WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Virtual Screening of Bacteriocins From Lactic Acid Bacteria Against Monkeypox DNA Polymerase: Sakacin P and Mundticin KS Emerge as Promising Candidates(Wiley, 2026) Göl, Karan; Ozdarendeli, Aykut; Yetiman, Ahmet E.; Karaman, Melisa Z.; Mujwar, Somdutt; Zanchi, Fernando Berton; Fidan, OzkanThe monkeypox virus (MPXV) has recently risen to be a significant global health threat and currently there are no approved antiviral agents, which makes it necessary to develop new antiviral strategies. In this study, we employed in silico techniques to investigate whether bacteriocins could be potent inhibitors of the MPXV DNA polymerase (MPDP). At first, the MPXV DNA polymerase enzyme chain was extracted from the Cryo-EM structure of MPXV DNA replication complex and was then assessed using SWISS-MODEL/QMEAN for structural quality. The structures of selected bacteriocins were either retrieved from the Protein Data Bank or predicted using AlphaFold. The quality of these predicted models was measured by LGscore. Moreover, the physicochemical properties of the selected bacteriocins, such as Sakacin P and Mundticin KS, were analyzed to assess their molecular stability and compatibility with the molecular docking process. The results of protein-peptide docking simulations on the HADDOCK platform showed that among all the bacteriocins tested, Sakacin P and Mundticin KS had the highest binding affinities toward MPXV DNA polymerase. The analysis of the docking experiments also revealed vital stabilizing interactions, such as the formation of hydrogen bonds, ionic linkages, and pi-pi stacking, which were crucial in the strength of the protein-ligand complexes. Subsequently, the molecular dynamics (MD) simulations further confirmed the stability of the protein-bacteriocin complexes. Our results indicate that bacteriocins, especially Sakacin P and Mundticin KS, can be considered potential antiviral agents against MPXV by interfering with its DNA replication mechanism. The present work shall serve as a reference for future laboratory testing and for the promising innovation of bacteriocin-based drugs targeting MPXV.Article Stevia Functionalized PVA-Chitosan Membranes as Novel Antimicrobial Wound Dressing Materials(MDPI, 2026) Badem, Merve; Pehlivan, Ilayda; Atakav, Yagmur; Kanbolat, SeydaWound dressings play a crucial role in promoting tissue regeneration while protecting damaged tissue from microbial infections. The aim of this study is to produce antimicrobial PVA-chitosan membranes for wound dressing applications using Stevia rebaudiana Bertoni leaf extract (stevia) as a natural bioactive agent. Although widely recognized as a natural sweetener, stevia contains a high concentration of diterpene glycosides, particularly stevioside and rebaudioside A, together with phenolic compounds that contribute to its biological activities. Therefore, stevia was first evaluated against selected microorganisms and demonstrated effective antimicrobial activity. PVA-chitosan (P/Ch) membranes were prepared by solvent casting method by incorporating different amounts of stevia (P/Ch, P/Ch@20, P/Ch@40, and P/Ch@60). The antimicrobial activity of stevia was also successfully maintained in P/Ch membranes. Good antibacterial activity was observed against Bacillus subtilis and Escherichia coli, whereas the antifungal activity against Candida albicans was comparatively modest. Contact surface analysis showed complete bactericidal activity in Gram-negative and spore-forming bacteria. The cytocompatibility of the membranes was investigated by MTT assay. All formulations maintained a high cell viability (>80%) while the P/Ch@40 membrane increased the metabolic activity to above 100% at higher extract concentrations. In conclusion, stevia-incorporated membranes exhibited high antimicrobial activity, acceptable characteristic properties, and good cytocompatibility, suggesting that they are promising alternative biomaterials for tissue engineering applications.Conference Object Microbiota-Derived Propionate Induces Deletional Tolerance and Decreases Graft-versus-Host Disease(Springer Nature, 2026) Chakka, Saranya; Turan, Ali; Wolfe, Alex E.; Dai, Angi; Markey, Kate A.; Mirimo-martinez, Mercy; Herman, AmaraArticle Overlap Mitigation Versus Classifier Selection in Imbalanced Classification: A Dual-Baseline Analysis(MDPI, 2026) Kaya, Sami; Ünlü, RamazanIn imbalanced binary classification, class overlap forces a familiar fork in the road: refine a preprocessing pipeline, or pivot to classifier selection. Which path dominates has remained empirically open. To address this question, a full factorial experiment was run across 58 KEEL datasets, 11 mitigation techniques, and 10 classifiers (yielding 6960 fitted models in total), with results read through a dual-baseline lens. Against the same classifier without mitigation, the techniques delivered a mean improvement of +4.13 points; gains concentrated on weak learners, with SMOTE and BorderlineSMOTE topping the rankings. Against an honest leave-one-dataset-out (LODO) baseline that selects the strongest standalone classifier from a held-out training pool, mean Delta F1 fell to -4.64 points, and 43.2% of mitigated configurations matched or beat the baseline. Among controllable factors, a Type II ANOVA placed classifier identity at eta(2) = 0.157 against mitigation technique at eta(2) = 0.010, so the choice of algorithm carried roughly sixteen times the explanatory weight of the choice of preprocessing. Dataset characteristics dominated the overall