IYTE GCRIS Database (Izmir Institute of Technology)
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Cx32 Cellular Localization Is Related To Epithelial To Mesenchymal Transition in Breast Cells
No full textConnexins (Cx) play both gap junction-related and -independent roles in cells, and their localization is essential for their function in cellular processes. Besides membrane localization, connexins can also be localized to the cytoplasm and nucleus, especially in cancer cells. The differential localization of connexins including Cx32 was observed in different stages of cancers. Cx32 was upregulated and observed in cytoplasms of cells in lymph-node metastasis of breast cancer samples compared to primary tumors. However, the significance of the increase in Cx32 expression and alteration of Cx32 cellular localization in epithelial-to-mesenchymal transition (EMT) is not known. To determine if Cx32 overexpression and/or localization over one week would induce the EMT process, we first examined the cellular localization of Cx32 in MCF10A and MDA-MB-231 cells at different time points using Western blot and RT-PCR as well as immunostaining with confocal microscopy. Then, we correlated the changes of Cx32 expression and localization with EMT marker expression. We showed that Cx32 had altered cellular localization and Cx32 overexpression increased Slug levels while it reduced E-cadherin and Snail expression in MDA-MB-231 for 7 days. In contrast, E-cadherin and Vimentin were reduced in MCF10A-Cx32 cells compared with controls over 7 days, and the expression pattern for nuclear Cx32 and Zeb2 was following similar pattern in MCF10A cells. Our results suggest a previously unknown time-dependent relation between Cx32 and the regulation of the EMT process
New Buildings in Historic Settings: Revisiting Renzo Piano's Design Approach
No full textContemporary design in historic settings is a controversial issue where it impacts on an existing historic context. Conservation charters and scholarly literature on architectural designs within the historic environment provide preliminary insights and guidance that necessitate further elaboration and development. The aim of this study is to generate a more detailed and clearer insight into design strategies that inform the design of new buildings in historic locations through the work of the architect Renzo Piano. From the Centre Pompidou to his most recently built ; Idot;stanbul Modern, he has seven new designs in the cities of Paris, Valetta, Athens, Beirut, and ; Idot;stanbul. These buildings, six of which are in the settings of UNESCO World Heritage Sites, the seventh within the historic site of Beirut, are described individually to trace the architect's design approach. Seven categories have been identified, from the use of an existing square to the scale, form, view creation, transparency preferences, opening designs, and colour choices revealing strategies have been found compatible with their historic settings. By listing them, seven core principles are proposed as policy guidelines
Vision Transformers-Based Deep Feature Generation Framework for Hydatid Cyst Classification in Computed Tomography Images
No full textHydatid cysts, caused by Echinococcus granulosus, form progressively enlarging fluid-filled cysts in organs like the liver and lungs, posing significant public health risks through severe complications or death. This study presents a novel deep feature generation framework utilizing vision transformer models (ViT-DFG) to enhance the classification accuracy of hydatid cyst types. The proposed framework consists of four phases: image preprocessing, feature extraction using vision transformer models, feature selection through iterative neighborhood component analysis, and classification, where the performance of the ViT-DFG model was evaluated and compared across different classifiers such as k-nearest neighbor and multi-layer perceptron (MLP). Both methods were evaluated independently to assess classification performance from different approaches. The dataset, comprising five cyst types, was analyzed for both five-class and three-class classification by grouping the cyst types into active, transition, and inactive categories. Experimental results showed that the proposed VIT-DFG method achieves higher accuracy than existing methods. Specifically, the ViT-DFG framework attained an overall classification accuracy of 98.10% for the three-class and 95.12% for the five-class classifications using 5-fold cross-validation. Statistical analysis through one-way analysis of variance (ANOVA), conducted to evaluate significant differences between models, confirmed significant differences between the proposed framework and individual vision transformer models (p0.05\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}\end{document}). These results highlight the effectiveness of combining multiple vision transformer architectures with advanced feature selection techniques in improving classification performance. The findings underscore the ViT-DFG framework's potential to advance medical image analysis, particularly in hydatid cyst classification, while offering clinical promise through automated diagnostics and improved decision-making
