RNAi, DRD1, and histone methylation actively target developmentally important Non-CG DNA methylation in Arabidopsis

Cytosine DNA methylation protects eukaryotic genomes by silencing transposons and harmful DNAs, but also regulates gene expression during normal development. Loss of CG methylation in the Arabidopsis thaliana met1 and ddm1 mutants causes varied and stochastic developmental defects that are often inherited independently of the original met1 or ddm1 mutation. Loss of non-CG methylation in plants with combined mutations in the DRM and CMT3 genes also causes a suite of developmental defects. We show here that the pleiotropic developmental defects of drm1 drm2 cmt3 triple mutant plants are fully recessive, and unlike phenotypes caused by met1 and ddm1, are not inherited independently of the drm and cmt3 mutations. Developmental phenotypes are also reversed when drm1 drm2 cmt3 plants are transformed with DRM2 or CMT3, implying that non-CG DNA methylation is efficiently re-established by sequence-specific signals. We provide evidence that these signals include RNA silencing though the 24-nucleotide short interfering RNA (siRNA) pathway as well as histone H3K9 methylation, both of which converge on the putative chromatin-remodeling protein DRD1. These signals act in at least three partially intersecting pathways that control the locus-specific patterning of non-CG methylation by the DRM2 and CMT3 methyltransferases. Our results suggest that non-CG DNA methylation that is inherited via a network of persistent targeting signals has been co-opted to regulate developmentally important genes. © 2006 Chan et al.

A comparison of four sustainable manufacturing strategies

Effective use of materials is one possible component of a sustainable manufacturing strategy. There are many such strategies proposed in the literature and used in practice, with confusion over what they are, what the differences among them may be and how they can be used by practitioners in design and manufacture to improve the sustainability of their product and processes. This paper reviews the literature on sustainable manufacturing strategies that deliver improved material performance. Four primary strategies were found: waste minimisation; material efficiency; resource efficiency; and eco-efficiency. The literature was analysed to determine the key characteristics of these sustainable manufacturing strategies and 17 characteristics were found. The four strategies were then compared and contrasted against all the characteristics. While current literature often uses these strategy titles in a confusing, occasionally inter-changeable manner, this study attempts to create clear separation between them. Definition, scope and practicality of measurement are shown to be key characteristics that impact upon the ability of manufacturing companies to make effective use of the proposed strategy. It is observed that the most actionable strategies may not include all of the dimensions of interest to a manufacturer wishing to become more sustainable, creating a dilemma between ease of implementation and breadth of impact. © 2008 Taylor & Francis.

Restoration of images and 3D data to higher resolution by deconvolution with sparsity regularization

Image convolution is conventionally approximated by the LTI discrete model. It is well recognized that the higher the sampling rate, the better is the approximation. However sometimes images or 3D data are only available at a lower sampling rate due to physical constraints of the imaging system. In this paper, we model the under-sampled observation as the result of combining convolution and subsampling. Because the wavelet coefficients of piecewise smooth images tend to be sparse and well modelled by tree-like structures, we propose the L0 reweighted-L2 minimization (L0RL2 ) algorithm to solve this problem. This promotes model-based sparsity by minimizing the reweighted L2 norm, which approximates the L0 norm, and by enforcing a tree model over the weights. We test the algorithm on 3 examples: a simple ring, the cameraman image and a 3D microscope dataset; and show that good results can be obtained. © 2010 IEEE.

Community-led resettlement: From a flood-affected slum to a new society in Pune, India

This paper describes the resettlement process of a community devastated by annual floods, to newly constructed housing in Pune, India. The relocation from Kamgar Putala slum to a housing society at Hadapsar was organized by a community-led NGO partnership in 2004. The housing development was coordinated by the local NGO Shelter Associates with significant community participation. The housing has been revisited in 2010 to evaluate the sustainability of the resettlement project's delivery model via stakeholder perception. The process of organizing for resettlement after natural disaster is described along with the implementation and evaluation of the new housing nearly six years after initial occupation. The strong partnership approach overcame a series of political and financial hurdles at various stages of the relocation project. The story of resettling Kamgar Putala is detailed alongside an outline of the current political climate for an alternative slum-upgrading policy in India and Pune. The advantages of an empowered community supported by an influential local NGO demonstrate a commendable team effort which has tackled the threat of floods. The paper highlights the merits of a community-led partnership approach to housing development for achieving sustainable urban development as well as the alleviation of poverty in a developing context. © 2011 Taylor & Francis.

A Comparative Picture of Corporate Social Responsibility Approaches by Leading Companies in the United Kingdom and Brazil

Purpose - This paper compares CSR strategy, stakeholder engagement and overseas approaches of six leading companies which have large potential environmental and social impacts, influential stakeholders and notable CSR actions. Design/methodology/approach - It is an exploratory survey based on interviews of senior executives from British and Brazilian companies operating in the steel, petroleum and retail sectors and makes comparisons between and within them. Findings - British companies interviewed are more rule-based, adopt an implicit CSR approach; react to stakeholder’s demands based on moral motives and focus on environmental issues. The Brazilian companies, reviewed in this study, adopt an explicit CSR approach, have relational motives to engage with stakeholders and are more concerned with building a responsible image and narrowing social gaps. Research limitations/implications - The survey is based on perceptions of senior executives interviewed which may or may not correspond to actual practices. The sample size restricts generalization of results and specific firms interviewed may not represent the prevailing CSR business strategy in their respective countries. Practical implications - British companies can learn from the Brazilian experience how to become more innovative in a broader approach to CSR. Brazil should reinforce its legal framework to provide a more systematic and rule-based approach to CSR close to the UK experience. Originality/value - The way CSR is conceived and implemented depends on the ethical, socioeconomic, legal and institutional environment of the country in which the firm operates

