Benign Fasciculation Syndrome and Migraine Aura without Headache: Possible Rare Side Effects of the BNT162b2 mRNA Vaccine? A Case Report and a Potential Hypothesis

The BNT162b2 (Pfizer BioNTech) mRNA vaccine is an effective vaccine against COVID-19 infection. Here, we report an adverse event following immunization (AEFI) in a 48-year-old female patient who presented with fasciculations, migraine auras without headaches and in an increased discomfort of previously present palpitations, as well as excitation and insomnia. Her fasciculations were intermittently present until the time this paper was written, starting from the 6th day post-vaccination; they changed localization and frequency, but most commonly they were generalized, affecting almost all muscle groups. The patient also suffered from two incidents of migraine auras with visual kaleidoscope-like phenomena without headaches a few months after the vaccination. These symptoms were considered to be AEFI and no causal relation with the vaccine could be proven.

When Elon Musk Changes his Tone, Does Bitcoin Adjust Its Tune?

We present a textual analysis that explains how Elon Musk’s sentiments in his Twitter content correlates with price and volatility in the Bitcoin market using the dynamic conditional correlation-generalized autoregressive conditional heteroscedasticity model, allowing less sensitive to window size than traditional models. After examining 10,850 tweets containing 157,378 words posted from December 2017 to May 2021 and rigorously controlling other determinants, we found that the tone of the world’s wealthiest person can drive the Bitcoin market, having a Granger causal relation with returns. In addition, Musk is likely to use positive words in his tweets, and reversal effects exist in the relationship between Bitcoin prices and the optimism presented by Tesla’s CEO. However, we did not find evidence to support linkage between Musk’s sentiments and Bitcoin volatility. Our results are also robust when using a different cryptocurrency, i.e., Ether this paper extends the existing literature about the mechanisms of social media content generated by influential accounts on the Bitcoin market.

Investigating the potential role of innovation and clean energy in mitigating the ecological footprint in N11 countries

This paper seeks to explore the potential function of technological innovation and clean power in mitigating the ecological footprint in the N-11 nations during the phase 1992–2015 by applying panel cointegration analysis. The outcomes of the panel cointegration test signify the occurrence of a long-run relation among the clean energy (CE) variable, the ecological footprint (EF) variable, the per capita GDP (Y) variable, the financial development (FIN) variable, and technological innovation (TI) variable. The outcomes of the VECM signify a long-run causal relation from the ecological footprint (EF) variable to the clean energy (CE) variable, the GDP per capita (Y) variable, and technological innovation (TI) variable. This implies that the environmental degradation faced by the N-11 countries leads to shifting toward clean energy sources and technological innovation in the long run. Thus, the N-11 countries are in need to design policies that enhance shifting toward environmentally friendly energy sources.

Case Report: Anorexia as a new type of adverse reaction caused by the COVID-19 vaccination: a case report applying detailed personal care records

Background: In Japan, more than 1,000 participants died shortly after receiving the coronavirus disease 2019 (COVID-19) vaccine, but the causal relation between the injection and death remains uncertain. Methods: Applying long-term personal vital care data for 28 months for an elderly patient, I investigated and evidenced adverse reactions after the first dose of the COVID-19 Pfizer vaccination. Results: The precise, detailed, and continuous data statistically clarified the long-term fevers associated with no meals or drinks. Interrupted time series analysis showed significant and fluctuating increases of body temperatures, pressures, and pulses, although solely long-term plots showed an abrupt and timely increase in these vital data after the vaccine. Conclusions: Anorexia was fatal, and newly reported in the present care records since the patient received the first dose of the COVID-19 vaccine.

Prioritizing Different Types of Barriers to Knowledge Sharing: A Cause-and-Effect Analysis of the Views of Academics in Turkey

This study aims to identify different types of barriers to knowledge sharing among academics in Turkey and to investigate the relationships between the barriers and their degree of impact. Accordingly, it implements qualitative and quantitative approaches in two phases. In the first phase, the knowledge sharing barriers are identified through the literature review and categorized under organizational, individual, and technological dimensions via expert opinions so as to determine current barriers for the Turkish academics. In the second phase, the identified barriers and their interactions are more deeply investigated by using the Interpretive Structural Modeling (ISM) and Decision Making Trial and Evaluation Laboratory Method (DEMATEL) methods. The findings of the study reveal that organizational and individual knowledge sharing barriers have a stronger effect than technological barriers. According to ISM and DEMATEL findings, “corporate structure”, “power relations"”, and “supportive corporate culture” are the driving forces for the knowledge sharing among the academics in Turkey. This study provides a hierarchical and causal relation model that may enable both performing the actions needed to promote academic knowledge sharing and advancing university performances. The findings offer useful insights on what the key barriers are and how these interrelate, so that they can be overcome. Thus, the findings hold significant potential to contribute both to the academic field and to the policymakers who are in charge of taking regulatory actions.

