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Tumor-associated macrophages (TAMs) are pivotal in cancer progression, influencing tumor growth, angiogenesis, and immune evasion. This review explores the spatial and temporal heterogeneity of TAMs within the tumor microenvironment (TME), highlighting their diverse subtypes, origins, and functions. Advanced technologies such as single-cell sequencing and spatial multi-omics have elucidated the intricate interactions between TAMs and other TME components, revealing the mechanisms behind their recruitment, polarization, and distribution. Key findings demonstrate that TAMs support tumor vascularization, promote epithelial-mesenchymal transition (EMT), and modulate extracellular matrix (ECM) remodeling, etc., thereby enhancing tumor invasiveness and metastasis. Understanding these complex dynamics offers new therapeutic targets for disrupting TAM-mediated pathways and overcoming drug resistance. This review underscores the potential of targeting TAMs to develop innovative cancer therapies, emphasizing the need for further research into their spatial characteristics and functional roles within the TME.

Human pose estimation (HPE) has made significant progress with deep learning; however, it still faces challenges in handling occlusions, complex poses, and complex multi-person scenarios. To address these issues, we propose PoseNet++, a novel approach based on a 3-stacked hourglass architecture, incorporating three key innovations: the multi-scale spatial pyramid attention hourglass module (MSPAHM), coordinate-channel prior convolutional attention (C-CPCA), and the PinSK Bottleneck Residual Module (PBRM). MSPAHM enhances long-range channel dependencies, enabling the model to better capture structural relationships between limb joints, particularly under occlusion. C-CPCA combines coordinate attention (CA) and channel prior convolutional attention (CPCA) to prioritize keypoints’ regions and reduce the confusion in complex multi-person scenarios. The PBRM improves pose estimation accuracy by optimizing the receptive field and convolutional kernel selection, thus enhancing the network’s feature extraction capabilities in multi-scale and complex poses. On the MPII validation set, PoseNet++ improves the PCKh score by 3.3% relative to the baseline 3-stacked hourglass network, while reducing the number of model parameters and the number of floating-point operations by 60.3% and 53.1%, respectively. Compared with other mainstream human pose estimation models in recent years, PoseNet++ achieves the state-of-the-art performance on the MPII, LSP, COCO and CrowdPose datasets. At the same time, the model complexity of PoseNet++ is much lower than that of methods with similar accuracy.

The various luminescent properties of carbon nanodots (CDs) reveal fascinating applications in several areas. Here, bright and multicolor chemiluminescence (CL) is realized from CDs, whose CL quantum yield can be optimized by adjusting the energy level alignment between the CDs and 1,2‐dioxetanedione intermediate generated from the reaction of peroxalate and hydrogen peroxide. A CL quantum yield of 9.32 × 10−3 Einsteins mol−1, maximal luminance of 3.28 cd m−2, and lifetime of 186.4 s are achieved in red CDs, all of which are the best values ever reported for CDs. As a proof‐of‐concept prototype, a high‐quality information encryption strategy is established via CD based CL imaging techniques by virtue of the high brightness and multicolor CL. Bright and Multicolour Chemiluminescence (CL) based on carbon nanodots (CDs) are developed by chemically initiated electron exchange luminescence. The CD based CL systems exhibit quantum yields up to 9.32 × 10‐3 Einsteins mol‐1, luminance up to 3.28 cd m‐2 and lifetimes up to 186.4 s. Information encryption and multicolour anti‐counterfeiting with active luminescence are demonstrated by the CL system.

