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Spectroscopic data, particularly diffraction data, are essential for materials characterization due to their comprehensive crystallographic information. The current crystallographic phase identification, however, is very time consuming. To address this challenge, we have developed a real-time crystallographic phase identifier based on a convolutional self-attention neural network (CPICANN). Trained on 692 190 simulated powder X-ray diffraction (XRD) patterns from 23 073 distinct inorganic crystallographic information files, CPICANN demonstrates superior phase-identification power. Single-phase identification on simulated XRD patterns yields 98.5 and 87.5% accuracies with and without elemental information, respectively, outperforming JADE software (68.2 and 38.7%, respectively). Bi-phase identification on simulated XRD patterns achieves 84.2 and 51.5% accuracies, respectively. In experimental settings, CPICANN achieves an 80% identification accuracy, surpassing JADE software (61%). Integration of CPICANN into XRD refinement software will significantly advance the cutting-edge technology in XRD materials characterization.

The atmospheric correction of hyperspectral data stands as a fundamental step in quantitative applications, crucial for the accurate analysis of hyperspectral information. Zhuhai-1 hyperspectral data, characterized by its high spatial and spectral resolution, holds substantial potential and advantages for the quantification of water body information. Nonetheless, the adoption of more precise physical models for atmospheric correction often demands extensive satellite and ground environmental parameters, which pose practical challenges in applying physical models The Dark Object Subtraction (DOS), leveraging the intrinsic spectral characteristics of the imagery, offers an efficient alternative for achieving improved atmospheric correction results tailored to the data and study area. In this context, this study presents a Dark Object Subtraction for Water body information extraction (DOSW), specifically designed to advance the quantification of water body information in Zhuhai-1 hyperspectral data. The proposed method is rigorously evaluated by comparing the correction results from the Foshan region and Feilaixia Reservoir with standard and measured spectra of typical objects. The results demonstrate the accuracy of DOSW in atmospheric correction, with correlation coefficients exceeding 0.7 when compared to standard spectra for three representative objects. Notably, DOSW achieves exceptional accuracy in water body correction, achieving a correlation coefficient of 0.95 and an RMSE of 0.002 in the Feilaixia Reservoir, and a correlation coefficient of 0.72 and an RMSE of 0.005 in the Foshan region. Overall, the results underscore the efficacy of DOSW in accurately addressing atmospheric correction challenges to Zhuhai-1 hyperspectral data, effectively meeting the requirements of hyperspectral quantification applications.

ABSTRACTRussula is the largest genus in the Russulales and is widespread throughout the world. Almost all Russula species are known to be ectomycorrhizal with high ecological and edible values, and some are lethal poisonous. In this study, four new species belonging to the subgenus Russula crown clade are identified based on morphological and phylogenetic evidence from the Xizang Autonomous Region and other provinces of China. Morphologically, Russula paragraveolens (sect. Polychromae, subsect. Xerampelinae) is mainly characterised by a cherry red to blood red pileus centre, a reddish orange pileus margin; R. pseudograveolens (sect. Polychromae, subsect. Xerampelinae) is characterised by a violet brown to brownish red pileus centre, a pale red to pastel red pileus margin and short basidia; R. shigatseensis (sect. Flavisiccantes, subsect. Lepidinae) is characterised by a brownish orange to madder red pileus centre, pinkish red pileus margin, and having lateral branches or branches of hyphal terminations in pileipellis; R. yadongensis (sect. Tenellae, subsect. Laricinae) is characterised by a dark purplish red pileus centre with brownish purple tints and having isolated to clustered spines of spore ornamentations. Their distinct taxonomic status is confirmed by the positions of the four new species in both the ITS and 4-locus (nucLSU, mtSSU, rpb2, tef1) phylogenetic trees.

Abstract A key factor limiting the development of silicone rubber insulating materials is their short service life in harsh operating environments. The establishment of a self‐healing system is desired to improve the long‐term operational performance of silicone rubber and effectively extend its service life. In this study, poly(urea‐formaldehyde) microcapsules containing polydimethylsiloxane healing agents are prepared and a dual‐component microcapsule self‐healing system for silicone rubber material is established and optimised. In addition, the key factors affecting the self‐healing performance (such as microcapsule size, rupture rate, and microcapsule doping amount) are investigated. It is found that when SYLGARDTM 184 is used as the healing agent and matrix, with a 4:6 (A:B) doping ratio of dual‐component microcapsules and 20 phr doping amount, the self‐healing efficiency is approximately 51%. Furthermore, increasing the microcapsule rupture rate and the interface strength between the healing agent and the matrix can effectively improve the self‐healing performance. The relationships between key factors and self‐healing performance are theoretically revealed. Based on the relationships, the microcapsule self‐healing system can be appropriately designed. The results of this paper can provide a reference for the development of long‐life silicone rubber insulating materials.

BackgroundThe active metabolite of benzo[a]pyrene (BaP), 7,8-dihydroxy-9,10-epoxybenzo[a]pyrene (BPDE), can form adducts with DNA, but the spectrum of BPDE-DNA adducts is unclear. ObjectiveTo identify the distribution of BPDE adduct sites and associated genes at the whole-genome level by chromatin immunoprecipitation followed by sequencing (ChIP-Seq), and serve as a basis for further exploring the toxicological mechanisms of BaP. MethodsHuman bronchial epithelial-like cells (16HBE) were cultured to the fourth generation inthe logarithmic growth phase. Cells were harvested and added to chromatin immunoprecipitation lysis buffer. The lysate was divided into experimental and control groups. The experimental group received a final concentration of 20 μmol·L−1 BPDE solution, while the control group received an equivalent volume of dimethyl sulfoxide solution. The cells were then incubated at 37 °C for 24 h. Chromatin fragments of 100-500 bp were obtained through sonication. BPDE-specific antibody (anti-BPDE 8E11) was used to enrich DNA fragments with BPDE adducts. High-throughput sequencing was conducted to detect BPDE adduct sites. The top 1000 peak sequences were subjected to motif analysis using MEME and DREME software. BPDE adduct target genes at the whole-genome level were annotated, and Gene Ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of BPDE adduct target genes were conducted using bioinformatics techniques. ResultsThe high-throughput sequencing detected a total of 842 BPDE binding sites, distributed across various chromosomes. BPDE covalently bound to both coding and non-coding regions of genes, with 73.9% binding sites located in intergenic regions, 19.6% in intronic regions, and smaller proportions in upstream 2 kilobase, exonic, downstream 2 kilobase, and 5' untranslated regions. Regarding the top 1000 peak sequences, four reliable motifs were identified, revealing that sites rich in adenine (A) and guanine (G) were prone to binding. Through the enrichment analysis of binding sites, a total of 199 BPDE-adduct target genes were identified, with the majority located on chromosomes 1, 5, 7, 12, 17, and X. The GO analysis indicated that these target genes were mainly enriched in nucleic acid and protein binding, participating in the regulation of catalytic activity, transport activity, translation elongation factor activity, and playing important roles in cell division, differentiation, motility, substance transport, and information transfer. The KEGG analysis revealed that these target genes were primarily enriched in pathways related to cardiovascular diseases, cancer, and immune-inflammatory responses. ConclusionUsing ChIP-Seq, 199 BPDE adduct target genes at genome-wide level are identified, impacting biological functions such as cell division, differentiation, motility, substance transport, and information transfer. These genes are closely associated with cardiovascular diseases, tumors, and immune-inflammatory responses.

