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.

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.

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.

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.

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 Background The goal of this study was to assess the effectiveness of machine learning models and create an interpretable machine learning model that adequately explained 3-year all-cause mortality in patients with chronic heart failure. Methods The data in this paper were selected from patients with chronic heart failure who were hospitalized at the First Affiliated Hospital of Kunming Medical University, from 2017 to 2019 with cardiac function class III-IV. The dataset was explored using six different machine learning models, including logistic regression, naive Bayes, random forest classifier, extreme gradient boost, K-nearest neighbor, and decision tree. Finally, interpretable methods based on machine learning, such as SHAP value, permutation importance, and partial dependence plots, were used to estimate the 3-year all-cause mortality risk and produce individual interpretations of the model's conclusions. Result In this paper, random forest was identified as the optimal aools lgorithm for this dataset. We also incorporated relevant machine learning interpretable tand techniques to improve disease prognosis, including permutation importance, PDP plots and SHAP values for analysis. From this study, we can see that the number of hospitalizations, age, glomerular filtration rate, BNP, NYHA cardiac function classification, lymphocyte absolute value, serum albumin, hemoglobin, total cholesterol, pulmonary artery systolic pressure and so on were important for providing an optimal risk assessment and were important predictive factors of chronic heart failure. Conclusion The machine learning-based cardiovascular risk models could be used to accurately assess and stratify the 3-year risk of all-cause mortality among CHF patients. Machine learning in combination with permutation importance, PDP plots, and the SHAP value could offer a clear explanation of individual risk prediction and give doctors an intuitive knowledge of the functions of important model components.

Abstract Ice-and-snow tourism (IST) is central to the Chinese ice-and-snow economy and can help achieve the green transformation and expansion of the Chinese economy. However, IST has not been defined conclusively in China. In this study, the IST concept was analyzed, and the growth of both tourism types was described using an Event–Time–Space comprehensive analysis method. Two temporal phases with large increases were highlighted in both tourisms. The factors promoting their increases were analyzed from market demand, the Beijing 2022 Olympics Winter Games, and scientific and technological developments for ice and snow. The 2020–2021 decrease in IST was primarily attributed to the COVID-19 pandemic. In addition, IST has dual challenges related to natural and socioeconomic factors. Climate warming has a long-term adverse effect on IST, particularly on glacier tourism. Traditional outdoor skiing tourism in Northeast China (NEC) may also be affected by more competition owing to increased ski tourism in the pan-Beijing-Tianjin-Hebei region, the Xinjiang Uygur Autonomous Region (XJUAR) and Southern China, as well as by the development of indoor skiing and ice/snow entertainment. These natural and socioeconomic factors currently limit the ability of IST to promote socioeconomic development in alpine areas, particularly in western China. However, there are many opportunities for future IST growth, including warmed comfort climate at high altitudes in the NEC, XJUAR and the Qinghai-Tibet Plateau, major glacier resource potentials, and IST development driven by policies. The findings of this study provide important information for decision-makers and investors in IST and its related industries.

Highlights The Cu-doped graphitic carbon nitride (g-C3N4) material is synthesized by an in situ thermal polymerization strategy, through which the atomic dispersion and coordination structure of Cu on g-C3N4 are realized by regulating the doping level of Cu. High Faraday efficiency of CH4 of 49.04% and a maximum CH4/C2H4 ratio up to 35.03 are achieved for the first time on g-C3N4-supported Cu single-atom catalyst. Structure–activity relationship analysis based on experimental and theoretical studies demonstrates the well-defined Cu single atoms coordinated with N atoms in the nitrogen cavity of g-C3N4 are active sites for CO2-to-CH4.

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.

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.

Maize growth and development are regulated by light quality, intensity and photoperiod. Cryptochromes are blue/ultraviolet-A light receptors involved in stem elongation, shade avoidance, and photoperiodic flowering. To investigate the function of cryptochrome 1 (CRY1) in maize, where it is encoded by ZmCRY1, we obtained two ZmCRY1a genes (ZmCRY1a1 and ZmCRY1a2), both of which share the highest similarity with other gramineous plants, in particular rice CRY1a by phylogenetic analysis. In Arabidopsis, overexpression of ZmCRY1a genes promoted seedling de-etiolation under blue and white light, resulting in dwarfing of mature plants. In seedlings of the maize inbred line Zong 31 (ZmCRY1a-OE), overexpression of ZmCRY1a genes caused a reduction in the mesocotyl and first leaf sheath lengths due to down-regulation of genes influencing cell elongation. In mature transgenic maize plants, plant height, ear height, and internode length decreased in response to overexpression of ZmCRY1a genes. Expression of ZmCRY1a were insensitive to low blue light (LBL)-induced shade avoidance syndrome (SAS) in Arabidopsis and maize. This prompted us to investigate the regulatory role of the gibberellin and auxin metabolic pathways in the response of ZmCRY1a genes to LBL treatment. We confirmed a link between ZmCRY1a expression and hormonal influence on the growth and development of maize under LBL-induced SAS. These results reveal that ZmCRY1a has a relatively conservative function in regulating maize photomorphogenesis and may guide new strategies for breeding high density-tolerant maize cultivars.

