Pushing the information states' acquisition efficiency has been a long-held goal to reach the measurement precision limit inside scattering spaces. Recent studies have indicated that maximal information states can be attained through engineered modes; however, partial intrusion is generally required. While non-invasive designs have been substantially explored across diverse physical scenarios, the non-invasive acquisition of information states inside dynamic scattering spaces remains challenging due to the intractable non-unique mapping problem, particularly in the context of multi-target scenarios. Here, we establish the feasibility of non-invasive information states' acquisition experimentally for the first time by introducing a tandem-generated adversarial network framework inside dynamic scattering spaces. To illustrate the framework's efficacy, we demonstrate that efficient information states' acquisition for multi-target scenarios can achieve the Fisher information limit solely through the utilization of the external scattering matrix of the system. Our work provides insightful perspectives for precise measurements inside dynamic complex systems.

The intestinal and intratumoral microbiota are closely associated with tumor progression and response to antitumor treatments. The antibacterial or tumor microenvironment (TME)-modulating approaches have been shown to markedly improve antitumor efficacy, strategies focused on normalizing the microbial environment are rarely reported. Here, we reported the development of an orally administered inulin-based hydrogel with colon-targeting and retention effects, containing hollow MnO₂ nanocarrier loaded with the chemotherapeutic drug Oxa (Oxa@HMI). On the one hand, beneficial bacteria in the colon specifically metabolized Oxa@HMI, resulting in the degradation of inulin and the generation of short-chain fatty acids (SCFAs). These SCFAs play a crucial role in modulating microbiota and stimulating immune responses. On the other hand, the hydrogel matrix underwent colon microbiota-specific degradation, enabling the targeted release of Oxa and production of reactive oxygen species in the acidic TME. In this study, we have established, for the first time, a microbiota-targeted drug delivery system Oxa@HMI that exhibited high efficiency in colorectal cancer targeting and colon retention. Oxa@HMI promoted chemotherapy efficiency and activated antitumor immune responses by intervening in the microbial environment within the tumor tissue, providing a crucial clinical approach for the treatment of colorectal cancer that susceptible to microbial invasion.

Complex diseases do not always follow gradual progressions. Instead, they may experience sudden shifts known as critical states or tipping points, where a marked qualitative change occurs. Detecting such a pivotal transition or pre-deterioration state holds paramount importance due to its association with severe disease deterioration. Nevertheless, the task of pinpointing the pre-deterioration state for complex diseases remains an obstacle, especially in scenarios involving high-dimensional data with limited samples, where conventional statistical methods frequently prove inadequate. In this study, we introduce an innovative quantitative approach termed sample-specific causality network entropy (SCNE), which infers a sample-specific causality network for each individual and effectively quantifies the dynamic alterations in causal relations among molecules, thereby capturing critical points or pre-deterioration states of complex diseases. We substantiated the accuracy and efficacy of our approach via numerical simulations and by examining various real-world datasets, including single-cell data of epithelial cell deterioration (EPCD) in colorectal cancer, influenza infection data, and three different tumor cases from The Cancer Genome Atlas (TCGA) repositories. Compared to other existing six single-sample methods, our proposed approach exhibits superior performance in identifying critical signals or pre-deterioration states. Additionally, the efficacy of computational findings is underscored by analyzing the functionality of signaling biomarkers.

Recent advancements in spatial transcriptomics (ST) technologies offer unprecedented opportunities to unveil the spatial heterogeneity of gene expression and cell states within tissues. Despite these capabilities of the ST data, accurately dissecting spatiotemporal structures (e.g., spatial domains, temporal trajectories, and functional interactions) remains challenging. Here, we introduce a computational framework, PearlST (partial differential equation [PDE]-enhanced adversarial graph autoencoder of ST), for accurate inference of spatiotemporal structures from the ST data using PDE-enhanced adversarial graph autoencoder. PearlST employs contrastive learning to extract histological image features, integrates a PDE-based diffusion model to enhance characterization of spatial features at domain boundaries, and learns the latent low-dimensional embeddings via Wasserstein adversarial regularized graph autoencoders. Comparative analyses across multiple ST datasets with varying resolutions demonstrate that PearlST outperforms existing methods in spatial clustering, trajectory inference, and pseudotime analysis. Furthermore, PearlST elucidates functional regulations of the latent features by linking intercellular ligand–receptor interactions to most contributing genes of the low-dimensional embeddings, as illustrated in a human breast cancer dataset. Overall, PearlST proves to be a powerful tool for extracting interpretable latent features and dissecting intricate spatiotemporal structures in ST data across various biological contexts.

