Effects of polyamines on the early development of cystocarps,spore release,survival,and germination rate of Gracilariopsis lemaneiformis

Three polyamines(PAs)(spermidine(Spd),spermine(Spm),and putrescine(Put))were used as growth regulators to the marine macroalgae Gracilariopsis lemaneiformis and to female gametophyte and reproductive tissues(cystocarps were constructed),which led to early maturation of cystocarps and spore release.Laboratory cultivation revealed that exogenous PA application accelerated the development of cystocarps,and the mean sporeling growth rate was 20%–50%/d during the initial week of cultivation.The spore count(SC),survival rate(SR),and germination rate(GR)of G.lemaneiformis showed different responses to PA(10-6 mol/L and 10-3 mol/L)treatments under different temperatures(18,26,and 34℃),light intensities(30,60,and 90μmol photons/(m^(2)·s)),salinities(25,30,and 35),and nutrient levels(25꞉2.5,50꞉5.0,and 75꞉7.5μmol/L NH4Cl꞉K2HPO4),and the optimal conditions were 26℃,light intensity of 60μmol/(m^(2)·s),salinity of 35,and nutrient level of 50꞉5.0μmol/L NH4Cl꞉K2HPO4.Under the optimum conditions,the SC per cystocarp increased by 82.38%,and the spore SR and GR increased by 33.07%and 46.44%,respectively.Each experiment lasted for 7 days,with the highest SC on Day 4 and plateauing on Day 7.The results indicate that PAs could promote the early maturation,spore release,survival,and germination of G.lemaneiformis under laboratory conditions.

Pectin methylesterase inhibitors GhPMEI53 and AtPMEI19 improve seed germination by modulating cell wall plasticity in cotton and Arabidopsis

The germination process of seeds is influenced by the interplay between two opposing factors,pectin methylesterase(PME)and pectin methylesterase inhibitor(PMEI),which collectively regulate patterns of pectin methylesterification.Despite the recognized importance of pectin methylesterification in seed germination,the specific mechanisms that govern this process remain unclear.In this study,we demonstrated that the overexpression of GhPMEI53is associated with a decrease in PME activity and an increase in pectin methylesterification.This leads to seed cell wall softening,which positively regulates cotton seed germination.AtPMEI19,the homologue in Arabidopsis thaliana,plays a similar role in seed germination to GhPMEI53,indicating a conserved function and mechanism of PMEI in seed germination regulation.Further studies revealed that GhPMEI53 and AtPMEI19 directly contribute to promoting radicle protrusion and seed germination by inducing cell wall softening and reducing mechanical strength.Additionally,the pathways of abscicic acid(ABA)and gibberellin(GA)in the transgenic materials showed significant changes,suggesting that GhPMEI53/AtPMEI19-mediated pectin methylesterification serves as a regulatory signal for the related phytohormones involved in seed germination.In summary,GhPMEI53 and its homologs alter the mechanical properties of cell walls,which influence the mechanical resistance of the endosperm or testa.Moreover,they impact cellular phytohormone pathways(e.g.,ABA and GA)to regulate seed germination.These findings enhance our understanding of pectin methylesterification in cellular morphological dynamics and signaling transduction,and contribute to a more comprehensive understanding of the PME/PMEI gene superfamily in plants.

Prediction model for corrosion rate of low-alloy steels under atmospheric conditions using machine learning algorithms

This work constructed a machine learning(ML)model to predict the atmospheric corrosion rate of low-alloy steels(LAS).The material properties of LAS,environmental factors,and exposure time were used as the input,while the corrosion rate as the output.6 dif-ferent ML algorithms were used to construct the proposed model.Through optimization and filtering,the eXtreme gradient boosting(XG-Boost)model exhibited good corrosion rate prediction accuracy.The features of material properties were then transformed into atomic and physical features using the proposed property transformation approach,and the dominant descriptors that affected the corrosion rate were filtered using the recursive feature elimination(RFE)as well as XGBoost methods.The established ML models exhibited better predic-tion performance and generalization ability via property transformation descriptors.In addition,the SHapley additive exPlanations(SHAP)method was applied to analyze the relationship between the descriptors and corrosion rate.The results showed that the property transformation model could effectively help with analyzing the corrosion behavior,thereby significantly improving the generalization ability of corrosion rate prediction models.

