Multi-photon three-dimensional (3D) nanoprinting technology, renowned for its 3D processing capability and nano-scale resolution beyond the diffraction limit, has garnered significant attention in the micro/nano-additive manufacturing field. This technology finds widespread applications in optics, biology, and mechanical engineering research. However, its broader adoption in industrial production and applications has been hindered by limitations such as relatively slow processing speed and restricted material formability and functionality. This paper presents the latest advancements in multi-photon 3D nanoprinting, with a focus on analyzing optical methods to enhance the processing speed of scanning and projection techniques. Additionally, it examines issues related to the formability and functionality of commonly used photosensitive materials, including organic polymers, inorganic compounds, and composite materials. In conclusion, this paper offers a comprehensive summary from the perspectives of productivity, cost, materials, and cross-scale processing, along with proposed routes and future directions.
The designing and manufacturing of micro/nanoscale tools for delivery, diagnostic, and therapeutic are essential for their multiscale integration in the precision medicine field. Conventional three-dimensional (3D) printing approaches are not suitable for such kind of tools due to the accuracy limitation. Multiphoton polymerization (MPP)-based micro/nanomanufacturing is a noncontact, high-precision molding technology that has been widely used in the micro/nano field is a promising tool for micro/nanoscale related precision medicine. In this article the fundamentals of MPP-based technology and the required materials in precision medicine are overviewed. The biomedical applications in various scenarios are then summarized and categorized as delivery systems, microtissue modeling, surgery, and diagnosis. Finally, the existing challenges and future perspectives on MPP-based micro/nanomanufacturing for precision medicine are discussed, focusing on material design, process optimization, and practical applications to overcome its current limitations.
A laser-induced periodic surface structure (LIPSS), which can be easily produced by femtosecond laser ablation, is a unique nanostructure with a visible refractive color that can be controlled by altering its orientation and uniformity, making it suitable for use in colorful marking, camouflage, and anti-counterfeiting measures. However, single-mode information camouflage can no longer meet increasingly higher-level security requirements. Therefore, metasurfaces offer revolutionary solutions. In this study, conceptual metasurfaces of pure tungsten are theoretically proposed and verified using hierarchical LIPSS/nanoparticle (NP) nanostructures as meta-atoms. The anisotropy of the LIPSS nanostructure enables polarization-sensitive optical modulation, whereas the spatial configuration, NPs size, and period of LIPSS in the LIPSS/NP meta-atoms provide flexibility for tailoring broadband optical responses. In x-polarization, the LIPSS/NP meta-atom system provides more visible colors and divergent infrared absorption (emission) than in y-polarized and unpolarized modes, paving the way for vividly colorful polarization-sensitive displays and information camouflage in infrared bands. A simplified rendition of the world-famous painting “The Starry Night” by Van Gogh is used as a proof-of-concept. Preliminary experimental results are presented, based on which the feasibility and challenges for laser nanomanufacturing of the proposed conceptual metasurfaces are discussed, aiming to provide inspiration for the development of novel metasurfaces through interdisciplinary studies.
High-entropy alloys (HEAs) are promising materials for photonic applications. In such applications, permittivity is essential for numerical studies. In this work, we experimentally determine the complex permittivity of an HEA composed of five noble metals—Au, Ag, Cu, Pd, and Pt. The measurements are conducted across a broad wavelength spectrum, spanning the ultraviolet, visible, and mid-infrared regions. The experiments, numerical simulations of reflection spectra, and analysis of absorption and scattering cross-sections reveal the potential for fabricating perfect absorber and emitter metasurfaces using this noble HEA. In addition, crystallography studies clearly show the formation of a uniform material. The lattice constant and electron work function of the alloy are found to be 0.396 nm and (4.8 ± 0.4) eV, respectively—results indicate that the formed HEA alloy is well mixed.
