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Wearable strain sensors that detect joint/muscle strain changes become prevalent at human–machine interfaces for full-body motion monitoring. However, most wearable devices cannot offer customizable opportunities to match the sensor characteristics with specific deformation ranges of joints/muscles, resulting in suboptimal performance. Adequate wearable strain sensor design is highly required to achieve user-designated working windows without sacrificing high sensitivity, accompanied with real-time data processing. Herein, wearable Ti 3 C 2 T x MXene sensor modules are fabricated with in-sensor machine learning (ML) models, either functioning via wireless streaming or edge computing, for full-body motion classifications and avatar reconstruction. Through topographic design on piezoresistive nanolayers, the wearable strain sensor modules exhibited ultrahigh sensitivities within the working windows that meet all joint deformation ranges. By integrating the wearable sensors with a ML chip, an edge sensor module is fabricated, enabling in-sensor reconstruction of high-precision avatar animations that mimic continuous full-body motions with an average avatar determination error of 3.5 cm, without additional computing devices. Wearable sensors with edge computing are desired for human motion monitoring. Here, the authors demonstrate a topographic design for wearable MXene sensor modules with wireless streaming or in-sensor computing models for avatar reconstruction.

BeiDou global navigation satellite system (BDS-3), a developed GNSS by China, has the ability to support five different signals, including B1I, B3I, B1C, B2a, and B2b. Meanwhile, BDS-3 has officially provided the satellite-based precise point positioning (PPP) service through the B2b signal (PPP-B2b) since 2021. It’s necessary to conduct a comprehensive analysis on multi-frequency PPP with PPP-B2b corrections. In this study, a multi-frequency undifferenced and uncombined PPP model (UDUC) using PPP-B2b corrections was employed to investigate dual-frequency to five-frequency real-time PPP performance. The results show that compared with the conventional dual-frequency solutions, multi-frequency solutions can improve both the convergence performances and positioning accuracy of PPP-B2b service, especially during the convergence stage. The quad-frequency and five-frequency solutions can achieve the best positioning performance. The static solutions of multi-frequency PPP models reach the centimeter-level accuracy after convergence. In kinematic mode, the convergence time of the five-frequency PPP results is reduced by 23.5% compared with the dual-frequency results. The root mean square (RMS) errors of the five-frequency PPP in the E, N, and U components are 7.1 cm, 4.8 cm, and 12.4 cm, which are improved by 6.8%, 11.5%, and 5.5%, respectively. Graphical Abstract

This study investigates the influence of game elements on active learning intentions within digital learning platforms. Drawing from a situational perspective, we developed a model and validated it using data from 492 respondents collected via questionnaires. Our findings suggest that while social elements enhance active learning intentions, both achievement and immersive elements tend to weaken them. Notably, the spirituality of the learning place serves as a mediator in the relationship between game elements and active learning intentions. Furthermore, we emphasize the moderating effects of different game behavior patterns. This research enriches our comprehension of game elements’ role in shaping active learning intentions on digital platforms, offering valuable insights for educators and platform developers.

Designing ultralight conductive aerogels with tailored electrical and mechanical properties is critical for various applications. Conventional approaches rely on iterative, time-consuming experiments across a vast parameter space. Herein, an integrated workflow is developed to combine collaborative robotics with machine learning to accelerate the design of conductive aerogels with programmable properties. An automated pipetting robot is operated to prepare 264 mixtures of Ti 3 C 2 T x MXene, cellulose, gelatin, and glutaraldehyde at different ratios/loadings. After freeze-drying, the aerogels’ structural integrity is evaluated to train a support vector machine classifier. Through 8 active learning cycles with data augmentation, 162 unique conductive aerogels are fabricated/characterized via robotics-automated platforms, enabling the construction of an artificial neural network prediction model. The prediction model conducts two-way design tasks: (1) predicting the aerogels’ physicochemical properties from fabrication parameters and (2) automating the inverse design of aerogels for specific property requirements. The combined use of model interpretation and finite element simulations validates a pronounced correlation between aerogel density and compressive strength. The model-suggested aerogels with high conductivity, customized strength, and pressure insensitivity allow for compression-stable Joule heating for wearable thermal management. Machine learning-assisted robots produce MXene aerogels containing cellulose, gelatin, and glutaraldehyde, fabricating 162 compositions. Inverse design from resulting properties affords tailored compression-stable materials for Joule heating.

