Search

Improving the utilization efficiency of sweetpotato on soil legacy phosphorus (P) is not only a need for sustainable utilization of P resources, but also an urgent need for cost saving and efficiency enhancement in the sweetpotato industry. Nano carbon dots (CDs), as an exogenous regulatory measure, were sprayed (10 mL, 1.5 mg mL− 1) on sweetpotato seedlings (Shangshu 19 and Xushu 32) grown in low P stress (spraying ultra-pure water as control), after which the root morphological, physiological parameters and rhizosphere properties were analyzed. Foliar-applied CDs promoted the root growth (e.g., increasing root length, projected area, surface area, etc.), and increased the expression of root phosphate transport genes (PHO1, PHT1-4). Meanwhile, foliar-applied CDs significantly reduced the expression level of inositol phosphate of root, and improved phoDgene abundance in Shangshu 19 rhizosphere. For Xushu 32, the expression level of roots citric acid and oxalic acid, and the abundance of rhizosphere pqqCgene increased after spaying CDs. Principal coordinates analysis (PCoA) showed that phoD-harboring Tuwongella, Prosthecobacter, Leptolyngbya, Rubinisphaera, and pqqC-harboring Nitrincola, Novimethylophilus, Ramlibacter, Mycobacterium, Thiomonasplayed key roles on the solubilization of insoluble P in Shangshu 19 and Xushu 32 rhizosphere, respectively. These alterations could increase available P content in Shangshu 19 (53%) and Xushu 32 (67%) rhizosphere, thus increasing the biomass and root P content of sweetpotato seedlings. Foliar-applied CDs would be a promising strategy for sweetpotato to respond low P stress, which provide scientific support for establishing regulation theories and new pathways of efficient P nutrients in sweetpotato cultivation.

As powerful tools deep neural networks have been successfully adopted for nuclei detection in histopathology images, whereas require the same probability distribution between training and testing data. However, domain shift among histopathology images widely exists in real-world applications and severely deteriorates the detection performance of deep neural networks. Despite encouraging results of existing domain adaptation methods, there remain challenges for cross-domain nuclei detection task. First, in view of the tiny size of nuclei, it is actually very difficult to obtain sufficient nuclei features, thus leading to a negative influence for feature alignment. Second, due to unavailable annotations in target domain, some extracted features contain background pixels and are thereby indiscriminative, which can largely confuse the alignment procedure. To address these challenges, in this article, we propose an end-to-end graph-based nuclei feature alignment (GNFA) method for boosting cross-domain nuclei detection. Concretely, sufficient nuclei features are generated from nuclei graph convolutional network (NGCN) by aggregating information of adjacent nuclei upon construction of nuclei graph for successful alignment. In addition, importance learning module (ILM) is designed to further select discriminative nuclei features for mitigating negative influence of background pixels in target domain during alignment. By utilizing sufficient and discriminative node features generated from GNFA, our method can successfully perform feature alignment and effectively alleviate domain shift problem for nuclei detection. Extensive experiments of multiple adaptation scenarios reveal that our method achieves state-of-the-art performance in cross-domain nuclei detection compared with existing domain adaptation methods.

Rhizosphere is the key part of belowground interaction between plant, microbes, and soil. Beneficial interactions between plant roots and rhizosphere microorganisms are pivotal for plant fitness. The aims of this study were to identify the response characteristics and their interactions of bacterial community structure and metabolites profiles in sweetpotato rhizosphere under long-term non-application of phosphorus (P) fertilizer. The research results can provide scientific basis for strengthening rhizosphere interactions and improving P utilization efficiency. Rhizosphere samples were collected from sweetpotato (Shangshu 19) grown in long-term (41 years) no fertilizer input (CK treatment), long-term application nitrogen (N) and potassium (K) fertilizers (NK treatment, as the long-term non-application of P fertilizer), and long-term application N, P, and K fertilizers (NPK treatment, as the long-term application of P fertilizer) soils. Then, soil bacterial community structure and metabolite profiles were analyzed by 16S rRNA high-throughput sequencing and liquid chromatography-mass spectrometry (LC-MS), respectively. Long-term non-application of P fertilizer significantly increased the relative abundances of Coprobacter, Cyclobacteriaceae, Pseudohongiella, Capnocytophaga, and Granulicatellain sweetpotato rhizosphere (P< 0.05). The cluster of orthologous groups of proteins (COG) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that the functions of these bacteria were mainly correlated to sugar metabolism and inositol phosphate metabolism. LC-MS analysis showed that long-term non-application of P fertilizer promoted the accumulation of lipid-like substances in sweetpotato rhizosphere, and galactose metabolism and phospholipase D signaling pathway were the top 2 differential metabolic pathways. There were significant negative correlations between bacteria with increased abundance with D-galactonate, mevalonate-P, and L-glutamate. Sweetpotato responds to low P stress through promoting sugar and inositol phosphate metabolism in the rhizosphere under long-term non-application of P fertilizer. This study linked the differential soil bacteria and metabolite profiles in the sweetpotato rhizosphere under long-term non-application of P fertilizer and provided new insights into plant–microbe interactions in soil.

Developing efficient catalysts for photocatalytic hydrogen evolution and pollutant degradation is a promising approach to address energy and environmental pollution issues. S-scheme heterojunctions exhibit strong redox capabilities and rapid charge separation rates, presenting enormous potential in realizing such dual-functional reaction systems. In this paper, the core-shell Cu2-xS@Zn0.5Cd0.5S (Cu2-xS@CZS) with S-scheme heterojunction was constructed by traditional hydrothermal reaction. The hydrogen production rate of Cu2-xS@CZS-2 is the best (20.73 mmol g−1h−1), which is 692 times and 4.82 times that of pure Cu2-xS and Zn0.5Cd0.5S. Furthermore, Cu2-xS@CZS-2 catalyst achieved a removal efficiency of 98.1 % for TC within 180 minutes under visible light. This study demonstrates a transition metal sulfide photocatalyst capable of efficiently harnessing solar energy for the production of clean energy and the remediation of water environments.