Quantifying Hydrogen Bonding Using Electrically Tunable Nanoconfined Water
No full textHydrogen bonding plays a crucial role in biology and technology, yet it remains poorly understood and quantified despite its fundamental importance. Traditional models, which describe hydrogen bonds as electrostatic interactions between electropositive hydrogen and electronegative acceptors, fail to quantitatively capture bond strength, directionality, or cooperativity, and cannot predict the properties of complex hydrogen-bonded materials. Here, we introduce a concept of hydrogen bonds as elastic dipoles in an electric field, which captures a wide range of hydrogen bonding phenomena in various water systems. Using gypsum, a hydrogen bond heterostructure with two-dimensional structural crystalline water, we calibrate the hydrogen bond strength through an externally applied electric field. We show that our approach quantifies the strength of hydrogen bonds directly from spectroscopic measurements and reproduces a wide range of key properties of confined water reported in the literature. Using only the stretching vibration frequency of confined water, we can predict hydrogen bond strength, local electric field, O-H bond length, and dipole moment. Our work also introduces hydrogen bond heterostructures - a class of electrically and chemically tunable materials that offer stronger, more directional bonding compared to van der Waals heterostructures, with potential applications in areas such as catalysis, separation, and energy storage
Investigating the Behavior of D-Glucose, D-Fructose, and D-Allulose in Aqueous Media by Molecular Dynamics Simulations
No full textIsomeric monosaccharides may have different hydration behaviors, leading to distinct physicochemical properties in solutions. In this work, the aqueous behavior, structure, and hydration properties of D-allulose, Dglucose, and D-fructose were investigated as a function of concentration by molecular dynamics simulations. This is the first computational study investigating D-allulose compared to its two isomers. The dynamics were analyzed through self-diffusion coefficients; hydration was characterized by hydrogen bond (HB) analyses. Radial distribution functions were used to probe water structuring around sugar oxygens. Results show the hydration number and the fraction of bound water in solution were the highest for glucose, followed by fructose and allulose. The C3 epimerization of fructose into allulose highly promotes the allulose pyranoses to form intramolecular HBs, significantly limiting their water-holding capacity. This may possibly explain the favorability of furanose forms over pyranose forms in aqueous allulose solutions, opposing glucose and fructose in solution
Emerging Trends of Biohydrogen Ecosystem on Environmental Sustainability: a Case Study
No full textThe greatest threat to humanity is now considered climate change. Biomass as a renewable energy source is treated as one of the clean energy sources that help meet humanity's energy needs. In the transition to a new energy system based on renewable energies, biomass can be crucial. This paper particularly focuses on a new biohydrogen (bioH2) ecosystem development concept for communities to provide global and local sustainable and green energy, considering the biomass-to-bioenergy nexus. In this regard, the paper further discusses the different bioH2 ecosystem concepts and emerging trends where biomass and renewable resources are utilized for energy production. In addition, the bioenergy production potentials of different agricultural crop wastes are evaluated for different end-use purposes like electricity, heat, cogeneration, and transport. In parallel to its high bioenergy yield, the highest total energy (83,686.8 GJ) and gross electricity (4686.5 MWh) production values were observed for the olive cake waste. Moreover, the biomethane and bioethanol production potentials of the crop wastes are evaluated. The highest biomethane yield of 253.7 m3/ha with a total bioenergy production of 40,662.6 GJ was obtained for the maize stover waste, while its bioethanol production was 505.7 L/ha. Consequently, the bioH2 ecosystem with biomass utilization reveales as a sustainable and green way of providing future energy for communities owing to the great potential of crop wastes for bioenergy production
Polycentricity and Regional Economic Resilience: a Ridge Regression Approach
