The L‐type channel CaV1.2 is essential for vascular smooth muscle contraction and arterial tone. Increased vascular CaV1.2 expression and function are related to high smooth muscle contractility and enhanced arterial tone during hypertension, which is characterized by enhanced angiotensin II (angII) signaling. Several pathways have been proposed to account for increased CaV1.2 expression during hypertension, including increased CaV1.2 trafficking. However, mechanisms underlying enhanced CaV1.2 function during angII signaling and hypertension remain a subject of intense investigation. In this study, we hypothesize that CaV1.2 phosphorylation at the S1928 site is a key event that mediates increased channel activity and vascular reactivity during angII signaling and hypertension. Initial experiments found increased S1928 phosphorylation in angII‐treated wild type arterial lysates. This was associated with elevated vascular smooth muscle whole‐cell L‐type channel currents, global intracellular Ca2+, and contraction. Similarly, ex vivo and in vivo experiments in mesenteric arteries reveal an increased arterial tone and decreased mesenteric blood flow from wild type mice. Moreover, smooth muscle cells treated with angII showed a redistribution of CaV1.2 into larger clusters. These functional changes were prevented or significantly ameliorated in arteries or cells from a knockin mouse expressing a mutant CaV1.2 in which serine was replaced with alanine at position 1928 (S1928A mouse) and by protein kinase C inhibition. In angII‐induced hypertensive mice, increased CaV1.2 clusters size and channel activity, enhanced arterial tone, and mean arterial pressure in wild type mice were prevented or significantly reduce in S1928A mice. Altogether, these results suggest a key role for phosphorylation of CaV1.2 at S1928 in regulating CaV1.2 distribution, activity, and vascular function during angII signaling and hypertension. Phosphorylation of this single vascular CaV1.2 amino acid could be an essential regulatory mechanism that could be exploited for therapeutic intervention.

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In videos, the human's actions are of three-dimensional (3D) signals. These videos investigate the spatiotemporal knowledge of human behavior. The promising ability is investigated using 3D convolution neural networks (CNNs). The 3D CNNs have not yet achieved high output for their well-established two-dimensional (2D) equivalents in still photographs. Board 3D Convolutional Memory and Spatiotemporal fusion face training difficulty preventing 3D CNN from accomplishing remarkable evaluation. In this paper, we implement Hybrid Deep Learning Architecture that combines STIP and 3D CNN features to enhance the performance of 3D videos effectively. After implementation, the more detailed and deeper charting for training in each circle of space-time fusion. The training model further enhances the results after handling complicated evaluations of models. The video classification model is used in this implemented model. Intelligent 3D Network Protocol for Multimedia Data Classification using Deep Learning is introduced to further understand space-time association in human endeavors. In the implementation of the result, the well-known dataset, i.e., UCF101 to, evaluates the performance of the proposed hybrid technique. The results beat the proposed hybrid technique that substantially beats the initial 3D CNNs. The results are compared with state-of-the-art frameworks from literature for action recognition on UCF101 with an accuracy of 95%.