A Smart Ski Pole for Skiing Pattern Recognition and Quantification Application.

In cross-country skiing, ski poles play a crucial role in technique, propulsion, and overall performance. The kinematic parameters of ski poles can provide valuable information about the skier's technique, which is of great significance for coaches and athletes seeking to improve their skiing performance. In this work, a new smart ski pole is proposed, which combines the uniaxial load cell and the inertial measurement unit (IMU), aiming to provide comprehensive data measurement functions more easily and to play an auxiliary role in training. The ski pole can collect data directly related to skiing technical actions, such as the skier's pole force, pole angle, inertia data, etc., and the system's design, based on wireless transmission, makes the system more convenient to provide comprehensive data acquisition functions, in order to achieve a more simple and efficient use experience. In this experiment, the characteristic data obtained from the ski poles during the Double Poling of three skiers were extracted and the sample t-test was conducted. The results showed that the three skiers had significant differences in pole force, pole angle, and pole time. Spearman correlation analysis was used to analyze the sports data of the people with good performance, and the results showed that the pole force and speed (r = 0.71) and pole support angle (r = 0.76) were significantly correlated. In addition, this study adopted the commonly used inertial sensor data for action recognition, combined with the load cell data as the input of the ski technical action recognition algorithm, and the recognition accuracy of five kinds of cross-country skiing technical actions (Diagonal Stride (DS), Double Poling (DP), Kick Double Poling (KDP), Two-stroke Glide (G2) and Five-stroke Glide (G5)) reached 99.5%, and the accuracy was significantly improved compared with similar recognition systems. Therefore, the equipment is expected to be a valuable training tool for coaches and athletes, helping them to better understand and improve their ski maneuver technique.

Smad4 Deficiency in Smooth Muscle Cells Initiates the Formation of Aortic Aneurysm.

RATIONALE: Aortic aneurysm is a life-threatening cardiovascular disorder caused by the predisposition for dissection and rupture. Genetic studies have proved the involvement of the transforming growth factor-ß (TGF-ß) pathway in aortic aneurysm. Smad4 is the central mediator of the canonical TGF-ß signaling pathway. However, the exact role of Smad4 in smooth muscle cells (SMCs) leading to the pathogenesis of aortic aneurysms is largely unknown. OBJECTIVE: To determine the role of smooth muscle Smad4 in the pathogenesis of aortic aneurysms. METHODS AND RESULTS: Conditional gene knockout strategy combined with histology and expression analysis showed that Smad4 or TGF-ß receptor type II deficiency in SMCs led to the occurrence of aortic aneurysms along with an upregulation of cathepsin S and matrix metallopeptidase-12, which are proteases essential for elastin degradation. We further demonstrated a previously unknown downregulation of matrix metallopeptidase-12 by TGF-ß in the aortic SMCs, which is largely abrogated in the absence of Smad4. Chemotactic assay and pharmacologic treatment demonstrated that Smad4-deficient SMCs directly triggered aortic wall inflammation via the excessive production of chemokines to recruit macrophages. Monocyte/macrophage depletion or blocking selective chemokine axis largely abrogated the progression of aortic aneurysm caused by Smad4 deficiency in SMCs. CONCLUSIONS: The findings reveal that Smad4-dependent TGF-ß signaling in SMCs protects against aortic aneurysm formation and dissection. The data also suggest important implications for novel therapeutic strategies to limit the progression of the aneurysm resulting from TGF-ß signaling loss-of-function mutations.