Increased cortical surface area and gyrification following long-term survival from early monocular enucleation

AbstractPurposeRetinoblastoma is typically diagnosed before 5 years of age and is often treated by enucleation (surgical removal) of the cancerous eye. Here, we sought to characterize morphological changes of the cortex following long-term survival from early monocular enucleation.MethodsNine adults with early right-eye enucleation (≤48 months of age) due to retinoblastoma were compared to 18 binocularly intact controls. Surface area, cortical thickness, and gyrification estimates were obtained from T1 weighted images and group differences were examined.ResultsEarly monocular enucleation was associated with increased surface area and/or gyrification in visual (i.e., V1, inferior temporal), auditory (i.e., supramarginal), and multisensory (i.e., superior temporal, inferior parietal, superior parietal) cortices compared with controls. Visual cortex increases were restricted to the right hemisphere contralateral to the remaining eye, consistent with previous subcortical data showing asymmetrical lateral geniculate nucleus volume following early monocular enucleation.ConclusionsAltered morphological development of visual, auditory, and multisensory regions occurs subsequent to long-time survival from early eye loss

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis

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