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Abstract Nipah virus (NiV) is a zoonotic emergent paramyxovirus that can cause severe encephalitis and respiratory infections in humans, with a high fatality rate ranging from 40% to 75%. Currently, there are no approved human vaccines or antiviral drugs against NiV. Here, we designed a ferritin-based self-assembling nanoparticle displaying the NiV G head domain on the surface (NiV G-ferritin) and assessed immune responses elicited by the soluble NiV G head domain (NiV sG) or NiV G-ferritin. Immunization with NiV G-ferritin or NiV sG conferred complete protection against lethal NiV challenge without detection of viral RNA in Syrian golden hamsters. Compared to NiV sG, NiV G-ferritin induced significantly faster, broader, and higher serum neutralizing responses against three pathogenic henipaviruses (NiV-Malaysia, NiV-Bangladesh, and Hendra virus). Moreover, NiV G-ferritin induced a durable neutralizing immunity in mice as antisera potently inhibited NiV infection even after six months of the third immunization. Additionally, we isolated a panel of 27 NiV G-binding monoclonal antibodies (mAbs) from NiV G-ferritin immunized mice and found that these mAbs targeted four distinct antigenic sites on NiV G head domain with two sites that have not been defined previously. Notably, 25 isolated mAbs have potent neutralizing activity with 50% inhibitory concentrations less than 10 ng/mL against NiV pseudovirus. Collectively, these findings provide new insights into the immunogenicity of NiV G protein and reveal that NiV G-ferritin is a safe and highly effective vaccine candidate against Nipah virus infection.

Abstract The rapid spread of monkeypox in multiple countries has resulted in a global public health threat and has caused international concerns since May 2022. Poxvirus encoded M2 protein is a member of the poxvirus immune evasion family and plays roles in host immunomodulation via the regulation of innate immune response mediated by the NF-κB pathway and adaptive immune response mediated by B7 ligands. However, the interaction of monkeypox virus (MPXV) M2 with B7 ligands and structural insight into poxviral M2 function have remained elusive. Here we reveal that MPXV M2, co-existing as a hexamer and a heptamer, recognizes human B7.1 and B7.2 (hB7.1/2) with high avidities. The binding of oligomeric MPXV M2 interrupts the interactions of hB7.1/2 with CD28 and CTLA4 and subverts T cell activation mediated by B7.1/2 costimulatory signals. Cryo-EM structures of M2 in complex with hB7.1/2 show that M2 binds to the shallow concave face of hB7.1/2 and displays sterically competition with CD28 and CTLA4 for the binding to hB7.1/2. Our findings provide structural mechanisms of poxviral M2 function and immune evasion deployed by poxviruses.

Object tracking based on Siamese networks has achieved great success in recent years, but increasingly advanced trackers are also becoming cumbersome, which will severely limit deployment on resource-constrained devices. To solve the above problems, we designed a network with the same or higher tracking performance as other lightweight models based on the SiamFC lightweight tracking model. At the same time, for the problems that the SiamFC tracking network is poor in processing similar semantic information, deformation, illumination change, and scale change, we propose a global attention module and different scale training and testing strategies to solve them. To verify the effectiveness of the proposed algorithm, this paper has done comparative experiments on the ILSVRC, OTB100, VOT2018 datasets. The experimental results show that the method proposed in this paper can significantly improve the performance of the benchmark algorithm.

In terms of small objects in traffic scenes, general object detection algorithms have low detection accuracy, high model complexity, and slow detection speed. To solve the above problems, an improved algorithm (named YOLO-MXANet) is proposed in this paper. Complete-Intersection over Union (CIoU) is utilized to improve loss function for promoting the positioning accuracy of the small object. In order to reduce the complexity of the model, we present a lightweight yet powerful backbone network (named SA-MobileNeXt) that incorporates channel and spatial attention. Our approach can extract expressive features more effectively by applying the Shuffle Channel and Spatial Attention (SCSA) module into the SandGlass Block (SGBlock) module while increasing the parameters by a small number. In addition, the data enhancement method combining Mosaic and Mixup is employed to improve the robustness of the training model. The Multi-scale Feature Enhancement Fusion (MFEF) network is proposed to fuse the extracted features better. In addition, the SiLU activation function is utilized to optimize the Convolution-Batchnorm-Leaky ReLU (CBL) module and the SGBlock module to accelerate the convergence of the model. The ablation experiments on the KITTI dataset show that each improved method is effective. The improved algorithm reduces the complexity and detection speed of the model while improving the object detection accuracy. The comparative experiments on the KITTY dataset and CCTSDB dataset with other algorithms show that our algorithm also has certain advantages.

