Immunisation against gonadotrophin-releasing hormone (GnRH) reduces agonistic behaviours in male rangeland goats

Rangeland goat bucks were used to evaluate the efficacy of a commercially available anti-gonadotrophin-releasing hormone vaccine, Improvac (Zoetis Australia, West Ryde, NSW, Australia). The hypothesis tested was that immunisation would suppress testosterone secretion by the testis and agonistic behaviour between male goats. We also compared intervals of 2 and 4 weeks between primary and booster immunisations and monitored responses over a 2-month period. The 45 goats were split into three groups (n = 15): one group receiving the vaccination booster on Day 14, one group receiving the vaccination booster on Day 28, and the Control group receiving sterile saline injections. Body mass, body condition score and scrotal circumference were measured fortnightly, and blood was collected at 2-week intervals and analysed for testosterone concentration. Behavioural interaction tests of 2-min duration were also conducted fortnightly. There was a significant decrease in paired testicular circumference (P < 0.05) and testosterone concentration (P < 0.01) in both vaccination groups by the end of the experiment at Day 60, compared with the Control group. Agonistic interactions measured at Day 60 were significantly reduced in both vaccination groups (P < 0.05) compared with the Control group. These results support the efficacy of Improvac in reducing agonistic behaviours in rangeland goat bucks and suggest that the use of the vaccine may assist in reducing social stress and possible injury in groups of confined male goats

Foreground removal from CMB temperature maps using an MLP neural network

One of the main obstacles in extracting the Cosmic Microwave Background (CMB) signal from observations in the mm-submm range is the foreground contamination by emission from galactic components: mainly synchrotron, free-free and thermal dust emission. Due to the statistical nature of the intrinsic CMB signal it is essential to minimize the systematic errors in the CMB temperature determinations. Following the available knowledge of the spectral behavior of the galactic foregrounds simple, power law-like spectra have been assumed. The feasibility of using a simple neural network for extracting the CMB temperature signal from the combined CMB and foreground signals has been investigated. As a specific example, we have analysed simulated data, like that expected from the ESA Planck Surveyor mission. A simple multilayer perceptron neural network with 2 hidden layers can provide temperature estimates, over more than 80 percent of the sky, that are to a high degree uncorrelated with the foreground signals. A single network will be able to cover the dynamic range of the Planck noise level over the entire sky.Comment: Accepted for publication in Astrophysics and Space Scienc

The Hamiltonian limit of (3+1)D SU(3) lattice gauge theory on anisotropic lattices

The extreme anisotropic limit of Euclidean SU(3) lattice gauge theory is examined to extract the Hamiltonian limit, using standard path integral Monte Carlo (PIMC) methods. We examine the mean plaquette and string tension and compare them to results obtained within the Hamiltonian framework of Kogut and Susskind. The results are a significant improvement upon previous Hamiltonian estimates, despite the extrapolation procedure necessary to extract observables. We conclude that the PIMC method is a reliable method of obtaining results for the Hamiltonian version of the theory. Our results also clearly demonstrate the universality between the Hamiltonian and Euclidean formulations of lattice gauge theory. It is particularly important to take into account the renormalization of both the anisotropy, and the Euclidean coupling $\beta_E$, in obtaining these results.Comment: 10 pages, 11 figure

An assessment of Evans' unified field theory I

Evans developed a classical unified field theory of gravitation and electromagnetism on the background of a spacetime obeying a Riemann-Cartan geometry. This geometry can be characterized by an orthonormal coframe theta and a (metric compatible) Lorentz connection Gamma. These two potentials yield the field strengths torsion T and curvature R. Evans tried to infuse electromagnetic properties into this geometrical framework by putting the coframe theta to be proportional to four extended electromagnetic potentials A; these are assumed to encompass the conventional Maxwellian potential in a suitable limit. The viable Einstein-Cartan(-Sciama-Kibble) theory of gravity was adopted by Evans to describe the gravitational sector of his theory. Including also the results of an accompanying paper by Obukhov and the author, we show that Evans' ansatz for electromagnetism is untenable beyond repair both from a geometrical as well as from a physical point of view. As a consequence, his unified theory is obsolete.Comment: 39 pages of latex, modified because of referee report, mistakes and typos removed, partly reformulated, taken care of M.W.Evans' rebutta

Bayesian classification of vegetation types with Gaussian mixture density fitting to indicator values.