Abstract Accelerating the discovery of advanced materials is crucial for modern industries, aerospace, biomedicine, and energy. Nevertheless, only a small fraction of materials are currently under experimental investigation within the vast chemical space. Materials scientists are plagued by time-consuming and labor-intensive experiments due to lacking efficient material discovery strategies. Artificial intelligence (AI) has emerged as a promising instrument to bridge this gap. Although numerous AI toolkits or platforms for material science have been developed, they suffer from many shortcomings. These include primarily focusing on material property prediction and being unfriendly to material scientists lacking programming experience, especially performing poorly with limited data. Here, we developed MLMD, an AI platform for materials design. It is capable of effectively discovering novel materials with high-potential advanced properties end-to-end, utilizing model inference, surrogate optimization, and even working in situations of data scarcity based on active learning. Additionally, it integrates data analysis, descriptor refactoring, hyper-parameters auto-optimizing, and properties prediction. It also provides a web-based friendly interface without need programming and can be used anywhere, anytime. MLMD is dedicated to the integration of material experiment/computation and design, and accelerate the new material discovery with desired one or multiple properties. It demonstrates the strong power to direct experiments on various materials (perovskites, steel, high-entropy alloy, etc). MLMD will be an essential tool for materials scientists and facilitate the advancement of materials informatics.

Developing materials with multiple desired characteristics is a tremendous challenge, particularly in an elaborate material system. Herein, a machine learning assisted material design strategy was applied to simultaneously optimize dual target attributes by considering γ′ solvus temperature and alloy density of multi-component Co-based superalloys. To verify the soundness of our strategy, four alloys were selected and experimentally synthesized from >510,000 candidates, each of them possessing γ′ solvus temperature exceeding 1200 °C and alloy density below 8.3 g/cm3. Of those, Co-35Ni-12Al-5Ti-3V-3Cr-2Ta-2Mo (at.%) possesses the highest γ′ solvus temperature of 1250 °C and lower density of 8.2 g/cm3. This article validates a straightforward strategy to guide rapid discovery and fabrication of multi-component materials with desired dual-performance characteristics.

Accurate and efficient urban traffic flow prediction can help drivers identify road traffic conditions in real-time, consequently helping them avoid congestion and accidents to a certain extent. However, the existing methods for real-time urban traffic flow prediction focus on improving the model prediction accuracy or efficiency while ignoring the training efficiency, which results in a prediction system that lacks the scalability to integrate real-time traffic flow into the training procedure. To conduct accurate and real-time urban traffic flow prediction while considering the latest historical data and avoiding time-consuming online retraining, herein, we propose a scalable system for Predicting short-term URban traffic flow in real-time based on license Plate recognition data (PURP). First, to ensure prediction accuracy, PURP constructs the spatio-temporal contexts of traffic flow prediction from License Plate Recognition (LPR) data as effective characteristics. Subsequently, to utilize the recent data without retraining the model online, PURP uses the nonparametric method k-Nearest Neighbor (namely KNN) as the prediction framework because the KNN can efficiently identify the top-k most similar spatio-temporal contexts and make predictions based on these contexts without time-consuming model retraining online. The experimental results show that PURP retains strong prediction efficiency as the prediction period increases.

Background: Anti-interferon-γ autoantibodies (AIGAs) syndrome is susceptible to disseminated opportunistic infections due to increased AIGAs, but its clinical immunological characteristics remain unrecognized.Methods: We conducted a prospective cohort study between January 2021 and December 2023, recruiting patients with opportunistic infections who were categorized into AIGAs-positive and AIGAs-negative groups. Clinical immunological data and outcomes were documented. A subset of AIGAs-positive patients received glucocorticoid treatment, and its effectiveness was evaluated.Results: A total of 238 patients were enrolled, with 135 AIGAs-positive and 103 AIGAs-negative patients. AIGAs-positive patients showed higher rates of multiple pathogen dissemination, shorter progression-free survival (PFS), and increased exacerbation frequency. They also showed elevated erythrocyte sedimentation rate (ESR), globulin (GLB), immunoglobulin (Ig)G, IgE, and IgG4 levels. Among the 70 AIGAs-positive patients monitored for at least six months, three subtypes were identified: high AIGAs titer with immune damage, high AIGAs titer without immune damage, and low AIGAs titer without immune damage. Of the 55 patients followed for 1 year, decreasing AIGAs titer and immune indices (GLB, IgG, IgE, IgG4) were observed. Among the 31 patients with high AIGAs titer and immune damage treated with low-dose glucocorticoids at the stable phase, reductions were observed in immune indices and AIGAs titer in 67.74% of cases.Conclusions: AIGAs-positive patients exhibit infectious and immunological characteristics. Elevated AIGAs, IgG, IgG4, and IgE indicate abnormal immune damages. AIGAs titer generally decrease over time. Stable-phase AIGAs-positive patients can be categorized into three subtypes, with those having high AIGAs titer and increased immune indices potentially benefitting from glucocorticoid treatment.

