Pseudomonas stutzeri A1501 is a non-fluorescent denitrifying bacteria that belongs to the gram-negative bacterial group.As a prominent strain in the fields of agriculture and bioengineering,there is still a lack of co...Pseudomonas stutzeri A1501 is a non-fluorescent denitrifying bacteria that belongs to the gram-negative bacterial group.As a prominent strain in the fields of agriculture and bioengineering,there is still a lack of comprehensive understanding regarding its metabolic capabilities,specifically in terms of central metabolism and substrate utilization.Therefore,further exploration and extensive studies are required to gain a detailed insight into these aspects.This study reconstructed a genome-scale metabolic network model for P.stutzeri A1501 and conducted extensive curations,including correcting energy generation cycles,respiratory chains,and biomass composition.The final model,iQY1018,was successfully developed,covering more genes and reactions and having higher prediction accuracy compared with the previously published model iPB890.The substrate utilization ability of 71 carbon sources was investigated by BIOLOG experiment and was utilized to validate the model quality.The model prediction accuracy of substrate utilization for P.stutzeri A1501 reached 90%.The model analysis revealed its new ability in central metabolism and predicted that the strain is a suitable chassis for the production of Acetyl CoA-derived products.This work provides an updated,high-quality model of P.stutzeri A1501for further research and will further enhance our understanding of the metabolic capabilities.展开更多
Building energy consumption is heavily dependent on its heating load(HL)and cooling load(CL).Therefore,an efficient building demand forecast is critical for ensuring energy savings and improving the operating efficacy...Building energy consumption is heavily dependent on its heating load(HL)and cooling load(CL).Therefore,an efficient building demand forecast is critical for ensuring energy savings and improving the operating efficacy of the heating,ventilation,and air conditioning(HVAC)system.Modern and specialized energy-efficient building modeling technologies may offer a fair estimate of the influence of different construction methods.However,deploying these tools could be time-consuming and complex for the user.Thus,in this article,an ensemble model based on decision trees and the least square-boosting(LS-boosting)algorithm known as the regression tree ensemble(RTE)is proposed for the accurate prediction of HL and CL.The hyper parameters of the RTE are optimized by shuffled frog leaping optimization(SFLA),which leads to SRTE.Stepwise regression(STR)and Gaussian process regression(GPR)based on different kernel functions are also designed for comparison purposes.Results demonstrate that the value of root mean squared error is reduced by 37%–68%and 30%–41%for HL and CL of residential buildings,respectively,by the proposed SRTE in comparison to other models.Furthermore,the findings from the real dataset support the proposed model’s effectiveness in predicting HVAC energy usage.It can be concluded that the proposed SRTE is more effective and accurate than other methods for predicting the energy consumption of HVAC systems.展开更多
基金funded by the National Key Research and Development Program of China(2018YFA0901400)the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0480000)+1 种基金Tianjin Synthetic Biotechnology Innovation Capacity Improvement Projects(TSBICIP-PTJS-001)Ministry of Science of China and Youth Innovation Promotion Association CAS(292023000018).
文摘Pseudomonas stutzeri A1501 is a non-fluorescent denitrifying bacteria that belongs to the gram-negative bacterial group.As a prominent strain in the fields of agriculture and bioengineering,there is still a lack of comprehensive understanding regarding its metabolic capabilities,specifically in terms of central metabolism and substrate utilization.Therefore,further exploration and extensive studies are required to gain a detailed insight into these aspects.This study reconstructed a genome-scale metabolic network model for P.stutzeri A1501 and conducted extensive curations,including correcting energy generation cycles,respiratory chains,and biomass composition.The final model,iQY1018,was successfully developed,covering more genes and reactions and having higher prediction accuracy compared with the previously published model iPB890.The substrate utilization ability of 71 carbon sources was investigated by BIOLOG experiment and was utilized to validate the model quality.The model prediction accuracy of substrate utilization for P.stutzeri A1501 reached 90%.The model analysis revealed its new ability in central metabolism and predicted that the strain is a suitable chassis for the production of Acetyl CoA-derived products.This work provides an updated,high-quality model of P.stutzeri A1501for further research and will further enhance our understanding of the metabolic capabilities.
基金supported by the National Research Foundation of Korea(NRF)funded by the Ministry of Education(NRF-2021R1A2C3013687)the GIST Research Institute(GRI)grant funded by the GIST in GIST Research Project.
文摘Building energy consumption is heavily dependent on its heating load(HL)and cooling load(CL).Therefore,an efficient building demand forecast is critical for ensuring energy savings and improving the operating efficacy of the heating,ventilation,and air conditioning(HVAC)system.Modern and specialized energy-efficient building modeling technologies may offer a fair estimate of the influence of different construction methods.However,deploying these tools could be time-consuming and complex for the user.Thus,in this article,an ensemble model based on decision trees and the least square-boosting(LS-boosting)algorithm known as the regression tree ensemble(RTE)is proposed for the accurate prediction of HL and CL.The hyper parameters of the RTE are optimized by shuffled frog leaping optimization(SFLA),which leads to SRTE.Stepwise regression(STR)and Gaussian process regression(GPR)based on different kernel functions are also designed for comparison purposes.Results demonstrate that the value of root mean squared error is reduced by 37%–68%and 30%–41%for HL and CL of residential buildings,respectively,by the proposed SRTE in comparison to other models.Furthermore,the findings from the real dataset support the proposed model’s effectiveness in predicting HVAC energy usage.It can be concluded that the proposed SRTE is more effective and accurate than other methods for predicting the energy consumption of HVAC systems.
