TR-Dizin İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/396
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Article Bazı Geleneksel Türk Gıdalarından Laktik Asit Bakterilerinin İzolasyonu(2021) Doğan, Osman; Aydın, Aysun CebeciAmaç: Bu çalışma ülkemizde geleneksel yöntemlerle üretilen gıda ürünlerindenlaktik asit bakterilerinin izolasyonunu ve tanımlanmasını sağlamak amacıylayapılmıştır.Materyal ve Yöntem: Çalışma kapsamında Van otlu peynir ve ekşi hamur örneğikullanılmıştır. Bu örnekler içerdikleri laktik asit bakterileri için çalışılmış vetanımlanmaları için biyokimyasal ve PCR bazlı moleküler biyolojik tekniklere tabitutulmuşlardır. Biyokimyasal testler kapsamında örnekler, Gram reaksiyonları,katalaz aktivitesi, gaz üretimi, 10oC ve 45oC'de, %6 ve %16 NaCl konsantrasyonda,pH 4.4 ve pH 9.6’da gelişim göstermeleri açısından incelenmiştir. Moleküler biyolojideneyleri kapsamında ise tür ve suş düzeyinde tanımlama için PCR-RFLP, 16SrRNA gen dizileme ve RAPD-PCR teknikleri kullanılmıştır.Araştırma Bulguları: Bir dizi mikrobiyolojik deneylerin sonucunda 26 adet bakteripotansiyel laktik asit bakterisi olarak izole edilmiştir. Bunlardan 25 adedininLactobacillus, Pediococcus ve Enterococcus cinslerine ait olduğu tespit edilmiş vetür ve suş düzeyinde tanımlanmaları sağlanmıştır. Kalan bir adet izolat iseStaphylococcus hominis olarak tanımlanmıştır.Sonuç: Çalışmamız sonucunda 25 adet laktik asit bakterisi gen dizileme ve RAPDPCR teknikleri kullanılarak tür ve suş düzeyinde başarıyla tanımlanmıştır.Article Developing a Label Propagation Approach for Cancer Subtype Classification Problem(TUBITAK, 2021) Güner, P.; Bakir-Güngör, B.; Coşkun, M.; Şahan, Pınar GünerCancer is a disease in which abnormal cells grow uncontrollably and invade other tissues. Several types of cancer have various subtypes with different clinical and biological implications. Based on these differences, treatment methods need to be customized. The identification of distinct cancer subtypes is an important problem in bioinformatics, since it can guide future precision medicine applications. In order to design targeted treatments, bioinformatics methods attempt to discover common molecular pathology of different cancer subtypes. Along this line, several computational methods have been proposed to discover cancer subtypes or to stratify cancer into informative subtypes. However, existing works do not consider the sparseness of data (genes having low degrees) and result in an ill-conditioned solution. To address this shortcoming, in this paper, we propose an alternative unsupervised method to stratify cancer patients into subtypes using applied numerical algebra techniques. More specifically, we applied a label propagation-based approach to stratify somatic mutation profiles of colon, head and neck, uterine, bladder, and breast tumors. We evaluated the performance of our method by comparing it to the baseline methods. Extensive experiments demonstrate that our approach highly renders tumor classification tasks by largely outperforming the state-of-the-art unsupervised and supervised approaches. © 2022 Elsevier B.V., All rights reserved.Research Project RNA İkincil Yapılarının Çok Boyutlu Gösterimi ve Pre-MiRNA Tespiti İçin Uygulamaları(2021) Demirci, Müşerref Duygu Saçar; Demirci, Yılmaz Mehmet-Article Testing a Metacognitive Regulation Approach for Judgment of Satiation: Might Hunger and Fullness Not Be the Polar Opposites of the Same Dimension(2020-12-31) Güzel, Mehmet Akif; Güngör, DuyguDespite the existence of several cognitive influences, metacognitive factors on eating and satiation still remain unclear. Therefore, we investigated a relatively recent metacognitive regulation approach and its measurement method in a lab-experiment.Participants (N=216) were shownphotografsof varying portions of common lunch foods (selected after a separate study, N=94) and asked to makepredicted judgmentsof satiation (JOS) for each via considering their actual hunger levels and whilst imagining other bodily states (e.g., extremely hungry and completely full). Differencescalculated between observed-JOS and their reference scores -thosepresumed to yield accurate matches for the cases-produced either deviances or none at all (discordant-or concordant-JOS). Hungry-group yielded significantly lower concordant-JOS percentage than full-group regardless of portion size, indicating a clearercognitive tendency to lose control over consumption when being hungry than satiated. Critically, full-group could notimagine extreme hunger as hungry-group whereas hungry-group imagined complete fullness just as full-group did, suggesting that whilst hunger was not an obstacle to imagine fullness,fullness hindered the ability to imagine hunger. These findingssuggestthathunger and fullness mightnotbe the polar opposites on the very same dimension, which would, for instance,reveal a need to revisit the treatments of eating disorders accordingly.Article Real-Effort Tasks in Laboratory Experiments(Economic and Financial Research Assoc - Efad, 2023-09-30) Demirtas, Burak KaganLaboratory experiments used in economics are differentiated in terms of many technical features. One of these technical features is whether the experiment involves a real-effort task. A real-effort task can be defined as a task in which the experiment participants work on aAreal job during the experiment, spend time and effort, determine their performance level and as a result earn a certain amount of money. This study aims to examine real-effort tasks that are frequently used in experimental economics studies, and to discuss potential problems that researchers may face when conducting experiments with real- effort tasks. Within the scope of this review, real-effort tasks commonly used in the literature are categorized under four groups: real-effort tasks based on mathematical operations, puzzles, slider task, and word encryption tasks. Choice of the real-effort task is important for an experimental study because it may lead to misinterpretation of the findings. AAsAa result of the study, the learning effect, the boredom of the task and the abilities required by the task are seen as possible sources of measurement error. While the learning effect and boredom may cause problems especially in within-subject designs, it was found that differences in the abilities of participants may cause measurement errors especially in between-subject designs.