Browsing by Subject "COVID-19"

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Open Access
Artificial intelligence-based hybrid anomaly detection and clinical decision support techniques for automated detection of cardiovascular diseases and Covid-19
(2023-10) Terzi, Merve Begüm
Coronary artery diseases are the leading cause of death worldwide, and early diagnosis is crucial for timely treatment. To address this, we present a novel automated arti cial intelligence-based hybrid anomaly detection technique com posed of various signal processing, feature extraction, supervised, and unsuper vised machine learning methods. By jointly and simultaneously analyzing 12-lead electrocardiogram (ECG) and cardiac sympathetic nerve activity (CSNA) data, the automated arti cial intelligence-based hybrid anomaly detection technique performs fast, early, and accurate diagnosis of coronary artery diseases. To develop and evaluate the proposed automated arti cial intelligence-based hybrid anomaly detection technique, we utilized the fully labeled STAFF III and PTBD databases, which contain 12-lead wideband raw recordings non invasively acquired from 260 subjects. Using the wideband raw recordings in these databases, we developed a signal processing technique that simultaneously detects the 12-lead ECG and CSNA signals of all subjects. Subsequently, using the pre-processed 12-lead ECG and CSNA signals, we developed a time-domain feature extraction technique that extracts the statistical CSNA and ECG features critical for the reliable diagnosis of coronary artery diseases. Using the extracted discriminative features, we developed a supervised classi cation technique based on arti cial neural networks that simultaneously detects anomalies in the 12-lead ECG and CSNA data. Furthermore, we developed an unsupervised clustering technique based on the Gaussian mixture model and Neyman-Pearson criterion that performs robust detection of the outliers corresponding to coronary artery diseases. By using the automated arti cial intelligence-based hybrid anomaly detection technique, we have demonstrated a signi cant association between the increase in the amplitude of CSNA signal and anomalies in ECG signal during coronary artery diseases. The automated arti cial intelligence-based hybrid anomaly de tection technique performed highly reliable detection of coronary artery diseases with a sensitivity of 98.48%, speci city of 97.73%, accuracy of 98.11%, positive predictive value (PPV) of 97.74%, negative predictive value (NPV) of 98.47%, and F1-score of 98.11%. Hence, the arti cial intelligence-based hybrid anomaly detection technique has superior performance compared to the gold standard diagnostic test ECG in diagnosing coronary artery diseases. Additionally, it out performed other techniques developed in this study that separately utilize either only CSNA data or only ECG data. Therefore, it signi cantly increases the detec tion performance of coronary artery diseases by taking advantage of the diversity in di erent data types and leveraging their strengths. Furthermore, its perfor mance is comparatively better than that of most previously proposed machine and deep learning methods that exclusively used ECG data to diagnose or clas sify coronary artery diseases. It also has a very short implementation time, which is highly desirable for real-time detection of coronary artery diseases in clinical practice. The proposed automated arti cial intelligence-based hybrid anomaly detection technique may serve as an e cient decision-support system to increase physicians' success in achieving fast, early, and accurate diagnosis of coronary artery diseases. It may be highly bene cial and valuable, particularly for asymptomatic coronary artery disease patients, for whom the diagnostic information provided by ECG alone is not su cient to reliably diagnose the disease. Hence, it may signi cantly improve patient outcomes, enable timely treatments, and reduce the mortality associated with cardiovascular diseases. Secondly, we propose a new automated arti cial intelligence-based hybrid clinical decision support technique that jointly analyzes reverse transcriptase polymerase chain reaction (RT-PCR) curves, thorax computed tomography im ages, and laboratory data to perform fast and accurate diagnosis of Coronavirus disease 2019 (COVID-19). For this purpose, we retrospectively created the fully labeled Ankara University Faculty of Medicine COVID-19 (AUFM-CoV) database, which contains a wide variety of medical data, including RT-PCR curves, thorax computed tomogra phy images, and laboratory data. The AUFM-CoV is the most comprehensive database that includes thorax computed tomography images of COVID-19 pneu monia (CVP), other viral and bacterial pneumonias (VBP), and parenchymal lung diseases (PLD), all of which present signi cant challenges for di erential diagnosis. We developed a new automated arti cial intelligence-based hybrid clinical de cision support technique, which is an ensemble learning technique consisting of two preprocessing methods, long short-term memory network-based deep learning method, convolutional neural network-based deep learning method, and arti cial neural network-based machine learning method. By jointly analyzing RT-PCR curves, thorax computed tomography images, and laboratory data, the proposed automated arti cial intelligence-based hybrid clinical decision support technique bene ts from the diversity in di erent data types that are critical for the reliable detection of COVID-19 and leverages their strengths. The multi-class classi cation performance results of the proposed convolu tional neural network-based deep learning method on the AUFM-CoV database showed that it achieved highly reliable detection of COVID-19 with a sensitivity of 91.9%, speci city of 92.5%, precision of 80.4%, and F1-score of 86%. There fore, it outperformed thorax computed tomography in terms of the speci city of COVID-19 diagnosis. Moreover, the convolutional neural network-based deep learning method has been shown to very successfully distinguish COVID-19 pneumonia (CVP) from other viral and bacterial pneumonias (VBP) and parenchymal lung diseases (PLD), which exhibit very similar radiological ndings. Therefore, it has great potential to be successfully used in the di erential diagnosis of pulmonary dis eases containing ground-glass opacities. The binary classi cation performance results of the proposed convolutional neural network-based deep learning method showed that it achieved a sensitivity of 91.5%, speci city of 94.8%, precision of 85.6%, and F1-score of 88.4% in diagnosing COVID-19. Hence, it has compara ble sensitivity to thorax computed tomography in diagnosing COVID-19. Additionally, the binary classi cation performance results of the proposed long short-term memory network-based deep learning method on the AUFM-CoV database showed that it performed highly reliable detection of COVID-19 with a sensitivity of 96.6%, speci city of 99.2%, precision of 98.1%, and F1-score of 97.3%. Thus, it outperformed the gold standard RT-PCR test in terms of the sensitivity of COVID-19 diagnosis Furthermore, the multi-class classi cation performance results of the proposed automated arti cial intelligence-based hybrid clinical decision support technique on the AUFM-CoV database showed that it diagnosed COVID-19 with a sen sitivity of 66.3%, speci city of 94.9%, precision of 80%, and F1-score of 73%. Hence, it has been shown to very successfully perform the di erential diagnosis of COVID-19 pneumonia (CVP) and other pneumonias. The binary classi cation performance results of the automated arti cial intelligence-based hybrid clinical decision support technique revealed that it diagnosed COVID-19 with a sensi tivity of 90%, speci city of 92.8%, precision of 91.8%, and F1-score of 90.9%. Therefore, it exhibits superior sensitivity and speci city compared to laboratory data in COVID-19 diagnosis. The performance results of the proposed automated arti cial intelligence-based hybrid clinical decision support technique on the AUFM-CoV database demon strate its ability to provide highly reliable diagnosis of COVID-19 by jointly ana lyzing RT-PCR data, thorax computed tomography images, and laboratory data. Consequently, it may signi cantly increase the success of physicians in diagnosing COVID-19, assist them in rapidly isolating and treating COVID-19 patients, and reduce their workload in daily clinical practice.
