Wave intensity analysis: A novel non-invasive method for determining arterial wave transmission

Wave intensity analysis is a novel technique for assessing wavelet transmission in the cardiovascular system. Using this tool, we have developed non-invasive techniques to study wave transmission in both central & peripheral arteries in man. The aim of this study was to determine the reproducibility of various haemodynamic measures in the carotid, brachial and radial arteries. 12 treated hypertensive men underwent applanation tonometry and pulsed Doppler ultrasound studies of the carotid, brachial and radial arteries on 2 occasions. Coefficients of variation for the local wave speed, cardiac compression wave intensity and main reflected wave intensity ranged between 3.7-6.6%, 8.2-11.4% and 12.5-19.6% respectively. We conclude that non-invasive methods used for wave intensity analysis are reproducible & provide additional information regarding the complex phenomenon of arterial wave transmission in man

Genotype assessment of grape regenerants from floral explants

A molecular typing of regenerant vines based on co-dominant simple sequence repeat (SSR) markers was applied for checking recombination events during somatic embryogenesis from floral explants. Twenty-one samples of somatic embryos and plantlets from embryogenic callus of both anthers and ovaries of the V. vinifera cv. Chardonnay, the rootstock Kober 125 AA, and the accession V. rupestris du Lot were randomly chosen from a number of regenerant lines. The genotype at polymorphic VVS2, VVMD5, VVMD7, VVMD27, VrZAG62 and VrZAG79 loci was produced and compared with reference patterns. No recombination events were detected in the cells involved in the somatic embryogenesis induction of all the checked samples, since all of them generated the same SSR profile of the grape variety from which explants were isolated.

Insulin for type 2 diabetes: choosing a second-line insulin regimen

Guidance has been published on the choice of initial insulin regimen for patients with type 2 diabetes [NPH (isophane) insulin or a long-acting insulin analogue] but not on how to choose a second regimen when glycaemic control becomes unsatisfactory

Study of one class boundary method classifiers for application in a video-based fall detection system

In this paper, we introduce a video-based robust fall detection system for monitoring an elderly person in a smart room environment. Video features, namely the centroid and orientation of a voxel person, are extracted. The boundary method, which is an example one class classification technique, is then used to determine whether the incoming features lie in the ‘fall region’ of the feature space, and thereby effectively distinguishing a fall from other activities, such as walking, sitting, standing, crouching or lying. Four different types of boundary methods, k-center, k-th nearest neighbor, one class support vector machine and single class minimax probability machine are assessed on representative test datasets. The comparison is made on the following three aspects: 1). True positive rate, false positive rate and geometric means in detection 2). Robustness to noise in the training dataset 3). The computational time for the test phase. From the comparison results, we show that the single class minimax probability machine achieves the best overall performance. By applying one class classification techniques with 3-d features, we can obtain a more efficient fall detection system with acceptable performance, as shown in the experimental part; besides, it can avoid the drawbacks of other traditional fall detection methods