Urban rail transit is a key enabler for optimizing urban spatial structures, and its interactive relationship with land use has long been a focus of attention. However, existing studies suffer from scattered methodologies, a lack of systematic analysis, and insufficient dynamic insights into global trends. This study comprehensively employs CiteSpace, VOSviewer, and Scimago Graphica to conduct bibliometric and knowledge map analysis on 1894 articles from the Web of Science database between 2004 and 2024, focusing on global research trends, collaboration networks, thematic evolution, and methodological advancements. Key findings include the following: (1) research on rail transit and land use has been steadily increasing, with a significant “US-China dual-core” distribution, where most studies are concentrated in the United States and China, with higher research density in Asia; (2) domestic and international research has primarily focused on themes such as the built environment, value capture, and public transportation, with a recent shift toward artificial intelligence and smart city technology applications; (3) research methods have evolved from foundational 3S technologies (GIS, GPS, RS) to spatial modeling tools (e.g., LUTI model, node-place model), and the current emergence of AI-driven analysis (e.g., machine learning, deep learning, digital twins). The study identifies three future research directions—technology integration, data governance, and institutional innovation—which provide guidance for the coordinated planning of transportation and land use in future smart city development.

To address the burgeoning demand for computing capacity in artificial intelligence, researchers have explored optical neural networks that show advantages of ultrafast speed, low power consumption, ultra-high bandwidth, and high parallelism. However, most existing optical networks are reciprocal, where forward and backward propagation are intrinsically coupled. This results in the backward pathway remaining largely unexplored, hindering the realization of integrated perception-response systems. Here, we present a nonreciprocal neural network leveraging enhanced magneto-optical effect in spoof surface plasmon polaritons transmission line to decouple forward and backward paths. Moreover, the computing function of the network can be flexibly modulated by the magnetization orientation in ferrites and variations in operating frequency. We demonstrate broadband bidirectional decoupled image processing across various operators, where the operator configuration can be precisely designed by encoding the input signals. This decoupling achieves independent control and signal isolation within the same structure, effectively emulating the unidirectional transmission of biological networks. Furthermore, matrix-solving operations can be facilitated by incorporating feedback waveguides for desired recursion paths. Our findings open pathways to nonreciprocal architectures for independent bidirectional algorithms in analogue computing. Unidirectional propagation is a characteristic of biological networks, but remains challenging in optical network. Here, authors present a nonreciprocal surface plasmonic network that shows asymmetric scattering matrices, promising decoupled computing in forward and backward paths.

Toxoplasma gondii (T. gondii) infection during pregnancy can cause severe placental damage and fetal impairment. Although triggering the receptor expressed on myeloid cells 2 (Trem2) confers protection against T. gondii infection, the precise molecular mechanisms underlying this immunoregulatory role remain incompletely understood. Using a mouse model, this study identifies a novel Trem2-MICAL1-P-ERK axis in macrophages that protects against T. gondii-induced adverse pregnancy outcomes (APO). RNA-seq of Trem2-overexpressing macrophages revealed significant upregulation of 1857 genes, with MICAL1 among the most markedly altered, highlighting its potential role in Trem2-mediated signaling. Mechanistically, correlation analysis, molecular docking, fluorescence co-localization, and immunoprecipitation assays demonstrate that Trem2 directly interacts with MICAL1, which modulates downstream phosphorylated ERK (P-ERK) signaling. In a T. gondii-infected murine pregnancy model, genetic ablation of Trem2 exacerbated pathogen-induced suppression of MICAL1 and P-ERK, whereas macrophage-specific overexpression of Trem2-DAP12 restored this signaling axis. Conversely, MICAL1 overexpression rescued P-ERK activation but failed to regulate Trem2 expression. Further studies in bone marrow-derived macrophages (BMDMs) revealed that Trem2 deficiency potentiated the inhibitory effects of soluble T. gondii antigens (TgAg) on MICAL1 and P-ERK. These findings elucidate how T. gondii disrupts placental immunity through targeted suppression of Trem2-mediated signaling and establish the Trem2-MICAL1-P-ERK cascade as a core regulatory pathway in immune homeostasis during pregnancy.

