Antibiotic cocktails (ABX) serve as potent therapeutic interventions for refractory ulcerative colitis (UC), yet invariably induce gut dysbiosis. This study demonstrates that pectin oligosaccharides synergistically enhance ABX efficacy by restoring gut microbiota balance and selectively enriched antibiotic-resistant Parabacteroides goldsteinii in a colitis mouse model. Our results further indicate that the gavage administration of P. goldsteinii AM58-2XD markedly alleviated colitis via enhancing the branched-chain amino acid metabolic pathway, particularly by facilitating valine metabolism. Notably, these anticolitis effects were partially attenuated in P. goldsteinii^ΔilvE mutants, which are defective in valine-derived isobutyrate (IBN) biosynthesis. We further demonstrated that exogenous IBN supplementation effectively alleviated colitis symptoms in mice and enhanced gut barrier function via activation of the peroxisome proliferator-activated receptor γ (PPARγ) pathway. Conditional knockout of PPARγ in Caco-2 intestinal epithelial cells markedly abrogated the IBN-induced enhancement of tight junctions, thereby substantiating the critical role of the IBN-PPARγ pathway in metabolite-mediated mucosal repair. Collectively, we delineate a prebiotic/probiotic–metabolite axis wherein P. goldsteinii facilitates mucosal repair via IBN/PPARγ-dependent epithelial metabolic reprogramming. This insight redefines antibiotic-resistant commensals as precise biotherapeutics for microbiota restoration in refractory UC management.

The elevated dielectric properties of carbonized cotton fibers with refined conductive networks result in substantial impedance mismatch, severely compromising their electromagnetic wave (EMW) absorption performance. Using cotton fibers and [Zn(pz)₂]_n complexes as easily prepared and low-cost raw materials, nano-hierarchically porous MnFeCuCe@C composites were constructed efficiently through optimization of the dynamic balance of element content and carbonization temperature. The results show that MnFeCuCe@C-20% exhibited a minimum reflection loss (RL_min) of −81.44 dB with a thickness of only 1.52 mm at an ultralow loading of 20 wt%, and the effective absorption bandwidth of MnFeCuCe@C-900 °C was also remarkably enlarged to 5.13 GHz at a thickness of 1.6 mm. Modifying only the metal content of the precursor and carbonization temperature can effectively reinforce the magnetic–dielectric synergy and impedance matching. The augmented EMW attenuation primarily stems from the inherent hierarchical porous structure of the MnFeCuCe@C nanocomposite and the rapid electron migration within its high-entropy metal particles. Furthermore, its excellent dissipation capability in practical application scenarios was demonstrated further through radar cross-section simulations. This work comprehensively delineates the optimization strategies for material dielectric properties as well as the convenient and environmental synthesis method.

U-structure has become a foundational approach in medical image segmentation, consistently demonstrating strong performance across various segmentation tasks. Most current models are based on this framework, customizing encoder–decoder components to achieve higher accuracy across various segmentation challenges. However, this often comes at the cost of increased parameter counts, which inevitably limit their practicality in real-world applications. In this study, we provide an E-shaped segmentation framework that discards the traditional step-by-step resolution recovery decoding process, instead directly aggregating multi-scale features extracted by the encoder at each stage for deep cross-level integration. Additionally, we propose an innovative multi-scale large-kernel convolution (MLKConv) module, designed to enhance high-level feature representation by effectively capturing both local and global contextual information. Compared to U-structure, the proposed E-structured approach substantially reduces parameters while delivering superior performance, especially in complex segmentation tasks. Based on this structure, we develop 2 segmentation networks specifically for 2-dimensional (2D) and 3D medical images. 2D E-SegNet is evaluated on four 2D segmentation benchmark datasets (Synapse multi-organ, ACDC, Kvasir-Seg, and BUSI), while 3D E-SegNet is assessed on four 3D segmentation benchmark datasets (Synapse, ACDC, NIH Pancreas, and Lung). Experimental results demonstrate that our approach outperforms the current leading U-shaped models across multiple datasets, achieving new state-of-the-art (SOTA) performance with fewer parameters. In summary, our research introduces a novel approach to medical image segmentation, offering potential improvements and contributing to ongoing advancements in the field. Our code is publicly available on https://github.com/zhaoqi106/E-SegNet.