variance at eta(2) = 0.620. The honest LODO selector returned LightGBM on every one of the 58 datasets, suggesting that under typical practitioner conditions, a strong-classifier default is the lower-effort, lower-variance path, with no expected performance cost relative to a pipeline-development investment.Article Genetic Background and Sex Moderate the Effects of Adolescent Nicotine Exposure on Adult Functional Neural Circuits and Behavior(SAGE Publications Ltd, 2026) Zhang, Nanyin; Ünsal, Hayreddin Said; Gould, Thomas J; Novoa, CarlosAdolescence is a critical neurodevelopmental stage marked by heightened plasticity and vulnerability to environmental influences such as nicotine. Nicotine disrupts neural function via nicotinic acetylcholine receptors, leading to long-term impairments in reward processing, cognition, and emotional regulation. While links between adolescent nicotine exposure and adult psychiatric or cognitive deficits are established, the influence of genetics and sex remains unclear. We examined the long-term effects of adolescent nicotine exposure on adult behavior and neural circuitry in male and female C57BL/6J and DBA/2J mice. Nicotine (24 mg/kg/day) or saline was administered via subcutaneous osmotic minipumps from postnatal day 37 for 12 days. Behavioral assessments 4 weeks later included the elevated plus maze for anxiety-related behavior and the open field test for locomotor activity after acute nicotine (0.81 mg/kg). Resting-state functional magnetic resonance imaging evaluated brain connectivity changes. Nicotine exposure altered both behavior and functional connectivity, depending on strain and sex. Adult female C57BL/6J mice exposed to nicotine during adolescence had lower anxiety-like behavior and higher locomotor activity, along with increased striatal-hippocampal connectivity, suggesting adaptations either directly related to nicotine exposure or compensatory adaptations. In contrast, adult female DBA/2J mice exposed to nicotine during adolescence showed widespread disruptions in cortico-striatal-thalamic, polymodal association, and hippocampal networks critical for cognitive and emotional regulation. Overall, greater effects were seen in female mice. Findings reveal that adolescent nicotine exposure drives enduring, strain- and sex-dependent changes in adult brain connectivity and behavior. Considering genetics and sex is essential for tailoring interventions for nicotine addiction and its neuropsychiatric consequences.Article Enhancing Bike-Sharing Demand Forecasting with Spatio-Temporal Learning: A Comparative Study on a Local Dataset(Golden Light Publ, 2026) Ozdemir, Suat; Cakiroglu, Melike AygunBike-sharing systems rely on accurate short-term demand forecasts to prevent shortages, surpluses, and costly rebalancing operations. Accurate predictions are also essential for enhancing the environmental, operational, and social sustainability of these systems, as improved forecasting may help reduce truck-based redistribution, potentially lowering emissions and supporting more efficient resource usage and more reliable urban mobility services. In this study, we evaluate a wide spectrum of forecasting approaches-ranging from classical time-series models (ARIMA, Prophet) and ensemble learners (Random Forest, XGBoost) to spatio-temporal deep learning models (LSTM variants, ST-GCN, GraphWaveNet, and GNN)-using a local station-level dataset. We incorporate both temporal history and spatial dependencies among stations under a unified evaluation protocol (24-hour look-back, one-hour-ahead prediction). Our findings show that integrating spatial context consistently improves accuracy. The spatio-augmented Random Forest (RF-ST) achieves the best performance, reducing error rates by over 10% compared to its temporal-only counterpart and by more than 35% relative to ARIMA and Prophet. Graph-based neural models (e.g., GNN) deliver comparable accuracy, further confirming the benefits of explicit spatial modeling. These results highlight the potential sustainability implications of spatio-temporal forecasting, suggesting that more accurate station-level predictions may support more sustainable rebalancing strategies, potentially help reduce operational inefficiencies, and strengthen the long-term viability of bike-sharing systems as a sustainable transportation mode.Article Discovering Potential Taxonomic Biomarkers of Gastrointestinal Cancers from Various Human Microbiota via G-S-M Machine Learning Approach(MDPI, 2026) Bakir-gungor, Burcu; Canakcimaksutoglu, Beyza; Ersoz, Nur Sebnem; Yousef, MalikAnalysis of microbial abundance profiles offers significant potential for improving cancer prediction and candidate biomarker discovery. This study aimed to identify cancer-associated microbial biomarkers across five gastrointestinal (GI) cancers: head and neck, esophagus, stomach, colon, and colorectal cancers by analyzing tissue and blood samples from the TCMA dataset in parallel. A novel machine learning model, MicrobiomeGSM, was developed to enhance biological interpretability and reduce computational complexity through a taxonomic grouping strategy. Classification performance of MicrobiomeGSM was rigorously evaluated using a Random Forest Classifier with 100-fold Monte Carlo Cross-Validation. MicrobiomeGSM model effectively identified colon adenocarcinoma (COAD) using a set of 30 genus-level species, achieving a 97% AUC and 97% specificity. Comparative analysis was also performed with six traditional feature selection (TFS) algorithms; CMIM, mRMR, FCBF, IG, XGB, and SKB. Comparison