Takeover safety draws increasing attention in the intelligent transportation as the new energy vehicles with cutting-edge autopilot capabilities vigorously blossom on the road. Despite recent studies highlighting the importance of drivers’ emotions in takeover safety, the lack of emotion-aware takeover datasets hinders further investigation, thereby constraining potential applications in this field. To this end, we introduce ViE-Take, the first Vision-driven (Vision is used since it constitutes the most cost-effective and user-friendly solution for commercial driver monitor systems) dataset for exploring the Emotional landscape in Takeovers of autonomous driving. ViE-Take enables a comprehensive exploration of the impact of emotions on drivers’ takeover performance through 3 key attributes: multi-source emotion elicitation, multi-modal driver data collection, and multi-dimensional emotion annotations. To aid the use of ViE-Take, we provide 4 deep models (corresponding to 4 prevalent learning strategies) for predicting 3 different aspects of drivers’ takeover performance (readiness, reaction time, and quality). These models offer benefits for various downstream tasks, such as driver emotion recognition and regulation for automobile manufacturers. Initial analysis and experiments conducted on ViE-Take indicate that (a) emotions have diverse impacts on takeover performance, some of which are counterintuitive; (b) highly expressive social media clips, despite their brevity, prove effective in eliciting emotions (a foundation for emotion regulation); and (c) predicting takeover performance solely through deep learning on vision data not only is feasible but also holds great potential.

The intricate relationship between cancer, circadian rhythms, and aging is increasingly recognized as a critical factor in understanding the mechanisms underlying tumorigenesis and cancer progression. Aging is a well-established primary risk factor for cancer, while disruptions in circadian rhythms are intricately associated with the tumorigenesis and progression of various tumors. Moreover, aging itself disrupts circadian rhythms, leading to physiological changes that may accelerate cancer development. Despite these connections, the specific interplay between these processes and their collective impact on cancer remains inadequately explored in the literature. In this review, we systematically explore the physiological mechanisms of circadian rhythms and their influence on cancer development. We discuss how core circadian genes impact tumor risk and prognosis, highlighting the shared hallmarks of cancer and aging such as genomic instability, cellular senescence, and chronic inflammation. Furthermore, we examine the interplay between circadian rhythms and aging, focusing on how this crosstalk contributes to tumorigenesis, tumor proliferation, and apoptosis, as well as the impact on cellular metabolism and genomic stability. By elucidating the common pathways linking aging, circadian rhythms, and cancer, this review provides new insights into the pathophysiology of cancer and identifies potential therapeutic strategies. We propose that targeting the circadian regulation of cancer hallmarks could pave the way for novel treatments, including chronotherapy and antiaging interventions, which may offer important benefits in the clinical management of cancer.

Accurately reconstructing the intricate structure of natural organisms is the long-standing goal of 3-dimensional (3D) bioprinting. Projection-based 3D printing boasts the highest resolution-to-manufacturing time ratio among all 3D-printing technologies, rendering it a highly promising technique in this field. However, achieving standardized, high-fidelity, and high-resolution printing of composite structures using bioinks with diverse mechanical properties remains a marked challenge. The root of this challenge lies in the long-standing neglect of multi-material printability research. Multi-material printing is far from a simple physical assembly of different materials; rather, effective control of material interfaces is a crucial factor that governs print quality. The current research gap in this area substantively hinders the widespread application and rapid development of multi-material projection-based 3D bioprinting. To bridge this critical gap, we developed a multi-material projection-based 3D bioprinter capable of simultaneous printing with 6 materials. Building upon this, we established a fundamental framework for multi-material printability research, encompassing its core logic and essential process specifications. Furthermore, we clarified several critical issues, including the cross-linking behavior of multicomponent bioinks, mechanical mismatch and interface strength in soft–hard composite structures, the penetration behavior of viscous bioinks within hydrogel polymer networks, liquid entrapment and adsorption phenomena in porous heterogeneous structures, and error source analysis along with resolution evaluation in multi-material printing. This study offers a solid theoretical foundation and guidance for the quantitative assessment of multi-material projection-based 3D bioprinting, holding promise to advance the field toward higher precision and the reconstruction of more intricate biological structures.

