The SKP1 gene is an important component of the SCF(SKP1-Cullin1-F-box)complex and serves as a bridge connecting the F-box and Cullin1genes(F-box-SKP1-Cullin1).The pattern of S-RNase being ubiquitously labelled by the ...The SKP1 gene is an important component of the SCF(SKP1-Cullin1-F-box)complex and serves as a bridge connecting the F-box and Cullin1genes(F-box-SKP1-Cullin1).The pattern of S-RNase being ubiquitously labelled by the SCF complex and degraded by the 26S protease accounts for the bulk of the available self-incompatibility studies.In this study,15 ClSKP1s from the‘Xiangshui'lemon genome and ubiquitome exist in the same SKP1 conserved domain(CD)as SKP1s in other species.The q PCR results showed that SKP1-6 and SKP1-14 have tissue expression patterns specific for expression in pollen.In addition,SKP1-6 and SKP1-14 in the stigma,style and ovary were significantly upregulated after self-pollination compared to those after cross-pollination.A subcellular location showed that SKP1-6 and SKP1-14 were located in the nucleus.In addition,yeast two-hybrid(Y2H)assays,bimolecular fluorescence complementation(BiFC)and luciferase complementation imaging(LCI)assays showed that SKP1-6 interacted with F-box1,F-box33,F-box34,F-box17,F-box19,Cullin1-2 and 26S proteasome subunit 4 homolog A(26S PS4HA).SKP1-14 interacted with F-box17,F-box19,F-box35,Cullin1-2 and 26S PS4HA.The interaction of Cullin1-2 and the F-box with SKP1 as a bridge was verified by a yeast three-hybrid experiment.The ability of S3-RNase to inhibit pollen and pollen tube growth and development was assessed using in vitro pollen co-culture experiments with recombinant S3-RNase proteins.Overall,this study provides important experimental evidence and theoretical basis for understanding the mechanism of self-incompatibility in plants by revealing the key role of the SCF complex in‘Xiangshui'lemon,which is bridged by ClSKP1-6,in self-incompatibility.The results of this study are of great significance for the future indepth exploration of the molecular mechanism of the SCF complex and its wide application in the self-incompatibility of plants.展开更多
Financial market liquidity is a popular research topic.Investor-driven research uses the turnover rate to measure liquidity and generally finds that the higher the stock turnover rate,the lower the returns.However,the...Financial market liquidity is a popular research topic.Investor-driven research uses the turnover rate to measure liquidity and generally finds that the higher the stock turnover rate,the lower the returns.However,the traditional financial liquidity theory has been impacted by new machine-driven quantitative trading models.To explore high machine-driven liquidity and the impact of high turnover rates on returns,this study establishes a dual-market quantitative trading system,introduces a variational modal decomposition(VMD)-bidirectional gated recurrent unit(BiGRU)model for data prediction,and uses the back-end Hong Kong foreign exchange market to develop a quantitative trading strategy using the same rotating funds in the U.S.and Chinese stock markets.The experimental results show that given a principal amount of 210,000.00 CNY,the final predicted net return is 226,538.30 CNY,a net return of 107.86%,which is 40.6%higher than the net return of a single Chinese market.We conclude that,under machine-driven trading,increasing liquidity and turnover increase returns.This study provides a new perspective on liquidity theory that is useful for future financial market research and quantitative trading practices.展开更多
Few-shot learning is the task of identifying new text categories from a limited set of training examples.The two key challenges in few-shot learning are insufficient understanding of new samples and imperfect modellin...Few-shot learning is the task of identifying new text categories from a limited set of training examples.The two key challenges in few-shot learning are insufficient understanding of new samples and imperfect modelling.The uniqueness of low-resource languages lies in their limited linguistic resources,which directly leads to the difficulty for models to learn sufficiently rich feature representations from limited samples.As a minority language,Tibetan few-shot learning requires further exploration.With limited data resources,if the model's understanding of text is noncontextual,it cannot provide sufficiently distinctive feature representations,limiting its performance in few-shot learning.Therefore,this paper proposed a few-shot learning architecture called two-level word embeddings matching networks(TWE-MN).TWE-MN is specifically designed to enhance the model's representational capacity and optimise its generalisation capabilities in data-scarce environments.As this paper focuses on Tibetan few-shot learning tasks,a pretrained Tibetan language model,BoBERT,was constructed.BoBERT,as the preembedding layer of TWE-MN,in combination with the BoBERT-augmented full-context embedding,can capture feature information from local to global levels.This paper evaluated the performance of TWE-MN in Tibetan few-shot learning tasks and Tibetan text classification tasks.The experimental results show that TWE-MN outperformed vanilla MN in all Tibetan few-shot learning tasks,with an average accuracy improvement of 4.5%–6.5%and up to 6.8%at most.In addition,this paper also explores the potential of TWE-MN in other NLP tasks,such as text classification and machine translation.展开更多
The emergence and growing popularity of Bitcoins have attracted the attention of the financial world.However,few empirical studies have considered the inclusion of the newly emerged commodity asset in the global commo...The emergence and growing popularity of Bitcoins have attracted the attention of the financial world.However,few empirical studies have considered the inclusion of the newly emerged commodity asset in the global commodity market.It is of great importance for investors and policymakers to take advantage of this asset and its potential benefits by incorporating it as a part of the broad commodity trading portfolio.In this study,we propose a novel ensemble portfolio optimization(NEPO)framework utilized for broad commodity assets,which integrates a hybrid variational mode decomposition-bidirectional long short-term memory deep learning model for future returns forecast and a reinforcement learning-based model for optimizing the asset weight allocation.Our empirical results indicate that the NEPO framework could effectively improve the prediction accuracy and trend prediction ability across various commodity assets from different sectors.In addition,it could effectively incorporate Bitcoins into the asset pool and achieve better financial performance compared to traditional asset allocation strategies,commodity funds,and indices.展开更多
