Kinetic modeling of gamma-aminobutyric acid production by Lactobacillus brevis based on pH-dependent model and rolling correction

Gamma-aminobutyric acid(GABA)is a natural non-protein functio nal amino acid,which has potential for fermentation industrial production by Lactobacillus brevis.This work investigated the batch fermentation process and developed a kinetic model based on substrate restrictive model established by experimental data from L25(5~6)orthogonal experiments.In this study,the OD600 value of fermentation broth was fixed to constant after reaching its maximum because the microorganism death showed no effect on the enzyme activity of glutamate decarboxylase(GAD).As pH is one of the key parameters in fermentation process,a pH-dependent kinetic model based on radial basis function was developed to enhance the practicality of the model.Furthermore,as to decrease the deviations between the simulated curves and the experimental data,the rolling correction strategy with OD600 values that was measured in real-time was introduced into this work to modify the model.Finally,the accu racy of the rolling corrected and pH-dependent model was validated by good fitness between the simulated curves and data of the initial batch fermentation(pH 5.2).As a result,this pH-dependent kinetic model revealed that the optimal pH for biomass growth is 5.6-5.7 and for GABA production is about 5,respectively.Therefore,the developed model is practical and convenient for the instruction of GABA fermentation production,and it has instructive significance for the industrial scale.

Development of an integrated process for the production of high-purityγ-aminobutyric acid from fermentation broth

γ-Aminobutyric acid(GABA),a natural non-protein amino acid,plays an irreplaceable role in regulating the life activities of organisms.Nowadays,the separation and purification of food-grade GABA from fermentation broth is still a great challenge.This research utilized monosodium glutamate as a substrate for the production of high-purity GABA via an integrated process incorporating fermentation,purification,and crystallization.Firstly,147 g·L^(-1) GABA with a yield of 99.8%was achieved through fed-batch fermentation by Lactobacillus brevis CE701.Secondly,three integrated purification methods by ethanol precipitation were compared,and crude GABA with a purity of 89,85%was obtained by the optimized method.Thirdly,GABA crystals with a purity of 98.69%and a yield of 60%were further obtained through a designed crystallization process.Furthermore,the GABA industrial production process model was established by Superproper Designer V10 software,and material balance and economic analysis were carried out.Ethanol used in the process was recovered with a recovery of 98.79%through Aspen simulated extractive distillation.Then the fixed investment(equipment purchase and installation costs)for an annual production of 80 t GABA will be about 833000 USD;the total annual production cost(raw material cost and utility cost)will be about 641000 USD.The annual sale of GABA may be at the range of 2400000-4000000 USD and the payback period will be about 1-2 year.This integrated process provides a potential way for the industrial-scale production of food-grade GABA.

Yangshan A-Type Granites in the Lower Yangtze River Belt Formed by Ridge Subduction:Radiogenic Ca and Nd Isotopic Constraints

The Early Cretaceous aluminous A-type granites in the Lower Yangtze River belt(LYRB)can provide important insights into the Mesozoic magmatism in eastern China,but their origin remains highly controversial.In this study,radiogenic Ca-Nd isotopic analysis was performed for syenite porphyry and alkali-feldspar granite porphyry of the Yangshan pluton,a typical aluminous A-type granitic intrusion in the LYRB,to constrain its source and geodynamic setting.The results show thatε_(Ca)(126 Ma),ε_(Nd)(126 Ma)and K/Ca_(source) of the syenite porphyry range from-0.24 to+0.96,-7.2 to-6.0,and 0.31 to 1.26,respectively.The corresponding values for the alkali-feldspar granite porphyry range from 0.26 to 0.84,-8.0 to-6.1,and 0.79 to 1.08,respectively.Binary mixing modeling indicates that they were originated from the same sources with different proportion,namely,a mixing of 50%to 75%Neoproterozoic crust and 50%to 25%asthenospheric mantle.Together with previous works,we propose that the Early Cretaceous subduction of the ridge between the Pacific and Izanagi plates was responsible for the formation of the aluminous A-type granites in the LYRB.

Distributionally Robust Learning Based on Dirichlet Process Prior in Edge Networks

In order to meet the real-time performance requirements,intelligent decisions in Internet of things applications must take place right here right now at the network edge.Pushing the artificial intelligence frontier to achieve edge intelligence is nontrivial due to the constrained computing resources and limited training data at the network edge.To tackle these challenges,we develop a distributionally robust optimization(DRO)-based edge learning algorithm,where the uncertainty model is constructed to foster the synergy of cloud knowledge and local training.Specifically,the cloud transferred knowledge is in the form of a Dirichlet process prior distribution for the edge model parameters,and the edge device further constructs an uncertainty set centered around the empirical distribution of its local samples.The edge learning DRO problem,subject to these two distributional uncertainty constraints,is recast as a single-layer optimization problem using a duality approach.We then use an Expectation-Maximization algorithm-inspired method to derive a convex relaxation,based on which we devise algorithms to learn the edge model.Furthermore,we illustrate that the meta-learning fast adaptation procedure is equivalent to our proposed Dirichlet process prior-based approach.Finally,extensive experiments are implemented to showcase the performance gain over standard approaches using edge data only.