On how religions could accidentally incite lies and violence: folktales as a cultural transmitter

Folklore has a critical role as a cultural transmitter, all the while being a socially accepted medium for the expressions of culturally contradicting wishes and conducts. In this study of Vietnamese folktales, through the use of Bayesian multilevel modeling and the Markov chain Monte Carlo technique, we offer empirical evidence for how the interplay between religious teachings (Confucianism, Buddhism, and Taoism) and deviant behaviors (lying and violence) could affect a folktale’s outcome. The findings indicate that characters who lie and/or commit violent acts tend to have bad endings, as intuition would dictate, but when they are associated with any of the above Three Teachings, the final endings may vary. Positive outcomes are seen in cases where characters associated with Confucianism lie and characters associated with Buddhism act violently. The results supplement the worldwide literature on discrepancies between folklore and real-life conduct, as well as on the contradictory human behaviors vis-à-vis religious teachings. Overall, the study highlights the complexity of human decision-making, especially beyond the folklore realm

How Digital Natives Learn and Thrive in the Digital Age: Evidence from an Emerging Economy

As a generation of ‘digital natives,’ secondary students who were born from 2002 to 2010 have various approaches to acquiring digital knowledge. Digital literacy and resilience are crucial for them to navigate the digital world as much as the real world; however, these remain under-researched subjects, especially in developing countries. In Vietnam, the education system has put considerable effort into teaching students these skills to promote quality education as part of the United Nations-defined Sustainable Development Goal 4 (SDG4). This issue has proven especially salient amid the COVID−19 pandemic lockdowns, which had obliged most schools to switch to online forms of teaching. This study, which utilizes a dataset of 1061 Vietnamese students taken from the United Nations Educational, Scientific, and Cultural Organization (UNESCO)’s “Digital Kids Asia Pacific (DKAP)” project, employs Bayesian statistics to explore the relationship between the students’ background and their digital abilities. Results show that economic status and parents’ level of education are positively correlated with digital literacy. Students from urban schools have only a slightly higher level of digital literacy than their rural counterparts, suggesting that school location may not be a defining explanatory element in the variation of digital literacy and resilience among Vietnamese students. Students’ digital literacy and, especially resilience, also have associations with their gender. Moreover, as students are digitally literate, they are more likely to be digitally resilient. Following SDG4, i.e., Quality Education, it is advisable for schools, and especially parents, to seriously invest in creating a safe, educational environment to enhance digital literacy among students

Evaluating an automatic data extraction tool for evidence synthesis through real-life case studies

Computer support tools are increasingly designed to help reduce the time required for evidence synthesis tasks such as manually extracting information from scientific papers. We present two case studies evaluating RobotReviewer, an automatic data extraction tool used to help synthesize evidence from primary research papers for use in review papers. We use primary research papers related to oral health and dental medicine for both our case studies. The first case study uses the same published review we presented at last year's Research Showcase, with a new evaluation metric and 3 novice annotators. Through manual annotation and a content analysis of the six studies synthesized in the review paper, we compare how well (1) novices and (2) the RobotReviewer data extraction compare to the Cochrane Review paper (seen here as a expert gold standard). (Feedback at last year's Research Showcase was instrumental, and we have improved the methodology we presented last year in several ways; for instance those preliminary results had just one novice annotator.) The second case study is based on a real systematic review project being conducted in part at the School of Dental Medicine at the University of Buffalo. Real reviewers collaborate by comparing RobotReviewer's results with their own manual extraction results and describing how well it meets their expectations. By evaluating existing tools such as RobotReviewer, we would be able to identify gaps between what computer support tools are available and how well these tools work. This could help propose new directions for automated support systems that would help to reduce the time and human labor required for evidence synthesis tasks such as data extraction.R01LM010817Ope

Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks

Federated Learning (FL) has recently become an effective approach for cyberattack detection systems, especially in Internet-of-Things (IoT) networks. By distributing the learning process across IoT gateways, FL can improve learning efficiency, reduce communication overheads and enhance privacy for cyberattack detection systems. Challenges in implementation of FL in such systems include unavailability of labeled data and dissimilarity of data features in different IoT networks. In this paper, we propose a novel collaborative learning framework that leverages Transfer Learning (TL) to overcome these challenges. Particularly, we develop a novel collaborative learning approach that enables a target network with unlabeled data to effectively and quickly learn knowledge from a source network that possesses abundant labeled data. It is important that the state-of-the-art studies require the participated datasets of networks to have the same features, thus limiting the efficiency, flexibility as well as scalability of intrusion detection systems. However, our proposed framework can address these problems by exchanging the learning knowledge among various deep learning models, even when their datasets have different features. Extensive experiments on recent real-world cybersecurity datasets show that the proposed framework can improve more than 40% as compared to the state-of-the-art deep learning based approaches.Comment: 12 page

Co-infection of human parvovirus B19 with Plasmodium falciparum contributes to malaria disease severity in Gabonese patients