Determinantal Clustering Processes - A Nonparametric Bayesian Approach to Kernel Based Semi-Supervised Clustering

Semi-supervised clustering is the task of clustering data points into clusters where only a fraction of the points are labelled. The true number of clusters in the data is often unknown and most models require this parameter as an input. Dirichlet process mixture models are appealing as they can infer the number of clusters from the data. However, these models do not deal with high dimensional data well and can encounter difficulties in inference. We present a novel nonparameteric Bayesian kernel based method to cluster data points without the need to prespecify the number of clusters or to model complicated densities from which data points are assumed to be generated from. The key insight is to use determinants of submatrices of a kernel matrix as a measure of how close together a set of points are. We explore some theoretical properties of the model and derive a natural Gibbs based algorithm with MCMC hyperparameter learning. The model is implemented on a variety of synthetic and real world data sets.

Nanotubes complexed with DNA and proteins for resistive-pulse sensing

We use a resistive-pulse technique to analyze molecular hybrids of single-wall carbon nanotubes (SWNTs) wrapped in either single-stranded DNA or protein. Electric fields confined in a glass capillary nanopore allow us to probe the physical size and surface properties of molecular hybrids at the single-molecule level. We find that the translocation duration of a macromolecular hybrid is determined by its hydrodynamic size and solution mobility. The event current reveals the effects of ion exclusion by the rod-shaped hybrids and possible effects due to temporary polarization of the SWNT core. Our results pave the way to direct sensing of small DNA or protein molecules in a large unmodified solid-state nanopore by using nanofilaments as carriers. © 2013 American Chemical Society.

Student-t processes as alternatives to Gaussian processes

We investigate the Student-t process as an alternative to the Gaussian process as a non-parametric prior over functions. We derive closed form expressions for the marginal likelihood and predictive distribution of a Student-t process, by integrating away an inverse Wishart process prior over the co-variance kernel of a Gaussian process model. We show surprising equivalences between different hierarchical Gaussian process models leading to Student-t processes, and derive a new sampling scheme for the inverse Wishart process, which helps elucidate these equivalences. Overall, we show that a Student-t process can retain the attractive properties of a Gaussian process - a nonparamet-ric representation, analytic marginal and predictive distributions, and easy model selection through covariance kernels - but has enhanced flexibility, and predictive covariances that, unlike a Gaussian process, explicitly depend on the values of training observations. We verify empirically that a Student-t process is especially useful in situations where there are changes in covariance structure, or in applications such as Bayesian optimization, where accurate predictive covariances are critical for good performance. These advantages come at no additional computational cost over Gaussian processes.

Low-bias terahertz amplitude modulator based on split-ring resonators and graphene.

Split-ring resonators represent the ideal route to achieve optical control of the incident light at THz frequencies. These subwavelength metamaterial elements exhibit broad resonances that can be easily tuned lithographically. We have realized a design based on the interplay between the resonances of metallic split rings and the electronic properties of monolayer graphene integrated in a single device. By varying the major carrier concentration of graphene, an active modulation of the optical intensity was achieved in the frequency range between 2.2 and 3.1 THz, achieving a maximum modulation depth of 18%, with a bias as low as 0.5 V.

A preliminary study on methods for retaining data quality problems in automatically generated test data

Data in an organisation often contains business secrets that organisations do not want to release. However, there are occasions when it is necessary for an organisation to release its data such as when outsourcing work or using the cloud for Data Quality (DQ) related tasks like data cleansing. Currently, there is no mechanism that allows organisations to release their data for DQ tasks while ensuring that it is suitably protected from releasing business related secrets. The aim of this paper is therefore to present our current progress on determining which methods are able to modify secret data and retain DQ problems. So far we have identified the ways in which data swapping and the SHA-2 hash function alterations methods can be used to preserve missing data, incorrectly formatted values, and domain violations DQ problems while minimising the risk of disclosing secrets. © (2012) by the AIS/ICIS Administrative Office All rights reserved.

Terahertz optical modulator based on metamaterial split-ring resonators and graphene

The integration of quantum cascade lasers with devices capable of efficiently manipulating terahertz light represents a fundamental step for many different applications. Split-ring resonators, subwavelength metamaterial elements exhibiting broad resonances that are easily tuned lithographically, represent the ideal route to achieve such optical control of the incident light. We have realized a design based on the interplay between metallic split rings and the electronic properties of a graphene monolayer integrated into a single device. By acting on the doping level of graphene, an active modulation of the optical intensity was achieved in the frequency range between 2.2 and 3.1 THz, with a maximum modulation depth of 18%. © 2014 Society of Photo-Optical Instrumentation Engineers.

Graphene-based optical modulator realized in metamaterial split-ring resonators operating in the THz frequency range

The integration of quantum cascade lasers with devices capable of efficiently manipulating terahertz light, represents a fundamental step for many different applications. Split-ring resonators, sub-wavelength metamaterial elements exhibiting broad resonances that are easily tuned lithographically, represent the ideal route to achieve such optical control of the incident light. We have realized a design based on the interplay between metallic split rings and the electronic properties of a graphene monolayer integrated into a single device. By acting on the doping level of graphene, an active modulation of the optical intensity was achieved in the frequency range between 2.2 THz and 3.1 THz, with a maximum modulation depth of 18%.