In this work, zinc oxide (ZnO)/ granular activated carbon (GAC) composites at different ZnO concentration (0.25M-ZnO@GAC, 0.5M-ZnO@GAC, and 0.75M-ZnO@GAC) were prepared by an in-situ hydrothermal method and demonstrated synergistic photocatalytic degradation and ad-sorption of Rhodamine B (RhB). The thermal stability, morphological structure, elemental com-position, crystallographic structure, and textural properties of developed catalysts were charac-terized by thermal gravimetric analysis (TGA/DTG), scanning electron microscopy equipped with energy dispersive-x-ray (SEM-EDS), x-ray diffraction (XRD), and Brunauer–Emmett–Teller (BET) analysis. Successful loading of ZnO onto GAC was confirmed by SEM-EDS and XRD analysis. The BET surface areas of GAC, 0.25M-ZnO@GAC, 0.5M-ZnO@GAC, and 0.75M-ZnO@GAC were 474 m2/g, 450 m2/g, 453 m2/g, and 421 m2/g, respectively. The decrease in GAC could be attributed to the successful loading of ZnO on the GAC surface. Notably, 0.5M-ZnO@GAC exhibited the best photocatalytic degradation efficiency of 82% and 97% under UV-A and UV-C light over 120 min, attributed to improved crystallinity and visible light absorption. The photocatalytic degradation parameters revealed that lowering the RhB concentration, raising the catalyst dosage and pH be-yond the point of zero charge (PZC) would favor the RhB degradation. Photocatalytic reusability was demonstrated over 5 cycles. Scavenger tests revealed that the hydroxyl radicals (•OH), su-peroxide radicals (O2−•), and photoinduced hole (h+) radicals play a major role during the RhB degradation process. Based on the TOC results, the RhB mineralization efficiency of 79.1% was achieved by 0.5M-ZnO@GAC. Additionally, GAC exhibited a strong adsorptive performance towards RhB with adsorption capacity and RhB removal of 487.1 mg/g and 99.5% achieved within 90 min equilibrium time. The adsorption characteristics were best described by pseu-do-second-order kinetics, suggesting chemical adsorption. This research offers a new strategy for the development of effective photocatalyst materials with potential for wider wastewater treat-ment applications.

Abiotic stress is a significant factor restricting the normal growth and development of plants. Under abiotic stress, plants maintain their life and continuous growth by reconfiguring transcriptional regulation and metabolic networks. In recent years, with the development of sequencing technology and mass spectrometry technology, transcriptomics and metabolomics have emerged as new disciplines following genomics and proteomics, which are conducive to identifying metabolites and regulatory genes in plants under abiotic stress. In this review, we mainly analyzes the technical characteristics, advantages, and disadvantages of transcriptomics and metabolomics, focusing on reviewing the research progress in the field of plant responses to abiotic stress (temperature stress, salt stress, water stress, and heavy metal stress) using transcriptomics and metabolomics both domestically and internationally in recent years. It also provides a prospective view on current issues, which helps accelerate the understanding of the mechanisms of plant responses to abiotic stress and provides new ideas for breeding stress-resistant varieties in the future.

The ethylene-regulated hypocotyl elongation of Arabidopsis thaliana's involves many transcription factors. The specific role of MYC transcription factors in ethylene signal transduction is not completely understood. The results here revealed that two MYCs, MYC2 and MYC3, act as negative regulators in ethylene-suppressed hypocotyl elongation. Etiolated seedlings of the lost-of-function mutant of MYC2 or MYC3 were longer than wild type seedlings. Single- or double-null mutants of MYC2 and MYC3 displayed an enhanced response to ACC(1-aminocyclopropane-1-carboxylate), the ethylene precursor, compared to wild type seedlings. MYC2 and MYC3 directly bind to the promoter zone of ERF1, suppressing its expression. Additionally, EIN3, a key component in ethylene signaling, interacts with MYC2 or MYC3 and suppresses their binding to ERF1’s promoter. MYC2 and MYC3 play crucial roles in the ethylene-regulated expression of functional genes. The results revealed these transcription factors’ novel role and functional mechanism in ethylene signal transduction.

Mulberry leaves, when infected by pathogens, can suffer significant yield loss or even death if early disease detection and timely spraying are not performed. To enhance the detection performance of mulberry leaf diseases in natural environments and to precisely locate early small lesions, we propose a high-precision, high-efficiency disease detection algorithm named YOLOv8-RFMD. Based on improvements to You Only Look Once version 8 (YOLOv8), we first proposed the Multi Dimension Feature Attention (MDFA) module, which integrates important features at the pixel-level, spatial and channel dimensions. Building on this, we designed the RFMD Module, which consists of the Conv-BatchNomalization-SiLU (CBS) module, Receptive-Field Coordinated Attention (RFCA) Conv, and MDFA, replacing the Bottleneck in the model’s Residual block. We then employed Adown down sampling structure to reduce the model size and computational complexity. Finally, to improve the detection precision of small lesion features, we replaced the complete intersection over union (CIOU) loss function with the Normalized Wasserstein Distance (NWD) loss function. Results show that the YOLOv8-RFMD model achieves an mAP50 of 94.3% and an mAP50:95 of 67.8% on experimental data, representing increases of 2.9% and 4.3% respectively compared to the original model. The model size is reduced by 0.53 MB to just 5.45 MB, and the number of floating-point operations is reduced by 0.3 G to only 7.8 G. The improved model meets the deployment requirements of mobile embedded devices and can provide a theoretical reference for the automated spraying operations of mulberry leaves.

When profit-driven renewal is difficult to implement, many historic districts are “frozen protected” in China. In the social context of “building cities for the people” in recent years, the public value-oriented renewal of urban historic areas plays an important role in the transformation of cities. This study investigates the strategies of historic district regeneration in China by integrating spatial form, urban governance, and public value. We propose a novel framework to analyze the regeneration of historic districts, using Taiyuan Bell Tower Street as a case study. The framework distinguishes between two dimensions: spatial and economic-social. In the spatial dimension, we focus on the transformation of spatial form to regenerate public value, emphasizing the attribute of "publicness" as central to urban regeneration efforts. This involves a tiered approach of excavating and upgrading historic districts at macro, meso, and micro levels. In the economic-social dimension, we explore urban governance to strengthen public value, involving government, market, and societal actors in a collaborative renewal process. The analysis highlights that a public value-oriented approach to heritage regeneration offers greater sustainability and could serve as a model for other cities seeking similar outcomes.

Utilizing deep learning for semantic segmentation of cropland from remote sensing imagery has become a crucial technique in land surveys. Cropland illustrates diverse morphologies and degrees of fragmentation on the Earth’s surface, underscoring the importance of accurately perceiving the complex boundaries of cropland which are crucial for effective segmentation. This paper introduces a UNet-like boundary-aware compensation model BAFormer. Cropland boundaries typically exhibit rapid transformations in pixel values and texture features, often appearing as high-frequency features in remote-sensing images. To enhance the recognition of these high-frequency features as represented by cropland boundaries, the proposed BAFormer integrates a Feature Adaptive Mixer (FAM) and develops a Deep Wide Large Kernel Multi-Layer Perceptron (DWLK-MLP) to enrich the global and local cropland boundaries features separately. Specifically, FAM adaptively mixes high-frequency and low-frequency features through the advantages of convolution and self-attention; DWLK-MLP expands the convolutional receptive field by deeply decomposing large kernel convolutions. The efficacy of BAFormer has been evaluated on the Vaihingen, Potsdam, and LoveDA public datasets, as well as the Mapcup dataset. It has demonstrated advanced performance, achieving mIoU scores of 84.5%, 87.3%, 53.5%, and 83.1% on these datasets respectively. Notably, BAFormer-T, the lightweight iteration of the model, surpasses other lightweight models on the Vaihingen dataset with scores of 91.3% F1 and 84.1% mIoU. The source code is available at https://github.com/WangYouM1999/BAFormer.

In patients with heart failure, the evaluation of left ventricular (LV) diastolic function is vital, offering crucial insights into hemodynamic impact and prognostic accuracy. Echocardiography stands as the primary imaging modality for diastolic function assessment, and using it effectively requires a profound understanding of the underlying pathology. This review covers four main topics: first, the fundamental driving forces behind each phase of normal diastolic dynamics, along with the physiological basis of two widely used echocardiographic assessment parameters, E/e' and mitral annulus early diastolic velocity (e'); second, the intricate functional relationship between the left atrium and LV in patients with varying degrees of LV diastolic dysfunction (LVDD); third, the role of stress echocardiography in diagnosing LVDD and the significance of parameter changes in this context; and fourth, the clinical utility of evaluating diastolic function from echocardiography images across diverse cardiovascular care areas.

Metabolites present in the hemolymph of honey bees play a crucial role in modulating the metamorphic process within the species. However, the precise alterations in metabolite composition, along with the associated variances and regulatory pathways implicated during the larvae-to-pupae metamorphosis of honey bees, remain incompletely elucidated. In this investigation, we gathered hemolymph samples from honey bee larvae at three distinct physiological stages-feeding, prepupal, and pupae and subjected them to metabolite analysis utilizing the liquid chromatography-mass spectrometry (LC-MS) technique. Employing principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA), we identified significant differential metabolites and conducted a metabolic pathway analysis on those differentially up-regulated during the prepupal and pupae stages. Notably, metabolites up-regulated in the hemolymph of prepupal stage larvae primarily governed glucose metabolism and fat digestion and absorption, while those in pupae stage larvae were involved in regulating chitin and lipopolysaccharide precursor formation, as well as the biosynthesis of phenylalanine, tyrosine, and tryptophan. These findings bear significant implications for advancing our comprehension of the metamorphic processes in honey bees.

The structure and expression of exogenous genes in transgenic crops are critical for the target traits. R7569 was a mutant event identified in this study with deletion at the 3' end of cry1Ac gene compared to the transgenic insect-resistant cotton MON531 event with commercial application. R7569 has the same exogenous insertion structure as MON531, but with a deletion in the 3' end of the cry1Ac gene and the terminator region. R7569 has a truncated cry1Ac gene with the length of 2,554 bp, encoding 881 amino acids. The transcription termination site was mainly concentrated downstream of the truncated position and extended 160-270 bp from the truncated position using rapid-amplification of cDNA ends (RACE). The transcript levels of cry1Ac genes of R7569 and MON531 decreased gradually at seedling, bud and bell stages, and the transcript levels of cry1Ac genes of R7569 were significantly higher than those of MON531 in seedling and bud stages, but there was no significant difference in the boll stage. The content of Cry1Ac protein in R7569 gradually decreased with the seedling, bud and boll stage, and the content of Cry1Ac protein in all three periods was higher than that of MON531. The insect resistance assay showed that the resistance levels of R7569 and MON531 were both at the high level, and the corrected mortality rate against bollworms was 99.5% and 95.2%, respectively, and there was no significant difference between them. The LC50 value of R7569 was 0.732ng/g dw, with a slope of 1.654 indicating a high level of resistance to bollworm. In this study, for the first time, we found a partial deletion of the target gene in commercially applied transgenic crops, and the partial deletion of the 3' end of the cry1Ac gene retained a better transcription, expression level and insecticidal activity, which can provide a specific case for the safety evaluation of transgenic crops.

Deep learning has increasingly been employed to identify minerals. However, deep learning can only be used to identify minerals in the distribution of the training set, while any mineral outside the spectrum of the training set is inevitably categorized erroneously within a predetermined class from the training set. To solve this problem, the study introduces the approach that amalgamates One-Class Support Vector Machines (OCSVM) with the ResNet architecture for the out-of-distribution mineral detection. Initially, ResNet undergoes training using a training set comprising well-defined minerals. Subsequently, the earlier layers of the trained ResNet are employed to extract the discriminative features of the mineral under consideration. These extracted mineral features then become the input for OCSVM. When OCSVM discerns the mineral in the training set's distribution, it triggers the subsequent layers within the trained ResNet, facilitating the accurate classification of the mineral into one of the predefined categories encompassing the known minerals. In the event OCSVM identifies the mineral out of the training set's distribution, it is unequivocally categorized as an unclassified or 'unknown' mineral. Empirical results substantiate the method's capability to identify out-of-distribution minerals while concurrently maintaining a commendably high accuracy rate for the classification of the 36 in-distribution minerals.

In numerical simulations, achieving high accuracy without significantly increasing computational costs is often challenging. To address this, this paper presents an improved finite volume weighted essentially non-oscillatory (WENO) scheme tailored for applicability in computational fluid dynamics (CFD) and implemented in the flow solver NUAA-Turbo for simulating turbomachinery flows using both RANS and RANS/LES coupling. Firstly, the new WENO scheme is validated against classic numerical test cases to assess its stability and reliability in handling discontinuities, the Double problem, and Raleigh-Taylor (RT) instability issues. Compared to the original format, this enhanced finite volume WENO scheme demonstrates superior stability near discontinuities and resolves flow features with the same grid resolution more effectively. Next, for engineering applications related to turbomachinery, such as compressor and turbine characteristics, computations are performed using RANS, and the results obtained using WENO-ZQ3 and WENO-JS3 are compared. Finally, the new fifth-order WENO scheme is applied to RANS/LES coupled simulations of turbine wakes and film cooling. The results show that the enhanced finite volume WENO scheme offers improved stability and accuracy in engineering applications, allowing for high-precision calculations with fewer grid points to capture more detailed flow physics, thereby reducing computational costs in aerodynamic applications.

In this study, carbon-quantum-dot (CQD)-decorated TiO2 was prepared using an ultrasonic doping method and applied in the photocatalytic degradation of naphthalene under sunlight irradiation. The CQDs were synthesized from a typical macroalgae via diluted sulfuric acid pretreatment and hydrothermal synthesis using an optimal design, i.e., 3 wt% and 200 oC, respectively. The CQD/TiO2 composite remarkably enhanced the photocatalytic activity. The degradation of naphthalene under a visible light environment indicated that there is a synergistic mechanism between CQDs and TiO2, in which the generation of reactive oxygen species is significantly triggered; in addition, the N that originated from the macroalgae accelerated the photocatalytic efficiency. Kinetic analysis showed that the photo-catalytic behavior of the CQD/TiO2 composite followed a pseudo-first-order equation. Consequently, our combined experimental approach not only provides a facile pretreatment process for bio-CQDs synthesis, but also delivers a suitable TiO2 photocatalyst for up-conversion, along with critical insights into the development of harmful macroalgae resources.

To conduct high-precision and high-resolution numerical simulation of complex flow structures in turbomachinery, a high-order finite volume weighted essentially non-oscillatory (WENO) scheme for the large eddy simulation (LES) is improved and embedded into the three-dimensional viscous unsteady CFD solver NUAA-Turbo. Firstly, the spectral characteristics and unsteady convergence of the improved WENO scheme are studied. Compared with the classical WENO scheme, the improved WENO scheme has better dissipation and dispersion characteristics and faster convergence speed. Then, the coefficient CW of the Wall-Adapting Local Eddy-viscosity (WALE) model is calibrated by decaying homogeneous isotropic turbulence (DHIT) test case. Finally, the scheme is applied to conduct high-precision LES calculations of turbulent boundary layer flow, supersonic compression corner flow, and low-pressure turbine cascade separation flow. The numerical simulation results show that this WENO scheme has excellent shock wave capture ability and turbulence resolution and can capture more flow field details with less mesh size, greatly reducing the computational cost and laying a foundation for large-scale engineering application of LES.

