Multiple paths extraction in images using a constrained expanded trellis

Single shortest path extraction algorithms have been used in a number of areas such as network flow and image analysis. In image analysis, shortest path techniques can be used for object boundary detection, crack detection, or stereo disparity estimation. Sometimes one needs to find multiple paths as opposed to a single path in a network or an image where the paths must satisfy certain constraints. In this paper, we propose a new algorithm to extract multiple paths simultaneously within an image using a constrained expanded trellis (CET) for feature extraction and object segmentation. We also give a number of application examples for our multiple paths extraction algorithm.

A class of self-stabilizing MCA learning algorithms

In this letter, we propose a class of self-stabilizing learning algorithms for minor component analysis (MCA), which includes a few well-known MCA learning algorithms. Self-stabilizing means that the sign of the weight vector length change is independent of the presented input vector. For these algorithms, rigorous global convergence proof is given and the convergence rate is also discussed. By combining the positive properties of these algorithms, a new learning algorithm is proposed which can improve the performance. Simulations are employed to confirm our theoretical results.

Seizure detection algorithm for neonates based on wave-sequence analysis

Objective: The description and evaluation of the performance of a new real-time seizure detection algorithm in the newborn infant. Methods: The algorithm includes parallel fragmentation of EEG signal into waves; wave-feature extraction and averaging; elementary, preliminary and final detection. The algorithm detects EEG waves with heightened regularity, using wave intervals, amplitudes and shapes. The performance of the algorithm was assessed with the use of event-based and liberal and conservative time-based approaches and compared with the performance of Gotman's and Liu's algorithms. Results: The algorithm was assessed on multi-channel EEG records of 55 neonates including 17 with seizures. The algorithm showed sensitivities ranging 83-95% with positive predictive values (PPV) 48-77%. There were 2.0 false positive detections per hour. In comparison, Gotman's algorithm (with 30 s gap-closing procedure) displayed sensitivities of 45-88% and PPV 29-56%; with 7.4 false positives per hour and Liu's algorithm displayed sensitivities of 96-99%, and PPV 10-25%; with 15.7 false positives per hour. Conclusions: The wave-sequence analysis based algorithm displayed higher sensitivity, higher PPV and a substantially lower level of false positives than two previously published algorithms. Significance: The proposed algorithm provides a basis for major improvements in neonatal seizure detection and monitoring. Published by Elsevier Ireland Ltd. on behalf of International Federation of Clinical Neurophysiology.

Extraction of texture features from arbitrary-shaped regions for image retrieval

Lots of work has been done in texture feature extraction for rectangular images, but not as much attention has been paid to the arbitrary-shaped regions available in region-based image retrieval (RBIR) systems. In This work, we present a texture feature extraction algorithm, based on projection onto convex sets (POCS) theory. POCS iteratively concentrates more and more energy into the selected coefficients from which texture features of an arbitrary-shaped region can be extracted. Experimental results demonstrate the effectiveness of the proposed algorithm for image retrieval purposes.

Real-Time Gene-Expression Extraction with Fast Classification (FC) Networks

Fast Classification (FC) networks were inspired by a biologically plausible mechanism for short term memory where learning occurs instantaneously. Both weights and the topology for an FC network are mapped directly from the training samples by using a prescriptive training scheme. Only two presentations of the training data are required to train an FC network. Compared with iterative learning algorithms such as Back-propagation (which may require many hundreds of presentations of the training data), the training of FC networks is extremely fast and learning convergence is always guaranteed. Thus FC networks may be suitable for applications where real-time classification is needed. In this paper, the FC networks are applied for the real-time extraction of gene expressions for Chlamydia microarray data. Both the classification performance and learning time of the FC networks are compared with the Multi-Layer Proceptron (MLP) networks and support-vector-machines (SVM) in the same classification task. The FC networks are shown to have extremely fast learning time and comparable classification accuracy.

