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Lithium metal, the ideal anode material for rechargeable batteries, suffers from the inherent limitations of sensitivity to the humid atmosphere and dendrite growth. Herein, low-cost fabrication of a metallic-lithium anode that is stable in air and plated dendrite-free from an organic-liquid electrolyte solves four key problems that have plagued the development of large-scale Li-ion batteries for storage of electric power. Replacing the low-capacity carbon anode with a safe, dendrite-free lithium anode provides a fast charge while reducing the cost of fabrication of a lithium battery, and increasing the cycle life of a rechargeable cell by eliminating the liquid-electrolyte ethylene-carbonate additive used to form a solid-electrolyte interphase passivation layer on the anode that is unstable during cycling. This solution is accomplished by formation of a hydrophobic solid-electrolyte interphase on a metallic-lithium anode that allows for handling of the treated lithium anode membrane in a standard dry room during cell fabrication. The lithium metal is a promising anode material for batteries; however, the growth of dendrite and its instability against moisture are two technical challenges. Here the authors address both issues by introducing a bifunctional layer consisting of hydrophobic graphite fluoride and lithium fluoride.

The challenge of enhancing the detection accuracy of widely adopted and stable object detectors while maintaining cost-effectiveness has long been a topic of significant interest and concern within the industry. To address this challenge, this paper proposes a general relation-based self-distillation framework suitable for object detection to help existing detectors achieve a better balance between accuracy and overhead. Compared to existing self-distillation methods, the framework we propose focuses on integrating relation-based knowledge into the self-distillation framework. To achieve this goal, we propose a relation-based self-distillation method within the framework and design a targeted optimization strategy in the form of an adaptive filtering strategy. The relation-based self-distillation method constrains the detector from focusing on the differences in the representation of the same type of object in different scenarios; and the adaptive filtering strategy filters the low-confidence results predicted by the detector before calling the matching mechanism, thereby ensuring the efficiency of the training process. A large number of experimental results show that our method can significantly improve the accuracy of existing detectors and reduce their redundant prediction results without increasing the computational resource overhead of existing detectors.

High-entropy alloys, with their unique structural characteristics and intrinsic properties, have evolved to be one of the most popular catalysts for energy-related applications. However, the geometry of the traditional nanoparticle morphology confines the majority of active atoms to the particle core, deeming them ineffective. In this study, we present a class of two-dimensional high-entropy alloys, namely, high-entropy metallenes, constructed by alloying various single-atom metals in atomically thin layers and reveal their great feasibility for electrocatalytic nitrate reduction to ammonia. Through multimetal interactions, various active centres are formed and sufficiently exposed over the metallene. Each element performs its own duties and jointly lowers the energy barrier of the rate-determining step. As expected, the proof-of-concept PdCuNiCoZn high-entropy metallene delivers satisfactory catalytic performance across wide pH ranges. In particular, in a strongly alkaline electrolyte, a maximum ammonia yield rate of 447 mg h −1 mg −1 and a high Faradaic efficiency of 99.0% are achieved. The conventional nanoparticle morphology in high-entropy alloys confines most active atoms to the particle core, making them inaccessible. Here, two-dimensional high entropy metallenes are reported, achieving maximized atom utilization and showing great feasibility for nitrate reduction.

Hepatocellular carcinoma (HCC) is a common malignant tumor with a complex immune evasion mechanism posing a challenge to treatment. The role of the S100A10 gene in various cancers has garnered significant attention. This study aims to elucidate the impact of S100A10 on CD8 + T cell exhaustion via the cPLA2 and 5-LOX axis, thereby elucidating its role in immune evasion in HCC. By analyzing the HCC-related data from the GEO and TCGA databases, we identified differentially expressed genes associated with lipid metabolism and developed a prognostic risk model. Subsequently, through RNA-seq and PPI analyses, we determined vital lipid metabolism genes and downstream factors S100A10, ACOT7, and SMS, which were significantly correlated with CD8 + T cell infiltration. Given the most significant expression differences, we selected S100A10 for further investigation. Both in vitro and in vivo experiments were conducted, including co-culture experiments of CD8 + T cells with MHCC97-L cells, Co-IP experiments, and validation in an HCC mouse model. S100A10 was significantly overexpressed in HCC tissues and potentially regulates CD8 + T cell exhaustion and lipid metabolism reprogramming through the cPLA2 and 5-LOX axis. Silencing S100A10 could inhibit CD8 + T cell exhaustion, further suppressing immune evasion in HCC. S100A10 may activate the cPLA2 and 5-LOX axis, initiating lipid metabolism reprogramming and upregulating LTB4 levels, thus promoting CD8 + T cell exhaustion in HCC tissues, facilitating immune evasion by HCC cells, ultimately impacting the growth and migration of HCC cells. This research highlights the critical role of S100A10 via the cPLA2 and 5-LOX axis in immune evasion in HCC, providing new theoretical foundations and potential targets for diagnosing and treating HCC.