Open Access
Correction to: Modelling personal cautiousness during the COVID-19 pandemic: a case study for Turkey and Italy
(Springer, 2021-06-14) Bulut, H.; Gölgeli, M.; Atay, Fatihcan M.
Although policy makers recommend or impose various standard measures, such as social distancing, movement restrictions, wearing face masks and washing hands, against the spread of the SARS-CoV-2 pandemic, individuals follow these measures with varying degrees of meticulousness, as the perceptions regarding the impending danger and the efficacy of the measures are not uniform within a population. In this paper, a compartmental mathematical model is presented that takes into account the importance of personal cautiousness (as evidenced, for example, by personal hygiene habits and carefully following the rules) during the COVID-19 pandemic. Two countries, Turkey and Italy, are studied in detail, as they share certain social commonalities by their Mediterranean cultural codes. A mathematical analysis of the model is performed to find the equilibria and their local stability, focusing on the transmission parameters and investigating the sensitivity with respect to the parameters. Focusing on the (assumed) viral exposure rate, possible scenarios for the spread of COVID-19 are examined by varying the viral exposure of incautious people to the environment. The presented results emphasize and quantify the importance of personal cautiousness in the spread of the disease.
Open Access
Could individuals from countries using BCG vaccination be resistant to SARS-CoV-2 induced infections?
(Turkish Society of Immunology, 2020) Ayanoğlu, İ. C.; İpekoğlu, E. M.; Yazar, Volkan; Yılmaz, İ. C.; Gürsel, İhsan; Gürsel, M.
The lower than expected number of SARS-CoV-2 cases in countries with fragile health systems is puzzling. Herein, we hypothesize that BCG vaccination policies and vaccine strain preferences adopted by different countries might influence the SARS-CoV-2 transmission patterns and/or COVID-19 associated morbidity and mortality. We also postulate that until a specific vaccine is developed, SARS-CoV-2 vulnerable populations could be immunized with BCG vaccines to attain heterologous nonspecific protection from the new coronavirus. In the lights of our investigations the most resistant countries appear to be the ones using Group I BCG strain. Within these countries, however, those who employs Russian strain is even more protected against COVID-19 infection.
Open Access
COVID-19 Detection from respiratory sounds with hierarchical spectrogram transformers
(Institute of Electrical and Electronics Engineers, 2023-12-05) Aytekin, Ayçe İdil; Dalmaz, Onat; Gönç, Kaan; Ankishan, H.; Sarıtaş, Emine Ülkü; Bağcı, U.; Çelik, H.; Çukur, Tolga
Monitoring of prevalent airborne diseases such as COVID-19 characteristically involves respiratory assessments. While auscultation is a mainstream method for preliminary screening of disease symptoms, its util ity is hampered by the need for dedicated hospital visits. Remote monitoring based on recordings of respi ratory sounds on portable devices is a promising alter native, which can assist in early assessment of COVID-19 that primarily affects the lower respiratory tract. In this study, we introduce a novel deep learning approach to distinguish patients with COVID-19 from healthy controls given audio recordings of cough or breathing sounds. The proposed approach leverages a novel hierarchical spectro gram transformer (HST) on spectrogram representations of respiratory sounds. HST embodies self-attention mech anisms over local windows in spectrograms, and window size is progressively grown over model stages to capture local to global context. HST is compared against state-of the-art conventional and deep-learning baselines. Demon strations on crowd-sourced multi-national datasets indicate that HST outperforms competing methods, achieving over 90% area under the receiver operating characteristic curve (AUC) in detecting COVID-19 cases.
Open Access
CoVrimer: a tool for aligning SARS-CoV-2 primer sequences and selection of conserved/degenerate primers
(Academic Press, 2021-07-20) Vural-Özdeniz, Merve; Aktürk, Aslınur; Demirdizen, Mert; Leka, Ronaldo; Acar, Rana; Konu, Özlen
As mutations in SARS-CoV-2 virus accumulate rapidly, novel primers that amplify this virus sensitively and specifically are in demand. We have developed a webserver named CoVrimer by which users can search for and align existing or newly designed conserved/degenerate primer pair sequences against the viral genome and assess the mutation load of both primers and amplicons. CoVrimer uses mutation data obtained from an online platform established by NGDC-CNCB (12 May 2021) to identify genomic regions, either conserved or with low levels of mutations, from which potential primer pairs are designed and provided to the user for filtering based on generalized and SARS-CoV-2 specific parameters. Alignments of primers and probes can be visualized with respect to the reference genome, indicating variant details and the level of conservation. Consequently, CoVrimer is likely to help researchers with the challenges posed by viral evolution and is freely available at http://konulabapps.bilkent.edu.tr:3838/CoVrimer/.
Open Access
Current community transmission and future perspectives on the COVID-19 process
(TÜBİTAK, 2021-03) Türk, S.; Türk, C.; Malkan, Ü. Y.; Temirci, Elif Sena; Peker, M. Ç.; Haznedaroĝlu, İ. C.