The metaverse enables immersive virtual healthcare environments, presenting opportunities for enhanced care delivery. A key challenge lies in effectively combining multimodal healthcare data and generative artificial intelligence abilities within metaverse-based healthcare applications, which is a problem that needs to be addressed. This paper proposes a novel multimodal learning framework for metaverse healthcare, MMLMH, based on collaborative intra- and intersample representation and adaptive fusion. Our framework introduces a collaborative representation learning approach that captures shared and modality-specific features across text, audio, and visual health data. By combining modality-specific and shared encoders with carefully formulated intrasample and intersample collaboration mechanisms, MMLMH achieves superior feature representation for complex health assessments. The framework’s adaptive fusion approach, utilizing attention mechanisms and gated neural networks, demonstrates robust performance across varying noise levels and data quality conditions. Experiments on metaverse healthcare datasets demonstrate MMLMH’s superior performance over baseline methods across multiple evaluation metrics. Longitudinal studies and visualization further illustrate MMLMH’s adaptability to evolving virtual environments and balanced performance across diagnostic accuracy, patient–system interaction efficacy, and data integration complexity. The proposed framework has a unique advantage in that a similar level of performance is maintained across various patient populations and virtual avatars, which could lead to greater personalization of healthcare experiences in the metaverse. MMLMH’s successful functioning in such complicated circumstances suggests that it can combine and process information streams from several sources. They can be successfully utilized in next-generation healthcare delivery through virtual reality.

Disruption of acylcarnitine homeostasis results in life-threatening outcomes in humans. Carnitine–acylcarnitine translocase deficiency (CACTD) is a scarce autosomal recessive genetic disease and may result in patients’ death due to heart arrest or respiratory insufficiency. However, the reasons and mechanism of CACTD inducing respiratory insufficiency have never been elucidated. Herein, we employed lipidomic techniques to create comprehensive lipidomic maps of entire lungs throughout both prenatal and postnatal developmental stages in mice. We found that the acylcarnitines manifested notable variations and coordinated the expression levels of carnitine–acylcarnitine translocase (Cact) across these lung developmental stages. Cact-null mice were all dead with a symptom of respiratory distress and exhibited failed lung development. Loss of Cact resulted in an accumulation of palmitoyl-carnitine (C16-acylcarnitine) in the lungs and promoted the proliferation of mesenchymal progenitor cells. Mesenchymal cells with elevated C16-acylcarnitine levels displayed minimal changes in energy metabolism but, upon investigation, revealed an interaction with sterile alpha motif domain and histidine-aspartate domain-containing protein 1 (Samhd1), leading to decreased protein abundance and enhanced cell proliferation. Thus, our findings present a mechanism addressing respiratory distress in CACTD, offering a valuable reference point for both the elucidation of pathogenesis and the exploration of treatment strategies for neonatal respiratory distress.

Exosomes (Exos) are emerging as noninvasive biomarkers for diagnosis and progression monitoring of gastric cancer (GC). However, the heterogeneity discrimination and ultrasensitive quantification of Exos presents a considerable analytical challenge, thereby impeding severely their clinical application. Herein, we propose an integrated terahertz metasensing platform for the discrimination of Exos in distinct subtypes of GC in a single step—through the simultaneous evaluation of the category and richness level of Exos membrane proteins. Characterized by dual-sided independent sensing capabilities with enhanced sensitivity (169 and 325 GHz per refractive index unit, respectively), the metasensor functionalized with antibodies simultaneously reflects the content of 2 membrane proteins in the terahertz spectral response. Our approach concurrently completes accurate differentiation and precise quantification of GC-subtype Exos by integrating dual-sided sensing information in merely a single assay. The dual-sided sensing design enhances the reliability of detection results. Moreover, combined with the signal amplification of gold nanoparticles, the platform experimentally demonstrates a superior dynamic response to Exos concentrations spanning from 1 × 10⁴ to 1 × 10⁸ particles/ml, with the limit of detection being 1 × 10⁴ particles/ml. This work provides new insights into multisensing metasurface design and paves the way for precise and personalized cancer treatment through the specific sensing of Exos.

Cardiovascular diseases constitute a marked threat to global health, and the emergence of spatial omics technologies has revolutionized cardiovascular research. This review explores the application of spatial omics, including spatial transcriptomics, spatial proteomics, spatial metabolomics, spatial genomics, and spatial epigenomics, providing more insight into the molecular and cellular foundations of cardiovascular disease and highlighting the critical contributions of spatial omics to cardiovascular science, and discusses future prospects, including technological advancements, integration of multi-omics, and clinical applications. These developments should contribute to the understanding of cardiovascular diseases and guide the progress of precision medicine, targeted therapies, and personalized treatments.

