Analysis of classification accuracy for pre-filtered multichannel remote sensing data

Noise is one of the main factors degrading the quality of original multichannel remote sensing data and its presence influences classification efficiency, object detection, etc. Thus, pre-filtering is often used to remove noise and improve the solving of final tasks of multichannel remote sensing. Recent studies indicate that a classical model of additive noise is not adequate enough for images formed by modern multichannel sensors operating in visible and infrared bands. However, this fact is often ignored by researchers designing noise removal methods and algorithms. Because of this, we focus on the classification of multichannel remote sensing images in the case of signal-dependent noise present in component images. Three approaches to filtering of multichannel images for the considered noise model are analysed, all based on discrete cosine transform in blocks. The study is carried out not only in terms of conventional efficiency metrics used in filtering (MSE) but also in terms of multichannel data classification accuracy (probability of correct classification, confusion matrix). The proposed classification system combines the pre-processing stage where a DCT-based filter processes the blocks of the multichannel remote sensing image and the classification stage. Two modern classifiers are employed, radial basis function neural network and support vector machines. Simulations are carried out for three-channel image of Landsat TM sensor. Different cases of learning are considered: using noise-free samples of the test multichannel image, the noisy multichannel image and the pre-filtered one. It is shown that the use of the pre-filtered image for training produces better classification in comparison to the case of learning for the noisy image. It is demonstrated that the best results for both groups of quantitative criteria are provided if a proposed 3D discrete cosine transform filter equipped by variance stabilizing transform is applied. The classification results obtained for data pre-filtered in different ways are in agreement for both considered classifiers. Comparison of classifier performance is carried out as well. The radial basis neural network classifier is less sensitive to noise in original images, but after pre-filtering the performance of both classifiers is approximately the same.

Simple shade plots aid better long-term choices of data pre-treatment in multivariate assemblage studies

Shade plots, simple visual representations of abundance matrices from multivariate species assemblage studies, are shown to be an effective aid in choosing an overall transformation (or other pre-treatment) of quantitative data for long-term use, striking an appropriate balance between dominant and less abundant taxa in ensuing resemblance-based multivariate analyses. Though the exposition is entirely general and applicable to all community studies, detailed illustrations of the comparative power and interpretative possibilities of shade plots are given in the case of two estuarine assemblage studies in south-western Australia: (a) macrobenthos in the upper Swan Estuary over a two-year period covering a highly significant precipitation event for the Perth area; and (b) a wide-scale spatial study of the nearshore fish fauna from five divergent estuaries. The utility of transformations of intermediate severity is again demonstrated and, with greater novelty, the potential importance seen of further mild transformation of all data after differential down-weighting (dispersion weighting) of spatially clumped' or schooled' species. Among the new techniques utilized is a two-way form of the RELATE test, which demonstrates linking of assemblage structure (fish) to continuous environmental variables (water quality), having removed a categorical factor (estuary differences). Re-orderings of sample and species axes in the associated shade plots are seen to provide transparent explanations at the species level for such continuous multivariate patterns.

CARBON MONOXIDE AND PREGNANCY: A SEARCH FOR A POSSIBLE THERAPEUTIC IN THE TREATMENT OF PRE-ECLAMPSIA

Pre-eclampsia (PE) is a pregnancy disorder that affects roughly 5-7% of all pregnancies and is a leading cause of both maternal and fetal/neonatal morbidity and mortality. With no present cure for the disease, researchers are interested in the lower incidence of PE observed among the cigarette smoking pregnant population. However, women who use smokeless tobacco do not experience the same decreased incidence of PE, leading to hypothesis of protection against PE from the largest combustible product of cigarette smoke, carbon monoxide (CO). Studies evaluated levels of CO in PE women and found that they were statistically lower than those of healthy pregnancy. Researchers have found CO to possess many cytoprotective and regulatory properties and specifically within the placenta, it has been found to increase perfusion pressure, decrease oxidative stress, decreases ischemia/reperfusion induced apoptosis and maintain endothelial functioning. The idea for use of CO as a possible therapeutic for PE has thus become a real possibility. This study determined CO levels in pregnant women ± smoking as well as in PE women±smoking, as to discover a possible therapeutic range for future treatments. The best correlated automated CO measurement device with blood CO levels was determined, for use in future clinical studies. This thesis also sought a possible CO delivery concentration, in order to achieve the CO levels observed in the human correlation study. A threshold level of maternal CO exposure in a murine animal model was found, for which fetal and maternal negative toxicities were not observed. The results of this thesis lend a few more pieces to the complicated puzzle involving CO and PE and offer another step toward the possibility of a therapeutic treatment/prevention using this gaseous molecule.

Challenge of culture, conscience, and contract to general practitioners' care of their own health: qualitative study

Objective: To explore general practitioners' perceptions of the effects of their profession and training on their attitudes to illness in themselves and colleagues. Design: Qualitative study using focus groups and indepth interviews. Setting: Primary care in Northern Ireland. Participants: 27 general practitioners, including six recently appointed principals and six who also practised occupational medicine part time. Main outcome measures: Participants' views about their own and colleagues' health. Results: Participants were concerned about the current level of illness within the profession. They described their need to portray a healthy image to both patients and colleagues. This hindered acknowledgement of personal illness and engaging in health screening. Embarrassment in adopting the role of a patient and concerns about confidentiality also influenced their reactions to personal illness. Doctors' attitudes can impede their access to appropriate health care for themselves, their families, and their colleagues. A sense of conscience towards patients and colleagues and the working arrangements of the practice were cited as reasons for working through illness and expecting colleagues to do likewise. Conclusions: General practitioners perceive that their professional position and training adversely influence their attitudes to illness in themselves and their colleagues. Organisational changes within general practice, including revalidation, must take account of barriers experienced by general practitioners in accessing health care. Medical education and culture should strive to promote appropriate self care among doctors.