Validation of the Cannabis Expectancy Questionnaire (CEQ) in adult cannabis users in treatment

Background Outcome expectancies are a key cognitive construct in the etiology, assessment and treatment of Substance Use Disorders. There is a research and clinical need for a cannabis expectancy measure validated in a clinical sample of cannabis users. Method The Cannabis Expectancy Questionnaire (CEQ) was subjected to exploratory (n = 501, mean age 27.45, 78% male) and confirmatory (n = 505, mean age 27.69, 78% male) factor analysis in two separate samples of cannabis users attending an outpatient cannabis treatment program. Weekly cannabis consumption was clinically assessed and patients completed the Severity of Dependence Scale-Cannabis (SDS-C) and the General Health Questionnaire (GHQ-28). Results Two factors representing Negative Cannabis Expectancies and Positive Cannabis Expectancies were identified. These provided a robust statistical and conceptual fit for the data. Internal reliabilities were high. Negative expectancies were associated with greater dependence severity (as measured by the SDS) and positive expectancies with higher consumption. The interaction of positive and negative expectancies was consistently significantly associated with self-reported functioning across all four GHQ-28 scales (Somatic Concerns, Anxiety, Social Dysfunction and Depression). Specifically, within the context of high positive cannabis expectancy, higher negative expectancy was predictive of more impaired functioning. By contrast, within the context of low positive cannabis expectancy, higher negative expectancy was predictive of better functioning. Conclusions The CEQ is the first cannabis expectancy measure to be validated in a sample of cannabis users in treatment. Negative and positive cannabis expectancy domains were uniquely associated with consumption, dependence severity and self-reported mental health functioning.

Adult learning in a workplace context

In light of the changing nature of contemporary workplaces, this chapter attempts to identify employer expectations and the associated skills required to workers to function effectively in such workplaces. Workers are required to participate in informed discussion about their specific jobs and to contribute to the overall development of organisations. This requires deep understanding of domain-specific knowledge, which at times can be very complex. Workers are also required to take responsibility for their actions and are expected to be flexible so that they can be deployed to other related jobs depending on demand. Finally, workers are expected to be pro-active, be able to anticipate situations and continuously update their knowledge to address new situations. This chapter discusses the nature of knowledge and skills that will facilitate the above qualities.

The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network

Sample complexity results from computational learning theory, when applied to neural network learning for pattern classification problems, suggest that for good generalization performance the number of training examples should grow at least linearly with the number of adjustable parameters in the network. Results in this paper show that if a large neural network is used for a pattern classification problem and the learning algorithm finds a network with small weights that has small squared error on the training patterns, then the generalization performance depends on the size of the weights rather than the number of weights. For example, consider a two-layer feedforward network of sigmoid units, in which the sum of the magnitudes of the weights associated with each unit is bounded by A and the input dimension is n. We show that the misclassification probability is no more than a certain error estimate (that is related to squared error on the training set) plus A3 √((log n)/m) (ignoring log A and log m factors), where m is the number of training patterns. This may explain the generalization performance of neural networks, particularly when the number of training examples is considerably smaller than the number of weights. It also supports heuristics (such as weight decay and early stopping) that attempt to keep the weights small during training. The proof techniques appear to be useful for the analysis of other pattern classifiers: when the input domain is a totally bounded metric space, we use the same approach to give upper bounds on misclassification probability for classifiers with decision boundaries that are far from the training examples.

Neural network learning:theoretical foundations

This important work describes recent theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. Chapters survey research on pattern classification with binary-output networks, including a discussion of the relevance of the Vapnik Chervonenkis dimension, and of estimates of the dimension for several neural network models. In addition, Anthony and Bartlett develop a model of classification by real-output networks, and demonstrate the usefulness of classification with a "large margin." The authors explain the role of scale-sensitive versions of the Vapnik Chervonenkis dimension in large margin classification, and in real prediction. Key chapters also discuss the computational complexity of neural network learning, describing a variety of hardness results, and outlining two efficient, constructive learning algorithms. The book is self-contained and accessible to researchers and graduate students in computer science, engineering, and mathematics

Aerial image analysis using spiking neural networks with application to power line corridor monitoring

