Department of Mechanical and Industrial Engineering, University of Massachusetts at Amherst, Amherst, MA 01003, USA.
J Exp Biol. 2010 Jun 1;213(11):1844-51. doi: 10.1242/jeb.041129.
Bite force is a measure of whole-organism performance that is often used to investigate the relationships between performance, morphology and fitness. When in vivo measurements of bite force are unavailable, researchers often turn to lever models to predict bite forces. This study demonstrates that bite force predictions based on two-dimensional (2-D) lever models can be improved by including three-dimensional (3-D) geometry and realistic physiological cross-sectional areas derived from dissections. Widely used, the 2-D method does a reasonable job of predicting bite force. However, it does so by over predicting physiological cross-sectional areas for the masseter and pterygoid muscles and under predicting physiological cross-sectional areas for the temporalis muscle. We found that lever models that include the three dimensional structure of the skull and mandible and physiological cross-sectional areas calculated from dissected muscles provide the best predictions of bite force. Models that accurately represent the biting mechanics strengthen our understanding of which variables are functionally relevant and how they are relevant to feeding performance.
咬合力是衡量整体生物性能的一种指标,通常用于研究性能、形态和适应性之间的关系。当无法进行体内咬合力测量时,研究人员通常会转向杠杆模型来预测咬合力。本研究表明,通过纳入三维(3D)几何形状和从解剖中得出的真实生理横截面积,可以改进基于二维(2D)杠杆模型的咬合力预测。广泛使用的 2D 方法在预测咬合力方面表现良好。然而,它通过过度预测咀嚼肌和翼状肌的生理横截面积,以及低估颞肌的生理横截面积来实现这一目标。我们发现,包含颅骨和下颌三维结构以及从解剖肌肉计算出的生理横截面积的杠杆模型可以更好地预测咬合力。准确表示咬合力学的模型增强了我们对哪些变量在功能上相关以及它们与进食性能的相关性的理解。