Size Doesn't Matter: Towards a More Inclusive Philosophy of Biology

notes: As the primary author, O’Malley drafted the paper, and gathered and analysed data (scientific papers and talks). Conceptual analysis was conducted by both authors.publication-status: Publishedtypes: ArticlePhilosophers of biology, along with everyone else, generally perceive life to fall into two broad categories, the microbes and macrobes, and then pay most of their attention to the latter. ‘Macrobe’ is the word we propose for larger life forms, and we use it as part of an argument for microbial equality. We suggest that taking more notice of microbes – the dominant life form on the planet, both now and throughout evolutionary history – will transform some of the philosophy of biology’s standard ideas on ontology, evolution, taxonomy and biodiversity. We set out a number of recent developments in microbiology – including biofilm formation, chemotaxis, quorum sensing and gene transfer – that highlight microbial capacities for cooperation and communication and break down conventional thinking that microbes are solely or primarily single-celled organisms. These insights also bring new perspectives to the levels of selection debate, as well as to discussions of the evolution and nature of multicellularity, and to neo-Darwinian understandings of evolutionary mechanisms. We show how these revisions lead to further complications for microbial classification and the philosophies of systematics and biodiversity. Incorporating microbial insights into the philosophy of biology will challenge many of its assumptions, but also give greater scope and depth to its investigations

Pelargonium quercetorum Agnew. bitkisinin antioksidan aktivitesinin belirlenmesi

This paper deals with a problem of recognition by gait when time-dependent covariates are added, i.e. when $6$ months have passed between recording of the gallery and the probe sets. We show how recognition rates fall significantly when data is captured between lengthy time intervals, for static and dynamic gait features. Under the assumption that it is possible to have some subjects from the probe for training and that similar subjects have similar changes in gait over time, a predictive model of changes in gait is suggested in this paper, which can improve the recognition capability. A small number of subjects were used for training and a much large number for classification and the probe contains the covariate data for a smaller number of subjects. Our new predictive model derives high recognition rates for different features which is a considerable improvement on recognition capability without this new approach