Throughout history, the influenza A virus has caused numerous devastating global pandemics. Macrophages, as pivotal innate immune cells, exhibit a wide range of immune functions characterized by distinct polarization states, reflecting their intricate heterogeneity. In this study, we employed the time-resolved single-cell sequencing technique coupled with metabolic RNA labelling to elucidate the dynamic transcriptional changes in distinct polarized states of bone marrow-derived macrophages (BMDMs) upon infection with the influenza A virus. Our approach not only captures the temporal dimension of transcriptional activity, which is lacking in conventional scRNA-seq methods, but also reveals that M2-polarized Arg1_macrophages is the sole state supporting successful replication of influenza A virus. Furthermore, we identified distinct antigen presentation capabilities to CD4+ T and CD8+ T cells across diverse polarized states of macrophages. Notably, the M1 phenotype, exhibited by both bone marrow-derived macrophages (BMDMs) and murine alveolar macrophages (AMs), demonstrated superior conventional and cross-presentation abilities for exogenous antigens, with a particular emphasis on cross-presentation capacity. Additionally, as CD8+ T cell differentiation progressed, M1 polarization exhibited an enhanced capacity for cross-presentation. All three phenotypes of BMDMs, including M1, demonstrated robust presentation of CD4+ regulatory T cells, while displaying limited ability to present naive CD4+ T cells. These findings offer novel insights into the immunological regulatory mechanisms governing distinct polarized states of macrophages, particularly their roles in restricting the replication of influenza A virus and modulating antigen-specific T cell responses through innate immunity.

Upon entering the marine environment, plastics are colonized by a plethora of microorganisms to form a plastisphere, influencing the fate and transport of the plastic debris and the health of marine ecosystems. The assembly of marine plastisphere is generally believed to be dominated by stochastic processes. However, it remains elusive whether microbial interaction in the assembly of plastisphere microbial communities is conserved or not. We analyzed the plastisphere microbiomes of 137 plastic debris samples from intertidal zones at different geographical locations and habitats (seagrass, coral, mangrove, beach, and open ocean) and compared them with the surrounding sediment and seawater microbiomes. Microbial community structures of the plastisphere from different locations were more similar to each other but differed substantially from the surrounding sediment and water microbiomes, implying a common mechanism of plastisphere assembly. We used different machine learning algorithms (Multinomial Logistic Regression, Support Vector Machine, Decision Trees, Random Forest, and Artificial Neural Networks) to classify plastic debris samples with high sensitivity based on the microbiome composition. Eukaryotic and prokaryotic phototrophic organisms such as green algae, diatoms, and cyanobacteria, were found to be enriched on the plastic surfaces. Network analysis revealed the central role of the phototrophic organisms in the formation and sustenance of the plastispheres. We found that phototrophs served as core members interacting strongly with heterotrophic organisms in marine plastisphere, irrespective of the sampling location, habitats, and polymer types. This would explain the stochastic assembly of the plastisphere along with conserved properties driven by the phototrophs in the surrounding environment. Our results highlight the importance of phototrophic organisms in shaping the marine plastisphere microbial communities.

Currently, cables are manually installed in aircraft manufacturing scenarios. The utilization of robots in cable assemblies can enhance efficiency and ensure quality, presenting great promise for the future. However, the assembly process must control the shape of the cables, which is a significant challenge for robots. On the one hand, cables have high degrees of freedom in space, making accurate modeling of cable dynamics for robotic arm manipulation difficult; on the other hand, cable deformation has uncertainty, and the applied forces cause simultaneous deformation and motion, which is difficult to control. To address these problems, this study proposes a cable‐shape control method based on graph neural networks and online visual shape‐servoing. The method first approximates cable dynamics with a graph neural network. Then, in practice, the learnt model is used alongside visual‐based shape‐servoing to generate optimal robotic arm movements, controlling the cable to attain the desired shape. In the experiments, precise control of three different cable types is realized, and an example of a completed cable assembly is shown.

Abstract Direct recycling is a novel approach to overcoming the drawbacks of conventional lithium‐ion battery (LIB) recycling processes and has gained considerable attention from the academic and industrial sectors in recent years. The primary objective of directly recycling LIBs is to efficiently recover and restore the active electrode materials and other components in the solid phase while retaining electrochemical performance. This technology's advantages over traditional pyrometallurgy and hydrometallurgy are cost‐effectiveness, energy efficiency, and sustainability, and it preserves the material structure and morphology and can shorten the overall recycling path. This review extensively discusses the advancements in the direct recycling of LIBs, including battery sorting, pretreatment processes, separation of cathode and anode materials, and regeneration and quality enhancement of electrode materials. It encompasses various approaches to successfully regenerate high‐value electrode materials and streamlining the recovery process without compromising their electrochemical properties. Furthermore, we highlight key challenges in direct recycling when scaled from lab to industries in four perspectives: (1) battery design, (2) disassembling, (3) electrode delamination, and (4) commercialization and sustainability. Based on these challenges and changing market trends, a few strategies are discussed to aid direct recycling efforts, such as binders, electrolyte selection, and alternative battery designs; and recent transitions and technological advancements in the battery industry are presented.

In the context of energy transition and carbon neutrality strategies, distributed renewable energy is widely emerging on the distribution side. However, due to the volatility and randomness characteristics of distributed renewable energy, its full absorption poses a huge challenge to the economy and stable operation of the power grid. Flexible resources on the demand side, represented by air conditioners and electric vehicles, can participate in grid dispatching, improving the economic efficiency and reliability of system operation. To this end, this paper proposes an intraday dispatch strategy for demand-side flexible resources based on two-stage optimization. First, based on a generalized energy storage model, the adjustable flexibility of demand-side flexible resources is modeled. Second, to hinder the uncertainties related with markets and demand-side flexible resources, the rolling optimization is adopted for the optimal bidding, thus enabling demand-side flexible resources to participate in the intraday market with low risks of profit losses. Third, based on the bidding decision, real-time dispatch optimization is carried out to cope with the system operation deviation caused by forecast errors. Then, in the real-time control stage, the dispatch instructions are decomposed to the control units to achieve power tracking. Finally, the proposed method is simulated and verified on a test system. The results show that by coordinating the operation of demand-side flexible resources through two-stage optimization, the uncertainties of renewable energy output, market price, etc. Can be effectively overcome, and the economics of system operation can be improved.