In the practical application scenarios of safety helmet detection, the lightweight algorithm You Only Look Once (YOLO) v3-tiny is easy to be deployed in embedded devices because its number of parameters is small. However, its detection accuracy is relatively low, which is why it is not suitable for detecting multi-scale safety helmets. The safety helmet detection algorithm (named SAS-YOLOv3-tiny) is proposed in this paper to balance detection accuracy and model complexity. A light Sandglass-Residual (SR) module based on depthwise separable convolution and channel attention mechanism is constructed to replace the original convolution layer, and the convolution layer of stride two is used to replace the max-pooling layer for obtaining more informative features and promoting detection performance while reducing the number of parameters and computation. Instead of two-scale feature prediction, three-scale feature prediction is used here to improve the detection effect about small objects further. In addition, an improved spatial pyramid pooling (SPP) module is added to the feature extraction network to extract local and global features with rich semantic information. Complete-Intersection over Union (CIoU) loss is also introduced in this paper to improve the loss function for promoting positioning accuracy. The results on the self-built helmet dataset show that the improved algorithm is superior to the original algorithm. Compared with the original YOLOv3-tiny, the SAS-YOLOv3-tiny has significantly improved all metrics (including Precision (P), Recall (R), Mean Average Precision (mAP), F1) at the expense of only a minor speed while keeping fewer parameters and amounts of calculation. Meanwhile, the SAS-YOLOv3-tiny algorithm shows advantages in accuracy compared with lightweight object detection algorithms, and its speed is faster than the heavyweight model.

The size of precious metal catalysts plays an important role in their activity, and inhibiting precious metal aggregation or even sintering is a major challenge in practical industrial applications. Herein, the physical limitation of fibrous mesoporous silica (KCC-1) and Pt–CeO2 interaction of CeO2 nanoislands were designed and used to inhibit the size of Pt particles, thereby improving their intrinsic activity and thermal stability. With the introduction of CeO2 nanoislands, the turnover frequency (TOF) value of the Pt–CeO2/KCC-1 catalyst for propane-catalyzed combustion at 210 °C was 1.5 times that of Pt/KCC-1 and 5.6 times that of Pt/CeO2. Moreover, after aging at 800 °C, Pt–CeO2/KCC-1 showed enhanced thermal stability compared with Pt/KCC-1 due to the smaller change in the Pt size. Through comprehensive characterization and research of the reaction mechanism, the results indicate that due to the Pt–CeO2 interaction, the Pt species were confined to the middle of the CeO2 nanoisland, which was further separated by fiber sheets of KCC-1, to advance the activity and thermal stability of the catalyst. The strategy of the nanoisland in this paper provides guidance for designing better catalysts for other harsh reaction conditions. [ABSTRACT FROM AUTHOR]

Objective To analyze the mechanism that Hcp1 protein in type Ⅵ secretion system of Burkholderia pseudomallei (B. pseudomallei) mediates the formation of multinucleated giant cells (MNGCs) when host cells are infected by the bacterium. Methods The mutant strain (BPC006 Δhcp1) and complementation strain (BPC006 Δhcp1:: hcp1) were constructed by homologous recombination and plasmid complement technology, respectively. After RAW264.7 cells were infected with B. pseudomallei, the localization of Hcp1 in host cells was analyzed by immunofluorescence staining. The localization was further verified by cytoplasmic-membrane isolation in 293T cells after transfecting pCDNA4.1-Hcp1. The biological significance and effect of Hcp1 were explored by the anti-Hcp1 polyclonal antibody blocking and the formation of MNGC was detected by Giemsa staining. Results Western blotting showed that BPC006 Δhcp1 could not express Hcp1, while BPC006 Δhcp1:: hcp1 restored Hcp1 expression. The above results proved that the mutant and complement strains were successfully constructed. Both cellular immunofluorescence co-localization and cytoplasmic-membrane isolation experiments showed that Hcp1 localized to host cell membranes. Last but not least, compared with the control group, anti-Hcp1 polyclonal antibodies inhibited the formation of MNGC (P < 0.01). Conclusion Hcp1 protein in type Ⅵ secretion system of B. pseudomallei is able to translocate to the RAW264.7 cell membranes and plays an important role in the formation of MNGCs.

The photocatalytic reforming of lignin for H2 evolution can simultaneously overcome environmental and energy issues. However, the photocatalytic activity is still far from useful, considering lignin's complex structure. Therefore, this poses a huge challenge for the design of photocatalysts via ingredient/heterojunction regulation synergistic effects. Herein, in situ Mo-doped and metallic Ni-supported ZnIn2S4-wrapped NiMoO4 step-scheme (S-scheme) heterojunctions (NMO@M–ZIS–N) were developed via a reduction strategy assisted by thermal dissolution. The introduction of NMO can significantly inhibit the agglomeration of ZIS and expose more active sites. The close contact of the two phases helps to form a S-scheme heterojunction, which effectively promotes the separation and transport of photogenerated electron–hole pairs. Due to its unique structural design, the optimal hybrid heterojunction can exhibit excellent photocatalytic hydrogen evolution reaction (HER) activity (5.14 mmol h−1 g−1 for TEOA and 0.53 mmol h−1 g−1 for lignin). The superior photocatalytic performance can be mainly attributed to the synergistic effect of S-scheme heterojunctions coupled with Mo doping and metallic Ni supports to broaden the light absorption, enhance the kinetic process of charge transport, and retain strong redox ability, thereby promoting the photocatalytic HER activity. [ABSTRACT FROM AUTHOR]