A New Neural Dynamic Learning Framework for Discrete-Time Strict-Feedback Systems: Internal Interaction-Based Weight Adaptive Laws.

This article investigates internal interaction-based dynamic learning control (LC) for uncertain discrete-time strict-feedback systems. On the basis of predict technology, the original system is converted into a common n -step-ahead input-output predict model. The predict model causes every estimated neural weight to converge to n different constants using the existing control framework.

HIPK2 inhibits cell metastasis and improves chemosensitivity in esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive and lethal malignancies worldwide. At present, the underlying mechanisms of ESCC development and progression are poorly understood. Previous studies have demonstrated that homeodomain-interacting protein kinase-2 (HIPK2) serves an important role in cancer biology, particularly in proliferation and metastasis.