Background/aim: COVID-19 syndrome due to the SARS-CoV-2 virus is a currently challenging situation ongoing worldwide. Since the current pandemic of the SARS-CoV-2 virus is a great concern for everybody in the World, the frequently asked question is how and when the COVID-19 process will be concluded. The aim of this paper is to propose hypotheses in order to answer this essential question. As recently demonstrated, SARS-CoV-2 RNAs can be reverse-transcribed and integrated into the human genome. Our main hypothesis is that the ultimate aim of the SARS-CoV-2 virus is the incorporation to human genome and being an element of the intestinal virobiota. Materials and methods: We propose that the SARS-CoV-2 genomic incorporation to be a part of human virobiota is essentially based on three pathobiological phases which are called as the ‘induction’, ‘consolidation’, and ‘maintenance phases’. The phase of ‘recurrence’ complicates any of these three disease phases based on the viral load, exposure time, and more contagious strains and/or mutants. We have performed the ‘random walk model’ in order to predict the community transmission kinetics of the virus. Results: Chimerism-mediated immunotherapy at the individual and community level with the help of vaccination seems to be the only option for ending the COVID-19 process. After the integration of SARS-CoV-2 virus into the human genome via the induction, consolidation, and maintenance phases as an element of intestinal virobiota, the chimerism would be concluded. The ‘viral load’, the ‘genomic strain of the SARS-CoV-2’, and ‘host immune reaction against the SARS-CoV-2’ are the hallmarks of this long journey. Conclusion: Elucidation of the functional viral dynamics will be helpful for disease management at the individual- and community based long-term management strategies.
Open Access
Democracies linked to greater universal health coverage compared with autocracies, even in an economic recession
(Project HOPE, 2021-08) Templin, T.; Dieleman, J. L.; Wigley, Simon; Mumford, J. E.; Miller-Petrie, M.; Kiernan, S.; Bollyky, T. J.
Despite widespread recognition that universal health coverage is a political choice, the roles that a country’s political system plays in ensuring essential health services and minimizing financial risk remain poorly understood. Identifying the political determinants of universal health coverage is important for continued progress, and understanding the roles of political systems is particularly valuable in a global economic recession, which tests the continued commitment of nations to protecting their health of its citizens and to shielding them from financial risk. We measured the associations that democracy has with universal health coverage and government health spending in 170 countries during the period 1990–2019. We assessed how economic recessions affect those associations (using synthetic control methods) and the mechanisms connecting democracy with government health spending and universal health coverage (using machine learning methods). Our results show that democracy is positively associated with universal health coverage and government health spending and that this association is greatest for low-income countries. Free and fair elections were the mechanism primarily responsible for those positive associations. Democracies are more likely than autocracies to maintain universal health coverage, even amid economic recessions, when access to affordable, effective health services matters most.
Open Access
Detecting COVID-19 from respiratory sound recordings with transformers
(S P I E - International Society for Optical Engineering, 2022-04-04) Aytekin, İdil; Dalmaz, Onat; Ankishan, Haydar; Sarıtaş, Emine Ü.; Bağcı, Ulaş; Çukur, Tolga; Çelik, Haydar
Auscultation is an established technique in clinical assessment of symptoms for respiratory disorders. Auscultation is safe and inexpensive, but requires expertise to diagnose a disease using a stethoscope during hospital or office visits. However, some clinical scenarios require continuous monitoring and automated analysis of respiratory sounds to pre-screen and monitor diseases, such as the rapidly spreading COVID-19. Recent studies suggest that audio recordings of bodily sounds captured by mobile devices might carry features helpful to distinguish patients with COVID-19 from healthy controls. Here, we propose a novel deep learning technique to automatically detect COVID-19 patients based on brief audio recordings of their cough and breathing sounds. The proposed technique first extracts spectrogram features of respiratory recordings, and then classifies disease state via a hierarchical vision transformer architecture. Demonstrations are provided on a crowdsourced database of respiratory sounds from COVID-19 patients and healthy controls. The proposed transformer model is compared against alternative methods based on state-of-the-art convolutional and transformer architectures, as well as traditional machine-learning classifiers. Our results indicate that the proposed model achieves on par or superior performance to competing methods. In particular, the proposed technique can distinguish COVID-19 patients from healthy subjects with over 94% AUC.
Unknown
Development of a combination vaccine against N. Meningitidis and SARS-CoV-2 and analysis of MIS-C plasma and extracellular vesicles in relation to disease severity in pediatric patients
(2024-12) Yıldırım, Tuğçe Canavar
Neisseria meningitidis is the causative agent of invasive meningococcal disease (IMD). Serogroup B remains the leading cause of IMD, representing 62% of documented serogroup cases overall, and is the most prevalent across all age groups under 65. In Türkiye, Serogroups B and W are responsible for most cases, accounting for over 75%. IMD has a case fatality rate of 10%, and 10% to 20% of survivors experience lifelong, disabling complications. Coronavirus disease 2019 (COVID-19), caused by the highly contagious SARS-CoV-2 virus, has had a devastating impact globally, leading to over 7 million deaths. It has become the most significant global health crisis since the influenza pandemic in 1918. Since the introduction of the first COVID-19 vaccine in the U.S., it is estimated that over 18 million hospitalizations and 3 million deaths have been prevented. Like flu vaccines, COVID-19 vaccines are expected to be introduced seasonally. In this thesis, we aim to develop a combination vaccine for populations where the prevalence of both COVID-19 and meningitis presents significant health risks. Our previous data indicated that our OMV-based bivalent vaccine, targeting B and W serogroups of Neisseria meningitidis, generates a broader humoral response and exhibits strong bactericidal activity. Similarly, our lab has developed a virus-like particle-based SARS-CoV-2 vaccine incorporating all four virus structural proteins, eliciting both humoral and cell-mediated immunity. Herein, we combined these two platforms and assessed their respective protective potencies in mice. Data revealed that combining the OMV and VLP vaccines with Alum adjuvant produced the most robust anti-meningococcal and anti-SARS-CoV-2 responses. The second part of the study focused on uncovering the immune parameters present in the plasma of MIS-C patients and investigating the role of extracellular vesicles in influencing disease severity. The data revealed that plasma from MIS-C patients contained significantly elevated levels of proinflammatory cytokines, including TNF-α, IFN-γ, IL-6, and IL-17. We also investigated the pathological role of extracellular vesicles (EVs) in MIS-C. Our findings showed that patient-derived EVs induced significantly higher levels of IRF9 in the THP1-dual reporter cell line. Contrary to our expectations, however, we did not observe EV-specific NF-κB induction in the THP1 cells.
