Cannabinoid influences on palatability: microstructural analysis of sucrose drinking after Delta(9)-tetrahydrocannabinol, anandamide, 2-arachidonoyl glycerol and SR141716

Rationale: Central cannabinoid systems have been implicated in appetite control through the respective hyperphagic and anorectic actions of CB1 agonists and antagonists. The motivational changes underlying these actions remain to be determined, but may involve alterations to food palatability. Objectives: The mode of action of cannabinoids on ingestion was investigated by examining the effects of exogenous and endogenous agonists, and a selective CB1 receptor antagonist, on licking microstructure in rats ingesting a palatable sucrose solution. Methods: Microstructural analyses of licking for a 10% sucrose solution was performed over a range of agonist and antagonist doses administered to non-deprived, male Lister hooded rats. Results: Delta(9)-tetrahydrocannabinol (0.5, 1 and 3 mg/kg) and anandamide (1 mg/kg and 3 mg/kg) significantly increased total number of licks. This was primarily due to an increase in bout duration rather than bout number. There was a nonsignificant increase in total licks following administration of 2-arachidonoyl glycerol (0.2, 1.0 and 2.0 mg/kg), whereas administration of the CB1 antagonist SR141716 (1 mg/kg and 3 mg/kg) significantly decreased total licks. All drugs, with the exception of anandamide, significantly decreased the intra-bout lick rate. An exponential function fitted to the cumulative lick rate curves for each drug revealed that all compounds altered the asymptote of this function without having any marked effects on the exponent. Conclusions: These data are consistent with endocannabinoid involvement in the mediation of food palatability.

Design of a minimum variance multiple input-multiple output neuro self-tuning proportional-integral-derivative controller for non-linear dynamic systems

In this study a minimum variance neuro self-tuning proportional-integral-derivative (PID) controller is designed for complex multiple input-multiple output (MIMO) dynamic systems. An approximation model is constructed, which consists of two functional blocks. The first block uses a linear submodel to approximate dominant system dynamics around a selected number of operating points. The second block is used as an error agent, implemented by a neural network, to accommodate the inaccuracy possibly introduced by the linear submodel approximation, various complexities/uncertainties, and complicated coupling effects frequently exhibited in non-linear MIMO dynamic systems. With the proposed model structure, controller design of an MIMO plant with n inputs and n outputs could be, for example, decomposed into n independent single input-single output (SISO) subsystem designs. The effectiveness of the controller design procedure is initially verified through simulations of industrial examples.

Electronic energy transfer in a dinuclear Ru/Os complex containing a photoresponsive dithienylethene derivative as bridging ligand

In order to build up a multicomponent system able to perform useful light-induced functions, a dithienylethene-bridged heterodinuclear metal complex (Ru/Os) has been prepared. The compound was characterized and its photophysical properties studied in detail.

A low delta-9-tetrahydrocannabinol cannabis extract induces hyperphagia in rats

Appetite stimulation via partial agonism of cannabinoid type 1 receptors by Δ9tetrahydrocannabinol (Δ9THC) is well documented and can be modulated by non-Δ9THC phytocannabinoids. Δ9THC concentrations sufficient to elicit hyperphagia induce changes to both appetitive (reduced latency to feed) and consummatory (increased meal one size and duration) behaviours. Here, we show that a cannabis extract containing too little Δ9THC to stimulate appetite can induce hyperphagia solely by increasing appetitive behaviours. Twelve, male Lister hooded rats were presatiated before treatment with a low-Δ9THC cannabis extract (0.5, 1.0, 2.0 and 4.0 mg/kg). Hourly intake and meal pattern data were recorded and analyzed using one-way analyses of variance followed by Bonferroni post-hoc tests. The cannabis extract significantly increased food intake during the first hour of testing (at 4.0 mg/kg) and significantly reduced the latency to feed versus vehicle treatments (at doses ≥1.0 mg/kg). Meal size and duration were unaffected. These results show only the increase in appetitive behaviours, which could be attributed to non-Δ9THC phytocannabinoids in the extract rather than Δ9THC. Although further study is required to determine the constituents responsible for these effects, these results support the presence of non-Δ9THC cannabis constituent(s) that exert a stimulatory effect on appetite and likely lack the detrimental psychoactive effects of Δ9THC.

