Glucocorticoid receptor and protein/RNA synthesis-dependent mechanisms underlie the control of synaptic plasticity by stress

Learning and memory are exquisitely sensitive to behavioral stress, but the underlying mechanisms are still poorly understood. Because activity-dependent persistent changes in synaptic strength are believed to mediate memory processes in brain areas such as the hippocampus we have examined the means by which stress affects synaptic plasticity in the CA1 region of the hippocampus of anesthetized rats, Inescapable behavioral stress (placement on an elevated platform for 30 min) switched the direction of plasticity, favoring low frequency stimulation-induced decreases in synaptic transmission (long-term depression, LTD), and opposing the induction of long-term potentiation by high frequency stimulation, We have discovered that glucocorticoid receptor activation mediates these effects of stress on LTD and longterm potentiation in a protein synthesis-dependent manner because they were prevented by the glucocorticoid receptor antagonist RU 38486 and the protein synthesis inhibitor emetine. Consistent with this, the ability of exogenously applied corticosterone in non-stressed rats to mimic the effects of stress on synaptic plasticity was also blocked by these agents, The enablement of low frequency stimulation-induced LTD by both stress and exogenous corticosterone was also blocked by the transcription inhibitor actinomycin D, Thus, naturally occurring synaptic plasticity is liable to be reversed in stressful situations via glucocorticoid receptor activation and mechanisms dependent on the synthesis of new protein and RNA, This indicates that the modulation of hippocampus-mediated learning by acute inescapable stress requires glucocorticoid receptor-dependent initiation of transcription and translation.

Interface Layer Effect On The Stress Distribution Of A Wafer-Bonded Bilayer Structure

The interface layer plays an important role in stress transfer in composite structures. However, many interface layer properties such as the modulus, thickness, and uniformity are difficult to determine. The model developed in this article links the influence of the interface layer on the normal stress distribution along the layer thickness with the layer surface morphology before bonding. By doing so, a new method of determining the interfacial parameter(s) is suggested. The effects of the layer thickness and the surface roughness before bonding on the normal stress distribution and its depth profile are also discussed. For ideal interface case with no interfacial shear stress, the normal stress distribution pattern can only be monotonically decreased from the interface. Due to the presence of interfacial shear stress, the normal stress distribution is much more complex, and varies dramatically with changes in the properties of the interface layer, or the dimensions of the bonding layers. The consequence of this dramatic stress field change, such as the shift of the maximum stress from the interface is also addressed. The size-dependent stress distribution in the thickness direction due to the interface layer effect is presented. When the interfacial shear stress is reduced to zero, the model presented in this article is also demonstrated to have the same normal stress distribution as obtained by the previous model, which does not consider the interface layer effect.

保重心脉冲压缩滤波及其在速度自动拾取中的应用

To pick velocity automatically is not only helpful to improve the efficiency of seismic data process, but also to provide quickly the initial velocity for prestack depth migration. In this thesis, we use the Viterbi algorithm to do automatic picking, but the velocity picked usually is immoderate. By thorough study and analysis, we think that the Viterbi algorithm has the function to do quickly and effectually automatic picking, but the data provided for picking maybe not continuous on derivative of its curved surface, viz., the curved face on velocity spectrum is not slick. Therefore, the velocity picked may include irrational velocity information. To solve the problem above, we develop a new method to filter signal by performing nonlinear transformation of coordinate and filter of function. Here, we call it as Gravity Center Preserved Pulse Compressed Filter (GCPPCF). The main idea to perform the GCPPCF as follows: separating a curve, such as a pulse, to several subsection, calculating the gravity center (coordinate displacement), and then assign the value (density) on the subsection to gravity center. When gravity center departure away from center of its subsection, the value assigned to gravity center is smaller than the actual one, but non other than gravity center anastomoses fully with its subsection center, the assigned value equal to the actual one. By doing so, the curve shape under new coordinate breadthwise narrows down compare to its original one. It is a process of nonlinear transformation of coordinate, due to gravity center changing with the shape of subsection. Furthermore, the gravity function is filter one, because it is a cause of filtering that the value assigned from subsection center to gravity center is obtained by calculating its weight mean of subsetion function. In addition, the filter has the properties of the adaptive time delay changed filter, owing to the weight coefficient used for weight mean also changes with the shape of subsection. In this thesis, the Viterbi algorithm inducted, being applied to auto pick the stack velocity, makes the rule to integral the max velocity spectrum ("energy group") forward and to get the optimal solution in recursion backward. It is a convenient tool to pick automatically velocity. The GCPPCF above not only can be used to preserve the position of peak value and compress the velocity spectrum, but also can be used as adaptive time delay changed filter to smooth object curved line or curved face. We apply it to smooth variable of sequence observed to get a favourable source data ta provide for achieving the final exact resolution. If there is no the adaptive time delay-changed filter to perform optimization, we can't get a finer source data and also can't valid velocity information, moreover, if there is no the Viterbi algorithm to do shortcut searching, we can't pick velocity automatically. Accordingly, combination of both of algorithm is to make an effective method to do automatic picking. We apply the method of automatic picking velocity to do velocity analysis of the wavefield extrapolated. The results calculated show that the imaging effect of deep layer with the wavefield extrapolated was improved dominantly. The GCPPCF above has achieved a good effect in application. It not only can be used to optimize and smooth velocity spectrum, but also can be used to perform a correlated process for other type of signal. The method of automatic picking velocity developed in this thesis has obtained favorable result by applying it to calculate single model, complicated model (Marmousi model) and also the practical data. The results show that it not only has feasibility, but also practicability.

Avoidant Learning Ability in Free Flying Housefly (Aldrichina grahami)by Electric Shock

下载PDF阅读器研究证实:蜜蜂和果蝇具有良好的学习记忆能力.利用自主改良的研究装置对另一种具有强大生存本能的双翅目昆虫--巨尾阿丽蝇(Aldrichina grahami)在自由状态下电击同避学习能力进行研究.结果表明,巨尾阿丽蝇具有良好的学习记忆能力,因为当刺激电压范围为5V到45V时,观察到巨尾阿丽蝇有显著的回避电刺激行为,而当电压达到60V时会受到明显伤害.由此推测,巨尾阿丽蝇适合作为神经系统研究的动物模型.该实验所采用的实验范例较以往有所改进,适合作为自由状态下研究昆虫的工具.

Effects of exposure to 50 Hz magnetic field of 1 mT on the performance of detour learning task by chicks

In the present study, we examined the effects of exposure to an extremely low-frequency magnetic field of 1 mT intensity on learning and memory in Lohmann brown domestic chicks using detour learning task. These results show that 20 h/day exposure to a low