Tarim University

IntroductionPopulus pruinosa is the key foundation tree species in desert riparian forests in arid areas of northwestern China. Timely and accurate monitoring of the physiological status of P. pruinosa is crucial for restoring the damaged ecosystem. Anthocyanins are one of the important physiological indicators that reflect the environmental adaptability of P. pruinosa under stress. Existing studies have extensively applied hyperspectral technology for the quantitative prediction of crop leaf pigments. However, research on hyperspectral prediction of anthocyanin concentration in woody halophytes is still lacking, particularly in the integration of spectral preprocessing, species-specific vegetation index construction, and machine learning modeling.MethodsIn this study, the hyperspectral technology was used to estimate the anthocyanin concentration of P. pruinosa leaves collected in five months (June - October) under five groundwater depth conditions (0-2, 2-4, 4-6, 6-8, and 8-10 m). Based on first-order (FD) and second-order (SD) derivative processing, competitive adaptive reweighted sampling (CARS), https://xueshu.baidu.com/usercenter/paper/show?paperid=bea4d6371f19161f21aac22941cc4408&site=xueshu_se shuffled frog leaping algorithm (SFLA), and recursive feature elimination with cross-validation (RFECV) were used to extract spectral features of P. pruinosa leaves to construct the anthocyanin reflectance index, composite index, difference vegetation index, and normalized anthocyanin reflectance index. After that, the top 10 sets of data with high correlation with anthocyanin concentration were selected from each vegetation index to form a total data set (40 sets in total) for modeling. Twelve models were constructed using support vector machine (SVM) and one-dimensional convolutional neural network (1D-CNN) methods.ResultsThe FD and SD derivative transformations of the spectral reflectance significantly enhanced the correlation with anthocyanin concentration. The feature extraction methods SFLA and RFECV were superior in extracting the bands highly related to anthocyanin concentration, and the vegetation indices constructed based on these two methods had a high correlation with anthocyanin concentration in the red and near-infrared regions. The optimal prediction model was FD-SFLA-SVM (R2 = 0.852, RMSE = 86.851 mg m-2, RPD = 2.596).DiscussionUnlike existing vegetation index-based studies, the research develops a systematic approach to construct vegetation indices and models for estimating the anthocyanin concentration in the woody halophyte P. pruinose in deserts. The research will provide technical support for non-destructive monitoring of the physiological status of P. pruinosa, and also contribute to the restoration of desert riparian ecosystems.

ABSTRACT In this work, we report the hydrothermal synthesis of NiFe2O4 (NFO) nanoparticles as high‐performance pseudocapacitive electrode materials. To engineer well-defined architectures, three different agents such as cetyltrimethylammonium bromide (CTAB), ammonium fluoride (NH4F), and sodium dodecyl sulphate (SDS) are employed . Ni-foam is used as the current collector, on to which the synthesised NFO nanostructures are directly grown . Distinct morphological features are obtained for each surfactant: CTAB produced an urchin-like structure, NH4F yielded a flower-like morphology, and SDS generates a sun coral-like architecture. Among the samples, the urchin-like NFO grown on NF exhibited the highest specific capacitance of 973 F g−1 in 1 M Na2SO4 electrolyte at room temperature, attributed to its larger accessible surface area and efficient ion diffusion pathways. Detailed kinetic analysis demonstrates that the charge storage mechanism is predominantly diffusion-controlled. These findings highlight the crucial role of morphology engineering in optimising the pseudocapacitive performance of NFO-based electrodes.

The jujube (Ziziphus jujuba Mill.) is a significant economic fruit tree, valued for its nutritional and medicinal properties. However, advances in functional genomics are hindered by the lack of an efficient transformation system. To overcome the limitations of conventional explant, we established a fruit-pulp-derived, callus-based Agrobacterium-mediated transformation system using fruit-pulp harvested 50 days after pollination. Through orthogonal experimental design, 6-benzylaminopurine and 2,4-dichlorophenoxyacetic acid were identified as key regulators for inducing high-quality, friable callus in two jujube genotypes, 'JZ60' and 'LWCZ'. This system revealed significant genotype-specific variation in auxin requirements for callus proliferation and in differential antibiotic sensitivity. Transformation efficiency, as evaluated by fluorescence screening, was primarily determined by acetosyringone concentration and the binary vector architecture. The results revealed that the compact pCY (kanamycin resistance) vector achieved higher transformation efficiency (up to 77.8%) than pCAMBIA1301, whereas the pCAMBIA1301 (hygromycin resistance) vector enabled more uniform transgene expression. Integration and expression of the ZjCBF3 transgene were confirmed by polymerase chain reaction (PCR), reverse transcription quantitative PCR, and green fluorescent protein fluorescence assays. This study established a fruit-pulp-based callus transformation system for jujube, providing a rapid platform for its functional genomic studies.