BackgroundD-2,3-butanediol has many industrial applications such as chiral reagents, solvents, anti-freeze agents, and low freezing point fuels. Traditional D-2,3-butanediol producing microorganisms, such as Klebsiella pneumonia and K. xoytoca, are pathogenic and not capable of producing D-2,3-butanediol at high optical purity. Bacillus licheniformis is a potential 2,3-butanediol producer but the wild type strain (WX-02) produces a mix of D- and meso-type isomers. BudC in B. licheniformis is annotated as 2,3-butanediol dehydrogenase or acetoin reductase, but no pervious experiment was performed to verify this hypothesis.ResultsWe developed a genetically modified strain of B. licheniformis (WX-02 ΔbudC) as a D-2,3-butanediol producer with high optimal purity. A marker-less gene deletion protocol based on a temperature sensitive knock-out plasmid T2-Ori was used to knock out the budC gene in B. licheniformis WX-02. The budC knock-out strain successfully abolished meso-2,3-butanediol production with enhanced D-2,3-butanediol production. No meso-BDH activity was detectable in cells of this strain. On the other hand, the complementary strain restored the characteristics of wild strain, and produced meso-2,3-butanediol and possessed meso-BDH activity. All of these data suggested that budC encoded the major meso-BDH catalyzing the reversible reaction from acetoin to meso-2,3-butanediol in B. licheniformis. The budC knock-out strain produced D-2,3-butanediol isomer only with a high yield of 30.76 g/L and a productivity of 1.28 g/L-h.ConclusionsWe confirmed the hypothesis that budC gene is responsible to reversibly transfer acetoin to meso-2,3-butanediol in B. licheniformis. A mutant strain of B. licheniformis with depleted budC gene was successfully developed and produced high level of the D-2,3-butanediol with high optimal purity.
Nitrate is an important nitrogen source for organism, but whether and how nitrate improves poly-γ-glutamic acid (γ-PGA) production of bacterial is not clear. The effect of nitrate on γ-PGA production of Bacillus licheniformis WX-02 was investigated. By addition of 50 mmol/L nitrate, the γ-PGA yield reached 12.3 ± 0.21 g/L, which increased 2.3-fold compared to the control. The mechanism of enhanced γ-PGA production was further investigated by analysis of nitrate reduction, physiology, pyruvate overflow metabolism and energy synthesis. Nitrate reduction was only carried out in the middle stage of γ-PGA fermentation. The result of consumption of nutrients showed that glucose uptake was not effected and the L-glutamic acid utilization efficiency increased from 48.3 to 77.0 %. The date of overflow metabolism obtained from high-performance liquid chromatography showed that the metabolism of pyruvate, formate, lactate and acetoin was both heightened by nitrate reduction, while the 2,3-butanediol biosynthesis was decreased. Meanwhile, the change of energy indicated that more ATP was synthesized during nitrate reduction. In summary, nitrate was a positive effector of γ-PGA biosynthesis in B. licheniformis WX-02 and nitrate reduction affected multi-metabolism pathways, including glycolysis, overflow metabolism and energy metabolism.
Backfat thickness (BF) is closely related to the service life and reproductive performance of sows. The dynamic monitoring of sows’ BF is a critical part of the production process in large-scale pig farms. This study proposed the application of a hybrid CNN-ViT (Vision Transformer, ViT) model for measuring sows’ BF to address the problems of high measurement intensity caused by the traditional contact measurement of sows’ BF and the low efficiency of existing non-contact models for measuring sows’ BF. The CNN-ViT introduced depth-separable convolution and lightweight self-attention, mainly consisting of a Pre-local Unit (PLU), a Lightweight ViT (LViT) and an Inverted Residual Unit (IRU). This model could extract local and global features of images, making it more suitable for small datasets. The model was tested on 106 pregnant sows with seven randomly divided datasets. The results showed that the CNN-ViT had a Mean Absolute Error (MAE) of 0.83 mm, a Root Mean Square Error (RMSE) of 1.05 mm, a Mean Absolute Percentage Error (MAPE) of 4.87% and a coefficient of determination (R-Square, R2) of 0.74. Compared to LviT-IRU, PLU-IRU and PLU-LviT, the CNN-ViT’s MAE decreased by more than 12%, RMSE decreased by more than 15%, MAPE decreased by more than 15% and R² improved by more than 17%. Compared to the Resnet50 and ViT, the CNN-ViT’s MAE decreased by more than 7%, RMSE decreased by more than 13%, MAPE decreased by more than 7% and R2 improved by more than 15%. The method could better meet the demand for the non-contact automatic measurement of pregnant sows’ BF in actual production and provide technical support for the intelligent management of pregnant sows.
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