Pulse rate estimation based on facial videos:an evaluation and optimization of the classical methods using both self-constructed and public datasets

Pulse rate is one of the important characteristics of traditional Chinese medicine pulse diagnosis,and it is of great significance for determining the nature of cold and heat in diseases.The prediction of pulse rate based on facial video is an exciting research field for getting palpation information by observation diagnosis.However,most studies focus on optimizing the algorithm based on a small sample of participants without systematically investigating multiple influencing factors.A total of 209 participants and 2,435 facial videos,based on our self-constructed Multi-Scene Sign Dataset and the public datasets,were used to perform a multi-level and multi-factor comprehensive comparison.The effects of different datasets,blood volume pulse signal extraction algorithms,region of interests,time windows,color spaces,pulse rate calculation methods,and video recording scenes were analyzed.Furthermore,we proposed a blood volume pulse signal quality optimization strategy based on the inverse Fourier transform and an improvement strategy for pulse rate estimation based on signal-to-noise ratio threshold sliding.We found that the effects of video estimation of pulse rate in the Multi-Scene Sign Dataset and Pulse Rate Detection Dataset were better than in other datasets.Compared with Fast independent component analysis and Single Channel algorithms,chrominance-based method and plane-orthogonal-to-skin algorithms have a more vital anti-interference ability and higher robustness.The performances of the five-organs fusion area and the full-face area were better than that of single sub-regions,and the fewer motion artifacts and better lighting can improve the precision of pulse rate estimation.

Dynamic interactive bitwise meta-holography with ultra-high computational and display frame rates

Interactive holography offers unmatched levels of immersion and user engagement in the field of future display.Despite of the substantial progress has been made in dynamic meta-holography,the realization of real-time,highly smooth interactive holography remains a significant challenge due to the computational and display frame rate limitations.In this study,we introduced a dynamic interactive bitwise meta-holography with ultra-high computational and display frame rates.To our knowledge,this is the first reported practical dynamic interactive metasurface holographic system.We spa-tially divided the metasurface device into multiple distinct channels,each projecting a reconstructed sub-pattern.The switching states of these channels were mapped to bitwise operations on a set of bit values,which avoids complex holo-gram computations,enabling an ultra-high computational frame rate.Our approach achieves a computational frame rate of 800 kHz and a display frame rate of 23 kHz on a low-power Raspberry Pi computational platform.According to this methodology,we demonstrated an interactive dynamic holographic Tetris game system that allows interactive gameplay,color display,and on-the-fly hologram creation.Our technology presents an inspiration for advanced dynamic meta-holography,which is promising for a broad range of applications including advanced human-computer interaction,real-time 3D visualization,and next-generation virtual and augmented reality systems.

Substrate,Hormone,Winnowing,and Stratification Influence the Seed Germination of Ilex asprella(Hook.et Arn.)Champ.ex Benth

Ilex asprella(Hook.et Arn.)Champ.ex Benth is one of the most important traditional Chinese medicines in southern China.The seeds of Ilex asprella usually have extremely low germination due to their dormancy characteristics,which severely impacts the efficiency of seedling raising and increases labor costs.In this study,to improve the seed germination of I.asprella,the effects of germination substrate,hormone,winnowing,and stratification treatments on the seed germination of I.asprella were investigated.The results of the germination matrix showed that the highest germination percentage of 45.2%was achieved under the 20℃/10℃day/night temperature and vermiculite germination medium conditions.The results of hormone treatments revealed that 100–400 mg/L of gibberellin(GA)and 50–100 mg/L of salicylic acid(SA)were found to be effective in releasing the dormancy of I.asprella seeds.Moreover,winnowing could effectively eliminate unsaturated seeds and impurities,thus improving the seed germination of I.asprella.Furthermore,warm temperature(15℃)stratification could expand the temperature range of I.asprella’s seed germination,which was beneficial for seed germination of I.asprella and for seed nursery at room temperature in production practice.The present study obtained a method to break dormancy and increase seed germination in I.asprella,thereby forming a groundwork for improving the efficiency of large-scale planting of I.asprella.

