Woodland strawberry WRKY71 acts as a promoter of flowering via a transcriptional regulatory cascade

The WRKY proteins are a large family of transcription factors that play important roles in stress responses and plant development.However,the roles of most WRKYs in strawberry are not well known.In this study,FvWRKY71 was isolated from the woodland strawberry‘Ruegen’.FvWRKY71 was highly expressed in the shoot apex and red fruit.Subcellular localization analysis showed that FvWRKY71 was located in the nucleus.Transactivation analysis showed that FvWRKY71 presented transcriptional activation activity in yeast.Overexpression of FvWRKY71 in Arabidopsis and woodland strawberry revealed early flowering in the transgenic plants compared with the wild-type control.Gene expression analysis indicated that the transcript levels of the flowering time and development integrator genes AP1,LFY,FT,AGL42,FUL,FPF1,SEP1,SEP2,and SEP3 were increased in FvWRKY71-overexpressing Arabidopsis and strawberry plants compared with the wild-type controls,which may result in accelerated flowering in transgenic plants.Furthermore,FvWRKY71 was proven to directly bind to the W-boxes(TTGACT/C)of the FvFUL,FvSEP1,FvAGL42,FvLFY,and FvFPF1 promoters in vitro and in vivo.Taken together,our results reveal a transcriptional regulatory cascade of FvWRKY71 involved in promoting flowering in woodland strawberry.

Predicting the Reflection Coefficient of a Viscoelastic Coating Containing a Cylindrical Cavity Based on an Artificial Neural Network Model

A cavity viscoelastic structure has a good sound absorption performance and is often used as a reflective baffle or sound absorption cover in underwater acoustic structures.The acoustic performance field has become a key research direction worldwide.Because of the time-consuming shortcomings of the traditional numerical analysis method and the high cost of the experimental method for measuring the reflection coefficient to evaluate the acoustic performance of coatings,this innovative study predicted the reflection coefficient of a viscoelastic coating containing a cylindrical cavity based on an artificial neural network(ANN).First,themapping relationship between the input characteristics and reflection coefficient was analysed.When the elastic modulus and loss factor value were smaller,the characteristics of the reflection coefficient curve were more complicated.These key parameters affected the acoustic performance of the viscoelastic coating.Second,a dataset of the acoustic performance of the viscoelastic coating containing a cylindrical cavity was generated based on the finite elementmethod(FEM),which avoided a large number of repeated experiments.The minmax normalization method was used to preprocess the input characteristics of the viscoelastic coating,and the reflection coefficient was used as the dataset label.The grid search method was used to fine-tune the ANNparameters,and the prediction error was studied based on a 10-fold cross-validation.Finally,the error distributions were analysed.The average root means square error(RMSE)and the mean absolute percentage error(MAPE)predicted by the improved ANN model were 0.298%and 1.711%,respectively,and the Pearson correlation coefficient(PCC)was 0.995,indicating that the improved ANN model accurately predicted the acoustic performance of the viscoelastic coating containing a cylindrical cavity.In practical engineering applications,by expanding the database of the material range,cavity size and backing of the coating,the reflection coefficient of more sound-absorbing layers was evaluated,which is useful for efficiently predicting the acoustic performance of coatings in a specific frequency range and has great application value.

A High-Efficiency Inversion Method for the Material Parameters of an Alberich-Type Sound Absorption Coating Based on a Deep Learning Model

Research on the acoustic performance of an anechoic coating composed of cavities in a viscoelastic material has recently become an area of great interest.Traditional forward research methods are unable to manipulate sound waves accurately and effectively,are difficult to analyse,have time-consuming solution processes,and have large optimization search spaces.To address these issues,this paper proposes a deep learning-based inverse research method to efficiently invert the material parameters of Alberich-type sound absorption coatings and rapidly predict their acoustic performance.First,an autoencoder(AE)model is pretrained to reconstruct the viscoelastic material parameters of an Alberich-type sound absorption coating,the material parameters are extracted into the latent feature space by the encoder,and the decoder model is saved.The internal relationship between the reflection coefficient and latent feature space is trained to establish a multilayer perceptron(MLP).Then,the reflection coefficients in the test set are input to the trained MLP and decoder models to automatically invert the material parameters.The accuracy of the inversion result is 95.08%.Finally,a predictive model is trained to rapidly predict the acoustic performance of the inverted material parameters.The speed of a single test target is 80 times faster than that of the finite element method(FEM).Furthermore,sound absorber material parameters with the best sound absorption performance and a three-band sound absorber are inverted,and their actual sound absorption performance is predicted by the proposed method.The proposed deep learning-based inversion research method provides a solution for low-frequency,wide-band,strong attenuation,and precisely controlled sound waves.It achieves an efficient inversion of material parameters and the rapid forecasting of acoustic performance.The training model can be used for a sound absorbing coating composed of irregular cavities in a viscoelastic material and predict its acoustic performance by only modifying the dataset.

