The localized surface plasmon resonance (LSPR) effect has been widely utilized in photocatalysis, but most reported LSPR materials are based on noble metals of gold or silver with high chemical stability. Plasmonic copper nanoparticles that exhibit an LSPR absorbance at 600 nm are promising for many applications, such as photocatalysis. Unfortunately, plasmonic copper nanoparticles are affected by serious surface oxidation in air. Herein, a novel lollipop-shaped Cu@Cu O/ZnO heterojunction nanostructure was designed, for the first time, to stabilize the plasmonic Cu core by decorating Cu@Cu O core-shell structures with ZnO nanorods. This Cu@Cu O/ZnO nanostructure exhibited significantly enhanced stability than that of regular Cu@Cu O, which accounted for the remarkably enhanced photocatalytic H evolution rate through water splitting, relative to pristine ZnO nanorods, over an extended wavelength range due to the plasmonic Cu core.
Purpose Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide. Most current therapeutic strategies primarily include localized treatment, lacking effective systemic strategies. Meanwhile, recent studies have suggested that RNA vaccines can effectively activate antigen-presenting cells (APCs) and lymphocytes to produce a strong systemic immune response and inhibit tumor growth. However, tumor vaccines loaded with a single tumor antigen may induce immunosuppression and immune evasion, while identifying tumor-specific antigens can require expensive and laborious procedures. Therefore, the use of whole tumor cell antigens are currently considered to be promising, potentially effective, methods. Previously, we developed a targeted liposome-polycation-DNA (LPD) complex nanoparticle that possess a small size, high RNA encapsulation efficiency, and superior serum stability. These particles were found to successfully deliver RNA to tumor sites. In the current study, we encapsulated total tumor-derived RNA in lipid nanoparticles (LNPs) to target dendritic cells (DCs) to incite expeditious and robust anti-tumor immunity. Methods Total tumor-derived RNA was extracted from liver cancer cells (Hepa1-6 cells). LNPs loaded with tumor RNA were then prepared thin-film hydration method. The ability of RNA LNPs to induce DC maturation, cytotoxicity, and anti-tumor activity, was investigated in vitro and in vivo. Results The average particle size of LNPs and RNA LNPs was 102.22 ± 4.05 nm and 209.68 ± 6.14 nm, respectively, while the zeta potential was 29.97 ± 0.61 mV and 42.03 ± 0.42 mV, respectively. Both LNPs and RNA LNP vaccines exhibited good distribution and stability. In vitro, RNA LNP vaccines were capable of promoting DC maturation and inducing T lymphocytes to kill Hepa1-6 cells. In vivo, RNA LNP vaccines effectively prevent and inhibit HCC growth. Conclusion RNA LNPs may serve as an effective antigen specific vaccine to induce anti-tumor immunity for HCC.
Apoptosis, a type of programmed cell death, plays crucial roles in various physiological processes, from development to adaptive responses. Key features of apoptosis have been verified in various fungal microbes but not yet in Fusarium species. Here, we identified 19 apoptosis-related genes in Fusarium pseudograminearum using a genome-wide survey. Expression profile analysis revealed that several apoptosis-related genes were significantly increased during conidiation and infection stages. Among these is FpBIR1, with two BIR (baculovirus inhibitor-of-apoptosis protein repeat) domains at the N-terminal end of the protein, a homolog of Saccharomyces cerevisiae BIR1, which is a unique apoptosis inhibitor. FpNUC1 is the ortholog of S. cerevisiae NUC1, which triggers AIF1- or YCA1-independent apoptosis. The functions of these two proteins were assessed by creating Δfpbir1 and Δfpnuc1 mutants via targeted gene deletion. The Δfpbir1 mutant had more cells with nuclear fragmentation and exhibited reduced conidiation, conidial formation, and infectivity. Correspondingly, the Δfpnuc1 mutant contained multiple nuclei, produced thicker and more branched hyphae, was reduced in conidiation, and exhibited faster conidial formation and higher infection rates. Taken together, our results indicate that the apoptosis-related genes FpBIR1 and FpNUC1 function in conidiation, conidial germination, and infection by F. pseudograminearum. IMPORTANCE The plant-pathogenic fungus F. pseudograminearum is the causal agent of Fusarium crown rot (FCR) in wheat and barley, resulting in substantial yield losses worldwide. Particularly, in the Huanghuai wheat-growing region of China, F. pseudograminearum was reported as the dominant Fusarium species in FCR infections. Apoptosis is an evolutionarily conserved mechanism in eukaryotes, playing crucial roles in development and cell responses to biotic and abiotic stresses. However, few reports on apoptosis in plant fungal pathogens have been published. In this study, we identified 19 conserved apoptosis-related genes in F. pseudograminearum, several of which were significantly increased during conidiation and infection stages. Potential apoptosis functions were assessed by deletion of the putative apoptosis inhibitor gene FpBIR1 and apoptosis trigger gene FpNUC1 in F. pseudograminearum. The FpBIR1 deletion mutant exhibited defects in conidial germination and pathogenicity, whereas the FpNUC1 deletion mutant experienced faster conidial formation and higher infection rates. Apoptosis appears to negatively regulate the conidial germination and pathogenicity of F. pseudograminearum. To our knowledge, this study is the first report of apoptosis contributing to infection-related morphogenesis and pathogenesis in F. pseudograminearum.
Synthetic aperture radar (SAR) image classification is an important part in the understanding and interpretation of SAR images. Patch-level labels are easy to achieve, and they require less expertise and lower resource consumption than pixellevel ones. Each patch has a scene category, but usually contains multiple land-cover classes or latent properties, which can be represented by topics in the probabilistic topic model (PTM). The representation and selection of discriminative features in PTM have a large impact on the classification results. Most of the existing feature learning methods do not make full use of high-level structure feature and the feature correlation within similar images to mine discriminative features. Therefore, this paper proposes a discriminative sketch topic model with structural constraint (C-SSTM) for SAR image classification. In the proposed model, each image patch is characterized by structural and texture features. In particular, the sketch structural feature is based on the sketch map to represent the image local structure pattern. Then the local image manifold information is preserved in terms of structure and texture. In the structural constraint, the texture and structure of each image patch are combined to learn discriminative latent semantic topics between image patches. The structural constraint enforces that the semantically similar features usually co-occur in similar images with a high probability. Finally, each image patch is quantified by discriminative latent semantic topics instead of lowlevel representation. The experimental results tested on synthetic and real SAR images demonstrate that the proposed C-SSTM is able to learn effective structural feature representation from SAR images. Compared with other related approaches, C-SSTM produces competitive classification accuracies with high time efficiency.
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