The Probability Density Function Related to Shallow Cumulus Entrainment Rate and Its Influencing Factors in a Large-Eddy Simulation

The process of entrainment-mixing between cumulus clouds and the ambient air is important for the development of cumulus clouds.Accurately obtaining the entrainment rate(λ)is particularly important for its parameterization within the overall cumulus parameterization scheme.In this study,an improved bulk-plume method is proposed by solving the equations of two conserved variables simultaneously to calculateλof cumulus clouds in a large-eddy simulation.The results demonstrate that the improved bulk-plume method is more reliable than the traditional bulk-plume method,becauseλ,as calculated from the improved method,falls within the range ofλvalues obtained from the traditional method using different conserved variables.The probability density functions ofλfor all data,different times,and different heights can be well-fitted by a log-normal distribution,which supports the assumed stochastic entrainment process in previous studies.Further analysis demonstrate that the relationship betweenλand the vertical velocity is better than other thermodynamic/dynamical properties;thus,the vertical velocity is recommended as the primary influencing factor for the parameterization ofλin the future.The results of this study enhance the theoretical understanding ofλand its influencing factors and shed new light on the development ofλparameterization.

Experimental study on the effect of unloading rate on gneiss rockburst

Rockburst are often encountered in tunnel construction due to the complex geological conditions.To study the influence of unloading rate on rockburst,gneiss rockburst experiments were conducted under three groups of unloading rates.A high-speed photography system and acoustic emission(AE)system were used to monitor the entire process of rockburst process in real-time.The results show that the intensity of gneiss rockburst decreases with decrease of unloading rate,which is manifested as the reduction of AE energy and fragments ejection velocity.The mechanisms are proposed to explain this effect:(i)The reduction of unloading rate changes the crack propagation mechanism in the process of rockburst.This makes the rockbursts change from the tensile failure mechanism at high unloading rate to the tension-shear mixed failure mechanism at low unloading rate,and more energy released in the form of shear crack propagation.Then,less strain energy is converted into kinetic energy of fragments ejection.(ii)Less plate cracking degree of gneiss has taken shape due to decrease of unloading rate,resulting in the destruction of rockburst incubation process.The enlightenments of reducing the unloading rate for the project are also described quantitatively.The rockburst magnitude is reduced from the medium magnitude at the unloading rate of 0.1 MPa/s to the slight magnitude at the unloading rate of 0.025 MPa/s,which was judged by the ejection velocity.

A new era of mutation rate analyses: Concepts and methods

The mutation rate is a pivotal biological characteristic,intricately governed by natural selection and historically garnering considerable attention.Recent advances in high-throughput sequencing and analytical methodologies have profoundly transformed our understanding in this domain,ushering in an unprecedented era of mutation rate research.This paper aims to provide a comprehensive overview of the key concepts and methodologies frequently employed in the study of mutation rates.It examines various types of mutations,explores the evolutionary dynamics and associated theories,and synthesizes both classical and contemporary hypotheses.Furthermore,this review comprehensively explores recent advances in understanding germline and somatic mutations in animals and offers an overview of experimental methodologies,mutational patterns,molecular mechanisms,and driving forces influencing variations in mutation rates across species and tissues.Finally,it proposes several potential research directions and pressing questions for future investigations.

Present-day Upper-crustal Strain Rate Field in Southeastern Tibet and its Geodynamic Implications:Constraints from GPS Measurements with ABIC Method

The Earth’s surface kinematics and deformation are fundamental to understanding crustal evolution.An effective research approach is to estimate regional motion field and deformation fields based on modern geodetic networks.If the discrete observed velocity field is obtained,the velocity related fields,such as dilatation rate and maximum shear strain rate,can be estimated by applying varied mathematical approaches.This study applied Akaike's Bayesian Information Criterion(ABIC)method to calculate strain rate fields constrained by GPS observations in the southeast Tibetan Plateau.Comparison with results derived from other three methods revealed that our ABIC-derived strain rate fields were more precise.The maximum shear strain rate highlighted the Xianshuihe–Xiaojiang fault system as the main boundary for the outward migration of material in southeastern Tibet,indicating rotation of eastern Tibet material around the eastern Himalaya rather than whole extrusion along a fixed channel.Additionally,distinct dilatation rate patterns in the northeast and southwest regions of the fault system were observed.The northeast region,represented by the Longmenshan area,exhibited negative dilatational anomalies;while the southwest region,represented by the Jinsha River area north of 29°N,displayed positive dilatational anomalies.This indicates compression in the former and extension in the latter.Combined with deep geophysical observations,we believe that the upper and lower crusts of the Jinsha River area north of 29°N are in an entire expanding state,probably caused by the escape-drag effect of material.The presence of a large,low-viscosity region south of 29°N may not enable the entire escape of the crust,but instead result in a differential escape of the lower crust faster than the upper crust.

