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The rapid development of urbanization has led to a continuous rise in number of elevators. This has led to elevator failures from time to time. At present, although there are some studies on elevator fault diagnosis, they are more or less limited by the lack of data to make the research more superficial. For such complex special equipment as elevator, it is difficult to obtain reliable and sufficient data to train the fault diagnosis model. To address this issue, this paper first establishes a numerical model of vertical vibration for elevators with three degrees of freedom. The obtained motion equations are then used as constraints to acquire simulated vibration data through PINNs. Next, the proposed e-RGCN is employed for elevator fault diagnosis. Finally, experimental validation shows that the fault diagnosis accuracy with the participation of digital twins exceeds 90%, and the accuracy of the proposed model reaches 96.61%, significantly higher than that of other comparative models.

Mapping tumor metabolic remodeling and their spatial crosstalk with surrounding non-tumor cells can fundamentally improve our understanding of tumor biology, facilitates the designing of advanced therapeutic strategies. Here, we present an integration of mass spectrometry imaging-based spatial metabolomics and lipidomics with microarray-based spatial transcriptomics to hierarchically visualize the intratumor metabolic heterogeneity and cell metabolic interactions in same gastric cancer sample. Tumor-associated metabolic reprogramming is imaged at metabolic-transcriptional levels, and maker metabolites, lipids, genes are connected in metabolic pathways and colocalized in the heterogeneous cancer tissues. Integrated data from spatial multi-omics approaches coherently identify cell types and distributions within the complex tumor microenvironment, and an immune cell-dominated “tumor-normal interface” region where tumor cells contact adjacent tissues are characterized with distinct transcriptional signatures and significant immunometabolic alterations. Our approach for mapping tissue molecular architecture provides highly integrated picture of intratumor heterogeneity, and transform the understanding of cancer metabolism at systemic level. The spatial signature of metabolic remodeling in tumours remains to be explored. Here, the integration of mass spectrometry imaging-based spatial metabolomics and lipidomics with microarray-based spatial transcriptomics allows the visualisation of metabolic heterogeneity in gastric cancer.

The breeding and large-scale adoption of hybrid seeds is an important achievement in agriculture. Rice hybrid seed production uses cytoplasmic male sterile lines or photoperiod/thermo-sensitive genic male sterile lines (PTGMS) as female parent. Cytoplasmic male sterile lines are propagated via cross-pollination by corresponding maintainer lines, whereas PTGMS lines are propagated via self-pollination under environmental conditions restoring male fertility. Despite huge successes, both systems have their intrinsic drawbacks. Here, we constructed a rice male sterility system using a nuclear gene named Oryza sativa No Pollen 1 (OsNP1). OsNP1 encodes a putative glucose–methanol–choline oxidoreductase regulating tapetum degeneration and pollen exine formation; it is specifically expressed in the tapetum and miscrospores. The osnp1 mutant plant displays normal vegetative growth but complete male sterility insensitive to environmental conditions. OsNP1 was coupled with an α-amylase gene to devitalize transgenic pollen and the red fluorescence protein (DsRed) gene to mark transgenic seed and transformed into the osnp1 mutant. Self-pollination of the transgenic plant carrying a single hemizygous transgene produced nontransgenic male sterile and transgenic fertile seeds in 1:1 ratio that can be sorted out based on the red fluorescence coded by DsRed. Cross-pollination of the fertile transgenic plants to the nontransgenic male sterile plants propagated the male sterile seeds of high purity. The male sterile line was crossed with ∼1,200 individual rice germplasms available. Approximately 85% of the F1s outperformed their parents in per plant yield, and 10% out-yielded the best local cultivars, indicating that the technology is promising in hybrid rice breeding and production.

Agrilinae is the largest subfamily in Buprestidae, which includes the four tribes, namely Coraebini, Agrilini, Aphanisticini, and Tracheini. However, there is a need to verify the evolutionary relationships among the taxa in Buprestidae. Thus, to explore the phylogenetic position of Aphanisticini, the mitochondrial genomes of Endelus continentalis and Cantonius szechuanensis were sequenced using next-generation sequencing technology. Three other mitogenomes of agriline beetles, Agrilus discalis , Sambus kanssuensis , and Habroloma sp., were also sequenced for the phylogenetic analyses. The divergence time of Buprestidae was estimated based on the mitogenomes. The general features of the known mitogenomes of Agrilinae were compared, analyzed, and summarized. Out of these five species, S . kanssuensis had the shortest mitogenome length (15,411), while Habroloma sp. had the longest (16,273). The gene arrangement of the five new sequences was identical to that of the reported buprestid mitogenomes. The Ka/Ks ratios of Meliboeus (0.79) and Endelus (0.78) were significantly larger than those of the other agriline genera. The results of the phylogeny indicated that Aphanisticini was more closely related to Tracheini and that the genus Sambus separated from the base of the Agrilinae clade at about 130 Ma. Moreover, Aphanisticini and Tracheini diverged at around 26 Ma.

