A portable device for studying the effects of fluid flow on degradation properties of biomaterials inside cell incubators.

A portable device was designed and constructed for studying the properties of biomaterials in physiologically relevant fluids under controllable flow conditions that closely simulate fluid flow inside the body. The device can fit entirely inside a cell incubator; and, thus, it can be used directly under standard cell culture conditions. An impedance-driven pump was built in the sterile flow loop to control the flow rates of fluids, which made the device small and portable for easy deployment in the incubator. To demonstrate the device functions, magnesium (Mg) as a representative biodegradable material was tested in the flow device for immersion degradation under flow versus static conditions, while the flow module was placed inside a standard cell incubator. The flow rate was controlled at 0.17 ± 0.06 ml/s for this study; and, the flow rate is adjustable through the controller module outside of incubators for simulating the flow rates in the ranges of blood flow in human artery (0.05 ∼0.43 ml/s) and vein (0.02 ∼0.08 ml/s). Degradation of Mg under flow versus static conditions was characterized by measuring the changes of sample mass and thickness, and Mg2+ ion concentrations in the immersion media. Surface chemistry and morphology of Mg after immersion under flow versus static conditions were compared. The portable impedance-driven flow device is easy to fit inside an incubator and much smaller than a peristaltic pump, providing a valuable solution for studying biomaterials and implants (e.g. vascular or ureteral stents) in body fluids under flow versus static conditions with or without cells

Towards an understanding of the impact of resources on the design process

Considerable effort has been devoted within the design research community to understanding the structure of design processes and their development for different design problems. Whilst much work has examined the impact of design goals upon the structure of a design process, less attention has been paid to the role that design resources can play. This paper describes an experiment directed towards gaining an understanding of the impact that both active resources (which perform design tasks) and passive resources (which are used by active resources) can have upon design process structure. Main outcomes from the experiment were the conclusive identification that resources can significantly impact design process structure and a number of examples of how these impacts manifest themselves. The main conclusion of the paper is that given the sizeable impact resources can have upon process structure, there is a considerable need to obtain a greater understanding of these impacts to facilitate the development of techniques that can support design process definition based upon an understanding of the design resources being used to solve a design problem

THE EFFECT OF ANKLE PLATFORM TRAINING ON ANKLE PROPRIOCEPTION IN SUBJECTS WITH UNILATERAL FUNCTIONAL ANKLE INSTABILITY

INTRODUCTION: Functional ankle instability (FAI) is defined as the subjective sensation of giving way or feeling joint instability after repeated episodes of ankle sprain (Riemann et al., 2003). The purpose of this study was to examine the effect of 12-week Biomechanical Ankle Platform System (BAPS) training on ankle reposition sense in subjects with unilateral FAI

THE EFFECT OF ANKLE PLATFORM TRAINING ON ANKLE PROPRIOCEPTION IN SUBJECTS WITH UNILATERAL FUNCTIONAL ANKLE INSTABILITY

INTRODUCTION: Functional ankle instability (FAI) is defined as the subjective sensation of giving way or feeling joint instability after repeated episodes of ankle sprain (Riemann et al., 2003). The purpose of this study was to examine the effect of 12-week Biomechanical Ankle Platform System (BAPS) training on ankle reposition sense in subjects with unilateral FAI

L1L^1 Estimation: On the Optimality of Linear Estimators

Consider the problem of estimating a random variable $X$ from noisy observations $Y = X+ Z$, where $Z$ is standard normal, under the $L^1$ fidelity criterion. It is well known that the optimal Bayesian estimator in this setting is the conditional median. This work shows that the only prior distribution on $X$ that induces linearity in the conditional median is Gaussian. Along the way, several other results are presented. In particular, it is demonstrated that if the conditional distribution $P_{X|Y=y}$ is symmetric for all $y$, then $X$ must follow a Gaussian distribution. Additionally, we consider other $L^p$ losses and observe the following phenomenon: for $p \in [1,2]$, Gaussian is the only prior distribution that induces a linear optimal Bayesian estimator, and for $p \in (2,\infty)$, infinitely many prior distributions on $X$ can induce linearity. Finally, extensions are provided to encompass noise models leading to conditional distributions from certain exponential families

