Molecular Biology and Genetics / Moleküler Biyoloji ve Genetik
Permanent URI for this collectionhttps://hdl.handle.net/11147/9
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Book Part Citation - Scopus: 1Automated Analysis of Phase-Contrast Optical Microscopy Time-Lapse Images: Application To Wound Healing and Cell Motility Assays of Breast Cancer(Elsevier, 2023) Erdem, Yusuf Sait; Ayanzadeh, Aydın; Mayalı, Berkay; Balıkçı, Muhammed; Belli, Özge Nur; Uçar, Mahmut; Yalçın Özuysal, Özden; Pesen Okvur, Devrim; Önal, Sevgi; Morani, Kenan; Iheme, Leonardo Obinna; Töreyin, Behçet UğurThis chapter describes a workflow for analyzing phase-contrast microscopy (PCM) data from two fundamental types of biomedical assays: assays for cell motility and assays for wound healing. The workflow of the analysis is composed of the methods for acquiring, restoring, segmenting, and quantifying biomedical data. In the literature, there have been separate methods aimed at specific stages of PCM data analysis. Nonetheless, there has never been a complete workflow for all stages of analysis. This work is an innovation that proposes an end-to-end workflow for image pre-processing, deep learning segmentation, tracking, and quantification stages in cell motility and wound healing assay analyses. The findings indicate that domain knowledge can be used to make simple but significant improvements to the results of cutting-edge methods. Furthermore, even for deep learning-based methods, pre-processing is clearly a necessary step in the workflow. © 2023 Elsevier Inc. All rights reserved.Article Assessment of Undergraduate Health Students' Perception and Satisfaction on Training and Participation in Community Health Outreach(Springer, 2023) Adegbore, Abidemi Kafayat; Adedokun, Amudatu Ambali; Adegoke, Juliet Ifeoluwa; Lawal, Maruf Ayobami; Oke, MuseAimThe need to improve training of health professionals has increased in recent years due to increasing frequencies of public health events. Consequently, a descriptive cross-sectional survey was carried out to determine the level of satisfaction and knowledge acquired by undergraduate students in the health sciences during a community health outreach program.Subject and methodsStudents were invited to complete an online-administered questionnaire (consisting of both open- and closed-ended questions) to assess their perceptions and experiences on the community health outreach program. Additionally, the survey was carried out to assess the quality of training provided and obtain suggestions for further improvements. Responses were collected and analysed using Microsoft Excel.ResultsMost respondents (>83%) reported satisfaction with the community diagnosis and community intervention briefing and training sessions. All respondents reported familiarity with standard community health outreach instruments and were capable of identifying environmental health risk factors that may contribute to the spread of communicable diseases. Interestingly, respondents reported greater appreciation of health challenges faced by rural communities. However, respondents expressed dissatisfaction with the duration of the outreach program (24%) and funding (15%).ConclusionAlthough respondents reported overall satisfaction with the organization and execution of the health outreach program, certain aspects of the program were deemed unsatisfactory. Despite the shortcomings, we believe that our student-centred learning strategy is readily adaptable for training future healthcare professionals and improving health literacy of rural communities, particularly in sub-Saharan Africa.Conference Object Citation - WoS: 3Citation - Scopus: 4Deep Learning Based Segmentation Pipeline for Label-Free Phase-Contrast Microscopy Images(IEEE, 2020) Ayanzadeh, Aydın; Yalçın Özuysal, Özden; Okvur, Devrim Pesen; Önal, Sevgi; Töreyin, Behçet Uğur; Ünay, DevrimThe segmentation of cells is necessary for biologists in the morphological statistics for quantitative and qualitative analysis in Phase-contrast Microscopy (PCM) images. In this paper, we address the cell segmentation problem in PCM images. Deep Neural Networks (DNNs) commonly is initialized with weights from a network pre-trained on a large annotated data set like ImageNet have superior performance than those trained from scratch on a small dataset. Here, we demonstrate how encoder-decoder type architectures such as U-Net and Feature Pyramid Network (FPN) can be improved by an alternative encoder which pre-trained on the ImageNet dataset. In particular, our experimental results confirm that the image descriptors from ResNet-18 are highly effective in accurate prediction of the cell boundary and have higher Intersection over Union (IoU) in comparison to the classical U-Net and require fewer training epochs.Conference Object