Risk factor disclosures in the US Airline Industry following the COVID-19 pandemic

This study examines how airlines in the United States report risk at a difficult and uncertain time as a result of the COVID-19 pandemic. The fundamental differences between the years 2019 and 2020 are identified using Leximancer, which is used to locate the key ideas and themes addressed in the risk reporting sections. Following the pandemic, the themes that addressed generic and recurring hazards were afforded less weight than themes that highlighted risks particular to day-to-day business and the stock market. The findings also point to the need for corporations to disclose future-oriented risks more fully in post-COVID-19 reporting, with an emphasis on unpredictability, stock volatility, and operational disruption. This study adds to the body of knowledge on risk profiling, particularly as it relates to the airline business, and it offers stakeholders and investors a glimpse into the general concerns of airlines. The inherent information imbalance between management and investors is lessened and transparency is increased because of this improved understanding of the market.info:eu-repo/semantics/publishedVersio

From little seeds to a big tree: A far-reaching assessment of the Integrated reporting stream

The purpose of this paper is to provide the first assessment of the Integrated Reporting stream using a broad sample of publications separated into research scopes (accounting and non-core accounting journals), and using a longitudinal perspective. This study proposes to identify its main contributors, evidencing both individual and collaborative work. Bibliometric tools supported by a milestone approach to IR history were used to address the first two research questions on the growth of this stream per scope. Density maps on keyword co-occurrence provided insights into the third question aimed at assessing differences in the scopes’ research topics. Number of publications, citation-based metrics, and network analysis based on co-authorship allowed us to answer the last question regarding the top contributors. The results endorse the acknowledged interest in this stream, exposing its incredible growth, which already amounts to over 1000 different scholars, 200 distinguished journals, and 7600 citations across 540 peer-reviewed publications. With the accounting scope leading on citation frequency, and the non-core accounting having more publications, an almost picture-perfect circle in a pooled density map supports the field’s advocated interdisciplinarity with its distinctive contributions. Lastly, the cluster analysis revealed that 140 publications belong exclusively to 10 research clusters that contribute to more than half of the total citation count. This rich analysis combines visualizing techniques with in-depth bibliometrics to provide the first far-reaching collation of publications on IR to offer a complementary view on this dynamic interdisciplinary stream.info:eu-repo/semantics/acceptedVersio

SDG9 and the competitiveness: Employing mixed methods to understand how countries can use science to compete

Policymakers seek the best ways to promote the development of their countries. With Agenda 2030 at the top of the conversation, this study aimed to analyze the relationship between SDG9 and GCI. The study uses two methodologies, the first of which uses panel data to explain how variables that make up SDG9 explain competitiveness across countries in the Eurozone from 2010 to 2019. A second approach is through qualitative methodology (fsQCA) to verify the differences in the possible combinations of variables for the same outcome – higher values of competitiveness. The findings suggest that R&D and patents are two factors that increase competitiveness. Additionally, the number of patents filed in each country is also considered a necessary condition for high GCI values, reinforcing the need for countries to protect their innovations. In conclusion, the variables of SDG9 show significant relationships to achieve high values of competitiveness and that there is not only one solution to achieve the outcome but several, and it is up to policymakers to define which strategy best fits their reality. The originality of the article lies in the way different methods are combined and the increased capacity to understand the reality that these different methods allow.info:eu-repo/semantics/publishedVersio

The effect of IFRS adoption on the business climate: A country perspective

Based on the ten areas that are measured by the ease of doing business (EDB) and based on the getting credit (GC) indicator, this study seeks to analyze factors that lead to a more favorable business climate in different countries. The methodology of fuzzy-set qualitative comparative analysis (fsQCA) was used to determine the paths taken by configurations or conditions in which variables affect an outcome. The results showed that high EDB and GC scores may be obtained under specified levels of IFRS (International Financial Reporting Standards) adoption degree and user experience requirements. Therefore, the adoption of IFRS could result in a better business climate in a nation since it would increase the comparability of financial statements, which will lower costs for investors, draw in foreign investors, and boost trust. Finally, the findings indicated that, depending on the presence of specific levels of GDP per capita, entrepreneurship, income group, and foreign direct investment (FDI) inflows, low or high values of IFRS adoption and high experience in applying IFRS are necessary to achieve high GC scores.info:eu-repo/semantics/publishedVersio

