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Abstract Neoadjuvant immunochemotherapy (nICT) has dramatically changed the treatment landscape of operable esophageal squamous cell carcinoma (ESCC), but factors influencing tumor response to nICT are not well understood. Here, using single-cell RNA sequencing paired with T cell receptor sequencing, we profile tissues from ESCC patients accepting nICT treatment and characterize the tumor microenvironment context. CXCL13+CD8+ Tex cells, a subset of exhausted CD8+ T cells, are revealed to highly infiltrate in pre-treatment tumors and show prominent progenitor exhaustion phenotype in post-treatment samples from responders. We validate CXCL13+CD8+ Tex cells as a predictor of improved response to nICT and reveal CXCL13 to potentiate anti-PD-1 efficacy in vivo. Post-treatment tumors from non-responders are enriched for CXCL13+CD8+ Tex cells with notably remarkable exhaustion phenotype and TNFRSF4+CD4+ Tregs with activated immunosuppressive function and a significant clone expansion. Several critical markers for therapeutic resistance are also identified, including LRRC15+ fibroblasts and SPP1+ macrophages, which may recruit Tregs to form an immunosuppressive landscape. Overall, our findings unravel immune features of distinct therapeutic response to nICT treatment, providing a rationale for optimizing individualized neoadjuvant strategy in ESCC.

Abstract Alzheimer’s disease (AD) is a common neurodegenerative disease characterized by progressive cognitive and physical impairment. Neuroinflammation is related to AD, and the misfolding and aggregation of amyloid protein in the brain creates an inflammatory microenvironment. Microglia are the predominant contributors to neuroinflammation, and abnormal activation of microglia induces the release of a large amount of inflammatory factors, promotes neuronal apoptosis, and leads to cognitive impairment. In this study, we used microglial membranes containing caffeic acid-coupled carbon quantum dots to prepare a novel biomimetic nanocapsule (CDs-CA-MGs) for the treatment of AD. The application of CDs-CA-MGs via nasal administration can bypass the blood‒brain barrier (BBB) and directly target the site of inflammation. After treatment with CDs-CA-MGs, AD mice showed reduced inflammation in the brain, decreased neuronal apoptosis, and significantly improved learning and memory abilities. In addition, CDs-CA-MGs affect inflammation-related JAK-STAT and Toll-like receptor signaling pathways in AD mice. CDs-CA-MGs significantly downregulated interleukins (IL-1β and IL-6) and tumor necrosis factor (TNF-α). This finding suggested that CDs-CA-MGs may improve cognitive impairment by modulating inflammatory responses. In conclusion, the use of CDs-CA-MGs provides a possible therapeutic strategy for the treatment of AD. Graphical abstract

Abstract Disruption of ciliary homeostasis in vascular endothelial cells has been implicated in the development of atherosclerosis. However, the molecular basis for the regulation of endothelial cilia during atherosclerosis remains poorly understood. Herein, we provide evidence in male mice that the accumulation of lipid droplets in vascular endothelial cells induces ciliary loss and contributes to atherosclerosis. Triglyceride accumulation in vascular endothelial cells differentially affects the abundance of free fatty acid species in the cytosol, leading to stimulated lipid droplet formation and suppressed protein S-palmitoylation. Reduced S-palmitoylation of ciliary proteins, including ADP ribosylation factor like GTPase 13B, results in the loss of cilia. Restoring palmitic acid availability, either through pharmacological inhibition of stearoyl-CoA desaturase 1 or a palmitic acid-enriched diet, significantly restores endothelial cilia and mitigates the progression of atherosclerosis. These findings thus uncover a previously unrecognized role of lipid droplets in regulating ciliary homeostasis and provide a feasible intervention strategy for preventing and treating atherosclerosis.

In order to solve the target ambiguity and information loss in the fusion results of traditional infrared and visible images, a fusion method of infrared and visible images based on the target enhancement and mouse swarm optimization, which is abbreviated as TERSFuse. Firstly, in order to reduce the loss of the original image details in the fusion results, the infrared contrast enhancement module and the visible image enhancement module based on the brightness perception are constructed respectively. Secondly, the infrared and visible enhanced images were decomposed by using the Laplace pyramid transform to obtain the corresponding high and low frequency images. In order to make the fusion result fully retain the original information, the "maximum absolute value" rule is used to fuse the infrared and visible high frequency images, and the low frequency images are fused by calculating the weight coefficient. Finally, the image reconstruction module based on the rat swarm optimization is designed to achieve the adaptive allocation of weight parameters of high frequency and low frequency image reconstruction, and then improve the visual effect of the fused image. In order to verify the advantages of the present algorithm, the experimental results show that the present algorithm not only obtains the good visual effects, but also can retains the rich edge texture and contrast information of the original image.

Abstract Fluctuations in gas emission or concentration at working face represent primary indicators of impending coal and gas outbursts, making them essential for monitoring processes. However, the direct use of original signals for predictive purposes may cause false warnings due to the inability to recognize valuable potential information. This can result in decreased prediction accuracy. Additionally, gas emission or concentration is affected by potential hazards such as sensor calibration, borehole spraying, and air duct damage, leading to complexity of their behavior and recognition process. This paper proposes an intelligent recognition method leveraging YOLOv8 neural network to discern coal and gas outburst precursors and potential hazards. By employing continuous wavelet transform (CWT) on the gas concentration signal processed by critical slowing down (CSD) method, a two-dimensional time–frequency representation is generated. This representation is then fed into YOLOv8 model to recognize the outburst precursor characteristics and potential hazards. The research results show that YOLOv8-based intelligent recognition adeptly identifies the relevant precursor characteristics and potential hazards, enhancing both coal mine safety protocols and the accuracy of early outburst warning mechanisms.

Abstract Multiplexing technology creates several orthogonal data channels and dimensions for high‐density information encoding and is irreplaceable in large‐capacity information storage, and communication, etc. The multiplexing dimensions are constructed by light attributes and spatial dimensions. However, limited by the degree of freedom of interaction between light and material structure parameters, the multiplexing dimension exploitation method is still confused. Herein, a 7D Spin‐multiplexing technique is proposed. Spin structures with four independent attributes (color center type, spin axis, spatial distribution, and dipole direction) are constructed as coding basic units. Based on the four independent spin physical effects, the corresponding photoluminescence wavelength, magnetic field, microwave, and polarization are created into four orthogonal multiplexing dimensions. Combined with the 3D of space, a 7D multiplexing method is established, which possesses the highest dimension number compared with 6 dimensions in the previous study. The basic spin unit is prepared by a self‐developed laser‐induced manufacturing process. The free state information of spin is read out by four physical quantities. Based on the multiple dimensions, the information is highly dynamically multiplexed to enhance information storage efficiency. Moreover, the high‐dynamic in situ image encryption/marking is demonstrated. It implies a new paradigm for ultra‐high‐capacity storage and real‐time encryption.