Nonlinear wavefront shaping is crucial for advancing optical technologies, enabling applications in optical computation, information processing, and imaging. However, a significant challenge is that once a metasurface is fabricated, the nonlinear wavefront it generates is fixed, offering little flexibility. This limitation often necessitates the fabrication of different metasurfaces for different wavefronts, which is both time-consuming and inefficient. To address this, we combine evolutionary algorithms with spatial light modulators (SLMs) to dynamically control wavefronts using a single metasurface, reducing the need for multiple fabrications and enabling the generation of arbitrary nonlinear wavefront patterns without requiring complicated optical alignment. We demonstrate this approach by introducing a genetic algorithm (GA) to manipulate visible wavefronts converted from near-infrared light via third-harmonic generation (THG) in a silicon metasurface. The Si metasurface supports multipolar Mie resonances that strongly enhance light-matter interactions, thereby significantly boosting THG emission at resonant positions. Additionally, the cubic relationship between THG emission and the infrared input reduces noise in the diffractive patterns produced by the SLM. This allows for precise experimental engineering of the nonlinear emission patterns with fewer alignment constraints. Our approach paves the way for self-optimized nonlinear wavefront shaping, advancing optical computation and information processing techniques.
Optical monitoring of object position and alignment with nanoscale precision is critical for ultra-precision measurement applications, such as micro/nano-fabrication, weak force sensing, and microscopic imaging. Traditional optical nanometry methods often rely on precision nanostructure fabrication, multi-beam interferometry, or complex post-processing algorithms, which can limit their practical use. In this study, we introduced a simplified and robust quantum measurement technique with an achievable resolution of 2.2 pm and an experimental demonstration of 1 nm resolution, distinguishing it from conventional interferometry, which depended on multiple reference beams. We designed a metasurface substrate with a mode-conversion function, in which an incident Gaussian beam is converted into higher-order transverse electromagnetic mode (TEM) modes. A theoretical analysis, including calculations of the Fisher information, demonstrated that the accuracy was maintained for nanoscale displacements. In conclusion, the study findings provide a new approach for precise alignment and metrology of nanofabrication and other advanced applications.
The fabrication of nanostructures beyond the diffraction limit has been the focus of nanotechnology research. Scanning probe microscopy (SPM) has attracted the attention of researchers for the detection and manufacture of nanostructures. Here, a nanosecond laser irradiated a cantilevered scanning near-field optical microscopy (SNOM) tip and directly wrote subwavelength nanostructures on Au nano-film, without the assistance of a mask or vacuum atmosphere. This method was stable and reproducible for long-term use. The in situ morphology detection was conducted after the writing process by atomic force microscope (AFM). A feature linewidth of approximately 83.6 nm (< λ/6) was confirmed using scanning electron microscopy (SEM). Linewidth of (167.8 ± 6.6) nm was reproduced stably. Theoretical calculations revealed that the elliptical heat distribution under the SNOM tip generated different linewidths when the tip scanned vertically and horizontally. It also interpreted the influential mechanism of single-pulse energy. The simulated linewidths were consistent with the fabricated linewidths. According to the elemental analysis by energy dispersive spectrometer (EDS), the mechanism of this method can be interpreted as melting of the Au nano-film instead of oxidation. Owing to its high positioning, machining accuracy, and instantaneous energy, this technology is considered convenient and economical for nanostructure fabrication and is proposed to be applied in nanolithography on multiple materials in the future.
Hard scales in scaly fish species ensure the structural and functional integrity of the inner skin and body, even when subjected to various types of external forces. Mucus and oil secreted from the inner layer of the fish skin to the surface exhibit resistance to a wide range of liquids, maintaining the antifouling properties of the fish skin surface. Inspired by these biological structures, ultra-sturdy and durable scale-armored-sliding surfaces (SASSs) were fabricated in this study using femtosecond laser electrodeposition (FED). In the FED method, a scaly structure is grown from the substrate across a sliding layer to form an SASS. The unique scale-armored structure offers protection against impact and abrasion while maintaining the performance and integrity of the structure. The mechanical sturdiness of the SASS improved by four orders of magnitude compared to that of the conventional antifouling surface. In addition, the SASS exhibited remarkable chemical durability, excellent hydraulic pressure resistance, liquid repellency, and good corrosion resistance based on characterization using various methods. FED enables the preparation of SASS on several materials, including Cu and Al and more. SASS fabricated using FED has great potential for the application of antifouling surfaces in extremely harsh environments.
