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Alzheimer’s disease (AD) is a progressive neurologic disorder that impacts a diverse population of older adults. As three-dimensional (3D) models are powerful tools for advancing AD studies, the authors have been developed AD cortical organoids to enable the observation of AD pathology at the cellular, tissue, and organ levels. For creating the model, APPSwe/Ind (APP) and PSEN1 (PS1) mutant genes were transfected into mouse induced pluripotent stem cells (iPSCs) following which the iPSC lines that expressed mutant APP and PS1 proteins were obtained. Then, using modified serum-free suspended embryoid body culture, AD cerebral organoids were made successfully at various ages. The AD model can show AD’s biochemical and pathological alterations, such as overexpressions of Aβ40 and Aβ42 and a decrease of GABAergic interneurons. The proposed model has the potential for implementation in many biomedical applications, including AD drug screening, stem cell transplant, and neuronal tissue engineering.

With the increase of the information level of SCADA system in recent years, the attacks against SCADA system are also increasing. Therefore, more and more scholars are beginning to study the safety of SCADA systems. Game theory is a balanced decision involving the main body of all parties. In recent years, domestic and foreign scholars have applied game theory to SCADA systems to achieve active defense. However, their research often focuses on the entire SCADA system, and the game theory is solved for the entire SCADA system, which is not flexible enough, and the calculation cost is also high. In this paper, a dynamic local game model (DLGM) for power SCADA system is proposed. This model first obtains normal data to form a whitelist, then dynamically detects each attack of the attacker's SCADA system, and through white list to determine the node location of the SCADA system attacked by the attacker, then obtains the smallest system attacked by SCADA system, and finally performs a local dynamic game algorithm to find the best defense path. Experiments show that DLGM model can find the best defense path more effectively than other game strategies.