The exogenous plant growth regulator,diethyl aminoethyl hexanoate(DA-6),in combination with suitable varieties and planting densities,is important to increase yield in the maize-soybean strip intercropping system.To i...The exogenous plant growth regulator,diethyl aminoethyl hexanoate(DA-6),in combination with suitable varieties and planting densities,is important to increase yield in the maize-soybean strip intercropping system.To identify the role of DA-6 in mitigating high-density stress and increasing yield,we conducted a two-year field experiment examining changes in branching architecture and other yield traits of soybeans in maize-soybean strip intercropping systems.In the planting system,two soybean cultivars(ND:Nandou 25 and QH:Qihuang 34)were grown under three planting densities(D1:102,000 plants ha^(-1),D2:130,000 plants ha^(-1),D3:158,000 plants ha^(-1))with DA-6 treatments(DA0:water control;DA60:60 mg L^(-1);DA100:100 mg L^(-1)).Applying DA-6 at 60 mg L^(-1)at the fourth trifoliolate leaf stage increased soybean yield,with QH yield rising by 22.4% and 29.5% at D3 density,and ND yield by 29.5% and 30.0% at D2 density in 2022 and 2023,respectively,compared with D1 under DA0.DA-6improved photosynthesis in both varieties under D2 density,with DA60 increasing ND canopy photosynthetic rate by 15.1%-16.4% and QG by 9.1%-20.6% over two years.In ND,DA-6 enhanced branching,raising the leaf area index by 37%,branch number from 3.6 to 4.7 per plant,and total pod number by 19.7%.In QH,yield grains were mainly due to a 17% increase in the number of stem pods and a 6.5% improvement in hundred-grain weight.In the maize-soybean strip intercropping system,QH achieved a high yield by forming a high-density(D2 to D3)main stem pod,and ND by combining moderate density(D1 to D2)with DA-6-induced branching.展开更多
Tetracycline(TC)residues from anthropogenic activities undesirably present in nature as an emerging sustainability challenge and thereby require innovations in remediation technologies.Herein,as inspired by the microc...Tetracycline(TC)residues from anthropogenic activities undesirably present in nature as an emerging sustainability challenge and thereby require innovations in remediation technologies.Herein,as inspired by the microcompartment structure in living organisms,we adopt a synthetic biology approach to engineer the FerTiG,a modular enzyme assembly,to robustly scavenge TC residues with improved performance.The FerTiG consists of three functional modules,namely,a TC degradation module(Tet(X4)),a cofactor recycling module glucose dehydrogenase(GDH),and a protection module(ferritin),to organize diverse catalytic processes simultaneously as a biological circuit.The incorporation of GDH suitably fuels the FerTiG-dependent TC degradation by regenerating expensive nicotinamide adenine dinucleotide phosphate(NADPH)cofactor with glucose.The ferritin shields the catalytic core of FerTiG to resiliently decompose TC under unfavorable conditions.Due to collaboration among functional modules,FerTiG strongly catalyzes the residual TC removal from multiple environmental matrices.The degradation pathways and environmental/biological safety of FerTiG are then elaborated,indicating the promising readiness for the application of FerTiG.In summary,this work presents a synthetic biology-based strategy to spontaneously impose residual antibiotic biodegradation for better sustainability management.The FerTiG is engineered as a proof-of-principle for TC removal;however,this'microcompartment-mimick ing'concept is of great interest in mitigating other sustainability challenges where modular catalytic machinery is applied.展开更多
There is a research gap in accurately predicting an individual stock’s finances from industry environment factors.Therefore,to predict trading strategies for a target stock’s closing price,this study constructed a p...There is a research gap in accurately predicting an individual stock’s finances from industry environment factors.Therefore,to predict trading strategies for a target stock’s closing price,this study constructed a prediction module and an environment module for a hybrid variational mode decomposition and stacked gated recurrent unit(VMD-StackedGRU)model,with individual stock information input into the prediction module and industry information input into the environment module.The results from the U.S.banking industry generalization tests proved that the proposed model could significantly improve prediction performances and that the environment module did not play an important role and was not equal to the prediction module.The hybrid neural network framework was a new application for financial price predictions based on an industry environment.Profitable trading strategies and accurate predictions can be valuable in hedging against market volatility risk and in assuring significant returns for investors and investment institutions.展开更多
Laser-accelerated ion beams(LIBs) have been increasingly applied in the field of material irradiation in recent years due to the unique properties of ultra-short beam duration, extremely high beam current, etc. Here w...Laser-accelerated ion beams(LIBs) have been increasingly applied in the field of material irradiation in recent years due to the unique properties of ultra-short beam duration, extremely high beam current, etc. Here we explore an application of using laser-accelerated ion beams to prepare graphene. The pulsed LIBs produced a great instantaneous beam current and thermal effect on the SiC samples with a shooting frequency of 1 Hz. In the experiment, we controlled the deposition dose by adjusting the number of shootings and the irradiating current by adjusting the distance between the sample and the ion source. During annealing at 1100℃, we found that the 190 shots ion beams allowed more carbon atoms to self-assemble into graphene than the 10 shots case. By comparing with the controlled experiment based on ion beams from a traditional ion accelerator, we found that the laser-accelerated ion beams could cause greater damage in a very short time. Significant thermal effect was induced when the irradiation distance was reduced to less than 1 cm, which could make partial SiC self-annealing to prepare graphene dots directly. The special effects of LIBs indicate their vital role to change the structure of the irradiation sample.展开更多