Unknown
Distress, anxiety, boredom, and their relation to the interior spaces under COVID-19 lockdowns
(Emerald Publishing Limited, 2022-08-12) Diker, Berk; Demirkan, Halime
Purpose – This research is based on the idea that interior elements leave a wide variety of impressions on their occupants and that some interiors are likely to have more positive impressions than others. These impressions are especially prevalent when an individual cannot leave their homes for extended periods. The architectural elements of an interior where people are isolated can mitigate the adverse psychological effects. Design/methodology/approach – The study was conducted by surveying individuals under lockdown because of the COVID-19 pandemic. A total of 140 participants completed three different scales (GAD-7, K10, FTB Scale) to measure mental health problems often experienced in isolated and confined environments. Their responses were then associated with the interior environments of the participants. Findings – Statistically significant relationships were identified between the reported interiors and the results of the psychological evaluations. The level of psychological distress was associated with Volume and Visual Variety factors. Susceptibility to generalized anxiety disorder was associated with Visual Variety and Airiness factors. Finally, free time boredom was associated with Volume, Visual Variety, and Airiness factors. The Furniture and Clutter factor did not significantly contribute to any of the psychological evaluations. Originality/value –The study was performed in response to the severe lockdown measures taken in response to the COVID-19 pandemic. It successfully highlighted the need for a rethinking of interior design approaches regarding the design for isolated and confined environments.
Unknown
Downside risk in Dow Jones Islamic equity indices: Precious metals and portfolio diversification before and after the COVID-19 bear market
(Elsevier, 2021-07-31) Ali, F.; Jiang, Y.; Şensoy, Ahmet
Besides great turmoil in financial markets, the COVID-19 pandemic also disrupted the global supply chain, putting the precious metal market into great uncertainty. In this study, we revisit the diversifying role of precious metals – gold, silver, and platinum – for six Dow Jones Islamic (DJI) equity index portfolios using a battery of tests: dynamic conditional correlations (DCCs), four-moment modified value at risk (VaR) and conditional VaR, and global minimum-variance (GMV) portfolio approach. Our empirical results exhibit drastically increased DCCs between sample assets during the COVID period; however, pairing gold with any of the DJI equity indices (except for the Asia-Pacific region) decreases the downside risk of these portfolios. Other precious metals (silver and platinum) do not provide such benefits. Furthermore, we find that a higher allocation of wealth in DJI Japanese equities and gold is required to achieve a GMV portfolio in the post-COVID-19 era, implying higher transaction (hedging) costs to rebalance portfolios (weights) accordingly. Our out-of-sample tests examining the global financial crisis, European debt crisis, and extended sample (2000–2020) periods yield similar findings as gold glitters across all market conditions. Overall, our findings provide notable practical implications for both domestic and international investors.
Unknown
Emergency remote teaching in Turkey: a systematic literature review
(2022-12) İnal, Selin
The aim of this study is to systematically review the literature on Emergency Remote Teaching (ERT) during COVID-19 period in Turkey which started on March 23, 2020 and continued until the end of the spring term 2019-2020 in K12 and higher education context. The study sample consisted of 52 articles which were located from Scopus, ERIC and DergiPark databases through search criteria and examined under systematic literature review procedures. Articles are categorized according to their demographic data; methodology, data collection tools, size of the sample, sample type, level of the sample, curricular area, and digital platforms. Results indicated that qualitative research methods were the most preferred amongst the studies, conducted mostly in the higher education level. Sample sizes of the studies differed between 0-400 and small-scale research was the most popular. Amongst the articles, video conferencing tool Zoom was the most encountered digital tool. The research articles were reviewed to locate the changes happened within the teaching-learning cycle during ERT period regarding students and teachers of K12 and higher education contexts. Findings included following patterns; concepts of context as accessibility, flexibility of time and space, characteristics of home environments, internet and infrastructure problems, inequalities in possession of required technology; concepts of classroom processes as participation, use of materials and communication between students and instructors; concepts of input as content, students’ levels of interest and motivation, students’ study habits and students’ learning style. Implications for practice and implications for further research were given.
Unknown
Fair allocation of personal protective equipment to health centers during early phases of a pandemic
(Elsevier, 2022-05) Dönmez, Zehranaz; Turhan, S.; Karsu, Özlem; Kara, Bahar Y.; Karaşan, Oya
We consider the problem of allocating personal protective equipment, namely surgical and respiratory masks, to health centers under extremely limited supply. We formulate a multi-objective multi-period non-linear resource allocation model for this problem with the objectives of minimizing the number of infected health workers, the number of infected patients and minimizing a deprivation cost function defined over shortages. We solve the resulting problem using the ε-constraint algorithm so as to obtain the exact Pareto set. We also develop a customized genetic algorithm to obtain an approximate Pareto frontier in reasonable time for larger instances. We provide a comparative analysis of the exact and heuristic methods under various scenarios and give insights on how the suggested allocations outperform the ones obtained through a set of rule-of-thumb policies, policies that are implemented owing to their simplicity and ease-of-implementation. Our comparative analysis shows that as the circumstances get worse, the trade-off between the deprivation cost and the ratio of infections deepens and that the proposed heuristic algorithm gives very close solutions to the exact Pareto frontier, especially under pessimistic scenarios. We also observed that while some rule-of-thumb policies such as a last-in-first-receives type policy work well in terms of deprivation costs in optimistic scenarios, others like split policies perform well in terms of number of infections under neutral or pessimistic settings. While favoring one of the objectives, these policies typically fail to provide good solutions in terms of the other objective; hence if such policies are to be implemented the choice would depend on the problem characteristics and the priorities of the policy makers. Overall, the solutions obtained by the proposed methods imply that more complicated distribution schemes that are not induced by these policies would be needed for best results.
