Search

Abstract It is of great significance to develop a novel approach to treat bacterial infections, as the frequent misuse of antibiotics leads to the serious problem of bacterial resistance. This study proposed antibiotic-free antibacterial nanoparticles for eliminating methicillin-resistant Staphylococcus aureus (MRSA) based on a multi-model synergistic antibacterial ability of chemodynamic therapy (CDT), photothermal effect, and innate immunomodulation. Specifically, a polydopamine (PDA) layer coated and Ag nanoparticles loaded core-shell structure Fe3O4 nanoparticles (Fe3O4@PDA-Ag) is prepared. The Fe3O4 catalyzes H2O2 present in acidic microenvironment of bacterial infection into more toxic reactive oxygen species (ROS) and synergizes with the released Ag ions to exert a stronger bactericidal capacity, which can be augmented by photothermal action of PDA triggered by near-infrared light and loosen the biofilm by photothermal action to promote the penetration of ROS and Ag ion into the biofilm, result in disrupting biofilm structure along with killing encapsulated bacteria. Furthermore, Fe3O4@PDA-Ag exerts indirect antibacterial effects by promoting M1 macrophage polarizing. Animal models demonstrated that Fe3O4@PDA-Ag effectively controlled MRSA-induced infections through photothermal enhanced CDT, Ag+ releasing, and macrophage-mediated bactericidal properties. The acid-triggered antibacterial nanoparticles are expected to combat drug-resistant bacteria infection. Graphical abstract

Abstract Due to the lack of direct assessment metrics, existing studies on the intensity of agricultural policies often utilize indicators such as Gross Domestic Product (GDP) of agriculture or the quantity of agricultural policies as measures. Optimizing methods for analyzing the intensity of agricultural policies will significantly impact parameter selection in agricultural policy research and the evaluation of policy effectiveness. In this study, we constructed a Chinese Agricultural Policy Corpus using agricultural policies released by various governmental agencies at the national level in China from 1982 to April 2023. We quantified the values of agricultural domain terms in the corpus and evaluated the intensity of each agricultural policy document. The validation results of this study indicate a strong correlation between the intensity of agricultural policies and agricultural GDP. The trend in agricultural GDP changes lags behind policy intensity by 2.5 years (at a 95% confidence level), thus validating the rationality of our constructed corpus, agricultural policy scoring dataset, and methodology.

Abstract Background Heterosis has successfully enhanced maize productivity and quality. Although significant progress has been made in delineating the genetic basis of heterosis, the molecular mechanisms underlying its genetic components remain less explored. Allele-specific expression (ASE), the imbalanced expression between two parental alleles in hybrids, is increasingly being recognized as a factor contributing to heterosis. ASE is a complex process regulated by both epigenetic and genetic variations in response to developmental and environmental conditions. Results In this study, we explored the differential characteristics of ASE by analyzing the transcriptome data of two maize hybrids and their parents under four light conditions. On the basis of allele expression patterns in different hybrids under various conditions, ASE genes were divided into three categories: bias-consistent genes involved in basal metabolic processes in a functionally complementary manner, bias-reversal genes adapting to the light environment, and bias-specific genes maintaining cell homeostasis. We observed that 758 ASE genes (ASEGs) were significantly overlapped with heterosis quantitative trait loci (QTLs), and high-frequency variations in the promoter regions of heterosis-related ASEGs were identified between parents. In addition, 10 heterosis-related ASEGs participating in yield heterosis were selected during domestication. Conclusions The comprehensive analysis of ASEGs offers a distinctive perspective on how light quality influences gene expression patterns and gene-environment interactions, with implications for the identification of heterosis-related ASEGs to enhance maize yield.

Abstract With the emergence of distributed energy resources (DER) and prosumers in the distribution networks, the traditional centralised operation structure has transformed into the decentralised operation and transaction structure, which is regarded as the transactive energy market. Furthermore, the planning problem in the distribution network is deeply affected by the transactive energy market. First, the literature and studies on distribution network planning with DERs are reviewed. Moreover, the multi‐stakeholder operation and planning framework under the transactive energy market are analysed. The transactive energy market provides a peer‐to‐peer (P2P) transaction for third‐party prosumers. Several techniques for multi‐stakeholders planning and P2P transaction are introduced. Finally, a tri‐layer distribution network expansion planning (DNEP) framework integrated with virtual P2P transactions is proposed and analysed to accommodate uncertainties and postpone investment. The key technology and difficulties are also discussed.