In recent years,Bitcoin has received substantial attention as potentially high-earning investment.However,its volatile price movement exhibits great financial risks.Therefore,how to accurately predict and capture chan...In recent years,Bitcoin has received substantial attention as potentially high-earning investment.However,its volatile price movement exhibits great financial risks.Therefore,how to accurately predict and capture changing trends in the Bitcoin market is of substantial importance to investors and policy makers.However,empirical works in the Bitcoin forecasting and trading support systems are at an early stage.To fill this void,this study proposes a novel data decomposition-based hybrid bidirectional deep-learning model in forecasting the daily price change in the Bitcoin market and conducting algorithmic trading on the market.Two primary steps are involved in our methodology framework,namely,data decomposition for inner factors extraction and bidirectional deep learning for forecasting the Bitcoin price.Results demonstrate that the proposed model outperforms other benchmark models,including econometric models,machine-learning models,and deep-learning models.Furthermore,the proposed model achieved higher investment returns than all benchmark models and the buy-and-hold strategy in a trading simulation.The robustness of the model is verified through multiple forecasting periods and testing intervals.展开更多
Background:To use an automated system exploiting the advantages of both a neural network and radiomics for analysis of non‐calcified predominant pla-que(NCPP).Methods:This study retrospectively included 234 patients....Background:To use an automated system exploiting the advantages of both a neural network and radiomics for analysis of non‐calcified predominant pla-que(NCPP).Methods:This study retrospectively included 234 patients.Using the work-flow of the previous study,the coronary artery was first segmented,images containing plaques were then extracted,and a classifier was built to identify non‐calcified predominant plaques.Radiomics feature analysis and a visuali-zation tool were used to better distinguish NCPP from other plaques.Results:Twenty‐six representative radiomics features were selected.Dense-Net achieved an area under the curve of 0.889,which was significantly larger(p=0.001)than that obtained using a gradient‐boosted decision tree(0.859).The feature variances and energy features in calcified predominant plaque were both different from those in NCPP.Conclusions:Our automated system provided high‐accuracy analysis of vulnerable plaques using a deep learning approach and predicted useful fea-tures of NCPP using a radiomics‐based approach.展开更多
Objective This study explored the therapeutic value of nutritional intervention in treating a patient with obesity complicated by primary hypertension.Methods A patient with obesity and primary hypertension received a...Objective This study explored the therapeutic value of nutritional intervention in treating a patient with obesity complicated by primary hypertension.Methods A patient with obesity and primary hypertension received an individualized nutritional intervention devised and continuously monitored by clinical nutritionists.The program included supplementation with probiotics,water-soluble vitamins,branched-chain amino acids,and whey protein powder.The nutritional plan was maintained for four weeks,with ongoing assessment of body weight and blood pressure.Antihypertensive medications were discontinued during follow-up.Results After four weeks of nutritional therapy,the patient lost 5.2 kg in weight,and blood pressure normalized and remained stable even after cessation of antihypertensive drugs.No adverse reactions were observed.Conclusion Nutritional therapy may provide a new,non-pharmacological option for managing obesity accompanied by primary hypertension.Its benefits may stem from improvements in metabolic function,insulin sensitivity,and inflammation,offering an alternative strategy to conventional pharmacotherapy.展开更多
Salience theory has been proposed as a new stock trading strategy.To assess the validity of this proposal,a complex decision trading system was constructed based on salience theory,a variational mode decomposition(VMD...Salience theory has been proposed as a new stock trading strategy.To assess the validity of this proposal,a complex decision trading system was constructed based on salience theory,a variational mode decomposition(VMD)model,a bidirectional gated recurrent unit(BiGRU)model,and high-frequency trading.The system selected 30 Chinese new energy concept stocks,ranked the stocks using salience theory,and selected the top and bottom three stocks for two portfolios.Twelve stages were established,following which the VMD and BiGRU models were applied to the predictions.The final predicted annualized returns for the high ST(salience theory value)group A(GA)and low ST group B(GB)were 194.06%and 165.88%,respectively.This finding validates the powerful utility of salience theory and deep learning to analyze the Chinese new energy market.Moreover,it explains the theoretical practicality issues that the short selling restriction is the essential reason,or even perhaps the only reason,that leads to the strength of salience theory.展开更多
High performance liquid chromatography coupled with quadruple-time-of-flight mass spectrometry(HPLC-Q-TOF-MS)method was developed for analyzing the hydrolytic mixtures of ginsenoside R_(g1) in acidic conditions(pH 3)....High performance liquid chromatography coupled with quadruple-time-of-flight mass spectrometry(HPLC-Q-TOF-MS)method was developed for analyzing the hydrolytic mixtures of ginsenoside R_(g1) in acidic conditions(pH 3). Three catalysts, a heteropolyacid(H_4SiW_(12)O_(40), SiW_(12) for short), its complex with γ-CD(SiW_(12)/γ-CD for short) and formic acid, were used for comparison. The chemical transformation products were identified based on the accurate mass measurement and the fragment ions obtained from tandem mass spectrometry. It was concluded that the catalytic efficiency of SiW_(12)(≈SiW_(12)/γ-CD) is ca. 410 times higher than that of formic acid, thus becoming the most efficient catalyst for chemical transformations of ginsenosides.展开更多
Thidiazuron(TDZ)is used for the expansion of fruits,but excessive levels of TDZ lead to a decline in fruit quality.The appropriate concentration of TDZ for mango expansion without a decline in quality is not clear.In ...Thidiazuron(TDZ)is used for the expansion of fruits,but excessive levels of TDZ lead to a decline in fruit quality.The appropriate concentration of TDZ for mango expansion without a decline in quality is not clear.In the present study,four different concentrations(5,10,15,and 20 mg/L)of TDZ were applied via spraying on mango plants,and several physiological and biochemical indicators were measured.The results showed that TDZ treatment significantly increased mango fruit size and single-fruit weight.In mango fruit,TDZ treatment decreased the disease index,delayed the increase in the malondialdehyde and H2O2 content,and maintained firmness and antioxidant capacities at a relatively high level during postharvest storage.At the same time,TDZ treatment delayed the decrease in the giberellin,indoleacetic acid and jasmonic acid content in mango,and reduced the accumulation of abscisic acid and ethylene.These trends are consistent with TDZ treatment leading to extension of the shelf life of mango.Furthermore,ethylene biosynthesis,signal transduction,and cell wall dismantling-related genes were investigated.The results indicated that the expression of the MiACS,MiETR2,MiERF113,MiERF010,MiERF054,MiEXP,MiPG14,MiPG21,MiCEL,and MiPEL genes in mango was inhibited under TDZ treatment compared with the control.In summary,TDZ treatment can significantly increase the size and weight of mango fruit and can extend its shelf life.The most suitable concentration is 10–15 mg/L TDZ,which will not affect the quality of mango fruit.展开更多
