WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article An Elementary Proof of Lucas's Theorem(Ramanujan Mathematical Society, 2025) Cinkir, ZubeyirLucas's Theorem is about finding the result of a binomial coefficient modulo a prime p efficiently. The result is expressed as a product of binomial coefficients involving the base p expansions of the parameters of the original binomial coefficient. We give an elementary proof of Lucas's Theorem by deriving an analogous Vander-monde identity modulo a prime number.Article Development of Resistant Starch Type-5 and Its Utilization in Cookie-Preparation(North University Center Baia Mare, 2025-11-30) Oskaybas-Emlek, Betul; Ozbey, Ayse; Kahraman, KevserThe objective of this study was the production of resistant starch type-5 (RS-5), its characterization, and utilization in cookie making. In first part of the study, the effects of starch-fatty acid complex formation (RS-5) between tapioca starch and lauric acid on the structure, digestibility, thermal and morphological properties of tapioca starch were investigated. X-ray diffraction revealed that the RS-5 had a V-type crystalline pattern. FT-IR analysis showed that a distinctive peak at 2846 cm-1 was only observed in RS-5. The resistant starch (RS) content of native starch increased from 22.76% to 28.02% with RS-5 formation. In the second part of the study, the RS-5 was added as a replacement for wheat flour with 10%, 20%, and 30% compared to control sample made with 100% wheat flour in cookie-making. The effects of RS-5 replacement of cookie samples on some physicochemical, estimated glycemic index (eGI) value, physical, and hardness properties were determined. Compared to control cookie, the cookie samples included RS-5 had lower hardness value, higher spread ratio. The eGI value of cookie samples was slightly decreased with the replacement with RS-5. The results demonstrated that the RS-5 has good potential for developing softer cookie with no adverse impact on eGI value.Article High-Accuracy Identification of Durian Leaf Diseases: A Convolutional Neural Network Approach Validated with K-Fold Cross-Validation and Bayesian Optimization(Springer, 2025-11-18) Soylemez, Ismet; Nalici, Mehmet Eren; Unlu, RamazanTo address the economic losses caused by plant diseases in durian farming, this study presents an optimized deep learning model that diagnoses diseases from leaf images with high accuracy. The model's performance is maximized through Bayesian optimization and hyperparameter tuning, while its reliability is maximized through layered five-fold cross-validation. Training the convolutional neural network model on 2595 leaf images displaying six different states (five diseased and one healthy) resulted in an average test accuracy of 91.98%. This high, consistent success rate demonstrates the model's generalizability to different datasets without overfitting. While the 'Healthy' and 'Algal' classes were successfully detected with high F1-scores, there are difficulties distinguishing between the 'Blight' and 'Colletotrichum' classes due to visual similarities. This study establishes a new reference point for durian disease classification and makes a significant contribution to the development of reliable artificial intelligence-based diagnostic tools for precision agriculture.Article Citation - WoS: 1Comprehensive Prediction of FBN1 Targeting Mirnas: A Systems Biology Approach for Marfan Syndrome(Galenos Publishing House, 2025-09-22) Orhan, M.E.; Demirci, Y.M.; Saçar Demirci, M.D.S.; Demirci, Muserref Duygu SacarObjective: Marfan syndrome (MFS) is a genetic connective tissue disorder primarily caused by mutations in the FBN1 gene. Emerging evidence highlights the regulatory role of microRNAs (miRNAs) in modulating gene expression in MFS, but a systematic investigation into miRNAs targeting FBN1 is lacking. This study aimed to comprehensively identify miRNAs interacting with the FBN1 transcript to reveal potential molecular regulators and therapeutic targets. Methods: Human miRNA sequences were retrieved from miRBase (Release 22.1), and the canonical FBN1 transcript (RefSeq: NM_000138.5) was used for target prediction. Computational interaction analysis was conducted using the psRNATarget server with stringent parameters to detect potential miRNA binding sites. Expression profiles and disease associations of the top candidate miRNAs were further investigated through database integration and literature review. Results: Out of 2656 human mature miRNAs analyzed, 251 were predicted to bind FBN1, with the hsa-miR-181 family exhibiting the highest number of