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.Article Citation - WoS: 3Citation - Scopus: 3MicroRNA Prediction Based on 3D Graphical Representation of RNA Secondary Structures(Tubitak Scientific & Technological Research Council Turkey, 2019-08-05) Sacar Demirci, Muserref Duygu; Demirci, Müşerref Duygu SaçarMicroRNAs (miRNAs) are posttranscriptional regulators of gene expression. While a miRNA can target hundreds of messenger RNA (mRNAs), an mRNA can be targeted by different miRNAs, not to mention that a single miRNA might have various binding sites in an mRNA sequence. Therefore, it is quite involved to investigate miRNAs experimentally. Thus, machine learning (ML) is frequently used to overcome such challenges. The key parts of a ML analysis largely depend on the quality of input data and the capacity of the features describing the data. Previously, more than 1000 features were suggested for miRNAs. Here, it is shown that using 36 features representing the RNA secondary structure and its dynamic 3D graphical representation provides up to 98% accuracy values. In this study, a new approach for ML-based miRNA prediction is proposed. Thousands of models are generated through classification of known human miRNAs and pseudohairpins with 3 classifiers: decision tree, naive Bayes, and random forest. Although the method is based on human data, the best model was able to correctly assign 96% of nonhuman hairpins from MirGeneDB, suggesting that this approach might be useful for the analysis of miRNAs from other species.Article Investigating the Impact of Birt–hogg–dubé Syndrome Associated Folliculin (Flcn) and Retinitis Pigmentosa 2 (Rp2) Loss on Cilia Function and Morphology(2024-06-30) Kaplan, Oktay IsmailFolliculin (FLCN), a GTPase-activating protein (GAP), has been linked to Birt–Hogg–Dubé syndrome, the mTORC1 signaling pathway and cilia. Disruptions in cilia structure and function lead to a group of diseases known as ciliopathies. Birt-Hogg-Dubé syndrome is one of 35 different ciliopathy diseases and there are more than 250 genes that cause ciliopathy diseases. FLCN interacts with kinesin-2 along cilia. The specific role of FLCN in regulating Kinesin-IFT trafficking has, however, remained unclear. In the current study, we investigated the effects of flcn-1 loss (the human ortholog of FLCN) on kinesin and IFT trafficking in C. elegans. The loss of flcn-1 alone did not result in any apparent alterations to kinesin or IFT trafficking within the cilia. However, when we combined the deletion of flcn-1 with the deletion of Retinitis Pigmentosa 2 (RP2), another GAP protein, the ciliary entry of a non-ciliary membrane protein TRAM-1 (Translocation Associated Membrane Protein 1) occured. Additionally, although cilia length was unaltered, our analysis of double mutants revealed the extra branch in wing AWB cilia morphology but not the single rod-like PHA/PHB cilia. In summary, our study reveals the previously unknown functions of FLCN in ciliary gating and cilia morphology in C. elegansArticle In Silico Evaluation of the Potential of Natural Products From Chili Pepper as Antiviral Agents Against DNA-Directed RNA Polymerase of the Monkeypox Virus(2024-03-24) Fidan, Ozkan; Mujwar, SomduttThis study focused on the discovery of new drug candidates effective against the monkeypox virus. Virtual screening was performed to evaluate the potential of chili pepper natural products against homology-modeled DNA-directed RNA polymerase of the monkeypox virus using molecular docking. Our findings revealed that structurally similar triterpenes such as α-amyrin, β-amyrin, and β-sitosterol had strong binding affinities towards the DNA-directed RNA polymerase and can inhibit this pivotal viral enzyme. The stability of one of the drug candidate molecules, α-amyrin with the strongest binding affinity towards the binding cavity of the enzyme was also confirmed via molecular dynamics simulation. This study showed that α-amyrin is a promising DNA-directed RNA polymerase inhibitor to treat monkeypox disease. It also paves the way for the idea of the potential dietary supplement candidate for monkeypox patients.Article Evaluation of Sub-Network Search Programs in Epilepsy-Related GWAS Dataset(Pamukkale Univ, 2022) Adanur Dedeturk, Beyhan; Bakir Gungor, Burcu; Dedeturk, Beyhan Adanur; Gungor, Burcu BakirThe active sub-network detection aims to find a group of interconnected genes of disease-related genes in a protein-protein interaction network. In recent years, several algorithms have been developed for this problem. In this study, the analysis of disease-specific sub-network identification programs is evaluated using epilepsy data set. Under the same conditions and with the same data set, 9 different programs are run and results of their Greedy algorithm, Genetic algorithm, Simulated Annealing Algorithm, MCC (Maximal Clique Centrality) algorithm, MCODE (Molecular Complex Detection) algorithm, and PEWCC (Protein Complex Detection using Weighted Clustering Coefficient) algorithm are shown. The top-scoring 5 modules of each program, are compared using fold enrichment analysis and normalized mutual information. Also, the identified subnetworks are functionally enriched using a hypergeometric test, and hence, disease-associated biological pathways are identified. In addition, running times and features of the programs are comparatively evaluated.