We demonstrate high efficiency and simplicity of the thiol-epoxy reaction towards preparation of a wide range of main-chain as well as end-chain multifunctional polymers.

In this study, we investigate the potential of an artificial structural motif, azobenzene, in the preparation of enzyme sensitive polymeric nanostructures. For this purpose, an azobenzene linkage is established at the copolymer junction of an amphiphilic diblock copolymer. This polymer assembles into a micellar structure in water. Treatment with the enzyme azoreductase, in the presence of coenzyme NADPH, results in the cleavage of the azo-based copolymer junction and disruption of the micellar assembly. These results suggest that azobenezene is a useful non-natural structural motif for the preparation of enzyme responsive polymer nanoparticles. Due to the presence of azoreductase in the human intestine, such nanomaterials are anticipated to find applicability in the arena of colon-specific delivery systems.

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Searching and mining large graphs today is critical to a variety of application domains, ranging from community detection in social networks, to de novo genome sequence assembly. Scalable processing of large graphs requires careful partitioning and distribution of graphs across clusters. In this paper, we investigate the problem of managing large-scale graphs in clusters and study access characteristics of local graph queries such as breadth-first search, random walk, and SPARQL queries, which are popular in real applications. These queries exhibit strong access locality, and therefore require specific data partitioning strategies. In this work, we propose a Self Evolving Distributed Graph Management Environment (Sedge), to minimize inter-machine communication during graph query processing in multiple machines. In order to improve query response time and throughput, Sedge introduces a two-level partition management architecture with complimentary primary partitions and dynamic secondary partitions. These two kinds of partitions are able to adapt in real time to changes in query workload. Sedge also includes a set of workload analyzing algorithms whose time complexity is linear or sublinear to graph size. Empirical results show that it significantly improves distributed graph processing on today's commodity clusters.

Know where to fold 'em: A foldamer exhibiting a light‐induced helix–coil transition (see scheme) can be constructed by introducing a photochromic azobenzene moiety (red) into the center of an amphiphilic phenylene ethynylene backbone (blue). This system gives insight into folding and unfolding mechanisms and promises applications in photoresponsive (bio)materials and “smart” delivery devices based on photoresponsive dynamic receptors.

ABSTRACT:We report on the strong segregation of core− shell Au nanoparticles, with a shell layer consisting of a random copolymer brush of styrene and vinylphenol (PS-rPVPh-SH), in poly(styrene-b-2-vinylpyridine) (PS-b-P2VP) diblock copolymer. Because of the formation of multiple hydrogen bonds between the hydroxyl groups within the shell of the nanoparticles and the pyridine group in PS-b-P2VP, the Au nanoparticles were strongly localized into P2VP domains with a very high volume fraction of nanoparticles (ϕ p ∼ 0.53). The spatial distribution of Au nanoparticles, observed by transmission electron microscopy (TEM), is compared with results of previous experiments where homopolymers were blended with block copolymers. If the diameter d of the nanoparticles is much less than the width D of the P2VP lamellar domains, these nanoparticles are more uniformly distributed across the P2VP domain than if d is comparable to D, in which case the nanoparticles are pushed toward the center of the P2VP domains. This behavior is similar to that observed when homopolymers are blended with block copolymers. Novel morphological transitions from spherical to cylindrical P2VP morphologies and from lamellae to cylindrical PS morphologies were observed during coassembly of these functional nanoparticles with block copolymers.

Cloud computing promises high scalability, flexibility and cost-effectiveness to satisfy emerging computing requirements. To efficiently provision computing resources in the cloud, system administrators need the capabilities of characterizing and predicting workload on the Virtual Machines (VMs). In this paper, we use data traces obtained from a real data center to develop such capabilities. First, we search for repeatable workload patterns by exploring cross-VM workload correlations resulted from the dependencies among applications running on different VMs. Treating workload data samples as time series, we develop a co-clustering technique to identify groups of VMs that frequently exhibit correlated workload patterns, and also the time periods in which these VM groups are active. Then, we introduce a method based on Hidden Markov Modeling (HMM) to characterize the temporal correlations in the discovered VM clusters and to predict variations of workload patterns. The experimental results show that our method can not only help better understand group-level workload characteristics, but also make more accurate predictions on workload changes in a cloud.

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