The reclamation disposal of oily sludge, which is a hazardous waste from the extraction, transportation, storage, and refining of crude oil, is a paramount challenge for environmental protection and resource recycle. Herein, a catalytic pyrolysis approach with the participation of CaO was adopted for oil resource recovery. The results show that the optimal pyrolysis temperature for recovering oil was 500 °C, in which the pyrolysis oil yield was 44.37%. CaO could act as a catalyst during the pyrolysis process, thus promoting the formation of light components in the pyrolysis oil. The light components in pyrolysis oil increased from 5.08% to 16.67% with the participation of CaO. Meanwhile, the addition of CaO immobilized As, Cr, Pb and Zn into the pyrolysis slag, thus decreasing their migration into pyrolysis oil and gas. The migration of Ni displayed a different trend, and part of Ni entered into the pyrolysis oil and gas. The BCR continuous extraction experiments display that the highly biological-activity heavy metals (i.e., F1, F2 and F3 form) was transformed to a more stable state (i.e., F4 form). These results demonstrate that the catalytic pyrolysis approach with the participation of CaO not only improve the yield and quality of pyrolysis oil, but also reduce the emission and mobility of heavy metals.

The harmless and reclamation disposal of overhaul slag, which is a hazardous waste from aluminum electrolysis, is a great challenge. Herein, a synergistic disposal strategy, i.e., adopting the overhaul slag as a raw material together with aluminum oxide in the aluminum electrolysis process to produce Al–Si alloy, was proposed. The dissolution behavior of overhaul slag in a cryolite-based molten salt system was investigated by thermodynamic calculation and dissolution experiments. The liquidus temperature of molten salt was decreased with the addition of SiO2 and Al2O3. This suggests that the synergistic disposal of overhaul slag did not cause an adverse effect on the aluminum electrolysis process. The possible dissolution products were Na3AlF6, Na5Al3F14, SiO2, NaAlSiO4, NaAlSi3O8, Na2SiO3, Na2SiF6, Na2 MgAlF7 and SiF4. The addition of Al2O3 could enhance the solubility and dissolution rate of SiO2 and hence shorten the dissolution time. The dissolution process of overhaul slag satisfied well with the mass transfer model, suggesting that the dissolution rate was controlled by the diffusion process. This work provides a basis for the synergistic disposal of overhaul slag in the aluminum electrolysis process.

The discarding of spent fluorescent lamps, one of the most significant sources of mercury usage in industry, might cause mercury release into the environment when suffering from breakage. The reclamation disposal of fluorescent lamps for mercury recycling is essential, while how to achieve highly efficient conversion of various mercury species into elemental mercury vapor is a key challenge. Herein, the distillation method was adopted for mercury recycling from phosphor powder. The results show that the contents of elemental mercury and oxidized mercury vapor filled in the lamps were 150–3500μgand 5–20μg, respectively. The mercury release sustained for 13(±2) h, with a maximum mercury concentration of 4500μg/m3. The mercury in phosphor can be desorbed to an undetected level when distilling at 600 °C under vacuum for 30 min, while that would be achieved at 700 °C when distilling under atmospheric pressure. This suggests that the vacuum distillation was favorable for the conversion of oxidized mercury into elemental mercury. The variation of mineral phase, particle size distribution and morphology of phosphor after distillation was insignificant, which guaranteed the recycling of phosphor simultaneously accompanying with mercury recycling. This work clarified the mercury release behavior during lamps crushing and provided a feasible way for mercury recycling.

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.

Wintergrazer-70 and Ganyin No1 are high-yield forage varieties suitable for cultivation in high-altitude areas of Tibet (4300 m above sea level). Ganyin No1 was developed from Wintergrazer-70, with the latter serving as its parent variety. Ganyin No1 was identified as a spring variety, and subsequent RNA sequencing was conducted. RNA sequencing analysis identified 4 differentially expressed genes related to vernalization and 28 genes related to photoperiod regulation. The Sc7296g5-i1G3 gene is related to the flowering inhibition of rye, which may be related to the phenotypic difference in the Ganyin No1 variety in winter and spring. This finding provides valuable insights for future research on Ganyin No1, especially in addressing feed shortages in Tibet during winter and spring.

