AGU GCRIS Premium Database (Abdullah Gül University)
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Discovery of New Candidates Targeting the SH2 Domains of Spleen Tyrosine Kinase (Syk) Through in Silico Studies
No full textSrc homology 2 (SH2) domains have become an increasingly popular candidate for researchers to search for novel therapeutics to target different diseases. Spleen tyrosine kinase (Syk) is one of the proteins with two SH2 domains that has a role in the pathogenesis of many diseases. Here, we report the discovery of a promising natural product (NP) inhibitor that targets the N-terminal SH2 (N-SH2) and C-terminal SH2 (C-SH2) domains of Syk simultaneously, through structure-based drug discovery approach. Molecular docking studies, followed by molecular dynamics (MD) simulations and molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) calculations, were utilized to reveal the interactions between NPs from "the COlleCtion of Open NatUral producTs (COCONUT)" database and Syk enzyme. Five natural products that have lowest Scoring and Minimization with AutoDock Vina (SMINA) scores against both SH2 domains of Syk were selected for further studies and compound CNP0265345 has the best binding free energies toward both C-SH2 and N-SH2 of Syk enzyme with -44.54 and -55.98 kcal/mol, respectively. Drug-likeness properties, absorption, distribution, metabolism, and excretion (ADME) and carcinogenicity predictions were also studied. In conclusion, our work highlights a novel drug candidate to target the Syk enzyme of SH2 domains using in silico methods.Health Institutes of Turkiye (TUSEB) [22905]This work was partially supported by the Health Institutes of Turkiye (TUSEB) with the Grant No. 22905
Optimizing Parameters for Efficient Computation With Fully Homomorphic Encryption Schemes
No full textIn this study, we aim to provide a parameter selection approach for the BFVrns scheme, one of the prominent fully homomorphic encryption (FHE) schemes. Selecting parameters for lattice-based FHE schemes poses a practical challenge for both experts and nonexperts. To solve this problem, we introduce a hybrid approach that combines theoretical approach with experimental analysis. First, we employ regression analysis to examine the impact of parameters on both performance and security. The varying behavior of FHE parameters in terms of performance, security, and ciphertext expansion factor (CEF) makes parameter selection more challenging. To address this issue, we employ a multi-objective optimization algorithm to determine the optimal parameter set for performance, CEF, and security simultaneously. As a result of this optimization, we obtain an improved parameter set that enhances performance at a given security level while ensuring correctness and resistance to lattice-based attacks, maintaining at least 128-bit security. Our results achieve an average similar to 5x reduction in CEF and generally better performance compared to the parameter sets in a previous BFVrns study. Our approach serves as a semi-automated parameter selection method for the PALISADE homomorphic encryption library, a widely recognized FHE library. This study sets a precedent for other FHE libraries
Accelerated Artificial Bee Colony Optimization for Cost-Sensitive Neural Networks in Multi-Class Problems
No full textMetaheuristics are advanced problem-solving techniques that develop efficient algorithms to address complex challenges, while neural networks are algorithms inspired by the structure and function of the human brain. Combining these approaches enables the resolution of complex optimization problems that traditional methods struggle to solve. This study presents a novel approach integrating the ABC algorithm with ANNs for weight optimization. The method is further enhanced by vectorization and parallelization techniques on both CPU and GPU to improve computational efficiency. Additionally, this study introduces a cost-sensitive fitness function tailored for multi-class classification to optimize results by considering relationships between target class levels. It validates these advancements in two critical applications: network intrusion detection and earthquake damage estimation. Notably, this study makes a significant contribution to earthquake damage assessment by leveraging machine learning algorithms and metaheuristics to enhance predictive models and decision-making in disaster response. By addressing the dynamic nature of earthquake damage, this research fills a critical gap in existing models and broadens the understanding of how machine learning and metaheuristics can improve disaster response strategies. In both domains, the ABC-ANN implementation yields promising results, particularly in earthquake damage estimation, where the cost-sensitive approach demonstrates satisfactory outcomes in macro-F1 and accuracy. The best results for macro-F1, weighted-F1, and overall accuracy provides best results with the