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The great advances in information technology (IT) have implications for many sectors, such as bioinformatics, and has considerably increased their possibilities. This book presents a collection of 11 original research papers, all of them related to the application of IT-related techniques within the bioinformatics sector: from new applications created from the adaptation and application of existing techniques to the creation of new methodologies to solve existing problems.

Technology: general issues --- machine learning --- metagenomics --- bioinformatics --- CTX-M --- data mining --- cluster --- clinical implications --- diabetes --- epidemiology --- forecast --- PART --- Decision table --- Weka --- real-life patients --- regression --- ear detection --- computer vision --- convolutional neural network --- image recognition --- video analysis --- gene clustering --- swarm intelligence --- biological functions detection --- informative genes --- fuel cell --- hydrogen energy --- intelligent systems --- hybrid systems --- Artificial Neural Networks --- power management --- Machine Learning --- personality assessment --- gradient boosting --- Affective Computing --- transposable elements --- metrics --- deep learning --- detection --- classification --- mitochondrial protein --- bi-directional LSTM --- plasmodium falciparum --- Particle Swarm Optimization --- Harmony Search --- parameter estimation --- Arabidopsis thaliana --- clinical data --- feature selection --- genetic programming --- evolutionary computation --- dynamic models --- evolutionary computing --- derivative-free optimization --- metabolism --- glycolysis --- yeast

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The great advances in information technology (IT) have implications for many sectors, such as bioinformatics, and has considerably increased their possibilities. This book presents a collection of 11 original research papers, all of them related to the application of IT-related techniques within the bioinformatics sector: from new applications created from the adaptation and application of existing techniques to the creation of new methodologies to solve existing problems.

machine learning --- metagenomics --- bioinformatics --- CTX-M --- data mining --- cluster --- clinical implications --- diabetes --- epidemiology --- forecast --- PART --- Decision table --- Weka --- real-life patients --- regression --- ear detection --- computer vision --- convolutional neural network --- image recognition --- video analysis --- gene clustering --- swarm intelligence --- biological functions detection --- informative genes --- fuel cell --- hydrogen energy --- intelligent systems --- hybrid systems --- Artificial Neural Networks --- power management --- Machine Learning --- personality assessment --- gradient boosting --- Affective Computing --- transposable elements --- metrics --- deep learning --- detection --- classification --- mitochondrial protein --- bi-directional LSTM --- plasmodium falciparum --- Particle Swarm Optimization --- Harmony Search --- parameter estimation --- Arabidopsis thaliana --- clinical data --- feature selection --- genetic programming --- evolutionary computation --- dynamic models --- evolutionary computing --- derivative-free optimization --- metabolism --- glycolysis --- yeast

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The use of non-Saccharomyces yeast species is currently a biotechnology trend in enology for which they are being broadly used to improve the sensory profile of wines because they affect aroma, color, and mouthfeel. They have become a powerful biotool to modulate the influence of global warming on grape varieties, helping to maintain the acidity, decrease the alcoholic degree, stabilize wine color, and increase freshness. In cool climates, some non-Saccharomyces can promote demalication or color stability by the formation of stable derived pigments. Additionally, non-Saccharomyces yeasts open new possibilities in biocontrol for removing spoilage yeast and bacteria or molds that can produce and release mycotoxins and, thereby, help in reducing applied SO2 levels.

Technology: general issues --- Metschnikowia pulcherrima --- Lachancea thermotolerans --- Torulaspora delbrueckii --- Grenache --- Graciano --- ochratoxin A (OTA) --- mycotoxins --- biogenic amines (BAs) --- ethyl carbamate (EC) --- organic wines --- non-Saccharomyces --- alcohol reduction --- native yeast --- sequential fermentation --- wine --- uninoculated fermentation --- yeast --- sulphur dioxide --- non-Saccharomyces yeasts --- mixed starter cultures --- fermentation --- Sangiovese --- sensory analysis --- antimicrobial peptides --- Brettanomyces bruxellensis --- Candida intermedia --- Pichia guilliermondii --- reactive oxygen species --- Hanseniaspora vineae --- alcoholic fermentation --- ageing on lees --- polysaccharides --- white wines --- winemaking --- aging-on-lees --- yeast assimilable nitrogen --- Saccharomyces non-cerevisiae --- ethanol --- glycerol --- glycolysis --- pyruvate kinase --- fermentation evolution clade --- sensory improvement --- dealcoholization --- SO2 --- grape variety --- Schizosaccharomyces pombe --- Metschnikowia pulcherrima --- Lachancea thermotolerans --- Torulaspora delbrueckii --- Grenache --- Graciano --- ochratoxin A (OTA) --- mycotoxins --- biogenic amines (BAs) --- ethyl carbamate (EC) --- organic wines --- non-Saccharomyces --- alcohol reduction --- native yeast --- sequential fermentation --- wine --- uninoculated fermentation --- yeast --- sulphur dioxide --- non-Saccharomyces yeasts --- mixed starter cultures --- fermentation --- Sangiovese --- sensory analysis --- antimicrobial peptides --- Brettanomyces bruxellensis --- Candida intermedia --- Pichia guilliermondii --- reactive oxygen species --- Hanseniaspora vineae --- alcoholic fermentation --- ageing on lees --- polysaccharides --- white wines --- winemaking --- aging-on-lees --- yeast assimilable nitrogen --- Saccharomyces non-cerevisiae --- ethanol --- glycerol --- glycolysis --- pyruvate kinase --- fermentation evolution clade --- sensory improvement --- dealcoholization --- SO2 --- grape variety --- Schizosaccharomyces pombe

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The great advances in information technology (IT) have implications for many sectors, such as bioinformatics, and has considerably increased their possibilities. This book presents a collection of 11 original research papers, all of them related to the application of IT-related techniques within the bioinformatics sector: from new applications created from the adaptation and application of existing techniques to the creation of new methodologies to solve existing problems.

