Assessment of the competence in electrocardiographic interpretation among Arabic resident doctors at the emergency medicine and internal medicine departments: A multi-center online cross-sectional study.

Background: This study aims to assess the electrocardiographic interpretation abilities of resident doctors at internal medicine and emergency medicine departments in eight Arabic countries. Methods: An online cross-sectional study was conducted between October 7, 2022 and October 21, 2022 in eight Arabic countries. The questionnaire consisted of two main sections: the first section included sociodemographic information, while the second section contained 12 clinical case questions of the most severe cardiac abnormalities with their electrocardiography (ECG) recordings. Results: Out of 2,509 responses, 630 were eligible for the data analysis. More than half of the participants were males (52.4%). Internal medicine residents were (n = 530, 84.1%), whereas emergency medicine residents were (n = 100, 15.9%). Almost participants were in their first or second years of residency (79.8%). Only 36.2% of the inquired resident doctors had attended an ECG course. Most participants, 85.6%, recognized the ECG wave order correctly, and 50.5% of the participants scored above 7.5/10 on the ECG interpretation scale. The proportions of participants who were properly diagnosed with atrial fibrillation, third-degree heart block, and atrial tachycardia were 71.1, 76.7, and 56.6%, respectively. No statistically significant difference was defined between the internal and emergency medicine residents regarding their knowledge of ECG interpretation (p value = 0.42). However, there was a significant correlation between ECG interpretation and medical residency year (p value < 0.001); the fourth-year resident doctors had the highest scores (mean = 9.24, SD = 1.6). As well, participants in the third and second years of postgraduate medical residency have a probability of adequate knowledge of ECG interpretation more than participants in the first year of residency (OR = 2.1, p value = 0.001) and (OR = 1.88, p value = 0.002), respectively. Conclusion: According to our research findings, resident doctors in departments of internal medicine and emergency medicine in Arabic nations have adequate ECG interpretation abilities; nevertheless, additional development is required to avoid misconceptions about critical cardiac conditions.

Green food supply chain management as a solution for the mitigation of food supply chain management risk for improving the environmental health level.

Purpose: In the context of the food industry, this research investigates the impact of green supply chain management (GSCM) on environmental health. This helps the practitioners and policy makers in mitigation of the supply chain (SC) risks and enhance the environmental health level. Design/methodology/approach: The study's model was structured using GSC risk factors of green purchasing, environmental cooperation, reverse logistics, eco design, internal environmental management, and investment recovery. A questionnaire-based survey was used to examine the proposed model; 102 data from the senior managers of food firms in Lebanon were collected. Using SPSS and AMOS statistical software, an exploratory factor analysis (EFA), a confirmatory factor analysis (CFA), and multiple regressions have been applied. Findings: The outcome of structural equation modeling (SEM) revealed that four of six GSC risk factors were significantly related to environmental health. The study findings can be applied to the external level through many green practices in cooperation with suppliers and customers like the collaboration with them on environmentally friendly design, purchasing, production, packaging and using less energy. This can increase the level of environmental health by decreasing the impact of SCM risks. Regarding the internal level, many procedures and decisions may lead to an environmentally friendly ambience in the firms like the commitment of GSCM practices from the management and the implementation of internal eco-performance evaluation system. This may enhance the environmental health provisions by setting up an action plan to mitigate the GSC risk and address the sustainable health objectives. Originality: The paper's distinctiveness comes from the fact that it fills a gap in the literature regarding the few numbers of studies that treat the green supply chain management GSCM as mitigation solution for the risks of SCM. In addition, there were no studies explain the relationship between GSCM and environmental health; this will be the first time the assess the impacts of GSCM practices on environmental health in the food industry.

Do the Right Thing: A Privacy Policy Adherence Analysis of over Two Million Apps in Apple iOS App Store.

