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Optimizing Ultrasonic-Assisted Extraction Process of Paralepista Flaccida: A Comparative Study of Antioxidant, Anticholinesterase, and Antiproliferative Activities via Response Surface Methodology and Artificial Neural Network Modeling
No full textIn this study, extraction conditions were optimized to maximize the biological activities of extracts obtained from Paralepista flaccida, an edible mushroom species. Extraction processes were carried out using an ultrasonically assisted system, and two different optimization approaches were used as follows: Response Surface Methodology (RSM) and Artificial Neural Network-Genetic Algorithm (ANN-GA). The antioxidant potentials of the optimized extracts were evaluated using DPPH, FRAP, TAS, TOS, and OSI parameters; anticholinesterase activities were measured against AChE and BChE enzymes; and antiproliferative activities were investigated in A549, MCF-7, and DU-145 human cancer cell lines. In addition, phenolic contents were determined by LC-MS/MS analysis. The findings revealed that the extracts obtained by the RSM method exhibited a superior biological profile compared to ANN-GA extracts in terms of antioxidant, anticholinesterase, and antiproliferative activities. The high cytotoxicity observed, particularly in the MCF-7 line, supports the anticancer potential of this extract. These results demonstrate that optimization strategies are crucial for increasing not only extract yield but also biological functionality
Analyzing Advertising Acceptance through the Diffusion of Innovations Theory: An Investigation into CGI Advertising
No full textAdoption is considered a critical element in marketing communication, as it reflects the extent to which consumers accept an advertisement and its influence on purchase intent and brand preference. Perceived usefulness, perceived value, advertisement content, visual design, and message clarity are key factors shaping how consumers adopt and evaluate an advertisement. This study examines advertising acceptance within the Diffusion of Innovations Theory framework, focusing on CGI (Computer-Generated Imagery) advertising, which is increasingly adopted in digital advertising. The study explores the adoption process of CGI advertisements by analyzing the perspectives of industry professionals regarding this innovation. A qualitative research approach was employed, utilizing in-depth interviews with 15 experienced professionals from the advertising industry. The collected data were analyzed to assess the diffusion process of CGI advertisements, their perceived advantages, and the challenges encountered in the sector. The findings reveal how the acceptance of CGI advertising is shaped within the Diffusion of Innovations Theory, contributing to the theoretical framework in this field. The findings offer strategic recommendations to enhance CGI advertising and highlight the key role of user perspectives on visual creativity, usability, and message clarity in its adoption
Non-Normative Childhood in Heteronormative Order
No full textThis study examines the childhood experiences of LGBT+ adults who were born and raised in Turkey and spent their childhood and adolescence in Turkey within the framework of family, friendship, education, life, and social spheres. The research group consists of 11 participants aged between 18 and 30, 10 of whom are still living in Turkey and 1 of whom moved abroad in adulthood. The participants were selected by snowball sampling method. The research was conducted based on a phenomenological design, which is a qualitative research method. One-to-one in-depth interviews were conducted with each participant. The findings reveal that the participants were exposed to gender norms in their family, friends, educational, and social environments and that this significantly affected their self-discovery processes from an early age. It was determined that the 'coming out' processes and social acceptance of the participants spread over a long period of time. The research highlights the urgent need for structural reforms to protect the rights of LGBT+ children and increase social acceptance
Computational Insights Into the Mechanism of Action of Bleomycin as Anticancer and Antibacterial Agent-Via Molecular Docking and Molecular Dynamics
No full textBleomycin (BLM) is the first-line clinical antibiotic used in the treatment of cancer. It inhibits DNA metabolism and is used in conjunction with other anticancer medications to treat various kinds of malignant tumors. This work focuses on examining more fully the bioactivity of BLM as both anticancer and antibacterial agents. Due to the structure-function relationship, the conformational study of the molecule was carried out first, and its potential conformations were identified. Afterwards, using the energy minimization feature of the YASARA structure program, the obtained lowest energy conformation of the molecule and the receptor taken from the protein databank (ligand-free) were optimized. BLM was subjected to molecular docking tests with two antibiotic-binding proteins (PDB IDs: 1ewj and 2zw7) to determine its action mechanism as a TN5 transposon inhibitor. Moreover, its binding affinities towards thymidylate kinase (TMK) (PDB ID: 4qgg) Escherichia coli DNA gyrase B (PDB ID: 6f86) were also evaluated to reveal its antibacterial potential. Additionally, ligand-receptor interactions were assessed via molecular dynamics (MD) process to confirm the stability of BLM docked into antibiotic binding protein (1ewj), TMK (4qgg) and E. coli DNA gyrase B (6f86) within 500 ns (for 1ewj and 6f86) or 200 ns of time (for 4qgg). Molecular mechanics/Poisson-Boltzmann Surface Area methods (MM/PBSA) were used to compute the binding energies through MD simulations. Dynamics cross correlation matrices (DCCM) analysis and principal component analysis (PCA) on the MD data were performed. Results have cleared the mechanism of action of BLM having anticancer and antibacterial properties.Bilimsel Arastirma Projeleri Birimi, Istanbul Universites
