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Photoactivatable carbon monoxide‐releasing molecules (CORMs), typically based on transition‐metal carbonyl complexes, have reliance on activation by UV or visible light that restricts their biomedical applications. To address this limitation, a near‐infrared (NIR)‐responsive nanoplatform is presented based on upconversion nanoparticles (UCNPs) loading with manganese carbonyl complex Mn2(CO)10 that concurrently releases CO and manganese ion (Mn2+). With the UCNPs, the more tissue‐penetrable NIR is used to locally generate UV light for photodecomposition of Mn2(CO)10 into CO and manganese oxide (MnOX), after which MnOX is reduced to Mn2+ by the overexpressed glutathione in cancer cells. Moreover, the released Mn2+ can serve as a magnetic resonance imaging contrast agent to monitor the NIR‐controlled corelease of CO and Mn2+ in real time. Therefore, this nanoplatform can provide a potential strategy for NIR‐enabled spatiotemporally release of CO and Mn2+, enhancing the controlled delivery and biomedical application of CORMs.

Abstract The considerable influence of crude oil prices on the international economy has motivated numerous scholars to develop various prediction models. Two difficulties are encountered in forecasting. One is that the time series of crude oil prices show massive jumps in high frequency. The other is that the time series of crude oil prices are characterised by nonlinearity, structural breaks and highly volatile states. Therefore, we propose a hybrid model that incorporates the principle of decomposition–integration. Its overall process can be divided into the steps of decomposition and integration. In decomposition step, modified ensemble empirical mode decomposition is used to decompose the data on crude oil prices. Next, K‐means and principal component analysis are utilised to extract the optimal intrinsic mode function (IMF) from diverse frequencies amongst decomposed IMFs. Following decomposition, different forecasting methods are used to match IMFs with various frequencies for prediction. In accordance with structural breaks and the highly volatile states of high‐frequency series, hidden Markov regression is applied to describe predicted values in a probabilistic manner, providing objective interpretation and forecasting. A neural network is taken to predict intermediate‐frequency IMFs by using a fully connected network. Owing to the low frequency and tendency of the residual sequence to be more stable than other frequency sequences, the autoregressive integrated moving average model is applied as the forecasting method for prediction. Finally, all individual predictions for the eventual results are ensembled as final predictions. Results confirm that the proposed model is better than several normal hybrid and single models in terms of prediction accuracy and Diebold–Mariano test results. All results confirm that the newly proposed hybrid model is a promising tool for the analysis and forecasting of crude oil prices.

Perception plays a vital role in autonomous driving as it serves as a prerequisite for downstream planning and decision tasks. Existing research has mainly focused on developing vehicle-side perception models using a single type of sensors. However, relying solely on one type of on-board sensors to perceive the surrounding environment leads to perceptual deficiencies owing to inherent characteristics and sensor sparsity. To address this bottleneck, we propose ViT-FuseNet, a novel vehicle-infrastructure cooperative perception framework that utilizes a Vision Transformer to fuse feature maps extracted from LiDAR and camera data. The key component is a multimodal fusion module designed based on a cross-attention mechanism. ViT-FuseNet has two distinct advantages: i) it incorporates roadside LiDAR point clouds as additional inputs to enhance the 3D object detection capability of the vehicle; and ii) for the effective fusion of data from two different modal sensors, we employ a cross-attention mechanism for feature fusion, rather than directly merging camera features with point clouds at the raw data level. Extensive experiments are conducted using the DAIR-V2X Dataset to demonstrate the effectiveness of the proposed method. Compared with advanced cooperative perception methods, our method achieves a 6.17% improvement in 3D-mAP (IoU=0.5) and an 8.72% improvement in 3D-mAP (IoU=0.7). Moreover, the framework achieves the highest 3D-mAP (IoU=0.5) in all three object categories of benchmarks for single-vehicle perception.

Abstract Background CD24+CK19+/CD24+SOX9+ resident liver cells are activated and expanded after chronic liver injury in a ductular reaction. However, the sources and functions of these cells in liver damage remain disputed. Results The current study combined genetic lineage tracing with in vitro small-molecule-based reprogramming to define liver progenitor cells (LPCs) derived from hepatic parenchymal and non-parenchymal tissues. tdTom+ hepatocytes were isolated from ROSA26 tdTomato mice following AAV8-Tbg-Cre-mediated recombination, EpCAM+ biliary epithelial cells (BECs) from wild-type intrahepatic bile ducts and ALB/GFP−EpCAM− cells were isolated from Alb CreERT /R26 GFP mice. A cocktail of small molecules was used to convert the isolated cells into LPCs. These in vitro cultured LPCs with CD24 and SOX9 expression regained the ability to proliferate. Transcriptional profiling showed that the in-vitro cultured LPCs derived from the resident LPCs in non-parenchymal tissues expressed Lipocalin-2 (Lcn2) at high levels. Accordingly, endogenous Cd24a + Lcn2 + LPCs were identified by integration of sc-RNA-sequencing and pathological datasets of liver dysfunction which indicates that LPCs produced by ductular reactions might also originate from the resident LPCs. Transplantation of in-vitro cultured Cd24a + Lcn2 + LPCs into CCl4-induced fibrotic livers exacerbated liver damage and dysfunction, possibly due to LCN2-dependent macrophage inflammatory response. Conclusions CD24+LCN2+ LPCs constituted the expanding ductular reaction and contributed to macrophage-mediated inflammation in chronic liver damage. The current findings highlight the roles of LPCs from distinct origins and expose the possibility of targeting LPCs in the treatment of chronic hepatic diseases. Graphical Abstract