Optical imaging in the second near-infrared (NIR-II; 900-1880 nm) window is currently a popular research topic in the field of biomedical imaging. This study aimed to explore the application value of NIR-II fluorescence imaging in foot and ankle surgeries. A lab-established NIR-II fluorescence surgical navigation system was developed and used to navigate foot and ankle surgeries which enabled obtaining more high-spatial-frequency information and a higher signal-to-background ratio (SBR) in NIR-II fluorescence images compared to NIR-I fluorescence images; our result demonstrates that NIR-II imaging could provide higher-contrast and larger-depth images to surgeons. Three types of clinical application scenarios (diabetic foot, calcaneal fracture, and lower extremity trauma) were included in this study. Using the NIR-II fluorescence imaging technique, we observed the ischemic region in the diabetic foot before morphological alterations, accurately determined the boundary of the ischemic region in the surgical incision, and fully assessed the blood supply condition of the flap. NIR-II fluorescence imaging can help surgeons precisely judge surgical margins, detect ischemic lesions early, and dynamically trace the perfusion process. We believe that portable and reliable NIR-II fluorescence imaging equipment and additional functional fluorescent probes can play crucial roles in precision surgery.
This paper introduces a systems theory-driven framework to integration artificial intelligence (AI) into traditional Chinese medicine (TCM) research, enhancing the understanding of TCM’s holistic material basis while adhering to evidence-based principles. Utilizing the System Function Decoding Model (SFDM), the research progresses through define, quantify, infer, and validate phases to systematically explore TCM’s material basis. It employs a dual analytical approach that combines top-down, systems theory-guided perspectives with bottom-up, elements-structure-function methodologies, provides comprehensive insights into TCM’s holistic material basis. Moreover, the research examines AI’s role in quantitative assessment and predictive analysis of TCM’s material components, proposing two specific AI-driven technical applications. This interdisciplinary effort underscores AI’s potential to enhance our understanding of TCM’s holistic material basis and establishes a foundation for future research at the intersection of traditional wisdom and modern technology.
The prevalence of metabolic-dysfunction-associated steatotic liver disease (MASLD) is alarmingly high; it is estimated to affect up to a quarter of the global population, making it the most common liver disorder worldwide. MASLD is characterized by excessive hepatic fat accumulation and is commonly associated with comorbidities such as obesity, dyslipidemia, and insulin resistance; however, it can also manifest in lean individuals. Therefore, it is crucial to develop effective therapies for this complex condition. Currently, there are no approved medications for MASLD treatment, so there is a pressing need to investigate alternative approaches. Extensive research has characterized MASLD as a multifaceted disease, frequently linked to metabolic disorders that stem from dietary habits. Evidence suggests that changes in the gut microbiome play a fundamental role in the development and progression of MASLD from simple steatosis to steatohepatitis and even hepatocellular carcinoma (HCC). In this review, we critically examine the literature on the emerging field of gut-microbiota-based therapies for MASLD and metabolic-dysfunction-associated steatohepatitis (MASH), including interventions such as fecal microbiota transplantation (FMT), probiotics, prebiotics, short-chain fatty acids, antibiotics, metabolic pathway targeting, and immune checkpoint kinase blockade.
Lianhua Qingke tablets, a patented traditional Chinese medicine that has validated clinical efficacy for treating cough caused by severe acute respiratory syndrome coronavirus 2 infection, lack rigorous evidence-based research evaluating their effect on long coronavirus disease (COVID) cough. A randomized, double-blind, placebo-controlled, multicenter clinical study was conducted among patients with long COVID cough from 19 hospitals and 23 community health centers in China. Patients were randomized 1:1 to receive either Lianhua Qingke tablets or placebo orally for 14 days (four tablets, 1.84 g, three times a day). The primary endpoint indicator was the disappearance of cough, with the remission of cough also considered. Among 482 randomized patients, 480 (full analysis set 480; per-protocol set 470; safety set 480) were included in the primary analysis. According to the full analysis, the time until cough disappearance was significantly shorter in the trial group than in the control group, with a significant increase in the 14-day cough disappearance rate. Accordingly, the time to cough remission was significantly shorter in the trial group than in the control group. The change in the total symptom score was significantly greater in the trial group than in the control group on days 7 and 14, consistent with the results indicated by the visual analog scale (VAS) and cough evaluation test (CET) scores. No serious adverse events were recorded during the study. Lianhua Qingke tablets significantly improved the clinical symptoms of patients with long COVID cough.
