The amount of astrocytic GABA positively correlates with the degree of tonic inhibition in hippocampal CA1 and cerebellum

A tonic form of synaptic inhibition occurs in discrete regions of the central nervous system and has an important role in controlling neuronal excitability. Recently, we reported that GABA present in astrocyte is the major source of tonic inhibition in cerebellum and that GABA is released through Bestrophin-1 channel by direct permeation. In this study, we screened for the presence of astrocytic GABA in various brain regions such as hippocampus, thalamus, hypothalamus and cerebellum using immunohistochemistry. We found that astrocytic GABA was present in the regions that were reported to show tonic inhibition. Because the existence of tonic inhibition in hippocampal CA1 is somewhat controversial, we compared the amount of astrocytic GABA and tonic inhibition between the hippocampal CA1 pyramidal cell layer and the cerebellar granule cell layer. Unlike cerebellar glial cells, hippocampal astrocytes did not contain GABA. The tonic inhibition was also much lower in the pyramidal neurons of hippocampal CA1 compared to the granule cells of cerebellum. Nevertheless, most of the hippocampal astrocytes expressed Bestrophin-1 channel. These data indicate that the absence of astrocytic GABA results in a low level of tonic inhibition in hippocampal CA1 region

Data-Driven Selection of Security Application Frameworks During Architectural Design

The selection of application frameworks is an important aspect of architectural design. Selection often requires satisficing, that is, searching a potentially large space of design alternatives until an acceptable solution is found. There is, however, little help for architects in selecting software frameworks. In this paper we investigate the criteria used by practicing software architects in selecting security frameworks. We also propose how information associated with some of the criteria that are important to architects can be obtained manually or in an automated way from online sources such as GitHub. Our ultimate goal is to identify measures associated with these criteria that can be helpful in providing support for architects to select software frameworks

Imiquimod enhances excitability of dorsal root ganglion neurons by inhibiting background (K2P) and voltage-gated (Kv1.1 and Kv1.2) potassium channels

<p>Abstract</p> <p>Background</p> <p>Imiquimod (IQ) is known as an agonist of Toll-like receptor 7 (TLR7) and is widely used to treat various infectious skin diseases. However, it causes severe itching sensation as its side effect. The precise mechanism of how IQ causes itching sensation is unknown. A recent report suggested a molecular target of IQ as TLR7 expressed in dorsal root ganglion (DRG) neurons. However, we recently proposed a TLR7-independent mechanism, in which the activation of TLR7 is not required for the action of IQ in DRG neurons. To resolve this controversy regarding the involvement of TLR7 and to address the exact molecular identity of itching sensation by IQ, we investigated the possible molecular target of IQ in DRG neurons.</p> <p>Findings</p> <p>When IQ was applied to DRG neurons, we observed an increase in action potential (AP) duration and membrane resistance both in wild type and TLR7-deficient mice. Based on these results, we tested whether the treatment of IQ has an effect on the activity of K⁺channels, K_v1.1 and K_v1.2 (voltage-gated K⁺channels) and TREK1 and TRAAK (K_2Pchannels). IQ effectively reduced the currents mediated by both K⁺channels in a dose-dependent manner, acting as an antagonist at TREK1 and TRAAK and as a partial antagonist at K_v1.1 and K_v1.2.</p> <p>Conclusions</p> <p>Our results demonstrate that IQ blocks the voltage-gated K⁺channels to increase AP duration and K_2Pchannels to increase membrane resistance, which are critical for the membrane excitability of DRG neurons. Therefore, we propose that IQ enhances the excitability of DRG neurons by blocking multiple potassium channels and causing pruritus.</p

Comprehensive understanding of cathodic and anodic polarization effects on stability of nanoscale oxygen electrode for reversible solid oxide cells

Whereas solid oxide cells (SOCs), which perform dual functions of power generation (fuel-cell mode) and energy storage (electrolysis mode) with high efficiency at high temperatures, are considered a potent candidate for future energy management systems, it is yet far from their practical use due to the fact that the stable long-term operations have not been achieved. Particularly, degradations of oxygen-electrode in the both electrolysis and fuel-cell operations are considered as the most imminent issues that should be overcome. Unfortunately, even the origins and mechanisms of degradation in the oxygen-electrode have not been clearly established due to the difficulties in precise assessments of microstructural/compositional changes of porous electrode, which is a typical form in actual solid oxide cells, and due to the diversities in operating conditions, electrode structure and material, fabrication history, and so on. We simultaneously investigated the degradation phenomena in electrolysis and fuel-cell operations for 540h using identical two half cells composed of a geometrically well-defined, nanoscale La0.6Sr0.4Co0.2Fe0.8O3-δ (LSCF) dense film with a thickness of ~ 70 nm on Ce0.9Gd0.1O2-δ electrolyte. Owing to the benefit of well-defined geometry of LSCF thin film, the microstructural/compositional changes in LSCF films were successfully analyzed in nanoscale, and the correlation between the components of electrochemical impedance and the major origins resulting in degradations was clarified. Furthermore, we suggest the most probable degradation mechanisms, and importantly, it is newly suggested that kinetic demixing/decomposition of LSCF, which is not readily observable in the typical porous-structured electrode, are highly probable to affect the both fuel-cell and electrolysis long-term degradations

