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Enregistrement W4213268689 · doi:10.3390/books978-3-03842-443-7

Yeast Biotechnology

2017· book· en· W4213268689 sur OpenAlexaff
Ronnie Willaert

Notice bibliographique

Revuenon disponible
Typebook
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueBioeconomy and Sustainability Development
Établissements canadiensUniversity of British Columbia
Organismes subventionnairesAgricultural Research ServiceU.S. Forest ServiceNational Institute of Food and AgricultureInstituto Madrileño de Investigación y Desarrollo Rural, Agrario y AlimentarioBelgian Federal Science Policy OfficeUniversitat de ValènciaRegione PugliaÉcole Polytechnique Fédérale de LausanneUniversiteit GentEuropean CommissionU.S. Department of AgricultureJunta de AndalucíaEuropean Space AgencyNorthwest Advanced Renewables AllianceVrije Universiteit BrusselInstituto Nacional de Investigación y Tecnología Agraria y Alimentaria
Mots-clésYeastBiotechnologyBiologyGenetics

Résumé

récupéré en direct d'OpenAlex

Yeasts are truly fascinating microorganisms.Due to their diverse and dynamic activities, they have been used for the production of many interesting products, such as beer, wine, bread, biofuels, and biopharmaceuticals.Saccharomyces cerevisiae (brewers' or bakers' yeast) is the yeast species that is surely the most exploited by man.Saccharomyces is a top choice organism for industrial applications, although its use for producing beer dates back to at least the 6 th millennium BC.Bakers' yeast has been a cornerstone of modern biotechnology, enabling the development of efficient production processes.Today, diverse yeast species are explored for industrial applications.This Special Issue is focused on some recent developments of yeast biotechnology, i.e., bioethanol, wine and beer, and enzyme production.Additionally, the new field of yeast nanobiotechnology is introduced and reviewed.New developments in efficient bio-ethanol production.Due to its low costs and wide distribution, lignocellulosic biomass is the most promising feedstock to be used in biorefineries and lignocellulose-derived fuels.The fermentation of sugars from lignocelluloses has been proposed as a viable pathway for the production of renewable biofuels.However, the feedstock is a major cost factor.Therefore, the use of low cost and underutilised feedstocks, such as harvest forest residues, to produce ethanol could be an interesting route.Yang and colleagues [1] evaluated several batch fermentation approaches under various conditions for ethanol production from softwood forest residues.Ranges of liquefaction time, cellulase, and yeast loadings were all evaluated in this study to improve ethanol production.A pretreatment of the lignocellulose feedstock is necessary to improve the saccharification and fermentation processes, but the current physical and/or chemical pretreatment procedures have several drawbacks.The use of laccases has been developed as an environmentally friendly alternative for improving the saccharification and fermentation stages of lignocellulosic biomass.Moreno et al. [2] evaluated a novel bacterial laccase for enhancing the hydrolysability and fermentability of steam-exploded wheat straw.To increase the productivity of the ethanol fermentation, new bioreactor designs and operation modes have been introduced.A novel textile bioreactor for improved ethanol production was developed by Osadolor and colleagues [3].Due to the efficient mixing, this fluidised-bed bioreactor allowed the procurement of a high cell density of flocculating yeast cells, resulting in a high ethanol productivity.Wine and beer yeasts.The increasing economic interest in the sector of sparkling wine has stimulated a renewed interest in microbial resource management.Starter cultures for sparkling wine production need to be selected in order to produce either quality base wine or to vigorously promote secondary fermentation.Garofalo and coworkers [4] reviewed the main characterisation for selecting Saccharomyces cerevisiae strains suitable as starter cultures and analysed the possible uses of selected Fermentation 2017, 3, 6 vii www.mdpi.com/journal/fermentationFermentation 2017, 3,6non-Saccharomyces and malolactic strains in order to differentiate specific productions, and highlighted the main safety aspects related to microbes of enological interest.In spontaneous wine fermentations, more than one Saccharomyces cerevisiae strain ferments the wine must.These strains affect flavour and aroma properties differently.Therefore, Gustafsson et al.[5] investigated the interaction of two S. cerevisiae strains.The results showed that the co-inoculation of strains creates a new chemical profile not seen in the pure cultures, which have implications for winemakers that are looking to control wine aroma and flavour profiles through strain selection.Wine quality can be improved by using locally-selected S. cerevisiae strains as starter cultures.Cordero-Bueso and coworkers [6] could improve the fruity and fresh character of Malvar wines by selecting two local strains.Non-Saccharomyces ("wild") yeasts are found on the grapes and are also present on cellar equipment.This wealth of yeast biodiversity with hidden potential, especially for oenology, is largely untapped.In this Special Issue, the applications of non-Saccharomyces yeasts to the wine-making process were reviewed by Mateo and Maicas [7].Schlander and coworkers [8] characterised acid proteases isolated from the wild yeasts Metschnikovia pulcherrima and Wickerhamomyces anomalus.These enzymes are of significant importance for medicine and biotechnology.Yeast strains that flocculate are of particular interest to brewers, since it simplifies the yeast removal at the end of the primary fermentation considerably.Conjaerts and Willaert [9] performed adaptive laboratory evolution with gravity imposed as selective pressure for evolving a weak flocculating industrial strain towards a more flocculent phenotype.They used 3D printing to construct a suitable mini tower fermenter.Yeast nanobiotechnology, which is a recent field where nanotechniques are used to manipulate and analyse yeast cells and cell constituents at the nanoscale, was reviewed by Willaert and coworkers [10].An overview and discussion of nanobiotechnological analysis and manipulation techniques that have been particularly applied to yeast cells, is given; i.e., nanoscale imaging techniques, single-molecule and single cell force spectroscopy, AFM (Atomic Force Microscopy)-cantilever-based nanomotion analysis of living cells, nano/microtechniques to pattern and manipulate yeast cells, and direct contact and non-contact cell manipulation methods were reviewed.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,070
Score d'incertitude au seuil0,233

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,002
Études des sciences et des technologies0,0010,001
Communication savante0,0040,002
Science ouverte0,0010,002
Intégrité de la recherche0,0010,003
Charge utile insuffisante (le modèle a refusé de juger)0,0700,097

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,015
Tête enseignante GPT0,201
Écart entre enseignants0,186 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2017
Routes d'admission1
Résumé présentoui

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