Self-driving vehicles rely closely on interactions with humans, vehicles, and the surrounding environment. However, the interactive analysis of self-driving is impacted by multiple perception sources, heterogeneous data, and complex environments in actual scenes. Due to the above issues, we are often unclear about the behavior of self-driving vehicles and do not understand their decisions, and it is also difficult to achieve synergy with our human intentions. We introduce the significance of research in the field of self-driving interactive cognition, detailing its components and underlying infrastructure. Furthermore, we demonstrate how self-driving interactive cognition, inspired by the Wiener model, embodies intelligence in complex environments with the purpose of stressing the importance of interactive cognition in complex environments and scientifically evaluating the analysis of machine interactive cognition. Then, a multidimensional analysis model of self-driving interactive cognition is established based on perceptual information acquisition, multichannel and cross-modal data registration, attention mechanism, visual recognition and understanding, and embodied dynamic control. Supported by the above, we build a multiview spatiotemporal graph convolutional network (MV-STGCN) model for action recognition to realize vehicle-to-human body language interactive cognition. Most importantly, we innovatively propose a nonlinear-CRITIC–technique for order preference by similarity to an ideal solution (TOPSIS)-based method to analyze the interactive cognition analyses of different action recognition algorithms efficiently, such as MV-STGCN. Future self-driving vehicles are bound to demonstrate multichannel and cross-modal intelligence perception and human–vehicle-friendly interaction, and we are committed to better realizing humanoid driving analysis and the embodied intelligence of self-driving vehicles. “Self-driving + interactive cognition” could make future vehicles become interactive wheeled robots that can be trusted and better serve human society.

As pivotal drivers of smart cities, mega-mobility systems integrate large-scale transportation networks, communication nodes, and energy circuits into a coupled multinetwork system. Urban megasystems epitomize the grand challenge of “organized complexity”, exhibiting characteristic features such as adaptive openness, nonlinear dynamics, hierarchical organization, and emergent properties. Analytical investigations, constrained by the rigid separation of macro- and microlevel paradigms, struggle to capture the nonlinear interdependencies across levels that define mega-mobility systems. In this review, we systematically advance macro–micro integration with feedback (MMIF) as a transformative paradigm for analyzing urban mega-mobility systems, synthesizing the state-of-the-art developments in typical constituent subsystems under this unified perspective. The MMIF paradigm bridges the gap between theoretical abstraction and empirical practice, contributing to scientifically sound urban development by harmonizing emergent patterns with granular behavioral dynamics. Building upon this paradigm, we investigate the key methods and technologies empowered by artificial intelligence that enable MMIF and critically analyze the enduring challenges and prospective research directions. As urban mobility systems increasingly serve as test beds for complexity science, the MMIF paradigm using artificial intelligence promises to reshape interdisciplinary collaboration, offering a blueprint for building intelligent, adaptive, and human-centric cities.

Stroke-induced motor and cognitive impairments substantially reduce the quality of life in elderly populations, driving the need for rehabilitation strategies that integrate neural plasticity and functional recovery. In this 4.5-year longitudinal study, we evaluated the efficacy of brain–computer interface combined with functional electrical stimulation (BCI-FES) versus FES only and conventional care (control) in 100 stroke survivors (60 to 90 years; 4,172 total screened, with 24 chronic-stage patients [>1 year post-onset] completing long-term follow-up). We integrated clinical metrics (Fugl-Meyer assessment [FMA], modified Barthel index [MBI], and Montreal Cognitive Assessment [MoCA]) with electroencephalography-based neurophysiological profiling to dissect recovery mechanisms. BCI-FES yielded superior and sustained improvements across all domains: motor function (FMA Δ = 4.5 ± 1.2 points, Cohen’s d = 1.2) versus FES (Δ = 1.7 ± 0.8, d = 0.4) and control (Δ = 0.9 ± 0.6, d = 0.2), functional independence (MBI Δ = 5.4 ± 1.5, d = 1.1) exceeding FES (Δ = 2.2 ± 1.1, d = 0.4) and control (Δ = 1.3 ± 0.5, d = 0.5), and cognitive function (MoCA Δ = 1.6 ± 0.5, d = 0.8 at 4 months), although cognitive gains declined to near baseline by 4.5 years. Hemorrhagic stroke patients showed exceptional BCI-FES responses, while ischemic patients exhibited higher variability. Neurophysiologically, BCI-FES induced theta (Cz and C4) and alpha (FC3 and CP3) power increases, with theta power at Cz strongly predicting FMA gains (r = 0.68), and enhanced theta/alpha band functional connectivity (clustering coefficient +22%, local efficiency +18%, and small-world index +15%). Predictive modeling identified that an optimal treatment window (3 to 12 months post-onset with 10 to 15 weeks of therapy) maximizes recovery via peak neuroplasticity, and a responder profile (stroke duration <23 months) includes patients with residual plasticity (age <70, baseline MBI >40), predicting 76% of favorable outcomes. These findings establish BCI-FES as a transformative rehabilitation tool, driving dual-phase recovery via early cortical plasticity and sustained network coherence while highlighting the need for age-tailored cognitive maintenance strategies. This work redefines precision stroke care by merging clinical outcomes with mechanistic insights, positioning BCI-FES as the standard of care for diverse stroke subtypes.

The absence of distinct biomarkers for oral squamous cell carcinoma (OSCC) hampers clinical diagnosis and treatment. Previous studies have demonstrated that sialylated immunoglobulin G (SIA-IgG) is up-regulated in a variety of epithelial-derived tumors and plays an important biological role. However, the function of SIA-IgG in OSCC progression is yet to be clarified. The expression characteristics and clinical significance of SIA-IgG were investigated using immunohistochemistry, western blot, and bioinformatics analysis. The biological functions of SIA-IgG were evaluated in vitro and in vivo. The underlying mechanisms of SIA-IgG in OSCC were elucidated using immunoprecipitation, mass spectrometry, and proteomic profiling. Furthermore, we explored the inhibitory effects of an exogenously added SIA-IgG-specific monoclonal antibody, RP215, on OSCC. An elevated level of SIA-IgG was observed in OSCC, and this was linked to poor survival among OSCC patients. Meanwhile, the up-regulation of SIA-IgG and valosin-containing protein (VCP) promoted the malignant progression of OSCC by activating the oxidative phosphorylation pathway. Moreover, SIA-IgG showed potential as a marker of OSCC cancer stem cells. Mechanistically, SIA-IgG enhanced the interaction between VCP and NADH:ubiquinone oxidoreductase subunit B6 (NDUFB6), thereby inhibiting the ubiquitin–proteasome-pathway-mediated degradation of NDUFB6 and sustaining the activation of the oxidative phosphorylation pathway. This inhibitory effect was abrogated by the overexpression of VCP. Furthermore, the SIA-IgG-specific antibody RP215 suppressed the malignant progression of OSCC, both in vitro and in vivo.

