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Formation of thick, high energy density, flexible solid supercapacitors is challenging because of difficulties infilling gel electrolytes into porous electrodes. Incomplete infilling results in a low capacitance and poor mechanical properties. Here we report a bottom-up infilling method to overcome these challenges. Electrodes up to 500 μm thick, formed from multi-walled carbon nanotubes and a composite of poly(3,4-ethylenedioxythiophene), polystyrene sulfonate and multi-walled carbon nanotubes are successfully infilled with a polyvinyl alcohol/phosphoric acid gel electrolyte. The exceptional mechanical properties of the multi-walled carbon nanotube-based electrode enable it to be rolled into a radius of curvature as small as 0.5 mm without cracking and retain 95% of its initial capacitance after 5000 bending cycles. The areal capacitance of our 500 μm thick poly(3,4-ethylenedioxythiophene), polystyrene sulfonate, multi-walled carbon nanotube-based flexible solid supercapacitor is 2662 mF cm –2 at 2 mV s –1 , at least five times greater than current flexible supercapacitors. The development of high performance flexible solid supercapacitors calls for an effective approach to infill gel electrolytes into porous electrodes. Here the authors report a bottom-up method to address this technical challenge, which leads to enhanced areal capacitance and durability.

Emerging SARS-CoV-2 variants of concern (VOCs) harboring multiple mutations in the spike protein raise concerns on effectiveness of current vaccines that rely on the ancestral spike protein. Here, we design a quadrivalent mosaic nanoparticle vaccine displaying spike proteins from the SARS-CoV-2 prototype and 3 different VOCs. The mosaic nanoparticle elicits equivalent or superior neutralizing antibodies against variant strains in mice and non-human primates with only small reduction in neutralization titers against the ancestral strain. Notably, it provides protection against infection with prototype and B.1.351 strains in mice. These results provide a proof of principle for the development of multivalent vaccines against pandemic and potential pre-emergent SARS-CoV-2 variants. Emerging SARS-CoV-2 variants with multiple mutations raise concerns on vaccine effectiveness. Here, Kang et al . report that a quadrivalent mosaic nanoparticle vaccine displaying spike proteins from the SARS-CoV-2 prototype and three different VOCs confer protection against SARS-CoV-2 variants in mice.

A bstract We construct Carrollian scalar field theories in general dimensions, mainly focusing on the boundaries of Minkowski and Rindler spacetime, whose quantum flux operators form a faithful representation of Carrollian diffeomorphism up to a central charge, respectively. At future/past null infinity, the fluxes are physically observable and encode rich information of the radiation. The central charge may be regularized to be finite by the spectral zeta function or heat kernel method on the unit sphere. For the theory at the Rindler horizon, the effective central charge is proportional to the area of the bifurcation surface after regularization. Moreover, the zero mode of supertranslation is identified as the modular Hamiltonian, linking Carrollian diffeomorphism to quantum information theory. Our results may hold for general null hypersurfaces and provide new insight in the study of the Carrollian field theory, asymptotic symmetry group and entanglement entropy.

As one of the most devastating disasters to pine forests, pine wilt disease (PWD) has caused tremendous ecological and economic losses in China. An effective way to prevent large-scale PWD outbreaks is to detect and remove the damaged pine trees at the early stage of PWD infection. However, early infected pine trees do not show obvious changes in morphology or color in the visible wavelength range, making early detection of PWD tricky. Unmanned aerial vehicle (UAV)-based hyperspectral imagery (HI) has great potential for early detection of PWD. However, the commonly used methods, such as the two-dimensional convolutional neural network (2D-CNN), fail to simultaneously extract and fully utilize the spatial and spectral information, whereas the three-dimensional convolutional neural network (3D-CNN) is able to collect this information from raw hyperspectral data. In this paper, we applied the residual block to 3D-CNN and constructed a 3D-Res CNN model, the performance of which was then compared with that of 3D-CNN, 2D-CNN, and 2D-Res CNN in identifying PWD-infected pine trees from the hyperspectral images. The 3D-Res CNN model outperformed the other models, achieving an overall accuracy (OA) of 88.11% and an accuracy of 72.86% for detecting early infected pine trees (EIPs). Using only 20% of the training samples, the OA and EIP accuracy of 3D-Res CNN can still achieve 81.06% and 51.97%, which is superior to the state-of-the-art method in the early detection of PWD based on hyperspectral images. Collectively, 3D-Res CNN was more accurate and effective in early detection of PWD. In conclusion, 3D-Res CNN is proposed for early detection of PWD in this paper, making the prediction and control of PWD more accurate and effective. This model can also be applied to detect pine trees damaged by other diseases or insect pests in the forest.

