Optimization on the orthogonal group for independent component analysis

This paper derives a new algorithm that performs independent component analysis (ICA) by optimizing the contrast function of the RADICAL algorithm. The core idea of the proposed optimization method is to combine the global search of a good initial condition with a gradient-descent algorithm. This new ICA algorithm performs faster than the RADICAL algorithm (based on Jacobi rotations) while still preserving, and even enhancing, the strong robustness properties that result from its contrast. © Springer-Verlag Berlin Heidelberg 2007.

Geometric optimization methods for independent component analysis applied on gene expression data

DNA microarrays provide a huge amount of data and require therefore dimensionality reduction methods to extract meaningful biological information. Independent Component Analysis (ICA) was proposed by several authors as an interesting means. Unfortunately, experimental data are usually of poor quality- because of noise, outliers and lack of samples. Robustness to these hurdles will thus be a key feature for an ICA algorithm. This paper identifies a robust contrast function and proposes a new ICA algorithm. © 2007 IEEE.

A duality principle for homogeneous vectorfields with applications

A duality transformation principle was proposed for converting a positive order homogeneous vectorfield into a negative order homogeneous vectorfield. The principle also converted a uniformly locally asymptotically stable differential equation into a uniformly bounded differential equation. The duality transformations included the geometric framework for homogeneity and the removal of origin from the state space.

Component level strategies for exploiting the lifespan of steel in products

Approximately 40% of annual demand for steel worldwide is used to replace products that have failed. With this percentage set to rise, extending the lifespan of steel in products presents a significant opportunity to reduce demand and thus decrease carbon dioxide emissions from steel production. This article presents a new, simplified framework with which to analyse product failure. When applied to the products that dominate steel use, this framework reveals that they are often replaced because a component/sub-assembly becomes degraded, inferior, unsuitable or worthless. In light of this, four products, which are representative of high steel content products in general, are analysed at the component level, determining steel mass and cost profiles over the lifespan of each product. The results show that the majority of the steel components are underexploited - still functioning when the product is discarded; in particular, the potential lifespan of the steel-rich structure is typically much greater than its actual lifespan. Twelve case studies, in which product or component life has been increased, are then presented. The resulting evidence is used to tailor life-extension strategies to each reason for product failure and to identify the economic motivations for implementing these strategies. The results suggest that a product template in which the long-lived structure accounts for a relatively high share of costs while short-lived components can be easily replaced (offering profit to the producer and enhanced utility to owners) encourages product life extension. © 2013 The Author.

Randomized nonlinear component analysis

Copyright © (2014) by the International Machine Learning Society (IMLS) All rights reserved. Classical methods such as Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA) are ubiquitous in statistics. However, these techniques are only able to reveal linear re-lationships in data. Although nonlinear variants of PCA and CCA have been proposed, these are computationally prohibitive in the large scale. In a separate strand of recent research, randomized methods have been proposed to construct features that help reveal nonlinear patterns in data. For basic tasks such as regression or classification, random features exhibit little or no loss in performance, while achieving drastic savings in computational requirements. In this paper we leverage randomness to design scalable new variants of nonlinear PCA and CCA; our ideas extend to key multivariate analysis tools such as spectral clustering or LDA. We demonstrate our algorithms through experiments on real- world data, on which we compare against the state-of-the-art. A simple R implementation of the presented algorithms is provided.

Molecular cloning, promoter analysis and induced expression of the complement component C9 gene in the grass carp Ctenopharyngodon idella

Complement-mediated killing of pathogens through lytic pathway is an important effector mechanism of innate immune response. C9 is the ninth member of complement components, creating the membrane attack complex (MAC). In the present study, a putative cDNA sequence encoding the 650 amino acids of C9 and its genomic organization were identified in grass carp Ctenopharyngodon idella. The deduced amino acid sequence of grass carp C9 (gcC9) showed 48% and 38.5% identity to Japanese flounder and human C9, respectively. Domain search revealed that gcC9 contains a LDL receptor domain, an EGF precursor domain, a MACPF domain and two TSP domain located in the N-terminal and C-terminal, respectively. Phylogenetic analysis demonstrated that gcC9 is clustered in a same clade with Japanese flounder, pufferfish and rainbow trout C9. The gcC9 gene consists of 11 exons with 10 introns, spacing over approximately 7 kb of genomic sequence. Analysis of gcC9 promoter region revealed the presence of a TATA box and some putative transcription factor such as C/EBP, HSF, NF-AT, CHOP-C, HNF-3B, GATA-2, IK-2, EVI- 1, AP-1, CP2 and OCT-1 binding sites. The first intron region contains C/EBPb, HFH-1 and Oct-1 binding sites. RT-PCR and Western blotting analysis demonstrated that the mRNA and protein of gcC9 gene have similar expression patterns, being constitutively expressed in all organs examined of healthy fish, with the highest level in hepatopancreas. By real-time quantitative RT-PCR analysis, gcC9 transcripts were significantly up-regulated in head kidney, spleen, hepatopancreas and down-regulated in intestine from inactivated fish bacterial pathogen Flavobacterium columnare-stimulated fish, demonstrating the role of C9 in immune response. (c) 2007 Elsevier B.V. All rights reserved.