Background: Lymphopenia is common in respiratory viral infection. However, no studies elucidated the impact of prolonged lymphopenia on worse outcome in the way of quantitative risk. Methods: Adult patients with laboratory-confirmed respiratory virus infection (influenza, SARS-CoV-2, and other viruses) between January 1st, 2016, and February 1st, 2023 were enrolled in this retrospective cohort study. Serial data of laboratory examination during hospitalization were acquired. The primary outcome was in-hospital all-cause death, and all information was obtained from the electronic medical records system. Legendre orthogonal polynomials (LOP), restricted cubic splines, and multivariable logistic regression were performed. Results: Finally, 2388 inpatients were involved in this study, including 436 patients with influenza, 1397 with SARS-CoV-2, and 319 with other respiratory virus infections. After being adjusted for age, corticosteroids, chronic kidney disease, chronic respiratory disease, cardiovascular disease, lymphopenia on admission and length of hospital stay, prolonged lymphopenia was significantly associated with death in influenza (OR 7.20, 95 % CI 2.27–22.77, p = 0. 0008 for lasting for 3–7 days; OR 17.80, 95 % CI 5.21–60.82, p

Summary: The decidua plays a crucial role in providing structural and trophic support to the developing conceptus before placentation. Following embryo attachment, embryonic components intimately interact with the decidual tissue. While evidence indicates the participation of embryo-derived factors in crosstalk with the uterus, the extent of their impact on post-implantation decidual development requires further investigation. Here, we utilize transgenic mouse models to selectively eliminate primary trophoblast giant cells (pTGCs), the embryonic cells that interface with maternal tissue at the forefront. pTGC ablation impairs decidualization and compromises decidual interferon response and lipid metabolism. Mechanistically, pTGCs release factors such as interferon kappa (IFNK) to strengthen the decidual interferon response and lipoprotein lipase (LPL) to enhance lipid accumulation within the decidua, thereby promoting decidualization. This study presents genetic and metabolomic evidence reinforcing the proactive role of pTGC-derived factors in mobilizing maternal resources to strengthen decidualization, facilitating the normal progression of early pregnancy.

This study employs text data analysis of annual government work reports from various provinces in China for the years 2015–2020. It constructs an index measuring the extent of government attention allocated to digital economic development (DED) at the government level. Simultaneously, it establishes a DED index system to measure the level of DED. The aim is to examine the impact of government attention on the level of DED. The research findings demonstrate that government attention significantly promotes the development of the DED. For every one-unit increase in government attention to the DED, the level of DED increases by 0.043 points. This result remains robust even after conducting sensitivity tests using instrumental variable methods and variable substitution methods. In further investigation, a threshold effect model is applied, revealing a marginal increasing effect of government attention on the level of DED. As the level of DED rises, the marginal contribution of government attention increases from 0.048 to 0.112. Using a spatial durbin model, it is found that government attention exhibits a positive spatial spillover effect on the level of DED.

Abstract As the fifth most common cancer in the world, gastric cancer (GC) ranks as the third major cause of cancer-related death globally. Although surgical resection and chemotherapy still remains the mainstay of potentially curative treatment for GC, chemotherapy resistance and adverse side effects limit their clinical applications. Thus, further investigation of the mechanisms of carcinogenesis in GC and discovery of novel biomarkers is of great concern. We herein report that the elevated expression of GPR137 is correlated with GC. Overexpression of GPR137 potentiates human gastric cancer AGS cell malignancy, including proliferation, migration, invasion, colony formation and xenograft growth in nude mice in vivo, whereas knockout of GPR137 by CRISPR/Cas9 gene editing exerts the opposite effects. Mechanistically, GPR137 could bind to MST, the upstream kinases in Hippo pathway, which disrupts the association of MST with LATS, subsequently activating the transcriptional co-activators, YAP and TAZ, and thereby triggering the target transcription and the alterations in GC cell biological actions consequently. Therefore, our findings may provide with the evidence of developing a potentially novel treatment method with specific target for GC.

Flexible load can optimize the load curve, which is an important means to promote renewable energy consumption. The peculiarities of electricity, heat, cooling and gas loads are analyzed in this paper, considering the fuzzy degree of human perception for water temperature, and the characteristic model of hot water load is established. Considering the fuzzy degree of human perception of ambient temperature, the characteristic model of cooling load is established by using PMV and PPD index. Meanwhile, considering four combinations of cut load, translatable load, transferable load and alternative load, and considering the coupling relationship of composite parts, different response models of load are established respectively. With the minimum cost of the system, including operation and compensation costs as the objective function, the optimization scheduling model of the regional integrated energy system is established, and the Gurobi solver is used for simulation analysis to solve the optimal output and load response curve of each piece of equipment. The results show that the load curve can be optimized, the flexible regulation ability of the regional integrated energy system can be enhanced, the energy loss of the system can be reduced, and the wind power consumption ability of the system can be increased by considering the integrated demand response.

Due to their outstanding anti-flashover characteristics, composite insulators have been extensively applied in power systems. A lot of research has investigated flashover characteristics of hydrophobic specimens with artificial water droplets. However, the phenomenon is not consistent with that of the contaminated composite insulators. On the test specimens covered with water droplets, there is no obvious leakage current before the flashover and no obvious relationship between flashover voltage and the conductivity of water droplets. On the contaminated composite insulator surface, there are short continuous arcs on the insulators before critical flashover, making insulators look like a luminous lantern. Considering that under these two conditions, the proportion of water along the insulation distance is different, the flashover characteristic of “dry band-water band” on a hydrophobicity surface is analyzed in the present study. The influence of the water band parameter (including length, width, and conductivity) as well as the length of dry band is studied. On this basis, the arc generation and development process of the surface covered with “dry band-water band” is analyzed. The research results improve the understanding of the flashover process on contaminated composite insulators.

Network slicing is envisioned as one of the key techniques to meet the extremely diversified service requirements of the Internet of Things (IoT) as it provides an enhanced user experience and elastic resource configuration. In the context of slicing enhanced IoT networks, both the Service Provider (SP) and Infrastructure Provider (InP) face challenges of ensuring efficient slice construction and high profit in dynamic environments. These challenges arise from randomly generated and departed slice requests from end-users, uncertain resource availability, and multi-dimensional resource allocation. Admission and resource allocation for distinct demands of slice requests are the key issues in addressing these challenges and should be handled effectively in dynamic environments. To this end, we propose an Opportunistic Admission and Resource allocation (OAR) policy to deal with the issues of random slicing requests, uncertain resource availability, and heterogeneous multi-resources. The key idea of OAR is to allow the SP to decide whether to accept slice requests immediately or defer them according to the load and price of resources. To cope with the random slice requests and uncertain resource availability, we formulated this issue as a Markov Decision Process (MDP) to obtain the optimal admission policy, with the aim of maximizing the system reward. Furthermore, the buyer-seller game theory approach was adopted to realize the optimal resource allocation, while motivating each SP and InP to maximize their rewards. Our numerical results show that the proposed OAR policy can make reasonable decisions effectively and steadily, and outperforms the baseline schemes in terms of the system reward.