Unknown
A global experiment on motivating social distancing during the COVID-19 pandemic
(National Academy of Sciences, 2022-05-27) Legate, N.; Nguyen, T.; Weinstein, N.; Moller, A.; Legault, L.; Vally, Z.; Tajchman, Z.; Zsido, A. N.; Zrimsek, M.; Chen, Z.; Ziano, I.; Gialitaki, Z.; Basnight-Brown, D. M.; Ceary, C. D.; Jang, Y.; Ijzerman, H.; Lin, Y.; Kunisato, Y.; Yamada, Y.; Xiao, Q.; Jiang, X.; Du, X.; Yao, E.; Ryan, W. S.; Wilson, J. P.; Cyrus-Lai, W.; Jimenez-Leal, W.; Law, W.; Unanue, W.; Collins, W. M.; Richard, K. L.; Vranka, M.; Ankushev, V.; Schei, V.; Lerche, V.; Kovic, V.; Krizanic, V.; Kadreva, V. H.; Adoric, V. C.; Tran, U. S.; Yeung, S. K.; Hassan, W.; Houston, R.; Urry, H. L.; Machin, M. A.; Lima, T. J. S.; Ostermann, T.; Frizzo, T.; Sverdrup, T. E.; House, T.; Gill, T.; Fedetov, M.; Paltrow, T.; Moshontz, H.; Jernsäther, T.; Rahman, T.; Machin, T.; Koptjevskaja-Tamm, M.; Hostler, T. J.; Ishii, T.; Szazsi, B.; Adamus, S.; Suter, L.; Von Bormann, S. M.; Habib, S.; Studzinska, A.; Stojanovska, D.; Jansenn, S. M. J.; Stieger, S.; Primbs, M. A.; Schulenberg, S. E.; Buchanan, E. M.; Tatachari, S.; Azouaghe, S.; Sorokowski, P.; Sorokowska, A.; Song, X.; Morbée, S.; Lewis, S.; Sinkolova, S.; Grigoryev, D.; Drexler, S. M.; Daches, S.; Levine, S. L.; Geniole, S. N.; Akter, S.; Vracar, S.; Massoni, S.; Costa, S.; Zorjan, S.; Sarioguz, E.; Izquierdo, S. M.; Tshonda, S. S.; Miller, J. K.; Alves, S. G.; Pöntinen, S.; Solas, S. A.; Ordoñez-Riaño, S.; Ocovaj, S. B.; Onie, S.; Lins, S.; Biberauer, T.; Çoksan, S.; Khumkom, S.; Sacakli, A.; Coles, N. A.; Ruiz-Fernández, S.; Geiger, S. J.; FatahModares, S.; Walczak, R. B.; Betlehem, R.; Vilar, R.; Cárcamo, R. A.; Ross, R. M.; McCarthy, R.; Ballantyne, T.; Westgate, E. C.; Ryan, R. M.; Gargurevich, R.; Afhami, R.; Ren, D.; Monteiro, R. P.; Reips, U.; Reggev, N.; Calin-Jageman, R. J.; Pourafshari, R.; Oliveira, R.; Nedelcheva-Datsova, M.; Rahal, R.; Ribeiro, R. R.; Radtke, T.; Searston, R.; Jai-Ai, R.; Habte, R.; Zdybek, P.; Chen, S; Wajanatinapart, P.; Maturan, P. L. G.; Perillo, J. T.; Isager, P. M.; Kacmár, P.; Macapagal, P. M.; Maniaci, M. R.; Szwed, P.; Hanel, P. H. P.; Forbes, P. A. G.; Arriaga, P.; Paris, B.; Parashar, N.; Papachristopoulos, K.; Chartier, C. R.; Correa, P. S.; Kácha, O.; Bernardo, M.; Campos, O.; Bravo, O. N.; Mallik, P. R.; Gallindo-Caballero, O. J.; Ogbonnaya, C. E.; Bialobrzeska, O.; Kiselnikova, N.; Simonovic, N.; Cohen, N.; Nock, N. L.; Hernandez, A.; Thogersen-Ntoumani, C.; Ntoumanis, N.; Johannes, N.; Albayrak-Aydemir, N.; Say, N.; Neubauer, A. B.; Martin, N. I.; Torunsky, N.; Van Antwerpen, N.; Van Doren, N.; Sunami, N.; Rachev, N. R.; Majeed, N. M.; Schmidt, N.; Nadif, K.; Forscher, P. S.; Corral-Frias, N. S.; Ouherrou, N.; Abbas, N.; Pantazi, M.; Lucas, M. Y.; Vasilev, M. R.; Ortiz, M. V.; Butt, M. M.; Kurfali, M.; Kabir, M.; Muda, R.; Del Carmen M. C. Tejada Rivera, M.; Sirota, M.; Seehuus, M.; Parzuchowski, M.; Toro, M.; Hricova, M.; Maldonado, M. A.; Arvanitis, A.; Rentzelas, P.; Vansteenkiste, M.; Metz, M. A.; Marszalek, M.; Karekla, M.; Mioni, G.; Bosma, M. J.; Westerlund, M.; Vdovic, M.; Bialek, M.; Silan, M. A.; Anne, M.; Misiak, M.; Gugliandolo, M. C.; Grinberg, M.; Capizzi, M.; Espinoza Barria, M. F.; Kurfali, Merve A.; Mensink, M. C.; Harutyunyan, M.; Khosla, M.; Dunn, M. R.; Korbmacher, M.; Adamkovic, M.; Ribeiro, M. F. F.; Terskova, M.; Hruška, M.; Martoncik, M.; Voracek, M.; Cadek, M.; Frias-Armenta, M.; Kowal, M.; Topor, M.; Roczniewska, M.; Oosterlinck, M.; Thomas, A. G.; Kohlová, M. B.; Paruzel-Czachura, M.; Sabristov, M.; Greenburgh, A.; Romanova, M.; Papadatou-Pastou, M.; Lund, M. L.; Antoniadi, M.; Magrin, M. E.; Jones, M. V.; Li, M.; Ortiz, M. S.; Manavalan, M.; Muminov, A.; Stoyanova, A.; Kossowska, M.; Friedemann, M.; Wielgus, M.; Van Hooff, M. L. M; Varella, M. A. C.; Standage, M.; Nicolotti, M.; Coloff, M. F.; Bradford, M.; Vaughn, L. A.; Eudave, L.; Vieira, L.; Lu, J. G.; Pineda, L. M. S.; Matos, L.; Pérez, L. C.; Lazarevic, L. B.; Jaremka, L. M.; Smit, E. S.; Kushnir, E.; Wichman, A. L.; Ferguson, L. J.; Anton-Boicuk, L.; De