Capturing Membrane Phase Separation by Dual Resolution Molecular Dynamics Simulations

[Image: see text] Understanding the lateral organization in plasma membranes remains an open problem and is of great interest to many researchers. Model membranes consisting of coexisting domains are commonly used as simplified models of plasma membranes. The coarse-grained (CG) Martini force field has successfully captured spontaneous separation of ternary membranes into a liquid-disordered and a liquid-ordered domain. With all-atom (AA) models, however, phase separation is much harder to achieve due to the slow underlying dynamics. To remedy this problem, here, we apply the virtual site (VS) hybrid method on a ternary membrane composed of saturated lipids, unsaturated lipids, and cholesterol to investigate the phase separation. The VS scheme couples the two membrane leaflets at CG and AA resolution. We found that the rapid phase separation reached by the CG leaflet can accelerate and guide this process in the AA leaflet

Effects of salinity and wet–dry treatments on C and N dynamics in coastal-forested wetland soils: Implications of sea level rise

Forested wetlands dominated by baldcypress (Taxodium distichum) and water tupelo (Nyssa aquatica) are commonly found in coastal regions of the southeastern United States. Global climate change and in particular sea level rise will alter the frequency and magnitude of wet/dry periods and salinity levels in these ecosystems. Soil microcosm experiments were set up to identify the effects of water level variations (0.4–3.0 g-water g-soil−1) and salinity changes (0, 1 and 5 ppt of NaCl) on greenhouse gas emissions (CH4, CO2, and N2O) and dissolved organic carbon (DOC) characteristics from forested wetland soils. Our results indicate that, the effect of water level was much greater than salt intrusion on C and N cycling. Wet–dry treatments significantly decreased DOC production and total CH4-C loss, aromatic and humic-like substance compounds in DOC were increased in both flooding and wet–dry treatments after 60-d incubation. The molecular weight (MW) of DOC, as indicated by E2/E3 ratio and spectral slope, after flooding treatments was higher than that in wet–dry treatments. A first order kinetic model showed there was a positive linear correlation (r2 = 0.73) between CO2 emission rate and DOC concentration which indicated that CO2was mainly generated from DOC. An exponential kinetic model was applied to describe the correlation between CH4 emission rate and DOC concentration (r2 = 0.41). This study demonstrates that an increase in salinity, and in particular variations in wet–dry cycles, will lead to changes in the formation of climate-relevant greenhouse gases, such as CH4, CO2, and N2O

Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns

We introduce Deep Thermal Imaging, a new approach for close-range automatic recognition of materials to enhance the understanding of people and ubiquitous technologies of their proximal environment. Our approach uses a low-cost mobile thermal camera integrated into a smartphone to capture thermal textures. A deep neural network classifies these textures into material types. This approach works effectively without the need for ambient light sources or direct contact with materials. Furthermore, the use of a deep learning network removes the need to handcraft the set of features for different materials. We evaluated the performance of the system by training it to recognise 32 material types in both indoor and outdoor environments. Our approach produced recognition accuracies above 98% in 14,860 images of 15 indoor materials and above 89% in 26,584 images of 17 outdoor materials. We conclude by discussing its potentials for real-time use in HCI applications and future directions.Comment: Proceedings of the 2018 CHI Conference on Human Factors in Computing System

Coupling Coarse-Grained to Fine-Grained Models via Hamiltonian Replica Exchange

The energy landscape of biomolecular systems contains many local minima that are separated by high energy barriers. Sampling this landscape in molecular dynamics simulations is a challenging task, and often requires the use of enhanced sampling techniques. Here, we increase the sampling efficiency by coupling the fine-grained (FG) GROMOS force field to the coarse-grained (CG) Martini force field via the Hamiltonian replica exchange method (HREM). We tested the efficiency of this procedure using a lutein/octane system. In traditional simulations, cis-trans transitions of lutein are barely observed due to the high energy barrier separating these states. However, many of these transitions are sampled with our HREM scheme. The proposed method offers new possibilities for enhanced sampling of biomolecular conformations, making use of CG models without compromising the accuracy of the FG model