Phytocannabinoids as novel therapeutic agents in CNS disorders

The Cannabis sativa herb contains over 100 phytocannabinoid (pCB) compounds and has been used for thousands of years for both recreational and medicinal purposes. In the past two decades, characterisation of the body's endogenous cannabinoid (CB) (endocannabinoid, eCB) system (ECS) has highlighted activation of central CB1 receptors by the major pCB, Δ9-tetrahydrocannabinol (Δ9-THC) as the primary mediator of the psychoactive, hyperphagic and some of the potentially therapeutic properties of ingested cannabis. Whilst Δ9-THC is the most prevalent and widely studied pCB, it is also the predominant psychotropic component of cannabis, a property that likely limits its widespread therapeutic use as an isolated agent. In this regard, research focus has recently widened to include other pCBs including cannabidiol (CBD), cannabigerol (CBG), Δ9tetrahydrocannabivarin (Δ9-THCV) and cannabidivarin (CBDV), some of which show potential as therapeutic agents in preclinical models of CNS disease. Moreover, it is becoming evident that these non-Δ9-THC pCBs act at a wide range of pharmacological targets, not solely limited to CB receptors. Disorders that could be targeted include epilepsy, neurodegenerative diseases, affective disorders and the central modulation of feeding behaviour. Here, we review pCB effects in preclinical models of CNS disease and, where available, clinical trial data that support therapeutic effects. Such developments may soon yield the first non-Δ9-THC pCB-based medicines.

A normative approach to multi-agent systems for intelligent buildings

Building Management Systems (BMS) are widely adopted in modern buildings around the world in order to provide high-quality building services, and reduce the running cost of the building. However, most BMS are functionality-oriented and do not consider user personalization. The aim of this research is to capture and represent building management rules using organizational semiotics methods. We implement Semantic Analysis, which determines semantic units in building management and their relationship patterns of behaviour, and Norm Analysis, which extracts and specifies the norms that establish how and when these management actions occur. Finally, we propose a multi-agent framework for norm based building management. This framework contributes to the design domain of intelligent building management system by defining a set of behaviour patterns, and the norms that govern the real-time behaviour in a building.

Pocket data mining: towards collaborative data mining in mobile computing environments

Pocket Data Mining (PDM) is our new term describing collaborative mining of streaming data in mobile and distributed computing environments. With sheer amounts of data streams are now available for subscription on our smart mobile phones, the potential of using this data for decision making using data stream mining techniques has now been achievable owing to the increasing power of these handheld devices. Wireless communication among these devices using Bluetooth and WiFi technologies has opened the door wide for collaborative mining among the mobile devices within the same range that are running data mining techniques targeting the same application. This paper proposes a new architecture that we have prototyped for realizing the significant applications in this area. We have proposed using mobile software agents in this application for several reasons. Most importantly the autonomic intelligent behaviour of the agent technology has been the driving force for using it in this application. Other efficiency reasons are discussed in details in this paper. Experimental results showing the feasibility of the proposed architecture are presented and discussed.

Distributed classification for pocket data mining

Distributed and collaborative data stream mining in a mobile computing environment is referred to as Pocket Data Mining PDM. Large amounts of available data streams to which smart phones can subscribe to or sense, coupled with the increasing computational power of handheld devices motivates the development of PDM as a decision making system. This emerging area of study has shown to be feasible in an earlier study using technological enablers of mobile software agents and stream mining techniques [1]. A typical PDM process would start by having mobile agents roam the network to discover relevant data streams and resources. Then other (mobile) agents encapsulating stream mining techniques visit the relevant nodes in the network in order to build evolving data mining models. Finally, a third type of mobile agents roam the network consulting the mining agents for a final collaborative decision, when required by one or more users. In this paper, we propose the use of distributed Hoeffding trees and Naive Bayes classifers in the PDM framework over vertically partitioned data streams. Mobile policing, health monitoring and stock market analysis are among the possible applications of PDM. An extensive experimental study is reported showing the effectiveness of the collaborative data mining with the two classifers.

Probability of survival in a random exchange economy with dependent agents

In this paper I analyze the general equilibrium in a random Walrasian economy. Dependence among agents is introduced in the form of dependency neighborhoods. Under the uncertainty, an agent may fail to survive due to a meager endowment in a particular state (direct effect), as well as due to unfavorable equilibrium price system at which the value of the endowment falls short of the minimum needed for survival (indirect terms-of-trade effect). To illustrate the main result I compute the stochastic limit of equilibrium price and probability of survival of an agent in a large Cobb-Douglas economy.

Two macrocyclic pentaaza compounds containing pyridine evaluated as novel chelating agents in copper(II) and nickel(II) overload

Two pentaaza macrocycles containing pyridine in the backbone, namely 3,6,9,12,18-pentaazabicyclo[12.3.1] octadeca-1(18),14,16-triene ([15]pyN(5)), and 3,6,10,13,19-pentaazabicyclo[13.3.1]nonadeca-1(19),15,17-triene ([16]pyN(5)), were synthesized in good yields. The acid-base behaviour of these compounds was studied by potentiometry at 298.2 K in aqueous solution and ionic strength 0.10 M in KNO3. The protonation sequence of [15]pyN(5) was investigated by H-1 NMR titration that also allowed the determination of protonation constants in D2O. Binding studies of the two ligands with Ca2+, Ni2+, Cu2+, Zn2+, Cd2+, and Pb2+ metal ions were performed under the same experimental conditions. The results showed that all the complexes formed with the 15-membered ligand, particularly those of Cu2+ and especially Ni2+, are thermodynamically more stable than with the larger macrocycle. Cyclic voltammetric data showed that the copper(II) complexes of the two macrocycles exhibited analogous behaviour, with a single quasi-reversible one-electron transfer reduction process assigned to the Cu(II)/Cu(I) couple. The UV-visible-near IR spectroscopic and magnetic moment data of the nickel(II) complexes in solution indicated a tetragonal distorted coordination geometry for the metal centre. X-band EPR spectra of the copper(II) complexes are consistent with distorted square pyramidal geometries. The crystal structure of [Cu([15]pyN(5))](2+) determined by X-ray diffraction showed the copper(II) centre coordinated to all five macrocyclic nitrogen donors in a distorted square pyramidal environment.