Abstract Rheumatoid arthritis (RA) is a chronic autoimmune disease of yet undetermined etiology that is accompanied by significant oxidative stress, inflammatory responses, and damage to joint tissues. In this study, we designed chondroitin sulfate (CS)-modified tragacanth gum–gelatin composite nanocapsules (CS-Cur-TGNCs) loaded with curcumin nanocrystals (Cur-NCs), which rely on the ability of CS to target CD44 to accumulate drugs in inflamed joints. Cur was encapsulated in the form of nanocrystals into tragacanth gum–gelatin composite nanocapsules (TGNCs) by using an inborn microcrystallization method, which produced CS-Cur-TGNCs with a particle size of approximately 80 ± 11.54 nm and a drug loading capacity of 54.18 ± 5.17%. In an in vitro drug release assay, CS-Cur-TGNCs showed MMP-2-responsive properties. During the treatment of RA, CS-Cur-TGNCs significantly inhibited oxidative stress, promoted the polarization of M2-type macrophages to M1-type macrophages, and decreased the expression of inflammatory factors (TNF-α, IL-1β, and IL-6). In addition, it also exerted excellent anti-inflammatory effects, and significantly alleviated the swelling of joints during the treatment of gouty arthritis (GA). Therefore, CS-Cur-TGNCs, as a novel drug delivery system, could lead to new ideas for clinical therapeutic regimens for RA and GA. Graphical Abstract

Abstract Converting elementary sulfur into sulfur-rich polymers provides a sustainable strategy to replace fossil-fuel-based plastics. However, the low ring strain of eight-membered rings, i.e., S8 monomers, compromises their ring-opening polymerization (ROP) due to lack of an enthalpic driving force and as a consequence, poly(sulfur) is inherently unstable. Here we report that copolymerization with cyclic disulfides, e.g., 1,2-dithiolanes, can enable a simple and energy-saving way to convert elementary sulfur into sulfur-rich thermoplastics. The key strategy is to combine two types of ROP—both mediated by disulfide bond exchange—to tackle the thermodynamic instability of poly(sulfur). Meanwhile, the readily modifiable sidechain of the cyclic disulfides provides chemical space to engineer the mechanical properties and dynamic functions over a large range, e.g., self-repairing ability and degradability. Thus, this simple and robust system is expected to be a starting point for the organic transformation of inorganic sulfur toward sulfur-rich functional and green plastics.

Abstract Transforming growth factor (TGF)-β is a multifunctional cytokine expressed by almost every tissue and cell type. The signal transduction of TGF-β can stimulate diverse cellular responses and is particularly critical to embryonic development, wound healing, tissue homeostasis, and immune homeostasis in health. The dysfunction of TGF-β can play key roles in many diseases, and numerous targeted therapies have been developed to rectify its pathogenic activity. In the past decades, a large number of studies on TGF-β signaling have been carried out, covering a broad spectrum of topics in health, disease, and therapeutics. Thus, a comprehensive overview of TGF-β signaling is required for a general picture of the studies in this field. In this review, we retrace the research history of TGF-β and introduce the molecular mechanisms regarding its biosynthesis, activation, and signal transduction. We also provide deep insights into the functions of TGF-β signaling in physiological conditions as well as in pathological processes. TGF-β-targeting therapies which have brought fresh hope to the treatment of relevant diseases are highlighted. Through the summary of previous knowledge and recent updates, this review aims to provide a systematic understanding of TGF-β signaling and to attract more attention and interest to this research area.

Abstract Investigating the intrinsic properties of the Schottky interface between graphene and 3D bulk silicon is crucial. However, the semiconductor technology introduces extra doping and defects in graphene, which significantly disturbs the property of the graphene‐silicon interface. Here, the interface parameters of graphene/n‐Si heterojunction are derived by the damage‐free Hg‐probe capacitance–voltage measurement. Due to its low‐density states, the Fermi level of graphene can be pushed upward, which results in a lower Schottky barrier height (ΦB0) of Hg/graphene/n‐Si (HGS) heterostructure than that of Hg/n‐Si (HS) structure. Additionally, the series resistance (Rs) of HGS becomes lower than that of HS, which can be attributed to the narrowed depletion layer width (WD) and the decreased interface state density (Nit). Furthermore, the frequency characteristic is also investigated. Because of the weak interface state charge trapping–detrapping process and the decreased Nit at high frequency, electrons will accumulate in graphene, and the Fermi level will be pushed up. Hence, the ΦB0 and Rs will decrease with increasing frequency. This study contributes to a deep understanding of the graphene/silicon heterojunction interfaces, which is crucial for designing and optimizing the new electronic and optoelectronic devices based on 2D/3D heterostructure.

Abstract With field effect transistor (FET) sustained to downscale to sub‐10 nm nodes, performance degradation originates from short channel effects (SCEs) degradation and power consumption increment attributed to inhibition of supply voltage (VDD) scaling down proportionally caused by thermionic limit subthreshold swing (SS) (60 mV dec−1) pose substantial challenges for today's semiconductor industry. To further sustain the Moore's law life, incorporation of new device concepts or new materials are imperative. 2D materials are predicted to be able to combat SCEs by virtue of high carrier mobility maintainability regardless of thickness thinning down, dangling bonds free surface and atomic thickness, which contributes to super gate electrostatic controllability. To overcome increasing power dissipation problem, new device structures including negative capacitance FET (NCFET), tunnel FET (TFET), dirac source FET (DSFET) and the like, which show superiority in decreasing VDD by lowering SS below thermionic limit of 60 mV dec−1 have been brought out. The combination of 2D materials and ultralow steep slope device structures holds great promise for low power‐dissipation electronics, which encompass both suppressed SCEs and reduced VDD simultaneously, leading to improved device performance and lowered power dissipation.

Highlights This review introduces the potential of 2D electronics for post-Moore era and discusses their current progress in digital circuits, analog circuits, heterogeneous integration, sensing circuits, artificial intelligence chips, and quantum chips in sequence. A comprehensive analysis of the current trends and challenges encountered in the development of 2D materials is summarized. An in-depth roadmap outlining the future development of 2D electronics is presented, and the most accessible and promising avenues for 2D electronics are suggested.

