Objective: For small abdominal aortic aneurysms (AAAs), a regular follow-up examination is recommended every 12 months for AAAs of 30-39 mm and every six months for AAAs of 40-55 mm. Follow-up diameters can determine if a patient follows the common growth model of the population. However, the rapid expansion of an AAA, often associated with higher rupture risk, may be overlooked even though it requires surgical intervention. Therefore, the prognosis of abdominal aortic aneurysm growth is clinically important for planning treatment. This study aims to build enhanced Bayesian inference methods to predict maximum aneurysm diameter.Methods: 106 CT scans from 25 Korean AAA patients were retrospectively obtained. A two-step approach based on Bayesian calibration was used, and an exponential abdominal aortic aneurysm growth model (population-based) was specified according to each individual patient's growth (patient-specific) and morphologic characteristics of the aneurysm sac (enhanced). The distribution estimates were obtained using a Markov Chain Monte Carlo (MCMC) sampler.
Evolutionary conservation is a fundamental resource for predicting the substitutability of amino acids and loss of function in proteins. The use of multiple sequence alignment alone—without considering the evolutionary relationships among sequences—results in the redundant counting of evolutionarily related alteration events as if they were independent. Here we propose a new method, PHACT that predicts the pathogenicity of missense mutations directly from the phylogenetic tree of proteins. PHACT travels through the nodes of the phylogenetic tree and evaluates the deleteriousness of a substitution based on the probability differences of ancestral amino acids between neighboring nodes in the tree. Moreover, PHACT assigns weights to each node in the tree based on their distance to the query organism. For each potential amino acid substitution, the algorithm generates a score that is used to calculate the effect of substitution on protein function. To analyze the predictive performance of PHACT, we performed various experiments over the subsets of two datasets that include 3023 proteins and 61662 variants in total. The experiments demonstrated that our method outperformed the widely used pathogenicity prediction tools (i.e., SIFT and PolyPhen-2) and achieved better predictive performance than did other conventional statistical approaches presented in dbNSFP. The PHACT source code is available at https://github.com/CompGenomeLab/PHACT.
Objective: The maximum diameter measurement of an abdominal aortic aneurysm (AAA), which depends on orthogonal and axial cross-sections or maximally inscribed spheres within the AAA, plays a significant role in the clinical decision making process. This study aims to build a large dataset of morphological parameters from longitudinal CT scans and analyze their correlations. Furthermore, this work explores the existence of a master curve of AAA growth, and tests which parameters serve to enhance its predictability for clinical use. Methods: 106 CT scan images from 25 Korean AAA patients were retrospectively obtained. We subsequently computed morphological parameters, growth rates, and pair-wise correlations, and attempted to enhance the predictability of the growth for high-risk aneurysms using non-linear curve fitting and least-square minimization. Results: An exponential AAA growth model was fitted to the maximum spherical diameter, as the best representative of the growth among all parameters (r-square: 0.985) and correctly predicted to 74 of 79 scans based on a 95% confidence interval. AAA volume expansion rates were highly correlated (r=0.80) with thrombus accumulation rates. Conclusions: The exponential growth model using spherical diameter provides useful information about progression of aneurysm size and enables AAA growth rate extrapolation during a given surveillance period.
Article Info:Graphical/Tabular Abstract Population growth and energy resource based on fossil fuel depletion increase the demand for renewable energy resources, especially for solar energy in the world. Smart grids have been developed in order to meet the growing energy need in the form of an intelligent structure with renewable energy sources. One key goal of the smart grid initiatives, therefore, increases the ratio of the renewable energy within overall energy power generation. However, the integration of renewable energies into the grid, whose power generation is intermittent and uncontrollable, leads to a number of challenges. It is critical to determine which renewable source will be dispatched to satisfy the variety of customer demands, and predict the energy power in advance. Figure A. Block schema of the systemPurpose: In this study, we aim to show that the energy generation could be modeled based on the weather measurements using the machine learning algorithms and the renewable energy production system oriented power generation could be, thus, predicted hourly. Theory and Methods:The model was created by machine learning approaches and an energy production estimate was made. A variety of methods such as multiple linear regression, Powell optimization and probabilistic programming based on Markov Chain Monte Carlo simulations were used and their capability of predictions were compared to each other. Results:The energy production is estimated with an accuracy of 80% with an analytical approach. Additionally, a probabilistic approach was used to predict the power associated with an uncertainty indicating the upper and lower limit of a power based on time. Conclusion:The power generated from a solar plant could be predictable based on the weather measurements. In addition, it is considered that estimation algorithms will facilitate the integration of renewable energy systems into the existing grid and make the smart grid more widespread.
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