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Given the high prevalence and relapse rates of hepatocellular carcinoma (HCC), an increased capacity for early identification of patients most at risk for post-resection recurrence would help improve patient outcomes and prioritize health care resources. Here, we combined spatial multi-transcriptomics and proteomics approaches to characterize the tumor and immunological landscape of 61 samples. We observed a spatial and HCC-recurrence-associated distribution of natural killer (NK) cells in the invasive front and tumor center. Using artificial-intelligence alongside an extreme gradient-boosting algorithm, we developed the Tumor Immune MicroEnvironment Spatial (“TIMES”) score based on the expression of five NK-associated markers (SPON2, ZFP36L2, ZFP36, VIM, and HLA-DRB1) to predict HCC recurrence. We also demonstrated that TIMES score (HR = 29.6, P < 0.001) outperforms the current standard tools for patient risk stratification including the TNM (HR = 1.93, P = 0.113) and BCLC (HR = 1.55, P = 0.253) systems. In the clinic, we validated the model in 103 patients from three multi-centered cohorts achieve a real-world sensitivity of 90.00% and specificity of 90.24%. In the lab, following up on the individual marker with the highest prediction accuracy, in vivo models revealed that SPON2 increases IFN-γ secretion and enhances infiltration potential of NK cells at the invasive front. Additionally, we established the TIMES score on a publicly accessible website that can be easily achieved by different levels of pathology labs to facilitate global prediction of HCC recurrence risk and stratification of high-risk patients. With its ability to efficiently stratify high-risk patients, it exemplifying the utility of artificial intelligence to improve our understanding on TIME features that underlie tumor progression.

A complete diagnostic autopsy is the gold-standard to gain insight into Coronavirus disease 2019 (COVID-19) pathogenesis. To delineate the in situ immune responses to SARS-CoV-2 viral infection, here we perform comprehensive high-dimensional transcriptional and spatial immune profiling in 22 COVID-19 decedents from Wuhan, China. We find TIM-3-mediated and PD-1-mediated immunosuppression as a hallmark of severe COVID-19, particularly in men, with PD-1+ cells being proximal rather than distal to TIM-3+ cells. Concurrently, lymphocytes are distal, while activated myeloid cells are proximal, to SARS-CoV-2 viral antigens, consistent with prevalent SARS-CoV-2 infection of myeloid cells in multiple organs. Finally, viral load positively correlates with specific immunosuppression and dendritic cell markers. In summary, our data show that SARS-CoV-2 viral infection induces lymphocyte suppression yet myeloid activation in severe COVID-19, so these two cell types likely have distinct functions in severe COVID-19 disease progression, and should be targeted differently for therapy., Postmortem analyses provide useful information for COVID-19 etiology. Here the authors profile 22 deceased severe COVID-19 patients with transcriptomic and histological approaches to find correlations between the presence of viral antigens with lymphocyte suppression yet myeloid activation, hinting distinct functions of these cells during pathogenesis.

A series of MIL-53(Fe)-graphene nanocomposite photocatalysts were synthesized by a facile one-pot solvothermal reaction. All materials have been thoroughly characterized by FT-IR, SEM, TEM, PXRD, UV–vis DRS and PL analysis. It was demonstrated that the introducing of graphene on the MIL-53(Fe) would minimize the recombination of photogenerated electron-hole pairs, thus, leading to the enhancement of photocatalytic activity. MIL-53(Fe)-graphene nanocomposite was an efficient catalyst towards the photocatalytic selectively oxidation of alcohols to the corresponding aldehydes or ketones under visible light and ambient conditions. High selectivity of the aldehydes or ketones (99%) were obtained. Meanwhile, the catalyst can be recycled at least four times without loss of catalytic efficiency. A possible photocatalytic reaction mechanism involving a direct photogenerated hole-oxidation process was also investigated in detail by the active species trapping experiments and the related electrochemical analysis.

Light emitting diode (LED) indicators used on automobile meters are essential for safe driving and few errors can be tolerated. The current manual inspection approach can achieve only 95% accuracy rate in weeding out errors occurring in the production process. It is imperative to improve the accuracy of the inspection process to better achieve the goal of safe driving. This paper proposes an automatic inspection method for LED indicators for use on automobile meters. Firstly, red-green-blue (RGB) color images of LED indicators are acquired and converted into R, G, and B intensity images. A seeded region growing (SRG) algorithm, which selects seeds automatically based on Otsu’s method, is then used to extract the LED indicator regions. Finally, a region matching process based on the seed and three area parameters of each region is applied to inspect the LED indicators one by one to locate any errors. Experiments on standard automobile meters showed that the inspection accuracy rate of this method was up to 99.52% and the inspection speed was faster compared with the manual method. Thus, the new method shows good prospects for practical application.

In order to improve the reusability and efficiency of software development for in-vehicle information platform, a reusable and extensible object-oriented framework was proposed. Firstly, by analyzing the characteristics and demands in the related field, the meta-model of in-vehicle information platform was designed. Then, based on the meta-model and according to the domain feature, the framework was set up and described with unified modeling language (UML). As this framework could completely depict the functional properties of various in-vehicle information equipments, it could be the basis for system modeling and provide guidance for the development of specific in-vehicle information equipment. Lastly, the development procedure of the automobile meters static model was elaborated, demonstrating that this framework has an efficient reusability and is able to improve the efficiency of in-vehicle information device.