Biological neural network (BNN) is the core brain for creature to accomplish its intelligent behaviors through unique network structure and mechanisms. It also inspires controlling human-designed autonomous agents including robots to generate more advanced intelligent behaviors by mimicking the neural control mechanism of creatures at a deeper and more interactive level. Here, we constructed a whole-brain neural network model of Caenorhabditis elegans (C. elegans), which characterizes the electrochemical processes at the level of cellular synapse. The neural network simulation integrates computational programming and visualization of neurons and synapse connections of C. elegans, containing the specific controllable circuits summarization with their dynamic characteristics within the whole network. To illustrate its particular intelligent control capability in terms of robotics, we introduce the first innovative methodology for applying the BNN model to a 12-legged robot’s movement control based on the established numerical simulation platforms. We accomplished and designed two methods and corresponding encoding processes, one involving orientation control and the other involving locomotion generation, to demonstrate the intelligent control performance of BNN. Both simulation and experiment results indicate that the robot exhibits stronger autonomy and more intelligent moving performance under the control of BNN. We then summarized the contributions for digitalizing the C. elegans’ whole-brain neural network in real-time and utilizing it to control robot in a closed loop to validate its advanced intelligence control ability in terms of a scientific robot.

In the case of a Cu-Mo-W polymetallic ore, the wastewater from the scheelite cleaning flowsheet contains large amounts of residual flocculants and metal ions, and the separation of chalcopyrite and molybdenite require a large number of environmentally harmful depressants. Therefore, it is necessary to find new methods to reduce the environmental and cost pressures of wastewater treatment and the use of depressants. In this work, the flotation wastewater from scheelite cleaning flowsheet for the separation of chalcopyrite and molybdenite by selective surface passivation was investigated for the first time. Flotations of single minerals and artificially mixed minerals with or without immersion pretreatment in the presence and absence of aeration were performed. The results showed that pulp pH had no effect on the flotation of either mineral, and a molybdenite recovery of 93.22% with chalcopyrite recovery of 10.77% was achieved under the conditions of 10 days of immersion pretreatment with aeration, 350 mg/L of kerosene, and 100 mg/L of MIBC. By combining the electrochemical cyclic voltammetry analysis and characterization by XRD and SEM, the selective surface passivation mechanism of chalcopyrite was discussed, which could be due to the coverage of the insoluble oxidation products, especially jarosite. This work has simultaneously achieved the depressant-free flotation separation of molybdenite and chalcopyrite and the reuse of scheelite flotation wastewater, which is of great significance for environmental protection and cost saving.

The hospital indoor environment plays a significant role in shaping the microbial exposures that humans encounter. Resistance to antibiotics is one of the mechanisms employed by bacteria to develop resilience against stress in indoor environments. The extensive usage of antibiotics has been a key factor in driving changes in the ecological function of antibiotic resistance genes (ARGs) and their acquisition by pathogenic bacteria. By integrating the 16S rRNA Illumina sequencing and high throughput-qPCR approaches with water and air dust samples across seven departments at Peking University Shenzhen Hospital, China, this study yielded intriguing findings regarding the department-specific variation, transmission, and correlation of bacteria, ARGs and MGEs within the hospital indoor environment. One notable observation was that the seasonal variation played a vital role in shaping the bacterial composition in the overall hospital indoor environment across seven studied departments. Nevertheless, a similar significant seasonal impact was only observed in the blood test room and emergency departments, rather than in the other five departments. Another intriguing finding was the correlation between ARGs and MGEs abundance. This correlation was evident in the overall hospital indoor environment, which was conversely not found in the emergency, pneumology and gynecology departments. Additionally, while there was no significant relationship that occurred between bacterial communities and ARGs/MGEs within the hospital, the gynecology department demonstrated a close link between bacterial communities and the presence of ARGs/MGEs. Notably, the gynecology department also stood out as an important source of bacteria and ARGs/MGEs for other departments. This study underscores the need for careful surveillance and monitoring of antibiotic resistance, particularly in Legionella spp. in man-made water systems. The findings also show the importance of monitoring and understanding the genetic elements such as Tp614 involved in gene transfer and recombination, as well as their impact on antimicrobial treatment efficacy.

This paper focus on the modeling and time-domain analyzing method of piecewise-smooth fractional-order circuit systems. Firstly, an analytical framework is constructed based on a generalized piecewise-smooth two-port impedance network model, which takes the topological characteristics of Dickson type switched-capacitor converters (SCCs) as examples, and considers the fractional-order characteristics of class-2 X7R ceramic capacitors, which are widely used in SCCs. Secondly, the time-domain analyzing method for such a piecewise-smooth fractional-order circuit system is developed, numerical solutions are obtained, and the influence of the fractional-order capacitor on the power conversion efficiency of the system are revealed. Finally, in order to validate the analysis, both numerical simulation and experiments are provided in the work, the results of which confirm that, the proposed fractional-order analytical framework can capture the power loss mechanism of piecewise-smooth fractional-order circuit systems more effectively than the traditional integer-order based approach, thus benefiting the design of circuit systems.

This paper proposed a three-layer distributed simulation network architecture: 1. Distributed virtual simulation network, 2. Perception and control sub-network, and 3. Collaborative communication service network. This simulation architecture runs on a distributed platform. The platform can provide a unique virtual scenario and several simulation services to verify the individual robot system's basic perception, control, and planning algorithms and the distributed collaborative algorithms of heterogeneous multi-robot systems. Furthermore, we designed a simulation experiment scenario for classical heterogeneous robot systems such as unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). At last, We gave a rough estimation model of the bandwidth.

Plants can utilize the different forms of nitrogen (N), including inorganic and organic N nutrients, and show the great difference in the utilizing efficiency of N-form among species and genotypes within a species. In our previous study, we found that Tibetan wild barley (XZ16) was better in utilization of organic nitrogen in comparison with cultivated barley (Hua30). In this study, metabolite profiles of the two barley genotypes were comprehensively compared in their response to four N forms, including nitrate (NO3-), ammonium (NH4+), urea and glycine. Macro-and micro-nutrient concentrations in shoots were mostly found higher in nitrate and urea treatments than in ammonium and glycine in both the genotypes. XZ16 had higher concentrations of nutrient ions in glycine treatment, but Hua30 accumulated more nutrients in ammonium treatment. Among the total 163 differentially expressed metabolites, highest up-regulation and highest down-regulation was found in XZ16 in glycine and urea treatments respectively. Some important metabolites such as proline, glutamine, serine, asparagine, L-homoserine, aspartic acid, putrescine, ornithine, 4-aminobutyrate were up-regulated in glycine treatment in both the genotypes with higher fold change in XZ16 than in Hua30. Similarly, fructose-6-PO4, aconitic acid and isocitrate were only up-regulated in XZ16 in glycine treatment. Here, we concluded that the genotype XZ16 exhibited higher response to glycine treatment, while Hua30 showed higher response to NH4+ treatment which is attributed to their better utilization of glycine-N and NH4+-N, respectively. .

BACKGROUND Studies demonstrate that T cells secrete interleukin and other cytokines to promote the growth and activation of B cells, and B lymphocytes release antibodies that attach to the PLA2R and THSD7A surface antigens on podocytes to create immunological complexes that are deposited beneath the glomerular epithelium, which damage the filtration barrier and trigger proteinuria.The combination regimen of Calcineurin inhibitors（CNIs）and rituximab(RTX) could simultaneously inhibit the proliferation of T and B lymphocytes and reduced the production of immune complexes.This kind of therapeutic method for idiopathic membranous nephropathy may achieve better clinical results and less complications. METHODS Fifty-eight patients who were diagnosed as idiopathic membranous nephropathy（IMN）through a biopsy suffering from nephrotic syndrome (NS) were recruited, and were divided into RTX treatment group(RTX group), CNIs combined with RTX treatment group(CNIs+RTX group) and CNIs combined with glucocorticoids(GC) treatment group(CNIs+CG group) according to their treatment background. All patients in 3 groups were followed up for at least 12 months. Clinical composite remission(including complete or partial remission) at 12 months was the main result; safety and the incidence of adverse events were the secondary outcomes. RESULTS The PLA2R antibody level of RTX+CNIs group at the baseline much higher than RTX and CNIs+GC groups (240.30(84.56,386.99)RU/ml vs. 44.94(11.48,143.94)RU/ml, 7.72(2.00,102.68) RU/ml, respectively, p=0.006). The PLA2R antibody concentrations among the three groups become comparable at the end of 3 months(36.80(2.10,88.05) RU/ml vs.14.32(2.00,47.98)RU/ml, 2.60(2.00,24.50) RU/ml, respectively, p>0.05). At 12 months, all patients in the CNIs+GC group achieved immune remission, and the PLA2R-ab concentration was less than 14RU/ml. At 3 months, 33of 58 patients(56.90%) had a composite remission(complete or partial remission), including 4 of 14 patients (27.85%) in the RTX group, 11 of 17 patients(64.71%) in the CNIs+RTX group and 18 of 27 patients (66.67%) in the CNIs+GC group (p=0.048). At 12 months, 51 of 58 patients(87.93%) had a composite remission, including 12 of 14 patients (85.71%) in the RTX group, 15 of 17 patients(88.24%) in the CNIs+RTX group and 24 of 27 patients (88.89%) in the CNIs+GC group (p=0.957). During the 12-month study, 7 patients did not response for the treatment. 1 patient relapsed during the 12-month treatment with a re-elevation of antibodies. Adverse events occurred in 18(31.03%)patients in the whole study, including 3 patients(5.17%) in the RTX group, 2 patients(3.45%) in the CNIs+RTX and 13patients(22.41%) in the CNIs+GC group(p< 0.05). CONCLUSIONS The combination of CNIs and RTX can effectively alleviate idiopathic membranous nephropathy with nephrotic syndrome in the first 3 months，as well as the combination of CNIs and GC, and the efficacy of both treatment methods for IMN was superior to that of RTX alone at the first 3 months. Furthermore, the RTX+CNIs group had less side effects compared with GC+CNIs group. So CNIs combined with RTX could be recommended as a superior therapeutic method for IMN treatment to shorten remission durion with less complications.

Intense climate change and rapid urbanization have increased the risk of urban flooding, seriously affecting urban economic and social stability. Enhancing the urban flood resilience (UFR) has required a new solution to cope with urban flood disasters. In this study, taking Yingtan city as an example, a system of indicators for evaluating UFR was constructed, with 17 representative indicators, comprising three subsystems: socio-economic, ecological and infrastructural. A hybrid (FAHP-EWM)-TOPSIS model was applied, to develop an index-based measurement to compare and evaluate UFR, and Gray Relational Analysis (GRA) was used to discover the main factors affecting UFR. In addition, the natural discontinuous method was innovatively used to divide the UFR grade interval into levels, and the grade change was evaluated based on the TOPSIS method. The results showed that (1) from 2010 to 2022, the UFR in Yingtan City increased by 80.69%, and the factors affecting UFR are highly correlated with urban infrastructure development. However, the ecological resilience in the subsystem showed a fluctuating downward trend, because of the influence of the surface area of lakes and rivers. (2) The grades of UFR for Yingtan City increased from Level III (2010 and 2016) to Level IV (2022), with local financial expenditures and the age structure of the population being the main factors currently limiting the development of UFR. The study provides a theoretical basis for the construction of an indicator system for assessing the UFR of Yingtan and proposes practical improvement directions for UFR.

Extraordinary high aerosol contamination observed in the atmosphere over Kyiv city, Ukraine, during the March – April 2020 period. The source of contamination was the large grass and forest fires in the northern part of Ukraine and the Kyiv region. The level of PM2.5 load investigated using newly established AirVisual sensors mini-network in five areas of the city. The aerosol data from the Kyiv AERONET sun-photometer site analyzed for that period. Aerosol optical depth, Angstrom exponent, and the aerosol particles properties (particles size distribution, single-scattering albedo, and complex refractive index) were analyzed using AERONET sun-photometer observations. The smoke particles observed at Kyiv site during the fires in general correspond to aerosol with optical properties of biomass burning particles. The variability of the optical properties and chemical composition indicates that the aerosol particles in the smoke plumes over Kyiv were produced by different burning material and phases of vegetation fires at different time. The case of enormous PM2.5 aerosol contamination in Kyiv city reveals the need to accept strong measures for forest fire control and prevention in Kyiv region, especially in the north-west region where radioactive contamination from Chornobyl disaster is still significant.

The urgent need for effective, safe and equitably accessible vaccines to tackle the ongoing spread of COVID-19 led researchers to generate vaccine candidates targeting varieties of immunogens of SARS-CoV-2. Because of its crucial role in mediating binding and entry to host cell and its proven safety profile, the subunit 1 (S1) of the spike protein represents an attractive immunogen for vaccine development. Here, we developed and assessed the immunogenicity of a DNA vaccine encoding the SARS-CoV-2 S1. Following in vitro confirmation and characterization, the humoral and cellular immune responses of our vaccine candidate (pVAX-S1) was evaluated in BALB/c mice using two different doses, 25 µg and 50 µg. Our data showed high levels of SARS-CoV-2 specific IgG and neutralizing antibodies in mice immunized with three doses of pVAX-S1. Analysis of the induced IgG subclasses showed a Th1-polarized immune response as demonstrated by the significant elevation of spike-specific IgG2a and IgG2b compared to IgG1. Furthermore, we found that immunization of mice with three doses of 50 µg of pVAX-S1 could elicit significant memory CD4+ and CD8+ T cell responses. Taken together, our data indicates that pVAX-S1 is immunogenic and safe in mice and is worthy of further preclinical and clinical evaluation.

Controlling Nutritional Status (CONUT) Score is useful for the nutritional screening. We aimed to explore whether the CONUT score may predict a 3-month functional outcome in hemorrhagic stroke (AHS). Totally, 349 patients with incident AHS were consecutively recruited, and their malnutrition risks were determined using a high CONUT score of ≥ 2. Poor functional outcomes were defined as the modified Rankin Scale (mRS) score of ≥ 3 at 3 months. A total of 328 patients (mean age, 60.4 ± 12.83 years; 66.8% male) were included, 172 (52.40%) patients at malnutrition risk and 104 (31.7%) patients with a poor prognosis. High-CONUT patients had lower total lymphocyte counts and total cholesterol levels than low-CONUT patients (p &lt; 0.001 and p = 0.012). At 3-month post discharge, patients with malnutrition risk had higher hospitalization costs (p = 0.021), lower Barthel Index (p = 0.001), and more infectious complications (p = 0.002) than those without, and there was a greater risk for poor functional outcomes in the high-CONUT compared with the low-CONUT patients at admission (adjusted odds ratio: 2.32, 95% confidence interval: 1.28-4.17). High-CONUT scores predict a 3-month poor prognosis in AHS, which may help identify the AHS patients who need additional nutritional managements.

By using the particle swarm optimization algorithm for crystal structure prediction, we reveal a newly orthorhombic Cmcm structure of HfB4, which is more energetically superior to the previously proposed YB4-, ReP4-, FeB4-, CrB4-, and MnB4-type structures in the considered pressure range. The phonon dispersion and elastic constants calculations confirm that the new phase is dynamically and mechanically stable. The calculated large shear modulus (240 GPa) and high hardness (45.7 GPa) imply that the predicted Cmcm-HfB4 is a potential superhard material. Meanwhile, the directional dependences of the Young's modulus, bulk modulus, and shear modulus for HfB4 are systematically investigated. Further analyses of the density of states and electronic localization function indicate that the strong B-B and B-Hf covalent bonds greatly contribute to its high hardness and stability.

Understory plants are important components of forest ecosystem productivity and diversity. Compared to biomass models of overstory canopy trees, few are available for understory saplings and shrubs and therefore their roles in estimation of forest carbon pools are often ignored. In this study, we harvested 24 understory species including 4 saplings, 9 tree-like shrubs and 11 typical shrubs in coniferous and broadleaved mixed forest in northeastern China and developed the best fit allometric equations of above- and below-ground and total biomass by species-specific or multispecies using morphological measurements of basal diameter, height and crown area as independent variables. The result showed that single basal diameter, height or crown area had good explanatory power for both species-specific and multispecies (p<0.001). The best-fit models included only basal diameter in sapling and tree-like shrubs, and combinations of crown area, height, and basal diameter in typical shrubs. The logarithmic model was most desired among the 4 model forms of linear, quadratic, multiple linear and logarithmic, for species-specific and multispecies. The models we developed should help the estimation of forest ecosystem carbon stocks, especially for belowground component, and provide tools for quantification of individual species biomass of understory plants.

