WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
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Article Identification of Turkish Extra Virgin Olive Oils Produced in Different Regions With Volatile Compounds(Innovhub SSI-Area SSOG, 2025) Sevim, Didar; Koseoglu, Oya; Ertan, Hasan; Ozdemir, Durmun; Ulan, MehmetThis study aims to characterize the composition of the volatile compounds in Turkish extra virgin olive oils (EVOOs) produced from three cultivars-Ayvalik, Gemlik, and Memecik-harvested in the South Marmara, South Aegean, and North Aegean regions during the 2014/15 and 2015/16 crop seasons. A total of 135 EVOO samples were obtained using industrial-scale 2-phase and 3-phase extraction systems. These samples were then analyzed using solid-phase microextraction (SPME) coupled with gas chromatography (GC). Among the twelve volatiles identified, trans-2-hexen-1-ol and cis-2-penten-1-ol exhibited the highest levels of abundance across all samples and seasons. Subsequently, 1-penten-3-one, hexanal, and cis-3-hexenyl acetate were identified, and it was determined that these contribute to the green and fruity sensory profile of high-quality olive oil. Two- and three-factor analyses of variance (ANOVA) revealed that volatile concentrations were significantly influenced by variety, harvest season, and extraction system. It is significant that 1-penten-3-one was found to be significantly influenced by both season and variety (p < 0.05), while 1-penten-3-ol exhibited a multifactorial dependency, with significant two-way interactions (season x variety, season x system, variety x system). Furthermore, PLS-DA-based classification successfully distinguished samples according to olive variety, indicating that volatile profiles could serve as reliable markers for authenticity and geographic origin. These findings underscore the potential of using volatile compounds as quality indicators and for geographic labelling in the olive oil industry.Article Dissecting the Metabolic Landscape of Breast Cancer Subtypes via Elastic Net Modeling and Examining Its Immune Correlates(Walter de Gruyter GmbH, 2026) Kus, M.E.; Ekiz, H.A.Objectives: Breast cancer is a heterogeneous disease, and the estrogen receptor (ER) status is a key factor in disease classification and treatment planning. While metabolomic profiling has revealed subtype-specific differences, cross-study comparisons have been limited, posing challenges for data extrapolation. This study aims to investigate metabolites that differentiate ER-positive and ER-negative tumors via integrative analyses of multi-omics data. Methods: We jointly analyzed two untargeted metabolomics datasets via elastic net modeling using consistent analysis pipelines tuned for low sample sizes, namely multiple bootstrapping and stability selection. Significant metabolite predictors from two studies were cross-examined to reveal distinctions and commonalities. We also performed differential gene expression analysis using RNA sequencing data from matching samples to link metabolic patterns with transcriptomic signatures and intratumoral immune cell signatures. Results: This study identified unique metabolite signatures in distinct datasets and a limited overlap of discriminating metabolites that can be broadly generalizable for subtyping. Nevertheless, several glycolysis and fatty acid metabolism intermediates exhibited variation depending on the tumor ER status. Consistently, genes related to fatty acid metabolism and glycolysis were enriched in ER-positive and ER-negative tumors respectively. Furthermore, we used multiple immune cell deconvolution algorithms to correlate various immune cell types with the metabolite levels within the tumor microenvironment. Conclusions: Together, these findings highlight the metabolic and immunological diversity of breast cancer and establish a reproducible machine-learning framework for integrating multi-omics data to interrogate tumor complexity. © 2025 the author(s), published by De Gruyter, Berlin/Boston.Article Soylulaştırmanın Kırsal Boyutu: Turizm, Tarım ve Sanayi(Kare Publ, 2025) Buldan, Ece; Akış, TonguçKırsal soylulaştırma, arazi özelleştirmesi ve yerinden edilme ile karakterize edilen bir süreç olup, 1990'larda devlet