of MicrobiomeGSM with TFS methods showed that while TFS methods capture statistical patterns, MicrobiomeGSM effectively leverages biological structures to identify clinically relevant candidate biomarkers. Also, MicrobiomeGSM competes with TFS methods in the analysis of high-dimensional datasets. In conclusion, these findings demonstrate that incorporating microbial abundance profiles with their taxonomic information into machine learning improve the interpretability and effectiveness of microbiome-based candidate biomarker discovery and may support future precision oncology application.Article Effect of Material Configuration on the Bending Behavior of 3D-Printed Adhesively Bonded Sandwich Core Structures(Springer Heidelberg, 2026) Bulum, M. Zahid; Atahan, M. Gokhan; Apalak, M. KemalThis study investigated the bending performance of 3D-printed adhesively bonded sandwich core structures produced from multiple materials and assessed their potential use as structural components and energy absorbers in UAVs, considering their load-carrying capability and crashworthiness characteristics. PLA, ABS, and TPU were selected for sandwich core structures because they provide rigidity, moderate strength with high deformability, and flexibility, respectively. The study aimed to integrate these mechanical properties into a single sandwich core structure using an adhesive bonding method. The novelty of this study lies in the use of an adhesive bonding method as a practical and effective approach to overcome weak interfacial bonding in multi-material 3D-printed honeycomb sandwich core structures. The bending behavior of ten honeycomb sandwich core configurations with various material stacking sequences was determined using a three-point bending test. While the use of TPU in sandwich core configurations decreased load-carrying capacity, it improved the deformation capability of the sandwich core structures. Configurations 5 and 6 showed 1.16-2.1 times higher energy absorption than monolithic and single-material adhesively bonded sandwich cores. Considering the load-carrying capacity and specific energy absorption capability, Configuration 6 exhibited promising potential for use in structural components and energy absorption applications in UAVs, as ABS was used in the top region and PLA in the bottom region, where normal stress reaches its maximum, while flexible TPU was placed in the middle region, where shear stress is highest. Among the hybrid material structures, Configuration 6 exhibited 46.2% and 439.4% higher load-carrying capacity and energy absorption capability, respectively, compared to Configuration 10, which showed the lowest mechanical performance.Article Bank Performance Under Varying Urban and Rural Governance: Evidence From the COVID-19 Pandemic(Wiley, 2026) Raz, Arisyi F.; Gokmen, SeyitThis paper examines how state capacity influences the effect of economic shocks on local bank performance in urban and rural areas. Our identification strategy exploits COVID-19's impact on Indonesia's community credit banks, which can only operate in a single region. Using loan loss provisions to represent credit risk and return on assets for profitability, we find that COVID-19 provoked more adverse impacts on the rural banking sector compared to urban areas. This heterogeneity is driven by greater state capacity in urban areas, allowing prompt and effective policy responses to mitigate the adverse effects of COVID-19 on the local economy.Article Commuting Flows and Regional Resilience: A Socio-Spatial Analysis of Population Change in Sweden(Springer, 2026) Osth, John; Marjanovic, Ivana; Turk, Umut; Dzunic, Marija; Stanojevic, Marina; Stankovic, JelenaThe interplay between population dynamics, resilience, and commuting patterns forms a critical nexus in regional economic and social development. This study investigates these intersections within the Swedish context, focusing on how spatial connectivity and labor market accessibility shape individual residential mobility and long-term population retention. The study addresses two central research questions: First, to what extent does a municipality's position within the commuting network influence regional resilience as expressed through population retention? Second, how do spatial accessibility conditions contribute to differences in out-migration risks across municipalities? The objective is to analyze resilience not merely as an outcome but as a dynamic interplay between socioeconomic and spatial variables. Using data from the Swedish PLACE database, the study uses a longitudinal panel of individual-level residential, migratory, social, and commuting information. The analysis applies survival models to examine how individual characteristics and municipal-level spatial structures jointly shape the hazard of residential out-migration. The results indicate that population retention is significantly associated with spatial connectivity and labor market accessibility. Municipalities with stronger network integration and better access to nearby employment opportunities exhibit lower out-migration risks. At the individual level, age, education, family structure, and migration background emerge as important predictors of mobility behavior. Commuting diversity is positively associated with migration hazards, suggesting that more dispersed commuting structures may signal weaker local anchoring. Overall, the findings highlight the importance of spatial embeddedness and functional labor market access in shaping demographic stability and regional resilience.