Excessive anthropogenic CO2 emission has caused a series of ecological and environmental issues, which threatens mankind's sustainable development. Mimicking the natural photosynthesis process (i.e., ...artificial photosynthesis) by electrochemically converting CO2 into value‐added products is a promising way to alleviate CO2 emission and relieve the dependence on fossil fuels. Recently, Sn‐based catalysts have attracted increasing research attentions due to the merits of low price, abundance, non‐toxicity, and environmental benignancy. In this review, the paradigm of nanostructure engineering for efficient electrochemical CO2 reduction (ECO2R) on Sn‐based catalysts is systematically summarized. First, the nanostructure engineering of size, composition, atomic structure, morphology, defect, surficial modification, catalyst/substrate interface, and single‐atom structure, are systematically discussed. The influence of nanostructure engineering on the electronic structure and adsorption property of intermediates, as well as the performance of Sn‐based catalysts for ECO2R are highlighted. Second, the potential chemical state changes and the role of surface hydroxides on Sn‐based catalysts during ECO2R are introduced. Third, the challenges and opportunities of Sn‐based catalysts for ECO2R are proposed. It is expected that this review inspires the further development of highly efficient Sn‐based catalysts, meanwhile offer protocols for the investigation of Sn‐based catalysts.
Mimicking artificial photosynthesis process by electrochemically converting CO2 from waste/flue gas into valuable chemicals that are powered by the renewable energies offers a promising choice to reach a carbon‐balanced sustainable society. The proper nanostructure engineering of Sn‐based catalysts with low cost and abundance enables the production of CO, HCOOH/formate, CH3OH, and C2H4 with considerable product selectivity by electrochemical CO2 reduction.
This work explores the role of geometric phase (GP), which results from light induced conical intersection (LICI), in photodissociation process of the D2+ molecule through solving the time‐dependent ...Schrödinger equation. The dissociation results between two cases including GP and excluding GP are compared. Different from the case including GP, the angular distribution of photofragments excluding GP is non‐vanishing at θ=π/2 which is the angle between the molecular axis and the polarization direction of laser field. Furthermore, in strong field, when the initial vibrational energy is higher than the energy of LICI point, not only the photofragments distributions present obvious quantum interference structures, but also the angular distributions of the photofragments of two cases have opposite oscillatory structure around θ=π/2. This also shows that the GP effect and nonadiabatic effect of LICI are unified in photodissociation processes.
The time‐independent Schrödinger equation is solved in two cases including GP and excluding GP for the D2+ molecule. In strong field, when the initial vibrational energy is higher than the energy of LICI point, not only the photofragments distributions present obvious quantum interference structures, but also the angular distribution of photofragments excluding GP is nonvanishing around θ=π/2. We show that the GP effect and nonadiabatic effect of LICI are unified in photodissociation processes.
With the rapid development of society, the pace of land change continues to accelerate. Consequently, remote sensing change detection (CD) has become a vital method for monitoring geographical ...information changes across various domains. However, the increasingly diverse and complex environments and structures where change targets exist pose significant challenges to CD tasks. To address these challenges, we propose a novel approach called stagewise short and long distance dependency network (SLDDNet). SLDDNet uses CNN-Transformer architecture and proposes the Transformer semantic selector to capture long-range feature dependencies and enhance semantic associations from a global perspective. Moreover, the pyramid structure feature stacking is proposed to capture short-range feature dependencies and emphasize local feature information. By integrating these two types of features at each layer, SLDDNet focuses on semantic information and improves its ability to attend to feature details. Furthermore, SLDDNet enhances target position information through axial semantic enhancement and optimizes the network training process using a deep supervision mechanism. Through extensive experiments, SLDDNet outperforms mainstream and state-of-the-art methods on three datasets. Specifically, on the LEVIR-CD dataset, SLDDNet achieves an F1 score of 91.75% and an intersection over union (IoU) of 84.76%. On the WHU-CD dataset, it achieves an F1 score of 92.76% and an IoU of 85.78%. Finally, on the GZ-CD dataset, SLDDNet achieves an F1 score of 86.61% and an IoU of 76.38%.
