As wave height is an important parameter in marine climate measurement,its accurate prediction is crucial in ocean engineering.It also plays an important role in marine disaster early warning and ship design,etc.Howev...As wave height is an important parameter in marine climate measurement,its accurate prediction is crucial in ocean engineering.It also plays an important role in marine disaster early warning and ship design,etc.However,challenges in the large demand for computing resources and the improvement of accuracy are currently encountered.To resolve the above mentioned problems,sequence-to-sequence deep learning model(Seq-to-Seq)is applied to intelligently explore the internal law between the continuous wave height data output by the model,so as to realize fast and accurate predictions on wave height data.Simultaneously,ensemble empirical mode decomposition(EEMD)is adopted to reduce the non-stationarity of wave height data and solve the problem of modal aliasing caused by empirical mode decomposition(EMD),and then improves the prediction accuracy.A significant wave height forecast method integrating EEMD with the Seq-to-Seq model(EEMD-Seq-to-Seq)is proposed in this paper,and the prediction models under different time spans are established.Compared with the long short-term memory model,the novel method demonstrates increased continuity for long-term prediction and reduces prediction errors.The experiments of wave height prediction on four buoys show that the EEMD-Seq-to-Seq algorithm effectively improves the prediction accuracy in short-term(3-h,6-h,12-h and 24-h forecast horizon)and long-term(48-h and 72-h forecast horizon)predictions.展开更多
Sika deer are known to prefer oak leaves,which are rich in tannins and toxic to most mammals;however,the genetic mechanisms underlying their unique ability to adapt to living in the jungle are still unclear.In identif...Sika deer are known to prefer oak leaves,which are rich in tannins and toxic to most mammals;however,the genetic mechanisms underlying their unique ability to adapt to living in the jungle are still unclear.In identifying the mechanism responsible for the tolerance of a highly toxic diet,we have made a major advancement by explaining the genome of sika deer.We generated the first high-quality,chromosome-level genome assembly of sika deer and measured the correlation between tannin intake and RNA expression in 15 tissues through 180 experiments.Comparative genome analyses showed that the UGT and CYP gene families are functionally involved in the adaptation of sika deer to high-tannin food,especially the expansion of the UGT family 2 subfamily B of UGT genes.The first chromosome-level assembly and genetic characterization of the tolerance to a highly toxic diet suggest that the sika deer genome may serve as an essential resource for understanding evolutionary events and tannin adaptation.Our study provides a paradigm of comparative expressive genomics that can be applied to the study of unique biological features in non-model animals.展开更多
基金The Project Supported by Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)under contract No.SML2020SP007the National Natural Science Foundation of China under contract Nos 42192562 and 62072249.
文摘As wave height is an important parameter in marine climate measurement,its accurate prediction is crucial in ocean engineering.It also plays an important role in marine disaster early warning and ship design,etc.However,challenges in the large demand for computing resources and the improvement of accuracy are currently encountered.To resolve the above mentioned problems,sequence-to-sequence deep learning model(Seq-to-Seq)is applied to intelligently explore the internal law between the continuous wave height data output by the model,so as to realize fast and accurate predictions on wave height data.Simultaneously,ensemble empirical mode decomposition(EEMD)is adopted to reduce the non-stationarity of wave height data and solve the problem of modal aliasing caused by empirical mode decomposition(EMD),and then improves the prediction accuracy.A significant wave height forecast method integrating EEMD with the Seq-to-Seq model(EEMD-Seq-to-Seq)is proposed in this paper,and the prediction models under different time spans are established.Compared with the long short-term memory model,the novel method demonstrates increased continuity for long-term prediction and reduces prediction errors.The experiments of wave height prediction on four buoys show that the EEMD-Seq-to-Seq algorithm effectively improves the prediction accuracy in short-term(3-h,6-h,12-h and 24-h forecast horizon)and long-term(48-h and 72-h forecast horizon)predictions.
基金This work was supported by the National Key R&D Program of China(Grant No.2018YFD0502204)the Agricultural Science and Technology Innovation Program of China(Grant No.CAAS-ASTIP-2019-ISAPS)+1 种基金the Special Animal Genetic Resources Platform of National Scientific and Technical Infrastructure Center(Grant No.NSTIC TZDWZYK2019)the Sika deer Genome Project of China(Grant No.20140309016YY).
文摘Sika deer are known to prefer oak leaves,which are rich in tannins and toxic to most mammals;however,the genetic mechanisms underlying their unique ability to adapt to living in the jungle are still unclear.In identifying the mechanism responsible for the tolerance of a highly toxic diet,we have made a major advancement by explaining the genome of sika deer.We generated the first high-quality,chromosome-level genome assembly of sika deer and measured the correlation between tannin intake and RNA expression in 15 tissues through 180 experiments.Comparative genome analyses showed that the UGT and CYP gene families are functionally involved in the adaptation of sika deer to high-tannin food,especially the expansion of the UGT family 2 subfamily B of UGT genes.The first chromosome-level assembly and genetic characterization of the tolerance to a highly toxic diet suggest that the sika deer genome may serve as an essential resource for understanding evolutionary events and tannin adaptation.Our study provides a paradigm of comparative expressive genomics that can be applied to the study of unique biological features in non-model animals.