In the present paper, the fast pulse loading at high temperature (FPLHT) was conducted on Stellite 6 coatings with and without the interlayer, and exerted great effects on the microstructure, distribution of elements, and mechanical property of the two coatings. The evident crack occurred in the coating without the interlayer in the 400-cycle condition, which was induced by the diffusion of the Fe, the formation of reprecipitation carbides, the coarsening grain, and the formation of Laves phase. The mechanisms of the formation of the Fe–Co phase was revealed by transmission electron microscopy, binary phase diagram, and diagrammatic sketch. To specify the functions of fast pulse loading on the diffusion of Fe and the transformation of the microstructure, the Stellite 6 coating without the interlayer aged for 2880 h was employed for comparison with that in the 400-cycle condition. The softened phenomena occurred in the Stellite 6 coating without the interlayer and the Stellite 6 with the interlayer, the former resulting from the diffusion of Fe and the latter resulting from the diffusion of Ni. The Stellite 6 coating with the interlayer exhibited no crack during the FPLHT, and the disparity of deformation between Stellite 6 coatings with and without the interlayer in the 100-cycle condition was less than that in the 400-cycle condition owing to the difference of diffusion speed between Fe and Ni.

Predicting the mechanical behaviors of structure and perceiving the anomalies in advance are essential to ensuring the safe operation of infrastructures in the long run. In addition to the incomplete consideration of influencing factors, the prediction time scale of existing studies is rough. Therefore, this study focuses on the development of a real-time prediction model by coupling the spatio-temporal correlation with external load through autoencoder network (ATENet) based on structural health monitoring (SHM) data. An autoencoder mechanism is performed to acquire the high-level representation of raw monitoring data at different spatial positions, and the recurrent neural network is applied to understanding the temporal correlation from the time series. Then, the obtained temporal-spatial information is coupled with dynamic loads through a fully connected layer to predict structural performance in next 12 h. As a case study, the proposed model is formulated on the SHM data collected from a representative underwater shield tunnel. The robustness study is carried out to verify the reliability and the prediction capability of the proposed model. Finally, the ATENet model is compared with some typical models, and the results indicate that it has the best performance. ATENet model is of great value to predict the real-time evolution trend of tunnel structure.

Background: We studied AKI incidence and prognosis in cardiac surgery patients under and over 60 years old. Methods: We studied AKI in patients who underwent cardiac surgery at the First Affiliated Hospital of Wenzhou Medical University between Jan 2020 and Dec 2021, using improved global prognostic criteria for diagnosis. Results: After analyzing 781 patients (402 males, 379 females), AKI incidence after surgery was 30.22 %. Adjusting for propensity scores revealed no significant difference in AKI incidence between young males (24.1 %) and females (19.3 %). However, young females had higher AKI stages. Among older patients, AKI incidence was comparable between males (43.4 %) and females (42.2 %), but females had longer intubation times. Independent risk factors for AKI included age, male gender, and BMI, while intraoperative hemoglobin level was protective. Conclusions: No gender gap in AKI frequency for

Objective To compare the modified strategy for the right-sided double-lumen tube (R-DLT) placement using a combination of CT measurements and flexible video bronchoscopy guidance with traditional bronchoscopy technique.Trial design, setting and participants Double-blind, parallel randomised control trial at a tertiary care medical centre in China. 100 patients undergoing video-assisted thoracoscopic surgery and requiring R-DLT were randomly allocated to the control group and the intervention group.Intervention The control group used the traditional bronchoscopy-guided technique. In the intervention group, the length and anteroposterior diameter of the right main bronchus (RMB) were measured on CT images to select the side and size of the Rüsch tube, and then a black depth marker was placed on the tube according to the difference between the length of the RMB and the bronchial cuff. Under the guidance of bronchoscopy, the depth marker should be placed parallel to the tracheal carina and a characteristic white line on the tube should be parallel to the midline of the tracheal carina.Main outcomes The primary endpoint was the positioning of right upper lobe (RUL) ventilatory slot and RUL bronchial orifice. The secondary endpoints included intubation data and perioperative adverse events.Results Compared with the control group, our modified strategy significantly increased the optimal and acceptable position rate (76% vs 98%, respectively; p