This paper proposes a new approach for multi-focus images fusion based on Region Mosaicing on Contrast Pyramids (REMCP). A density-based region growing method is developed to construct a focused region mask for multi-focus images. The segmented focused region mask is decomposed into a mask pyramid, which is then used for supervised region mosaicking on a contrast pyramid. In this way, the focus measurement and the continuity of focused regions are incorporated and the pixel level pyramid fusion is improved at the region level. Objective and subjective experiments show that the proposed REMCP is more robust to noise than compared algorithms and can fully preserves the focus information of the multi-focus images meanwhile reducing distortions of the fused images.

The electroencephalogram (EEG) is a record of brain activity. Brain Computer Interface (BCI) technology formed by the EEG signal has become one of the hotspots at present. How to extract the feature signal of EEG is the most basic research of BCI technology. In this paper, A new method of recognizing fatigue, conscious, concentrated state of human brain is proposed by the combination of discrete wavelet transform and the neural network based on EEG signal. First of all, the law signal is preprocessed by the wavelet denoising method because the law EEG signal contains a large number of high frequency noise, which is decomposed into multi-layer high frequency signal and low frequency signal. thus, δ wave, θ wave, α wave, β wave are obtained by the wavelet transform. And then, frequency band energy of the different wave is regards as the feature signal of EEG. In the experiment, the feature signal is classified by radial basic function (RBF) and annealed chaotic competitive learning network (ACCLN). RBF and ACCLN networks are trained with 500 sets of sample data and are tested by 100 sets of samples in different mental states. The experimental results show that the average accuracy of RBF network under three conditions are 88.75%, 88.25%, 88.5%, respectively, and the correct rate of ACCLN network is 97%, 98%, 98%, respectively.

Fabrication of durable materials with excellent oil adsorption capacity and separation performance for the treatment of oily wastewater is meaningful based on smart responsiveness. Herein, a solvent-responsive melamine sponge (MS) was developed via silanization followed by the in-situ growth of zeolitic imidazolate framework-8 (ZIF-8). Detailed characterization of the resultant composite MS was conducted using scanning electron microscopy (SEM), energy dispersive spectrometer (EDS), X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FTIR). The multiscale hierarchical MS substrate exhibited highly hydrophobic property in the pH range of 1-11, along with a satisfactory adsorption capacity in the range of 65.4-134.2 g/g for different oils. The modified surface transformed from superhydrophobicity/superlipophilicity to superhydrophilicity/underwater superoleophobicity upon ethanol wetting, reverting to its original superhydrophobic state upon drying. The separation flux of the MS substrate was above 1.5×104L/m2h for both oil and water removal, along with the separation efficiency more than 98.7%. No obvious changes of the separation performance after 50 successive immiscible oil/water separation indicated the excellent durability and robustness of the anchored ZIF-8 nanoparticles on the surface of modified MS substrate. More importantly, oil-in-water emulsion separation was successfully carried out via the ZIF-8 MS composite with a high separation efficiency over 99.1%. The developed smart sponge with high oil absorption capacity, excellent chemical stability and fire resistance exhibited a wide range of potential practical applications in the convenient treatment of oily wastewater.

Keywords: domain adversarial; channel reconstruction; transfer learning; fault diagnosis

In the present study, we aimed to observed hair follicle development and describe Wnt10b/β-catenin signaling pathways related to hair follicle development in different ages of Rex rabbits. Six Rex rabbits (3 male and 3 female) of 15, 30, 60, 90, 120 and 150 days of age were selected and skin samples were collected for hair follicle development and Wnt/β-catenin signaling pathway genes expresion detection. The results showed that the the values for body weight, coat length, skin thickness, skin area, and skin weight of Rex rabbits were increased significantly with age (p &lt; 0.05). The total hair follicle density, secondary hair follicle density and the ratio of the secondary hair follicle density to the primary hair follicle density in 120 days of Rex rabbits was higher than that in the other ages (p &lt; 0.05), and the major hair follicles were in the growing period at 15 days and 30 days of age, some of them had already entered the degeneration period at 60 days of age, and most of them entered the degeneration period at 90 days of age, the presence of quiescent hair follicles and the enlargement of primary hair follicles were also observed. At 120 and 150 days of age, the back hair follicles entered the growing period, and the secondary hair follicles had obvious differentiation. Furthermore, the expression of the Wnt10b, CTNNB1, adenomatosis polyposis protein (APC), Disheveled 2 (DVL2), glycogen synthase kinase 3β（GSK-3β）, Lymphoid enhancer-binding factor 1 (LEF1), dickkopf-1 (DKK1) and transforming growth factor beta 1 (TGFβ-1) genes in the skin tissue of Rex rabbits at different age was significantly differences (p &lt; 0.05), and the expression of the Wnt10b, CTNNB1, DVL2, GSK-3β, and TGFβ-1 genes at 90 days was higher than that in the other days, the expression of the LEF1 and DKK1 genes at 60 days was higher than that in the other days. As western blotting indicated, the β-catenin protein phosphorylation (P-CTNNB1) levels in the skin tissue of Rex rabbits shows the trend of first increasing, then decreasing and then increasing, 60 days, 120 days and 150 days significantly higher than 15 days, 30 days or 90 days (p &lt; 0.05). With the increasing of ages, the GSK-3β protein phosphorylation (P-GSK-3β) levels in the skin tissue of Rex rabbits were increasing, and maximum at 120 days of age (p &lt; 0.05). These results show that ages could influence on the fur quality and hair follicle traits of Rex rabbits, and Wnt10b/β-catenin signaling pathways is involved in the biological process of periodic hair follicle development in Rex rabbits. Considering the rule of fur growth, the slaughter age of Rex rabbits should not be earlier than 120 days.

The high flammability and lack of charrability of polystyrene (PS) pose significant limitations on its broader applications. Therefore, there is an urgent need for a straightforward and efficient method for the synthesis of flame-retardant PS, which still poses a considerable challenge. In this study, we present an efficient approach to enhance the flame-retardant properties of PS through direct phosphorylating aromatic C(sp2)-H bonds using commercially available and inexpensive manganese catalyst. A range of phosphonates served as reactive substrates to enable a tunable degree of polymer functionalization. The corresponding phosphorylated PS specimens were determined by means of 1H NMR, 31P NMR, FTIR spectroscopy and GPC. Microscale combustion calorimetry (MCC) tests indicate that this protocol indeed enhances the flame-retardant performance of PS. Moreover, other advantages associated with the incorporation of the phosphonate group have been observed, including improved thermal resistance and wettability. This cost-effective strategy of Mn-catalyzed C-H can also be utilized to directly obtain phosphonate modification of waste foamed PS and styrene acrylonitrile copolymer, providing a new method for the purpose of recycling and upgrading PS plastics.

Currently there are fewer depth models applied to pepper picking detection, while the existing generalized neural networks have problems such as large model parameters, long training time, and low model accuracy.In order to solve the above problems, this paper proposes a Yolo-chili target detection algorithm for chili pepper detection. First, the classical target detection algorithm yolov5 is used as a benchmark model, and an adaptive spatial feature pyramid structure combining the attention mechanism and the idea of multi-scale prediction is introduced to improve the model's detection effect on occluded peppers and small target peppers. Secondly, a three-channel attention mechanism module is introduced to improve the algorithm's long-distance recognition ability and reduce the interference of redundant testers. Finally, the quantized pruning method is used to reduce the model parameters and realize the lightweight processing of the model. Applying the method to the homemade chili pepper dataset, the AP value of chili pepper reaches 93.11%; the accuracy rate is 93.51% and the recall rate is 92.55%.The experimental results show that yolo-chili is able to achieve accurate and real-time pepper detection under complex orchards.

A new quinazolinone alkaloid, named peniquinazolinone A (1), as well as eleven known com-pounds, 2-(2-hydroxy-3-phenylpropionamido)-N-methylbenzamide (2), viridicatin (3), viridicatol (4), (±)-cyclopeptin (5a/5b), dehydrocyclopeptin (6), cyclopenin (7), cyclopenol (8), methyl-indole-3-carboxylate (9), 2,5-dihydroxyphenyl acetate (10), methyl m-hydroxyphenylacetate (11), conidiogenone B (12), was isolated from the endophytic Penicillium sp. HJT-A-6. The chemical structures of all compounds were elucidated by comprehensive spectroscopic analysis, including 1D, 2D NMR, and HRESIMS. The absolute configuration at C-13 of peniquinazolinone A (1) was established by applying the modified Mosher’s method. Compounds 2, 3, and 7 exhibited an op-timal promoting effect on the seed germination of Rhodiola tibetica at a concentration of 0.01 mg/mL, while the optimal concentration for compounds 4 and 9 to promote Rhodiola tibetica seed germination was 0.001 mg/mL. Compound 12 showed optimal seed-germination-promoting activity at a concentration of 0.1 mg/mL. Compared with the positive drug 6-benzyladenine (6-BA), compounds 2, 3, 4, 7, 9, and 12 could extend the seed-germination period of Rhodiola tibetica up to the 11th day.

The branch prediction units (BPUs) generally have security vulnerabilities, which can be used by attackers to obtain the execution status, jump directions, and jump address of conditional branches, and the existing protection methods cannot defend against these attacks. Therefore, this article proposes a hardware security protection method for conditional branches of embedded systems. This method calculates the number of updates to the branch target buffer (BTB). When it exceeds the threshold, BTB is locked to prevent attackers from analyzing the execution status of branches based on the time difference of whether BTB is updated. Moreover, the hybrid physical unclonable function (PUF) circuit is designed to provide confidentiality protection for the jump directions, jump addresses, and their indexes, preventing attackers from stealing these critical data. If these mechanism fails and attackers successfully tamper with conditional branches, this paper proposes a control flow integrity (CFI) protection mechanism based on branch labels to timely detect tampering with instruction codes, jump addresses, and jump directions. The proposed method is implemented and tested on FPGA. The experimental results show that this method can achieve fine-grained security protection for conditional branches, with about 5.4% resource overhead and less than 5.5% performance overhead.

Generalized zero-shot learning (GZSL) aims to simultaneously recognize both seen classes and unseen classes by training only on seen class samples and auxiliary semantic descriptions. Recent state-of-the-art methods infer unseen classes based on semantic information or synthesize unseen classes using generative models based on semantic information, all of which rely on the correct alignment of visual-semantic features. However, they often overlook the inconsistency between original visual features and semantic attributes. Additionally, due to the existence of cross-modal dataset biases, the visual features extracted and synthesized by the model may also mismatch with some semantic features, which could hinder the model from properly aligning visual-semantic features. To address this issue, this paper proposes a GZSL framework that enhances the consistency of visual-semantic features using self-distillation and disentanglement network (SDDN). The aim is to utilize self-distillation and disentanglement network to obtain semantically consistent refined visual features and non-redundant semantic features to enhance the consistency of visual-semantic features. Firstly, SDDN utilizes self-distillation technology to refine the extracted and synthesized visual features of the model. Subsequently, the visual-semantic features are then disentangled and aligned using a disentanglement network to enhance the consistency of the visual-semantic features. Finally, the consistent visual-semantic features are fused to jointly train a GZSL classifier. Extensive experiments demonstrate that the proposed method achieves more competitive results on four challenging benchmark datasets (AWA2, CUB, FLO, and SUN).

The state-of-the-art smart city has been calling for an economic but efficient energy management over large-scale network, especially for the electric power system. It is a critical issue to monitor, analyze and control electric loads of all users in system. In this paper, we employ the popular computer vision techniques of AI to design a non-intrusive load monitoring method for smart electric energy management. First of all, we utilize both signal transforms (including wavelet transform and discrete Fourier transform) and Gramian Angular Field (GAF) methods to map one-dimensional current signals onto two-dimensional color feature images. Second, we propose to recognize all electric loads from color feature images using a deep neural network with multi-scale feature extraction and attention mechanism. Third, we design our method as a cloud-based, non-intrusive monitoring of all users, thereby saving energy cost during electric power system control. Experimental results on both public and our private datasets have demonstrated our method achieves superior performances than its peers, and thus supports efficient energy management over large-scale Internet of Things.

: To improve the efficiency of rhizobial inoculation operations and address the current situation of rhizobial inoculation being labor-intensive, a spraying and control system for variable rate application (VRA) of liquid rhizobia was developed. The system can accurately regulate the spraying rate of liquid rhizobia according to the desired rate of rhizobia by incremental Proportional Integral Derivative (PID) closed-loop control algorithm, making it particularly suitable for precise spraying with small amounts. In the laboratory experiment of precise flow control, variation coefficient of spraying nozzle from 0.10 MP to 0.20 MP pressure were no more than 1.2%. A clear linear relationship was observed between the spraying rate and pressure. The maximum response time was 1.93 seconds, while the average response time was 1.62 seconds when controlling the spraying system for small amounts. Field trial was conducted in Heilongjiang Agricultural Xianghe Farm of China. Average seeds number and 100-seed weight of soybean plant with rhizobia inoculation treatment were better than control group, and average yield of spraying liquid rhizobia agent with full base fertilizer increased by 232.5 kg/hm2, representing a significant 7.4% improvement over the control group. The results of these trails clearly showed the positive effect of the spraying and control system for liquid rhizobial inoculation, which has superior to existing inoculation manners, and provides an important technical support for the widespread adoption of rhizobia technology.

The wellness economy has developed rapidly recently, particularly in wellness tourism. Wellness tourism transforms sustainable strategy through the availability of extended multi-actor engagement in service innovation to further the tourism experience. Therefore, when operating a wellness tourism destination, it is necessary to encourage multi-actor engagement in service innovation to achieve sustainable development. This paper introduces a mixed research method with the SWOT-AHP-QSPM, as it qualitatively analyzes the advantages, disadvantages, opportunities, and threats, constructs an evaluation index system, conducts quantitative analysis using the analytic hierarchy process, determines the index weight and weighted score, and calculate the sustainable development strategy, which is appropriate for wellness development of Chongqing Tea Mountain and Bamboo Forest National Forest Park. Purposive field surveys and personal interviews were conducted in Chongqing forest tourism destinations, involving 356 effective respondents. The research reveals that multi-actor engagement significantly enhances sustainable development. These findings bridge service innovation knowledge gaps and provide valuable insights for forest tourism destination management and sustainable policy strategies to elevate multi-actor engagement in forest sites.

Alternative fertilizations are essential to minimize the deteriorating effects of chemical fertilizers on soil and water quality/health. Accordingly, the present work investigated the effects of combined organic-inorganic fertilization (COIF) on wheat and rice yields, soil nutrients, and soil Cd accumulation. Hence, seven different treatments were set up: control (CK); conventional fertilization (CF); optimized fertilization (OF); organic fertilizer replacing 25% (T1) and 50% (T2) of OF; and organic nitrogen (N) replacing 25% (M1) and 50% (M2) of OF-N. Overall, significant increases occurred in the yields of COIF crops. Compared with the CF, the highest wheat and rice yields happened in M1 treatment (approximately by18.5%) (P &lt; 0.05). COIF slightly alleviated soil acidification, and improved the cation exchange capacity (CEC) of the study soils. Besides, COIF treatments significantly increased the contents of total phosphorus, total potassium, available phosphorus, and available potassium by 6.35 to 16.9 %, 3.17 to 10.9 %, 5.53 to 28.7 %, and 2.6 to 12 %, respectively (P &lt;0.05). Nevertheless, negligible increases took place in Cd content of COIF soils compared with the CK. Altogether, our results concluded on 25% replacement of OF-N by organic N (M1) to effectively improve the fertility/ecological sustainability of the study soils.

