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Educators have increasingly turned to social media for their instructional, social, and emotional needs during the COVID-19 pandemic. In order to see where support and professional development would be needed and how the educational community interacted online, we sought to use existing Twitter data to examine potential educators' networking and discourse patterns. Specifically, this mixed-methods study explores how educators used Twitter as a platform to seek and share resources and support during the transition to remote teaching around the start of massive school closures due to the pandemic. Based on a public COVID-19 Twitter chatter database, tweets from late March to early April 2020 were searched using educational keywords and analyzed using social network analysis and thematic analysis. Social network analysis findings indicate that the support networks for educators on Twitter were sparse and consisted of mainly small, exclusive communities. The networks featured one-on-one interactions during the early pandemic, highlighting that there were few large conversations that most educators were part of but rather many small ones. Thematic analysis findings further suggest that both informational and nurturant support were relatively equally present on Twitter among educators, particularly pedagogical content knowledge and gratitude. This study adds to an understanding of the educational networks as a means of professional and personal support. Additionally, findings present the discourse featured in educator networks at the onset of an educational emergency (i.e., COVID-19) as decentralized as well as desiring pedagogical content knowledge and emotional sharing.

Background: STEM minority participation programs have been widely implemented in higher education with the goal of diversifying the global STEM workforce. Informed by research highlighting the potential of targeted exploration of STEM roles and refection on the self in relation to STEM (identity exploration), this work examines how engagement in a government funded STEM minority participation program shaped these processes in current students and program alumni. Results: Epistemic network analysis (ENA) was used to visualize conceptual connections between identity themes that emerged from interviews with present and past program participants. Network models were developed for current students and alumni for cross-group comparisons. Diferences were found in how participants at diferent stages of their careers enact and describe their identity exploration processes. Summative network models highlighted how students discussed action-taking (sometimes through participation in STEM minority program initiatives) as they explored less-certain possible future STEM roles, while alumni integrated more diverse and holistic facets of their identities when conceptualizing their futures. To close the interpretive loop, a qualitative interpretation of interview discourse was used to give context to network patterns. Conclusions: Results highlight the differences between novices' and professionals' conceptualizations of their future selves and illustrate how minoritized individuals describe their long-term patterns of identity exploration related to STEM majors and careers. Implications for future STEM identity research and practice, including higher education programming as a tool to support students' STEM identity exploration processes, are discussed.

Prior to 2020, empirical research and reports on approaches to education during health crises were limited. They focused primarily on reporting local-level response and provided only limited analysis. Various historic epidemics, like SARS, Ebola, and HIV/AIDS, provided important lessons about educational efficacy during major health emergencies. However, the sudden emergence of the COVID-19 pandemic led to an explosion of research on educating during a worldwide health crisis. This integrative literature review (Torraco 2005) uses the INEE Minimum Standards framework to conceptualize the response to pandemics and epidemics from 1990 to 2020. The research analyzes 124 empirical studies, practitioner and governmental reports, and historic accounts of Ebola, SARS, and other epidemics, as well as early responses to COVID-19, in order to understand how education stakeholders continued educating during widespread communicable illnesses. The high-level themes that emerged included the foundational role of context and community support; access to an equitable education in the digital age; the social-emotional wellbeing of teachers and students; teachers' role in adapting curriculum and pedagogy; the need for additional training and support for teachers; and the opportunity for a creative shift in practices and policies in education.

In recent years, edible oil safety problems have occurred frequently. In order to reduce the threat of such incidents, it is of great significance to research edible oil safety risk assessment models. Considering that high-dimensional, non-linear and discrete data containing noise are currently obtained from the detection of edible oils, and the existing risk assessment models have several problems such as poor noise suppression, inaccurate evaluation, and strong subjectivity in model parameter adjustment, a risk assessment model for pollutants in edible oils was proposed in this paper. First, risk indicators were selected and data were preprocessed and input into a filtering module based on the wavelet threshold method for filtering. Second, grey relational analysis (GRA) was used to calculate the weight of each risk index and develop a multi-index comprehensive risk label. Extreme learning machine (ELM) was adopted to predict the comprehensive risk value. Third, the practical Bayesian optimization (PBO) algorithm was used to optimize the parameters of filtering module and ELM network. Finally, the fuzzy comprehensive analysis was applied to classify the risk grade of the predicted comprehensive risk value. The application of the proposed model to 150 groups of edible oil data was described in detail. The coefficient of determination (R2) and root mean square error (RMSE) of this model were 0.056 3 and 0.946 1, respectively, indicating its superiority and effectiveness. This study provides reasonable evidence for relevant departments to formulate risk control and sample inspection strategies and optimize the supply chain of edible oils.

Generative artificial intelligence (GenAI) tools developed to support teaching and learning are widely available. Trustworthiness concerns, however, have prompted calls for researchers to study their effectiveness and for educators and educational researchers to be involved in their creation and piloting processes. This study investigated one type of GenAI created to support educators: multiple-choice question generators (MCQ GenAI). Among the nine MCQ GenAI tools investigated, a variety of useful options were available, but only one indicated teacher involvement and none mentioned testing experts in development processes. MCQ GenAI-created items (n = 270) were coded based on MCQ quality item-writing guidelines. Results showed 80.00% of items (n = 216) violated at least one guideline, with 73.70% (n = 199) likely to produce major measurement error (should not use without revision), 6.30% (n = 17) likely to elicit minor measurement error (consider modifying), and 20.00% (n = 54) acceptable (usable as created). Implications suggest multidisciplinary teams are needed in educational GenAI tool development. [ABSTRACT FROM AUTHOR]

Introduction: Micronutrients perform a wide range of physiological functions essential for growth and development. However, most people still need to meet the estimated average requirement worldwide. Globally, 2 billion people suffer from micronutrient deficiency, most of which are co-occurring deficiencies in children under age five. Despite decades of research, animal models studying multiple micronutrient deficiencies within the early-life period are lacking, which hinders our complete understanding of the long-term health implications and may contribute to the inefficacy of some nutritional interventions. Evidence supporting the Developmental Origins of Health and Disease (DOHaD) theory demonstrates that early-life nutritional deficiencies carry life-long consequences mediated through various mechanisms such as abnormal metabolic programming, stunting, altered body composition, and the gut microbiome. However, this is largely unexplored in the multiple micronutrient deficient host., Methods: we developed a preclinical model to examine undernutrition's metabolic and functional impact on the host and gut microbiome early in life. Three-week-old weanling C57BL/6N male mice were fed a low-micronutrient diet deficient in zinc, folate, iron, vitamin A, and vitamin B12 or a control diet for 4-weeks., Results: Our results showed that early-life multiple micronutrient deficiencies induced stunting, altered body composition, impaired glucose and insulin tolerance, and altered the levels of other micronutrients not depleted in the diet within the host. In addition, functional metagenomics profiling and a carbohydrate fermentation assay showed an increased microbial preference for simple sugars rather than complex ones, suggestive of a less developed microbiome in the low-micronutrient-fed mice. Moreover, we found that a zinc-only deficient diet was not sufficient to induce these phenotypes, further supporting the importance of studying co-occurring deficiencies., Discussion: Together, these findings highlight a previously unappreciated role of early-life multiple micronutrient deficiencies in shaping the metabolic phenome of the host and gut microbiome through altered glucose energy metabolism, which may have implications for metabolic disease later in life in micronutrient-deficient survivors., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Littlejohn, Bar-Yoseph, Edwards, Li, Ramirez-Contreras, Holani, Metcalfe-Roach, Fan, Yang, Radisavljevic, Hu, Johnson and Finlay.)