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An accurate assessment of grassland carbon stocks is essential for understanding their role in China's terrestrial carbon cycle. At regional scales, combining remote sensing technology with carbon density has become a common approach. However, substantial variability among remote sensing inversion models, particularly in theoretical foundations, variable selection, and algorithmic implementation, introduces significant uncertainty into estimating grassland carbon density. This study focuses on the grassland ecosystems in Gansu Province, China, employing both an overall approach (without distinguishing between grassland types) and a stratified approach, classifying the grassland into seven distinct types: alpine meadow steppe, temperate steppe, lowland meadow, alpine meadow, mountain meadow, shrubby grassland, and temperate desert. Using remote sensing, topography, climate, and 490 measured sample data points, this study employs five representative inversion models from three model categories: parametric (single-factor model and stepwise multivariate linear regression), spatial (geographically weighted regression (GWR) and multi-scale geographically weighted regression (MGWR)), and non-parametric (random forest (RF)). Inversion models were constructed for four components of the grassland ecosystem: aboveground (AGBC) and belowground biomass carbon density (BGBC), dead organic matter carbon density (DOMC), and soil organic carbon density (SOC). The applicability of each model was then systematically compared and analyzed. The main conclusions are as follows: (1) The overall estimation results demonstrate that the GWR model is the optimal choice for inverting AGBC, DOMC, and SOC, with coefficients of determination (R2) of 0.67, 0.60, and 0.92, respectively. In contrast, the MGWR model is best suited for BGBC, with an R2 value of 0.73. (2) The stratified estimation results suggest that the optimal inversion models for AGBC and BGBC are predominantly the MGWR and RF models selected through the recursive feature elimination algorithm. For DOMC, the optimal model is a spatial model, while SOC is most accurately estimated using the GWR and RF models selected via the Boruta algorithm. (3) When comparing the inversion results of the optimal overall and stratified approaches, the stratified estimations of AGBC, BGBC, and DOMC (R2 = 0.80, 0.78, and 0.73, respectively) outperformed those of the overall approach. In contrast, the SOC estimates followed an opposite trend, with the overall approach yielding a higher R2 value of 0.92. (4) Generally, variable selection significantly enhanced model accuracy, with spatial and non-parametric models demonstrating superior precision and stability in estimating the four carbon density components of grassland. These findings provide methodological guidance for converting sample point carbon density data into regional-scale estimates of grassland carbon storage. [ABSTRACT FROM AUTHOR]

Higher alcohol synthesis through the Fischer–Tropsch (F–T) process was considered a promising route for the efficient utilization of fossil resources could be achieved. The CuCo catalysts were proven to be efficient candidates and attracted much interest. Great efforts have been made to investigate the active sites and mechanisms of CuCo catalysts. However, the industrialized application of CuCo catalysts in this process was still hindered. The poor stability of this catalyst was one of the main reasons. This short review summarized the recent development of active sites on the CuCo catalysts for higher alcohol synthesis, including CuCo alloy particles, CuCo core–shell particles, and unsaturated particles. The complex active sites and their continual changes during the reaction led to the poor stability of the catalysts. The effect of active sites on catalytic performance was discussed. Furthermore, the key factors in fabricating stable CuCo catalysts were proposed. Finally, reasonable proposals were proposed for designing efficient and stable CuCo catalysts in higher alcohol synthesis. [ABSTRACT FROM AUTHOR]