Background: High seroprevalence of parvovirus B19 (B19V) coinfection with Plasmodium falciparum has been previously reported. However, the impact of B19V-infection on the clinical course of malaria is still elusive. In this study, we investigated the prevalence and clinical significance of B19V co-infection in Gabonese children with malaria. Methods: B19V prevalence was analyzed in serum samples of 197 Gabonese children with P. falciparum malaria and 85 healthy controls using polymerase chain reaction (PCR), enzyme-linked immunosorbent assay (ELISA), and direct DNA-sequencing. Results: B19V was detected in 29/282 (10.28%) of Gabonese children. B19V was observed more frequently in P. falciparum malaria patients (14.21%) in comparison to healthy individuals (1.17%) (

Large scale physiological readjustment during growth enables rapid, comprehensive and inexpensive systems analysis

Abstract Background Rapidly characterizing the operational interrelationships among all genes in a given organism is a critical bottleneck to significantly advancing our understanding of thousands of newly sequenced microbial and eukaryotic species. While evolving technologies for global profiling of transcripts, proteins, and metabolites are making it possible to comprehensively survey cellular physiology in newly sequenced organisms, these experimental techniques have not kept pace with sequencing efforts. Compounding these technological challenges is the fact that individual experiments typically only stimulate relatively small-scale cellular responses, thus requiring numerous expensive experiments to survey the operational relationships among nearly all genetic elements. Therefore, a relatively quick and inexpensive strategy for observing changes in large fractions of the genetic elements is highly desirable. Results We have discovered in the model organism Halobacterium salinarum NRC-1 that batch culturing in complex medium stimulates meaningful changes in the expression of approximately two thirds of all genes. While the majority of these changes occur during transition from rapid exponential growth to the stationary phase, several transient physiological states were detected beyond what has been previously observed. In sum, integrated analysis of transcript and metabolite changes has helped uncover growth phase-associated physiologies, operational interrelationships among two thirds of all genes, specialized functions for gene family members, waves of transcription factor activities, and growth phase associated cell morphology control. Conclusions Simple laboratory culturing in complex medium can be enormously informative regarding the activities of and interrelationships among a large fraction of all genes in an organism. This also yields important baseline physiological context for designing specific perturbation experiments at different phases of growth. The integration of such growth and perturbation studies with measurements of associated environmental factor changes is a practical and economical route for the elucidation of comprehensive systems-level models of biological systems

Depression, anxiety and stress among healthcare workers in the context of the COVID-19 pandemic: a cross-sectional study in a tertiary hospital in Northern Vietnam

IntroductionThe outbreak of coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) had significant effects on the mental well-being in general, particularly for healthcare professionals. This study examined the prevalence of depression, anxiety, and stress, and identified the associated risk factors amongst healthcare workers during the COVID-19 outbreak in a tertiary hospital located in Vietnam.MethodsWe conducted a cross-sectional study at a tertiary-level hospital, where the Depression Anxiety and Stress Scale 21 (DASS-21) web-based questionnaire was employed. We analyzed the determinant factors by employing multivariate logistic models.ResultsThe prevalence of depression, anxiety, and stress symptoms were 19.2%, 24.7%, and 13.9%, respectively. Factors such as engaging in shift work during the pandemic, taking care of patients with COVID-19, and staff’s health status were associated with mental health issues among health professionals. In addition, having alternate rest periods was likely to reduce the risk of stress.ConclusionThe prevalence of mental health problems in healthcare workers during the COVID-19 pandemic was relatively high. Having resting periods could potentially mitigate the development of stress among health professionals. Our findings could be taken into account for improving mental health of the health professional population

Caspase-1 causes truncation and aggregation of the Parkinson's disease-associated protein α-synuclein

The aggregation of α-synuclein (aSyn) leading to the formation of Lewy bodies is the defining pathological hallmark of Parkinson's disease (PD). Rare familial PD-associated mutations in aSyn render it aggregation-prone; however, PD patients carrying wild type (WT) aSyn also have aggregated aSyn in Lewy bodies. The mechanisms by which WT aSyn aggregates are unclear. Here, we report that inflammation can play a role in causing the aggregation of WT aSyn. We show that activation of the inflammasome with known stimuli results in the aggregation of aSyn in a neuronal cell model of PD. The insoluble aggregates are enriched with truncated aSyn as found in Lewy bodies of the PD brain. Inhibition of the inflammasome enzyme caspase-1 by chemical inhibition or genetic knockdown with shRNA abated aSyn truncation. In vitro characterization confirmed that caspase-1 directly cleaves aSyn, generating a highly aggregation-prone species. The truncation-induced aggregation of aSyn is toxic to neuronal culture, and inhibition of caspase-1 by shRNA or a specific chemical inhibitor improved the survival of a neuronal PD cell model. This study provides a molecular link for the role of inflammation in aSyn aggregation, and perhaps in the pathogenesis of sporadic PD as well