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Abstract This study explores the social, economic and environmental dimensions of human‐wildlife conflict (HWC) in four districts of Bhutan. Looking beyond the common unidimensional approach to the evaluation of HWCs, it documents the multifaceted nature of HWCs through a social‐ecological system (SES) lens. We carried out a mixed method analysis of HWC based on 96 interviews consisting of qualitative and quantitative data and eight focus group discussions conducted in four districts representing four regions of Bhutan. Our case study finding reveals that HWC scenarios are complex and there is spatial variation in both social and ecological factors that contribute to HWC. In some areas, ecological aspects such as the composition and behaviour of wildlife species exacerbate the conflict, while in other regions the co‐occurrence of wildlife and livestock in the same ‘wild’ landscape drives HWC. The socio‐economic dimensions of HWC are largely driven by attitudes, tolerance, and behaviour of individuals underpinned by multiple factors. We propose that the SES approach improves our understanding of key social, economic and ecological factors, their interactions, and the feedback that either exacerbates or ameliorates HWC in contested landscapes. Read the free Plain Language Summary for this article on the Journal blog.

Part 1 of this article described THE FUSED Technique (Ford & Knutson, 2024) as a new pedagogical approach to teaching K-12 health education, blending or fusing the best aspects from the standards-based and skill-based health education teaching models. THE stands for teaching health education and FUSED stands for functional (data driven) information, using health-behavior outcomes (HBOs), skill-based cues, engaging teaching strategies, and daily application in developing health-literate K-12 students. The FUSED Hourglass (Figure 1) was designed to provide health educators with a progressive itinerary to design a transformative health learning experience for students. There are two main parts to the FUSED Hourglass -- planning and implementation -- and each is divided into phases. Part 1 of the article described how to follow Phases 1 through 7 to build a FUSED unit and lesson. This article, Part 2, describes how to implement a FUSED lesson by applying Phases 9 through 14.

THE FUSED health education series was developed as a one-stop guide for developing a quality curriculum for teaching health education. This first, two-part article introduces a new approach to teaching health education called THE FUSED Technique. THE stands for Teaching Health Education and FUSED stands for Functional (data driven) information, Using health-behavior outcomes (HBOs), Skill-based cues, Engaging teaching strategies, and Daily application in developing health-literate K-12 students. THE FUSED Technique was developed to teach health skills and content to today's K-12 learners. It focuses on lifelong health literacy and helps bridge standards-based and skill-based health education. At its core, FUSED is a skill-based health education teaching technique that engages K-12 students by using a variety of participatory methods to ensure that students have ample opportunities to practice and develop the knowledge, skills and attitudes needed to lead healthy lives. Part 1 of this article describes how to build a FUSED unit and lesson, and Part 2 will describe how to implement a FUSED lesson.

Background: Hospitalisation can be a traumatic experience, where inpatients are exposed to an abundance of physical and psychological stressors. Evidence suggests that these hospital-related stressors negatively impact health: a phenomenon known as post-hospital syndrome. The current study aimed to identify hospital-related stressors, and to develop and provide initial validation for a new measure of in-hospital stress.Methods: Measure development occurred in three stages: (i) semi-structured interviews, (ii) item generation, and (iii) pilot testing. Twenty-one patients were interviewed regarding their recent hospital experiences, and a list of hospital-related stressors was produced. These stressors were compiled into a questionnaire and piloted on 200 recent inpatients to provide initial evidence of internal consistency and construct validity.Results: Stressors identified from the interviews captured all relevant questions from three previous hospital stress measures, plus 12 more. The most reported stressor was ‘poor sleep’. These hospital-related stressors were developed into 67 questions, forming the Hospital Stress Questionnaire (HSQ). The HSQ showed excellent internal consistency and construct validity, and correlated with feelings of vulnerability and being unprepared to go home.Conclusion: The HSQ is a promising self-report tool for measuring in-hospital stress. Future research ought to investigate its psychometric properties further in larger and more diverse samples. The measure has potential to be used to monitor patient risk of post-hospital syndrome.

With the generation of spatially resolved transcriptomics of microbial biofilms, computational tools can be used to integrate this data to elucidate the multi-scale mechanisms controlling heterogeneous biofilm metabolism. This work presents a Multi-scale model of Metabolism In Cellular Systems (MiMICS) which is a computational framework that couples a genome-scale metabolic network reconstruction (GENRE) with Hybrid Automata Library (HAL), an existing agent-based model and reaction-diffusion model platform. A key feature of MiMICS is the ability to incorporate multiple -omics-guided metabolic models, which can represent unique metabolic states that yield different metabolic parameter values passed to the extracellular models. We used MiMICS to simulate Pseudomonas aeruginosa regulation of denitrification and oxidative stress metabolism in hypoxic and nitric oxide (NO) biofilm microenvironments. Integration of P. aeruginosa PA14 biofilm spatial transcriptomic data into a P. aeruginosa PA14 GENRE generated four PA14 metabolic model states that were input into MiMICS. Characteristic of aerobic, denitrification, and oxidative stress metabolism, the four metabolic model states predicted different oxygen, nitrate, and NO exchange fluxes that were passed as inputs to update the agent's local metabolite concentrations in the extracellular reaction-diffusion model. Individual bacterial agents chose a PA14 metabolic model state based on a combination of stochastic rules, and agents sensing local oxygen and NO. Transcriptome-guided MiMICS predictions suggested microscale denitrification and oxidative stress metabolic heterogeneity emerged due to local variability in the NO biofilm microenvironment. MiMICS accurately predicted the biofilm's spatial relationships between denitrification, oxidative stress, and central carbon metabolism. As simulated cells responded to extracellular NO, MiMICS revealed dynamics of cell populations heterogeneously upregulating reactions in the denitrification pathway, which may function to maintain NO levels within non-toxic ranges. We demonstrated that MiMICS is a valuable computational tool to incorporate multiple -omics-guided metabolic models to mechanistically map heterogeneous microbial metabolic states to the biofilm microenvironment.