A remarkable marine heatwave,known as the“Blob”,occurred in the Northeast Pacific Ocean from late 2013 to early 2016,which displayed strong warm anomalies extending from the surface to a depth of 300 m.This study em...A remarkable marine heatwave,known as the“Blob”,occurred in the Northeast Pacific Ocean from late 2013 to early 2016,which displayed strong warm anomalies extending from the surface to a depth of 300 m.This study employed two assimilation schemes based on the global Climate Forecast System of Nanjing University of Information Science(NUIST-CFS 1.0)to investigate the impact of ocean data assimilation on the seasonal prediction of this extreme marine heatwave.The sea surface temperature(SST)nudging scheme assimilates SST only,while the deterministic ensemble Kalman filter(EnKF)scheme assimilates observations from the surface to the deep ocean.The latter notably improves the forecasting skill for subsurface temperature anomalies,especially at the depth of 100-300 m(the lower layer),outperforming the SST nudging scheme.It excels in predicting both horizontal and vertical heat transport in the lower layer,contributing to improved forecasts of the lower-layer warming during the Blob.These improvements stem from the assimilation of subsurface observational data,which are important in predicting the upper-ocean conditions.The results suggest that assimilating ocean data with the EnKF scheme significantly enhances the accuracy in predicting subsurface temperature anomalies during the Blob and offers better understanding of its underlying mechanisms.展开更多
Intraseasonal Oscillation (ISO) which is the eastward-propagating disturbance with a period of 10 - 60 days has been the topic of interest since its discovery by Madden-Julian in 1972. Many researchers have published ...Intraseasonal Oscillation (ISO) which is the eastward-propagating disturbance with a period of 10 - 60 days has been the topic of interest since its discovery by Madden-Julian in 1972. Many researchers have published their work on ISO, yet they all agree that there is no clear understanding of this matter. By using daily observed surface temperature (T2m), this study reveals the presence of significant biweekly ISO over Tanzania, a period shorter than the anticipated Madden-Julian Oscillation (MJO) period of 30 to 60 days. It also reveals significant changes in wind direction when comparing the cold phase to the warm phase, highlighting a distinct atmospheric circulation pattern associated with each phase. Furthermore, the analysis reveals the presence of MJO-like eastward movement of pressure systems in the Subtropical High region, which is associated with this variability. This study presents a new analysis by providing a detailed analysis of the intraseasonal variability (ISV) of temperature over Tanzania, focusing on understanding the 2020 spatial-temporal patterns within the October-November-December (OND) season that may play a role in weather forecasting, agricultural planning, climate adaptation, reducing heat-related illnesses and contributing to the international effort to refine climate models and predictability.展开更多
Forecasting tropical cyclone(TC)activities has been a topic of great interest and research.Taiwan Island(TW)is one of the key regions that is highly exposed to TCs originated from the western North Pacific.Here,the au...Forecasting tropical cyclone(TC)activities has been a topic of great interest and research.Taiwan Island(TW)is one of the key regions that is highly exposed to TCs originated from the western North Pacific.Here,the authors utilize two mainstream reanalysis datasets for the period 1979-2013 and propose an effective statistical seasonal forecasting model-namely,the Sun Yat-sen University(SYSU)Model-for predicting the number of TC landfalls on TW based on the environmental factors in the preseason.The comprehensive predictor sampling and multiple linear regression show that the 850-hPa meridional wind over the west of the Antarctic Peninsula in January,the 300-hPa specific humidity over the open ocean southwest of Australia in January,the 300-hPa relative vorticity over the west of the Sea of Okhotsk in March,and the sea surface temperature in the South Indian Ocean in April,are the most significant predictors.The correlation coefficient between the modeled results and observations reaches 0.87.The model is validated by the leave-one-out and nine-fold cross-validation methods,and recent 9-yr observations(2014-2022).The Antarctic Oscillation,variabilities of the western Pacific subtropical high,Asian summer monsoon,and oceanic tunnel are the possible physical linkages or mechanisms behind the model result.The SYSU Model exhibits a 98%hit rate in 1979-2022(43 out of 44),suggesting an operational potential in the seasonal forecasting of TC landfalls on TW.展开更多
Seasonal prediction of summer rainfall in China plays a crucial role in decision-making,environmental protection,and socio-economic development,while it currently has a low prediction skill.We developed a deep learnin...Seasonal prediction of summer rainfall in China plays a crucial role in decision-making,environmental protection,and socio-economic development,while it currently has a low prediction skill.We developed a deep learning-based seasonal prediction bias correction method for summer rainfall in China.Based on prediction fields from the flexible Global Ocean-Atmosphere-Land System Model finite volume version 2(FGOALS-f2),we optimized the loss function of U-Net,trained with different hyperparameters,and selected the optimum model.U-Net model can extract multi-scale feature information and preserve spatial information,making it suitable for processing meteorological data.With this endto-end model,the precipitation distribution can be obtained directly without using the traditional method of data dimensionality reduction(e.g.,Empirical Orthogonal Function),which could maximize the retention of spatio-temporal information of the input data.Optimization of the loss function enhances the prediction results and mitigates model overfitting.The independent prediction shows a significant skill improvement measured by the anomalous correlation coefficient score.The skill has an average value of 0.679 in China(0°–63°N,73°–133°E)and 0.691 in the region of the Chinese mainland,which significantly improves the dynamical prediction skill by 1357%and 4836%.This study suggests that the deep learning(U-Net)-based seasonal prediction bias correction method is a promising approach for improving rainfall prediction of the dynamical model.展开更多
Seasonal precipitation has always been a key focus of climate prediction.As a dynamic-statistical combined method,the existing observational constraint correction establishes a regression relationship between the nume...Seasonal precipitation has always been a key focus of climate prediction.As a dynamic-statistical combined method,the existing observational constraint correction establishes a regression relationship between the numerical model outputs and historical observations,which can partly predict seasonal precipitation.However,solving a nonlinear problem through linear regression is significantly biased.This study implements a nonlinear optimization of an existing observational constrained correction model using a Light Gradient Boosting Machine(LightGBM)machine learning algorithm based on output from the Beijing National Climate Center Climate System Model(BCC-CSM)and station observations to improve the prediction of summer precipitation in China.The model was trained using a rolling approach,and LightGBM outperformed Linear Regression(LR),Extreme Gradient Boosting(XGBoost),and Categorical Boosting(CatBoost).Using parameter tuning to optimize the machine learning model and predict future summer precipitation using eight different predictors in BCC-CSM,the mean Anomaly Correlation Coefficient(ACC)score in the 2019–22 summer precipitation predictions was 0.17,and the mean Prediction Score(PS)reached 74.The PS score was improved by 7.87%and 6.63%compared with the BCC-CSM and the linear observational constraint approach,respectively.The observational constraint correction prediction strategy with LightGBM significantly and stably improved the prediction of summer precipitation in China compared to the previous linear observational constraint solution,providing a reference for flood control and drought relief during the flood season(summer)in China.展开更多