As the key link connecting the earth’s spheres,continental weathering plays an important role in regulating the global biogeochemical cycle and long-term climate change.Siliciclastic sediments derived from large rive...As the key link connecting the earth’s spheres,continental weathering plays an important role in regulating the global biogeochemical cycle and long-term climate change.Siliciclastic sediments derived from large river basins can record continental weathering and erosion signals,and are thus widely used to investigate weathering processes.However,sediment grain size,hydrodynamic sorting and sedimentary recycling complicate the interpretation of sediment weathering proxies.This study presents elemental and lithium isotope compositions of estuarine surface sediments(SS)and suspended particulate matters(SPM)collected from the Changjiang(Yangtze River)Estuary.Based on a simple mass balance model,the proportions of different end-members(i.e.,igneous rocks,modern weathering products and inherited weathering products)in sediments were quantitatively calculated and thus the silicate weathering process can be estimated.Overall,the sediments in the Changjiang Estuary are mainly eroded from un-weathered rock fragments(>60%),while modern weathering products account for less than 40%.The fine-grained SPM contain more shale components(52–66%),and the modern weathering products account for 21–40%.Comparatively,the coarse-grained surface sediments contain more un-weathered igneous rock fragments(63–84%)and less modern weathering products(only 4–18%).The comparison ofδ^(7)Li values with the weathering proxy(Chemical Index of Alteration,CIA)suggests that sediment weathering intensity declines with increasing proportion of un-weathered igneous rock fragments.Additionally,the occurrence of inherited weathering products(i.e.,shale)in modern sediments makes it a challenge to simply use CIA andδ^(7)Li as indicators of weathering intensity.This study confirms that fine-grained particles are more suitable for tracing contemporary weathering process,albeit with the influence of sedimentary recycling.Lithium isotopes combining with the mass balance model can quantitatively constrain the continental weathering processes in large river basins.展开更多
Due to relative poverty,China’s rural areas face challenges in preventing and controlling infectious diseases.Close contact data are essential for understanding the spread of infections;however,there is currently a l...Due to relative poverty,China’s rural areas face challenges in preventing and controlling infectious diseases.Close contact data are essential for understanding the spread of infections;however,there is currently a lack of quantitative analysis and assessment of infection risk associated with human behavior in rural areas.This study addresses the challenges rural areas face in controlling respiratory infectious diseases due to underdeveloped economies and vulnerable populations.It focuses on close contact as the primary transmission route and bridges the gap in real indoor close contact behavior data,providing a scientific basis for effective prevention and control.This study developed a model based on real indoor close contact behaviors to simulate infectious disease spread and quantify viral exposure and infection risk in rural China.Effectiveness of non-pharmaceutical interventions in high-risk indoor environments was quantified.In rural areas with a 1%disease prevalence,the highest hourly infection risk was 0.28%in restaurants,followed by clinics and classrooms.Close contact risks in rural homes,offices,clinics,and classrooms were up to 1.6 times higher than in urban areas.These risks could be reduced below 0.1%with targeted interventions:(1)in restaurants,set air change rate to 5.8 ACH,with 1.5-m seat spacing and mandatory mask-wearing during non-meal times;(2)in classrooms,set air change rate to 14 ACH,with mask-wearing and online/offline blended learning ensuring 1.5-m spacing between desks for offline students;(3)in clinics,set air change rate to 8.8 ACH,encourage telemedicine for half the population,and require mask-wearing.The interventions reduce infection risk by 69.9%in restaurants,67.8%in classrooms,and 77.4%in clinics.This study highlights the high infection risk in rural restaurants,classrooms,homes,and clinics,and suggests targeted measures to support effective epidemic prevention in these indoor environments.展开更多
Background:Regional ecosystem health assessments are the basis for the sustainable development of society.However,an ecosystem is a complex integration of ecosystem mosaics and subsystems that influence each other,mak...Background:Regional ecosystem health assessments are the basis for the sustainable development of society.However,an ecosystem is a complex integration of ecosystem mosaics and subsystems that influence each other,making it difficult to evaluate them using traditional assessment methods of linear and explicit functions.We introduce a back-propagation neural network model optimized by a genetic algorithm to evaluate ecosystem health in 16 districts in Yunnan Province.Result:(1)The model required fewer inputs to evaluate complex and nonlinear systems,avoided the need for subjective weights,and performed well in this practical application to regional ecosystem health assessment.(2)The ecosystem health in Yunnan Province was increasing,and there was a significant positive spatial autocorrelation during 2000-2020,showing that districts with high Ecosystem Health cluster together and the ecological protection policy of the region has produced a diffusion effect,leading to continuous improvement of the ecological health of the surrounding areas.High-low outlier areas of ecosystem health should be paid more attention,because of the increasing instability of local health levels.Conclusion:This study provides a methodological exploration for assessing spatial mosaics of different ecosystems at a regional scale.展开更多
A complex system is composed of many interrelated elements,and the interaction between these elements makes the overall performance of the system greater than the sum of member performance[1-5].In the context of manag...A complex system is composed of many interrelated elements,and the interaction between these elements makes the overall performance of the system greater than the sum of member performance[1-5].In the context of management,various forms of organizations,from micro enterprises to macroeconomic systems,can be seen as systems formed by a large number of interactive individuals acting on their own limited information.展开更多