Holanda Coelho, G. L.; Ahlgren, L.; Liga, F.; Levitan, C. A.; Micheli, L.; Gunton, L.; Volz, L.; Stojanovska, M.; Boucher, L.; Samojlenko, L.; Delgado, L. G. J.; Kaliska, L.; Beatrix, L.; Warmelink, L.; Rojas-Berscia, L. M.; Yu, K.; Wylie, K.; Wachowicz, J.; Charyate, A. C.; Desai, K.; Barzykowski, K.; Kozma, L.; Evans, K.; Kirgizova, K.; Belaus, A.; Emmanuel Agesin, B. B.; Koehn, M. A.; Wolfe, K.; Korobova, T.; Morris, K.; Klevjer, K.; Van Schie, K.; Vezirian, K.; Damnjanovic, K.; Thommesen, K. K.; Schmidt, K.; Filip, K.; Staniaszek, K.; Adetula, A.; Grzech, K.; Hoyer, K.; Moon, K.; Khaobunmasiri, S.; Rana, K.; Janjic, K.; Suchow, J. W.; Kielinska, J.; Cruz Vásquez, J. E.; Chanal, J.; Beitner, J.; Vargas-Nieto, J. C.; Roxas, J. C. T.; Taber, J.; Urriago-Rayo, J.; Askelund, A. D.; Pavlacic, J. M.; Benka, J.; Bavolar, J.; Soto, J. A.; Olofsson, J. K.; Vilsmeier, J. K.; Messerschmidt, J.; Czamanski-Cohen, J.; Waterschoot, J.; Moss, J. D.; Boudesseul, J.; Lee, J. M.; Kamburidis, J.; Joy-Gaba, J. A.; Zickfeld, J.; Miranda, J. F.; Verharen, J. P. H.; Hristova, E.; Beshears, J. E.; Djordjevic, J. M.; Bosch, J.; Valentova, J. V.; Antfolk, J.; Berkessel, J. B.; Schrötter, J.; Urban, J.; Röer, J. P.; Norton, J. O.; Silva, J. R.; Pickerin, J. S.; Vintr, J.; Uttly, J.; Kunst, J. R.; Ndukaihe, I. L. G.; Iyer, A.; Vilares, I.; Ivanov, A.; Ropovik, I.; Sula, I.; Groyecka-Bernard, A.; Sarieva, I.; Metin-Orta, I.; Prusova, I.; Pinto, I.; Bozdoc, A. I.; Almeida, I. A. T.; Pit, I. L.; Dalgar, I.; Zakharov, I.; Arinze, A. I.; Ihaya, K.; Stephen, I. D.; Gjoneska, B.; Brohmer, H.; Flowe, H.; Godbersen, H.; Kocalar, H. E.; Hedgebeth, M. V.; Chuan-Peng, H.; Sharifian, M.; Manley, H.; Akkas, H.; Hajdu, N.; Azab, H.; Kaminski, G.; Nilsonne, G.; Anjum, G.; Travaglino, G. A.; Feldman, G.; Pfuhl, G.; Czarnek, G.; Marcu, G. M.; Hofer, G.; Banik, G.; Adetula, G. A.; Bijlstra, G.; Verbruggen, F.; Kung, F. Y. H.; Martela, F.; Foroni, F.; Forest, J.; Singer, G.; Muchembled, F.; Azevedo, F.; Mosannenzadeh, F.; Marinova, E.; Strukelj, E.; Etebari, Z.; Bradshaw, E. L.; Baskin, E.; Garcia, E. O. L.; Musser, E.; Van Steenkiste, I. M. M.; Ahn, E. R.; Quested, E.; Pronizius, E.; Jackson, E. A.; Manunta, E.; Agadullina, E.; Sakan, D.; Dursun, P.; Dujols, O.; Dubrov, D.; Willis, M.; Tümer, M.; Beaudry, J. L.; Popovic, D.; Dunleavy, D.; Djamal, I.; Krupic, D.; Herrera, D.; Vega, D.; Du, H.; Mola, D.; Chakarova, D.; Davis, W. E.; Holford, D. L.; Lewis, D. M. G.; Vaidis, D. C.; Ozery, D. H.; Ricaurte, D. Z.; Storage, D.; Sousa, D.; Alvarez, D. S.; Boller, D.; Rosa, A. D.; Dimova, D.; Krupic, D.; Marko, D.; Moreau, D.; Reeck, C.; Correia, R. C.; Whitt, C. M.; Lamm, C.; Solorzano, C. S.; Von Bastian, C. C.; Sutherland, C. A. M.; Ebersole, C. R.; Overkott, C.; Aberson, C. L.; Wang, C.; Niemiec, C. P.; Karashiali, C.; Noone, C.; Chiu, F.; Picchiocchi, C.; Brownlow, C.; Karaarslan, C.; Cellini, N.; Esteban-Serna, C.; Reyna, C.; Ferreyra, C.; Batres, C.; Li, R.; Grano, C.; Carpentier, J.; Tamnes, C. K.; Fu, C. H. Y.; Ishkhanyan, B.; Bylinina, L.; Jaeger, B.; Bundt, C.; Allred, T. B.; Vermote, B. J.; Bokkour, A.; Bogatyreva, N.; Shi, J.; Chopik, W. J.; Antazo, B.; Behzadnia, B.; Becker, M.; Bayyat, M. M.; Cocco, B.; Ahmed, A.; Chou, W.; Barkoukis, V.; Hubena, B.; Khaoudi, A.; Žuro, B.; Aczel, B.; Baklanova, E.; Bai, H.; Balci, B. B.; Babincák, P.; Soenens, B.; Dixson, B. J. W.; Mokady, A.; Kappes, H. B.; Atari, M.; Szala, A.; Szabelska, A.; Aruta, J. J. B.; Domurat, A.; Arinze, N. C.; Modena, A.; Adiguzel, A.; Monajem, A.; Ait El Arabi, K.; Özdogru, A. A.; Rothbaum, A. O.; Torres, A. O.; Theodoropoulou, A.; Skowronek, A.; Urooj, A.; Jurkovic, A. P.; Singh, A.; Kassianos, A. P.; Findor, A.; Hartanto, A.; Landry, A. T.; Ferreira, A.; Santos, A. C.; De La Rosa-Gomez, A.; Gourdon-Kanhukamwe, A.; Luxon, A. M.; Todsen, A. L.; Karababa, A.; Janak, A.; Pilato, A.; Bran, A.; Tullett, A. M.; Kuzminska, A. O.; Krafnik, A. J.; Massey, D.