Real-time wearable sleep monitors process diverse biological signals while operating under tight energy and computation budgets. The existing algorithms are facing problems of high energy consumption due to separate hardware storage and computation units. In this work, textile-integrated in-memory neuromorphic computing electronics based on MoS₂ quantum dot fiber memristors was proposed for physical reservoir computing for the first time. Textile electronics convert raw electroencephalogram (EEG)and snoring audio directly into rich, high-dimensional state vectors based on intrinsic nonlinear dynamics. Leveraging 16 pulse-programmable conductance levels, the reservoir realizes an accuracy of 94.8%, 95.4%, and 93.5% in snoring events, sleep stages, and multimodal fusion, respectively. To enhance the robustness of feature extraction and improve classification performance under noisy conditions, the linear readout layer was replaced with a lightweight convolutional neural network. The hybrid neural network is 6 times faster than traditional deep-learning methods in 24-h segment EEG analysis. The memristors switch at ±1 V and sub-nanoampere currents, providing picowatt energy consumption suited to continuous on-body use. The results establish fiber memristor reservoir computing as an energy-efficient path to in-fabric, multimodal intelligence for next-generation home sleep analysis and wearable health care.

Most human viral pandemics are caused by animal-originated viruses with human adaptation. It is challenging to infer adaptation from viral genes or their coded protein sequences, particularly when the data labels for modeling are inadequate or the input sequence to be predicted is incomplete. Here, we developed a semi-supervised General Intelligence framework to predict Virus Adaptation based on Language-model-embedded protein sequences (GIVAL) for blind input of virus sequences. The language model in GIVAL, named virus Bidirectional Encoder Representations from Transformers (vBERT), was pretrained for embedding using hidden Markov model-contextualized tokens of viral protein sequences. vBERT outperformed prevalent pretrained models like DNABERT-2, proteinBERT, ESM-2, Transformer, and Word2Vec on distinguishing viral proteins with various-grained labels, such as serotypes and single phenotype-altering mutation. The semi-supervised GIVAL obtained higher accuracy in virus adaptation prediction and better fault tolerance on raw labels in the training dataset, overcoming the obstacle of modeling with insufficient labels and predicting blind input. GIVAL was applicable to the adaptation prediction of diverse viruses. For influenza A viruses (IAVs), higher human adaptation was predicted for equine-origin H3N8 IAVs and bovine H5N1 IAVs with simulated mutations. For coronaviruses, GIVAL predicted an adaptation shift of receptor binding from Middle East respiratory syndrome–related coronavirus (MERS-CoV) receptor to severe acute respiratory syndrome coronavirus receptor of 2 recently reported MERS-CoV-like virus variants. For monkeypox viruses, GIVAL quantified an incremental adaptation shift of viral variants, matching the rise in human monkeypox cases. Summarily, GIVAL provides a generally intelligent framework for predicting virus adaptation based on its genotype, with the potential to extend to more genotype-to-phenotype prediction scenarios.

Abdominal aortic aneurysm (AAA) is a potentially fatal vascular disease with no effective therapeutic intervention. Vascular smooth muscle cell (VSMC) phenotypic switching and elevated matrix metalloproteinase (MMP) levels are key pathogeneses of AAA, although the underlying regulatory mechanisms remain to be fully elucidated. In our study, single-cell RNA-sequencing data analysis demonstrated a substantial elevation in modulated VSMCs in patients with aortic aneurysm, accompanied by Dickkopf 3 (DKK3) up-regulation. Both systemic DKK3 knockout and VSMC-specific DKK3 knockdown led to a marked decrease in both the incidence and mortality of AAA in mice. Reintroduction of DKK3 in Dkk3^−/−Apoe^−/− mice via adeno-associated virus (AAV) exacerbated AAA development. DKK3 deficiency maintained the contractile phenotype of VSMC and inhibited MMP production. Given the critical role of TGF-β signaling in VSMC phenotypic switching and the progression of AAA, its regulatory mechanisms exhibit spatiotemporal heterogeneity, and the precise underlying mechanisms require further investigation. Next, we aim to investigate the regulators of this pathway. Mechanistically, DKK3 deficiency activates the TGFβ3–Smad2/3 signaling pathway by down-regulating ATF6, thereby inhibiting VSMC phenotype switching. In summary, these findings indicate that DKK3 drives the phenotypic transition of VSMCs to a synthetic phenotype through the ATF6–TGFβ3–Smad2/3 signaling pathway during the development of AAA, which represents a potential target for therapeutic intervention to maintain VSMC homeostasis in AAA.

