Quality control autophagy: a joint effort of ubiquitin, protein deacetylase and actin cytoskeleton.

Autophagy has been predominantly studied as a nonselective self-digestion process that recycles macromolecules and produces energy in response to starvation. However, autophagy independent of nutrient status has long been known to exist. Recent evidence suggests that this form of autophagy enforces intracellular quality control by selectively disposing of aberrant protein aggregates and damaged organelles--common denominators in various forms of neurodegenerative diseases. By definition, this form of autophagy, termed quality-control (QC) autophagy, must be different from nutrient-regulated autophagy in substrate selectivity, regulation and function. We have recently identified the ubiquitin-binding deacetylase, HDAC6, as a key component that establishes QC. HDAC6 is not required for autophagy activation per se; rather, it is recruited to ubiquitinated autophagic substrates where it stimulates autophagosome-lysosome fusion by promoting F-actin remodeling in a cortactin-dependent manner. Remarkably, HDAC6 and cortactin are dispensable for starvation-induced autophagy. These findings reveal that autophagosomes associated with QC are molecularly and biochemically distinct from those associated with starvation autophagy, thereby providing a new molecular framework to understand the emerging complexity of autophagy and therapeutic potential of this unique machinery.

Using decision analysis to improve malaria control policy making.

Malaria and other vector-borne diseases represent a significant and growing burden in many tropical countries. Successfully addressing these threats will require policies that expand access to and use of existing control methods, such as insecticide-treated bed nets (ITNs) and artemesinin combination therapies (ACTs) for malaria, while weighing the costs and benefits of alternative approaches over time. This paper argues that decision analysis provides a valuable framework for formulating such policies and combating the emergence and re-emergence of malaria and other diseases. We outline five challenges that policy makers and practitioners face in the struggle against malaria, and demonstrate how decision analysis can help to address and overcome these challenges. A prototype decision analysis framework for malaria control in Tanzania is presented, highlighting the key components that a decision support tool should include. Developing and applying such a framework can promote stronger and more effective linkages between research and policy, ultimately helping to reduce the burden of malaria and other vector-borne diseases.

Integrated vector management for malaria control in Uganda: knowledge, perceptions and policy development.

BACKGROUND: Integrated vector management (IVM) is increasingly being recommended as an option for sustainable malaria control. However, many malaria-endemic countries lack a policy framework to guide and promote the approach. The objective of the study was to assess knowledge and perceptions in relation to current malaria vector control policy and IVM in Uganda, and to make recommendations for consideration during future development of a specific IVM policy. METHODS: The study used a structured questionnaire to interview 34 individuals working at technical or policy-making levels in health, environment, agriculture and fisheries sectors. Specific questions on IVM focused on the following key elements of the approach: integration of chemical and non-chemical interventions of vector control; evidence-based decision making; inter-sectoral collaboration; capacity building; legislation; advocacy and community mobilization. RESULTS: All participants were familiar with the term IVM and knew various conventional malaria vector control (MVC) methods. Only 75% thought that Uganda had a MVC policy. Eighty percent (80%) felt there was inter-sectoral collaboration towards IVM, but that it was poor due to financial constraints, difficulties in involving all possible sectors and political differences. The health, environment and agricultural sectors were cited as key areas requiring cooperation in order for IVM to succeed. Sixty-seven percent (67%) of participants responded that communities were actively being involved in MVC, while 48% felt that the use of research results for evidence-based decision making was inadequate or poor. A majority of the participants felt that malaria research in Uganda was rarely used to facilitate policy changes. Suggestions by participants for formulation of specific and effective IVM policy included: revising the MVC policy and IVM-related policies in other sectors into a single, unified IVM policy and, using legislation to enforce IVM in development projects. CONCLUSION: Integrated management of malaria vectors in Uganda remains an underdeveloped component of malaria control policy. Cooperation between the health and other sectors needs strengthening and funding for MVC increased in order to develop and effectively implement an appropriate IVM policy. Continuous engagement of communities by government as well as monitoring and evaluation of vector control programmes will be crucial for sustaining IVM in the country.

Bayesian Model Averaging in the M-Open Framework

This chapter presents a model averaging approach in the M-open setting using sample re-use methods to approximate the predictive distribution of future observations. It first reviews the standard M-closed Bayesian Model Averaging approach and decision-theoretic methods for producing inferences and decisions. It then reviews model selection from the M-complete and M-open perspectives, before formulating a Bayesian solution to model averaging in the M-open perspective. It constructs optimal weights for MOMA:M-open Model Averaging using a decision-theoretic framework, where models are treated as part of the ‘action space’ rather than unknown states of nature. Using ‘incompatible’ retrospective and prospective models for data from a case-control study, the chapter demonstrates that MOMA gives better predictive accuracy than the proxy models. It concludes with open questions and future directions.