Forest park tourism ecological security is the cornerstone of ensuring ecological tourism safety. Delineating the ecological carrying capacity within forest parks is crucial for enhancing the security of forest tourism resources. This study utilizes statistical data from China’s forest parks spanning 2004 to 2019, employing methodologies to comprehensively depict the spatiotemporal dynamic characteristics of forest park tourism ecology in China. Subsequently, this research forecasts the prospective trajectory of forest park tourism ecology in China from 2020 to 2029. The research findings reveal that China’s forest park tourism ecological footprint exhibits oscillating characteristics, while the overall touristic ecological carrying capacity shows a sustained upward trend. However, a significant portion of regions experience deficits in tourism ecology. Notably, the coldspot regions with ecological security features demonstrate relative stability, while the hotspot areas gradually transition from inland to eastern coastal regions. Spatially and temporally, the disparities in touristic ecological profit and deficit depict a “U”-shaped distribution, more pronounced along the east–west axis than the north–south orientation. The migratory shift in the touristic ecological surplus and deficit center gravitates towards the southwest, demonstrating a fluctuating trend characterized by varying migration speeds. The discernible difference between the east and west concerning touristic ecological profit and deficit amplifies the likelihood of imbalance, surpassing disparities between the north and south. Projections suggest a deepening forest park tourism ecological deficit in China from 2020 to 2029, particularly accentuating the unsustainable development of forest park resources in economically developed regions. Through this study, a more comprehensive understanding of the current status and changing trends in the ecological carrying capacity of forest park tourism can be obtained. This research provides theoretical and practical support to promote sustainable tourism development and establishes a solid foundation for the ecological security of future forest park tourism.

To address the challenges of high passenger density, severe object occlusion, and limited computational resources in escalator scenarios, this paper proposes SAL-YOLO, a lightweight detection model, and develops a safety warning system. The model is optimized in multiple dimensions based on the YOLOv8n architecture: (1) A lightweight backbone network, StarNet, is designed by integrating depth-wise separable convolutions and residual connections to improve feature extraction efficiency. (2) A Transformer-based Adaptive Image Feature Integration (AIFI) module is integrated to capture global contextual dependencies through a self-attention mechanism. (3) The C2f_star feature fusion module is designed to enhance the discriminative power of local features in scenarios with occlusion and small objects. (4) A Lightweight Shared Convolution (LWSC) detection head is proposed that uses shared convolution and a dynamic stride mechanism to optimize computational load. Experiments on a custom-built dataset show that SAL-YOLO achieves 92.6% Precision (P), 95.4% mAP@50, and 79.9% mAP@(0.5:0.95). The computational complexity and parameter count are reduced by 47.1% and 50%, respectively, compared to the baseline model YOLOv8n. Furthermore, by introducing a knowledge distillation framework with a Similarity-Preserving (SP) feature distillation strategy, the model’s performance improved to 94.6% P and 80.6% mAP@(0.5:0.95), achieving an inference speed of 500 FPS. Building on the aforementioned model, an innovative safety warning system is developed by integrating the DeepSeek large language model, establishing an end-to-end mechanism of ’behavior recognition - risk assessment - response plan generation’. When abnormal behaviors, such as falls or bending over, are detected, the system dynamically generates graded response strategies and triggers multi-modal alerts. Empirical validation shows that the system is highly robust in complex scenarios, such as varying illumination and dense occlusions, offering both a theoretical methodology and an application paradigm for intelligent public safety surveillance.

Background Diagnosing seronegative rheumatoid arthritis (RA) can be challenging due to complex diagnostic criteria. We sought to discover diagnostic biomarkers for seronegative RA cases by studying metabolomic and lipidomic changes in RA patient serum. Methods We performed comprehensive metabolomic and lipidomic profiling in serum of 225 RA patients and 100 normal controls. These samples were divided into a discovery set (n = 243) and a validation set (n = 82). A machine-learning-based multivariate classification model was constructed using distinctive metabolites and lipids signals. Results Twenty-six metabolites and lipids were identified from the discovery cohort to construct a RA diagnosis model. The model was subsequently tested on a validation set and achieved accuracy of 90.2%, with sensitivity of 89.7% and specificity of 90.6%. Both seropositive and seronegative patients were identified using this model. A co-occurrence network using serum omics profiles was built and parsed into six modules, showing significant association between the inflammation and immune activity markers and aberrant metabolism of energy metabolism, lipids metabolism and amino acid metabolism. Acyl carnitines (20:3), aspartyl-phenylalanine, pipecolic acid, phosphatidylethanolamine PE (18:1) and lysophosphatidylethanolamine LPE (20:3) were positively correlated with the RA disease activity, while histidine and phosphatidic acid PA (28:0) were negatively correlated with the RA disease activity. Conclusions A panel of 26 serum markers were selected from omics profiles to build a machine-learning-based prediction model that could aid in diagnosing seronegative RA patients. Potential markers were also identified in stratifying RA cases based on disease activity.

