Bitschkessler2915

We strongly believe that the PQ technique developed here can be adapted to the RTS hydrides and other materials of value with minimal effort.A remarkable molecular and functional heterogeneity of the primary sensory neurons and dorsal horn interneurons transmits pain- and or itch-relevant information, but the molecular signature of the projection neurons that convey the messages to the brain is unclear. Here, using retro-TRAP (translating ribosome affinity purification) and RNA sequencing, we reveal extensive molecular diversity of spino- and trigeminoparabrachial projection neurons. Among the many genes identified, we highlight distinct subsets of Cck + -, Nptx2 + -, Nmb + -, and Crh + -expressing projection neurons. By combining in situ hybridization of retrogradely labeled neurons with Fos-based assays, we also demonstrate significant functional heterogeneity, including both convergence and segregation of pain- and itch-provoking inputs into molecularly diverse subsets of NK1R- and non-NK1R-expressing projection neurons.Heterogeneous selection is often proposed as a key mechanism maintaining repeatable behavioral variation ("animal personality") in wild populations. Previous studies largely focused on temporal variation in selection within single populations. The relative importance of spatial versus temporal variation remains unexplored, despite these processes having distinct effects on local adaptation. Using data from >3,500 great tits (Parus major) and 35 nest box plots situated within five West-European populations monitored over 4 to 18 y, we show that selection on exploration behavior varies primarily spatially, across populations, and study plots within populations. Exploration was, simultaneously, selectively neutral in the average population and year. These findings imply that spatial variation in selection may represent a primary mechanism maintaining animal personalities, likely promoting the evolution of local adaptation, phenotype-dependent dispersal, and nonrandom settlement. Selection also varied within populations among years, which may counteract local adaptation. Our study underlines the importance of combining multiple spatiotemporal scales in the study of behavioral adaptation.Damage to the microtubule lattice, which serves as a rigid cytoskeletal backbone for the axon, is a hallmark mechanical initiator of pathophysiology after concussion. Understanding the mechanical stress transfer from the brain tissue to the axonal cytoskeleton is essential to determine the microtubule lattice's vulnerability to mechanical injury. selleck chemical Here, we develop an ultrastructural model of the axon's cytoskeletal architecture to identify the components involved in the dynamic load transfer during injury. Corroborative in vivo studies were performed using a gyrencephalic swine model of concussion via single and repetitive head rotational acceleration. Computational analysis of the load transfer mechanism demonstrates that the myelin sheath and the actin/spectrin cortex play a significant role in effectively shielding the microtubules from tissue stress. We derive failure maps in the space spanned by tissue stress and stress rate to identify physiological conditions in which the microtubule lattice can rupture. We establish that a softer axonal cortex leads to a higher susceptibility of the microtubules to failure. Immunohistochemical examination of tissue from the swine model of single and repetitive concussion confirms the presence of postinjury spectrin degradation, with more extensive pathology observed following repetitive injury. Because the degradation of myelin and spectrin occurs over weeks following the first injury, we show that softening of the myelin layer and axonal cortex exposes the microtubules to higher stress during repeated incidences of traumatic brain injuries. Our predictions explain how mechanical injury predisposes axons to exacerbated responses to repeated injuries, as observed in vitro and in vivo.Circadian clocks regulate ∼24-h oscillations in gene expression, behavior, and physiology. While the genetic and molecular mechanisms of circadian rhythms are well characterized, what remains poorly understood are the intracellular dynamics of circadian clock components and how they affect circadian rhythms. Here, we elucidate how spatiotemporal organization and dynamics of core clock proteins and genes affect circadian rhythms in Drosophila clock neurons. Using high-resolution imaging and DNA-fluorescence in situ hybridization techniques, we demonstrate that Drosophila clock proteins (PERIOD and CLOCK) are organized into a few discrete foci at the nuclear envelope during the circadian repression phase and play an important role in the subnuclear localization of core clock genes to control circadian rhythms. Specifically, we show that core clock genes, period and timeless, are positioned close to the nuclear periphery by the PERIOD protein specifically during the repression phase, suggesting that subnuclear localization of core clock genes might play a key role in their rhythmic gene expression. Finally, we show that loss of Lamin B receptor, a nuclear envelope protein, leads to disruption of PER foci and per gene peripheral localization and results in circadian rhythm defects. These results demonstrate that clock proteins play a hitherto unexpected role in the subnuclear reorganization of core clock genes to control circadian rhythms, revealing how clocks function at the subcellular level. Our results further suggest that clock protein foci might regulate dynamic clustering and spatial reorganization of clock-regulated genes over the repression phase to control circadian rhythms in behavior and physiology.With the advent of machine learning and its overarching pervasiveness it is imperative to devise ways to represent large datasets efficiently while distilling intrinsic features necessary for subsequent analysis. The primary workhorse used in data dimensionality reduction and feature extraction has been the matrix singular value decomposition (SVD), which presupposes that data have been arranged in matrix format. A primary goal in this study is to show that high-dimensional datasets are more compressible when treated as tensors (i.e., multiway arrays) and compressed via tensor-SVDs under the tensor-tensor product constructs and its generalizations. We begin by proving Eckart-Young optimality results for families of tensor-SVDs under two different truncation strategies. Since such optimality properties can be proven in both matrix and tensor-based algebras, a fundamental question arises Does the tensor construct subsume the matrix construct in terms of representation efficiency? The answer is positive, as proven by showing that a tensor-tensor representation of an equal dimensional spanning space can be superior to its matrix counterpart.