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Pseudomonas aeruginosa as an Opportunistic Pathogen
Assembling the latest research by an international group of contributors, this volume covers the epidemiology, pathogenesis, clinical features, and control measures of this elusive microorganism. It will provide a deeper understanding of the pathogen to physicians and surgeons caring for patients infected, or at risk of becoming infected, with Pseudomonas Aeruginosa.
MALDI-TOF MS Application for Susceptibility Testing of Microorganisms
This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.
Spores and Spore Formers
Bacterial spore formers have been the focus of intense study for almost half a century centered primarily on Bacillus subtilis. This research has given us a detailed picture of the genetic, physiological and biochemical mechanisms that allow bacteria to survive harsh environmental conditions by forming highly robust spores. Although, many basic aspects of this process are now understood in great detail, bacterial sporulation still continues to be a highly attractive model for studying various cell processes at a molecular level. There are several reasons for such scientific interest. First, some of the complex steps in sporulation are not fully understood and/or only are only described by 'c...
Acta Cytologica
Includes abstracts of papers of various symposia.
Microbiome and Machine Learning, Volume II
Due to the success of Microbiome and Machine Learning, which collected research results and perspectives of researchers working in the field of machine learning (ML) applied to the analysis of microbiome data, we are launching the second volume to collate any new findings in the field to further our understanding and encourage the participation of experts worldwide in the discussion. The success of ML algorithms in the field is substantially due to their capacity to process high-dimensional data and deal with uncertainty and noise. However, to maximize the combinatory potential of these emerging fields (microbiome and ML), researchers have to deal with some aspects that are complex and inherently related to microbiome data. Microbiome data are convoluted, noisy and highly variable, and non-standard analytical methodologies are required to unlock their clinical and scientific potential. Therefore, although a wide range of statistical modelling and ML methods are available, their application is only sometimes optimal when dealing with microbiome data.