At present, the treatment options available for idiopathic pulmonary fibrosis are both limited and often come with severe side effects, emphasizing the pressing requirement for innovative therapeutic ...alternatives. Myofibroblasts, which hold a central role in pulmonary fibrosis, have a close association with the Smad signaling pathway induced by transforming growth factor-β1 (TGF-β1) and the transformation of myofibroblasts driven by oxidative stress. Liquiritigenin, an active compound extracted from the traditional Chinese herb licorice, boasts a wide array of biomedical properties, such as anti-fibrosis and anti-oxidation. The primary objective of this study was to examine the impact of liquiritigenin on bleomycin-induced pulmonary fibrosis in mice and the underlying mechanisms.
The anti-pulmonary fibrosis and anti-oxidant effects of liquiritigenin in vivo were tested by HE staining, Masson staining, DHE staining and bio-chemical methods. In vitro, primary mouse lung fibroblasts were treated with TGF-β1 with or without liquiritigenin, the effects of liquiritigenin in inhibiting differentiation of myofibroblasts and facilitating the translocation of Nrf2 were valued using Quantitative real-time polymerase chain reaction (Q-PCR), western blotting and immunofluorescence. Nrf2 siRNA and SIRT1 siRNA were used to investigate the mechanism underlies liquiritigenin's effect in inhibiting myofibroblast differentiation.
Liquiritigenin displayed a dose-dependent reduction effect in bleomycin-induced fibrosis. In laboratory experiments, it was evident that liquiritigenin possessed the ability to enhance and activate sirtuin1 (SIRT1), thereby facilitating the nuclear translocation of Nrf2 and mitigating the oxidative stress-induced differentiation of primary mouse myofibroblasts. Moreover, our investigation unveiled that SIRT1 not only regulated myofibroblast differentiation via Nrf2-mediated antioxidant responses against oxidative stress but also revealed liquiritigenin's activation of SIRT1, enabling direct binding to Smad. This led to decreased phosphorylation of the Smad complex, constrained nuclear translocation, and suppressed acetylation of the Smad complex, ultimately curtailing the transcription of fibrotic factors. Validation in live subjects provided substantial evidence for the anti-fibrotic efficacy of liquiritigenin through the SIRT1/Nrf2 signaling pathway.
Our findings imply that targeting myofibroblast differentiation via the SIRT1/Nrf2 signaling pathway may constitute a pivotal strategy for liquiritigenin-based therapy against pulmonary fibrosis.
The purpose of pan-sharpening is to generate high-resolution multispectral (HRMS) images by combining multispectral images (MS) and panchromatic images (PAN), which have become an important part of ...remote sensing image processing. Therefore, how to better extract complete feature information from MS images and PAN images has become the focus of our attention. In this paper, we propose a new pansharpening method, called Transformer-based Dual-path cross fusion network (TDF) for Pan-sharpening remote sensing images, which aims to extract the spatial details of PAN images while maintaining the spectral fidelity of MS images. The whole network structure can be divided into two parts: in the encoder part, we adopt the Swin-Transformer module for the downsampling operation, which expands the sensory field of the network to the feature map, and then extracts the global information. However, since the Swin-Transformer module is not good at extracting pixel-level details, we introduce the Base Feature Extraction (BFE) and the Invertible Neural Network Block (INNB) modules for the interaction between the local and the global feature information. we also introduce the Edge-Enhancement Block (EEB) to further enhance the feature extraction at multi-scales during the image fusion process. In the decoder section, we once again employ the Swin Transformer module for downsampling. After the convolution operation and activation function, we utilize the Sub-Pixel Convolutional Neural Network for upsampling to generate the ultimate high-resolution multispectral images. Simulation experiments and real experiments are conducted on QuickBird (QB) and WorldView2 (WV2) datasets, which demonstrated our method are superior to the current methods.
Aberrant AKT activation contributes to cancer stem cell (CSC) traits in hepatocellular carcinoma (HCC). We previously reported that CD73 activated AKT signaling via the Rap1/P110β cascade. Here, we ...further explored the roles of CD73 in regulating CSC characteristics of HCC.
CD73 expression modulations were conducted by lentiviral transfections. CD73+ fractions were purified by magnetic-based sorting, and fluorescent-activated cell sorting was used to assess differentiation potentials. A sphere-forming assay was performed to evaluate CSC traits in vitro, subcutaneous NOD/SCID mice models were generated to assess in vivo CSC features, and colony formation assays assessed drug resistance capacities. Stemness-associated gene expression was also determined, and underlying mechanisms were investigated by evaluating immunoprecipitation and ubiquitylation.
