The sites of neural adaptation induced by resistance training in humans

Although it has long been supposed that resistance training causes adaptive changes in the CNS, the sites and nature of these adaptations have not previously been identified. In order to determine whether the neural adaptations to resistance training occur to a greater extent at cortical or subcortical sites in the CNS, we compared the effects of resistance training on the electromyographic (EMG) responses to transcranial magnetic (TMS) and electrical (TES) stimulation. Motor evoked potentials (MEPs) were recorded from the first dorsal interosseous muscle of 16 individuals before and after 4 weeks of resistance training for the index finger abductors (n = 8), or training involving finger abduction-adduction without external resistance (n = 8). TMS was delivered at rest at intensities from 5 % below the passive threshold to the maximal output of the stimulator. TMS and TES were also delivered at the active threshold intensity while the participants exerted torques ranging from 5 to 60 % of their maximum voluntary contraction (MVC) torque. The average latency of MEPs elicited by TES was significantly shorter than that of TMS MEPs (TES latency = 21.5 ± 1.4 ms; TMS latency = 23.4 ± 1.4 ms; P < 0.05), which indicates that the site of activation differed between the two forms of stimulation. Training resulted in a significant increase in MVC torque for the resistance-training group, but not the control group. There were no statistically significant changes in the corticospinal properties measured at rest for either group. For the active trials involving both TMS and TES, however, the slope of the relationship between MEP size and the torque exerted was significantly lower after training for the resistance-training group (P < 0.05). Thus, for a specific level of muscle activity, the magnitude of the EMG responses to both forms of transcranial stimulation were smaller following resistance training. These results suggest that resistance training changes the functional properties of spinal cord circuitry in humans, but does not substantially affect the organisation of the motor cortex.

A hybrid planning method for transmission networks in a deregulated environment

The reconstruction of power industries has brought fundamental changes to both power system operation and planning. This paper presents a new planning method using multi-objective optimization (MOOP) technique, as well as human knowledge, to expand the transmission network in open access schemes. The method starts with a candidate pool of feasible expansion plans. Consequent selection of the best candidates is carried out through a MOOP approach, of which multiple objectives are tackled simultaneously, aiming at integrating the market operation and planning as one unified process in context of deregulated system. Human knowledge has been applied in both stages to ensure the selection with practical engineering and management concerns. The expansion plan from MOOP is assessed by reliability criteria before it is finalized. The proposed method has been tested with the IEEE 14-bus system and relevant analyses and discussions have been presented.

Visualization of learning in multilayer perceptron networks using principal component analysis

This paper is concerned with the use of scientific visualization methods for the analysis of feedforward neural networks (NNs). Inevitably, the kinds of data associated with the design and implementation of neural networks are of very high dimensionality, presenting a major challenge for visualization. A method is described using the well-known statistical technique of principal component analysis (PCA). This is found to be an effective and useful method of visualizing the learning trajectories of many learning algorithms such as back-propagation and can also be used to provide insight into the learning process and the nature of the error surface.

Inter-subband redundancy prediction using neural network for video coding

High performance video codec is mandatory for multimedia applications such as video-on-demand and video conferencing. Recent research has proposed numerous video coding techniques to meet the requirement in bandwidth, delay, loss and Quality-of-Service (QoS). In this paper, we present our investigations on inter-subband self-similarity within the wavelet-decomposed video frames using neural networks, and study the performance of applying the spatial network model to all video frames over time. The goal of our proposed method is to restore the highest perceptual quality for video transmitted over a highly congested network. Our contributions in this paper are: (1) A new coding model with neural network based, inter-subband redundancy (ISR) prediction for video coding using wavelet (2) The performance of 1D and 2D ISR prediction, including multiple levels of wavelet decompositions. Our result shows a short-term quality enhancement may be obtained using both 1D and 2D ISR prediction.

Segmenting the market of West Australian senior tourists using an artificial neural network

Measuring perceptions of customers can be a major problem for marketers of tourism and travel services. Much of the problem is to determine which attributes carry most weight in the purchasing decision. Older travellers weigh many travel features before making their travel decisions. This paper presents a descriptive analysis of neural network methodology and provides a research technique that assesses the weighting of different attributes and uses an unsupervised neural network model to describe a consumer-product relationship. The development of this rich class of models was inspired by the neural architecture of the human brain. These models mathematically emulate the neurophysical structure and decision making of the human brain, and, from a statistical perspective, are closely related to generalised linear models. Artificial neural networks or neural networks are, however, nonlinear and do not require the same restrictive assumptions about the relationship between the independent variables and dependent variables. Using neural networks is one way to determine what trade-offs older travellers make as they decide their travel plans. The sample of this study is from a syndicated data source of 200 valid cases from Western Australia. From senior groups, active learner, relaxed family body, careful participants and elementary vacation were identified and discussed. (C) 2003 Published by Elsevier Science Ltd.

Tyrosinase-Cre mice for tissue-specific gene ablation in neural crest and neuroepithelial-derived tissues

This study describes the derivation of two new lines of transgenic mice that express Cre recombinase under the control of tyrosinase transcriptional elements. To determine the suitability of the Tyrosinase-Cre transgene for tissue-specific gene ablation studies, a fate map of Cre expression domains was determined using the Z/AP reporter strain. It was shown that Cre-expressing cells contribute to a wide array of neural crest and neuroepithelial-derived lineages. The melanocytes of the harderian gland and eye choroid, sympathetic cephalic ganglia, leptomeninges of the telencephalon, as well as cranial nerves (V), (VII), and (IX) are derived either fully or partly from Cre-expressing cephalic crest. The cells contributing to the cranial nerves were the first to exhibit Cre expression at E10.5 as they were migrating into the branchial arches. The melanocytes, chromaffin cells of the adrenal medulla, and dorsal root ganglia are derived from trunk neural crest that either express Cre or were derived from Cre-expressing precursors. An array of brain tissue including the basal forebrain, hippocampus, olfactory bulb, and the granule cell layer of the lateral cerebellum, as well as the retinal pigmented epithelium and glia of the optic nerve originate from Cre-expressing neuroepithelial cells. (C) 2003 Wiley-Liss, Inc.