MICAL-1 is a negative regulator of MST-NDR kinase signaling and apoptosis.

MICALs (molecules interacting with CasL) are atypical multidomain flavoenzymes with diverse cellular functions. The molecular pathways employed by MICAL proteins to exert their cellular effects remain largely uncharacterized. Via an unbiased proteomics approach, we identify MICAL-1 as a binding partner of NDR (nuclear Dbf2-related) kinases. NDR1/2 kinases are known to mediate apoptosis downstream of the mammalian Ste-20-like kinase MST1, and ablation of NDR1 in mice predisposes the mice to cancer as a result of compromised apoptosis. MST1 phosphorylates NDR1/2 kinases at their hydrophobic motif, thereby facilitating full NDR kinase activity and function. However, if and how this key phosphorylation event is regulated are unknown. Here we show that MICAL-1 interacts with the hydrophobic motif of NDR1/2 and that overexpression or knockdown of MICAL-1 reduces or augments NDR kinase activation or activity, respectively. Surprisingly, MICAL-1 is a phosphoprotein but not an NDR or MST1 substrate. Rather, MICAL-1 competes with MST1 for NDR binding and thereby antagonizes MST1-induced NDR activation. In line with this inhibitory effect, overexpression or knockdown of MICAL-1 inhibits or enhances, respectively, NDR-dependent proapoptotic signaling induced by extrinsic stimuli. Our findings unveil a previously unknown biological role for MICAL-1 in apoptosis and define a novel negative regulatory mechanism of MST-NDR signaling.

Estimating root–soil contact from 3D X-ray microtomographs

Adequate contact with the soil is essential for water and nutrient adsorption by plant roots, but the determination of root–soil contact is a challenging task because it is difficult to visualize roots in situ and quantify their interactions with the soil at the scale of micrometres. A method to determine root–soil contact using X-ray microtomography was developed. Contact areas were determined from 3D volumetric images using segmentation and iso-surface determination tools. The accuracy of the method was tested with physical model systems of contact between two objects (phantoms). Volumes, surface areas and contact areas calculated from the measured phantoms were compared with those estimated from image analysis. The volume was accurate to within 0.3%, the surface area to within 2–4%, and the contact area to within 2.5%. Maize and lupin roots were grown in soil (<2 mm) and vermiculite at matric potentials of −0.03 and −1.6 MPa and in aggregate fractions of 4–2, 2–1, 1–0.5 and < 0.5 mm at a matric potential of −0.03 MPa. The contact of the roots with their growth medium was determined from 3D volumetric images. Macroporosity (>70 µm) of the soil sieved to different aggregate fractions was calculated from binarized data. Root-soil contact was greater in soil than in vermiculite and increased with decreasing aggregate or particle size. The differences in root–soil contact could not be explained solely by the decrease in porosity with decreasing aggregate size but may also result from changes in particle and aggregate packing around the root.

Conditionally unbiased estimation in phase II/III clinical trials with early stopping for futility

Seamless phase II/III clinical trials combine traditional phases II and III into a single trial that is conducted in two stages, with stage 1 used to answer phase II objectives such as treatment selection and stage 2 used for the confirmatory analysis, which is a phase III objective. Although seamless phase II/III clinical trials are efficient because the confirmatory analysis includes phase II data from stage 1, inference can pose statistical challenges. In this paper, we consider point estimation following seamless phase II/III clinical trials in which stage 1 is used to select the most effective experimental treatment and to decide if, compared with a control, the trial should stop at stage 1 for futility. If the trial is not stopped, then the phase III confirmatory part of the trial involves evaluation of the selected most effective experimental treatment and the control. We have developed two new estimators for the treatment difference between these two treatments with the aim of reducing bias conditional on the treatment selection made and on the fact that the trial continues to stage 2. We have demonstrated the properties of these estimators using simulations

Efficient calculation of two-dimensional periodic and waveguide acoustic Green’s functions

New representations and efficient calculation methods are derived for the problem of propagation from an infinite regularly spaced array of coherent line sources above a homogeneous impedance plane, and for the Green's function for sound propagation in the canyon formed by two infinitely high, parallel rigid or sound soft walls and an impedance ground surface. The infinite sum of source contributions is replaced by a finite sum and the remainder is expressed as a Laplace-type integral. A pole subtraction technique is used to remove poles in the integrand which lie near the path of integration, obtaining a smooth integrand, more suitable for numerical integration, and a specific numerical integration method is proposed. Numerical experiments show highly accurate results across the frequency spectrum for a range of ground surface types. It is expected that the methods proposed will prove useful in boundary element modeling of noise propagation in canyon streets and in ducts, and for problems of scattering by periodic surfaces.

Solvability and spectral properties of integral equations on the real line: I. Weighted spaces of continuous functions

We consider in this paper the solvability of linear integral equations on the real line, in operator form (λ−K)φ=ψ, where and K is an integral operator. We impose conditions on the kernel, k, of K which ensure that K is bounded as an operator on . Let Xa denote the weighted space as |s|→∞}. Our first result is that if, additionally, |k(s,t)|⩽κ(s−t), with and κ(s)=O(|s|−b) as |s|→∞, for some b>1, then the spectrum of K is the same on Xa as on X, for 01. As an example where kernels of this latter form occur we discuss a boundary integral equation formulation of an impedance boundary value problem for the Helmholtz equation in a half-plane.

