Contributions of aesthetics

Journal ArticleThe most tempting answer to the question posed as the topic for these remarks -- "what can aesthetics contribute to a young person's ability to understand and value art?" -- is "nothing", or, at least, "embarrassingly little". Aesthetics, after all, is a field of philosophy, and hence a field dedicated to the analysis of abstract, foundational questions rarely raised in everyday life. The concerns of aesthetics include metaphysical questions about the ontology of art, epistemological questions about valuational judgments in art, and ethical questions about the intersection of aesthetic and other values. But, as we all know, you can live your life, and you can furthermore enjoy and contribute to the arts, without any formal examination of questions like these

Marshall University Music Department Presents Variazioni, Facutly Recital, Wendell Dobbs, flute, Stephen Lawson, horn, Júlio Ribeiro Alves, guitar

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Marshall University Music Department Presents Music from the Age of Invention, The Maelzel Woodwind Quintet

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Marshall University Music Department Presents a Faculty Recital, Woodwind Quintets

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Marshall University Music Department Presents Music from the Age of Invention, The Maelzel Woodwind Quintet, performing on historical instruments from the early 19th century, Wendell Dobbs, flute, Richard Kravchak, oboe, Richard Spece, clarinet, Stephen Lawson, horn, Kay Lawson, bassoon

https://mds.marshall.edu/music_perf/1726/thumbnail.jp

Dynamic somite cell rearrangements lead to distinct waves of myotome growth

The myogenic precursors responsible for muscle growth in amniotes develop from thedermomyotome, an epithelium at the external surface of the somite. In teleosts, themyogenic precursors responsible for growth have not been identified. We have usedsingle cell lineage labeling in zebrafish to show that anterior border cells of epithelialsomites are myogenic precursors responsible for zebrafish myotome growth. These cellsmove to the external surface of the embryonic myotome and express the transcriptionfactor Pax7. Some remain on the external surface and some incorporate into the fastmyotome, apparently by moving between differentiated slow fibres. The posterior cellsof the somite, in contrast, elongate into medial muscle fibres. The surprising movementof the anterior somite cells to the external somite surface transforms a segmentallyrepeated arrangement of myogenic precursors into a medio-lateral arrangement similar tothat seen in amniotes.Fil: Stellabotte, Frank. Ohio Wesleyan University.; Estados UnidosFil: Dobbs McAuliffe, Betsy. Ohio Wesleyan University.; Estados UnidosFil: Fernandez, Daniel Alfredo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas; Argentina. Universidad Nacional de Tierra del Fuego; ArgentinaFil: Feng, Xuesong. Ohio Wesleyan University.; Estados UnidosFil: Devoto, Stephen Henry. Ohio Wesleyan University.; Estados Unido

Comparative study of ChatGPT and human evaluators on the assessment of medical literature according to recognized reportingstandards

Introduction Amid clinicians’ challenges in staying updated with medical research, artificial intelligence (AI) tools like the large language model (LLM) ChatGPT could automate appraisal of research quality, saving time and reducing bias. This study compares the proficiency of ChatGPT3 against human evaluation in scoring abstracts to determine its potential as a tool for evidence synthesis.Methods We compared ChatGPT’s scoring of implant dentistry abstracts with human evaluators using the Consolidated Standards of Reporting Trials for Abstracts reporting standards checklist, yielding an overall compliance score (OCS). Bland-Altman analysis assessed agreement between human and AI-generated OCS percentages. Additional error analysis included mean difference of OCS subscores, Welch’s t-test and Pearson’s correlation coefficient.Results Bland-Altman analysis showed a mean difference of 4.92% (95% CI 0.62%, 0.37%) in OCS between human evaluation and ChatGPT. Error analysis displayed small mean differences in most domains, with the highest in ‘conclusion’ (0.764 (95% CI 0.186, 0.280)) and the lowest in ‘blinding’ (0.034 (95% CI 0.818, 0.895)). The strongest correlations between were in ‘harms’ (r=0.32, p<0.001) and ‘trial registration’ (r=0.34, p=0.002), whereas the weakest were in ‘intervention’ (r=0.02, p<0.001) and ‘objective’ (r=0.06, p<0.001).Conclusion LLMs like ChatGPT can help automate appraisal of medical literature, aiding in the identification of accurately reported research. Possible applications of ChatGPT include integration within medical databases for abstract evaluation. Current limitations include the token limit, restricting its usage to abstracts. As AI technology advances, future versions like GPT4 could offer more reliable, comprehensive evaluations, enhancing the identification of high-quality research and potentially improving patient outcomes