Fever is a serious condition that can lead to various consequences ranging from prolonged illness to death. Tetrastigma hemsleyanum Diels et Gilg (T. hemsleyanum) has been used for centuries to treat fever, but the specific chemicals responsible for its antipyretic effects are not well understood. This study aimed to isolate and identify the chemicals with antipyretic bioactivity in T. hemsleyanum extracts and to provide an explanation for the use of T. hemsleyanum as a Chinese herbal medicine for fever treatment. Our results demonstrate that kaempferol 3-rutinoside (K3OR) could be successfully isolated and purified from the roots of T. hemsleyanum. Furthermore, K3OR exhibited a significant reduction in rectal temperature in a mouse model of fever. Notably, a 4 μM concentration of K3OR showed more effective antipyretic effects than ibuprofen and acetaminophen. To explore the underlying mechanism, we conducted an RNA sequencing analysis, which revealed that PXN may act as a key regulator in the fever process induced by lipopolysaccharide (LPS). In the mouse model of fever, K3OR significantly promoted the secretion of IL-6 and TNF-α during the early stage in the LPS-treated group. However, during the middle to late stages, K3OR facilitated the elimination of IL-6 and TNF-α in the LPS-treated group. Overall, our study successfully identified the chemicals responsible for the antipyretic bioactivity in T. hemsleyanum extracts, and it answered the question as to why T. hemsleyanum is used as a traditional Chinese herbal medicine for treating fever. These findings contribute to a better understanding of the therapeutic potential of T. hemsleyanum in managing fever, and they provide a basis for further research and development in this field.

Efficient and intuitive programming strategies are essential for enabling robots to adapt to small-batch, high-mix production scenarios. Mixed reality (MR) and programming by demonstration (PbD) have shown great potential to lower the programming barrier and enhance human–robot interaction by leveraging natural human guidance. However, traditional offline programming methods, while capable of generating industrial-grade trajectories, remain time-consuming, costly to debug, and heavily dependent on expert knowledge. Conversely, existing MR-based PbD approaches primarily focus on improving intuitiveness but often suffer from low trajectory quality due to hand jitter and the lack of refinement mechanisms. To address these limitations, this paper introduces a coarse-to-fine human–robot collaborative programming paradigm. In this paradigm, the operator’s role is elevated from a low-level “trajectory drawer” to a high-level “task guider”. By leveraging sparse key points as guidance, the paradigm decouples high-level human task intent from machine-level trajectory planning, enabling their effective integration. The feasibility of the proposed system is validated through two industrial case studies and comparative quantitative experiments against conventional programming methods. The results demonstrate that the coarse-to-fine paradigm significantly improves programming efficiency and usability while reducing operator cognitive load. Crucially, it achieves this without compromising the final output, automatically generating smooth, high-fidelity trajectories from simple user inputs. This work provides an effective pathway toward reconciling programming intuitiveness with final trajectory quality.

Mass spectrometry (MS) is a core analytical tool in proteomics, and the quality of the generated data directly determines the effectiveness of downstream analyses and the reliability of final research conclusions. While MS is also widely used in other omics applications, this study focuses on label-free quantitative proteomics, where samples are represented as protein-abundance matrices derived from MaxQuant. However, MS data are typically characterized by high dimensionality and substantial noise, posing serious challenges for quality control (QC). Existing QC methods have limited feature extraction capabilities and struggled to capture the key information embedded in the data, resulting in poor performance in identifying anomalous samples. Here, we propose the Multi-Scale Feature Fusion-based Autoencoder (MFF-AE). This deep learning-based anomaly detection model achieves precise identification of anomalous samples by integrating both global and local data features. The model consists of three modules: an autoencoder-based backbone network that efficiently embeds raw data into a low-dimensional semantic space, a local feature extraction and fusion module designed to capture and integrate multi-scale features within MS data, and a sample identification module that enhances discriminative representations to enable accurate anomaly detection. To evaluate the effectiveness of the proposed model, we conduct extensive experiments on a benchmark dataset with synthesized anomalies. Quantitative results on the benchmark dataset show that, compared with 15 baseline models from statistical learning, deep learning, and ensemble learning, our model consistently achieves the best performance across key metrics. Furthermore, through linear relationship analysis on real-world clinical datasets, the exclusion of outlier samples significantly increased the statistical significance and fold change in the identified differential proteins. Overall, the proposed model establishes a solid data foundation, paving the way for downstream mechanistic studies and target discovery.