Viscoelastic analysis of single-component and composite PEG and alginate hydrogels

Hydrogels, three-dimensional hydrophilic polymer networks, are appealing candidate materials for studying the cellular microenvironment as their substantial water content helps to better mimic soft tissue. However, hydrogels can lack mechanical stiffness, strength, and toughness. Composite hydrogel systems have been shown to improve upon mechanical properties compared to their singlecomponent counterparts. Poly (ethylene glycol) dimethacrylate (PEGDMA) and alginate are polymers that have been used to form hydrogels for biological applications. Singlecomponent and composite PEGDMA and alginate systems were fabricated with a range of total polymer concentrations. Bulk gels were mechanically characterized using spherical indentation testing and a viscoelastic analysis framework. An increase in shear modulus with increasing polymer concentration was demonstrated for all systems. Alginate hydrogels were shown to have a smaller viscoelastic ratio than the PEGDMA gels, indicating more extensive relaxation over time. Composite alginate and PEGDMA hydrogels exhibited a combination of the mechanical properties of the constituents, as well as a qualitative increase in toughness. Additionally, multiple hydrogel systems were produced that had similar shear moduli, but different viscoelastic behaviors. Accurate measurement of the mechanical properties of hydrogels is necessary in order to determine what parameters are key in modeling the cellular microenvironment. © 2014 The Chinese Society of Theoretical and Applied Mechanics; Institute of Mechanics, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg.

A novel method of extraction of blend component structure from SANS measurements of homopolymer bimodal blends

A new method is presented for the extraction of single-chain form factors and interchain interference functions from a range of small-angle neutron scattering (SANS) experiments on bimodal homopolymer blends. The method requires a minimum of three blends, made up of hydrogenated and deuterated components with matched degree of polymerization at two different chain lengths, but with carefully varying deuteration levels. The method is validated through an experimental study on polystyrene homopolymer bimodal blends with M A≈1/2MB. By fitting Debye functions to the structure factors, it is shown that there is good agreement between the molar mass of the components obtained from SANS and from chromatography. The extraction method also enables, for the first time, interchain scattering functions to be produced for scattering between chains of different lengths. © 2014 The Authors. Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

alr0117, a two-component histidine kinase gene, is involved in heterocyst development in Anabaena sp PCC 7120

Anabaena sp. PCC; 7120 was mutagenized by transposon Tn5-1087b, generating a mutant whose heterocysts lack the envelope polysaccharide layer. The transposon was located between nucleotides 342 and 343 of alr0117, a 918 bp gene encoding a histidine kinase for a two-component regulatory system. Complementation of the mutant with a DNA fragment containing alr0117 and targeted inactivation of the gene confirmed that alr0117 is involved in heterocyst development. RT-PCR showed that alr0117 was constitutively expressed in the presence or absence of a combined-nitrogen source. hepA and patB, the two genes turned on during wild-type heterocyst development, were no longer activated in an alr0117-null mutant. The two-component signal transduction system involving alr0117 may control the formation of the envelope polysaccharide layer and certain late events essential to the function of heterocysts.

First-principles study of UC2 and U2C3

The electronic structure and mechanical properties Of UC2 and U2C3 have been systematically investigated using first-principles calculations by the projector-augmented-wave (PAW) method. Furthermore, in order to describe precisely the strong on-site Coulomb repulsion among the localized U 5f electrons, we adopt the generalized gradient approximation +U formalisms for the exchange-correlation term. We show that our calculated structural parameters and electronic properties for UC2 and U2C3 are in good agreement with the experimental data by choosing an appropriate Hubbard U = 3 eV. As for the chemical bonding nature, the contour plot of charge density and total density of states suggest that UC2 and U2C3 are metallic mainly contributed by the 5f electrons, mixed with significant covalent component resulted from the strong C-C bonds. The present results also illustrate that the metal-carbon (U-C) bonding and the carbon-carbon covalent bonding in U2C3 are somewhat weaker than those in UC2, leading to the weaker thermodynamic stability at high temperature as observed by experiments.

First-principle study of native defects in CuScO2 and CuYO2

This paper studies the electronic structure and native defects intransparent conducting oxides CuScO2 and CuYO2 using the first-principle calculations. Some typical native copper-related and oxygen-related defects, such as vacancy, interstitials, and antisites in their relevant charge state are considered. The results of calculation show that, CuMO2 (M = Sc, Y) is impossible to shown-type conductivity ability. It finds that copper vacancy and oxygen interstitial have relatively low formation energy and they are the relevant defects in CuScO2 and CuYO2. Copper vacancy is the most efficient acceptor, and under O-rich condition oxygen antisite also becomes important acceptor and plays an important role in p-type conductivity.

First-principle study of extrinsic defects in CuScO2 and CuYO2

Using first-principles methods, we studied the extrinsic defects doping in transparent conducting oxides CuMO2 (M=Sc, Y). We chose Be, Mg, Ca, Si, Ge, Sn as extrinsic defects to substitute for M and Cu atoms. By systematically calculating the impurity formation energy and transition energy level, we find that Be-Cu is the most prominent extrinsic donor and Ca-M is the prominent extrinsic acceptor. In addition, we find that Mg atom substituting for Sc is the most prominent extrinsic acceptor in CuSCO2. Our calculation results are expected to be a guide for preparing n-type and p-type materials through extrinsic doping in CuMO2 (M=SC, y). (C) 2008 Elsevier B.V. All rights reserved.