Notes on Mount Fuji

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Mount Fuji sign following subdural haematoma evacuation.

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Tension Pneumocephalus with Mount Fuji Sign

The 81-year-old male presented with progressive headache and was diagnosed as bilateral chronic subdural hemorrhage (SDH). He has history of hypertension, coronary artery disease and hyperlipidemia. Burr-hole craniostomy for removal of SDH was performed. However, deteriorated consciousness and seizure happened after removal of bilateral external ventricular drainage (EVD) on postoperative Day 3...

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Mount Fuji sign in tension pneumocephalus.

The Mount Fuji sign is seen in bilateral subdural hypertensive pneumocephalus. These air collections cause compression of the frontal lobes, which take a shape similar to the silhouette of Mount Fuji. It is most commonly seen after surgical decompression of a chronic subdural hematoma. However, it may also be observed following head trauma, otogenic infections, nitrous oxide anesthesia and diving.

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Predicting C4 Photosynthesis Evolution: Modular, Individually Adaptive Steps on a Mount Fuji Fitness Landscape

An ultimate goal of evolutionary biology is the prediction and experimental verification of adaptive trajectories on macroevolutionary timescales. This aim has rarely been achieved for complex biological systems, as models usually lack clear correlates of organismal fitness. Here, we simulate the fitness landscape connecting two carbon fixation systems: C3 photosynthesis, used by most plant spe...

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Adaptation in tunably rugged fitness landscapes: the rough Mount Fuji model.

Much of the current theory of adaptation is based on Gillespie's mutational landscape model (MLM), which assumes that the fitness values of genotypes linked by single mutational steps are independent random variables. On the other hand, a growing body of empirical evidence shows that real fitness landscapes, while possessing a considerable amount of ruggedness, are smoother than predicted by th...

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ژورنال

عنوان ژورنال: The Journal of the Geological Society of Japan

سال: 1898

ISSN: 0016-7630,1349-9963

DOI: 10.5575/geosoc.5.60_492