No full textResilience and "polycentricity" have surged as popular concepts over the recent decades, although the link between the two has not yet been investigated empirically. Identification of this relationship and its theoretical justification are politically crucial to shed light on prospective policies for urbanization and regionalization. Thus, the aim of this study is to investigate the impact of polycentricity/monocentricity on the regional resilience of Turkish (Nuts-2) regions against the global financial crisis in 2008/09. This paper also identifies the channels through which it can influence resilience. Through the application of a rich set of empirical tools, including computation of monocentricity degree, resistance, recovery, and adaptability indexes based on national and regional business cycle turning points, LOESS, RIDGE regressions, and inferential mediation tests, three main conclusions were obtained. First, polycentric regions were evidently more resistant to the crisis compared to monocentric morphologies; the later were more industrialized and open to trade, which made them more vulnerable to the crisis. Second, polycentric spatial structures were found to recover more quickly compared to monocentric regions. Third, monocentric regions clearly adapt better to long-term trajectories. In sum, the wellknown strategy of the European Union rooted in "polycentric development" can still be valid for the purposes such as resisting to and recovering from economic disruptions. However, in the long-run, polycentrilization can hardly be seen as an optimal strategy, particularly in the context of adapting to the future trajectories
A Novel Framework for Droplet/Particle Size Distribution in Suspension Polymerization Using Physics-Informed Neural Network (PINN)
No full textA Machine Learning (ML) based neural network can capture the complex evolution of polymer chain distributions, accounting for factors such as initiation, propagation, and termination steps in a suspension polymerization process, by integrating stagewise molar balance model (MBM) and population balance model (PBM) with Physics-Informed Neural Network (PINN). The integrated PINN framework is proposed to efficiently solve these equations, incorporating known physical laws as constraints and minimizing errors in both the distribution and dynamics of the polymer chains. By optimizing the neural network parameters such as weight matrices and bias vector, the model reproduces the moments of the polymer molecular weight distribution in close alignment with numerical solutions, and it generates population balance solutions that exhibit excellent agreement with their analytical counterparts. Sensitivity analyses for the depth of the neural network architecture to quantify how structural choices affect model fidelity has been performed. The resulting MBM-PINN and PBM-PINN integrated framework demonstrates robustness and versatility in accurately capturing (96-97%) droplet/particle dynamics. The proposed methodology has the capability to provide a powerful tool for faster and scalable simulations of polymerization reactions, enabling better prediction of product properties which could be used for optimizing reaction conditions in industrial applications
Nanostructured Ox-MWCNT-Ppy-Au Electrochemical Sensor for Ultralow Detection of Retrorsine and Evaluation of Its Cytotoxic Effects on Liver Cells
No full textThis study presents the development of a novel retrorsine (RTS)-imprinted sensor utilizing oxidized multi-walled carbon nanotubes (Ox-MWCNTs), polypyrrole (PPy), and gold nanoparticles (AuNPs), employing square wave voltammetry for the sensitive and selective detection of RTS which causes oxidative-stress and DNA damage. The fabricated Ox-MWCNT-PPy-AuNP sensor demonstrated a surface-area of (0.218 cm2) is 4.25 times larger than a bare glassy carbon electrode, with a low charge transfer resistance (10.9 Omega), enhancing electron transfer kinetics. The sensor showed excellent sensitivity in detecting retrorsine, with a limit of detection of 0.035 nM in synthetic matrices and -0.030 nM in HepaRG cell culture medium. Toxicity assays in HepaRG cells revealed dose-dependent oxidative-stress, with glutathione levels decreasing from 23.08 +/- 0.21 mu mol/109 to 21.21 +/- 0.02 mu mol/109 at 35 mu M retrorsine. Concurrently, GSSG levels increased from 1.32 +/- 0.26 mu mol/109 to 2.22 +/- 0.02 mu mol/109. DNA-damage assessed via comet assay, showed significant increases in tail-moment (2.53 mu m) and tail-migration (16.13 mu m). Oxidative DNA-damage, indicated by 8-OHdG levels, increased significantly from 0.29 +/- 0.02 ng.mL- (control) to 0.47 +/- 0.07 ng.mL- at 35 mu M retrorsine. These findings demonstrate the sensor's effectiveness for retrorsine detection and its applicability in toxicological studies. The integration of nanomaterial engineering and molecular imprinting provides a highly sensitive, selective, and eco-friendly solution for monitoring toxic agents and assessing their biological impacts