Abstract The electrochemical nitrate reduction reaction (NO3RR) holds promise for ecofriendly nitrate removal. However, the challenge of achieving high selectivity and efficiency in electrocatalyst systems still significantly hampers the mechanism understanding and the large‐scale application. Tandem catalysts, comprising multiple catalytic components working synergistically, offer promising potential for improving the efficiency and selectivity of the NO3RR. This review highlights recent progress in designing tandem catalysts for electrochemical NO3RR, including the noble metal‐related system, transition metal electrocatalysts, and pulsed electrocatalysis strategies. Specifically, the optimization of active sites, interface engineering, synergistic effects between catalyst components, various in situ technologies, and theory simulations are discussed in detail. Challenges and opportunities in the development of tandem catalysts for scaling up electrochemical NO3RR are further discussed, such as stability, durability, and reaction mechanisms. By outlining possible solutions for future tandem catalyst design, this review aims to open avenues for efficient nitrate reduction and comprehensive insights into the mechanisms for energy sustainability and environmental safety.

Low-temperature and low-salt fermented Chinese kohlrabi (LSCK) represents a novel approach to producing low-salt kohlrabi without the need for desalination during processing, as compared to traditional techniques. However, the profile of its non-volatile metabolites remains unclear. In order to investigate the non-volatile metabolites and their changes in LSCK during fermentation, the LSCKs fermented for 0 day (0D), 45 days (45D) and 90 days (90D) were analyzed using LC-MS/MS non-targeted metabolomics coupled with multivariate statistical analysis. The results showed that 60, 74, and 68 differential metabolites were identified in the three groups A1 (0D and 45D), A2 (0D and 90D), and A3 (45D and 90D) (VIP >1, p 1), respectively. The differential metabolites were mainly amino acids, peptides, and analogues, fatty acyls, organic acids and derivatives, and carbohydrates and carbohydrate conjugates. Seventeen common differential metabolites were identified in A1, A2, and A3 groups. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis suggested that the alanine, aspartate and glutamate metabolism, butanoate metabolism, α-linolenic acid metabolism, arginine biosynthesis, and phenylalanine metabolism were significantly correlated with the differential metabolites. The present study elucidates for the first time the changes in non-volatile differential metabolites and their associated metabolic pathways in the novel Chinese low-salt kohlrabi, providing a theoretical basis for improving the industrial fermentation process of this innovative product.

Objective Explainable machine learning (XAI) was introduced in this study to improve the interpretability, explainability and transparency of the modelling results. The survex package in R was used to interpret and compare two survival models – the Cox proportional hazards regression (coxph) model and the random survival forest (rfsrc) model – and to estimate overall survival (OS) and its determinants in heart failure (HF) patients using these models. Methods We selected 1159 HF patients hospitalised at the First Affiliated Hospital of Kunming Medical University. First, the performance of the two models was investigated using the C-index, the integrated C/D AUC, and the integrated Brier score. Second, a global explanation of the whole cohort was carried out using the time-dependent variable importance and the partial dependence survival profile. Finally, the SurvSHAP(t) and SurvLIME plots and the ceteris paribus survival profile were used to obtain a local explanation for each patient. Results By comparing the C-index, the C/D AUC, and the Brier score, this study showed that the model performance of rfsrc was better than coxph. The global explanation of the whole cohort suggests that the C-reactive protein, lg BNP (brain natriuretic peptide), estimated glomerular filtration rate, albumin, age and blood chloride were significant unfavourable predictors of OS in HF patients in both the cxoph and the rfsrc models. By including individual patients in the model, we can provide a local explanation for each patient, which guides the clinician in individualising the patient's treatment. Conclusion By comparison, we conclude that the model performance of rfsrc is better than that of coxph. These two predictive models, which address not only the whole population but also selected patients, can help clinicians personalise the treatment of each HF patient according to his or her specific situation.