One of the fundamental principles of electrostatics is that an uncharged object will be attracted to a charged object through electrostatic induction as the two approaches one another. We refer to the charged object as a single electrode and examine the scenario where a positive voltage is applied. Because of electrostatic induction phenomenon, single-electrode electrostatics only generates electrostatic attraction forces. Here, we discover that single-electrode electrostatics can generate electrostatic repulsion forces and define this new phenomenon as single-electrode electrostatic repulsion phenomenon. We investigate the fundamental electrostatic phenomena, giving a curve of electrostatic force versus voltage and then defining 3 regions. Remote actuation and manipulation are essential technologies that are of enormous concern, with tweezers playing an important role. Various tweezers designed on the basis of external fields of optics, acoustics, and magnetism can be used for remote actuation and manipulation, but some inherent drawbacks still exist. Tweezers would benefit greatly from our discovery in electrostatics. On the basis of this discovery, we propose the concept of electrostatic tweezers, which can achieve noncontact and remote actuation and manipulation. Experimental characterizations and successful applications in metamaterials, robots, and manipulating objects demonstrated that electrostatic tweezers can produce large deformation rates (>6,000%), fast actuation (>100 Hz), and remote manipulating distance (~15 cm) and have the advantages of simple device structure, easy control, lightweight, no dielectric breakdown, and low cost. Our work may deepen people's understanding of single-electrode electrostatics and opens new opportunities for remote actuation and manipulation.

Adeno-associated virus (AAV)-mediated gene therapy is widely applied to treat numerous hereditary diseases in animal models and humans. The specific expression of AAV-delivered transgenes driven by cell type-specific promoters should further increase the safety of gene therapy. However, current methods for screening cell type-specific promoters are labor-intensive and time-consuming. Herein, we designed a “multiple vectors in one AAV” strategy for promoter construction in vivo. Through this strategy, we truncated a native promoter for Myo15 expression in hair cells (HCs) in the inner ear, from 1,611 bp down to 1,157 bp, and further down to 956 bp. Under the control of these 2 promoters, green fluorescent protein packaged in AAV-PHP.eB was exclusively expressed in the HCs. The transcription initiation ability of the 2 promoters was further verified by intein-mediated otoferlin recombination in a dual-AAV therapeutic system. Driven by these 2 promoters, human otoferlin was selectively expressed in HCs, resulting in the restoration of hearing in treated Otof ^−/− mice for at least 52 weeks. In summary, we developed an efficient screening strategy for cell type-specific promoter engineering and created 2 truncated Myo15 promoters that not only restored hereditary deafness in animal models but also show great potential for treating human patients in future.

The thalamus and its cortical connections play a pivotal role in pain information processing, yet the exploration of its electrophysiological responses to nociceptive stimuli has been limited. Here, in 2 experiments we recorded neural responses to nociceptive laser stimuli in the thalamic (ventral posterior lateral nucleus and medial dorsal nucleus) and cortical regions (primary somatosensory cortex [S1] and anterior cingulate cortex) within the lateral and medial pain pathways. We found remarkable similarities in laser-evoked brain responses that encoded pain intensity within thalamic and cortical regions. Contrary to the expected temporal sequence of ascending information flow, the recorded thalamic response (N1) was temporally later than its cortical counterparts, suggesting that it may not be a genuine thalamus-generated response. Importantly, we also identified a distinctive component in the thalamus, i.e., the early negativity (EN) occurring around 100 ms after the onset of nociceptive stimuli. This EN component represents an authentic nociceptive thalamic response and closely synchronizes with the directional information flow from the thalamus to the cortex. These findings underscore the importance of isolating genuine thalamic neural responses, thereby contributing to a more comprehensive understanding of the thalamic function in pain processing. Additionally, these findings hold potential clinical implications, particularly in the advancement of closed-loop neuromodulation treatments for neurological diseases targeting this vital brain region.

