Nitrogen Deficiency Enhances Eggplant Defense against Western Flower Thrips via the Induction of the Jasmonate Pathway.

Plant nutrition is connected to defense against insect herbivores, but the exact mechanism underlying the effect of the nitrogen (N) supply on the anti-herbivore capacity of eggplants (Solanum melongena) has not been studied in detail. Therefore, we examined the impact of low (LN, 0.5 mM) and high (HN, 5 mM) nitrate levels on eggplant resistance against the western flower thrips Frankliniella occidentalis (WFT), a major destructive eggplant pest. Our results showed that LN plants displayed enhanced defense responses to WFT compared to HN plants. This included increased transcript levels of key genes in the jasmonic acid (JA) pathway, the accumulation of JA-amido conjugates (jasmonoyl-isoleucine, jasmonoyl-phenylalanine, and jasmonoyl-valine), JA precursor (12-oxophytodienoic acid), and methyl jasmonate, higher transcript levels of defense marker genes (MPK3, MPK7, and WRKY53), and increased activities of polyphenol oxidase and peroxidase upon a WFT attack. Our findings suggest that N deficiency can prime JA-mediated defense responses in eggplants, resulting in increased anti-herbivore resistance.

Semiquantitative Fingerprinting Based on Pseudotargeted Metabolomics and Deep Learning for the Identification of Listeria monocytogenes and Its Major Serotypes.

The rapid identification of pathogenic microorganism serotypes is still a bottleneck problem to be solved urgently. Compared with proteomics technology, metabolomics technology is directly related to phenotypes and has higher specificity in identifying pathogenic microorganism serotypes. Our study combines pseudotargeted metabolomics with deep learning techniques to obtain a new deep semiquantitative fingerprinting method for Listeria monocytogenes identification at the serotype levels. We prescreened 396 features with orthogonal partial least-squares discrimination analysis (OPLS-DA), and 200 features were selected for deep learning model building. A residual learning framework for L. monocytogenes identification was established. There were 256 convolutional filters in the initial convolution layer, and each hidden layer contained 128 filters. The total depth included seven layers, consisting of an initial convolution layer, a residual layer, and two final fully connected classification layers, with each residual layer containing four convolutional layers. In addition, transfer learning was used to predict new isolates that did not participate in model training to verify the method's feasibility. Finally, we achieved prediction accuracies of L. monocytogenes at the serotype level exceeding 99%. The prediction accuracy of the new strain validation set was greater than 97%, further demonstrating the feasibility of this method. Therefore, this technology will be a powerful tool for the rapid and accurate identification of pathogens.

Pseudotargeted Metabolomic Fingerprinting and Deep Learning for Identification and Visualization of Common Pathogens.

Matrix-assisted laser desorption/ionization time-of-flight mass (MALDI-TOF) spectrometry fingerprinting has reduced turnaround times, costs, and labor as conventional procedures in various laboratories. However, some species strains with high genetic correlation have not been directly distinguished using conventional standard procedures. Metabolomes can identify these strains by amplifying the minor differences because they are directly related to the phenotype. The pseudotargeted metabolomics method has the advantages of both non-targeted and targeted metabolomics. It can provide a new semi-quantitative fingerprinting with high coverage. We combined this pseudotargeted metabolomic fingerprinting with deep learning technology for the identification and visualization of the pathogen. A variational autoencoder framework was performed to identify and classify pathogenic bacteria and achieve their visualization, with prediction accuracy exceeding 99%. Therefore, this technology will be a powerful tool for rapidly and accurately identifying pathogens.

Modified Nanopillar Arrays for Highly Stable and Efficient Photoelectrochemical Water Splitting.

Atomically modified graphitic carbon nitride quantum dots (QDs), characterized by strongly increased reactivity and stability, are developed. These are deposited on arrays of TiO2 nanopillars used as a photoanode for the photoelectrochemical water splitting. This photoanode shows excellent stability, with 111 h of continuous work without any performance loss, which outperforms the best-reported results by a factor of 10. Remarkably, our photoanode produces hydrogen even at zero bias. The excellent performance is attributed to the enhancement of photoabsorption, as well as to the promotion of charge separation between TiO2 nanopillars and the QDs.

