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People around the world are living longer. For the first time in history, most humans will live to be sixty and beyond. By 2050, the world's population aged 60 and over will reach a total of 2 billion, up from 900 million in 2015. Today, 125 million people are 80 years of age or older. By 2050, there will be 434 million people in this age group worldwide. In addition, the pace of aging of the world population is also increasing. However, there is not enough evidence to show that older people have better health than their parents. While rates of severe disability have declined over the past 30 years (but only in high-income countries), there have been no significant changes in mild to moderate disability over the same period of time. Indeed, the increase in the duration of life (lifespan) does not coincide with the increase in the duration of health (healthspan), that is, the period of life free from serious chronic diseases and disabilities. Therefore, the identification of the factors that predispose to a long and healthy life, as discussed in the papers of this book, is of enormous interest for translational medicine.

aging --- alternative therapy --- composition of royal jelly --- dietary interventions --- healthspan --- lifespan --- longevity --- royal jelly --- IGF-1 --- oxidative stress --- ageing --- nematode --- immunosenescence --- probiotic bacteria --- pathogen protection --- food allergy --- elderly --- hypersensitivity --- gut --- allergy --- inflammation --- redoxomics --- glutathione --- meniere’s disease --- neurodegenerative diseases --- healthy aging --- DNA methylation --- epigenetic clocks --- telomere length --- centenarians --- exosomes --- serum --- functional enrichment analysis --- ingenuity pathway analysis --- miRNA-mRNA networks --- aging-related disease --- Di (2-Ethylhexyl) pthalate --- Hericium erinaceus --- vitagenes --- apoptosis --- mitochondrial respiratory complexes --- C. elegans --- polyphenols --- olive oil --- Parkinson’s disease --- β-Dystroglycan --- cellular senescence --- lamin B1 --- DNA-damage response --- defective mitosis --- n/a --- meniere's disease --- Parkinson's disease

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Metabolomics data analysis strategies are central to transforming raw metabolomics data files into meaningful biochemical interpretations that answer biological questions or generate novel hypotheses. This book contains a variety of papers from a Special Issue around the theme “Best Practices in Metabolomics Data Analysis”. Reviews and strategies for the whole metabolomics pipeline are included, whereas key areas such as metabolite annotation and identification, compound and spectral databases and repositories, and statistical analysis are highlighted in various papers. Altogether, this book contains valuable information for researchers just starting in their metabolomics career as well as those that are more experienced and look for additional knowledge and best practice to complement key parts of their metabolomics workflows.

Research & information: general --- metabolic networks --- mass spectral libraries --- metabolite annotation --- metabolomics data mapping --- nontarget analysis --- liquid chromatography mass spectrometry --- compound identification --- tandem mass spectral library --- forensics --- wastewater --- gut microbiome --- meta-omics --- metagenomics --- metabolomics --- metabolic reconstructions --- genome-scale metabolic modeling --- constraint-based modeling --- flux balance --- host–microbiome --- metabolism --- global metabolomics --- LC-MS --- spectra processing --- pathway analysis --- enrichment analysis --- mass spectrometry --- liquid chromatography --- MS spectral prediction --- metabolite identification --- structure-based chemical classification --- rule-based fragmentation --- combinatorial fragmentation --- time series --- PLS --- NPLS --- variable selection --- bootstrapped-VIP --- data repository --- computational metabolomics --- reanalysis --- lipidomics --- data processing --- triplot --- multivariate risk modeling --- environmental factors --- disease risk --- chemical classification --- in silico workflows --- metabolome mining --- molecular families --- networking --- substructures --- mass spectrometry imaging --- metabolomics imaging --- biostatistics --- ion selection algorithms --- liquid chromatography high-resolution mass spectrometry --- data-independent acquisition --- all ion fragmentation --- targeted analysis --- untargeted analysis --- R programming --- full-scan MS/MS processing --- R-MetaboList 2 --- liquid chromatography–mass spectrometry (LC/MS) --- fragmentation (MS/MS) --- data-dependent acquisition (DDA) --- simulator --- in silico --- untargeted metabolomics --- liquid chromatography–mass spectrometry (LC-MS) --- experimental design --- sample preparation --- univariate and multivariate statistics --- metabolic pathway and network analysis --- LC–MS --- metabolic profiling --- computational statistical --- unsupervised learning --- supervised learning

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• Reference Manager
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Metabolomics data analysis strategies are central to transforming raw metabolomics data files into meaningful biochemical interpretations that answer biological questions or generate novel hypotheses. This book contains a variety of papers from a Special Issue around the theme “Best Practices in Metabolomics Data Analysis”. Reviews and strategies for the whole metabolomics pipeline are included, whereas key areas such as metabolite annotation and identification, compound and spectral databases and repositories, and statistical analysis are highlighted in various papers. Altogether, this book contains valuable information for researchers just starting in their metabolomics career as well as those that are more experienced and look for additional knowledge and best practice to complement key parts of their metabolomics workflows.

Research & information: general --- metabolic networks --- mass spectral libraries --- metabolite annotation --- metabolomics data mapping --- nontarget analysis --- liquid chromatography mass spectrometry --- compound identification --- tandem mass spectral library --- forensics --- wastewater --- gut microbiome --- meta-omics --- metagenomics --- metabolomics --- metabolic reconstructions --- genome-scale metabolic modeling --- constraint-based modeling --- flux balance --- host–microbiome --- metabolism --- global metabolomics --- LC-MS --- spectra processing --- pathway analysis --- enrichment analysis --- mass spectrometry --- liquid chromatography --- MS spectral prediction --- metabolite identification --- structure-based chemical classification --- rule-based fragmentation --- combinatorial fragmentation --- time series --- PLS --- NPLS --- variable selection --- bootstrapped-VIP --- data repository --- computational metabolomics --- reanalysis --- lipidomics --- data processing --- triplot --- multivariate risk modeling --- environmental factors --- disease risk --- chemical classification --- in silico workflows --- metabolome mining --- molecular families --- networking --- substructures --- mass spectrometry imaging --- metabolomics imaging --- biostatistics --- ion selection algorithms --- liquid chromatography high-resolution mass spectrometry --- data-independent acquisition --- all ion fragmentation --- targeted analysis --- untargeted analysis --- R programming --- full-scan MS/MS processing --- R-MetaboList 2 --- liquid chromatography–mass spectrometry (LC/MS) --- fragmentation (MS/MS) --- data-dependent acquisition (DDA) --- simulator --- in silico --- untargeted metabolomics --- liquid chromatography–mass spectrometry (LC-MS) --- experimental design --- sample preparation --- univariate and multivariate statistics --- metabolic pathway and network analysis --- LC–MS --- metabolic profiling --- computational statistical --- unsupervised learning --- supervised learning