Crosstalk with insulin and dependence on PI3K/Akt/mTOR rather than MAPK pathways in upregulation of basal growth following long-term oestrogen deprivation in three human breast cancer cell lines

Background: MCF-7, T-47-D, ZR-75-1 human breast cancer cell lines are dependent on oestrogen for growth but can adapt to grow during long-term oestrogen deprivation. This serves as a model for identification of therapeutic targets in endocrine-resistant breast cancer. Methods: An overlooked complication of this model is that it involves more than non-addition of oestrogen, and inadequate attention has been given to separating molecular events associated with each of the culture manipulations. Results: Insulin and oestradiol were shown to protect MCF-7 cells against upregulation of basal growth, demonstrating a crosstalk in the growth adaptation process. Increased phosphorylation of p44/42MAPK and c-Raf reflected removal of insulin from the medium and proliferation of all three cell lines was inhibited to a lesser extent by PD98059 and U0126 following long-term oestrogen/insulin withdrawal, demonstrating a reduced dependence on the MAPK pathway. By contrast, long-term oestrogen/insulin deprivation did not alter levels of phosphorylated Akt and did not alter the dose-response of growth inhibition with LY294002 in any of the three cell lines. The IGF1R inhibitor picropodophyllin inhibited growth of all MCF-7 cells but only in the long-term oestrogen/insulin-deprived cells was this paralleled by reduction in phosphorylated p70S6K, a downstream target of mTOR. Long-term oestrogen/insulin-deprived MCF-7 cells had higher levels of phosphorylated p70S6K and developed increased sensitivity to growth inhibition by rapamycin. Conclusions: The greater sensitivity to growth inhibition by rapamycin in all three cell lines following long-term oestrogen/insulin deprivation suggests rapamycin-based therapies might be more effective in breast cancers with acquired oestrogen resistance. Keywords Akt, breast cancer cells, endocrine resistance, insulin, MAPK, MCF-7 cells, mTOR, oestrogen, oestrogen-deprived, PI3K, picropodophyllin, rapamycin, T-47-D cells, ZR-75-1 cells

Grey-box radial basis function modelling

A fundamental principle in data modelling is to incorporate available a priori information regarding the underlying data generating mechanism into the modelling process. We adopt this principle and consider grey-box radial basis function (RBF) modelling capable of incorporating prior knowledge. Specifically, we show how to explicitly incorporate the two types of prior knowledge: (i) the underlying data generating mechanism exhibits known symmetric property, and (ii) the underlying process obeys a set of given boundary value constraints. The class of efficient orthogonal least squares regression algorithms can readily be applied without any modification to construct parsimonious grey-box RBF models with enhanced generalisation capability.

On combination of SMOTE and particle swarm optimization based radial basis function classifier for imbalanced problems

The combination of the synthetic minority oversampling technique (SMOTE) and the radial basis function (RBF) classifier is proposed to deal with classification for imbalanced two-class data. In order to enhance the significance of the small and specific region belonging to the positive class in the decision region, the SMOTE is applied to generate synthetic instances for the positive class to balance the training data set. Based on the over-sampled training data, the RBF classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier structure and the parameters of RBF kernels are determined using a particle swarm optimization algorithm based on the criterion of minimizing the leave-one-out misclassification rate. The experimental results on both simulated and real imbalanced data sets are presented to demonstrate the effectiveness of our proposed algorithm.

Dependence of Indian monsoon rainfall on moisture fluxes across the Arabian Sea and the impact of coupled model sea surface temperature biases

The Arabian Sea is an important moisture source for Indian monsoon rainfall. The skill of climate models in simulating the monsoon and its variability varies widely, while Arabian Sea cold sea surface temperature (SST) biases are common in coupled models and may therefore influence the monsoon and its sensitivity to climate change. We examine the relationship between monsoon rainfall, moisture fluxes and Arabian Sea SST in observations and climate model simulations. Observational analysis shows strong monsoons depend on moisture fluxes across the Arabian Sea, however detecting consistent signals with contemporaneous summer SST anomalies is complicated in the observed system by air/sea coupling and large-scale induced variability such as the El Niño-Southern Oscillation feeding back onto the monsoon through development of the Somali Jet. Comparison of HadGEM3 coupled and atmosphere-only configurations suggests coupled model cold SST biases significantly reduce monsoon rainfall. Idealised atmosphere-only experiments show that the weakened monsoon can be mainly attributed to systematic Arabian Sea cold SST biases during summer and their impact on the monsoon-moisture relationship. The impact of large cold SST biases on atmospheric moisture content over the Arabian Sea, and also the subsequent reduced latent heat release over India, dominates over any enhancement in the land-sea temperature gradient and results in changes to the mean state. We hypothesize that a cold base state will result in underestimation of the impact of larger projected Arabian Sea SST changes in future climate, suggesting that Arabian Sea biases should be a clear target for model development.