Finding communication strategies that effectively motivate social distancing continues to be a global public health priority during the COVID-19 pandemic. This crosscountry, preregistered experiment (n = 25,718 from 89 countries) tested hypotheses concerning generalizable positive and negative outcomes of social distancing messages that promoted personal agency and reflective choices (i.e., an autonomy-supportive message) or were restrictive and shaming (i.e., a controlling message) compared with no message at all. Results partially supported experimental hypotheses in that the controlling message increased controlled motivation (a poorly internalized form of motivation relying on shame, guilt, and fear of social consequences) relative to no message. On the other hand, the autonomy-supportive message lowered feelings of defiance compared with the controlling message, but the controlling message did not differ from receiving no message at all. Unexpectedly, messages did not influence autonomous motivation (a highly internalized form of motivation relying on one's core values) or behavioral intentions. Results supported hypothesized associations between people's existing autonomous and controlled motivations and self-reported behavioral intentions to engage in social distancing. Controlled motivation was associated with more defiance and less long-term behavioral intention to engage in social distancing, whereas autonomous motivation was associated with less defiance and more short- and long-term intentions to social distance. Overall, this work highlights the potential harm of using shaming and pressuring language in public health communication, with implications for the current and future global health challenges.
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Habits of mind of primary school teachers implementing technology during emergency remote teaching
(2023-06) Tuysuzoglu, Amber Lee
This study used narrative inquiry to analyze how four primary school English teachers described the challenges they faced while teaching remotely during the COVID-19 pandemic and how they handled these challenges, particularly with technology during online instruction. The purpose of this exploration was to identify the ways of thinking or Habits of Mind participants exhibited when dealing with the challenges. Through thematic analysis, participants’ narratives were aligned with intelligent behaviors. The study found that there were seven Habits of Mind commonly used by teachers to address the problems they faced during emergency remote teaching. A conceptual framework was used to link these Habits of Mind to educational theories. Constructivism, self-regulated learning, and incremental learning were among the theories that could be used to explain teachers’ persistence, inquiry, and continuous efforts to learn technology, aself-improvement. The outcomes indicate that these educational theories can be emphasized in teacher education programs to foster constructive and supportive Habits of Mind that will help teachers address and overcome technological challenges.
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How to weather the storm: a comparative case study on populist trajectorıes to COVID-19 pandemic
(2021-07) Güllüoğlu, Tümay H.
Why populist leaders have responded to the COVID-19 pandemic differently? This thesis addresses this question through a paired comparison of Brazil, Mexico, and Turkey. These three countries, despite being similar, have employed unique responses to the COVID-19 pandemic. The cases are explained based on two sets of indicators. First, the national indicators address the overall conduct within the case where vaccination efforts, closures, quarantines, local and national policies are assessed. The second set of indicators address the leaders’ attitude, their recognition of the seriousness of the pandemic, and assess their populist tendencies during the crisis. Crisis management, populism, and recently emerging COVID-19 politics literature have been used alongside articles in media to trace the change in the policies and leaders’ evolving discourses. These changes are explained through three factors: the left-right continuum, social aid capabilities, and opposition strength. After the analyses, the thesis suggests new terms for unique trajectories. Adversarial denialism for Brazil indicates an environment where during the downplaying of the pandemic, efforts of other parties are demonized. Defective inclusionism for Mexico denotes a setting where the leader gradually defects his denialistic positions after popular pressure while failing to provide inclusionist policies. Partisan affirmation for Turkey signifies a monopolization of the conduct where efforts of others are systemically prevented.
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Human genetic and immunological determinants of critical COVID-19 pneumonia
(Springer Nature, 2022-03-24) Zhang, Qian; Bastard, Paul; Karbuz, Adem; Gervais, Adrian; Tayoun, Ahmad Abou; Aiuti, Alessandro; Belot, Alexandre; Bolze, Alexandre; Gaudet, Alexandre; Bondarenko, Anastasiia; Liu, Zhiyong; Spaan, András N.; Guennoun, Andrea; Arias, Andres Augusto; Planas, Anna M.; Sediva, Anna; Shcherbina, Anna; Neehus, Anna-Lena; Puel, Anne; Froidure, Antoine; Novelli, Antonio; Parlakay, Aslınur Özkaya; Pujol, Aurora; Yahşi, Aysun; Gülhan, Belgin; Bigio, Benedetta; Boisson, Bertrand; Drolet, Beth A.; Franco, Carlos Andres Arango; Flores, Carlos; Rodríguez-Gallego, Carlos; Prando, Carolina; Biggs, Catherine M.; Luyt, Charles-Edouard; Dalgard, Clifton L.; O’Farrelly, Cliona; Matuozzo, Daniela; Dalmau, David; Perlin, David S.; Mansouri, Davood; van de Beek, Diederik; Vinh, Donald C.; Dominguez-Garrido, Elena; Hsieh, Elena W. Y.; Erdeniz, Emine Hafize; Jouanguy, Emmanuelle; Şevketoglu, Esra; Talouarn, Estelle; Quiros-Roldan, Eugenia; Andreakos, Evangelos; Husebye, Eystein; Alsohime, Fahad; Haerynck, Filomeen; Casari, Giorgio; Novelli, Giuseppe; Aytekin, Gökhan; Morelle, Guillaume; Alkan, Gulsum; Bayhan, Gulsum Iclal; Feldman, Hagit Baris; Su, Helen C.; von Bernuth, Horst; Resnick, Igor; Bustos, Ingrid; Meyts, Isabelle; Migeotte, Isabelle; Tancevski, Ivan; Bustamante, Jacinta; Fellay, Jacques; El