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Target detection (TD) is one of the major tasks in hyperspectral image (HSI) processing, and its performance is greatly affected by the background. Feature extraction (FE) has been an effective way to mine discriminative information, especially FE based on deep learning, which can learn the intrinsic properties of data to further improve the detection performance. Unlike supervised networks, unsupervised stacked sparse autoencoders (SSAEs) can learn deep and nonlinear features without any labeled data. However, SSAEs usually require a supervised fine-tuned model to obtain better discrimination, which is not feasible for TD, since the prior information is generally insufficient. In this letter, we introduce a distance constraint that is added to the SSAE to form a new distance constrained SSAE (DCSSAE) network. Specifically, the distance constraint maximizes the distinction between the target pixels and other background pixels in the feature space. Then, using the discriminative features learned from the DCSSAE, a simple detector using radial basis function kernel is derived for background suppression. Experiments on two HSIs demonstrate that the deep spectral features learned from the DCSSAE are more distinguishable, and our proposed detector, namely, the DCSSAE detector, outperforms several popular detectors, especially in background suppression. [ABSTRACT FROM AUTHOR]

In recent years, the notion of sustainable development has begun to figure prominently in the regional, as well as the national, policy concerns of many industrialized countries. Indicators have typically been used to monitor changes in economic, environmental and social variables to show whether economic development is on a sustainable path. In this paper we endogenize individual and composite environmental indicators within an appropriately specified computable general equilibrium modelling framework for Scotland. In principle, at least, this represents a very powerful modelling tool that can inform the policy making process by identifying the impact of any exogenous policy change on the key endogenous environmental and economic indicators. It can also identify the effects of any binding environmental targets on economic activity. [ABSTRACT FROM AUTHOR]

Abstract: The 5-hydroxytryptamine 7 (5-HT7) receptor is the most recently classified member of the serotonin receptor family. The localization of 5-HT7 receptors and the biological activity of its ligands have suggested that 5-HT7 receptors might be involved in the pathogenesis of epilepsy. In the present study, we investigated the correlation between temporal lobe epilepsy and 5-HT7 receptors using pilocarpine-induced rat models of temporal lobe epilepsy and surgical samples of temporal neocortex from intractable epilepsy patients. An analysis of electroencephalogram (EEG) and behavioral changes before and after the treatment of SB269970 hydrochloride (a selective 5-HT7 receptor antagonist, 10mg/kg, i.p.) and AS19 (a selective 5-HT7 receptor agonist, 10mg/kg, s.c.) demonstrated that in epileptic rats the activation of 5-HT7 receptors could increase the number of seizures, which could be reduced by a 5-HT7 receptor antagonist. Moreover, the expression of 5-HT7 receptors was higher in the epilepsy group compared with the nonepileptic group in both rat and human brain tissues. The present results suggested that 5-HT7 receptors participate in the pathogenesis of temporal lobe epilepsy, and a 5-HT7 receptor antagonist may be used as a therapeutic alternative for temporal lobe epilepsy. [Copyright &y& Elsevier]