, Statlog collection and IJCNN challenge 2001, respectively. Experimental results show that the proposed SVFNN for pattern classification can achieve good classification performance with drastically reduced number of fuzzy kernel functions.     

Variable-Gain Controllers for Nonlinear Systems Using the T–S Fuzzy Model

This correspondence proposes two novel control schemes with variable state-feedback gain to stabilize a Takagi-Sugeno (T-S) fuzzy system. The T-S fuzzy model is expressed as a linear plant with nonlinear disturbance terms in both schemes. In controller I, the T-S fuzzy model is expressed as a linear plant around a nominal plant arbitrarily selected from the set of linear subsystems that the T-S fuzzy model consists of. The variable gain then becomes a function of a gain parameter that is computed to neutralize the effect of disturbance term, which is, in essence, the deviation of the actual system dynamics from the nominal plant as the system traverses a specific trajectory. This controller is shown to stabilize the T-S fuzzy model. In controller II, individual linear subsystems are locally stabilized. Fuzzy blending of individual control actions is shown to make the T-S fuzzy system Lyapunov stable. Although applicability of both control schemes depends on the norm bound of unmatched state disturbance, this constraint is relaxed further in controller II. The efficacy of controllers I and II has been tested on two nonlinear systems     

(2D)LDA: An efficient approach for face recognition

Although 2DLDA algorithm obtains higher recognition accuracy, a vital unresolved problem of 2DLDA is that it needs huge feature matrix for the task of face recognition. To overcome this problem, this paper presents an efficient approach for face image feature extraction, namely, (2D)²LDA method. Experimental results on ORL and Yale database show that the proposed method obtains good recognition accuracy despite having less number of coefficients.     

Performance optimization problem in speculative prefetching

Speculative prefetching has been proposed to improve the response time of network access. Previous studies in speculative prefetching focus on building and evaluating access models for the purpose of access prediction. This paper investigates a complementary area which has been largely ignored, that of performance modeling. We analyze the performance of a prefetcher that has uncertain knowledge about future accesses. Our performance metric is the improvement in access time, for which we derive a formula in terms of resource parameters (time available and time required for prefetching) and speculative parameters (probabilities for next access). We develop a prefetch algorithm to maximize the improvement in access time. The algorithm is based on finding the best solution to a stretch knapsack problem, using theoretically proven apparatus to reduce the search space. An integration between speculative prefetching and caching is also investigated