A mesh of cytoskeletal fibers, consisting of microtubules, intermediate filaments, and fibrous actin, prevents the Brownian diffusion of particles with a diameter larger than 0.10 μm, such as vesicular stomatitis virus ribonucleoprotein (RNP) particles, in mammalian cells. Nevertheless, RNP particles do move in random directions but at a lower rate than Brownian diffusion, which is thermally driven. This nonthermal biological transport process is called “active diffusion” because it is driven by ATP. The ATP powers motor proteins such as myosin II. The motor proteins bend and cross-link actin fibers, causing the mesh to jiggle. Until recently, little was known about how RNP particles get through the mesh. It has been customary to analyze the tracks of particles like RNPs by computing the slope of the ensemble-averaged mean-squared displacement of the particles as a signature of mechanism. Although widely used, this approach “loses information” about the timing of the switches between physical mechanisms. It has been recently shown that machine learning composed of variational Bayesian analysis, Gaussian mixture models, and hidden Markov models can use “all the information” in a single track to reveal that that the positions of RNP particles are spatially clustered. Machine learning assigns a number, called a state, to each cluster. RNP particles remain in one state for 0.2–1.0 s before switching (hopping) to a different state. This earlier work is here extended to analyze the movements of a particle within a state and to determine particle directionality within and between states.

中文翻译：

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病毒核糖核蛋白颗粒通过细胞质主动扩散过程中的方向变化

细胞骨架纤维网由微管、中间丝和纤维状肌动蛋白组成，可防止哺乳动物细胞中直径大于 0.10 μm 的颗粒（例如水泡性口炎病毒核糖核蛋白 (RNP) 颗粒）的布朗扩散。尽管如此，RNP 粒子确实会沿随机方向移动，但速度低于热驱动的布朗扩散。这种非热生物运输过程被称为“主动扩散”，因为它是由 ATP 驱动的。 ATP 为肌球蛋白 II 等运动蛋白提供动力。运动蛋白使肌动蛋白纤维弯曲并交联，导致网状结构抖动。直到最近，人们对 RNP 粒子如何穿过网格还知之甚少。人们习惯上通过计算粒子的整体平均均方位移的斜率作为机制的特征来分析 RNP 等粒子的轨迹。尽管广泛使用，但这种方法“丢失了有关物理机制之间切换时间的信息”。最近的研究表明，由变分贝叶斯分析、高斯混合模型和隐马尔可夫模型组成的机器学习可以使用单个轨迹中的“所有信息”来揭示RNP粒子的位置在空间上聚集。机器学习为每个集群分配一个数字，称为状态。 RNP 粒子在切换（跳跃）到不同状态之前保持在一种状态 0.2-1.0 秒。这项早期工作在这里扩展到分析状态内粒子的运动并确定状态内和状态之间的粒子方向性。