Accurate seasonal precipitation forecasts,especially for extreme events,are crucial to preventing meteorological hazards and their potential impacts on national development,social activity,and security.However,the int...Accurate seasonal precipitation forecasts,especially for extreme events,are crucial to preventing meteorological hazards and their potential impacts on national development,social activity,and security.However,the intensity of summer precipitation is often largely underestimated in many current dynamic models.This study uses a deep learning method called Cycle-Consistent Generative Adversarial Networks(CycleGAN)to improve the seasonal forecasts for June-JulyAugust precipitation in southeastern China by the Nanjing University of Information Science and Technology Climate Forecast System(NUIST-CFS 1.0).The results suggest that the CycleGAN-based model significantly improves the accuracy in predicting the spatiotemporal distribution of summer precipitation compared to the traditional quantile mapping(QM)method.Using the unpaired bias-correction model,we can also obtain advanced forecasts of the frequency,intensity,and duration of extreme precipitation events over the dynamic model predictions.This study expands the potential applications of deep learning models toward improving seasonal precipitation forecasts.展开更多
Using monthly observations and ensemble hindcasts of the Nanjing University of Information Science and Technology Climate Forecast System(NUIST-CFS1.0) for the period 1983–2020, this study investigates the forecast s...Using monthly observations and ensemble hindcasts of the Nanjing University of Information Science and Technology Climate Forecast System(NUIST-CFS1.0) for the period 1983–2020, this study investigates the forecast skill of marine heatwaves(MHWs) over the globe and the predictability sources of the MHWs over the tropical oceans. The MHW forecasts are demonstrated to be skillful on seasonal-annual time scales, particularly in tropical oceans. The forecast skill of the MHWs over the tropical Pacific Ocean(TPO) remains high at lead times of 1–24 months, indicating a forecast better than random chance for up to two years. The forecast skill is subject to the spring predictability barrier of El Nino-Southern Oscillation(ENSO). The forecast skills for the MHWs over the tropical Indian Ocean(TIO), tropical Atlantic Ocean(TAO), and tropical Northwest Pacific(NWP) are lower than that in the TPO. A reliable forecast at lead times of up to two years is shown over the TIO, while a shorter reliable forecast window(less than 17 months) occurs for the TAO and NWP.Additionally, the forecast skills for the TIO, TAO, and NWP are seasonally dependent. Higher skills for the TIO and TAO appear in boreal spring, while a greater skill for the NWP emerges in late summer-early autumn. Further analyses suggest that ENSO serves as a critical source of predictability for MHWs over the TIO and TAO in spring and MHWs over the NWP in summer.展开更多
Background:Heterologous synthesis presents a promising new approach for accessing the active ingredients of traditional Chinese medicine(TCM),contributing to the conservation of natural medicinal resources.Compound Da...Background:Heterologous synthesis presents a promising new approach for accessing the active ingredients of traditional Chinese medicine(TCM),contributing to the conservation of natural medicinal resources.Compound Danshen preparation,a widely used TCM formulation,is designed to treat coronary heart disease and angina pectoris.It is primarily composed of Salviae Miltiorrhizae Radix et Rhizoma(Danshen),Notoginseng Radix et Rhizoma(Sanqi),and borneol(Bingpian).Danshen primarily yields tanshinones and phenolic acids,while Sanqi produces notoginsenosides.Borneol serves as an auxiliary agent to promote mental clarity,dissipate heat,and relieve pain.Objectives:The objective is to employ heterologous synthesis in a single yeast strain to produce the active ingredients of Compound Danshen preparation,and these include notoginsenosides,tanshinones,and borneol.Methods:Firstly,the“plug-and-play”terpene synthase screening framework in Saccharomyces cerevisiae was used for the identification of a novel,highly efficient bornyl diphosphate synthase.Furthermore,by leveraging the Compound Danshen preparation as a basis to concurrently reconstruct the biosynthetic pathways for the notoginsenoside precursor protopanaxadiol(a triterpene),the tanshinone precursor miltiradiene(a diterpene),and borneol(a monoterpene)within a single yeast strain.Results:This engineered strain,termed Compound Danshen Yeast 1.0,successfully produced protopanaxadiol at 62.34 mg/L,miltiradiene at 15.38 mg/L,and borneol at 2.54 mg/L in shake-flask fermentation.Conclusions:This research signifies the inaugural cross-species andmulticomponent synthetic biology endeavor that enables the synthesis of active ingredients in engineered cells,setting the stage for the industrial manufacture of TCM compounds.展开更多
基金supported by the National Key Research and Development Program of China(Grant No.2020YFA0608000)the National Natural Science Foundation of China(Grant No.42030605)+1 种基金CAAI-MindSpore Academic Fund Research Projects(CAAIXSJLJJ2023MindSpore11)the program of China Scholarships Council(No.CXXM2101180001)。
文摘Accurate seasonal precipitation forecasts,especially for extreme events,are crucial to preventing meteorological hazards and their potential impacts on national development,social activity,and security.However,the intensity of summer precipitation is often largely underestimated in many current dynamic models.This study uses a deep learning method called Cycle-Consistent Generative Adversarial Networks(CycleGAN)to improve the seasonal forecasts for June-JulyAugust precipitation in southeastern China by the Nanjing University of Information Science and Technology Climate Forecast System(NUIST-CFS 1.0).The results suggest that the CycleGAN-based model significantly improves the accuracy in predicting the spatiotemporal distribution of summer precipitation compared to the traditional quantile mapping(QM)method.Using the unpaired bias-correction model,we can also obtain advanced forecasts of the frequency,intensity,and duration of extreme precipitation events over the dynamic model predictions.This study expands the potential applications of deep learning models toward improving seasonal precipitation forecasts.