predicted interactions. Evidence from the literature highlighted dysregulation of hsa-miR-181 expression in MFS patients, suggesting a functional role in disease pathophysiology. Conclusion: This study identifies key members of the hsa-miR-181 family as post-transcriptional regulators of FBN1, offering new insights into miRNA-driven mechanisms in MFS. These findings support the potential of RNA-based diagnostics and therapeutic strategies targeting miRNA-FBN1 interactions. ©Copyright 2025 The Author.Article Use of Confocal Microscopy to Monitor Structural Transformations in Nanopillars Based on DNA and CdSe/CdZnSe Quantum Dots(Springer, 2023-06-24) Motevich, I. G.; Erdem, T.; Akrema, A.; Maskevich, S. A.; Strekal, N. D.Chip system prototypes in the form of nanopillars were created from DNA complexes with CdSe/CdZnSe/ZnS quantum dots immobilized on a plasmonic gold fi lm by the use of vacuum deposition technology and inorganic synthesis. The design and presence of terminal DNA labeled with Cy3 cyanine dyes makes it possible to carry out the hybridization reaction of this terminal strand with complementary DNA and to control the process by variation of the giant Raman scattering (GRS) and the fluorescence signal. The effect of molecular recognition of complementary DNA is accompanied by a change in the GRS spectrum, a 20-fold increase in the fluorescence intensity, and a decrease in the duration of fluorescence decay.Article Citation - WoS: 1Citation - Scopus: 2Tomatidine, a Steroidal Alkaloid, Synergizes With Cisplatin to Inhibit Cell Viability and Induce Cell Death Selectively on FLT3-ITD+ Acute Myeloid Leukemia Cells(Humana Press inc, 2024-07-11) Ayvaz, Havva Berre; Yenigul, Munevver; Akcok, Emel Basak Gencer; Gencer Akçok, Emel BaşakBackgroundAcute Myeloid Leukemia (AML) is a hematological cancer that frequently presents with a range of side effects and drug resistance during anticancer drug treatment. The current study aims to achieve increased efficacy by combining lower doses of cisplatin with increasing concentrations of tomatidine in AML cells to increase efficacy.MethodsAnti-proliferative effects of single and combination of cisplatin and tomatidine were assessed via MTT cell viability assay. The Annexin V/Propidium Iodide Double Staining method was used to measure the apoptotic effects of combined tomatidine and cisplatin treatment. Then, Western Blot analysis was performed to measure Poly (ADP-ribose) polymerase (PARP) and Caspase-3 protein expression levels.ResultsCisplatin treatment with lower concentrations displayed high cytotoxic effects on AML cells, compared with tomatidine. The combination of the Inhibitory Concentration (IC) 20 value of cisplatin and increasing doses of tomatidine exhibited a significant decrease in cell viability relative to single treatments. The combination index analysis revealed a mild synergistic effect of cisplatin IC20 and varying tomatidine doses. The apoptosis induced when cisplatin was combined with 500 mu M tomatidine by almost 20%, while the percentage of apoptosis in combination with 1 mM tomatidine was measured by 50% for both cell lines. The upregulation of proapoptotic cleaved-PARP (3.2 and 1.08-fold for THP-1 and MOLM-13, respectively) and downregulation in Caspase-3 (0.23 and 0.13-fold for THP-1 and MOLM-13, respectively) was detected.ConclusionsTogether, the study indicated that when tomatidine combined with cisplatin on AML cell lines, a combinatorial anti-proliferative and apoptotic effect is observed. The combination of cisplatin with tomatidine may be a promising approach.Conference Object Text Classification Experiments on Contextual Graphs Built by N-Gram Series(Springer International Publishing AG, 2025) Sen, Tarik Uveys; Yakit, Mehmet Can; Gumus, Mehmet Semih; Abar, Orhan; Bakal, GokhanTraditional n-gram textual features, commonly employed in conventional machine learning models, offer lower performance rates on high-volume datasets compared to modern deep learning algorithms, which have been intensively studied for the past decade. The main reason for this performance disparity is that deep learning approaches handle textual data through the word vector space representation by catching the contextually hidden information in a better way. Nonetheless, the potential of the n-gram feature set to reflect the context is open to further investigation. In this sense, creating graphs using discriminative ngram series with high classification power has never been fully exploited by researchers. Hence, the main goal of this study is to contribute to the classification power by including the