Mobile app developers are often obliged by regulatory frameworks to provide a privacy policy in natural comprehensible language to describe their apps' privacy practices. However, prior research has revealed that: (1) not all app developers offer links to their privacy policies; and (2) even if they do offer such access, it is difficult to determine if it is a valid link to a (valid) policy. While many prior studies looked at this issue in Google Play Store, Apple App Store, and particularly the iOS store, is much less clear. In this paper, we conduct the first and the largest study to investigate the previous issues in the iOS app store ecosystem. First, we introduce an App Privacy Policy Extractor (APPE), a system that embraces and analyses the metadata of over two million apps to give insightful information about the distribution of the supposed privacy policies, and the content of the provided privacy policy links, store-wide. The result shows that only 58.5% of apps provide links to purported privacy policies, while 39.3% do not provide policy links at all. Our investigation of the provided links shows that only 38.4% of those links were directed to actual privacy policies, while 61.6% failed to lead to a privacy policy. Further, for research purposes we introduce the App Privacy Policy Corpus (APPC-451K); the largest app privacy policy corpus consisting of data relating to more than 451K verified privacy policies.

Cancer Burden Among Arab World Males in 2020: The Need for a Better Approach to Improve Outcome.

PURPOSE: Cancer is a leading cause of increased morbidity and mortality worldwide. This work aims to study the Arab world males' cancers (AMCs) and the similarities and disparities with the world males' cancers (WMCs) from different burden points of view. MATERIALS AND METHODS: A descriptive review of the 2020 Global Cancer Observatory revealed AMCs compared with the 2020 WMCs and the 2018 AMCs. Data on the top 27 AMCs were compared among the region's countries and the world groups. RESULTS: In 2020, a total estimate of 217,203 new AMCs, 2.2% of WMCs, with an average age-standardized rate of 133.5/100,000 population, compared with 222/100,000 population of WMCs, was observed. Death estimates were 148,395, 2.7% of WMCs, with an average age-standardized rate of 95/100,000 population, compared with 120.8/100,000 population of WMCs. The five-year prevalence was observed in 442,014, 1.8% of WMCs. The average AMC mortality to incidence ratio (MIR) was 0.68, compared with 0.55 in WMCs and 0.54 in Arab females. Lung cancer was the top in incidence and mortality, whereas penile cancer was the lowest. The range of MIRs among the 27 cancer types was 0.19-0.96. CONCLUSION: The descriptive review of the 2020 males' cancers in the Arab world revealed a relatively high MIR, compared with males' cancers worldwide and the females' cancers in the Arab world. This requires further evaluation to discern the underlying causes and address them systematically. More cancer control actions are warranted.

p53 directs leader cell behavior, migration, and clearance during epithelial repair.

Epithelial cells migrate across wounds to repair injured tissue. Leader cells at the front of migrating sheets often drive this process. However, it is unclear how leaders emerge from an apparently homogeneous epithelial cell population. We characterized leaders emerging from epithelial monolayers in cell culture and found that they activated the stress sensor p53, which was sufficient to initiate leader cell behavior. p53 activated the cell cycle inhibitor p21WAF1/CIP1, which in turn induced leader behavior through inhibition of cyclin-dependent kinase activity. p53 also induced crowding hypersensitivity in leader cells such that, upon epithelial closure, they were eliminated by cell competition. Thus, mechanically induced p53 directs emergence of a transient population of leader cells that drive migration and ensures their clearance upon epithelial repair.

AutoSEIR: Accurate Forecasting from Real-time Epidemic Data Using Machine Learning

Since the SARS-CoV-2 virus outbreak has been recognized as a pandemic on March 11, 2020, several models have been proposed to forecast its evolution following the governments interventions. In particular, the need for fine-grained predictions, based on real-time and fluctuating data, has highlighted the limitations of traditional SEIR models and parameter fitting, encouraging the study of new models for greater accuracy. In this paper we propose a novel approach to epidemiological parameter fitting and epidemic forecasting, based on an extended version of the SEIR compartmental model and on an auto-differentiation technique for partially observable ODEs (Ordinary Differential Equations). The results on publicly available data show that the proposed model is able to fit the daily cases curve with greater accuracy, obtaining also a lower forecast error. Furthermore, the forecast accuracy allows to predict the peak with an error margin of less than one week, up to 50 days before the peak happens.

