This paper reviews the progress in the field of block copolymer-templated mesoporous materials, including synthetic methods, morphological and pore size control and their potential applications in energy storage and conversion devices.
Stat6 is known to drive macrophage M2 polarization. However, how macrophage polarization is fine-tuned by Stat6 is poorly understood. Here, we find that Lys383 of Stat6 is acetylated by the acetyltransferase CREB-binding protein (CBP) during macrophage activation to suppress macrophage M2 polarization. Mechanistically, Trim24, a CBP-associated E3 ligase, promotes Stat6 acetylation by catalyzing CBP ubiquitination at Lys119 to facilitate the recruitment of CBP to Stat6. Loss of Trim24 inhibits Stat6 acetylation and thus promotes M2 polarization in both mouse and human macrophages, potentially compromising antitumor immune responses. By contrast, Stat6 mediates the suppression of TRIM24 expression in M2 macrophages to contribute to the induction of an immunosuppressive tumor niche. Taken together, our findings establish Stat6 acetylation as an essential negative regulatory mechanism that curtails macrophage M2 polarization.
Free-standing 2D porous nanomaterials have attracted considerable interest as ideal candidates of 2D film electrodes for planar energy storage devices.N evertheless,t he construction of well-defined mesopore arrays parallel to the lateral surface,w hichf acilitate fast in-plane ionic diffusion, is achallenge.Now,auniversal interface self-assembly strategy is used for patterning 2D porous polymers,f or example, polypyrrole,p olyaniline,a nd polydopamine,w ith cylindrical mesopores on graphene nanosheets.T he resultant 2D sandwich-structured nanohybrids are employed as the interdigital microelectrodes for the assembly of planar micro-supercapacitors (MSCs), whichd eliver outstanding volumetric capacitance of 102 Fcm À3 and energy density of 2.3 mWh cm À3 , outperforming most reported MSCs.T he MSCs display remarkable flexibility and superior integration for boosting output voltage and capacitance.
Efficient structure optimization is one of the key factors for improving the oxygen reduction reaction (ORR) catalytic performance of carbon materials.
Multi-modal pre-training models have been intensively explored to bridge vision and language in recent years. However, most of them explicitly model the cross-modal interaction between image-text pairs, by assuming that there exists strong semantic correlation between the text and image modalities. Since this strong assumption is often invalid in real-world scenarios, we choose to implicitly model the cross-modal correlation for large-scale multi-modal pretraining, which is the focus of the Chinese project 'Wen-Lan' led by our team. Specifically, with the weak correlation assumption over image-text pairs, we propose a twotower pre-training model called BriVL within the crossmodal contrastive learning framework. Unlike OpenAI CLIP that adopts a simple contrastive learning method, we devise a more advanced algorithm by adapting the latest method MoCo into the cross-modal scenario. By building a large queue-based dictionary, our BriVL can incorporate more negative samples in limited GPU resources. We further construct a large Chinese multi-source imagetext dataset called RUC-CAS-WenLan for pre-training our BriVL model. Extensive experiments demonstrate that the pre-trained BriVL model outperforms both UNITER and OpenAI CLIP on various downstream tasks.
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