Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
Browse
115 results
Search Results
Article Predicting Respiratory Infection and Symptoms Development Using Gene Set Enrichment Scores and Machine Learning(Elsevier Sci Ltd, 2026) Aydin, Zafer; Isik, Yunus EmreRecent advancements in precision medicine enable personalized predictions grounded in individual-level genetic data. However, relying solely on a single type of data can decrease prediction accuracy and limit the biological interpretability of the resulting models. Incorporating predefined genetic knowledge, such as derived gene sets, can improve performance and provide deeper biological insights for complex diseases, including respiratory infections. This study aimed to evaluate the usability of enrichment scores (ES), calculated using gene sets from the Molecular Signatures Database (MSigDB), as a feature representation for machine learning models to predict respiratory viral infections and symptom development. In addition, the proposed feature representation approach was extensively compared with the de facto gene-level expression representation. A total of 36,834 predefined gene sets were compiled from the MSigDB, and their ES values were calculated. Experiments used the GSE73072 dataset from Gene Expression Omnibus, containing gene expression profiles before and after virus exposure. Various machine learning and feature selection algorithms were applied to ES-based and probe-level feature sets. The results showed that both feature representation approaches achieved an area under the precision-recall curve (AUPRC) value greater than 0.90 for all tasks. Compared with the Respiratory Viral DREAM Challenge leaderboard phase, our models showed a 14.8% improvement in pre-exposure predictions (T0) and a 17.4% improvement in symptom classification. Using enrichment scores as a feature representation generally resulted in better performance than probe-level representation when predicting respiratory infections and symptom development. Identifying key gene sets through feature selection and comparing them with essential genes for respiratory viruses enabled a more comprehensive analysis, providing deeper insights into the pathways that contribute to these predictions.Article Spatial Proximity and Accessibility Patterns of X-Minute Cities(Elsevier Ltd, 2026) Östh, John; Türk, Umut; Kourtit, Karima; Nijkamp, PeterThe 15-minute city concept has gained prominence in urban planning as a framework linking proximity, mobility, and quality of life at the intra-urban scale. An open question is whether this concept remains meaningful when applied to spatially connected urban regions or national urban systems. To address this issue, the new X-minute city concept allows proximity thresholds to vary with spatial context, mobility conditions, and service distribution. This paper develops an operational framework with the aim to examine and highlight spatial accessibility patterns from an X-minute city perspective, using the relatively urbanized area of the Netherlands as a national-scale case study. Accessibility is analyzed at the building level using OpenStreetMap data and a combination of object-based k-nearest-neighbor measures, distance-decay functions, inequality metrics, Average Nearest Neighbor analysis, and quantile regression. Concepts from central place theory are employed as an interpretive lens to understand service clustering and accessibility patterns, rather than as a formal model to be tested. The results show pronounced spatial disparities in accessibility between urban and rural areas, as well as substantial variation within cities. Major urban centers such as Amsterdam, Rotterdam, and Utrecht exhibit high accessibility levels due to dense service provision, while peripheral and rural areas face systematically lower accessibility. Next accessibility outcomes also differ sharply across population groups. Elderly residents and groups reliant on walking experience the highest levels of inequality, with accessibility distributions remaining highly uneven at short-distance thresholds. Cycling expands the range of effective activity spaces and reduces inequality, but does not offset structural disadvantages in areas with sparse service provision. The findings point to the limits of uniform proximity targets and reveal the need for context-sensitive accessibility planning that explicitly accounts for service distribution, mobility constraints, and population heterogeneity within national urban systems.Article Functional Characterization of Loss of RNF43 Reveals Neuronal Defects in a Caenorhabditis Elegans Model(MDPI, 2026) Kazan, Hasan Huseyin; Turkyilmaz, Zafer; Ekim, Burcu; Kaya, Cem; Ergun, Mehmet Ali; Sonmez, Kaan; Güzel, SinemRing finger protein 43 (RNF43) encodes a transmembrane E3 ubiquitin ligase that negatively regulates canonical Wnt signaling and is classically associated with serrated polyposis syndrome