Exploiting affinity propagation for energy-efficient information discovery in sensor networks

Wireless sensor networks (WSN) are attractive for information gathering in large-scale data rich environments. Emerging WSN applications require dissemination of information to interested clients within the network requiring support for differing traffic patterns. Further, in-network query processing capabilities are required for autonomic information discovery. In this paper, we formulate the information discovery problem as a load-balancing problem, with the combined aim being to maximize network lifetime and minimize query processing delay. We propose novel methods for data dissemination, information discovery and data aggregation that are designed to provide significant QoS benefits. We make use of affinity propagation to group "similar" sensors and have developed efficient mechanisms that can resolve both ALL-type and ANY-type queries in-network with improved energy-efficiency and query resolution time. Simulation results prove the proposed method(s) of information discovery offer significant QoS benefits for ALL-type and ANY-type queries in comparison to previous approaches.

Discovery of latent subcommunities in a blog's readership

The blogosphere has grown to be a mainstream forum of social interaction as well as a commercially attractive source of information and influence. Tools are needed to better understand how communities that adhere to individual blogs are constituted in order to facilitate new personal, socially-focused browsing paradigms, and understand how blog content is consumed, which is of interest to blog authors, big media, and search. We present a novel approach to blog subcommunity characterization by modeling individual blog readers using mixtures of an extension to the LDA family that jointly models phrases and time, Ngram Topic over Time (NTOT), and cluster with a number of similarity measures using Affinity Propagation. We experiment with two datasets: a small set of blogs whose authors provide feedback, and a set of popular, highly commented blogs, which provide indicators of algorithm scalability and interpretability without prior knowledge of a given blog. The results offer useful insight to the blog authors about their commenting community, and are observed to offer an integrated perspective on the topics of discussion and members engaged in those discussions for unfamiliar blogs. Our approach also holds promise as a component of solutions to related problems, such as online entity resolution and role discovery.

Flickr hypergroups

The amount of multimedia content available online constantly increases, and this leads to problems for users who search for content or similar communities. Users in Flickr often self-organize in user communities through Flickr Groups. These groups are particularly interesting as they are a natural instantiation of the content + relations social media paradigm. We propose a novel approach to group searching through hypergroup discovery. Starting from roughly 11,000 Flickr groups' content and membership information, we create three different bag-of-word representations for groups, on which we learn probabilistic topic models. Finally, we cast the hypergroup discovery as a clustering problem that is solved via probabilistic affinity propagation. We show that hypergroups so found are generally consistent and can be described through topic-based and similarity-based measures. Our proposed solution could be relatively easily implemented as an application to enrich Flickr's traditional group search.

A framework for identifying affinity classes of inorganic materials binding peptide sequences

With the rapid development of bionanotechnology, there has been a growing interest recently in identifying the affinity classes of the inorganic materials binding peptide sequences. However, there are some distinct characteristics of inorganic materials binding sequence data that limit the performance of many widely-used classification methods. In this paper, we propose a novel framework to predict the affinity classes of peptide sequences with respect to an associated inorganic material. We first generate a large set of simulated peptide sequences based on our new amino acid transition matrix, and then the probability of test sequences belonging to a specific affinity class is calculated through solving an objective function. In addition, the objective function is solved through iterative propagation of probability estimates among sequences and sequence clusters. Experimental results on a real inorganic material binding sequence dataset show that the proposed framework is highly effective on identifying the affinity classes of inorganic material binding sequences.

Identifying affinity classes of inorganic materials binding sequences via a graph-based model

Rapid advances in bionanotechnology have recently generated growing interest in identifying peptides that bind to inorganic materials and classifying them based on their inorganic material affinities. However, there are some distinct characteristics of inorganic materials binding sequence data that limit the performance of many widely-used classification methods when applied to this problem. In this paper, we propose a novel framework to predict the affinity classes of peptide sequences with respect to an associated inorganic material. We first generate a large set of simulated peptide sequences based on an amino acid transition matrix tailored for the specific inorganic material. Then the probability of test sequences belonging to a specific affinity class is calculated by minimizing an objective function. In addition, the objective function is minimized through iterative propagation of probability estimates among sequences and sequence clusters. Results of computational experiments on two real inorganic material binding sequence data sets show that the proposed framework is highly effective for identifying the affinity classes of inorganic material binding sequences. Moreover, the experiments on the structural classification of proteins (SCOP) data set shows that the proposed framework is general and can be applied to traditional protein sequences.

