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e., insert, delete, update) queries on Big Social Data. We evaluate the performance of proposed framework in terms of provenance capturing overhead for different query sets including select, aggregate, and data update queries, and average execution time for various provenance queries.

The ancestral background of human cells may play a role in cells' behavior and response to therapeutic interventions in vitro. We investigate the prevalence of ancestry reporting in current biological research and suggest that increased reporting would be beneficial to the field.

Articles published over a six-month period in ten different journals were reviewed for their use of human primary cells and immortalized cell lines, and were analyzed based on whether or not the ancestral or ethnic information of cell donors was ascertainable.

The vast majority of literature published in the journals and timeframe we investigated did not report on the ancestral or ethnic origins of the human cells used.

There is currently a substantial lack of reporting on the ancestral background of human cells used for research. We suggest that increased ancestral reporting should be implemented in order to improve the development of precision medicine.

Many diseases affect patients of different ancestral backgrounds in a variety of ways. In this perspective article, we raise the concern that, since many scientists do not consider ancestry when designing their studies, their results may not apply to all patients. We use data to show that very few scientists report on the ancestry of the donors who contribute cells and tissues to their research. We suggest that broader reporting on donor ancestry would improve biomedical research and would help doctors to personalize treatments for their patients.Future work includes further increasing awareness of the importance of including ancestry as a variable in experimental design, as well as promoting increased reporting on ancestry in the research community.

The online version contains supplementary material available at 10.1007/s40883-021-00237-8.

The online version contains supplementary material available at 10.1007/s40883-021-00237-8.This article synthesizes findings from an international virtual conference, funded by the National Science Foundation (NSF), focused on the home mathematics environment (HME). In light of inconsistencies and gaps in research investigating relations between the HME and children's outcomes, the purpose of the conference was to discuss actionable steps and considerations for future work. The conference was composed of international researchers with a wide range of expertise and backgrounds. Presentations and discussions during the conference centered broadly on the need to better operationalize and measure the HME as a construct - focusing on issues related to child, family, and community factors, country and cultural factors, and the cognitive and affective characteristics of caregivers and children. Results of the conference and a subsequent writing workshop include a synthesis of core questions and key considerations for the field of research on the HME. Findings highlight the need for the field at large to use multi-method measurement approaches to capture nuances in the HME, and to do so with increased international and interdisciplinary collaboration, open science practices, and communication among scholars.In this paper, an empirical analysis of linear state space models and long short-term memory neural networks is performed to compare the statistical performance of these models in predicting the spread of COVID-19 infections. Data on the pandemic daily infections from the Arabian Gulf countries from 2020/03/24 to 2021/05/20 are fitted to each model and a statistical analysis is conducted to assess their short-term prediction accuracy. The results show that state space model predictions are more accurate with notably smaller root mean square errors than the deep learning forecasting method. The results also indicate that the poorer forecast performance of long short-term memory neural networks occurs in particular when health surveillance data are characterized by high fluctuations of the daily infection records and frequent occurrences of abrupt changes. One important result of this study is the possible relationship between data complexity and forecast accuracy with different models as suggested in the entropy analysis. It is concluded that state space models perform better than long short-term memory networks with highly irregular and more complex surveillance data.

The coronavirus disease 2019 pandemic has prompted rapid restructuring of the health-care system in an effort to stop the spread of the pandemic. Thus, telemedicine is more preferable in order to prevent the COVID-19 pandemic when face to face meeting is forbidden, allowing provision of health service over a distance. This study aimed to assess willingness to use telemedicine and factors that will determine their extent of willingness during COIVID-19 among healthcare providers working in south west of Ethiopia.

Institutional based cross-sectional study design was applied to assess willingness to use telemedicine among healthcare providers working at public health hospitals in south west of Ethiopia. Self-administered questionnaires were used. We have used Epi-info for data entry and Analysis of Moment Structure (AMOS) for analysis. A structural equation modeling was performed to identify factors associated with willingness to use telemedicine at 95% confidence interval (CI).

