Intrinsically Photosensitive Retinal Ganglion Cells

Intrinsically photosensitive retinal ganglion cells (ipRGCs) respond to light in the absence of all rod and cone photoreceptor input. The existence of these ganglion cell photoreceptors, although predicted from observations scattered over many decades, was not established until it was shown that a novel photopigment, melanopsin, was expressed in retinal ganglion cells of rodents and primates. Phototransduction in mammalian ipRGCs more closely resembles that of invertebrate than vertebrate photoreceptors and appears to be mediated by transient receptor potential channels. In the retina, ipRGCs provide excitatory drive to dopaminergic amacrine cells and ipRGCs are coupled to GABAergic amacrine cells via gap junctions. Several subtypes of ipRGC have been identified in rodents based on their morphology, physiology and expression of molecular markers. ipRGCs convey irradiance information centrally via the optic nerve to influence several functions including photoentrainment of the biological clock located in the hypothalamus, the pupillary light reflex, sleep and perhaps some aspects of vision. In addition, ipRGCs may also contribute irradiance signals that interface directly with the autonomic nervous system to regulate rhythmic gene activity in major organs of the body. Here we review the early work that provided the motivation for searching for a new mammalian photoreceptor, the ground-breaking discoveries, current progress that continues to reveal the unusual properties of these neuron photoreceptors, and directions for future investigation

Water quality assessment of the Jinshui River (China) using multivariate statistical techniques

Multivariate statistical techniques have been widely utilized to assess water quality and evaluate aquatic ecosystem health. In this study, cluster analysis, discriminant analysis, and factor analysis techniques are applied to analyze the physical and chemical variables in order to evaluate water quality of the Jinshui River, a water source area for an interbasin water transfer project of China. Cluster analysis classifies 12 sampling sites with 22 variables into three clusters reflecting the geo-setting and different pollution levels. Discriminant analysis confirms the three clusters with nine discriminant variables including water temperature, total dissolved solids, dissolved oxygen, pH, ammoniacal nitrogen, nitrate nitrogen, turbidity, bicarbonate, and potassium. Factor analysis extracts five varifactors explaining 90.01% of the total variance and representing chemical component, oxide-related process, natural weathering and decomposition processes, nutrient process, and physical processes, respectively. The study demonstrates the capacity of multivariate statistical techniques for water quality assessment and pollution factors/sources identification for sustainable watershed management