Despite the widespread applications of manganese oxide nanomaterials (MONs) in biomedicine, the intrinsic immunogenicity of MONs is still unclear. MnOx nanospikes (NSs) as tumor microenvironment (TME)‐responsive nanoadjuvants and immunogenic cell death (ICD) drugs are proposed for cancer nanovaccine‐based immunotherapy. MnOx NSs with large mesoporous structures show ultrahigh loading efficiencies for ovalbumin and tumor cell fragment. The combination of ICD via chemodynamic therapy and ferroptosis inductions, as well as antigen stimulations, presents a better synergistic immunopotentiation action. Furthermore, the obtained nanovaccines achieve TME‐responsive magnetic resonance/photoacoustic dual‐mode imaging contrasts, while effectively inhibiting primary/distal tumor growth and tumor metastasis.
Artificial photosynthesis of H2O2 from H2O and O2, as a spotless method, has aroused widespread interest. Up to date, most photocatalysts still suffer from serious salt-deactivated effects with huge consumption of photogenerated charges, which severely limit their wide application. Herein, by using a phenolic condensation approach, carbon dots, organic dye molecule procyanidins and 4-methoxybenzaldehyde are composed into a metal-free photocatalyst for the photosynthetic production of H2O2 in seawater. This catalyst exhibits high photocatalytic ability to produce H2O2 with the yield of 1776 μmol g−1h−1 (λ ≥ 420 nm; 34.8 mW cm−2) in real seawater, about 4.8 times higher than the pure polymer. Combining with in-situ photoelectrochemical and transient photovoltage analysis, the active site and the catalytic mechanism of this composite catalyst in seawater are also clearly clarified. This work opens up an avenue for a highly efficient and practical, available catalyst for H2O2 photoproduction in real seawater.
We present a deep learning-based technique to infer high-quality facial reflectance and geometry given a single unconstrained image of the subject, which may contain partial occlusions and arbitrary illumination conditions. The reconstructed high-resolution textures, which are generated in only a few seconds, include high-resolution skin surface reflectance maps, representing both the diffuse and specular albedo, and medium- and high-frequency displacement maps, thereby allowing us to render compelling digital avatars under novel lighting conditions. To extract this data, we train our deep neural networks with a high-quality skin reflectance and geometry database created with a state-of-the-art multi-view photometric stereo system using polarized gradient illumination. Given the raw facial texture map extracted from the input image, our neural networks synthesize complete reflectance and displacement maps, as well as complete missing regions caused by occlusions. The completed textures exhibit consistent quality throughout the face due to our network architecture, which propagates texture features from the visible region, resulting in high-fidelity details that are consistent with those seen in visible regions. We describe how this highly underconstrained problem is made tractable by dividing the full inference into smaller tasks, which are addressed by dedicated neural networks. We demonstrate the effectiveness of our network design with robust texture completion from images of faces that are largely occluded. With the inferred reflectance and geometry data, we demonstrate the rendering of high-fidelity 3D avatars from a variety of subjects captured under different lighting conditions. In addition, we perform evaluations demonstrating that our method can infer plausible facial reflectance and geometric details comparable to those obtained from high-end capture devices, and outperform alternative approaches that require only a single unconstrained input image.
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