Beta cells in the pancreatic islets of Langerhans are precise biological sensors for glucose and play a central role in balancing the organism between catabolic and anabolic needs. A hallmark of the beta cell response to glucose are oscillatory changes of membrane potential that are tightly coupled with oscillatory changes in intracellular calcium concentration which, in turn, elicit oscillations of insulin secretion. Both membrane potential and calcium changes spread from one beta cell to the other in a wave-like manner. In order to assess the properties of the abovementioned responses to physiological and pathological stimuli, the main challenge remains how to effectively measure membrane potential and calcium changes at the same time with high spatial and temporal resolution, and also in as many cells as possible. To date, the most wide-spread approach has employed the electrophysiological patch-clamp method to monitor membrane potential changes. Inherently, this technique has many advantages, such as a direct contact with the cell and a high temporal resolution. However, it allows one to assess information from a single cell only. In some instances, this technique has been used in conjunction with CCD camera-based imaging, offering the opportunity to simultaneously monitor membrane potential and calcium changes, but not in the same cells and not with a reliable cellular or subcellular spatial resolution. Recently, a novel family of highly-sensitive membrane potential reporter dyes in combination with high temporal and spatial confocal calcium imaging allows for simultaneously detecting membrane potential and calcium changes in many cells at a time. Since the signals yielded from both types of reporter dyes are inherently noisy, we have developed complex methods of data denoising that permit for visualization and pixel-wise analysis of signals. Combining the experimental approach of high-resolution imaging with the advanced analysis of noisy data enables novel physiological insights and reassessment of current concepts in unprecedented detail.

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This paper introduces a new algorithm for losslessly compressing voxel-based 3D CT medical images. Firstly, medical data is segmented according to selected ranges of the Hounsfield scale. The data is then arranged into two data streams. The first stream identifies the positions of the remaining data after segmentation. This information is compressed by the JBIG standard. The second stream contains the exact data values and it is losslessly compressed by our algorithm. The efficiency of this approach has been evaluated by a prototype application. This approach represents an interesting alternative for the long-term storage of medical 2D and 3D images, and for applications in telemedicine. The compression method can be used either for 2D or 3D medical data.

The incremental nearest-point search successively inserts query points into the space partition data structure, and the nearest point for each of them is simultaneously found among the previously inserted ones. The paper introduces a new approach which solves this task in 2-D space in a nearly optimal manner. The proposed dynamic partition into parallel strips, each containing a limited number of points structured in the deterministic skip list, successfully prevents situations with over-populated strips, while its further advanced version with two perpendicular partitions and four categories of deterministic skip lists efficiently decreases the number of strips to be examined in a great majority of practical cases.Povzetek: V članku je predstavljen algoritem za reševanje inkrementalnega problema najbližje točke, ki z dinamično delitvijo ravnine v vzporedne trakove preprečuje prenaseljenost le-teh, z dodatno delitvijo, pravokotno na prvo, pa se večinoma izogne tudi preiskovanju prevelikega števila trakov.

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