First-time comparison between NO2 vertical columns from GEMS and Pandora measurements

The Geostationary Environmental Monitoring Spectrometer (GEMS) is a UV&ndash;visible spectrometer onboard the GEO-KOMPSAT-2B satellite launched into geostationary orbit in February 2020. To evaluate GEMS NO2 column data, comparison was carried out using NO2 vertical column density (VCD) measured using direct-sunlight observations by the Pandora spectrometer system at four sites in Seosan, South Korea, during November 2020 to January 2021. Correlation coefficients between GEMS and Pandora NO2 data at four sites ranged from 0.35 to 0.48, with root mean square errors (RMSEs) from 4.7 &times; 1015 molec. cm-2 to 5.5 &times; 1015 molec. cm-2 for cloud fraction (CF) &lt; 0.7. Higher correlation coefficients of 0.62&ndash;0.78 with lower RMSEs from 3.3 &times; 1015 molec. cm-2 to 4.3 &times; 1015 molec. cm-2 were found with CF &lt; 0.3, indicating the higher sensitivity of GEMS to atmospheric NO2 in less-cloudy conditions. Overall, GEMS NO2 column data tend to be lower than those of Pandora due to differences in representative spatial coverage, with a large negative bias under high-CF conditions. With correction for horizontal representativeness in Pandora measurement coverage, the correlation coefficients range from 0.69 to 0.81 with RMSEs from 3.2 &times; 1015 molec. cm-2 to 4.9 &times; 1015 molec. cm-2 were achieved for CF &lt; 0.3, showing the better correlation with the correction than that without the correction.</p

First-time comparison between NO2 vertical columns from Geostationary Environmental Monitoring Spectrometer (GEMS) and Pandora measurements

The Geostationary Environmental Monitoring Spectrometer (GEMS) is a UV-visible (UV-Vis) spectrometer on board the GEO-KOMPSAT-2B (Geostationary Korea Multi-Purpose Satellite 2B) satellite launched into a geostationary orbit in February 2020. To evaluate the GEMS NO2 total column data, a comparison was carried out using the NO2 vertical column density (VCD) that measured direct sunlight using the Pandora spectrometer system at four sites in Seosan, South Korea, from November 2020 to January 2021. Correlation coefficients between GEMS and Pandora NO2 data at four sites ranged from 0.35 to 0.48, with root mean square errors (RMSEs) from 4.7×1015 to 5.5×1015 molec. cm−2 for a cloud fraction (CF) &lt;0.7. Higher correlation coefficients of 0.62–0.78 with lower RMSEs from 3.3×1015 to 5.0×1015 molec. cm−2 were found with CF &lt;0.3, indicating the higher sensitivity of GEMS to atmospheric NO2 in less cloudy conditions. Overall, the GEMS NO2 total column data tended to be lower than the Pandora data, owing to differences in the representative spatial coverage, with a large negative bias under high CF conditions. With a correction for horizontal representativeness in the Pandora measurement coverage, correlation coefficients ranging from 0.69 to 0.81, with RMSEs from 3.2×1015 to 4.9×1015 molec. cm−2, were achieved for CF &lt;0.3, showing a better correlation with the correction than without the correction.</p

Chapter 6 MARINE BIODIVERSITY IN KOREA: A REVIEW OF MACROZOOBENTHIC ASSEMBLAGES, THEIR DISTRIBUTIONS, AND LONG-TERM COMMUNITY CHANGES FROM HUMAN IMPACTS

oceanography, climate change, reefs, marine science, marine conservation, marine researc

The geometric evolution of aortic dissections: Predicting surgical success using fluctuations in integrated Gaussian curvature

Clinical imaging modalities are a mainstay of modern disease management, but the full utilization of imaging-based data remains elusive. Aortic disease is defined by anatomic scalars quantifying aortic size, even though aortic disease progression initiates complex shape changes. We present an imaging-based geometric descriptor, inspired by fundamental ideas from topology and soft-matter physics that captures dynamic shape evolution. The aorta is reduced to a two-dimensional mathematical surface in space whose geometry is fully characterized by the local principal curvatures. Disease causes deviation from the smooth bent cylindrical shape of normal aortas, leading to a family of highly heterogeneous surfaces of varying shapes and sizes. To deconvolute changes in shape from size, the shape is characterized using integrated Gaussian curvature or total curvature. The fluctuation in total curvature (δK) across aortic surfaces captures heterogeneous morphologic evolution by characterizing local shape changes. We discover that aortic morphology evolves with a power-law defined behavior with rapidly increasing δK forming the hallmark of aortic disease. Divergent δK is seen for highly diseased aortas indicative of impending topologic catastrophe or aortic rupture. We also show that aortic size (surface area or enclosed aortic volume) scales as a generalized cylinder for all shapes. Classification accuracy for predicting aortic disease state (normal, diseased with successful surgery, and diseased with failed surgical outcomes) is 92.8±1.7%. The analysis of δK can be applied on any three-dimensional geometric structure and thus may be extended to other clinical problems of characterizing disease through captured anatomic changes