Algorithms for sequence analysis via mutagenesis

Despite many successes of conventional DNA sequencing methods, some DNAs remain difficult or impossible to sequence. Unsequenceable regions occur in the genomes of many biologically important organisms, including the human genome. Such regions range in length from tens to millions of bases, and may contain valuable information such as the sequences of important genes. The authors have recently developed a technique that renders a wide range of problematic DNAs amenable to sequencing. The technique is known as sequence analysis via mutagenesis (SAM). This paper presents a number of algorithms for analysing and interpreting data generated by this technique.

Selection bias in gene extraction on the basis of microarray gene-expression data

In the context of cancer diagnosis and treatment, we consider the problem of constructing an accurate prediction rule on the basis of a relatively small number of tumor tissue samples of known type containing the expression data on very many (possibly thousands) genes. Recently, results have been presented in the literature suggesting that it is possible to construct a prediction rule from only a few genes such that it has a negligible prediction error rate. However, in these results the test error or the leave-one-out cross-validated error is calculated without allowance for the selection bias. There is no allowance because the rule is either tested on tissue samples that were used in the first instance to select the genes being used in the rule or because the cross-validation of the rule is not external to the selection process; that is, gene selection is not performed in training the rule at each stage of the cross-validation process. We describe how in practice the selection bias can be assessed and corrected for by either performing a cross-validation or applying the bootstrap external to the selection process. We recommend using 10-fold rather than leave-one-out cross-validation, and concerning the bootstrap, we suggest using the so-called. 632+ bootstrap error estimate designed to handle overfitted prediction rules. Using two published data sets, we demonstrate that when correction is made for the selection bias, the cross-validated error is no longer zero for a subset of only a few genes.

A framework for electricity price spike analysis with advanced data mining methods

There are many techniques for electricity market price forecasting. However, most of them are designed for expected price analysis rather than price spike forecasting. An effective method of predicting the occurrence of spikes has not yet been observed in the literature so far. In this paper, a data mining based approach is presented to give a reliable forecast of the occurrence of price spikes. Combined with the spike value prediction techniques developed by the same authors, the proposed approach aims at providing a comprehensive tool for price spike forecasting. In this paper, feature selection techniques are firstly described to identify the attributes relevant to the occurrence of spikes. A simple introduction to the classification techniques is given for completeness. Two algorithms: support vector machine and probability classifier are chosen to be the spike occurrence predictors and are discussed in details. Realistic market data are used to test the proposed model with promising results.

Comparison of BR and QR Eigenvalue Algorithms for Power System Small Signal Stability Analysis

The BR algorithm is a novel and efficient method to find all eigenvalues of upper Hessenberg matrices and has never been applied to eigenanalysis for power system small signal stability. This paper analyzes differences between the BR and the QR algorithms with performance comparison in terms of CPU time based on stopping criteria and storage requirement. The BR algorithm utilizes accelerating strategies to improve its performance when computing eigenvalues of narrowly banded, nearly tridiagonal upper Hessenberg matrices. These strategies significantly reduce the computation time at a reasonable level of precision. Compared with the QR algorithm, the BR algorithm requires fewer iteration steps and less storage space without depriving of appropriate precision in solving eigenvalue problems of large-scale power systems. Numerical examples demonstrate the efficiency of the BR algorithm in pursuing eigenanalysis tasks of 39-, 68-, 115-, 300-, and 600-bus systems. Experiment results suggest that the BR algorithm is a more efficient algorithm for large-scale power system small signal stability eigenanalysis.

Chemical treatment of Escherichia coli. II. Direct extraction of recombinant protein from cytoplasmic inclusion bodies in intact cells

A method is presented for the direct extraction of the recombinant protein Long-R-3-IGF-I from inclusion bodies located in the cytoplasm of intact Escherichia coli cells. Chemical treatment with 6M urea, 3 mM EDTA, and 20 mM dithiothreitol (DTT) at pH 9.0 proved an effective combination for extracting recombinant protein from intact cells. Comparable levels of Long-R-3-IGF-I were recovered by direct extraction as achieved by in vitro dissolution following mechanical disruption. However, the purity of directly extracted recombinant protein was lower due to contamination by bacterial cell components. The kinetics of direct extraction are described using a first-order equation with the time constant of 3 min. Urea appears important for permeabilization of the cell and dissolution of the inclusion body. Conversely, EDTA is involved in permeabilization of the cell wall and DTT enhances protein release. pH proved to be important with lower levels of protein release achieved at low pH values (