The 5-HT_1A receptor is a critical target in the treatment of depression and anxiety. Bufotenine derivatives, such as 5-methoxy-N,N-dimethyltryptamine (5-MeO-DMT), 5-hydroxy-N,N-dimethyltryptamine (5-OH-DMT), and 5-hydroxy-N,N,N-dimethyltryptamine—derived from traditional Chinese medicine—have shown antidepressant potential. However, the structural basis of their interaction with 5-HT_1A and their pharmacological profiles remain incompletely understood. This study investigated bufotenine derivatives acting on multiple serotonin receptors, highlighting 5-HT_1A as a key mediator of antidepressant effects while recognizing 5-HT_2A as primarily responsible for hallucinogenic outcomes, to identify candidates with therapeutic efficacy but reduced hallucinogenic liability. We determined the cryo-electron microscopy structures of 5-HT_1A bound to selected bufotenine derivatives. Functional assays in mice, including behavioral tests and receptor activation studies, were used to evaluate the antidepressant of each compound. Structural analysis revealed that all bufotenine derivatives engage conserved motifs within the 5-HT_1A binding pocket, with 5-OH-DMT displaying a distinct interaction pattern. Behavioral assays showed that 5-OH-DMT and 5-MeO-DMT retained strong antidepressant and anxiolytic effects. These pharmacological differences correlate with their unique receptor binding conformation. This study delineated the structural pharmacology of bufotenine derivatives at the 5-HT_1A receptor, identifying 5-OH-DMT and 5-MeO-DMT as promising antidepressant and anxiolytic candidates. The findings establish a molecular framework for the development of next-generation nonhallucinogenic therapeutics aimed at 5-HT_1A.

As an essential branch of smart materials, shape memory polymer materials (SMPs) have made substantial advances in fabrication strategies, microstructure design, and response methods. Shape memory polymer porous materials (SMPPMs), combining the advantages of SMPs and porous materials, feature lightweight properties, tunable micro/nanostructures, large specific surface areas, and programmable shapes, which have attracted important attention across a wide range of applications. This study focuses on 2 types of SMPPMs: shape memory foams and shape memory aerogels. This review systematically examines the fabrication strategies for SMPPMs, including gas foaming, template, freeze-drying, and 4-dimensional printing methods, deeply analyzes the impact of fabrication strategies on their micro/nanostructures, and summarizes their latest applications in areas such as smart thermal protection systems for aerospace, minimally invasive biomedical devices, and high-sensitivity smart sensors. This review analyzes the current state of research and future trends in SMPPMs from multiple perspectives, including material design, structural design, and response strategies. The design of SMPPMs requires integration with actual application needs, achieved through appropriate selection of polymer matrices and optimized micro/nanostructure designs to improve material performance. Furthermore, the review also introduces current challenges and development trends related to SMPPMs, including advanced, sophisticated, large-scale preparation strategies, and efficient, rapid, precise driving methods, as well as applications that integrate multiple disciplines and fields.

This study overcomes 2 critical barriers to the clinical translation of nano-adsorbents for sepsis blood purification: the weakening of adsorption function caused by nanoparticle biofouling and the limitations in clinical translation of recovery devices. We pioneer electrically neutral phosphocholine zwitterions as selective lipopolysaccharide (LPS) ligands. Their precise charge orientation resolves the core conflict between anti-fouling efficacy and LPS capture via differential dipole realignment upon LPS binding, enabling unprecedented selective LPS capture capacity with minimal protein adsorption. To address the persistent challenges of nano-adsorbent retrieval from blood, and clinical incompatibility of existing retrieval devices with blood purification systems, we developed a discretely assembled magnetic nanocomposite platform (PCAPAN-Fe) and an extracorporeal LPS-targeting magnetic array system (ELMAS), eliminating key risks inherent in monolithic designs while ensuring complete nanoparticle harvest. In septic rabbit models, the integrated platform exhibited 100% survival with early intervention, 84.7% LPS clearance (versus 20% survival and 45.6% LPS clearance for commercial adsorbents), and marked reduction of key proinflammatory cytokines. Crucially, the therapy achieved 82.2% LPS clearance efficacy in progressive sepsis, extending survival to 40% (versus 0% for commercial adsorbents). By ingeniously integrating molecular-level ligand design with a clinically viable device, this work pioneers a paradigm shifts in sepsis nanotherapeutic, resolving the performance–biosafety paradox in blood purification.

Organonitrogen chemicals with C=N bonds are one of the most important groups of chemicals with broad applications, but their synthesis via reductive coupling remains a great challenge, because of the favorable hydrogenation of C=N bonds into C–N bonds. In this study, a nitrogen-doped carbon-supported β-MoO₃ catalyst with abundant oxygen vacancies (O_v) was discovered to be robust in the reductive coupling of nitro compounds with biomass-derived alcohols toward the synthesis of organonitrogen chemicals, including imines and N-heterocycles with C=N bonds. The O_v in β-MoO₃ serves a crucial role in the adsorption and activation of substrates via the electronic interaction between the negatively charged oxygen atoms in these substrates and the O_v sites in β-MoO₃. The presence of O_v greatly lowers the energy barriers of the reductive coupling reaction, and the electron transfer from alcohols to nitro compounds is mediated by the Mo⁵⁺/Mo⁶⁺ redox cycle. Our method demonstrates excellent selectivity to C=N bonds and is effective for a wide substrate scope including the highly inert methanol and ethanol. This study highlights the use of non-noble metal oxides as alternatives to traditional metal nanoparticles for various challenging organic transformations.

Achieving stable n-type conductivity in diamond has remained a central challenge due to the deep donor levels and compensation associated with conventional dopants. Here, we show that oxygen-assisted B–N codoping during microwave plasma chemical vapor deposition activates shallow BN₂ donors within a narrow thermal window ~(1,020 K), yielding reproducible electron conduction. First-principles calculations identify the BN₂ complex—stabilized via oxygen-mediated regulation of N site occupancy that suppresses deeper N-related defects—as a shallow and strain-tolerant donor, consistent with the experimentally optimized growth window for BN₂ codoping. The resulting films exhibit electron concentrations above 10¹⁹ cm⁻³ with a mobility of 4.05 cm²/(V·s) and a shallow activation energy of ~21.1 meV, confirming effective n-type transport. Integration with p-type 2H-MoTe₂ yields a diamond-based pn junction with clear rectifying behavior. While device performance is presently limited by interface states, these findings establish BN₂ codoping as a viable strategy for n-type diamond and highlight interface quality as the decisive frontier for diamond electronics, opening opportunities for high-frequency, high-power, and radiation-hard device platforms.

