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Rationale and objectives
Previous research with an animal model of relapse has shown that acute food deprivation will reinstate extinguished drug seeking. Recent evidence with humans, however, suggests that chronic food restriction rather than acute food deprivation is related to increases in drug taking and relapse, emphasizing a need for an animal model to elucidate the neural mechanisms mediating the effects of chronic food restriction on drug seeking. Here we studied the effects of chronic food restriction during a period of abstinence on heroin seeking in rats.Methods
Rats were trained to self-administer heroin over 10 days (0.1 mg/kg/infusion; i.v.). Rats were then removed from the operant conditioning chambers and exposed to a mild food restriction (resulting in 10–15 % decrease in body weight) or given unrestricted access to food for 14 days while abstinent. The abstinence period was followed by a drug-seeking test under extinction conditions. Subsequent experiments manipulated the length of restriction and test conditions.Results
Rats that were food restricted throughout the abstinence period demonstrated a robust increase in cue-induced heroin seeking compared to sated rats. Re-feeding prior to testing or decreasing the length of the food restriction period prevented the augmentation of drug seeking.Conclusions
A combination of chronic food restriction and a concurrent state of hunger appears to be necessary for an increase in cue-induced heroin seeking following abstinence. The procedure presented here may serve as a useful model to study the increased risk for relapse following dietary manipulations in abstinent subjects. 相似文献Nowadays, analyzing, detecting, and visualizing abnormal power consumption behavior of householders are among the principal challenges in identifying ways to reduce power consumption. This paper introduces a new solution to detect energy consumption anomalies based on extracting micro-moment features using a rule-based model. The latter is used to draw out load characteristics using daily intent-driven moments of user consumption actions. Besides micro-moment features extraction, we also experiment with a deep neural network architecture for efficient abnormality detection and classification. In the following, a novel anomaly visualization technique is introduced that is based on a scatter representation of the micro-moment classes, and hence providing consumers an easy solution to understand their abnormal behavior. Moreover, in order to validate the proposed system, a new energy consumption dataset at appliance level is also designed through a measurement campaign carried out at Qatar University Energy Lab, namely, Qatar University dataset. Experimental results on simulated and real datasets collected at two regions, which have extremely different climate conditions, confirm that the proposed deep micro-moment architecture outperforms other machine learning algorithms and can effectively detect anomalous patterns. For example, 99.58% accuracy and 97.85% F1 score have been achieved under Qatar University dataset. These promising results establish the efficacy of the proposed deep micro-moment solution for detecting abnormal energy consumption, promoting energy efficiency behaviors, and reducing wasted energy.
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