güdümlü mü- dahalelerin sisteme daha fazla entegre edilmesiyle yoğunlaştı. Soy- lulaştırma tarihsel referanslarını kaybederek, genelleşmiş bir olgu olarak kent merkezlerinin ötesindeki kırsal alanları da etkilemek - tedir. Bu özel gelişme, kentsel ve kırsal temeller üzerine yoğunla- şan çalışmalar üzerinden ele alınmaktadır. Günümüz söyleminde kırsal alanlar, sanayi kenti ve doğal kırsal ortamlar arasında birer hibritleşme alanı olarak tasvir edilmekte, bu alanların tüketimi, sermaye akışının etkilerini dışlamakta ve giderek daha fazla din - lenme ve turizm üzerinden şekillenmektedir. Küresel Güney ül - kelerindeki çalışmalar incelendiğinde, sanayileşme fonksiyonu da turizm ve ikincil konut sürecinin yanı sıra kırsal soylulaştırmaya katkı sağlamaktadır. Bu süreçler, hizmet ve üretim sektörlerinde ağırlıklı olarak ortaya çıkmakta, kentsel ve kırsal alanlar arasın - daki ayrımı bulanıklaştırarak araştırma ve kavramsallaştırmada zorluklar sunmaktadır. Bu boşluğu ele alarak, bu çalışma, güncel mekânsal sorunlar üzerinden kentsel-kırsal durumun bulanıklığını teorik bir katkı dahilinde bağlamsallaştırmayı amaçlamaktadır. Tu- rizm, sanayi ve tarımdaki dönüşüm süreçlerini inceleyerek, kırsal alan üretimine dair gelişmiş ve gelişmekte olan bağlamlarda tar- tışmalar sunmayı hedeflemektedir. Literatür taraması ve önemli örnekler üzerinde nitel araştırma yöntemleri kullanılarak, bu çalış- ma yeni demografik veriler aracılığıyla kentsel ve kırsal çalışmalar çerçevesinde kırsal soylulaştırmayı inceleyerek, bunun etkilerine ve uygulamalarına dair içgörüler sunacaktır. Buna uygun olarak, kırsal alana giren yeni sermaye akışının etkileri de araştırılacaktır.Article New Bifunctional Catalysts for the Synthesis of Dimethyl Ether Via Carbon Dioxide Utilization(Y H Mammadaliyev inst Petrochemical Proc, Natl Acad Sci, Baku, Azerbaijan, 2025) Guliyev, Bilal, V; Zahidova, Aysel; Tuncer, Bashak; Sheker, Erol; Nasirov, Fizuli A.The increasing demand for sustainable energy sources has intensified research into carbon dioxide (CO2) utilization for the synthesis of clean alternative fuels such as dimethyl ether. This study investigates the direct synthesis of dimethyl ether from CO2 using bifunctional copper-based hybrid catalysts (SCR-A, SCR-B, and SCR-C) synthesized via the sol-gel method. These catalysts integrate oxidative and acidic functionalities within a single system, enabling methanol synthesis and subsequent dehydration into dimethyl ether in a one-step process. Experimental evaluations were conducted under varying pressures ranging from atmospheric to 40 bar and temperatures between 200-350 degrees C, using both low-and high-pressure reactors to assess performance. The results indicate that under atmospheric conditions, methanol conversion reached 87%, with 82% dimethyl ether selectivity, demonstrating the bifunctional character of the catalysts. Among them, SCR-A exhibited the most favorable performance in terms of conversion and product distribution. Under high-pressure conditions (5 and 7 bar), CO2 conversion remained constant at 50%, while selectivity was influenced by temperature and reactor pressure. At 40 bar and 300 degrees C, dimethyl ether selectivity reached its peak at 60%, confirming this range as the optimal operational window for maximizing dimethyl ether yield. However, a notable decrease in selectivity was observed at 350 degrees C, likely due to catalyst deactivation or the promotion of undesired side reactions. These findings underline the thermodynamic and operational benefits of direct dimethyl ether synthesis over the conventional two-step route, as it simplifies process design, enhances CO2 utilization, and reduces energy and cost demandsArticle The OpenAIRE Guide for Research Institutions(Turkish Librarians Assoc, 2012) Gurdal, Gultekin; Turkfidani, Ata; Kutluturk, Levent; Celik, Sonmez; Keten, BurcuThis text is transcript of OpenAIRE Guide which is prepared in order to help research institutions was released on 13.04.2011and translated with the cooperation of ANKOS Open Access and Institutional Repositories Grup members and OpenAIREplus project team of Turkey which is coordinated from Izmir Institute of Technology Library. OpenAIRE Project aims to support researchers in complying