Super strength and toughness, excellent deformation resistance, and high-temperature service performance are the key factors to determine the practical application of new thermal barrier coatings (TBCs). The limited mobility of dislocations and the internal inherent defects in ceramics will inevitably lead to the decline of strength–plasticity and the reduction of service performance. Introducing preexisting twin boundaries and stacking faults (SFs) or preparing ceramic materials with high configuration entropy has demonstrated to be an effective strategy for enhancing the mechanical properties of ceramics. However, due to the positive thermal expansion coefficient of most ceramics and the remarkable increase of structural disorder at elevated temperature, the problem of elastic softening has become a bottleneck restricting the high-temperature service life of new TBCs. In this paper, the deformation behavior of high configuration entropy Zr₆Ta₂O₁₇ ceramics at 25 to 1,200 °C was in situ monitored via digital image correlation technique and three-point bending test platform in high-temperature environment. A remarkable Elinvar-like effect appears in the Zr₆Ta₂O₁₇ ceramic. More interestingly, mechanical deformation dominates the severe lattice distortion (deformation twins, SFs) and the disorder–order transition of chemical order at the atomic scale, while temperature can further enhance the degree of lattice distortion and ordering of Zr₆Ta₂O₁₇ ceramics. Furthermore, the atomic fluctuations at high temperature promotes the comprehensive improvement of mechanical properties in the Zr₆Ta₂O₁₇ ceramics.

Due to the absence of definitive diagnostic criteria, there remains a lack of consensus regarding the risk assessment of central lymph node metastasis (CLNM) and the necessity for prophylactic lymph node surgery in ultrasound-diagnosed thyroid cancer. The localization of thyroid nodules is a recognized predictor of CLNM; however, quantifying this relationship is challenging due to variable measurements. In this study, we developed a differential isomorphism-based alignment method combined with a graph transformer to accurately extract localization and morphological information of thyroid nodules, thereby predicting CLNM. We collected 88,796 ultrasound images from 48,969 patients who underwent central lymph node (CLN) surgery and utilized these images to train our predictive model, ACE-Net. Furthermore, we employed an interpretable methodology to explore the factors influencing CLNM and generated a risk heatmap to visually represent the distribution of CLNM risk across different thyroid regions. ACE-Net demonstrated superior performance in 6 external multicenter tests (AUC = 0.826), surpassing the predictive accuracy of human experts (accuracy = 0.561). The risk heatmap enabled the identification of high-risk areas for CLNM, likely correlating with lymphatic metastatic pathways. Additionally, it was observed that the likelihood of metastasis exceeded 80% when the nodal margin's minimum distance from the thyroid capsule was less than 1.25 mm. ACE-Net's capacity to effectively predict CLNM and provide interpretable disease-related insights can importantly reduce unnecessary lymph node dissections by 37.9%, without missing positive cases, thus offering a valuable tool for clinical decision-making.

The rapid development of neuromorphic computing has led to widespread investigation of artificial synapses. These synapses can perform parallel in-memory computing functions while transmitting signals, enabling low-energy and fast artificial intelligence. Robots are the most ideal endpoint for the application of artificial intelligence. In the human nervous system, there are different types of synapses for sensory input, allowing for signal preprocessing at the receiving end. Therefore, the development of anthropomorphic intelligent robots requires not only an artificial intelligence system as the brain but also the combination of multimodal artificial synapses for multisensory sensing, including visual, tactile, olfactory, auditory, and taste. This article reviews the working mechanisms of artificial synapses with different stimulation and response modalities, and presents their use in various neuromorphic tasks. We aim to provide researchers in this frontier field with a comprehensive understanding of multimodal artificial synapses.