and colorectal cancer. In this study, regarding a homozygous truncating RNF43 variant (NM_001305545.1:c.1906C>T; p.Gln636Ter) in a patient segregating with a severe neurodevelopmental phenotype characterized by developmental delay, neonatal hypotonia, recurrent seizures, progressive microcephaly, and bilateral optic atrophy, the loss of polarity defective 1 (plr-1), an ortholog of RNF43, was modeled in Caenorhabditis elegans and the phenotype was primarily characterized. The results demonstrated that loss of the plr-1 disrupted gamma aminobutyric acid (GABA)ergic axon organization, reduced locomotor speed calculated from 60 s recordings, and altered developmental growth. These findings expand the phenotypic spectrum of RNF43 and support a dosage-dependent developmental role.Article Citation - WoS: 5Citation - Scopus: 5Targeting Cholinergic Dysfunction and Neuroinflammation through Rationally Designed Thieno[3,2-d]Pyrimidine Hybrids(Academic Press Inc Elsevier Science, 2026-07) Acar, Ozden Ozgun; Acar, Busra; Senol, Halil; Tokali, Feyzi Sinan; Sen, Alaattin; Demir, Yeliz; Cakir, FurkanNeurodegenerative diseases involve the convergence of cholinergic dysfunction, neuronal loss, and sustained neuroinflammatory responses, necessitating the development of multifunctional therapeutic agents. In this study, a series of novel thieno[3,2-d]pyrimidine-phenolic Mannich base hybrids were rationally designed, synthesized, and evaluated as dual cholinesterase inhibitors with neuroprotective and anti-neuroinflammatory potential. The synthesized compounds exhibited potent inhibition against acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), with inhibition constants in the low nanomolar range. Among them, compounds 5 and 9 emerged as the most active derivatives, displaying Ki values of 8.79 and 14.11 nM for AChE and 7.04 and 11.75 nM for BChE, surpassing the reference inhibitors tacrine and donepezil. Molecular docking and molecular dynamics simulations supported the experimental findings, and Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) binding free energy calculations further confirmed their superior binding affinities compared with donepezil. Cytotoxicity profiling in SH-SY5Y neuronal cells and RAW 264.7 and THP-1 immune cells identified a narrow sub-cytotoxic concentration window (EC05-EC10 = 1.2-2.1 mu M), ensuring biological effects independent of nonspecific cell damage. Within this range, both compounds exerted pronounced antineuroinflammatory activity. Notably, compound 9 significantly downregulated pro-inflammatory mediators, reducing IL-1 beta, IL-6, and NF-kappa B1 gene expression by up to 2.78-, 3.37-, and 4.84-fold, respectively. Consistently, it suppressed nitric oxide production in LPS-stimulated macrophages to levels comparable with ascorbic acid and markedly decreased Iba1 expression in activated THP-1 cells. This integrated enzymatic, computational, and cellular investigation identifies compounds 5 and 9 as promising multifunctional lead combining dual cholinesterase inhibition with robust anti-neuroinflammatory activity. The results provide a strong foundation for future in vivo studies and further optimization toward disease-modifying agents for neurodegenerative disorders.Article Citation - Scopus: 5Integrated Querying and Version Control of Context-Specific Biological Networks(Oxford University Press, 2020-01-01) Coşkun, Mustafa; Grama, Ananth; Koyutürk, Mehmet; Cowman, TylerArticle Harnessing Raw Materials in Cement Plants for the Production of Limestone Calcined Clay Cement(Taylor & Francis Ltd, 2026-04-06) Atasever, Muhammet; Erdogan, Sinan TurhanReliance on kaolinite clay in limestone calcined clay cement (LC3) can create availability issues. This study compared a kaolinitic clay obtained from an industrial pit with four other clays used as sources of silica in cement plants, exploring their potential for LC3 production. Three cements were prepared; gray, white, and a composite cement with slag, to investigate the interaction and synergy between the clinker and clay in LC3. Inert quartz was used to replace the clay, limestone, or both components of the mixtures, to evaluate their separate and combined effects on strength, mineralogy, and heat evolution. Several of the LC3 mortars made with the non-kaolinite clays had higher 1-7 d strengths, by up to 60 %, than the LC3 with gray cement and kaolinite which reached similar to 48 MPa strength at 28 d). Carboaluminate formation depends on clinker type, while its quantity is influenced more by the type of calcined clay.Article Strategic Modeling of Hybrid Smart Micro Energy Communities: A Decision-Oriented Approach(MDPI, 2026-02-10) Perez-Sanchez, Modesto; Coronado-Hernandez, Oscar E.; McNabola, Aonghus; Erdfarb, Alex; Ramos, Helena M.; Demircan, Isil; Koca, KemalHybrid renewable energy systems are increasingly important for enabling sustainable and resilient energy supply in rural smart