Welcome to IKU Academic Digital Archive System
OpenAccess@IKU is Istanbul Kultur University's Academic Digital Archive System, established in June 2014 to digitally store and provide open access to academic and artistic outputs in line with international standards and intellectual property rights. The system includes various outputs such as articles, presentations, theses, books, book chapters, reports, encyclopedias, and works of art produced by the university's faculty members and students.
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Recent Submissions
The naa40 Deletion Is Associated with Extended Chronological Lifespan and Changes in Energy and Lipid Metabolism in Fission Yeast
(Pleiades Publishing, 2025) Çalici Z; BUSE, ÖZDEN; Tarhan Ç.
Abstract: Aging is associated with various physiological and molecular changes, including epigenetic reprogramming and metabolic remodeling. N-alpha-acetyltransferase 40 (NAA40), encoded by naa40, has emerged as a key factor connecting chromatin dynamics to cellular metabolism among epigenetic regulators. NAA40 specifically acetylates the N-termini of histones H4 and H2A, thereby influencing gene expression and maintaining the metabolic balance. In mammalian systems, the depletion of NAA40 increases intracellular acetyl-CoA levels, which impacts lipid metabolism and stress response pathways. However, the overall effects of naa40 deletion in organisms are not fully understood. In this study, we examined the functional consequences of naa40 deletion in Schizosaccharomyces pombe by analyzing changes in chronological lifespan, glucose consumption, lipid metabolism, and oxidative stress. Our findings indicate that the loss of NAA40 significantly extends the lifespan, decreases glucose utilization, alters lipid distribution, and reduces lipid peroxidation. These metabolic changes are similar to the cellular adaptations observed under calorie restriction. Our results support a model in which naa40 deletion triggers chromatin remodeling and reprograms the metabolic pathways, ultimately leading cells to a quiescent-like state characterized by increased stress resistance and extended survival. These findings underscore the importance of NAA40 as a critical link between epigenetic regulation and the mechanisms that promote metabolic longevity.
COŞKUN, ASLIHAN
Dr. Öğr. Üyesi
Genetic Codes of Housing: Morphological Reading of Traditional Antalya Houses
(MDPI, 2025) COŞKUN, ASLIHAN; Haştemoğlu, Hasan Şehmuz
This study focuses on the traditional houses of Kaleiçi, Haşim İşcan, and Balbey neighborhoods, which constitute the historical center of Antalya and reflect the cultural continuity of the organic urban texture dating back to the Hellenistic period. The aim is to reveal how climatic, topographic and cultural factors shape the spatial organization of these traditional houses. Using the space syntax method, the study analyzes nine sample houses to examine their morphological structure. The findings show that while the ground floors are mostly arranged as service areas and the upper floors as living spaces, the sofa emerges as the most integrated space despite varying depths across neighborhoods. Moreover, spatial differences between the neighborhoods indicate contextual diversity, yet the persistence of sofa-centered organization underlines the role of cultural continuity in housing morphology. By emphasizing the centrality of the sofa and demonstrating how cultural factors sustain typological patterns over time, this study contributes to the literature on space syntax-based morphology and offers practical insights for contemporary housing design that considers climatic and cultural contexts.
Multimodal Insights Into Diverse Pain Experiences: Physiopain Dataset
(Elsevier Inc., 2025) TOKTAY, BORAN; ORHAN, İKBAL IŞIK; Yıldırım, Elif; AKBULUT, FATMA PATLAR; Çatal, Cağatay
PhysioPain dataset comprises several physiological data of different kinds of pain: no pain, headache, menstrual cycle pain and back/neck/waist pain in search of a sophisticated and complete approach to pain representation. The study comprised 99 individuals, of whom 93 participants contributed real-time physiological data. These participants underwent experiment process to gather real-time physiological data including electroencephalogram (EEG), skin temperature, electrodermal activity (EDA), blood volume pulse (BVP), and accelerometer data. Combining objective physiological data with subjective information acquired by the survey using the McGill questionnaire and customized questions produces a complete dataset fit for the tasks related to pain estimate, pain classification, and other approaches to pain observation. This method seeks to offer a fresh viewpoint on pain intensity and catch a more complete knowledge of the intricate character of pain experiences.
Deep Ensemble Learning and Explainable AI for Multi-Class Classification of Earthstar Fungal Species
(MDPI, 2025) Kumru, Eda; KORKMAZ, ARAS FAHRETTİN; Ekinci, Fatih; Aydoğan, Abdullah; Güzel, Mehmet Serdar
The current study presents a multi-class, image-based classification of eight morphologically similar macroscopic Earthstar fungal species (Astraeus hygrometricus, Geastrum coronatum, G. elegans, G. fimbriatum, G. quadrifidum, G. rufescens, G. triplex, and Myriostoma coliforme) using deep learning and explainable artificial intelligence (XAI) techniques. For the first time in the literature, these species are evaluated together, providing a highly challenging dataset due to significant visual overlap. Eight different convolutional neural network (CNN) and transformer-based architectures were employed, including EfficientNetV2-M, DenseNet121, MaxViT-S, DeiT, RegNetY-8GF, MobileNetV3, EfficientNet-B3, and MnasNet. The accuracy scores of these models ranged from 86.16% to 96.23%, with EfficientNet-B3 achieving the best individual performance. To enhance interpretability, Grad-CAM and Score-CAM methods were utilised to visualise the rationale behind each classification decision. A key novelty of this study is the design of two hybrid ensemble models: EfficientNet-B3 + DeiT and DenseNet121 + MaxViT-S. These ensembles further improved classification stability, reaching 93.71% and 93.08% accuracy, respectively. Based on metric-based evaluation, the EfficientNet-B3 + DeiT model delivered the most balanced performance, with 93.83% precision, 93.72% recall, 93.73% F1-score, 99.10% specificity, a log loss of 0.2292, and an MCC of 0.9282. Moreover, this modeling approach holds potential for monitoring symbiotic fungal species in agricultural ecosystems and supporting sustainable production strategies. This research contributes to the literature by introducing a novel framework that simultaneously emphasises classification accuracy and model interpretability in fungal taxonomy. The proposed method successfully classified morphologically similar puffball species with high accuracy, while explainable AI techniques revealed biologically meaningful insights. All evaluation metrics were computed exclusively on a 10% independent test set that was entirely separate from the training and validation phases. Future work will focus on expanding the dataset with samples from diverse ecological regions and testing the method under field conditions.