UNSW-NB15 and earthquake datasets, showing values of 64%, 72%, 68%, and 60%, 80%, and 79%, respectively. Comparative performance evaluations reveal that the proposed parallel ABC-ANN model, incorporating the novel cost-sensitive fitness function and enhanced by vectorization and parallelization techniques, significantly reduces training time and outperforms state-of-the-art methods in terms of macro-F1 and accuracy in both network intrusion detection and earthquake damage estimation.The Scientific and Technological Research Council of Turkiye (TUBIdot;TAK); Republic of Turkiye; [121E406]This work was supported by The Scientific and Technological Research Council of Turkiye (TUB & Idot;TAK), 121E406 and The Council of Higher Education 100/2000 Scholarship by the Republic of Turkiye
Determination and Analysis of Characteristics of Dysphagia Disease From EEG Signals
No full textDisfaji, genellikle nörolojik hastalıklarla ilişkilendirilen ve özellikle yaşlı bireylerde yaşam kalitesini olumsuz yönde etkileyen bir yutma bozukluğudur. Bu çalışma, EEG verileri kullanılarak yutma ve yutmayı hayal etme süreçlerinin nörofizyolojik analizini yapmayı ve bu verilerin disfaji rehabilitasyonunda nasıl kullanılabileceğini araştırmaktadır. Otuz adet sağ elini kullanan birey üzerinde gerçekleştirilen deneylerde, doğal yutma, indüklenmiş tükürük yutma, indüklenmiş su yutma ve indüklenmiş dil dışarı çıkarma gibi farklı deneysel paradigmalar kullanılmıştır. Verilerin ön işlenmesinde Bağımsız Bileşen Analizi (ICA), Empirik Mod Ayrıştırma (EMD), bant geçiren filtreleme ve Ortak Uzamsal Desen (CSP) analizi gibi teknikler uygulanmıştır. Bu ön işleme yöntemleri, EEG verilerindeki gürültüyü azaltarak daha doğru bir analiz sağlamak amacıyla kullanılmıştır. Geleneksel makine öğrenmesi teknikleri ve derin öğrenme yöntemleriyle yapılan sınıflandırma görevlerinde, dinlenme ve hayal etme evreleri arasındaki farklar belirgin bir şekilde ayrılmıştır. Random Forest, AdaBoost ve Bagging gibi topluluk tabanlı algoritmaların yanı sıra, derin öğrenme yöntemlerinden Konvolüsyonel Sinir Ağları (CNN) da uygulanmıştır. Ayrıca, çok ölçekli mekânsal dikkat ağı (MS-SAN) modeli, özellikle delta ve teta frekans bantlarında hareketi hayal etme ile dinlenme durumları arasındaki nörofizyolojik farkları yüksek doğrulukla ayırt etmiştir. Sonuçlar, hareketi hayal etme ve dinlenme evrelerinin EEG verileriyle tespit edilmesinin disfaji tedavisinde ve motor rehabilitasyon uygulamalarında büyük bir potansiyel taşıdığını göstermektedir. Bu çalışma, EEG tabanlı beyin-bilgisayar arayüzleri (BBA) teknolojilerinin, makine öğrenimi ve derin öğrenme yöntemlerinin disfaji rehabilitasyonundaki potansiyelini vurgulamakta ve bu alandaki araştırmaların klinik uygulamalar açısından önemini ortaya koymaktadır. Anahtar kelimeler: Elektroensefalografi, Makine Öğrenmesi, Derin Öğrenme, BBA, YutkunmaDysphagia is a swallowing disorder that is usually associated with neurological diseases and negatively affects the quality of life, especially in elderly individuals. This study investigates the neurophysiological analysis of swallowing and motor imagery processes using EEG data and how this data can be used in dysphagia rehabilitation. Different experimental paradigms, such as natural swallowing, induced saliva swallowing, induced water swallowing, and induced tongue protrusion, were used in the experiments conducted on 30 right-handed individuals. Techniques such as Independent Component Analysis (ICA), Empirical Mode Decomposition (EMD), band-pass filtering, and Common Spatial Pattern (CSP) analysis were applied in the preprocessing of the data. These preprocessing methods provided a more accurate analysis by reducing the noise in the EEG data. The differences between the resting and imagery stages were clearly separated in the classification tasks performed with traditional machine learning techniques and deep learning methods. In addition to ensemble-based algorithms such as Random Forest, AdaBoost, and Bagging, Convolutional Neural Networks (CNN) from deep learning methods were also applied. In addition, the multi-scale spatial attention network (MS-SAN) model distinguished neurophysiological differences between motor imagery and resting states, especially in delta and theta frequency bands, with high accuracy. The results show that detecting motor imagery and resting stages with EEG data has great potential in dysphagia treatment and motor rehabilitation applications. This study highlights the potential of EEG-based brain-computer interface (BCI) technologies, machine learning, and deep learning methods in dysphagia rehabilitation. It reveals the importance of research in this area for clinical applications. Keywords: Electroencephalography, Machine Learning, Deep Learning, BCI, Swallowin
Prediction of Colorectal Cancer Based on Taxonomic Levels of Microorganisms and Discovery of Taxonomic Biomarkers Using the Grouping-Scoring (G-S-M) Approach
