Control of plant growth is an important aspect of crop productivity and yield in agriculture. Overexpression of the At CHR12/ 23 genes in Arabidopsis thaliana reduced growth habit without other morphological changes. These two genes encode Snf2 chromatin remodelling ATPases. Here, we translate this approach to the horticultural crop tomato ( Solanum lycopersicum). We identified and cloned the single tomato ortholog of the two Arabidopsis Snf2 genes, designated Sl CHR1. Transgenic tomato plants (cv. Micro-Tom) that constitutively overexpress the coding sequence of Sl CHR1 show reduced growth in all developmental stages of tomato. This confirms that Sl CHR1 combines the functions of both Arabidopsis genes in tomato. Compared to the wild type, the transgenic seedlings of tomato have significantly shorter roots, hypocotyls and reduced cotyledon size. Transgenic plants have a much more compact growth habit with markedly reduced plant height, severely compacted reproductive structures with smaller flowers and smaller fruits. The results indicate that either GMO-based or non- GMO-based approaches to modulate the expression of chromatin remodelling ATPase genes could develop into methods to control plant growth, for example to replace the use of chemical growth retardants. This approach is likely to be applicable and attractive for any crop for which growth habit reduction has added value.
Aging is associated with a decline in the ability to carry out daily tasks. Physical activity can delay or diminish the decline and increase the ability of older adults to live independently at home. Performing home-based exercises can help older adults achieve the recommended levels of physical activity. Technology allows exercise programs to be tailored to individual needs. This thesis describes a blended intervention that was developed and evaluated according to the Medical Research Council framework. The principal findings are that older adults are motivated to perform technology-supported home-based exercises if they help them maintain self-reliance and there is sufficient guidance, safety is taken into account, and adherence is stimulated. To meet those conditions, a blended intervention was developed that was based on functional exercises, behavior change theory and human guidance. A custom-made tablet application appears to be usable by the target audience. A process evaluation has shown that the tablet as well as the coach support older adults in the various phases of self-regulating their exercise behavior. The blended intervention stimulates intrinsic motivation by supporting the autonomy of participants, fostering competence and, for some, meeting the need for relatedness by offering emotional support. Data derived from the tablet demonstrate that older adults participating in the intervention exhibit exercise behavior that is in line with WHO guidelines and that engagement with the tablet was a contributing factor. Future work should include assessment of intervention fidelity and explore which aspects of coaching can and cannot be further automated.
MULTIFILE
Over the past two months, the Going Hybrid Publishing group has convened for two days of design sprints. The discussions we had during these sprints were informed by our previous state-of-the-art analysis and survey of relevant tools and practices. This blog post is a recap of two design sprint days, sharing both process and outcomes.
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Developing a framework that integrates Advanced Language Models into the qualitative research process.Qualitative research, vital for understanding complex phenomena, is often limited by labour-intensive data collection, transcription, and analysis processes. This hinders scalability, accessibility, and efficiency in both academic and industry contexts. As a result, insights are often delayed or incomplete, impacting decision-making, policy development, and innovation. The lack of tools to enhance accuracy and reduce human error exacerbates these challenges, particularly for projects requiring large datasets or quick iterations. Addressing these inefficiencies through AI-driven solutions like AIDA can empower researchers, enhance outcomes, and make qualitative research more inclusive, impactful, and efficient.The AIDA project enhances qualitative research by integrating AI technologies to streamline transcription, coding, and analysis processes. This innovation enables researchers to analyse larger datasets with greater efficiency and accuracy, providing faster and more comprehensive insights. By reducing manual effort and human error, AIDA empowers organisations to make informed decisions and implement evidence-based policies more effectively. Its scalability supports diverse societal and industry applications, from healthcare to market research, fostering innovation and addressing complex challenges. Ultimately, AIDA contributes to improving research quality, accessibility, and societal relevance, driving advancements across multiple sectors.