Federated Learning Model for Auto Insurance Rate Setting Based on Tweedie Distribution

In the assessment of car insurance claims,the claim rate for car insurance presents a highly skewed probability distribution,which is typically modeled using Tweedie distribution.The traditional approach to obtaining the Tweedie regression model involves training on a centralized dataset,when the data is provided by multiple parties,training a privacy-preserving Tweedie regression model without exchanging raw data becomes a challenge.To address this issue,this study introduces a novel vertical federated learning-based Tweedie regression algorithm for multi-party auto insurance rate setting in data silos.The algorithm can keep sensitive data locally and uses privacy-preserving techniques to achieve intersection operations between the two parties holding the data.After determining which entities are shared,the participants train the model locally using the shared entity data to obtain the local generalized linear model intermediate parameters.The homomorphic encryption algorithms are introduced to interact with and update the model intermediate parameters to collaboratively complete the joint training of the car insurance rate-setting model.Performance tests on two publicly available datasets show that the proposed federated Tweedie regression algorithm can effectively generate Tweedie regression models that leverage the value of data fromboth partieswithout exchanging data.The assessment results of the scheme approach those of the Tweedie regressionmodel learned fromcentralized data,and outperformthe Tweedie regressionmodel learned independently by a single party.

A performance-based hybrid deep learning model for predicting TBM advance rate using Attention-ResNet-LSTM

The technology of tunnel boring machine(TBM)has been widely applied for underground construction worldwide;however,how to ensure the TBM tunneling process safe and efficient remains a major concern.Advance rate is a key parameter of TBM operation and reflects the TBM-ground interaction,for which a reliable prediction helps optimize the TBM performance.Here,we develop a hybrid neural network model,called Attention-ResNet-LSTM,for accurate prediction of the TBM advance rate.A database including geological properties and TBM operational parameters from the Yangtze River Natural Gas Pipeline Project is used to train and test this deep learning model.The evolutionary polynomial regression method is adopted to aid the selection of input parameters.The results of numerical exper-iments show that our Attention-ResNet-LSTM model outperforms other commonly-used intelligent models with a lower root mean square error and a lower mean absolute percentage error.Further,parametric analyses are conducted to explore the effects of the sequence length of historical data and the model architecture on the prediction accuracy.A correlation analysis between the input and output parameters is also implemented to provide guidance for adjusting relevant TBM operational parameters.The performance of our hybrid intelligent model is demonstrated in a case study of TBM tunneling through a complex ground with variable strata.Finally,data collected from the Baimang River Tunnel Project in Shenzhen of China are used to further test the generalization of our model.The results indicate that,compared to the conventional ResNet-LSTM model,our model has a better predictive capability for scenarios with unknown datasets due to its self-adaptive characteristic.

Effects of Scarification and Substrates on Seed Germination Quality for Domestication of Stereospermum kunthianum(Bignoniaceae)

The demand for medicinal plants at the origin of phyto-drugs,like all ethnobotanical plants,threatens their existence today.The aim of this work was to determine suitable methods for optimizing the possibilities of regenerating Stereospermum kunthianum.To achieve this objective,3 levels of mechanical scarification(light scarification,medium scarification and deep scarification)were applied to the seeds of this species using a rough instrument,and 5 days after germination,the seedlings were transplanted into 5 types of substrate to study their effects on seedling growth.The length of the seedlings was measured using a graduated ruler.The results obtained showed that the best latency time was 2 days for average seed scarifications.The highest germination half-time was 4.00±0.06 days observed for average seed scarification,the highest germination speed was 22.50±1.00 and the highest germination rate was 100.00%±0.00%for average scarification.The homogeneous substrate mixes black earth/woodsilk and Fine sand/woodsilk were more effective for seedling growth(1.9 cm/day in the juvenile state)and development.Medium seed scarification is the most effective method for regenerating Stereospermum kunthianum,and the black earth/wood shavings substrate mix is the best for its cultivation.

Construction of a Computational Scheme for the Fuzzy HIV/AIDS Epidemic Model with a Nonlinear Saturated Incidence Rate

This work aimed to construct an epidemic model with fuzzy parameters.Since the classical epidemic model doesnot elaborate on the successful interaction of susceptible and infective people,the constructed fuzzy epidemicmodel discusses the more detailed versions of the interactions between infective and susceptible people.Thenext-generation matrix approach is employed to find the reproduction number of a deterministic model.Thesensitivity analysis and local stability analysis of the systemare also provided.For solving the fuzzy epidemic model,a numerical scheme is constructed which consists of three time levels.The numerical scheme has an advantage overthe existing forward Euler scheme for determining the conditions of getting the positive solution.The establishedscheme also has an advantage over existing non-standard finite difference methods in terms of order of accuracy.The stability of the scheme for the considered fuzzy model is also provided.From the plotted results,it can beobserved that susceptible people decay by rising interaction parameters.

