End-To-End Alzheimer's Disease Diagnosis and Biomarker Identification

As shown in computer vision, the power of deep learning lies in automatically learning relevant and powerful features for any perdition task, which is made possible through end-to-end architectures. However, deep learning approaches applied for classifying medical images do not adhere to this architecture as they rely on several pre- and post-processing steps. This shortcoming can be explained by the relatively small number of available labeled subjects, the high dimensionality of neuroimaging data, and difficulties in interpreting the results of deep learning methods. In this paper, we propose a simple 3D Convolutional Neural Networks and exploit its model parameters to tailor the end-to-end architecture for the diagnosis of Alzheimer's disease (AD). Our model can diagnose AD with an accuracy of 94.1\% on the popular ADNI dataset using only MRI data, which outperforms the previous state-of-the-art. Based on the learned model, we identify the disease biomarkers, the results of which were in accordance with the literature. We further transfer the learned model to diagnose mild cognitive impairment (MCI), the prodromal stage of AD, which yield better results compared to other methods

Bayesian parameter identification in plasticity

To evaluate the cyclic behaviour under different loading conditions using the kinematic and isotropic hardening theory of steel a Chaboche visco-plastic material model is employed. The parameters of a constitutive model are usually identified by minimization of the distance between model response and experimental data. However, measurement errors and differences in the specimens lead to deviations in the determined parameters. In this article the Choboche model is used and a stochastic simulation technique is applied to generate artificial data which exhibit the same stochastic behaviour as experimental data. Then the model parameters are identified by applying a variaty of Bayes’s theorem. Identified parameters are compared with the true parameters in the simulation and the efficiency of the identification method is discussed

Complexity of functional connectivity networks in mild cognitive impairment subjects during a working memory task

Objectives: The objective is to study the changes of brain activity in patients with mild cognitive impairment (MCI). Using magneto-encephalogram (MEG) signals, the authors investigate differences of complexity of functional connectivity network between MCI and normal elderly subjects during a working memory task. Methods: MEGs are obtained from 18 right handed patients with MCI and 19 age-matched elderly participants without cognitive impairment used as the control group. The brain networks’ complexities are measured by Graph Index Complexity (Cr) and Efficiency Complexity (Ce). Results: The results obtained by both measurements show complexity of functional networks involved in the working memory function in MCI subjects is reduced at alpha and theta bands compared with subjects with control subjects, and at the theta band this reduction is more pronounced in the whole brain and intra left hemisphere. Conclusions: Ce would be a better measurement for showing the global differences between normal and MCI brains compared with Cr. Significance: The high accuracy of the classification shows Ce at theta band can be used as an index for assessing deficits associated with working memory, a good biomarker for diagnosis of MC

The Impact of export restrictions on the structure of Iran's non-oil export with an emphasis on mining sector

Thesis(Master) -- KDI School: Master of Public Policy, 2020The present study investigated the factors affecting Iran’s non-oil exports especially in exportation of mineral products with an emphasis on export restrictions. For this purpose and in the first step, the literature on the export restrictions was reviewed and the main restrictions imposed on Iran’s non-oil exports in the recent decade were taken into consideration. In the next step and in a descriptive examination, the structure of Iran’s non-oil export was considered in terms of factor intensity of production and the technology level, and the impact of international sanctions was identified as the main export restriction on this structure. In the last step, the demand for and the supply of Iran’s non-oil exports in the period 1987-2017 were estimated in the framework of Simultaneous Equation Model (SEM) and using Two-Stage least squares (2SLS) for total Iran’s non-oil exports and its mining sector for the purpose of modeling to determine the factors affecting the exportation and examining the impacts of sanctions. The results show that Iran’s non-oil exports have decreased as a result of the international sanctions despite the foreign exchange surge in Iran in 2012. In addition to these sanctions, the structure of Iran’s non-oil export has not undergone any manifest change in terms of the extent of the factor intensity of production, while as far as technology is concerned, the exportation of higher-technology products has been impacted by the sanctions to a greater extent. According to the estimated coefficients in the function of demand for non-oil exports, the price of the foreign goods and the revenues of other countries are some of the main factors affecting the demand for Iran’s exports, and the price and income elasticities in the minerals exportation sector have been obtained as higher than those of the total non-oil exports. The important point in estimating the demand function is that the coefficient of dummy variable is different in estimating the impact of the sanctions such that this coefficient is negative and significant for Iran’s total non-oil exports indicating the effectiveness of the sanctions in restricting Iran’s non-oil exports while the coefficient for the mineral products exports is positive for mineral product exportation. In conclusion, it should be mentioned that according to the achievements of the present study and the estimated model, more than two thirds of Iran’s non-oil exports are composed of the raw and resource-based products, and the factors affecting the demand and supply in exportation show that Iran’s non-oil exports are mainly demand-oriented and some factors such as the world prices, revenues of other countries and sanctions were more effective in restricting them than the supply factors such as investment or roductivity. The results of the model and structure analysis show the small impact of the sanctions on raw materials sectors.1. INTRODUCTION 2. THEORETICAL FOUNDATIONS AND REVIEW OF LITERATURE 3. DATA AND METHODOLOGY 4. EMPIRICAL RESULTS AND DISCUSSION 5. CONCLUSION AND POLICY RECOMMENDATIONSmasterpublishedAbdolhamid ADEL