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Microbial composition and functions in the rhizosphere—an important microbial hotspot—are among the most fascinating yet elusive topics in microbial ecology. We used 557 pairs of published 16S rDNA amplicon sequences from the bulk soils and rhizosphere in different ecosystems around the world to generalize bacterial characteristics with respect to community diversity, composition, and functions. The rhizosphere selects microorganisms from bulk soil to function as a seed bank, reducing microbial diversity. The rhizosphere is enriched in Bacteroidetes, Proteobacteria, and other copiotrophs. Highly modular but unstable bacterial networks in the rhizosphere (common for r -strategists) reflect the interactions and adaptations of microorganisms to dynamic conditions. Dormancy strategies in the rhizosphere are dominated by toxin–antitoxin systems, while sporulation is common in bulk soils. Functional predictions showed that genes involved in organic compound conversion, nitrogen fixation, and denitrification were strongly enriched in the rhizosphere (11–182%), while genes involved in nitrification were strongly depleted. Understanding soil microbiota dynamics is key the development of soil-based sustainable agriculture and conservation strategies. This meta-analysis shows that bulk soil functions as a seed bank for the rhizosphere, which encompasses a rich microbiota adapted to dynamic conditions in hotpots.

The vulnerability, exposure and resilience of socioeconomic activities to future climate extremes call for high-resolution gridded GDP in climate change adaptation and mitigation research. While global socioeconomic projections are provided mainly at the national level, and downscaling approaches using nighttime light (NTL) images or gridded population data can increase the uncertainty due to limitations. Therefore, we adopt an NTL-population-based approach, which exhibits higher accuracy in socioeconomic disaggregation. Gross regional product of over 800 provinces, which covering over 60% of the global land surface and accounted for more than 80% of GDP in 2005, were used as input. We present a first set of comparable spatially explicit global gridded GDP projections with fine spatial resolutions of 30 arc-seconds and 0.25 arc-degrees for the historical period of 2005 and for 2030–2100 at 10-year intervals under the five SSPs, accounting for the two-child policy in China. This gridded GDP projection dataset can broaden the applicability of GDP data, the availability of which is necessary for socioeconomic and climate change research.Measurement(s)GDP • population • nighttime light imagesTechnology Type(s)SSP database • LandScan Global Population database • Remote sensingSample Characteristic - LocationGlobe

Engaging students in Chinese character learning tasks poses significant challenges for instructional design, especially for learners from alphabetic language backgrounds, due to Chinese characters’ complex, morpho-syllabic structure. Traditional teaching methods, such as rote memorization and hand copying, have long dominated instruction but often result in limited task engagement. This study explores a gamified approach to teaching Chinese characters grounded in task engagement principles. Specifically, it examines how task engagement principles could be integrated into a digital game designed for beginner-level students on their understanding of Chinese character structure and engagement in learning tasks. The study details the development process and analyzes quantitative and qualitative data to assess students’ learning outcomes. Findings demonstrate that the Chinese character game significantly improves character recognition and student engagement, fostering collaborative learning and enhancing overall academic performance. This study highlights the importance of incorporating engagement-driven principles and a radical-driven approach into game-based language instruction, offering educators practical insights into creating effective educational tools that blend content with interactive and collaborative elements.

Plenty of immune cells infiltrate into the tumor microenvironment (TME) during tumor progression, in which myeloid-derived suppressor cells (MDSCs) represent a heterogeneous population of immature myeloid cells with immunosuppressive activity. Tumor cells and stromal cells facilitate the activation and expansion of MDSCs in TME via intercellular communication, and expanded MDSCs suppress anti-tumor immune responses through direct and indirect mechanisms. Currently, exosomes, which are a kind of extracellular vesicles (EVs) that can convey functional components, are demonstrated to participate in the local and distal intercellular communication between cells. Numerous studies have supposed that tumor-derived exosomes (TEXs), whose assembly and release can be modulated by TME, are capable of modulating the cell biology of MDSCs, including facilitating their activation, promoting the expansion, and enhancing the immunosuppressive function. Therefore, in this review, we mainly focus on the role of TEXs in the cell-cell communication between tumor cells and MDSCs, and discuss their clinical applications.