Sequence and Phylogenetic Analysis of the First Complete Genome of Ricetungro spherical virus in Malaysia.

BACKGROUND: Rice tungro disease (RTD) is a viral disease mainly affecting rice in Asia. RTD caused by Rice tungro bacilliform virus and Rice tungro spherical virus. To date, there are only 5 RTSV isolates have been reported. OBJECTIVES: In this study, we aimed to report the complete nucleotide sequence of Malaysian isolate of Rice tungro spherical virus Seberang Perai (RTSV-SP) for the first time. RTSV-SP was characterized and its evolutionary relationship with previously reported Indian and Philippines isolates were elucidated. MATERIALS AND METHODS: RTSV-SP isolate was isolated from a recent outbreak in a paddy field in Seberang Perai zone of Malaysia. Its complete genome was amplified by RT-PCR, cloned and sequenced. RESULTS: Sequence analysis indicated that the genome of RTSV-SP consisted of 12,173 nucleotides (nt). Comparative analysis of 6 complete genome sequences using Clustal Omega showed that Seberang Perai isolate shared the highest nucleotide identity (96.04%) with Philippine-A isolate, except that the sORF-2 of RTSV-SP is shorter than RTSV Philippine-A by 27 amino acid residues. RTSV-SP found to cluster in Southeast Asia (SEA) group based on the whole genome sequence phylogenetic analysis using MEGA X software. CONCLUSIONS: Phylogenetic classification of RTSV isolates based on the complete nucleotide sequences showed more distinctive clustering pattern with the addition of RTSV-SP whole genome to the available isolates. Present study described the isolation and molecular characterization of RTSV-SP.

Neural correlates of eye contact in face-to-face verbal interaction: An EEG-based study of the extraversion personality trait.

The extraversion personality trait has a positive correlation with social interaction. In neuroimaging studies, investigations on extraversion in face-to-face verbal interactions are still scarce. This study presents an electroencephalography (EEG)-based investigation of the extraversion personality trait in relation to eye contact during face-to-face interactions, as this is a vital signal in social interactions. A sample of healthy male participants were selected (consisting of sixteen more extraverted and sixteen less extraverted individuals) and evaluated with the Eysenck's Personality Inventory (EPI) and Big Five Inventory (BFI) tools. EEG alpha oscillations in the occipital region were measured to investigate extraversion personality trait correlates of eye contact during a face-to-face interaction task and an eyes-open condition. The results revealed that the extraversion personality trait has a significant positive correlation with EEG alpha coherence in the occipital region, presumably due to its relationship with eye contact during the interaction task. Furthermore, the decrease in EEG alpha power during the interaction task compared to the eyes-open condition was found to be greater in the less extraverted participants; however, no significant difference was observed between the less and more extraverted participants. Overall, these findings encourage further research towards the understanding of neural mechanism correlates of the extraversion personality trait-particularly in social interaction.

Mitigation of stress: new treatment alternatives.

Complaints of stress are common in modern life. Psychological stress is a major cause of lifestyle-related issues, contributing to poor quality of life. Chronic stress impedes brain function, causing impairment of many executive functions, including working memory, decision making and attentional control. The current study sought to describe newly developed stress mitigation techniques, and their influence on autonomic and endocrine functions. The literature search revealed that the most frequently studied technique for stress mitigation was biofeedback (BFB). However, evidence suggests that neurofeedback (NFB) and noninvasive brain stimulation (NIBS) could potentially provide appropriate approaches. We found that recent studies of BFB methods have typically used measures of heart rate variability, respiration and skin conductance. In contrast, studies of NFB methods have typically utilized neurocomputation techniques employing electroencephalography, functional magnetic resonance imaging and near infrared spectroscopy. NIBS studies have typically utilized transcranial direct current stimulation methods. Mitigation of stress is a challenging but important research target for improving quality of life.