The identification of plant leaf diseases is crucial in precision agriculture, playing a pivotal role in advancing the modernization of agriculture. Timely detection and diagnosis of leaf diseases for preventive measures significantly contribute to enhancing both the quantity and quality of agricultural products, thereby fostering the in-depth development of precision agriculture. However, despite the rapid development of research on plant leaf disease identification, it still faces challenges such as insufficient agricultural datasets and the problem of deep learning-based disease identification models having numerous training parameters and insufficient accuracy. This paper proposes a plant leaf disease identification method based on improved SinGAN and improved ResNet34 to address the aforementioned issues. Firstly, an improved SinGAN called Reconstruction-Based Single Image Generation Network (ReSinGN) is proposed for image enhancement. This network accelerates model training speed by using an autoencoder to replace the GAN in the SinGAN and incorporates a Convolutional Block Attention Module (CBAM) into the autoencoder to more accurately capture important features and structural information in the images. Random pixel Shuffling are introduced in ReSinGN to enable the model to learn richer data representations, further enhancing the quality of generated images. Secondly, an improved ResNet34 is proposed for plant leaf disease identification. This involves adding CBAM modules to the ResNet34 to alleviate the limitations of parameter sharing, replacing the ReLU activation function with LeakyReLU activation function to address the problem of neuron death, and utilizing transfer learning-based training methods to accelerate network training speed. This paper takes tomato leaf diseases as the experimental subject, and the experimental results demonstrate that: (1) ReSinGN generates high-quality images at least 44.6 times faster in training speed compared to SinGAN. (2) The Tenengrad score of images generated by the ReSinGN model is 67.3, which is improved by 30.2 compared to the SinGAN, resulting in clearer images. (3) ReSinGN model with random pixel Shuffling outperforms SinGAN in both image clarity and distortion, achieving the optimal balance between image clarity and distortion. (4) The improved ResNet34 achieved an average recognition accuracy, recognition precision, recognition accuracy (redundant as it’s similar to precision), recall, and F1 score of 98.57, 96.57, 98.68, 97.7, and 98.17%, respectively, for tomato leaf disease identification. Compared to the original ResNet34, this represents enhancements of 3.65, 4.66, 0.88, 4.1, and 2.47%, respectively.

Abstract Highly efficient decontamination of elemental mercury (Hg0) remains an enormous challenge for public health and ecosystem protection. The artificial conversion of Hg0 into mercury chalcogenides could achieve Hg0 detoxification and close the global mercury cycle. Herein, taking inspiration from the bio-detoxification of mercury, in which selenium preferentially converts mercury from sulfoproteins to HgSe, we propose a biomimetic approach to enhance the conversion of Hg0 into mercury chalcogenides. In this proof-of-concept design, we use sulfur-rich polyphenylene sulfide (PPS) as the Hg0 transporter. The relatively stable, sulfur-linked aromatic rings result in weak adsorption of Hg0 on the PPS rather than the formation of metastable HgS. The weakly adsorbed mercury subsequently migrates to the adjacent selenium sites for permanent immobilization. The sulfur-selenium pair affords an unprecedented Hg0 adsorption capacity and uptake rate of 1621.9 mg g−1 and 1005.6 μg g−1 min−1, respectively, which are the highest recorded values among various benchmark materials. This work presents an intriguing concept for preparing Hg0 adsorbents and could pave the way for the biomimetic remediation of diverse pollutants.