C─H bond activation represents a ubiquitous transformation in chemistry, yet challenging owing to the complex requirements for proton and electron transfer. A general strategy for constructing proton–electron dual‐transport‐channel photocatalysts: hollow hierarchical Co3S4/Sv‐chalcogenide/Ti3C2 nanoreactors (Sv = sulfur vacancies, chalcogenide = CdIn2S4, ZnIn2S4, CdS) is developed via lateral epitaxy and defect‐mediated heterocomponent anchorage. These ternary‐component nanoreactors integrate dynamic hydrogen‐bonding nanonetworks and asymmetric dual‐interface built‐in electric fields (BIEFs), acting as the strong proton/electron extractors for steering proton‐coupled electron transfer (PCET) in C─H activation of biomass‐derived molecules. The BIEFs‐induced electron transport channel is featured by powerful photocarrier enrichment and feeble photocarrier recombination at Co3S4/chalcogenide S‐scheme heterointerface, and photocarrier localization and delocalized‐electron transport at Sv‐chalcogenide/Ti3C2 Schottky heterointerface. The hydrogen bond network‐induced proton transport channel lies in electron‐enriched interfacial lattice oxygen for mediating the substrate deprotonation via nucleophilic ion, and the hydrophilic MXene for guiding proton transfer along modified dynamic hydrogen‐bonding nanonetworks. By virtue of dynamically optimized molecular catalytic behavior accomplished by pivotal intermediate adsorption/activation regulation, representative Co3S4/Sv‐CdIn2S4/Ti3C2 HNR exhibits remarkable C─H activation performance and broad substrate compatibility. This work establishes a pioneering paradigm for manipulating proton–electron dual‐transport‐channel by hydrogen‐bonding nanonetworks and BIEFs, offering novel strategies for regulating molecular catalytic behavior in complex reaction pathways. The general strategy for constructing proton–electron dual‐transport‐channel photocatalysts (hollow Co3S4/Sv‐chalcogenide/Ti3C2 nanoreactors) is developed. Theses ternary‐component nanoreactors feature dynamic hydrogen‐bonding nanonetworks as proton‐transport channels and asymmetric dual‐interface BIEFs as electron‐transport channels, serving as a catalytic platform for activating proton‐coupled electron transfer (PCET) in biomass C─H activation.