Clustered heavy precipitation(CHP)events can severely impact human society,infrastructure,and natural ecosystems.Consequently,short-term climate prediction of CHP events is vital for the prevention and mitigation of a...Clustered heavy precipitation(CHP)events can severely impact human society,infrastructure,and natural ecosystems.Consequently,short-term climate prediction of CHP events is vital for the prevention and mitigation of associated hazards.Employing year-to-year increment(DY)and multiple linear regression approaches,this study developed a seasonal prediction model for pre-summer(i.e.,May and June)CHP frequency in South China(SC)during 1981–2022.Three robust predictor factors were identified:March sea surface temperature in Southwestern Atlantic,early-winter snow depth in East Europe,and winter soil moisture in Central Asia.Three predictors exert substantial impacts on presummer precipitation in SC via modulation of an anomalous anticyclone(cyclone)over the(subtropical)western North Pacific.In leave-one-out cross-validation test during 1981–2022,the prediction model exhibited reasonable performance in predicting the interannual and interdecadal variations and trends of CHP days.The temporal correlation coefficient(TCC)was 0.66 between the observations and predictions.In the independent hindcast for 2013–2022,the TCC was as high as 0.85.Moreover,coherent covariations were observed between the frequency and the amounts of CHP,with a TCC of 0.99 for 1981–2022.Those three predictors show good performance in forecasting CHP amounts over SC,with a TCC of 0.68 between the predictions and observations in the cross-validation test during 1981–2022 and of 0.86 in the independent hindcasts during 2013–2022.Notably,the predictors also showed good predictive skill for years with high CHP occurrence(e.g.,1998 and 2019).The predicted high-incidence areas of heavy precipitation days were highly consistent with observations,with a pattern correlation coefficient of 0.44(0.55)for 1998(2019).This study provides valuable insights to improve seasonal prediction of pre-summer CHP frequency in SC.展开更多
Wetland degradation is an escalating global challenge with profound impacts on animal diversity,particularly during successional processes.Birds,as highly mobile and environmentally sensitive organisms,serve as effect...Wetland degradation is an escalating global challenge with profound impacts on animal diversity,particularly during successional processes.Birds,as highly mobile and environmentally sensitive organisms,serve as effective indicators of ecological change.While previous studies have primarily focused on local community structures and species diversity during a specific season,there is a need to extend the research timeframe and explore broader spatial variations.Additionally,expanding from simple species diversity indices to more multidimensional diversity indices would provide a more comprehensive understanding of wetland health and resilience.To address these gaps,we investigated the effects of wetland degradation on bird diversity across taxonomic,phylogenetic,and functional dimensions in the Zoige Wetland,a plateau meadow wetland biodiversity hotspot.Surveys were conducted during both breeding(summer)and overwintering(winter)seasons across 20 transects in 5 sampling areas,representing 4 degradation levels(pristine,low,medium,and high).Our study recorded a total of 106 bird species from 32 families and 14 orders,revealing distinct seasonal patterns in bird community composition and diversity.Biodiversity indices were significantly higher in pristine and low-degraded wetlands,particularly benefiting waterfowl(Anseriformes,Ciconiiformes)and wading birds(Charadriiformes)in winter,when these areas provided superior food resources and habitat conditions.In contrast,medium and highly degraded wetlands supported increased numbers of terrestrial birds(Passeriformes)and raptors(Accipitriformes,Falconiformes).Seasonal differences in taxonomic,phylogenetic,and functional diversity indices highlighted the contrasting ecological roles of wetlands during breeding and overwintering periods.Furthermore,indicator species analysis revealed key species associated with specific degradation levels and seasons,providing valuable insights into wetland health.This study underscores the importance of spatiotemporal dynamics in understanding avian responses to wetland degradation.By linking seasonal patterns of bird diversity to habitat conditions,our findings contribute to conservation efforts and provide a framework for assessing wetland degradation and its ecological impacts.展开更多
Background Ovarian follicular fluid(FF)is a dynamic environment that changes with the seasons,affecting follicle development,ovulation,and oocyte quality.Cells in the follicles release tiny particles called extracellu...Background Ovarian follicular fluid(FF)is a dynamic environment that changes with the seasons,affecting follicle development,ovulation,and oocyte quality.Cells in the follicles release tiny particles called extracellular vesicles(EVs)containing vital regulatory molecules,such as microRNAs(miRNAs).These miRNAs are pivotal in facilitating commu-nication within the follicles through diverse signaling and information transfer forms.EV-coupled miRNA signaling is implicated to be associated with ovarian function,follicle and oocyte growth and response to various environmen-tal insults.Herein,we investigated how seasonal variations directly influence the ovulatory and anovulatory states of ovarian follicles and how are they associated with follicular fluid EV-coupled miRNA dynamics in horses.Results Ultrasonographic monitoring and follicular fluid aspiration of preovulatory follicles in horses during the ano-vulatory(spring:non-breeding)and ovulatory(spring,summer,and fall:breeding)seasons and subsequent EV isola-tion and miRNA profiling identified significant variation in EV-miRNA cargo content.We identified 97 miRNAs with dif-ferential expression among the groups and specific clusters of miRNAs involved in the spring transition(miR-149,-200b,-206,-221,-328,and-615)and peak breeding period(including miR-143,-192,-451,-302b,-100,and let-7c).Bioinformatic analyses showed enrichments in various biological functions,e.g.,transcription factor activity,transcrip-tion and transcription regulation,nucleic acid binding,sequence-specific DNA binding,p53 signaling,and post-trans-lational modifications.Cluster analyses revealed distinct sets of significantly up-and down-regulated miRNAs associ-ated with spring anovulatory(Cluster 1)and summer ovulation–the peak breeding season(Clusters 4 and 6).Conclusions The findings from the current study shed light on the dynamics of FF-EV-coupled miRNAs in relation to equine ovulatory and anovulatory seasons,and their roles in understanding the mechanisms involved in seasonal shifts and ovulation during the breeding season warrant further investigation.展开更多
The time-varying periodic variations in Global Navigation Satellite System(GNSS)stations affect the reliable time series analysis and appropriate geophysical interpretation.In this study,we apply the singular spectrum...The time-varying periodic variations in Global Navigation Satellite System(GNSS)stations affect the reliable time series analysis and appropriate geophysical interpretation.In this study,we apply the singular spectrum analysis(SSA)method to characterize and interpret the periodic patterns of GNSS deformations in China using multiple geodetic datasets.These include 23-year observations from the Crustal Movement Observation Network of China(CMONOC),displacements inferred from the Gravity Recovery and Climate Experiment(GRACE),and loadings derived from Geophysical models(GM).The results reveal that all CMONOC time series exhibit seasonal signals characterized by amplitude and phase modulations,and the SSA method outperforms the traditional least squares fitting(LSF)method in extracting and interpreting the time-varying seasonal signals from the original time series.The decrease in the root mean square(RMS)correlates well with the annual cycle variance estimated by the SSA method,and the average reduction in noise amplitudes is nearly twice as much for SSA filtered results compared with those from the LSF method.With SSA analysis,the time-varying seasonal signals for all the selected stations can be identified in the reconstructed components corresponding to the first ten eigenvalues.Moreover,both RMS reduction and correlation analysis imply the advantages of GRACE solutions in explaining the GNSS periodic variations,and the geophysical effects can account for 71%of the GNSS annual amplitudes,and the average RMS reduction is 15%.The SSA method has proved to be useful for investigating the GNSS timevarying seasonal signals.It could be applicable as an auxiliary tool in the improvement of nonlinear variations investigations.展开更多