It remains a challenge to obtain biocompatible afterglow materials with long emission wavelengths, durable lifetimes, and good water solubility. Herein we develop a photooxidation strategy to construct near-infrared afterglow carbon nanodots with an extra-long lifetime of up to 5.9 h, comparable to that of the well-known rare-earth or organic long-persistent luminescent materials. Intriguingly, size-dependent afterglow lifetime evolution from 3.4 to 5.9 h has been observed from the carbon nanodots systems in aqueous solution. With structural/ultrafast dynamics analysis and density functional theory simulations, we reveal that the persistent luminescence in carbon nanodots is activated by a photooxidation-induced dioxetane intermediate, which can slowly release and convert energy into luminous emission via the steric hindrance effect of nanoparticles. With the persistent near-infrared luminescence, tissue penetration depth of 20 mm can be achieved. Thanks to the high signal-to-background ratio, biological safety and cancer-specific targeting ability of carbon nanodots, ultralong-afterglow guided surgery has been successfully performed on mice model to remove tumor tissues accurately, demonstrating potential clinical applications. These results may facilitate the development of long-lasting luminescent materials for precision tumor resection. Biocompatible afterglow materials have potential in imaging applications, but are challenging to prepare. Here the authors report the development of carbon nanodots with near-infrared afterglow, and demonstrate their use in imaging for tumour resection.

Runways in permafrost regions face significant stability challenges due to their flat geometry, wide pavement area, and pronounced heat absorption effects. To address this issue, this study proposes a novel parallel perforated ventilation system for thermal regulation. The applicability and reliability of the numerical model are validated by comparing the parallel perforated ventilation’s air velocity, crushed rock layer performance, and temperature-depth profiles with existing experimental data. Key findings demonstrate that, under combined global warming and geothermal influence, the parallel perforated ventilation system maintains subgrade temperatures below 10 m depth in a frozen state for 30 years. The cooling efficacy of parallel perforated ventilation diminishes gradually with depth and time before stabilizing, with the most pronounced effect observed in the crushed rock layer, followed by silty clay, and least in strongly weathered rock. The study offers a scientific foundation for sustainable runway construction in permafrost areas, with implications for engineering practices under climate change scenarios.

Streak cameras, essential for ultrahigh temporal resolution diagnostics in laser-driven inertial confinement fusion, underpin the streak tube imaging LiDAR (STIL) system—a flash LiDAR technology offering high spatiotemporal resolution, precise ranging, enhanced sensitivity, and wide field of view. This study establishes a theoretical model of the STIL system, with numerical simulations predicting limits of temporal and spatial resolutions of ~6 ps and 22.8 lp/mm, respectively. Dynamic simulations of laser backscatter signals from targets at varying depths demonstrate an optimal distance reconstruction accuracy of 98%. An experimental STIL platform was developed, with the key parameters calibrated as follows: scanning speed (16.78 ps/pixel), temporal resolution (14.47 ps), and central cathode spatial resolution (20 lp/mm). The system achieved target imaging through streak camera detection of azimuth-resolved intensity profiles, generating raw streak images. Feature extraction and neural network-based three-dimensional (3D) reconstruction algorithms enabled target reconstruction from the time-of-flight data of short laser pulses, achieving a minimum distance reconstruction error of 3.57%. Experimental results validate the capability of the system to detect fast, low-intensity optical signals while acquiring target range information, ultimately achieving high-frame-rate, high-resolution 3D imaging. These advancements position STIL technology as a promising solution for applications that require micron-scale depth discrimination under dynamic conditions.

Myeloid cells accumulate extensively in most tumors and play a critical role in immunosuppression of the tumor microenvironment (TME). Like T cells, myeloid cells also express immune checkpoint molecules, which induce the immunosuppressive phenotype of these cells. In this review, we summarize the tumor-promoting function and immune checkpoint expression of four types of myeloid cells: macrophages, neutrophils, dendritic cells, and myeloid-derived suppressor cells, which are the main components of the TME. By summarizing the research status of myeloid checkpoints, we propose that blocking immune checkpoints on myeloid cells might be an effective strategy to reverse the immunosuppressive status of the TME. Moreover, combining nanotechnology, cellular therapy, and bispecific antibodies to achieve precise targeting of myeloid immune checkpoints can help to avoid the adverse effects of systemic administration, ultimately achieving a balance between efficacy and safety in cancer therapy. Graphical abstract

