Seven key actions to eradicate rheumatic heart disease in Africa: the Addis Ababa communiqué

Acute rheumatic fever (ARF) and rheumatic heart disease (RHD) remain major causes of heart failure, stroke and death among African women and children, despite being preventable and imminently treatable. From 21 to 22 February 2015, the Social Cluster of the Africa Union Commission (AUC) hosted a consultation with RHD experts convened by the Pan-African Society of Cardiology (PASCAR) in Addis Ababa, Ethiopia, to develop a 'roadmap' of key actions that need to be taken by governments to eliminate ARF and eradicate RHD in Africa. Seven priority areas for action were adopted: (1) create prospective disease registers at sentinel sites in affected countries to measure disease burden and track progress towards the reduction of mortality by 25% by the year 2025, (2) ensure an adequate supply of high-quality benzathine penicillin for the primary and secondary prevention of ARF/RHD, (3) improve access to reproductive health services for women with RHD and other non-communicable diseases (NCD), (4) decentralise technical expertise and technology for diagnosing and managing ARF and RHD (including ultrasound of the heart), (5) establish national and regional centres of excellence for essential cardiac surgery for the treatment of affected patients and training of cardiovascular practitioners of the future, (6) initiate national multi-sectoral RHD programmes within NCD control programmes of affected countries, and (7) foster international partnerships with multinational organisations for resource mobilisation, monitoring and evaluation of the programme to end RHD in Africa. This Addis Ababa communiqué has since been endorsed by African Union heads of state, and plans are underway to implement the roadmap in order to end ARF and RHD in Africa in our lifetime.

The African National Congress and its Critics: 'Predatory Liberalism', Black Empowerment and Intra-Alliance Tensions in Post-Apartheid South Africa

Post-apartheid South Africa is characterized by centralized, neo-liberal policymaking that perpetuates, and in some cases exaggerates, socio-economic inequalities inherited from the apartheid era. The African National Congress (ANC) leadership’s alignment with powerful international and domestic market actors produces tensions within the Tripartite Alliance and between government and civil society. Consequently, several characteristics of ‘predatory liberalism’ are evident in contemporary South Africa: neo-liberal restructuring of the economy is combined with an increasing willingness by government to assert its authority, to marginalize and delegitimize those critical of its abandonment of inclusive governance. A new form of oligarch power, combining entrenched economic interests with those of a new ‘black bourgeoisie’ promoted by narrowly implemented Black Economic Empowerment policies, diminishes prospects for broad-based socio-economic transformation. Because the new policy environment is failing to resolve tensions between global market demands for increasing market liberalization and domestic popular demands for poverty-alleviation and socio-economic transformation, the ANC leadership is forced increasingly to confront ‘ultra-leftists’ who are challenging its credentials as defender of the National Democratic Revolution which was the cornerstone in the anti-apartheid struggle.

Speech Recognition with unknown partial feature corruption - a review of the union model

This paper provides a summary of our studies on robust speech recognition based on a new statistical approach – the probabilistic union model. We consider speech recognition given that part of the acoustic features may be corrupted by noise. The union model is a method for basing the recognition on the clean part of the features, thereby reducing the effect of the noise on recognition. To this end, the union model is similar to the missing feature method. However, the two methods achieve this end through different routes. The missing feature method usually requires the identity of the noisy data for noise removal, while the union model combines the local features based on the union of random events, to reduce the dependence of the model on information about the noise. We previously investigated the applications of the union model to speech recognition involving unknown partial corruption in frequency band, in time duration, and in feature streams. Additionally, a combination of the union model with conventional noise-reduction techniques was studied, as a means of dealing with a mixture of known or trainable noise and unknown unexpected noise. In this paper, a unified review, in the context of dealing with unknown partial feature corruption, is provided into each of these applications, giving the appropriate theory and implementation algorithms, along with an experimental evaluation.