Radiative cooling is an environmentally friendly, passive cooling technology that operates without energy consumption. Current research primarily focuses on optimizing the optical properties of radiative cooling films to enhance their cooling performance. In practical applications, thermal contact between the radiative cooling film and the object significantly influences the ultimate cooling performance. However, achieving optimal thermal contact has received limited attention. In this study, we propose and experimentally demonstrate a high-power, flexible, and magnetically attachable and detachable radiative cooling film. This film consists of polymer metasurface structures on a flexible magnetic layer. The monolithic design allows for convenient attachment to and detachment from steel or iron surfaces, ensuring optimal thermal contact with minimal thermal resistance and uniform temperature distribution. Our magnetic radiative cooling film exhibits superior cooling performance compared to non-magnetic alternatives. It can reduce the temperature of stainless steel plates under sunlight by 15.2 °C, which is 3.6 °C more than that achieved by non-magnetic radiative cooling films. The radiative cooling power can reach 259 W∙m−2 at a working temperature of 70 °C. Unlike other commonly used attachment methods, such as thermal grease or one-off tape, our approach allows for detachment and reusability of the cooling film according to practical needs. This method offers great simplicity, flexibility, and cost-effectiveness, making it promising for broad applications, particularly on non-horizontal irregular surfaces previously considered challenging.
Tamm plasmon polaritons (TPPs) are localized photonic states at the interface between a metal layer and one-dimensional (1D) photonic crystal substrate. Unlike surface plasmon polaritons (SPPs), TPPs can be excited by both transverse magnetic and electric waves without requiring additional coupling optics. TPPs offer robust color filtering, making them ideal for applications such as complementary metal oxide semiconductor (CMOS) image detectors. However, obtaining a large-area, reversible, and reconfigurable filter remains challenging. This study demonstrates a dynamically reconfigurable reflective color filter by integrating an ultrathin antimony trisulfide (Sb2S3) layer with Tamm plasmonic photonic crystals. Reconfigurable tuning was achieved by inducing Sb2S3 crystallization and reamorphization via thermal and optical activation, respectively. The material exhibited good stability after multiple switching cycles. The reflectance spectrum can be tuned across the visible range, with a shift of approximately 50 nm by switching Sb2S3 between its amorphous and crystalline phases. This phase transition is nonvolatile and substantially minimizes the energy consumption, enhancing efficiency for practical applications. Tamm plasmonic photonic crystals are low-cost and large-scale production, offering a platform for compact color display systems and customizable photonic crystal filters for realistic system integration.
In recent years, physical unclonable function (PUF) has emerged as a lightweight solution in the Internet of Things security. However, conventional PUFs based on complementary metal oxide semiconductor (CMOS) present challenges such as insufficient randomness, significant power and area overhead, and vulnerability to environmental factors, leading to reduced reliability. In this study, we realize a strong, highly reliable and reconfigurable PUF with resistance against machine-learning attacks in a 1 kb spin-orbit torque magnetic random access memory fabricated using a 180 nm CMOS process. This strong PUF achieves a challenge–response pair capacity of 109 through a computing-in-memory approach. The results demonstrate that the proposed PUF exhibits near-ideal performance metrics: 50.07% uniformity, 50% diffuseness, 49.89% uniqueness, and a bit error rate of 0%, even in a 375 K environment. The reconfigurability of PUF is demonstrated by a reconfigurable Hamming distance of 49.31% and a correlation coefficient of less than 0.2, making it difficult to extract output keys through side-channel analysis. Furthermore, resistance to machine-learning modeling attacks is confirmed by achieving an ideal accuracy prediction of approximately 50% in the test set.