Plastics are ubiquitous and indispensable in our daily life because of their low cost, portability, durability, and processability. However, due to the overuse, short service life, and chemical inert character, the accumulated discarded plastics pose a great threat to the sustainable development of ecology and environment. Photocatalysis represents highly promising technology in transforming plastic wastes into value‐added products via green and mild method. In this perspective, the advantages of photocatalysis are discussed and compared with other catalysis technologies including thermal catalysis, electrocatalysis, and enzyme catalysis. Then the possible photocatalytic upcycling path of plastic wastes is clarified under different experimental conditions. The types of plastic wastes that can be upcycled by photocatalysis, the integrating modus between plastic wastes and the photocatalysts as well as the modulation of the product selectivity are also emphasized. Finally, the challenges and insights into the future development of photocatalytic plastic waste upcycling fields are presented. It is expected that this timely and critical review provides the instructive guidance for the design of photocatalysts with high efficiency and high selectivity toward plastic waste upcycling.

Abstract Purpose Repeated exposures to sevoflurane could induce epigenetic modifications in specific brain regions and cognitive impairments in the immature mice. Conflicting findings make neurobehavioral manifestations intricate and potential mechanisms elusive. Influence of neonatal anesthesia with sevoflurane on the expression of synaptic scaffold proteins and neuronal activity remains to be determined. Methods C57BL/6 male and female mice in breeding ages were used to produce next generation. The offspring male mice were randomly scheduled to receive 3.0% sevoflurane plus 60% oxygen for 2 h daily at postnatal day (P) 6–8. Three-chambered social paradigm was used to test social affiliation and social memory. Morris water maze was used to test learning and memory. Whole genome bisulfite sequencing (WGBS), differentially methylated regions (DMRs) and KEGG enrichment analysis were performed to screen target gene in sequence context of CG. RT-PCR and immunoblotting analysis were used to assess expression of the Shank gene family, as well as DNA methylases. Results The male mice undergoing sevoflurane anesthesia at P6-8 showed diminished preference for novel conspecific and prolonged escape latency and decreased platform-crossing times. The sevoflurane-exposed mice showed reduced mRNA and protein levels of the Shank2 gene. KEGG analysis disclosed the role of DNA hypermethylation of Shank2 gene in the pathway of glutamatergic synapse. In addition, sevoflurane anesthesia reduced mRNA and protein levels of the TET3 enzyme. Conclusion Repeated exposures to sevoflurane in neonatal period could impair social recognition memory and spatial reference memory in the male mice. Reduction of hippocampal SHANK2 protein could contribute to sevoflurane-induced neurotoxicity in the immature mice. Reduction of the TET3 enzyme should be responsible for DNA hypermethylation-related silencing of the Shank2 gene. Graphical Abstract

There is an urgent need to develop efficient strategies to remove elemental mercury from industrial flue gases. Here the authors develop a simple, versatile, and scalable strategy to obtain a 100-fold enhancement of the elemental mercury accommodation capacities of a wide range of transition metal sulfides by in situ acid etching.

Abstract Background Heterosis, or hybrid vigor, refers to the phenotypic superiority of an F1 hybrid relative to its parents in terms of growth rate, biomass production, grain yield, and stress tolerance. Light is an energy source and main environmental cue with marked impacts on heterosis in plants. Research into the production applications and mechanism of heterosis has been conducted for over a century and a half, but little is known about the effect of light on plant heterosis. Results In this study, an integrated transcriptome and metabolome analysis was performed using maize (Zea mays L.) inbred parents, B73 and Mo17, and their hybrids, B73 × Mo17 (BM) and Mo17 × B73 (MB), grown in darkness or under far-red, red, or blue light. Most differentially expressed genes (73.72–92.50%) and differentially accumulated metabolites (84.74–94.32%) exhibited non-additive effects in BM and MB hybrids. Gene Ontology analysis revealed that differential genes and metabolites were involved in glutathione transfer, carbohydrate transport, terpenoid biosynthesis, and photosynthesis. The darkness, far-red, red, and blue light treatments were all associated with phenylpropanoid–flavonoid biosynthesis by Weighted Gene Co-expression Network Analysis and Kyoto Encyclopedia of Genes and Genomes enrichment analysis. Five genes and seven metabolites related to phenylpropanoid–flavonoid biosynthesis pathway were identified as potential contributors to the interactions between maize heterosis and light conditions. Consistent with the strong mid-parent heterosis observed for metabolites, significant increases in both fresh and dry weights were found in the MB and BM hybrids compared with their inbred parents. Unexpectedly, increasing light intensity resulted in higher biomass heterosis in MB, but lower biomass heterosis in BM. Conclusions The transcriptomic and metabolomic results provide unique insights into the effects of light quality on gene expression patterns and genotype–environment interactions, and have implications for gene mining of heterotic loci to improve maize production.