Estimating interchannel observation-error correlations for IASI radiance data in the Met Office system

The optimal utilisation of hyper-spectral satellite observations in numerical weather prediction is often inhibited by incorrectly assuming independent interchannel observation errors. However, in order to represent these observation-error covariance structures, an accurate knowledge of the true variances and correlations is needed. This structure is likely to vary with observation type and assimilation system. The work in this article presents the initial results for the estimation of IASI interchannel observation-error correlations when the data are processed in the Met Office one-dimensional (1D-Var) and four-dimensional (4D-Var) variational assimilation systems. The method used to calculate the observation errors is a post-analysis diagnostic which utilises the background and analysis departures from the two systems. The results show significant differences in the source and structure of the observation errors when processed in the two different assimilation systems, but also highlight some common features. When the observations are processed in 1D-Var, the diagnosed error variances are approximately half the size of the error variances used in the current operational system and are very close in size to the instrument noise, suggesting that this is the main source of error. The errors contain no consistent correlations, with the exception of a handful of spectrally close channels. When the observations are processed in 4D-Var, we again find that the observation errors are being overestimated operationally, but the overestimation is significantly larger for many channels. In contrast to 1D-Var, the diagnosed error variances are often larger than the instrument noise in 4D-Var. It is postulated that horizontal errors of representation, not seen in 1D-Var, are a significant contributor to the overall error here. Finally, observation errors diagnosed from 4D-Var are found to contain strong, consistent correlation structures for channels sensitive to water vapour and surface properties.

Estimating the potential yield of small wind turbines in urban areas: a case study for Greater London, UK

To optimise the placement of small wind turbines in urban areas a detailed understanding of the spatial variability of the wind resource is required. At present, due to a lack of observations, the NOABL wind speed database is frequently used to estimate the wind resource at a potential site. However, recent work has shown that this tends to overestimate the wind speed in urban areas. This paper suggests a method for adjusting the predictions of the NOABL in urban areas by considering the impact of the underlying surface on a neighbourhood scale. In which, the nature of the surface is characterised on a 1 km2 resolution using an urban morphology database. The model was then used to estimate the variability of the annual mean wind speed across Greater London at a height typical of current small wind turbine installations. Initial validation of the results suggests that the predicted wind speeds are considerably more accurate than the NOABL values. The derived wind map therefore currently provides the best opportunity to identify the neighbourhoods in Greater London at which small wind turbines yield their highest energy production. The model does not consider street scale processes, however previously derived scaling factors can be applied to relate the neighbourhood wind speed to a value at a specific rooftop site. The results showed that the wind speed predicted across London is relatively low, exceeding 4 ms-1 at only 27% of the neighbourhoods in the city. Of these sites less than 10% are within 10 km of the city centre, with the majority over 20 km from the city centre. Consequently, it is predicted that small wind turbines tend to perform better towards the outskirts of the city, therefore for cities which fit the Burgess concentric ring model, such as Greater London, ‘distance from city centre’ is a useful parameter for siting small wind turbines. However, there are a number of neighbourhoods close to the city centre at which the wind speed is relatively high and these sites can only been identified with a detailed representation of the urban surface, such as that developed in this study.

Structural spoilers or structural supports? Unions and the strategic integration of HR functions

The strategic integration of the human resource (HR) function is regarded as crucial in the literature on (strategic) human resource management ((S)HRM). Evidence on the contextual or structural influences on this integration is, however, limited. The structural implications of unionism are particularly intriguing given the evolution of study of the employment relationship. Pluralism is typically seen as antithetical to SHRM, and unions as an impediment to the strategic integration of HR functions, but there are also suggestions in the literature that unionism might facilitate the strategic integration of HR. This paper deploys large-scale international survey evidence to examine the organization-level influence of unionism on this strategic integration, allowing for other established and plausible influences. The analysis reveals that exceptionally, where the organization-level role of unions is particularly contested, unionism does impede the strategic integration of HR. However, it is the predominance of the facilitation of the strategic integration of HR by unionism which is most remarkable.

Conditional mean functions of non-linear models of US output

We compare a number of models of post War US output growth in terms of the degree and pattern of non-linearity they impart to the conditional mean, where we condition on either the previous period's growth rate, or the previous two periods' growth rates. The conditional means are estimated non-parametrically using a nearest-neighbour technique on data simulated from the models. In this way, we condense the complex, dynamic, responses that may be present in to graphical displays of the implied conditional mean.

Forecasting economic and financial time series with non-linear models

In this paper we discuss the current state-of-the-art in estimating, evaluating, and selecting among non-linear forecasting models for economic and financial time series. We review theoretical and empirical issues, including predictive density, interval and point evaluation and model selection, loss functions, data-mining, and aggregation. In addition, we argue that although the evidence in favor of constructing forecasts using non-linear models is rather sparse, there is reason to be optimistic. However, much remains to be done. Finally, we outline a variety of topics for future research, and discuss a number of areas which have received considerable attention in the recent literature, but where many questions remain.