Computational solid mechanics has become an indispensable approach in engineering, and numerical investigation of fracturing in composites is essential, as composites are widely used in structural applications. Crack evolution in composites is the path to elucidating the relationship between microstructures and fracture performance, but crack-based finite-element methods are computationally expensive and time-consuming, which limits their application in computation-intensive scenarios. Consequently, this study proposes a deep learning framework called Crack-Net for instant prediction of the dynamic crack growth process, as well as its strain-stress curve. Specifically, Crack-Net introduces an implicit constraint technique, which incorporates the relationship between crack evolution and stress response into the network architecture. This technique substantially reduces data requirements while improving predictive accuracy. The transfer learning technique enables Crack-Net to handle composite materials with reinforcements of different strengths. Trained on high-accuracy fracture development datasets from phase field simulations, the proposed framework is capable of tackling intricate scenarios, involving materials with diverse interfaces, varying initial conditions, and the intricate elastoplastic fracture process. The proposed Crack-Net holds great promise for practical applications in engineering and materials science, in which accurate and efficient fracture prediction is crucial for optimizing material performance and microstructural design.
In deep oil reservoir development, enhanced oil recovery (EOR) techniques encounter significant challenges under high-temperature and high-salinity conditions. Traditional profile-control agents often fail to maintain stable blocking under extreme conditions and exhibit poor resistance to high temperature and high salinity. This study develops a functionalized nanographite system (the MEGO system) with superior high-temperature dispersibility and thermosalinity-responsive capability through polyether amine (PEA) grafting and noncovalent interactions with disodium naphthalene sulfonate (DNS) molecules. The grafted PEA and DNS provide steric hindrance and electrostatic repulsion, enhancing thermal and salinity resistance. After ten days of aggregation, the MEGO system forms stable particle aggregates (55.51–61.80 µm) that are suitable for deep reservoir migration and profile control. Both experiments and simulations reveal that particle size variations are synergistically controlled by temperature and salt ions (Na+, Ca2+, and Mg2+). Compared with monovalent ions, divalent ions promote nanographite aggregation more strongly through double-layer compression and bridging effects. In core displacement experiments, the MEGO system demonstrated superior performance in reservoirs with permeabilities ranging from 21.6 to 103 mD. The aggregates formed within the pore throats significantly enhanced flow resistance, expanded the sweep volume, and increased the overall oil recovery to 56.01%. This research indicates that the MEGO system holds excellent potential for EOR in deep oil reservoirs.
The thawing and warming of ice-rich permafrost present a considerable threat to the long-term stability of the Qinghai–Xizang Railway (QXR) on the roof of the world—that is, the Qinghai–Xizang Plateau (QXP). In this review, we explore the extent of the observed permafrost degradation and embankment damage under the coupled impacts of climate change and engineering construction. The ice-rich permafrost beneath the embankment presents a substantial threat to the thermal–mechanical stability of the embankment due to the permafrost’s accelerated and amplified degradation. The observed embankment deformation has a potential high risk of thaw settlement, especially for 656 embankment–bridge sections, whose potential high risk of thaw settlement may be as great as 25%. Several techniques for roadbed cooling can be used to alleviate these impacts, including crushed rock structure embankments (CRSEs), thermosyphons, and reinforcement measures, which have been demonstrated to be successful in cooling the underlying permafrost and stabilizing an embankment. Under future climate change and permafrost degradation, however, the QXR still faces a high risk of embankment damage caused by permafrost degradation and requires more effective methods to reinforce the thermal–mechanical stability of permafrost. Therefore, a better understanding of such high-risk regions is needed, and roadbed cooling techniques will require further adaption in order to address the issues brought by climate change.
