The frequency of Duchenne muscular dystrophy/Becker muscular dystrophy and Pompe disease in children with isolated transaminase elevation: results from the observational VICTORIA study

IntroductionElevated transaminases and/or creatine phosphokinase can indicate underlying muscle disease. Therefore, this study aims to determine the frequency of Duchenne muscular dystrophy/Becker muscular dystrophy (DMD/BMD) in male children and Pompe disease (PD) in male and female children with isolated hypertransaminasemia.MethodsThis multi-center, prospective study enrolled patients aged 3–216 months with serum alanine transaminase (ALT) and/or aspartate transaminase (AST) levels >2× the upper limit of normal (ULN) for ≥3 months. Patients with a known history of liver or muscle disease or physical examination findings suggestive of liver disease were excluded. Patients were screened for creatinine phosphokinase (CPK) levels, and molecular genetic tests for DMD/BMD in male patients and enzyme analysis for PD in male and female patients with elevated CPK levels were performed. Genetic analyses confirmed PD. Demographic, clinical, and laboratory characteristics of the patients were analyzed.ResultsOverall, 589 patients [66.8% male, mean age of 63.4 months (standard deviation: 60.5)] were included. In total, 251 patients (188 male and 63 female) had CPK levels above the ULN. Of the patients assessed, 47% (85/182) of male patients were diagnosed with DMD/BMD and 1% (3/228) of male and female patients were diagnosed with PD. The median ALT, AST, and CPK levels were statistically significantly higher, and the questioned neurological symptoms and previously unnoticed examination findings were more common in DMD/BMD patients than those without DMD/BMD or PD (p < 0.001).DiscussionQuestioning neurological symptoms, conducting a complete physical examination, and testing for CPK levels in patients with isolated hypertransaminasemia will prevent costly and time-consuming investigations for liver diseases and will lead to the diagnosis of occult neuromuscular diseases. Trial RegistrationClinicaltrials.gov NCT04120168

Hava hareket planı kaynak paylaştırma problemi için optimizasyon algoritmaları.

In recent years, evolving technology has provided a wide range of resources for Military forces. However, that wideness also caused resource management difficulties in combat missions. Air Tasking Order (ATO) is prepared for various missions of air combats in order to reach objectives by an optimized resource management. Considering combinatorial military aspects with dynamic objectives and various constraints; computer support became inevitable for optimizing the resource management in air force operations. In this thesis, we study different optimization approaches for resource allocation problem of ATO preparation and analyze their performance. We proposed a genetic algorithm formulation with customized encoding, crossover and fitness calculation mechanisms by using the domain knowledge. A linear programming formulation of the problem is developed by integer decision variables and it is used for effectivity and efficiency analysis of genetic algorithm formulations.M.S. - Master of Scienc

Contextual Feature Analysis to Improve Link Prediction for Location Based Social Networks

In recent years, people started to communicate, interact, maintain relationship and share data (image, video, note, location, etc.) with their acquaintances through varying online social network sites. Online social networks with location and time sharing/interaction among people are called Location Based Social Networks (LBSNs). Link prediction in social networks aims at predicting future possible links for representing the real life relations better. In this work, we studied the link prediction problem and proposed new contextual features that improve the link prediction performance for LBSNs

A New Approach for Threat Evaluation and Weapon Assignment Problem, Hybrid Learning with Multi-Agent Coordination

The use of intelligent agents is one of the popular topics in defense industry. Agent usage would be beneficial for defense industry especially in decision making phase of the domain procedures. In Threat Evaluation Weapon Assignment System (TEWAS), we tried to develop learning agents working in coordination for the decision process of command and control systems. This paper describes all details of TEWAS Project in the scope of machine learning techniques

Mining Individual Features to Enhance Link Prediction Efficiency in Location Based Social Networks

One of the most attractive problems of social network analysis is the link prediction. Social networks' user growth is mostly supported with data driven friend recommendations which are provided by link predictors. Previously, we had studied new features to improve prediction accuracy in Location Based Social Networks (LBSNs) where users share temporal location information with check-in interactions. In this paper, we focused on the efficiency of link predictors as the speed of prediction is as critical as its accuracy in LBSNs. Extraction time costs and prediction accuracy of individual LBSN features are mined to pick a feature subset that is achieving faster link prediction while not losing from accuracy

Employment of an evolutionary heuristic to solve the target allocation problem efficiently

In this study, we investigated different optimization approaches for the resource allocation problem in the preparation of Air Tasking Orders (ATOs) and analyzed their performances. We developed a genetic algorithm with customized encoding, crossover and fitness calculation mechanisms making use of domain knowledge. We also developed an integer programming model, a simple greedy algorithm and a brute-force algorithm for the same problem to assess the performance of the proposed algorithm and demonstrate our contribution to the resource allocation's effectiveness and efficiency. ATOs are designed to meet the objectives of various air combat missions by optimized resource management. Considering combinatorial aspects with dynamic objectives and various constraints, computer support has become essential for the optimization of resource management in air force operations. We developed a novel solution to this real life time critical problem, which is a time-consuming and gain-optimized decision problem

Effective feature reduction for link prediction in location-based social networks

In this study, we investigated feature-based approaches for improving the link prediction performance for location-based social networks (LBSNs) and analysed their performances. We developed new features based on time, common friend detail and place category information of check-in data in order to make use of information in the data which cannot be utilised by the existing features from the literature. We proposed a feature selection method to determine a feature subset that enhances the prediction performance with the removal of redundant features by clustering them. After clustering features, a genetic algorithm is used to determine the ones to select from each cluster. A non-monotonic and feasible feature selection is ensured by the proposed genetic algorithm. Results depict that both new features and the proposed feature selection method improved link prediction performance for LBSNs

Examining Place Categories for Link Prediction in Location Based Social Networks

The day mankind met with smart-phones, a new era started. Since then, daily mobile internet usage rates are increasing everyday and people have developed new habits like frequently sharing information (photo, video, location, etc.) on online social networks. Location Based Social Networks (LBSNs) are the platforms that empowers users to share place/location information with friends. As all other social networks, LBSNs aim to acquire more users with a smart friend recommendation. Solution for smart friend recommendation problem is studied under link prediction field by researchers. Check-in information is the main data for link prediction in LBSNs. Data extracted from check-in information plays vital role for predictor performance. In this study, we attempt to make use of detailed analysis of place category in order to exploit possible information gain enhancements through such semantic information. We proposed two new feature groups; Common Place Check-in Count Product Sum and Common Category Check-in Count Sum Product. For any link candidate pair; those features are calculated for each category. Use of new features improved the link prediction performance for multiple data subsets