Approximately 3.44 billion tons of copper mine tailings(MT)were produced globally in 2018 with an increase of 45%from 2010.Significant efforts are being made to manage these tailings through storage facilities,recycli...Approximately 3.44 billion tons of copper mine tailings(MT)were produced globally in 2018 with an increase of 45%from 2010.Significant efforts are being made to manage these tailings through storage facilities,recycling,and reuse in different industries.Currently,a large portion of tailings are managed through the tailing storage facilities(TSF)where these tailings undergo hydro-thermal-mechanical stresses with seasonal cycles which are not comprehensively understood.This study presents an investigative study to evaluate the performance of control and cement-stabilized copper MT under the influence of seasonal cycles,freeze-thaw(F-T)and wet-dry(W-D)conditions,representing the seasonal variability in the cold and arid regions.The control and cement-stabilized MT samples were subjected to a maximum of 12 F-T and 12 W-D cycles and corresponding micro-and-macro behavior was investigated through scanning electron microscope(SEM),volumetric strain(εvT,wet density(r),moisture content loss,and unconfined compressive strength(UCS)tests.The results indicated the vulnerability of Copper MT to 67%and 75%strength loss reaching residual states with 12 F-T and 8 W-D cycles,respectively.Whereas the stabilized MT retained 39%-55%and 16%-34%strength with F-T and W-D cycles,demonstrating increased durability.This research highlights the impact of seasonal cycles and corresponding strength-deformation characteristics of control and stabilized Copper MT in cold and arid regions.展开更多
Gross primary production(GPP)is a crucial indicator representing the absorption of atmospheric CO_(2) by vegetation.At present,the estimation of GPP by remote sensing is mainly based on leaf-related vegetation indexes...Gross primary production(GPP)is a crucial indicator representing the absorption of atmospheric CO_(2) by vegetation.At present,the estimation of GPP by remote sensing is mainly based on leaf-related vegetation indexes and leaf-related biophysical para-meter leaf area index(LAI),which are not completely synchronized in seasonality with GPP.In this study,we proposed chlorophyll content-based light use efficiency model(CC-LUE)to improve GPP estimates,as chlorophyll is the direct site of photosynthesis,and only the light absorbed by chlorophyll is used in the photosynthetic process.The CC-LUE model is constructed by establishing a linear correlation between satellite-derived canopy chlorophyll content(Chlcanopy)and FPAR.This method was calibrated and validated utiliz-ing 7-d averaged in-situ GPP data from 14 eddy covariance flux towers covering deciduous broadleaf forest ecosystems across five dif-ferent climate zones.Results showed a relatively robust seasonal consistency between Chlcanopy with GPP in deciduous broadleaf forests under different climatic conditions.The CC-LUE model explained 88% of the in-situ GPP seasonality for all validation site-year and 56.0% of in-situ GPP variations through the growing season,outperforming the three widely used LUE models(MODIS-GPP algorithm,Vegetation Photosynthesis Model(VPM),and the eddy covariance-light use efficiency model(EC-LUE)).Additionally,the CC-LUE model(RMSE=0.50 g C/(m^(2)·d))significantly improved the underestimation of GPP during the growing season in semi-arid region,re-markably decreasing the root mean square error of averaged growing season GPP simulation and in-situ GPP by 75.4%,73.4%,and 37.5%,compared with MOD17(RMSE=2.03 g C/(m^(2)·d)),VPM(RMSE=1.88 g C/(m^(2)·d)),and EC-LUE(RMSE=0.80 g C/(m^(2)·d))model.The chlorophyll-based method proved superior in capturing the seasonal variations of GPP in forest ecosystems,thereby provid-ing the possibility of a more precise depiction of forest seasonal carbon uptake.展开更多
A long-term field experiment （started at 2003） was conducted to determine the effects of different dce cultivation methods on growth characteristics and grain yield of late-season rice under double-rice cropping sys...A long-term field experiment （started at 2003） was conducted to determine the effects of different dce cultivation methods on growth characteristics and grain yield of late-season rice under double-rice cropping system in seasonal drought region of southeast China （Yujiang County, Jiangxi Province）. The rice cultivation methods included no-tillage and flooded rice cultivation （N-F）, no-tillage and non-flooded rice cultivation with straw mulching （N-SM）, and no-tillage and non-flooded rice cultivation without straw mulching （N-ZM）. There was no significant difference in rice grain yield between the N-SM and N-F treatments. However, the rice grain yields in the N-SM and N-F treatments were significantly higher than that in the N-ZM treatment. The late-season rice plants in the N-SM treatment had significantly higher numbers of effective panicles and total grains per hill compared with those in the N-ZM treatment. The above-ground dry matter of late-season rice was similar between the N-SM and N-F treatments. Compared with the N-F treatment, the N-ZM and N-SM treatments significantly decreased the leaf area at the heading stage. Moreover, the N-SM treatment could significantly increase total root length and root tip number at the grain-filling stage compared with the N-ZM treatment.展开更多
Mesoscale eddies are a prominent oceanic phenomenon that plays an important role in oceanic mass transport and energy conversion.Characterizing by rotational speed,the eddy intensity is one of the most fundamental pro...Mesoscale eddies are a prominent oceanic phenomenon that plays an important role in oceanic mass transport and energy conversion.Characterizing by rotational speed,the eddy intensity is one of the most fundamental properties of an eddy.However,the seasonal spatiotemporal variation in eddy intensity has not been examined from a global ocean perspective.In this study,we unveil the seasonal spatiotemporal characteristics of eddy intensity in the global ocean by using the latest satellite-altimetry-derived eddy trajectory data set.The results suggest that the eddy intensity has a distinct seasonal variation,reaching a peak in spring while attaining a minimum in autumn in the Northern Hemisphere and the opposite in the Southern Hemisphere.The seasonal variation of eddy intensity is more intense in the tropical-subtropical transition zones within latitudinal bands between 15°and 30°in the western Pacific Ocean,the northwestern Atlantic Ocean,and the eastern Indian Ocean because baroclinic instability in these areas changes sharply.Further analysis found that the seasonal variation of baroclinic instability precedes the eddy intensity by a phase of 2–3 months due to the initial perturbations needing time to grow into mesoscale eddies.展开更多
在当今世界文化交流愈发频繁的背景下,推动中华文明走向世界成为重中之重。翻译作为推动跨文化交流的重要手段之一,是连接不同文化的桥梁。而文化负载词由于其涵盖的大量民族特色词汇,成为翻译过程中的挑战,同时也是文化传播的重要工具...在当今世界文化交流愈发频繁的背景下,推动中华文明走向世界成为重中之重。翻译作为推动跨文化交流的重要手段之一,是连接不同文化的桥梁。而文化负载词由于其涵盖的大量民族特色词汇,成为翻译过程中的挑战,同时也是文化传播的重要工具。文章基于目的论视角,以纪录片Seasons of China中的中文文化负载词英译为例,探讨译者在翻译过程中采取的恰当翻译方法,以期为纪录片的文化负载词翻译研究提供一定的借鉴与参考。展开更多
Two-line hybrid rice with excellent quality is preferred in the Chinese market.However,there is a trade-off between reducing costs for hybrid seed production and lowering the outcrossing rate of the sterile line,which...Two-line hybrid rice with excellent quality is preferred in the Chinese market.However,there is a trade-off between reducing costs for hybrid seed production and lowering the outcrossing rate of the sterile line,which is largely determined by the stigma exsertion rate(SER).In this study,we constructed mutants of male sterility lines with improved grain length(GL)and SER in three elite early-season indica rice varieties through targeted manipulation of the TMS5 and GS3 genes using CRISPR/Cas9-mediated multiplex systems.We obtained a series of marker-free gs3 single mutants and gs3tms5 double mutants with significantly higher SER,longer grains,and increased 1000-grain weight compared with the wild type(WT).Importantly,the typically thermo-sensitive genic male sterile(TGMS)trait with a higher SER was observed in gs3tms5 mutants,and their F1 hybrids exhibited remarkable improvements in grain shape and yield-related traits.Our findings provided an efficient method to generate new valuable TGMS germplasm with improved SER through the mutagenesis of GS3 and TMS5 synergistically,and demonstrated that GS3 had pleiotropic effects on grain size,SER,and grain quality in early-season indica rice.展开更多
Despite a considerable global effort to eradicate malaria in the last few years,the disease burden in 2020 was 241 million,with 627000 deaths.India accounted for 83%of cases reported in the World Health Organization(W...Despite a considerable global effort to eradicate malaria in the last few years,the disease burden in 2020 was 241 million,with 627000 deaths.India accounted for 83%of cases reported in the World Health Organization(WHO)South-East Asia Region(WHO malaria report 2021).Nevertheless,India has shown its commitment to eliminating malaria from the country by framing the National Strategic Plan(NSP),according to which the districts have been stratified into four categories based on the malaria transmission intensity and the indicator used for categorization of the districts was annual parasite incidence(API).In North-East India,many highly endemic districts(API>2)are in the“Intensified Control Phase”and fall under category栿[1].A few pockets in such districts have disproportionate(API>10)due to forest and tribal dominance in those regions.The previous reports demonstrated a high malaria burden(API>10)in the district East Garo Hills[2];however,a few pockets of the district Udalguri are malaria hotspots with the API>5[3].展开更多
