Araştırma Çıktıları | WoS | Scopus | TR-Dizin | PubMed
Permanent URI for this communityhttps://hdl.handle.net/20.500.14719/1741
Browse
130 results
Search Results
Publication Open Access Legume genomics and transcriptomics: From classic breeding to modern technologies(Elsevier B.V., 2020) Afzal, Muhammad Zohaib; Alghamdi, Salem Safer; Migdadi, Hussein M.; Khan, Muhammad Altaf; Nurmansyah, undefined; Mirza, Shaher Bano; El-Harty, Ehab H.; Afzal, Muhammad Zohaib, Plant Production Department, King Saud University, Riyadh, Saudi Arabia; Alghamdi, Salem Safer, Plant Production Department, King Saud University, Riyadh, Saudi Arabia; Migdadi, Hussein M., Plant Production Department, King Saud University, Riyadh, Saudi Arabia; Khan, Muhammad Altaf, Plant Production Department, King Saud University, Riyadh, Saudi Arabia; Nurmansyah, undefined, Plant Production Department, King Saud University, Riyadh, Saudi Arabia; Mirza, Shaher Bano, Department of Biophysics, Bahçeşehir Üniversitesi, Istanbul, Turkey, Department of Biosciences, COMSATS University Islamabad, Islamabad, Pakistan; El-Harty, Ehab H., Plant Production Department, King Saud University, Riyadh, Saudi ArabiaLegumes are essential and play a significant role in maintaining food standards and augmenting physiochemical soil properties through the biological nitrogen fixation process. Biotic and abiotic factors are the main factors limiting legume production. Classical breeding methodologies have been explored extensively about the problem of truncated yield in legumes but have not succeeded at the desired rate. Conventional breeding improved legume genotypes but with more resources and time. Recently, the invention of next-generation sequencing (NGS) and high-throughput methods for genotyping have opened new avenues for research and developments in legume studies. During the last decade, genome sequencing for many legume crops documented. Sequencing and re-sequencing of important legume species have made structural variation and functional genomics conceivable. NGS and other molecular techniques such as the development of markers, genotyping, high density genetic linkage maps, quantitative trait loci (QTLs) identification, expressed sequence tags (ESTs), single nucleotide polymorphisms (SNPs), and transcription factors incorporated into existing breeding technologies have made possible the accurate and accelerated delivery of information for researchers. The application of genome sequencing, RNA sequencing (transcriptome sequencing), and DNA sequencing (re-sequencing) provide considerable insights for legume development and improvement programs. Moreover, RNA-Seq helps to characterize genes, including differentially expressed genes, and can be applied for functional genomics studies, especially when there is limited information available for the studied genomes. Genome-based crop development studies and the availability of genomics data as well as decision-making gears look be specific for breeding programs. This review mainly presents an overview of the path from classical breeding to new emerging genomics tools, which will trigger and accelerate genomics-assisted breeding for recognition of novel genes for yield and quality characters for sustainable legume crop production. © 2019 Elsevier B.V., All rights reserved.Publication Open Access Artificial intelligence and ophthalmology(Turkish Ophthalmology Society info@oftalmoloji.com, 2020) Keskinbora, Kadircan H.; Guven, Fatih; Keskinbora, Kadircan H., Department of Ophthalmology, Bahçeşehir Üniversitesi, Istanbul, Turkey; Guven, Fatih, Clinic of Ophthalmology, University of Health Sciences, Istanbul, TurkeyArtificial intelligence is advancing rapidly and making its way into all areas of our lives. This review discusses developments and potential practices regarding the use of artificial intelligence in the field of ophthalmology, and the related topic of medical ethics. Various artificial intelligence applications related to the diagnosis of eye diseases were researched in books, journals, search engines, print and social media. Resources were cross-checked to verify the information. Artificial intelligence algorithms, some of which were approved by the US Food and Drug Administration, have been adopted in the field of ophthalmology, especially in diagnostic studies. Studies are being conducted that prove that artificial intelligence algorithms can be used in the field of ophthalmology, especially in diabetic retinopathy, age-related macular degeneration, and retinopathy of prematurity. Some of these algorithms have come to the approval stage. The current point in artificial intelligence studies shows that this technology