The pathological features of intraductal oncocytic papillary neoplasm (IOPN) of the bile duct include tumor cells that are rich in eosinophilic cytoplasm and arranged in papillary structures. Herein, we report a missed case of IOPN of the bile duct because of concomitant gallstones. A 70-year-old woman was hospitalized with upper abdominal discomfort. The primary diagnosis was choledocholithiasis following imaging examination. However, an unidentified mass was detected after the gallstones were removed. The mass appeared as many papillary protuberances surrounded by fish-egg-like mucosa when viewed by the choledochoscope and was confirmed as IOPN by pathological examination. The patient underwent choledochectomy and no recurrence was observed at the 6-month follow-up examination. In this report, peroral choledochoscopy demonstrated its advantages for the diagnosis of biliary diseases and acquisition of tissue specimens. Therefore, it may solve the challenge related to the lack of preoperative pathological evidence for bile duct tumors.

Developing new materials through traditional trial-and-error is time-consuming and costly due to the vast potential compositions to explore. Moreover, some desirable properties are mutually exclusive, presenting challenges for materials design. Herein, we employed an active learning strategy to address the strength-ductility trade-off in SAC105 lead-free solders. We developed two Gaussian regression models, one for strength and one for elongation representing ductility. The Gaussian Upper Confidence Boundary algorithm, a Bayesian optimization method, was used to balance exploitation and exploration by considering prediction uncertainty. The variance weight was designed to decay with adaptive iterations. The two models were linearly combined and the maximal combined model recommended alloys on the Pareto front for experiments. After just three iterations, a new 91.4Sn-1.0Ag-0.5Cu-1.5Bi-4.4In-0.2Ti low-silver SAC solder was discovered, exhibiting 73.94±5.05 MPa strength and 24.37±5.92% elongation, representing the best comprehensive mechanical properties. Weldability experiments also showed excellent performance. Based on Ab initio molecular dynamics, a precise machine learning potential revealed the alloying mechanism in Sn-In solid solutions with quantum accuracy in systems of over 103 atoms, closely matching experimental X-ray diffraction pattern. To facilitate materials informatics development, all active learning algorithms was made open-source in our designed framework, Bgolearn.

In order to solve the high latency of traditional cloud computing and the processing capacity limitation of Internet of Things (IoT) users, Multi-access Edge Computing (MEC) migrates computing and storage capabilities from the remote data center to the edge of network, providing users with computation services quickly and directly. In this paper, we investigate the impact of the randomness caused by the movement of the IoT user on decision-making for offloading, where the connection between the IoT user and the MEC servers is uncertain. This uncertainty would be the main obstacle to assign the task accurately. Consequently, if the assigned task cannot match well with the real connection time, a migration (connection time is not enough to process) would be caused. In order to address the impact of this uncertainty, we formulate the offloading decision as an optimization problem considering the transmission, computation and migration. With the help of Stochastic Programming(SP), we use the posteriori recourse to compensate for inaccurate predictions. Meanwhile, in heterogeneous networks, considering multiple candidate MEC servers could be selected simultaneously due to overlapping, we also introduce the Multi-Arm Bandit (MAB) theory for MEC selection. The extensive simulations validate the improvement and effectiveness of the proposed SP-based Multi-arm bandit Method (SMM) for offloading in terms of reward, cost, energy consumption and delay. The results show that SMM can achieve about 20% improvement compared with the traditional offloading method that does not consider the randomness, and it also outperforms the existing SP/MAB based method for offloading.

Abstract The attainment of both high strength and high ductility is always the goal for structure materials, because the two properties generally are mutually competing, called strength-ductility trade-off. Nowadays, the data-driven paradigm combined with expert domain knowledge provides the state-of-the-art methodology to design and discovery for structure materials with high strength and high ductility. To enhance both strength and ductility, a joint feature is proposed here to be the product of strength multiplying ductility. The strategy of “divide and conquer” is developed to solve the contradictory problem, that material experimental data of mechanical behaviors are, in general, small in size and big in noise, while the design space is huge, by a newly developed data preprocessing algorithm, named the Tree-Classifier for Gaussian Process Regression (TCGPR). The TCGPR effectively divides an original dataset in a huge design space into three appropriate sub-domains and then three Machine Learning (ML) models conquer the three sub-domains, achieving significantly improved prediction accuracy and generality. After that the Bayesian sampling is applied to design next experiments by balancing exploitation and exploration. Finally, the experiment results confirm the ML predictions, exhibiting novel lead-free solder alloys with high strength high ductility. Various material characterizations were also conducted to explore the mechanism of high strength and high ductility of the alloys.

Data on the viral rebound and safety of nirmatrelvir/ritonavir in lung transplant (LTx) recipients are limited. The study prospectively followed four LTx recipients. Clinical characteristics, viral RNA dynamic in throat swabs, and tacrolimus blood concentration were monitored regularly. All four LTx recipients, aged 35–74 years, were not vaccinated against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). They got coronavirus disease 2019 (COVID-19) after more than one week of admission during the era of Omicron. All cases received nirmatrelvir/ritonavir (NM/r) within two days of infection, and the relative viral RNA copies dropped quickly. Viral load rebound was observed in all four cases after discontinuation of the first five days of NM/r treatment. Three of them received another 5-days antiviral therapy with NM/r. The duration of positive viral PCR testing was 25-28 days. None of them progressed into severe or critical COVID-19. Tacrolimus was stopped 12 h before NM/r and held during the 5-day course of antiviral therapy. Blood concentration of tacrolimus were maintained at a baseline level during these five days. Tacrolimus was re‐initiated at its baseline daily dose 3-4 days after NM/r therapy. However, during the second round of antiviral therapy with NM/r, the concentration of tacrolimus fluctuated wildly. In conclusion, the 5-day course of NM/r treatment was not sufficient for LTx recipients and the viral rebound was common. More data are needed to clarify whether LTx recipients with SARS-CoV-2 viral rebound could benefit from additional treatment with NM/r.

