ACUTE EFFECTS OF WHOLE-BODY VIBRATION ON KNEE JOINT DROP LANDING KINEMATICS AND DYNAMIC POSTURAL STABILITY

Whole-body vibration (WBV) is being increasingly utilized in addition to other training modalities in order to prevent and rehabilitate athletic injuries. Excessive knee joint movement has been reported to be a contributing factor to many traumatic and overuse knee joint injuries (Sigward et al., 2008). However the effects of WBV on sensorimotor function and consequent knee joint kinematics is unknown. Thus, the aim of the present study was to examine the effects of an acute WBV exposure on knee joint drop landing kinematics and dynamic postural stability in healthy participants. The null hypothesis was that acute WBV exposure would not influence lower limb drop landing kinematics or dynamic postural stability

PySINDy: A comprehensive Python package for robust sparse system identification

Automated data-driven modeling, the process of directly discovering the governing equations of a system from data, is increasingly being used across the scientific community. PySINDy is a Python package that provides tools for applying the sparse identification of nonlinear dynamics (SINDy) approach to data-driven model discovery. In this major update to PySINDy, we implement several advanced features that enable the discovery of more general differential equations from noisy and limited data. The library of candidate terms is extended for the identification of actuated systems, partial differential equations (PDEs), and implicit differential equations. Robust formulations, including the integral form of SINDy and ensembling techniques, are also implemented to improve performance for real-world data. Finally, we provide a range of new optimization algorithms, including several sparse regression techniques and algorithms to enforce and promote inequality constraints and stability. Together, these updates enable entirely new SINDy model discovery capabilities that have not been reported in the literature, such as constrained PDE identification and ensembling with different sparse regression optimizers

What Have We Learnt from Quantitative Case Reports of Acute Lateral Ankle Sprains Injuries and Episodes of \u27Giving-Way\u27 of the Ankle Joint, and What Shall We Further Investigate?

Lateral ankle sprains are a commonly incurred injury in sports. They have a high recurrence rate and can lead to the development of persistent injury associated symptoms. We performed a quantitative synthesis of published case reports documenting the kinematics of acute lateral ankle sprains and episodes of ‘giving-way’ of the ankle joint to provide a comprehensive description of the mechanisms. A systematic literature search was conducted to screen records within MEDLINE® and EMBASE®. Additional strategies included manual search of specific journals, as well as contacting researchers in relevant communities to retrieve unpublished data. Twenty-four cases were included in the quantitative synthesis, 11 from individual case reports and 13 from four separate case series. Two authors independently reviewed all the articles and extracted ankle joint kinematic data. Excessive ankle inversion was the most pronounced kinematic pattern observed across all included cases, with a mean peak inversion angle of 67.5° (range 2.0 to 142) and a mean peak inversion velocity of 974°/s (range 468 to 1752). This was followed by internal rotation and plantar flexion, respectively. A homogeneous linear function revealed a mean inversion velocity across all cases of 337°/s (range 117 to 1400; R2 = 0.78; p \u3c 0.0001)