A key step in the study of tree pathology is the identification of appropriate method for inoculating pathogens of diseases in branch and trunk. Pathogens of diseases in branch and trunk are commonly inoculated through punching, burning, and toothpick inoculation. However, there is a lack of comparative analysis of the inoculation outcomes of these three methods. In this work, Six‐year‐old P. alba var. pyramidalis were inoculated with V. sordida, using punching, burning, and toothpick techniques, respectively, to investigate the differences in the effectiveness of these inoculation methods. Results reveal that the incidence rate was 93.55% in the toothpick inoculation group, significantly higher than 80.65% in the burning inoculation group (p = 0.007), while punching inoculation exhibited significant pathological responses in the early stages, with spontaneous healing in the later stage. Addi-tionally, toothpick inoculation was more efficient in inducing Valsa canker when inoculating the pathogen at the bottom of the tree, with lower intra‐ and inter‐row spacing (stand density) providing better outcomes than higher intra‐ and inter‐row spacing. The results of this study demonstrate that toothpick inoculation is an optimal option for studying the artificial inoculation of V. sordida in six‐year‐old P. alba var. pyramidalis, providing technical support for research on poplar diseases and offering a theoretical basis for the inoculation of other diseases in branch and trunk.

Aiming at effectively generating safe and reliable motion paths for quadruped robots, a hierar-chical path planning approach driven by dynamic 3D point cloud is proposed in this article. The developed path planning model is essentially constituted of two layers: global path planning layer and local path planning layer. At the global path planning layer, a new method is proposed for calculating terrain potential field based on point cloud height segmentation. Variable step size is employed to improve the path smoothness. At the local path planning layer, a real-time prediction method for potential collision area and a strategy for temporary target point selection is developed. Quadruped robot experiments were carried out in an outdoor complex environment. The experi-mental results verified that, for global path planning, the smoothness of the path is improved and the complexity of the passing ground is reduced. The effective step size is increased by a maxi-mum of 13.4 times, and the number of iterations is decreased by up to 1/6, compared with the traditional fixed step size planning algorithm. For local path planning, the path length is short-ened by 20%, more efficient dynamic obstacle avoidance and more stable velocity planning are achieved by using the improved dynamic window approach (DWA).

Preventing the occurrence of secondary caries serves as one of the significant issues in dental clinic, thus make it indispensable to improving the properties of conventional composite resin (CR) by developing a novel CR. In present study, two groups of experimental CRs loaded with different contents of fluoride-doped nano-zirconia fillers (25 wt% and 50 wt%) were fabricated. The surface topography, mechanical performance, fluoride release, antibacterial effect, aging property and cytotoxicity of the experimental CRs were evaluated subsequently. A uniform distribution of the F-zirconia fillers over the whole surface of resin matrix could be observed. The experimental CRs showed continuous fluoride release within 28 days, which was positively correlated with the content of F-zirconia fillers. Moreover, the amount of fluoride release increased in the acidic buffer. Addition of F-zirconia fillers could improve the color stability, wear resistance and microhardness of the experimental CRs, without reducing the flexure strength. Furtherly, the fluoride ions released continuously from the experimental CRs resulted in effective contact and antibacterial properties, while they showed no cytotoxicity. As a consequence, considerations can be made to employ this new kind of composite resin loaded with fluoride-doped nano-zirconia fillers to meet clinical requirements when the antimicrobial benefits are desired.

Owing to the rising demand for vegetable soybean products, there is an increasing need for high-yield soybean varieties to raise the output. However, complex correlation patterns among quantitative traits with genetic interactions bring a challenge for vegetable soybean breeding. Here, a genome-wide association study (GWAS) was applied for six yield-related traits with 188 vegetable soybean accessions. A total of 81 SNPs were identified for pod length, plant height, pods number, pod thickness, pod width, and seed weight per plant by Blink model. Among these significant SNPs, 79 were novel SNPs, while 2 overlapped with previously reported SNPs. A total of 220 genes were found in 100 kb upstream and downstream regions of significant SNPs, of which 17 genes were functional proteins. Among these function proteins, four candidate genes, Glyma.13G109100, Glyma.03G183200, Glyma.09G102200, and Glyma.09G102300 were analyzed as significant haplotype variations, which encode MYB-related transcription factor, auxin-responsive family protein, F-box protein and CYP450, respectively. In addition, the relative expression with four candidate genes in the V030 and V071 (Among them, the plant height, pod number and fresh pod weight of V030 were lower than V071 strains) vegetable soybean were significantly different and these genes could be involved in plant growth and development via various pathways. Therefore, we proposed four genes from vegetable soybean germplasm that could be the key candidate genes for pod yield and plant height and these studies will accelerate the process of high-yield vegetable soybean breeding.

The swelling behavior of titanium-modified austenitic stainless steel 15-15Ti was investigated by pre-implantation of He at room temperature followed by Ni irradiation at 580 °C to peak doses of 120, 240 and 400 dpa. Relatively smaller cavities were observed in the zone of helium implantation while large cavities appeared in the region near the damage peak. A correction formula of the dpa curve was proposed and applied for samples with large swelling. It is found that the steady-state swelling rate of 15-15Ti keeps at ~1% /dpa even to high doses. By comparing the swelling data of the helium-implanted and helium-free regions at same doses, 70 dpa and 122 dpa, the suppression of excessive helium on swelling can be deduced at such doses.

In recent years, metabolomics has been used as a powerful tool to better understand the physiology of neurodegenerative diseases and identify potential biomarkers for progression. We used targeted and untargeted aqueous, and lipidomic profiles of the metabolome from human cerebrospinal fluid to build multivariate predictive models distinguishing patients with Alzheimer’s disease (AD), Parkinson’s disease (PD), and healthy age-matched controls. We emphasize several statistical challenges associated with metabolomic studies where the number of measured metabolites far exceeds sample size. We found strong separation in the metabolome between PD and controls, as well as between PD and AD, with weaker separation between AD and controls. Consistent with existing literature, we found alanine, kynurenine, tryptophan, and serine to be associated with PD classification against controls, while alanine, creatine, and long chain ceramides were associated with AD classification against controls. We conducted a univariate pathway analysis of untargeted and targeted metabolite profiles and find that vitamin E and urea cycle metabolism pathways are associated with PD, while the aspartate/asparagine and c21-steroid hormone biosynthesis pathways are associated with AD. We also found that the amount of metabolite missingness varied by phenotype, highlighting the importance of examining missing data in future metabolomic studies.

Objective: The objective of this study is to determine the epidemic dynamics and clinical features of COVID-19 in southern Hainan Island, China, and provide experience for other tropical areas of the world. Methods: This retrospective study included confirmed cases of COVID-19 in southern Hainan. All enrolled patients were treated in Sanya, and data on epidemiological and clinical features of the disease and infection prevention and control measures adopted by the local government during the epidemic were collected. Results: Of the 74 cases, 71 (95.95%) were imported from Wuhan, Hubei Province (47, 63.51%), other cities in Hubei Province (11, 14.86%), or provinces other than Hubei and Hainan (13, 17.57%). Three (4.06%) patients were infected in southern Hainan, including one autochthonous case in Sanya. Fifty-four cases (72.97%) were detected in Sanya, and 27 cases (27.03%) were diagnosed in other cities. The rate of severe or critical cases was 28.38% (21/74), and mortality was 2.7% (2/74). The serum lactate levels and base excess of severe-critical patients were higher than those of patients with mild-moderate disease. Multivariate logistic regression analysis showed that chronic conditions were risk factors for severe and critical COVID-19. Seventy-four patients were diagnosed with COVID-19 over a 22-day period in Sanya, and the epidemic period in the city was 48 days. The outbreak was controlled rapidly because the local government adopted strict infection prevention and control measures. Conclusions: The clinical characteristics of COVID-19 in Hainan Island were similar to those reported in other regions. In Sanya, the rate of severe and very severe cases was higher than in other regions; however, most cases were imported, and there was only one autochthonous case. The rapid control of the outbreak in Sanya may be related to the tropical climate, adoption of strict infection prevention and control measures, daily reporting of new cases, increased public awareness about the epidemic, and other emergency actions implemented by the local government.

The real systems are often complex, nonlinear, and noisy in various areas including mathematics, natural science, and social science. We present the symplectic entropy (SymEn) measure as well as an analysis method based on SymEn to estimate the nonlinearity of the complex system by analyzing the given time series. The SymEn estimation is a kind of entropy based on symplectic principal component analysis (SPCA) which represent organized but unpredictable behaviors of systems. The key to SPCA is to preserve the global submanifold geometrical properties of the systems through symplectic transform in the phase space, which is a kind of the measure-preserving transforms. The capability of preserving the global geometrical characteristics makes the SymEn a test statistic to detect the nonlinear characteristics in several typical chaotic time series and the stochastic characteristic in the Gaussian white noise. The results are in agreement with findings in the approximate entropy (ApEn), the sample entropy (SampEn) and the fuzzy entropy (FuzzyEn). Moreover, the SymEn method is also used to analyze the nonlinearities of the real signals (including the EEG signals for ASD and healthy subjects, and the sound and vibration signals for the mechanical systems). The results indicate that the SymEn estimation can be taken as a measure for describing the nonlinear characteristics in the data collected from the natural complex systems.

(1) Background: We aim to construct a machine learning (ML) algorithm to predict the risk of distant metastasis (DM) of T1 and T2gallbladder cancer (GBC); (2) Demographic and clinical pathological data of T1 and T2 GBC patients were extracted from the National Institutes of Health (NIH)’s Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015 to develop seven ML algorithm models. Models were evaluated based on accuracy, precision, recall rate, F1- score, and area under the receiver operating characteristic curve (AUC); (3) Results:A total of 4371 patients were included in the study, of whom 764 (17.4%) developed DM. Multivariate logistic regression showed that age, histology, tumor size, T and N stages were independent factors in GBC with DM. A novel nomogram was established to predict distant metastasis in early T stage GBC patients. Evaluation indicators of the best model Random Forest (RF) were as follows: accuracy (0.828), recall rate (0.862), precision (0.811), F1- score (0.836), and AUC value (0.913); (4) Con-clusions: The RF model constructed in this study could accurately predict distant metastasis in GBC patients, which may provide clinicians with more personalized clinical decision-making recom-mendations.

The evolution of the H5 highly pathogenic avian influenza (HPAI) viruses has led to the emergence of distinct groups with genetically similar clusters of hemagglutinin (HA) sequences. In this study, a consensus H5 HA sequence was cloned into the baculovirus expression system. The HA protein was expressed in baculovirus-infected insect cells and utilized as the antigen for the production of an oil emulsion-based H5 avian influenza vaccine (rBacH5Con5Mut). 21-day old SPF chickens were immunized with this vaccine and then challenged at 21 days post-vaccination (dpv) with clade 2.3.2.1, clade 2.3.4.4 and clade 7.2 of H5 HPAI virus, respectively. The chicken sera of vaccinated chickens exhibited high hemagglutination inhibition (HI) titers against the rBacH5 vaccine antigen, while lower HI titers were observed against the different challenge virus H5 hemagglutinins. Furthermore, the rBacH5Con5Mut vaccine provided 100% protection from mortality and clinical signs. Virus isolation results showed that oropharyngeal and cloacal shedding was prevented in 100% of the vaccinated chickens when challenged with clade 2.3.2.1 and clade 2.3.4.4 H5 viruses. When the rBacH5Con5Mut vaccine candidate was administrated at one day of age, 100% protection was demonstrated against challenge of clade 2.3.4.4 virus at 3 weeks of age, indicating the potential of this vaccine for hatchery vaccination. Overall, the rBacH5Con5Mut vaccine candidate with a consensus HA antigen and a single dose can protect chickens against different clades of H5 HPAI viruses throughout the rearing period of broiler chickens without a boost, thus fulfilling the criteria for an efficacious broad-spectrum H5 avian influenza vaccine.

The CARA program has been developed since 2008 by the French reference laboratory for air quality monitoring (LCSQA) and the regional monitoring networks to gain a better knowledge at the national level on the particulate matter (PM) chemistry and its diverse origins in urban environments. It results of strong collaborations with international-level academic partners, allowing to bring state-of-the-art, straightforward and robust results and methodologies within operational air quality stakeholders (and subsequently, decision makers). Here, we illustrate some of the main outputs obtained over the last decade thanks to this program, regarding methodological aspects (both in terms of measurement techniques and data treatment procedures) as well as acquired knowledge on the predominant PM sources. Offline and online methods are used following well-suited quality assurance and quality control procedures, notably including inter-laboratory comparison exercises. Source apportionment studies are conducted using various receptor modeling approaches. Overall, the results presented herewith underline the major influences of residential wood burning (during the cold period) and road transport emissions (exhaust and non-exhaust ones, all along the year), as well as substantial contributions of mineral dust and primary biogenic particles (mostly during the warm period). Long-range transport phenomena, e.g., advection of secondary inorganic aerosols from the European continental sector and of Saharan dust into the French West Indies, are also discussed in this paper. Finally, we briefly address the use of stable isotope measurements (δ15N) and of various organic molecular markers for a better understanding of the origins of ammonium and of the different organic aerosol fractions, respectively.

A novel determination method using flow-through cartridge purification and ultra-high-performance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS) was developed for the identification and quantification of 30 mycotoxins (aflatoxin B1, B2, G1, G2, sterigmatocysin, T-2 toxin, diacetoxyscirpenol, roquefortine C, methylergonovine, ergocornine, lysergol, enniatin A, enniatin A1, enniatin B, enniatin B1, beauvericin, deoxynivalenol, 3-acetylDeoxynivalenol, 15-acetylDeoxynivalenol, patulin, verruculogen, neosolaniol, gliotoxin, HT-2 toxin, wortmannin, zearalenone, α-zearalenol, β-zearalenol, α-zearalanol, and β-zearalanol) in feedstuffs. Mycotoxins were extracted from sample by 0.1% formic acid aqueous solution/acetonitrile (16/84 v/v), then purified by a Cleanert® MC clean-up column. As a result, the pigments and other matrices were efficient removed from the sample extract solution. The purified eluate was collected, then evaporated and redissolved by 0.1% formic acid aqueous solution/acetonitrile (70:30 v/v) for UPLC-MS/MS detection. A BEH Shield RP18 chromatographic column was employed for separation of 30 mycotoxins in a total of 14 min. Multiple-reaction monitoring (MRM) mode was applied for qualitative and quantitative analysis, and matrix calibration curves obtained with the external-standard method was used for quantitation of target analytes. Under optimized conditions, the linearity range was from 2 to 1000 ng/g, and the limit of quantification of the developed method was from 2 to 50 ng/g. The recoveries of 30 mycotoxins spiked in urine samples were from 72.0% to 118.5%, and the relative standard deviation was below 20%. The method was also well approved by certified reference sample, and applied on the real feedstuff samples testing successfully.