Chemoresistance is a primary cause of cancer treatment failure, due to the lack of specific regulatory strategies arising from unclear mechanisms. Here, we uncover the pivotal role of the PRMT1/SOX2 axis in regulating cancer stemness, a key factor contributing to cancer chemoresistance. In light of this, we construct a DNA nanomachine (DNM) to overcome chemoresistance by reversing cancer stemness. This DNM is constructed using a programmable DNA origami framework, incorporating CD44-targeting aptamers and glutathione (GSH)-responsive stemness inhibitors (DCLX069) as functional components. The DNM exhibits a specific affinity toward CD44-overexpressing tumor cells, enabling the effective delivery of the loaded cisplatin (CDDP) to the tumor cells. Upon entering the tumor cells, DCLX069 is rapidly released from the DNM due to high intracellular GSH levels, leading to swift regulation of the PRMT1/SOX2 axis. In contrast, CDDP exhibits a gradual enzymatic release profile. This temporally programmed release enables the reversal of cancer stemness before chemotherapy initiation, resulting in a substantial improvement in CDDP chemosensitivity and a significant increase in the median survival of tumor-bearing mice from 27 to over 56 d with DNM assistance. This study highlights the promising potential of this DNA nanotechnology-empowered therapy in addressing chemoresistance in malignant tumors.

Large-scale proteomics enables the identification of biomarkers and undulations in metabolic aging. This study aimed to develop a metabolic age (MA) and identify proteomic biomarkers and their undulating changes during metabolic aging. Using UK Biobank data, MA was developed from mortality-associated metabolomic profiles (nuclear magnetic resonance platform) in 203,491 participants. Associations between 2,923 plasma proteins (Olink Explore 3072 platform) and metabolic aging phenotypes, including MA, telomere length, frailty index, incident type 2 diabetes, cardiovascular disease, and mortality, were examined in 24,920 participants via Cox proportional hazards or linear models. Differential expression–sliding window analysis captured protein waves during metabolic aging in 7,092 participants. MA improved the predictions of mortality, cardiovascular disease, and type 2 diabetes beyond conventional risk factors (C-index up to 0.786) and correlated strongly with chronological age (Spearman’s r: 0.876). Sixty proteins were associated with all metabolic aging phenotypes. Among them, growth differentiation factor 15 (GDF15), urokinase plasminogen activator surface receptor (PLAUR), tumor necrosis factor receptor superfamily member 10A (TNFRSF10A), tumor necrosis factor receptor superfamily member 10B (TNFRSF10B), gamma-interferon-inducible lysosomal thiol reductase (IFI30), hepatocyte growth factor (HGF), WAP 4-disulfide core domain protein 2 (WFDC2), collagen alpha-3(VI) chain (COL6A3), polymeric immunoglobulin receptor (PIGR), insulin-like growth factor-binding protein 4 (IGFBP4), and tumor necrosis factor receptor superfamily member 27 (EDA2R) ranked within the top 20 for at least 4 phenotypes based on P values. Pathway analysis highlighted symbiont entry into host cell and cytokine–cytokine receptor interaction in metabolic aging. Proteins showed undulating changes during metabolic aging, with 3 peaks at 44, 51, and 63 years. MA–protein trajectories clustered into 3 groups. Groups 1 and 3 exhibited linear increases with MA, whereas group 2 showed nonlinear increases. In conclusion, the identification of plasma proteomic biomarkers and their undulating changes in metabolic aging provides a critical foundation for developing clinical markers and precision interventions to prevent accelerated metabolic aging.

Vascular stents require biointerfaces that simultaneously prevent thrombosis and promote endothelialization, yet achieving a balance of these conflicting demands on a single surface remains a challenge. While cell membrane coatings have demonstrated biomimetic potential, their application to macroscale devices has been largely limited to unstable noncovalent nanoparticle-focused approaches, which suffer from disordered assembly and poor stability under physiological flow. In contrast, we introduce a platelet membrane armor strategy facilitated by epigallocatechin gallate (EGCG) cross-linking, creating a robust macroscale coating that integrates both antithrombogenicity and proendothelialization. This covalent stabilization, achieved through EGCG’s unique chemistry, effectively anchors platelet membrane vesicles to form a stable coating that ensures uniform modification across various substrates. The coating is resistant to mechanical deformation and maintains its structural continuity even after over 30 d of rinsing. Importantly, the EGCG-cross-linked platelet membrane (EPM) coating preserves functional membrane proteins (CD47 and integrins). When stents coated with EPM armor were implanted in rabbits, they exhibited a marked reduction in acute thrombus formation, alongside enhanced endothelial cell proliferation and anti-inflammation effects, without introducing smooth muscle cell hyperplasia. This EPM coating strategy offers a promising approach to biomimetic interfaces for cardiovascular implants.

Alkenes are fundamental structural motifs in organic synthesis, valued for their prevalence in natural products and their broad reactivity profile. While traditional de novo synthesis offers access to diverse alkene frameworks, strategies enabling the direct structural remodeling of alkenes offer more concise and efficient alternatives, yet remain underdeveloped. Herein, we present a molecular recasting strategy for alkene diversification, enabled by N-heterocyclic carbene/photoredox dual catalysis. The transformation involves a sequential dealkenylative acylation and realkenylation cascade, realizing precise and modular reassembly of a broad spectrum of alkenes under mild conditions. This approach demonstrates exceptional generality, accommodating up to trisubstituted alkenes, and supports interconversion among diverse alkene frameworks, as well as late-stage functionalization of complex bioactive molecules. Mechanistic investigations reveal a radical-mediated catalytic pathway and shed light on the synergy of photoredox and N-heterocyclic carbene dual catalysis. This work provides a conceptually different and synthetically valuable platform for alkene editing.