Recently, the rapid development of deep learning has greatly improved the performance of image classification. However, a central problem in hyperspectral image (HSI) classification is spectral uncertainty, where spectral features alone cannot accurately and robustly identify a pixel point in a hyperspectral image. This paper presents a novel HSI classification network called MS-RPNet, i.e., multiscale superpixelwise RPNet, which combines superpixel-based S[sup.3] -PCA with two-dimensional singular spectrum analysis (2D-SSA) based on the Random Patches Network (RPNet). The proposed frame can not only take advantage of the data-driven method, but can also apply S[sup.3] -PCA to efficiently consider more global and local spectral knowledge at the super-pixel level. Meanwhile, 2D-SSA is used for noise removal and spatial feature extraction. Then, the final features are obtained by random patch convolution and other steps according to the cascade structure of RPNet. The layered extraction superimposes the different sparial information into multi-scale spatial features, which complements the features of various land covers. Finally, the final fusion features are classified by SVM to obtain the final classification results. The experimental results in several HSI datasets demonstrate the effectiveness and efficiency of MS-RPNet, which outperforms several current state-of-the-art methods.

Recently, the rapid development of deep learning has greatly improved the performance of image classification. However, a central problem in hyperspectral image (HSI) classification is spectral uncertainty, where spectral features alone cannot accurately and robustly identify a pixel point in a hyperspectral image. This paper presents a novel HSI classification network called MS-RPNet, i.e., multiscale superpixelwise RPNet, which combines superpixel-based S3-PCA with two-dimensional singular spectrum analysis (2D-SSA) based on the Random Patches Network (RPNet). The proposed frame can not only take advantage of the data-driven method, but can also apply S3-PCA to efficiently consider more global and local spectral knowledge at the super-pixel level. Meanwhile, 2D-SSA is used for noise removal and spatial feature extraction. Then, the final features are obtained by random patch convolution and other steps according to the cascade structure of RPNet. The layered extraction superimposes the different sparial information into multi-scale spatial features, which complements the features of various land covers. Finally, the final fusion features are classified by SVM to obtain the final classification results. The experimental results in several HSI datasets demonstrate the effectiveness and efficiency of MS-RPNet, which outperforms several current state-of-the-art methods.

Vaccines to induce effective and sustained antitumor immunity have great potential for postoperative cancer therapy. However, a robust cancer vaccine simultaneously eliciting tumor-specific immunity and abolishing immune resistance continues to be a challenge. Here we present a personalized cancer vaccine (PVAX) for postsurgical immunotherapy. PVAX is developed by encapsulating JQ1 (a BRD4 inhibitor) and indocyanine green (ICG) co-loaded tumor cells with a hydrogel matrix. Activation of PVAX by 808 nm NIR laser irradiation significantly inhibits the tumor relapse by promoting the maturation of dendritic cells and eliciting tumor infiltration of cytotoxic T lymphocytes. A mechanical study reveals that NIR light-triggered antigen release and JQ1-mediated PD-L1 checkpoint blockade cumulatively contribute to the satisfied therapeutic effect. Furthermore, PVAX prepared from the autologous tumor cells induces patient-specific memory immune response to prevent tumor recurrence and metastasis. The PVAX model might provide novel insights for postoperative immunotherapy. Cancer vaccines represent a promising personalized therapeutic approach to treating cancer. Here, the authors report the efficacy in a metastatic model of a cancer vaccine-mediated postoperative immunotherapy, based on the coencapsulation of the JQ1 and a photosensitizer ICG together with inactivated tumor cells into a hydrogel matrix.

