Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Citation - WoS: 3Citation - Scopus: 3Stress Regulation via Being in Nature and Social Support in Adults, a Meta-Analysis(Univ California Press, 2023) Sparacio, Alessandro; Ropovik, Ivan; Jiga-Boy, Gabriela; Lagap, Adar Cem; IJzerman, HansIn this meta-analysis, the authors investigated whether being in nature and emotional social support are reliable strategies to downregulate stress. We retrieved all the relevant articles that investigated a connection between one of these two strategies and stress. For being in nature we found 54 effects reported in 16 papers (total N = 1,697, MdnN = 52.5), while for emotional social support we found 18 effects reported in 13 papers (total N = 3,787, MdnN = 186). Although we initially found an effect for being in nature and emotional social support on stress (Hedges' g =-.42; Hedges' g =-.14, respectively), the effect only held for being in nature after applying our main publication bias correction technique (Hedges' g =-.60). The emotional social support literature also had a high risk of bias. Although the being-in-nature literature was moderately powered (.72) to detect effects of Cohen's d = .50 or larger, the risk of bias was considerable, and the reporting contained numerous statistical reporting errors.Conference Object Population Specific Classification of Colorectal Cancer With Meta-Analysis of Metagenomic Data(Institute of Electrical and Electronics Engineers Inc., 2023-10-11) Temiz, Mustafa; Yousef, Malik; Bakir-Güngör, BurcuAdvances in next-generation sequencing and '-omics' technologies makes it possible to characterize the human gut microbiome. While some of these microorganisms are important regulators of our immune system, modulation of the microbiota leads to a variety of diseases. Colorectal cancer (CRC), the third most common cancer worldwide, is caused by genetic mutations, environmental conditions, and abnormalities in the gut microbiota. Using various machine learning methods and meta-analysis techniques, this study aims to build a classification model that can help in CRC diagnosis by analyzing metagenomic datasets of different populations obtained at the species level. Using 8 different countries and 9 different metagenomic datasets, 3 different meta-analyzes are performed: within-population, cross-population, and one population is selected for testing and the rest is used as a training dataset (LODO). For CRC classification, 4 different classification algorithms (Random Forest (RF), Logitboost, Adaboost, and Decision Tree (DT)) are used. The best performance among these methods was obtained with the Random Forest algorithm with an AUC of 0.98 by using JP for the training data set and JPN populations for the test data set in the cross-population performance evaluation. © 2023 Elsevier B.V., All rights reserved.