Development of a UAV-mounted Light Source for Fluorescence Detector Calibration of the Telescope Array Experiment

ArticleThe Physical Society of Japanjournal articl

Maintenance of GLUT4 expression in smooth muscle prevents hypertension‐induced changes in vascular reactivity

Previous studies have shown that expression of GLUT4 is decreased in arterial smooth muscle of hypertensive rats and mice and that total body overexpression of GLUT4 in mice prevents enhanced arterial reactivity in hypertension. To demonstrate that the effect of GLUT4 overexpression on vascular responses is dependent on vascular smooth muscle GLUT4 rather than on some systemic effect we developed and tested smooth‐muscle‐specific GLUT4 transgenic mice (SMG4). When made hypertensive with angiotensin II, both wild‐type and SMG4 mice exhibited similarly increased systolic blood pressure. Responsiveness to phenylephrine, serotonin, and prostaglandin F2α was significantly increased in endothelium‐intact aortic rings from hypertensive wild‐type mice but not in aortae of SMG4 mice. Inhibition of Rho‐kinase equally reduced serotonin‐stimulated contractility in aortae of hypertensive wild‐type and SMG4‐mice. In addition, acetylcholine‐stimulated relaxation was significantly decreased in aortic rings of hypertensive wild‐type mice, but not in rings of SMG4 mice. Inhibition of either prostacylin receptors or cyclooxygenase‐2 reduced relaxation in rings of hypertensive SMG4 mice. Inhibition of cyclooxygenase‐2 had no effect on relaxation in rings of hypertensive wild‐type mice. Cyclooxygenase‐2 protein expression was decreased in hypertensive wild‐type aortae but not in hypertensive SMG4 aortae compared to nonhypertensive controls. Our results demonstrate that smooth muscle expression of GLUT4 exerts a major effect on smooth muscle contractile responses and endothelium‐dependent vasorelaxation and that normal expression of GLUT4 in vascular smooth muscle is required for appropriate smooth muscle and endothelial responses.e12299In the smooth muscle of aortae of hypertensive mice, expression of GLUT4 is decreased. Maintenance of aortic smooth muscle GLUT4 expression prevents hypertension‐mediated changes in vasomotor response. These effects include decreasing/preventing endothelial dysfunction.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/110755/1/phy212299.pd

Glacial fjord environment and ecosystem reconstructed from sediments deposited in Bowdoin Fjord, northwestern Greenland.

The Tenth Symposium on Polar Science/Ordinary sessions: [OG] Polar Geosciences, Wed. 4 Dec. / 3F Seminar room, National Institute of Polar Researc

Magnetic anisotropy driven by ligand in 4d transition metal oxide SrRuO3

The origin of magnetic anisotropy in magnetic compounds is a longstanding issue in solid state physics and nonmagnetic ligand ions are considered to contribute little to magnetic anisotropy. Here, we introduce the concept of ligand driven magnetic anisotropy in a complex transition-metal oxide. We conducted X ray absorption and X ray magnetic circular dichroism spectroscopies at the Ru and O edges in the 4d ferromagnetic metal SrRuO3. Systematic variation of the sample thickness in the range below 10 nm allowed us to control the localization of Ru 4d t2g states, which affects the magnetic coupling between the Ru and O ions. We found that the orbital magnetization of the ligand induced via hybridization with the Ru 4d orbital determines the magnetic anisotropy in SrRuO3

Higher-order modulations in the skyrmion-lattice phase of Cu2_2OSeO3_3

Using small angle neutron scattering, we have investigated higher-order peaks in the skyrmion-lattice phase of Cu$_2$OSeO$_3$, in which two different skyrmion lattices, SkX1 and SkX2, are known to form. For each skyrmion-lattice phase, we observed two sets of symmetrically inequivalent peaks at the higher-order-reflection positions with the indices $(110)$ and $(200)$. Under the condition where the SkX1 and SkX2 coexist, we confirmed the absence of the scattering at $\mathbf{Q}$ positions combining reflections from the two phases, indicating a significantly weak double-scattering component. Detailed analysis of the peak profile, as well as the temperature and magnetic-field dependence of the peak intensity, also supports the intrinsic higher-order modulation rather than the parasitic double scattering. The two higher-order modulations show contrasting magnetic-field dependence; the former $(110)$ increases as the field is increased, whereas the latter $(200)$ decreases. This indicates that, in Cu$_2$OSeO$_3$, skyrmions are weakly distorted, and the distortion is field-dependent in a way that the dominant higher-order modulation switches from $(110)$ to $(200)$ under field. Monte Carlo simulations under sweeping external magnetic field qualitatively reproduce the observed magnetic-field dependence, and suggests that the higher-order modulations correspond to the superlattices of weak swirlings appearing in the middle of the original triangular-latticed skyrmions.Comment: 13 pages, 14 figure

Task-adaptive physical reservoir computing

Reservoir computing is a neuromorphic architecture that may offer viable solutions to the growing energy costs of machine learning. In software-based machine learning, computing performance can be readily reconfigured to suit different computational tasks by tuning hyperparameters. This critical functionality is missing in 'physical' reservoir computing schemes that exploit nonlinear and history-dependent responses of physical systems for data processing. Here we overcome this issue with a 'task-adaptive' approach to physical reservoir computing. By leveraging a thermodynamical phase space to reconfigure key reservoir properties, we optimize computational performance across a diverse task set. We use the spin-wave spectra of the chiral magnet Cu2OSeO3 that hosts skyrmion, conical and helical magnetic phases, providing on-demand access to different computational reservoir responses. The task-adaptive approach is applicable to a wide variety of physical systems, which we show in other chiral magnets via above (and near) room-temperature demonstrations in Co8.5Zn8.5Mn3 (and FeGe)

Task-adaptive physical reservoir computing

Reservoir computing is a neuromorphic architecture that potentially offers viable solutions to the growing energy costs of machine learning. In software-based machine learning, neural network properties and performance can be readily reconfigured to suit different computational tasks by changing hyperparameters. This critical functionality is missing in ``physical" reservoir computing schemes that exploit nonlinear and history-dependent memory responses of physical systems for data processing. Here, we experimentally present a `task-adaptive' approach to physical reservoir computing, capable of reconfiguring key reservoir properties (nonlinearity, memory-capacity and complexity) to optimise computational performance across a broad range of tasks. As a model case of this, we use the temperature and magnetic-field controlled spin-wave response of Cu$_2$OSeO$_3$ that hosts skyrmion, conical and helical magnetic phases, providing on-demand access to a host of different physical reservoir responses. We quantify phase-tunable reservoir performance, characterise their properties and discuss the correlation between these in physical reservoirs. This task-adaptive approach overcomes key prior limitations of physical reservoirs, opening opportunities to apply thermodynamically stable and metastable phase control across a wide variety of physical reservoir systems, as we show its transferable nature using above(near)-room-temperature demonstration with Co$_{8.5}$Zn$_{8.5}$Mn$_{3}$ (FeGe).Comment: Main manuscript: 14 pages, 5 figures. Supplementary materials: 13 pages, 10 figure