Background An in-depth understanding of immune evasion mechanisms in tumors is crucial to overcome resistance and enable innovative advances in immunotherapy. Circular RNAs (circRNAs) have been implicated in cancer progression. However, much remains unknown regarding whether circRNAs impact immune escape in non-small-cell lung carcinoma (NSCLC). Methods We performed bioinformatics analysis to profile and identify the circRNAs mediating immune evasion in NSCLC. A luciferase reporter assay, RNA immunoprecipitation (RIP), RNA pulldown assays and fluorescence in situ hybridization were performed to identify the interactions among circIGF2BP3, miR-328-3p, miR-3173-5p and plakophilin 3 (PKP3). In vitro T cell-mediated killing assays and in vivo syngeneic mouse models were used to investigate the functional roles of circIGF2BP3 and its downstream target PKP3 in antitumor immunity in NSCLC. The molecular mechanism of PKP3-induced PD-L1 upregulation was explored by immunoprecipitation, RIP, and ubiquitination assays. Results We demonstrated that circIGF2BP3 (hsa_circ_0079587) expression was increased in NSCLC and negatively correlated with CD8 + T cell infiltration. Functionally, elevated circIGF2BP3 inactivated cocultured T cells in vitro and compromised antitumor immunity in an immunocompetent mouse model, and this effect was dependent on CD8 + T cells. Mechanistically, METTL3 mediates the N 6 -methyladenosine (m 6 A) modification of circIGF2BP3 and promotes its circularization in a manner dependent on the m 6 A reader protein YTHDC1. circIGF2BP3 competitively upregulates PKP3 expression by sponging miR-328-3p and miR-3173-5p to compromise the cancer immune response. Furthermore, PKP3 engages with the RNA-binding protein FXR1 to stabilize OTUB1 mRNA, and OTUB1 elevates PD-L1 abundance by facilitating its deubiquitination. Tumor PD-L1 deletion completely blocked the impact of the circIGF2BP3/PKP3 axis on the CD8 + T cell response. The inhibition of circIGF2BP3/PKP3 enhanced the treatment efficacy of anti-PD-1 therapy in a Lewis lung carcinoma mouse model. Collectively, the PKP3/PD-L1 signature and the infiltrating CD8 + T cell status stratified NSCLC patients into different risk groups. Conclusion Our results reveal the function of circIGF2BP3 in causing immune escape from CD8 + T cell-mediated killing through a decrease in PD-L1 ubiquitination and subsequent proteasomal degradation by stabilizing OTUB1 mRNA in a PKP3-dependent manner. This work sheds light on a novel mechanism of PD-L1 regulation in NSCLC and provides a rationale to enhance the efficacy of anti-PD-1 treatment in NSCLC.

The virulence of Pseudomonas aeruginosa , a Gram-negative opportunistic pathogen, is regulated by many transcriptional factors (TFs) that control the expression of quorum sensing and protein secretion systems. Here, we report a genome-wide, network-based approach to dissect the crosstalk between 20 key virulence-related TFs. Using chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq), as well as RNA-seq, we identify 1200 TF-bound genes and 4775 differentially expressed genes. We experimentally validate 347 of these genes as functional target genes, and describe the regulatory relationships of the 20 TFs with their targets in a network that we call ‘ Pseudomonas aeruginosa genomic regulatory network’ (PAGnet). Analysis of the network led to the identification of novel functions for two TFs (ExsA and GacA) in quorum sensing and nitrogen metabolism. Furthermore, we present an online platform and R package based on PAGnet to facilitate updating and user-customised analyses. The virulence of Pseudomonas aeruginosa is regulated by many transcriptional factors (TFs). Here, the authors study the crosstalk between 20 key virulence-related TFs, validate 347 functional target genes, and describe the regulatory relationships of the 20 TFs with their targets in a network that is available as an online platform.

HighlightsThe interfacial engineering strategies of surface and electrolyte modifications for high-performance Zn metal anodes are reviewed.The reaction mechanisms for inhibiting dendrite growth and side reactions in interface engineering are systematically summarized.An outlook on future reference directions for new interface strategies to advance this field is provided.Due to their high safety and low cost, rechargeable aqueous Zn-ion batteries (RAZIBs) have been receiving increased attention and are expected to be the next generation of energy storage systems. However, metal Zn anodes exhibit a limited-service life and inferior reversibility owing to the issues of Zn dendrites and side reactions, which severely hinder the further development of RAZIBs. Researchers have attempted to design high-performance Zn anodes by interfacial engineering, including surface modification and the addition of electrolyte additives, to stabilize Zn anodes. The purpose is to achieve uniform Zn nucleation and flat Zn deposition by regulating the deposition behavior of Zn ions, which effectively improves the cycling stability of the Zn anode. This review comprehensively summarizes the reaction mechanisms of interfacial modification for inhibiting the growth of Zn dendrites and the occurrence of side reactions. In addition, the research progress of interfacial engineering strategies for RAZIBs is summarized and classified. Finally, prospects and suggestions are provided for the design of highly reversible Zn anodes.