Wastewater surveillance (WWS) can leverage its wide coverage, population-based sampling, and high monitoring frequency to capture citywide pandemic trends independent of clinical surveillance. Here we conducted a nine months daily WWS for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from 12 wastewater treatment plants (WWTPs), covering approximately 80% of the population, to monitor infection dynamics in Hong Kong, China. We found that the SARS-CoV-2 virus concentration in wastewater was correlated with the daily number of reported cases and reached two pandemic peaks three days earlier during the study period. In addition, two different methods were established to estimate the prevalence/incidence rates from wastewater measurements. The estimated results from wastewater were consistent with findings from two independent citywide clinical surveillance programmes (rapid antigen test (RAT) surveillance and serology surveillance), but higher than the cases number reported by the Centre for Health Protection (CHP) of Hong Kong, China. Moreover, the effective reproductive number (Rt) was estimated from wastewater measurements to reflect both citywide and regional transmission dynamics. Our findings demonstrate that large-scale intensive WWS from WWTPs provides cost-effective and timely public health information, especially when the clinical surveillance is inadequate and costly. This approach also provides insights into pandemic dynamics at higher spatiotemporal resolutions, facilitating the formulation of effective control policies and targeted resource allocation.
Microparticles have demonstrated value for regenerative medicine. Attempts in this field tend to focus on the development of intelligent multifunctional microparticles for tissue regeneration. Here, inspired by erythrocytes-associated self-repairing process in damaged tissue, we present novel biomimetic erythrocyte-like microparticles (ELMPs). These ELMPs, which are composed of extracellular matrix-like hybrid hydrogels and the functional additives of black phosphorus, hemoglobin, and growth factors (GFs), are generated by using a microfluidic electrospray. As the resultant ELMPs have the capacity for oxygen delivery and near-infrared-responsive release of both GFs and oxygen, they would have excellent biocompatibility and multifunctional performance when serving as microscaffolds for cell adhesion, stimulating angiogenesis, and adjusting the release profile of cargoes. Based on these features, we demonstrate that the ELMPs can stably overlap to fill a wound and realize controllable cargo release to achieve the desired curative effect of tissue regeneration. Thus, we consider our biomimetic ELMPs with discoid morphology and cargo-delivery capacity to be ideal for tissue engineering.
The industrial sector is the primary source of carbon emissions in China. In pursuit of meeting its carbon reduction targets, China aims to promote resource consumption sustainability, reduce energy consumption, and achieve carbon neutrality within its processing industries. An effective strategy to promote energy savings and carbon reduction throughout the life cycle of materials is by applying life cycle engineering technology. This strategy aims to attain an optimal solution for material performance, resource consumption, and environmental impact. In this study, five types of technologies were considered: raw material replacement, process reengineering, fuel replacement, energy recycling and reutilization, and material recycling and reutilization. The meaning, methodology, and development status of life cycle engineering technology abroad and domestically are discussed in detail. A multidimensional analysis of ecological design was conducted from the perspectives of resource and energy consumption, carbon emissions, product performance, and recycling of secondary resources in a manufacturing process. This coupled with an integrated method to analyze carbon emissions in the entire life cycle of a material process industry was applied to the nonferrous industry, as an example. The results provide effective ideas and solutions for achieving low or zero carbon emission production in the Chinese industry as recycled aluminum and primary aluminum based on advanced technologies had reduced resource consumption and emissions as compared to primary aluminum production.
