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The 'Bridging Imaging Users to Imaging Analysis' survey was conducted in 2022 by the Center for Open Bioimage Analysis (COBA), BioImaging North America (BINA) and the Royal Microscopical Society Data Analysis in Imaging Section (RMS DAIM) to understand the needs of the imaging community. Through multichoice and open‐ended questions, the survey inquired about demographics, image analysis experiences, future needs and suggestions on the role of tool developers and users. Participants of the survey were from diverse roles and domains of the life and physical sciences. To our knowledge, this is the first attempt to survey cross‐community to bridge knowledge gaps between physical and life sciences imaging. Survey results indicate that respondents' overarching needs are documentation, detailed tutorials on the usage of image analysis tools, user‐friendly intuitive software, and better solutions for segmentation, ideally in a format tailored to their specific use cases. The tool creators suggested the users familiarise themselves with the fundamentals of image analysis, provide constant feedback and report the issues faced during image analysis while the users would like more documentation and an emphasis on tool friendliness. Regardless of the computational experience, there is a strong preference for 'written tutorials' to acquire knowledge on image analysis. We also observed that the interest in having 'office hours' to get an expert opinion on their image analysis methods has increased over the years. The results also showed less‐than‐expected usage of online discussion forums in the imaging community for solving image analysis problems. Surprisingly, we also observed a decreased interest among the survey respondents in deep/machine learning despite the increasing adoption of artificial intelligence in biology. In addition, the community suggests the need for a common repository for the available image analysis tools and their applications. The opinions and suggestions of the community, released here in full, will help the image analysis tool creation and education communities to design and deliver the resources accordingly. [ABSTRACT FROM AUTHOR]

Background: Recent evidence suggests that sarcopenia and subsequent changes in muscle mass and functional outcomes are linked to disruption to the gastrointestinal microbiota composition and/or function via the microbiota-gut-muscle axis. Despite growing interest, few studies have systemically analysed (1) the relationship between the gut microbiota, muscle mass and physical performance and (2) the effects of gut-modulating dietary interventions on these outcomes within older individuals with or without sarcopenia., Methods: Four electronic databases (PubMed, MEDLINE, Embase and Scopus) were searched for articles published from the year 2004 until July 2023. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) were followed. Revised Cochrane Risk of Bias (RoB 2.0) and Joanna Briggs Institute (JBI) critical appraisal checklist were utilised to evaluate the risk of bias within intervention and observational studies, respectively., Results: A total of 20 studies (14 observational and 6 interventional) involving 4071 older participants (mean age 69.9 years, 51.6% female) were included. There was significant heterogeneity regarding interventions and outcome measures used in these studies. Correlations between microbiota diversity and composition and sarcopenia-related functional outcomes were observed. Interventional studies targeting the gut microbiota resulted in improved muscle strength, body composition or physical function in some, but not all, studies., Conclusions: Despite limitations in the studies reviewed, the findings provide further evidence that the development of sarcopenia is likely influenced by an altered gut microbial environment and that interventions targeting the microbiome could hold therapeutic potential for the treatment or management of sarcopenia.

Modern microscopy systems allow researchers to generate large volumes of image data with relative ease. However, the challenge of analyzing these data effectively is often hindered by a lack of computational skills. This bottleneck negatively impacts both research reproducibility and efficiency, as researchers frequently rely on manual or semi-automated analysis methods. Interactive image analysis workshops offer a valuable solution, equipping researchers with the skills and tools needed to automate image processing tasks. In this paper, we share our experiences and best practices from conducting such workshops, which emphasize the use of open-source software like ImageJ, FIJI, and Python-based tools such as JupyterLab and napari. We discuss key considerations for workshop design, logistics, and outcomes, while highlighting common pitfalls to avoid. Using two recent workshops as case studies, we also present strategies for optimizing participant engagement and learning. Our insights offer practical guidance for planning and conducting image analysis workshops and serve as a starting point for researchers looking to establish similar training initiatives and enrich their local imaging communities., (© 2024 The Author(s). Microscopy Research and Technique published by Wiley Periodicals LLC.)