Lambert-Eaton sera reduce low-voltage and high-voltage activated Ca2+ currents in murine dorsal root ganglion neurons.

Voltage-gated Ca2+ channels are categorized as either high-voltage activated (HVA) or low-voltage activated (LVA), and a subtype (or subtypes) of HVA Ca2+ channels link the presynaptic depolarization to rapid neuro-transmitter release. Reductions in transmitter release are characteristic of the autoimmune disorder, Lambert-Eaton syndrome (LES). Because antibodies from LES patients reduce Ca2+ influx in a variety of cell types and disrupt the intramembrane organization of active zones at neuromuscular synapses, specificity of LES antibodies for the Ca2+ channels that control transmitter release has been suggested as the mechanism for disease. We tested sera from four patients with LES. Serum samples from three of the four patients reduced both the maximal LVA and HVA Ca2+ conductances in murine dorsal root ganglion neurons. Thus, even though LES is expressed as a neuromuscular and autonomic disorder, our studies suggest that Ca2+ channels may be broadly affected in LES patients. To account for the specificity of disease expression, we suggest that incapacitation of only a fraction of the Ca2+ channels clustered at active zones would severely depress transmitter release. In particular, if several Ca2+ channels in a cluster are normally required to open simultaneously before transmitter release becomes likely, the loss of a few active zone Ca2+ channels would exponentially reduce the probability of transmitter release. This model may explain why LES is expressed as a neuromuscular disorder and can account for a clinical hallmark of LES, facilitation of neuromuscular transmission produced by vigorous voluntary effort.

Changes in voltage activation, Cs+ sensitivity, and ion permeability in H5 mutants of the plant K+ channel KAT1.

KAT1 is a voltage-dependent inward rectifying K+ channel cloned from the higher plant Arabidopsis thaliana [Anderson, J. A., Huprikar, S. S., Kochian, L. V., Lucas, W. J. & Gaber, R. F. (1992) Proc. Natl. Acad. Sci. USA 89, 3736-3740]. It is related to the Shaker superfamily of K+ channels characterized by six transmembrane spanning domains (S1-S6) and a putative pore-forming region between S5 and S6 (H5). The 115 region between Pro-247 and Pro-271 in KAT1 contains 14 additional amino acids when compared with Shaker [Aldrich, R. W. (1993) Nature (London) 362, 107-108]. We studied various point mutations introduced into H5 to determine whether voltage-dependent plant and animal K+ channels share similar pore structures. Through heterologous expression in Xenopus oocytes and voltage-clamp analysis combined with phenotypic analysis involving a potassium transport-defective Saccharomyces cerevisiae strain, we investigated the selectivity filter of the mutants and their susceptibility toward inhibition by cesium and calcium ions. With respect to electrophysiological properties, KAT1 mutants segregated into three groups: (i) wild-type-like channels, (ii) channels modified in selectivity and Cs+ or Ca2+ sensitivity, and (iii) a group that was additionally affected in its voltage dependence. Despite the additional 14 amino acids in H5, this motif in KAT1 is also involved in the formation of the ion-conducting pore because amino acid substitutions at Leu-251, Thr-256, Thr-259, and Thr-260 resulted in functional channels with modified ionic selectivity and inhibition. Creation of Ca2+ sensitivity and an increased susceptibility to Cs+ block through mutations within the narrow pore might indicate that both blockers move deeply into the channel. Furthermore, mutations close to the rim of the pore affecting the half-activation potential (U1/2) indicate that amino acids within the pore either interact with the voltage sensor or ion permeation feeds back on gating.

Real-time detection of DNA hybridization and melting on oligonucleotide arrays by using optical wave guides.

The challenge of the Human Genome Project is to increase the rate of DNA sequence acquisition by two orders of magnitude to complete sequencing of the human genome by the year 2000. The present work describes a rapid detection method using a two-dimensional optical wave guide that allows measurement of real-time binding or melting of a light-scattering label on a DNA array. A particulate label on the target DNA acts as a light-scattering source when illuminated by the evanescent wave of the wave guide and only the label bound to the surface generates a signal. Imaging/visual examination of the scattered light permits interrogation of the entire array simultaneously. Hybridization specificity is equivalent to that obtained with a conventional system using autoradiography. Wave guide melting curves are consistent with those obtained in the liquid phase and single-base discrimination is facile. Dilution experiments showed an apparent lower limit of detection at 0.4 nM oligonucleotide. This performance is comparable to the best currently known fluorescence-based systems. In addition, wave guide detection allows manipulation of hybridization stringency during detection and thereby reduces DNA chip complexity. It is anticipated that this methodology will provide a powerful tool for diagnostic applications that require rapid cost-effective detection of variations from known sequences.

