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Shengli Oilfield has abundant low-permeability oil reservoirs with proven reserves of 1.18 billion tons， presenting a good prospect for development. However， there is a problem of inability to inject and produce during the development process of the oil reservoirs due to the low porosity， poor permeability， and strong heterogeneity of the low-permeability oil reservoir. A systematic review was conducted on the prediction and analysis methods for low-permeability oil reservoirs in Shengli Oilfield to achieve efficient development of low-permeability oil reservoirs， as well as efficient development technologies， and the advantages and disadvantages of existing technologies were analyzed to provide an outlook for the development technologies of low-permeability oil reservoirs. Based on research and practice， the following conclusions are drawn： ① Shengli Oilfield has developed fine reservoir characterization technologies such as reservoir prediction， in-situ stress prediction， and evaluation of reservoir heterogeneity. ② Based on theoretical and technological innovation， the physical limit of reserve production was constantly broken， forming differential development technology series， such as water injection development technology of horizontal wells in ultra-low permeability oil reservoirs， long well section multi-stage fracturing development technology of horizontal wells in tight oil reservoirs， well pattern adaptation technology for enhanced oil recovery of general low-permeability oil reservoirs， pressure flooding technology in low-permeability tight oil reservoirs， and viscous emulsion-surfactant flooding technology. Good development results have been achieved. ③ Combined with the actual needs of high-quality development and speed and efficiency improvement development of low-permeability oil reservoirs in Shengli Oilfield， the well pattern reconstruction technology of dual control streamline variation， CCUS technology， and nanobubble flooding technology were proposed， pointing out the development direction for improving oil recovery of low-permeability oil reservoirs.

Nanozymes can improve reactive oxygen species (ROS)-based cancer therapies by targeting cancer cells' antioxidant defense mechanisms, particularly glutathione (GSH) depletion, to overcome ROS-resistant cancer cells. Nanozymes, innovative enzyme-mimetic nanomaterials, can generate ROS, alter the tumor microenvironment (TME), and synergize with photodynamic therapy (PDT), chemodynamic therapy (CDT), radiotherapy, and immunotherapy. This review shows how nanozymes catalyze ROS generation, selectively deplete GSH, and target cancer elimination, offering clear advantages over standard therapies. Nanozymes selectively target cancer cells' antioxidant defenses to improve PDT, CDT, and radiation therapies. To maximize nanozyme-based cancer treatment efficacy, biodistribution, biocompatibility, and tumor heterogeneity must be assessed. To improve cancer treatment, multifunctional, stimuli-responsive nanozymes and synergistic combination drugs should be developed. [ABSTRACT FROM AUTHOR]

High-frequency workpieces are specialized items characterized by complex internal textures and minimal variance in properties. Under intricate lighting conditions, existing mainstream image recognition models struggle with low precision when applied to the identification of high-frequency workpiece images. This paper introduces a high-frequency workpiece image recognition model based on a hybrid attention mechanism, HAEN. Initially, the high-frequency workpiece dataset is enhanced through geometric transformations, random noise, and random lighting adjustments to augment the model's generalization capabilities. Subsequently, lightweight convolution, including one-dimensional and dilated convolutions, is employed to enhance convolutional attention and reduce the model's parameter count, extracting original image features with robustness to strong lighting and mitigating the impact of lighting conditions on recognition outcomes. Finally, lightweight re-estimation attention modules are integrated at various model levels to reassess spatial information in feature maps and enhance the model's representation of depth channel features. Experimental results demonstrate that the proposed model effectively extracts features from high-frequency workpiece images under complex lighting, outperforming existing models in image classification tasks with a precision of 97.23%. [ABSTRACT FROM AUTHOR]

Summary: Monitoring and localizing molecules on living plants is critical for understanding their growth, development and disease. However, current techniques for molecular imaging of living plants often lack spatial information or require tedious pre‐labelling. Here, we proposed a novel molecular imaging platform that combines sliver nanowire‐doped Ti3C2 MXene (Ag NWs@MXene) flexible film substrate with laser desorption/ionization mass spectrometry imaging (AMF‐LDI‐MSI) to study the spatial distribution of biomolecules on the surface of living plants. This platform overcomes the MSI challenges posed by difficult‐to‐slice plant tissues (e.g., tough or water‐rich roots and fragile flowers) and enables precisely transfer and visualize the molecule. Comparisons of the measurement results to those from matrix‐assisted LDI‐MSI (MALDI‐MSI) technology demonstrate the accuracy and reliability of the platform. Biocompatibility evaluations indicated that the platform without observable adverse effects on the health of living plants. The distribution of growth and disease‐associated signalling molecules, such as choline, organic acids and carbohydrates, can be in situ non‐destructively detected on the surfaces of living plants, which is important for tracking the health of plants and their diseased areas. AMF‐LDI‐MSI platform can serve as a promising tool for label‐free, in situ and non‐destructive monitoring of functional biomolecules and plant growth from a spatial perspective. [ABSTRACT FROM AUTHOR]

