Physiological and metabolomic analyses reveal that Fe3O4 nanoparticles ameliorate cadmium and arsenic toxicity in Panax notoginseng.

Heavy metal(loid)-contaminated available arable land seriously affects crop development and growth. Engineered nanomaterials have great potential in mitigating toxic metal(loid) stress in plants. However, there are few details of nanoparticles (NPs) involved in Panax notoginseng response to cadmium (Cd) and arsenic (As). Herein, integrating physiological and metabolomic analyses, we investigated the effects of Fe3O4 NPs on plant growth and Cd/As responses in P. notoginseng. Cd/As treatment caused severe growth inhibition. However, foliar application of Fe3O4 NPs increased beneficial elements in the roots and/or leaves, decreased Cd/As content by 10.38% and 20.41% in the roots, reduced membrane damage and regulated antioxidant enzyme activity, thereby alleviating Cd/As-induced growth inhibition, as indicated by increased shoot fresh weight (FW), the rootlet length and root FW by 40.14%, 15.74%, and 46.70% under Cd stress and promoted the shoot FW by 27.00% under As toxicity. Metabolomic analysis showed that 227 and 295 differentially accumulated metabolites (DAMs) were identified, and their accumulation patterns were classified into 8 and 6 clusters in the roots and leaves, respectively. Fe3O4 NPs altered metabolites significantly involved in key pathways, including amino sugar and nucleotide sugar metabolism, flavonoid biosynthesis and phenylalanine metabolism, thus mediating the trade-off between plant growth and defense under stress. Interestingly, Fe3O4 NPs recovered more Cd/As-induced DAMs to normal levels, further supporting that Fe3O4 NPs positively affected seedling growth under metal(loid)s stress. In addition, Fe3O4 NPs altered terpenoids when the seedlings were subjected to Cd/As stress, thus affecting their potential medicinal value. This study provides insights into using nanoparticles to improve potential active ingredients of medicinal plants in metal(loid)-contaminated areas.

Effect of Nitrogen Addition on Selection of Germination Trait in an Alpine Meadow on the Tibet Plateau.

Seed germination requirements may determine the kinds of habitat in which plants can survive. We tested the hypothesis that nitrogen (N) addition can change seed germination trait-environmental filter interactions and ultimately redistribute seed germination traits in alpine meadows. We determined the role of N addition on germination trait selection in an alpine meadow after N addition by combining a 3-year N addition experiment in an alpine meadow and laboratory germination experiments. At the species level, germination percentage, germination rate (speed) and breadth of temperature niche for germination (BTN) were positively related to survival of a species in the fertilized community. In addition, community-weighted means of germination percentage, germination rate, germination response to alternating temperature and BTN increased. However, germination response to wet-cold storage (cold stratification) and functional richness of germination traits was lower in alpine meadows with high-nitrogen addition than in those with no, low and medium N addition. Thus, N addition had a significant influence on environmental filter-germination trait interactions and generated a different set of germination traits in the alpine meadow. Further, the effect of N addition on germination trait selection by environmental filters was amount-dependent. Low and medium levels of N addition had less effect on redistribution of germination traits than the high level.

Impacts of growth form and phylogenetic relatedness on seed germination: A large-scale analysis of a subtropical regional flora.

Plant regeneration strategy plays a critical role in species survival and can be used as a proxy for the evolutionary response of species to climate change. However, information on the effects of key plant traits and phylogenetic relatedness on seed germination is limited at large regional scales that vary in climate. To test the hypotheses that phylogenetic niche conservatism plays a critical force in shaping seed ecophysiological traits across species, and also drives their response to climatic fluctuation, we conducted a controlled experiment on seed germination and determined the percentage and rate of germination for 249 species in subtropical China under two temperature regimes (i.e., daily 25°C; daily alternating 25/15°C for each 12 hr). Germination was low with a skewed distribution (mean = 38.9% at 25°C, and 43.3% at 25/15°C). One fifth of the species had low (<10%) and slow (4-30 days) germination, and only a few (8%) species had a high (>80%) and rapid (1.2-6.6 days) germination. All studied plant traits (including germination responses) showed a significant phylogenetic signal, with an exception of seed germination percentage under the alternating temperature scenario. Generalized linear models (GLMs) and phylogenetic generalized estimation equations (GEEs) demonstrated that growth form and seed dispersal mode were strong drivers of germination. Our experimental study highlights that integrating plant key traits and phylogeny is critical to predicting seed germination response to future climate change.

A New Deep Learning Algorithm for SAR Scene Classification Based on Spatial Statistical Modeling and Features Re-Calibration.

Synthetic Aperture Radar (SAR) scene classification is challenging but widely applied, in which deep learning can play a pivotal role because of its hierarchical feature learning ability. In the paper, we propose a new scene classification framework, named Feature Recalibration Network with Multi-scale Spatial Features (FRN-MSF), to achieve high accuracy in SAR-based scene classification. First, a Multi-Scale Omnidirectional Gaussian Derivative Filter (MSOGDF) is constructed. Then, Multi-scale Spatial Features (MSF) of SAR scenes are generated by weighting MSOGDF, a Gray Level Gradient Co-occurrence Matrix (GLGCM) and Gabor transformation. These features were processed by the Feature Recalibration Network (FRN) to learn high-level features. In the network, the Depthwise Separable Convolution (DSC), Squeeze-and-Excitation (SE) Block and Convolution Neural Network (CNN) are integrated. Finally, these learned features will be classified by the Softmax function. Eleven types of SAR scenes obtained from four systems combining different bands and resolutions were trained and tested, and a mean accuracy of 98.18% was obtained. To validate the generality of FRN-MSF, five types of SAR scenes sampled from two additional large-scale Gaofen-3 and TerraSAR-X images were evaluated for classification. The mean accuracy of the five types reached 94.56%; while the mean accuracy for the same five types of the former tested 11 types of scene was 96%. The high accuracy indicates that the FRN-MSF is promising for SAR scene classification without losing generality.