Many important physiological processes operate at time and space scales far beyond those accessible to atom-realistic simulations, and yet discrete stochastic rather than continuum methods may best represent finite numbers of molecules interacting in complex cellular spaces. We describe and validate new tools and algorithms developed for a new version of the MCell simulation program (MCell3), which supports generalized Monte Carlo modeling of diffusion and chemical reaction in solution, on surfaces representing membranes, and combinations thereof. A new syntax for describing the spatial directionality of surface reactions is introduced, along with optimizations and algorithms that can substantially reduce computational costs (e.g., event scheduling, variable time and space steps). Examples for simple reactions in simple spaces are validated by comparison to analytic solutions. Thus we show how spatially realistic Monte Carlo simulations of biological systems can be far more cost-effective than often is assumed, and provide a level of accuracy and insight beyond that of continuum methods.
A relatively compact, lightweight, and programmable spectropolarimetric imager was used to collect spectral and polarization data from various objects and backgrounds, both in the laboratory and in field tests. This imager uses a tellurium dioxide (TeO2) acousto-optic tunable filter and a liquid-crystal retardation plate with a CCD camera. The spectral images were collected 450-1000 nm at 10- or 20-nm intervals at two or four polarization settings for each spectral interval. We analyzed a portion of these data to assess the effectiveness of this system for object detection. We present our measurements and discuss the analysis results.
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