Trees, shrubs and other vegetation are of continued importance to the environment and our daily life. They provide shade around our roads and houses, offer a habitat for birds and wildlife, and absorb air pollutants. However, vegetation touching power lines is a risk to public safety and the environment, and one of the main causes of power supply problems. Vegetation management, which includes tree trimming and vegetation control, is a significant cost component of the maintenance of electrical infrastructure. For example, Ergon Energy, the Australia’s largest geographic footprint energy distributor, currently spends over $80 million a year inspecting and managing vegetation that encroach on power line assets. Currently, most vegetation management programs for distribution systems are calendar-based ground patrol. However, calendar-based inspection by linesman is labour-intensive, time consuming and expensive. It also results in some zones being trimmed more frequently than needed and others not cut often enough. Moreover, it’s seldom practicable to measure all the plants around power line corridors by field methods. Remote sensing data captured from airborne sensors has great potential in assisting vegetation management in power line corridors. This thesis presented a comprehensive study on using spiking neural networks in a specific image analysis application: power line corridor monitoring. Theoretically, the thesis focuses on a biologically inspired spiking cortical model: pulse coupled neural network (PCNN). The original PCNN model was simplified in order to better analyze the pulse dynamics and control the performance. Some new and effective algorithms were developed based on the proposed spiking cortical model for object detection, image segmentation and invariant feature extraction. The developed algorithms were evaluated in a number of experiments using real image data collected from our flight trails. The experimental results demonstrated the effectiveness and advantages of spiking neural networks in image processing tasks. Operationally, the knowledge gained from this research project offers a good reference to our industry partner (i.e. Ergon Energy) and other energy utilities who wants to improve their vegetation management activities. The novel approaches described in this thesis showed the potential of using the cutting edge sensor technologies and intelligent computing techniques in improve power line corridor monitoring. The lessons learnt from this project are also expected to increase the confidence of energy companies to move from traditional vegetation management strategy to a more automated, accurate and cost-effective solution using aerial remote sensing techniques.

Frequency response function based structural damage detection using artificial neural networks

Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, few attempts have been made to explore the structure damage with frequency response functions (FRFs). This paper illustrates the damage identification and condition assessment of a beam structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). In practice, usage of all available FRF data as an input to artificial neural networks makes the training and convergence impossible. Therefore one of the data reduction techniques Principal Component Analysis (PCA) is introduced in the algorithm. In the proposed procedure, a large set of FRFs are divided into sub-sets in order to find the damage indices for different frequency points of different damage scenarios. The basic idea of this method is to establish features of damaged structure using FRFs from different measurement points of different sub-sets of intact structure. Then using these features, damage indices of different damage cases of the structure are identified after reconstructing of available FRF data using PCA. The obtained damage indices corresponding to different damage locations and severities are introduced as input variable to developed artificial neural networks. Finally, the effectiveness of the proposed method is illustrated and validated by using the finite element modal of a beam structure. The illustrated results show that the PCA based damage index is suitable and effective for structural damage detection and condition assessment of building structures.

Caregiving experience and its influencing factors : rural versus urban adult-child caregivers caring for parents with dementia in China

Background: It is predicted that China will have the largest number of cases of dementia in the world by 2025 (Ferri et al., 2005). Research has demonstrated that caring for family members with dementia can be a long-term, burdensome activity resulting in physical and emotional distress and impairment (Pinquart & Sorensen, 2003b). The establishment of family caregiver supportive services in China can be considered urgent; and the knowledge of the caregiving experience and related influencing factors is necessary to inform such services. Nevertheless, in the context of rapid demographic and socioeconomic change, the impact of caregiving for rural and urban Chinese adult-child caregivers may be different, and different needs in supportive services may therefore be expected. Objectives: The aims of this research were 1) to examine the potential differences existing in the caregiving experience between rural and urban adult-child caregivers caring for parents with dementia in China; and 2) to examine the potential differences existing in the influencing factors of the caregiving experience for rural as compared with urban adult-child caregivers caring for parents with dementia in China. Based on the literature review and Kramer.s (1997) caregiver adaptation model, six concepts and their relationships of caregiving experience were studied: severity of the care receivers. dementia, caregivers. appraisal of role strain and role gain, negative and positive well-being outcomes, and health related quality of life. Furthermore, four influencing factors (i.e., filial piety, social support, resilience, and personal mastery) were studied respectively. Methods: A cross-sectional, comparative design was used to achieve the aims of the study. A questionnaire, which was designed based on the literature review and on Kramer.s (1997) caregiver adaptation model, was completed by 401 adult-child caregivers caring for their parents with dementia from the mental health outpatient departments in five hospitals in the Yunnan province, P.R. China. Structural equation modelling (SEM) was employed as the main statistical technique for data analyses. Other statistical techniques (e.g., t-tests and Chi-Square tests) were also conducted to compare the demographic characteristics and the measured variables between rural and urban groups. Results: For the first research aim, the results indicated that urban adult-child caregivers in China experienced significantly greater strain and negative well-being outcomes than their rural peers; whereas, the difference on the appraisal of role gain and positive outcomes was nonsignificant between the two groups. The results also indicated that the amounts of severity of care receivers. dementia and caregivers. health related quality of life do not have the same meanings between the two groups. Thus, the levels of these two concepts were not comparable between the rural and urban groups in this study. Moreover, the results also demonstrated that the negative direct effect of gain on negative outcomes in urban caregivers was stronger than that in rural caregivers, suggesting that the urban caregivers tended to use appraisal of role gain to protect themselves from negative well-being outcomes to a greater extent. In addition, the unexplained variance in strain in the urban group was significantly more than that in the rural group, suggesting that there were other unmeasured variables besides the severity of care receivers. dementia which would predict strain in urban caregivers compared with their rural peers. For the second research aim, the results demonstrated that rural adult-child caregivers reported a significantly higher level of filial piety and more social support than their urban counterparts, although the two groups did not significantly differ on the levels of their resilience and personal mastery. Furthermore, although the mediation effects of these four influencing factors on both positive and negative aspects remained constant across rural and urban adult-child caregivers, urban caregivers tended to be more effective in using personal mastery to protect themselves from role strain than rural caregivers, which in turn protects them more from the negative well-being outcomes than was the case with their rural peers. Conclusions: The study extends the application of Kramer.s caregiving adaptation process model (Kramer, 1997) to a sample of adult-child caregivers in China by demonstrating that both positive and negative aspects of caregiving may impact on the caregiver.s health related quality of life, suggesting that both aspects should be targeted in supportive interventions for Chinese family caregivers. Moreover, by demonstrating partial mediation effects, the study provides four influencing factors (i.e., filial piety, social support, resilience, and personal mastery) as specific targets for clinical interventions. Furthermore, the study found evidence that urban adult-child caregivers had more negative but similar positive experience compared to their rural peers, suggesting that the establishment of supportive services for urban caregivers may be more urgent at present stage in China. Additionally, since urban caregivers tended to use appraisal of role gain and personal mastery to protect themselves from negative well-being outcomes than rural caregivers to a greater extend, interventions targeting utility of gain or/and personal mastery to decrease negative outcomes might be more effective in urban caregivers than in rural caregivers. On the other hand, as cultural expectations and expression of filial piety tend to be more traditional in rural areas, interventions targeting filial piety could be more effective among rural caregivers. Last but not least, as rural adult-child caregivers have more existing natural social support than their urban counterparts, mobilising existing natural social support resources may be more beneficial for rural caregivers, whereas, formal supports (e.g., counselling services, support groups and adult day care centres) should be enhanced for urban caregivers.