Background Biliary atresia (BA) is a rare pediatric cholestatic disorder characterized by progressive bile duct inflammation and fibrosis. The underlying molecular mechanisms of BA remain poorly defined. This study aimed to identify susceptibility genes causally associated with BA by integrating genome-wide association study (GWAS) and transcriptomic data, and to explore their potential immunopathological roles. Methods Two independent BA transcriptomic datasets from the Gene Expression Omnibus (GEO) were analyzed, and Mendelian randomization (MR) was applied to assess causal associations between differentially expressed genes (DEGs) and BA. Co-expressed genes were further evaluated for biological pathway enrichment and immune cell infiltration patterns. Expression levels of candidate genes were validated using quantitative real-time PCR (qRT-PCR) in liver tissues from 20 BA patients and 10 normal controls. Representative liver histopathology was also examined. Results We identified 816 DEGs, including 458 upregulated and 358 downregulated genes. MR analysis highlighted seven co-expressed genes with potential causal relevance to BA, including C12orf75, PSD3, CRIM1, CHIT1 (upregulated), and SEC14L4, MAPRE3, TCEA3 (downregulated). qRT-PCR validation confirmed significantly elevated expression of C12orf75, PSD3, and CHIT1, and reduced expression of TCEA3 in BA liver tissues compared to controls ( P  < 0.05), consistent with MR predictions. Histopathological analysis revealed severe portal fibrosis, bile duct proliferation, and pseudolobule formation in BA samples, whereas normal controls exhibited preserved hepatic architecture with minimal fibrotic changes. Conclusion This study identifies a panel of immune- and transcription-related genes with potential causal roles in BA and validates their expression in human liver tissue. These findings offer new insights into the genetic and molecular basis of BA, supporting future efforts in subtype classification and immunomodulatory therapeutic development.

A growing number of web APIs published on the Internet allows mashup developers to discover appropriate web APIs for polishing mashups. Developers often have to manually pick and choose several web APIs from extremely massive candidates, which is a laborious and cumbersome task. Fortunately, recommender system comes into existence. Some approaches perform recommendations in cloud platforms by utilizing historical records of Mashup-API interactions stored in edge nodes. However, many of these methods often pay more attention to recommendation accuracy while ignoring recommendation diversity, i.e., there are usually popular web APIs in recommendation list while most of the other novel web APIs are absent. The poor recommendation diversity may limit the usefulness of the recommendation results due to the lack of novelty. In order to implement an accurate and diversified web API recommendation, a novel MF-based recommendation approach named Div_PreAPI is put forward in this paper. Div_PreAPI integrates a weighting mechanism and neighborhood information into matrix factorization (MF) to implement diversified and personalized APIs recommendations. Finally, we conduct a series of experiments on a real-world dataset. Experimental results show the effectiveness of our proposal.

Nitrogen use efficiency (NUE) is the efficiency with which plants acquire and use nitrogen. Plants have high-affinity nitrate transport systems, which involve certain nitrate transporter (NRT) genes. However, limited data are available on the contribution of the NRT2/3 gene family in barley nitrate transport. In the present study, ten putative NRT2 and three putative NRT3 genes were identified using bioinformatics methods. All the HvNRT2/3 genes were located on chromosomes 3H, 5H, 6H or 7H. Remarkably, the presence of tandem repeats indicated that duplication events contributed to the expansion of the NRT2 gene family in barley. In addition, the HvNRT2/3 genes displayed various expression patterns at selected developmental stages and were induced in the roots by both low and high nitrogen levels. Furthermore, the overexpression of HvNRT2.1 improved the yield related traits in Arabidopsis. Taken together, the data generated in the present study will be useful for genome-wide analyses to determine the precise role of the HvNRT2/3 genes during barley development, with the ultimate goal of improving NUE and crop production.