In the face of the unrelenting challenge posed by earthquakes—a natural hazard of unpredictable nature with a legacy of significant loss of life, destruction of infrastructure, and profound economic and social impacts—the scientific community has pursued advancements in earthquake early warning systems (EEWSs). These systems are vital for pre-emptive actions and decision-making that can save lives and safeguard critical infrastructure. This study proposes and validates a domain-informed deep learning-based EEWS called the hybrid earthquake early warning framework for estimating response spectra (HEWFERS), which represents a significant leap forward in the capabilities to predict ground shaking intensity in real-time, aligning with the United Nations’ disaster risk reduction goals. HEWFERS ingeniously integrates a domain-informed variational autoencoder for physics-based latent variable (LV) extraction, a feed-forward neural network for on-site prediction, and Gaussian process regression for spatial prediction. Adopting explainable artificial intelligence-based Shapley explanations further elucidates the predictive mechanisms, ensuring stakeholder-informed decisions. By conducting an extensive analysis of the proposed framework under a large database of approximately 14 000 recorded ground motions, this study offers insights into the potential of integrating machine learning with seismology to revolutionize earthquake preparedness and response, thus paving the way for a safer and more resilient future.
Accurate and efficient prediction of the distribution of surface loads on buildings subjected to explosive effects is crucial for rapidly calculating structural dynamic responses, establishing effective protective measures, and designing civil defense engineering solutions. Current state-of-the-art methods face several issues: Experimental research is difficult and costly to implement, theoretical research is limited to simple geometries and lacks precision, and direct simulations require substantial computational resources. To address these challenges, this paper presents a data-driven method for predicting blast loads on building surfaces. This approach increases both the accuracy and computational efficiency of load predictions when the geometry of the building changes while the explosive yield remains constant, significantly improving its applicability in complex scenarios. This study introduces an innovative encoder–decoder graph neural network model named BlastGraphNet, which uses a message-passing mechanism to predict the overpressure and impulse load distributions on buildings with conventional and complex geometries during explosive events. The model also facilitates related downstream applications, such as damage mode identification and rapid assessment of virtual city explosions. The calculation results indicate that the prediction error of the model for conventional building tests is less than 2%, and its inference speed is 3–4 orders of magnitude faster than that of state-of-the-art numerical methods. In extreme test cases involving buildings with complex geometries and building clusters, the method achieved high accuracy and excellent generalizability. The strong adaptability and generalizability of BlastGraphNet confirm that this novel method enables precise real-time prediction of blast loads and provides a new paradigm for damage assessment in protective engineering.
Satellite observations are widely used to estimate the concentrations of surface air pollutants, but the temporal coverage of these datasets is relatively short. To overcome this limitation, we propose a wide–deep ensemble machine learning framework to reconstruct the fine particulate matter (particulate matter lower than 2.5 μm (PM2.5)) dataset of East Asia (EA) over the past four decades (1981–2020). The results indicate that the framework effectively leveraged the advantages of satellite observations (higher accuracy) and model-based estimations (longer temporal coverage) of surface air pollutants. The reconstructed PM2.5 concentrations agreed well with the ground measurements, with coefficient of determination (R2) and root-mean-square error (RMSE) values of 0.99 and 1.38 μg·m−3, respectively, which outperformed the satellite-based PM2.5 estimates. As more ground measurements were incorporated into the model for training, the average RMSE in Japan and the Korean Peninsula decreased to 0.83 and 1.50 μg·m−3, respectively. Simultaneously, on the basis of the reconstructed datasets, we investigated the exposure level to PM2.5 in EA from 1981 to 2020. Since 2000, the increase in anthropogenic emissions has substantially worsened the air quality in EA, and nearly 50% of the population resided in areas where the annual average PM2.5 concentrations exceeded 50 μg·m−3 from 2009 to 2010. Despite the implementation of various mitigation strategies by local authorities to lower the ambient PM2.5 concentrations, the entire exposure level in EA is still implausible to meet the World Health Organization (WHO) air quality guidelines. In addition, population aging and climate change have the potential to increase PM2.5 exposure risk in the future. For policy-makers in EA, it is essential to consider the effects of these factors and develop more effective mitigation strategies that aim to lessen the health impact associated with PM2.5 exposure.