The traditional method of screening plants for disease resistance phenotype is both time-consuming and costly. Genomic selection offers a potential solution to improve efficiency, but accurately predicting plant disease resistance remains a challenge. In this study, we evaluated eight different machine learning (ML) methods, including random forest classification (RFC), support vector classifier (SVC), light gradient boosting machine (lightGBM), random forest classification plus kinship (RFC_K), support vector classification plus kinship (SVC_K), light gradient boosting machine plus kinship (lightGBM_K), deep neural network genomic prediction (DNNGP), and densely connected convolutional networks (DenseNet), for predicting plant disease resistance. Our results demonstrate that the three plus kinship (K) methods developed in this study achieved high prediction accuracy. Specifically, these methods achieved accuracies of up to 95% for rice blast (RB), 85% for rice black-streaked dwarf virus (RBSDV), and 85% for rice sheath blight (RSB) when trained and applied to the rice diversity panel I (RDPI). Furthermore, the plus K models performed well in predicting wheat blast (WB) and wheat stripe rust (WSR) diseases, with mean accuracies of up to 90% and 93%, respectively. To assess the generalizability of our models, we applied the trained plus K methods to predict RB disease resistance in an independent population, rice diversity panel II (RDPII). Concurrently, we evaluated the RB resistance of RDPII cultivars using spray inoculation. Comparing the predictions with the spray inoculation results, we found that the accuracy of the plus K methods reached 91%. These findings highlight the effectiveness of the plus K methods (RFC_K, SVC_K, and lightGBM_K) in accurately predicting plant disease resistance for RB, RBSDV, RSB, WB, and WSR. The methods developed in this study not only provide valuable strategies for predicting disease resistance, but also pave the way for using machine learning to streamline genome-based crop breeding.
Decarbonization and decontamination of the iron and steel industry (ISI), which contributes up to 15% to anthropogenic CO2 emissions (or carbon emissions) and significant proportions of air and water pollutant emissions in China, are challenged by the huge demand for steel. Carbon and pollutants often share common emission sources, indicating that emission reduction could be achieved synergistically. Here, we explored the inherent potential of measures to adjust feedstock composition and technological structure and to control the size of the ISI to achieve carbon emission reduction (CER) and pollution emission reduction (PER). We investigated five typical pollutants in this study, namely, petroleum hydrocarbon pollutants and chemical oxygen demand in wastewater, particulate matter, SO2, and NOx in off gases, and examined synergies between CER and PER by employing cross elasticity for the period between 2022 and 2035. The results suggest that a reduction of 8.7%-11.7% in carbon emissions and 20%-31% in pollution emissions (except for particulate matter emissions) could be achieved by 2025 under a high steel scrap ratio (SSR) scenario. Here, the SSR and electric arc furnace (EAF) ratio serve critical roles in enhancing synergies between CER and PER (which vary with the type of pollutant). However, subject to a limited volume of steel scrap, a focused increase in the EAF ratio with neglection of the available supply of steel scrap to EAF facilities would lead to an increase carbon and pollution emissions. Although CER can be achieved through SSR and EAF ratio optimization, only when the crude steel production growth rate remains below 2.2% can these optimization measures maintain the emissions in 2030 at a similar level to that in 2021. Therefore, the synergistic effects between PER and CER should be considered when formulating a development route for the ISI in the future.
Ice pigging is an emerging technique for pipe cleaning in drinking water distribution systems. However, substantial confusion and controversy exist on the potential impacts of ice pigging on bulk water quality. This study monitored the microstructural features and composition of sediments and microbial community structures in bulk water in eight multimaterial Chinese networks. Chloride concentration analysis demonstrated that separate cleaning of pipes with different materials in complex networks could mitigate the risk of losing ice pigs and degrading water quality. The microstructural and trace element characterization results showed that ice pigs would scarcely disturb the inner surfaces of long-used pipes. The bacterial richness and diversity of bulk water decreased significantly after ice pigging. Furthermore, correlations were established between pipe service age, temperature, and chloride and total iron concentrations, and the 15 most abundant taxa in bulk water, which could be used to guide practical ice pigging operations.
