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This study investigates thermomechanical stress in cryopreservation by vitrification of the heart, while exploring the effects of nanowarming-assisted recovery from cryogenic storage. This study expands upon a recently published study, combining experimental investigation and thermal analysis of cryopreservation on a rat heart model. Specifically, this study focuses on scenarios with variable concentrations of silica-coated iron-oxide nanoparticles (sIONPs), while accounting for loading limitations associated with the heart physiology, as well as the properties of cryoprotective agent (CPA) solution and the geometry of the container. Results of this study suggest that variable sIONP concentration based on the heart physiology will elevate mechanical stresses when compared with the mathematically simplified, uniform distribution case. The most dangerous part of rewarming is below glass transition and at the onset of nanowarming past the glass transition temperature on the way for organ recovery from cryogenic storage. Throughout rewarming, regions that rewarm faster, such as the chambers of the heart (higher sIONP concentration), undergo compressive stresses, while the slower rewarming regions, such as the heart myocardium (low sIONP concentration), undergo tension. Being a brittle material, the vitrified organ is expected to fail under tension in lower stresses than in compression. Unfortunately, the location and magnitude of the maximum stress in the investigated cases varied, while general rules were not identified. This investigation demonstrates the need to tailor the thermal protocol of heart cryopreservation on a case-by-case basis, since the location, orientation, magnitude, and instant at which the maximum mechanical stress is found cannot be predicteda priori. While thermomechanical stress poses a significant risk to organ integrity, careful design of the thermal protocol can be instrumental in reducing the likelihood of structural damage, while taking full advantage of the benefits of nanowarming. 

The nematode Caenorhabditis elegans ( C. elegans ) is a model organism used frequently in developmental biology and neurobiology [White, (1986), Sulston, (1983), Chisholm, (2016) and Rapti, (2020)]. The C. elegans embryo can be used for cell tracking studies to understand how cell movement drives the development of specific embryonic tissues. Analyses in late-stage development are complicated by bouts of rapid twitching motions which invalidate traditional cell tracking approaches. However, the embryo possesses a small set of cells which may be identified, thereby defining the coiled embryo’s posture [Christensen, 2015]. The posture serves as a frame of reference, facilitating cell tracking even in the presence of twitching. Posture identification is nevertheless challenging due to the complete repositioning of the embryo between sampled images. Current approaches to posture identification rely on time-consuming manual efforts by trained users which limits the efficiency of subsequent cell tracking. Here, we cast posture identification as a point-set matching task in which coordinates of seam cell nuclei are identified to jointly recover the posture. Most point-set matching methods comprise coherent point transformations that use low order objective functions [Zhou, (2016) and Zhang, (2019)]. Hypergraphs, an extension of traditional graphs, allow more intricate modeling of relationships between objects, yet existing hypergraphical point-set matching methods are limited to heuristic algorithms which do not easily scale to handle higher degree hypergraphs [Duchenne, (2010), Chertok, (2010) and Lee, (2011)]. Our algorithm, Exact Hypergraph Matching ( EHGM ), adapts the classical branch-and-bound paradigm to dynamically identify a globally optimal correspondence between point-sets under an arbitrarily intricate hypergraphical model. EHGM with hypergraphical models inspired by C. elegans embryo shape identified posture more accurately (56%) than established point-set matching methods (27%), correctly identifying twice as many sampled postures as a leading graphical approach. Posterior region seeding empowered EHGM to correctly identify 78% of postures while reducing runtime, demonstrating the efficacy of the method on a cutting-edge problem in developmental biology.