Objective The aim of this work was to investigate the immunological effect of MENK by analyzing the protein spectrum and bioinformatics of macrophage RAW264.7, and to explore the relationship between macrophage and ferroptosis. Result We employed proteomic analysis to identify differentially expressed proteins (DEPs) between macrophages and macrophages intervened by MENK. A total of 208 DEPs were identified. Among these, 96 proteins had upregulated expression and 112 proteins had downregulated expression. Proteomic analysis revealed a significant enrichment of DEPs associated with iron metabolism. The identification of hub genes was conducted using KEGG pathway diagrams and protein-protein interaction (PPI) analysis. The hub genes identified in this study include HMOX1 and Ferritin (FTH and FTL). A correlation was established between HMOX1, FTH, and FTL in the GO and KEGG databases. The results of PCR, WB and immunofluorescence showed that MENK downregulated the level of HMOX1 and FTH. Conclusion MENK had the potential to become an adjuvant chemotherapy drug by regulating iron metabolism in macrophages, reducing levels of HMOX1 and ferritin. We proposed an innovative research direction on the therapeutic potential of MENK, focusing on the relationship between ferroptosis and macrophage activity.

The expansion of Global Navigation Satellite Systems (GNSS) and Regional Navigation Satellite Systems (RNSS), along with broadcasting of additional signals, offers more options for Precise Point Positioning (PPP). Utilizing a number of constellations and frequencies provides more precise and reliable Observable-Specific Bias (OSBs) and atmospheric products, thereby enhancing the performance of PPP with Ambiguity Resolution (PPP-AR) and PPP-RTK systems. Additionally, the emergence and refinement of satellite-based augmentation services targeting PPP, such as BDS-3 Precise Point Positioning (PPP-B2b) and High Accuracy Service (HAS), have further expanded the applications of multi-GNSS and multi-frequency PPP. In this contribution, we comprehensively overview the developmental history of major systems and the construction of global satellite-based augmentation systems. The model of multi-GNSS and multi-frequency PPP-RTK is then established, encompassing both the server and user sides. Benefiting from the modernization of GPS, refinement of Galileo, and stable operation of BDS, the convergence time of PPP float solutions is less than 9 min, while the convergence time of fixed solutions is under 5 min. The PPP-RTK system can achieve instantaneous convergence with high-confidence atmospheric products, maintaining positioning accuracy within 2.5 cm. Furthermore, vehicular experiments demonstrate that the PPP-RTK technology can maintain a positioning accuracy of around 10 cm even in rapidly changing observational conditions. For augmentation services such as PPP-B2b and HAS, after correcting for orbit, clock, and code biases, the convergence time can be reduced to under 15 min, with positioning accuracy of approximately 16 cm. Finally, we present our outlook for the future of BDS.

Amur ide ( Leuciscus waleckii ), which inhabits Lake Dali, a soda lake in Northeast China with extremely high alkalinity (~ 53.57 mmol/L) and pH value (~ 9.6), is considered to be an ideal model for elucidating alkaline adaption mechanisms. To uncover the molecular mechanisms underlying this adaptation, we conducted a comparative study between the alkaline water ecotype (JY) and freshwater ecotype (DY). Both groups were exposed to a gradient of NaHCO 3 stress levels (0, 10, 30, and 50 mmol/L), and their responses were systematically assessed through integrated multi-omics analyses alongside physiological assays. Our results revealed that under low and moderate alkaline stress (10 and 30 mmol/L), JY group significantly upregulated the gene anpep , facilitating the hydrolysis of cysteinyl-glycine to release l -cysteine, thereby enhancing antioxidant capacity. Under high stress conditions (50 mmol/L), JY further synergistically upregulated gpx to activated the glutathione peroxidase (GPx) pathway to eliminate excess ROS. In contrast, the DY group predominantly relied on upregulating chac1 -mediated γ-glutamyltransferase activity to facilitate glutathione cycling. Notably, while cysteinyl-glycine content significantly increased in the alkaline water ecotype (JY) under moderate and high alkalinity stress (30 and 50 mmol/L), the expression of its upstream gene chac1 was significantly downregulated. This paradox suggests alternative sources or regulatory mechanisms for cysteinyl-glycine accumulation in JY. Microbial tracing analysis revealed a positive correlation between cysteinyl-glycine levels and the gut microbiota genus Stenotrophomonas in JY, whose relative abundance increased progressively with elevated alkalinity. It is speculated that Stenotrophomonas may modulate host glutathione metabolism by regulating cysteinyl-glycine levels, thereby facilitating alkaline adaptation.