As the core of leaf functional traits,the trade-off relationship between the petiole and lamina expresses the plant's adaptability to the environment in terms of support structure and photosynthesis.We investigate...As the core of leaf functional traits,the trade-off relationship between the petiole and lamina expresses the plant's adaptability to the environment in terms of support structure and photosynthesis.We investigated the proportions of allometric growth in the relationship between the petiole and the lamina of broadleaf woody plants in temperate highland Tianshan Mountains montane forests through three dimensions(length,area,and mass),including the length of the lamina(LL)and the length of the petiole(PL),and the area of the lamina(LA)and petiole cross sectional area(PCA)versus the mass of the lamina(LM)and the mass of the petiole(PM),as well as exploring the characteristics of the variance in response to seasonal changes.We found that the functional traits in all three dimensions showed a clear convergent evolution as the seasons progressed,that is,a“seasonal effect”of increasing and then decreasing.The effect of the petioleelamina relationship under spring was minimal in the area dimension;the effects of the threeedimensional relationships of the traits were all highest in summer,and the effect of the petioleelamina relationship was lower in autumn.We also found that petiole traits are simultaneously and multiply affected by lamina traits,with LA and LM having additional effects on the length/mass and area dimensions,respectively.Compared to tree species,shrub species significantly require more light intensity and support capacity.Compound-leaved plants would invest more in photoluminescence,increasing leaf light capture efficiency and static load and dynamic resistance.Our results suggest that plants have rather complex trade-off mechanisms at the leaf level influencing their ability to adapt to the environment,emphasize the need for leaf-level studies on the relationships between functional traits in plants,and illustrate the importance of the season as a distinct time scale for plant trade-off mechanisms.展开更多
In many songbird species,birdsong features phonological syntax,meaning that the units within their vocal se-quences are ordered in a non-random way that adheres to a rule.While such syntactical patterns have been rich...In many songbird species,birdsong features phonological syntax,meaning that the units within their vocal se-quences are ordered in a non-random way that adheres to a rule.While such syntactical patterns have been richly described in many species,comparatively little is known about how those patterns contribute to song achieving its important functions.For each of song’s main functions,territorial defense and mate attraction,evidence of a role for syntax is limited.One species for which syntax has been thoroughly described is the Hermit Thrush(Catharus guttatus),which presents song types from their repertoires in a semi-predictable order and,in doing so,rapidly cycle up and down the frequency spectrum.The objective of the present study was to explore the importance of song syntax in the Hermit Thrush through a within-subject examination of how measures of syntax,such as the predictability of song type order within song sequences,shift over the breeding season.We hypothesized that,if such syntactical characteristics are important to breeding behaviour,they would be most prominent at the start of the breeding season when activity associated with territory establishment and mate attraction is most intense.Analysis revealed that,as predicted,the rigidness of song type ordering within se-quences was highest at the start of the season and declined thereafter.That song type sequences were most predictable at the vitally important early part of the breeding season fit our hypothesis that this aspect of song syntax is important to song’s functions related to territory establishment and/or mate attraction.Future work will clarify whether that role relates to one of song’s two main functions or serves song transmission in some broader way.展开更多
The rapidly changing Antarctic sea ice has garnered significant interest. To enhance the prediction skill for sea ice and respond to the Sea Ice Prediction Network-South's latest call, this study presents the refo...The rapidly changing Antarctic sea ice has garnered significant interest. To enhance the prediction skill for sea ice and respond to the Sea Ice Prediction Network-South's latest call, this study presents the reforecast results of Antarctic sea-ice area and extent from December to June of the coming year with a Convolutional Long Short-Term Memory(Conv LSTM)Network. The reforecast experiments demonstrate that Conv LSTM captures the interannual and interseasonal variability of Antarctic sea ice successfully, and performs better than the European Centre for Medium-Range Weather Forecasts. Based on this, we present the prediction from December 2023 to June 2024, indicating that the Antarctic sea ice will remain at lows, but may not create a new record low. This research highlights the promising application of deep learning in Antarctic sea-ice prediction.展开更多
Seasonal variation of hearing sensitivity has been observed in many vertebrate groups with obvious vocal behaviors.Circulating hormones,conspecific calling signals,and temperature are potential factors that drive thes...Seasonal variation of hearing sensitivity has been observed in many vertebrate groups with obvious vocal behaviors.Circulating hormones,conspecific calling signals,and temperature are potential factors that drive these plasticity patterns.Turtles have a hearing range that appears to be limited to under 1.5 kHz and are often thought to be non-vocal;thus,they are commonly neglected in vocal communication research.In this study,we aimed to determine whether the auditory phenotype exhibits seasonal variation in sensitivity and to analyze the potential factors driving such variation patterns in turtles.We measured hearing sensitivity and sex hormone levels in female(estradiol)and male(testosterone and dihydrotestosterone)Red-eared sliders(Trachemys scripta elegans)during spring and winter.The results showed that auditory brainstem response(ABR)thresholds were significantly lower in spring than in winter at a frequency range of 0.5-0.9 kHz.The hearing-sensitivity bandwidth was wider,and the ABR latency was significantly shorter in spring than in winter.No significant differences were found in estradiol,testosterone,and dihydrotestosterone levels in T.scripta elegans between spring and winter.This study is the first to reveal the seasonal variation of peripheral hearing sensitivity in turtles,a special animal group with limited hearing range and less vocalization.Temperature variations may be used to explain these seasonal effects,but further research is required to confirm our findings.展开更多
基金supported by the National Natural Science Foundation of China [grant number 42030605]the National Key R&D Program of China [grant number 2020YFA0608004]。
文摘A remarkable marine heatwave,known as the“Blob”,occurred in the Northeast Pacific Ocean from late 2013 to early 2016,which displayed strong warm anomalies extending from the surface to a depth of 300 m.This study employed two assimilation schemes based on the global Climate Forecast System of Nanjing University of Information Science(NUIST-CFS 1.0)to investigate the impact of ocean data assimilation on the seasonal prediction of this extreme marine heatwave.The sea surface temperature(SST)nudging scheme assimilates SST only,while the deterministic ensemble Kalman filter(EnKF)scheme assimilates observations from the surface to the deep ocean.The latter notably improves the forecasting skill for subsurface temperature anomalies,especially at the depth of 100-300 m(the lower layer),outperforming the SST nudging scheme.It excels in predicting both horizontal and vertical heat transport in the lower layer,contributing to improved forecasts of the lower-layer warming during the Blob.These improvements stem from the assimilation of subsurface observational data,which are important in predicting the upper-ocean conditions.The results suggest that assimilating ocean data with the EnKF scheme significantly enhances the accuracy in predicting subsurface temperature anomalies during the Blob and offers better understanding of its underlying mechanisms.