has advanced considerably and shows promise for future work. It is believed that artificial intelligence applications will be effective in identifying patients with preventable vision loss and directing them to physicians, especially in developing countries where there are fewer trained professionals and physicians are difficult to reach. When we consider the possibility that some future artificial intelligence systems may be candidates for moral/ethical status, certain ethical issues arise. Questions about moral/ethical status are important in some areas of applied ethics. Although it is accepted that current intelligence systems do not have moral/ethical status, it has yet to be determined what the exact the characteristics that confer moral/ethical status are or will be. © 2020 Elsevier B.V., All rights reserved.Publication Open Access Vitamin D in autism spectrum disorder, Otizm Spektrum Bozukluğunda D Vitamini(Ortadog u Reklam Tanitim Yayincilik Turizm Egitim Insaat Sanayi ve Ticaret A.S. aysea@turkiyeklinikleri.com Turkocagi Caddesi No. 30 Balgat 06520, 2020) Ozlu Karahan, Tugce; Arslan, Ezgi; Kenger, Emre Batuhan; Ergün, Can; Ozlu Karahan, Tugce, University of Health Sciences, Istanbul, Turkey; Arslan, Ezgi, University of Health Sciences, Istanbul, Turkey; Kenger, Emre Batuhan, University of Health Sciences, Istanbul, Turkey; Ergün, Can, University of Health Sciences, Istanbul, TurkeyAutism spectrum disorder (ASD) is a neurodevelopmental disorder which is characterized by impaired socio-communicative functionality, limited interests, recurrent or stereotyped behaviors. Epidemiological studies have shown that the prevalence of ASD has increased in the last 50 years even if the increase of awareness of ASD and the expansion of diagnostic criteria and diagnostic methods of ASD. Many studies demonstrated that there is an association between the risk of ASD and vitamin D deficiency. In this, review, studies examined the relationship between vitamin D and ASD were considered. All of the examined cross-sectional studies showed that the serum vitamin D level was low among children with ASD. In addition, it was determined that as a therapeutic purpose, vitamin D had positive impacts on children with ASD. On the other hand, investigated studies demonstrated that reduced maternal vitamin D levels during pregnancy and exposure to sunlight may increase the risk of ASD. It has been believed that vitamin D deficiency affects ASD pathogenesis due to the formation of many de novo gene mutations by not repairing the mutated genes. According to another opinion, it has been suggested that the decreased antioxidant defenses as a result of vitamin D deficiency affect the neuroglial activation and neuroinflammation process in the brain of individuals with ASD. As the relationship of vitamin D with ASD, it is crucial to screen pregnant women who are at risk of deficiency and children with ASD in terms of vitamin D deficiency and to plan appropriate treatment if necessary. Randomized controlled trials are required to be carried out in the prevention and/or treatment of ASD. © 2020 Elsevier B.V., All rights reserved.Publication Open Access Public health and tourism, a personalist approach to community well-being: A narrative review(Iranian Journal of Public Health, 2020) Alipour, Habib; Rezapouraghdam, Hamed; Esmaeili, Banafshe; Alipour, Habib, Faculty of Tourism, Eastern Mediterranean University, Famagusta, Turkey; Rezapouraghdam, Hamed, School of Tourism and Hotel Management, Bahçeşehir Üniversitesi, Istanbul, Turkey; Esmaeili, Banafshe, Faculty of Tourism, Eastern Mediterranean University, Famagusta, TurkeyGiven concerns over the public and individual health status of modern society and the scarcity of research on mobility and the health nexus, taking a personalist perspective grounded in spillover theory integrated with broaden-and-build theory, this study uses preventive science ideology and explores the links between tourism and public health through the illustration of the effects of travel on people’s personal, mental, and social wellbeing (PMS-web). A comprehensive review of the literature which is based on themes initiated from WHO (1948) statement: Health is a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity was adopted. Physical, mental, and social (PMS) well-being and tourism/travel keywords were used to search top tier journal articles via the Web of Science and google scholars’ search engines. Findings revealed that a positive linkage exists between travel/tourism and the PMS well-being of individuals that contribute considerably to their state of health per se and is vital to the public health in societies. Although