Enhancing rehabilitation of motor deficits with peripheral nerve stimulation

A number of different neurorehabilitation strategies include manipulation of the somatosensory system, e.g. in the form of training by passive movement. Recently, peripheral electrical nerve stimulation has been proposed as a simple, painless method of enhancing rehabilitation of motor deficits. Several physiological studies both in animals and in humans indicate that a prolonged period of patterned peripheral electrical stimulation induces short-term plasticity at multiple levels of the motor system. Small-scale studies in humans indicate that these plastic changes are linked with improvement in motor function, particularly in patients with chronic motor deficits after stroke. Somatosensory-mediated disinhibition of motor pathways is a possible underlying mechanism and might explain why peripheral electrical stimulation is more effective when combined with active training. Further large-scale studies are needed to identify the optimal stimulation protocol and the patient groups that stand to benefit the most from this technique.

The General Geology of the Cardwell Mining District

The Cardwell Mining District is part of the greater Whitehall Mining District. The district is situated about four miles to the east and northeast of Whitehall in the southern end of the Bull Mountains which are near the Continental Divide. The first reported production was in 1896 after the dis­covery of the Mayflower Mine. Mining has been carried on in­termittently and on a small scale since that time.

Electrolysis of Molten Mixtures of Galena and Lead Chloride.

Electrolysis of molten mixtures of lead chloride and galena was carried out under various conditions of temperature, time, composition, and current densities; without a diaphram, and with various diaphrams. Continuous runs, with necessary additions of lead sulfide and lead chloride to maintain a melt of the proper composition, were attempted on a small scale.

Laboratory Model Shaking Table; An Investigation of Defects and Suggested Improvements

The laboratory model is considered in this thesis. Information gained from this investigation has not been trans­ferred to the larger industrial machines. Some of the factors noted concerning the efficiency of the laboratory shaking table are inherent in this small scale model only.

Sampling-Design Effects on Properties of Species-Area Relationships - A Case Study from Estonian Dry Grassland Communities

Despite widespread use of species-area relationships (SARs), dispute remains over the most representative SAR model. Using data of small-scale SARs of Estonian dry grassland communities, we address three questions: (1) Which model describes these SARs best when known artifacts are excluded? (2) How do deviating sampling procedures (marginal instead of central position of the smaller plots in relation to the largest plot; single values instead of average values; randomly located subplots instead of nested subplots) influence the properties of the SARs? (3) Are those effects likely to bias the selection of the best model? Our general dataset consisted of 16 series of nested-plots (1 cm(2)-100 m(2), any-part system), each of which comprised five series of subplots located in the four corners and the centre of the 100-m(2) plot. Data for the three pairs of compared sampling designs were generated from this dataset by subsampling. Five function types (power, quadratic power, logarithmic, Michaelis-Menten, Lomolino) were fitted with non-linear regression. In some of the communities, we found extremely high species densities (including bryophytes and lichens), namely up to eight species in 1 cm(2) and up to 140 species in 100 m(2), which appear to be the highest documented values on these scales. For SARs constructed from nested-plot average-value data, the regular power function generally was the best model, closely followed by the quadratic power function, while the logarithmic and Michaelis-Menten functions performed poorly throughout. However, the relative fit of the latter two models increased significantly relative to the respective best model when the single-value or random-sampling method was applied, however, the power function normally remained far superior. These results confirm the hypothesis that both single-value and random-sampling approaches cause artifacts by increasing stochasticity in the data, which can lead to the selection of inappropriate models.