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Capacitance recording provides a readout of membrane area that can be used to monitor exo- and endocytosis in neurons and secretory cells in real time. By interfacing a lock-in amplifier to a patch-clamp amplifier, the capacitance of cell-attached membrane patches can be measured with sufficient sensitivity to reveal the fusion and retrieval of single vesicles as unitary stepwise changes in capacitance. The small size of many secretory vesicles, especially of synaptic vesicles, places a premium on the reduction of noise in a capacitance recording. With care, the capacitance noise in cell-attached patches can be reduced to below 10 aF root-mean-square (rms), thus bringing into view steps resulting from the fusion of vesicles as small as about 18 nm in diameter. Thus, the lowest achievable noise level enables the resolution of changes in capacitance associated with the smallest secretory vesicles. This chapter presents the method of capacitance recording from cell-attached patches with an emphasis on noise reduction. It also addresses the closely related issue of extracting fusion pore properties from these recordings. [ABSTRACT FROM AUTHOR]

In many decision-making applications, the skyline query is frequently used to find a set of dominating data points (called skyline points) in a multi-dimensional dataset. In a high-dimensional space skyline points no longer offer any interesting insights as there are too many of them. In this paper, we introduce a novel metric, called skyline frequency that compares and ranks the interestingness of data points based on how often they are returned in the skyline when different number of dimensions (i.e., subspaces) are considered. Intuitively, a point with a high skyline frequency is more interesting as it can be dominated on fewer combinations of the dimensions. Thus, the problem becomes one of finding top-k frequent skyline points. But the algorithms thus far proposed for skyline computation typically do not scale well with dimensionality. Moreover, frequent skyline computation requires that skylines be computed for each of an exponential number of subsets of the dimensions. We present efficient approximate algorithms to address these twin difficulties. Our extensive performance study shows that our approximate algorithm can run fast and compute the correct result on large data sets in high-dimensional spaces. [ABSTRACT FROM AUTHOR]