IntroductionLight use efficiency (LUE) is a crucial determinant of plant productivity, while leaf functional traits directly affect ecosystem functions. However, it remains unclear how climate warming affects LUE and leaf functional traits of dominant species in alpine meadows.MethodsWe conducted a 4-year in-situ field warming experiment to investigate the eco-physiological characteristics for a dominant species (Elymus nutans) and a common species (Potentilla anserina) on the Tibetan Plateau. The leaf traits, photosynthesis and fluorescence characteristics were measured, along with the soil physical-chemical properties associated with the two species.Results and discussionsExperimental warming increased the leaf LUE, maximum photochemical efficiency, non-photochemical quenching, relative water content and specific leaf area for both species. However, there was a decrease in leaf and soil element content. Different species exhibit varying adaptability to warming. Increasing temperature significantly increased the photosynthetic rate, stomatal conductance, transpiration rate, total water content, and specific leaf volume of E. nutans; however, all these traits exhibited an opposite trend in P. anserina. Warming has a direct negative impact on leaf LUE and an indirectly enhances LUE through its effects on leaf traits. The impact of warming on plant photosynthetic capacity is primarily mediated by soil nutrients and leaf traits. These results indicate that the two different species employ distinct adaptive strategies in response to climate change, which are related to their species-specific variations. Such changes can confer an adaptive advantage for plant to cope with environmental change and potentially lead to alterations to ecosystem structure and functioning.

Three-dimensional (3D) single-object tracking (3D SOT) is a fundamental yet not well-solved problem in 3D vision, where the complexity of feature matching and the sparsity of point clouds pose significant challenges. To handle abrupt changes in appearance features and sparse point clouds, we propose a novel 3D SOT network, dubbed CDTracker. It leverages both cosine similarity and an attention mechanism to enhance the robustness of feature matching. By combining similarity embedding and attention assignment, CDTracker performs template and search area feature matching in a coarse-to-fine manner. Additionally, CDTracker addresses the problem of sparse point clouds, which commonly leads to inaccurate tracking. It incorporates relatively dense sampling based on the concept of point cloud segmentation to retain more target points, leading to improved localization accuracy. Extensive experiments on both the KITTI and Waymo datasets demonstrate clear improvements in CDTracker over its competitors.

IntroductionThe study addresses challenges in detecting cotton leaf pests and diseases under natural conditions. Traditional methods face difficulties in this context, highlighting the need for improved identification techniques.MethodsThe proposed method involves a new model named CFNet-VoV-GCSP-LSKNet-YOLOv8s. This model is an enhancement of YOLOv8s and includes several key modifications: (1) CFNet Module. Replaces all C2F modules in the backbone network to improve multi-scale object feature fusion. (2) VoV-GCSP Module. Replaces C2F modules in the YOLOv8s head, balancing model accuracy with reduced computational load. (3) LSKNet Attention Mechanism. Integrated into the small object layers of both the backbone and head to enhance detection of small objects. (4) XIoU Loss Function. Introduced to improve the model's convergence performance.ResultsThe proposed method achieves high performance metrics: Precision (P), 89.9%. Recall Rate (R), 90.7%. Mean Average Precision (mAP@0.5), 93.7%. The model has a memory footprint of 23.3MB and a detection time of 8.01ms. When compared with other models like YOLO v5s, YOLOX, YOLO v7, Faster R-CNN, YOLOv8n, YOLOv7-tiny, CenterNet, EfficientDet, and YOLOv8s, it shows an average accuracy improvement ranging from 1.2% to 21.8%.DiscussionThe study demonstrates that the CFNet-VoV-GCSP-LSKNet-YOLOv8s model can effectively identify cotton pests and diseases in complex environments. This method provides a valuable technical resource for the identification and control of cotton pests and diseases, indicating significant improvements over existing methods.

Grain legumes play a significant global role and are integral to agriculture and food production worldwide. Therefore, comprehending and analyzing the factors that influence grain legume yield are of paramount importance for guiding agricultural management and decision making. Traditional statistical analysis methods present limitations in interpreting results, but explainable artificial intelligence (AI) provides a visual representation of model results, offering insights into the key factors affecting grain legume yield. In this study, nine typical grain legume species were selected from a published global experimental dataset: garden pea (Pisum sativum), chickpea (Cicer arietinum), cowpea (Vigna unguiculata), garden vetch (Vicia sativa), faba bean (Vicia faba), lentil (Lens culinaris), pigeon pea (Cajanus cajan), peanut (Arachis hypogaea), and white lupine (Lupinus albus). Seven commonly used models were constructed for each legume species, and model performance evaluation was conducted using accuracy, AUC, recall, precision, and F1 score metrics. The best classification model was selected for each grain legume species. Employing Decision Tree analysis, Feature Importance Evaluation, and SHapley Additive exPlanations (SHAP) as explainable techniques, our study conducted both individual and comprehensive analyses of nine leguminous crops. This approach offers a novel perspective, unveiling not only the unique responses of each crop to the influencing factors but also demonstrating the common factors across different crops. According to the experimental results, XGboost (XGB) and Random Forests (RF) are the best-performing models among the nine types of grain legumes, and the classification accuracy of a specific species is as high as 87.33%. Insights drawn from the feature importance map reveal that several factors, including aerial biomass, precipitation, sunshine duration, soil conditions, growth cycle, and fertilization strategy, have a pivotal influence. However, it was found from the SHAP graph that the responses of various crops to these factors are not the same. This research furnishes novel perspectives and insights into understanding the factors influencing grain legume yields. The findings provide a robust scientific foundation for agricultural managers, experts, and policymakers in the pursuit of optimizing pulse yields and advancing agricultural sustainability.