Specificity and affinity motifs for Grb2 SH2-ligand interactions

Protein–protein interactions are often mediated by the recognition of short continuous amino acid stretches on target proteins by specific binding domains. Affinity-based selection strategies have successfully been used to define recognition motifs for a large series of such protein domains. However, in many biological systems specificity of interaction may be of equal or greater importance than affinity. To address this issue we have developed a peptide library screening technology that can be used to directly define ligands for protein domains based on both affinity and specificity of interaction. We demonstrate the value of this approach by the selection of peptide ligands that are either highly specific for the Grb2 Src homology 2 (SH2) domain or that are cross-reactive between a group of related SH2 domains. Examination of previously identified physiological ligands for the Grb2 SH2 domain suggests that for these ligands regulation of the specificity of interaction may be an important factor for in vivo ligand selection.

Propagation characteristics of lamb waves in stringer-stiffened panels

Stringer-stiffened plate-like structure is a typical engineering structure and its structural integrity is critical. A guided Lamb wave-based damage identification scheme and an online structural health monitoring (SHM) system with an integrated PZT-sensor network were developed. In the previous studies, the specimens were relatively simple. In this paper, the above mentioned method was extended to the stiffened plate-like structure—a flat plate reinforced by stringer. FE dynamic simulation was applied to investigate the Lamb wave propagation characteristics due to the existence of stringer with the consideration of its material and geometric configurations.

The propagation of technology management taxonomies for evaluating investments in information systems

To provide managers with a critical insight into the management of new technology, this paper uses a case study research strategy to examine the technology management experiences of a leading UK manufacturing organization during its adoption of a vendor-supplied Manufacturing Resource Planning information system.

Ni2+-based immobilized metal ion affinity chromatography of lactose operon repressor protein from Escherichia coli

A two-step chromatographic sequence is described for the purification of native lactose operon repressor protein from Escherichia coli cells. The first step involves Ni2+-based immobilized metal ion affinity chromatography of the soluble cytoplasmic extract. This method provides superior speed, resolution and yield than the established phosphocellulose cation-exchange chromatographic procedure. Anion-exchange chromatography is used for further purification to >95% purity. The identity and purity of the lactose repressor protein were demonstrated using sodium dodecylsulphate polyacrylamide electrophoresis, crystallization, tryptic finger-printing mass spectrometry, and inducer binding assays. The purified lac repressor exhibited inducer sensitivity for operator DNA binding and undergoes a conformational change upon inducer binding. By all these extensive biochemical criteria, the purified protein behaves exactly as that described for the Escherichia coli lactose operon repressor.

Apparent in vivo Δ-6 desaturase activity, efficiency, and affinity are affected by total dietary C18 PUFA in the freshwater fish Murray cod

Dietary fatty acids are known to modulate fatty acid metabolism in fish. However, the innate capability of fish to bioconvert short chain fatty acids to health promoting long chain fatty acids (LCPUFA) is insufficient to compensate for a reduced dietary intake. While many studies have focused on the dietary regulation of the fatty acid bioconversion pathways, there is little known regarding the effects of the dietary levels of C18 polyunsaturated fatty acids (PUFA) on fatty acid metabolism. Here, we show a greater degree of apparent enzyme activity (Δ-6 desaturase) in fish fed a diet with higher amounts of dietary C18 PUFA. In particular, fish receiving high amounts of dietary C18 PUFA had a greater amount of Δ-6 desaturase activity acting on 18:3n-3 than 18:2n-6. However, with the gradual reduction of dietary C18 PUFA there was a shift in substrate preference of Δ-6 desaturase from 18:3n-3 to 18:2n-6. This information will provide valuable insight for the implementation of low fish oil diets, which permit the maintenance of n-3 LCPUFA levels in farmed Murray cod.

Nutritional related aspects of the artificial propagation and larval rearing of Macquarie perch, Macquaria Australasica (Cuvier, 1830)

Studies the underlying role of nutrition in the lack of response of captive fish to hypophysation. Aspects studied include morphological characteristics, histology of ovaries, proximate analysis, fatty and amino acid profiles of oocytes, muscle, liver and diets of wild and tank-reared fish, egg and larval quality, amino acid composition of eggs and larvae at different developmental stages, larval feeding and hormone treatments.