Improving 3D Keypoint Detection from Noisy Data Using Growing Neural Gas

3D sensors provides valuable information for mobile robotic tasks like scene classification or object recognition, but these sensors often produce noisy data that makes impossible applying classical keypoint detection and feature extraction techniques. Therefore, noise removal and downsampling have become essential steps in 3D data processing. In this work, we propose the use of a 3D filtering and down-sampling technique based on a Growing Neural Gas (GNG) network. GNG method is able to deal with outliers presents in the input data. These features allows to represent 3D spaces, obtaining an induced Delaunay Triangulation of the input space. Experiments show how the state-of-the-art keypoint detectors improve their performance using GNG output representation as input data. Descriptors extracted on improved keypoints perform better matching in robotics applications as 3D scene registration.

Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps

Feature selection is an important and active issue in clustering and classification problems. By choosing an adequate feature subset, a dataset dimensionality reduction is allowed, thus contributing to decreasing the classification computational complexity, and to improving the classifier performance by avoiding redundant or irrelevant features. Although feature selection can be formally defined as an optimisation problem with only one objective, that is, the classification accuracy obtained by using the selected feature subset, in recent years, some multi-objective approaches to this problem have been proposed. These either select features that not only improve the classification accuracy, but also the generalisation capability in case of supervised classifiers, or counterbalance the bias toward lower or higher numbers of features that present some methods used to validate the clustering/classification in case of unsupervised classifiers. The main contribution of this paper is a multi-objective approach for feature selection and its application to an unsupervised clustering procedure based on Growing Hierarchical Self-Organising Maps (GHSOMs) that includes a new method for unit labelling and efficient determination of the winning unit. In the network anomaly detection problem here considered, this multi-objective approach makes it possible not only to differentiate between normal and anomalous traffic but also among different anomalies. The efficiency of our proposals has been evaluated by using the well-known DARPA/NSL-KDD datasets that contain extracted features and labelled attacks from around 2 million connections. The selected feature sets computed in our experiments provide detection rates up to 99.8% with normal traffic and up to 99.6% with anomalous traffic, as well as accuracy values up to 99.12%.

Research on vehicle-based driver status/performance monitoring; development, validation, and refinement of algorithms for detection of driver drowsiness.

National Highway Traffic Safety Administration, Washington, D.C.

Ciguatoxins: Cyclic polyether modulators of voltage-gated Iion channel function

Ciguatoxins are cyclic polyether toxins, derived from marine dinoflagellates, which are responsible for the symptoms of ciguatera poisoning. Ingestion of tropical and subtropical fin fish contaminated by ciguatoxins results in an illness characterised by neurological, cardiovascular and gastrointestinal disorders. The pharmacology of ciguatoxins is characterised by their ability to cause persistent activation of voltage-gated sodium channels, to increase neuronal excitability and neurotransmitter release, to impair synaptic vesicle recycling, and to cause cell swelling. It is these effects, in combination with an action to block voltage-gated potassium channels at high doses, which are believed to underlie the complex of symptoms associated with ciguatera. This review examines the sources, structures and pharmacology of ciguatoxins. In particular, attention is placed on their cellular modes of actions to modulate voltage-gated ion channels and other Na+-dependent mechanisms in numerous cell types and to current approaches for detection and treatment of ciguatera.

Presentation and detection of invasive melanoma in a high-risk population

Background: Early detection of melanoma has been encouraged in Queensland for many years, yet little is known about the patterns of detection and the way in which they relate to tumor thickness. Objective: Our purpose was to describe current patterns of melanoma detection in Queensland. Methods: This was a population-based study, comprising 3772 Queensland residents diagnosed with a histologically confirmed melanoma between 2000 and 2003. Results: Almost half (44.0%) of the melanomas were detected by the patients themselves, with physicians detecting one fourth (25.3%) and partners one fifth (18.6%). Melanomas detected by doctors were more likely to be thin (\0.75 mm) than those detected by the patient or other layperson. Melanomas detected during a deliberate skin examination were thinner than those detected incidentally. Limitations: Although a participation rate of 78% was achieved, as in any survey, nonresponse bias cannot be completely excluded, and the ability of the results to be generalized to other geographical areas is unknown. Conclusion: There are clear differences in the depth distribution of melanoma in terms of method of detection and who detects the lesions that are consistent with, but do not automatically lead to, the conclusion that promoting active methods of detection may be beneficial. ( J Am Acad Dermatol 2006;54:783-92.)

