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• A Spatial Neural Network (SNN) model is proposed for automatic property appraisal as required by Basel II and Ш, called Property Appraisal 4.0. • We uses disruptive technology and discover hidden neighbourhood features of real estate information in the satellite embedding vectors. • The latest deep learning technologies are also employed, such as knowledge distillation, incremental learning, and Deep-Automated Optical Inspection. • Class Activation Mapping is also adapted to reinforce the proposed spatial neural network in the model. • Experimental results show that our approach's performance is better than that of previous mainstream models, Property valuation is a complex issue that has always been the focal point for the real estate industry. The traditional valuation models used for appraisals cannot meet real-world demand anymore due to the improper processing of correlated information of nearby facilities. In this study, we propose a Spatial Neural Network (SNN) model, called Property Appraisal 4.0, that uses disruptive technology to forecast property values and discover hidden neighbourhood features of real estate information in the satellite embedding vectors. The latest deep learning technologies are also employed, such as knowledge distillation, incremental learning, and Deep-Automated Optical Inspection. Class Activation Mapping is also adapted to reinforce the proposed spatial neural network in the model. Experimental results show that our approach's performance is better than that of previous mainstream models, such as the Hedonic Pricing Model and Support Vector Machines. [ABSTRACT FROM AUTHOR]

The effect of leucine-rich repeat kinase 2 (LRRK2) mutation I2020T on its kinase activity has been controversial, with both increased and decreased effects being reported. We conducted steady-state and pre-steady-state kinetic studies on LRRKtide and its analog LRRKtideS. Their phosphorylation differs by the rate-limiting steps: product release is rate-limiting for LRRKtide and phosphoryl transfer is rate-limiting for LRRKtideS. As a result, we observed that the I2020T mutant is more active than wild type (WT) LRRK2 for LRRKtideS phosphorylation, whereas it is less active than WT for LRRKtide phosphorylation. Our pre-steady-state kinetic data suggest that (i) the I2020T mutant accelerates the rates of phosphoryl transfer of both reactions by 3-7-fold; (ii) this increase is masked by a rate-limiting product release step for LRRKtide phosphorylation; and (iii) the observed lower activity of the mutant for LRRKtide phosphorylation is a consequence of its instability: the concentration of the active form of the mutant is 3-fold lower than WT. The I2020T mutant has a dramatically low KATP and therefore leads to resistance to ATP competitive inhibitors. Two well known DFG-out or type II inhibitors are also weaker toward the mutant because they inhibit the mutant in an unexpected ATP competitive mechanism. The I2020 residue lies next to the DYG motif of the activation loop of the LRRK2 kinase domain. Our modeling and metadynamic simulations suggest that the I2020T mutant stabilizes the DYG-in active conformation and creates an unusual allosteric pocket that can bind type II inhibitors but in an ATP competitive fashion. [ABSTRACT FROM AUTHOR]