Sepsis is a life-threatening syndrome caused by dysregulated host response that culminates in organ dysfunction. Recognizing that many prior reviews treated neutrophils as largely homogeneous or discussed their functions in isolation, we present a framework centered on emergency granulopoiesis that unifies groundbreaking single-cell multi-omics discoveries, bone marrow niche remodeling, and bedside biomarkers such as the immature-to-total neutrophil ratio and sepsis endotypes to explain the shift from defense to dysfunction. As a hallmark adaptation, emergency granulopoiesis rapidly replenishes neutrophils but paradoxically yields immature neutrophils with impaired chemotaxis, excessive neutrophil extracellular trap formation (NETosis)/reactive oxygen species, and immunosuppressive activity that aggravate tissue damage, organ failure, and late immunoparalysis. We synthesize subset-level diversity and map this diversity to transcriptional shifts coupled with epigenetic and metabolic reprogramming as well as stromal inflammation. Clinically, elevated immature-to-total neutrophil ratios and these immature subsets align with disease severity and mortality, supporting biomarker-guided stratification. Stage-specific therapeutic strategies are evaluated along the continuum of emergency granulopoiesis, calibrating NETosis, and modulating metabolic reprogramming and epigenetics to restore neutrophil homeostasis. Furthermore, we delineate testable priorities for future research such as standardizing immature neutrophil phenotyping across cohorts and defining harmonized immature-to-total neutrophil ratio thresholds. By explicitly centering emergency granulopoiesis and integrating single-cell biology with bedside indicators, this review clarifies why a protective program becomes maladaptive in sepsis and outlines practical avenues for targeted interventions and trial design.

With the significant transformation in the classification, risk stratification, and therapy standards for gliomas in recent years, we sought to reintegrate clinical data, whole-exome sequencing data, and magnetic resonance imaging data from glioma patients to further analyze their impact on overall survival. We identified 798 primary gliomas: 355 glioblastomas, 179 IDH1/2-mutant astrocytomas, 135 oligodendrogliomas, and 129 other IDH1/2-wild-type gliomas. Kaplan–Meier analysis revealed that our cohort showed significantly prolonged survival compared to the CGGA/TCGA cohorts (median: 85.2, 60.4, and 50.5 months; P < 0.0001). Molecular reclassification criteria yielded altered final histopathologic classification for 23.7% of gliomas. Molecular alterations differ among glioma subtypes. Among the 5 tumorigenic pathways analyzed, glioblastomas exhibited the highest average number of activated pathways (mean: 2.17), followed by astrocytomas (mean: 1.40) and oligodendrogliomas (mean: 0.42). In one glioma subtype, upstream and downstream gene activations in the same pathway are mutually exclusive. In this large-scale Chinese cohort, we first confirmed a strong link between tumor location and molecular subtype: Frontal gliomas had IDH1/2 mutations in 63.5% of cases, while temporal (80.3%) and thalamic/basal ganglia gliomas (90.4%) were predominantly IDH1/2-wild-type. Age stratification confirmed these patterns: 74.7% of frontal gliomas in younger patients (<46 years) had IDH1/2 mutations versus 91.4% of temporal and 100% of thalamic/basal ganglia tumors in older patients (≥46 years) being IDH1/2-wild-type. Contemporary molecular criteria modified diagnoses in ~25% of cases. Contemporary glioma cohorts showed prolonged survival outcomes compared to historical cohorts. An association between anatomic localization and molecular subtypes was also established in this Chinese glioma cohort.

Programmable metasurfaces show great application potential in many fields, so studying their radar cross-section reduction (RCSR) characteristics is very important. Metasurfaces based on polarization conversion and artificial magnetic conductors are widely used for RCSR, but they cannot scan beams. Programmable metasurfaces can dynamically steer beams in-band and reduce RCS by changing the phase of electromagnetic waves. But to meet practical needs, they must be stealthy both in-band and out-of-band. This paper presents a low-profile self-stealth programmable metasurface. Self-stealth describes the capability of the metasurface to achieve in-band and out-of-band RCSR through its intrinsic design, without requiring additional structures such as radomes or absorbing materials. The design principle is to achieve out-of-band RCS reduction without increasing the profile height or adding extra radio frequency components. The meta-atom has a positive-intrinsic-negative (PIN) diode to control the in-band (f₀) x-polarization phase. Switching the PIN diode states gives 1-bit phase modulation. The in-band y-polarization uses a bridge design. Changing the size can get 1-bit phase, and the x- and y-polarizations can be adjusted separately. Also, the top layer has 4 cross-shaped dumbbells and 4 branch structures for out-of-band high (fh₁) and low (fl₁) frequencies. The middle layer has four 45° rotated cross-shaped dumbbells and 4 branch structures for out-of-band high (fh₂) and low (fl₂) frequencies. So, the meta-atom can modulate phase across 5 frequency bands (fl₂ < fl₁ < f₀ < fh₂ < fh₁). The profile height is 0.065λ of the in-band center frequency, which is more conducive to conformal design. A 12 × 12 prototype was made. Simulation and measurement show that the metasurface reduces RCS in 5 bands and its in-band radiation can be used in communication transmission mode, which is good for programmable metasurface technology development.

Radiation-induced skin injury (RISI) is a common and debilitating complication of radiotherapy, characterized by persistent inflammation and delayed wound healing. Macrophages play a central role in this process; however, the molecular mechanisms governing their dysfunction under radiation stress remain poorly understood. To elucidate the role of triggering receptor expressed on myeloid cells 2 (TREM2) in macrophage regulation after irradiation, we combined single-cell RNA sequencing, in vivo mouse models, and in vitro macrophage assays. Conditional knockout mice (LysM^CreTrem2^flox/flox) were used to selectively delete Trem2 in macrophages. Radiation induced a distinct TREM2⁺ macrophage subset; however, despite elevated Trem2 mRNA, protein levels declined due to ADAM17-mediated shedding driven by radiation-induced reactive oxygen species (ROS) accumulation and NRF2 activation. Inhibition or small interfering RNA (siRNA)-mediated knockdown of ADAM17 restored TREM2 protein expression, reduced soluble TREM2 release, improved macrophage survival, and promoted anti-inflammatory M2 polarization. Conversely, Trem2 deficiency enhanced apoptosis, sustained inflammation, and delayed wound healing, whereas Trem2 overexpression or local adoptive transfer of TREM2⁺ macrophages accelerated tissue repair. Mechanistically, TREM2 conferred radioprotection through extracellular signal-regulated kinase (ERK) pathway activation, linking the ROS–NRF2–ADAM17 axis to TREM2–ERK signaling in macrophage survival and polarization. Collectively, these findings identify a novel regulatory cascade, ROS–NRF2–ADAM17–TREM2–ERK, that governs macrophage fate under irradiation. Targeting this pathway or supplementing TREM2⁺ macrophages may offer promising therapeutic strategies for mitigating RISI.