With the rapid advancement of communication technologies, issues of electromagnetic pollution and electromagnetic compatibility have become increasingly severe, heightening the demand for high-performance electromagnetic wave absorption materials. Metal-organic frameworks (MOFs) have flourished in this field owing to their chemical tunability, high porosity, tailored topological structures, and functionality. MOF-derived composites exhibit diverse loss mechanisms and heterogeneous structures, achieving lightweight, broadband, and highly efficient absorption. Notably, recent developments in conductive MOFs (cMOFs) have positioned pristine MOFs as promising intrinsic absorbers. Accordingly, this review comprehensively classifies and summarizes recent progresses in MOF derivatives and cMOF-based absorbers, with a focus on 3 critical aspects: design strategies (compositional and structural engineering), absorption performance (reflection loss and bandwidth), and loss mechanisms (dielectric and magnetic loss). Finally, perspectives on future development directions for MOF-based absorption materials are proposed. This review provides methodological guidelines for constructing high-performance MOF-based absorption materials in the future, while highlighting persisting challenges in their development. Ultimately, it charts a course toward designing and fabricating lightweight, broadband, and high-efficiency MOF absorption materials with structural–functional integration.

Dysfunction of motor behavior during spaceflight is linked to alterations in neuronal activities. However, the longitudinal functional changes in the motor cortex triggered by simulated weightlessness remain ambiguous. In this study, we utilized a miniaturized 2-photon microscope to examine the dynamic shifts in neuronal activities within the hindlimb motor cortex during simulated weightlessness and its subsequent recovery period at the single-cell level. Our results demonstrated that simulated weightlessness led to a progressive decline in motor behavior during open-field and rotarod tasks, which was fully reversed after a 2-week recovery period. Single-cell analysis revealed that hindlimb motor neurons could be classified as activated, inhibited, or unchanged. During active locomotion in the open field, the activity of locomotion-activated neurons increased, while the activity of locomotion-inhibited neurons decreased, despite their numbers remaining constant. Conversely, during passive rotation on the rotarod test, the number of rotation-activated neurons decreased, while their activity increased, and the number of rotation-inhibited neurons increased along with their activity. These changes were largely restored after reloading. These findings elucidate motor dysfunction under simulated weightlessness and the heterogeneous changes in neuronal activities within the hindlimb motor cortex, offering valuable insights into understanding behavioral changes regulated by the motor cortex during spaceflight.

Plasma wakefield acceleration in the nonlinear blowout regime has achieved marked milestones in electron beam acceleration, demonstrating high acceleration gradients and energy efficiency while preserving excellent beam quality. However, this regime is deemed unsuitable for achieving positron acceleration of comparable results, which is vital for future compact electron–positron colliders. In this article, we find that an intense positron beam loaded at the back of beam-driven blowout cavity can self-consistently induce the focusing field and flatten the longitudinal wakefield, leading to stable, high-efficiency, and high-quality positron acceleration. This is achieved through the formation of an on-axis electron filament induced by positron beam load, which shapes the plasma wakefield in a distinct way compared to electron beam load in the blowout regime. Via a nonlinear analytic model and numerical simulations, we explain the novel beam loading effects of the interaction between the on-axis filament and the blowout cavity. High-fidelity simulations show that a high-charge positron beam can be accelerated with >20% energy transfer efficiency, ~1% energy spread, and ~1 mm·mrad normalized emittance, while considerably depleting the energy of the drive beam. The concept can also be extended to simultaneous acceleration of electron and positron beams and high transformer ratio positron acceleration as well. This development offers a new route for the application of plasma wakefield acceleration into particle physics.

Cancer persists as one of the most formidable global public health crises and socioeconomic burdens of our era, compelling the scientific community to develop innovative and diversified therapeutic modalities to revolutionize clinical management and enhance patient outcomes. The recent seminal discovery by Swamynathan et al. has unveiled menadione, a vitamin K precursor, as a potent inducer of triaptosis—a novel regulated cell death pathway mediated through the oxidative modulation of phosphatidylinositol 3-kinase PIK3C3/VPS34. This mechanistically distinct cell death paradigm, characterized by its intimate association with endosomal dysfunction and oxidative stress-induced cellular catastrophe, has demonstrated remarkable therapeutic efficacy in preclinical prostate cancer models, outperforming conventional therapeutic regimens and emerging as a potential paradigm-shifting strategy in oncology. This comprehensive review provides a critical synthesis of the triaptosis discovery landscape, elucidating its molecular intricacies and pathophysiological implications. We systematically examine the multifaceted roles of endosomal biology in oncogenesis and tumor progression, while offering a nuanced perspective on redox homeostasis in malignant cells and the therapeutic potential of oxidative stress modulation. Furthermore, we address the inherent dichotomy of oxidative stress induction in cancer therapy, balancing its therapeutic promise against potential adverse effects. Looking toward the horizon of cancer research, we explore transformative therapeutic strategies leveraging triaptosis induction and its potential applications beyond oncology, aiming to catalyze a new era of precision medicine that ultimately enhances patient survival and quality of life.