Effectiveness of landmark analysis for establishing locality in p2p networks

Locality to other nodes on a peer-to-peer overlay network can be established by means of a set of landmarks shared among the participating nodes. Each node independently collects a set of latency measures to landmark nodes, which are used as a multi-dimensional feature vector. Each peer node uses the feature vector to generate a unique scalar index which is correlated to its topological locality. A popular dimensionality reduction technique is the space filling Hilbert’s curve, as it possesses good locality preserving properties. However, there exists little comparison between Hilbert’s curve and other techniques for dimensionality reduction. This work carries out a quantitative analysis of their properties. Linear and non-linear techniques for scaling the landmark vectors to a single dimension are investigated. Hilbert’s curve, Sammon’s mapping and Principal Component Analysis have been used to generate a 1d space with locality preserving properties. This work provides empirical evidence to support the use of Hilbert’s curve in the context of locality preservation when generating peer identifiers by means of landmark vector analysis. A comparative analysis is carried out with an artificial 2d network model and with a realistic network topology model with a typical power-law distribution of node connectivity in the Internet. Nearest neighbour analysis confirms Hilbert’s curve to be very effective in both artificial and realistic network topologies. Nevertheless, the results in the realistic network model show that there is scope for improvements and better techniques to preserve locality information are required.

A suite of parallel vectors for baculovirus expression

The expression of proteins using recombinant baculoviruses is a mature and widely used technology. However, some aspects of the technology continue to detract from high throughput use and the basis of the final observed expression level is poorly understood. Here, we describe the design and use of a set of vectors developed around a unified cloning strategy that allow parallel expression of target proteins in the baculovirus system as N-terminal or C-terminal fusions. Using several protein kinases as tests we found that amino-terminal fusion to maltose binding protein rescued expression of the poorly expressed human kinase Cot but had only a marginal effect on expression of a well-expressed kinase IKK-2. In addition, MBP fusion proteins were found to be secreted from the expressing cell. Use of a carboxyl-terminal GFP tagging vector showed that fluorescence measurement paralleled expression level and was a convenient readout in the context of insect cell expression, an observation that was further supported with additional non-kinase targets. The expression of the target proteins using the same vectors in vitro showed that differences in expression level were wholly dependent on the environment of the expressing cell and an investigation of the time course of expression showed it could affect substantially the observed expression level for poorly but not well-expressed proteins. Our vector suite approach shows that rapid expression survey can be achieved within the baculovirus system and in addition, goes some way to identifying the underlying basis of the expression level obtained. (c) 2006 Elsevier Inc. All rights reserved.

A family of promoter probe vectors incorporating autofluorescent and chromogenic reporter proteins for studying gene expression in Gram-negative bacteria

A series of promoter probe vectors for use in Gram-negative bacteria has been made in two broad-host-range vectors, pOT (pBBR replicon) and pJP2 (incP replicon). Reporter fusions can be made to gfpUV, gfprnut3.1, unstable gfpmut3.1 variants (LAA, LVA, AAV and ASV), gfp+, dsRed2, dsRedT3, dsRedT4, mRFP1, gusA or lacZ. The two vector families, pOT and pJP2, are compatible with one another and share the same polylinker for facile interchange of promoter regions. Vectors based on pJP2 have the advantage of being ultra-stable in the environment due to the presence of the parABCDE genes. As a confirmation of their usefulness, the dicarboxylic acid transport system promoter (dctA(p)) was cloned into a pOT (pRU1097)- and a pJP2 (pRU1156)-based vector and shown to be expressed by Rhizobium leguminosarum in infection threads of vetch. This indicates the presence of dicarboxylates at the earliest stages of nodule formation.