Objectives: Cancer cells with 'stemness' are generally resistant to chemoradiotherapy. This study aims to compare the differences in radiation sensitivity of A549 and CD44+A549 stem-like cells to X-rays and carbon ion radiation (C-ions), and to find a target that can kill cancer stem-like cells (CSCs) of non-small cell lung cancer (NSCLC). Methods: The study used two cell lines (A549 and CD44+A549). The tumorigenicity of cells was tested with animal experiments. The cells were irradiated with X-rays and C-ions. Cell viability was detected using the CCK-8 and EdU assay. A liquid chromatograph-mass spectrometer (LC–MS) helped detect metabolic differences. Protein and mRNA expression were detected using a Western blot, reverse transcription-quantitative (RT-qPCR), and PCR array. The autophagic activity was monitored with a CYTO-ID® Autophagy Detection Kit 2.0. Immunofluorescence and co-immunoprecipitation helped to observe the localization and interaction relationships. Results: First, we verified the radio-resistance of CD44+A549 stem-like cells. LC-MS indicated the difference in autophagy between the two cells, followed by establishing a correlation between the radio-resistance and autophagy. Subsequently, the PCR array proved that TGM2 is significantly upregulated in CD44+A549 stem-like cells. Moreover, the TGM2 knockdown by small interfering RNA could decrease the radio-resistance of CD44+A549 cells. Bioinformatic analyses and experiments showed that TGM2 is correlated with the expression of CD44 and LC3B. Additionally, TGM2 could directly interact with LC3B. Conclusions: We established the CD44-TGM2-LC3 axis: CD44 mediates radio-resistance of CD44+A549 stem-like cells through TGM2 regulation of autophagy. Our study may provide new biomarkers and strategies to alleviate the radio-resistance of CSCs in NSCLC. [ABSTRACT FROM AUTHOR]

With the decrease of crude oil reserves and the aggravation of environmental problems, it is urgent to find new technologies for producing fuels and chemicals. Cu-based catalyst is one of the important catalysts for the direct synthesis of ethanol and higher alcohols (C2+ alcohols) from syngas, but it has the problem of low selectivity for target products. A series of CuZnAl catalysts (Cat-Alx) with different Al component contents (n(Al)/n(Zn)) were prepared by the complete liquid-phase method. The phase compositions, texture properties and reduction properties of the catalysts were characterized by X-ray diffraction, N2 adsorption/desorption and H2-programmed temperature reduction, etc. The catalytic performances of Cat-Alx for CO hydrogenation to ethanol and C2+ alcohols were studied in a simulated slurry bed reactor. The results show that Cat-Al0.8 has the most abundant Cu+ species and more oxygen vacancies on its surface, showing relatively optimal catalytic performance. Reaction under the conditions of 280 °C, 4 MPa and V(H2)/V(CO) = 2/1 for 24 h, the CO conversion rate of Cat-Al0.8 is 18.45%, and the total alcohols selectivity is 30.34%, and the proportion of ethanol in the total alcohols is 30.19%, and the proportion of C2+ alcohols in the total alcohols is 48.08%. [ABSTRACT FROM AUTHOR]

With the rapid development of the shipping industry, the number of ships is continuously increasing, and maritime accidents happen frequently. In recent years, computer vision and drone flight control technology have continuously developed, making drones widely used in related fields such as maritime target detection. Compared to the cameras fixed on ships, a greater flexibility and a wider field of view is provided by cameras equipped on drones. However, there are still some challenges in high-altitude detection with drones. Firstly, from a top-down view, the shapes of ships are very different from ordinary views. Secondly, it is difficult to achieve faster detection speeds because of limited computing resources. To solve these problems, we propose YOLOv7-DyGSConv, a deep learning-based model for detecting ships in real-time videos captured by drones. The model is built on YOLOv7 with an attention mechanism, which enhances the ability to capture targets. Furthermore, the Conv in the Neck of the YOLOv7 model is replaced with the GSConv, which reduces the complexity of the model and improves the detection speed and detection accuracy. In addition, to compensate for the scarcity of ship datasets in top-down views, a ship detection dataset containing 2842 images taken by drones or with a top-down view is constructed in the research. We conducted experiments on our dataset, and the results showed that the proposed model reduced the parameters by 16.2%, the detection accuracy increased by 3.4%, and the detection speed increased by 13.3% compared with YOLOv7. [ABSTRACT FROM AUTHOR]

Inflammatory myofibroblastic tumor (IMT) infrequently involves the sigmoid colon, and has not previously been described in an infant sigmoid colon. An inflammatory myofibroblastic tumor arose from the sigmoid colon of an 11-month-old boy, confirmed by anaplastic lymphoma kinase (ALK), smooth muscle actin (SMA) and desmin immunohistochemical staining. The patient recovered well after complete resection of the tumor. Sigmoid IMT can occur in infancy. This eighth case is the youngest so far. The child did well after surgical resection. [ABSTRACT FROM AUTHOR]