No full textColorectal cancer (CRC) is one of the most prevalent forms of cancer globally. The human gut microbiome plays an important role in the development of CRC and serves as a biomarker for early detection and treatment. This research effort focuses on the identification of potential taxonomic biomarkers of CRC using a grouping-based feature selection method. Additionally, this study investigates the effect of incorporating biological domain knowledge into the feature selection process while identifying CRC-associated microorganisms. Conventional feature selection techniques often fail to leverage existing biological knowledge during metagenomic data analysis. To address this gap, we propose taxonomy-based Grouping Scoring Modeling (G-S-M) method that integrates biological domain knowledge into feature grouping and selection. In this study, using metagenomic data related to CRC, classification is performed at three taxonomic levels (genus, family and order). The MetaPhlAn tool is employed to determine the relative abundance values of species in each sample. Comparative performance analyses involve six feature selection methods and four classification algorithms. When experimented on two CRC associated metagenomics datasets, the highest performance metric, yielding an AUC of 0.90, is observed at the genus taxonomic level. At this level, 7 out of top 10 groups (Parvimonas, Peptostreptococcus, Fusobacterium, Gemella, Streptococcus, Porphyromonas and Solobacterium) were commonly identified for both datasets. Moreover, the identified microorganisms at genus, family, and order levels are thoroughly discussed via refering to CRC-related metagenomic literature. This study not only contributes to our understanding of CRC development, but also highlights the applicability of taxonomy-based G-S-M method in tackling various diseases. © 2025 Elsevier B.V., All rights reserved
Development and Characterization of Starch-Fatty Acid Complexes Produced with Buckwheat Starch and Capric/Stearic Acid Using Different Reaction Conditions
No full textThe aim of present study was to investigate the impact of reaction parameters on the complex formation between buckwheat starch and capric acid (B-Capric) or stearic acid (B-Stearic). The most effective parameters on complex formation indicator (Complex index (CI) value) were found as reaction temperature (60-90 degrees C) and pH (5-8). Additionally, the effect of these parameters on physicochemical, pasting, and in-vitro digestibility properties of complex samples were evaluated. XRD and FTIR was also used in characterize the complex samples. In general, increasing pH increased the CI values of B-Stearic samples while decreasing those of B-Capric samples. Syneresis of buckwheat starch increased after complexation while paste clarity and swelling power diminished. The pasting properties of native starch significantly changed after complex formation. The FTIR results showed that starch structure changed with complex formation. XRD revealed that buckwheat starch, having an A-type pattern, converted to V-type pattern after complexation. Complex formation of buckwheat starch with capric and stearic acid significantly increased the RS content of buckwheat starch (19.01 %) by up to 36.25 % and 30.60 %, respectively. These results highlight the possibility of using buckwheat starch-capric acid/stearic acid complexes in food formulation to enhance the RS content.Scientific and Technological Research Council of Turkey (TUBITAK) [119O031]This study was a part of the project supported by the Scientific and Technological Research Council of Turkey (TUBITAK) (Project Number: 119O031)
Comprehensive Optimization of Shot Peening Intensity Using a Hybrid Model With AI-Based Techniques via Almen Tests
No full textShot peening is a crucial surface treatment technique that significantly improves the mechanical properties of metallic components, particularly their fatigue resistance and ability to withstand corrosion cracking. This study aims to optimize the shot peening process for aviation applications by evaluating and comparing various mathematical modeling and optimization techniques. Seven mathematical models were analyzed using a neuro-regression method (NRM), among which the second-order trigonometric non-linear (SOTN) model exhibited the highest reliability, achieving R2 values of 0.93 and 0.90 for training and testing datasets, respectively. To improve the model's robustness, four optimization algorithms - differential evolution (DE), simulated annealing (SA), Nelder-Mead (NM), and random search (RS) - were applied to the SOTN model. Although each technique offered valuable insights, performance fluctuations across different intensity ranges necessitated the development of a hybrid optimization model that combines the strengths of all four methods. The hybrid model achieved a mean error of approximately 2.69 %, outperforming individual approaches and demonstrating strong potential for reliable shot peening optimization across a wide range of target intensities. These findings provide a comprehensive methodology for AI-based optimization of surface treatment processes in engineering applications.Turk Havacimath;limath;k ve Uzay Sanayii (Turkish Aerospace Industries) [2021-TUSAS-BAP-01]This research was funded by Turk Havac & imath;l & imath;k ve Uzay Sanayii (Turkish Aerospace Industries), Award Number: 2021-TUSAS-BAP-01