Highlights The photoelectric response of violet phosphorus (VP) is significantly enhanced by the MXene/VP van der Waals heterojunctions and the highest photoresponsivity of VP is demonstrated. The first VP-based optoelectronic synapse with essential synaptic behaviours is demonstrated. Proof-of-concept visual-olfactory crossmodal perception based on MXene/VP optoelectronic synapses is explored to mimic neuromorphic vision with multi-sensory interactions.

In order to study the influence of loading rate on infrared radiation energy of siltstone during loading failure, and quantitatively analyze the change law of infrared radiation energy of siltstone during loading process, the infrared radiation experiments of siltstone under uniaxial compression at different loading rates were carried out. The results show that the peak stress decreases with the increase of loading rate. Under different loading rates, in difference mode, the maximum infrared radiation temperature ΔTmax has a good correspondence with the stress change, which can better reflect the damage of the sample, and the maximum temperature difference of ΔTmax increases by 1.0-1.7 ℃. During the main rupture, cumulative infrared radiant energy increment ΔQ increases slowly with time. There is a power function relationship between ΔQ and mechanical work. With the increase of mechanical work, the cumulative infrared radiation energy increment increases first and then increases slowly. The range of infrared precursor ratio of specimen damage under different loading rates is 0.02-0.285, and decreases with the loading rate.

Abstract Lung squamous cell carcinoma (LUSC) is the second most common lung cancer worldwide, leading to millions of deaths annually. Although immunotherapy has expanded the therapeutic choices for LUSC and achieved considerable efficacy in a subset of patients, many patients could not benefit, and resistance was pervasive. Therefore, it is significant to investigate the mechanisms leading to patients’ poor response to immunotherapies and explore novel therapeutic targets. Using multiple public LUSC datasets, we found that Kallikrein-8 (KLK8) expression was higher in tumor samples and was correlated with inferior survival. Using a LUSC cohort (n = 190) from our center, we validated the bioinformatic findings about KLK8 and identified high KLK8 expression as an independent risk factor for LUSC. Function enrichment showed that several immune signaling pathways were upregulated in the KLK8 low-expression group and downregulated in the KLK8 high-expression group. For patients with low KLK8 expression, they were with a more active TME, which was both observed in the TCGA database and immune marker immunohistochemistry, and they had extensive positive relations with immune cells with tumor-eliminating functions. This study identified KLK8 as a risk factor in LUSC and illustrated the associations between KLK8 and cancer immunity, suggesting the potentiality of KLK8 as a novel immune target in LUSC.

IntroductionAn unprecedented surge of Omicron infections appeared nationwide in China in December 2022 after the adjustment of the COVID-19 response policy. Here, we report the clinical and genomic characteristics of SARS-CoV-2 infections among children in Shanghai during this outbreak.MethodsA total of 64 children with symptomatic COVID-19 were enrolled. SARS-CoV-2 whole genome sequences were obtained using next-generation sequencing (NGS) technology. Patient demographics and clinical characteristics were compared between variants. Phylogenetic tree, mutation spectrum, and the impact of unique mutations on SARS-CoV-2 proteins were analysed in silico.ResultsThe genomic monitoring revealed that the emerging BA.5.2.48 and BF.7.14 were the dominant variants. The BA.5.2.48 infections were more frequently observed to experience vomiting/diarrhea and less frequently present cough compared to the BF.7.14 infections among patients without comorbidities in the study. The high-frequency unique non-synonymous mutations were present in BA.5.2.48 (N:Q241K) and BF.7.14 (nsp2:V94L, nsp12:L247F, S:C1243F, ORF7a:H47Y) with respect to their parental lineages. Of these mutations, S:C1243F, nsp12:L247F, and ORF7a:H47Y protein were predicted to have a deleterious effect on the protein function. Besides, nsp2:V94L and nsp12:L247F were predicted to destabilize the proteins.DiscussionFurther in vitro to in vivo studies are needed to verify the role of these specific mutations in viral fitness. In addition, continuous genomic monitoring and clinical manifestation assessments of the emerging variants will still be crucial for the effective responses to the ongoing COVID-19 pandemic.

Summary: Background: In recent years, a growing number of patients with multiple primary lung cancer (MPLC) are being diagnosed, and a subset of these patients is found to have a large number of lesions at the time of diagnosis, which are referred to as ‘super MPLC’. Methods: Here, we perform whole exome sequencing (WES) and immunohistochemistry (IHC) analysis of PD-L1 and CD8 on 212 tumor samples from 42 patients with super MPLC. Findings: We report the genomic alteration landscape of super MPLC. EGFR, RBM10 and TP53 mutation and TERT amplification are important molecular events in the evolution of super MPLC. We propose the conception of early intrapulmonary metastasis, which exhibits different clinical features from conventional metastasis. The IHC analyses of PD-L1 and CD8 reveal a less inflamed microenvironment of super MPLC than that of traditional non-small cell lung cancer (NSCLC). We identify the potentially susceptible germline mutations for super MPLC. Interpretation: Our study depicts the genomic characteristics and immune landscape, providing insights into the pathogenesis and possible therapeutic guidance of super MPLC. Funding: A full list of funding bodies that supported this study can be found in the Acknowledgements section.

Studying the anisotropic mechanical properties of cartilage induced by inhomogeneous structure is significant to prevent cartilage damage induced by overloading or fatigue and to develop biomaterials for tissue engineering. The depth-dependent fracture behavior of knee articular cartilage was studied by conducting the fracture tests on the clamped single-edge notched tensile (SENT) samples. The articular cartilage exhibited excellent load bearing capacity and ductility due to its unique depth-dependent matrix structure. The necking length of cartilage decreases through the depth of cartilage, while the peak tensile load first increases, then decreases in depth. The grow of crack in full-thickness cartilage starts in superficial layer at about peak load and the cartilage fails gradually once the crack begins to grow. The fibril network experiences irreversible damage with large area of collagen fibrils yielding before the fracture of full-thickness cartilage. Three fracture parameters, namely the stress intensity factor (K), the critical total work (CTW) and the crack tip opening displacement (CTOD) are calculated to study the fracture properties of cartilage, and CTODΔa = 0.2 is employed to evaluate the depth-dependent initiation fracture toughness of cartilage. The fracture toughness of cartilage decreases along depth direction and the fracture toughness of full-thickness cartilage is close to that of deep layer. In addition, the superficial layer with external layer exhibits larger ductility and larger crack-tip blunting degree than the middle and deep layer of cartilage, and plays a vital role in fracture of cartilage. In addition, the fracture toughness of cartilage increases slightly with the increase of loading rate. The study could provide mechanical reference for repairing cartilage defects with artificial cartilage produced by tissue engineering.