Análise bibliométrica às revisões de literatura sobre relato integrado

Sensivelmente desde 2015 tem-se assistido a um crescimento exponencial do Relato Integrado (RI). Um pouco por todo o mundo, inúmeras empresas começaram a preparar a sua informação anual utilizando a abordagem do RI, dando uma visão holística de como o seu negócio tem capacidade para criar (ou destruir) valor ao longo do tempo. A par do contexto empresarial, também a academia tem oferecido uma evolução considerável nos estudos que atestam como o RI tem evoluído ao longo do tempo, passando de estudos concetuais e normativos, para análises empíricas, com o intuito de estudar o seu efeito nas organizações. O número crescente de artigos publicados em revistas prestigiadas conduz, naturalmente, a que comecem a ser publicadas, também, revisões de literatura sobre RI, que englobam diversos temas, e que atingiram em 2022 um número considerável. Neste contexto, este estudo visa efetuar uma análise bibliométrica às sucessivas revisões de literatura sobre o RI publicadas por diferentes investigadores. Após a aplicação do PRISMA, a análise incide sobre 36 revisões de literatura, provenientes de 25 journals indexados na Scopus, entre 2014 e 2022. São também utilizados o método CASP e o Readability Analyzer para avaliar a qualidade do artigo e a legibilidade do texto, respetivamente. Este estudo tem como objetivo responder a duas questões de investigação fulcrais: (i) Como é que a literatura tem evoluído ao longo do tempo em termos de citações, publicações e journals; (ii) como se caracteriza a literatura relativamente à qualidade. Os principais resultados sugerem que as revisões de literatura registam um número considerável de citações e numa trajetória ascendente ao longo dos anos sendo a grande maioria publicada em journals de Contabilidade, o que demonstra a importante contribuição desta área para o crescimento do RI. Em termos de qualidade, são bem estruturadas, apresentam um bom conteúdo e são úteis no desenvolvimento da literatura do RI. Adicionalmente, os artigos apresentam valores consistentes em todas as métricas de avaliação da legibilidade do texto, o que significa ter sido utilizado um tipo de vocabulário avançado e congruente.info:eu-repo/semantics/publishedVersio

Training machine learning models with synthetic data improves the prediction of ventricular origin in outflow tract ventricular arrhythmias

In order to determine the site of origin (SOO) in outflow tract ventricular arrhythmias (OTVAs) before an ablation procedure, several algorithms based on manual identification of electrocardiogram (ECG) features, have been developed. However, the reported accuracy decreases when tested with different datasets. Machine learning algorithms can automatize the process and improve generalization, but their performance is hampered by the lack of large enough OTVA databases. We propose the use of detailed electrophysiological simulations of OTVAs to train a machine learning classification model to predict the ventricular origin of the SOO of ectopic beats. We generated a synthetic database of 12-lead ECGs (2,496 signals) by running multiple simulations from the most typical OTVA SOO in 16 patient-specific geometries. Two types of input data were considered in the classification, raw and feature ECG signals. From the simulated raw 12-lead ECG, we analyzed the contribution of each lead in the predictions, keeping the best ones for the training process. For feature-based analysis, we used entropy-based methods to rank the obtained features. A cross-validation process was included to evaluate the machine learning model. Following, two clinical OTVA databases from different hospitals, including ECGs from 365 patients, were used as test-sets to assess the generalization of the proposed approach. The results show that V2 was the best lead for classification. Prediction of the SOO in OTVA, using both raw signals or features for classification, presented high accuracy values (>0.96). Generalization of the network trained on simulated data was good for both patient datasets (accuracy of 0.86 and 0.84, respectively) and presented better values than using exclusively real ECGs for classification (accuracy of 0.84 and 0.76 for each dataset). The use of simulated ECG data for training machine learning-based classification algorithms is critical to obtain good SOO predictions in OTVA compared to real data alone. The fast implementation and generalization of the proposed methodology may contribute towards its application to a clinical routine.Copyright © 2022 Doste, Lozano, Jimenez-Perez, Mont, Berruezo, Penela, Camara and Sebastian

Training machine learning models with synthetic data improves the prediction of ventricular origin in outflow tract ventricular arrhythmias

In order to determine the site of origin (SOO) in outflow tract ventricular arrhythmias (OTVAs) before an ablation procedure, several algorithms based on manual identification of electrocardiogram (ECG) features, have been developed. However, the reported accuracy decreases when tested with different datasets. Machine learning algorithms can automatize the process and improve generalization, but their performance is hampered by the lack of large enough OTVA databases. We propose the use of detailed electrophysiological simulations of OTVAs to train a machine learning classification model to predict the ventricular origin of the SOO of ectopic beats. We generated a synthetic database of 12-lead ECGs (2,496 signals) by running multiple simulations from the most typical OTVA SOO in 16 patient-specific geometries. Two types of input data were considered in the classification, raw and feature ECG signals. From the simulated raw 12-lead ECG, we analyzed the contribution of each lead in the predictions, keeping the best ones for the training process. For feature-based analysis, we used entropy-based methods to rank the obtained features. A cross-validation process was included to evaluate the machine learning model. Following, two clinical OTVA databases from different hospitals, including ECGs from 365 patients, were used as test-sets to assess the generalization of the proposed approach. The results show that V2 was the best lead for classification. Prediction of the SOO in OTVA, using both raw signals or features for classification, presented high accuracy values (>0.96). Generalization of the network trained on simulated data was good for both patient datasets (accuracy of 0.86 and 0.84, respectively) and presented better values than using exclusively real ECGs for classification (accuracy of 0.84 and 0.76 for each dataset). The use of simulated ECG data for training machine learning-based classification algorithms is critical to obtain good SOO predictions in OTVA compared to real data alone. The fast implementation and generalization of the proposed methodology may contribute towards its application to a clinical routine