Enhancing Bioink Potential of Hyaluronic Acid by Microwave-Induced Methacrylation
No full textThis study reports the development of a light-curable methacrylated hyaluronic acid (HAMA) synthesized using microwave irradiation. The methacrylation process was carried out with AEMA as the methacrylating agent via an EDC/NHS protocol at varying microwave energy levels and compared comprehensively with those synthesized using the conventional heating method. The HAMA synthesis by microwave was optimized by applying different power levels (100 W, 250 W, and 800 W). The products were characterized by 1H NMR to determine the degree of methacrylation (DoM). The microwave-assisted synthesis significantly reduced the reaction time from 24 h to 6 min, improved reaction efficiency, and shortened the purification period from 3 days to 1 day. Additionally, it enhanced the mechanical, rheological, and swelling properties of the resulting hydrogels. The highest DoM was achieved at 78 % for HAMA-100 hydrogels synthesized at 100 W microwave energy. Rheological analysis demonstrated that microwave-assisted HAMA hydrogels could withstand nearly 100 % strain, outperforming those produced by conventional methods. This indicated the presence of an improved energy distribution mechanism at the molecular level within the polymer network structure of the microwave-assisted hydrogels. It was also observed that the microwave-assisted hydrogels exhibited strain-hardening behavior, ensuring the stability of bioactive structures in bioinks. Furthermore, the printing conditions for HAMA-100 gels were optimized in terms of printing pressure and speed. These findings highlight the significant role of microwave energy in achieving superior hydrogel properties, making it a promising green method for preparing bioinks for 3D printing applications.Drug Application and Research Center (ILMER) at Beemialem Vakif University; Turkish Academy of Sciences (TUBA)The authors acknowledge resources and support from the Drug Application and Research Center (ILMER) at Beemialem Vakif University. D.C. thanks the Turkish Academy of Sciences (TUBA) for the suppor
Modeling and Simulation of Dynamic Energy Management Systems for Smart Buildings
No full textThis study presents a dynamic energy management system tailored for smart residential buildings, integrating thermal and electrical models to achieve both natural gas and electricity bill cost reduction. By harnessing wind and solar energy sources, the system aims to meet the diverse energy needs of modern homes. Through load shifting and thermal storage strategies, known as power-to-heat (P2H) approaches, the system ensures efficient renewable energy utilization while maintaining resident comfort. Validation of the proposed system was conducted using real-world data from the Y & imath;ld & imath;z Technical University Smart Home Laboratory, demonstrating its practical applicability and effectiveness. Results indicate significant reductions in both natural gas and electricity consumption, leading to substantial cost savings. Specifically, the proposed system reduced natural gas consumption by 3.79% and electricity consumption by 35.62%, highlighting its potential to enhance energy efficiency and sustainability in residential settings
Fluctuations in the European Housing Market: Forecasting the House Price Index Change with Time-Series Models
No full textThis study presents a comparative analysis of a time series models for forecasting changes in the Housing Price Index (HPI) in 27 European countries. Accurate HPI forecasting is essential for the development of effective policies and investment strategies. The study uses quarterly data from Q4 2013 to Q3 2024. Methodologically, the stationarity of the data is tested using the Dickey-Fuller test and differencing is applied to non-stationary series. The ARIMA, Holt Linear Trend, Additive Damped Trend and Exponential Smoothing models are evaluated based on the lowest mean squared error (MSE) value for each country. The findings confirmed the heterogeneous structure of the European housing market, showing that no single model is suitable for all countries. The ARIMA model provided the most accurate results for nine countries, while the Holt Linear Trend and Additive Damped Trend models performed best in seven countries each. Forecasts for the period 2025-2026 are generated based on these results. This study highlights the importance of adopting country-specific and adaptable forecasting approaches to accommodate the varying dynamics of European housing markets