Recently, the development of the Metaverse has become a frontier spotlight, which is an important demonstration of the integration innovation of advanced technologies in the Internet. Moreover, artificial intelligence (AI) and 6G communications will be widely used in our daily lives. However, the effective interactions with the representations of multimodal data among users via 6G communications is the main challenge in the Metaverse. In this work, we introduce an intelligent cross-modal graph semantic communication approach based on generative AI and 3-dimensional (3D) point clouds to improve the diversity of multimodal representations in the Metaverse. Using a graph neural network, multimodal data can be recorded by key semantic features related to the real scenarios. Then, we compress the semantic features using a graph transformer encoder at the transmitter, which can extract the semantic representations through the cross-modal attention mechanisms. Next, we leverage a graph semantic validation mechanism to guarantee the exactness of the overall data at the receiver. Furthermore, we adopt generative AI to regenerate multimodal data in virtual scenarios. Simultaneously, a novel 3D generative reconstruction network is constructed from the 3D point clouds, which can transfer the data from images to 3D models, and we infer the multimodal data into the 3D models to increase realism in virtual scenarios. Finally, the experiment results demonstrate that cross-modal graph semantic communication, assisted by generative AI, has substantial potential for enhancing user interactions in the 6G communications and Metaverse.

Resistin plays an important role in the pathophysiology of obesity-mediated insulin resistance in mice. However, the biology of resistin in humans is quite different from that in rodents. Therefore, the association between resistin and insulin resistance remains unclear in humans. Here, we tested whether and how the endocannabinoid system (ECS) control circulating peripheral blood mononuclear cells (PBMCs) that produce resistin and infiltrate into the adipose tissue, heart, skeletal muscle, and liver, resulting in inflammation and insulin resistance. Using human PBMCs, we investigate whether the ECS is connected to human resistin. To test whether the ECS regulates inflammation and insulin resistance in vivo, we used 2 animal models such as “humanized” nonobese diabetic/Shi-severe combined immunodeficient interleukin-2Rγ (null) (NOG) mice and “humanized” resistin mouse models, which mimic human body. In human atheromatous plaques, cannabinoid 1 receptor (CB1R)-positive macrophage was colocalized with the resistin expression. In addition, resistin was exclusively expressed in the sorted CB1R-positive cells from human PBMCs. In CB1R-positive cells, endocannabinoid ligands induced resistin expression via the p38–Sp1 pathway. In both mouse models, a high-fat diet increased the accumulation of endocannabinoid ligands in adipose tissue, which recruited the CB1R-positive cells that secrete resistin, leading to adipose tissue inflammation and insulin resistance. This phenomenon was suppressed by CB1R blockade or in resistin knockout mice. Interestingly, this process was accompanied by mitochondrial change that was induced by resistin treatment. These results provide important insights into the ECS–resistin axis, leading to the development of metabolic diseases. Therefore, the regulation of resistin via the CB1R could be a potential therapeutic strategy for cardiometabolic diseases.

In the evolving landscape of robotics and visual navigation, event cameras have gained important traction, notably for their exceptional dynamic range, efficient power consumption, and low latency. Despite these advantages, conventional processing methods oversimplify the data into 2 dimensions, neglecting critical temporal information. To overcome this limitation, we propose a novel method that treats events as 3D time-discrete signals. Drawing inspiration from the intricate biological filtering systems inherent to the human visual apparatus, we have developed a 3D spatiotemporal filter based on unsupervised machine learning algorithm. This filter effectively reduces noise levels and performs data size reduction, with its parameters being dynamically adjusted based on population activity. This ensures adaptability and precision under various conditions, like changes in motion velocity and ambient lighting. In our novel validation approach, we first identify the noise type and determine its power spectral density in the event stream. We then apply a one-dimensional discrete fast Fourier transform to assess the filtered event data within the frequency domain, ensuring that the targeted noise frequencies are adequately reduced. Our research also delved into the impact of indoor lighting on event stream noise. Remarkably, our method led to a 37% decrease in the data point cloud, improving data quality in diverse outdoor settings.