Modelling Acetification with Artificial Neural Networks and Comparison with Alternative Procedures

Modelling techniques allow certain processes to be characterized and optimized without the need for experimentation. One of the crucial steps in vinegar production is the biotransformation of ethanol into acetic acid by acetic bacteria. This step has been extensively studied by using two predictive models: first-principles models and black-box models. The fact that first-principles models are less accurate than black-box models under extreme bacterial growth conditions suggests that the kinetic equations used by the former, and hence their goodness of fit, can be further improved. By contrast, black-box models predict acetic acid production accurately enough under virtually any operating conditions. In this work, we trained black-box models based on Artificial Neural Networks (ANNs) of the multilayer perceptron (MLP) type and containing a single hidden layer to model acetification. The small number of data typically available for a bioprocess makes it rather difficult to identify the most suitable type of ANN architecture in terms of indices such as the mean square error (MSE). This places ANN methodology at a disadvantage against alternative techniques and, especially, polynomial modelling

Optimization of the Acetification Stage in the Production of Wine Vinegar by Use of Two Serial Bioreactors

In the scope of a broader study about wine acetification, previous works concluded that using a single bioreactor hindered simultaneously reaching high productivities with high substrate consumption and the use of two serially arranged bioreactors (TSAB) could achieve such goal. Then, the aim of this work is the optimization, using Karush–Kuhn–Tucker (KKT) conditions, of this TSAB using polynomial models previously obtained. The ranges for the operational variables leading to either maximum and minimum mean rate of acetification of 0.11 ≤ (rA)global ≤ 0.27 g acetic acid·(100 mL·h)−1 and acetic acid production of 14.7 ≤ Pm ≤ 36.6 g acetic acid·h−1 were identified; the results show that simultaneously maximizing (rA)global and Pm is not possible so, depending on the specific objective, different operational ranges must be used. Additionally, it is possible to reach a productivity close to the maximum one (34.6 ≤ Pm ≤ 35.5 g acetic acid·h−1) with an almost complete substrate use [0.2% ≤ Eu2 ≤ 1.5% (v/v)]. Finally, comparing the performance of the bioreactors operating in series and in parallel revealed that the former choice resulted in greater production

Modelling of the Acetification Stage in the Production of Wine Vinegar by Use of Two Serial Bioreactors

In the scope of a broader study about modelling wine acetification, the use of polynomial black-box models seems to be the best choice. Additionally, the use of two serially arranged bioreactors was expected to result in increased overall acetic acid productivity. This paper describes the experiments needed to obtain enough data for modelling the process and the use of second-order polynomials for this task. A fractional experimental design with central points was used with the ethanol concentrations during loading of the bioreactors, their operation temperatures, the ethanol concentrations at unloading time, and the unloaded volume in the first one as factors. Because using two serial reactors imposed some constraints on the operating ranges for the process, an exhaustive combinatorial analysis was used to identify a working combination of such ranges. The obtained models provided highly accurate predictions of the mean overall rate of acetic acid formation, the mean total production of acetic acid of the two-reactor system, and ethanol concentration at the time the second reactor is unloaded. The operational variables associated with the first bioreactor were the more strongly influential to the process, particularly the ethanol concentration at the time the first reactor was unloaded, the unloaded volume, and the ethanol concentration when loading

Aplicaciones y concentraciones de plaguicidas en la zona regable de La Violada tras la modernización

Peer ReviewedPublishe

PTGDR gene expression and response to dexamethasone treatment in an in vitro model

[EN]Asthma is a multifactorial pathology influenced by environmental and genetic factors. Glucocorticoid treatment decreases symptoms by regulating genes involved in the inflammatory process through binding to specific DNA sequences. Polymorphisms located in the promoter region of the Prostaglandin D Receptor (PTGDR) gene have been related to asthma. We aimed to analyze the effect of PTGDR promoter haplotypes on gene expression and response to corticosteroid therapy. A549 lung epithelial cells were transfected with vectors carrying four different PTGDR haplotypes (CTCT, CCCC, CCCT and TCCT), and treated with dexamethasone. Different approaches to study the promoter activity (Dual Luciferase Reporter System), gene expression levels (qPCR) and cytokine secretion (Multiplexed Bead-based Flow Cytometric) were used. In addition, in silico analysis was also performed. Cells carrying the TCCT haplotype showed the lowest promoter activity (p-value<0.05) and mRNA expression levels in basal conditions. After dexamethasone treatment, cells carrying the wild-type variant CTCT showed the highest response, and those carrying the TCCT variant the lowest (p-value<0.05) in luciferase assays. Different transcription factor binding patterns were identified in silico. Moreover, differences in cytokine secretion were also found among different promoter haplotypes. Polymorphisms of PTGDR gene influence basal promoter activity and gene expression, as well as the cytokine secretory pattern. Furthermore, an association between these positions and response to corticoid treatment was observed

Latest trends in industrial vinegar production and the role of acetic acid bacteria: classification, metabolism, and applications—a comprehensive review

Vinegar is one of the most appreciated fermented foods in European and Asian countries. In industry, its elaboration depends on numerous factors, including the nature of starter culture and raw material, as well as the production system and operational conditions. Furthermore, vinegar is obtained by the action of acetic acid bacteria (AAB) on an alcoholic medium in which ethanol is transformed into acetic acid. Besides the highlighted oxidative metabolism of AAB, their versatility and metabolic adaptability make them a taxonomic group with several biotechnological uses. Due to new and rapid advances in this field, this review attempts to approach the current state of knowledge by firstly discussing fundamental aspects related to industrial vinegar production and then exploring aspects related to AAB: classification, metabolism, and applications. Emphasis has been placed on an exhaustive taxonomic review considering the progressive increase in the number of new AAB species and genera, especially those with recognized biotechnological potential

Valorisation of the invasive alga Rugulopteryx okamurae through the production of monomeric sugars.