This study investigated the neuroprotective effects of methyl 3,4-dihydroxybenzoate (MDHB) against t-butylhydroperoxide(TBHP) induced oxidative damage in SH-SY5Y (human neuroblastoma cells) and the underlying mechanisms. SH-SY5Y were cultured in DMEM+10% FBS for 24 hours and pretreated with different concentrations of MDHB or N-acetyl-L-cysteine (NAC) for 4 hours prior to the addition of 40 μM TBHP for 24 hours. Cell viability was analyzed using the methyl thiazolyl tetrazolium (MTT) and lactate dehydrogenase (LDH) assays. An annexin V-FITC assay was used to detect cell apoptosis rate. The 2',7'-dichlorofluorescin diacetate (DCFH-DA) assay was used to determine intracellular ROS levels. The activities of antioxidative enzymes (GSH-Px and SOD) were measured using commercially available kits. The oxidative DNA damage marker 8-OHdG was detected using ELISA. Western blotting was used to determine the expression of Bcl-2, Bax, caspase 3, p-Akt and Akt proteins in treated SH-SY5Y cells. Our results showed that MDHB is an effective neuroprotective compound that can mitigate oxidative stress and inhibit apoptosis in SH-SY5Y cells

With the development of educational technology, machine learning and deep learning provide technical support for traditional classroom observation assessment. However, in real classroom scenarios, the technique faces challenges such as lack of clarity of raw images, complexity of datasets, multi-target detection errors and complexity of character interactions.Based on the above problems, a student classroom behavior recognition network incorporating super-resolution and target detection is proposed. To cope with the problem of unclear original images in the classroom scenario, SRGAN (Super Resolution Generative Adversarial Network for Images) is used to improve the image resolution and thus the recognition accuracy.To address the dataset complexity and multi-targeting problems, feature extraction is optimized and multi-scale feature recognition is enhanced by introducing AKConv and LASK attention mechanisms into the Backbone module of YOLOv8s algorithm. To improve the character interaction complexity problem, the CBAM attention mechanism is integrated to enhance the recognition of important feature channels and spatial regions. Experiments show that it can detect six behaviors of students raising their hands, reading, writing, playing cell phones, looking down, and lying on the table in high-definition images. And the accuracy and robustness of this network are verified.Compared with small target detection algorithms such as Faster R-CNN, YOLOv5, and YOLOv8s, this network has better detection performance and can efficiently deal with the behavior recognition of multiple students.

In this work, we report the synthesis of a new thiosemicarbazone-based drug of N'-(di(pyridin-2-yl)methylene)-4-(thiazol-2-yl)piperazine-1-carbothiohydrazide (HL) featuring a thiazole spectator for the efficient coordination with Cu(II) to give [CuCl(L)]2 (1) and [Cu(NO3)(L)]2 (2). Both 1 and 2 exhibit dimeric structures ascribed to the presence of di-2-pyridylketone moieties that demonstrate dual functions of chelation and intermolecular bridging. HL, 1 and 2 are highly toxic against hepatocellular carcinoma cell lines Hep-G2, PLC/PRF/5, and HuH-7, with half maximal inhibitory concentration (IC50) values as low as 3.26 nmol/mL (HL), 2.18 nmol/mL (1), and 2.54 × 10−5 nmol/mL (2) for PLC/PRF/5. While the free ligand HL may elicit its anticancer effect via sequestration of bio-relevant metal ions (i.e. Fe3+ and Cu2+), 1 and 2 are also capable of generating cytotoxic reactive oxygen species (ROS) to inhibit cancer cell proliferation. Our preliminary pharmacokinetic studies revealed that oral administration (per os, PO) of HL has a significantly longer half-life t1/2 of 21.61 ± 9.4 h, nearly doubled as compared to that of the intravenous (i.v.) administration of 11.88 ± 1.66 h, certifying HL as an effective chemotherapeutic drug via PO administration.

A mass mortality event of Asian seabass Lates calcarifer juveniles occurred in Zhuhai, the main Asian seabass cultured area in mainland China. The fish samples were pooled for pathogen identification and both high viral loads of ISKNV and NNV were detected by real-time microfluidic quantitative PCR. Immunohistochemistry and immunofluorescence showed that strong ISKNV signals were examined in spleen and liver, while strong NNV signals were detected in brain and eye. The multi-tissue homogenates were inoculated into MFF-1 cell and SSN-1 cell, respectively. After several viral passages, both ISKNV and NNV were purely isolated from each other, and designated as ASB-ISKNV-23 and ASB-NNV-23, respectively. The whole genome sequences of ASB-ISKNV-23 and ASB-NNV-23 were determined and annotated. The result showed that ASB-ISKNV-23 and ASB-NNV-23 are composed of 112,236 bp and 4,538 bp, respectively. Phylogeny analysis showed that ASB-ISKNV-23 belongs to ISKNV-II sub-genotype and ASB-NNV-23 belongs to RGNNV genotype. Collectedly, coinfection of ISKNV-II and RGNNV were firstly documented in mass mortality of Asian seabass in mainland China. Importantly, both ISKNV-II and RGNNV were purely isolated using two different permissive cell lines. Our study provides useful information for better understanding the complex pathogenesis regarding the coinfection with ISKNV and NNV in farmed fish.

The integration of Artificial Intelligence (AI) in healthcare has transformed clinical practices by improving the accuracy of diagnosis, optimizing surgical procedures, improving the patient experience, and accelerating drug development. This article provides a comprehensive overview of the many applications of artificial intelligence (AI) across several industries, including fast diagnostics, where ML algorithms greatly enhance the accuracy and velocity of disease diagnosis. State-of-the-art robotic-assisted minimally invasive surgical procedures that shorten patients' hospitalization and improve their chances of recovery, cutting-edge AI applications in healthcare monitoring, and medication development. The article also looks at the primary challenges that AI in healthcare will inevitably encounter, such as differences in product quality, a shortage of skilled workers, privacy and ethical issues, and the need for improved regulatory frameworks. Despite the challenges, AI contributes significant advantages to the healthcare industry, providing novel and remarkable contributions to the efficiency of medical procedures and the progress of medical outcomes. The paper emphasizes the importance of cooperation in overcoming current challenges and enhancing the acceptability of AI technology in clinical settings. This will ensure that AI-driven innovations continue to enhance the standards of patient care.

In order to further control the equivalent circulating density,a new type of annular pres-sure-reducing downhole tool for high temperature and high pressure wells-liquid-absorbing ECD tool is designed.Through this tool,the bottom hole pressure of the equivalent circulating drilling fluid can be close to its hydrostatic pres-sure,therebyachieving a deeper drilling depth.The tool is mainly composed of screw mo-tor,vortex blade,annular seal,universal joint and drill string.Its working principle is mainly to use the liquid absorption effect of the tool and the hydraulic energy of the circu-lating fluid extracted by the screw motor to convert it into mechanical energy,and to gen-erate suction force to improve the flow energy of the drilling fluid in the bottom hole an-nulusand reduce the equivalent circulation density.Based on ANSYS-FLUENT analysis andsimulation,the change of pressure drop characteristics under different drilling fluid density,displacement and tool size was simulated.The simulation results show that thedepressurization effect is 1.0MPa when the drilling fluid density is 1.2g/cm3. When the drilling fluid density is 1.5g/cm3,the depressurization is about 1.7MPa,and when thedrill-ing fluid density is 1.8g/cm3,the depressurization is about 1.9MPa.When the drilling fluid density is 2.0g/cm3,the pressure drop is about 2.3MPa.When the flow rate is from 1500-2500L/min,the maximum pressure can be reduced by 2.3MPa.When the viscosity is 40mPa•S and 60mPa•S,the pressure drop almost does not change.The increase of viscosi-ty has little effect on the pressure drop,and it is mainly affected by the flow rate under equal displacement.

Urbanization is a dynamic process. At different stages of urbanization, polices of managing car-bon emissions should be adjusted. In this paper, two types of nighttime light data and statistical data of 68 cities in China from 2002 to 2021 are used to establish an urban development evalua-tion system comprising population, economy, construction, and society subsystems. Then, cities were categorized into expanding, contracting, and stable types to understand the uncertainty of urbanization's impact on carbon emissions. The main conclusions are as follows: (1) Integrated nighttime light data is used to extract built-up area boundaries, achieving an overall accuracy ranging from 68.75% to 98.4%, with kappa coefficients ranging between 0.60 and 0.94. (2) Ex-panding cities were concentrated in the eastern coastal regions, while contracting cities were mainly located in inland cities with traditional industries. Urban agglomerations in the Beijing-Tianjin-Hebei region, Yangtze River Delta region, and Chengdu-Chongqing metropolitan demonstrate synchronized characteristic of urbanization development. (3)Economy and con-struction have a greater impact on carbon emissions than population and society. For expanding cities, the focus of carbon emission governance should prioritize rational land use planning. Conversely, for contracting cities, emphasis should be placed on adjusting economic structures.

With the rapid development of intelligent logistics and network environment, it has become an urgent problem to efficiently and accurately handle and analyse the huge amount of uncertain decision information. Traditional decision making methods often fail to make the best use of complex and incomplete information, especially in the field of cyber defence. To address these problems, this paper introduces a new mathematical tool, discrete multidimensional Z-numbers (MZs), for expressing and dealing with uncertainty and reliability in network defence decisions. In this paper, we first introduce the synthesis method of discrete multidimensional Z-numbers, which allows us to consider the uncertainty and reliability of multi-source information in network defence strategy. Then, the hidden probability of MZs is calculated by using the hidden probability model, and the multidimensional Z+−number is proposed, which improves the expressiveness of the model in dealing with information uncertainty. Based on MZs, we define a variety of utility functions and build a multi-attribute group decision framework for network defence around these functions. The MZs provides a novel perspective for analysing and designing response strategies, especially against highly adaptive and covert attacks. The method in this paper is verified by a case of network security assessment of an intelligent logistics company. The results show that this method can significantly improve the accuracy and efficiency of decision making, demonstrating its unique advantages and wide application potential in network defence. This integrated discrete multidimensional Z-number group decision method is not only suitable for intelligent logistics decision making, but also provides an efficient decision support tool for network defence, which helps to improve the intelligence and adaptability of network security defence.

Exploring the growth and physiological response mechanisms of rice under continuous drought stress circumstances can provide a significant scientific foundation and technological assistance for meeting drought difficulties, improving drought resistance and rice (Oryza sativa L.) output, and ensuring food security. In this study, a rice field experiment was conducted under a rain shelter with five different treatments set up: P1 (drought stress from tillering stage), P2 (drought stress from jointing-booting stage), P3 (drought stress from heading-flowering stage), P4 (drought stress from grain filling stage), and CK (adequate water management throughout the growth stage). Continuous drought stress from different growth stages with four levels (mild, medium, serious, and extreme). The results showed that the effects of different drought stress treatments on rice growth varied significantly. Compared with the CK treatment, plant height was reduced by 12.10%, 8.14%, 3.83%, and 1.06% in the P1, P2, P3, and P4 treatments, respectively; and the number of tillers was reduced by 23.83%, 18.91%, 13.47%, and 8.68%, respectively. With the increase in drought stress levels, SPAD values and Rubisco activity of rice leaf continued to decrease; SOD activity showed a decreasing trend, but the decreasing trend of POD and CAT activities was not significant, while MDA content showed an increasing trend. For yield components, continuous drought stress significantly reduced spike length, effective number of spikes, thousand grain weight, grain number per spike, and fruiting rate of rice compared to CK treatments during the growth period. In general, continuous drought stress during the early growth period affected the effective spike number and the grain number per spike. Continuous drought stress after the grain filling stage had the least effect on yield and water use efficiency was much higher than other treatments. The results of these studies reveal the response of rice growth and physiology to continuous drought stress, which is important for agricultural practices.

The high penetration of distributed energy resources poses significant challenges to the dispatch and operation of power systems. Improving the accuracy of short-term load forecasting (STLF) can optimize grid management, leading to increased economic and social benefits. Currently, some simple AI and hybrid models has issues to deal with and struggle with multivariate dependencies, long-term dependencies, and non-linear relationships. This paper proposes a novel hybrid model for short-term load forecasting (STLF) that integrates multiple AI models with Lasso regression using the stacking technique. The base learners include ANN, XgBoost, LSTM, Stacked LSTM, and Bi-LSTM, while Lasso regression serves as the meta-learner. By considering factors such as temperature, rainfall, and daily electricity prices, the model aims to more accurately reflect real-world conditions and enhance predictive accuracy. Empirical analyses on real-world datasets from Australia and Spain show significant improvements in forecasting accuracy, with a substantial reduction in Mean Absolute Percentage Error (MAPE) compared to existing hybrid models and individual AI models. This research highlights the efficiency of the stacking technique in improving STLF accuracy, suggesting potential operational efficiency benefits for the power industry.

China’s greenhouse industry has thousands of years of development history, although the development of modern greenhouses has come late. After decades of development of China, its greenhouse industry is in the forefront of the world. China’s greenhouse industry is experiencing rapid development, transformation and upgrading. China ranks first in the world in terms of greenhouse area, used mainly for vegetable cultivation and plays an important role in the utilization of agricultural resources and food production. In 2023, the cumulative area of new greenhouses nationwide was 1835.87 million ha. In this paper we present the latest developments in the greenhouse sector. More specifically, we present a greenhouse market development overview, the greenhouse production, the greenhouse industry, and technology development. Current problems are underpinned, development opportunities and prospects are analyzed, and measures and specific proposals are detailed for the technological development of the greenhouse sector both for increasing product quantity and quality.

Habenaria, a member of Orchidaceae family is the cosmopolitan distributions, which has significant medicinal and ornamental values. Regardless of morphology and molecular data that have been studied in recent times, the phylogenetic relationship is still under debate. Here, we sequenced, assembled, and annotated the whole chloroplast (cp) genome of two species (Habenaria aitchisonii Rchb.f. and Habenaria tibetica Schltr.ex Limpricht) of Habenaria grown on the Qinghai-Tibetan Plateau (QTP), and combined with seven already published cp genomes which may assist to uncover their genomic profiling. The two genomes ranged from 155259-155269 bp in length and both encoded 132 genes, including 86 protein, 38 tRNA and 8 rRNA. In the cp genomes, the tandem repeats (797), SSRs (2195) and diverse loci (3214) were identified. Comparative analyses of codon usage, amino frequency, microsatellite, oligo repeats and transition and transversion substitutions showed similarities among the species. Moreover, we identified 16 highly polymorphic regions with nucleotide diversity above 0.02, which may be suitable for robust authentic barcoding and inferring in the phylogeny of Habenaria species. Among the polymorphic regions, positive selection was significantly exerted on the several genes such as cemA, petA, and ycf1. This may suggest that the important adaptation stratagem for two Habenaria species on the QTP. The phylogenetic relationship displayed that H.aitchisonii and H. tibetica have closer relationship than others and the rest seven species clustered in the other three groups. Our findings also supported the idea that Habenaria could be divided into different sections. This study enriched the genomics resources of Habenaria, which may be helpful for the conservation efforts of these endangered species.

Deep learning methods paired with sidescan sonar (SSS) are commonly used in underwater search and rescue operations for drowning victims, wrecks, and airplanes. However, these techniques are primarily designed to detect mine-like objects and are rarely applied to identifying features in dynamic dredge pit environments, due to a lack of datasets. In this study, we present a Sandy Point Dredge Pit (SPDP) dataset, in which high-resolution SSS data were collected from the west flank of Mississippi bird foot delta of the Louisiana inner shelf. This dataset contains a total of 385 SSS images. We introduce an Effective Geomorphology Classification model (EGC). By ablation studies, we analyze the utility of transfer learning on different model architectures and the impact of data augmentations on model performance. This EGC model will make geomorphic features identification in dredge pit environments quick and efficient which requires extensive experience and professional knowledge. The combination of SSS images and EGC model is a cost-effective and valuable toolkit for hazards monitoring in marine dredge pit environment. The SPDP SSS images dataset, especially the feature of pit wall without rotational slump, is also valuable for other machine learning modelers.