long-range neighborhood relationships for each word in the word embedding representations. To achieve this goal, we transformed the textual data by employing n-gram series into a graph structure and then trained a graph convolution network model. Consequently, we obtained contextually enriched word embeddings and observed F1-score performance improvements from 0.78 to 0.80 when we integrated those convolution-based word embeddings into an LSTM model. This research contributes to improving classification capabilities by leveraging graph structures derived from discriminative n-gram series.Conference Object TextNetTopics+: Enhancing Text Classification Through Classifier Diversity and Model Ensembling(Springer International Publishing AG, 2025) Voskergian, Daniel; Bakir-Gungor, Burcu; Yousef, MalikTextNetTopics is an innovative text classification framework that integrates topic modeling with feature selection to improve model accuracy and interpretability. Unlike traditional methods that rely on individual words, TextNetTopics selects cohesive topics extracted via Latent Dirichlet Allocation as features for document representation, effectively reducing dimensionality while preserving the semantic structure of the text. This study evaluates the performance of TextNetTopics utilizing multiple machine learning algorithms in the M (Modeling) component, including Random Forest, Support Vector Machine, Gradient Boosting, eXtreme Gradient Boosting, and Logistic Regression. To further enhance classification performance, we introduce TextNetTopics+, an ensemblebased extension that leverages both hard voting and soft voting mechanisms to combine the strengths of multiple classifiers. Comprehensive experiments on the LitCovid and WOS datasets demonstrate that ensemble learning in TextNetTopics + significantly outperforms individual classifiers in TextNetTopics, confirming its effectiveness in improving model robustness and generalization.Conference Object Temporal Logic-Based Intrusion Detection for Securing Connected Vehicles(Springer International Publishing AG, 2024) Bozdal, MehmetEnsuring the security and integrity of in-vehicle communication networks (IVCNs) is paramount. The increasing connectivity of vehicles exposes them to unprecedented security vulnerabilities, necessitating innovative methodologies to safeguard against cyberattacks and unauthorized access. This research presents a novel approach to enhance IVCN security through the deployment of a Signal Temporal Logic (STL)-based Intrusion Detection System (IDS). Considering the limited resources of Electronic Control Units (ECUs), this approach offers an adaptive and lightweight solution that addresses the unique challenges posed by the dynamic nature of vehicular networks. The proposed STL-based IDS effectively detects a broad spectrum of intrusions while maintaining acceptable overhead for resource-constrained ECUs, thanks to its distributed architecture. Comprehensive experimental evaluations demonstrate significant performance improvements in detecting Denial of Service (DoS) attacks, achieving the highest accuracy of 0.996 and recall of 1.000. The system also excels in detecting fuzzy attacks, with the highest accuracy of 0.996.Article Citation - WoS: 1Citation - Scopus: 1RPI-1 (Human DCDC2) Displays Functional Redundancy With Nephronophthisis 4 in Regulating Cilia Biogenesis in C. Elegans(Tubitak Scientific & Technological Research Council Turkey, 2023-01-01) Kaplan, Oktay I.Projecting from most cell surfaces, cilia serve as important hubs for sensory and signaling processes and have been linked to a variety of human disorders, including Bardet-Biedl Syndrome (BBS), Meckel-Gruber Syndrome (MKS), Nephronophthisis (NPHP), and Joubert Syndrome, and these diseases are collectively known as a ciliopathy. DCDC2 is a ciliopathy protein that localizes to cilia; nevertheless, our understanding of the role of DCDC2 in cilia is still limited. We employed C. elegans to investigate the function of C. elegans RPI-1, a Caenorhabditis elegans ortholog of human DCDC2, in cilia and found that C. elegans RPI-1 localizes to the entire ciliary axoneme, but is not present in the transition zone and basal body. We generated a null mutant of C. elegans rpi-1, and our analysis with a range of fluorescence-based ciliary markers revealed that DCDC2 and nephronophthisis 4 (NPHP-4/NPHP4) display functional redundant roles in regulating cilia length and cilia positions. Taken together, our analysis discovered a novel genetic interaction between two ciliopathy disease genes (RPI-1/DCDC2 and NPHP-4/NPHP4) in C. elegans.