基金supported by grants from the National Natural Science Foundation of China(Grant No.31960585)Science and Technology Major Project of Guangxi(Grant No.Guike AA22068092)+1 种基金Guangxi Science and Technology Vanguard Special Action Project(Grant No.202204)State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources(Grant Nos.SKLCUSA-a201906,SKLCU-SA-c201901)。
文摘The SKP1 gene is an important component of the SCF(SKP1-Cullin1-F-box)complex and serves as a bridge connecting the F-box and Cullin1genes(F-box-SKP1-Cullin1).The pattern of S-RNase being ubiquitously labelled by the SCF complex and degraded by the 26S protease accounts for the bulk of the available self-incompatibility studies.In this study,15 ClSKP1s from the‘Xiangshui'lemon genome and ubiquitome exist in the same SKP1 conserved domain(CD)as SKP1s in other species.The q PCR results showed that SKP1-6 and SKP1-14 have tissue expression patterns specific for expression in pollen.In addition,SKP1-6 and SKP1-14 in the stigma,style and ovary were significantly upregulated after self-pollination compared to those after cross-pollination.A subcellular location showed that SKP1-6 and SKP1-14 were located in the nucleus.In addition,yeast two-hybrid(Y2H)assays,bimolecular fluorescence complementation(BiFC)and luciferase complementation imaging(LCI)assays showed that SKP1-6 interacted with F-box1,F-box33,F-box34,F-box17,F-box19,Cullin1-2 and 26S proteasome subunit 4 homolog A(26S PS4HA).SKP1-14 interacted with F-box17,F-box19,F-box35,Cullin1-2 and 26S PS4HA.The interaction of Cullin1-2 and the F-box with SKP1 as a bridge was verified by a yeast three-hybrid experiment.The ability of S3-RNase to inhibit pollen and pollen tube growth and development was assessed using in vitro pollen co-culture experiments with recombinant S3-RNase proteins.Overall,this study provides important experimental evidence and theoretical basis for understanding the mechanism of self-incompatibility in plants by revealing the key role of the SCF complex in‘Xiangshui'lemon,which is bridged by ClSKP1-6,in self-incompatibility.The results of this study are of great significance for the future indepth exploration of the molecular mechanism of the SCF complex and its wide application in the self-incompatibility of plants.
基金supported by the National Natural Science Foundation of China(Grant Nos.:72032006 and 92146005).
文摘Financial market liquidity is a popular research topic.Investor-driven research uses the turnover rate to measure liquidity and generally finds that the higher the stock turnover rate,the lower the returns.However,the traditional financial liquidity theory has been impacted by new machine-driven quantitative trading models.To explore high machine-driven liquidity and the impact of high turnover rates on returns,this study establishes a dual-market quantitative trading system,introduces a variational modal decomposition(VMD)-bidirectional gated recurrent unit(BiGRU)model for data prediction,and uses the back-end Hong Kong foreign exchange market to develop a quantitative trading strategy using the same rotating funds in the U.S.and Chinese stock markets.The experimental results show that given a principal amount of 210,000.00 CNY,the final predicted net return is 226,538.30 CNY,a net return of 107.86%,which is 40.6%higher than the net return of a single Chinese market.We conclude that,under machine-driven trading,increasing liquidity and turnover increase returns.This study provides a new perspective on liquidity theory that is useful for future financial market research and quantitative trading practices.
基金supported in part by the National Science and Technology Major Project(Grant No.2022ZD0116100)in part by the National Natural Science Foundation of China(Grant No.62276055)in part by the Sichuan Science and Technology Program(Grant Nos.23ZDYF0755 and 24NSFSC5679).
文摘Few-shot learning is the task of identifying new text categories from a limited set of training examples.The two key challenges in few-shot learning are insufficient understanding of new samples and imperfect modelling.The uniqueness of low-resource languages lies in their limited linguistic resources,which directly leads to the difficulty for models to learn sufficiently rich feature representations from limited samples.As a minority language,Tibetan few-shot learning requires further exploration.With limited data resources,if the model's understanding of text is noncontextual,it cannot provide sufficiently distinctive feature representations,limiting its performance in few-shot learning.Therefore,this paper proposed a few-shot learning architecture called two-level word embeddings matching networks(TWE-MN).TWE-MN is specifically designed to enhance the model's representational capacity and optimise its generalisation capabilities in data-scarce environments.As this paper focuses on Tibetan few-shot learning tasks,a pretrained Tibetan language model,BoBERT,was constructed.BoBERT,as the preembedding layer of TWE-MN,in combination with the BoBERT-augmented full-context embedding,can capture feature information from local to global levels.This paper evaluated the performance of TWE-MN in Tibetan few-shot learning tasks and Tibetan text classification tasks.The experimental results show that TWE-MN outperformed vanilla MN in all Tibetan few-shot learning tasks,with an average accuracy improvement of 4.5%–6.5%and up to 6.8%at most.In addition,this paper also explores the potential of TWE-MN in other NLP tasks,such as text classification and machine translation.
基金supported by the National Natural Science Foundation of China under Grants No.71801213 and No.71988101the National Center for Mathematics and Interdisciplinary Sciences,CAS.
文摘The emergence and growing popularity of Bitcoins have attracted the attention of the financial world.However,few empirical studies have considered the inclusion of the newly emerged commodity asset in the global commodity market.It is of great importance for investors and policymakers to take advantage of this asset and its potential benefits by incorporating it as a part of the broad commodity trading portfolio.In this study,we propose a novel ensemble portfolio optimization(NEPO)framework utilized for broad commodity assets,which integrates a hybrid variational mode decomposition-bidirectional long short-term memory deep learning model for future returns forecast and a reinforcement learning-based model for optimizing the asset weight allocation.Our empirical results indicate that the NEPO framework could effectively improve the prediction accuracy and trend prediction ability across various commodity assets from different sectors.In addition,it could effectively incorporate Bitcoins into the asset pool and achieve better financial performance compared to traditional asset allocation strategies,commodity funds,and indices.