Abstract Histone H3 lysine 4 trimethylation (H3K4me3) is a canonical chromatin modification associated with active gene transcription, playing a pivotal role in regulating various cellular functions. Components of the H3K4me3 methyltransferase complex, known as the proteins associated with SET1 (COMPASS), have been implicated in exerting cancer-protective or cancer-inhibitory effects through inducive H3K4me3 modification. However, the role of the indispensable non-catalytic component of COMPASS CXXC-type zinc finger protein 1 (CFP1) in malignant progression remains unclear. We have unveiled that CFP1 promote lung adenocarcinoma (LUAD) cell proliferation, migration, and invasion while impairing cell apoptosis through in vitro and in vivo models. In addition, high CFP1 expression was identified as emerged as an adverse prognostic indicator across multiple public and in-house LUAD datasets. Notably, CFP1 deficiency led to dual effects on cancer cell transcriptome including extensive inactivation of cancer-promoting as well as activation of cancer repressors. Combining this with the chromatin immunoprecipitation sequencing (ChIP-seq) analysis, we showed that CFP1 ablation reshaped the genomic H3K4me3 distribution signature, with prominent effects on TGF-β and WNT signaling pathways. Collectively, our study proposes that CFP1 mediates tumorigenesis by genomic histone methylation reprogramming, offering insights for future investigations into epigenetic modifications in cancer progression and potential therapeutic advancements.

Self-supervised learning has made significant progress in point cloud processing. Currently, the primary tasks of self-supervised learning, which include point cloud reconstruction and representation learning, are trained separately due to their structural differences. This separation inevitably leads to increased training costs and neglects the potential for mutual assistance between tasks. In this paper, a self-supervised method named PointUR-RL is introduced, which integrates point cloud reconstruction and representation learning. The method features two key components: a variable masked autoencoder (VMAE) and contrastive learning (CL). The VMAE is capable of processing input point cloud blocks with varying masking ratios, ensuring seamless adaptation to both tasks. Furthermore, CL is utilized to enhance the representation learning capabilities and improve the separability of the learned representations. Experimental results confirm the effectiveness of the method in training and its strong generalization ability for downstream tasks. Notably, high-accuracy classification and high-quality reconstruction have been achieved with the public datasets ModelNet and ShapeNet, with competitive results also obtained with the ScanObjectNN real-world dataset.

Abstract In ferroelectrics, complex interactions among various degrees of freedom enable the condensation of topologically protected polarization textures. Known as ferroelectric solitons, these particle-like structures represent a new class of materials with promise for beyond-CMOS technologies due to their ultrafine size and sensitivity to external stimuli. Such polarization textures have scarcely been demonstrated in multiferroics. Here, we present evidence for ferroelectric solitons in (BiFeO3)/(SrTiO3) superlattices. High-resolution piezoresponse force microscopy and Cs-corrected high-angle annular dark-field scanning transmission electron microscopy reveal a zoo of topologies, and polarization displacement mapping of planar specimens reveals center-convergent/divergent topological defects as small as 3 nm. Phase-field simulations verify that some of these structures can be classed as bimerons with a topological charge of ±1, and first-principles-based effective Hamiltonian computations show that the coexistence of such structures can lead to non-integer topological charges, a first observation in a BiFeO3-based system. Our results open new opportunities in multiferroic topotronics.

Abstract Two‐dimensional material‐based field‐effect transistors (2DM‐FETs) are playing a revolutionary role in electronic devices. However, before electronic design automation (EDA) for 2DM‐FETs can be achieved, it remains necessary to determine how to incorporate contact transports into model. Reported methods compromise between physical intelligibility and model compactness due to the heterojunction nature. To address this, quasi‐Fermi‐level phase space theory (QFLPS) is generalized to incorporate contact transports using the Landauer formula. It turns out that the Landauer‐QFLPS model effectively overcomes the issue of concern. The proposed new formula can describe 2DM‐FETs with Schottky or Ohmic contacts with superior accuracy and efficiency over previous methods, especially when describing non‐monotonic drain conductance characteristics. A three‐bit threshold inverter quantizer (TIQ) circuit is fabricated using ambipolar black phosphorus and it is demonstrated that the model accurately predicts circuit performance. The model could be very effective and valuable in the development of 2DM‐FET‐based integrated circuits.

Abstract Memristor-based circuits offer low hardware costs and in-memory computing, but full-memristive circuit integration for different algorithm remains limited. Cellular automata (CA) has been noticed for its well-known parallel, bio-inspired, computational characteristics. Running CA on conventional chips suffers from low parallelism and high hardware costs. Establishing dedicated hardware for CA remains elusive. We propose a recirculated logic operation scheme (RLOS) using memristive hardware and 2D transistors for CA evolution, significantly reducing hardware complexity. RLOS’s versatility supports multiple CA algorithms on a single circuit, including elementary CA rules and more complex majority classification and edge detection algorithms. Results demonstrate up to a 79-fold reduction in hardware costs compared to FPGA-based approaches. RLOS-based reservoir computing is proposed for edge computing development, boasting the lowest hardware cost (6 components/per cell) among existing implementations. This work advances efficient, low-cost CA hardware and encourages edge computing hardware exploration.

Abstract Backgroud The role of epigenetic modifications in tumorigenesis has been widely reported. However, the role and mechanism of H3K4me3 modification in lung adenocarcinoma (LUAD) are rarely reported systematically. We, therefore, sought to analyze the characteristics of LUAD associated with H3K4me3 modification, build an H3K4me3-lncRNAs score model to predict the prognosis of patients with LUAD and clarify the potential value of H3K4me3 in immunotherapy of LUAD. Methods We evaluated H3K4me3-lncRNA patterns and H3K4me3-lncRNA scores of 477 LUAD samples based on 53 lncRNAs closely correlated to H3K4me3 regulators and comprehensive analyzed the role of these patterns in tumorigenesis and tumor immunity. Using Gene set variation analysis (GSVA), we systematically evaluated the H3K4me3 level of every sample and deeply analyzed the effect of H3K4me3 on the prognosis of LUAD. In addition, we included two independent immunotherapy cohorts to study the impact of high H3K4me3 score on the prognosis of patients. We also used an independent cohort with 52 matched paraffin specimens of LUAD to verify the impact of high H3K3me3 expression on the prognosis of patients. Results We identified three H3K4me3-lncRNA patterns that exhibited specific immune characteristics. Characterized by immunosuppressive and increased TGFβ-mediated epithelial-mesenchymal transition (EMT), patients with high H3K4me3-lncRNA score had a poor overall survival and decreased H3K4me3 score. H3K4me3 score was significantly positively correlated with CD4+T-cell and CD8+T-cell activation, programmed cell death and immune checkpoints (ICs) expression, and was negatively correlated with MYC pathway, TP53 pathway, and cell proliferation. Patients with high H3K4me3 score showed elevated expression of ICs, potentiated CD4 T-cell and CD8 T-cell activation, increased programmed cell death, and suppressed cell proliferation and TGFβ-mediated EMT. Patients with high H3K4me3 score and high expression of CTLA4, ICOS, TIGIT, PDCD1LG2, IDO1, CD274, PDCD1, LAG3, or HAVCR2 had the best survival advantage. Two independent immunotherapy cohorts verified that patients with high H3K4me3 score showed an increased inflamed tumor microenvironment (TME) phenotype and enhanced anti-PD-1/L1 immunotherapy response. Immunohistochemistry (IHC) data from 52 matched paraffin specimens of LUAD confirmed that the protein level of H3K4me3 in tumor was significantly lower than that of paracancerous tissues and H3K4me3 brought significant survival benefits to patients with LUAD. Conclusions We build an H3K4me3-lncRNAs score model to predict the prognosis of patients with LUAD. More importantly, this study revealed characteristics of H3K4me3 modification in LUAD and clarified the important potential role of H3K4me3 on tumor immunotherapy and patients’ survival.