In the contemporary world of science, continuous observation of specific fixed regions is required, awaiting the emergence of targets or phenomena within those areas to locate and identify them. Astronomy requires continuous monitoring of stars, swiftly pinpointing occurrences, such as flares, and acquiring their spectral data to aid identification. In military contexts, continuous observation of specific areas is essential for promptly locating enemy movements and obtaining target spectral data for deciphering enemy disguises. Additionally, continuous observation of experimental samples in biochemistry is mandatory to pinpoint localized changes and acquire spectral data from those areas. Hence, this paper develops a novel imaging spectrometer solution to monitor localized regions continuously. Specifically, this paper first elaborates on the developed instrument’s characteristics and optical structure, followed by optical design software simulations. The instrument has an F-number of 5, a focal length of 100mm, and a field of view covering the wavelength range from 400nm to 600nm. The optical path diagram indicates that the system’s dispersion and imaging images can be separated, meeting the system’s requirements. Besides, the Modulation Transfer Function (MTF) graph confirms that the image quality meets the requirements. Furthermore, a region-monitoring type imaging spectrometer was constructed to assess the instrument’s capability for spectral observation of fixed regions. The instrument can detect a fixed area, with the target placed within the detection area. Spectral data were successfully obtained by conducting image processing on the acquired images. The correlation coefficient between this data and reference data was as high as 0.9226, indicating the instrument’s successful measurement of the target’s spectrum. Thus, the developed instrument demonstrated that it can image the observed areas and acquire spectral data from targets within those regions.

This study explores novel techniques to improve the detection accuracy of skarn iron deposits using advanced image processing methodologies. Leveraging the capabilities of Aster image, Band Ratio (BR) images, and Principal Component Analysis (PCA), alongside the power of 3D Convolutional Neural Networks (3D-CNN), the research aims to enhance the precision and efficiency of ore detection in complex geological environments. The proposed method employs a specific 3D-CNN architecture, accepting input as a 7×7×C image patch, where C represents the combined number of selected Aster image bands, Principal Component (PC) bands, and computed BR images. To evaluate the accuracy of the proposed method, five distinct image bands combinations, including the proposed bands combination, were tested and evaluated based on the overall accuracy (OA), average accuracy (AA), and kappa coefficient. The results demonstrated that while the incorporation of BR images alongside Aster bands initially seemed promising, it introduced significant confusion in certain classifications, leading to unexpected mis-classification rates. Surprisingly, utilizing solely Aster bands as input parameters yielded higher accuracy rates (OA= 93.13%, AA = 91.96%, Kappa = 90.91%) compared to scenarios involving the integration with band ratios (OA = 87.02%, AA = 79.15, and Kappa = 82.60%) or the integration of BR images to PC bands (OA= 87.78%, AA = 82.39%, Kappa = 83.81%). However, the amalgamation of Aster bands with selected PC bands showed slight improvements in accuracy (OA= 94.65%, AA = 92.93%, Kappa = 93.45%), although challenges in accurately classifying certain features persisted. Ultimately, the proposed combination of Aster bands, PC bands, and BR images (proposed bands combination) presented the most visually appealing and statistically accurate results (OA = 96.95%, AA= 94.87%, and Kappa = 95.93%), effectively addressing misclassifications observed in the other combinations. These findings underscore the synergistic contributions of each of the Aster bands, PC bands, and BR images, with Aster bands proving pivotal for optimal skarn classification, PC bands enhancing intrusions classification accuracy, and BR images strengthening wall rock classification accuracy. In conclusion, the proposed combination of input image bands emerges as a robust and comprehensive methodology, demonstrating unparalleled accuracy in the remote sensing detection of skarn iron minerals.

Rice blast and brown planthopper (BPH) are two of the most destructive diseases and pests in rice (Oryza sativa L.) production in China. The development of multi-resistant varieties is widely recognized as the most efficient and environmentally friendly approach to controlling crop diseases and pests. Functional molecular markers (FMMs) have been developed from functional variants in the genic region associated with trait variation, greatly enhancing the efficiency of identifying and pyramiding valuable genes in crop breeding. In this study, two FMMs and a multiplex PCR amplification system were developed for two major broad-spectrum BPH resistance genes, Bph6 and Bph9. By assistance of FMMs in multi-resistant rice restorer line development pipeline, two lines (Huahui7713 and Huahui3006) with blast and BPH resistance were developed by pyramiding three resistance genes Pigm, Bph6 and Bph9. Huahui7713 and Huahui3006 as male lines, two new hybrid rice varieties (WeiLiangYou7713 and Xuanliangyou3006) were certificated and commercial released in China. WeiLiangYou7713 and Xuanliangyou3006 showed improved blast and BPH resistance, and no penalty on yield and grain-quality. Annual promotion area of WeiLiangYou7713 has exceeded 53.3 thousand hectares, indicating its widespread adoption and success in the market. Huahui7713 and Huahui3006 could have great potential for utilization in blast and BPH resistance rice breeding.

Aiming at the problems of high labor intensity and high cost of manual picking, high loss rate of mechanical picking seeds, low mechanization level and difficult to guarantee the quality of picking plates in the process of picking plates of edible sunflower. Based on the principle of manual plate taking, a plate taking control system for sunflower harvesting table was designed. Through the research and analysis of the principle of taking the plate of the edible sunflower harvesting table. According to the actual operation requirements, the overall scheme of the sunflower plate control system is determined, and the control strategy of imitating artificial low loss harvesting is designed. To reduce the grain loss in the process of taking the plate and improve the control accuracy of the system on the movement speed and displacement of the key components in the process of taking the plate, a trapezoidal acceleration and deceleration control algorithm is designed as the control algorithm of taking the plate. To verify the working performance of the plate control system, with the absolute error, relative error and total loss rate of the harvest as objectives. The bench and field verification experiments were both carried out. The bench experiment showed that the speed error of the plate parts was not more than 0.028 m/s. In the bench experiments of the device, the maximum positioning error was 1.25 mm, the average relative error is only 0.94 %, and the grain loss rate was not more than 2.26 %. It result showed that the system algorithm was reliable, the positioning accuracy was high, and the taking plate operation can be completed well. The field verification experiment showed that the forward speed of the unit was in the range of 0.4~0.8 m/s, and the total loss rate of harvest was less than 5 %. When the forward speed is 0.6 m/s, the minimum harvest loss rate is 2.32 %, and which indicated the control system meets the requirements of sunflower harvesting operation.

The study comprehensively evaluated the prognostic roles of PLR, NLR, MLR, BLR, and ELR in patients with acute exacerbation chronic obstructive pulmonary disease (AECOPD). 619 patients with AECOPD and 300 healthy volunteers were retrospectively included into the study. The clinical characteristics containing laboratory findings of the AECOPD patients and the blood cell counts (CBCs) of the healthy volunteers were collected. Compared with the healthy volunteers, PLR, NLR, and MLR were elevated in COPD patients in stable condition, and were further ele-vated during exacerbation. ELR showed the opposite trend. PLR, NLR, and MLR were all posi-tively correlated with hospital LOS as well as CRP. In contrast, ELR was negatively correlated with hospital LOS as well as CRP. Elevated PLR, NLR, and MLR were all associated with more serious airflow limitation in AECOPD. Elevated PLR, NLR, and MLR were all associated with increased in-hospital mortality while Elevated ELR was associated with decreased in-hospital mortality in AECOPD. A nomogram was construct to predict in-hospital mortality in AECOPD. The nomo-gram had a C-index of 0.850 (95% CI: 0.799 – 0.901) with good predictive value and clinical ap-plicability. In summary, PLR, NLR, MLR, and ELR served as predictors for clinical outcomes in patients with AECOPD.

Glial fibrillary acidic protein (GFAP) is the major intermediate filament III protein of astroglia cells which is upregulated in traumatic brain injury (TBI). Here we reported that GFAP is truncated at both the C- and N-terminals by cytosolic protease calpain to GFAP breakdown products (GBDP) of 46-40K then 38K following pro-necrotic (A23187) and pro-apoptotic (staurosporine) challenges to primary cultured astroglia or neuron-glia mixed cells. In addition, with another pro-apoptotic challenge (EDTA) where caspases are activated but not calpain, GFAP was fragmented internally, generating a C-terminal GBDP of 20 kDa. Following controlled cortical impact in mice, GBDP of 46-40K and 38K were formed from day 3 to 28 post-injury. Purified GFAP protein treated with calpain-1 and -2 generates (i) major N-terminal cleavage sites between A-56 and A-75, and (ii) major C-terminal cleavage sites between T-383 and Q-388, producing a limit fragment of 38K. Caspase-6 treated GFAP was cleaved at D-78, R-79, D-266 and A-267, where GFAP was relatively resistant to caspase-3. We also derived a GBDP-38K N-terminal-specific antibody which only labels injured astroglia cell body in both cultured astroglia and mouse cortex and hippocampus after TBI. As a clinical translation, we observed that CSF samples collected from severe human TBI have elevated levels of GBDP-38K as well as two C-terminally released GFAP peptides (DGEVIKES and DGEVIKE). Thus, in addition to intact GFAP, both the GBDP-38K as well as unique GFAP released C-terminal proteolytic peptides species might have the potential in tracking brain injury progression.

Considering characteristics of high river density, special underlying surface in plain river network region, and municipal drainage system function, river network drainage unit model is proposed, which is defined as a region surrounded by main river or embankment. Flood storage variety and control projects regulation of small rivers in each unit is simulated. With drainage catchment as an object, according to its drainage capacity, simplified municipal drainage model was developed. Coupling river network drainage unit model, simplified municipal drainage model and 2D flood routing model, urban flood analysis model for plain tidal river network region was developed, which could be applied to analyzing flood from upstream river, storm surge and local rainfall. Demonstration research was carried out in Puxi flood protected area in Shanghai.

In March 2013, a novel avian influenza A H7N9 virus was emerged in China, which cause rapidly progressive pneumonia and with a high fatality rate. Serologic studies to evaluate neutralizing antibodies of infected patients and birds are invaluable tools for immunogenicity research of H7N9 and epidemiological investigation. Conventional neutralization assays are laborious and time-consuming which also hampered by biosafety requirement. In this study, We construct and produce pseudovirus bearing the full-length hemagglutinin (HA) of H7N9 virus in the Env-defective, luciferase-expressing HIV-1 backbone. The production of lentiviral pseudovirus was analysed by HA gene specific real-time reverse-transcription PCR, transmission electron microscopy (TEM), and Western Blot assay to prove the nucleic acid replication, the morphology of virus, and the expression of HA protein in pseudovirus. After that pseudovirus based inhibition assay was established to detect neutralizing antibodies of a panel of serum samples. Our results demonstrated that H7N9 pseudovirus which had single-cycle infection was generated. By comparing the neutralization antibody titers, pseudovirus based neutralization test could be recognized as an alternative of conventional microneutralization (MN). Hence, we conclude that it is possible to use pseudovirus inhibition assay to screen sera samples, as well as evaluate vaccine-induced neutralizing antibodies against H7N9 virus.

Network Function Virtualization (NFV) allows for the dynamic provisioning of Virtualized Network Functions, adapting services to the complex and real-time network environment for enhanced network performance. Despite the benefits of NFV, virtual network functions (VNFs) migration and the energy consumption pose significant challenges due to the dynamic nature of the physical network, especially in the edge-cloud continuum network. Currently, people tackle this challenge by predicting the network traffic and then proactively migrating the VNFs using the predicted values. There are two challenges here. First, when SFCs belong to different network service providers, the historical network traffic data is held by the service providers, and they are reluctant to share the historical data due to privacy concerns. Without the historical network traffic data of each SFC, the resource provider that owns the underlying network cannot effectively predict the network traffic; in addition, without the feedback of the migration results of the underlying physical network, the network traffic prediction will be difficult to match the needs of the physical network. In this manuscript, we solve the problem of network traffic prediction in the NFV-based edge-cloud continuum network for multiple network service providers. We aim to improve migration performance and reduce energy consumption while ensuring resource and latency requirements. To protect the privacy of each network service provider, we apply an FL algorithm. After that, to improve the migration performance, we tailor the loss function. In the loss function, factors such as the number of migrations, the number of migration failures, and the energy consumption of the NFV-based edge-cloud continuum network are added. Additionally, to obtain the results of the SFC migration, we develop a mathematical model for the problem of multiple network service providers in the NFV-based network. The effectiveness of our algorithm is evaluated through extensive simulations, and the results show a significant reduction in the number of migrated nodes and energy consumption while improving the SFC acceptance ratio compared to state-of-the-art schemes which only use the difference between predicted and actual flows to define the loss function.

Ice accumulation poses considerable challenges in transportation, notably in the domain of general aviation. The present study combines the strengths and limitations of conventional aircraft de-icing techniques with the emerging trend toward all-electric aircraft. The study aims to utilize laser-induced graphene (LIG) technology to create a multifunctional surface, seamlessly integrating hydrophobic properties with efficient electrical heating to mitigate surface icing effectively. We investigated the utilization of a 10.6 μm CO2 laser for direct writing on polyimide (PI), a widely-used insulating encapsulation material. From the thermomechanical perspective, our initial analysis using COMSOL Multiphysics software revealed that when the laser power P exceeds 5 W, the PI substrate experience ablative damage. The experimental results show that when P ≤ 5W, an increase in power has a positive impact on the quality, surface porosity, roughness reduction, line-spacing reduction, and water contact-angle enhancement of the graphene. Conversely, when P &gt; 5 W, higher power negatively affects both the substrate and the graphene structure by inducing excessive ablation. However, it influences the graphene line height positively and is consistent with overall experimental-simulation congruence. Furthermore, the incorporation of high-quality graphene resulted in a surface that exhibited higher contact angles (CA &gt; 120°), lower energy consumption, and higher heating efficiency compared to the use of traditional electrically heated materials for anti-icing applications. The potential applications of this one-step fabrication method extend across various industries, particularly aviation, marine engineering, and other ice-prone domains. Moreover, the method has extensive prospects for addressing pivotal challenges associated with ice formation and serves an innovative and efficient anti-icing technology.

Efficient gene transfer and expression tool is lacking for shrimps and shrimp cells. To solve this, this study has developed a shrimp DNA virus-mediated gene transfer and expression system, consisting of insect Sf9 cells for viral packaging, shrimp viral vector of pUC19-IHHNV-PH-GUS and baculoviral vector of Bacmid or Bacmid-VP28 encoding shrimp WSSV envelope protein VP28. The pUC19-IHHNV-PH-GUS vector was constructed by assembling the genomic DNA of shrimp infectious hypodermal and hematopoietic necrosis virus (IHHNV), which had shortened inverted terminal repeats, into pUC19 backbone, and then an expression cassette of baculoviral polyhedron (PH) promoter-driven GUS reporter gene was inserted immediately downstream of IHHNV for proof-of-concept. It was found that the viral vector of pUC19-IHHNV-PH-GUS could be successfully packaged into IHHNV-like infective virions in the Sf9 cells, and gene transfer efficiency of this system had been evaluated and verified in three systems of Sf9 cells, shrimp hemolymph cells and tissues of infected shrimps, but GUS expression could be detected only in the cases when the viral vector was co-transfected or co-infected with a baculovirus of Bacmid or Bacmid-VP28 due to the Bacmid-dependence of PH promoter. Moreover, the packaging and infection efficiencies could be significantly improved when Bacmid-VP28 was used instead of Bacmid.

The antimicrobial peptide LRGG (LLRLLRRGGRRLLRLL-NH2) was designed and chemically synthesized in a study conducted by Jia et al. Gram-negative bacteria were found to be sensitive to LRGG and exhibited a high therapeutic index. Genetic engineering methods were used to create the prokaryotic fusion expression vector pQE-GFP-LRGG, and the resulting corresponding fusion protein GFP-LRGG was subsequently expressed and purified. The precursor GFP was then removed by TEV proteolysis, and pure LRGG was obtained after another round of purification and endotoxin removal. The prokaryotic-expressed antimicrobial peptide LRGG displays a broad-spectrum antibacterial effect on gram-negative bacteria and can eliminate 99.99% of E. coli within 100 minutes at the minimal MIC concentration. Compared to the chemically synthesized LRGG, the prokaryotic-expressed LRGG exhibits similar temperature, pH, salt ion, serum stability, and cell selectivity. Furthermore, prokaryotic-expressed LRGG showed excellent therapeutic effects in both the infection model of cell selectivity and no embryotoxicity in a Galleria mellonella infection model. The mechanism by which LRGG causes bacterial death was found to be disruption of Gram-negative cell membrane.