Since the scarcity of bandwidth resources has become increasingly critical in modern communication systems, orbital angular momentum (OAM) with a higher degree of freedom in information modulation has become a promising solution to alleviate the shortage of spectrum resources. Consequently, the integration of OAM with millimeter-wave technology has emerged as a focal point in next-generation communication research. Recently, programmable metasurfaces have gained considerable attention as essential devices for OAM generation due to real-time tunability, but their profiles are relatively high as a result of the external feed source. This paper proposes a conformal radiation-type programmable metasurface operating in the millimeter-wave band. By employing a series–parallel hybrid feed network to replace conventional external feed sources, the overall profile of the metasurface system can be reduced to less than 0.1λ. Furthermore, the proposed innovation design could also achieve a conformal cross-shaped architecture, which is ultraportable and very effective in integrating with the front ends of satellites or aircraft and eliminating issues such as feed source blockage as well as energy spillover losses in conventional metasurfaces. The proposed metasurface could achieve a realized gain of 22.54 dB with an aperture efficiency of 21.75%, thus generating high-purity OAM waves with topological charges of l = 0, l = +1, l = +2, and l = +3. Additionally, by incorporating beam scanning techniques, OAM waves could be deflected to accommodate scenarios with moving receivers, demonstrating substantial potential for future high-speed wireless communication applications.

Robo-pigeons, a novel class of hybrid robotic systems developed using brain–computer interface technology, hold marked promise for search and rescue missions due to their superior load-bearing capacity and sustained flight performance. However, current research remains largely confined to laboratory environments, and precise control of their flight behavior, especially flight altitude regulation, in a large-scale spatial range outdoors continues to pose a challenge. Herein, we focus on overcoming this limitation by using electrical stimulation of the locus coeruleus (LoC) nucleus to regulate outdoor flight altitude. We investigated the effects of varying stimulation parameters, including stimulation frequency (SF), interstimulus interval (ISI), and stimulation cycles (SC), on the flight altitude of robo-pigeons. The findings indicate that SF functions as a pivotal switch controlling the ascending and descending flight modes of the robo-pigeons. Specifically, 60 Hz stimulation effectively induced an average ascending flight of 12.241 m with an 87.72% success rate, while 80 Hz resulted in an average descending flight of 15.655 m with a 90.52% success rate. SF below 40 Hz did not affect flight altitude change, whereas over 100 Hz caused unstable flights. The number of SC was directly correlated with the magnitude of altitude change, enabling quantitative control of flight behavior. Importantly, electrical stimulation of the LoC nucleus had no significant effects on flight direction. This study is the first to establish that targeted variation of electrical stimulation parameters within the LoC nucleus can achieve precise altitude control in robo-pigeons, providing new insights for advancing the control of flight animal–robot systems in real-world applications.

Cerebral small vessel disease (SVD) involves ischemic white matter damage and choroid plexus (CP) dysfunction for cerebrospinal fluid (CSF) production. Given the vascular and CSF links between the eye and brain, this study explored whether retinal vascular morphology can indicate cerebrovascular injury and CP dysfunction in SVD. We assessed SVD burden using imaging phenotypes like white matter hyperintensities (WMH), perivascular spaces, lacunes, and microbleeds. Cerebrovascular injury was quantified by WMH volume and peak width of skeletonized mean diffusivity (PSMD), while CP volume measured its dysfunction. Retinal vascular markers were derived from fundus images, with associations analyzed using generalized linear models and Pearson correlations. Path analysis quantified contributions of cerebrovascular injury and CP volume to retinal changes. Support vector machine models were developed to predict SVD severity using retinal and demographic data. Among 815 participants, 578 underwent ocular imaging. Increased SVD burden markedly correlated with both cerebral and retinal biomarkers, with retinal alterations equally influenced by cerebrovascular damage and CP enlargement. Machine learning models showed robust predictive power for severe SVD burden (AUC was 0.82), PSMD (0.81), WMH volume (0.77), and CP volume (0.80). These findings suggest that retinal imaging could serve as a cost-effective, noninvasive tool for SVD screening based on vascular and CSF connections.