Development of Channeled Nanofibrous Scaffolds for Oriented Tissue Engineering

A tissue‐engineering scaffold resembling the structure of the natural extracellular matrix can often facilitate tissue regeneration. Nerve and tendon are oriented micro‐scale tissue bundles. In this study, a method combining injection molding and thermally induced phase separation techniques is developed to create single‐ and multiple‐channeled nanofibrous poly( L ‐lactic acid) scaffolds. The overall shape, the number and spatial arrangement of channels, the channel wall matrix architecture, the porosity and mechanical properties of the scaffolds are all tunable. The porous NF channel wall matrix provides an excellent microenvironment for protein adsorption and the attachment of PC12 neuronal cells and tendon fibroblast cells, showing potential for neural and tendon tissue regeneration. A method combining injection molding and thermally induced phase separation is developed to create single‐ and multiple‐channeled nanofibrous polymer scaffolds. The porous nanofibrous channel wall provides an excellent microenvironment for protein adsorption and cell attachment, showing potential for nerve and tendon regeneration.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/92054/1/761_ftp.pd

Influence of scale effect on flow field offset for ships in confined waters

To investigate the flow field characteristics of full-scale ships advancing through confined waters, the international standard container ship (KRISO Container Ship) was considered as a research object in this study. Using the RANS equation, the volume of fluid method and the body force method were selected to investigate the hydrodynamic characteristics of a model-scale ship (the model-scale ratio λ=31.6) and a full-scale ship advancing through confined waters at low speed. A virtual disk was used in the full-scale model to determine the influence of the propeller on the ship’s flow field. First, the feasibility of the numerical calculations was verified. This proves the feasibility of the numerical and grid division methods. The self-propulsion point of the full-scale ship at Fr=0.108 is determined. The calculation cases of model-scale and full-scale ships (with or without virtual disks) at different water depths and distances between the ship and the shore were calculated, and the changes in the hull surface pressure, the flow field around the ship, and the wake fraction near the ship propeller disk in different calculation cases were determined and compared. The variations in the surge force, sway force, and yaw moment between the model- scale and full-scale ships were generally consistent. In very shallow water (H/T=1.3), the non-dimensional force and moment coefficients for model-scale ships increase more rapidly with decreasing distance from shore, suggesting that using model-scale ships to investigate the wall effect in very shallow water will result in predictions that are biased towards safety. By comparing full-scale ships with and without propellers, it was discovered that the surge force, sway force, and yaw moment were marginally greater in the propeller-equipped ship due to the suction effect, and the accompanying flow before and after the propeller was slightly smaller, with less asymmetry

Deep Learning on Abnormal Chromosome Segments: An Intelligent Copy Number Variants Detection System Design

Gene testing emerged as a business in the last two decades, and the testing cost has been reduced from 100 million to 1000 dollars for the development of technologies. Preimplantation genetic screening (PGS) is a popular genetic profiling of embryos prior to implantation in gene testing. Copy number variants (CNVs) detection is a key task in PGS which still needs the manual operation and evaluation. At the same time, deep learning technology earns a booming development and wide application in recent years for its strong computing and learning capability. This research redesigns the PGS workflow with the intelligent CNVs detection system, and proposes the corresponding system framework. Deep learning is selected as the proper technology in the system design for CNVs detection, which also fit the task of denoising. The evaluation is conducted on simulation dataset with high accuracy and low time cost, which may achieve the requirements of clinical application and reduce the workload of bioinformatics experts. Moreover, the redesigned process and proposed framework may enlighten the intelligent system design for gene testing in following work, and provide a guidance of deep learning application in AI healthcar

Improving Mechanical Properties of Magnesium Phosphate Cement-Based Ultra-High Performance Concrete by Ultrafine Fly Ash Incorporation

Magnesium phosphate cement-based ultra-high-performance concrete (MPC-UHPC) develops strength rapidly but shows unsatisfactory ultimate strength (\u3c150 \u3eMPa). In this study, ultrafine fly ash (UFA) was incorporated in MPC-UHPC to improve mechanical properties. The effect of UFA content, varying from 0 % to 15 %, by weight of binder, on microstructure, fiber pullout behavior, mechanical strengths, flexural and tensile properties of MPC-UHPC reinforced with 2 vol% steel fibers was investigated. Experimental results indicate that the incorporation of 10 %–15 % UFA with mean particle size of 1.4 µm was effective in reducing porosity and microcracks of binder matrix and fiber–matrix interface, thereby resulting in significantly improved fiber–matrix bond properties. The 28-d average bond strength and pullout energy could increase by 27 % and 57 % at 15 % UFA addition, respectively. Such UFA addition level was found to yield a mix with 158.3 MPa compressive strength and substantially enhance the flexural and tensile fracture properties. The result reported herein will further promote the utilization of UFA for the development of MPC materials with outstanding mechanical properties

Inverse Martensitic Transformation in Zr Nanowires

Like martensitic transformations (MTs), inverse martensitic transformations (IMTs) are shear-dominant diffusionless transformations, but are driven by reduction in interfacial energies rather than bulk free energies, and exhibit distinctive behavior such as instantaneous initiation (like spinodal decomposition) and self-limiting lengthscale. Bulk Zr metal is known to undergo normal MT from the high-temperature bcc phase to the low-temperature hcp phase. Using molecular dynamics simulations we demonstrate that, unlike in the bulk, an IMT to the bcc structure can occur in (1100)-oriented hcp Zr nanowires at low temperatures, which is driven by the reduction in the nanowire surface energy. The bcc domains subsequently become distorted and transform into a new (1120)-oriented hcp domain, leading to reorientation of the nanowire. This behavior has implications for the study of structural transformations at the nanoscale and surface patterning