WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
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Article Citation - WoS: 1Citation - Scopus: 1The Soft Nanodots as Fluorescent Probes for Cell Imaging: Analysis of Cell and Spheroid Penetration Behavior of Single Chain Polymer Dots(Wiley, 2024) Yıldız, Ümit Hakan; Arslan Yıldız, Ahu; Arslan Yıldız, Ahu; Yıldız, Ümit Hakan; 04.01. Department of Chemistry; 03.01. Department of Bioengineering; 03. Faculty of Engineering; 04. Faculty of Science; 01. Izmir Institute of TechnologyThis study describes the formation, size control, and penetration behavior of polymer nanodots (Pdots) consisting of single or few chain polythiophene-based conjugated polyelectrolytes (CPEs) via nanophase separation between good solvent and poor solvent of CPE. Though the chain singularity may be associated with dilution nanophase separation suggests that molecules of a good solvent create a thermodynamically driven solvation layer surrounding the CPEs and thereby separating the single chains even in their poor solvents. This statement is therefore corroborated with emission intensity/lifetime, particle size, and scattering intensity of polyelectrolyte in good and poor solvents. Regarding the augmented features, Pdots are implemented into cell imaging studies to understand the nuclear penetration and to differentiate the invasive characteristics of breast cancer cells. The python based red, green, blue (RGB) color analysis depicts that Pdots have more nuclear penetration ability in triple negative breast cancer cells due to the different nuclear morphology in shape and composition and Pdots have penetrated cell membrane as well as extracellular matrix in spheroid models. The current Pdot protocol and its utilization in cancer cell imaging are holding great promise for gene/drug delivery to target cancer cells by explicitly achieving the very first priority of nuclear intake. The penetration capability of cationic soft nanodots in to tumor models of breast cancer is demonstrated. The image analysis based on fluorescence intensity variation reveals the characteristics of translocation of nanodots in dense mediums such as tumor models.imageArticle Citation - WoS: 6Citation - Scopus: 7Time-Resolved Eeg Signal Analysis for Motor Imagery Activity Recognition(Elsevier, 2023) Olcay, Bilal Orkan; Karaçalı, Bilge; Karaçalı, Bilge; Olcay, Bilal Orkan; 01.01. Units Affiliated to the Rectorate; 03.05. Department of Electrical and Electronics Engineering; 01. Izmir Institute of Technology; 03. Faculty of EngineeringAccurately characterizing brain activity requires detailed feature analysis in the temporal, spatial, and spectral domains. While previous research has proposed various spatial and spectral feature extraction methods to distinguish between different cognitive tasks, temporal feature analysis for each separate brain region and frequency band has been largely overlooked. This study introduces two novel approaches for recognizing cognitive activity: temporal entropic profiling and time-aligned common spatio-spectral patterns analysis. These approaches capture and use discriminative short-lived signal segments for motor imagery activity recognition. In Approach-1, we evaluated nine different measures to determine timing parameters that showed altered behavior associated with maximal inter-activity differences, which we then used in a machine-learning framework. In Approach-2, we used the best-performing signal characteristic measures from Approach-1 to determine the optimum latency of each channel at each frequency band for a CSP-based activity recognition strategy. We evaluated both approaches on two online available motor imagery EEG datasets and achieved average recognition accuracy levels of 86%. We compared our methods with four established BCI methods. The performance results show that our approaches exceeded the benchmark methods' performances, with notable improvements in the proposed time-aligned common spatio-spectral patterns approach. This study demonstrates that motor imagery recognition performance is improved when a temporal analysis is adopted alongside spatio-spectral neural feature analysis and that timing parameters associated with the maximal entropic difference of EEG segments to the cognitive tasks varied between different brain regions and subjects. © 2023 Elsevier LtdArticle Citation - WoS: 6Citation - Scopus: 10Determination of Volume of Alaska Pollock (theragra Chalcogramma) by Image Analysis(Taylor and Francis Ltd., 2011) Balaban, Murat Ömer; Chombeau, Melanie; Gümüş, Bahar; Cırban, Dilşat; 01. Izmir Institute of TechnologyThe objective of this study was to develop two methods to predict the volume of whole Alaska pollock and to compare the results with the experimentally measured volumes. One hundred fifty-five whole pollock, obtained from a Kodiak processor, were individually immersed in a graduated cylinder equipped with an outflow tube to catch the displaced water as a result of immersion. The weight of the water was recorded. Then the fish were placed in a light box equipped with a digital video camera, and the side view and top view recorded (2 images for each fish). A reference square of known surface area was placed by the fish. A cubic spline method to predict volume by integration of cross-sectional area slices based on the top and side views and an empirical equation using dimensional (length L, width W, depth D) measurements at three locations of the fish image were developed. The R 2 value for the correlation between the L × W × D versus measured volume was 0.987. The best R 2 for the correlation of the predicted volume by the cubic spline method versus the measured volume was 0.99. Image analysis can be used reliably to predict the volume of whole Alaska pollock. © Taylor & Francis Group, LLC.