communities, yet existing tools often lack the ability to integrate environmental variability, multi-technology interactions, and economic-environmental assessment in a unified framework. This study presents Hybrid Smart Micro Energy Community (HySMEC), a novel modeling approach that combines high-resolution meteorological data, technology-specific generation models, detailed demand characterization, and financial analysis to evaluate hybrid configurations of hydropower, solar PV, wind, battery storage, and grid interaction. Hourly simulations capture seasonal dynamics and system behavior under realistic technical efficiencies, investment costs, and emission factors, enabling a transparent assessment of energy flows, self-consumption, and grid dependence. The results show that hybrid systems can achieve competitive economic performance, low Levelized Costs of Energy, and significant CO2 emission reductions across diverse rural community profiles, even when space or demand constraints are present. The analysis confirms the technical feasibility and environmental benefits of integrating multiple renewable sources with storage, highlighting the importance of self-consumption ratios in improving system profitability. Overall, HySMEC provides a robust and scalable tool to support data-driven design and optimization of distributed energy systems, offering valuable insights for researchers, planners, and decision-makers involved in sustainable rural energy development.Article Frequency-Based Deep Occlusion Awareness Instance Segmentation(MDPI, 2026-02-26) Guzel, Yasin; Aydin, Zafer; Talu, Muhammed FatihOne major challenge faced by deep learning-based methods that detect target objects in the form of bounding boxes is object occlusion. High degrees of occlusion significantly diminish the accuracy of instance segmentation. Nonetheless, complex-valued Fourier descriptors can robustly represent object boundaries using minimal information. In this study, the impact of integrating Fourier descriptors-renowned for their strong representational capacity-with deep network models (UNet) that exhibit high generalization performance on instance segmentation accuracy was investigated. Within the scope of the research, nine network models were designed based on different strategies for utilizing frequency components. These variants fall into four strategy families: (i) UNet-style spectrum regression on fixed low-frequency windows (FUNet), (ii) magnitude-guided frequency selection/ROI construction (FUNet-Thr, FUNet-BBox), (iii) sequence models over tokenized FFT coefficients (BiLSTM Patch/Sorted), and (iv) encoder-only spectrum predictors with different depth/capacity (EncoderFFT1/2). To fairly evaluate the models' performance in segmenting objects subjected to disruptive factors (e.g., occlusion, blurring, noise), a specialized synthetic dataset was prepared. The task is formulated as single-target (single-instance), single-class segmentation. This dataset, automatically generated according to initial parameter values, contains images of objects moving at various speeds within a single frame. Among these models, the one termed FUNet, which relies on partial matching of central frequency components, achieved the highest segmentation accuracy despite the disruptive effects. Under the challenging Dataset 8 setting, the proposed FUNet achieved the highest overlap-based performance (Dice = 0.9329, IoU = 0.8842) among Attention U-Net, U-Net, and FourierNet, with statistically significant gains confirmed by paired per-image tests.Article Citation - Scopus: 20Computational Fluid Dynamics for the Optimization of Internal Bioprinting Parameters and Mixing Conditions(AccScience Publishing, 2023-06-22) Bartolo, Paulo; Ates, GokhanArticle Comparative Assessment of Smooth and Non-Smooth Optimization Solvers in HANSO Software(Balikesir University, 2021-10-27) Tor, Ali HakanThe aim of this study is to compare the performance of smooth and nonsmooth mization) software. The smooth optimization solver is the implementation of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method and the nonsmooth optimization solver is the Hybrid Algorithm for Nonsmooth Optimization. More precisely, the nonsmooth optimization algorithm is the combination of the BFGS and the Gradient Sampling Algorithm (GSA). We use well-known collection of academic test problems for nonsmooth optimization containing both convex and nonconvex problems. The motivation for this research is the importance of the comparative assessment of smooth optimization methods for solving nonsmooth optimization problems. This assessment will demonstrate how successful is the BFGS method for solving nonsmooth optimization problems in comparison with the nonsmooth optimization solver from HANSO. Performance profiles using the number iterations, the number of function evaluations and the number of subgradient evaluations are used to compare solvers.