D–π–A-type fluorescent materials are crucial tools in life sciences and medicine, with their development hinging on a precise understanding of fluorophore mechanisms, particularly twisted intramolecular charge transfer (TICT) and planar intramolecular charge transfer (PICT) processes. These fluorophores exhibit unique charge transfer properties, making them highly valuable in organic optoelectronics, fluorescent probes, and sensors. However, despite their growing applications, the structural essence of TICT and PICT fluorophores remains poorly understood. This often results in molecules with similar structures displaying charge transfer modes that contradict design expectations, substantially hindering the application of TICT and PICT fluorescent probes. In this study, we meticulously designed various computational strategies based on interpretable machine learning to thoroughly deconstruct the chemical structural essence of TICT and PICT fluorophores. Utilizing the first real-world TICT and PICT dataset, we constructed predictive models that balance both interpretability and accuracy (area under the receiver operating characteristic curve = 0.846) using a range of algorithms, including deep learning. We established artificial intelligence (AI)-guided rules comprising 5 structural factors—electron-donating group strength, electron-withdrawing group strength, alkyl cyclization, steric hindrance, and solvent–solute interactions—that influence TICT and PICT. These rules provide obvious guidance for probe design based on molecular rigidity and charge transfer driving forces. Compared to community-suggested rules, the AI-guided rules achieved an over 20% improvement in accuracy in a controlled evaluation. By applying these rules, we successfully synthesized and validated several representative fluorophores that are challenging to distinguish using chemical intuition alone. Both quantitative calculations and experimental results confirmed the accuracy of the model and the practicality of the AI-guided rules. This novel approach is expected to establish a novel paradigm for exploring ideal TICT and PICT molecules, offering a robust framework for future research and application in fluorescent materials.

Accurate prediction of drug–target molecular recognition is essential for early-stage drug discovery, spanning binding occurrence, binding site localization, and binding affinity estimation. However, current approaches frequently treat these tasks independently, thereby overlooking the shared mechanistic principles that underlie them. We present DTBind, a unified and mechanism-driven framework that hierarchically adapts to sequence, structure, and complex-level inputs for predicting binding occurrence, site, and affinity, grounded in the shared mechanistic determinants of molecular recognition. Comprehensive benchmarking demonstrates that DTBind achieves superior performance over state-of-the-art methods in terms of accuracy and generalizability. Analysis of hierarchical protein encodings demonstrates that determinant-based protein representations gradually drive the accurate prediction of molecular recognition. Validation based on molecular dynamics simulations shows that DTBind reliably predicts drug–target binding regions for 3 famous proteins that lack experimental structures.

Terahertz (THz) amplification presents promising potential for various innovative applications. Here, we achieve an ultra-broadband and high-gain THz amplification from 0.1 to 25 THz with a saturated electric field peaking at 10⁷ V/m by leveraging drift-induced plasmon instability in a single-layer gated graphene system. The plasmon instability arises from the electron concentration modulation due to the nonuniformity of the electron–phonon scattering rate distribution on the Fermi surface. The modulation will introduce increments to compensate for the plasmon loss and even achieve plasmon amplification. Specifically, we derive the nonreciprocal plasmon dispersion using Maxwell’s equations in conjunction with the Boltzmann transport equation, incorporating interactions between electrons and phonons. We find that the amplifying plasmon mode propagates against the electron drift velocity, and the amplification efficiency can be tuned by adjusting either the electron concentration or the electron drift velocity. Furthermore, the simulation shows that when the fundamental mode is amplified to saturation, higher harmonics are generated and amplified, forming a THz frequency comb. These results provide a promising avenue for developing tunable on-chip THz light sources, amplifiers, and frequency comb generators within integrated plasmonic systems.

The origin of life (OoL) is a fundamental and long-standing scientific question. Although a variety of plausible hypotheses had been put forward, how life began on the prebiotic Earth from a pile of prehistoric inert chemicals (gases) is still a puzzle to us. Here, to unify the existing hypotheses to cover the entire scenarios, the author proposed the “nanozymes hypothesis” of the OoL on Earth, in which natural mineral nanozymes (MN-zymes) and their later upgraded organic/inorganic hybridized nanozymes played multiple key roles in the initial emergence of life molecules, especially in the manner of “inorganic photosynthesis” under primitive Earth conditions. Under the hypothesis framework, proteins, DNA, and RNA might emerged near-simultaneously, as a result of the diversity of nanozymes and catalyses, and multiple physical and chemical key roles of the MN-zymes. Besides nanozyme aspects, several fundamental and key issues on the topic are briefly discussed and several essential elements and conditions for the natural selection and survival of life molecules are proposed.

Hypertensive individuals are concurrently at risk of heavy metal exposure, particularly lead (Pb). However, it remains unclear whether individuals with hypertension are more sensitive to the neurotoxicity induced by Pb exposure. Here, we established a hypertension model of mice with Pb exposure for 2, 4, 8, 12, and 24 weeks to detect the temporal and spatial changes in microglial polarization. Pb-exposed and hypertensive mice exhibited a notably higher proportion of M1 as early as 4 and 8 weeks, but a lower proportion of M2 as early as 4 and 12 weeks. Notably, hypertension exacerbated Pb-induced microglial polarization, which reached a plateau at 12 weeks. Elevated inducible nitric oxide synthase and reduced arginase-1 protein expressions were observed in the prefrontal cortex, hippocampus, and hypothalamus of the Pb, hypertension (HTN), and Pb+HTN groups, with the most pronounced alterations occurring in the prefrontal cortex. Furthermore, treatment with minocycline (microglial polarization inhibitor) markedly alleviated the depression-like behavior of mice in the Pb+HTN group. Bioinformatics analysis revealed that the differentially expressed genes of microglia following Pb and angiotensin II combined exposure were mainly enriched in the cyclic GMP-AMP synthase/stimulator of interferon genes (cGAS/STING) pathway. In addition, the protein expression of the cGAS/STING pathway was significantly upregulated both in vitro and in vivo, especially in the Pb+HTN group. Inhibition of cGAS significantly alleviated the increase in M1-type microglia and the decrease in M2-type microglia in hypertensive mice with Pb exposure. Meanwhile, mitochondrial DNA (mtDNA) leakage of microglia was observed under Pb and angiotensin II exposure, and the activation of the cGAS/STING pathway by mtDNA was proven to play an important role in microglial polarization. Adenine nucleotide translocase 1 (ANT1) was identified as the pivotal protein regulating mtDNA leakage via bioinformatics analyses and was proven by in vitro and in vivo studies. Collectively, hypertensive mice are more sensitive to Pb-induced neurotoxicity, which is mediated by the ANT1-regulated mtDNA/cGAS/STING signaling pathway, which then subsequently induces microglial polarization.

