Do gamma oscillations play a role in cerebral cortex?

Gamma rhythm (which has a center frequency between 30 and 80 Hz) is modulated by cognitive mechanisms such as attention and memory, and has been hypothesized to play a role in mediating these processes by supporting communication channels between cortical areas or encoding information in its phase. We highlight several issues related to gamma rhythms, such as low and inconsistent power, its dependence on low-level stimulus features, problems due to conduction delays, and contamination due to spike-related activity that makes accurate estimation of gamma phase difficult. Gamma rhythm could be a potentially useful signature of excitation-inhibition interactions in the brain, but whether it also provides a mechanism for information processing or coding remains an open question.

Simultaneously targeting inflammatory response and parasite sequestration in brain to treat Experimental Cerebral Malaria

Malaria afflicts around 200 million people annually, with a mortality number close to 600,000. The mortality rate in Human Cerebral Malaria (HCM) is unacceptably high (15-20%), despite the availability of artemisinin-based therapy. An effective adjunct therapy is urgently needed. Experimental Cerebral Malaria (ECM) in mice manifests many of the neurological features of HCM. Migration of T cells and parasite-infected RBCs (pRBCs) into the brain are both necessary to precipitate the disease. We have been able to simultaneously target both these parameters of ECM. Curcumin alone was able to reverse all the parameters investigated in this study that govern inflammatory responses, CD8(+) T cell and pRBC sequestration into the brain and blood brain barrier (BBB) breakdown. But the animals eventually died of anemia due to parasite build-up in blood. However, arteether-curcumin (AC) combination therapy even after the onset of symptoms provided complete cure. AC treatment is a promising therapeutic option for HCM.

Average Overlap for Clustering Incomplete Data using Symmetric Non-Negative Matrix Factorization

Clustering techniques which can handle incomplete data have become increasingly important due to varied applications in marketing research, medical diagnosis and survey data analysis. Existing techniques cope up with missing values either by using data modification/imputation or by partial distance computation, often unreliable depending on the number of features available. In this paper, we propose a novel approach for clustering data with missing values, which performs the task by Symmetric Non-Negative Matrix Factorization (SNMF) of a complete pair-wise similarity matrix, computed from the given incomplete data. To accomplish this, we define a novel similarity measure based on Average Overlap similarity metric which can effectively handle missing values without modification of data. Further, the similarity measure is more reliable than partial distances and inherently possesses the properties required to perform SNMF. The experimental evaluation on real world datasets demonstrates that the proposed approach is efficient, scalable and shows significantly better performance compared to the existing techniques.