This article reviews the anti-penetration principles and strengthening mechanisms of metal materials, ranging from macroscopic failure modes to microscopic structural characteristics, and further summarizes the micro-macro correlation in the anti-penetration process. Finally, it outlines the constitutive models and numerical simulation studies utilized in the field of impact and penetration. From the macro perspective, nine frequent penetration failure modes of metal materials are summarized, with a focus on the analysis of the cratering, compression shear, penetration, and plugging stages of the penetration process. The reasons for the formation of adiabatic shear bands (ASBs) in metal materials with different crystal structures are elaborated, and the formation mechanism of the equiaxed grains in the ASB is explored. Both the strength and the toughness of metal materials are related to the materials’ crystal structures and microstructures. The toughness is mainly influenced by the deformation mechanism, while the strength is explained by the strengthening mechanism. Therefore, the mechanical properties of metal materials depend on their microstructures, which are subject to the manufacturing process and material composition. Regarding numerical simulation, the advantages and disadvantages of different constitutive models and simulation methods are summarized based on the application characteristics of metal materials in high-speed penetration practice. In summary, this article provides a systematic overview of the macroscopic and microscopic characteristics of metal materials, along with their mechanisms and correlation during the anti-penetration and impact-resistance processes, thereby making an important contribution to the scientific understanding of anti-penetration performance and its optimization in metal materials.
Strain gradient is a normal phenomenon around a heterostructural interface in ultrathin film, and it is important to determine its effect on magnetic interactions to understand interfacial coupling. In this work, ultrathin Pr0.67Sr0.33MnO3 (PSMO) films on different substrates are studied. For PSMO film under different in-plane strain conditions, the saturated magnetization and Curie temperature can be qualitatively explained by double-exchange interaction and the Jahn-Teller distortion. However, the difference in the saturated magnetization with zero field cooling and 5 T field cooling is proportional to the strain gradient. Strain-gradient-induced structural disorder is proposed to enhance phonon-electron antiferromagnetic interactions and the corresponding antiferromagnetic-to-ferromagnetic phase transition via a strong magnetic field during the field cooling process. A non-monotonous structural transition of the MnO6 octahedral rotation can enlarge the strain gradient in PSMO film on a SrTiO3 substrate. This work demonstrates the existence of the flexomagnetic effect in ultrathin manganite film, which should be applicable to other complex oxide systems.
The incorporation of commercial flame retardants into fiber-reinforced polymer (FRP) composites has been proposed as a potential solution to improve the latter’s poor flame resistance. However, this approach often poses a challenge, as it can adversely affect the mechanical properties of the FRP. Thus, balancing the need for improved flame resistance with the preservation of mechanical integrity remains a complex issue in FRP research. Addressing this critical concern, this study introduces a novel additive system featuring a combination of one-dimensional (1D) hollow tubular structured halloysite nanotubes (HNTs) and two-dimensional (2D) polygonal flake-shaped nano kaolinite (NKN). By employing a 1D/2D hybrid kaolinite nanoclay system, this research aims to simultaneously improve the flame retardancy and mechanical properties. This innovative approach offers several advantages. During combustion and pyrolysis processes, the 1D/2D hybrid kaolinite nanoclay system proves effective in reducing heat release and volatile leaching. Furthermore, the system facilitates the formation of reinforcing skeletons through a crosslinking mechanism during pyrolysis, resulting in the development of a compact char layer. This char layer acts as a protective barrier, enhancing the material’s resistance to heat and flames. In terms of mechanical properties, the multilayered polygonal flake-shaped 2D NKN plays a crucial role by impeding the formation of cracks that typically arise from vulnerable areas, such as adhesive phase particles. Simultaneously, the 1D HNT bridges these cracks within the matrix, ensuring the structural integrity of the composite material. In an optimal scenario, the homogeneously distributed 1D/2D hybrid kaolinite nanoclays exhibit remarkable results, with a 51.0% improvement in mode II fracture toughness (GIIC), indicating increased resistance to crack propagation. In addition, there is a 34.5% reduction in total heat release, signifying improved flame retardancy. This study represents a significant step forward in the field of composite materials. The innovative use of hybrid low-dimensional nanomaterials offers a promising avenue for the development of multifunctional composites. By carefully designing and incorporating these nanoclays, researchers can potentially create a new generation of FRP composites that excel in both flame resistance and mechanical strength.