Neural interface systems : a new legal frontier?

In an age where digital innovation knows no boundaries, research in the area of brain-computer interface and other neural interface devices go where none have gone before. The possibilities are endless and as dreams become reality, the implications of these amazing developments should be considered. Some of these new devices have been created to correct or minimise the effects of disease or injury so the paper discusses some of the current research and development in the area, including neuroprosthetics. To assist researchers and academics in identifying some of the legal and ethical issues that might arise as a result of research and development of neural interface devices, using both non-invasive techniques and invasive procedures, the paper discusses a number of recent observations of authors in the field. The issue of enhancing human attributes by incorporating these new devices is also considered. Such enhancement may be regarded as freeing the mind from the constraints of the body, but there are legal and moral issues that researchers and academics would be well advised to contemplate as these new devices are developed and used. While different fact situation surround each of these new devices, and those that are yet to come, consideration of the legal and ethical landscape may assist researchers and academics in dealing effectively with matters that arise in these times of transition. Lawyers could seek to facilitate the resolution of the legal disputes that arise in this area of research and development within the existing judicial and legislative frameworks. Whether these frameworks will suffice, or will need to change in order to enable effective resolution, is a broader question to be explored.

Human neural cell interactions with orientated electrospun nanofibers in vitro

Aim: Electrospun nanofibers represent potent guidance substrates for nervous tissue repair. Development of nanofiber-based scaffolds for CNS repair requires, as a first step, an understanding of appropriate neural cell type-substrate interactions. Materials & methods: Astrocyte–nanofiber interactions (e.g., adhesion, proliferation, process extension and migration) were studied by comparing human neural progenitor-derived astrocytes (hNP-ACs) and a human astrocytoma cell line (U373) with aligned polycaprolactone (PCL) nanofibers or blended (25% type I collagen/75% PCL) nanofibers. Neuron–nanofiber interactions were assessed using a differentiated human neuroblastoma cell line (SH-SY5Y). Results & discussion: U373 cells and hNP-AC showed similar process alignment and length when associated with PCL or Type I collagen/PCL nanofibers. Cell adhesion and migration by hNP-AC were clearly improved by functionalization of nanofiber surfaces with type I collagen. Functionalized nanofibers had no such effect on U373 cells. Another clear difference between the U373 cells and hNP-AC interactions with the nanofiber substrate was proliferation; the cell line demonstrating strong proliferation, whereas the hNP-AC line showed no proliferation on either type of nanofiber. Long axonal growth (up to 600 µm in length) of SH-SY5Y neurons followed the orientation of both types of nanofibers even though adhesion of the processes to the fibers was poor. Conclusion: The use of cell lines is of only limited predictive value when studying cell–substrate interactions but both morphology and alignment of human astrocytes were affected profoundly by nanofibers. Nanofiber surface functionalization with collagen significantly improved hNP-AC adhesion and migration. Alternative forms of functionalization may be required for optimal axon–nanofiber interactions.