Bi-clustering of metabolic data using matrix factorization tools

Metabolic phenotyping technologies based on Nuclear Magnetic Spectroscopy (NMR) and Mass Spectrometry (MS) generate vast amounts of unrefined data from biological samples. Clustering strategies are frequently employed to provide insight into patterns of relationships between samples and metabolites. Here, we propose the use of a non-negative matrix factorization driven bi-clustering strategy for metabolic phenotyping data in order to discover subsets of interrelated metabolites that exhibit similar behaviour across samples. The proposed strategy incorporates bi-cross validation and statistical segmentation techniques to automatically determine the number and structure of bi-clusters. This alternative approach is in contrast to the widely used conventional clustering approaches that incorporate all molecular peaks for clustering in metabolic studies and require a priori specification of the number of clusters. We perform the comparative analysis of the proposed strategy with other bi-clustering approaches, which were developed in the context of genomics and transcriptomics research. We demonstrate the superior performance of the proposed bi-clustering strategy on both simulated (NMR) and real (MS) bacterial metabolic data

Multipole polarizability of a graded spherical particle

We have studied the multipole polarizability of a graded spherical particle in a nonuniform electric field, in which the conductivity can vary radially inside the particle. The main objective of this work is to access the effects of multipole interactions at small interparticle separations, which can be important in non-dilute suspensions of functionally graded materials. The nonuniform electric field arises either from that applied on the particle or from the local field of all other particles. We developed a differential effective multipole moment approximation (DEMMA) to compute the multipole moment of a graded spherical particle in a nonuniform external field. Moreover, we compare the DEMMA results with the exact results of the power-law graded profile and the agreement is excellent. The extension to anisotropic DEMMA will be studied in an Appendix.Comment: LaTeX format, 2 eps figures, submitted for publication

Nonlinear alternating current responses of graded materials

When a composite of nonlinear particles suspended in a host medium is subjected to a sinusoidal electric field, the electrical response in the composite will generally consist of alternating current (AC) fields at frequencies of higher-order harmonics. The situation becomes more interesting when the suspended particles are graded, with a spatial variation in the dielectric properties. The local electric field inside the graded particles can be calculated by the differential effective dipole approximation, which agrees very well with a first-principles approach. In this work, a nonlinear differential effective dipole approximation and a perturbation expansion method have been employed to investigate the effect of gradation on the nonlinear AC responses of these composites. The results showed that the fundamental and third-harmonic AC responses are sensitive to the dielectric-constant and/or nonlinear-susceptibility gradation profiles within the particles. Thus, by measuring the AC responses of the graded composites, it is possible to perform a real-time monitoring of the fabrication process of the gradation profiles within the graded particles.Comment: 18 pages, 4 figure

Active Semi-Supervised Learning Using Sampling Theory for Graph Signals

We consider the problem of offline, pool-based active semi-supervised learning on graphs. This problem is important when the labeled data is scarce and expensive whereas unlabeled data is easily available. The data points are represented by the vertices of an undirected graph with the similarity between them captured by the edge weights. Given a target number of nodes to label, the goal is to choose those nodes that are most informative and then predict the unknown labels. We propose a novel framework for this problem based on our recent results on sampling theory for graph signals. A graph signal is a real-valued function defined on each node of the graph. A notion of frequency for such signals can be defined using the spectrum of the graph Laplacian matrix. The sampling theory for graph signals aims to extend the traditional Nyquist-Shannon sampling theory by allowing us to identify the class of graph signals that can be reconstructed from their values on a subset of vertices. This approach allows us to define a criterion for active learning based on sampling set selection which aims at maximizing the frequency of the signals that can be reconstructed from their samples on the set. Experiments show the effectiveness of our method.Comment: 10 pages, 6 figures, To appear in KDD'1

Nonlinear ER effects in an ac applied field

The electric field used in most electrorheological (ER) experiments is usually quite high, and nonlinear ER effects have been theoretically predicted and experimentally measured recently. A direct method of measuring the nonlinear ER effects is to examine the frequency dependence of the same effects. For a sinusoidal applied field, we calculate the ac response which generally includes higher harmonics. In is work, we develop a multiple image formula, and calculate the total dipole moments of a pair of dielectric spheres, embedded in a nonlinear host. The higher harmonics due to the nonlinearity are calculated systematically.Comment: Presented at Conference on Computational Physics (CCP2000), held at Gold Coast, Australia from 3-8, December 200

Transient Sub-Poissonian Distribution for Single-Mode Lasers

In this paper, the transient photon statistics for single-mode lasers is investigated by making use of the theory of quantum electrodynamics. By taking into account of the transitive time l,we obtain the master equation for Jaynes-Cummings model. The relation between the Mandel factor and the time is obtained by directly solving the master equation. The result shows that a transient phenomenon from the transient super-Poissonian distribution to the transient sub-Poissonian distribution occurs for single-mode lasers. In addition, the influences of the thermal light field and the cavity loss on the transient sub-Poissonian distribution are also studied

An Atlas of Warm AGN and Starbursts from the IRAS Deep Fields

We present 180 AGN candidates based on color selection from the IRAS slow-scan deep observations, with color criteria broadened from the initial Point-Source Catalog samples to include similar objects with redshifts up to z=1 and allowing for two-band detections. Spectroscopic identifications have been obtained for 80 (44%); some additional ones are secure based on radio detections or optical morphology, although yet unobserved spectroscopically. These spectroscopic identifications include 13 Sy 1 galaxies, 17 Sy 2 Seyferts, 29 starbursts, 7 LINER systems, and 13 emission-line galaxies so heavily reddened as to remain of ambiguous classification. The optical magnitudes range from R=12.0-20.5; counts suggest that incompleteness is important fainter than R=15.5. Redshifts extend to z=0.51, with a significant part of the sample at z>0.2. The sample includes slightly more AGN than star-forming systems among those where the spectra contain enough diagnostic feature to make the distinction. The active nuclei include several broad-line objects with strong Fe II emission, and composite objects with the absorption-line signatures of fading starbursts. These AGN with warm far-IR colors have little overlap with the "red AGN" identified with 2MASS; only a single Sy 1 was detected by 2MASS with J-K > 2. Some reliable IRAS detections have either very faint optical counterparts or only absorption-line galaxies, potentially being deeply obscured AGN. The IRAS detections include a newly identified symbiotic star, and several possible examples of the "Vega phenomenon", including dwarfs as cool as type K. Appendices detail these candidate stars, and the optical-identification content of a particularly deep set of high-latitude IRAS scans (probing the limits of optical identification from IRAS data alone).Comment: ApJ Suppl, in press. Figures converted to JPEG/GIF for better compression; PDF with full-resolution figures available before publication at http://www.astr.ua.edu/keel/aoagn.pd

Optimization of Agrobacterium-mediated transformation parameters for sweet potato embryogenic callus using &#946-glucuronidase (GUS) as a reporter

Agrobacterium-mediated transformation factors for sweet potato embryogenic calli were optimized using -glucuronidase (GUS) as a reporter. The binary vector pTCK303 harboring the modified GUS genedriven by the CaMV 35S promoter was used. Transformation parameters were optimized including bacterial concentration, pre-culture period, co-cultivation period, immersion time, acetosyringone (AS)concentration and mannitol treated time. Results were obtained based on the percentage of GUS expression. Agrobacterium tumefaciens strain EHA105 at concentration OD600 nm = 0.8 showed the highest virulence on sweet potato embryogenic callus. Four days of pre-culture, four days ofco-cultivation, 10 min of immersion, 200 M acetosyringone and 60 min of mannitol-treated embryogenic callus gave the highest percentage of GUS positive transformants