Kjeldgaardpuggaard3895

Results The sequence of wind direction anomalies indicated that multiple air blasts passed the AWS, each associated with a distinct avalanche source, suggesting that earthquake likely caused a number of distinct avalanches from different source regions along this ridge. Discussion Results suggest that a swarm of avalanches collectively lead to the death and destruction at EBC, suggesting the need for improvement in our understanding of avalanches in the region as well as in our ability to model and forecast such events.As evolutionary algorithms (EAs) are general-purpose optimization algorithms, recent theoretical studies have tried to analyze their performance for solving general problem classes, with the goal of providing a general theoretical explanation of the behavior of EAs. Particularly, a simple multi-objective EA, i.e., GSEMO, has been shown to be able to achieve good polynomial-time approximation guarantees for submodular optimization, where the objective function is only required to satisfy some properties and its explicit formulation is not needed. Submodular optimization has wide applications in diverse areas, and previous studies have considered the cases where the objective functions are monotone submodular, monotone nonsubmodular, or non-monotone submodular. To complement this line of research, this paper studies the problem class of maximizing monotone approximately submodular minus modular functions (i.e., g - c ) with a size constraint, where g is a so-called non-negative monotone approximately submodular function and c is a socalled non-negative modular function, resulting in the objective function ( g - c ) being non-monotone non-submodular in general. Different from previous analyses, we prove that by optimizing the original objective function ( g - c ) and the size simultaneously, the GSEMO fails to achieve a good polynomial-time approximation guarantee. However, we also prove that by optimizing a distorted objective function and the size simultaneously, the GSEMO can still achieve the best-known polynomialtime approximation guarantee. Empirical studies on the applications of Bayesian experimental design and directed vertex cover show the excellent performance of the GSEMO.The two-machine permutation flow shop scheduling problem with buffer is studied for the special case that all processing times on one of the two machines are equal to a constant c. This case is interesting because it occurs in various applications, e.g., when one machine is a packing machine or when materials have to be transported. compound 991 research buy Different types of buffers and buffer usage are considered. It is shown that all considered buffer flow shop problems remain NP-hard for the makespan criterion even with the restriction to equal processing times on one machine. However, the special case where the constant c is larger or smaller than all processing times on the other machine is shown to be polynomially solvable by presenting an algorithm (2BF-OPT) that calculates optimal schedules in O ( n log n ) steps. Two heuristics for solving the NP-hard flow shop problems are proposed i) a modification of the commonly used NEH heuristic (mNEH) and ii) an Iterated Local Search heuristic (2BF-ILS) that uses the mNEH heuristic for computing its initial solution. It is shown experimentally that the proposed 2BF-ILS heuristic obtains better results than two state-of-the-art algorithms for buffered flow shop problems from the literature and an Ant Colony Optimization algorithm. In addition, it is shown experimentally that 2BF-ILS obtains the same solution quality as the standard NEH heuristic, however, with a smaller number of function evaluations.A fundamental aspect of learning in biological neural networks is the plasticity property which allows them to modify their configurations during their lifetime. Hebbian learning is a biologically plausible mechanism for modeling the plasticity property in artificial neural networks (ANNs), based on the local interactions of neurons. However, the emergence of a coherent global learning behavior from local Hebbian plasticity rules is not very well understood. The goal of this work is to discover interpretable local Hebbian learning rules that can provide autonomous global learning. To achieve this, we use a discrete representation to encode the learning rules in a finite search space. These rules are then used to perform synaptic changes, based on the local interactions of the neurons. We employ genetic algorithms to optimize these rules to allow learning on two separate tasks (a foraging and a prey-predator scenario) in online lifetime learning settings. The resulting evolved rules converged into a set of well-defined interpretable types, that are thoroughly discussed. Notably, the performance of these rules, while adapting the ANNs during the learning tasks, is comparable to that of offline learning methods such as hill climbing.The warfarin dose requirement and therapeutic response of a 42-year-old African-American male with genotype CYP2C9 *11/*11, VKORC1 -1639GG and CYP4F2 433Val/Val anticoagulated for ischemic stroke is described herein. Warfarin was dosed according to the institution's personalized medicine program recommendations of a 10 mg mini-load dose, followed by dose decreases to 4-6 mg/day through discharge. Stable international normalized ratio was achieved after eight doses, with good overall long-term maintenance of therapeutic international normalized ratio over several years with warfarin doses of 3.1-4.3 mg/day. This case report sheds further light on the clinical impact of CYP2C9 *11/*11 on warfarin dose requirements, short- and long-term treatment response and practical considerations for warfarin management in suspected carriers of rare variant CYP2C9 alleles.Interleukin (IL) 6 contributes to atherosclerotic plaque development through IL6 membrane-bound (IL6R and gp130) and soluble (sIL6R and sgp130) receptors. We investigated IL6 receptor expression in carotid plaques and its correlation with circulating IL6 and soluble receptor levels. Plasma samples and carotid plaques were obtained from 78 patients in the Biobank of Karolinska Endarterectomies study. IL6, sIL6R, and sgp130 were measured in plasma and IL6, IL6R, sIL6R, GP130, and sGP130-RAPS (sGP130) gene expression assessed in carotid plaques. Correlations between plaque IL6 signaling gene expression and plasma levels were determined by Spearman's correlation. Differences in plasma and gene expression levels between patients with (n = 53) and without (n = 25) a history of a cerebral event and statin-treated (n = 65) and non-treated (n = 11), were estimated by Kruskal-Wallis. IL6 and its receptors were all expressed in carotid plaques. There was a positive, borderline significant, moderate correlation between plasma IL6 and sIL6R and the respective gene expression levels (rho 0.