Citation - Scopus: 2Yara İyileşmesi Mikroskopi Görüntü Serilerinin Otomatik Analizi - Bir Ön-çalışma(IEEE, 2020) Mayalı, Berkay; Şaylığ, Orkun; Yalçın Özuysal, Özden; Pesen Okvur, Devrim; Töreyin, Behçet Uğur; Ünay, DevrimCollective cell analysis from microscopy image series is important for wound healing research. Computer-based automation of such analyses may help in rapid acquisition of reliable and reproducible results. In this study phase -contrast optical microscopy image series of an in-vitro wound healing essay is manually delineated by two experts and its analysis is realized, traditional image processing and deep learning based approaches for automated segmentation of wound area are developed and their perlOrmance comparisons are carried out.Conference Object Assessment of Cell Cycle and Viability of Magnetic Levitation Assembled Cellular Structures(IEEE, 2020) Anıl İnevi, Müge; Ünal, Yağmur Ceren; Yaman, Sena; Tekin, H. Cumhur; Meşe, Gülistan; Meşe, GülistanLabel-free magnetic levitation is one of the most recent Earth-based in vitro techniques that simulate the microgravity. This technique offers a great opportunity to biofabricate scaffold-free 3-dimensional (3D) structures and to study the effects of microgravity on these structures. In this study, self-assembled 3D living structures were fabricated in a paramagnetic medium by magnetic levitation technique and effects of the technique on cellular health was assessed. This magnetic force-assisted assembly system applied here offers broad applications in several fields, such as space biotechnology and bottom-up tissue engineering.Article Citation - WoS: 6Citation - Scopus: 3Determining Element Accumulations in Turkish Red Pine Used as a Bioindicator for Estimating of Existing Pollution on Both Sides of Bosphorus in İstanbul(Parlar Scientific Publications, 2020) Yalçın, İbrahim Ertuğrul; Özyiğit, İbrahim İlker; Doğan, İlhan; Demir, Göksel; Yarcı, CelalEvergreen Turkish red pine tree shows wide distribution around Aegean and Mediterranean regions in Turkey. Herein work investigated the mineral nutrient status of Turkish red pine as a bioindicator for revealing the impact of existing pollution on both sides of Bosphorus in Istanbul. For this, Al, B, Ca, Cu, Fe, K, Mg, Na and Zn concentrations were determined in unwashed and washed leaves and barks of the plant and soil samples. The standard procedures were applied and the determinations of element concentrations in all samples were done using ICP-OES. The sample collections were performed at five different locations in Istanbul, 4 from the Bosphorus region and one from Prince Island (as control). The highest element concentrations (in mg kg(-1) DW) in plant parts were recorded between 109.10 +/- 1.68 and 120.58 +/- 1.75 for Al, 10.18 +/- 0.14 and 12.52 +/- 0.17 for B, 8765.42 +/- 92.41 and 9600.69 +/- 102.22 for Ca, 10.91 +/- 0.13 and 11.73 +/- 0.16 for Cu, 226.85 +/- 3.01 and 254.07 +/- 3.20 for Fe, 4050.69 +/- 48.51 and 4477.08 +/- 52.34 for K, 794.58 +/- 9.82 and 878.33 +/- 10.07 for Mg, 1255.14 +/- 14.67 and 1374.31 +/- 18.55 for Na and 34.92 +/- 0.49 and 37.25 +/- 0.57 for Zn while the highest element concentrations (in mg kg(-1) DW) in co-located soil samples were measured between 5470.42 +/- 66.48 and 6046.25 +/- 73.54 for Al, 14.86 +/- 0.20 and 16.43 +/- 0.29 for B, 4600.56 +/- 55.22 and 4984.86 +/- 62.71 for Ca, 22.33 +/- 0.36 and 25.07 +/- 0.48 for Cu, 5500.01 +/- 71.05 and 5953.06 +/- 80.16 for Fe, 1819.44 +/- 23.51 and 2029.17 +/- 27.04 for K, 4108.75 +/- 50.77 and 4714.17 +/- 58.09 for Mg, 111.11 +/- 1.82 and 122.08 +/- 2.45 for Na and 117.10 +/- 2.33 and 126.86 +/- 2.61 for Zn.Conference Object Citation - WoS: 3Citation - Scopus: 8Distinguishing Between Microrna Targets From Diverse Species Using Sequence Motifs and K-Mers(SCITEPRESS, 2017) Yousef, Malik; Khalifa, Waleed; Acar, İlhan Erkin; Allmer, JensA disease phenotype is often due to dysregulation of gene expression. Post-translational regulation of protein abundance by microRNAs (miRNAs) is, therefore, of high importance in, for example, cancer studies. MicroRNAs provide a complementary sequence to their target messenger RNA (mRNA) as part of a complex molecular machinery. Known miRNAs and targets are listed in miRTarBase for a variety of organisms. The experimental detection of such pairs is convoluted and, therefore, their computational detection is desired which is complicated by missing negative data. For machine learning, many features for parameterization of the miRNA targets are available and k-mers and sequence motifs have previously been used. Unrelated organisms like intracellular pathogens and their hosts may communicate via miRNAs and, therefore, we investigated whether miRNA targets from one species can be differentiated from miRNA targets of another. To