Publications by authors named "E Iu Gracheva"

Water deprivation is a life-threatening condition that engages a protective physiological response to couple osmolyte retention with potentiation of thirst. This response, typical for most mammals, safeguards against short-term water deprivation but fails in the long term. Thirteen-lined ground squirrels () use the short-term response during summer, whereas during winter, they lack thirst and survive without water for months.

View Article and Find Full Text PDF

Pacinian corpuscles are rapidly adapting mechanoreceptor end-organs that detect transient touch and high-frequency vibration. In the prevailing model, these properties are determined by the outer core, which acts as a mechanical filter limiting static and low-frequency stimuli from reaching the afferent terminal-the sole site of touch detection in corpuscles. Here, we determine the detailed 3D architecture of corpuscular components and reveal their contribution to touch detection.

View Article and Find Full Text PDF

In order to facilitate cardiovascular research to develop non-invasive optical heart pacing methods, we have generated a double-transgenic Drosophila melanogaster (fruit fly) model suitable for optogenetic pacing. We created a fly stock with both excitatory H134R-ChR2 and inhibitory eNpHR2.0 opsin transgenes.

View Article and Find Full Text PDF

Mammalian hibernators survive prolonged periods of cold and resource scarcity by temporarily modulating normal physiological functions, but the mechanisms underlying these adaptations are poorly understood. The hibernation cycle of thirteen-lined ground squirrels (Ictidomys tridecemlineatus) lasts for 5-7 months and comprises weeks of hypometabolic, hypothermic torpor interspersed with 24-48-h periods of an active-like interbout arousal (IBA) state. We show that ground squirrels, who endure the entire hibernation season without food, have negligible hunger during IBAs.

View Article and Find Full Text PDF

Optical coherence microscopy (OCM) imaging of the (fruit fly) heart tube has enabled the non-invasive characterization of fly heart physiology . OCM generates large volumes of data, making it necessary to automate image analysis. Deep-learning-based neural network models have been developed to improve the efficiency of fly heart image segmentation.

View Article and Find Full Text PDF