presented a significant association with the Engel classifications for the Year 2∼3 visit (r = -0.466, p = 0.004) or the latest visit with >1 year follow-up (r = -0.374, p = 0.003) while controlling for disease duration and follow-up duration. The HSI index and HV presented comparable good performance in HS detection, and HSI may have better sensitivity than HV in differentiating pathological HS severity. Higher magnitude of HV dissymmetry may indicate better post-surgical outcomes for HS patients.The HSI index and HV presented comparable good performance in HS detection, and HSI may have better sensitivity than HV in differentiating pathological HS severity. Higher magnitude of HV dissymmetry may indicate better post-surgical outcomes for HS patients.Physiological processes such as blood clotting and wound healing as well as pathologies such as fibroses and musculoskeletal contractures, all involve biological materials composed of a contracting cellular population within a fibrous matrix, yet how the microscale interactions among the cells and the matrix lead to the resultant emergent behavior at the macroscale tissue level remains poorly understood. Platelets, the anucleate cell fragments that do not divide nor synthesize extracellular matrix, represent an ideal model to study such systems. During blood clot contraction, microscopic platelets actively pull fibers to shrink the macroscale clot to less than 10% of its initial volume. We discovered that platelets utilize a new emergent behavior, asynchrono-mechanical amplification, to enhanced volumetric material contraction and to magnify contractile forces. This behavior is triggered by the heterogeneity in the timing of a population of actuators. This result indicates that cell heterogeneity, often attributed to stochastic cell-to-cell variability, can carry an essential biophysical function, thereby highlighting the importance of considering 4 dimensions (space + time) in cell-matrix biomaterials. This concept of amplification via heterogeneity can be harnessed to increase mechanical efficiency in diverse systems including implantable biomaterials, swarm robotics, and active polymer composites.Medical image registration is a critical process for automated image computing, and ideally, the deformation field from one image to another should be smooth and inverse-consistent in order to bidirectionally align anatomical structures and to preserve their topology. BPTES cell line Consistent registration can reduce bias caused by the order of input images, increase robustness, and improve reliability of subsequent quantitative analysis. Rigorous differential geometry constraints have been used in traditional methods to enforce the topological consistency but require comprehensive optimization and are time consuming. Recent studies show that deep learning-based registration methods can achieve comparable accuracy and are much faster than traditional registration. However, the estimated deformation fields do not necessarily possess inverse consistency when the order of two input images is swapped. To tackle this problem, we propose a new deep registration algorithm by employing the inverse consistency training strategy, so the forward and backward deformations of a pair of images can consistently align anatomical structures. In addition, since fine-tuned deformations among the training images reflect variability of shapes and appearances in a high-dimensional space, we formulate a group prior data modeling framework so that such statistics can be used to improve accuracy and consistency for registering new input image pairs. Specifically, we implement the wavelet principle component analysis (w-PCA) model of deformation fields and incorporate such prior constraints into the inverse-consistent deep registration network. We refer the proposed algorithm as consistent deep registration with group data modeling. Experiments on 3D brain magnetic resonance (MR) images showed that the unsupervised consistent deep registration and data modeling strategy yield consistent deformations after switching the input images and tolerated image variations well.In kidney transplantations, pathologists evaluate the architecture of both glomeruli, interstitium and tubules to assess the nephron status. An accurate assessment of glomerulosclerosis and tubular atrophy is crucial for determining kidney acceptance, which is currently based on the pathologists' histological evaluations on renal biopsies in addition to clinical data. In this work, we present an automated algorithm, called RENTAG (Robust EvaluatioN of Tubular Atrophy & Glomerulosclerosis), for the segmentation and classification of glomerular and tubular structures in histopathological images. The proposed novel strategy combines the accuracy of a level-set with the semantic segmentation of convolutional neural networks to detect the glomeruli and tubules contours. In the TEST set, our method exhibited excellent performance in both glomeruli (dice score 0.9529) and tubule (dice score 0.9174) detection and outperformed all the compared methods. To the best of our knowledge, the RENTAG algorithm is the first fully automated method capable of quantifying glomerulosclerosis and tubular atrophy in digital histological images. The developed software can be employed for the analysis of pre-transplantation biopsies to support the pathologists' diagnostic activity. Use of virtual reality (VR) in healthcare has expanded in recent years. The challenges faced by patients with prolonged COVID-19-related hospitalizations - social isolation, disability, neurologic sequelae, adjustment-related anxiety, depression, and stress - may be mitigated by the novel use of VR as one modality of a comprehensive rehabilitation plan. This descriptive study aimed to understand patient satisfaction and perceived benefit of virtual reality on a COVID-19 recovery unit, as well as the logistical and operational feasibility of providing VR content for patients and staff. During the COVID-19 surge in New York City in 2020, the COVID-19 Recovery Unit (CRU) of a large academic hospital invited patients and staff to participate in VR sessions with three categories of experience (1) Guided meditation, (2) Exploration of natural environments, (3) Cognitive stimulation games. Patients and staff were surveyed about satisfaction and perceived benefit. 13 patients and 11 staff were surveyed, with median patient satisfaction scores of 9 out of 10, with ten representing "extremely satisfied," and median staff satisfaction scores of 10.