Technology: general issues --- machine learning --- metagenomics --- bioinformatics --- CTX-M --- data mining --- cluster --- clinical implications --- diabetes --- epidemiology --- forecast --- PART --- Decision table --- Weka --- real-life patients --- regression --- ear detection --- computer vision --- convolutional neural network --- image recognition --- video analysis --- gene clustering --- swarm intelligence --- biological functions detection --- informative genes --- fuel cell --- hydrogen energy --- intelligent systems --- hybrid systems --- Artificial Neural Networks --- power management --- Machine Learning --- personality assessment --- gradient boosting --- Affective Computing --- transposable elements --- metrics --- deep learning --- detection --- classification --- mitochondrial protein --- bi-directional LSTM --- plasmodium falciparum --- Particle Swarm Optimization --- Harmony Search --- parameter estimation --- Arabidopsis thaliana --- clinical data --- feature selection --- genetic programming --- evolutionary computation --- dynamic models --- evolutionary computing --- derivative-free optimization --- metabolism --- glycolysis --- yeast --- machine learning --- metagenomics --- bioinformatics --- CTX-M --- data mining --- cluster --- clinical implications --- diabetes --- epidemiology --- forecast --- PART --- Decision table --- Weka --- real-life patients --- regression --- ear detection --- computer vision --- convolutional neural network --- image recognition --- video analysis --- gene clustering --- swarm intelligence --- biological functions detection --- informative genes --- fuel cell --- hydrogen energy --- intelligent systems --- hybrid systems --- Artificial Neural Networks --- power management --- Machine Learning --- personality assessment --- gradient boosting --- Affective Computing --- transposable elements --- metrics --- deep learning --- detection --- classification --- mitochondrial protein --- bi-directional LSTM --- plasmodium falciparum --- Particle Swarm Optimization --- Harmony Search --- parameter estimation --- Arabidopsis thaliana --- clinical data --- feature selection --- genetic programming --- evolutionary computation --- dynamic models --- evolutionary computing --- derivative-free optimization --- metabolism --- glycolysis --- yeast

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Women and men have probably never been concerned as much by their health as during this COVID-19 pandemic. In this context, lifestyle habits continue to be promoted as allies for daily prevention against diseases. This is valid also for metabolic diseases, among which many affect the liver and are risk factors for aggravating the disease course of COVID-19. In fact, liver diseases are currently a major global health problem. There is a huge range of liver diseases and non-alcoholic fatty liver disease (NAFLD) is the most common chronic hepatic condition, which in some patients progresses to cirrhosis and liver cancer. Currently, substantial efforts are being made to better understand NAFLD, especially, because there is no U.S. Food and Drug Administration (FDA)-approved pharmacological therapy. To explore this disease, metabolomics is the most recently developed omics technology after genomics, transcriptomics, and proteomics. Metabolomics is the large-scale analysis of molecules, known as metabolites that are intermediate or end products of metabolism found within cells, tissues, and biofluids. This technology has a very high potential to identify biomarker candidates for the future development of new therapeutics. The book features articles that address metabolomics technology and its use to document different liver functions and dysfunctions, with a major focus on NAFLD.

nonalcoholic fatty liver disease --- nonalcoholic steatohepatitis --- Fibrosis --- Liver biopsy --- Genomics --- Metabolomics --- Proteomics --- Transcriptomics --- nicotinamide --- NAFLD --- steatosis --- heat stress --- primary mouse hepatocytes --- metabolic profile --- GC-MS --- multivariate statistical analysis --- arachidonic acid --- docosahexaenoic acid --- inflammation --- fibrosis --- lipidomics --- mass spectrometry --- in vitro --- HepaRG --- sodium saccharin --- reference toxicants --- de novo lipogenesis --- carbohydrate response element-binding protein --- ChREBP --- diabetes --- glucose production --- glycogen --- glycolysis --- glycogen storage disease type I --- hexosamine --- pentose phosphate pathway --- acupuncture --- imflammation --- lipid metabolism --- oxidative stress --- metabolomics quantitative profiling --- 1H-NMR spectroscopy --- liver --- bile acids --- metabolomics --- rat plasma --- tandem mass spectrometry --- liquid chromatography --- acetaminophen --- hepatotoxicity --- biomarker --- premalignant --- alcoholic liver disease --- cholestasis --- cirrhosis --- NAFL --- NASH --- standard operating procedures --- urine --- blood --- feces --- tissue --- cells --- liver function --- nonalcoholic fatty liver --- liquid chromatography-mass spectrometry --- nuclear magnetic resonance spectroscopy --- metabolic pathway --- non-alcoholic fatty liver disease --- non-alcoholic steatohepatitis --- transcription factors --- metabolic stress --- lipid homeostasis --- glucose homeostasis