Machine learning-based comparison of factors influencing estimated glomerular filtration rate in Chinese women with or without nonalcoholic fatty liver

BACKGROUND The prevalence of non-alcoholic fatty liver(NAFLD)has increased recently.Subjects with NAFLD are known to have higher chance for renal function impairment.Many past studies used traditional multiple linear regression(MLR)to identify risk factors for decreased estimated glomerular filtration rate(eGFR).However,medical research is increasingly relying on emerging machine learning(Mach-L)methods.The present study enrolled healthy women to identify factors affecting eGFR in subjects with and without NAFLD(NAFLD+,NAFLD-)and to rank their importance.AIM To uses three different Mach-L methods to identify key impact factors for eGFR in healthy women with and without NAFLD.METHODS A total of 65535 healthy female study participants were enrolled from the Taiwan MJ cohort,accounting for 32 independent variables including demographic,biochemistry and lifestyle parameters(independent variables),while eGFR was used as the dependent variable.Aside from MLR,three Mach-L methods were applied,including stochastic gradient boosting,eXtreme gradient boosting and elastic net.Errors of estimation were used to define method accuracy,where smaller degree of error indicated better model performance.RESULTS Income,albumin,eGFR,High density lipoprotein-Cholesterol,phosphorus,forced expiratory volume in one second(FEV1),and sleep time were all lower in the NAFLD+group,while other factors were all significantly higher except for smoking area.Mach-L had lower estimation errors,thus outperforming MLR.In Model 1,age,uric acid(UA),FEV1,plasma calcium level(Ca),plasma albumin level(Alb)and T-bilirubin were the most important factors in the NAFLD+group,as opposed to age,UA,FEV1,Alb,lactic dehydrogenase(LDH)and Ca for the NAFLD-group.Given the importance percentage was much higher than the 2nd important factor,we built Model 2 by removing age.CONCLUSION The eGFR were lower in the NAFLD+group compared to the NAFLD-group,with age being was the most important impact factor in both groups of healthy Chinese women,followed by LDH,UA,FEV1 and Alb.However,for the NAFLD-group,TSH and SBP were the 5th and 6th most important factors,as opposed to Ca and BF in the NAFLD+group.

Prediction of corrosion rate for friction stir processed WE43 alloy by combining PSO-based virtual sample generation and machine learning

The corrosion rate is a crucial factor that impacts the longevity of materials in different applications.After undergoing friction stir processing(FSP),the refined grain structure leads to a notable decrease in corrosion rate.However,a better understanding of the correlation between the FSP process parameters and the corrosion rate is still lacking.The current study used machine learning to establish the relationship between the corrosion rate and FSP process parameters(rotational speed,traverse speed,and shoulder diameter)for WE43 alloy.The Taguchi L27 design of experiments was used for the experimental analysis.In addition,synthetic data was generated using particle swarm optimization for virtual sample generation(VSG).The application of VSG has led to an increase in the prediction accuracy of machine learning models.A sensitivity analysis was performed using Shapley Additive Explanations to determine the key factors affecting the corrosion rate.The shoulder diameter had a significant impact in comparison to the traverse speed.A graphical user interface(GUI)has been created to predict the corrosion rate using the identified factors.This study focuses on the WE43 alloy,but its findings can also be used to predict the corrosion rate of other magnesium alloys.

A comparative study on the role of conventional,chemical,and nanopriming for better salt tolerance during seed germination of direct seeding rice

