Valvular heart diseases are complex disorders, varying in pathophysiological mechanism and affected valve components. Understanding the effects of these diseases on valve functionality requires a thorough characterization of the mechanics and structure of the healthy heart valves. In this study, we performed biaxial mechanical experiments with extensive testing protocols to examine the mechanical behaviors of the mitral valve and tricuspid valve leaflets. We also investigated the effect of loading rate, testing temperatures, species (porcine versus ovine hearts), and age (juvenile vs adult ovine hearts) on the mechanical responses of the leaflet tissues. In addition, we evaluated the structure of chordae tendineae within each valve and performed histological analysis on each atrioventricular leaflet. We found all tissues displayed a characteristic nonlinear anisotropic mechanical response, with radial stretches on average 30.7% higher than circumferential stretches under equibiaxial physiological loading. Tissue mechanical responses showed consistent mechanical stiffening in response to increased loading rate and minor temperature dependence in all five atrioventricular heart valve leaflets. Moreover, our anatomical study revealed similar chordae quantities in the porcine mitral (30.5 ± 1.43 chords) and tricuspid valves (35.3 ± 2.45 chords) but significantly more chordae in the porcine than the ovine valves (p < 0.010). Our histological analyses quantified the relative thicknesses of the four distinct morphological layers in each leaflet. This study provides a comprehensive database of the mechanics and structure of the atrioventricular valves, which will be beneficial to development of subject-specific atrioventricular valve constitutive models and toward multi-scale biomechanical investigations of heart valve function to improve valvular disease treatments.

Intracranial aneurysms (ICAs) are focal dilations in the brain's arteries. When left untreated, ICAs can grow to the point of rupture, accounting for 50-80% of subarachnoid hemorrhage cases. Current treatments include surgical clipping and endovascular coil embolization to block circulation into the aneurysmal space for preventing aneurysm rupture. As for endovascular embolization, patients could experience aneurysm recurrence due to an incomplete coil filling or compaction over time. The use of shape memory polymers (SMPs) in place of conventional platinum coils could provide more control and predictability for mitigating these complications. This study was focused on characterization of an aliphatic urethane-based SMP to evaluate its potential as a novel biomaterial for endovascular embolization. Twelve compositions of the SMP were synthesized and their thermomechanical properties together with the shape recovery behavior were comprehensively investigated. Our results showed that the SMPs experienced a significant decrease in storage and loss moduli as heated above their glass transition temperatures (32.3-83.2 °C), and that all SMPs were thermally stable up to 265 °C. Moreover, the SMPs exhibited both composition-dependent stress relaxation and a decrease in elastic modulus during cyclic loading. The shape recovery time was less than 11 s for all SMP compositions, which is sufficiently short for shape changing during embolization procedures. Several candidate compositions were identified, which possess a glass transition temperature above body temperature (37 °C) and below the threshold of causing tissue damage (45 °C). They also exhibit high material strength and low stress relaxation behavior, suggesting their potential applicability to endovascular embolization of ICAs.

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Conventional endovascular embolization of intracranial (or brain) aneurysms using helical detachable platinum coils can be time-consuming and occasionally requires retreatment due to incomplete coil packing. These shortcomings create a need for new biomedical devices and methods of achieving brain aneurysm occlusion. This paper presents a biocompatible and highly porous shape memory polymer (SMP) material with potential applications in the development of novel endovascular devices for treating complex intracranial aneurysms. The novel highly porous polyurethane SMP is synthesized as an open cell foam material with a glass transition temperature (Tg) of 39 °C using a sugar particle leaching method. Once heated above the Tg, the compressed SMP foam is able to quickly return to its original shape. An electrical resistance heating method is also employed to demonstrate a potential triggering design for the shape recovery process in future medical applications. The mechanical properties of the developed SMP foam are characterized at temperatures up to 10 °C above the respective Tg. The results from this work demonstrate that the porous SMP material developed in this study and the electrical resistance heating trigger mechanism provide a solid foundation for future design of biomedical devices to enhance the long-term therapeutic outcomes of endovascular intracranial aneurysm treatments.

This dataset contains the anisotropic tissue responses of porcine atrioventricular valve leaflets to force-controlled biaxial mechanical testing. The set includes the first Piola-Kirchhoff Stress and the specimen stretches (λ) in both circumferential and radial tissue directions (C and R, respectively) for the mitral valve anterior and posterior leaflets (MVAL and MVPL), and the tricuspid valve anterior, posterior, and septal leaflets (TVAL, TVPL, and TVSL) from six porcine hearts at five separate force-controlled biaxial loading protocols. This dataset is associated with a companion journal article, which can be consulted for further information about the methodology, results, and discussion of this biaxial mechanical testing (Jett et al., in press) [1].

This article presents data from the investigation of the thermal characteristics and mechanical behaviors of twelve different compositions of a polyurethane shape memory polymer (SMP). Each of the SMP compositions has a unique molar ratio of three monomers: (i) hexamethylene diisocyanate (HDI), (ii) N,N,N′,N′-Tetrakis(2-Hydroxypropyl)ethylenediamine (HPED), and (iii) Triethanolamine (TEA). The thermal characteristic datasets for each composition include the glass transition temperatures, as obtained from differential scanning calorimetry (DSC) and dynamic mechanical analysis (DMA), and the thermal degradation thresholds, as found from thermogravimetric analysis (TGA). The mechanical behaviors of the SMPs are represented by the failure stresses and strains, as obtained by cyclic tensile testing and failure testing, respectively. The interpretation of these measurements as well as a discussion of the potential usage of candidate SMP compositions for medical devices can be found in the companion article by Kunkel et al . (2018) [1] , “Synthesis and characterization of bio-compatible shape memory polymers with potential applications to endovascular embolization of intracranial aneurysms.”

Machine learning has become a critical component of modern datadriven online services. Typically, the training phase of machine learning techniques requires to process large-scale datasets which may contain private and sensitive information of customers. This imposes significant security risks since modern online services rely on cloud computing to store and process the sensitive data. In the untrusted computing infrastructure, security is becoming a paramount concern since the customers need to trust the thirdparty cloud provider. Unfortunately, this trust has been violated multiple times in the past.To overcome the potential security risks in the cloud, we answer the following research question: how to enable secure executions of machine learning computations in the untrusted infrastructure? To achieve this goal, we propose a hardware-assisted approach based on Trusted Execution Environments (TEEs), specifically Intel SGX, to enable secure execution of the machine learning computations over the private and sensitive datasets. More specifically, we propose a generic and secure machine learning framework based on Tensorflow, which enables secure execution of existing applications on the commodity untrusted infrastructure. In particular, we have built our system called TensorSCONE from ground-up by integrating TensorFlow with SCONE, a shielded execution framework based on Intel SGX. The main challenge of this work is to overcome the architectural limitations of Intel SGX in the context of building a secure TensorFlow system. Our evaluation shows that we achieve reasonable performance overheads while providing strong security properties with low TCB.

A procedure giving reproducible data has been devised for the preparation, exposure, and testing of yarns in fabric form for resistance to actinic degradation. Effects of season, test location, and sample presentation have been considered. It has been shown that ultraviolet radiation is a more useful index of exposure than total incident radiation. It is suggested that the cyclical seasonal variation of ozone in the upper atmosphere is responsible for the observed seasonal variations in ultraviolet radiation. It is also suggested that the difference in degradation rate noted between South Florida and Arizona are primarily a function of the observed large difference in relative humidity between those sites. Preliminary accelerated tests in Xenon and Sunshine Carbon Arc Weatherometers are shown to correlate with direct-to-weather tests based on an ultraviolet radiation index.