'The effect of different genres of music on the stress levels of kennelled dogs'

Classical music has been shown to reduce stress in kennelled dogs; however, rapid habituation of dogs to this form of auditory enrichment has also been demonstrated. The current study investigated the physiological and behavioural response of kennelled dogs (n = 38) to medium-term (5 days) auditory enrichment with five different genres of music including Soft Rock, Motown, Pop, Reggae and Classical, to determine whether increasing the variety of auditory stimulation reduces the level of habituation to auditory enrichment. Dogs were found to spend significantly more time lying and significantly less time standing when music was played, regardless of genre. There was no observable effect of music on barking, however, dogs were significantly (z = 2.2, P < 0.05) more likely to bark following cessation of auditory enrichment. Heart Rate Variability (HRV) was significantly higher, indicative of decreased stress, when dogs were played Soft Rock and Reggae, with a lesser effect observed when Motown, Pop and Classical genres were played. Relative to the silent period prior to auditory enrichment, urinary cortisol:creatanine (UCCR) values were significantly higher during Soft Rock (t = 2.781, P < 0.01) and the second silent control period following auditory enrichment (t = 2.46, P < 0.05). Despite the mixed response to different genres, the physiological and behavioural changes observed remained constant over the 5d of enrichment suggesting that the effect of habituation may be reduced by increasing the variety of auditory enrichment provided

Activation of phospholipase A2 by cannabinoids Lack of correlation with CNS effects

AbstractCannabinoids Δ1-tetrahydrocannabinol, cannabinol, cannabidiol and cannabigerol have been shown to affect directly the activity of phospholipase A2 in a cell-free assay. The compounds produced a biphasic activation of the enzyme, with EC50 values in the range 6.0–20.0 × 10−6 M and IC50 values in the range 50.0–150.0 × 10−6M. These results correlated well with the relative potencies reported for the stimulation of prostaglandin release from human synovial cells in vitro, confirming that activation of phospholipase A2 is the predominant action of cannabinoids on arachidonate metabolism in tissue culture. However, since Δ1-tetrahydrocannabinol is unique among these compounds in possessing cataleptic activity, it is unlikely that phospholipase A2 is the major receptor mediating the psychotropic effects of cannabis

TPA and resiniferatoxin-mediated activation of NADPH-oxidase A possible role for Rx-kinase augmentation of PKC

AbstractThe non-tumour promoting irritant, resiniferatoxin, was capable of activating the NADPH-oxidase respiratory burst of starch-elicited, but not resident mouse peritoneal macrophages in vitro. Unlike TPA, the response was synergised by incubation with zymosan. The Rx-stimulated NADPH-oxidase activity in a cell-free assay was selectively enhanced in the presence of exogenous Rx-kinase rather than PKC and in the absence of Ca2+. Since resiniferatoxin is a poor activator of PKC, it is probable that the Ca2+-independent Rx-kinase plays a role in activation of the macrophage respiratory burst following stimulation by zymosan

Neural network approach to the classification of urban images

Over the past few years considerable research effort has been devoted to the study of pattern recognition methods applied to the classification of remotely sensed images. Neural network methods have been widely explored, and been shown to be generally superior to conventional statistical methods. However, the classification of objects shown on greylevel high resolution images in urban areas presents significant difficulties. This thesis presents the results of work aimed at reducing some of these difficulties. High resolution greylevel aerial images are used as the raw material, and methods of processing using neural networks are presented. If a per-pixel approach were used there would be only one input neuron, the pixel greylevel, which would not provide a sufficient basis for successful object identification. The use of spatial neighbourhoods providing an m x m input vector centred on each pixel is investigated; this method takes into account the texture of the pixel's neighbourhood. The pixel's neighbourhood could be considered to contain more that textural information. Second order methods using mean greylevel, Laplacian and variance values derived from the pixel neighbourhood are developed to provide the neural network with a three neuron input vector. This method provides the neural network with additional information, improving the strength of the relationship between the input and output neurons, and therefore reducing the training time and improving the classification accuracy. A third method using a hierarchical set of two or more neural networks is proposed as a method of identifying the high level objects in the images. The methods were applied to representative data sets and the results were compared with manually classified images to quantify the results. Classification accuracy varied from 69% with a window of raw pixel values and 84% with a three neuron input vector of second order values

Construction material classification using multi-spectral terrestrial laser scanning