The emergence of multi-petawatt laser facilities is expected to push forward the maximum energy gain that can be achieved in a single stage of a laser wakefield acceleration (LWFA) to tens of giga-electron volts, which begs the question—is it likely to impact particle physics by providing a truly compact particle collider? Colliders have very stringent requirements on beam energy, acceleration efficiency, and beam quality. In this article, we propose an LWFA scheme that can for the first time simultaneously achieve hitherto unrealized acceleration efficiency from the laser to the electron beam of >20% and a sub-1% energy spread using a stepwise plasma structure and a nonlinearly chirped laser pulse. Three-dimensional high-fidelity simulations show that the nonlinear chirp can effectively mitigate the laser waveform distortion and lengthen the acceleration distance. This, combined with an interstage rephasing process in the stepwise plasma, can triple the beam energy gain compared to that in a uniform plasma for a fixed laser energy, thereby dramatically increasing the efficiency. A dynamic beam loading effect can almost perfectly cancel the energy chirp that arises during the acceleration, leading to the sub-percent energy spread. This scheme is highly scalable and can be applied to petawatt LWFA scenarios. Scaling laws are obtained, which suggest that electron beams with parameters relevant for a Higgs factory could be reached with the proposed high-efficiency, low-energy-spread scheme.

Problem: Chest radiography is a crucial tool for diagnosing thoracic disorders, but interpretation errors and a lack of qualified practitioners can cause delays in treatment. Aim: This study aimed to develop a reliable multi-classification artificial intelligence (AI) tool to improve the accuracy and efficiency of chest radiograph diagnosis. Methods: We developed a convolutional neural network (CNN) capable of distinguishing among 26 thoracic diagnoses. The model was trained and externally validated using 795,055 chest radiographs from 13 datasets across 4 countries. Results: The CNN model achieved an average area under the curve (AUC) of 0.961 across all 26 diagnoses in the testing set. COVID-19 detection achieved perfect accuracy (AUC 1.000, [95% confidence interval {CI}, 1.000 to 1.000]), while effusion or pleural effusion detection showed the lowest accuracy (AUC 0.8453, [95% CI, 0.8417 to 0.8489]). In external validation, the model demonstrated strong reproducibility and generalizability within the local dataset, achieving an AUC of 0.9634 for lung opacity detection (95% CI, 0.9423 to 0.9702). The CNN outperformed both radiologists and nonradiological physicians, particularly in trans-device image recognition. Even for diseases not specifically trained on, such as aortic dissection, the AI model showed considerable scalability and enhanced diagnostic accuracy for physicians of varying experience levels (all P < 0.05). Additionally, our model exhibited no gender bias (P > 0.05). Conclusion: The developed AI algorithm, now available as professional web-based software, substantively improves chest radiograph interpretation. This research advances medical imaging and offers substantial diagnostic support in clinical settings.

Removing trace amounts of acetylene (C₂H₂) from ethylene (C₂H₄)-rich gas mixtures is vital for the supply of high-purity C₂H₄ to the chemical industry and plastics sector. However, selective removal of C₂H₂ is challenging due to the similar physical and chemical properties of C₂H₂ and C₂H₄. Here, we report a “single-molecule trap” strategy that utilizes electrostatic interactions between the one-dimensional (1D) channel of a covalent organic framework (denoted as COF-1) and C₂H₂ molecules to massively enhance the adsorption selectivity toward C₂H₂ over C₂H₄. C₂H₂ molecules are immobilized via interactions with the O atom of C=O groups, the N atom of C≡N groups, and the H atom of phenyl groups in 1D channels of COF-1. Due to its exceptionally high affinity for C₂H₂, COF-1 delivered a remarkable C₂H₂ uptake of 7.97 cm³/g at 298 K and 0.01 bar, surpassing all reported COFs and many other state-of-the-art adsorbents under similar conditions. Further, COF-1 demonstrated outstanding performance for the separation of C₂H₂ and C₂H₄ in breakthrough experiments under dynamic conditions. COF-1 adsorbed C₂H₂ at a capacity of 0.17 cm³/g at 2,000 s/g when exposed to 0.5 ml/min C₂H₄-rich gas mixture (99% C₂H₄) at 298 K, directly producing high-purity C₂H₄ gas at a rate of 3.95 cm³/g. Computational simulations showed that the strong affinity between C₂H₂ and the single-molecule traps of COF-1 were responsible for the excellent separation performance. COF-1 is also robust, providing a promising new strategy for the efficient removal of trace amounts of C₂H₂ in practical C₂H₄ purification.

