University of Westminster

About: University of Westminster is a education organization based out in London, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 2944 authors who have published 8426 publications receiving 200236 citations. The organization is also known as: Westminster University & Royal Polytechnic Institution.

Our Common Interest: Report of the Commission for Africa

Abstract: n 11 March 2005, the British Prime Minister, Tony Blair, launched the Commission for Africa report, ‘Our Common Interest’, at the British Museum in London. The Commission, which comprised 17 people (the majority from Africa) drawn from politics, public service and the private sector, had been set an ambitious task: to define the challenges facing Africa, and to provide clear recommendations on how the developed world could support the changes needed to reduce poverty. Its report was widely welcomed as one of the most thorough and rigorous analyses of Africa’s problems ever undertaken. Its detailed and practical set of recommendations – directed, most immediately, to the G8 Summit in Gleneagles in July, the UN High Level Plenary on the Millennium Development Goals in New York in September, and the WTO Ministerial Conference in Hong Kong in December – constitutes a comprehensive programme for collective action to lift Africa from poverty, famine and disease, and to unlock its productive potential. At the outset, the report makes a strong case for urgent action. It highlights the positive developments already underway in Africa, in areas such as governance and economic growth, and argues that rich countries should support this progress to ensure that precious gains are not reversed. While encouraged by these signs of progress, the Commission is realistic about the challenge facing many African countries. On current trends, Africa is set to halve poverty, not in 2015 as envisaged with the Millennium Development Goals, but in 2150. Referring to African poverty and stagnation as “the greatest tragedy of our time”, the Commission cautions that failure to act now could lead to irreversible damage to the prospects of future generations. In its analysis of the causes of the current crisis, the report argues that the present situation is the result of a complex interplay of numerous interrelated factors, which form interlocking cycles that affect each country in different ways. Action is therefore required in several areas at once if vicious circles are to be broken. Subsequent chapters provide details of specific actions required in each of these areas. The subject of cultural awareness is given prominence in an early chapter, setting the Commission’s approach apart from many that have gone before. The report calls on the international community to make greater efforts to understand the values, norms and allegiances of the cultures of Africa, and in policy-making to display greater flexibility, open-mindedness, willingness to learn, and humility. An action plan that fails to take proper account of the role of culture is doomed to failure.

Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm

Abstract: Computer-aided detection/diagnosis (CAD) systems can enhance the diagnostic capabilities of physicians and reduce the time required for accurate diagnosis. The objective of this paper is to review the recent published segmentation and classification techniques and their state-of-the-art for the human brain magnetic resonance images (MRI). The review reveals the CAD systems of human brain MRI images are still an open problem. In the light of this review we proposed a hybrid intelligent machine learning technique for computer-aided detection system for automatic detection of brain tumor through magnetic resonance images. The proposed technique is based on the following computational methods; the feedback pulse-coupled neural network for image segmentation, the discrete wavelet transform for features extraction, the principal component analysis for reducing the dimensionality of the wavelet coefficients, and the feed forward back-propagation neural network to classify inputs into normal or abnormal. The experiments were carried out on 101 images consisting of 14 normal and 87 abnormal (malignant and benign tumors) from a real human brain MRI dataset. The classification accuracy on both training and test images is 99% which was significantly good. Moreover, the proposed technique demonstrates its effectiveness compared with the other machine learning recently published techniques. The results revealed that the proposed hybrid approach is accurate and fast and robust. Finally, possible future directions are suggested.

Prediction of Breast Cancer Risk Based on Profiling With Common Genetic Variants

Abstract: Background: Data for multiple common susceptibility alleles for breast cancer may be combined to identify women at different levels of breast cancer risk. Such stratification could guide preventive and screening strategies. However, empirical evidence for genetic risk stratification is lacking. Methods: We investigated the value of using 77 breast cancer-associated single nucleotide polymorphisms (SNPs) for risk stratification, in a study of 33 673 breast cancer cases and 33 381 control women of European origin. We tested all possible pair-wise multiplicative interactions and constructed a 77-SNP polygenic risk score (PRS) for breast cancer overall and by estrogen receptor (ER) status. Absolute risks of breast cancer by PRS were derived from relative risk estimates and UK incidence and mortality rates. Results: There was no strong evidence for departure from a multiplicative model for any SNP pair. Women in the highest 1% of the PRS had a three-fold increased risk of developing breast cancer compared with women in the middle quintile (odds ratio [OR] = 3.36, 95% confidence interval [CI] = 2.95 to 3.83). The ORs for ER-positive and ER-negative disease were 3.73 (95% CI = 3.24 to 4.30) and 2.80 (95% CI = 2.26 to 3.46), respectively. Lifetime risk of breast cancer for women in the lowest and highest quintiles of the PRS were 5.2% and 16.6% for a woman without family history, and 8.6% and 24.4% for a woman with a first-degree family history of breast cancer. Conclusions: The PRS stratifies breast cancer risk in women both with and without a family history of breast cancer. The observed level of risk discrimination could inform targeted screening and prevention strategies. Further discrimination may be achievable through combining the PRS with lifestyle/environmental factors, although these were not considered in this report.