Fisheries and bottom seawater data were collected in July in 2022 and 2023 from 15 sampling stations in the Huanghe River Estuary and adjacent seas in China. The patterns of functional alpha- and beta-diversity of fish communities in this area and their relationship with environmental factors were studied using trait-based analysis. Five functional classifications, which included 16 functional traits, four functional alpha-diversity indices (functional richness, functional evenness, functional divergence, and community-weighted mean), three functional beta-diversity indices (functional beta-diversity and its turnover and nestedness components), and 14 bottom environmental factors were considered. The dominant traits of fish communities were: benthivorous feeding habits, small and front-facing mouths, high trophic level, demersal, anguilliform, low growth coefficient, medium resilience, low vulnerability, and adapted to warm temperate conditions. The dominant migration traits and types of fish eggs varied by year, and fish abundance was the main factor affecting the dominant traits of the communities. The results of multiple regression on distance matrices and variance partitioning analysis indicated that ammonia nitrogen content, total phosphorus content, and pH were the main environmental factors that affected fish functional diversity. The N/P ratio had an upward control effect on fish functional diversity. Our findings also revealed that high pH gradients and distant geographical distances can inhibit trait turnover in fish communities.

The long-term use of fertilizers and pesticides in conventional cultivation has resulted in a decrease in soil productivity and vegetable yields in greenhouses. However, only a limited number of studies have explored the impacts of organic cultivation on greenhouse soil. Specifically, there is a scarcity of research examining the alterations in soil aggregate mediated organic carbon and microbial communities, which are known to enhance soil productivity. This study investigated the properties of soil aggregates, including the levels of organic carbon fractions, microbial community, and enzyme activity with the three aggregate classes microaggregates (&lt;0.25 mm), small macroaggregates (2-0.25 mm) and large macroaggregates (&gt;2 mm). These investigations were conducted under three different cultivation methods in a greenhouse: conventional cultivation (CC), integrated cultivation (IC), and organic cultivation (OC) in greenhouse. The results showed that (1) OC and IC promoted the formation of small macro-aggregates and enhanced aggregate stability compared to CC, (2) SOC in the three size fractions of OC increased by 98.99%-92.06% compared to CC; EOC increased by 98.47%-117.59%; POC increased by 138.59%-208.70%; MBC increased by 104.71%-230.61%; DOC increased by 21.93%-40.90%, respectively; (3) Organic cultivation significantly increased enzyme activity in all three particle size aggregates and increased the relative abundance of bacteria in micro-aggregates as well as the relative abundance of fungi in small macro-aggregates. Structural equation model (SEM) analysis revealed that organic farming practices fostered the development of smaller macro-aggregates, elevated microbial and enzyme activities within soil aggregates, and facilitated the conversion of soil nutrients and carbon sequestration. consequently, this led to an increase in soil organic carbon content and improved vegetable yield in greenhouse environments. In conclusion, the soil carbon content and vegetable yield are enhanced through long-term organic cultivation in greenhouses, primarily by increasing the proportion of small macro-aggregates.

The relationship between the structure characteristics and performances of coal based hydrogenation isomeric base oil (CTL) and metallocene catalyzed coal based polyalphaolefin (mPAO) base oil was clarified here. CTL and mPAO were compared with typical petroleum based and natural gas based commercial API Class III and IV base oils. Pressurized differential scanning calorimetry (PDSC), Rotary Bomb Oxidation Test (RBOT) and four-balls friction tester were used to evaluate the oxidation stability and lubrication performance of base oils under different working conditions. The sensitivity of different base oils to typical antioxidants and extreme pressure antiwear agents was compared. Specially, the composition and structure of CTL base oil are obviously different from GTL and mineral base oil. The coal-based CTL and mPAO base oils exhibit commendable viscosity-temperature properties, characteristics, coupled with low-temperature fluidity, fire safety, and minimal evaporation loss. The lubricating properties, oxidation stability and the sensitivity to extreme pressure antiwear agents of CTL are close to those of similar base oils. But the sensitivity of CTL to typical antioxidants is relatively poor. Besides, compared with commercial PAO base oil, mPAO has lower isomerization degree and fewer isomerization types. The oxidation stability and sensitivity to typical antioxidants of mPAO base oil are comparable with those of commercial PAO base oil, while its lubrication performance and sensitivity to typical extreme pressure antiwear agents are significantly better than those of commercial PAO base oil.

Due to the extraordinary mechanical properties of ultra-high-performance concrete (UHPC), the shear stirrups in UHPC beams could potentially be removed. The aim of this study is to determine the effect of beam height and steel fiber volume content on the shear behavior of non-stirrup UHPC beams under a larger shear span-depth ratio (up to 2.8). Eight beams were designed and fabricated, including six non-stirrup UHPC beams and two comparing stirrup-reinforced normal concrete (NC) beams. The experimental results demonstrated that the volume content of steel fiber could be a crucial factor affecting the ductility, cracking strength, and shear strength of non-stirrup UHPC beams and altering their failure modes. Additionally, the height of the beam had a considerable effect on its shear resistance. This study revealed that the French standard formulae were more accurate for the UHPC beams with larger shear span-depth ratios and PCI-2021 formulae greatly overestimate the shear capacity of UHPC beams with larger shear span-depth ratios. Xu’s formulae were more accurate for the steel fiber-reinforced UHPC beams with larger shear span-depth ratios.

Pituitary adenylate cyclase-activating polypeptide receptor 1 (PAC1-R) has a higher affinity for pituitary adenylate cyclase- activating polypeptide (PACAP) than for vasoactive intestinal peptide (VIP). PACAP exerts its neuroprotective effects through PAC1-R. PAC1-R has become a target for drug development, and we targeted PAC1-R to screen small positive allosteric modulator 1 (SPAM1). SPAM1 has a neuroprotective effects, and in the D-galactose (D-gal) mouse model, SPAM1 improved the structure of the hippocampal dentate gyrus and restored the number of neurons. Compared with D-gal model mice, SPAM1 treatment upregulated the expression of Sirtuin 6 (SIRT6) and Lamin B1, and downregulated the expression of YinYang 1 (YY1) and p16. Consistent with this, the same results were obtained in senescent RGC-5 cells. Meanwhile, SPAM1 was also found to up-regulate SIRT6 and down-regulate YY1 expression in normal RGC-5 cells. Then, we further explored the mechanism of SPAM1 neuroprotection by whole transcriptome sequencing and proteomic analysis, and found that SPAM is involved in the longevity regulating pathway, and confirmed the up-regulation of neurofilament light polypeptide and neurofilament medium polypeptide by western blot, thus laying a pharmacological foundation for SPAM1 as a therapeutic agent for neurodegenerative diseases. polypeptide and neurofilament medium polypeptide. Thus, it lays a pharmacological foundation for SPAM1 as a therapeutic drug for neurodegenerative diseases.

Three kinds of inorganic alkali are introduced into tetraethylenepentamine (TPEA) and polyethyleneimine (PEI) modified MCM-41 as the CO2 adsorbents. FT-IR and TGA are used to characterize the surface groups and the thermal stability of adsorbents. Chemical titration method is used to measure the alkali amounts of adsorbents. Thermo-gravimetric analysis with 10%CO2/N2 as the simulated flue gas is used to test the CO2 adsorption performance of adsorbents. The results show that all three kinds of inorganic alkali containing adsorbents exhibit promoted CO2 adsorption capability than traditional TPEA and PEI modified samples. Ca(OH)2 and PEI modified samples exhibit the highest adsorption capacity and stable regeneration property. The introduction of inorganic alkali changes the chemical adsorption mechanism between CO2 and increases the effective alkali amounts due to its hydroxyl groups. The CO2 adsorption capacities have a linear dependence relation with the alkali amounts of adsorbents, indicating that alkali amount is a critical factor for novel adsorbents exploring.

Banana wilt caused by Fusarium oxysporum f. sp. cubense tropical race 4 (Foc TR4) is a devastating fungal disease. Biocontrol strategies owned immense potential in inhibiting the spread of Foc TR4. Here, thirty actinobacteria were isolated from soils and screened for their antagonistic activity against Foc TR4. Strain SCA4-21T exhibited broad-spectrum antifungal activity against nine phytopathogenic fungi including Foc TR4. The strain was identified as the genus Streptomyces according to phenotypic characteristics and 16S rRNA gene phylogenetic analysis. Multi-locus sequence analysis based on five house-keeping gene alleles (atpD, gyrB, recA, rpoB, and trpB) revealed that strain SCA4-21T clustered into S. hygroscopicus subsp. hygroscopicus NBRC 13472T with 100% of bootstrap value. Average nucleotide identity and dDDH were 98.49% and 44.30% above the threshold of novel species. Hence, strain SCA 4-21T represented a novel species within the genus Streptomyces, named as Streptomyces luomodiensis sp. nov.. The type strain is SCA4-21T (=GDMCC4.340T= JCM36555T). By the CAZymes analysis, 348 carbohydrate-active enzymes were detected, including 15 chitinases and 8 β-1,3-glucanases. The fermentation broth of strain SCA4-21T showed high activities of chitinase and β-1,3-glucanase, contributing to the antifungal activity. Our results showed an innovative potential biocontrol agent for managing plant fungal diseases, specifically banana fusarium wilt.

The characteristics of tight sandstone, such as small pores, complex pore structures, low permeability, and low porosity, make it difficult to accurately characterize its pore structure. This represents a major challenge in the exploration and development of tight sandstone oil and gas reservoirs. This article conducts a simulation study on the impact of characteristic parameters such as pore size, coordination number, pore-throat ratio, and wettability on microscopic remaining oil after polymer flooding, thereby quantifying the types of remaining oil.

Plant activators have emerged as promising alternatives to conventional pesticides for crop disease manage-ment due to their unique mode of action. By priming the plant's innate immune system, these compounds can induce systemic acquired resistance against a broad spectrum of pathogens without directly inhibiting their proliferation. Key advantages of plant activators include prolonged defense activity, lower effective dosages, and negligible risk of developing pathogen resistance. Among the various defensive pathways tar-geted, the salicylic acid (SA) signaling cascade has been extensively explored, leading to the successful devel-opment of commercial activators like benzothiadiazole for widespread application in crop protection. While the action sites of many SA-targeting activators have been preliminarily mapped to different steps along the pathway, a comprehensive understanding of their precise mechanisms remains elusive. This review provides a historical perspective on plant activator development and outlines diverse screening strategies employed across multiple levels, from whole-plant bioassays to molecular and transgenic approaches. We expound on the intricate components, biological relevance, and regulatory circuits governing the SA pathway, while criti-cally examining the structural features, bioactivities, and proposed modes of action of classical activators such as benzothiadiazole derivatives, salicylic acid analogs, and other small molecules. Insights from field trials as-sessing their practical applicability are also discussed. Furthermore, we highlight the current status, challenges, and future prospects in the realm of SA-targeting activator development globally, with a focus on recent en-deavors in China. Collectively, this comprehensive review aims to synthesize existing knowledge and provide a roadmap for future research toward developing more potent and mechanistically understood plant activa-tors that fortify crop immunity against disease.

The application of OpenAI's advanced artificial intelligence language model, ChatGPT, in education has garnered widespread attention from the academic community. The openness and intelligence features of ChatGPT demonstrate great potential and value in supporting students' learning, daily life, and communication. This study, grounded in the theoretical frameworks of Innovation Diffusion Theory (IDT) and the Technology Acceptance Model (TAM), conducted a systematic empirical analysis through a questionnaire survey of 677 university students who had used or were currently using ChatGPT. The results indicate that the six perceived attributes of innovation significantly influence university students' behavioral intention to use ChatGPT, while attitudes towards ChatGPT, perceived usefulness, and perceived ease of use indirectly influence students' behavioral intention to continue using ChatGPT. Based on the findings of this study, we propose an extended Technology Acceptance Model (TAM) that incorporates key elements of Innovation Diffusion Theory (IDT) to optimize the application of advanced technologies, such as ChatGPT, in education, thereby improving students' learning efficiency and behavioral performance. These findings not only provide important insights into the complex relationships between college students’ perceptions of ChatdGPT technology and predictors of behavioral intentions, but can also help relevant education departments, higher education institutions, and university teachers to effectively plan and evaluate the use of ChatGPT as a learning tool.

The Sanhetun tellurium-gold (Te-Au) deposit, located in the Duobaoshan polymetallic metallogenic belt (DPMB) within the eastern section of the Central Asian Orogenic Belt (CAOB), is a newly discovered small scale gold deposit. The mineralization, with resource of ≥ 4t Au, is mainly hosted in 3 NNE-trending alteration zones between granitic mylonite and volcanogenic-sedimentary formations. It’s the genesis and timing of formation of this deposit are poorly constrained. Here, we report the results of petrographic studies, TESCAN Integrated Mineral Analyzer (TIMA), major and trace element concentrations, and in situ S isotope of pyrite and U-Pb ages of calcite. The results show that there are five species of telluride, including native tellurium, tetradymite, tsumoite, hessite and petzite and four types of pyrite, including coarse-grained euhedral Py1, fine-grained quartz-Py2 vein crosscutting Py1 and anhedral aggregated Py3 and Py4. The pre-ore Py1, containing negligible Au, Te and other trace elements, have a relatively narrow range of δ34S values ranging from -1.20 to -0.57‰. The Py2 have slightly high concentrations of Au and Te and distinctly high concentrations of Mo, Sb, Zn and Mn with markedly positive δ34S values of 4.67 to 14.43‰. The main ore stage Py3 contains high Au and Te concentrations and shows narrow δ34S values ranging from -5.69 to 0.19‰. The post-ore stage Py4 contains low Au concentrations with the δ34S values ranging from 2.66 to 3.86‰. Calcite from ore stage yield a U-Pb age of 135 ± 22 Ma, interpreted as the age of Te-Au mineralization. Our geochemical and geochronological data indicate that the Te was derived from partial melting of mantle metasomatized by the Te-rich fluids that originated from oceanic sediments. We correlate the tectonic setting with the subduction of Paleo Pacific plate in Early Cretaceous.

Due to the significant price difference, expensive oils like olive oil are often blended with cheaper edible oils. This practice of adulteration in edible oils, aimed at increasing profits for producers, poses a major concern for consumers. Furthermore, adulteration in edible oils can lead to various health issues impacting consumer well-being. In order to meet the requirements of fast, non-destructive, universal, accurate and reliable quality testing for edible oil, the oblique-incidence reflectivity difference (OIRD) method combined with machine learning algorithms was introduced to detect a variety of edible oils. The prediction accuracy of Gradient Boosting, K-Nearest Neighbor, and Random Forest models exceeded 95%. Experimental results indicate that the OIRD method can serve as a powerful tool for detecting edible oils.