Abstract Heating and cooling in buildings accounts for over 20% of total energy consumption in China. Therefore, it is essential to understand the thermal requirements of building occupants when establishing building energy codes that would save energy while maintaining occupants’ thermal comfort. This paper introduces the Chinese thermal comfort dataset, established by seven participating institutions under the leadership of Xi’an University of Architecture and Technology. The dataset comprises 41,977 sets of data collected from 49 cities across five climate zones in China over the past two decades. The raw data underwent careful quality control procedure, including systematic organization, to ensure its reliability. Each dataset contains environmental parameters, occupants’ subjective responses, building information, and personal information. The dataset has been instrumental in the development of indoor thermal environment evaluation standards and energy codes in China. It can also have broader applications, such as contributing to the international thermal comfort dataset, modeling thermal comfort and adaptive behaviors, investigating regional differences in indoor thermal conditions, and examining occupants’ thermal comfort responses.

With the development of UHV transmission technology, large-tonnage disc-type suspension porcelain insulators have been widely used. Insulators with cylindrical heads have the advantages of light weight and high strength compared with traditional insulators with conical heads. Cement is an important part of disc-type suspension porcelain insulators. The drying shrinkage of cement can lead to sliding between cement and porcelain. It can also transform the stress distribution of the porcelain, thus influencing the overall long-term mechanical performance of the insulators. In this paper, the effect of the shrinkage of cement on the stress distribution of porcelain insulators was studied by simulation. In addition, the effect of the drying shrinkage rates of cement on insulators with cylindrical and conical heads were compared. The simulation results show that cylindrical-head insulators can better resist the stress change caused by cement shrinkage than the conical-head insulators, which was beneficial to the long-term performance of the insulators. Based on the tensile stress of porcelain parts, the recommended range of the drying shrinkage rate of cement for insulators with cylindrical heads was presented. The research results are of practical value for the selection of cement.

Based on phylogenetic analysis, Candolleomyces (Psathyrellaceae, Agaricales) was established with Psathyrella candolleana as the type species. The basidiomes range from small to large and are typically terrestrial, lignicolous, and rarely fimicolous. We analysed the Candolleomyces species collected during five years in China, and based on morphological and molecular data (nrITS, nrLSU, and tef-1α), we propose seven new Candolleomyces species viz. C. brevisporus, C. gyirongicus, C. lignicola, C. luridus, C. shennongdingicus, C. shennongjianus, and C. sichuanicus. Full descriptions, colour photographs, illustrations, phylogenetic analyses results, and comparisons with related Candolleomyces species of the new taxa are provided. This study enriches the species diversity of Candolleomyces in China.

Abstract Aqueous zinc‐ion batteries (AZIBs) are gaining popularity for their cost‐effectiveness, safety, and utilization of abundant resources. MXenes, which possess outstanding conductivity, controllable surface chemistry, and structural adaptability, are widely recognized as a highly versatile platform for AZIBs. MXenes offer a unique set of functions for AZIBs, yet their significance has not been systematically recognized and summarized. This review article provides an up‐to‐date overview of MXenes‐based electrode materials for AZIBs, with a focus on the unique functions of MXenes in these materials. The discussion starts with MXenes and their derivatives on the cathode side, where they serve as a 2D conductive substrate, 3D framework, flexible support, and coating layer. MXenes can act as both the active material and a precursor to the active material in the cathode. On the anode side, the functions of MXenes include active material host, zinc metal surface protection, electrolyte additive, and separator modification. The review also highlights technical challenges and key hurdles that MXenes currently face in AZIBs.

BackgroundMaternal smoking during pregnancy (MSDP) is a known risk factor for offspring developing chronic obstructive pulmonary disease (COPD), but the underlying mechanism remains unclear. ObjectiveThis study aimed to explore whether the increased COPD risk associated with MSDP could be attributed to tobacco dependence (TD). MethodsThis case-control study used data from the nationwide cross-sectional China Pulmonary Health study, with controls matched for age, sex, and smoking status. TD was defined as smoking within 30 minutes of waking, and the severity of TD was assessed using the Fagerstrom Test for Nicotine Dependence. COPD was diagnosed when the ratio of forced expiratory volume in 1 second to forced vital capacity was

The gut microbiota undergoes substantial changes in COVID-19 patients; yet, the utility of these alterations as prognostic biomarkers at the time of hospital admission, and its correlation with immunological and hematological parameters, remains unclear. The objective of this study is to investigate the gut microbiota's dynamic change in critically ill patients with COVID-19 and evaluate its predictive capability for clinical outcomes alongside immunological and hematological parameters. In this study, anal swabs were consecutively collected from 192 COVID-19 patients (583 samples) upon hospital admission for metagenome sequencing. Simultaneously, blood samples were obtained to measure the concentrations of 27 cytokines and chemokines, along with hematological and biochemical indicators. Our findings indicate a significant correlation between the composition and dynamics of gut microbiota with disease severity and mortality in COVID-19 patients. Recovered patients exhibited a higher abundance of Veillonella and denser interactions among gut commensal bacteria compared to deceased patients. Furthermore, the abundance of gut commensal bacteria exhibited a negative correlation with the concentration of proinflammatory cytokines and organ damage markers. The gut microbiota upon admission showed moderate prognostic prediction ability with an AUC of 0.78, which was less effective compared to predictions based on immunological and hematological parameters (AUC 0.80 and 0.88, respectively). Noteworthy, the integration of these three datasets yielded a higher predictive accuracy (AUC 0.93). Our findings suggest the gut microbiota as an informative biomarker for COVID-19 prognosis, augmenting existing immune and hematological indicators.

Conclusion: Impaired central sensitivity to thyroid hormones was significantly associated with the reduction of VFA and SFA, while impaired peripheral sensitivity was associated with an increase of SFA in euthyroid individuals with T2DM.

Gut microbial communities of plastic-munching worms provide novel insights for the development of plastic-processing biotechnologies. Considering the complexity of worm maintenance and the gut microbial communities, it is challenging to apply the worms directly in plastic processing. Harnessing the power of microbial communities derived from the worm gut microbiomes in vitro may enable a promising bioprocess for plastic degradation. Here, we established stable and reproducible plastic-associated biofilm communities derived from the gut microbiome of a superworm, Zophobas atratus, through a two-stage enrichment process: feeding with plastics (HDPE, PP, and PS) and in vitro incubation of gut microbiomes obtained from the plastic-fed worms. Plastic feeding exhibited marginal influence on bacterial diversity but substantially changed the relative abundance of different bacterial groups, and intriguingly, enriched potential plastic degraders. More prominent shifts of microbial communities were observed during the in vitro incubation of the gut microbiomes. Taxa containing plastic-degrading strains were further enriched, while other taxa represented by lactic acid bacteria were depleted. Additionally, the plastic characterization confirmed the degradation of the incubated plastics by the plastic-associated microbial communities. Community functional inference for both gene abundance and community phenotype suggested that the in vitro incubation enhanced plastic-degrading potential. Deterministic ecological effects, in particular, selection processes, were identified as the main driving force of the observed community shifts. Our findings provide novel insights into plastic-munching-worm-inspired bioprocessing of plastic wastes.

