Archiv Fur Entwicklungsmechanik Der Organismen最新文献

Ground falls in longwall gateroad entries remain a concern in modern longwall operations. The gateroads are subject to changing horizontal and vertical ground stress induced by longwall extraction. These stress changes can result in failure of the strata around an entry leading to large deformations of the entry roof, floor, and ribs. The gateroad support systems are required to control the failed strata while maintaining safe access to the longwall face and unimpeded ventilation. This paper presents research that was conducted to better understand the stability issues in gateroad excavations and to develop procedures for evaluating support and layout alternatives for longwall gateroads. Using the results of a field-monitoring program and numerical model analysis of case histories, a conceptual model of gateroad support needs was developed. The conceptual model formed the basis for developing a set of equations that can be used to estimate likely roof sag and support loading for given roof geology and longwall-induced loading conditions. The developed equations were used to compare predicted gateroad stability to field study results, showing satisfactory agreement. The calculation procedures are used to demonstrate their application in assessing support alternatives at a case study mine. It is concluded that the developed analysis procedures provide realistic assessments of likely ground stability and can be used to evaluate alternative gateroad support systems at operating longwall mines.

Statins have been effective in reducing adverse cardiovascular events. Their benefits have been proportional to the level of plasma LDL-cholesterol reduction and seem to extend to patients with 'normal' levels of cholesterol at outset. Statins are also inexpensive and have a favourable side-effect profile. As a result, they are used widely (almost indiscriminately) in patients with atherosclerotic vascular disease, and in those at risk of disease. Next generation lipid-modifying drugs seem unlikely to offer the same simplicity of application. The recent trials of new classes of lipid modifying drugs underline the need for a risk stratification tool which is not based on patients' category of diagnosis (for example, post-myocardial infarction) but based on the characterization of disease in that individual patient. Mechanistic staging, a process that matches the target of the drug action with an identifiable disease characteristic, may offer an opportunity to achieve more precise intervention. The upshots of this targeted approach will be greater efficacy, requiring smaller clinical trials to demonstrate effectiveness; a reduced number needed to treat to yield benefits and more cost-effective prescribing. This will be important, as purchasers require ever more rigorous demonstration of both efficacy and cost-effectiveness. In this context, we will discuss available pharmacological strategies of lipid reduction in anti-atherosclerotic treatment and how plaque imaging techniques may provide an ideal method in stratifying patients for new lipid-modifying drugs.

Rey's Auditory Verbal Learning Test (RAVLT) is a powerful neuropsychological tool for testing episodic memory, which is widely used for the cognitive assessment in dementia and pre-dementia conditions. Several studies have shown that an impairment in RAVLT scores reflect well the underlying pathology caused by Alzheimer's disease (AD), thus making RAVLT an effective early marker to detect AD in persons with memory complaints. We investigated the association between RAVLT scores (RAVLT Immediate and RAVLT Percent Forgetting) and the structural brain atrophy caused by AD. The aim was to comprehensively study to what extent the RAVLT scores are predictable based on structural magnetic resonance imaging (MRI) data using machine learning approaches as well as to find the most important brain regions for the estimation of RAVLT scores. For this, we built a predictive model to estimate RAVLT scores from gray matter density via elastic net penalized linear regression model. The proposed approach provided highly significant cross-validated correlation between the estimated and observed RAVLT Immediate (R = 0.50) and RAVLT Percent Forgetting (R = 0.43) in a dataset consisting of 806 AD, mild cognitive impairment (MCI) or healthy subjects. In addition, the selected machine learning method provided more accurate estimates of RAVLT scores than the relevance vector regression used earlier for the estimation of RAVLT based on MRI data. The top predictors were medial temporal lobe structures and amygdala for the estimation of RAVLT Immediate and angular gyrus, hippocampus and amygdala for the estimation of RAVLT Percent Forgetting. Further, the conversion of MCI subjects to AD in 3-years could be predicted based on either observed or estimated RAVLT scores with an accuracy comparable to MRI-based biomarkers.