Hyperglycemic Myocardial Damage Is Mediated by Proinflammatory Cytokine: Macrophage Migration Inhibitory Factor

Diabetes has been regarded as an inflammatory condition which is associated with left ventricular diastolic dysfunction (LVDD). The purpose of this study was to examine the expression levels of macrophage migration inhibitory factor (MIF) and G protein-coupled receptor kinase 2 (GRK2) in patients with early diabetic cardiomyopathy, and to investigate the mechanisms involved in MIF expression and GRK2 activation.83 patients in the age range of 30-64 years with type 2 diabetes and 30 matched healthy men were recruited. Left ventricular diastolic function was evaluated by cardiac Doppler echocardiography. Plasma MIF levels were determined by ELISA. To confirm the clinical observation, we also studied MIF expression in prediabetic rats with impaired glucose tolerance (IGT) and relationship between MIF and GRK2 expression in H9C2 cardiomyoblasts exposed to high glucose.Compared with healthy subjects, patients with diabetes have significantly increased levels of plasma MIF which was further increased in diabetic patients with Left ventricular diastolic dysfunction (LVDD). The increased plasma MIF levels in diabetic patients correlated with plasma glucose, glycosylated hemoglobin and urine albumin levels. We observed a significant number of TUNEL-positive cells in the myocardium of IGT-rats but not in the control rats. Moreover, we found higher MIF expression in the heart of IGT with cardiac dysfunction compared to that of the controls. In H9C2 cardiomyoblast cells, MIF and GRK2 expression was significantly increased in a glucose concentration-dependant manner. Furthermore, GRK2 expression was abolished by siRNA knockdown of MIF and by the inhibition of CXCR4 in H9C2 cells.Our findings indicate that hyperglycemia is a causal factor for increased levels of pro-inflammatory cytokine MIF which plays a role in the development of cardiomyopathy occurring in patients with type 2 diabetes. The elevated levels of MIF are associated with cardiac dysfunction in diabetic patients, and the MIF effects are mediated by GRK2

A Dual Receptor Crosstalk Model of G-Protein-Coupled Signal Transduction

Macrophage cells that are stimulated by two different ligands that bind to G-protein-coupled receptors (GPCRs) usually respond as if the stimulus effects are additive, but for a minority of ligand combinations the response is synergistic. The G-protein-coupled receptor system integrates signaling cues from the environment to actuate cell morphology, gene expression, ion homeostasis, and other physiological states. We analyze the effects of the two signaling molecules complement factors 5a (C5a) and uridine diphosphate (UDP) on the intracellular second messenger calcium to elucidate the principles that govern the processing of multiple signals by GPCRs. We have developed a formal hypothesis, in the form of a kinetic model, for the mechanism of action of this GPCR signal transduction system using data obtained from RAW264.7 macrophage cells. Bayesian statistical methods are employed to represent uncertainty in both data and model parameters and formally tie the model to experimental data. When the model is also used as a tool in the design of experiments, it predicts a synergistic region in the calcium peak height dose response that results when cells are simultaneously stimulated by C5a and UDP. An analysis of the model reveals a potential mechanism for crosstalk between the Gαi-coupled C5a receptor and the Gαq-coupled UDP receptor signaling systems that results in synergistic calcium release

Neutrophil Paralysis in Plasmodium vivax Malaria

Plasmodium vivax is responsible for approximately 60–80% of the malaria cases in the world, and contributes to significant social and economic instability in the developing countries of Latin America and Asia. The pathogenesis of P. vivax malaria is a consequence of host derived inflammatory mediators. Hence, a better understanding of the mechanisms involved in induction of systemic inflammation during P. vivax malaria is critical for the clinical management and prevention of severe disease. The innate immune receptors recognize Plasmodium sp. and initiate a broad spectrum of host defense mechanisms that mediate resistance to infection. However, the innate immune response is the classic “two-edged sword”, and clinical malaria is associated with high levels of circulating pro-inflammatory cytokines. Our findings show that both monocytes and neutrophils are highly activated during malaria. Monocytes produced high levels of IL-1β, IL-6 and TNF-α during acute malaria. On the other hand, neutrophils were a poor source of cytokines, but displayed an enhanced phagocytic activity and superoxide production. Unexpectedly, we noticed an impaired chemotaxis of neutrophils towards an IL-8 (CXCL8) gradient. We proposed that neutrophil paralysis is in part responsible for the enhanced susceptibility to bacterial infection observed in malaria patients