We found CD73 expression was positively associated with sphere-forming capacity and elevated in HCC spheroids. CD73 knockdown hindered sphere formation, Lenvatinib resistance, and stemness-associated gene expression, while CD73 overexpression achieved the opposite effects. Moreover, CD73 knockdown significantly inhibited the in vivo tumor propagation capacity. Notably, we found that CD73+ cells exhibited substantially stronger CSC traits than their CD73- counterparts. Mechanistically, CD73 exerted its pro-stemness activity through dual AKT-dependent mechanisms: activating SOX9 transcription via c-Myc, and preventing SOX9 degradation by inhibiting glycogen synthase kinase 3β. Clinically, the combined analysis of CD73 and SOX9 achieved a more accurate prediction of prognosis.
Collectively, CD73 plays a critical role in sustaining CSCs traits by upregulating SOX9 expression and enhancing its protein stability. Targeting CD73 might be a promising strategy to eradicate CSCs and reverse Lenvatinib resistance in HCC.
We report the total syntheses of daphenylline (1), daphnipaxianine A (5), and himalenine D (6), three Daphniphyllum alkaloids from the calyciphylline A subfamily. A pentacyclic triketone was prepared ...by using atom‐transfer radical cyclization and the Lu 3+2 cycloaddition as key steps. Inspired by the proposed biosynthetic relationship between 1 and another calyciphylline A type alkaloid, we developed a ring‐expansion/aromatization/aldol cascade to construct the tetrasubstituted benzene moiety of 1. The versatile triketone intermediate was also elaborated into 5 and 6 through a C=C bond migration/aldol cyclization approach.
Hat trick: Total syntheses of three Daphniphyllum alkaloids of the calyciphylline A subfamily were inspired by the proposed biosynthetic relationship between 1 and another calyciphylline A type alkaloid. Thus, the tetrasubstituted benzene moiety of 1 was constructed by a ring‐expansion/aromatization/aldol cascade, and the versatile triketone intermediate was also elaborated into 2 and 3 through C=C bond migration and aldol cyclization.
In the current era, diffusion models have emerged as a groundbreaking force in the realm of medical image segmentation. Against this backdrop, we introduce the Diffusion Text-Attention Network ...(DTAN), a pioneering segmentation framework that amalgamates the principles of text attention with diffusion models to enhance the precision and integrity of medical image segmentation. Our proposed DTAN architecture is designed to steer the segmentation process towards areas of interest by leveraging a text attention mechanism. This mechanism is adept at identifying and zeroing in on the regions of significance, thus improving the accuracy and robustness of the segmentation. In parallel, the integration of a diffusion model serves to diminish the influence of noise and irrelevant background data in medical images, thereby improving the quality of the segmentation results. The diffusion model is instrumental in filtering out extraneous factors, allowing the network to more effectively capture the nuances and characteristics of the target regions, which in turn enhances segmentation precision. We have subjected DTAN to rigorous evaluation across three datasets: Kvasir-Sessile, Kvasir-SEG, and GlaS. Our focus was particularly drawn to the Kvasir-Sessile dataset due to its relevance to clinical applications. When benchmarked against other state-of-the-art methods, our approach demonstrated significant improvements on the Kvasir-Sessile dataset, with a 2.77% increase in mean Intersection over Union (mIoU) and a 3.06% increase in mean Dice Similarity Coefficient (mDSC). These results provide strong evidence of the DTAN's generalizability and robustness, and its distinct advantages in the task of medical image segmentation.
This brief studies Nash equilibrium (NE) seeking problems among groups of players in non-cooperative games. Instead of having global information, this group of players exchanges their information via ...a topology graph. By integrating the leader-follower consensus protocol with the gradient descent method, we design a new finite-time event-triggered NE seeking strategy. The NE is practical finite-time stable under the proposed algorithm, even though estimate updates occur only when the triggering condition is satisfied. Moreover, players' estimates on the actions converge to sufficiently small circles centered at the actual actions in a fixed time, which can be predefined by adjusting the design parameters. The strictly positive inter-execution time is proven to reveal the inexistence of Zeno behavior. Lastly, a simulation is provided to demonstrate the feasibility and efficiency of the designed algorithm.
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