This research tackles the intricate challenges of detecting densely distributed maize leaf diseases and the constraints inherent in YOLO-based detection algorithms. It introduces the GhostNet_Triplet_YOLOv8s algorithm, enhancing YOLO v8s by integrating the lightweight GhostNet (Ghost Convolutional Neural Network) structure, which replaces the YOLO v8s backbone. This adaptation involves swapping the head’s C2f (Coarse-to-Fine) and Conv (Convolutional) modules with C3 Ghost and GhostNet, simplifying the model architecture while significantly amplifying detection speed. Additionally, a lightweight attention mechanism, Triplet Attention, is incorporated to refine the accuracy in identifying the post-neck layer output and to precisely define features within disease-affected areas. By introducing the ECIoU_Loss (EfficiCLoss Loss) function, replacing the original CIoU_Loss, the algorithm effectively mitigates issues associated with aspect ratio penalties, resulting in marked improvements in recognition and convergence rates. The experimental outcomes display promising metrics with a precision rate of 87.50%, a recall rate of 87.70%, and an mAP@0.5 of 91.40% all within a compact model size of 11.20 MB. In comparison to YOLO v8s, this approach achieves a 0.3% increase in mean average precision (mAP), reduces the model size by 50.2%, and significantly decreases FLOPs by 43.1%, ensuring swift and accurate maize disease identification while optimizing memory usage. Furthermore, the practical deployment of the trained model on a WeChat developer mini-program underscores its practical utility, enabling real-time disease detection in maize fields to aid in timely agricultural decision-making and disease prevention strategies.

Recently, there has been a notable surge in interest regarding reclaiming valuable chemicals from waste plastics. However, the energy-intensive conventional thermal catalysis does not align with the concept of sustainable development. Herein, we report a sustainable electrocatalytic approach allowing the selective synthesis of glycolic acid (GA) from waste polyethylene terephthalate (PET) over a Pd67Ag33 alloy catalyst under ambient conditions. Notably, Pd67Ag33 delivers a high mass activity of 9.7 A mgPd -1 for ethylene glycol oxidation reaction (EGOR) and GA Faradaic efficiency of 92.7 %, representing the most active catalyst for selective GA synthesis. In situ experiments and computational simulations uncover that ligand effect induced by Ag incorporation enhances the GA selectivity by facilitating carbonyl intermediates desorption, while the lattice mismatch-triggered tensile strain optimizes the adsorption of *OH species to boost reaction kinetics. This work unveils the synergistic of strain and ligand effect in alloy catalyst and provides guidance for the design of future catalysts for PET upcycling. We further investigate the versatility of Pd67Ag33 catalyst on CO2 reduction reaction (CO2RR) and assemble EGOR//CO2RR integrated electrolyzer, presenting a pioneering demonstration for reforming waste carbon resource (i.e., PET and CO2) into high-value chemicals. [ABSTRACT FROM AUTHOR]