Enhancing ecological security for sustainable social, economic, and environmental development is a key focus of current research and a practical necessity for ecological management. However, the integration of retrospective ecological security assessments with future trend predictions and fine-scale targeted regulations remains inadequate, limiting effective ecological governance and sustainable regional development. Guided by Social–Economic–Natural Complex Ecosystems (SENCE) theory, this study proposes an analytical framework that integrates ecological security assessment, prediction, and zoning management. The Daqing River Basin, a typical river basin in the North China Plain, was selected as a case study. The results indicate that overall ecological security in the Daqing River Basin improved from a “Moderate” level to a “Relatively Safe” level between 2000 and 2020; however, spatial heterogeneity persisted, with higher ecological security in northwestern and eastern regions and lower ecological security in the central region. Approximately 62% of the Basin experienced an improvement in ecological security level, except in the major urban areas of Beijing, Tianjin, and Hebei, where ecological security deteriorated. From 2025 to 2040, the overall ecological security of the Daqing River Basin is expected to improve and remain at the “Relatively Safe” level. However, spatial heterogeneity will be further aggravated as the ecological security of major urban areas continues to deteriorate. Ecological security management zones and regulation strategies are proposed at the regional and county scales to emphasize integrated regulation for the entire basin and major urban areas. The proposed analytical framework provides valuable insights for advancing theoretical research on ecological security. The case study offers a practical reference for ecological security enhancement in river basins and other regions facing significant human–land conflicts.
Catalytic amine-solvent regeneration has been validated as an energy-saving strategy for CO2 chemisorption by boosting reaction kinetics under mild conditions. The upscale performance evaluation and long-term durability are indispensable steps for industrial application but have been scarcely reported thus far. Here, we report a ZrO2/Al2O3 pack catalyst that possesses strong metal oxide-support interactions, a porous structure, active and stable Zr–O–Al coordination, promoted proton transfer and a 40.7% decrease in the energy activation of carbamate decomposition, which significantly accelerates CO2 desorption kinetics. The upscale experiment and cost evaluation based on industrial flue gas revealed that the use of packing catalysts can reduce energy consumption by 27.56% and optimize the overall cost by 10.49%. The active sites present excellent stability in alkaline solvents. This work is the first to investigate the ability of high-technology readiness (technology readiness level at 6 (TRL 6)) for catalytic amine-solvent regeneration, providing valuable insights for potential applications involving efficient CO2 capture with catalyst assistance.
Three-dimensional (3D) braided composites have significant potential for use in engineering structural materials. However, conventional 3D braiding machines are insufficient for designing composites with complex geometries. This paper proposes a programmable design methodology for 3D rotary braiding machines using circle-cutting and combination strategies. By introducing varying numbers of incisions on the circle, a diverse range of horn gears can be designed. Different combinations of these cut-circles allow the horn gears to be assembled into various 3D rotary braiders. The parametric equation for the braider plate is derived, showing that a combination strategy involving two cut-circles is feasible for braider design, whereas integrating three cut-circles simultaneously is impossible for a single machine. The construction of an automatic 6-3 type 3D braiding machine demonstrates the effectiveness of the proposed design strategy. This flexible braider design approach provides a practical solution for producing 3D braided composites with complex geometries.
