Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Conference Object Citation - Scopus: 9Production of Concrete Compatible Biogranules for Self-Healing Concrete Applications(EDP Sciences, 2019) Sonmez, Merve; Erşan, Yusuf ÇaǧatayRecently, cost-efficient nitrate reducing biogranules were suggested as an alternative to axenic microbial cultures for development of microbial self-healing concrete. In a marine environment, biogranule containing microbial self-healing concrete showed simultaneous self-healing of cracks and immunisation against rebar corrosion. Yet, information about the production strategy of these biogranules and their compatibility with a mortar matrix is limited. This study presents the production of biogranules and their compatibility with mortar specimens when incorporated at dosages between 0.36% to 4.30% w/w cement (0.25% to 3% of bacteria w/w cement). In-house produced biogranules composed of 70% bacteria and 30% of minerals w/w of biogranule were used for the compatibility tests. In test mortars, calcium formate (CF) and calcium nitrate (CN) were used as regular nutrient admixtures, and nutrient content was set identical in every batch. Up to 2.9% incorporation, biogranules had no significant influence on the fresh properties of mortar. More than 2.9% incorporation caused poor workability and a 26% decrease in 3-Day compressive strength of biomortar specimens. Overall, the biogranules produced are compatible with a cementitious matrix up to 2.9% w/w cement, and even up to 3.6% if early age strength is not essential, which makes biogranules one of the most compatible microbial healing agents among the suggested agents in the literature. © 2021 Elsevier B.V., All rights reserved.Conference Object Citation - WoS: 5Citation - Scopus: 6Optimizing Nutrient Content of Microbial Self-Healing Concrete(CRC Press-Taylor & Francis Group, 2019) Ersan, Y. C.; Akin, Y.Cracks in microbial self-healing concrete are autonomously sealed through microbial induced calcium carbonate precipitation (MICP). The biogenic production of dissolved inorganic carbon (i.e. CO2) is the main drive for MICP and it is limited by the bioavailability of the nutrients. When added as admixtures bioavailability of the nutrients becomes even more significant for crack sealing as they disperse in mortar and a considerable portion stays far from an individual crack. Therefore, determination of the nutrient bioavailability and optimization of the nutrient content is necessary to enhance self-healing performance of bioconcrete. This study defines an optimum nutrient content range for nitrate reduction based microbial self-healing concrete. Ca-formate and Ca-nitrate were used as nutrient admixtures and their wt/wt ratio was kept constant at 2.50: 1.00 while testing various nutrient doses. Variation in mortar properties and nutrient bioavailability was observed and the optimum nutrient content range was defined as 3.5% to 7% depending on the expectations.Conference Object Citation - Scopus: 5Identifying Taxonomic Biomarkers of Colorectal Cancer in Human Intestinal Microbiota Using Multiple Feature Selection Methods(Institute of Electrical and Electronics Engineers Inc., 2022-09-07) Jabeer, Amhar; Kocak, Aysegul; Akkaş, Huseyin; Yenisert, Ferhan; Nalbantoĝlu, Özkan Ufuk; Yousef, Malik; Bakir-Güngör, Burcu; Bakir Gungor, BurcuA variety of bacterial species called gut microbiota work together to maintain a steady intestinal environment. The gastrointestinal tract contains tremendous amount of different species including archaea, bacteria, fungi, and viruses. While these organisms are crucial immune system stabilizers, the dysbiosis of the intestinal flora has been related to gastrointestinal disorders including Colorectal cancer (CRC), intestinal cancer, irritable bowel syndrome and inflammatory bowel disease. In the last decade, next-generation sequencing (NGS) methods have accelerated the identification of human gut flora. CRC is a deathly condition that has been on the rise in the last century, affecting half a million people each year. Since early CRC diagnosis is critical for an effective treatment, there is an immediate requirement for a classification system that can expedite CRC diagnosis. In this study, via analyzing the available metagenomics data on CRC, we aim to facilitate the CRC diagnosis via finding biomarkers linked with CRC, and via building a classification model. We have obtained the metagenomic sequencing data of the healthy individuals and CRC patients from a metagenome-wide association analysis and we have classified this data according to the disease stages. Conditional Mutual Information Maximization (CMIM), Fast Correlation Based Filter (FCBF), Extreme Gradient Boosting (XGBoost), min redundancy max relevance (mRMR), Information Gain (IG) and Select K Best (SKB) feature selection algorithms were utilized to cope with the complexity of the features. We observed that the SKB, IG, and XGBoost techniques made significant contributions to decrease the microbiota in use for CRC diagnosis, thereby reducing cost and time. We realized that our Random Forest classifier outperformed Adaboost, Support Vector Machine, Decision Tree, Logitboost and stacking ensemble classifiers in terms of CRC classification performance. Our results reiterated some known and some potential microbiome associated mechanisms in CRC, which could aid the design of new diagnostics based on the microbiome. © 2022 Elsevier B.V., All rights reserved.