Abstract Solution growth of single-crystal ferroelectric oxide films has long been pursued for the low-cost development of high-performance electronic and optoelectronic devices. However, the established principles of vapor-phase epitaxy cannot be directly applied to solution epitaxy, as the interactions between the substrates and the grown materials in solution are quite different. Here, we report the successful epitaxy of single-domain ferroelectric oxide films on Nb-doped SrTiO3 single-crystal substrates by solution reaction at a low temperature of ~200 oC. The epitaxy is mainly driven by an electronic polarization screening effect at the interface between the substrates and the as-grown ferroelectric oxide films, which is realized by the electrons from the doped substrates. Atomic-level characterization reveals a nontrivial polarization gradient throughout the films in a long range up to ~500 nm because of a possible structural transition from the monoclinic phase to the tetragonal phase. This polarization gradient generates an extremely high photovoltaic short-circuit current density of ~2.153 mA/cm2 and open-circuit voltage of ~1.15 V under 375 nm light illumination with power intensity of 500 mW/cm2, corresponding to the highest photoresponsivity of ~4.306×10−3 A/W among all known ferroelectrics. Our results establish a general low-temperature solution route to produce single-crystal gradient films of ferroelectric oxides and thus open the avenue for their broad applications in self-powered photo-detectors, photovoltaic and optoelectronic devices.

Ferroelectric hafnia-based thin films are promising for applications in memories and neuromorphic devices due to their robust ferroelectricity at reduced dimensions. Here, the authors demonstrate stabilization of the metastable orthorhombic phase in Hf0.5 Zr0.5O2 films by interface engineering with a hole doping mechanism.

Highlights This review introduces soft electronics for health monitoring assisted by machine learning, and discusses soft materials, physiological signals, and machine learning algorithms in sequence and their relationships. The principles of classic machine learning algorithms and neural network algorithms are summarized and explained by representative examples combining with soft electronics. The potential challenges of soft electronics assisted by machine learning especially in health monitoring field are outlined, and future research directions are outlooked.

Oceanic targets, including ripples, islands, vessels, and coastlines, display distinct sparse characteristics, rendering the ocean a significant arena for sparse Synthetic Aperture Radar (SAR) imaging rooted in sparse signal processing. Deep neural networks (DNNs), a current research emphasis, have, when integrated with sparse SAR, attracted notable attention for their exceptional imaging capabilities and high computational efficiency. Yet, the efficiency of traditional unfolding techniques is impeded by their architecturally inefficient design, which curtails their information transmission capacity and consequently detracts from the quality of reconstruction. This paper unveils a novel Memory-Augmented Deep Unfolding Network (MADUN) for SAR imaging in marine environments. Our methodology harnesses the synergies between deep learning and algorithmic unfolding, enhanced with a memory component, to elevate SAR imaging’s computational precision. At the heart of our investigation is the incorporation of High-Throughput Short-Term Memory (HSM) and Cross-Stage Long-Term Memory (CLM) within the MADUN framework, ensuring robust information flow across unfolding stages and solidifying the foundation for deep, long-term informational correlations. Our experimental results demonstrate that our strategy significantly surpasses existing methods in enhancing the reconstruction of sparse marine scenes.

Stroke is a high-morbidity central nervous system disease. Its complex pathological process often leads to impaired consciousness, disability and even death. Currently, stroke is treated mainly by thrombolysis, and treatment for its complications, and treatment for pathological processes within the stroke lesion is lacking. This is mainly because most drugs are unable to cross the blood–brain barrier (BBB) to reach the site of injury to exert their therapeutic effects. Therefore, this study aims to develop biomimetic nanocladding devices capable of crossing the BBB, targeting the site of injury, and blocking the pathological process. Since the injury site is a highly inflammatory area, we selected the apoptotic body membrane (ABm) of activated T cells with inflammatory targeting ability to prepare biomimetic nanocladding devices (BND) and use them as the carriers to carry catalase (CAT) (BND-CAT) for the treatment of stroke rats. The results show that BND can not only target inflammatory lesions, but also regulate the transformation of microglia subtypes, thereby inhibiting inflammation within the lesions. In addition, BND-CAT could also inhibit oxidative stress, protect the mitochondria, increase the ATP content in the lesion, suppress the endoplasmic reticulum stress, and ultimately inhibit the apoptosis of neurons in the lesion.

Potassium niobate (KNbO3, KNO) has been intensively investigated for photovoltaic performance because of its non-toxicity and excellent nonlinear optical properties. The corresponding photovoltaic current density, however, remains very low due to a wide bandgap. Chemical doping and strain engineering strategies have been employed to tailor the band structure to enhance photovoltaic current density. Nevertheless, the original current density is still at a level of several tens of nA/cm2, significantly limiting device applications. In this work, we report a lattice-gradient KNO film on (100) single-crystal 0.7 wt. % Nb doped SrTiO3 (NSTO) substrate processed by annealing, generating a polarization-gradient that allows us to generate a large current density via a built-in field. The film exhibits a remarkable short-circuit current density (Jsc) of 58.63 µA/cm2 under the 375 nm irradiation of 500 mW/cm2 light intensity, where the corresponding responsivity (117.26 µA/W) is ∼3.82 times higher than those of reported KNO-based materials. It was revealed that the annealing process driven interfacial structure evolution from disorder to atomic-scale smoothness, accompanied by the transformation of the polarization shielding mechanism. After this process, an intriguing lattice-gradient throughout the film was established to have a uniform polarization direction, possibly accounting for the improved photovoltaic current density of KNO film. These findings may trigger interest in developing KNO as a potential key material for lead-free optoelectronic or photodetector devices.

