Doping bioactive elements into a collagen scaffold based on synchronous self-assembly/mineralization for bone tissue engineering

Pure collagen is biocompatible but lacks inherent osteoinductive,osteoimmunomodulatory and antibacterial activities.To obtain collagen with these characteristics,we developed a novel methodology of doping bioactive elements into collagen through the synchronous self-assembly/mineralization(SSM)of collagen.In the SSM model,amorphous mineral nanoparticles(AMN)(amorphous SrCO3,amorphous Ag3PO4,etc.)stabilized by the polyampholyte,carboxymethyl chitosan(CMC),and collagen molecules were the primary components under acidic conditions.As the pH gradually increased,intrafibrillar mineralization occurred via the self-adaptive interaction between the AMNs and the collagen microfibrils,which were self-assembling;the AMNs wrapped around the microfibrils became situated in the gap zones of collagen and finally transformed into crystals.Srdoped collagen scaffolds(Sr-CS)promoted in vitro cell proliferation and osteogenic differentiation of rat bone marrow mesenchymal stromal cells(rBMSCs)and synergistically improved osteogenesis of rBMSCs by altering the macrophage response.Ag-doped collagen scaffolds(Ag-CS)exhibited in vitro antibacterial effects on S.aureus,as well as cell/tissue compatibility.Moreover,Sr-CS implanted into the calvarial defect of a rat resulted in improved bone regeneration.Therefore,the SSM model is a de novo synthetic strategy for doping bioactive elements into collagen,and can be used to fabricate multifunctional collagen scaffolds to meet the clinical challenges of encouraging osteogenesis,boosting the immune response and fighting severe infection in bone defects.

DEEPEYE: An Automatic Big Data Visualization Framework

Data visualization transforms data into images to aid the understanding of data; therefore, it is an invaluable tool for explaining the significance of data to visually inclined people. Given a(big) dataset, the essential task of visualization is to visualize the data to tell compelling stories by selecting, filtering, and transforming the data, and picking the right visualization type such as bar charts or line charts. Our ultimate goal is to automate this task that currently requires heavy user intervention in the existing visualization systems. An evolutionized system in the field faces the following three main challenges:(1) Visualization verification: to determine whether a visualization for a given dataset is interesting, from the viewpoint of human understanding;(2) Visualization search space: a "boring" dataset may become interesting after an arbitrary combination of operations such as selections,joins, and aggregations, among others;(3) On-time responses: do not deplete the user's patience. In this paper,we present the DEEPEYE system to address these challenges. This system solves the first challenge by training a binary classifier to decide whether a particular visualization is good for a given dataset, and by using a supervised learning to rank model to rank the above good visualizations. It also considers popular visualization operations, such as grouping and binning, which can manipulate the data, and this will determine the search space. Our proposed system tackles the third challenge by incorporating database optimization techniques for sharing computations and pruning.