Malaria is arguably the most important vector-borne disease worldwide, affecting 300 million people and killing 1-2 million people every year. The lack of an effective vaccine and the emergence of the parasites' resistance to many existing anti-malarial drugs have aggravated the situation. Clearly, development of novel strategies for control of the disease is urgently needed. Mosquitoes are obligatory vectors for the disease and inhibition of parasite development in the mosquito has considerable promise as a new approach in the fight against malaria. Based on recent advances in the genetic engineering of mosquitoes, the concept of generating genetically modified (GM) mosquitoes that hinder transmission by either killing or interfering with parasite development is a potential means of controlling the disease. To generate these GM mosquitoes, the authors focused on a unique lectin isolated from the sea cucumber, which has both hemolytic and cytotoxic activities, as an anti-parasite effector molecule. A transgenic mosquito expressing the lectin effectively caused erythrocyte lysis in the midgut after ingestion of an infectious blood meal and severely impaired parasite development. This laboratory-acquired finding may provide significant implications for future malaria control using GM mosquitoes refractory to the parasites.
To deal with the vast complexity of the brain and its many degrees of freedom, many reductionist methods have been designed that can be used to simplify neural interactions to just a few key underlying macroscopic variables. Despite these theoretical advances, even today relatively few population models have been subjected to stringent experimental tests. We explore whether second-order spike correlations measured in songbirds can be explained by single-neuron statistics and population dynamics, both reflecting hypotheses on network connectivity. We formulate a Markov population model with essentially two degrees of freedom and associated with different behavioral states of birds such as waking, singing, or sleeping. Excellent agreement between spike-train data and model is achieved, given a few connectivity assumptions that strengthen the view of a hierarchical organization of songbird motor networks. This work is an important demonstration that a broad range of neural activity patterns can be compatible at the population level with few underlying degrees of freedom.