Abstract Consider general minimum variance distortionless response (MVDR) robust adaptive beamforming problems based on the optimal estimation for both the desired signal steering vector and the interference‐plus‐noise covariance (INC) matrix. The optimal robust adaptive beamformer design problem is an array output power maximization problem, subject to three constraints on the steering vector, namely, a (convex or nonconvex) quadratic constraint ensuring that the direction‐of‐arrival (DOA) of the desired signal is separated from the DOA region of all linear combinations of the interference steering vectors, a double‐sided norm constraint, and a similarity constraint; as well as a ball constraint on the INC matrix, which is centered at a given data sample covariance matrix. To tackle the nonconvex problem, a new tightened semidefinite relaxation (SDR) approach is proposed to output a globally optimal solution; otherwise, a sequential convex approximation (SCA) method is established to return a locally optimal solution. The simulation results show that the MVDR robust adaptive beamformers based on the optimal estimation for the steering vector and the INC matrix have better performance (in terms of, e.g., the array output signal‐to‐interference‐plus‐noise ratio) than the existing MVDR robust adaptive beamformers by the steering vector estimation only.

The ability of cartilage to regenerate and repair is limited. N-acetyl- d-glucosamine (GlcNAc) is a nutritional supplement commonly used to activate chondrocytes. To prolong the duration of action of GlcNAc and improve its curative effect after cartilage injury, a GlcNAc thermosensitive hydrogel is prepared based on Pluronic F127 (PF127). The physicochemical properties results indicate that this hydrogel is injectable and retards the release of GlcNAc. Further, the therapeutic benefits of GlcNAc hydrogel are detected through intra-articular injection in rat specimens with cartilage injury. Behavioral experiments results indicate that the rats treated with GlcNAc hydrogel had longer step lengths, smaller foot angles and slower fall times. Compared with the sham group, the expression of Sox9 was 1.5 times and the level of collagen II was 2.4 times in the hydrogel treated group. Terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) staining result confirmed that the GlcNAc hydrogel reduce apoptosis by about 50%. Our results of immunohistochemical staining, Western blotting assays and enzyme activity detection all suggested that GlcNAc hydrogel reduce the expression of cleaved-caspase3 and caspase8 (Compared to the sham group, the protein contents were reduced by about 50% in the GlcNAc hydrogel group). We also found that GlcNAc hydrogel activates autophagy through ERK signal pathway. The results of Western blotting indicated that GlcNAc hydrogel increase the levels of LC3B and Becline1 (hydrogel group & sham group, LC3B: 1.56 ± 0.07 & 1.00 ± 0.14; Becline1: 1.98 ± 0.07 & 1.00 ± 0.13). Whereas, the content of P62 reduced after GlcNAc hydrogel treatment, the relative level in sham group and hydrogel group are 1.00 ± 0.02 and 0.73 ± 0.06. Our results revealed that the number of P-ERK positive cells in the hydrogel group (57.36 ± 3.56%) was higher when compared with the sham (24.82 ± 2.72%). And, the ratio of P-ERK and ERK was higher than that in the sham group (1.48 ± 0.07 & 1.00 ± 0.08). The GlcNAc thermosensitive hydrogel is a promising and sustainable drug delivery system for intra-articular injection in the treatment of cartilage injury.

Spinal cord injury (SCI) can cause very serious bodily harm or even paralysis. However, all clinical drugs require long-term use to relieve the disease, causing toxicity and side effects. Zinc tends to activate the self-defense system to repair SCI, but the currently reported zinc supplementation is not efficient to cure SCI due to the poor targeting effects. The zinc delivery system has been employed to improve the targeting efficiency, which also increases the complexity and toxicity of drugs. On the other hand, we envisage that if there is a drug that can accumulate and activate endogenous zinc in the lesions of the spinal cord and stimulate immunotherapy, it can not only reduce long-term drug dependence but also significantly improve the therapeutic effect on SCI. Herein, we found zinc deficiency for SCI mice, but more zinc accumulated in the lesions of the spinal cord after the administration of GSH-AuNCs in SCI mice. Meanwhile, more microglia/macrophages polarize toward M2 from M1 phenotypes after zinc accumulation which enhances immunotherapy effects. Thereafter, the inflammatory response was inhibited, which exerts a neuroprotective effect. As well as this, the current treatment showed memory effects for zinc accumulation even after stopping the drug administration, allowing endogenous zinc to continue its induction of multiple therapeutic effects. Due to the direct and memory curative function, the lesions of SCI mice were well recovered and the motor functions were also significantly improved.

Organic fluorophores with dynamic conformations in the excited state have played a significant role in applications of organic functional dyes. Among them, dihydrophenazine-based dynamic fluorophores involving a photoinduced structural planarization process in the excited state exhibited large Stokes shifts and conformation-dependent multicolor emissions. With the developments of synthetic strategies, precise modifications on dihydrophenazine-based scaffolds have successfully afforded a variety of precise molecular structures of varying sizes and compositions, which have delicately modulated their photophysical properties. Herein, this Perspective summarizes the precise modulations of dihydrophenazine-based dynamic fluorophores, including the development of the synthetic methodologies, and tailor-made molecular models to reveal the luminescence–structure relationships. Insights about the future molecular design and applications are also presented.

Abstract To accurately identify atoms on noisy transmission electron microscope images, a deep learning (DL) approach is employed to estimate the map of probabilities at each pixel for being an atom with element discernment. Thanks to a delicately-designed loss function and the ability to extract features, the proposed DL networks can be trained by a small dataset created from approximately 30 experimental images, each with a size of 256 × 256 pixels2. The accuracy and robustness of the network were verified by resolving the structural defects of graphene and polar structures in PbTiO3/SrTiO3 multilayers from both the general TEM images and their imitated images on which intensities of some pixels lost randomly. Such a network has the potential to identify atoms from very few images of beam-sensitive material and explosive images recorded in a dynamical atomic process. The idea of using a small-dataset-trained DL framework to resolve a specific problem may prove instructive for practical DL applications in various fields.

‘Pure organic room-temperature phosphorescence (RTP) materials with circularly polarized luminescence (CPL) gained increasing interest. Here, the authors propose a photoinduced circularly polarized RTP material by homogeneously dispersing phosphorescent chiral polyacetylenes into a processable polymer matrix.