Varietal Resistance Against the Rice Water Weevil in Field and Greenhouse Studies.

The resistance of commercial rice (Oryza sativa L.) varieties widely grown in Louisiana was assessed against the rice water weevil, Lissorhoptrus oryzophilus Kuschel (Coleoptera: Curculionidae), the major insect pest of rice in the United States, in a 5-yr field study that included conventional inbred, herbicide-tolerant, and hybrid varieties. Resistance was evaluated by comparing densities of immature rice water weevils (larvae and pupae) in root-soil core samples taken at two time points after flooding. Randomized block experiments were conducted in two different locations to enable identification of potentially resistant varieties over diverse environments. There were small but significant differences in the resistance of commercial varieties over the 5-yr field study. The variety 'Jefferson' was found to support larval densities 6-70% lower than other varieties, while 'Jupiter' often supported higher larval densities. Greenhouse experiments evaluated adult preference for oviposition and survivorship of larvae on different varieties. Females exhibited limited ovipositional preference for varieties: numbers of weevil eggs per plant differed significantly among varieties in choice tests but not in no-choice tests, while first instar densities in both choice and no-choice tests showed no significant differences among varieties. Analysis of data from both choice and no-choice tests showed that numbers of late instars and pupae differed significantly among varieties, suggesting presence of antibiosis in some cultivars. Our results suggest that none of the varieties tested possess high levels of resistance to rice water weevil infestation, although 'Jupiter' appears to be more susceptible than other varieties and 'Jefferson' appears to be somewhat more resistant.

A Bi-Criteria Active Learning Algorithm for Dynamic Data Streams.

Active learning (AL) is a promising way to efficiently build up training sets with minimal supervision. A learner deliberately queries specific instances to tune the classifier's model using as few labels as possible. The challenge for streaming is that the data distribution may evolve over time, and therefore the model must adapt. Another challenge is the sampling bias where the sampled training set does not reflect the underlying data distribution. In the presence of concept drift, sampling bias is more likely to occur as the training set needs to represent the whole evolving data. To tackle these challenges, we propose a novel bi-criteria AL (BAL) approach that relies on two selection criteria, namely, label uncertainty criterion and density-based criterion. While the first criterion selects instances that are the most uncertain in terms of class membership, the latter dynamically curbs the sampling bias by weighting the samples to reflect on the true underlying distribution. To design and implement these two criteria for learning from streams, BAL adopts a Bayesian online learning approach and combines online classification and online clustering through the use of online logistic regression and online growing Gaussian mixture models, respectively. Empirical results obtained on standard synthetic and real-world benchmarks show the high performance of the proposed BAL method compared with the state-of-the-art AL methods.

Active learning for classifying data streams with unknown number of classes.

The classification of data streams is an interesting but also a challenging problem. A data stream may grow infinitely making it impractical for storage prior to processing and classification. Due to its dynamic nature, the underlying distribution of the data stream may change over time resulting in the so-called concept drift or the possible emergence and fading of classes, known as concept evolution. In addition, acquiring labels of data samples in a stream is admittedly expensive if not infeasible at all. In this paper, we propose a novel stream-based active learning algorithm (SAL) which is capable of coping with both concept drift and concept evolution by adapting the classification model to the dynamic changes in the stream. SAL is the first AL algorithm in the literature to explicitly take account of these concepts. Moreover, using SAL, only labels of samples that are expected to reduce the expected future error are queried. This process is done while tackling the problem of sampling bias so that samples that induce the change (i.e., drifting samples or samples coming from new classes) are queried. To efficiently implement SAL, the paper proposes the application of non-parametric Bayesian models allowing to cope with the lack of prior knowledge about the data stream. In particular, Dirichlet mixture models and the stick breaking process are adopted and adapted to meet the requirements of online learning. The empirical results obtained on real-world benchmarks demonstrate the superiority of SAL in terms of classification performance over the state-of-the-art methods using average and average class accuracy.