Anglo-American corporate governance and the employment relationship: a case to answer?

The corporate governance environment in the UK and US is generally thought to be hostile to the emergence of cooperative employment relations of the kind exemplified by labour-management partnerships. We discuss case study evidence from the UK which suggests that, contrary to this widespread perception, enduring and proactive partnerships may develop, in conditions where management can convince shareholders of the long-term gains from this approach, and where other regulatory factors operate to extend the time-horizon for financial returns. We conclude that there is more scope than is commonly allowed for measures which could reconcile liquidity in capital markets with cooperation in labour relations competition rather than EC legislationcorporate governance, labour-management partnerships, stakeholding

Parallel Attribute Computation for Distributed Component Forests

Component trees are powerful image processing tools to analyze the connected components of an image. One attractive strategy consists in building the nested relations at first and then deriving the components' attributes afterward, such that the user can switch between different attribute functions without having to re-compute the entire tree. Only sequential algorithms allow such an approach, while no parallel algorithm is available. In this paper, we extend a recent method using distributed memory techniques to enable posterior attribute computation in a parallel or distributed manner. This novel approach significantly reduces the computational time needed for combining several attribute functions interactively in Giga and Tera-Scale data sets

ABC for ancestral inference

ABC for ancestral inferenc

CCPG1, an unconventional cargo receptor for ER-phagy, maintains pancreatic acinar cell health

ER stress-mediated induction of a new vertebrate-specific autophagy cargo receptor, CCPG1 (cell-cycle progression gene 1), drives degradation of endoplasmic reticulum. CCPG1 acts via ATG8-family interaction and, non-canonically, via discrete interactions with FIP200. CCPG1 ameliorates ER stress in the exocrine pancreas. This has potential implications for inflammation and cancer, discussed here

Model fitting for small skin permeability data sets: hyperparameter optimisation in Gaussian Process Regression

This is the pre-peer reviewed version of the following article: Parivash Ashrafi, Yi Sun, Neil Davey, Roderick G. Adams, Simon C. Wilkinson, and Gary Patrick Moss, ‘Model fitting for small skin permeability data sets: hyperparameter optimisation in Gaussian Process Regression’, Journal of Pharmacy and Pharmacology, Vol. 70 (3): 361-373, March 2018, which has been published in final form at https://doi.org/10.1111/jphp.12863. Under embargo until 17 January 2019. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.Objectives The aim of this study was to investigate how to improve predictions from Gaussian Process models by optimising the model hyperparameters. Methods Optimisation methods, including Grid Search, Conjugate Gradient, Random Search, Evolutionary Algorithm and Hyper-prior, were evaluated and applied to previously published data. Data sets were also altered in a structured manner to reduce their size, which retained the range, or ‘chemical space’ of the key descriptors to assess the effect of the data range on model quality. Key findings The Hyper-prior Smoothbox kernel results in the best models for the majority of data sets, and they exhibited significantly better performance than benchmark quantitative structure–permeability relationship (QSPR) models. When the data sets were systematically reduced in size, the different optimisation methods generally retained their statistical quality, whereas benchmark QSPR models performed poorly. Conclusions The design of the data set, and possibly also the approach to validation of the model, is critical in the development of improved models. The size of the data set, if carefully controlled, was not generally a significant factor for these models and that models of excellent statistical quality could be produced from substantially smaller data sets.Peer reviewedFinal Accepted Versio