Background Long non-coding RNAs (lncRNAs) are increasingly implicated in the regulation of the progression of malignancy. Aim To clarify the relations among BCYRN1 (brain cytoplasmic RNA 1, a long non-coding RNA), c-MYC and cell metastasis of non-small-cell lung cancer (NSCLC). Methods Real-time PCR was used to measure expression of BCYRN1 in NSCLC. Knockdown and overexpression of c-MYC were respectively performed using shRNA and lentivirus to investigate its effect on BCYRN1 expression. BCYRN1 was respectively knockdown and overexpressed by siRNA and BCYRN1 mimics to investigate its role in regulating cell metastasis in vitro . ChIP (chromatin immunoprecipitation) assay was performed to confirm the binding of c-MYC to the promoter of BCYRN1. Expression levels of matrix metalloproteinases (MMP9 and MMP13) were determined using real-time PCR and Western blotting. Results BCYRN1 is upregulated and targeted by c-MYC in NSCLC, leading to the increase of cell motility and invasiveness. RNA interference and lentivirus infection showed a positive correlation between the expressions of c-MYC and BCYRN1. ChIP assay confirmed the binding of c-MYC to the promoter region of BCYRN1 gene. In-vitro cell metastasis experiments demonstrated that BCYRN1 was necessary in the c-MYC-regulated cell migration and invasion. The mRNA and protein expression levels of MMP9 and MMP13 descended with the decreasing BCYRN1 level and ascended with the upregulation of BCYRN1. Conclusion These findings uncover a regulatory mechanism in NSCLC cells involving the metastasis-promoting lncRNA BCYRN1 that improves expressions of the key metastasis-supporting proteins MMP9 and MMP13.

Background: Pine wilt disease (PWD) is a major ecological concern in China that has caused severe damage to millions of Chinese pines (Pinus tabulaeformis). To control the spread of PWD, it is necessary to develop an effective approach to detect its presence in the early stage of infection. One potential solution is the use of Unmanned Airborne Vehicle (UAV) based hyperspectral images (HIs). UAV-based HIs have high spatial and spectral resolution and can gather data rapidly, potentially enabling the effective monitoring of large forests. Despite this, few studies examine the feasibility of HI data use in assessing the stage and severity of PWD infection in Chinese pine. Method: To fill this gap, we used a Random Forest (RF) algorithm to estimate the stage of PWD infection of trees sampled using UAV-based HI data and ground-based data (data directly collected from trees in the field). We compared relative accuracy of each of these data collection methods. We built our RF model using vegetation indices (VIs), red edge parameters (REPs), moisture indices (MIs), and their combination. Results: We report several key results. For ground data, the model that combined all parameters (OA: 80.17%, Kappa: 0.73) performed better than VIs (OA: 75.21%, Kappa: 0.66), REPs (OA: 79.34%, Kappa: 0.67), and MIs (OA: 74.38%, Kappa: 0.65) in predicting the PWD stage of individual pine tree infection. REPs had the highest accuracy (OA: 80.33%, Kappa: 0.58) in distinguishing trees at the early stage of PWD from healthy trees. UAV-based HI data yielded similar results: the model combined VIs, REPs and MIs (OA: 74.38%, Kappa: 0.66) exhibited the highest accuracy in estimating the PWD stage of sampled trees, and REPs performed best in distinguishing healthy trees from trees at early stage of PWD (OA: 71.67%, Kappa: 0.40). Conclusion: Overall, our results confirm the validity of using HI data to identify pine trees infected with PWD in its early stage, although its accuracy must be improved before widespread use is practical. We also show UAV-based data PWD classifications are less accurate but comparable to those of ground-based data. We believe that these results can be used to improve preventative measures in the control of PWD.

The continuous emergence of SARS-CoV-2 variants highlights the need of developing vaccines with broad protection. Here, according to the immune-escape capability and evolutionary convergence, the representative SARS-CoV-2 strains carrying the hotspot mutations were selected. Then, guided by structural and computational analyses, we present a mutation-integrated trimeric form of spike receptor-binding domain (mutI-tri-RBD) as a broadly protective vaccine candidate, which combined heterologous RBDs from different representative strains into a hybrid immunogen and integrated immune-escape hotspots into a single antigen. When compared with a homo-tri-RBD vaccine candidate in the stage of phase II trial, of which all three RBDs are derived from the SARS-CoV-2 prototype strain, mutI-tri-RBD induced significantly higher neutralizing antibody titers against the Delta and Beta variants, and maintained a similar immune response against the prototype strain. Pseudo-virus neutralization assay demonstrated that mutI-tri-RBD also induced broadly strong neutralizing activities against all tested 23 SARS-CoV-2 variants. The in vivo protective capability of mutI-tri-RBD was further validated in hACE2-transgenic mice challenged by the live virus, and the results showed that mutI-tri-RBD provided potent protection not only against the SARS-CoV-2 prototype strain but also against the Delta and Beta variants.