Load profiling and data mining techniques in electricity deregulated market

This paper presents load profiles of electricity customers, using the knowledge discovery in databases (KDD) procedure, a data mining technique, to determine the load profiles for different types of customers. In this paper, the current load profiling methods are compared using data mining techniques, by analysing and evaluating these classification techniques. The objective of this study is to determine the best load profiling methods and data mining techniques to classify, detect and predict non-technical losses in the distribution sector, due to faulty metering and billing errors, as well as to gather knowledge on customer behaviour and preferences so as to gain a competitive advantage in the deregulated market. This paper focuses mainly on the comparative analysis of the classification techniques selected; a forthcoming paper will focus on the detection and prediction methods.

Off-line signature verification and forgery detection system based on fuzzy modeling

This paper presents an innovative approach for signature verification and forgery detection based on fuzzy modeling. The signature image is binarized and resized to a fixed size window and is then thinned. The thinned image is then partitioned into a fixed number of eight sub-images called boxes. This partition is done using the horizontal density approximation approach. Each sub-image is then further resized and again partitioned into twelve further sub-images using the uniform partitioning approach. The features of consideration are normalized vector angle (α) from each box. Each feature extracted from sample signatures gives rise to a fuzzy set. Since the choice of a proper fuzzification function is crucial for verification, we have devised a new fuzzification function with structural parameters, which is able to adapt to the variations in fuzzy sets. This function is employed to develop a complete forgery detection and verification system.

Influential DMUs and outlier detection in data envelopment analysis with an application to health care

This paper explains some drawbacks on previous approaches for detecting influential observations in deterministic nonparametric data envelopment analysis models as developed by Yang et al. (Annals of Operations Research 173:89-103, 2010). For example efficiency scores and relative entropies obtained in this model are unimportant to outlier detection and the empirical distribution of all estimated relative entropies is not a Monte-Carlo approximation. In this paper we developed a new method to detect whether a specific DMU is truly influential and a statistical test has been applied to determine the significance level. An application for measuring efficiency of hospitals is used to show the superiority of this method that leads to significant advancements in outlier detection. © 2014 Springer Science+Business Media New York.

Simulation and Evaluation of High-Voltage Power Systems for Civil Aircraft

A status report of the modelling and simulation work that is being undertaken as part of the TIMES (Totally Integrated More Electric Systems) project is presented. Dynamic power quality simulations have been used to asses the performance of the electrical system of a EMA based actuation system for an Airbus A330 size aircraft, for both low voltage 115 V, and high voltage 230 V three-phase AC systems. The high voltage system is shown to have benefits in terms of power quality and reduced size and weight of equipment.

A Method for Classification of Doubly Resolvable Designs and Its Application

This article presents the principal results of the Ph.D. thesis Investigation and classification of doubly resolvable designs by Stela Zhelezova (Institute of Mathematics and Informatics, BAS), successfully defended at the Specialized Academic Council for Informatics and Mathematical Modeling on 22 February 2010.

Fractal Analysis for Cancer Research: Case Study and Simulation of Fractals

2010 Mathematics Subject Classification: 65D18.

A new unsupervised convolutional neural network model for Chinese scene text detection

As one of the most popular deep learning models, convolution neural network (CNN) has achieved huge success in image information extraction. Traditionally CNN is trained by supervised learning method with labeled data and used as a classifier by adding a classification layer in the end. Its capability of extracting image features is largely limited due to the difficulty of setting up a large training dataset. In this paper, we propose a new unsupervised learning CNN model, which uses a so-called convolutional sparse auto-encoder (CSAE) algorithm pre-Train the CNN. Instead of using labeled natural images for CNN training, the CSAE algorithm can be used to train the CNN with unlabeled artificial images, which enables easy expansion of training data and unsupervised learning. The CSAE algorithm is especially designed for extracting complex features from specific objects such as Chinese characters. After the features of articficial images are extracted by the CSAE algorithm, the learned parameters are used to initialize the first CNN convolutional layer, and then the CNN model is fine-Trained by scene image patches with a linear classifier. The new CNN model is applied to Chinese scene text detection and is evaluated with a multilingual image dataset, which labels Chinese, English and numerals texts separately. More than 10% detection precision gain is observed over two CNN models.