Baghdadi, Jamila; Martinez-Picado, Javier; Casanova, Jean-Laurent; Rosain, Jeremie; Manry, Jeremy; Chen, Jie; Christodoulou, John; Bohlen, Jonathan; Franco, José Luis; Li, Juan; Anaya, Juan Manuel; Rojas, Julian; Ye, Junqiang; Uddin, K. M. Furkan; Yasar, Kadriye Kart; Kisand, Kai; Okamoto, Keisuke; Chaïbi, Khalil; Mironska, Kristina; Maródi, László; Abel, Laurent; Renia, Laurent; Lorenzo, Lazaro; Hammarström, Lennart; Ng, Lisa F. P.; Quintana-Murci, Lluis; Erazo, Lucia Victoria; Notarangelo, Luigi D.; Reyes, Luis Felipe; Allende, Luis M.; Imberti, Luisa; Renkilaraj, Majistor Raj Luxman Maglorius; Moncada-Velez, Marcela; Materna, Marie; Anderson, Mark S.; Gut, Marta; Chbihi, Marwa; Ogishi, Masato; Emiroglu, Melike; Seppänen, Mikko R. J.; Uddin, Mohammed J.; Shahrooei, Mohammed; Alexander, Natalie; Hatipoglu, Nevin; Marr, Nico; Akçay, Nihal; Boyarchuk, Oksana; Slaby, Ondrej; Akcan, Ozge Metin; Zhang, Peng; Soler-Palacín, Pere; Gregersen, Peter K.; Brodin, Petter; Garçon, Pierre; Morange, Pierre-Emmanuel; Pan-Hammarström, Qiang; Zhou, Qinhua; Philippot, Quentin; Halwani, Rabih; de Diego, Rebeca Perez; Levy, Romain; Yang, Rui; Öz, Şadiye Kübra Tüter; Muhsen, Saleh Al; Kanık-Yüksek, Saliha; Espinosa-Padilla, Sara; Ramaswamy, Sathishkumar; Okada, Satoshi; Bozdemir, Sefika Elmas; Aytekin, Selma Erol; Karabela, Şemsi Nur; Keles, Sevgi; Senoglu, Sevtap; Zhang, Shen-Ying; Duvlis, Sotirija; Constantinescu, Stefan N.; Boisson-Dupuis, Stephanie; Turvey, Stuart E.; Tangye, Stuart G.; Asano, Takaki; Özcelik, Tayfun; Le Voyer, Tom; Maniatis, Tom; Morio, Tomohiro; Mogensen, Trine H.; Sancho-Shimizu, Vanessa; Beziat, Vivien; Solanich, Xavier; Bryceson, Yenan; Lau, Yu-Lung; Itan, Yuval; Cobat, Aurélie; Casanova, Jean-Laurent
SARS-CoV-2 infection is benign in most individuals but, in around 10% of cases, it triggers hypoxaemic COVID-19 pneumonia, which leads to critical illness in around 3% of cases. The ensuing risk of death (approximately 1% across age and gender) doubles every five years from childhood onwards and is around 1.5 times greater in men than in women. Here we review the molecular and cellular determinants of critical COVID-19 pneumonia. Inborn errors of type I interferons (IFNs), including autosomal TLR3 and X-chromosome-linked TLR7 deficiencies, are found in around 1–5% of patients with critical pneumonia under 60 years old, and a lower proportion in older patients. Pre-existing auto-antibodies neutralizing IFNα, IFNβ and/or IFNω, which are more common in men than in women, are found in approximately 15–20% of patients with critical pneumonia over 70 years old, and a lower proportion in younger patients. Thus, at least 15% of cases of critical COVID-19 pneumonia can be explained. The TLR3- and TLR7-dependent production of type I IFNs by respiratory epithelial cells and plasmacytoid dendritic cells, respectively, is essential for host defence against SARS-CoV-2. In ways that can depend on age and sex, insufficient type I IFN immunity in the respiratory tract during the first few days of infection may account for the spread of the virus, leading to pulmonary and systemic inflammation. © 2022, Springer Nature Limited.
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Identifying diversifiers, hedges, and safe havens among Asia Pacific equity markets during COVID-19: New results for ongoing portfolio allocation
(Elsevier BV, 2023-02-22) Ali, F.; Şensoy, Ahmet; Goodell, J. W.
We identify diversification benefits among Asian equity markets in the COVID-19 era. We find that such benefits among Asia-Pacific markets changed considerably during the pandemic, and most changes were persistent. In most cases, any of the sample equities had at least one safe-haven protection. The exceptions are Pakistan, Thailand, and Singapore, where diversification benefits are limited and vary across subperiods. The Hong Kong equity market provides safe-haven protection to most markets during periods of extreme negative returns. Further, we find that greater (lower) weightings on the Bangladeshi, Taiwanese, and Malaysian (Thai) markets provide important diversification in terms of maximizing Sharpe ratio and minimizing variance during the pandemic.
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Impact of COVID-19 outbreak on asymmetric multifractality of gold and oil prices
(Elsevier, 2020) Mensi, W.; Şensoy, Ahmet; Vo, X. V.; Kang, S. H.
This paper examines the impacts of COVID-19 on the multifractality of gold and oil prices based on upward and downward trends. We apply the Asymmetric Multifractal Detrended Fluctuation Analysis (A-MF-DFA) approach to 15-min interval intraday data. The results show strong evidence of asymmetric multifractality that increases as the fractality scale increases. Moreover, multifractality is especially higher in the downside (upside) trend for Brent oil (gold), and this excess asymmetry has been more accentuated during the COVID-19 outbreak. Before the outbreak, the gold (oil) market was more inefficient during downward (upward) trends. During the COVID-19 outbreak period, we see that the results have changed. More precisely, we find that gold (oil) is more inefficient during upward (downward) trends. Gold and oil markets have been inefficient, particularly during the outbreak. The efficiency of gold and oil markets is sensitive to scales, market trends, and to the pandemic outbreak, highlighting the investor sentiment effect.
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Impact of media hype and fake news on commodity futures prices: A deep learning approach over the COVID-19 period
(2023-10-30) Banerjee, Ameet Kumar; Şensoy, Ahmet; Goodell, John W.; Mahapatra, Biplab
We investigate the reactions of eight commodity futures to media hype and fake news during COVID-19, utilising the Ravenpack news database, along with deep learning algorithms. Results identify a significant impact on commodity prices of media hype and fake news, with this reaction amplified during COVID-19. Compared to alternative deep learning algorithms, bi-directional long-short-term memory is adaptive to forecasting the returns of the commodity futures contracts with lower mean absolute error and root mean square error. Findings, confirmed by Diebold-Mariano testing, as well as alternative data partitioning, show commodity markets are susceptible to fake news and media hype.