The objective of cross-scene hyperspectral image (HSI) classification is to develop models capable of adapting to the “domain gap” that exists between different scenes, enabling accurate object classification in previously unseen scenes. Many researchers have devised various domain adaptation techniques aimed at aligning the statistical or spectral distributions of data from diverse scenes. However, many previous studies have overlooked the potential benefits of incorporating spatial topological information from hyperspectral imagery, which could provide a more accurate representation of the inherent data structure in HSIs. To overcome this issue, we introduce an innovative approach for cross-scene HSI classification, founded on hierarchical prototype graph alignment. Specifically, this method leverages prototypes as representative embedded representations of all samples within the same class. By employing multiple graph convolution and pooling operations, multi-scale domain alignment is attained. Beyond statistical distribution alignment, we integrate graph matching to effectively reconcile semantic and topological information. Experimental results on several datasets achieve significantly improved accuracy and generalization capabilities for cross-scene HSI classification tasks.

Comparative study on micro-deep hole drilling by picosecond ultra-short pulse laser in four metals, including copper, aluminum alloy, titanium alloy, and nickel alloy, was presented. Destructive testing was performed to measure the depth, shape, and side-wall morphology of micro-deep holes in this study. Diameters and depths of micro-deep holes in four metals ablated using the same processing parameters were compared. The multiple thresholds of metals had been calculated. Relationships between hole dimensions and multiple thresholds (the average ablation threshold, the ablation threshold and the thermal threshold) and physical properties (for example, the heat capacity, the melting temperature, the optical penetration depth and the absorbance, etc.) of the four metals are examined. Furthermore, the surface and side-wall morphologies of the micro-deep holes in four metals were demonstrated. The influence mechanisms, especially the side-wall morphology, were discussed, such as the thresholds, conical emission, self-cleaning effect, physical properties of metals, the energy transmission, the plasma effect, thermochemical reaction, and so on.

Triple-negative breast Cancer (TNBC) is a highly malignant cancer with unclear pathogenesis. Within the tumor microenvironment (TME), cancer-associated fibroblasts (CAFs) vitally influence tumor onset and progression. Thus, this research aimed to identify distinct subgroups of CAF using single-cell and TNBC-related information from the GEO and TCGA databases, respectively. The primary aim was to establish a novel predictive model based on the CAF features and their clinical relevance. Moreover, the CAFs were analyzed for their immune characteristics, response to immunotherapy, and sensitivity to different drugs. The developed predictive model demonstrated significant effectiveness in determining the prognosis of patients with TNBC, TME, and the immune landscape of the tumor. Of note, the expression of GPR34 was significantly higher in TNBC tissues compared to that in other breast cancer (non-TNBC) tissues, indicating that GPR34 plays a crucial role in the onset and progression of TNBC. In summary, this research has yielded a novel predictive model for TNBC that holds promise for the accurate prediction of prognosis and response to immunotherapy in patients with TNBC.

Machine learning has been introduced to assist humans in handling complex computations across various domains. However, concerns about inadequate user privacy have hindered its widespread adoption in communication systems. To enhance communication efficiency and reduce the overhead associated with machine learning, reconfigurable intelligent surfaces (RIS) and over-the-air computation (AirComp) have been utilized as supportive technologies. Simultaneously, federated learning (FL) emerged as an alternative approach to preserve user privacy and mitigate communication latency issues by requiring only the exchange of gradient information. Research has demonstrated that integrating FL with RIS leads to improved performance in system models. In this context, we propose a multiple RIS-assisted over-the-air FL system that incorporates a device selection algorithm based on channel quality. This algorithm identifies optimal devices for participation in the training process according to their communication channel conditions. Simulation results confirm that our proposed model outperforms the benchmark designs in terms of test accuracy and convergence speed.