the reviewed tourism literature includes plentiful studies on health/medical tourism or the health issues of host/guests, the lack of focus on the nexus of tourism and public health is sensible. © 2024 Elsevier B.V., All rights reserved.Publication Open Access ATLAS data quality operations and performance for 2015-2018 data-taking(Institute of Physics Publishing helen.craven@iop.org, 2020) Aad, Georges; Abbott, Brad K.; Abbott, D. C.; Abed Abud, Adam; Abeling, Kira; Abhayasinghe, D. K.; Abidi, Syed Hani; AbouZeid, O. S.; Abraham, Nicola L.; Abramowicz, Halina; Aad, Georges, Centre de Physique des Particules de Marseille, Marseille, France; Abbott, Brad K., Homer L. Dodge Department of Physics and Astronomy, The University of Oklahoma, Norman, United States; Abbott, D. C., Department of Physics, University of Massachusetts Amherst, Amherst, United States; Abed Abud, Adam, Organisation Européenne pour la Recherche Nucléaire, Geneva, Switzerland; Abeling, Kira, Institute of Physics, Georg-August-Universität Göttingen, Gottingen, Germany; Abhayasinghe, D. K., Royal Holloway, University of London, Egham, United Kingdom; Abidi, Syed Hani, Department of Physics, University of Toronto, Toronto, Canada; AbouZeid, O. S., Niels Bohr Institutet, Copenhagen, Denmark; Abraham, Nicola L., University of Sussex, Brighton, United Kingdom; Abramowicz, Halina, Raymond and Beverly Sackler School of Physics and Astronomy, Tel Aviv University, Tel Aviv-Yafo, IsraelThe ATLAS detector at the Large Hadron Collider reads out particle collision data from over 100 million electronic channels at a rate of approximately 100 kHz, with a recording rate for physics events of approximately 1 kHz. Before being certified for physics analysis at computer centres worldwide, the data must be scrutinised to ensure they are clean from any hardware or software related issues that may compromise their integrity. Prompt identification of these issues permits fast action to investigate, correct and potentially prevent future such problems that could render the data unusable. This is achieved through the monitoring of detector-level quantities and reconstructed collision event characteristics at key stages of the data processing chain. This paper presents the monitoring and assessment procedures in place at ATLAS during 2015-2018 data-taking. Through the continuous improvement of operational procedures, ATLAS achieved a high data quality efficiency, with 95.6% of the recorded proton-proton collision data collected at s=13 TeV certified for physics analysis. © 2020 Elsevier B.V., All rights reserved.Publication Open Access Neurosurgical Practice During Coronavirus Disease 2019 (COVID-19) Pandemic(Elsevier Inc. usjcs@elsevier.com, 2020) Ozoner, Barış; Gungor, Abuzer; Hasanov, Teyyub; Toktaş, Zafer Orkun; Kilic, Turker D.; Ozoner, Barış, Department of Neurosurgery, Bahçeşehir Üniversitesi, Istanbul, Turkey; Gungor, Abuzer, Department of Neurosurgery, Okmeydani Research and Education Hospital, University of Medical Sciences, Istanbul, Turkey; Hasanov, Teyyub, Department of Neurosurgery, Bahçeşehir Üniversitesi, Istanbul, Turkey; Toktaş, Zafer Orkun, Department of Neurosurgery, Bahçeşehir Üniversitesi, Istanbul, Turkey; Kilic, Turker D., Department of Neurosurgery, Bahçeşehir Üniversitesi, Istanbul, TurkeyCoronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a highly contagious life-threatening condition with unprecedented impacts for worldwide societies and health care systems. Since the first detection in China, it has spread rapidly worldwide. The increased burden has substantially affected neurosurgical practice and intensive modifications have been required in surgical scheduling, inpatient and outpatient clinics, management of emergency cases, and even in academic activities. In some systems, nonoverlapping teams have been created to minimize transmission among health care workers. In cases of a massive burden, neurosurgeons may need to be reassigned to COVID-19 wards, or teams from other regions may need to be sent to severely affected areas. Recommendations are as following. In outpatient practice, if possible, appointments should be undertaken via telemedicine. All staff assigned to the non-COVID treatment unit should be clothed in level 1 personal protective equipment. If possible, postponement is recommended for operations that do not require urgent or emergent intervention. All patients indicated for surgery must receive COVID-19 screening, including a nasopharyngeal swab and thorax computed tomography. Level 2 protection measures are appropriate during COVID-19–negative patients' operations. Operations of COVID-19–positive patients and emergency