基金supported by the earmarked fund for the China Agriculture Research System(CARS-04-PS21)National Key Research and Development Program of China(2024YFD2300401)a recipient of a joint Ph.D.scholarship supported by the China Scholarship Council(CSC)(202306910067)。
文摘The exogenous plant growth regulator,diethyl aminoethyl hexanoate(DA-6),in combination with suitable varieties and planting densities,is important to increase yield in the maize-soybean strip intercropping system.To identify the role of DA-6 in mitigating high-density stress and increasing yield,we conducted a two-year field experiment examining changes in branching architecture and other yield traits of soybeans in maize-soybean strip intercropping systems.In the planting system,two soybean cultivars(ND:Nandou 25 and QH:Qihuang 34)were grown under three planting densities(D1:102,000 plants ha^(-1),D2:130,000 plants ha^(-1),D3:158,000 plants ha^(-1))with DA-6 treatments(DA0:water control;DA60:60 mg L^(-1);DA100:100 mg L^(-1)).Applying DA-6 at 60 mg L^(-1)at the fourth trifoliolate leaf stage increased soybean yield,with QH yield rising by 22.4% and 29.5% at D3 density,and ND yield by 29.5% and 30.0% at D2 density in 2022 and 2023,respectively,compared with D1 under DA0.DA-6improved photosynthesis in both varieties under D2 density,with DA60 increasing ND canopy photosynthetic rate by 15.1%-16.4% and QG by 9.1%-20.6% over two years.In ND,DA-6 enhanced branching,raising the leaf area index by 37%,branch number from 3.6 to 4.7 per plant,and total pod number by 19.7%.In QH,yield grains were mainly due to a 17% increase in the number of stem pods and a 6.5% improvement in hundred-grain weight.In the maize-soybean strip intercropping system,QH achieved a high yield by forming a high-density(D2 to D3)main stem pod,and ND by combining moderate density(D1 to D2)with DA-6-induced branching.
基金supported by the National Natural Science Foundation of China(32121004 and 32102720)the Guangzhou Science and Technology Plan Project(2024A04J6509)+3 种基金the National Key Research and Development Program of China(2023YFD1800100)the Local Innovative and Research Teams Project of Guangdong Pearl River Talents Program(2019BT02N054)the Double First-Class Discipline Promotion Project(2023B10564003)the 111 Project(D20008)。
文摘Tetracycline(TC)residues from anthropogenic activities undesirably present in nature as an emerging sustainability challenge and thereby require innovations in remediation technologies.Herein,as inspired by the microcompartment structure in living organisms,we adopt a synthetic biology approach to engineer the FerTiG,a modular enzyme assembly,to robustly scavenge TC residues with improved performance.The FerTiG consists of three functional modules,namely,a TC degradation module(Tet(X4)),a cofactor recycling module glucose dehydrogenase(GDH),and a protection module(ferritin),to organize diverse catalytic processes simultaneously as a biological circuit.The incorporation of GDH suitably fuels the FerTiG-dependent TC degradation by regenerating expensive nicotinamide adenine dinucleotide phosphate(NADPH)cofactor with glucose.The ferritin shields the catalytic core of FerTiG to resiliently decompose TC under unfavorable conditions.Due to collaboration among functional modules,FerTiG strongly catalyzes the residual TC removal from multiple environmental matrices.The degradation pathways and environmental/biological safety of FerTiG are then elaborated,indicating the promising readiness for the application of FerTiG.In summary,this work presents a synthetic biology-based strategy to spontaneously impose residual antibiotic biodegradation for better sustainability management.The FerTiG is engineered as a proof-of-principle for TC removal;however,this'microcompartment-mimick ing'concept is of great interest in mitigating other sustainability challenges where modular catalytic machinery is applied.
基金supported by the National Natural Science Foundation(NSFC)Programs of China(Grant Nos.:91646113,71722014,71471141,and 71771182)support of the Youth Innovation Team of Shaanxi Universities“Big data and Business Intelligent Innovation Team”and Shaanxi Superiority Funding Project for Scientific and Technological Activities of Overseas Scholars(Grant No.:2018017).
文摘There is a research gap in accurately predicting an individual stock’s finances from industry environment factors.Therefore,to predict trading strategies for a target stock’s closing price,this study constructed a prediction module and an environment module for a hybrid variational mode decomposition and stacked gated recurrent unit(VMD-StackedGRU)model,with individual stock information input into the prediction module and industry information input into the environment module.The results from the U.S.banking industry generalization tests proved that the proposed model could significantly improve prediction performances and that the environment module did not play an important role and was not equal to the prediction module.The hybrid neural network framework was a new application for financial price predictions based on an industry environment.Profitable trading strategies and accurate predictions can be valuable in hedging against market volatility risk and in assuring significant returns for investors and investment institutions.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11875077,11975037,and 11921006)the National Grand Instrument Project of China(Grant Nos.2019YFF01014400 and 2019YFF01014404).
文摘Laser-accelerated ion beams(LIBs) have been increasingly applied in the field of material irradiation in recent years due to the unique properties of ultra-short beam duration, extremely high beam current, etc. Here we explore an application of using laser-accelerated ion beams to prepare graphene. The pulsed LIBs produced a great instantaneous beam current and thermal effect on the SiC samples with a shooting frequency of 1 Hz. In the experiment, we controlled the deposition dose by adjusting the number of shootings and the irradiating current by adjusting the distance between the sample and the ion source. During annealing at 1100℃, we found that the 190 shots ion beams allowed more carbon atoms to self-assemble into graphene than the 10 shots case. By comparing with the controlled experiment based on ion beams from a traditional ion accelerator, we found that the laser-accelerated ion beams could cause greater damage in a very short time. Significant thermal effect was induced when the irradiation distance was reduced to less than 1 cm, which could make partial SiC self-annealing to prepare graphene dots directly. The special effects of LIBs indicate their vital role to change the structure of the irradiation sample.