A Multi-Pretraining U-Net Architecture for Semantic Segmentation
No full textPathological cancer research relies heavily on different domain-specific applications including nucleus segmentation from histopathology images. Nucleus segmentation is one of the most challenging tasks because of the many hurdles involved such as masking operations, inaccurate and erroneous annotations, unclear boundaries, poor colours, and overlapping cells. New developments in the deep learning field contributed to the development of new application domains and this has made segmenting nuclei possible. In this research, we propose and evaluate a modified version of a deep learning algorithm called U-Net architecture for partitioning histopathological images. Particularly, we present a novel non-sequential multi-pretraining U-Net architecture and demonstrate that employing a number of persistent parallel models can boost the effectiveness of the segmentation procedures. The proposed approach makes advantage of data augmentation to generate newly synthesized images, which are subsequently processed using a watershed mask. For the validation of the proposed model, we used data from 21,000 cell nuclei at a resolution of 1000 by 1000 pixels. Experimental results demonstrate that the suggested architecture successfully segments nuclei with minimal loss in accuracy
Phase Equilibrium of n-Nonane plus n-Decane for Low-Temperature Thermal Energy Storage: Insights into Odd-Even Effects
No full textThe present article presents the solid–liquid phase diagram of a binary system composed of an n-alkane with an odd number of carbon atoms, namely, n-nonane (n-C9), with an even-numbered one, namely n-decane (n-C10). This work is part of a series of phase equilibrium studies on n-alkanes for low-temperature thermal energy storage (TES) applications. The ultimate purpose of this work is to investigate the adequacy of this binary system to be used as a Phase Change Material (PCM) at low temperatures. Additionally, along the equilibrium studies on linear alkanes, an interesting feature has emerged: the solid–liquid phase diagrams of binary n-alkane systems apparently show a striking dependence on the odd or even number of carbon atoms in their chains. The phase diagram for the system n-C9 + n-C10 has primarily been obtained using Differential Scanning Calorimetry (DSC), whose results have been complemented by Hot-Stage Microscopy (HSM) and low-temperature Raman Spectroscopy results. The DSC analysis provided both the temperature and enthalpy values for the observed solid–liquid and solid–solid phase transitions. The n-C9 + n-C10 binary system seems to display a peritectic solid–liquid phase diagram at low temperatures. The peritectic temperature found was 222.41 K, with a peritectic composition around xnonane = 0.60. Those results confirmed the initial suggestions that this would be a peritectic system, based on previously observed odd–even effects on phase equilibrium behavior of alkane mixtures. The goal of this work is to extend new insights into the solid–liquid phase equilibrium behavior of the binary system n-C9 + n-C10, a topic not yet covered in the literature. This information, consequently, provides practical and essential information on the potential use of this system as PCM for low-temperature TES applications. Additionally, it contributes to support the important discussion on the effect of odd–even number of carbons of the individual n-alkanes in the solid–liquid phase equilibrium behavior of their binary mixtures. The solid–liquid diagram of this system is being published for the first time, as far as the authors are aware. © The Author(s) 2025.FCT|FCCN (b-on)
Performance of Rapid Seismic Safety Assessment Methods on Estimation of Seismic Vulnerability of Relatively New RC Buildings Affected by the February 2023 Türkiye Earthquake Sequence
No full textThis study evaluates the performance of five rapid seismic safety assessment methods for relatively new reinforced concrete (RC) buildings affected by February 2023 T & uuml;rkiye earthquakes. Methods demonstrated reasonable performance in grading seismic vulnerabilities, whereas approaches incorporating deformation-based criteria, structural characteristics of newer buildings and seismic demand exhibited better accuracy. Methods, originally calibrated for older buildings and moderate earthquakes, are prone to under- or over-estimations of vulnerabilities. This underscores the need for refinements of methods which do not consider the characteristics of newer buildings and strong ground motions beyond the design level of earthquakes.Istanbul Kultur University
Istanbul Technical Universit
Infrared Spectrum and UV-Triggered Transformations of Matrix-Isolated Meta-Fluorothiophenol Supported by Ground and Excited State Theoretical Calculations
