The ctenophore Mnemiopsis leidyi is an opportunistic species that can be extremely abundant and invasive in many parts of the world. It is well known for its bright bioluminescence, but its light emission response to flow stimulation has not been rigorously quantified. The objective of this study was to determine the luminescent response of cydippid larvae of M. leidyi to two types of mechanical stimuli, including an impeller pump within the UBAT bathyphotometer and stirring as the stimulus within an integrating sphere. Tests were conducted with less than one week old cydippid larvae, analyzing flash parameters of rise time, peak intensity, decay slope, decay time, total integrated emission (TMSL), integrated flash emission, and flash duration. Cydippid larval size had a positive correlation with peak intensity. There were four patterns of bioluminescent responses from the UBAT but they did not have statistically different flash kinetics. For the integrating sphere, the average peak intensity and TMSL were much greater than for the UBAT, possibly due to the two forms of stimulation. However, a constant phosphorescent emitter was 2.6 times brighter when measured with the integrating sphere compared to the UBAT, suggesting inaccurate photon calibration of the UBAT perhaps due to light measurement geometry. This study provides a well-defined baseline of cydippid larvae flash responses that can be used for interpreting field measurements made with bathyphotometers and to determine their contribution to the bioluminescence potential of waters where they are present.
We develop a remote hyperspectral (HS) imaging work flow that relays spectral and spatial information of a scene via a minimal amount of encoded samples along with a robust data reconstruction scheme. To fully exploit the redundant and multidimensional structure of HS images, we adopt the canonical polyadic (CP) decomposition of multiway tensors. This approach represents our HS cube in a compressive manner while being naturally suitable for the linear mixing model, commonly used by practitioners to analyze the spectral content of each pixel. Under this low CP rank model we achieve frugal HS sensing by attenuating and encoding the incoming spectrum, thereby faithfully capturing the information with few measurements relative to its ambient dimensions. To further reduce the complexity of HS data, we apply image segmentation techniques to our encoded observations. By clustering the pixels into groups of endmembers with similar structure, we obtain a set of simplified data cubes each well approximated by a low CP rank tensor. To decode the measurements, we apply CP alternating least squares to each set of clustered pixels and combine the outputs to obtain our final HS image. We present several numerical experiments on synthetic and real HS data with various levels of input noise. We demonstrate that the approach outperforms state of the art methods, achieving noise attenuation while reducing the amount of collected data by a factor of 1/14.
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