基金supported by the National Natural Science Foundation of China(Grant numbers 71988101,71901205).
文摘In recent years,Bitcoin has received substantial attention as potentially high-earning investment.However,its volatile price movement exhibits great financial risks.Therefore,how to accurately predict and capture changing trends in the Bitcoin market is of substantial importance to investors and policy makers.However,empirical works in the Bitcoin forecasting and trading support systems are at an early stage.To fill this void,this study proposes a novel data decomposition-based hybrid bidirectional deep-learning model in forecasting the daily price change in the Bitcoin market and conducting algorithmic trading on the market.Two primary steps are involved in our methodology framework,namely,data decomposition for inner factors extraction and bidirectional deep learning for forecasting the Bitcoin price.Results demonstrate that the proposed model outperforms other benchmark models,including econometric models,machine-learning models,and deep-learning models.Furthermore,the proposed model achieved higher investment returns than all benchmark models and the buy-and-hold strategy in a trading simulation.The robustness of the model is verified through multiple forecasting periods and testing intervals.
文摘Background:To use an automated system exploiting the advantages of both a neural network and radiomics for analysis of non‐calcified predominant pla-que(NCPP).Methods:This study retrospectively included 234 patients.Using the work-flow of the previous study,the coronary artery was first segmented,images containing plaques were then extracted,and a classifier was built to identify non‐calcified predominant plaques.Radiomics feature analysis and a visuali-zation tool were used to better distinguish NCPP from other plaques.Results:Twenty‐six representative radiomics features were selected.Dense-Net achieved an area under the curve of 0.889,which was significantly larger(p=0.001)than that obtained using a gradient‐boosted decision tree(0.859).The feature variances and energy features in calcified predominant plaque were both different from those in NCPP.Conclusions:Our automated system provided high‐accuracy analysis of vulnerable plaques using a deep learning approach and predicted useful fea-tures of NCPP using a radiomics‐based approach.
文摘Objective This study explored the therapeutic value of nutritional intervention in treating a patient with obesity complicated by primary hypertension.Methods A patient with obesity and primary hypertension received an individualized nutritional intervention devised and continuously monitored by clinical nutritionists.The program included supplementation with probiotics,water-soluble vitamins,branched-chain amino acids,and whey protein powder.The nutritional plan was maintained for four weeks,with ongoing assessment of body weight and blood pressure.Antihypertensive medications were discontinued during follow-up.Results After four weeks of nutritional therapy,the patient lost 5.2 kg in weight,and blood pressure normalized and remained stable even after cessation of antihypertensive drugs.No adverse reactions were observed.Conclusion Nutritional therapy may provide a new,non-pharmacological option for managing obesity accompanied by primary hypertension.Its benefits may stem from improvements in metabolic function,insulin sensitivity,and inflammation,offering an alternative strategy to conventional pharmacotherapy.
基金supported by the National Natural Science Foundation of China(Grant Nos.72032006 and 92146005).
文摘Salience theory has been proposed as a new stock trading strategy.To assess the validity of this proposal,a complex decision trading system was constructed based on salience theory,a variational mode decomposition(VMD)model,a bidirectional gated recurrent unit(BiGRU)model,and high-frequency trading.The system selected 30 Chinese new energy concept stocks,ranked the stocks using salience theory,and selected the top and bottom three stocks for two portfolios.Twelve stages were established,following which the VMD and BiGRU models were applied to the predictions.The final predicted annualized returns for the high ST(salience theory value)group A(GA)and low ST group B(GB)were 194.06%and 165.88%,respectively.This finding validates the powerful utility of salience theory and deep learning to analyze the Chinese new energy market.Moreover,it explains the theoretical practicality issues that the short selling restriction is the essential reason,or even perhaps the only reason,that leads to the strength of salience theory.
基金supported by the National Natural Science Foundation of China(21371025),the 111 Project(B07012)the degree and postgraduate education development research project(YJYJG2015B07)by Beijing Institute of Technology
文摘High performance liquid chromatography coupled with quadruple-time-of-flight mass spectrometry(HPLC-Q-TOF-MS)method was developed for analyzing the hydrolytic mixtures of ginsenoside R_(g1) in acidic conditions(pH 3). Three catalysts, a heteropolyacid(H_4SiW_(12)O_(40), SiW_(12) for short), its complex with γ-CD(SiW_(12)/γ-CD for short) and formic acid, were used for comparison. The chemical transformation products were identified based on the accurate mass measurement and the fragment ions obtained from tandem mass spectrometry. It was concluded that the catalytic efficiency of SiW_(12)(≈SiW_(12)/γ-CD) is ca. 410 times higher than that of formic acid, thus becoming the most efficient catalyst for chemical transformations of ginsenosides.
基金supported by the Innovation Team of Guangxi Mango Industry Project(No.nycytxgxcxtd-2021-06-02)Guangxi Key Laboratory of Biology for Mango(No.GKLBM02204)+2 种基金Science and Technology Major Projects of Guangxi(No.GXKJ-AA22068098-2)the Six One’Special Action of‘Strengthening Agriculture and Enriching People’by Science and Technology Vanguard(No.202304-04)the Youth Talent Fund Project of Guangxi Natural Science Foundation(Nos.2018GXNSFBA035558 and 2018GXNSFBA050026),China.
