New treatment strategies for Alzheimer's disease: is there a hope?

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease, and corresponds to the most common cause of dementia worldwide. Although not fully understood, the pathophysiology of AD is largely represented by the neurotoxic events triggered by the beta-amyloid cascade and by cytoskeletal abnormalities subsequent to the hyperphosphorylation of microtubule-associated Tau protein in neurons. These processes lead respectively to the formation of neuritic plaques and neurofibrillary tangles, which are the pathological hallmarks of the disease. Clinical benefits of the available pharmacological treatment for AD with antidementia drugs (namely cholinesterase inhibitors and memantine) are unquestionable, although limited to a temporary, symptomatic support to cognitive and related functions. Over the past decade, substantial funding and research have been dedicated to the search and development of new pharmaceutical compounds with disease-modifying properties. The rationale of such approach is that by tackling key pathological processes in AD it may be possible to attenuate or even change its natural history. In the present review, we summarize the available evidence on the new therapeutic approaches that target amyloid and Tau pathology in AD, focusing on pharmaceutical compounds undergoing phase 2 and phase 3 randomized controlled trials.

Critically evaluating rural women’s participation and empowerment : An interdisciplinary feminist framework for research and analysis

Community development is increasingly using participatory processes that aim to be inclusive and empowering. However, researchers have found that such processes can have contradictory effects. Australian research has highlighted the significant leadership of rural women in sustainable community and economic development and in the adoption of new communication technologies such as the Internet. A focus on gender in participatory development may therefore lead to more effective programs and policies. This chapter outlines an interdisciplinary feminist framework for critically evaluating the participation and empowerment of rural women. This framework was found effective in evaluating an Australian project that aimed to enhance rural women’s access to communication technologies and to empower its participants. Its multiple theoretical and methodological approaches are outlined. The framework advocates an analysis of diversity and difference and the macro and micro contexts. Some principles and strategies for rural women’s inclusion, participation, empowerment, and for participatory feminist evaluation are outlined.

Optimisation of maintenance expenditure for buildings : refurbish or demolish

With an increase in growing number of aging public building infrastructure globally, there is an opportunity for an efficient life care management rather then mere demolition and rebuild. By carefully implementing appropriate structural engineering practices with facility management, the whole of life cycle costs for public building assets can be optimised and public money can be saved and better utilised elsewhere. A need of decision support tool/methodology which can assist asset manager make better decision among demolish, refurbish, do nothing or rebuilt option for any typical building under consideration is growing in order to optimise maintenance funds. The paper is part of research project focusing on development of such methodology known as residual service life prediction. The paper is mainly focusing on following three major aspects of public building infrastructure; first, issues and challenges in optimisation of maintenance funds, second, residual service life prediction methodology and issues and challenges in the development of such methodology. The paper concludes with the authors’ observations and further research potentials

A user driven data mining process model and learning system

This paper deals with the problem of using the data mining models in a real-world situation where the user can not provide all the inputs with which the predictive model is built. A learning system framework, Query Based Learning System (QBLS), is developed for improving the performance of the predictive models in practice where not all inputs are available for querying to the system. The automatic feature selection algorithm called Query Based Feature Selection (QBFS) is developed for selecting features to obtain a balance between the relative minimum subset of features and the relative maximum classification accuracy. Performance of the QBLS system and the QBFS algorithm is successfully demonstrated with a real-world application