operations, in which screening cannot be obtained, should be performed after level 3 protective measures. During surgery, the use of high-speed drills and electrocautery should be reduced to minimize aerosol production. Screening is crucial in all patients because the surgical outcome is highly mortal in patients with COVID-19. All educational and academic conferences can be undertaken as virtual webinars. © 2020 Elsevier B.V., All rights reserved.Publication Open Access A new risk factor for cardiometabolic syndrome: Chrononutrition, Kardiyometabolik Sendrom İçin Yeni Bir Risk Faktörü: Krono-Beslenme(Ortadog u Reklam Tanitim Yayincilik Turizm Egitim Insaat Sanayi ve Ticaret A.S., 2020) Arslan, Ezgi; Ozlu Karahan, Tugce; Kenger, Emre Batuhan; Ergün, Can; Arslan, Ezgi, Bahçeşehir Üniversitesi, Istanbul, Turkey; Ozlu Karahan, Tugce, Bahçeşehir Üniversitesi, Istanbul, Turkey; Kenger, Emre Batuhan, Bahçeşehir Üniversitesi, Istanbul, Turkey; Ergün, Can, Bahçeşehir Üniversitesi, Istanbul, TurkeyCircadian system regulates energy homeostasis, including nutrient intake and energy expenditure. Chrononutrition is a novel developing discipline based on the close relationship between endogenous circadian rhythm and metabolism. In this review, it was aimed to reveal the effects of nutritional intake and diet on cardiometabolic heath through circadian systems. Chrononutrition covers 3 dimensions of eating behaviour: timing, frequency and regularity. Sleep patterns are defined as a risk factor for cardiovascular diseases because of circadian disruption, glucose and lipid metabolism and physiological conditions that occur betwen hunger/feeding cycles and light/dark cycles. The most of studies about chrononutrition focused on meal timing and frequency. Regarding this, meal timing patterns such as skipping breakfast or higher energy intake in the evening might be linked to the risk of overweight/obesity and negative metabolic effects on individuals. Insufficient and poor quality sleep is risk factor for cardiometabolic health. It has been explained that inadequate sleep disrupts the rhythms of body, and deteriorated rhythms cause increased nutritional intake and irregular feeding profiles. It has been explained that inadequate sleep disrupts the rhythms of body, and deteriorated rhythms cause increased nutritional intake and irregular feeding profiles. In conclusion, there are cycles that affect between nutrition, sleep and circadian rhythm. It has been suggested that there are mechanisms and pathways that have not yet been revealed among the cycles. More research is required to understand the interaction between chrononutrition and cardiometabolic health. © 2021 Elsevier B.V., All rights reserved.Publication Open Access Crop yield prediction using machine learning: A systematic literature review(Elsevier B.V., 2020) van Klompenburg, Thomas; Kassahun, Ayalew; Catal, Cagatay; van Klompenburg, Thomas, Information Technology Group, Wageningen University & Research, Wageningen, Netherlands; Kassahun, Ayalew, Information Technology Group, Wageningen University & Research, Wageningen, Netherlands; Catal, Cagatay, Department of Computer Engineering, Bahçeşehir Üniversitesi, Istanbul, TurkeyMachine learning is an important decision support tool for crop yield prediction, including supporting decisions on what crops to grow and what to do during the growing season of the crops. Several machine learning algorithms have been applied to support crop yield prediction research. In this study, we performed a Systematic Literature Review (SLR) to extract and synthesize the algorithms and features that have been used in crop yield prediction studies. Based on our search criteria, we retrieved 567 relevant studies from six electronic databases, of which we have selected 50 studies for further analysis using inclusion and exclusion criteria. We investigated these selected studies carefully, analyzed the methods and features used, and provided suggestions for further research. According to our analysis, the most used features are temperature, rainfall, and soil type, and the most applied algorithm is Artificial Neural Networks in these models. After this observation based on the analysis of machine learning-based 50 papers, we performed an additional search in electronic databases to identify deep learning-based studies, reached 30 deep learning-based papers, and extracted the applied deep learning algorithms. According to this additional analysis, Convolutional Neural Networks (CNN) is the most widely used deep learning algorithm in these studies, and the other widely used deep learning algorithms are Long-Short Term Memory (LSTM) and Deep Neural Networks (DNN). © 