A novel method has been devised for the study of swimming organisms by using speckle patterns produced by their scattering of coherent laser light. The speckle patterns show fluctuations in space and time which may be correlated with the activity of the organisms. The fluctuations give an immediate indication of mobility and a more detailed analysis of the frequency spectrum of the speckle fluctuations shows characteristic resonance-like features which are specific to the organism. The speckle patterns produced by several protozoans, including Paramecium bursaria, Entosiphon sulcatum, and by the alga Chlamydomonas reinhardii and the rotifer Brachionus calyciflorus have been studied. Laser speckle spectroscopy (LSS) allows a rapid non-invasive monitoring of the activity of the organisms and could find application in ecotoxicity studies and environmental biomonitoring. The results presented here are the first reports of LSS and its use in this way and demonstrate its viability and potential for further development.
A recent article on syncytial nuclear divisions in early embryos of Drosophila melanogaster presented evidence that the complex spatio–temporal data obtained from time-lapse images were patterned, contrary to previous suggestions based on preliminary observations. However, the reasoning that led to the hypothesis of patterning was informal and unsystematic. Here, we propose a systematic and computerized method for detecting subtle spatio–temporal mitotic patterns, under a general formulation of mitosis as three-dimensional asynchronous processes, of which the earlier data are a special case. The approach involves a rather elaborate application of the concept of permutation test from nonparametric statistics. Far from being limited to mitotic processes, the approach holds general promise for other classes of mass, time-lapse imaging phenomena in cell and developmental biology, such as migration and cell death.
An efficient algorithm for discrete signal sinc-interpolation that is suitable for use in image and signal processing is described. Being mathematically equivalent to the commonly used zero padding interpolation method, the algorithm surpasses it in terms of flexibility, computational complexity and usage of computer memory.