文摘Intraseasonal Oscillation (ISO) which is the eastward-propagating disturbance with a period of 10 - 60 days has been the topic of interest since its discovery by Madden-Julian in 1972. Many researchers have published their work on ISO, yet they all agree that there is no clear understanding of this matter. By using daily observed surface temperature (T2m), this study reveals the presence of significant biweekly ISO over Tanzania, a period shorter than the anticipated Madden-Julian Oscillation (MJO) period of 30 to 60 days. It also reveals significant changes in wind direction when comparing the cold phase to the warm phase, highlighting a distinct atmospheric circulation pattern associated with each phase. Furthermore, the analysis reveals the presence of MJO-like eastward movement of pressure systems in the Subtropical High region, which is associated with this variability. This study presents a new analysis by providing a detailed analysis of the intraseasonal variability (ISV) of temperature over Tanzania, focusing on understanding the 2020 spatial-temporal patterns within the October-November-December (OND) season that may play a role in weather forecasting, agricultural planning, climate adaptation, reducing heat-related illnesses and contributing to the international effort to refine climate models and predictability.
基金jointly supported by the Innovation Group Project of the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number 316323005]the Guangdong Basic and Applied Basic Research Foundation[grant numbers 2023A1515010741 and 2024B1515020035]the Science and Technology Planning Project of Guangdong Province[grant number 2023B1212060019]。
文摘Forecasting tropical cyclone(TC)activities has been a topic of great interest and research.Taiwan Island(TW)is one of the key regions that is highly exposed to TCs originated from the western North Pacific.Here,the authors utilize two mainstream reanalysis datasets for the period 1979-2013 and propose an effective statistical seasonal forecasting model-namely,the Sun Yat-sen University(SYSU)Model-for predicting the number of TC landfalls on TW based on the environmental factors in the preseason.The comprehensive predictor sampling and multiple linear regression show that the 850-hPa meridional wind over the west of the Antarctic Peninsula in January,the 300-hPa specific humidity over the open ocean southwest of Australia in January,the 300-hPa relative vorticity over the west of the Sea of Okhotsk in March,and the sea surface temperature in the South Indian Ocean in April,are the most significant predictors.The correlation coefficient between the modeled results and observations reaches 0.87.The model is validated by the leave-one-out and nine-fold cross-validation methods,and recent 9-yr observations(2014-2022).The Antarctic Oscillation,variabilities of the western Pacific subtropical high,Asian summer monsoon,and oceanic tunnel are the possible physical linkages or mechanisms behind the model result.The SYSU Model exhibits a 98%hit rate in 1979-2022(43 out of 44),suggesting an operational potential in the seasonal forecasting of TC landfalls on TW.
基金Guangdong Major Project of Basic and Applied Basic Research(2020B0301030004)Postdoctoral Fellowship Program of CPSF(GZC20232598)+1 种基金China Postdoctoral Science Foundation(2024M753168)National Key Scientific and Technological Infrastructure Project“Earth System Numerical Simulation Facility”(EarthLab)。
文摘Seasonal prediction of summer rainfall in China plays a crucial role in decision-making,environmental protection,and socio-economic development,while it currently has a low prediction skill.We developed a deep learning-based seasonal prediction bias correction method for summer rainfall in China.Based on prediction fields from the flexible Global Ocean-Atmosphere-Land System Model finite volume version 2(FGOALS-f2),we optimized the loss function of U-Net,trained with different hyperparameters,and selected the optimum model.U-Net model can extract multi-scale feature information and preserve spatial information,making it suitable for processing meteorological data.With this endto-end model,the precipitation distribution can be obtained directly without using the traditional method of data dimensionality reduction(e.g.,Empirical Orthogonal Function),which could maximize the retention of spatio-temporal information of the input data.Optimization of the loss function enhances the prediction results and mitigates model overfitting.The independent prediction shows a significant skill improvement measured by the anomalous correlation coefficient score.The skill has an average value of 0.679 in China(0°–63°N,73°–133°E)and 0.691 in the region of the Chinese mainland,which significantly improves the dynamical prediction skill by 1357%and 4836%.This study suggests that the deep learning(U-Net)-based seasonal prediction bias correction method is a promising approach for improving rainfall prediction of the dynamical model.
基金jointly supported by the National Natural Science Foundation of China(Grant Nos.42122034,42075043,42330609)the Second Tibetan Plateau Scientific Expedition and Research program(2019QZKK0103)+2 种基金Key Talent Project in Gansu and Central Guidance Fund for Local Science and Technology Development Projects in Gansu(No.24ZYQA031)the Youth Innovation Promotion Association of Chinese Academy of Sciences(2021427)West Light Foundation of the Chinese Academy of Sciences(xbzg-zdsys-202215)。
文摘Seasonal precipitation has always been a key focus of climate prediction.As a dynamic-statistical combined method,the existing observational constraint correction establishes a regression relationship between the numerical model outputs and historical observations,which can partly predict seasonal precipitation.However,solving a nonlinear problem through linear regression is significantly biased.This study implements a nonlinear optimization of an existing observational constrained correction model using a Light Gradient Boosting Machine(LightGBM)machine learning algorithm based on output from the Beijing National Climate Center Climate System Model(BCC-CSM)and station observations to improve the prediction of summer precipitation in China.The model was trained using a rolling approach,and LightGBM outperformed Linear Regression(LR),Extreme Gradient Boosting(XGBoost),and Categorical Boosting(CatBoost).Using parameter tuning to optimize the machine learning model and predict future summer precipitation using eight different predictors in BCC-CSM,the mean Anomaly Correlation Coefficient(ACC)score in the 2019–22 summer precipitation predictions was 0.17,and the mean Prediction Score(PS)reached 74.The PS score was improved by 7.87%and 6.63%compared with the BCC-CSM and the linear observational constraint approach,respectively.The observational constraint correction prediction strategy with LightGBM significantly and stably improved the prediction of summer precipitation in China compared to the previous linear observational constraint solution,providing a reference for flood control and drought relief during the flood season(summer)in China.
