Amplitude/frequency of spontaneous mEPSC correlates to the degree of long-term depression in the CA1 region of the hippocampal slice

Prior synaptic or cellular activity influences degree or threshold for subsequent induction of synaptic plasticity, a process known as metaplasticity. Thus, the continual synaptic activity, spontaneous miniature excitatory synaptic current (mEPSC) may correlate to the induction of long-teen depression (LTD). Here, we recorded whole-cell EPSC and mEPSC alternately in the Schaffer-CA1 synapses in brain slice of young rats, and found that this recording configuration affected neither EPSC nor mEPSC. Low frequency stimulation (LFS) induced variable magnitudes of LTD. Remarkably, larger magnitudes of LTD were significantly correlated to smaller amplitude/lower frequency of the basal mEPSC. Furthermore, under the conditions reduced amplitude/frequency of the basal mEPSC by exposure to behavioral stress immediately before slice preparation or low concentration of calcium in bath solution, the magnitudes of LTD were still inversely correlated to mEPSC amplitude/frequency. These new findings suggest that spontaneous mEPSC may reflect functional and/or structural aspects of the synapses, the synaptic history ongoing metaplasticity. (C) 2005 Elsevier B.V. All rights reserved.

Physical properties of a hybrid and a nanohybrid dental light-cured resin composite.

This work was aimed at the study of some physical properties of two current light-cured dental resin composites, Rok (hybrid) and Ice (nanohydrid). As filler they both contain strontium aluminosilicate particles, however, with different size distribution, 40 nm-2.5 mum for Rok and 10 nm-1 mum for Ice. The resin matrix of Rok consists of UDMA, that of Ice of UDMA, Bis-EMA and TEGDMA. Degree of conversion was determined by FT-IR analysis. The flexural strength and modulus were measured using a three-point bending set-up according to the ISO-4049 specification. Sorption, solubility and volumetric change were measured after storage of composites in water or ethanol/water (75 vol%) for 1 day, 7 or 30 days. Thermogravimetric analysis was performed in air and nitrogen atmosphere from 30 to 700 degrees C. Surface roughness and morphology of the composites was studied by atomic force microscopy (AFM). The degree of conversion was found to be 56.9% for Rok and 61.0% for Ice. The flexural strength of Rok does not significantly differ from that of Ice, while the flexural modulus of Rok is higher than that of Ice. The flexural strengths of Rok and Ice did not show any significant change after immersion in water or ethanol solution for 30 days. The flexural modulus of Rok and Ice did not show any significant change either after immersion in water for 30 days, while it decreased significantly, even after 1 day immersion, in ethanol solution. Ice sorbed a higher amount of water and ethanol solution than Rok and showed a higher volume increase. Thermogravimetric analysis showed that Rok contains about 80 wt% inorganic filler and Ice about 75 wt%.

Degree of fourth-order coherence by double Hanbury Brown-Twiss detections

Photon quantum statistics of light can be shown by the high-order coherence. The fourth-order coherences of various quantum states including Pock states, coherent states, thermal states and squeezed vacuum states are investigated based on a double Banbury Brown Twiss (HBT) scheme. The analytical results are obtained by taking the overall efficiency and background into account.

Entanglement degree of electron pairs out of Andreev reflection

We study the entanglement degree of electron pairs emitted from an s-wave Superconductor, which Couples to two normal leads via a single-level quantum dot. Within the framework of scattering matrix theory. the concurrence is used to quantify the entanglement. And the result shows that the entanglement degree is generally influenced by the initial separation of the two electrons in a Cooper pair and the normal transmission eigenvalues T-1, T-2. But it is only determined by the eigenvalues in the tunnelling limit, T-1. T-2 << 1, what is more. it is measurable. (C) 2008 Elsevier B.V. All rights reserved.

A study of the degree of relaxation of AlGaN epilayers on GaN template

The strain state of 570nm AlXGa1-xN layers grown on 600nm GaN template by metal organic chemical vapor deposition was studied using Rutherford backscattering (RBS)/channeling and triple-axis X-ray diffraction measurements. The results showed that the degree of relaxation (R) of AlxGa1-xN layers increased almost linearly when x less than or equal to 0.42 and reached to 70% when x = 0.42. Above 0.42, the value of R varied slowly and AI(x)Ga(1-x)N layers almost full relaxed when x = 1 (AIN). In this work the underlying GaN layer was in compressive strain, which resulted in the reduction of lattice misfit between GaN and AlxGa1-xN, and a 570nm AlxGa1-xN layer with the composition of about 0.16 might be grown on GaN coherently from the extrapolation. The different shape of (0004) diffraction peak was discussed to be related to the relaxation. (C) 2004 Elsevier B.V. All rights reserved.

Geometry property of multi-degree of freedom neurons

In this paper, we study a problem of geometric inequalities for a Multi-degree of Freedom Neurons. Some new geometric inequalities for a Multi-degree of Freedom Neurons are established. As special cases, some known inequalities are deduced.

Research on multi-degree-of-freedom neurons with weighted graphs

In this paper, we redefine the sample points set in the feature space from the point of view of weighted graph and propose a new covering model - Multi-Degree-of-Freedorn Neurons (MDFN). Base on this model, we describe a geometric learning algorithm with 3-degree-of-freedom neurons. It identifies the sample points secs topological character in the feature space, which is different from the traditional "separation" method. Experiment results demonstrates the general superiority of this algorithm over the traditional PCA+NN algorithm in terms of efficiency and accuracy.

Face recognition based on PCA and multi-degree of freedom neurons

An algorithm of PCA face recognition based on Multi-degree of Freedom Neurons theory is proposed, which based on the sample sets' topological character in the feature space which is different from "classification". Compare with the traditional PCA+NN algorithm, experiments prove its efficiency.

Research on multi-degree-of-freedom neurons with weighted graphs

In this paper, we redefine the sample points set in the feature space from the point of view of weighted graph and propose a new covering model - Multi-Degree-of-Freedorn Neurons (MDFN). Base on this model, we describe a geometric learning algorithm with 3-degree-of-freedom neurons. It identifies the sample points secs topological character in the feature space, which is different from the traditional "separation" method. Experiment results demonstrates the general superiority of this algorithm over the traditional PCA+NN algorithm in terms of efficiency and accuracy.