Luoshenia tenuis , a newly identified gut commensal microbe from the family Christensenellaceae , has shown therapeutic effects on weight control and metabolic disorders in model mice. Bacterial strains are essential for investigations on the host-microbe interaction and further development of medical applications. In this study, we collected 27 strains of L. tenuis from the Christensenellaceae Gut Microbial Biobank (ChrisGMB) and sequenced their complete genomes. Our analysis revealed considerable genetic diversity and genomic plasticity. Metabolic prediction indicated that L. tenuis had a preference for metabolizing plant-derived carbohydrates and the ability to synthesize various amino acids and cofactors. In silico analysis, along with in vitro experiments, validated that L. tenuis strains possessed strong acid tolerance and limited antibiotic resistance, suitable traits for oral probiotic development. Further volatile metabolomics and bile acid transformation profiling revealed that L. tenuis was capable of producing metabolites with previously-identified beneficial effects, along with extensive bile acid modification, potentially contributing to its positive impact on host metabolism. This study provides essential insight into strain-level functional and genomic features, laying a foundation for future research towards the development of L. tenuis -based therapies for metabolic disease.

Background Malignant peritoneal mesothelioma (MPM) is a highly aggressive peritoneal malignancy with a significant recurrence rate following cytoreductive surgery (CRS) combined with hyperthermic intraperitoneal chemotherapy (HIPEC). There is an urgent need to investigate novel therapeutic strategies for MPM. Natural killer (NK) cells exhibit rapid responsiveness in anti-tumor immunity; however, NK cells’ dynamic evolution and clinical significance in MPM remain unclear. Methods This study retrospectively enrolled 80 newly diagnosed MPM patients (preoperative group) and 64 patients who underwent CRS + HIPEC (postoperative group). The level of NK cells (CD3 − CD56 dim CD16 + ) in peripheral blood was quantified using flow cytometry. Univariate and multivariate regression analyses were performed to evaluate the association between NK cell counts and clinicopathological characteristics, intraoperative events, and prognosis. A multivariate prediction model for NK cell recovery was established. Results 41 patients (51.3%) exhibited decreased NK cell levels preoperatively, which were significantly associated with an increased risk of thrombosis ( P  = 0.023), intraoperative plasma transfusion ( P  = 0.004), and prolonged hospitalization duration ( P  = 0.023). Postoperative dynamic changes in NK cell levels were found to correlate with Karnofsky performance scale (KPS) scores ( P  = 0.048) and elevated levels of IL-4, IL-5, IL-6, and IL-8 ( P  < 0.05). Multivariate analysis revealed that the volume of intraoperative plasma transfusion was an independent correlated factor for preoperative NK cell reduction ( P  = 0.013), while a low KPS score was an independent predictor of postoperative NK cell decline ( P  = 0.048). Survival analysis indicated that a high perioperative stress score (PSS) ( P  = 0.015), lymph node metastasis ( P  = 0.015), significant intraoperative blood loss ( P  = 0.013), low preoperative CD8⁺ T cell levels ( P  = 0.001), and reduced postoperative IL-17 expression ( P  = 0.013) were independent adverse prognostic factors for overall survival (OS). Furthermore, the dynamic NK cell recovery model demonstrated that baseline NK cell levels, peritoneal cancer index (PCI), CD8⁺ T cell status, and postoperative recovery time all significantly influenced the immune remodeling process (all P  < 0.001). Conclusions Preoperative NK depletion correlated with thrombosis and surgical risks, while postoperative NK recovery was influenced by KPS, specific cytokines (IL-4/5/6/8), and was significantly enhanced after CRS + HIPEC.