In this study, less than ha during COVID-19 in this setting is 46.5%. Addressing the problem related with ease of use, attitude and patient-physician relationships will help to increase the overall willingness to use telemedicine during COVID-19. An attempt to improving patient-physician relationship, provision of technical training for ease of use and working on healthcare providers' attitude will help to improve the willingness to use telemedicine.[This corrects the article DOI 10.1016/j.onehlt.2020.100185.].Assessing the possibility of Coronavirus infection and its risk on an individual's life, estimating the spatial transmission risk based on the dynamic condition of a particular place into consideration, and communicating the same to the public is crucial for minimizing the potential impact of COVID-19. With the increase in cases world-wide, new patterns are being unfolded. Nevertheless, an application for risk assessment will not only help the researcher to quickly verify the proof of concept but also is a powerful tool to bring into notice the results immediately as one of the perfect tools for risk communication. Covira (https//covira.info) is an open-source web-based software that captures the response, calculates personal as well as regional risk, and displays the result to the end-user in the form of maps and risk cards.The face-to-face university classes were abruptly transferred to virtual environments during the pandemic of COVID-19, which generated changes in teaching routine and environmental impacts associated with them. Considering this reality, studies comparing the environmental impacts of face-to-face and remote classes can be of great value. In this sense, this study performed a Life Cycle Assessment (LCA) of face-to-face and remote university classes in a Higher Education institution in the context of COVID-19. Inputs of energy and materials (food, office materials), outputs (air and water emissions, and solid waste) were gathered in situ for the functional unit of 2 hours of face-to-face or virtual class per week for 60 engineering students. Thirteen midpoint impact categories were selected by using the recent Impact World+ midpoint method for Continental Latin America, version 1.251. In the literature, most papers about the environmental management of educational activities focus on the energy efficiency of buildings and electronic equipment during their use. But this study revealed other environmental hotspots primarily associated with meal consumption followed by ethanol fuel use. Meal consumption patterns can be explained by the fact that people usually eat more often during home-office activities. Otherwise, the transportation impacts due to ethanol use are related mainly to face-to-face classes, as much transport is required such as for food supply and student transportation. Finally, an uncertainty and a sensitivity analysis were designed for the LCA conclusions. We concluded that remote classes during the COVID-19 pandemic tend to minimize the overall evaluated impacts to ten of the thirteen impact categories. An optimal scenario was also proposed showing an overall minimization of the impacts by up to 57%, if a hybrid class model were to be adopted.In response to the current COVID-19 pandemic, universal face masking represents one of the most important strategies to limit the spread of infection. However, their use in children is still highly debated (Esposito and Principi, 2020; Esposito et al., 2020) and there are few data (Lubrano et al., 2021a, 2021b) describing their possible effects on respiratory function in children. A dataset in this paper presents a comparison of the data related to the effects on respiratory function of children wearing a filtering facepiece 2 (N95 mask) with or without exhalation valve. 22 healthy children were randomly assigned to two groups, both groups wearing an N95 mask one without an exhalation valve (group A), another with an exhalation valve (group B). Children were subjected to a 72 min test the first 30 min without mask, then 30 min wearing face mask while practiced their usual play activity; finally, 12 min, with face mask in place, while they walked as in a walking test. They were monitored through to microstream capnography system (Rad-97TM with Nomo-Line Capnography, Masimo, Irvine, CA, USA) to log oxygen saturation (SpO2) and respiratory rate (RR). We use the Wilcoxon test to analyzed the differences between the parameters recorded during the study in group A and B. Data analysis was performed using JMP14.3.0 program for Mac by SAS Institute inc.The COVID-19 pandemic led to a state-imposed lockdown in the UK; there are many psychosocial consequences of pandemics, with older adults potentially at an increased risk. The current study assessed psychosocial functioning in a sample of older adults in the UK with baseline data collected pre-lockdown and follow-up 12 weeks later during lockdown. Thus, allowing investigation of the effect of COVID-19 and associated lockdown on psychosocial well-being. Thirty-five older adults (Mean age = 76.06, sex = 12 males) participated in this repeated measures study. selleck kinase inhibitor A final follow-up was then conducted post-lockdown to capture any factors that were viewed as helpful to well-being during lockdown. From pre- to during lockdown, perceived stress, well-being, depressive symptoms, mood disturbance and memory were all significantly worsened. There were significant improvements in self-reported physical health symptoms, social interaction, time spent engaging in physical activity and certain aspects of relationship quality. Follow-up showed that well-being, depression and mood were still negatively affected post-lockdown. Given the sample were all 'healthy' at baseline in comparison to established norms, there may be greater impairment in more vulnerable populations. Support for older populations is needed to mitigate the negative effects shown, particularly in light of the endurance of some of these effects post-lockdown.