ObjectiveTo observe the effectiveness and safety of Lianhua Qingwen granule in the treatment of non-influenza viral pneumonia.MethodsThis study was a multicenter, randomized, double-blind, placebo-controlled trial. Subjects who met the inclusion and exclusion criteria and were clinically diagnosed with viral pneumonia (negative for influenza virus) were randomly divided into the Lianhua Qingwen granule trial group and placebo control group. Patients in the trial group was given Lianhua Qingwen granule, 2 bags at a time, 3 times a day, and the controls were given placebo, with a treatment course of 7 days. Patients’ clinical symptoms and signs, and treatment-associated adverse events were observed. Subjects should be included in the full analysis set (FAS) as long as they were all given the medication and had an effectiveness test performed after randomization. Subjects should be included in the Per Protocol Set (PPS),a subset of the total analysis set, which should contain those with strong compliance, no protocol violations, and complete baseline values for the primary indicators.ResultsA total of 169 subjects were enrolled in 12 subcenters, including 151 (76 in the trial group and 75 in the control group) in the FAS and 140 (68 in the trial group and 72 in the control group) in the PPS. After 7 days of treatment, the clinical symptom relief rates were 82.98% (FAS) and 87.12% (PPS) in the trial group, and 75.11% (FAS) and 76.02% (PPS) in the control group, respectively. The clinical symptom relief rates in the trial group were significantly higher than those in the control group (p 0.05).SafetyThere were no significant differences in body weight, vital signs, blood routine, urine routine, stool routine, and blood biochemical indicators (CK, AST, ALT, Cr, and Bun) between the two groups before and after treatment (p > 0.05). During treatment, there were no significant differences in the incidence of adverse events and serious adverse events between the two groups (p > 0.05).ConclusionLianhua Qingwen granules improved the clinical symptoms of patients with non-influenza virus pneumonia, especially ameliorating cough and expectoration. Lianhua Qingwen granules were associated with good safety.

Objectives This study aimed to assess the prevalence and related factors of myopia among school-aged children after COVID-19 pandemic.Design Cross-sectional study.Setting Pudong New Area, Shanghai.Participants 1722 children aged 7–9 randomly selected from 8 primary schools were screened from 1 February 2023 to 30 April 2023.Main outcome measures Children’s height, weight and eye parameters were examined. Myopia was defined as a cycloplegic spherical equivalent ≤−0.50 dioptres in either eye. A vision-related behaviour questionnaire was applied to investigate the associations between myopia and its risk factors.Results Of the 1722 individuals enrolled, 25.6% (456) had myopia. After adjusting other characteristics, the following factors were associated with an increased rate of myopia: age (9 years vs 7 years, adjusted OR (AOR) 1.84, 95% CI 1.18 to 2.85, p=0.007), parental myopia status (both myopia vs none, AOR 5.66, 95% CI 3.71 to 8.63, p

In the context of innovation-driven development, the effective transformation of the low-carbon economy increasingly relies on the innovation ecosystem in which it exists. In order to explore the spatial effect of the innovation ecosystem on low-carbon transformation, this study comprehensively measures the innovation ecosystem and low-carbon transformation level based on China’s provincial panel data from 2015 to 2020 and use the spatial Durbin model to analyze the impact of the innovation ecosystem on low-carbon transition. The main conclusions are as follows: (1) There is apparent spatial heterogeneity in the development of innovation ecosystems, and the development pattern shows “cluster-type” agglomeration characteristics. However, the development level gap between provinces has remained the same. The southeast coastal economic zone has become an essential high-level development level of innovation ecosystems. (2) The innovation ecosystem has a significant spatial spillover effect on low-carbon transition. This conclusion still holds after multiple robustness tests. (3) In the process of low-carbon transition, the driving effect of the innovation ecosystem shows a “U” shaped stage characteristic of first decreasing and then increasing. (4) The impact of the innovation ecosystem on low-carbon transition varies within different economic circles. Spatial spillover has a boundary effect and the spillover peaks at 1200 km. This study provides theoretical support and empirical evidence based on China’s situation for transforming and developing a low-carbon economy. It provides important policy implications for accelerating low-carbon technological innovation, promoting sustainable development, and responding to climate change.

ObjectiveIn order to set or improve the lightning protection measures of ships, it is necessary to evaluate the strike risk of critical equipment and the protection effect of the lightning rod.MethodsIn this paper, a lightning leader fractal model is constructed and applied to a simulation study on the lightning attachment process of a ship. The configuration method of the simulation boundary of the model and the quantitative evaluation method of lightning strike risk are improved, and a quantitative study on the influence of different factors on the lightning strike risk of the ship is performed.ResultsThe simulation results show that the protection probability of the lightning rod in the middle of the ship is more than 75% under the basic configuration. The bow, stern and antenna far away from the lightning rod have a relatively large risk of circling strike. The lightning strike probability of the shipboard antenna is lower if the lightning initiation position is closer to the lightning rod, the lightning current is greater or the lightning rod is higher.Conclusions The lightning leader fractal model constructed in this paper can be used to investigate the weak links of direct lightning protection on the surface of ships, which can provide useful references for the direct lightning protection design of ships.

Abstract Aim This study aimed to describe the different trajectories groups of HbA1c during the long-term treatment of diabetes and explore the effect of glycemic control on the progression of arterial stiffness. Method The study participants registered at the National Metabolic Management Center (MMC) of Beijing Luhe hospital. The latent class mixture model (LCMM) was used to identify distinct trajectories of HbA1c. We calculated the change value of baPWV (ΔbaPWV) of each participant between the whole follow-up time as the primary outcome. Then we examined the associations between each HbA1c trajectory pattern and ΔbaPWV using covariate-adjusted means (SE) of ΔbaPWV, which were calculated by multiple linear regression analyses adjusted for the covariates. Results After data cleaning, a total of 940 type 2 diabetes patients aged 20–80 years were included in this study. According to the BIC, we identified four discrete trajectories of HbA1c: Low-stable, U-shape, Moderate-decrease, High-increase, respectively. Compared with the low-stable group of HbA1c, the adjusted mean values of baPWV were significantly higher in U-shape, Moderate-decrease, and High-increase groups (all P

Raindrops contain abundant renewable energy including both kinetic energy and electrostatic energy, and how to effectively harvest it becomes a hot research topic. Recently, a triboelectric nanogenerator (TENG) using liquid–solid contact electrification has been demonstrated for achieving an ultra-high instantaneous power output. However, when harvesting the energy from the dense raindrops instead of a single droplet, a more rational structure to eliminate the mutual influence of individual generation units is needed for maximize the output. In this work, a “solar panel-like” bridge array generators (BAGs) is proposed. By adopting array lower electrodes (ALE) and bridge reflux structure (BRS), BAGs could minimize the sharp drop in the peak power output for large-scale energy harvesting devices. When the area of the raindrop energy harvesting device is 15 × 15 cm2, the peak power output of BAGs reached 200 W/m2, which is remarkable for paving a potential industrial approach for effective harvesting raindrop energy at a large scale.