Improved one-step solid-phase extraction method for morphine, morphine-3-glucuronide, and morphine-6-glucuronide from plasma and quantitation using high-performance liquid chromatography with electrochemical detection

This communication describes an improved one-step solid-phase extraction method for the recovery of morphine (M), morphine-3-glucuronide (M3G), and morphine-6-glucuronide (M6G) from human plasma with reduced coextraction of endogenous plasma constituents, compared to that of the authors' previously reported method. The magnitude of the peak caused by endogenous plasma components in the chromatogram that eluted immediately before the retention time of M3G has been reduced (similar to 80%) significantly (p < 0.01) while achieving high extraction efficiencies for the compounds of interest, viz morphine, M6G, and M3G (93.8 +/- 2.5, 91.7 +/- 1.7, and 93.1 +/- 2.2%, respectively). Furthermore, when the improved solid-phase extraction method was used, the extraction cartridge-derived late-eluting peak (retention time 90 to 100 minutes) reported in our previous method, was no longer present in the plasma extracts. Therefore the combined effect of reducing the recovery of the endogenous components of plasma that chromatographed just before the retention time of M3G and the removal of the late-eluting, extraction cartridge-derived peak has resulted in a decrease in the chromatographic run-time to 20 minutes, thereby increasing the sample throughput by up to 100%.

A study of the stability of subcycling algorithms in structural dynamics

Algorithms for explicit integration of structural dynamics problems with multiple time steps (subcycling) are investigated. Only one such algorithm, due to Smolinski and Sleith has proved to be stable in a classical sense. A simplified version of this algorithm that retains its stability is presented. However, as with the original version, it can be shown to sacrifice accuracy to achieve stability. Another algorithm in use is shown to be only statistically stable, in that a probability of stability can be assigned if appropriate time step limits are observed. This probability improves rapidly with the number of degrees of freedom in a finite element model. The stability problems are shown to be a property of the central difference method itself, which is modified to give the subcycling algorithm. A related problem is shown to arise when a constraint equation in time is introduced into a time-continuous space-time finite element model. (C) 1998 Elsevier Science S.A.

Pilot-scale extraction of PHB from recombinant E-coli by homogenization and centrifugation

A new method of poly-beta-hydroxybutyrate (PHB) extraction from recombinant E. coli is proposed, using homogenization and centrifugation coupled with sodium hypochlorite treatment. The size of PHB granules and cell debris in homogenates was characterised as a function of the number of homogenization passes. Simulation was used to develop the PHB and cell debris fractionation system, enabling numerical examination of the effects of repeated homogenization and centrifuge-feedrate variation. The simulation provided a good prediction of experimental performance. Sodium hypochlorite treatment was necessary to optimise PHB fractionation. A PHB recovery of 80% at a purity of 96.5% was obtained with the final optimised process. Protein and DNA contained in the resultant product were negligible. The developed process holds promise for significantly reducing the recovery cost associated with PHB manufacture.

Solid-phase extraction method with high-performance liquid chromatography and electrochemical detection for the quantitative analysis of oxycodone in human plasma

A sensitive and reproducible solid-phase extraction (SPE) method for the quantification of oxycodone in human plasma was developed. Varian Certify SPE cartridges containing both C-8 and benzoic acid functional groups were the most suitable for the extraction of oxycodone and codeine (internal standard), with consistently high (greater than or equal to 80%) and reproducible recoveries. The elution mobile phase consisted of 1.2 ml of butyl chloride-isopropanol (80:20, v/v) containing 2% ammonia. The quantification limit for oxycodone was 5.3 pmol on-column. Within-day and inter-day coefficients of variation were 1.2% and 6.8% respectively for 284 nM oxycodone and 9.5% and 6.2% respectively for 28.4 nM oxycodone using 0.5-ml plasma aliquots. (C) 1998 Elsevier Science BN. All rights reserved.