In order to analyze the genetic diversity of Atractylodes chinensis (DC.) Koidz., 158 A. chinensis germplasm resources from 16 populations were used as materials. The variation coefficient and diversity index of 13 phenotypic traits were analyzed, and the correlation analysis, principal component analysis and cluster analysis were performed. The genetic diversity was analyzed by SSR molecular markers. and the population structure was analyzed using Structure 2.3.4 software. The results showed that the coefficient of variation of 13 phenotypic traits of 158 A. chinensis germplasms ranged from 0.63% to 89.54%, and the variation coefficient of stem number was the largest. The Shannon-Wiener diversity index (H) ranged from 0.58 to 2.95, and main stem diameter was the highest diversity index. Correlation analysis showed that 13 phenotypic traits were correlated, among which plant height was significantly positively correlated with main stem diameter, leaf length, leaf width, stem number and crown width (P<0.05) . Principal component analysis showed that leaf width, crown width, stem number, lodging condition, flowering period and leaf color were the key factors affecting phenotypic traits of A. chinensis. Based on 13 phenotypic characters, 158 A. chinensis germplasms were divided into 3 groups. A total of 128 polymorphic bands were amplified by 17 SSR core primers, and the polymorphism rate was 88.56%. The variation ranges of Nei' s gene diversity index (h), Shannon' s information index (I) and polymorphic information content were 0.03-0.41, 0.29-0.60 and 0.30-0.86, respectively. Based on 17 pairs of SSR markers, 158 A. chinensis germplasms were divided into 3 groups by UPGMA (unweighted pair group method with arithmetic mean) method. The analysis of population structure showed that 158 germplasms were divided into 3 groups, which 10 germplasms were mixed populations with complex genetic background. Comprehensive analysis showed that 60 germplasms of 158 A. chinensis germplasms were classified according to phenotype and SSR markers, and 20 germplasms were closely related, which indicated that geographical origin had a certain influence on the classification, but it was not a decisive factor. Above results provided theoretical reference for germplasm resources identification and selection of excellent germplasms for A. chinensis. [ABSTRACT FROM AUTHOR]

Landfilling is one of the important methods in hazardous waste treatment. In order to prevent the landfill leachate from connecting with external groundwater and prevent leachate from flowing into external polluted water bodies and soil, vertical anti-seepage membranes will be installed along the perimeter of the reservoir area. Given the structural characteristics of vertical HDPE membranes and site conditions, this study proposes the application of three-dimensional high-density electrical resistivity (3D-HDER) tomography for the detection of these membranes. Compared with the traditional resistivity method, the high-density resistivity method can realize automatic switching of electrode device type, electrode spacing and measuring points during the measurement process, thereby improving detection accuracy and work efficiency. By employing 3D-HDER for data collection on vertical HDPE membranes and inverting the subsurface resistivity distribution, the presence of leaks can be inferred based on the distribution of low-resistivity anomalies in the target underground area, thereby facilitating the integrity assessment of the vertical HDPE membranes. This research was applied in a practical investigation at a municipal landfill site in Hunan Province, where field tests were conducted, and the effectiveness of 3D-HDER in detecting vertical HDPE membranes was validated through excavation. The experimental results indicate that: (1) 3DHDER is capable of effectively and accurately locating shallow leaks, and (2) low resistivity anomalies were observed in the vicinity of the leaks.

Curcumin is widely recognized for its diverse antitumor properties, ranging from breast cancer to many other types of cancers. However, its role in the tumor microenvironment remains to be elucidated. In this study, we established a 3D tumor spheroids model that can simulate the growth environment of tumor cells and visualized the antitumor metabolic alteration caused by curcumin using mass spectrometry imaging technology. Our results showed that curcumin not only exerts a profound impact on the growth and proliferation of breast cancer cells but in situ multivariate statistical analysis also reveals the significant effect on the overall metabolic profile of tumor spheroids. Meanwhile, our visualization map characterized curcumin metabolic processes of reduction and glucuronidation in tumor spheroids. More importantly, abnormal metabolic pathways related to lipid metabolism and polyamine metabolism were also remodeled at the metabolite and gene levels after curcumin intervention. These insights deepen our comprehension of the regulatory mechanism of curcumin on the tumor metabolic network, furnishing powerful references for antitumor treatment. [ABSTRACT FROM AUTHOR]