This paper proposes a bearing fault diagnosis method based on Kolmogorov–Arnold Convolutional Network: Adaptive Context-aware Graph Channel Attention with Squeeze-and-Excitation Networks (KANConv-ACGCA-SENet). Firstly, a new structure of KANs is applied to Convolutional Neural Networks (CNN) for replacing traditional linear convolutional kernels. Secondly, a dual-branch fusion attention module, comprising the ACGCA modules, is proposed for use in learning fault features. This is achieved by capturing feature differences and utilising non-local(NL) operations, thereby enhancing the feature representation ability under different working conditions. Subsequently, context-aware features and non-local aggregation features are combined with the objective of obtaining global features. Finally, the SENet module is introduced with the aim of further enhancing the key information in the global features and improving the robustness of the model. The experimental results demonstrate that the method proposed in this paper achieves an average accuracy of 99.63% in a single load scenario and 99.05% in a variable working condition scenario. It exhibits high diagnostic accuracy and a superior capacity for generalization, proves that the KANConv represents a formidable alternative to the existing CNN-based variants for bearing fault diagnosis.

The development of multifunctional biomaterials to repair bone defects after neoplasm removal and inhibit tumor recurrence remained huge clinical challenges. Here, we demonstrate a kind of innovative and multifunctional magnetic mesoporous calcium sillicate/chitosan (MCSC) porous scaffolds, made of M-type ferrite particles (SrFe 12 O 19 ), mesoporous calcium silicate (CaSiO 3 ) and chitosan (CS), which exert robust anti-tumor and bone regeneration properties. The mesopores in the CaSiO 3 microspheres contributed to the drug delivery property, and the SrFe 12 O 19 particles improved photothermal therapy (PTT) conversion efficacy. With the irradiation of NIR laser, doxorubicin (DOX) was rapidly released from the MCSC/DOX scaffolds. In vitro and in vivo tests demonstrated that the MCSC scaffolds possessed the excellent anti-tumor efficacy via the synergetic effect of DOX drug release and hyperthermia ablation. Moreover, BMP-2/Smad/Runx2 pathway was involved in the MCSC scaffolds promoted proliferation and osteogenic differentiation of human bone marrow stromal cells (hBMSCs). Taken together, the MCSC scaffolds have the ability to promote osteogenesis and enhance synergetic photothermal-chemotherapy against osteosarcoma, indicating MCSC scaffolds may have great application potential for bone tumor-related defects.

Alopecia areata (AA) is a common autoimmune disorder causing patchy hair loss. Epidemiological observations and molecular studies collectively suggest an underrecognized interplay between AA and cardiovascular disease (CVD). However, the relationship between them remains controversial and requires further investigation. To evaluate the association between AA and CVD through a meta-analysis of combinable results. We systematically searched four databases (MEDLINE, Embase, Web of Science, and Cochrane Library) for relevant studies from inception to December 6, 2024. Studies included in the analysis were cohort or case-control studies that focused on the relationship between AA and CVD. Two independent reviewers extracted the data. The study quality was evaluated using the Newcastle-Ottawa Scale. A random-effects model was used for meta-analysis to calculate the odds ratio (OR) and 95% confidence intervals (CIs). Our search yielded five studies involving 238,270 AA patients from three countries. The meta-analysis revealed that AA patients had an increased OR (OR = 1.71; 95% CI: 1.0 to 2.92; < 0.01) for CVD outcomes compared to the control group. Subgroup analysis revealed a stronger risk in patients with alopecia totalis or alopecia universalis (OR = 3.80; 95% CI: 1.65 to 8.73; < 0.01). Associations were not observed between patch-type AA and CVD, nor between AA and ischemic stroke or myocardial infarction. This meta-analysis suggests that AA patients, especially those with alopecia totalis or alopecia universalis, may have an elevated risk of developing CVD. Given the shared immunological mechanisms, systemic inflammation in AA may contribute to the development of atherosclerosis and increased cardiovascular risk. Further studies are needed to validate these findings and clarify the underlying mechanisms.