Defining Causality in Covid-19 and Google Search Trends in Java, Indonesia Cases: A Retrospective Analysis

The Coronavirus disease 2019 (Covid-19) has led all countries around the world to the unpredicted situation. It is such a crucial to investigate novel approaches in predicting the future behaviour of the outbreak. In this paper, Google trend analysis will be employed to analyse the seek pattern of Covid-19 cases. The first method to investigate the seek information behaviour related to Covid-19 outbreak is using lag-correlation between two time series data per regional data. The second method is used to encounter the cause-effect relation between time series data. We apply statistical methods for causal inference in epidemics. Our focus is on predicting the causal-effect relationship between information-seeking patterns and Google search in the Covid-19 pandemic. We propose the using of Granger Causality method to analyse the causal relation between incidence data and Google Trend Data.

Test of Asymmetric Information in the Nepalese Reinsurance Market

Background: The imperfect information can cause an imbalance of power which may lead to market failure thus collection of information is very essential in today’s business world therefore, the availability of the correct and accurate information is very crucial for making sound economic decisions. Thus, information asymmetry has been a very pertinent issue where economic transaction takes place insurance market is not far behind. As, reinsurance provides huge indirect capital to the insurance industry, providing correct information’s like premium earned, claim by the insurer to the reinsurer’s for fair pricing of reinsurance premium along ensuring top rated reinsurance company remain in Nepalese insurance industry. Objectives: This study aims to examine whether there remains asymmetric information in Nepalese insurance market with reinsurer’s perspective in different portfolios such as fire, marine, motor, engineering and miscellaneous as well as combining all portfolio in aggregate. Methods: The study uses descriptive and causal relation research design. Further, the study uses secondary data of 14 nonlife insurance from 2008/09 to 2018/19 with 168 firm year observations Result: Results of the study revealed that that only in fire, marine and overall portfolios there exists strong asymmetric information. Rest of the portfolio like motor, engineering and miscellaneous there is no evidence of existence of asymmetric information. Conclusion: Existence of asymmetric information is mostly an inevitable part as one party always tries to avoid information to others for the sake of benefit. However, the existence of asymmetric information to a large extent may lead to unhealthy relations between the parties and may bring the cold war distortion of relations. Thus, the finding of the studies is beneficial to the Nepalese nonlife insurers as insurers need to provide correct and accurate information to the reinsures Implication: To cope with asymmetric information in the Nepalese insurance industry, this study provides strong evidence to provide correct and accurate information’s to the reinsurers else top rated reinsurers might withdraw their presence from the Nepalese market which will have adverse effect in the insurance industry.

Causal Discovery with Confounding Cascade Nonlinear Additive Noise Models

Identification of causal direction between a causal-effect pair from observed data has recently attracted much attention. Various methods based on functional causal models have been proposed to solve this problem, by assuming the causal process satisfies some (structural) constraints and showing that the reverse direction violates such constraints. The nonlinear additive noise model has been demonstrated to be effective for this purpose, but the model class does not allow any confounding or intermediate variables between a cause pair–even if each direct causal relation follows this model. However, omitting the latent causal variables is frequently encountered in practice. After the omission, the model does not necessarily follow the model constraints. As a consequence, the nonlinear additive noise model may fail to correctly discover causal direction. In this work, we propose a confounding cascade nonlinear additive noise model to represent such causal influences–each direct causal relation follows the nonlinear additive noise model but we observe only the initial cause and final effect. We further propose a method to estimate the model, including the unmeasured confounding and intermediate variables, from data under the variational auto-encoder framework. Our theoretical results show that with our model, the causal direction is identifiable under suitable technical conditions on the data generation process. Simulation results illustrate the power of the proposed method in identifying indirect causal relations across various settings, and experimental results on real data suggest that the proposed model and method greatly extend the applicability of causal discovery based on functional causal models in nonlinear cases.

The Causal Relation between Revenue and Expenditure of Bangladesh

Bangladesh is a developing country that has been experiencing budget deficits since its independence in 1971. It means the government spending has been exceeding the government revenue. This phenomenon calls for a study of government spending or expenditure and government revenue. This study tries to establish a causal relation between expenditure and revenue of governments of Bangladesh. To accomplish this, this study uses the Vector Autoregressive (VAR) model and the Granger Causality model on the data for the financial year from 1993-1994 to 2017-2018. The study reveals that in the context of Bangladesh, total revenue affects total expenditure, whereas total expenditure does not affect total revenue.

Brief Mindfulness Instruction Increases Prosocial Helping of an Ostracized Racial Outgroup Member

Two experiments tested whether brief instruction in mindfulness increased helping behavior toward an ostracized racial outgroup member by enhancing empathic concern. In Study 1, brief mindfulness instruction, relative to active and inactive control conditions, increased helping behavior toward an ostracized racial outgroup member in a private (but not in a public) context. In Study 2, which involved greater anonymity, mindfulness instruction increased both private and public helping behavior toward an ostracized racial outgroup member relative to the two control conditions. Importantly, measured empathic concern accounted for a portion of the variance in the causal relation between mindfulness and interracial helping behavior in Study 2. Together these studies suggest that brief mindfulness training increases interracial prosocial responsiveness in a digitally mediated context, particularly when personal anonymity was greater.