Neurabin Contributes to Hippocampal Long-Term Potentiation and Contextual Fear Memory

Neurabin is a scaffolding protein that interacts with actin and protein phosphatase-1. Highly enriched in the dendritic spine, neurabin is important for spine morphogenesis and synaptic formation. However, less is known about the role of neurabin in hippocampal plasticity and its possible effect on behavioral functions. Using neurabin knockout (KO) mice, here we studied the function of neurabin in hippocampal synaptic transmission, plasticity and behavioral memory. We demonstrated that neurabin KO mice showed a deficit in contextual fear memory but not auditory fear memory. Whole-cell patch clamp recordings in the hippocampal CA1 neurons showed that long-term potentiation (LTP) was significantly reduced, whereas long-term depression (LTD) was unaltered in neurabin KO mice. Moreover, increased AMPA receptor but not NMDA receptor-mediated synaptic transmission was found in neurabin KO mice, and is accompanied by decreased phosphorylation of GluR1 at the PKA site (Ser845) but no change at the CaMKII/PKC site (Ser831). Pre-conditioning with LTD induction rescued the following LTP in neurabin KO mice, suggesting the loss of LTP may be due to the saturated synaptic transmission. Our results indicate that neurabin regulates contextual fear memory and LTP in hippocampal CA1 pyramidal neurons

Cross-Correlation of Diffuse Synchrotron and Large-Scale Structures

We explore for the first time the method of cross-correlation of radio synchrotron emission and tracers of large-scale structure in order to detect the diffuse IGM/WHIM. We performed a cross-correlation of a 34 x 34 degree area of 2MASS galaxies for two redshift slices (0.03 < z < 0.04 and 0.06 < z < 0.07) with the corresponding region of the 1.4 GHz Bonn survey. For this analysis, we assumed that the synchrotron surface brightness is linearly proportional to surface density of galaxies. We also sampled the cross-correlation function using 24 distant fields of the same size from the Bonn survey, to better assess the noise properties. Though we obtained a null result, we found that by adding a signal weighted by the 2MASS image with a filament (peak) surface brightness of 1 (7) mK and 7 (49) mK would produce a 3 sigma positive correlation for the 0.03 < z < 0.04 and 0.06 < z < 0.07 redshift slices respectively. These detection thresholds correspond to minimum energy magnetic fields as low as 0.2 microG, close to some theoretical expectations for filament field values. This injected signal is also below the rms noise of the Bonn survey, and demonstrates the power of this technique and its utility for upcoming sensitive continuum surveys such as GALFACTS at Arecibo and those planned with the Murchison Widefield Array (MWA).Comment: 6 pages, 4 figures, accepted to MNRA

Electrochemical Reduction of CO2 on Compositionally Variant Au-Pt Bimetallic Thin Films

The electrocatalytic reduction of CO2 on Au-Pt bimetallic catalysts with different compositions was evaluated, offering a platform for uncovering the correlation between the catalytic activity and the surface composition of bimetallic electrocatalysts. The Au-Pt alloy films were synthesized by a magnetron sputtering co-deposition technique with tunable composition. It was found that the syngas ratio (CO:H2) on the Au-Pt films is able to be tuned by systematically controlling the binary composition. This tunable catalytic selectivity is attributed to the variation of binding strength of COOH and CO intermediates, influenced by the surface electronic structure (d-band center energy) which is linked to the surface composition of the bimetallic films. Notably, a gradual shift of the d-band center away from the Fermi level was observed with increasing Au content, which correspondingly reduces the binding strength of the COOH and CO intermediates, leading to the distinct catalytic activity for the reduction of CO2 on the compositionally variant Au-Pt bimetallic films. In addition, the formation of formic acid in the bimetallic systems at reduced overpotentials and higher yield indicates that synergistic effects can facilitate reaction pathways for products that are not accessible with the individual components.</p

The sleep-inducing lipid oleamide deconvolutes gap junction communication and calcium wave transmission in glial cells.

Oleamide is a sleep-inducing lipid originally isolated from the cerebrospinal fluid of sleep-deprived cats. Oleamide was found to potently and selectively inactivate gap junction-mediated communication between rat glial cells. In contrast, oleamide had no effect on mechanically stimulated calcium wave transmission in this same cell type. Other chemical compounds traditionally used as inhibitors of gap junctional communication, like heptanol and 18beta-glycyrrhetinic acid, blocked not only gap junctional communication but also intercellular calcium signaling. Given the central role for intercellular small molecule and electrical signaling in central nervous system function, oleamide- induced inactivation of glial cell gap junction channels may serve to regulate communication between brain cells, and in doing so, may influence higher order neuronal events like sleep induction

An assessment of precipitation adjustment and feedback computation methods

The precipitation adjustment and feedback framework is a useful tool for understanding global and regional precipitation changes. However, there is no definitive method for making the decomposition. In this study we highlight important differences which arise in results due to methodological choices. The responses to five different forcing agents (CO2, CH4, SO4, black carbon, and solar insolation) are analyzed using global climate model simulations. Three decomposition methods are compared: using fixed sea surface temperature experiments (fSST), regressing transient climate change after an abrupt forcing (regression), and separating based on timescale using the first year of coupled simulations (YR1). The YR1 method is found to incorporate significant SST-driven feedbacks into the adjustment and is therefore not suitable for making the decomposition. Globally, the regression and fSST methods produce generally consistent results; however, the regression values are dependent on the number of years analyzed and have considerably larger uncertainties. Regionally, there are substantial differences between methods. The pattern of change calculated using regression reverses sign in many regions as the number of years analyzed increases. This makes it difficult to establish what effects are included in the decomposition. The fSST method provides a more clear-cut separation in terms of what physical drivers are included in each component. The fSST results are less affected by methodological choices and exhibit much less variability. We find that the precipitation adjustment is weakly affected by the choice of SST climatology

Optimal mathematical programming and variable neighborhood search for k-modes categorical data clustering

The conventional k-modes algorithm and its variants have been extensively used for categorical data clustering. However, these algorithms have some drawbacks, e.g., they can be trapped into local optima and sensitive to initial clusters/modes. Our numerical experiments even showed that the k-modes algorithm could not identify the optimal clustering results for some special datasets regardless the selection of the initial centers. In this paper, we developed an integer linear programming (ILP) approach for the k-modes clustering, which is independent to the initial solution and can obtain directly the optimal results for small-sized datasets. We also developed a heuristic algorithm that implements iterative partial optimization in the ILP approach based on a framework of variable neighborhood search, known as IPO-ILP-VNS, to search for near-optimal results of medium and large sized datasets with controlled computing time. Experiments on 38 datasets, including 27 synthesized small datasets and 11 known benchmark datasets from the UCI site were carried out to test the proposed ILP approach and the IPO-ILP-VNS algorithm. The experimental results outperformed the conventional and other existing enhanced k-modes algorithms in literature, updated 9 of the UCI benchmark datasets with new and improved results

Explanation of the Colour Change in Alexandrites.

Alexandrites are remarkable and rare gemstones. They display an extraordinary colour change according to the ambient lighting, from emerald green in daylight to ruby red in incandescent light from tungsten lamps or candles. While this colour change has been correctly attributed to chromium impurities and their absorption band in the yellow region of the visible light spectrum, no adequate explanation of the mechanism has been given. Here, the alexandrite effect is fully explained by considering the von Kries model of the human colour constancy mechanism. This implies that our colour constancy mechanism is real (objective) and primarily attuned to correct for the colour temperature of black-body illuminants