achieve this end, we employed target information of one species as positive and the other as negative training and testing data. Models of species with higher evolutionary distance generally achieved better results of up to 97% average accuracy (mouse versus Caenorhabditis elegans) while more closely related species did not lead to successful models (human versus mouse; 60%). In the future, when more targeting data becomes available, models can be established which will be able to more precisely determine miRNA targets in hostpathogen systems using this approach.Conference Object Citation - WoS: 7Citation - Scopus: 11Cell Segmentation of 2d Phase-Contrast Microscopy Images With Deep Learning Method(Institute of Electrical and Electronics Engineers Inc., 2019) Ayanzadeh, Aydın; Yağar, Hüseyin Onur; Yalçın Özuysal, Özden; Pesen Okvur, Devrim; Töreyin, Behçet Uğur; Unay, Devrim; Önal, SevgiThe quantitative and qualitative ascertainment of cell culture is integral to the robust determination of the cell structure analysis. Microscopy cell analysis and the epithet structures of cells in cell cultures are momentous in the fields of the biological research process. In this paper, we addressed the problem of phase-contrast microscopy under cell segmentation application. In our proposed method, we utilized the state-of-the-art deep learning models trained on our proposed dataset. Due to the low number of annotated images, we propose a multi-resolution network which is based on the U-Net architecture. Moreover, we applied multi-combination augmentation to our dataset which has increased the performance of segmentation accuracy significantly. Experimental results suggest that the proposed model provides superior performance in comparison to traditional state-of-the-art segmentation algorithms.Article Evaluation of Aluminum Stress Induced Antibacterial and Antifungal Activities in Roman Nettle(Parlar Scientific Publications, 2019) Özyiğit, İbrahim İlker; Doğan, İlhan; Yalçın, İbrahim Ertuğrul; Severoğlu, ZekiIn this current work, Roman nettle (Urtica pilulifera L.), a traditional medicinal plant that is very common and widespread species throughout Asia, Europe, and Northern Africa, was used as a model plant to investigate changes in antimicrobial activity following the application of aluminum stress. U pilulifera seedlings were grown in growth-room conditions and 0, 100, and 200 M AlCl3 were applied to the plants together with Hoagland solution (20 ml) for two months. The antimicrobial activities were tested against nine strains of bacteria (Salmonella sp., Staphyllococcus aureus, Escherichia coli, E. coli O157:H7 and Bacillus cereus) and fungus (Penicillum sp., Saccharomyces cerevisiae, Candida tropicans and C. albicans) by using the disc diffusion and agar well methods. The accumulated Al was measured by using ICP-OES in the leaves of studied plant samples. Additionally, a control group (water + 11.31 mg l(-1) Al) was prepared and applied to selected bacteria and fungi in order to understand the reason for obtained antimicrobial activities of Roman nettle is whether because of the compounds isolated from nettle leaves exposed to Al stress, or Al itself accumulated in leaves. The data proved that inhibitory antimicrobial effects were altered in U pilulifera upon the application of Al stress, especially on fungi species.Book Part Citation - Scopus: 9Differential Expression of Toxoplasma Gondii Micrornas in Murine and Human Hosts(Springer, 2016) Allmer, Jens; Saçar Demirci, Müşerref Duygu; Bağcı, CanerMicroRNAs are short RNA sequences involved in post-transcriptional gene regulation. MicroRNAs are known for a wide variety of species ranging from bacteria to plants. It has become clear that some cross-kingdom regulation is possible especially between viruses and their hosts. We hypothesized that intracellular parasites, like Toxoplasma gondii, similar to viruses would be able to modulate their host’s gene expression. We were able to show that T. gondii produces many putative pre-miRNAs which are actually transcribed. Furthermore, some of these expressed pre-miRNAs have a striking resemblance to host mature miRNAs. Previous studies indicated that T. gondii infection coincides with increased abundance of some miRNAs. Here we were able to show that many of these miRNAs have close relatives in T. gondii which may not be distinguishable using PCR. Taken together, the similarity to host miRNAs, their confirmed expression, and their upregulation during infection, it suggests that T. gondii actively transfers miRNAs to regulate its host. We conclude, that this type of cross-kingdom regulation may be possible, but that targeted analysis is necessary to consolidate our computational findings. © Springer International Publishing Switzerland 2016. All rights are reserved.