Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
Browse
69 results
Search Results
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 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.Conference Object Twist-Bend Instability of a Cantilever Beam Subjected to an End Load via Homotopy Perturbation Method(Amer Inst Physics, 2018) Yucesoy, Ahmet; Coskun, Safa Bozkurt; Atay, Mehmet Tarik; Cesoy, AhmetIn this article, twist-bend buckling analysis of a cantilever beam subjected to a concentrated end load is conducted using Homotopy Perturbation Method (HPM). Even in the linear stability analysis, obtaining an exact solution for some cases is not an easy task. However, by the use of HPM this difficulty can be overcome easily. This issue is presented with a case study and the results show that HPM can be used successfully in the analysis of twist-bend buckling of beams.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 Citation - WoS: 2Citation - Scopus: 1The Numerical Solutions for Stiff Ordinary Differential Equations by Using Interpolated Variational Iteration Method With Comparison to Exact Solutions(Amer Inst Physics, 2018) Ciftci, Cihan; Cayci, Hatice Sinem Sas; Atay, Mehmet Tarik; Toker, Batuhan; Guncan, Berkay; Yildirim, Afsin TalhaRecently proposed Interpolated Variational Iteration Method (IVIM) is used to find numerical solutions of stiff ordinary differential equations for both linear and nonlinear problems. The examples are given to illustrate the accuracy and effectiveness of IVIM method and IVIM results are compared with exact results. In recent analytical approximate methods based studies related to stiff ordinary differential equations, problems were solved by Adomian Decomposition Method and VIM and Homotopy Perturbation Method, Homotopy Analysis Method etc. In this study comparisons with exact solutions reveal that the Interpolated Variational Iteration Method (IVIM) is easy to implement. In fact, this method is promising methods for various systems of linear and nonlinear stiff ordinary differential equations as an initial value problem. Furthermore, IVIM is giving very satisfactory solutions when compared to exact solutions for nonlinear cases depending on the stiffness ratio of the stiff system to be solved.Article Citation - WoS: 3Citation - Scopus: 1The New Visual Culture in Eighteenth-Century Istanbul: Building Up New Shore Kiosks and Gardens on the Outskirts of the Royal Palace(Routledge Journals, Taylor & Francis Ltd, 2019-11-14) Tozoglu, Ahmet ErdemThis article examines the construction and expansion of a less-known royal shore kiosk complex in Istanbul, namely the Shore Palace near the Cannon Gate (Topkapisi Sahil Sarayi) or Summer Harem, which was built on the outskirts of the royal palace complex in the eighteenth century, to interpret the changing features of royal residential culture and spatial practices. In this article, I aim to propose a new thematic frame based on the central role of the issue of visuality to examine the shifting cultural paradigm of eighteenth-century royal patronage. The eighteenth century witnessed the physical expansion of the complex and renovation of the furnishings several times and the official records of these activities provide us with invaluable information for the visual construction of these buildings, which were torn down after a devastating fire in 1862. Furthermore, the choice of location and all physical changes in the interiors and gardens demonstrate the spatial results of the changing codes of visual culture in the cityscape. In this respect, examination of this case enables us to discuss how the new visual culture was adopted and exercised in and around the royal palace gardens by the royal court members.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 Citation - WoS: 3Citation - Scopus: 3Template Scoring Methods for Protein Torsion Angle Prediction(Springer-Verlag Berlin, 2015) Aydin, Zafer; Baker, David; Noble, William StaffordPrediction of backbone torsion angles provides important constraints about the 3D structure of a protein and is receiving a growing interest in the structure prediction community. In this paper, we introduce a three-stage machine learning classifier to predict the 7-state torsion angles of a protein. The first two stages employ dynamic Bayesian and neural networks to produce an ab-initio prediction of torsion angle states starting from sequence profiles. The third stage is a committee classifier, which combines the ab-initio prediction with a structural frequency profile derived from templates obtained by HHsearch. We develop several structural profile models and obtain significant improvements over the Laplacian scoring technique through: (1) scaling templates by integer powers of sequence identity score, (2) incorporating other alignment scores as multiplicative factors (3) adjusting or optimizing parameters of the profile models with respect to the similarity interval of the target. We also demonstrate that the torsion angle prediction accuracy improves at all levels of target-template similarity even when templates are distant from the target. The improvement is at significantly higher rates as template structures gradually get closer to target.