Detection of avian influenza virus subtype H5 using a biosensor based on imaging ellipsometry

A novel method is reported for the detection of avian influenza virus subtype H5 using a biosensor based on high spatial resolution imaging ellipsometry (IE). Monoclonal antibodies specific to H5 hemagglutinin protein were immobilized on silicon wafers and used to capture virus particles. Resultant changes on the surface of the wafers were visualized directly in gray-scale on an imaging ellipsometry image. This preliminary study has shown that the assay is rapid and specific for the identification of avian influenza virus subtype H5. Compared with lateral-flow immunoassays, this biosensor not only has better sensitivity, but can also simultaneously perform multiplexed tests. These results suggest that this biosensor might be a valuable diagnostic toot for avian influenza virus detection. (c) 2009 Elsevier B.V. All rights reserved.

Diagnostic experiments for transport mechanisms of suspended sediment discharged from the Yellow River in the Bohai Sea

Five diagnostic experiments with a 3D baroclinic hydrodynamic and sediment transport model ECOMSED in couple with the third generation wave model SWAN and the Grant-Madsen bottom boundary layer model driven by the monthly sediment load of the Yellow River, were conducted to separately diagnose effects of different hydrodynamic factors on transport of suspended sediment discharged from the Yellow River in the Bohai Sea. Both transport and spatio-temporal distribution of suspended sediment concentration in the Bohai Sea were numerially simulated. It could be concluded that suspended sediment discharged from the Yellow River cannot be delivered in long distance under the condition of tidal current. Almost all of sediments from the Yellow River are deposited outside the delta under the condition of wind-driven current, and only very small of them are transported faraway. On the basis of wind forcing, sediments from the Yellow River are mainly transported north-northwestward, and others which are first delivered to the Laizhou Bay are continuously moved northward. An obvious 3D structure characteristic of sediment transport is produced in the wind-driven and tide-induced residual circulation condition. Transport patterns at all layers are generally consistent with circulation structure, but there is apparent deviation between the depth-averaged sediment flux and the circulation structure. The phase of temporal variation of sediment concentration is consistent with that of the bottom shear stress, both of which are proved to have a ten-day cycle in wave and current condition.

Evaluation of a combined wavelet and a combined principal component analysis classification system for BCG diagnostic problem

Heart disease is one of the main factor causing death in the developed countries. Over several decades, variety of electronic and computer technology have been developed to assist clinical practices for cardiac performance monitoring and heart disease diagnosis. Among these methods, Ballistocardiography (BCG) has an interesting feature that no electrodes are needed to be attached to the body during the measurement. Thus, it is provides a potential application to asses the patients heart condition in the home. In this paper, a comparison is made for two neural networks based BCG signal classification models. One system uses a principal component analysis (PCA) method, and the other a discrete wavelet transform, to reduce the input dimensionality. It is indicated that the combined wavelet transform and neural network has a more reliable performance than the combined PCA and neural network system. Moreover, the wavelet transform requires no prior knowledge of the statistical distribution of data samples and the computation complexity and training time are reduced.

Permutation Tests for Classification

We introduce and explore an approach to estimating statistical significance of classification accuracy, which is particularly useful in scientific applications of machine learning where high dimensionality of the data and the small number of training examples render most standard convergence bounds too loose to yield a meaningful guarantee of the generalization ability of the classifier. Instead, we estimate statistical significance of the observed classification accuracy, or the likelihood of observing such accuracy by chance due to spurious correlations of the high-dimensional data patterns with the class labels in the given training set. We adopt permutation testing, a non-parametric technique previously developed in classical statistics for hypothesis testing in the generative setting (i.e., comparing two probability distributions). We demonstrate the method on real examples from neuroimaging studies and DNA microarray analysis and suggest a theoretical analysis of the procedure that relates the asymptotic behavior of the test to the existing convergence bounds.

Generating Circuit Tests by Exploiting Designed Behavior

This thesis describes two programs for generating tests for digital circuits that exploit several kinds of expert knowledge not used by previous approaches. First, many test generation problems can be solved efficiently using operation relations, a novel representation of circuit behavior that connects internal component operations with directly executable circuit operations. Operation relations can be computed efficiently by searching traces of simulated circuit behavior. Second, experts write test programs rather than test vectors because programs are more readable and compact. Test programs can be constructed automatically by merging program fragments using expert-supplied goal-refinement rules and domain-independent planning techniques.

Winkler springs (p-y curves) for liquefied soil from element tests.

In practice, piles are most often modelled as "Beams on Non-Linear Winkler Foundation" (also known as “p-y spring” approach) where the soil is idealised as p-y springs. These p-y springs are obtained through semi-empirical approach using element test results of the soil. For liquefied soil, a reduction factor (often termed as p-multiplier approach) is applied on a standard p-y curve for the non-liquefied condition to obtain the p-y curve liquefied soil condition. This paper presents a methodology to obtain p-y curves for liquefied soil based on element testing of liquefied soil considering physically plausible mechanisms. Validation of the proposed p-y curves is carried out through the back analysis of physical model tests.

Computer-Based Diagnostic Systems: Computer-Based Troubleshooting

ISBN: 3-540-76198-5 (out of print)

The Use of Graph Grammars for Model-based Reasoning in Diagnostic Expert Systems

Flasinski M. and Lee M.H., The Use of Graph Grammars for Model-based Reasoning in Diagnostic Expert Systems, Prace Informatyczne, Zeszyty Naukowe Uniwersytetu Jagiellonskiego, 9, 1999, pp147-165.

Digital signal processing and artificial intelligence for the automated classification of food allergy

As a by-product of the ‘information revolution’ which is currently unfolding, lifetimes of man (and indeed computer) hours are being allocated for the automated and intelligent interpretation of data. This is particularly true in medical and clinical settings, where research into machine-assisted diagnosis of physiological conditions gains momentum daily. Of the conditions which have been addressed, however, automated classification of allergy has not been investigated, even though the numbers of allergic persons are rising, and undiagnosed allergies are most likely to elicit fatal consequences. On the basis of the observations of allergists who conduct oral food challenges (OFCs), activity-based analyses of allergy tests were performed. Algorithms were investigated and validated by a pilot study which verified that accelerometer-based inquiry of human movements is particularly well-suited for objective appraisal of activity. However, when these analyses were applied to OFCs, accelerometer-based investigations were found to provide very poor separation between allergic and non-allergic persons, and it was concluded that the avenues explored in this thesis are inadequate for the classification of allergy. Heart rate variability (HRV) analysis is known to provide very significant diagnostic information for many conditions. Owing to this, electrocardiograms (ECGs) were recorded during OFCs for the purpose of assessing the effect that allergy induces on HRV features. It was found that with appropriate analysis, excellent separation between allergic and nonallergic subjects can be obtained. These results were, however, obtained with manual QRS annotations, and these are not a viable methodology for real-time diagnostic applications. Even so, this was the first work which has categorically correlated changes in HRV features to the onset of allergic events, and manual annotations yield undeniable affirmation of this. Fostered by the successful results which were obtained with manual classifications, automatic QRS detection algorithms were investigated to facilitate the fully automated classification of allergy. The results which were obtained by this process are very promising. Most importantly, the work that is presented in this thesis did not obtain any false positive classifications. This is a most desirable result for OFC classification, as it allows complete confidence to be attributed to classifications of allergy. Furthermore, these results could be particularly advantageous in clinical settings, as machine-based classification can detect the onset of allergy which can allow for early termination of OFCs. Consequently, machine-based monitoring of OFCs has in this work been shown to possess the capacity to significantly and safely advance the current state of clinical art of allergy diagnosis