文摘Thidiazuron(TDZ)is used for the expansion of fruits,but excessive levels of TDZ lead to a decline in fruit quality.The appropriate concentration of TDZ for mango expansion without a decline in quality is not clear.In the present study,four different concentrations(5,10,15,and 20 mg/L)of TDZ were applied via spraying on mango plants,and several physiological and biochemical indicators were measured.The results showed that TDZ treatment significantly increased mango fruit size and single-fruit weight.In mango fruit,TDZ treatment decreased the disease index,delayed the increase in the malondialdehyde and H2O2 content,and maintained firmness and antioxidant capacities at a relatively high level during postharvest storage.At the same time,TDZ treatment delayed the decrease in the giberellin,indoleacetic acid and jasmonic acid content in mango,and reduced the accumulation of abscisic acid and ethylene.These trends are consistent with TDZ treatment leading to extension of the shelf life of mango.Furthermore,ethylene biosynthesis,signal transduction,and cell wall dismantling-related genes were investigated.The results indicated that the expression of the MiACS,MiETR2,MiERF113,MiERF010,MiERF054,MiEXP,MiPG14,MiPG21,MiCEL,and MiPEL genes in mango was inhibited under TDZ treatment compared with the control.In summary,TDZ treatment can significantly increase the size and weight of mango fruit and can extend its shelf life.The most suitable concentration is 10–15 mg/L TDZ,which will not affect the quality of mango fruit.
基金funded by the National Natural Science Foundation of China(Grant Nos.41991324,41730531,40830107)The sampling cruise(No.YEC2017)was supported by the State Key Laboratory of Marine Geology,Tongji University.
文摘As the key link connecting the earth’s spheres,continental weathering plays an important role in regulating the global biogeochemical cycle and long-term climate change.Siliciclastic sediments derived from large river basins can record continental weathering and erosion signals,and are thus widely used to investigate weathering processes.However,sediment grain size,hydrodynamic sorting and sedimentary recycling complicate the interpretation of sediment weathering proxies.This study presents elemental and lithium isotope compositions of estuarine surface sediments(SS)and suspended particulate matters(SPM)collected from the Changjiang(Yangtze River)Estuary.Based on a simple mass balance model,the proportions of different end-members(i.e.,igneous rocks,modern weathering products and inherited weathering products)in sediments were quantitatively calculated and thus the silicate weathering process can be estimated.Overall,the sediments in the Changjiang Estuary are mainly eroded from un-weathered rock fragments(>60%),while modern weathering products account for less than 40%.The fine-grained SPM contain more shale components(52–66%),and the modern weathering products account for 21–40%.Comparatively,the coarse-grained surface sediments contain more un-weathered igneous rock fragments(63–84%)and less modern weathering products(only 4–18%).The comparison ofδ^(7)Li values with the weathering proxy(Chemical Index of Alteration,CIA)suggests that sediment weathering intensity declines with increasing proportion of un-weathered igneous rock fragments.Additionally,the occurrence of inherited weathering products(i.e.,shale)in modern sediments makes it a challenge to simply use CIA andδ^(7)Li as indicators of weathering intensity.This study confirms that fine-grained particles are more suitable for tracing contemporary weathering process,albeit with the influence of sedimentary recycling.Lithium isotopes combining with the mass balance model can quantitatively constrain the continental weathering processes in large river basins.
基金supported by Capital's Funds for Health Improvement and Research(No.2024–1G-5011)the National Natural Science Foundation of China(Grant No.52478074).
文摘Due to relative poverty,China’s rural areas face challenges in preventing and controlling infectious diseases.Close contact data are essential for understanding the spread of infections;however,there is currently a lack of quantitative analysis and assessment of infection risk associated with human behavior in rural areas.This study addresses the challenges rural areas face in controlling respiratory infectious diseases due to underdeveloped economies and vulnerable populations.It focuses on close contact as the primary transmission route and bridges the gap in real indoor close contact behavior data,providing a scientific basis for effective prevention and control.This study developed a model based on real indoor close contact behaviors to simulate infectious disease spread and quantify viral exposure and infection risk in rural China.Effectiveness of non-pharmaceutical interventions in high-risk indoor environments was quantified.In rural areas with a 1%disease prevalence,the highest hourly infection risk was 0.28%in restaurants,followed by clinics and classrooms.Close contact risks in rural homes,offices,clinics,and classrooms were up to 1.6 times higher than in urban areas.These risks could be reduced below 0.1%with targeted interventions:(1)in restaurants,set air change rate to 5.8 ACH,with 1.5-m seat spacing and mandatory mask-wearing during non-meal times;(2)in classrooms,set air change rate to 14 ACH,with mask-wearing and online/offline blended learning ensuring 1.5-m spacing between desks for offline students;(3)in clinics,set air change rate to 8.8 ACH,encourage telemedicine for half the population,and require mask-wearing.The interventions reduce infection risk by 69.9%in restaurants,67.8%in classrooms,and 77.4%in clinics.This study highlights the high infection risk in rural restaurants,classrooms,homes,and clinics,and suggests targeted measures to support effective epidemic prevention in these indoor environments.
基金This work was supported by the National Natural Science Foundation of China[52078160]。
文摘Background:Regional ecosystem health assessments are the basis for the sustainable development of society.However,an ecosystem is a complex integration of ecosystem mosaics and subsystems that influence each other,making it difficult to evaluate them using traditional assessment methods of linear and explicit functions.We introduce a back-propagation neural network model optimized by a genetic algorithm to evaluate ecosystem health in 16 districts in Yunnan Province.Result:(1)The model required fewer inputs to evaluate complex and nonlinear systems,avoided the need for subjective weights,and performed well in this practical application to regional ecosystem health assessment.(2)The ecosystem health in Yunnan Province was increasing,and there was a significant positive spatial autocorrelation during 2000-2020,showing that districts with high Ecosystem Health cluster together and the ecological protection policy of the region has produced a diffusion effect,leading to continuous improvement of the ecological health of the surrounding areas.High-low outlier areas of ecosystem health should be paid more attention,because of the increasing instability of local health levels.Conclusion:This study provides a methodological exploration for assessing spatial mosaics of different ecosystems at a regional scale.
文摘A complex system is composed of many interrelated elements,and the interaction between these elements makes the overall performance of the system greater than the sum of member performance[1-5].In the context of management,various forms of organizations,from micro enterprises to macroeconomic systems,can be seen as systems formed by a large number of interactive individuals acting on their own limited information.