As terahertz (THz) science and technology continue to advance, THz biophysics and chemistry have emerged as a pivotal interface, linking molecular-scale insights with transformative innovations in biological, physical, and chemical sciences. This review elucidates the fundamental mechanisms underlying THz–matter interactions, emphasizing the relationship between THz wave physics and molecular motions. We highlight molecular-level effects of THz radiation on water, ion channels, DNA, and proteins, and summarize its biological impacts spanning molecular, cellular, and neurological scales. These mechanistic insights underpin diverse applications, including the control of water transport and molecular dynamics, diagnostics, neurobiology, and emerging therapeutic strategies. Furthermore, we examine advanced methods for detection and characterization in THz biophysics and chemistry, including THz spectroscopy, imaging, and sensing, which bridge fundamental principles with practical implementations, positioning THz technology as a versatile platform for probing and manipulating complex biological and chemical systems. By integrating these recent advances, this review aims to catalyze further research in this rapidly evolving field.

Generative artificial intelligence (AI) is rapidly transforming medical imaging by enabling capabilities such as data synthesis, image enhancement, modality translation, and spatiotemporal modeling. This review presents a comprehensive and forward-looking synthesis of recent advances in generative modeling—including generative adversarial networks (GANs), variational autoencoders (VAEs), diffusion models, and emerging multimodal foundation architectures—and evaluates their expanding roles across the clinical imaging continuum. We systematically examine how generative AI contributes to key stages of the imaging workflow, from acquisition and reconstruction to cross-modality synthesis, diagnostic support, treatment planning, and prognosis prediction. Emphasis is placed on both retrospective and prospective clinical scenarios, where generative models help address longstanding challenges such as data scarcity, standardization, and integration across modalities. To promote rigorous benchmarking and translational readiness, we propose a 3-tiered evaluation framework encompassing pixel-level fidelity, feature-level realism, and task-level clinical relevance. We also identify critical obstacles to real-world deployment, including limited generalization under domain shift, risks of hallucinated or unreliable features, data scarcity and privacy concerns, as well as stringent regulatory and ethical constraints. Finally, we explore the convergence of generative AI with large-scale foundation models, highlighting how this synergy may enable the next generation of scalable, reliable, and clinically integrated imaging systems. By charting technical progress and translational pathways, this review aims to guide future research and foster interdisciplinary collaboration at the intersection of AI, medicine, and biomedical engineering.

The ventral tegmental area (VTA) is a crucial brain region for dopamine synthesis and reward processing, yet the molecular diversity and functional roles of nondopaminergic VTA neurons remain poorly characterized. While ARVCF (a member of ARVCF delta catenin family) has been implicated in dopamine release and reward-related behaviors (e.g., nicotine/alcohol and natural rewards), its influence on VTA neuronal subpopulations at single-cell resolution is unknown. Based on the Arvcf-knockout (Arvcf-KO) mouse model and nicotine exposure, we constructed a reward behavior-related profile at the single-cell transcriptome level and explored the cell subpopulations associated with reward behavior in VTA, as well as how these populations communicate with dopaminergic neurons. Following single-nucleus RNA sequencing (snRNA-seq) from VTA, we obtained 96,240 cells of wild-type and Arvcf-KO mice with and without nicotine treatment. Subsequent cell-type abundance analysis revealed a significant reduction in the abundance of neuronal populations upon Arvcf-KO (FDR < 0.05). By integrating single-cell transcriptomics, neurology proteomics, and multiplex immunofluorescence imaging, we discovered a subpopulation of glutamatergic–dopaminergic combinatorial neurons, which is significantly associated with reward. Further cell communication analysis indicated that Arvcf-KO reduced the signal output from these neurons to dopaminergic neurons, represented by Wnt signaling. Finally, integrative analyses of metabolite detection and 2-way analysis of variance based on snRNA-seq indicated that the glutamatergic properties represent a key regulator of reward learning behaviors induced by nicotine and related stimuli. Taken together, our single-cell analysis identified that VTA combinatorial neurons are crucial for nicotine-induced reward through cellular signaling and glutamatergic properties, implying potential new therapeutic targets for addiction treatment.

The mechanism of and effective treatment for the headache, with the global prevalence of 52%, and its common anxiety comorbidity remain elusive. Here, we found that chronic isosorbide dinitrate (ISDN) injections induce c-fos expression in the anterior insula (AI), prelimbic cortex (PrL), and oval nucleus of the bed nucleus of the stria terminalis (ovBNST), suggesting their contributions to headache and anxiety comorbidity. This hypothesis is substantiated by our findings that chronic ISDN injection-induced headache and anxiety are blocked by inhibition of ventral AI (vAI)-PrL and dorsal AI (dAI)-ovBNST circuits, respectively. Headache and anxiety stimuli in chronic ISDN-injected mice markedly increase endocannabinoid (eCB) release at both glutamatergic vAI-PrL synapses and dAI-ovBNST synapses, indicating the role of eCB signaling in modulating headache and anxiety. Indeed, presynaptic knockdown of eCB hydrolase or presynaptic activation of cannabinoid type 1 receptors (CB1Rs) in vAI-PrL and dAI-ovBNST circuits separately alleviates headache and anxiety. A systemic application of eCB degradation enzyme inhibitors blocks chronic ISDN-induced headache and anxiety comorbidity, which are separately blocked by CB1R antagonist application in PrLs and ovBNSTs. Our findings reveal divergent counteracting effects of elevated eCB signaling in vAI-PrL and dAI-ovBNST circuits on comorbid headache and anxiety.

Multipartite entanglement enables secure group key distribution among multiple users while providing immunity against hacking attacks targeting source devices, thereby realizing source-independent quantum conference key agreement (SI-QCKA). However, previous experimental demonstrations of SI-QCKA have encountered substantial technical challenges, primarily due to the low efficiency and scalability limitations inherent in the generation and distribution of multipartite entanglement. Here, we experimentally demonstrate a scalable and efficient SI-QCKA protocol using polarization-entangled photon pairs in a 3-user star network, where Greenberger–Horne–Zeilinger correlations are realized via a post-matching method. We achieve a secure group key rate of 2.11 × 10⁴ bits/s under the single-user channel transmission of 1.64 × 10⁻¹ in a symmetric channel loss network. Additionally, we conduct 6 sets of experiments to investigate the impact of varying channel transmission and random basis selection probabilities on secure key rates. Our work establishes an efficient pathway for SI-QCKA and demonstrates potential scalability for future large-scale multiuser quantum networks.