Chromatographic alignment of LC-MS and LC-MS/MS datasets by genetic algorithm feature extraction

Liquid chromatography-mass spectrometry (LC-MS) datasets can be compared or combined following chromatographic alignment. Here we describe a simple solution to the specific problem of aligning one LC-MS dataset and one LC-MS/MS dataset, acquired on separate instruments from an enzymatic digest of a protein mixture, using feature extraction and a genetic algorithm. First, the LC-MS dataset is searched within a few ppm of the calculated theoretical masses of peptides confidently identified by LC-MS/MS. A piecewise linear function is then fitted to these matched peptides using a genetic algorithm with a fitness function that is insensitive to incorrect matches but sufficiently flexible to adapt to the discrete shifts common when comparing LC datasets. We demonstrate the utility of this method by aligning ion trap LC-MS/MS data with accurate LC-MS data from an FTICR mass spectrometer and show how hybrid datasets can improve peptide and protein identification by combining the speed of the ion trap with the mass accuracy of the FTICR, similar to using a hybrid ion trap-FTICR instrument. We also show that the high resolving power of FTICR can improve precision and linear dynamic range in quantitative proteomics. The alignment software, msalign, is freely available as open source.

Determining feature relevance for the grouping of motor unit action potentials through generative topographic mapping

The study of motor unit action potential (MUAP) activity from electrornyographic signals is an important stage on neurological investigations that aim to understand the state of the neuromuscular system. In this context, the identification and clustering of MUAPs that exhibit common characteristics, and the assessment of which data features are most relevant for the definition of such cluster structure are central issues. In this paper, we propose the application of an unsupervised Feature Relevance Determination (FRD) method to the analysis of experimental MUAPs obtained from healthy human subjects. In contrast to approaches that require the knowledge of a priori information from the data, this FRD method is embedded on a constrained mixture model, known as Generative Topographic Mapping, which simultaneously performs clustering and visualization of MUAPs. The experimental results of the analysis of a data set consisting of MUAPs measured from the surface of the First Dorsal Interosseous, a hand muscle, indicate that the MUAP features corresponding to the hyperpolarization period in the physisiological process of generation of muscle fibre action potentials are consistently estimated as the most relevant and, therefore, as those that should be paid preferential attention for the interpretation of the MUAP groupings.

Evolving features-algorithms knowledge map to support NIDS data intelligence and learning loop architecting – a generalised approach to NIDS pattern feature space knowledge processing

This paper describes a proposed new approach to the Computer Network Security Intrusion Detection Systems (NIDS) application domain knowledge processing focused on a topic map technology-enabled representation of features of the threat pattern space as well as the knowledge of situated efficacy of alternative candidate algorithms for pattern recognition within the NIDS domain. Thus an integrative knowledge representation framework for virtualisation, data intelligence and learning loop architecting in the NIDS domain is described together with specific aspects of its deployment.

A comparison between feature-based and EM-based contour tracking

Most active-contour methods are based either on maximizing the image contrast under the contour or on minimizing the sum of squared distances between contour and image 'features'. The Marginalized Likelihood Ratio (MLR) contour model uses a contrast-based measure of goodness-of-fit for the contour and thus falls into the first class. The point of departure from previous models consists in marginalizing this contrast measure over unmodelled shape variations. The MLR model naturally leads to the EM Contour algorithm, in which pose optimization is carried out by iterated least-squares, as in feature-based contour methods. The difference with respect to other feature-based algorithms is that the EM Contour algorithm minimizes squared distances from Bayes least-squares (marginalized) estimates of contour locations, rather than from 'strongest features' in the neighborhood of the contour. Within the framework of the MLR model, alternatives to the EM algorithm can also be derived: one of these alternatives is the empirical-information method. Tracking experiments demonstrate the robustness of pose estimates given by the MLR model, and support the theoretical expectation that the EM Contour algorithm is more robust than either feature-based methods or the empirical-information method. (c) 2005 Elsevier B.V. All rights reserved.

