Background: Salt stress hinders the growth of plants and reduces crop production worldwide. However, different plant species might possess different adaptive mechanisms to mitigate salt stress. We conducted a detailed pathway analysis of transcriptional dynamics in the roots of Medicago truncatula seedlings under salt stress and selected a transcription factor gene, MtCBF4, for experimental validation. Results: A microarray experiment was conducted using root samples collected 6, 24, and 48 h after application of 180 mM NaCl. Analysis of 11 statistically significant expression profiles revealed different behaviors between primary and secondary metabolism pathways in response to external stress. Secondary metabolism that helps to maintain osmotic balance was induced. One of the highly induced transcription factor genes was successfully cloned, and was named MtCBF4. Phylogenetic analysis revealed that MtCBF4, which belongs to the AP2-EREBP transcription factor family, is a novel member of the CBF transcription factor in M. truncatula. MtCBF4 is shown to be a nuclear-localized protein. Expression of MtCBF4 in M. truncatula was induced by most of the abiotic stresses, including salt, drought, cold, and abscisic acid, suggesting crosstalk between these abiotic stresses. Transgenic Arabidopsis over-expressing MtCBF4 enhanced tolerance to drought and salt stress, and activated expression of downstream genes that contain DRE elements. Over-expression of MtCBF4 in M. truncatula also enhanced salt tolerance and induced expression level of corresponding downstream genes. Conclusion: Comprehensive transcriptomic analysis revealed complex mechanisms exist in plants in response to salt stress. The novel transcription factor gene MtCBF4 identified here played an important role in response to abiotic stresses, indicating that it might be a good candidate gene for genetic improvement to produce stress-tolerant plants.

A sensitive and selective chemiluminescence (CL) sensor based on the peroxidase-like activity of copper nanoclusters was established for the detection of cholesterol. Copper nanoclusters catalyse the CL reaction between luminol and H₂O₂. Because H₂O₂ is the oxidative product of cholesterol in the presence of cholesterol oxidase, the oxidation of cholesterol can be quantitatively converted to a CL response by combining the two reactions. The proposed method is simple and can be completed in a few minutes with high sensitivity. Under the optimal conditions, the CL intensity was proportional to the concentration of cholesterol over a wide range of 0.05–10 mM, with a detection limit of 1.5 μM. Furthermore, the method was successfully applied to determine cholesterol in milk powder and human serum with satisfactory accuracy and precision. This method expands the applications of nano-mimic enzymes in the field of CL-based sensors.

Angelica L. has attracted global interest for its traditional medicinal uses and commercial values. However, few studies have focused on the metabolomic differences among the Angelica species. In this study, employing the widely targeted metabolomics based on gas chromatography-tandem mass spectrometry, the metabolomes of four Angelica species were analyzed ( Angelica sinensis (Oliv.) Diels ( A. sinensis ), Angelica biserrata (R.H.Shan & Yuan) C.Q.Yuan & R.H.Shan ( A. biserrata ), Angelica dahurica (Hoffm.) Benth. & Hook.f. ex Franch. & Sav. ( A. dahurica ), Angelica keiskei Koidz. ( A. keiskei )). A total of 698 volatile metabolites were identified and classified into fifteen different categories. The metabolomic analysis indicated that 7-hydroxycoumarin and Z-ligustilide were accumulated at significantly higher levels in A. sinensis , whereas the opposite pattern was observed for bornyl acetate. In addition, a high correspondence between the dendrogram of metabolite contents and phylogenetic positions was detected in the four species. This study provides a biochemical map for the exploitation, application and development of the Angelica species as medicinal plants or health-related dietary supplements.

Background: The geo-traceability of cotton is crucial for ensuring the quality and integrity of cotton brands. However, effective methods for achieving this traceability are currently lacking. This study investigates the potential of explainable machine learning for the geo-traceability of raw cotton. Results: The findings indicate that principal component analysis (PCA) exhibits limited effectiveness in tracing cotton origins. In contrast, partial least squares discriminant analysis (PLS-DA) demonstrates superior classification performance, identifying seven discriminating variables: Na, Mn, Ba, Rb, Al, As, and Pb. The use of decision tree (DT), support vector machine (SVM), and random forest (RF) models for origin discrimination yielded accuracies of 90%, 87%, and 97%, respectively. Notably, the light gradient boosting machine (LightGBM) model achieved perfect performance metrics, with accuracy, precision, and recall rate all reaching 100% on the test set. The output of the LightGBM model was further evaluated using the SHapley Additive exPlanation (SHAP) technique, which highlighted differences in the elemental composition of raw cotton from various countries. Specifically, the elements Pb, Ni, Na, Al, As, Ba, and Rb significantly influenced the model's predictions. Conclusion: These findings suggest that explainable machine learning techniques can provide insights into the complex relationships between geographic information and raw cotton. Consequently, these methodologies enhances the precision and reliability of geographic traceability for raw cotton.