Synthesis and Investigation of CuGeO3 Nanowires as Anode Materials for Advanced Sodium-Ion Batteries.

Germanium is considered as a potential anode material for sodium-ion batteries due to its fascinating theoretical specific capacity. However, its poor cyclability resulted from the sluggish kinetics and large volume change during repeated charge/discharge poses major threats for its further development. One solution is using its ternary compound as an alternative to improve the cycling stability. Here, high-purity CuGeO3 nanowires were prepared via a facile hydrothermal method, and their sodium storage performances were firstly explored. The as-obtained CuGeO3 delivered an initial charge capacity of 306.7 mAh g-1 along with favorable cycling performance, displaying great promise as a potential anode material for sodium ion batteries.

Flower-like Cu2SnS3 Nanostructure Materials with High Crystallinity for Sodium Storage.

In this study, ternary Cu2SnS3 (CTS) nanostructure materials with high crystallinity were successfully prepared via a facile solvothermal method, which was followed by high-temperature treatment. The morphology of the as-synthesized samples is uniform flower-like spheres, with these spheres consisting of hierarchical nanosheets and possessing network features. Sodium storage measurements demonstrate that the annealed CTS electrodes have high initial reversible capacity (447.7 mAh·g−1 at a current density of 100 mA·g−1), good capacity retention (200.6 mAh·g−1 after 50 cycles at a current density of 100 mA·g−1) and considerable rate capability because of their high crystallinity and unique morphology. Such good performances indicate that the high crystallinity CTS is a promising anode material for sodium ion batteries.

Biomass-Derived Oxygen and Nitrogen Co-Doped Porous Carbon with Hierarchical Architecture as Sulfur Hosts for High-Performance Lithium/Sulfur Batteries.

In this work, a facile strategy to synthesize oxygen and nitrogen co-doped porous carbon (ONPC) is reported by one-step pyrolysis of waste coffee grounds. As-prepared ONPC possesses highly rich micro/mesopores as well as abundant oxygen and nitrogen co-doping, which is applied to sulfur hosts as lithium/sulfur batteries' appropriate cathodes. In battery testing, the sulfur/oxygen and nitrogen co-doped porous carbon (S/ONPC) composite materials reveal a high initial capacity of 1150 mAh·g-1 as well as a reversible capacity of 613 mAh·g-1 after the 100th cycle at 0.2 C. Furthermore, when current density increases to 1 C, a discharge capacity of 331 mAh·g-1 is still attainable. Due to the hierarchical porous framework and oxygen/nitrogen co-doping, the S/ONPC composite exhibits a high utilization of sulfur and good electrochemical performance via the immobilization of the polysulfides through strong chemical binding.

Synthesis of Mesoporous ZnO Nanosheets via Facile Solvothermal Method as the Anode Materials for Lithium-ion Batteries.

Mesoporous ZnO nanosheets are synthesized through a room temperature solvothermal method. Transmission and scanning electronic microscopy observations indicate that as-prepared ZnO hierarchical aggregates are composed and assembled by nanosheets with a length of 1-2 µm and a thickness of 10-20 nm, and interlaced ZnO nanosheets irregularly stack together, forming a three-dimensional network. Furthermore, large mesopores are embedded in the walls of ZnO nanosheets, confirmed by Brunauer-Emmett-Teller (BET) measurement. Accordingly, the resulting ZnO anode exhibits a high and stable specific discharge capacity of 421 mAh g(-1) after 100 cycles at 200 mA g(-1) and a good rate capability. Such electrochemical performance could be attributed to the multiple synergistic effects of its mesoporous nanosheet structure, which can not only provide a large specific surface area for lithium storage, but also favor the ion transport and electrolyte diffusion.