Localization to the nucleolus is a common feature of coronavirus nucleoproteins and the protein may disrupt host cell division

The subcellular localization of transmissible gastroenteritis virus (TGEV) and mouse hepatitis virus (MHV) (group I and group II coronaviruses, respectively) nucleoproteins (N proteins) were examined by confocal microscopy. The proteins were shown to localize either to the cytoplasm alone or to the cytoplasm and a structure in the nucleus. This feature was confirmed to be the nucleolus by using specific antibodies to nucleolin, a major component of the nucleolus, and by confocal microscopy to image sections through a cell expressing N protein. These findings are consistent with our previous report for infectious bronchitis virus (group III coronavirus) (J. A. Hiscox et al., J. Virol. 75:506-512, 2001), indicating that nucleolar localization of the N protein is a common feature of the coronavirus family and is possibly of functional significance. Nucleolar localization signals were identified in the domain III region of the N protein from all three coronavirus groups, and this suggested that transport of N protein to the nucleus might be an active process. In addition, our results suggest that the N protein might function to disrupt cell division. Thus, we observed that approximately 30% of cells transfected with the N protein appeared to be undergoing cell division. The most likely explanation for this is that the N protein induced a cell cycle delay or arrest, most likely in the G2/M phase. In a fraction of transfected cells expressing coronavirus N proteins, we observed multinucleate cells and dividing cells with nucleoli (which are only present during interphase). These findings are consistent with the possible inhibition of cytokinesis in these cells.

Estimating climatically relevant singular vectors for decadal predictions of the Atlantic Ocean

A key aspect in designing an ecient decadal prediction system is ensuring that the uncertainty in the ocean initial conditions is sampled optimally. Here, we consider one strategy to address this issue by investigating the growth of optimal perturbations in the HadCM3 global climate model (GCM). More specically, climatically relevant singular vectors (CSVs) - the small perturbations which grow most rapidly for a specic initial condition - are estimated for decadal timescales in the Atlantic Ocean. It is found that reliable CSVs can be estimated by running a large ensemble of integrations of the GCM. Amplication of the optimal perturbations occurs for more than 10 years, and possibly up to 40 years. The identi ed regions for growing perturbations are found to be in the far North Atlantic, and these perturbations cause amplication through an anomalous meridional overturning circulation response. Additionally, this type of analysis potentially informs the design of future ocean observing systems by identifying the sensitive regions where small uncertainties in the ocean state can grow maximally. Although these CSVs are expensive to compute, we identify ways in which the process could be made more ecient in the future.

Riemann solvers with primitive parameter vectors for two-dimensional compressible flows of a real gas

An analysis of averaging procedures is presented for an approximate Riemann solver for the equations governing the compressible flow of a real gas. This study extends earlier work for the Euler equations with ideal gases.

A novel shape feature for fast region-based pedestrian recognition

A new class of shape features for region classification and high-level recognition is introduced. The novel Randomised Region Ray (RRR) features can be used to train binary decision trees for object category classification using an abstract representation of the scene. In particular we address the problem of human detection using an over segmented input image. We therefore do not rely on pixel values for training, instead we design and train specialised classifiers on the sparse set of semantic regions which compose the image. Thanks to the abstract nature of the input, the trained classifier has the potential to be fast and applicable to extreme imagery conditions. We demonstrate and evaluate its performance in people detection using a pedestrian dataset.

Neural network feature maps for Chinese phonemes

It has been shown through a number of experiments that neural networks can be used for a phonetic typewriter. Algorithms can be looked on as producing self-organizing feature maps which correspond to phonemes. In the Chinese language the utterance of a Chinese character consists of a very simple string of Chinese phonemes. With this as a starting point, a neural network feature map for Chinese phonemes can be built up. In this paper, feature map structures for Chinese phonemes are discussed and tested. This research on a Chinese phonetic feature map is important both for Chinese speech recognition and for building a Chinese phonetic typewriter.

Using self-organising feature maps for the control of artificial organisms

Variations on the standard Kohonen feature map can enable an ordering of the map state space by using only a limited subset of the complete input vector. Also it is possible to employ merely a local adaptation procedure to order the map, rather than having to rely on global variables and objectives. Such variations have been included as part of a hybrid learning system (HLS) which has arisen out of a genetic-based classifier system. In the paper a description of the modified feature map is given, which constitutes the HLSs long term memory, and results in the control of a simple maze running task are presented, thereby demonstrating the value of goal related feedback within the overall network.