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Recombinant protein polymers were synthesized and examined under various loading conditions to assess the mechanical stability and deformation responses of physically cross-linked, hydrated, protein polymer networks designed as triblock copolymers with central elastomeric and flanking plastic-like blocks. Uniaxial stress-strain properties, creep and stress(More)
Synthesis and characterization of cinnamated Type I collagen and its related mechanical properties after photomediated crosslinking were investigated in detail. Using an EDC/NHS conjugation method, collagen was chemically modified to incorporate a photosensitive cinnamate moiety. The cinnamated collagen was fully characterized by 1H NMR, UV-vis, and(More)
Physically cross-linked protein-based materials possess a number of advantages over their chemically cross-linked counterparts, including ease of processing and the ability to avoid the addition or removal of chemical reagents or unreacted intermediates. The investigations reported herein sought to examine the nature of physical cross-links within two-phase(More)
A one-pot affinity precipitation purification of carbohydrate-binding protein was demonstrated by designing thermally responsive glyco-polypeptide polymers, which were synthesized by selective coupling of pendant carbohydrate groups to a recombinant elastin-like triblock protein copolymer (ELP). The thermally driven inverse transition temperature of the(More)
An optimal control approach is used to solve the problem of routing in sensor networks where the goal is to maximize the network’s lifetime. We show that in a fixed topology case there exists an optimal policy consisting of fixed routing probabilities which may be obtained by solving a set of relatively simple Non-Linear Programming (NLP) problems. An(More)
Most language models used for natural language processing are continuous. However, the assumption of such kind of models is too simple to cope with data sparsity problem. Although many useful smoothing techniques are developed to estimate these unseen sequences, it is still important to make full use of contex-tual information in training data. In this(More)
For relieving data sparsity problem, Hierarchical Word Sequence (abbreviated as HWS) language model, which uses word frequency information to convert raw sentences into special n-gram sequences, can be viewed as an effective alternative to normal n-gram method. In this paper, we use directional information to make HWS models more syntactically appropriate(More)
Language modeling is a fundamental research problem that has wide application for many NLP tasks. For estimating probabilities of natural language sentences, most research on language modeling use n-gram based approaches to factor sentence probabilities. However, the assumption under n-gram models is not robust enough to cope with the data sparseness(More)