No full textThe infrared (IR) spectrum of meta-fluorothiophenol (mFTP) isolated in a low-temperature N-2 matrix was recorded and interpreted with the aid of B3LYP vibrational frequency calculations for both cis and trans conformers. Then, photochemical transformations in the matrix-isolated compound were triggered through UV-Vis laser irradiations and their outcomes were monitored by IR spectroscopy. Upon excitation at lambda = 285 nm, thiol-to-thione phototautomerization was identified as the sole reaction pathway, leading to the formation of three thione isomers. Among them, the ortho-isomer where the hydrogen atom reattaches to the fluorine-substituted side of the aromatic ring was identified as the predominant photoproduct. Identification of the photoproducts was confirmed by comparing the emerging experimental spectra with the IR absorptions predicted for the candidate structures. The photoreaction was found to be reversible, as irradiation at lambda = 405 nm partially restored the reactant. The experimental results were complemented with the application of multireference/multiconfigurational (CASSCF, CASPT2, MR-CIS) and TD-DFT (TD-M062X, omega B97XD, and tau-HCTHhyb) methods to investigate the excited state properties of mFTP, including the simulation of its UV photoabsorption spectra. A comparative analysis of the results obtained by the different methods was performed. This combined experimental and theoretical approach provided valuable insights into the photochemical behavior and electronic structure of fluorinated thiophenols.Fundacao para a Ciencia e a Tecnologia (FCT
Deep Ensemble Learning and Explainable AI for Multi-Class Classification of Earthstar Fungal Species
No full textThe current study presents a multi-class, image-based classification of eight morphologically similar macroscopic Earthstar fungal species (Astraeus hygrometricus, Geastrum coronatum, G. elegans, G. fimbriatum, G. quadrifidum, G. rufescens, G. triplex, and Myriostoma coliforme) using deep learning and explainable artificial intelligence (XAI) techniques. For the first time in the literature, these species are evaluated together, providing a highly challenging dataset due to significant visual overlap. Eight different convolutional neural network (CNN) and transformer-based architectures were employed, including EfficientNetV2-M, DenseNet121, MaxViT-S, DeiT, RegNetY-8GF, MobileNetV3, EfficientNet-B3, and MnasNet. The accuracy scores of these models ranged from 86.16% to 96.23%, with EfficientNet-B3 achieving the best individual performance. To enhance interpretability, Grad-CAM and Score-CAM methods were utilised to visualise the rationale behind each classification decision. A key novelty of this study is the design of two hybrid ensemble models: EfficientNet-B3 + DeiT and DenseNet121 + MaxViT-S. These ensembles further improved classification stability, reaching 93.71% and 93.08% accuracy, respectively. Based on metric-based evaluation, the EfficientNet-B3 + DeiT model delivered the most balanced performance, with 93.83% precision, 93.72% recall, 93.73% F1-score, 99.10% specificity, a log loss of 0.2292, and an MCC of 0.9282. Moreover, this modeling approach holds potential for monitoring symbiotic fungal species in agricultural ecosystems and supporting sustainable production strategies. This research contributes to the literature by introducing a novel framework that simultaneously emphasises classification accuracy and model interpretability in fungal taxonomy. The proposed method successfully classified morphologically similar puffball species with high accuracy, while explainable AI techniques revealed biologically meaningful insights. All evaluation metrics were computed exclusively on a 10% independent test set that was entirely separate from the training and validation phases. Future work will focus on expanding the dataset with samples from diverse ecological regions and testing the method under field conditions.TUBITA
Erken Çocukluk Döneminde Sanat: Deneyimler ve Etkiler
No full textArt education, which contributes to a wide range of activities from supporting children's fine motor development to increasing aesthetic sensitivity, is a basic learning area that supports children's creativity and strengthens their sensory and cognitive development in the preschool period. The purpose of the research is to create a roadmap for educators engaged in art activities with children from an early age on how these activities can be conducted more effectively for the benefit of children. The study uses a qualitative design, employing phenomenology. Data was collected through in-depth interviews with two participants engaged in art activities with children, using a semi-structured interview form developed by the researcher and refined based on expert opinions. Descriptive analysis was conducted, and codes and themes were generated from the results. The findings were presented under the themes of: (1) the definition and function of art, (2) the implementation process with children, (3) its contribution to the children's development and learning, (4) evaluation process, and (5) recommendations for practitioners. According to the results obtained from the research, it is emphasized that artistic activities in early childhood should be planned according to the interests, needs, and desires of children