The in-band full-duplex (IBFD) wireless system is a promising candidate for 6G and beyond, as it can double data throughput and enormously lower transmission latency by supporting simultaneous in-band transmission and reception of signals. Enabling IBFD systems requires a substantial mitigation of a transmitter (Tx)’s strong self-interference (SI) signal into the receiver (Rx) channel. However, current state-of-the-art approaches to tackle this challenge are inefficient in terms of performance, cost, and complexity, hindering the commercialization of IBFD techniques. In this work, we devise and demonstrate an innovative approach to realize IBFD systems that exhibit superior performance with a low-cost and less-complex architecture in an all-passive module. Our scheme is based on meticulously combining polarization-division multiplexing (PDM) with ferromagnetic nonreciprocity to achieve ultra-high isolation between Tx and Rx channels. Such an unprecedented conception has become feasible thanks to a concurrent dual-mode circulator—a new component introduced for the first time—as a key feature of our module, and a dual-mode waveguide that transforms two orthogonally polarized waves into two orthogonal waveguide modes. In addition, we propose a unique passive tunable secondary SI cancellation (SIC) mechanism, which is embedded within the proposed module and boosts the isolation over a relatively broad bandwidth. We report, solely in the analog domain, experimental isolation levels of 50, 70, and 80 dB over 340, 101, and 33 MHz bandwidth at the center frequency of interest, respectively, with excellent tuning capability. Furthermore, the module is tested in two real IBFD scenarios to assess its performance in connection with Tx-to-Rx leakage and modulation error in the presence of a Tx’s strong interference signal.
Practical real-world scenarios such as the Internet, social networks, and biological networks present the challenges of data scarcity and complex correlations, which limit the applications of artificial intelligence. The graph structure is a typical tool used to formulate such correlations, it is incapable of modeling high-order correlations among different objects in systems; thus, the graph structure cannot fully convey the intricate correlations among objects. Confronted with the aforementioned two challenges, hypergraph computation models high-order correlations among data, knowledge, and rules through hyperedges and leverages these high-order correlations to enhance the data. Additionally, hypergraph computation achieves collaborative computation using data and high-order correlations, thereby offering greater modeling flexibility. In particular, we introduce three types of hypergraph computation methods: ① hypergraph structure modeling, ② hypergraph semantic computing, and ③ efficient hypergraph computing. We then specify how to adopt hypergraph computation in practice by focusing on specific tasks such as three-dimensional (3D) object recognition, revealing that hypergraph computation can reduce the data requirement by 80% while achieving comparable performance or improve the performance by 52% given the same data, compared with a traditional data-based method. A comprehensive overview of the applications of hypergraph computation in diverse domains, such as intelligent medicine and computer vision, is also provided. Finally, we introduce an open-source deep learning library, DeepHypergraph (DHG), which can serve as a tool for the practical usage of hypergraph computation.
Intelligent chatbots powered by large language models (LLMs) have recently been sweeping the world, with potential for a wide variety of industrial applications. Global frontier technology companies are feverishly participating in LLM-powered chatbot design and development, providing several alternatives beyond the famous ChatGPT. However, training, fine-tuning, and updating such intelligent chatbots consume substantial amounts of electricity, resulting in significant carbon emissions. The research and development of all intelligent LLMs and software, hardware manufacturing (e.g., graphics processing units and supercomputers), related data/operations management, and material recycling supporting chatbot services are associated with carbon emissions to varying extents. Attention should therefore be paid to the entire life-cycle energy and carbon footprints of LLM-powered intelligent chatbots in both the present and future in order to mitigate their climate change impact. In this work, we clarify and highlight the energy consumption and carbon emission implications of eight main phases throughout the life cycle of the development of such intelligent chatbots. Based on a life-cycle and interaction analysis of these phases, we propose a system-level solution with three strategic pathways to optimize the management of this industry and mitigate the related footprints. While anticipating the enormous potential of this advanced technology and its products, we make an appeal for a rethinking of the mitigation pathways and strategies of the life-cycle energy usage and carbon emissions of the LLM-powered intelligent chatbot industry and a reshaping of their energy and environmental implications at this early stage of development.