A neural network enhanced generalized minimum variance self-tuning proportional, integral and derivative control algorithm for complex dynamic systems

A neural network enhanced proportional, integral and derivative (PID) controller is presented that combines the attributes of neural network learning with a generalized minimum-variance self-tuning control (STC) strategy. The neuro PID controller is structured with plant model identification and PID parameter tuning. The plants to be controlled are approximated by an equivalent model composed of a simple linear submodel to approximate plant dynamics around operating points, plus an error agent to accommodate the errors induced by linear submodel inaccuracy due to non-linearities and other complexities. A generalized recursive least-squares algorithm is used to identify the linear submodel, and a layered neural network is used to detect the error agent in which the weights are updated on the basis of the error between the plant output and the output from the linear submodel. The procedure for controller design is based on the equivalent model, and therefore the error agent is naturally functioned within the control law. In this way the controller can deal not only with a wide range of linear dynamic plants but also with those complex plants characterized by severe non-linearity, uncertainties and non-minimum phase behaviours. Two simulation studies are provided to demonstrate the effectiveness of the controller design procedure.

Self-tuning proportional, integral and derivative controller based on genetic algorithm least squares

A self-tuning proportional, integral and derivative control scheme based on genetic algorithms (GAs) is proposed and applied to the control of a real industrial plant. This paper explores the improvement in the parameter estimator, which is an essential part of an adaptive controller, through the hybridization of recursive least-squares algorithms by making use of GAs and the possibility of the application of GAs to the control of industrial processes. Both the simulation results and the experiments on a real plant show that the proposed scheme can be applied effectively.

Eliminativism about derivative prima facie duties

Ross divides prima facie duties into derivative and foundational ones, but seems to understand the notion of a derivative prima facie duty in two very different ways. Sometimes he understands them in a non-eliminativist way. According to this understanding, basic prima facie duties ground distinct derivative ones. According to the eliminativist understanding, basic duties do not ground distinct derivative duties, but replace (eliminate) them. On the eliminativist view, discovering that a prima facie duty is derivative is discovering that it is not genuine. The genuine one is the basic one. I argue that Ross is best understood as an eliminativist.

The synthesis of dicobalt and dinickel complexes of trimethylsilylethynyl-1, 2-dihydrofullerene; characterisation by n.m.r. and structure of the first acyclic metal fullerene derivative: molecular structure of [η2-{2-H-1-(Me3SiC ≡ C)-C60}Ni2(η-C5H5)2]

The synthesis and characterisation of the complexes [η2-{2-H-1-(Me3SiC ≡ C)-C60}Co2(CO)6] (2)} and [η-2-{2-H-1-(Me3SiC ≡ C)-C60}Ni2η-C5H5)2] (3)} is reported, together with a single-crystal molecular structure for (3). This provides the first structural data for an acyclic metal derivative of [60]-fullerene.

Regularization of Descriptor Systems by Derivative and Proportional State Feedback

For linear multivariable time-invariant continuous or discrete-time singular systems it is customary to use a proportional feedback control in order to achieve a desired closed loop behaviour. Derivative feedback is rarely considered. This paper examines how derivative feedback in descriptor systems can be used to alter the structure of the system pencil under various controllability conditions. It is shown that derivative and proportional feedback controls can be constructed such that the closed loop system has a given form and is also regular and has index at most 1. This property ensures the solvability of the resulting system of dynamic-algebraic equations. The construction procedures used to establish the theory are based only on orthogonal matrix decompositions and can therefore be implemented in a numerically stable way. The problem of pole placement with derivative feedback alone and in combination with proportional state feedback is also investigated. A computational algorithm for improving the “conditioning” of the regularized closed loop system is derived.