A heterogeneous peer-to-peer network testbed

In this paper, we describe a heterogeneous peer-to-peer network testbed, which is developed as part of a joint research project to investigate novel resource discovery and content distribution protocols in a heterogeneous wired/wireless environment. We describe the testbed requirements, the testbed architecture, the multi-functional wireless node, and the software architecture. We also describe some of the proposed protocols to be developed and tested on the testbed. © 2009 IEEE.published_or_final_versionThe 1st International Conference on Ubiquitous and Future Networks (ICUFN 2009), Hong Kong, 7-9 June 2009. In Proceedings of the 1st ICUFN, 2009, p. 46-5

Pre-classification module for an all-season image retrieval system

Author name used in this publication: Zheru ChiAuthor name used in this publication: Dagan FengCentre for Multimedia Signal Processing, Department of Electronic and Information EngineeringRefereed conference paper2007-2008 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Identifying rodent olfactory bulb structures with micro-DTI

Conference Theme: Personalized Healthcare Through TechnologyOlfactory bulb (OB) is one of the most developed systems in rodent models with complex neuronal organization and anatomical structures. MR diffusion tensor imaging (DTI) is a non-invasive technique to probe tissue microstructures by examining the diffusion characteristics of water molecules. This paper presents how different OB layers can be identified and quantitatively characterized by micro-DTI using a specially constructed micro-imaging radio frequency (RF) coil. High spatial resolution and high signal to noise ratio (SNR) DTI images of ex vivo rat OBs were obtained. Distinct contrasts were observed between various olfactory bulb layers in trace map, fractional anisotropy (FA) map and FA color map, all in consistence with the known OB neuroanatomy. These experimental results demonstrate the utility of micro-DTI in investigation of complex OB organization. © 2008 IEEE.published_or_final_versio

Large magnetic entropy and electron-phonon coupling in Gd-based metallic glass

Author name used in this publication: Chan, K. C.2012-2013 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Molecular lens applied to benzene and carbon disulfide molecular beams

A molecular lens of the nonresonant dipole force formed by focusing a nanosecond IR laser pulse has been applied to benzene and CS2 molecular beams. Using the velocity map imaging technique for molecular ray tracing, characteristic molecular lens parameters including the focal length (f ), minimum beam width (W), and distance to the minimum beam width position (D) were determined. The laser intensity dependence of the observed lens parameters was in good agreement with theoretical predictions. W was independent of the laser peak intensity (I-0), whereas f and D varied linearly with 1/I-0. The differences in lens parameters between the molecular species were well correlated with the polarizability per mass values of the molecules. A high chromatographic resolution of Rs = 0.84 was achieved between the images of benzene molecular beams undeflected and deflected by the lens. The possibilities for a new type of chromatography are discussed.open293

Prediction of Protein Domain with mRMR Feature Selection and Analysis

The domains are the structural and functional units of proteins. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop effective methods for predicting the protein domains according to the sequences information alone, so as to facilitate the structure prediction of proteins and speed up their functional annotation. However, although many efforts have been made in this regard, prediction of protein domains from the sequence information still remains a challenging and elusive problem. Here, a new method was developed by combing the techniques of RF (random forest), mRMR (maximum relevance minimum redundancy), and IFS (incremental feature selection), as well as by incorporating the features of physicochemical and biochemical properties, sequence conservation, residual disorder, secondary structure, and solvent accessibility. The overall success rate achieved by the new method on an independent dataset was around 73%, which was about 28–40% higher than those by the existing method on the same benchmark dataset. Furthermore, it was revealed by an in-depth analysis that the features of evolution, codon diversity, electrostatic charge, and disorder played more important roles than the others in predicting protein domains, quite consistent with experimental observations. It is anticipated that the new method may become a high-throughput tool in annotating protein domains, or may, at the very least, play a complementary role to the existing domain prediction methods, and that the findings about the key features with high impacts to the domain prediction might provide useful insights or clues for further experimental investigations in this area. Finally, it has not escaped our notice that the current approach can also be utilized to study protein signal peptides, B-cell epitopes, HIV protease cleavage sites, among many other important topics in protein science and biomedicine

Epigenetic memory in induced pluripotent stem cells.

