The timely and accurate assessment of soil nutrient information is essential for ensuring global food security and sustainable agricultural development.  This study evaluated the individual and fusion performance of mid-infrared (MIR) and portable X-ray fluorescence (pXRF) spectroscopy for predicting selected soil properties.  Four sensor fusion strategies were implemented: direct concatenation (DC), feature-level fusion using stability competitive adaptive reweighted sampling (sCARS) and least absolute shrinkage and selection operator (LASSO) algorithms (sCARS-C and LASSO-C), multi-block fusion via sequential orthogonal partial least squares (SO-PLS), and Granger-Ramanathan model averaging (GRA) method to enhance prediction accuracy for 13 soil properties.  The findings revealed that single sensor models using either MIR or pXRF provided accurate estimations for soil organic matter (SOM), total nitrogen (TN), available phosphorus (AP), calcium (Ca), iron (Fe), manganese (Mn), and pH, but showed limitations for total potassium (TK), magnesium (Mg), copper (Cu), zinc (Zn), available potassium (AK), and total phosphorus (TP).  The DC model significantly improved predictions for Mg (Rp²=0.76, RMSEp=358.76 mg kg^–1, RPDp=2.03) and TK (Rp²=0.75, RMSEp=775.96 mg kg^–1, RPDp=2.00).  The LASSO-C model demonstrated superior prediction accuracy compared to the DC model for AP, AK, TP, Zn, Mn, and Cu, achieving optimal results for AP (Rp²=0.89, RMSEp=21.37 mg kg^–1, RPDp=3.01) and Zn (Rp²=0.80, RMSEp=9.88 mg kg^–1, RPDp=2.32).  This enhancement is attributed to LASSO's effective selection of feature information from the complete MIR and pXRF spectra.  The GRA models achieved the highest prediction accuracy for TP, pH, AK, and Cu, with Rp² values of 0.80, 0.82, 0.82, and 0.65, RMSEp values of 129.21 mg kg^–1, 0.13, 48.38 mg kg^–1, and 3.87 mg kg^–1, and RPDp values of 2.23, 2.34, 2.37, and 1.67, respectively.  For single-sensor applications, MIR spectra are recommended for predicting SOM, TN, and Ca (Rp²≥0.88, RPDp≥2.87), while pXRF is more cost-effective for measuring Ca, Fe, and Mn (Rp²≥0.80, RPDp≥2.22).  This research demonstrates the effectiveness of MIR and pXRF sensor fusion in enhancing soil nutrient assessment accuracy, particularly for available nutrients and micronutrients.

Flavonoid compounds, including anthocyanins and proanthocyanidins, are significant secondary metabolites in plants and play crucial roles in various aspects of plant growth, development, and environmental stress responses. In the present study, we identified a key transcription factor from the NAC family, designated as MdNAC72-like, which had a strong correlation with the anthocyanin content during the apple fruit ripening process. Through techniques including yeast one-hybrid analysis, electrophoretic mobility shift assays, and luciferase reporter assays, we illustrated that MdNAC72-like directly interacts with the promoters of the MdMYB9, MdLAR, and MdUFGT genes. This interaction enhances their transcriptional activity, leading to a favorable impact on the biosynthesis of anthocyanins and proanthocyanidins in plants. Furthermore, utilizing yeast two-hybrid, pull-down, and bimolecular fluorescence complementation assays, we demonstrated that MdERF1B forms an interaction with MdNAC72-like, which in turn augments the transcriptional activation ability of MdNAC72-like on the downstream structural genes MdMYB9, MdLAR, and MdUFGT. In conclusion, MdNAC72-like presents significant research potential, and these results offer a theoretical framework for understanding the regulatory mechanisms governing anthocyanin and proanthocyanidin synthesis in apple.