Synergistic extraction of rare earth elements and alumina from coal fly ash by potassium pyrosulfate

Although coal fly ash(CFA)contains a high content of rare earth elements(REEs),the related extraction methods have limitations because of their low efficiencies,high levels of energy consumption,and other drawbacks.To address these problems,in this study,we examined the coextraction of REEs and Al_(2)O_(3)from two types of Al_(2)O_(3)-rich CFA,pulverized CFA(PCFA)and circulating fluidized bed fly ash(CFBFA)using low-temperature calcination in the presence of K_(2)S_(2)O_(7).The total REEs,heavy REEs(HREEs),and light REEs(LREEs)extraction efficiencies were determined using different K_(2)S_(2)O_(7)/Al_(2)O_(3) molar ratios and calcination temperatures and correlated with the Al_(2)O_(3) extraction efficiency using Pearson correlation coefficient analysis.The REEs are concentrated within CFA particles encapsulated in an aluminosilicate glass phase,and the REEs extraction efficiency is related to the form of Al in CFA.The extraction efficiencies of Al_(2)O_(3) and REEs increase as the K_(2)S_(2)O_(7)/Al_(2)O_(3) molar ratio and calcination temperature increase,and the extraction selectivity of the more industrially valuable HREEs from CFBFA is higher.At high K_(2)S_(2)O_(7)/Al_(2)O_(3) molar ratios,the extraction of REEs from PCFA is more efficient than that from CFBFA with the regeneration of the highly active Al-O-Si bonds in CFBFA.The Al_(2)O_(3) extraction efficiencies of PCFA as well as CFBFA correlate strongly with the total REEs,HREEs,and LREEs extraction efficiencies.The developed extraction technology has the potential to promote CFA valorization and expand REEs resources,thus mitigating the bottlenecks of REEs procurement.

煤气化渣改性工艺及吸附Cd^(2+)性能

以固体废弃物煤气化渣(CGS)为材料,通过水热法制备改性煤气化渣(MCGS)吸附材料,并用于吸附Cd^(2+)。由于CGS吸附Cd^(2+)能力较低,利用Box-Behnken响应面模型方法优化改性条件,X射线衍射仪、傅里叶变换红外光谱仪等表征CGS及制备的MCGS的物理化学性质。改性结果表明,MCGS最佳反应条件为碱度6.20%~8.10%、温度102~108℃和时间138~192 min,温度对MCGS吸附Cd^(2+)性能影响最大。改性后Si-O-Si键断裂,MCGS表面含有丰富的孔隙结构,比表面积、孔容和孔径分别为255.08 m^(2)/g,0.24 cm^(3)/g和3.72 nm;吸附结果表明,当Cd^(2+)浓度50 mg/L、MCGS投加量为0.10 g时,Cd^(2+)饱和吸附量为13.96 mg/g;当Cd^(2+)浓度40 mg/L、MCGS投加量为0.20 g时,Cd^(2+)去除率98.08%;MCGS对重金属Cd^(2+)的吸附过程符合准二级动力学模型和Langmuir模型。本研究可为CGS处理含Cd^(2+)废水提供理论依据。

Protective effects of nicotine on gamma-aminobutyricacid neurons and dopaminergic neurons in micewith Parkinson disease

This study aimed to investigate the protective effect of nicotine on dopaminergic neurons and its mechanisms in mice with Parkinson disease(PD)induced by 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine(MPTP).C57BL/6J mice were injected with MPTP for 8 days to establish a PD model.Nicotine was given for 10 days in the nicotine therapeutic group.Animals were examined behaviorally with the pole test and traction test.Tyrosine hydroxylase(TH)andγ-aminobutyric acid(GABA)were determined by using the immunocytochem-istry(ICC)method.The ultrastructural changes of the caudate nucleus(CN)were observed under electron microscopy.The results showed that pretreatment with nicotine could improve the dyskinesia of PD mice markedly.Simultaneously,TH-positive(P<0.01)neurons and GABA-positive(P<0.05)neurons in the nicotine therapeutic group were significantly more than those in the model group.The ultrastructural injury of the nicotine therapeutic group was also ameliorated.Nicotine has protective effects on theγ-aminobutyric acid neurons and dopaminergic neurons in the MPTP-treated mice.