RF radiation hazards to space station personnel

RF radiation hazards to space station personne

The role of the ventral intraparietal area (VIP/pVIP) in parsing optic flow into visual motion caused by self-motion and visual motion produced by object-motion

Retinal image motion is a composite signal that contains information about two behaviourally significant factors: self-motion and the movement of environmental objects. It is thought that the brain separates the two relevant signals, and although multiple brain regions have been identified that respond selectively to the composite optic flow signal, which brain region(s) perform the parsing process remains unknown. Here, we present original evidence that the putative human ventral intraparietal area (pVIP), a region known to receive optic flow signals as well as independent self-motion signals from other sensory modalities, plays a critical role in the parsing process and acts to isolate object-motion. We localised pVIP using its multisensory response profile, and then tested its relative responses to simulated object-motion and self-motion stimuli; results indicated that responses were much stronger in pVIP to stimuli that specified object-motion. We report two further observations that will be significant for the future direction of research in this area; firstly, activation in pVIP was suppressed by distant stationary objects compared to the absence of objects or closer objects. Secondly, we describe several other brain regions that share with pVIP selectivity for visual object-motion over visual self-motion as well as a multisensory response

PSF Sampling in Fluorescence Image Deconvolution

All microscope imaging is largely affected by inherent resolution limitations because of out-of-focus light and diffraction effects. The traditional approach to restoring the image resolution is to use a deconvolution algorithm to “invert” the effect of convolving the volume with the point spread function. However, these algorithms fall short in several areas such as noise amplification and stopping criterion. In this paper, we try to reconstruct an explicit volumetric representation of the fluorescence density in the sample and fit a neural network to the target z-stack to properly minimize a reconstruction cost function for an optimal result. Additionally, we do a weighted sampling of the point spread function to avoid unnecessary computations and prioritize non-zero signals. In a baseline comparison against the Richardson-Lucy method, our algorithm outperforms RL for images affected with high levels of noise

Alien Registration- Inman, Hattie A. (Mapleton, Aroostook County)

https://digitalmaine.com/alien_docs/34135/thumbnail.jp