A series of Zn-modified HBeta (Zn/HBeta) catalysts were prepared via the wetness impregnation method with different zinc precursors such as ZnSO4·7H2O, ZnCl2, C4H6O4Zn·2H2O and Zn(NO3)2·6H2O, and their catalytic performance in the conversion of ethanol to propylene reaction was evaluated. Results indicate that the amount and strength distribution of the acid sites of the Zn/HBeta catalysts were easily tuned by employing different types of zinc precursors. More importantly, when the zinc species were introduced to the HBeta, the propylene yield was significantly enhanced, whereas the yields of ethylene and C2–C4 alkanes were remarkably suppressed. For the catalyst prepared by using the ZnCl2 precursor, a higher propylene yield of up to 43.4% for Zn/HBeta-C was achieved as a result of the moderate amount and strength distribution of acid sites. The average coking rate of the used Zn/HBeta catalysts strongly depended on the amount of total acid sites, especially the strong acid sites, i.e., the higher the amount of total acid sites of the catalyst, the greater the average coking rate. For the catalyst prepared by using the ZnSO4·7H2O precursor, Zn/HBeta-S exhibited a better stability even after depositing more coke, which was due to the higher amount of strong acid sites.

Water splitting is an important way to obtain hydrogen applied in clean energy, which mainly consists of two half-reactions: hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). However, the kinetics of the OER of water splitting, which occurs at the anode, is slow and inefficient, especially in acid. Currently, the main OER catalysts are still based on noble metals, such as Ir and Ru, which are the main active components. Hence, the exploration of new OER catalysts with low cost, high activity, and stability has become a key issue in the research of electrolytic water hydrogen production technology. In this paper, the reaction mechanism of OER in acid was discussed and summarized, and the main methods to improve the activity and stability of non-noble metal OER catalysts were summarized and categorized. Finally, the future prospects of OER catalysts in acid were made to provide a little reference idea for the development of advanced OER catalysts in acid in the future.

ObjectivesAiming at the current situation in which it is difficult to efficiently evaluate protection probability through traditional lightning rod evaluation methods, an efficient numerical evaluation algorithm is developed on the basis of an electrogeometric model (EGM) and attractive volume to realize the efficient calculation of lightning protection probability at any point in space.MethodsThis method first determines the attractive volume boundary of the lightning rod and protection object according to the interception process of the upward and downward leaders. The collection surface and exposure arc of the lightning stroke distance are then calculated, enabling the attractive risk and interception effect of the lightning rod to be quantified. Finally, the attraction and interception characteristics of the lightning rod are integrated to establish a numerical evaluation model of protection probability. To verify the accuracy of this method, the general rule of lightning rod protection probability is analyzed and the results compared with the existing analysis method.ResultThe evaluation results of this method show good agreement with those of classical leader progression model (LPM) theory.Conclusions The method proposed herein has a high degree of quantification and can realize the efficient calculation of lightning protection probability at any point in space, which can provide useful references for lightning protection design work.

The annual increase in installation capacity and electrical production of renewable energy sources, primarily wind turbine generators (WTG), is shaping a renewable energy dominated power system. WTGs are susceptible to the temporary overvoltage caused by reactive power surplus following low-voltage ride through (LVRT). This can lead to the large-scale trip-off of WTGs and pose significant risks to the secure and stable operation of power systems. An insightful elaboration of the underlying mechanisms determining the occurrence of temporary overvoltage, and an analysis of influencing factors, is pivotal to ensure the reliable integration of WTGs. This paper investigates the temporary overvoltage in the AC systems integrated with multiple renewable energy stations. A temporary overvoltage model that accounts for various types of equipment has been derived. Resorting to the model, the influence of LVRT parameters of WTGs, SCR and IR of the AC system on the maximum terminal overvoltage has been quantitatively assessed. Simulations and semi-physical validations have been conducted to verify the effectiveness and accuracy of the theoretical analysis.

Summary: Background: Severe community-acquired pneumonia (SCAP) results in high mortality as well as massive economic burden worldwide, yet limited knowledge of the bio-signatures related to prognosis has hindered the improvement of clinical outcomes. Pathogen, microbes and host are three vital elements in inflammations and infections. This study aims to discover the specific and sensitive biomarkers to predict outcomes of SCAP patients. Methods: In this study, we applied a combined metagenomic and transcriptomic screening approach to clinical specimens gathered from 275 SCAP patients of a multicentre, prospective study. Findings: We found that 30-day mortality might be independent of pathogen category or microbial diversity, while significant difference in host gene expression pattern presented between 30-day mortality group and the survival group. Twelve outcome–related clinical characteristics were identified in our study. The underlying host response was evaluated and enrichment of genes related to cell activation, immune modulation, inflammatory and metabolism were identified. Notably, omics data, clinical features and parameters were integrated to develop a model with six signatures for predicting 30-day mortality, showing an AUC of 0.953 (95% CI: 0.92–0.98). Interpretation: In summary, our study linked clinical characteristics and underlying multi-omics bio-signatures to the differential outcomes of patients with SCAP. The establishment of a comprehensive predictive model will be helpful for future improvement of treatment strategies and prognosis with SCAP. Funding: National Natural Science Foundation of China (No. 82161138018), Shanghai Municipal Key Clinical Specialty (shslczdzk02202), Shanghai Top-Priority Clinical Key Disciplines Construction Project (2017ZZ02014), Shanghai Key Laboratory of Emergency Prevention, Diagnosis and Treatment of Respiratory Infectious Diseases (20dz2261100).