10 research outputs found

    Enhancing the student learning experience through memes

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    Meme-making is an effective method for engaging students and enhancing the learning environment. Memes are a social media cultural phenomenon that the majority of those in Higher Education are exposed to on an almost daily occurrence. This research examined the use of meme-making within the forensic sciences to allow students to reflect on their knowledge. Students studying modules in forensic science across six universities in the UK and USA participated in the study. At the end of a teaching session, students produced a meme (using Meme Generator) to reflect on what they had learned; memes were then shared with the class anonymously via Padlet. This allowed all class members to see and engage with the memes created. At the end of the activity students were anonymously surveyed on their experience using Microsoft Forms and analysis of the results were undertaken using SPSS software. Meme-making was found to be an inclusive learning activity with no limitations, including age (part-time, distance learning and visually impaired students were not part of the study parameters). Results showed that not only did students find the practice fun, but it also helped with the retention of the class content suggesting that the meme-making process is an effective way to enhance the learning environment while engaging students. Student feedback suggests that to maximise participation the educator should stress reflection and learning as the key purpose of generating a meme, rather than being witty or entertaining. The forensic science educator should be mindful of selecting appropriate subject matter for this often-humorous activity

    Social prescribing in cardiology : rediscovering the nature of and within us

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    Personalised care is integral to the delivery of the NHSE Long Term Plan. Enabling choice and supporting patients to make decisions predicated on what matters to them, rather than what is the matter with them, is a fundamental part of the NHS vision. Social prescribing uses nonmedical, asset based, salutogenic approaches to promote this personalised paradigm, and places the patient central to decision making. We discuss how Personalised care can be used to help people with Cardiovascular Disease (CVD) using socially prescribed ‘nature-based’ interventions to support the prehabilitation and rehabilitation of patients with CVD. The concept of Personalised care outlined and the significance of salutogenic principles as complementary approach to the pathogenic model is discussed. We argue that this seemingly novel approach to using nature-based interventions can help promote wellbeing for people with CVD as part of the wider personalised agenda

    Unraveling the functional dark matter through global metagenomics

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    Metagenomes encode an enormous diversity of proteins, reflecting a multiplicity of functions and activities1,2. Exploration of this vast sequence space has been limited to a comparative analysis against reference microbial genomes and protein families derived from those genomes. Here, to examine the scale of yet untapped functional diversity beyond what is currently possible through the lens of reference genomes, we develop a computational approach to generate reference-free protein families from the sequence space in metagenomes. We analyse 26,931 metagenomes and identify 1.17 billion protein sequences longer than 35 amino acids with no similarity to any sequences from 102,491 reference genomes or the Pfam database3. Using massively parallel graph-based clustering, we group these proteins into 106,198 novel sequence clusters with more than 100 members, doubling the number of protein families obtained from the reference genomes clustered using the same approach. We annotate these families on the basis of their taxonomic, habitat, geographical and gene neighbourhood distributions and, where sufficient sequence diversity is available, predict protein three-dimensional models, revealing novel structures. Overall, our results uncover an enormously diverse functional space, highlighting the importance of further exploring the microbial functional dark matter
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