This research addresses the problem of populating Building Information Model databases with information on building construction materials using a new classification method which uses multi-spectral laser scanning intensity and geometry data. Research in multi-spectral laser scanning will open up a new era in survey and mapping; the 3D surface spectral response sensitive to the transmitted wavelengths could be derived day or night in complex environments using a single sensor. At the start of this research a commercial multi-spectral sensor did not exist, but a few prototype level instruments had been developed; this work wished to get ahead of the hardware development and assess capability and develop applications from multi-spectral laser scanning. These applications could include high density topographic surveying, seamless shallow water bathymetry, environmental modelling, urban surface mapping, or vegetative classification. This was achieved by using from multiple terrestrial laser scanners, each with a different laser wavelength. The fused data provided a spectral and geometric signature of each material which was subsequently classified using a supervised neural network. The multi-spectral data was created by precise co-positioning of the scanner optical centres and sub-centimetre registration using common sphere targets. A common point cloud, with reflected laser intensity values for each laser wavelength, was created from the data. The three intensity values for each point were then used as input to the classifier; ratios of the actual intensities were used to reduce the effect of range and incidence angle differences. Analysis of five classes of data showed that they were not linearly separable; an artificial neural network classifier was the chosen classifier has been shown to separate this type of data. The classifier training dataset was manually created from a small section of the original scan; five classes of building materials were selected for training. The performance of the classification was tested against a reference point cloud of the complete scene. The classifier was able to distinguish the chosen test classes with a mean rate of 84.9% and maximum for individual classes of 100%. The classes with the highest classification rate were brick, gravel and pavement. The success rate was found to be affected by several factors, among these the most significant, inter-scan registration, limitation on available wavelengths and the number of classes of material chosen. Additionally, a method which included a measure of texture through variations in intensity was tested successfully. This research presents a new method of classifying materials using multi-spectral laser scanning, a novel method for registering dissimilar point clouds from different scanners and an insight into the part played by laser speckle interpretation of reflected intensity

Neural network approach to the classification of urban images

Over the past few years considerable research effort has been devoted to the study of pattern recognition methods applied to the classification of remotely sensed images. Neural network methods have been widely explored, and been shown to be generally superior to conventional statistical methods. However, the classification of objects shown on greylevel high resolution images in urban areas presents significant difficulties. This thesis presents the results of work aimed at reducing some of these difficulties. High resolution greylevel aerial images are used as the raw material, and methods of processing using neural networks are presented. If a per-pixel approach were used there would be only one input neuron, the pixel greylevel, which would not provide a sufficient basis for successful object identification. The use of spatial neighbourhoods providing an m x m input vector centred on each pixel is investigated; this method takes into account the texture of the pixel's neighbourhood. The pixel's neighbourhood could be considered to contain more that textural information. Second order methods using mean greylevel, Laplacian and variance values derived from the pixel neighbourhood are developed to provide the neural network with a three neuron input vector. This method provides the neural network with additional information, improving the strength of the relationship between the input and output neurons, and therefore reducing the training time and improving the classification accuracy. A third method using a hierarchical set of two or more neural networks is proposed as a method of identifying the high level objects in the images. The methods were applied to representative data sets and the results were compared with manually classified images to quantify the results. Classification accuracy varied from 69% with a window of raw pixel values and 84% with a three neuron input vector of second order values

Collider phenomenology of Higgs bosons in Left-Right symmetric Randall-Sundrum models

We investigate the collider phenomenology of a left-right symmetric Randall-Sundrum model with fermions and gauge bosons in the bulk. We find that the model is allowed by precision electroweak data as long as the ratio of the (unwarped) Higgs vev to the curvature scale is $v/k \le 1/4$. In that region there can be substantial modifications to the Higgs properties. In particular, the couplings to $WW$ and $ZZ$ are reduced, the coupling to gluons is enhanced, and the coupling to $\gamma\gamma$ can receive shifts in either direction. The Higgs mass bound from LEP II data can potentially be relaxed to $m_H \gtrsim 80$ GeV.Comment: 21 pages, 11 figures. Minor changes to numerics; replaced with published versio

Ordering dynamics of the driven lattice gas model

The evolution of a two-dimensional driven lattice-gas model is studied on an L_x X L_y lattice. Scaling arguments and extensive numerical simulations are used to show that starting from random initial configuration the model evolves via two stages: (a) an early stage in which alternating stripes of particles and vacancies are formed along the direction y of the driving field, and (b) a stripe coarsening stage, in which the number of stripes is reduced and their average width increases. The number of stripes formed at the end of the first stage is shown to be a function of L_x/L_y^\phi, with \phi ~ 0.2. Thus, depending on this parameter, the resulting state could be either single or multi striped. In the second, stripe coarsening stage, the coarsening time is found to be proportional to L_y, becoming infinitely long in the thermodynamic limit. This implies that the multi striped state is thermodynamically stable. The results put previous studies of the model in a more general framework