Question: Is it possible to mathematically classify relevés into vegetation types on the basis of their average indicator values, including the uncertainty of the classification? Location: The Netherlands. Method: A large relevé database was used to develop a method for predicting vegetation types based on indicator values. First, each relevé was classified into a phytosociological association on the basis of its species composition. Additionally, mean indicator values for moisture, nutrients and acidity were computed for each relevé. Thus, the position of each classified relevé was obtained in a three-dimensional space of indicator values. Fitting the data to so called Gaussian Mixture Models yielded densities of associations as a function of indicator values. Finally, these density functions were used to predict the Bayesian occurrence probabilities of associations for known indicator values. Validation of predictions was performed by using a randomly chosen half of the database for the calibration of densities and the other half for the validation of predicted associations. Results and Conclusions: With indicator values, most relevés were classified correctly into vegetation types at the association level. This was shown using confusion matrices that relate (1) the number of relevés classified into associations based on species composition to (2) those based on indicator values. Misclassified relevés belonged to ecologically similar associations. The method seems very suitable for predictive vegetation models

Can You Hear us Now? Voices from the Margin: Using Indigenous Methodologies in Geographic Research

Indigenous methodologies are an alternative way of thinking about research processes. Although these methodologies vary according to the ways in which different Indigenous communities express their own unique knowledge systems, they do have common traits. This article argues that research on Indigenous issues should be carried out in a manner which is respectful and ethically sound from an Indigenous perspective. This naturally challenges Western research paradigms, yet it also affords opportunities to contribute to the body of knowledge about Indigenous peoples. It is further argued that providing a mechanism for Indigenous peoples to participate in and direct these research agendas ensures that their communal needs are met, and that geographers then learn how to build ethical research relationships with them. Indigenous methodologies do not privilege Indigenous researchers because of their Indigeneity, since there are many ‘insider’ views, and these are thus suitable for both Indigenous and non-Indigenous researchers. However, there is a difference between research done within an Indigenous context using Western methodologies and research done using Indig- enous methodologies which integrates Indigenous voices. This paper will discuss those differences while presenting a historical context of research on Indigenous peoples, providing further insights into what Indigenous methodologies entail, and proposing ways in which the academy can create space for this discourse

Neural networks in petroleum geology as interpretation tools

Abstract Three examples of the use of neural networks in analyses of geologic data from hydrocarbon reservoirs are presented. All networks are trained with data originating from clastic reservoirs of Neogene age located in the Croatian part of the Pannonian Basin. Training always included similar reservoir variables, i.e. electric logs (resistivity, spontaneous potential) and lithology determined from cores or logs and described as sandstone or marl, with categorical values in intervals. Selected variables also include hydrocarbon saturation, also represented by a categorical variable, average reservoir porosity calculated from interpreted well logs, and seismic attributes. In all three neural models some of the mentioned inputs were used for analyzing data collected from three different oil fields in the Croatian part of the Pannonian Basin. It is shown that selection of geologically and physically linked variables play a key role in the process of network training, validating and processing. The aim of this study was to establish relationships between log-derived data, core data, and seismic attributes. Three case studies are described in this paper to illustrate the use of neural network prediction of sandstone-marl facies (Case Study # 1, Okoli Field), prediction of carbonate breccia porosity (Case Study # 2, Beničanci Field), and prediction of lithology and saturation (Case Study # 3, Kloštar Field). The results of these studies indicate that this method is capable of providing better understanding of some clastic Neogene reservoirs in the Croatian part of the Pannonian Basin

Technology-Supported Storytelling (TSST) Strategy in Virtual World for Multicultural Education

Learning culture through stories is an effective way for multicultural education, since stories are one of the most powerful and personal ways that we learn about the world. Storytelling, the process of telling stories, is a form of communication and a universal expression of culture. With the development of technology, storytelling emerges out of diverse ways. This study explores the storytelling in virtual worlds for multicultural education, and devises a Technology-Supported storytelling (TSST) strategy by examining and considering the characteristics of virtual worlds which could be incorporated into the storytelling, and then uses this strategy to teach Korean culture to students with different culture background. With this innovative TSST strategy in virtual world, this study expects to provide a guide to practice for teaching multicultural in digital era