Photocatalytic conversion of CO2 into value-added hydrocarbon fuels is a promising approach to alleviate the energy crisis caused by the overuse of fossil fuels. Here, a flexible Zr/TiO2-x nanofiber photocatalyst with abundant oxygen-vacancies (OVs) has been fabricated and employed in the CO2 photocatalytic reduction process. The selectivity for the photocatalytic reduction of CO2 to CH4 over H400-Zr/TiO2-x nanofiber films could reach up to 87.6% which is rather high compared with currently reported photocatalytic systems. In-depth experiments demonstrate that the high product selectivity of CH4 originated from the synergetic effect between vacancy and nanofiber structure in H400-Zr/TiO2-x. Density functional theory (DFT) simulation reveals that the existence of a vacancy in H400-Zr/TiO2-x facilitates a reduction in the surface free energy barrier from the intermediate CO* to CHO* during the production of CH4, which is further confirmed by the observed obvious CHO* signal in in situ FTIR spectra. Additionally, the characteristic of one-dimensional long-range orientation and large surface area of nanofiber structure of H400-Zr/TiO2-x is beneficial to providing more catalytic active sites which help to promote the CO2 photoreduction property. This work paves the way for the efficient design of photocatalytic systems towards high conversion of CO2 to CH4. [ABSTRACT FROM AUTHOR]

This study proposed a weighted sampling hierarchical classification learning method based on an efficient backbone network model to address the problems of high costs, low accuracy, and time-consuming traditional tea disease recognition methods. This method enhances the feature extraction ability by conducting hierarchical classification learning based on the EfficientNet model, effectively alleviating the impact of high similarity between tea diseases on the model's classification performance. To better solve the problem of few and unevenly distributed tea disease samples, this study introduced a weighted sampling scheme to optimize data processing, which not only alleviates the overfitting effect caused by too few sample data but also balances the probability of extracting imbalanced classification data. The experimental results show that the proposed method was significant in identifying both healthy tea leaves and four common leaf diseases of tea (tea algal spot disease, tea white spot disease, tea anthracnose disease, and tea leaf blight disease). After applying the "weighted sampling hierarchical classification learning method" to train 7 different efficient backbone networks, most of their accuracies have improved. The EfficientNet-B1 model proposed in this study achieved an accuracy rate of 99.21% after adopting this learning method, which is higher than EfficientNet-b2 (98.82%) and MobileNet-V3 (98.43%). In addition, to better apply the results of identifying tea diseases, this study developed a mini-program that operates on WeChat. Users can quickly obtain accurate identification results and corresponding disease descriptions and prevention methods through simple operations. This intelligent tool for identifying tea diseases can serve as an auxiliary tool for farmers, consumers, and related scientific researchers and has certain practical value. [ABSTRACT FROM AUTHOR]

Background Primary care physicians (PCPs), who play a crucial role in providing primary healthcare services, utilize clinical practice guidelines as valuable tools for guiding their practice. By investigating these physicians' awareness and usage of guidelines, we can offer innovative strategies for promoting guideline adherence and enhancing physicians' training. This, in turn, can ultimately improve the quality of primary healthcare. Objective To explore the level of awareness and the current status of reference use of traditional Chinese medicine (TCM) guidelines for diabetes among PCPs, and to identify the influencing factors. Methods From September 22 to October 29, 2021, an online questionnaire survey was conducted among physicians affiliated with the Specialist Committee for Primary Diabetes Care of China Association of Chinese Medicine, using the snowball sampling method to ensure a broader representation of physicians. The questionnaire consists of three parts: basic information, current status and needs of physicians in using TCM for diabetes prevention and treatment, and a survey on physicians' current awareness and needs regarding diabetes guidelines. Ordered multiclass Logistic regression analysis was used to identify the influencing factors of PCPs' awareness and usage of TCM guidelines for diabetes. Results A total of 382 questionnaires were collected from PCPs, 35.34% (135/382) of the physicians were very familiar or fairly familiar with TCM guidelines for diabetes, while 28.80% (110/382) of the physicians referred to these guidelines regularly for diabetes management. The results of the univariate analysis showed that, PCPs with differences in gender, specialty, department, number of diabetic patients treated per week, evaluation of TCM treatment efficacy, availability of TCM herbs and Chinese patent drugs in physicians' institutions, and physicians' awareness of guidelines, showed statistically significant differences in their reference status of TCM guidelines for diabetes (P