with the European Commission Seventh Framework Programme Open Access Pilot through a European Helpdesk System; support researchers in depositing their research publications in an institutional or disciplinary repository; build up an OpenAIRE portal and e-infrastructure for repository networks. The project will work in tadem with OpeanAIREplus Project which has the principal goal of creating a robust, participatory service for the cross-linking of peer-reviewed scientific publications and associated datasets.Article Citation - WoS: 1The Final Declaration of 3rd National Open Access Workshop (21 October 2014)(Turkish Librarians Assoc, 2014) Tonta, Yasar; Gurdal, GultekinThe article shares with the public the recommendations of the Final Declaration prepared within the scope of the Third (Turkish) National Open Access Workshop organized by ANKOS (Anatolian University Libraries Consortium), AEKA (ANKOS Open Access and Consortial Archives), YOK (Higher Education Council), and HU BBY (Hacettepe University Department of Information Management). The article urges that as soon as possible the necessary arrangements be made for the acceptance of the free circulation of information-one of the priorities of the European Union's European Research Area. and for the transition to implementation of open access at the national level. important in order to integrate with the Horizon 2020 project.Article Exploring the Contemporary Dynamics of Extended Urbanisation: a Comprehensive Analysis on the Case of Denizli, Turkey(Kare Publ, 2025) Kolaoglu, Busra; Penpecioglu, Mehmet; Ogur, Aysun AyguenContemporary discussions about extended urbanization and its inherent practices of suburbanization particularly focus on metropolitan cities in the Global South. There is inadequate empirical evidence on the rapidly developing Anatolian cities in Turkey. To address this gap, this article analyzes Denizli's extended urban development process, elaborates on the dominant practices, and examines the driving forces shaping its rapid, contested, and fragmented socio-spatial landscape. As one of the most ubiquitous cases among rapidly developing Anatolian cities, Denizli highlights the leading role of fragmented urban development planning interventions, the stimulating impact of transportation and infrastructure investments, and the pivotal role of private sector projects. The research consists of urban spatial analysis using statistical data and urban planning documents, detecting land use/cover changes over time, and identifying the driving factors that have influenced and shaped the patterns of urban development in Denizli. The findings indicate that fragmented urban development planning interventions have both triggered and sustained extended urban development in Merkezefendi, Denizli. Moreover, key public investments and real estate projects have fostered this extended urban development process, leading to disjointed fragments in a socioeconomically polarized geography. As a diversified and relational formation of extended urbanization, Denizli provides genuine research findings, and includes remarkable similarities as well as differences in the comparative analysis of global urbanism practices.Article Housing Instability and Roma Children's Educational Engagement: Perspectives From Teachers and Volunteers(Istanbul Univ, Fac Letters, dept Sociology, 2024) Uştuk, OzanInequalities experienced by the Roma remain a complex challenge, particularly in education. Despite various initiatives, their impact on the Roma communities’ daily lives has been limited. This article is based on applied research aimed at reducing early school-leaving rates among Roma children in Türkiye. The study highlights that the high rates of early school leaving cannot be fully understood without considering the profound impact of housing instability on their educational experiences. By exploring the intersection of educational challenges and housing insecurity through the perspectives of elementary school teachers, preschool teachers, and volunteering university students engaged with a Roma community, the findings reveal that the constant threat of displacement and inadequate living conditions severely disrupt educational engagement, undermining