基金Guangdong Major Project of Basic and Applied Basic Research(2020B0301030004)Science and Technology Development Plan in Jilin Province of China(20230203135SF)+1 种基金National Natural Science Foundation of China(41875119)Special Fund for Innovative Development of China Meteorological Administration(CXFZ2022J007)。
文摘Clustered heavy precipitation(CHP)events can severely impact human society,infrastructure,and natural ecosystems.Consequently,short-term climate prediction of CHP events is vital for the prevention and mitigation of associated hazards.Employing year-to-year increment(DY)and multiple linear regression approaches,this study developed a seasonal prediction model for pre-summer(i.e.,May and June)CHP frequency in South China(SC)during 1981–2022.Three robust predictor factors were identified:March sea surface temperature in Southwestern Atlantic,early-winter snow depth in East Europe,and winter soil moisture in Central Asia.Three predictors exert substantial impacts on presummer precipitation in SC via modulation of an anomalous anticyclone(cyclone)over the(subtropical)western North Pacific.In leave-one-out cross-validation test during 1981–2022,the prediction model exhibited reasonable performance in predicting the interannual and interdecadal variations and trends of CHP days.The temporal correlation coefficient(TCC)was 0.66 between the observations and predictions.In the independent hindcast for 2013–2022,the TCC was as high as 0.85.Moreover,coherent covariations were observed between the frequency and the amounts of CHP,with a TCC of 0.99 for 1981–2022.Those three predictors show good performance in forecasting CHP amounts over SC,with a TCC of 0.68 between the predictions and observations in the cross-validation test during 1981–2022 and of 0.86 in the independent hindcasts during 2013–2022.Notably,the predictors also showed good predictive skill for years with high CHP occurrence(e.g.,1998 and 2019).The predicted high-incidence areas of heavy precipitation days were highly consistent with observations,with a pattern correlation coefficient of 0.44(0.55)for 1998(2019).This study provides valuable insights to improve seasonal prediction of pre-summer CHP frequency in SC.
基金supported by the Southwest Minzu University Research Startup Funds (No.16011221038,RQD2022021)Double World-Class Project (No.CX2023010)。
文摘Wetland degradation is an escalating global challenge with profound impacts on animal diversity,particularly during successional processes.Birds,as highly mobile and environmentally sensitive organisms,serve as effective indicators of ecological change.While previous studies have primarily focused on local community structures and species diversity during a specific season,there is a need to extend the research timeframe and explore broader spatial variations.Additionally,expanding from simple species diversity indices to more multidimensional diversity indices would provide a more comprehensive understanding of wetland health and resilience.To address these gaps,we investigated the effects of wetland degradation on bird diversity across taxonomic,phylogenetic,and functional dimensions in the Zoige Wetland,a plateau meadow wetland biodiversity hotspot.Surveys were conducted during both breeding(summer)and overwintering(winter)seasons across 20 transects in 5 sampling areas,representing 4 degradation levels(pristine,low,medium,and high).Our study recorded a total of 106 bird species from 32 families and 14 orders,revealing distinct seasonal patterns in bird community composition and diversity.Biodiversity indices were significantly higher in pristine and low-degraded wetlands,particularly benefiting waterfowl(Anseriformes,Ciconiiformes)and wading birds(Charadriiformes)in winter,when these areas provided superior food resources and habitat conditions.In contrast,medium and highly degraded wetlands supported increased numbers of terrestrial birds(Passeriformes)and raptors(Accipitriformes,Falconiformes).Seasonal differences in taxonomic,phylogenetic,and functional diversity indices highlighted the contrasting ecological roles of wetlands during breeding and overwintering periods.Furthermore,indicator species analysis revealed key species associated with specific degradation levels and seasons,providing valuable insights into wetland health.This study underscores the importance of spatiotemporal dynamics in understanding avian responses to wetland degradation.By linking seasonal patterns of bird diversity to habitat conditions,our findings contribute to conservation efforts and provide a framework for assessing wetland degradation and its ecological impacts.
基金Southern Illinois University,Carbondale,ILMinistry of Higher Education&Scientific Research,Baghdad,Iraq+2 种基金NIFA-USDA Hatch project accession#1016077(Multistate#W4171)USDAARS project 6066-31000-015-00DNIH MS-IDeA network of Biomedical Research Excellence award 5P20GMI03476-19.GMI received a PhD scholarship from the Ministry of Higher Education&Scientific Research,Baghdad,Iraq.
文摘Background Ovarian follicular fluid(FF)is a dynamic environment that changes with the seasons,affecting follicle development,ovulation,and oocyte quality.Cells in the follicles release tiny particles called extracellular vesicles(EVs)containing vital regulatory molecules,such as microRNAs(miRNAs).These miRNAs are pivotal in facilitating commu-nication within the follicles through diverse signaling and information transfer forms.EV-coupled miRNA signaling is implicated to be associated with ovarian function,follicle and oocyte growth and response to various environmen-tal insults.Herein,we investigated how seasonal variations directly influence the ovulatory and anovulatory states of ovarian follicles and how are they associated with follicular fluid EV-coupled miRNA dynamics in horses.Results Ultrasonographic monitoring and follicular fluid aspiration of preovulatory follicles in horses during the ano-vulatory(spring:non-breeding)and ovulatory(spring,summer,and fall:breeding)seasons and subsequent EV isola-tion and miRNA profiling identified significant variation in EV-miRNA cargo content.We identified 97 miRNAs with dif-ferential expression among the groups and specific clusters of miRNAs involved in the spring transition(miR-149,-200b,-206,-221,-328,and-615)and peak breeding period(including miR-143,-192,-451,-302b,-100,and let-7c).Bioinformatic analyses showed enrichments in various biological functions,e.g.,transcription factor activity,transcrip-tion and transcription regulation,nucleic acid binding,sequence-specific DNA binding,p53 signaling,and post-trans-lational modifications.Cluster analyses revealed distinct sets of significantly up-and down-regulated miRNAs associ-ated with spring anovulatory(Cluster 1)and summer ovulation–the peak breeding season(Clusters 4 and 6).Conclusions The findings from the current study shed light on the dynamics of FF-EV-coupled miRNAs in relation to equine ovulatory and anovulatory seasons,and their roles in understanding the mechanisms involved in seasonal shifts and ovulation during the breeding season warrant further investigation.
基金supported by the National Natural Science Foundation of China(NO.42104028,42174030 and 42004017)the Open Fund of Hubei Luojia Laboratory(No.220100048 and 230100021)the Scientific Research Project of Hubei Provincial Department of Education,and Research Foundation of the Department of Natural Resources of Hunan Province(No.20230104CH)。
文摘The time-varying periodic variations in Global Navigation Satellite System(GNSS)stations affect the reliable time series analysis and appropriate geophysical interpretation.In this study,we apply the singular spectrum analysis(SSA)method to characterize and interpret the periodic patterns of GNSS deformations in China using multiple geodetic datasets.These include 23-year observations from the Crustal Movement Observation Network of China(CMONOC),displacements inferred from the Gravity Recovery and Climate Experiment(GRACE),and loadings derived from Geophysical models(GM).The results reveal that all CMONOC time series exhibit seasonal signals characterized by amplitude and phase modulations,and the SSA method outperforms the traditional least squares fitting(LSF)method in extracting and interpreting the time-varying seasonal signals from the original time series.The decrease in the root mean square(RMS)correlates well with the annual cycle variance estimated by the SSA method,and the average reduction in noise amplitudes is nearly twice as much for SSA filtered results compared with those from the LSF method.With SSA analysis,the time-varying seasonal signals for all the selected stations can be identified in the reconstructed components corresponding to the first ten eigenvalues.Moreover,both RMS reduction and correlation analysis imply the advantages of GRACE solutions in explaining the GNSS periodic variations,and the geophysical effects can account for 71%of the GNSS annual amplitudes,and the average RMS reduction is 15%.The SSA method has proved to be useful for investigating the GNSS timevarying seasonal signals.It could be applicable as an auxiliary tool in the improvement of nonlinear variations investigations.
基金the W.M.Keck Center for Nano-Scale Imaging in the Department of Chemistry and Biochemistry at the University of Arizona(Grant No.RRID:SCR_022884),with funding from the W.M.Keck Foundation Grant.
