Ethanol and Phencyclidine Interact with Respect to Nucleus Accumbens Dopamine Release: Differential Effects of Administration Order and Pretreatment Protocol

Executive dysfunction is a common symptom among alcohol-dependent individuals. Phencyclidine (PCP) injection induces dysfunction in the prefrontal cortex of animals but little is known about how PCP affects the response to ethanol. Using the in vivo microdialysis technique in male Wistar rats, we investigated how systemic injection of 5 mg/kg PCP would affect the dopamine release induced by local infusion of 300 mM ethanol into the nucleus accumbens. PCP given 60 min before ethanol entirely blocked ethanol-induced dopamine release. However, when ethanol was administered 60 min before PCP, both drugs induced dopamine release and PCP's effect was potentiated by ethanol (180% increase vs 150%). To test the role of prefrontal cortex dysfunction in ethanol reinforcement, animals were pretreated for 5 days with 2.58 mg/kg PCP according to previously used ‘PFC hypofunction protocols’. This, however, did not change the relative response to PCP or ethanol compared to saline-treated controls. qPCR illustrated that this low PCP dose did not significantly change expression of glucose transporters Glut1 (SLC2A1) or Glut3 (SLC2A3), monocarboxylate transporter MCT2 (SLC16A7), glutamate transporters GLT-1 (SLC1A2) or GLAST (SLC1A3), the immediate early gene Arc (Arg3.1) or GABAergic neuron markers GAT-1 (SLC6A1) and parvalbumin. Therefore, we concluded that PCP at a dose of 2.58 mg/kg for 5 days did not induce hypofunction in Wistar rats. However, PCP and ethanol do have overlapping mechanisms of action and these drugs differentially affect mesolimbic dopaminergic transmission depending on the order of administration

Numerical computation for vibration characteristics of long-span bridges with considering vehicle-wind coupling excitations based on finite element and neural network models

CA (Cellular Automaton) model was applied to the simulation of random traffic flow to develop a model considering the randomness of traffic flow and apply it to wind-vehicle-bridge coupling vibration. Finite element and neural network models were adopted respectively to numerically compute the vibration characteristics of bridges under wind and vehicle loads, verify the correctness of model. Subspace iteration method was used for the modal analysis of bridges. Natural frequencies of the top 8 orders were 0.21 Hz, 0.27 Hz, 0.36 Hz, 0.45 Hz, 0.56 Hz, 0.66 Hz, 0.87 Hz and 1.02 Hz respectively. The vibration frequency of the long-span bridge was consistent with the vibration characteristics of large-scale complex structures. Natural modes mainly reflected the torsion and bending of main beam and the swinging vibration of side and main towers. Fluctuation wind time-history presented periodic characteristics. The maximum and minimum values of fluctuation wind were about 20 m/s and –20 m/s respectively. The target and simulation values of power spectral density of wind speed were basically the same in change trend, which indicated that the fluctuation wind time-history computed in this paper was reliable. The model of dense traffic flow based on CA more truly described the running status like accelerating, decelerating and changing lanes of vehicles on the bridge, also contained the density information of vehicles and more truly reflected traffic characteristics. Vibration accelerations of the long-span bridge were symmetrically distributed. Vibration acceleration of central position in the left main span was the largest and near 50 cm/s2; vibration acceleration on the main tower was the smallest. The curve of vibration displacement with considering wind loads presented some fluctuations, while the vibration displacement of bridges without considering wind loads was very smooth. In addition, the amplitude of vibration displacement without considering wind loads moved laterally towards the left compared with that with considering wind loads. Therefore, wind loads must be considered when the vibration characteristics of the long-span bridge were computed. Otherwise, the accuracy of computational results would be reduced. It only took 0.5 hours to use neural network to predict the vibration acceleration of the long-span bridge. In the case of the same computer performance, it took 5 hours to use finite element model to predict the vibration acceleration of the long-span bridge. The advantage of neural network model in predicting the performance of large-scale complex structures like a long-span bridge could be obviously found. In the future, we will consider using neural network model to systematically study and optimize the long-span bridge

Research on the computational method of vibration impact coefficient for the long-span bridge and its application in engineering

To compute vibration impact coefficient at each part of the long-span bridge more accurately, this paper proposed a computational method based on vehicle-bridge coupling vibration. Firstly, the general equations of vehicle-bridge coupling vibration were derived based on the standard fatigue vehicle and multi-scale model of bridges. Secondly, the corresponding program of vehicle-bridge coupling vibration was designed. Thirdly, the computational method of vibration impact coefficient for the long-span bridge was introduced and obtained. The proposed computation method of vibration impact coefficient based on vehicle-bridge coupling vibration was finally verified by the corresponding experiment. They were consistent with each other, and the computational method was reliable and can be used to analyze the bridge. Based on the verified method, a lot of influence factors on vibration impact coefficient were analyzed. As a result, we can obtain a bridge with the smallest vibration impact coefficient. Finally, the remaining life of bridges was computed and evaluated based on the smallest vibration impact coefficient

Poly[bis­[8-ethyl-5-oxo-2-(piperazin-1-yl)-5,8-dihydro­pyrido[2,3-d]pyrimidine-6-carboxyl­ato]cadmium]

The title layered coordination polymer, [Cd(C14H16N5O3)2]n or [Cd(ppa)2]n, where ppa is 8-ethyl-5-oxo-2-(piperazin-1-yl)-5,8-dihydro­pyrido[2,3-d]pyrimidine-6-carboxyl­ate, was syn­thesized under hydro­thermal conditions. The CdII atom (site symmetry 2) exhibits a distorted cis-CdN2O4 octa­hedral geometry defined by two N-monodentate and two O,O′-bidentate ppa monoanions. The extended two-dimensional structure resulting from the bridging ppa species is a grid lying parallel to (001). An N—H⋯O hydrogen bond helps to establish the crystal packing