Spinal cord injury (SCI) is a serious central nervous system disease, and secondary injury, including oxidative stress, the inflammatory response and accompanying neuronal apoptosis, will aggravate the condition. Due to the existence of the blood–spinal cord barrier (BSCB), the existing drugs for SCI treatment are difficulty to reach the injury site and thus their efficacy is limited. In this study, we designed chitosan-modified hollow manganese dioxide nanoparticles (CM) for the delivery of resveratrol to help it pass through the BSCB. Resveratrol (Res), a poorly soluble drug, was adsorbed into CM with a particle size of approximately 130 nm via the adsorption method, and the drug loading reached 21.39 ± 2.53%. In vitro dissolution experiment, the Res release of the loaded sample (CMR) showed slowly release behavior and reached about 87% at 36 h. In vitro at the cellular level and in vivo at the animal level experiments demonstrated that CMR could alleviate significantly oxidative stress by reducing level of reactive oxygen species (ROS), malondialdehyde (MDA), superoxide dismutase (SOD), and increasing glutathione peroxidase (GSH) level. Additionally, immunofluorescence (iNOS, IL-1β, and Cl caspase-3) and western blot (iNOS, cox-2, IL-1β, IL-10, Cl caspase-3, bax, and bcl-2) were used to detect the expression of related factors, which verified that CMR could also reduce inflammation and neuronal apoptosis. These results indicated that CM, as a potential central nervous system drug delivery material, was suitable for SCI treatment.

Different cities have significant variations in terms of economic development, population size, geographical features, energy endowment, and total amount and structure of carbon dioxide (CO2) emission from transport. It is essential to understand the types and characteristics of urban transport CO2 emissions and propose differentiated CO2 emission reduction measures and formulate renewable energy use strategy, with a view to sequentially achieving the peak of urban transport CO2 emissions. Based on the analysis of driving factors of urban transport CO2 emission, this paper establishes a classified index system and then adopts the Gaussian mixture model (GMM) and expectation–maximization (EM) algorithm to cluster the trends of transport CO2 emissions peak in 672 municipal cities in China. The results show that the model can effectively identify the differences between different cities and divide them into five types, namely the types of public transport demonstration, emission pressure, low carbon potential, population loss, and high carbon pressure, the proportion of urban transportation carbon emission in these five cities is 44.39%, 22.19%, 18.21%, 7.66% and 7.55%. Moreover, by analyzing different energy endowment characteristics and geographical features, optimization suggestions are put forward for the transportation energy consumption of different cities to realize the efficient utilization of renewable energy.

Abstract Lead halide perovskites have made remarkable progress in the field of radiation detection owing to the excellent and unique optoelectronic properties. However, the instability and the toxicity of lead‐based perovskites have greatly hindered its practical applications. Alternatively, lead‐free perovskites with high stability and environmental friendliness thus have fascinated significant research attention for direct X‐ray detection. In this review, the current research progress of X‐ray detectors based on lead‐free halide perovskites is focused. First, the synthesis methods of lead‐free perovskites including single crystals and films are discussed. In addition, the properties of these materials and the detectors, which can provide a better understanding and designing satisfactory devices are also presented. Finally, the challenge and outlook for developing high‐performance lead‐free perovskite X‐ray detectors are also provided.

Abstract Aurivillius‐type compounds ((Bi2O2)2+(An–1BnO3n+1)2−) with alternately stacked layers of bismuth oxide (Bi2O2)2+ and perovskite (An−1BnO3n+1)2− are promising photocatalysts for overall water splitting due to their suitable band structures and adjustable layered characteristics. However, the self‐reduction of Bi3+ at the top (Bi2O2)2+ layers induced by photogenerated electrons during photocatalytic processes causes inactivation of the compounds as photocatalysts. Here, using Bi3TiNbO9 as a model photocatalyst, its surface termination is modulated by acid etching, which well suppresses the self‐corrosion phenomenon. A combination of comprehensive experimental investigations together with theoretical calculations reveals the transition of the material surface from the self‐reduction‐sensitive (Bi2O2)2+ layer to the robust (BiTiNbO7)2− perovskite layer, enabling effective electron transfer through surface trapping and effective hole transfer through surface electric field, and also efficient transfer of the electrons to the cocatalyst for greatly enhanced photocatalytic overall water splitting. Moreover, this facile modification strategy can be readily extended to other Aurivillius compounds (e.g., SrBi2Nb2O9, Bi4Ti3O12, and SrBi4Ti4O15) and therefore justify its usefulness in rationally tailoring surface structures of layered photocatalysts for high photocatalytic overall water‐splitting activity and stability.

Abstract In this paper, the application of a hybrid representation in wide‐angle synthetic aperture radar (WASAR) imaging is investigated, addressing the challenge of achieving sparse representation in the presence of complex electromagnetic scattering characteristics and highly anisotropic targets. A convolutional neural network is utilized to represent the two‐dimensional data within each azimuth aperture, while employing blind compressed sensing (BCS) to achieve sparse representation across different azimuth apertures. Convolutional neural network (CNN) excels in learning spatial hierarchies and local dependencies of two‐dimensional data but requires a large amount of training data. Isotropic targets within each azimuth aperture can be used for training conventional SAR data, while acquiring training samples for anisotropic targets poses challenges. To address this issue, BCS are integrated into WASAR imaging, eliminating the need for additional training data. By integrating these methods, a novel approach called hybrid‐WASAR is proposed, which incorporates two regularization terms into WASAR imaging and employs the alternating direction method of multipliers to iteratively solve the imaging model. Compared to traditional WASAR imaging techniques, hybrid‐WASAR improves the accuracy of reconstructing the backscattering coefficients of the targets, leading to a significant enhancement in overall imaging quality.

With the advances in cancer immunity regulation and immunotherapy, the effects of histone modifications on establishing antitumor immunological ability are constantly being uncovered. Developing combination therapies involving epigenetic drugs (epi-drugs) and immune checkpoint blockades or chimeric antigen receptor-T cell therapies are promising to improve the benefits of immunotherapy. Histone H3 lysine 4 trimethylation (H3K4me3) is a pivotal epigenetic modification in cancer immunity regulation, deeply involved in modulating tumor immunogenicity, reshaping tumor immune microenvironment, and regulating immune cell functions. However, how to integrate these theoretical foundations to create novel H3K4 trimethylation-based therapeutic strategies and optimize available therapies remains uncertain. In this review, we delineate the mechanisms by which H3K4me3 and its modifiers regulate antitumor immunity, and explore the therapeutic potential of the H3K4me3-related agents combined with immunotherapies. Understanding the role of H3K4me3 in cancer immunity will be instrumental in developing novel epigenetic therapies and advancing immunotherapy-based combination regimens.