文摘Approximately 3.44 billion tons of copper mine tailings(MT)were produced globally in 2018 with an increase of 45%from 2010.Significant efforts are being made to manage these tailings through storage facilities,recycling,and reuse in different industries.Currently,a large portion of tailings are managed through the tailing storage facilities(TSF)where these tailings undergo hydro-thermal-mechanical stresses with seasonal cycles which are not comprehensively understood.This study presents an investigative study to evaluate the performance of control and cement-stabilized copper MT under the influence of seasonal cycles,freeze-thaw(F-T)and wet-dry(W-D)conditions,representing the seasonal variability in the cold and arid regions.The control and cement-stabilized MT samples were subjected to a maximum of 12 F-T and 12 W-D cycles and corresponding micro-and-macro behavior was investigated through scanning electron microscope(SEM),volumetric strain(εvT,wet density(r),moisture content loss,and unconfined compressive strength(UCS)tests.The results indicated the vulnerability of Copper MT to 67%and 75%strength loss reaching residual states with 12 F-T and 8 W-D cycles,respectively.Whereas the stabilized MT retained 39%-55%and 16%-34%strength with F-T and W-D cycles,demonstrating increased durability.This research highlights the impact of seasonal cycles and corresponding strength-deformation characteristics of control and stabilized Copper MT in cold and arid regions.
基金Under the auspices of the National Key Research and Development Program of China(No.2019YFA0606603)。
文摘Gross primary production(GPP)is a crucial indicator representing the absorption of atmospheric CO_(2) by vegetation.At present,the estimation of GPP by remote sensing is mainly based on leaf-related vegetation indexes and leaf-related biophysical para-meter leaf area index(LAI),which are not completely synchronized in seasonality with GPP.In this study,we proposed chlorophyll content-based light use efficiency model(CC-LUE)to improve GPP estimates,as chlorophyll is the direct site of photosynthesis,and only the light absorbed by chlorophyll is used in the photosynthetic process.The CC-LUE model is constructed by establishing a linear correlation between satellite-derived canopy chlorophyll content(Chlcanopy)and FPAR.This method was calibrated and validated utiliz-ing 7-d averaged in-situ GPP data from 14 eddy covariance flux towers covering deciduous broadleaf forest ecosystems across five dif-ferent climate zones.Results showed a relatively robust seasonal consistency between Chlcanopy with GPP in deciduous broadleaf forests under different climatic conditions.The CC-LUE model explained 88% of the in-situ GPP seasonality for all validation site-year and 56.0% of in-situ GPP variations through the growing season,outperforming the three widely used LUE models(MODIS-GPP algorithm,Vegetation Photosynthesis Model(VPM),and the eddy covariance-light use efficiency model(EC-LUE)).Additionally,the CC-LUE model(RMSE=0.50 g C/(m^(2)·d))significantly improved the underestimation of GPP during the growing season in semi-arid region,re-markably decreasing the root mean square error of averaged growing season GPP simulation and in-situ GPP by 75.4%,73.4%,and 37.5%,compared with MOD17(RMSE=2.03 g C/(m^(2)·d)),VPM(RMSE=1.88 g C/(m^(2)·d)),and EC-LUE(RMSE=0.80 g C/(m^(2)·d))model.The chlorophyll-based method proved superior in capturing the seasonal variations of GPP in forest ecosystems,thereby provid-ing the possibility of a more precise depiction of forest seasonal carbon uptake.
基金the National High-Tech Research and Development Program of China(Grant No.2002AA2Z4331)for generous financial support
文摘A long-term field experiment （started at 2003） was conducted to determine the effects of different dce cultivation methods on growth characteristics and grain yield of late-season rice under double-rice cropping system in seasonal drought region of southeast China （Yujiang County, Jiangxi Province）. The rice cultivation methods included no-tillage and flooded rice cultivation （N-F）, no-tillage and non-flooded rice cultivation with straw mulching （N-SM）, and no-tillage and non-flooded rice cultivation without straw mulching （N-ZM）. There was no significant difference in rice grain yield between the N-SM and N-F treatments. However, the rice grain yields in the N-SM and N-F treatments were significantly higher than that in the N-ZM treatment. The late-season rice plants in the N-SM treatment had significantly higher numbers of effective panicles and total grains per hill compared with those in the N-ZM treatment. The above-ground dry matter of late-season rice was similar between the N-SM and N-F treatments. Compared with the N-F treatment, the N-ZM and N-SM treatments significantly decreased the leaf area at the heading stage. Moreover, the N-SM treatment could significantly increase total root length and root tip number at the grain-filling stage compared with the N-ZM treatment.
基金The National Key R&D Program of China under contract No.2022YFC2807604the Basic Scientific Fund for National Public Research Institutes of China under contract Nos 2022S02,2022Q03 and 2018S02+3 种基金the Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology(Qingdao)under contract No.2018SDKJ0105-3the National Natural Science Foundation of China under contract Nos 41876030,41976021,41876231,4190060432 and 41706220the program Impact and Response of Antarctic Seas to Climate Change under contract No.IRASCC 01-01-01Athe Taishan Scholars Project Fund under contract No.ts20190963。
文摘Mesoscale eddies are a prominent oceanic phenomenon that plays an important role in oceanic mass transport and energy conversion.Characterizing by rotational speed,the eddy intensity is one of the most fundamental properties of an eddy.However,the seasonal spatiotemporal variation in eddy intensity has not been examined from a global ocean perspective.In this study,we unveil the seasonal spatiotemporal characteristics of eddy intensity in the global ocean by using the latest satellite-altimetry-derived eddy trajectory data set.The results suggest that the eddy intensity has a distinct seasonal variation,reaching a peak in spring while attaining a minimum in autumn in the Northern Hemisphere and the opposite in the Southern Hemisphere.The seasonal variation of eddy intensity is more intense in the tropical-subtropical transition zones within latitudinal bands between 15°and 30°in the western Pacific Ocean,the northwestern Atlantic Ocean,and the eastern Indian Ocean because baroclinic instability in these areas changes sharply.Further analysis found that the seasonal variation of baroclinic instability precedes the eddy intensity by a phase of 2–3 months due to the initial perturbations needing time to grow into mesoscale eddies.
文摘在当今世界文化交流愈发频繁的背景下,推动中华文明走向世界成为重中之重。翻译作为推动跨文化交流的重要手段之一,是连接不同文化的桥梁。而文化负载词由于其涵盖的大量民族特色词汇,成为翻译过程中的挑战,同时也是文化传播的重要工具。文章基于目的论视角,以纪录片Seasons of China中的中文文化负载词英译为例,探讨译者在翻译过程中采取的恰当翻译方法,以期为纪录片的文化负载词翻译研究提供一定的借鉴与参考。
基金the Natural Science Foundation of Zhejiang Province,China(Grant Nos.LY24C130004,LY22C135104,and LY23C130002)the National Natural Science Foundation of China(Grant No.31501288)+1 种基金the Open Project Program of State Key Laboratory of Rice Biology and Breeding,China(Grant No.20210207)Central Publicinterest Scientific Institution Basal Research Fund,China(Grant No.CPSIBRF-CNRRI-202203).
