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Significance: The spatial organization of collagen fibers has been used as a biomarker for assessing injury and disease progression. However, quantifying this organization for complex structures is challenging. Aim: To quantify and classify complex collagen fiber organizations. Approach: Using quantitative second-harmonic generation (SHG) microscopy, we show that collagen-fiber orientation can be viewed as pseudovector fields. Subsequently, we analyze them using fluid mechanic metrics, such as energy U, enstrophy E, and tortuosity tau. Results: We show that metrics used in fluid mechanics for analyzing fluid flow can be adapted to analyze complex collagen fiber organization. As examples, we consider SHG images of collagenous tissue for straight, wavy, and circular fiber structures. Conclusions: The results of this study show the utility of the chosen metrics to distinguish diverse and complex collagen organizations. We find that the distribution of values for E and U increases with collagen fiber disorganization, where they divide between low and high values corresponding to uniformly aligned fibers and disorganized collagen fibers, respectively. We also confirm that the values of tau cluster around 1 when the fibers are straight, and the range increases up to 1.5 when wavier fibers are present. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Published version

Many complex physical systems exhibit a rich variety of discrete behavioural modes. Often, the system complexity limits the applicability of standard modelling tools. Hence, understanding the underlying physics of different behaviours and distinguishing between them is challenging. Although traditional machine learning techniques could predict and classify behaviour well, typically they do not provide any meaningful insight into the underlying physics of the system. In this paper we present a novel method for extracting physically meaningful clusters of discrete behaviour from limited experimental observations. This method obtains a set of physically plausible functions that both facilitate behavioural clustering and aid in system understanding. We demonstrate the approach on the V-shaped falling paper system, a new falling paper type system that exhibits four distinct behavioural modes depending on a few morphological parameters. Using just 49 experimental observations, the method discovered a set of candidate functions that distinguish behaviours with an error of 2.04%, while also aiding insight into the physical phenomena driving each behaviour. © 2019 Howison et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Objectives: Despite many hypotheses, the pathophysiology of syringomyelia is still not well understood. In this report, the authors propose a hypothesis based on analysis of cerebrospinal fluid dynamics in the spine. Methods: An electric circuit model of the CSF dynamics of the spine was constructed based on a technique of computational fluid mechanics. With this model, the authors calculated how a pulsatile CSF wave coming from the cranial side is propagated along the spinal cord. Results: Reducing the temporary fluid storage capacity of the cisterna magna dramatically increased the pressure wave propagated along the central canal. The peak of this pressure wave resided in the mid-portion of the spinal cord. Conclusions: The following hypotheses are proposed. The cisterna magna functions as a shock absorber against the pulsatile CSF waves coming from the cranial side. The loss of shock absorbing capacity of the cisterna magna and subsequent increase of central canal wall pressure leads to syrinx formation in patients with Chiari I malformation.