Yazılım Mühendisliği Bölümü Koleksiyonu
Permanent URI for this collectionhttps://acikerisim2.beykoz.edu.tr/handle/20.500.12879/72
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Article Alterations in Niban Gene Expression as a Response to Stress Conditions in 3t3-L1 Adipocytes(Springer, 2020) Cevik, Mehtap; Gunduz, Meliha Koldemir; Deliorman, Gokce; Susleyici, BelginAdipocyte death is important in obesity development. Understanding and prevention of adipocyte deaths may be a molecular approach in the treatment. In the study, we aimed to understand role of Niban gene, which acts as an anti-apoptotic molecule as a response to stress conditions, in adipocytes. 3T3-L1 adipocytes were treated with different doses of linoleic acid, hydrogen peroxide and ethanol; and proliferation of the cells examined with real time monitoring iCELLingence system. Gene expression levels were measured by q-PCR. As a response to 24h 480 mu M linoleic acid treatment, Niban gene expression was found to be higher than control group (p = 0.008), whereas 24 h 90 mM ethanol treatment was determined to be lower than control group (p = 0.008). The highest value of Niban gene expression among H2O2 treatment groups was detected in 4h 600 mu M H2O2 in comparison to control group (p = 0.008). To understand role of Niban in adipogenesis, Niban gene expressions were compared between pre-adipocytes and advanced fat accumulated adipocytes and determined to be significantly different (p = 0.042). Our results suggest that Niban might be involved in stress response process in adipocytes. However, the exact molecular role of Niban needs to be investigated in further studies.Article Binary Particle Swarm Optimization as a Detection Tool for Influential Subsets in Linear Regression(Taylor & Francis Ltd, 2021) Deliorman, G.; Inan, D.An influential observation is any point that has a huge effect on the coefficients of a regression line fitting the data. The presence of such observations in the data set reduces the sensitivity and validity of the statistical analysis. In the literature there are many methods used for identifying influential observations. However, many of those methods are highly influenced by masking and swamping effects and require distributional assumptions. Especially in the presence of influential subsets most of these methods are insufficient to detect these observations. This study aims to develop a new diagnostic tool for identifying influential observations using the meta-heuristic binary particle swarm optimization algorithm. This proposed approach does not require any distributional assumptions and also not affected by masking and swamping effects as the known methods. The performance of the proposed method is analyzed via simulations and real data set applications.