Hydrogen has emerged as a promising alternative to meet the growing demand for sustainable and renewable energy sources. Underground hydrogen storage (UHS) in depleted gas reservoirs holds significant potential for large-scale energy storage and the seamless integration of intermittent renewable energy sources, due to its capacity to address challenges associated with the intermittent nature of renewable energy sources, ensuring a steady and reliable energy supply. Leveraging the existing infrastructure and well-characterized geological formations, depleted gas reservoirs offer an attractive option for large-scale hydrogen storage implementation. However, significant knowledge gaps regarding storage performance hinder the commercialization of UHS operation. Hydrogen deliverability, hydrogen trapping, and the equation of state are key areas with limited understanding. This literature review critically analyzes and synthesizes existing research on hydrogen storage performance during underground storage in depleted gas reservoirs; it then provides a high-level risk assessment and an overview of the techno-economics of UHS. The significance of this review lies in its consolidation of current knowledge, highlighting unresolved issues and proposing areas for future research. Addressing these gaps will advance hydrogen-based energy systems and support the transition to a sustainable energy landscape. Facilitating efficient and safe deployment of UHS in depleted gas reservoirs will assist in unlocking hydrogen’s full potential as a clean and renewable energy carrier. In addition, this review aids policymakers and the scientific community in making informed decisions regarding hydrogen storage technologies.
Underground salt cavern CO2 storage (SCCS) offers the dual benefits of enabling extensive CO2 storage and facilitating the utilization of CO2 resources while contributing the regulation of the carbon market. Its economic and operational advantages over traditional carbon capture, utilization, and storage (CCUS) projects make SCCS a more cost-effective and flexible option. Despite the widespread use of salt caverns for storing various substances, differences exist between SCCS and traditional salt cavern energy storage in terms of gas-tightness, carbon injection, brine extraction control, long-term carbon storage stability, and site selection criteria. These distinctions stem from the unique phase change characteristics of CO2 and the application scenarios of SCCS. Therefore, targeted and forward-looking scientific research on SCCS is imperative. This paper introduces the implementation principles and application scenarios of SCCS, emphasizing its connections with carbon emissions, carbon utilization, and renewable energy peak shaving. It delves into the operational characteristics and economic advantages of SCCS compared with other CCUS methods, and addresses associated scientific challenges. In this paper, we establish a pressure equation for carbon injection and brine extraction, that considers the phase change characteristics of CO2, and we analyze the pressure during carbon injection. By comparing the viscosities of CO2 and other gases, SCCS’s excellent sealing performance is demonstrated. Building on this, we develop a long-term stability evaluation model and associated indices, which analyze the impact of the injection speed and minimum operating pressure on stability. Field countermeasures to ensure stability are proposed. Site selection criteria for SCCS are established, preliminary salt mine sites suitable for SCCS are identified in China, and an initial estimate of achievable carbon storage scale in China is made at over 51.8-77.7 million tons, utilizing only 20%-30% volume of abandoned salt caverns. This paper addresses key scientific and engineering challenges facing SCCS and determines crucial technical parameters, such as the operating pressure, burial depth, and storage scale, and it offers essential guidance for implementing SCCS projects in China.
To reduce CO2 emissions from coal-fired power plants, the development of low-carbon or carbon-free fuel combustion technologies has become urgent. As a new zero-carbon fuel, ammonia (NH3) can be used to address the storage and transportation issues of hydrogen energy. Since it is not feasible to completely replace coal with ammonia in the short term, the development of ammonia-coal co-combustion technology at the current stage is a fast and feasible approach to reduce CO2 emissions from coal-fired power plants. This study focuses on modifying the boiler and installing two layers of eight pure-ammonia burners in a 300-MW coal-fired power plant to achieve ammonia-coal co-combustion at proportions ranging from 20% to 10% (by heat ratio) at loads of 180- to 300-MW, respectively. The results show that, during ammonia-coal co-combustion in a 300-MW coal-fired power plant, there was a more significant change in NOx emissions at the furnace outlet compared with that under pure-coal combustion as the boiler oxygen levels varied. Moreover, ammonia burners located in the middle part of the main combustion zone exhibited a better high-temperature reduction performance than those located in the upper part of the main combustion zone. Under all ammonia co-combustion conditions, the NH3 concentration at the furnace outlet remained below 1 parts per million (ppm). Compared with that under pure-coal conditions, the thermal efficiency of the boiler slightly decreased (by 0.12%-0.38%) under different loads when ammonia co-combustion reached 15 t·h−1. Ammonia co-combustion in coal-fired power plants is a potentially feasible technology route for carbon reduction.