Real-time thermal sensing through flexible temperature sensors in extreme environments is critically essential for precisely monitoring chemical reactions, propellant combustions, and metallurgy processes. However, despite their low response speed, most existing thermal sensors and related sensing materials will degrade or even lose their sensing performances at either high or low temperatures. Achieving a microsecond response time over an ultrawide temperature range remains challenging. Here, we design a flexible temperature sensor that employs ultrathin and consecutive Mo_1−xW_xS₂ alloy films constructed via inkjet printing and a thermal annealing strategy. The sensing elements exhibit a broad work range (20 to 823 K on polyimide and 1,073 K on flexible mica) and a record-low response time (about 30 μs). These properties enable the sensors to detect instantaneous temperature variations induced by contact with liquid nitrogen, water droplets, and flames. Furthermore, a thermal sensing array offers the spatial mapping of arbitrary shapes, heat conduction, and cold traces even under bending deformation. This approach paves the way for designing unique sensitive materials and flexible sensors for transient sensing under harsh conditions.

Radiotherapy (RT) serves as the primary treatment for solid tumors. Its potential to incite an immune response against tumors both locally and distally profoundly impacts clinical outcomes. However, RT may also promote the accumulation of immunosuppressive cytokines and immunosuppressive cells, greatly impeding the activation of antitumor immune responses and substantially limiting the effectiveness of RT. Therefore, regulating post-RT immunosuppression to steer the immune milieu toward heightened activation potentially enhances RT's therapeutic potential. Cytokines, potent orchestrators of diverse cellular responses, play a pivotal role in regulating this immunosuppressive response. Identifying and promptly neutralizing early released immunosuppressive cytokines are a crucial development in augmenting RT's immunomodulatory effects. To this end, we conducted a screen of immunosuppressive cytokines following RT and identified macrophage colony-stimulating factor (MCSF) as an early up-regulated and persistent immune suppressor. Single-cell sequencing revealed that the main source of up-regulated MCSF derived from tumor cells. Mechanistic exploration revealed that irradiation-dependent phosphorylation of the p65 protein facilitated its binding to the MCSF gene promoter, enhancing transcription. Knockdown and chemical inhibitor experiments conclusively demonstrated that suppressing tumor cell-derived MCSF amplifies RT's immune-activating effects, with optimal results achieved by early MCSF blockade after irradiation. Additionally, we validated that MCSF acted on macrophages, inducing the secretion of a large number of inhibitory cytokines. In summary, we propose a novel approach to enhance the immune activation effects of RT by blocking the MCSF-CSF1R signaling pathway early after irradiation.

Metal-organic frameworks (MOFs) have been widely considered as ideal platforms for the preparation of biomimetic catalysts, but it remains challenging to fabricate MOF-based enzyme-like catalysts with optimal activity. Here, we leverage the inherent flexibility of MOFs and propose a novel trans-functionalization strategy to construct a carbonic anhydrase (CA) mimic by the structural transformation from ZIF-L to ZIF-8. Theoretical and experimental results reveal that during the structural transformation, the hydroxyl group will preferentially coordinate with the interlayer Zn clusters to form the CA-like active center Zn-N₃-OH. Therefore, more accessible active centers are generated on the as-prepared ZIF-8-OH, resulting in substantially enhanced catalytic activity in the hydrolysis of para-nitrophenyl acetate.

Bone is a dynamic tissue reshaped by constant bone formation and bone resorption to maintain its function. The skeletal system accounts for approximately 70% of the total volume of the body, and continuous bone remodeling requires quantities of energy and material consumption. Adipose tissue is the main energy storehouse of the body and has a strong adaptive capacity to participate in the regulation of various physiological processes. Considering that obesity and metabolic syndrome have become major public health challenges, while osteoporosis and osteoporotic fractures have become other major health problems in the aging population, it would be interesting to explore these 2 diseases together. Currently, an increasing number of researchers are focusing on the interactions between multiple tissue systems, i.e., multiple organs and tissues that are functionally coordinated together and pathologically pathologically interact with each other in the body. However, there is lack of detailed reviews summarizing the effects of lipid metabolism on bone homeostasis and the interactions between adipose tissue and bone tissue. This review provides a detailed summary of recent advances in understanding how lipid molecules and adipose-derived hormones affect bone homeostasis, how bone tissue, as a metabolic organ, affects lipid metabolism, and how lipid metabolism is regulated by bone-derived cytokines.