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Forensic SNP Genotyping with SNaPshot: Development of a Novel In-house SBE Multiplex SNP Assay,
Zar, Mian Sahib,Shahid, Ahmad Ali,Shahzad, Muhammad Saqib,Shin, Kyoung-Jin,Lee, Hwan Young,Lee, Sang-Seob,Israr, Muhammad,Wiegand, Peter,Kulstein, Galina Wiley (Blackwell Publishing) 2018 Journal of forensic sciences Vol.63 No.6
Cytokine Pattern is Affected by Training Intensity in Women Futsal Players
Abdossaleh Zar,Fatemeh Ahmadi,Maryamosadat Miri,Hassan Ali Abedi,Mohsen Salesi 대한면역학회 2016 Immune Network Vol.16 No.2
To find the relation between exercise and cytokines, we examined the effect of the training intensity on the levels of cytokines, including interferon-gamma (IFN-γ), interlukine- 4 (IL-4) and interlukine-4/interferon-gamma ratio (IL-4/IFN-γ ratio) in female Futsal players. Twelve well-trained female college Futsal players aged 19∼22 participated in this study. The athletes completed 30-min of running at 60∼65% maximal heart rate [moderate-intensity exercise], and 30-min of running at 75∼80% maximal heart rate [high-intensity exercise]. peripheral blood samples were collected 24 h before and 24 h and 48 h after each of the exercise bouts. finding showed that The 30-min bout of moderate-intensity exercise induced a significant increase in IFN-γ (p=0.01) and significant decreases in IL-4 (p=0.001) and IL-4/IFN-γ ratio (p=0.003). And also, 30-min of running at 75∼80% maximal heart rate induced increase in IFN-γ (p=0.07) and decreased in IL-4 (p=0.01) and IL-4/IFN-γ ratio (p=0.06) that these changes not significantly. In summary, exercise intensity can effect on the magnitude of changes in cytokines. It seems that moderate intensity exercise enhances cytokine pattern in female college Futsal players.
Zahoor Hussain,Ali Zar,Muhammad Akbar,Bassam A. Tayeh,Zhibin Lin 국제구조공학회 2023 Smart Structures and Systems, An International Jou Vol.32 No.5
The study presents a new hybrid data-driven method by combining radial basis functions neural networks (RBFNN) with the Jaya algorithm (JA) to provide effective structural health monitoring of arch dams. The novelty of this approach lies in that only one user-defined parameter is required and thus can increase its effectiveness and efficiency, as compared to other machine learning techniques that often require processing a large amount of training and testing model parameters and hyper-parameters, with high time-consuming. This approach seeks rapid damage detection in arch dams under dynamic conditions, to prevent potential disasters, by utilizing the RBF-NNN to seamlessly integrate the dynamic elastic modulus (DEM) and modal parameters (such as natural frequency and mode shape) as damage indicators. To determine the dynamic characteristics of the arch dam, the JA sequentially optimizes an objective function rooted in vibration-based data sets. Two case studies of hyperbolic concrete arch dams were carefully designed using finite element simulation to demonstrate the effectiveness of the RBF-NN model, in conjunction with the Jaya algorithm. The testing results demonstrated that the proposed methods could exhibit significant computational time-savings, while effectively detecting damage in arch dam structures with complex nonlinearities. Furthermore, despite training data contaminated with a high level of noise, the RBF-NN and JA fusion remained the robustness, with high accuracy.