Summary: As one of the most potential ways to manipulate heat, thermal functional devices have achieved several breakthroughs in recent years, but are still limited to theoretical simulations. One of its theoretical bases is the existence of the negative differential thermal resistance (NDTR). However, most of the existing systems where the phenomenon of NDTR is found are atomic-level systems. In order to realize the macroscopic NDTR and provide effective theoretical guidance and support for the practical realization of thermal functional devices, we construct the overlapping graphene homojunction model, using the negative thermal expansion property of graphene to modify the overlapping area, and thus regulating the heat flow. The COMSOL-MATLAB co-simulation is used to perform calculations through negative feedback loops. It is found that the NDTR phenomenon exists under certain parameter conditions, which can provide new ideas and bring more opportunities for the experimental realization of nonlinear thermal functional devices.

Understanding and tuning charge transport over a single molecule is a fundamental topic in molecular electronics. Single-molecule junctions composed of individual molecules attached to two electrodes are the most common components built for single-molecule charge transport studies. During the past two decades, rapid technical and theoretical advances in single-molecule junctions have increased our understanding of the conductance properties and functions of molecular devices. In this perspective article, we introduce the basic principles of charge transport in single-molecule junctions, then give an overview of recent progress in modulating single-molecule transport through external stimuli such as electric field and potential, light, mechanical force, heat, and chemical environment. Lastly, we discuss challenges and offer views on future developments in molecular electronics.

Neuromorphic computing (NC), considered as a promising candidate for future computer architecture, can facilitate more biomimetic intelligence while reducing energy consumption. Neuron is one of the critical building blocks of NC systems. Researchers have been engaged in promoting neuron devices with better electrical properties and more biomimetic functions. Two-dimensional (2D) materials, with ultrathin layers, diverse band structures, featuring excellent electronic properties and various sensing abilities, are promised to realize these requirements. Here, the progress of artificial neurons brought by 2D materials is reviewed, from the perspective of electrical performance of neuron devices, from stability, tunability to power consumption and on/off ratio. Rose up to system-level applications, algorithms and hardware implementation of spiking neural network, stochastic neural network and artificial perception system based on 2D materials are reviewed. 2D materials not only facilitate the realization of NC systems but also increase the integration density. Finally, current challenges and perspectives on developing 2D material-based neurons and NC systems are systematically analyzed, from the bottom 2D materials fabrication to novel neural devices, more brain-like computational algorithms and systems.

The integration of deep neural networks into sparse synthetic aperture radar (SAR) imaging is explored to enhance SAR imaging performance and reduce the system’s sampling rate. However, the scarcity of training samples and mismatches between the training data and the SAR system pose significant challenges to the method’s further development. In this paper, we propose a novel SAR imaging approach based on deep image prior powered by RED (DeepRED), enabling unsupervised SAR imaging without the need for additional training data. Initially, DeepRED is introduced as the regularization technique within the sparse SAR imaging model. Subsequently, variable splitting and the alternating direction method of multipliers (ADMM) are employed to solve the imaging model, alternately updating the magnitude and phase of the SAR image. Additionally, the SAR echo simulation operator is utilized as an observation model to enhance computational efficiency. Through simulations and real data experiments, we demonstrate that our method maintains imaging quality and system downsampling rate on par with deep-neural-network-based sparse SAR imaging but without the requirement for training data.

In response to the volatility of photovoltaic power generation, this paper proposes a short-term photovoltaic power generation prediction model (HWOA-MVMD-TPA-TCN) based on a Hybrid Whale Optimization Algorithm (HWOA), multivariate variational mode decomposition (MVMD), temporal pattern attention mechanism (TPA), and temporal convolutional network (TCN). In order to improve the accuracy of photovoltaic power generation forecasting, HWOA-MVMD is used for data decomposition, the Minimum Mode Overlap Component (MMOC) is used as the objective function, the photovoltaic power generation sequence is decomposed into finite Intrinsic Mode Functions (IMFs) according to the optimal solution, and the training set is formed with key meteorological variable data such as total radiation (unit: W/m2), ambient temperature, and humidity. Then, the TPA-TCN model is used to train the sub-sequences, the final predicted values are obtained after superimposing the reconstruction of the prediction results, and finally the prediction error of the photovoltaic power generation data is studied. The proposed method is applied to real photovoltaic power generation data from a commercial center in Tianjin and is compared with HWOA-MVMD-BiLSTM, GWO-MVMD-TPA-TCN, and TPA-TCN prediction models. The simulation results demonstrate that the MAE value of the forecast method proposed in this paper is 1.95 MW and the RMSE value is 2.55 MW, which can be reduced by up to 33.74% and 38.85%, respectively. The HWOA-MVMD-TPA-TCN-based short-term photovoltaic power generation prediction model presented in this paper achieves higher prediction accuracy and superior performance, serving as a valuable reference for related research.

The human gut microbiome has primarily been studied through the use of fecal samples, a practice that has generated vital knowledge on the composition and functional capacities of gastrointestinal microbial communities. However, this reliance on fecal materials limits the investigation of microbial dynamics in other locations along the gastrointestinal tract (in situ), and the infrequent availability of fecal samples prevents analysis at finer temporal scales (e.g., hours). In our study, we utilized colonic transendoscopic enteral tubing, a technology originally developed for fecal microbiota transplantation, to sample the ileocecal microbiome twice daily; metagenomic and metatranscriptomic analyses were then conducted on these samples. A total of 43 ileocecal and 28 urine and fecal samples were collected from five healthy volunteers. The ileocecal and fecal microbiomes, as profiled in the five volunteers, were found to be similar in metagenomic profiling, yet their active genes (metatranscriptome) were found to be highly distinct. Both microbiomes were perturbed after laxative exposure; over time, they exhibited reduced dissimilarity to their pre-treatment state, thereby demonstrating resilience as an innate property of the gut microbiome, although they did not fully recover within our observation time window. Sampling of the ileocecal microbiome during the day and at night revealed the existence of diurnal rhythms in a series of bacterial species and functional pathways, particularly those related to short-chain fatty acid production, such as Propionibacterium acnes and coenzyme A biosynthesis II. Autocorrelation analysis and fluctuations decomposition further indicated the significant periodicity of the diurnal oscillations. Metabolomic profiling in the fecal and urine samples mirrored the perturbance and recovery in the gut microbiome, indicating the crucial contribution of the gut microbiome to many key metabolites involved in host health. This study provides novel insights into the human gut microbiome and its inner resilience and diurnal rhythms, as well as the potential consequences of these to the host.