Organelle morphology and dynamics are closely linked to cellular function and fate, yet their relationships remain poorly defined across physiological and pathological contexts. Live-cell imaging enables the visualization of subcellular structures and dynamic processes but often requires extensive manual analysis, introducing variability and limiting reproducibility and throughput. Image segmentation partitions digital images into meaningful regions, facilitating the quantification of organelle morphology and molecular behavior for precise subcellular analysis. Herein, this review surveys recent advances in live-cell imaging segmentation algorithms across diverse organelles, from traditional thresholding-based methods to deep learning approaches that enhance accuracy and adaptability in complex biological environments. We discuss key challenges, including 3-dimensional imaging, multi-organelle segmentation, and generalization across diverse imaging modalities. We also highlight label-efficient strategies, synthetic data, and physics-guided modeling that reduce reliance on manual annotations and large annotated datasets. By advancing generalist models, these innovations improve quantitative cell biology, accelerate disease research, and drive therapeutic discovery, underscoring the transformative role of artificial intelligence in biomedical microscopy.

MgH₂ (magnesium hydride) has attracted attention as a potential hydrogen storage material owing to its rich availability and high theoretical hydrogen capacity. Nevertheless, its practical utilization is restricted by its high thermodynamic stability and slow hydrogen sorption kinetics. Recent advancements have demonstrated that incorporating various catalytic systems—such as transition metals, metal oxides, metal halides, metal sulfides, and carbon-supported materials—effectively improves hydrogen dissociation, diffusion, and Mg–H bond modulation. Structural transformations, interfacial interactions, and synergistic effects in multicomponent systems substantially enhance MgH₂ performance. Furthermore, cutting-edge computational techniques such as DFT (density functional theory) and ML (machine learning) have become indispensable for expediting catalyst development and forecasting their performance. These computational techniques enable high-throughput screening, provide atomic-scale insights into catalytic mechanisms, and substantially reduce experimental workloads. This review systematically summarizes recent progress in catalytic modifications of MgH₂, elucidates the underlying enhancement mechanisms, highlights the contributions of DFT and ML methodologies, and discusses future directions such as nanostructuring, multifunctional composite catalysts, and computational-driven rational catalyst design for next-generation hydrogen storage technologies.

Probing early-stage crystallization in hydrated environments is crucial for elucidating the microscopic mechanism of crystal growth. However, capturing these processes remains challenging because of the nanometric dimensions of nanocrystals and the dynamic role of water in solvation and ion–ion interactions. Here, we employ a cryogenic scanning probe microscopy platform, which integrates qPlus-type atomic force microscopy with a frozen-solution preparation technique, to directly visualize hydrated sodium chloride (NaCl) nanocrystals at atomic resolution. We observe double- to 5-stranded ionic chain structures, which are hydrated by water dimers. Those structures promote the anisotropic growth of NaCl nanocrystals. Density functional theory calculations reveal that the water dimer can substantially stabilize the chain-shaped configurations by optimizing the water–water and water–ion interactions. In contrast, larger crystals favor isotropic crystalline lattices due to dominant bulk ionic interactions. These findings highlight the unique role of water dimers in the initial crystallization process of salts. Furthermore, this work demonstrates the potential of cryogenic scanning probe microscopy as a powerful tool for probing the crystallization process at atomic resolution, particularly in hydrated environments.

Hepatocellular carcinoma (HCC) is characterized by a profoundly hypoxic microenvironment, which drives tumor aggressiveness and poor clinical outcomes. However, the precise regulatory mechanisms through which hypoxia promotes HCC progression remain incompletely understood. Here, we identified ubiquitin conjugating enzyme E2 variant 1 (UBE2V1) as a novel hypoxia-responsive gene that is transcriptionally activated by hypoxia-inducible factor-1α (HIF-1α) through direct binding to a hypoxia-response element located between −208 and −201 bp in the UBE2V1 promoter. Overexpression of UBE2V1 was frequently detected in HCC tissues and correlated strongly with advanced tumor stage and unfavorable patient prognosis. Moreover, UBE2V1 facilitated the proliferation and migration of HCC cells. Further investigation revealed that up-regulated UBE2V1 competes with HIF-1α for binding to the β-domain of von Hippel–Lindau (VHL) protein and, in complex with UBE2S, catalyzes K11/K48-linked ubiquitination at VHL K196, leading to its proteasomal degradation. This disruption of VHL function attenuates HIF-1α ubiquitination and degradation, resulting in sustained HIF-1α stabilization, increased nuclear accumulation, and enhanced transcriptional activity. Consistent with these findings, genetic knockdown of UBE2V1 or pharmacological inhibition of HIF-1α markedly suppresses HCC tumorigenesis and metastasis in vivo. Altogether, our study unveils a previously unrecognized HIF-1α–UBE2V1 positive feedback loop that is self-reinforcing and critically sustains the hypoxic microenvironment to drive HCC progression, highlighting UBE2V1 as both a promising prognostic biomarker and a compelling therapeutic target for HCC.

Human activities have had complex, long-term impacts on forest function across New England—a trend expected to continue. To assess these impacts, we conducted a regional-scale modeling study using the PnET-CN-daily model, simulating multiple scenarios that reflect projected changes in land cover, climate, and air quality. The results suggest that while New England will continue to serve as a regional carbon sink, carbon accumulation in the southern portion of the region will slow and may shift to a net carbon source due to aggressive urban expansion. Carbon dioxide (CO₂) fertilization and carbon loss associated with urbanization are the dominant factors controlling future carbon dynamics. However, CO₂ fertilization may diminish over time due to nutrient limitations, while rising temperatures are expected to accelerate soil decomposition, further increasing carbon loss. The forecasts also show that urbanization will increasingly affect ecosystem nitrogen storage. Climate change and CO₂ fertilization along with declining nitrogen deposition from decreases in fossil fuel use are projected to drive nitrogen oligotrophication—slowing forest growth and becoming more severe as nitrogen inputs decrease. In addition, urbanization and climate change are expected to substantially reduce snowpack and shorten snow cover duration in southern New England, with potential consequences for regional water dynamics. These trends highlight the need to integrate future climate, air quality, and land-use projections into forest management strategies for both urban and rural ecosystems.

The emergence of embodied intelligence has brought a fundamental shift to the robotics field, emphasizing the integration of perception, cognition, and control in dynamic physical environments. Although substantial progress has been made in artificial intelligence models, control middleware, and industrial communication protocols within their respective domains, the fragmentation in semantic interaction and task-level coordination still limits the scalability and deployment of embodied intelligence systems. This review synthesizes the current research on system deployment and coordination in embodied intelligence, particularly focusing on the challenges in achieving task-level coordination and semantic interoperability across heterogeneous components. We examine key coordination requirements, such as context semantics, capability declaration, and workflow composition, and highlight the existing gaps in addressing these issues within current systems. In response, we propose the Embodied Context Protocol (ECP) as an emerging solution, designed to bridge these gaps and enhance interoperability across various subsystems. It then presents the design philosophy, interface specification, and execution workflow of ECP, followed by its current implementation progress validated through practical deployments, while also highlighting the future directions and unresolved challenges that will shape its standardization and large-scale adoption. As an interface protocol, ECP aims to evolve into a standardized interoperability ecosystem for embodied intelligence and industrial automation. Realizing this vision will require collaboration across academic and industrial communities to jointly advance the development, adoption, and standardization of ECP.

