Saccharomyces cerevisiae is not naturally capable of efficiently utilizing xylose as a carbon source.When cultured with lignocellulosic hydrolysates containing pretreatment-derived inhibitors,S.cerevisiae suffers from...Saccharomyces cerevisiae is not naturally capable of efficiently utilizing xylose as a carbon source.When cultured with lignocellulosic hydrolysates containing pretreatment-derived inhibitors,S.cerevisiae suffers from much lower sugar uptake,ethanol yield and fermentation efficiency.Thus,considering efficient xylose conversion into ethanol during non-detoxified hydrolysate culture,genetic engineering and adaptive evolution of S.cerevisiae might be a promising joint strategy for improving xylose uptake and ethanol production.In this study,an inhibitor-tolerant strain S.cerevisiae SPSC01-TAF94 was genetically engineered by overexpressing both xylose transport-and metabolism-related genes(N360F,Ru-xyl A,TAL1,TKL1,RKI1 and RPE1),yielding the xylose-utilizing strain TAF94-X,followed by three-stage adaptation in non-detoxified corn stover hydrolysate containing 5 g·L^(-1)acetic acid,0.32 g·L^(-1)furfural,0.17 g·L(-1)HMF and 0.19 g·L^(-1)vanillin as the major inhibitors as well as 20,40 and 60 g·L^(-1)xylose adjusted as the major carbon source,respectively.Finally,an active xylose-utilizing and ethanolproducing strain TAF94-X60 was obtained,which achieved 44.9 g·L^(-1)ethanol with yield of0.41 g·g^(-1),productivity of 0.62 g·L^(-1)·h^(-1)and xylose consumption rate of 0.42 g·L^(-1)·h^(-1)during hydrolysate culture,compared to those of 36.5 g·L^(-1),0.38 g·g^(-1),0.50 g·L^(-1)·h^(-1)and 0.20 g·L^(-1)·h^(-1)obtained with the control strain TAF94-X.The proposed joint strategy effectively utilizes hydrolyzed sugars while eliminating the need for conventional detoxification or water washing processes,thus enhancing the economic feasibility of large-scale lignocellulosic ethanol production.展开更多
The on-demand food delivery(OFD)service has gained rapid development in the past decades but meanwhile encounters challenges for further improving operation quality.The order dispatching problem is one of the most con...The on-demand food delivery(OFD)service has gained rapid development in the past decades but meanwhile encounters challenges for further improving operation quality.The order dispatching problem is one of the most concerning issues for the OFD platforms,which refer to dynamically dispatching a large number of orders to riders reasonably in very limited decision time.To solve such a challenging combinatorial optimization problem,an effective matching algorithm is proposed by fusing the reinforcement learning technique and the optimization method.First,to deal with the large-scale complexity,a decoupling method is designed by reducing the matching space between new orders and riders.Second,to overcome the high dynamism and satisfy the stringent requirements on decision time,a reinforcement learning based dispatching heuristic is presented.To be specific,a sequence-to-sequence neural network is constructed based on the problem characteristic to generate an order priority sequence.Besides,a training approach is specially designed to improve learning performance.Furthermore,a greedy heuristic is employed to effectively dispatch new orders according to the order priority sequence.On real-world datasets,numerical experiments are conducted to validate the effectiveness of the proposed algorithm.Statistical results show that the proposed algorithm can effectively solve the problem by improving delivery efficiency and maintaining customer satisfaction.展开更多
基金supported by the National Key Research and Development Program of China(2021YFC2101303)the National Natural Science Foundation of China(U22A20424 and 22378048)+5 种基金the Major scientific and technological projects of Sinopecthe Dalian Technology Talents Project for Distinguished Young Scholars(2021RJ03)the Fundamental Research Funds for the Central Universities(DUT25LAB104)the Liaoning Revitalization Talents Program(XLYC2202049)the Ningbo Natural Science Foundation(2022J013)the Ningbo Municipal Public Welfare Science and Technology Foundation(2024S004)。
文摘Saccharomyces cerevisiae is not naturally capable of efficiently utilizing xylose as a carbon source.When cultured with lignocellulosic hydrolysates containing pretreatment-derived inhibitors,S.cerevisiae suffers from much lower sugar uptake,ethanol yield and fermentation efficiency.Thus,considering efficient xylose conversion into ethanol during non-detoxified hydrolysate culture,genetic engineering and adaptive evolution of S.cerevisiae might be a promising joint strategy for improving xylose uptake and ethanol production.In this study,an inhibitor-tolerant strain S.cerevisiae SPSC01-TAF94 was genetically engineered by overexpressing both xylose transport-and metabolism-related genes(N360F,Ru-xyl A,TAL1,TKL1,RKI1 and RPE1),yielding the xylose-utilizing strain TAF94-X,followed by three-stage adaptation in non-detoxified corn stover hydrolysate containing 5 g·L^(-1)acetic acid,0.32 g·L^(-1)furfural,0.17 g·L(-1)HMF and 0.19 g·L^(-1)vanillin as the major inhibitors as well as 20,40 and 60 g·L^(-1)xylose adjusted as the major carbon source,respectively.Finally,an active xylose-utilizing and ethanolproducing strain TAF94-X60 was obtained,which achieved 44.9 g·L^(-1)ethanol with yield of0.41 g·g^(-1),productivity of 0.62 g·L^(-1)·h^(-1)and xylose consumption rate of 0.42 g·L^(-1)·h^(-1)during hydrolysate culture,compared to those of 36.5 g·L^(-1),0.38 g·g^(-1),0.50 g·L^(-1)·h^(-1)and 0.20 g·L^(-1)·h^(-1)obtained with the control strain TAF94-X.The proposed joint strategy effectively utilizes hydrolyzed sugars while eliminating the need for conventional detoxification or water washing processes,thus enhancing the economic feasibility of large-scale lignocellulosic ethanol production.
基金supported in part by the National Natural Science Foundation of China(No.62273193)Tsinghua University-Meituan Joint Institute for Digital Life,and the Research and Development Project of CRSC Research&Design Institute Group Co.,Ltd.
文摘The on-demand food delivery(OFD)service has gained rapid development in the past decades but meanwhile encounters challenges for further improving operation quality.The order dispatching problem is one of the most concerning issues for the OFD platforms,which refer to dynamically dispatching a large number of orders to riders reasonably in very limited decision time.To solve such a challenging combinatorial optimization problem,an effective matching algorithm is proposed by fusing the reinforcement learning technique and the optimization method.First,to deal with the large-scale complexity,a decoupling method is designed by reducing the matching space between new orders and riders.Second,to overcome the high dynamism and satisfy the stringent requirements on decision time,a reinforcement learning based dispatching heuristic is presented.To be specific,a sequence-to-sequence neural network is constructed based on the problem characteristic to generate an order priority sequence.Besides,a training approach is specially designed to improve learning performance.Furthermore,a greedy heuristic is employed to effectively dispatch new orders according to the order priority sequence.On real-world datasets,numerical experiments are conducted to validate the effectiveness of the proposed algorithm.Statistical results show that the proposed algorithm can effectively solve the problem by improving delivery efficiency and maintaining customer satisfaction.
