VEIDase is a principal caspase-like activity involved in plant programmed cell death and essential for embryonic pattern formation

Plant embryogenesis is intimately associated with programmed cell death. The mechanisms of initiation and control of programmed cell death during plant embryo development are not known. Proteolytic activity associated with caspase-like proteins is paramount for control of programmed cell death in animals and yeasts. Caspase family of proteases has unique strong preference for cleavage of the target proteins next to asparagine residue. In this work, we have used synthetic peptide substrates containing caspase recognition sites and corresponding specific inhibitors to analyse the role of caspase-like activity in the regulation of programmed cell death during plant embryogenesis. We demonstrate that VEIDase is a principal caspase-like activity implicated in plant embryogenesis. This activity increases at the early stages of embryo development that coincide with massive cell death during shape remodeling. The VEIDase activity exhibits high sensitivity to pH, ionic strength and Zn2+ concentration. Altogether, biochemical assays show that VEIDase plant caspase-like activity resembles that of both mammalian caspase-6 and yeast metacaspase, YCA1. In vivo, VEIDase activity is localised specifically in the embryonic cells during both the commitment and in the beginning of the execution phase of programmed cell death. Inhibition of VEIDase prevents normal embryo development via blocking the embryo-suspensor differentiation. Our data indicate that the VEIDase activity is an integral part in the control of plant developmental cell death programme, and that this activity is essential for the embryo pattern formation.

Improved principal component monitoring using the local approach

This paper shows that current multivariate statistical monitoring technology may not detect incipient changes in the variable covariance structure nor changes in the geometry of the underlying variable decomposition. To overcome these deficiencies, the local approach is incorporated into the multivariate statistical monitoring framework to define two new univariate statistics for fault detection. Fault isolation is achieved by constructing a fault diagnosis chart which reveals changes in the covariance structure resulting from the presence of a fault. A theoretical analysis is presented and the proposed monitoring approach is exemplified using application studies involving recorded data from two complex industrial processes. © 2007 Elsevier Ltd. All rights reserved.

A New Principal Curve Algorithm for Nonlinear Principal Component Analysis

This paper emerged from work supported by EPSRC grant GR/S84354/01 and proposes a method of determining principal curves, using spline functions, in principal component analysis (PCA) for the representation of non-linear behaviour in process monitoring. Although principal curves are well established, they are difficult to implement in practice if a large number of variables are analysed. The significant contribution of this paper is that the proposed method has minimal complexity, assuming simple spline geometry, thus enabling efficient computation. The paper provides a foundation for further work where multiple curves may be required to represent underlying non-linear information in complex data.

SWAT 1:what effects do site visits by the principal investigator have on recruitment in a multicentre randomized trial?

The SWAT (Study Within A Trial) programme has been established to develop a series of studies that would embed research within research, so as to resolve uncertainties about the effects of different ways of designing, conducting, analyzing and interpreting evaluations of health and social care. It was described in an Education piece in the Journal of Evidence-Based Medicine in 2012. We have now prepared the first example of the design summary for a SWAT, using the template that will be used for other SWAT. This is presented in this article.

Community Engagement in Ireland's Developmental Welfare State: a study of the life cycle approach

The idea that people matter in modern democracies, often referred to as 'civic engagement' is recognised at the highest international level (United Nations 2008: 9). Civic or community engagement is essential to how budgets are decided, policy is developed and public services delivered. Significantly, community engagement is crucial in developing policy for sustained economic and social development. In Ireland the idea of the Developmental Welfare State (DWS) is based on the premise that the social policy system should support citizens so as to reach their full potential. Such a system comprises three overlapping elements: tax and welfare transfer, the provision of services and activist initiatives (National Economic and Social Council, 2005: ix-xviii). Civil Society Organisations have been challenged to 'operationalise the DWS' using a 'life cycle framework' as part of Ireland's corporatist partnership model (Department of Taoiseach, 2006: 40).

Principal Component Analysis of Wide Area Phasor Measurements for Islanding Detection – A Geometric View

This paper presents a new technique for the detectionof islanding conditions in electrical power systems. This problem isespecially prevalent in systems with significant penetrations of distributedrenewable generation. The proposed technique is based onthe application of principal component analysis (PCA) to data setsof wide-area frequency measurements, recorded by phasor measurementunits. The PCA approach was able to detect islandingaccurately and quickly when compared with conventional RoCoFtechniques, as well as with the frequency difference and change-ofangledifference methods recently proposed in the literature. Thereliability and accuracy of the proposed PCA approach is demonstratedby using a number of test cases, which consider islandingand nonislanding events. The test cases are based on real data,recorded from several phasor measurement units located in theU.K. power system.