Rugulopteryx okamurae is an invasive brown alga causing severe environmental and economic problems on the western Mediterranean coasts. Thus, in addition to the difficulties caused to the fishing and tourism sectors, there is a need to manage its accumulation on the beaches. This work aims to valorise this waste by using it as raw material for producing monosaccharides through a two-stage sequential process. These sugars could be used for different fermentative processes to obtain high-value-added bioproducts. In this work, biological pretreatment of the previously conditioned seaweed with the fungus Aspergillus awamori in solid-state fermentation (SSF), followed by enzymatic hydrolysis with a commercial enzyme cocktail, was performed. The effect of the extension of the biological pretreatment (2, 5, 8 and 12 days) on the subsequent release of total reducing sugars (TRS) in the enzymatic hydrolysis stage was studied. To analyse this effect, experimental data of TRS produced along the hydrolysis were fitted to simple first-order kinetics. Also, the secretion of cellulase and alginate lyase by the fungus, along with the biological pretreatment, was determined. The results suggest that 5 days of biological pretreatment of the macroalgae with A. awamori followed by enzymatic saccharification for 24 h with Cellic CTec2 (112 FP units/g of dry biomass) are the best conditions tested, allowing the production of around 240 g of TRS per kg of dried biomass. The main sugars obtained were glucose (95.8 %) and mannitol (1.5 %), followed by galactose (1 %), arabinose (0.9 %) and fucose (0.5 %). KEY POINTS: Five-day SSF by A. awamori was the best condition to pretreat R. okamurae. Five-day SSF was optimal for alginate lyase production (1.63 ±0.011 IU/g biomass). A maximum yield of 239 mg TRS/g biomass was obtained (with 95.8 % glucose)

Use of macroalgal waste from the carrageenan industry as feedstock for the production of polyhydroxybutyrate

The industrial waste generated in the extraction of carrageenan from red seaweed, Eucheuma spinosum, was tested in this study to produce fermentable sugars that could be used for the production of high-value-bioproducts with a biorefinery approach. A sequential process was used: thermochemical pretreatment with HCl and enzymatic hydrolysis. Hydrogen chloride concentrations in the range from 0 mol L−1 to 0.5 mol L−1 and pretreatment times from 15 to 100 min were assayed. The best conditions found for pretreatment were HCl 0.3 mol L−1 for 60 min, leading to reducing sugar concentrations of 21.4 g L−1 (274 mg of reducing sugars per gram of algal residue). The hydrolysates coming from the sequential process under the pretreatment conditions of HCl 0.3 mol L−1 for 60 and 80 min have been used successfully for the production of polyhydroxyalkanoates by Cupriavidus necator. The yields of polyhydroxybutyrate were 0.21–0.26 g PHB g−1 reducing sugar consumed and the accumulation of the biopolymer was of the order of 58% dry cell weight. © 2023 The Authors. Biofuels, Bioproducts and Biorefining published by Society of Industrial Chemistry and John Wiley & Sons Ltd

Interleukin-4 (IL4) and Interleukin-4 receptor (IL4RA) polymorphisms in asthma: a case control study

BACKGROUND: IL4/IL4RA pathway plays an important role in atopy and asthma. Different polymorphisms in IL4 and IL4RA genes have been described. Particularly, -33C>TIL4 and 576Q>RIL4RA SNPs have been independently associated to atopy and asthma. The purpose of this study was to analyse these polymorphisms in a population of patients with a well-characterized asthma phenotype. METHODS: A total of 212 unrelated Caucasian individuals, 133 patients with asthma and 79 healthy subjects without symptoms or history of asthma or atopy and with negative skin prick tests were recruited. Lung function was measured by spirometry and asthma was specialist physician-diagnosed according to the ATS (American Thoracic Society) criteria and classified following the GINA (Global Initiative for Asthma) guidelines. Skin prick tests were performed according to EAACI recommendations. -33C>TIL4 was studied with TaqMan assay and 576Q>RIL4RA by PCR-RFLP technique. Hardy-Weinberg equilibrium was analysed in all groups. Dichotomous variables were analysed using χ(2), Fisher exact test, Monte Carlo simulation test and odds ratio test. To model the effects of multiple covariates logistic regression was used. RESULTS: No statistically significant differences between the group of patients with asthma and the controls were found when the allele and genotype distribution of -33C>TIL4 and 576Q>RIL4RA polymorphisms were compared. However, the T allele of the -33C>TIL4 SNP was more frequent in patients with persistent asthma. Multivariate analysis adjusted for age and sex confirmed that carriers of allele T had an increased risk of persistent asthma (OR:2.77, 95%CI:1.18–6.49; p = 0.019). Analysis of combination of polymorphisms showed that patients carrying both the T allele of -33C>TIL4 and the A allele of 576Q>RIL4RA had an increased risk of asthma. This association was particularly observed in persistent asthma [Fisher's p value = 0.0021, Monte Carlo p value (after 10(4 )simulations) = 0.0016, OR:3.39; 95% CI:1.50–7.66]. CONCLUSION: Our results show a trend of association between the genetic combination of the T allele of -33C>TIL4 and the A allele of 576Q>RIL4RA with asthma. This genetic variant was more frequently observed in patients with persistent asthma. As long as this study was performed in a small population, further studies in other populations are needed to confirm these results