Aiming at the development of the application of massive and heterogeneous LEO constellations, the influence of typical perturbation forces on the orbit prediction of LEO satellites at different altitudes is analyzed. Firstly, the main perturbation force model of LEO satellites is introduced. Then experiments for analyzing the influence of orbit prediction on LEO satellites at different altitudes are designed. Finally, the influence of typical perturbation forces on the orbit prediction under different prediction durations by numerical analysis is assessed systematically, and the orbit prediction of LEO satellites with different non-spherical gravitational field orders, ocean tide orders and atmospheric density models were compared and calculated. The research results show that different orders of non-spherical gravitational fields should be considered when the altitude of satellites is different, the reasonable gravitational field orders for LEO satellites at 500km, 1000km, 1500km and 2000km under 1-hour forecast time are 140x140, 80x80, 60x60 and 40x40 orders, the reasonable orders of ocean tide perturbation for 1500km and 2000km satellites are 20x20 orders. Under the 24-hour forecast time, atmospheric resistance has a great influence on the precision of LEO satellites at orbit altitude below 1,000km. The forecast deviation of the LEO satellites orbit at 500km reaches 8751.576m. The difference of orbit precision between different atmospheric density models is in the meter level. The orbit deviation by the Standard Atmosphere 1976 and NRLMSISE 2000 atmospheric models respectively reaches 25.494m. Relevant conclusions provide support for the optimization and selection of dynamical models in high precision orbit determination and fast orbit prediction of LEO satellites.

This study aimed to identify miRNA editing sites during adipose tissue development in Ningxiang pigs and analyze their characteristics and functions. Based on the small RNA-seq data from subcutaneous adipose tissue samples of Ningxiang pigs at four stages (30, 90, 150 and 210 days), we constructed a developmental atlas of the miRNA editome in adipose tissue of Ningxiang pigs. The characteristics and functions of this editome were further explored through bioinformatics analysis. The findings revealed that a total of 505 miRNA editing sites were identified with C-to-U editing types being the most prevalent, followed by U-to-C, A-to-G and G-to-U. Importantly, these four types of miRNA editing exhibited a base preference. The number of editing sites showed obvious differences among age groups, with the highest occurrence of miRNA editing events observed at 90 days of age and the lowest at 150 days of age. In the miRNA seed region, a total of 9 miRNA editing sites were identified with significant differences in editing levels (P &lt; 0.05), and located in ssc-miR-23a, ssc-miR-27a, ssc-miR-30b-5p, ssc-miR-15a, ssc-miR-497, ssc-miR-15b, and ssc-miR-425-5p, respectively. Target gene prediction and enrichment analysis revealed that the editing of miR-497 may potentially regulate fat deposition by inhibiting adipose synthesis via influencing target binding. These results provide new insights into the regulatory mechanism of pig fat deposition.

This study aimed to investigate the fatigue performance and interfacial strengthening mechanisms of Al6061-to-Cu dissimilar lap joints via electromagnetic pulse welding (EMPW). The load-bearing capacity of the joints at discharge voltages of 14 kV and 16 kV was superior to that of 12 kV. Furthermore, the fatigue life of the 14 kV joints was one order of magnitude higher compared to the 12 kV joints. SEM observation of the fatigue fracture surface revealed the presence of typical "tire-mark" fatigue striations only in the 14 kV joint, indicating higher ductility and the ability to withstand extended plastic deformation. Notably, ultra-fine nanocrystalline Al2Cu phase, amorphous phase, and numerous dislocations hindered EBSD acquisition in the transition zone. Nevertheless, dense low-angle grain boundaries (LAGBs) and refined grains were examined at the bonding interface. This suggests that the high-velocity collision caused severe plastic deformation and led to the formation of substructures. The interfacial strength was attributed not only to the "wave-like" or "inverted hook-like" diffusion layer but also to the presence of refined grains along the interface and nanocrystalline Al2Cu in the transition zone. Furthermore, the hybrid nanocrystalline-amorphous microstructure strengthened the joint by regulating the balance between strength and ductility.

In order to improve contrast and restore color for underwater image captured by camera sensors without suffering from insufficient details and color cast, a fusion algorithm for image enhancement in different color spaces based on contrast limited adaptive histogram equalization (CLAHE) is proposed in this article. The original color image is first converted from RGB color space to two different special color spaces: YIQ and HSI. The color space conversion from RGB to YIQ is a linear transformation, while the RGB to HSI conversion is nonlinear. Then, the algorithm separately operates CLAHE in YIQ and HSI color spaces to obtain two different enhancement images. The luminance component (Y) in the YIQ color space and the intensity component (I) in the HSI color space are enhanced with CLAHE algorithm. The CLAHE has two key parameters: Block Size and Clip Limit, which mainly control the quality of CLAHE enhancement image. After that, the YIQ and HSI enhancement images are respectively converted backward to RGB color. When the three components of red, green, and blue are not coherent in the YIQ-RGB or HSI-RGB images, the three components will have to be harmonized with the CLAHE algorithm in RGB space. Finally, with 4 direction Sobel edge detector in the bounded general logarithm ratio operation, a self-adaptive weight selection nonlinear image enhancement is carried out to fuse YIQ-RGB and HSI-RGB images together to achieve the final fused image. The enhancement fusion algorithm has two key factors: average of Sobel edge detector and fusion coefficient, and these two factors determine the effects of enhancement fusion algorithm. A series of evaluate metrics such as mean, contrast, entropy, colorfulness metric (CM), mean square error (MSE) and peak signal to noise ratio (PSNR) are used to assess the proposed enhancement algorithm. The experiments results showed that the proposed algorithm provides more detail enhancement and higher values of colorfulness restoration as compared to other existing image enhancement algorithms. The proposed algorithm can suppress effectively noise interference, improve the image quality for underwater image availably.

The advancement in science and technology has led to luffa sponge (LS) being widely used as a natural material in industrial application as its polyporous structure and light texture. In order to enhance the utility of LS fibers as the reinforcement of lightweight composite materials, this study investigate its water absorption, mechanical properties, anatomical characteristic and thermal performance. Hence, moisture regain, tensile properties of LS fiber bundles were measured in accordance with standards and the structural characteristics were investigated via microscopic observation. Scanning electron microscopy (SEM) was used to observe the surface morphology and fracture surface of fiber bundles. Test results shows that the special structure where the phloem tissues degenerate to cavities had a significant influence on the mechanical properties of LS fiber bundles. Additionally, the transverse sectional area occupied by fibers in a fiber bundle (SF), wall thickness and ratio of wall to lumen of fiber cell, and crystallinity of cellulose had an impact on the mechanical properties of LS fiber bundles. Furthermore, the fiber bundles density of LS varies range of 385.46-468.70 kg/m³, much less than that of jute (1360.40 kg/m³) and Arenga engleri (950.20 kg/m³) while LS fiber bundles has superior specific modulus.

Background: in this paper, a new physical penetration technology for transdermal administration with traditional Chinese medicine (TCM) characteristics - Fu’s cupping therapy (FCT) - was established and studied by in-vitro and in-vivo experiments; the penetration effect and mechanism of FCT physical penetration technology (FCT-PPT) was preliminarily discussed. Method: Indomethacin (IM) as a model drug，by transdermal in vitro tests the establishment of the high，medium and low reference were finished as the chemical permeation system；chemical penetration enhancers and iontophoresis as a reference，the percutaneous penetration effect of FCT for IM patch was evaluated with 7 species diffusion kinetics model and in vitro drug distribution；naproxen as an internal standard，using UPLC-MS/MS technology，the IM quantitative analysis method in vivo was established，and pharmacokinetic parameters (AUC0-t，AUC0-∞，AUMC0-t，AUMC0-∞, Cmax and MRT) as indicators were used evaluate to FCT penetration role in vivo；in the same time，the group used 3K factorial design to study joint synergistic penetration effect on FCT and chemical penetration enhancers (CPEs)；by SEM and TEM，the skin micro and ultrastructural changes of the stratum corneum (SC) surface were observed, to explore pay tank penetration mechanism. Results: In vitro and in-vivo skin permeation experiments revealed that both the total cumulative percutaneous amount and in-vivo percutaneous absorption amount (AUC and AUMC) of indomethacin that permeated SD mouse skin using FCT techniques were greater than the amount observed using CPE and iontophoresis: Firstly, in contrast to the control group, the indomethacin percutaneous rate (PR) of the FCT lower group (FCTL) was 35.52%, and the enhancement ratio (ER) at 9h was 1.76X, which was roughly equivalent to the penetration enhancing effect of the CPEs and iontophoresis; secondly, the indomethacin PR of the FCT middle (FCTM) group and the FCT high intensity group (FCTH) were respectively 47.36% and 54.58%, ER at 9h were separately 3.58X and 8.39X; thirdly, pharmacokinetic studies showed that in-vivo indomethacin percutaneous absorption of the FCTs was higher than that of the control group, that of the FCTM group was slightly higher than that of the CPEs group, and that of the FCTM group was significantly higher than that of the others. Meanwhile, the variance analysis indicated that the combination of the FCT penetration enhancement method and the CPE method had beneficial effects in penetration enhancing of the skin: the significance level of the CPE method was 0.0004, which was apparently lower than the 0.001, meaning the difference was markedly significant; the significance level of the FCT was under 0.0001, its difference markedly significant; and the significance level of factor interaction A×B was lower than 0.0001, indicating that its difference of the synergism was markedly significant. Moreover, SEM and TEM images showed that the SC surfaces of SD rats treaded with FCT-PPT was damaged, and hard to observe the complete surface structure with its SC pores growing bigger and its special “brick structure” becoming looser, indicating that it broke the barrier function of skin, which revealed potentially a major route of skin penetration. Conclusion: FCT, as percutaneous penetration new technologies, has penetration effects significantly, with Chinese characteristics and highly clinical value, worth promoting development.

In this paper, we first discuss the research status and application progress of the finite element method and the smoothed particle method. By analyzing the advantages of the smoothed particle method and the finite element method, a new coupling algorithm, namely FEM-SPH algorithm, is proposed. By the method of comparison, it shows that finite element method and SPH method in the simulation of large deformation problems each have advantages and disadvantages, the finite element method smoothed particle coupling algorithm is effective to achieve the performance of high computational efficiency and can naturally simulate large deformation problems across. In the process of calculation, the large deformation unit can be freely into an algorithm to facilitate the calculation accuracy and efficiency of three methods of numerical simulation. Through the study found, FEM-SPH algorithm not only overcome the defect of smooth particle tensile instability, but also overcomes the problem of low efficiency of finite element computation. To further test the FEM-SPH algorithm has advantages in the practical engineering, we have carried out the actual test to the example of the super high speed collision, concluded that, since the target of most of the computational domain is always finite element, smoothed particle focused only in contact with the projectile and target of local area, particle number is not much, the whole calculation process just ten minutes, computational efficiency has been greatly improved, at the same time in the simulation of large deformation, the advantage is very obvious .This provides a criterion for the actual project, depending on the specific material deformation mode and choose a more appropriate conversion algorithm.

Dynamic sparse training is an effective strategy to alleviate the training and inference demands of artificial neural networks. However, current sparse training methods face a challenge in achieving high levels of sparsity while maintaining performance comparable to that of their fully connected counterparts. The Cannistraci-Hebb training (CHT) method produces an ultra-sparse advantage compared to fully connected training in various tasks by using a gradient-free link regrowth method, which relies solely on the network topology. However, its rigid selection based on link prediction scores may lead to epitopological local minima, especially at the beginning of the training process when the network topology might be noisy and unreliable. In this article, we introduce the Cannistraci-Hebb training soft rule (CHTs), which applies a flexible approach to both the removal and regrowth of links during training, fostering a balance between exploring and exploiting network topology. Additionally, we investigate the network topology initialization using several approaches, including the bipartite scale-free and bipartite small-world network models. Empirical results show that CHTs can surpass the performance of fully connected networks with MLP architecture by using only 1% of the connections (99% sparsity) on the MNIST, EMNIST, and Fashion MNIST datasets and can provide remarkable results with only 0.1% of the links (99.9% sparsity). In some MLPs for image classification tasks, CHTs can reduce the active neuron network size to 20% of the original nodes (neurons), demonstrating a remarkable ability to generalize better than fully connected architectures, reducing the entire model size. This represents a relevant result for dynamic sparse training. Finally, we present evidence from larger network models such as Transformers, with 10% of the connections (90% sparsity), where CHTs outperform other prevalent dynamic sparse training methods in machine translation tasks.

Iron plays a critical role in lung infections due to its function in the inflammatory immune response but also as an important factor for bacterial growth. Iron chelation represents a potential therapeutic approach to inhibit bacterial growth and pathologically increased pro-inflammatory mediator production. The present study was designed to investigate the impact of the iron chelator, DIBI, in murine lung infection induced by intratracheal Pseudomonas aeruginosa (strain PA14) administration. DIBI (80mg/kg) was given by intraperitoneal injection either as a single dose immediately after PA14 administration or a double dose (second dose 4 h after PA14 administration). The results showed that lung NF-κBp65 levels, as well as levels of various inflammatory cytokines (TNFα, IL-1β, IL-6) both in lung tissue and bronchoalveolar lavage fluid (BALF), were significantly increased 24h after PA14 administration. Single-dose DIBI did not affect the bacterial load or inflammatory response in the lungs or BALF. However, two doses of DIBI significantly decreased bacterial load, attenuated NF-κBp65 upregulation, reduced inflammatory cytokines production, and relieved lung tissue damage. Our findings support the conclusion that the iron chelator, DIBI, can reduce lung injury induced by P. aeruginosa, via its anti-bacterial and anti-inflammatory effects.

The use of tuning forks to measure fluid density and viscosity is widely employed in fields such as food, medicine, textiles, automobiles, petrochemicals, and deep drilling. The explicit analytical model based on Euler-Bernoulli cantilever beam theory for the relationship between tuning fork resonance characteristics and the density and viscosity of fluid is only applicable to the situation where the fluid viscous effect is very small. In this paper, the finite element method is used to simulate the influence of large variations in fluid density and viscosity on the resonance characteristic parameters (resonant frequency and quality factor) of the tuning fork. The numerical simulation results are compared with the analytical analysis results and experimental measurement results. Then, the sensitivity of tuning fork resonance characteristic parameters to fluid density and viscosity is studied. The results show that compared with the analytical results, the numerical simulation results have a higher degree of agreement with the experimental measurement results. The relative difference in resonant frequency is less than 2%, and the relative difference in quality factor is less than 4%. This indicates that the finite element method includes the influence of fluid viscosity on tuning fork resonance parameters, which is more in line with the actual conditions than the analytical model. Simulating and analyzing the sensitivity of the tuning fork to fluid density and viscosity by the finite element method, it is possible to consider the situation where fluid density and viscosity vary over a large range. Compared with experimental measurements, this method has higher efficiency and can significantly save time and economic costs. This study can overcome the limitation of existing explicit analytical models, which are only applicable when the viscous effects of the fluid are very small. It enables a more accurate simulation of the coupling vibration between tuning forks and fluids, thereby providing theoretical references for further optimizing tuning fork structural parameters to enhance the accuracy of measuring fluid characteristic parameters.

With its unique properties and potential applications, nanoparticle-based delivery platforms for vac-cines have gained significant attention in recent years. Nanoparticles have the advantages of enhancing immunogenicity, targeting delivery and improving stability, providing a new solution for drug and vaccine delivery. And in some clinical studies, a variety of nanoparticle delivery platforms have been gradually applied to a wide range of vaccine applications. Current research priorities are exploring various types of nanoparticles as vaccine delivery system to enhance vaccine stability and immunogen-icity. Lipid nanoparticles have shown promising potential in preclinical and clinical studies to efficiently deliver antigens to immune cells. Moreover, lipid nanoparticles (LNPs) and other nanoparticles as nu-cleic acids especially mRNA delivery system, have shown vast potential for vaccine development. In this review, we present various vaccine platforms with an emphasis on nanoparticles for mRNA nano-vaccine delivery vehicles. We introduce several novel nanoparticles delivery platforms for mRNA vac-cine such as lipid, polymer, protein-based nanoparticle and so on. In addition, we overview the an-ti-tumor immunity of nanovaccine against different tumors in cancer immunotherapy. Finally, we give an outlook future perspectives and remaining challenges for this promising technolo-gy-nanoparticle-based delivery platforms for vaccines.