Multi-layer radial basis function networks: an extension to the radial basis function

This paper presents the initial research carried out into a new neural network called the multilayer radial basis function network (MRBF). The network extends the radial basis function (RBF) in a similar way to that in which the multilayer perceptron extends the perceptron. It is hoped that by connecting RBFs together in a layered fashion, an equivalent increase in ability can be gained, as is gained from using MLPs instead of single perceptrons. The results of a practical comparison between individual RBFs and MRBF's are also given.

An introduction to radial basis functions for system identification: a comparison with other neural network methods

A look is taken at the use of radial basis functions (RBFs), for nonlinear system identification. RBFs are firstly considered in detail themselves and are subsequently compared with a multi-layered perceptron (MLP), in terms of performance and usage.

Integrating spatial dependence into stochastic frontier analysis

An approach to incorporate spatial dependence into stochastic frontier analysis is developed and applied to a sample of 215 dairy farms in England and Wales. A number of alternative specifications for the spatial weight matrix are used to analyse the effect of these on the estimation of spatial dependence. Estimation is conducted using a Bayesian approach and results indicate that spatial dependence is present when explaining technical inefficiency.

Path dependence and Britain’s "coal wagon problem"

This article explores the nature and impact of path dependence in British rail coal haulage before 1939. It examines the factors which locked Britain's railways into a system of small coal wagons with highly fragmented ownership, the cost penalties of this system, and the reasons that attempts at modernization were unsuccessful. The analysis highlights the importance of decentralized ownership of a highly durable installed base of complementary infrastructure. Technical and institutional interrelatedness blocked incremental modernization, while the political requirement to compensate private wagon owners for the loss of their wagon stock made wholesale rationalization financially unattractive.

The dependence of contrail formation on the weather pattern and altitude in the North Atlantic

Aircraft flying through cold ice-supersaturated air produce persistent contrails which contribute to the climate impact of aviation. Here, we demonstrate the importance of the weather situation, together with the route and altitude of the aircraft through this, on estimating contrail coverage. The results have implications for determining the climate impact of contrails as well as potential mitigation strategies. Twenty-one years of re-analysis data are used to produce a climatological assessment of conditions favorable for persistent contrail formation between 200 and 300 hPa over the north Atlantic in winter. The seasonal-mean frequency of cold ice-supersaturated regions is highest near 300 hPa, and decreases with altitude. The frequency of occurrence of ice-supersaturated regions varies with large-scale weather pattern; the most common locations are over Greenland, on the southern side of the jet stream and around the northern edge of high pressure ridges. Assuming aircraft take a great circle route, as opposed to a more realistic time-optimal route, is likely to lead to an error in the estimated contrail coverage, which can exceed 50% for westbound north Atlantic flights. The probability of contrail formation can increase or decrease with height, depending on the weather pattern, indicating that the generic suggestion that flying higher leads to fewer contrails is not robust.

Assessment of surface winds over the Atlantic, Indian and Pacific Ocean sectors of the Southern Ocean in CMIP5 models: historical bias, forcing response, and state dependence

An assessment of the fifth Coupled Models Intercomparison Project (CMIP5) models’ simulation of the near-surface westerly wind jet position and strength over the Atlantic, Indian and Pacific sectors of the Southern Ocean is presented. Compared with reanalysis climatologies there is an equatorward bias of 3.7° (inter-model standard deviation of ± 2.2°) in the ensemble mean position of the zonal mean jet. The ensemble mean strength is biased slightly too weak, with the largest biases over the Pacific sector (-1.6±1.1 m/s, 27 -22%). An analysis of atmosphere-only (AMIP) experiments indicates that 41% of the zonal mean position bias comes from coupling of the ocean/ice models to the atmosphere. The response to future emissions scenarios (RCP4.5 and RCP8.5) is characterized by two phases: (i) the period of most rapid ozone recovery (2000-2049) during which there is insignificant change in summer; and (ii) the period 2050-2098 during which RCP4.5 simulations show no significant change but RCP8.5 simulations show poleward shifts (0.30, 0.19 and 0.28°/decade over the Atlantic, Indian and Pacific sectors respectively), and increases in strength (0.06, 0.08 and 0.15 m/s/decade respectively). The models with larger equatorward position biases generally show larger poleward shifts (i.e. state dependence). This inter-model relationship is strongest over the Pacific sector (r=-0.89) and insignificant over the Atlantic sector (r=-0.50). However, an assessment of jet structure shows that over the Atlantic sector jet shift is significantly correlated with jet width whereas over the Pacific sector the distance between the sub-polar and sub-tropical westerly jets appears to be more important.

‘What Price Solidarity? Nuclear Dependence in NATO, 1949-1967’

Archive-based study of the dependence of NATO member states on the USA as nuclear guarantor, and the problems this entailed.