the stability necessary for academic success. However, these perspectives also expose critical gaps in understanding, particularly among educators who often overlook the significance of housing insecurity in shaping educational outcomes. By situating these challenges within the broader context of systemic housing issues, this research underscores the need for comprehensive, community-based interventions that address the root causes of educational inequities among Roma students. The study advocates for a holistic approach to educational equity—one that addresses both the material and psychological dimensions of housing insecurity, thereby creating pathways for genuine social mobility and inclusion for Roma children.Article Citation - Scopus: 1Improving the Stability of Ink-Jet Printed Red Qleds by Optimizing the Device Fabrication Process(Eurasia Academic Publishing Group, 2024) Diker, H.; Unluturk, S.S.; Ozcelik, S.; Varlikli, C.Red-light emitting Cadmium Sulfide0.8 Selenide0.2 / Zinc Sulfide (CdS0.8 Se0.2 /ZnS) based quantum dots (QDs) were synthesized by hot injection method and utilized as the emissive layer in the quantum dot light emitting diode (QLED) with the device structure of Indium Tin Oxide/Poly(3,4-ethylenedioxythiophene): Polystyrene Sulfonate /Polyvinylcarbazole(or Poly (N,N'-bis-4-butylphenyl-N,N'-bisphenyl)benzidin)/QD/ZincOxide/ LithiumFluoride/ Aluminum [ITO/ PEDOT: PSS/PVK(or p-TPD)/ QD/ZnO/LiF/Al]. QD inks were formulated and prepared in octane: decane; (1/1, v/v) solvent system and mixed with the nonionic surfactant, TritonX-100, to make the QD inks inkjet printable. In addition to the inkjet printing technique, spin coating was also employed to form the QD emissive layer for comparing device performance. Compared to the p-TPD-based QLED device, the PVK-based device fabricated via spin coating exhibited ~6-fold higher performance in terms of luminance and efficiency values. In the case of using the ink-jet printer, ~2-fold higher maximum luminance value and slightly lower external quantum efficiency at the lower current density region were obtained in the p-TPD-based device. Furthermore, compared to the PVK layer, the p-TPD layer provided higher device stability regardless of the coating method at the higher current density regions. We suggest that the coating method applied and the choice of hole transport layer (HTL) materials may control the device parameters. © The Author(s), 2024.Article Ai-Assisted Survival Prediction in Colorectal Cancer: a Clinical Decision Support Tool(Dokuz Eylul Univ inst Health Sciences, 2024) Misirlioglu, Huseyin Koray; Leblebici, Asim; Calibasi-Kocal, Gizem; Ellidokuz, Hulya; Basbinar, YaseminPurpose: This study was planned to determine the problems and affecting factors that children encounter Purpose: Colorectal cancer (CRC) is a leading cause of cancer-related mortality worldwide. Accurate survival prediction is crucial for advanced-stage patients to optimize treatment strategies and improve clinical outcomes. This study aimed to develop an artificial intelligence-assisted clinical decision support system (CDSS) for survival prediction in CRC patients using clinical and genomic data from the Cancer Genome Atlas Colon Adenocarcinoma Collection (TCGA-COAD) dataset. Methods: Machine learning algorithms, including C4.5 Decision Tree, Support Vector Machines (SVM), Random Forest, and Naive Bayes, were employed to create survival prediction models. Clinical parameters and genomic data from key pathways, such as glycolysis/gluconeogenesis and mTORC1, were integrated into the models. The models were evaluated based on accuracy and performance. Results: The Random Forest algorithm achieved the highest accuracy (82.3%) when only clinical parameters were used. When clinical data were combined with gene expression data, the model's accuracy increased further. The resulting models were incorporated into a user-friendly web interface, SurvCOCA, for clinical use. Conclusions: This study demonstrates the potential of AI-based tools to improve prognosis predictions in CRC patients. Further research is needed, with larger datasets and additional machine learning algorithms, to enhance clinical decision-making and optimize treatment strategies.