文摘Two-line hybrid rice with excellent quality is preferred in the Chinese market.However,there is a trade-off between reducing costs for hybrid seed production and lowering the outcrossing rate of the sterile line,which is largely determined by the stigma exsertion rate(SER).In this study,we constructed mutants of male sterility lines with improved grain length(GL)and SER in three elite early-season indica rice varieties through targeted manipulation of the TMS5 and GS3 genes using CRISPR/Cas9-mediated multiplex systems.We obtained a series of marker-free gs3 single mutants and gs3tms5 double mutants with significantly higher SER,longer grains,and increased 1000-grain weight compared with the wild type(WT).Importantly,the typically thermo-sensitive genic male sterile(TGMS)trait with a higher SER was observed in gs3tms5 mutants,and their F1 hybrids exhibited remarkable improvements in grain shape and yield-related traits.Our findings provided an efficient method to generate new valuable TGMS germplasm with improved SER through the mutagenesis of GS3 and TMS5 synergistically,and demonstrated that GS3 had pleiotropic effects on grain size,SER,and grain quality in early-season indica rice.
基金funded by Indian Council of Medical Research India(grant No.NER/55/2015-ECD-I).
文摘Despite a considerable global effort to eradicate malaria in the last few years,the disease burden in 2020 was 241 million,with 627000 deaths.India accounted for 83%of cases reported in the World Health Organization(WHO)South-East Asia Region(WHO malaria report 2021).Nevertheless,India has shown its commitment to eliminating malaria from the country by framing the National Strategic Plan(NSP),according to which the districts have been stratified into four categories based on the malaria transmission intensity and the indicator used for categorization of the districts was annual parasite incidence(API).In North-East India,many highly endemic districts(API>2)are in the“Intensified Control Phase”and fall under category栿[1].A few pockets in such districts have disproportionate(API>10)due to forest and tribal dominance in those regions.The previous reports demonstrated a high malaria burden(API>10)in the district East Garo Hills[2];however,a few pockets of the district Udalguri are malaria hotspots with the API>5[3].
基金supported by The Third Comprehensive Scientific Investigation Project in Xinjiang(2021XJKK0900).
文摘As the core of leaf functional traits,the trade-off relationship between the petiole and lamina expresses the plant's adaptability to the environment in terms of support structure and photosynthesis.We investigated the proportions of allometric growth in the relationship between the petiole and the lamina of broadleaf woody plants in temperate highland Tianshan Mountains montane forests through three dimensions(length,area,and mass),including the length of the lamina(LL)and the length of the petiole(PL),and the area of the lamina(LA)and petiole cross sectional area(PCA)versus the mass of the lamina(LM)and the mass of the petiole(PM),as well as exploring the characteristics of the variance in response to seasonal changes.We found that the functional traits in all three dimensions showed a clear convergent evolution as the seasons progressed,that is,a“seasonal effect”of increasing and then decreasing.The effect of the petioleelamina relationship under spring was minimal in the area dimension;the effects of the threeedimensional relationships of the traits were all highest in summer,and the effect of the petioleelamina relationship was lower in autumn.We also found that petiole traits are simultaneously and multiply affected by lamina traits,with LA and LM having additional effects on the length/mass and area dimensions,respectively.Compared to tree species,shrub species significantly require more light intensity and support capacity.Compound-leaved plants would invest more in photoluminescence,increasing leaf light capture efficiency and static load and dynamic resistance.Our results suggest that plants have rather complex trade-off mechanisms at the leaf level influencing their ability to adapt to the environment,emphasize the need for leaf-level studies on the relationships between functional traits in plants,and illustrate the importance of the season as a distinct time scale for plant trade-off mechanisms.
基金partly funded by an NSERC Discovery Grant received by LS Phillmorea UNB University Research Fund grant received by SP Roach
文摘In many songbird species,birdsong features phonological syntax,meaning that the units within their vocal se-quences are ordered in a non-random way that adheres to a rule.While such syntactical patterns have been richly described in many species,comparatively little is known about how those patterns contribute to song achieving its important functions.For each of song’s main functions,territorial defense and mate attraction,evidence of a role for syntax is limited.One species for which syntax has been thoroughly described is the Hermit Thrush(Catharus guttatus),which presents song types from their repertoires in a semi-predictable order and,in doing so,rapidly cycle up and down the frequency spectrum.The objective of the present study was to explore the importance of song syntax in the Hermit Thrush through a within-subject examination of how measures of syntax,such as the predictability of song type order within song sequences,shift over the breeding season.We hypothesized that,if such syntactical characteristics are important to breeding behaviour,they would be most prominent at the start of the breeding season when activity associated with territory establishment and mate attraction is most intense.Analysis revealed that,as predicted,the rigidness of song type ordering within se-quences was highest at the start of the season and declined thereafter.That song type sequences were most predictable at the vitally important early part of the breeding season fit our hypothesis that this aspect of song syntax is important to song’s functions related to territory establishment and/or mate attraction.Future work will clarify whether that role relates to one of song’s two main functions or serves song transmission in some broader way.
基金supported by the National Key R&D Program of China (Grant No.2022YFE0106300)the National Natural Science Foundation of China (Grant Nos.41941009 and 42006191)+2 种基金the China Postdoctoral Science Foundation (Grant No.2023M741526)the Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (Grant Nos.SML2022SP401 and SML2023SP207)the Program of Marine Economy Development Special Fund under Department of Natural Resources of Guangdong Province (Grant No.GDNRC [2022]18)。
文摘The rapidly changing Antarctic sea ice has garnered significant interest. To enhance the prediction skill for sea ice and respond to the Sea Ice Prediction Network-South's latest call, this study presents the reforecast results of Antarctic sea-ice area and extent from December to June of the coming year with a Convolutional Long Short-Term Memory(Conv LSTM)Network. The reforecast experiments demonstrate that Conv LSTM captures the interannual and interseasonal variability of Antarctic sea ice successfully, and performs better than the European Centre for Medium-Range Weather Forecasts. Based on this, we present the prediction from December 2023 to June 2024, indicating that the Antarctic sea ice will remain at lows, but may not create a new record low. This research highlights the promising application of deep learning in Antarctic sea-ice prediction.
基金funded by the Natural Science Foundation of Hainan Province(320QN256 to TW)the High-level Talent Project of the Hainan Natural Science Foundation(322RC661 to TW)+1 种基金the National Natural Science Foundation of China(31860608 to JW)the Specific Research Fund of the Innovation Platform for Academicians of Hainan Province.
文摘Seasonal variation of hearing sensitivity has been observed in many vertebrate groups with obvious vocal behaviors.Circulating hormones,conspecific calling signals,and temperature are potential factors that drive these plasticity patterns.Turtles have a hearing range that appears to be limited to under 1.5 kHz and are often thought to be non-vocal;thus,they are commonly neglected in vocal communication research.In this study,we aimed to determine whether the auditory phenotype exhibits seasonal variation in sensitivity and to analyze the potential factors driving such variation patterns in turtles.We measured hearing sensitivity and sex hormone levels in female(estradiol)and male(testosterone and dihydrotestosterone)Red-eared sliders(Trachemys scripta elegans)during spring and winter.The results showed that auditory brainstem response(ABR)thresholds were significantly lower in spring than in winter at a frequency range of 0.5-0.9 kHz.The hearing-sensitivity bandwidth was wider,and the ABR latency was significantly shorter in spring than in winter.No significant differences were found in estradiol,testosterone,and dihydrotestosterone levels in T.scripta elegans between spring and winter.This study is the first to reveal the seasonal variation of peripheral hearing sensitivity in turtles,a special animal group with limited hearing range and less vocalization.Temperature variations may be used to explain these seasonal effects,but further research is required to confirm our findings.