2020 Elsevier B.V., All rights reserved.Publication Open Access Editorial: Blockchain Ecosystem - Technological and Management Opportunities and Challenges(Institute of Electrical and Electronics Engineers Inc., 2020) Choo, Kim Kwang Raymond; Ozcan, Sercan; Dehghantanha, Ali; Parizi, Reza Meimandi; Choo, Kim Kwang Raymond, Department of Information Systems and Cyber Security, The University of Texas at San Antonio, San Antonio, United States; Ozcan, Sercan, Portsmouth Business School, Portsmouth, United Kingdom, Department of Engineering Management, Bahçeşehir Üniversitesi, Istanbul, Turkey, HSE University, Moscow, Russian Federation; Dehghantanha, Ali, Cyber Science Lab, University of Guelph, Guelph, Canada; Parizi, Reza Meimandi, Department of Software Engineering and Game Development, Kennesaw State University, Kennesaw, United StatesBlockchain is increasingly deployed in a broad range of sectors, ranging from banking and finance to manufacturing to energy to transportation, and so on. While many technological and business related blockchain developments and challenges have been identified, many of these engineering and management challenges have not been addressed. The ongoing interest in this topic is also partly evidenced by the large number of submissions we received in this special issue. Of the 200 submissions, only 39 articles were eventually accepted after several rounds of rigorous reviews (i.e., acceptance rate of 19.5%). In this editorial, we report on the findings from the first 36 articles on a broad range of topics (e.g., supply chain, financial technology, Internet of Things, smart city, healthcare, security, privacy, and blockchain building blocks such as consensus algorithms). Hopefully, the findings reported in these 36 accepted articles will provide sustainable solutions for existing and future blockchain systems and platforms. © 2020 Elsevier B.V., All rights reserved.Publication Open Access Democratising systems of innovations based on Blockchain platform technologies(Emerald Group Holdings Ltd., 2020) Unalan, Serhan; Ozcan, Sercan; Unalan, Serhan, Enterprise and Innovation, Portsmouth Business School, Portsmouth, United Kingdom; Ozcan, Sercan, Enterprise and Innovation, Portsmouth Business School, Portsmouth, United Kingdom, Department of Engineering Management, Bahçeşehir Üniversitesi, Istanbul, TurkeyPurpose: Blockchain is expected to have a significant impact on Systems of Innovation as the new General Purpose Technology. The purpose of this study is to investigate how Blockchain can revolutionise the Systems of Innovation by investigating its overall structure, actors and relationships. Design/methodology/approach: This study used the systematic mapping method to explore and integrate the Blockchain and Systems of Innovation literature for the creation of a new conceptual model of Blockchain-enabled Systems of Innovation. In that scope, 37 Blockchain-related and 32 Systems of Innovation-related papers, besides two major books in the field of Blockchain, have been reviewed and then integrated based on the Systems Thinking approach. Findings: The key findings for Blockchain-enabled Systems of Innovation are that there is (1) an increased distribution of networks and collaborations, (2) increased trust through the use of reputation systems, (3) an emerging new nature of platform characteristics, (4) a democratisation of entrepreneurship by the new funding landscape and (5) an increased significance of technological drivers, such as energy. Research limitations/implications: The study shows new Systems of Innovation-related research implications. Accordingly, a new type of actor, relationship and attribute has been introduced where the boundaries of the role definitions are blurred and more distributed. This is where larger organisations can expect to lose their central position. The different types of actors are replaced by a network of actors as a result of the distributed new Blockchain-based system. The threshold for the Bottom of the Pyramid is expected to be reduced, leading to a more democratised innovation system. Practical Implications: Blockchain appears to reduce the effects of distrust in collaborative innovation practices with its consensus mechanisms and the new Blockchain-enabled Systems of Innovation is expected to revolutionise the interactions in the future. Originality/value: There are very few studies that have been found to integrate innovation management practices with Blockchain. This is the first Blockchain-based Systems of Innovation study enabling the fundamental revision of its structure, types of relationships and actors. © 2021 Elsevier B.V., All rights reserved.