Somatic cell nuclear transfer and transcription-factor-based reprogramming revert adult cells to an embryonic state, and yield pluripotent stem cells that can generate all tissues. Through different mechanisms and kinetics, these two reprogramming methods reset genomic methylation, an epigenetic modification of DNA that influences gene expression, leading us to hypothesize that the resulting pluripotent stem cells might have different properties. Here we observe that low-passage induced pluripotent stem cells (iPSCs) derived by factor-based reprogramming of adult murine tissues harbour residual DNA methylation signatures characteristic of their somatic tissue of origin, which favours their differentiation along lineages related to the donor cell, while restricting alternative cell fates. Such an 'epigenetic memory' of the donor tissue could be reset by differentiation and serial reprogramming, or by treatment of iPSCs with chromatin-modifying drugs. In contrast, the differentiation and methylation of nuclear-transfer-derived pluripotent stem cells were more similar to classical embryonic stem cells than were iPSCs. Our data indicate that nuclear transfer is more effective at establishing the ground state of pluripotency than factor-based reprogramming, which can leave an epigenetic memory of the tissue of origin that may influence efforts at directed differentiation for applications in disease modelling or treatment

A Pair of Measures of Rotational Error for Axisymmetric Robot End-Effectors

International audienceThis paper deals with the problem of representing the rotational error of spatial robots with three orientational degrees of freedom (DOF). Typically, the errors on each of three Euler angles defining the orientation of an end-effector are analysed separately. However, this is wrong since an accuracy measure should depend only on the "distance" between the nominal pose and the actual one, and not on the choice of reference frame in which these are represented. Several bi-invariant metrics for rotational error exist but are single-parameter and, by definition, disregard the shape of the robot end-effector. Yet, robot end-effectors are typically axisymmetric. Therefore, we propose a two-parameter measure of rotational errors that is better suited for such robot end-effectors

UNCLES: Method for the identification of genes differentially consistently co-expressed in a specific subset of datasets

Background: Collective analysis of the increasingly emerging gene expression datasets are required. The recently proposed binarisation of consensus partition matrices (Bi-CoPaM) method can combine clustering results from multiple datasets to identify the subsets of genes which are consistently co-expressed in all of the provided datasets in a tuneable manner. However, results validation and parameter setting are issues that complicate the design of such methods. Moreover, although it is a common practice to test methods by application to synthetic datasets, the mathematical models used to synthesise such datasets are usually based on approximations which may not always be sufficiently representative of real datasets. Results: Here, we propose an unsupervised method for the unification of clustering results from multiple datasets using external specifications (UNCLES). This method has the ability to identify the subsets of genes consistently co-expressed in a subset of datasets while being poorly co-expressed in another subset of datasets, and to identify the subsets of genes consistently co-expressed in all given datasets. We also propose the M-N scatter plots validation technique and adopt it to set the parameters of UNCLES, such as the number of clusters, automatically. Additionally, we propose an approach for the synthesis of gene expression datasets using real data profiles in a way which combines the ground-truth-knowledge of synthetic data and the realistic expression values of real data, and therefore overcomes the problem of faithfulness of synthetic expression data modelling. By application to those datasets, we validate UNCLES while comparing it with other conventional clustering methods, and of particular relevance, biclustering methods. We further validate UNCLES by application to a set of 14 real genome-wide yeast datasets as it produces focused clusters that conform well to known biological facts. Furthermore, in-silico-based hypotheses regarding the function of a few previously unknown genes in those focused clusters are drawn. Conclusions: The UNCLES method, the M-N scatter plots technique, and the expression data synthesis approach will have wide application for the comprehensive analysis of genomic and other sources of multiple complex biological datasets. Moreover, the derived in-silico-based biological hypotheses represent subjects for future functional studies.The National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (Grant Reference Number RP-PG-0310-1004)