Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Novel Strut-Based Mechanical Analysis: Flow Stress Determination of Electron Beam Melt (EBM) Lattice Structures(Springernature, 2025) Bin Riaz, Muhammad Arslan; Erten, Hacer Irem; Guden, MustafaIn modeling lattices, the material flow stress equation, such as the Johnson and Cook (JC) equation, is usually determined from the mechanical tests conducted on bulk, relatively large test size specimens which were manufactured using the same process parameters with the lattices. However, the flow stresses of struts were shown in several studies to be significantly lower than those of large size test specimens. To overcome this discrepancy, a novel approach that combined the strut compression test, the strut double shear test (DST) and the numerical model of the strut DST using the JC equation was proposed. The study confirmed that the flow stress determined from the machined bulk tension test specimens overestimated the experimental compression stress-strain behavior of a body centered cubic (BCC) Ti6Al4V lattice. The flow stress parameters determined from the compression stress-strain curves of the as-printed strut specimens, on the other side, showed the best match to the experimental compression stress-strain behavior of the BCC lattice. The fidelity of the determined parameters of the JC equation was further verified with the experimental and numerical DSTs. It was also shown that the numerical iterations of DST model could be used for the fine-tuning the flow stress parameters.Article Meal Timing Trajectories in Older Adults and Their Associations With Morbidity, Genetic Profiles, and Mortality(Springernature, 2025) Dashti, Hassan S.; Liu, Chloe; Deng, Hao; Sharma, Anushka; Payton, Antony; Maharani, Asri; Didikoglu, AltugBackgroundOlder adults are vulnerable to mistimed food intake due to health and environmental changes; characterizing meal timing may inform strategies to promote healthy aging. We investigated longitudinal trajectories of self-reported meal timing in older adults and their associations with morbidity, genetic profiles, and all-cause mortality.MethodsWe analyzed data from 2945 community-dwelling older adults from the University of Manchester Longitudinal Study of Cognition in Normal Healthy Old Age, with up to five repeated assessments of meal timing and health behaviors conducted between 1983 and 2017. Linear mixed-effects models, latent class analysis, and Cox regression were used to examine relationships between meal timing with illness and behavioral factors, genetic scores for chronotype and obesity, and mortality.ResultsHere we show older age is associated with later breakfast and dinner times, a later eating midpoint, and a shorter daily eating window. Physical and psychological illnesses, including fatigue, oral health problems, depression, anxiety, and multimorbidity, are primarily associated with later breakfast. Genetic profiles related to an evening chronotype, but not obesity, are linked to later meals. Later breakfast timing is also associated with increased mortality. Latent class analysis of meal timing trajectories identify early and late eating groups, with 10-year survival rates of 86.7% in the late eating group compared to 89.5% in the early eating group.ConclusionsMeal timing, particularly later breakfast, shifts with age and may reflect broader health changes in older adults, with implications for morbidity and longevity.Article Citation - WoS: 1Tcgex: a Powerful Visual Interface for Exploring and Analyzing Cancer Gene Expression Data(Springernature, 2025) Kus, M. Emre; Sahin, Cagatay; Kilic, Emre; Askin, Arda; Ozgur, M. Mert; Karahanogullari, Gokhan; Ekiz, H. AtakanAnalyzing gene expression data from the Cancer Genome Atlas (TCGA) and similar repositories often requires advanced coding skills, creating a barrier for many researchers. To address this challenge, we developed The Cancer Genome Explorer (TCGEx), a user-friendly, web-based platform for conducting sophisticated analyses such as survival modeling, gene set enrichment analysis, unsupervised clustering, and linear regression-based machine learning. TCGEx provides access to preprocessed TCGA data and immune checkpoint inhibition studies while allowing integration of user-uploaded data sets. Using TCGEx, we explore molecular subsets of human melanoma and identify microRNAs associated with intratumoral immunity. These findings are validated with independent clinical trial data on immune checkpoint inhibitors for melanoma and other cancers. In addition, we identify cytokine genes that can be used to predict treatment responses to various immune checkpoint inhibitors prior to treatment. Built on the R/Shiny framework, TCGEx offers customizable features to adapt analyses for diverse research contexts and generate publication-ready visualizations. TCGEx is freely available at https://tcgex.iyte.edu.tr, providing an accessible tool to extract insights from cancer transcriptomics data.