In 2023, the global soybean trade volume with China reached 99.41 million tonnes without any contribution from Africa. With its vast arable land, Africa has the potential to develop a strong soybean industry to increase food security, create employment opportunities, and position itself as a key exporter to China. However, soybean growth and yield are stringently linked to nodulation and nitrogen gas (N2) fixation, as well as to photothermal effects. The soil bacteria that nodulate and fix N2 for soybean growth are absent in African soils, which is a major constraint to soybean cultivation. However, the breeding of promiscuous soybean varieties that freely nodulate and fix N2 with native rhizobia in African soils has been achieved. The photothermal constraint limiting soybean production in Africa has also been resolved with the discovery and testing of several genes regulating photoperiodism at the laboratory and field levels. Large-scale soybean production in Africa will nonetheless still require science, technology, and innovation (STI) partnerships for the easy transfer and/or exchange of biological materials for research among Chinese and African scientists. This study aims to identify opportunities to boost soybean production in Africa, with potential benefits including increased food security, enhanced economic growth, improved continental gross domestic product, reduced unemployment, and greater poverty alleviation through job creation while enhancing China–Africa trade. It also explores the advantages soybean production in Africa could derive from the China–Africa STI partnership under the Forum on China–Africa Cooperation (FOCAC) Beijing Action Plan (2025–2027) and the China–Africa Agricultural Science and Technology Innovation Alliance (CAASTIA), which is implemented by the Chinese Academy of Agricultural Sciences and the African Academy of Sciences.
Mapping potential areas for finfish mariculture, particularly high-yield regions, is crucial for the proper utilization of marine space and global food security. Physiological models (growth performance models) that consider the spatiotemporal heterogeneity of the marine environment are a potentially effective approach to achieving this goal. In the present study, we developed an integrated model that combines the thermal performance curve and spatiotemporal heterogeneity of the marine environment to map the global high-yield potential mariculture areas for 27 commercial finfish species. Our results showed that the current sizes of the potentially suitable areas (achieving 50% of the maximum growth rate for at least six months annually) and high-yield areas (achieving 75% of the maximum growth rate throughout a year) are (8.00 ± 0.30) × 106 and (5.96 ± 0.13) × 106 km2, respectively. Currently, the sizes of suitable and high-yield areas for warm-water mariculture fish are larger than those for other species. The growth potential of suitable mariculture areas is higher at mid and low latitudes than at high latitudes. Under the two shared socioeconomic pathway scenarios (SSP1-2.6 and SSP5-8.5), the sizes of both suitable and high-yield areas will increase by 2050. However, there is the potential for finfish mariculture to respond differently to climate change among species and regions, and cold-water fish may benefit from global warming. Overall, the global potential for suitable high-yield mariculture areas continues to increase, making finfish mariculture an important contributor to global food security.
Hyperglycemia in individuals with diabetes causes cognitive impairment, called diabetic encephalopathy (DE). The pathogenesis of DE is closely related to angiopathy, and effective treatment is highly desirable. The botanical agent berberine (BBR) effectively lowers blood glucose in diabetic patients. Here, we show for the first time that BBR significantly improved cognitive function in type 2 diabetic encephalopathy KK-Ay (2DEK) mice. High-resolution imaging via fluorescence micro-optical sectioning tomography (fMOST) revealed that the integrity of brain vessels was improved by BBR treatment. The improvements in average vessel diameter, vessel length, and total vessel volume were significant in the parietal association cortex (PtA), as well as in the CA1 and CA3 regions. A mechanistic study revealed that oral BBR inhibited δ-valerobetaine (δ-VB, a metabolite of the gut microbiota) production in the intestine. As intestinal δ-VB can enter the circulation and activate the Toll-like receptor-4 (TLR-4)/myeloid differentiation factor 88 (MyD88)/nuclear factor kappa B (NF-κB) inflammatory pathway in the epithelial cells of blood vessels through interacting with TLR-4, BBR might reduce the intestinal level of δ-VB to protect the cerebral blood vessels of DE mice and improve their brain function. Fecal microbiota transplantation (FMT) using the gut microbiota from BBR-treated mice confirmed the vital role of the gut microbiota. BBR showed a wide range of effects on the gut flora, also increasing short-chain fatty acid (SCFA) production and decreasing lipopolysaccharide (LPS) levels in the intestine by adjusting the abundance of SCFA- or LPS-producing bacteria. The observed therapeutic efficacy in vivo revealed a synergistic effect of BBR on the gut microbiota. Conclusively, we found an association between the gut microbiota and blood vessels, of which intestinal δ-VB might be a chemical link. Mainly through downregulating δ-VB in the intestine, BBR protected cerebral vessels and alleviated DE.