Alzheimer’s disease (AD) is a prevalent neurodegenerative disorder affecting the elderly, leading to important impairments in cognitive function and the ability to live independently. This results in substantial disability and places an increasing burden on families and society. Currently, the therapeutic approaches adopted in clinical practice predominantly hinge upon cholinesterase inhibitors and the N-methyl-d-aspartate (NMDA) receptor antagonist memantine. Nevertheless, these medications merely alleviate symptoms and fail to tackle the pathological characteristics of AD. In recent years, monoclonal antibodies such as lecanemab and donanemab against β-amyloid (Aβ) have shown good efficacy in clinical practice for early-stage AD patients. However, the early diagnosis of AD remains a challenge. Against this backdrop, endovascular brain–computer interface (EBCI) offers an integrated solution for the early diagnosis and neuroregulatory treatment of AD patients, with minimal invasiveness. This review comprehensively examines the safety and feasibility of EBCI for AD patients, focusing on 3 major application areas: early diagnosis, deep brain stimulation targeting specific brain regions, such as the fornix and the basal nuclei of Meynert, and the use of external neurofeedback devices. Furthermore, we explore future development trends in this field, including miniaturization, integration, and the exploration of deep brain regions.

Early mammalian embryos undergo crucial cell divisions despite lacking fully functional centrosomes, presenting a paradox of how mitotic fidelity is maintained. This commentary discusses a seminal study by Hernandez et al. that overturns the microtubule-centric model of early embryonic mitosis by revealing 2 essential, complementary F-actin networks. First, a formin-dependent nuclear F-actin network, cross-linked by anillin, positions prophase chromosomes and then drives their centripetal congregation upon nuclear envelope breakdown via a novel depolymerization-based contraction mechanism. Second, a branched, Arp2/3-dependent peri-spindle actin shell acts as a physical brake to restrain spindle elongation. Using advanced live imaging and targeted perturbations in mouse and human embryos, the study demonstrates that these actin assemblies collectively ensure accurate chromosome segregation by compensating for weak centrosomal activity. These findings redefine the mechanical landscape of early embryonic cell division, with profound implications for understanding the origins of aneuploidy and advancing reproductive medicine.

Magnetic resonance imaging (MRI) signal acquisition relies heavily on radio frequency (RF) coils, which play a critical role in obtaining high-quality images. However, traditional RF coils are often rigid and bulky and require complex decoupling mechanisms, limiting their adaptability and effectiveness for anatomically curved or dynamic regions. In this study, we present a novel high-impedance nonlinear metasurface (HINM) coil based on a “building bricks” concept to overcome these limitations. The HINM coil features a lightweight, flexible, wireless, and compact design that enables direct attachment to various anatomical regions, providing localized signal enhancement without the need for intricate adjustments for decoupling. This innovative approach utilized a flexible coaxial cable with a shielded design, ensuring stable frequency characteristics under bending, stretching, and dynamic conditions. Furthermore, the HINM coil with passive detuning did not alter the RF transmit field distribution while improving the signal-to-noise ratio (SNR) up to 87% in the surface region, as demonstrated by the phantom studies. In the knee and hand imaging, the SNR in the joint and finger areas was also doubled when using the HINM arrays. Enhanced image quality was achieved, and more subtle blood vessels were revealed in the hand vascular imaging with the use of the HINM arrays in combination with the commercial RF coils. Additionally, the HINM arrays were applied for the knee and hand imaging at 0.5 T, which achieved up to 74% SNR improvement, demonstrating its effectiveness for both low-field and ultrahigh-field MRI. By offering a versatile and adaptable solution, the HINM coil demonstrates its potential to transform MRI coil designs, particularly for imaging anatomically complex and dynamic environments.

The drive for complementary transistors with ultra-steep subthreshold swing (SS) and minimal power consumption has intensified interest in integrating low-dimensional semiconductors with wide-bandgap materials, as silicon scaling approaches its physical limits in the post-Moore era. A central bottleneck, however, lies in dielectric/semiconductor interface engineering: existing strategies based on van der Waals transfer or nonnative dielectric deposition compromise interface quality, wafer-scale uniformity, and process compatibility, preventing practical large-scale integration. Here, we demonstrate a previously unexplored in situ oxidation strategy that directly forms a high-κ gallium oxide (GaO_x) dielectric on heavily doped n-type gallium nitride (GaN) substrates, enabling high-performance, energy-efficient complementary inverters based on n-type molybdenum disulfide (MoS₂) and p-type carbon nanotube transistors. Leveraging the excellent dielectric properties of the GaN/GaO_x heterostructure, MoS₂ field-effect transistors achieve an interface trap density of 1.18 × 10¹¹ cm⁻² eV⁻¹, an SS at the thermionic limit of 60 mV dec⁻¹, an on/off ratio above 10⁸ at 0.87 V, and a voltage gain of 134.5 at 2 V for inverters. These results establish GaN/GaO_x as a native, scalable, and complementary metal-oxide-semiconductor-compatible integration platform, offering a transformative route toward wafer-scale, low-power electronics in the post-Moore era.

Cell fate determination is a complex and dynamic process. Across diverse cellular contexts, various protein modifications (PTMs) are involved throughout the entire process of cell fate determination. Linear ubiquitination is a specific type of PTM. Under the catalysis of linear ubiquitin chain assembly complex, multiple ubiquitin molecules form a unique linear ubiquitin chain and modify the substrate. A growing number of studies have shown that linear ubiquitination is an important regulatory target in the cell fate determination pathway, and that once the linear ubiquitination process of key proteins is defective, the cell fate determination process will be disrupted and lead to the development of various diseases. In this review, we systematically elaborated the key links between linear ubiquitination and cell fate determination from the aspects of cell proliferation, differentiation, senescence, and survival/death. Furthermore, we provide an in-depth exploration of the molecular mechanisms through which homeostatic imbalance of linear ubiquitination contributes to numerous diseases, including neoplastic, neurodegenerative, infectious, inflammatory, and metabolic disorders. Finally, we fully affirm the significance of linear ubiquitination as a promising therapeutic target for future drug discovery and development. Our critical appraisal and synthesis of the literature provide a foundational framework for future investigations and inform novel strategies for the treatment and management of related diseases.