Distribution of long chain heterocyst glycolipids in n2-fixing cyanobacteria of the order stigonematales

N2-fixing heterocystous cyanobacteria have been shown to hold a suite of unique glycolipids, so-called heterocyst glycolipids (HGs), as part of the heterocyst cell envelope. It was also demonstrated that the distribution of these components bears a high level of chemotaxonomic information, which allows distinguishing heterocystous cyanobacteria of the order Nostocales on a family or even genus level. Here we report the heterocyst glycolipid composition of five representatives of the order Stigonematales (Fischerella muscicola, Fischerella sp., Nostochopsis lobatus, Westiellopsis prolifica and Westiellopsis sp.), which have largely escaped a detailed investigation of their HG content so far. All analyzed strains contained a similar qualitative mixture of HGs with 1-(O-hexose)-3,29,31-dotriacontanetriol (HG32 triol) dominating over minor quantities of 1-(O-hexose)-29-keto-3,31-dotriacontanediol (HG32 keto-diol). When viewed in conjunction with previous culture studies on the HG composition of heterocystous cyanobacteria, our results demonstrate that HG32 triols and their corresponding keto-diol varieties are characteristic biological markers for heterocystous cyanobacteria of the order Stigonematales. Given that these N2-fixers primarily occur in tropical to subtropical freshwater lakes and subaerial habitats, the presence of HG32 triols and keto-diols in sedimentary sequences may offer additional information on climatic conditions in palaeoenvironmental studies

Interfilum and Klebsormidium are closely related streptophycean algae

Proceedings of the international meeting Algal Culture Collections (Oban, 8-11 Giugno 2008) (http://www.ccap.ac.uk/algalculturecollections2008.htm) - Abstrac

Perspectives on the use of modelling and economic analysis to guide HIV programmes in sub-Saharan Africa

HIV modelling and economic analyses have had a prominent role in guiding programmatic responses to HIV in sub-Saharan Africa. However, there has been little reflection on how the HIV modelling field might develop in future. HIV modelling should more routinely align with national government and ministry of health priorities, recognising their legitimate mandates and stewardship responsibilities, for HIV and other wider health programmes. Importance should also be placed on ensuring collaboration between modellers, and that joint approaches to addressing modelling questions, becomes the norm rather than the exception. Such an environment can accelerate translation of modelling analyses into policy formulation because areas where models agree can be prioritised for action, whereas areas over which uncertainty prevails can be slated for additional study, data collection, and analysis. HIV modelling should increasingly be integrated with the modelling of health needs beyond HIV, particularly in allocative efficiency analyses, where focusing on one disease over another might lead to worse health overall. Such integration might also enhance partnership with national governments whose mandates extend beyond HIV. Finally, we see a need for there to be substantial and equitable investment in capacity strengthening within African countries, so that African researchers will increasingly be leading modelling exercises. Building a critical mass of expertise, strengthened through external collaboration and knowledge exchange, should be the ultimate goal

Perspectives on the use of modelling and economic analysis to guide HIV programmes in sub-Saharan Africa

HIV modelling and economic analyses have had a prominent role in guiding programmatic responses to HIV in sub-Saharan Africa. However, there has been little reflection on how the HIV modelling field might develop in future. HIV modelling should more routinely align with national government and ministry of health priorities, recognising their legitimate mandates and stewardship responsibilities, for HIV and other wider health programmes. Importance should also be placed on ensuring collaboration between modellers, and that joint approaches to addressing modelling questions, becomes the norm rather than the exception. Such an environment can accelerate translation of modelling analyses into policy formulation because areas where models agree can be prioritised for action, whereas areas over which uncertainty prevails can be slated for additional study, data collection, and analysis. HIV modelling should increasingly be integrated with the modelling of health needs beyond HIV, particularly in allocative efficiency analyses, where focusing on one disease over another might lead to worse health overall. Such integration might also enhance partnership with national governments whose mandates extend beyond HIV. Finally, we see a need for there to be substantial and equitable investment in capacity strengthening within African countries, so that African researchers will increasingly be leading modelling exercises. Building a critical mass of expertise, strengthened through external collaboration and knowledge exchange, should be the ultimate goal