The Role of Ca2+/Calmodulin Dependent Protein Kinase II Alpha in Group 1 Metabotropic Glutamate Receptor Endocytosis and Signalling

Group 1 metabotropic glutamate receptors (mGluR1 and mGluR5) are G-protein coupled receptors (GPCRs) activated by glutamate. mGluR1/5 couples to Gαq/11 and releases Ca2+ from the endoplasmic reticulum. Ca2+/calmodulin-dependent protein kinase II alpha (CaMKIIα) can be activated by Gαq/11-mediated Ca2+ release through binding of Ca2+/calmodulin. Results from a proteomic screen identified CaMKII as a novel mGluR-interacting protein. Therefore, we hypothesized that CaMKIIα associates with group 1 mGluRs and this association alters mGluR1/5 signalling and internalization. Firstly, we demonstrated the novel association between CaMKIIα and mGluR1/5 by co-immunoprecipitation of transiently transfected proteins in HEK293 cells and of endogenous proteins in mouse hippocampal tissue. Next, we showed that the second intracellular loop of the mGluR1a receptor is sufficient for this association. Furthermore, CaMKIIα significantly enhances agonist-induced internalization of group 1 mGluRs. Yet, it does not appear that CaMKIIα plays a significant role in receptor signalling by either ERK1/2 phosphorylation or inositol phosphosphate formation. Both CaMKIIα and mGluR1/5 play an important role in memory, learning and synaptic transmission. Understanding how these two players work together could provide a mechanism for reducing excitotoxicity through desensitization of mGluR1/5 by CaMKIIα

Mineralogische Untersuchungen zum komplexen Reaktionsverhalten von C-S-H Phasen in zementgebundenen Baustoffen

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Cyclic Multi-Directional Response of Clay Deposits: Evaluating a Constitutive Model

The focus of this study is to verify the capabilities of a constitutive model to mimic a wide range of monotonic and cyclic multi-directional stress paths in clays. The generalized elasto-plastic constitutive formulation of the model enables to describe stress-strain response, accumulation of permanent deformations and excess pore pressure in monotonic and multi-directional cyclic loading. The model is calibrated based on the experimental database on the Gulf of Mexico clay developed at Texas A&M, including the constant rate strain (CRS) consolidation as well as monotonic triaxial tests. Capabilities of the calibrated model to predict the cyclic multi-directional stress paths are then evaluated through comparison with the results of cyclic, circular, and figure 8 multi-directional simple shear tests as a part of the Gulf of Mexico clay experimental database. We also used the extensive database for Boston Blue Clay (BBC) to calibrate model constants and verify its capabilities to mimic the monotonic and cyclic response of lower plasticity clays. The model proves successful to predict a wide range of complicated cyclic multi-directional stress paths for clays

Testing of Drilled Shafts Socketed Into Limestone

Construction of the first phase of the South Parking Garage at the Tampa International Airport in Florida was completed in December 1991. A full-scale field load testing program was used in design of the drilled shaft foundations, with specific goals of determining the bond strength and characterizing the load-displacement behavior of the rock sockets. The displacement behavior and bond strength of the test shafts were predicted from elastic solutions and semiempirical methods. Input parameters included the rock uniaxial compressive strength and elastic modulus. The load test results are compared to the predictions herein, and the practical application of these comparisons is demonstrated by a sample evaluation of a drilled shaft supporting the South Parking Garage

Load-settlement modelling of axially loaded drilled shafts using CPT-based recurrent neural networks

The design of pile foundations requires good estimation of the pile load-carrying capacity and settlement. Design for bearing capacity and design for settlement have been traditionally carried out separately. However, soil resistance and settlement are influenced by each other, and the design of pile foundations should thus consider the bearing capacity and settlement inseparably. This requires the full load–settlement response of piles to be well predicted. However, it is well known that the actual load–settlement response of pile foundations can be obtained only by load tests carried out in situ, which are expensive and time-consuming. In this paper, recurrent neural networks (RNNs) were used to develop a prediction model that can resemble the full load–settlement response of drilled shafts (bored piles) subjected to axial loading. The developed RNN model was calibrated and validated using several in situ full-scale pile load tests, as well as cone penetration test (CPT) data. The results indicate that the developed RNN model has the ability to reliably predict the load–settlement response of axially loaded drilled shafts and can thus be used by geotechnical engineers for routine design practice

Regressive approach for predicting bearing capacity of bored piles from cone penetration test data

© 2015 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. In this study, the least square support vector machine (LSSVM) algorithm was applied to predicting the bearing capacity of bored piles embedded in sand and mixed soils. Pile geometry and cone penetration test (CPT) results were used as input variables for prediction of pile bearing capacity. The data used were collected from the existing literature and consisted of 50 case records. The application of LSSVM was carried out by dividing the data into three sets: a training set for learning the problem and obtaining a relationship between input variables and pile bearing capacity, and testing and validation sets for evaluation of the predictive and generalization ability of the obtained relationship. The predictions of pile bearing capacity by LSSVM were evaluated by comparing with experimental data and with those by traditional CPT-based methods and the gene expression programming (GEP) model. It was found that the LSSVM performs well with coefficient of determination, mean, and standard deviation equivalent to 0.99, 1.03, and 0.08, respectively, for the testing set, and 1, 1.04, and 0.11, respectively, for the validation set. The low values of the calculated mean squared error and mean absolute error indicated that the LSSVM was accurate in predicting the pile bearing capacity. The results of comparison also showed that the proposed algorithm predicted the pile bearing capacity more accurately than the traditional methods including the GEP model