Alcohol consumption poses an escalating public health challenge. However, the impact of alcoholic liver disease (ALD) on post-transplant hepatitis B virus (HBV) reactivation and surgical outcomes remains inadequately characterized. Herein, we retrospectively analyzed our cohort (NCT06114251) comprising 453 patients with an HBV background. Propensity score matching (PSM) and sensitivity analyses were employed to assess the influence of ALD on surgical outcomes. Benchmark analysis compared the predictive performance of 21 models for post-transplant HBV reactivation. The Shapley additive explanation (SHAP) algorithm facilitated feature ranking and model interpretation. Patients were stratified into three subgroups based on the alcohol-modified HBV reactivation index (AMBRI). Among the cohort, 113 patients (24.9%) had concurrent pre-transplant diagnoses of ALD and HBV infection, while 340 (75.1%) had HBV infection alone. The presence of ALD was associated with an elevated risk of HBV reactivation and liver metastasis. PSM and sensitivity analyses revealed significantly lower five-year HBV reactivation-free survival (74.9% vs 85.4%), overall survival (OS, 56.2% vs 70.5%), and tumor recurrence-free survival (RFS, 47.8% vs 63.3%) in the ALD cohort. In recipients without HBV reactivation, hepatocellular carcinomas (HCCs) arising from both ALD and HBV exhibited inferior RFS (log-rank P = 0.026) and OS beyond one year (landmark P = 0.032) compared to HBV-related HCC alone. Benchmark analysis identified the surv.cforest model as the optimal predictor, achieving an area under the receiver operating characteristic (AUC) curve of 0.914 in internal validation and 0.884 in external validation, outperforming the published Cox model (AUC = 0.78). AMBRI-based stratification delineated three distinct risk subgroups, with the intermediate- and high-risk groups exhibiting significantly worse OS and RFS than the low-risk group. In this study, stratification by AMBRI identified intermediate- and high-risk groups with poorer post-transplant outcomes, underscoring the necessity for intensified surveillance and enhanced HBV treatment regimens, particularly in recipients with pre-transplant ALD.
The use of unmanned aerial system (UAS) in congested airspace and/or in the proximity of critical infrastructure poses several challenges as far as safe and secure operations are concerned. The paper provides a detailed description of the architecture and workflow of a platform for UAS traffic management (UTM), designed to pave the way for increased, improved and safer UAS operations in the civil airspace. In particular, access to low-altitude airspace for UAS operations is managed, while facilitating the implementation of beyond visual line-of-sight (BVLOS) operations, and ensuring a safe and efficient integration of UAS into both controlled and uncontrolled airspace. Detection and management of unidentified or uncooperative UAS’s is also taken care of. To this end, an architecture based on three interacting layers is proposed, with the air traffic control at the highest level, the UAS operator(s) at the bottom, and a UAS service supplier acting as an interface. The platform, with its physical and digital elements, guarantees the effective and efficient interaction among these three layers, including management of contingency scenarios, which require a variation of admissible flight volumes for UAS operations and/or fast trajectory re-planning. The platform, developed within a research project which involved several partners, was tested in a relevant operational scenario at the Grottaglie–Taranto airport in Italy. The operators involved in the tests provided positive feedback on the services provided by the platform and the usability of the interfaces, while also making suggestions for adding new features in future developments.