Salinity is one of the most significant risks to crop production and food security as it harms plant physiology and biochemistry.The salt stress during the rice emergence stages severely hampers the seed germination and seedling growth of direct-seeded rice.Recently,nanoparticles(NPs)have been reported to be effectively involved in many plant physiological processes,particularly under abiotic stresses.To our knowledge,no comparative studies have been performed to study the efficiency of conventional,chemical,and seed nanopriming for better plant stress tolerance.Therefore,we conducted growth chamber and field experiments with different salinity levels(0,1.5,and 3‰),two rice varieties(CY1000 and LLY506),and different priming techniques such as hydropriming,chemical priming(ascorbic acid,salicylic acid,and γ-aminobutyric acid),and nanopriming(zinc oxide nanoparticles).Salt stress inhibited rice seed germination,germination index,vigor index,and seedling growth.Also,salt stress increased the over accumulation of reactive oxygen species(H_(2)O_(2) and O_(2)^(-)·)and malondialdehyde(MDA)contents.Furthermore,salt-stressed seedlings accumulated higher sodium(Na^(+))ions and significantly lower potassium(K^(+))ions.Moreover,the findings of our study demonstrated that,among the different priming techniques,seed nanopriming with zinc oxide nanoparticles(NanoZnO)significantly contributed to rice salt tolerance.ZnO nanopriming improved rice seed germination and seedling growth in the pot and field experiments under salt stress.The possible mechanism behind ZnO nanopriming improved rice salt tolerance included higher contents of α-amylase,soluble sugar,and soluble protein and higher activities of antioxidant enzymes to sustain better seed germination and seedling growth.Moreover,another mechanism of ZnO nanopriming induced rice salt tolerance was associated with better maintenance of(K^(+))ions content.Our research concluded that NanoZnO could promote plant salt tolerance and be adopted as a practical nanopriming technique,promoting global crop production in saltaffected agricultural lands.

Arginine promotes seed energy metabolism,increasing wheat seed germination at low temperature

Low temperatures during germination inhibit seed growth,lead to small and weak seedlings,and significantly reduce the wheat yield.Alleviating the adverse effects of low temperature on wheat seed germination is highly important for achieving high and stable wheat yields.In this study,Tongmai 6(insensitive)and Zhengmai 113(sensitive),which have different low-temperature sensitivities during germination were treated with low temperature during germination.The transcriptome,metabolome and physiological data revealed that low temperature decreased the germination rate,downregulated the expression of a large number of genes involved in regulating glycometabolism,and inhibited carbon,nitrogen(especially amino acids)and energy metabolism in the seeds.Arginine content increased at low temperature,and its increase in the low-temperature-tolerant variety was significantly greater than that in the sensitive variety.Arginine priming experiment showed that treatment with an appropriate concentration of arginine improved the seed germination rate.The conversion of starch to soluble sugar significantly increased under exogenous arginine conditions,the content of key metabolites in energy metabolism increased,and the utilization of ATP in the seeds increased.Taken together,arginine priming increased seed germination at low temperature by relieving inhibition of seed carbon and nitrogen metabolism and improving seed energy metabolism.

Fluorinated semi-interpenetrating polymer networks for enhancing the mechanical performance and storage stability of polymer-bonded explosives by controlling curing and phase separation rates

Herein, the effect of fluoropolymer binders on the properties of polymer-bonded explosives(PBXs) was comprehensively investigated. To this end, fluorinated semi-interpenetrating polymer networks(semiIPNs) were prepared using different catalyst amounts(denoted as F23-CLF-30-D). The involved curing and phase separation processes were monitored using Fourier-transform infrared spectroscopy, differential scanning calorimetry, a haze meter and a rheometer. Curing rate constant and activation energy were calculated using a theoretical model and numerical method, respectively. Results revealed that owing to its co-continuous micro-phase separation structure, the F23-CLF-30-D3 semi-IPN exhibited considerably higher tensile strength and elongation at break than pure fluororubber F2314 and the F23-CLF-30-D0 semi-IPN because the phase separation and curing rates matched in the initial stage of curing.An arc Brazilian test revealed that F23-CLF-30-D-based composites used as mock materials for PBXs exhibited excellent mechanical performance and storage stability. Thus, the matched curing and phase separation rates play a crucial role during the fabrication of high-performance semi-IPNs;these factors can be feasibly controlled using an appropriate catalyst amount.

Genome-wide association study reveals that JASMONATE ZIM-DOMAIN 5 regulates seed germination in rice

Seed germination is a complex trait regulated by multiple genes in rice.However,the regulators of rice seed germination have yet to be sufficiently determined.Here,a quantitative trait locus(QTL)for rice seed germination was identified in a genome-wide association study.The candidate gene JASMONATE ZIM-DOMAIN 5(OsJAZ5)of the QTL was verified that positively regulates seed germination.OsJAZ5 regulation of seed germination involves an OsABI3-mediated abscisic acid pathway.Overexpression of OsJAZ5 facilitated seed germination.The application of OsJAZ5 might be useful for increasing seed germination for rice direct seeding.