Immunopathology and Pathophysiology of Experimental Autoimmune Encephalomyelitis

Experimental autoimmune encephalomyelitis (EAE) is an inflammatory demyelinating disease of the central nervous system (CNS), and is widely studied as an animal model of the human CNS demyelinating diseases, including multiple sclerosis (Raine, 1984). EAE can be induced by inoculation with whole CNS tissue, purified myelin basic protein (MBP) or myelin proteolipid protein (PLP), together with adjuvants. It may also be induced by the passive transfer of T cells specifically reactive to these myelin antigens. EAE may have either an acute or a chronic relapsing course. Acute EAE closely resembles the human disease acute disseminated encephalomyelitis, while chronic relapsing EAE resembles multiple sclerosis. EAE is also the prototype for T-cell-mediated autoimmune disease in general. This chapter will focus on the immunopathology and pathophysiology of EAE, which are the subjects of investigation in my laboratory.

Endangerment and likeability of wildlife species: How important are they for proposed payments for conservation

Examines empirically the relative influence of the degree of endangerment of wildlife species and their stated likeability on individuals’ willingness to pay (WTP) for their conservation. To do this, it utilises data obtained from the IUCN Red List and likeability and WTP data obtained from two serial surveys of a sample of the Australian public who were requested to assess 24 Australian wildlife species in each of three animal classes: mammals, birds and reptiles. Between the first and second survey, respondents were provided with extra information about the focal species. This information resulted in the clear dominance of endangerment as the major influence on the WTP of respondents for the conservation of the focal wildlife species. Our results throw doubts on the proposition in the literature that the likeability of species is the dominant influence on WTP for conservation of wildlife species. Furthermore, our results suggest that the relationship between WTP for the conservation of wildlife in relation to their population levels may be more complex and different to that suggested in some of the literature on ecological economics.

Economic, social and cultural influences on the status and wellbeing of Indian rural wives

Examines the status and well being of wives in rural India using results from interviews. Surveys were conducted in three villages in the southwest of West Bengal and in three villages in the west of Orissa. The survey in West Bengal contained a large proportion of Santal tribals and the Oriyan survey was dominated by Kondh tribals. The relationships between variables representing economic, social and cultural factors are compared with ‘dependent’ variables representing the status of wives within their family and their wellbeing. Wellbeing is indicated by whether the basic needs of wives for food and medical care are met. Status of wives is indicated by their ‘control’ over family resources, whether or not they are restricted in joining social groups and in working outside their home, and the extent of their involvement in family decision-making. Cultural factors are found to be the dominant influence on the status of wives. Family income and other economic factors are found to be associated cross sectionally with greater restrictions on wives within their family. Nevertheless, the higher in the economic status of the household, the more likely are the basic needs of wives to be met.

Development and evaluation of neural network freeway incident detection models using field data

This paper discusses a multi-layer feedforward (MLF) neural network incident detection model that was developed and evaluated using field data. In contrast to published neural network incident detection models which relied on simulated or limited field data for model development and testing, the model described in this paper was trained and tested on a real-world data set of 100 incidents. The model uses speed, flow and occupancy data measured at dual stations, averaged across all lanes and only from time interval t. The off-line performance of the model is reported under both incident and non-incident conditions. The incident detection performance of the model is reported based on a validation-test data set of 40 incidents that were independent of the 60 incidents used for training. The false alarm rates of the model are evaluated based on non-incident data that were collected from a freeway section which was video-taped for a period of 33 days. A comparative evaluation between the neural network model and the incident detection model in operation on Melbourne's freeways is also presented. The results of the comparative performance evaluation clearly demonstrate the substantial improvement in incident detection performance obtained by the neural network model. The paper also presents additional results that demonstrate how improvements in model performance can be achieved using variable decision thresholds. Finally, the model's fault-tolerance under conditions of corrupt or missing data is investigated and the impact of loop detector failure/malfunction on the performance of the trained model is evaluated and discussed. The results presented in this paper provide a comprehensive evaluation of the developed model and confirm that neural network models can provide fast and reliable incident detection on freeways. (C) 1997 Elsevier Science Ltd. All rights reserved.