Natural springs have the potential to provide important information on hydrogeochemical processes within aquifers. This study used traditional and classic technical methods and procedures to determine the characteristics and evolution of springs to gain further knowledge on the differences between hot saline springs and cold fresh springs. In a short river segment near Wenquanzhen in the eastern Sichuan Basin, southwest China, several natural springs coexist with total dissolved solids (TDS) ranging from less than 1 to 15 g/L and temperatures from 15 to 40 °C. The springs emanate from the outcropping Lower and Middle Triassic carbonates in the river valley cutting the core of an anticline. The cold springs are of Cl·HCO3-Na·Ca and Cl·SO4-Na types, and the hot saline springs are mainly of Cl-Na type. The chemistry of the springs has undergone some changes with time. The stable hydrogen and oxygen isotopes indicate that the spring waters are of a meteoric origin. The salinity of the springs originates from dissolution of minerals, including halite, gypsum, calcite and dolomite. The evolution of the springs involves the following mechanisms: the groundwater receives recharge from infiltration of precipitation, then undergoes deep circulation in the core of the anticline (incongruent dissolution of the salt-bearing strata occurs), and emerges in the river valley in the form of hot springs with high TDS. Groundwater also undergoes shallow circulation in the northern and southern flanks of the anticline and appears in the river valley in the form of cold springs with low TDS.

Purpose Recent studies have revealed an increase in the incidence rate of non-alcoholic fatty liver disease-related hepatocellular carcinoma (NAFLD-HCC). Furthermore, the association of Sphingosine 1-phosphate receptor 2 (S1PR2) with various types of tumours is identified, and the metabolism of conjugated bile acids (CBAs) performs an essential function in the onset and development of HCC. However, the association of CBA and S1PR2 with NAFLD-HCC is unclear. Methods The relationship between the expression of S1PR2 and the prognosis of patients suffering from NAFLD-HCC was investigated by bioinformatics techniques. Subsequently, the relationship between S1PR2 and the biological behaviours of HCC cell lines Huh 7 and HepG2 was explored by conducting molecular biology assays. Additionally, several in vivo animal experiments were carried out for the elucidation of the biological impacts of S1PR2 inhibitors on HCC cells. Finally, We used Glycodeoxycholic acid (GCDA) of CBA to explore the biological effects of CBA on HCC cell and its potential mechanism. Results High S1PR2 expression was linked to poor prognosis of the NAFLD-HCC patients. According to cellular assay results, S1PR2 expression could affect the proliferation, invasion, migration, and apoptosis of Huh 7 and HepG2 cells, and was closely associated with the G1/G2 phase of the cell cycle. The experiments conducted in the In vivo conditions revealed that the overexpression of S1PR2 accelerated the growth of subcutaneous tumours. In addition, JTE-013, an antagonist of S1PR2, effectively inhibited the migration and proliferation of HCC cells. Furthermore, the bioinformatics analysis highlighted a correlation between S1PR2 and the PI3K/AKT/mTOR pathway. GCDA administration further enhanced the expression levels of p-AKT, p-mTOR, VEGF, SGK1, and PKCα. Moreover, both the presence and absence of GCDA did not reveal any significant change in the levels of S1PR2, p-AKT, p-mTOR, VEGF, SGK1, and PKCα proteins under S1PR2 knockdown, indicating that CBA may regulates the PI3K/AKT/mTOR pathway by mediating S1PR2 expression. Conclusion S1PR2 is a potential prognostic biomarker in NAFLD-HCC. In addition, We used GCDA in CBAs to treat HCC cell and found that the expression of S1PR2 was significantly increased, and the expression of PI3K/AKT/mTOR signalling pathway-related signal molecules was also significantly enhanced, indicating that GCDA may activate PI3K/AKT/mTOR signalling pathway by up-regulating the expression of S1PR2, and finally affect the activity of hepatocellular carcinoma cells. S1PR2 can be a candidate therapeutic target for NAFLD-HCC. Collectively, the findings of this research offer novel perspectives on the prevention and treatment of NAFLD-HCC.