基金jointly supported by the National Natural Science Foundation of China (Grant Nos.42192562 and 42030605)。
文摘Using monthly observations and ensemble hindcasts of the Nanjing University of Information Science and Technology Climate Forecast System(NUIST-CFS1.0) for the period 1983–2020, this study investigates the forecast skill of marine heatwaves(MHWs) over the globe and the predictability sources of the MHWs over the tropical oceans. The MHW forecasts are demonstrated to be skillful on seasonal-annual time scales, particularly in tropical oceans. The forecast skill of the MHWs over the tropical Pacific Ocean(TPO) remains high at lead times of 1–24 months, indicating a forecast better than random chance for up to two years. The forecast skill is subject to the spring predictability barrier of El Nino-Southern Oscillation(ENSO). The forecast skills for the MHWs over the tropical Indian Ocean(TIO), tropical Atlantic Ocean(TAO), and tropical Northwest Pacific(NWP) are lower than that in the TPO. A reliable forecast at lead times of up to two years is shown over the TIO, while a shorter reliable forecast window(less than 17 months) occurs for the TAO and NWP.Additionally, the forecast skills for the TIO, TAO, and NWP are seasonally dependent. Higher skills for the TIO and TAO appear in boreal spring, while a greater skill for the NWP emerges in late summer-early autumn. Further analyses suggest that ENSO serves as a critical source of predictability for MHWs over the TIO and TAO in spring and MHWs over the NWP in summer.
基金supported by National Key Research and Development Program of China(2020YFA0908000)Prof.Zhubo Dai was supported by Youth Innovation Promotion Association of CAS(2015138)+1 种基金Haihe Laboratory Disruptive Innovation Talent Program(22HHSWSS00026)supported by the Key Project at Central Government Level:The ability establishment of sustainable use for valuable Chinese medicine resources(2060302)。
文摘Background:Heterologous synthesis presents a promising new approach for accessing the active ingredients of traditional Chinese medicine(TCM),contributing to the conservation of natural medicinal resources.Compound Danshen preparation,a widely used TCM formulation,is designed to treat coronary heart disease and angina pectoris.It is primarily composed of Salviae Miltiorrhizae Radix et Rhizoma(Danshen),Notoginseng Radix et Rhizoma(Sanqi),and borneol(Bingpian).Danshen primarily yields tanshinones and phenolic acids,while Sanqi produces notoginsenosides.Borneol serves as an auxiliary agent to promote mental clarity,dissipate heat,and relieve pain.Objectives:The objective is to employ heterologous synthesis in a single yeast strain to produce the active ingredients of Compound Danshen preparation,and these include notoginsenosides,tanshinones,and borneol.Methods:Firstly,the“plug-and-play”terpene synthase screening framework in Saccharomyces cerevisiae was used for the identification of a novel,highly efficient bornyl diphosphate synthase.Furthermore,by leveraging the Compound Danshen preparation as a basis to concurrently reconstruct the biosynthetic pathways for the notoginsenoside precursor protopanaxadiol(a triterpene),the tanshinone precursor miltiradiene(a diterpene),and borneol(a monoterpene)within a single yeast strain.Results:This engineered strain,termed Compound Danshen Yeast 1.0,successfully produced protopanaxadiol at 62.34 mg/L,miltiradiene at 15.38 mg/L,and borneol at 2.54 mg/L in shake-flask fermentation.Conclusions:This research signifies the inaugural cross-species andmulticomponent synthetic biology endeavor that enables the synthesis of active ingredients in engineered cells,setting the stage for the industrial manufacture of TCM compounds.