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.

Summary: Temperature increases as light intensity rises, but whether light signals can be directly linked to high temperature response in plants is unclear. Here, we find that light pre-treatment enables plants to survive better under high temperature, designated as light-induced thermotolerance (LIT). With short-term light treatment, plants induce light-signaling pathway genes and heat shock genes. Blue light photoreceptor cryptochrome 1 (CRY1) is required for LIT. We also find that CRY1 physically interacts with the heat shock transcription factor A1d (HsfA1d) and that HsfA1d is involved in thermotolerance under light treatment. Furthermore, CRY1 promotes HsfA1d nuclear localization through importin alpha 1 (IMPα1). Consistent with this, CRY1 shares more than half of the chromatin binding sites with HsfA1d. Mutation of CRY1 (cry1-304) diminishes a large number of HsfA1d binding sites that are shared with CRY1. We present a model where, by coupling light sensing to high-temperature stress, CRY1 confers thermotolerance in plants via HsfA1d.

Highlights Yield, N content, and specific surface area of biochar predicted by machine learning Gradient boosting regression outperformed random forest, with test R2 of 0.81–0.95 Temperature, nitrogen, and ash were top features for predicting the three targets The yield and properties of biochar were engineered and experimentally verified

Orthopedic implants account for 99% of orthopedic surgeries, however, orthopedic implant-related infection is one of the most serious complications owing to the potential for limb-threatening sequelae and mortality. Current antibiotic treatments still lack the capacity to target bone infection sites, thereby resulting in unsatisfactory therapeutic effects. Here, the bone infection site targeting efficacy of D6 and UBI29-41 peptides was investigated, and bone-and-bacteria dual-targeted nanoparticles (NPs) with D6 and UBI29-41 peptides were first fabricated to target bone infection site and control the release of vancomycin in bone infection site. The results of this study demonstrated that the bone-and-bacteria dual-targeted mesoporous silica NPs exhibit excellent bone and bacteria targeting efficacy, excellent biocompatibility and effective antibacterial properties in vitro. Furthermore, in a rat model of orthopedic implant-related infection with methicillin-resistant Staphylococcus aureus, the growth of bacteria was evidently inhibited without cytotoxicity, thus realizing the early treatment of implant-related infection. Hence, the bone-and-bacteria dual-targeted molecule-modified NPs may target bacteria-infected bone sites and act as ideal candidates for the therapy of orthopedic implant-related infections.

With the rapid development of clean energy and the deepening of the interaction between supply and demand, power grid investment upgrading measures involve many new elements, such as clean energy installation and distribution automation. Traditional investment decision-making models are difficult to establish and solve. In view of this, this paper analyzes the investment benefit mechanism directly from the perspective of investment input–output relationship, and designs an interactive auxiliary investment decision-making system based on correlation rule mining. The system constructs an investment benefit mapping model from power grid investment measures to benefit output by means of deep transfer learning, and provides three objective functions, which consider the optimal economy, performance improvement and comprehensive index optimization, thus assisting decision makers to formulate investment alternatives according to different investment needs. A case demonstrates the decision-making process based on an actual power grid, and verifies the practicability and effectiveness of the system.

Abstract Oxygen vacancies (V o) in electrocatalysts are closely correlated with the hydrogen evolution reaction (HER) activity. The role of vacancy defects and the effect of their concentration, however, yet remains unclear. Herein, Bi2O3, an unfavorable electrocatalyst for the HER due to a less than ideal hydrogen adsorption Gibbs free energy (ΔG H*), is utilized as a perfect model to explore the function of V o on HER performance. Through a facile plasma irradiation strategy, Bi2O3 nanosheets with different V o concentrations are fabricated to evaluate the influence of defects on the HER process. Unexpectedly, while the generated oxygen vacancies contribute to the enhanced HER performance, higher V o concentrations beyond a saturation value result in a significant drop in HER activity. By tunning the V o concentration in the Bi2O3 nanosheets via adjusting the treatment time, the Bi2O3 catalyst with an optimized oxygen vacancy concentration and detectable charge carrier concentration of 1.52 × 1024 cm−3 demonstrates enhanced HER performance with an overpotential of 174.2 mV to reach 10 mA cm−2, a Tafel slope of 80 mV dec−1, and an exchange current density of 316 mA cm−2 in an alkaline solution, which approaches the top-tier activity among Bi-based HER electrocatalysts. Density-functional theory calculations confirm the preferred adsorption of H* onto Bi2O3 as a function of oxygen chemical potential (∆μ O) and oxygen partial potential (P O2) and reveal that high V o concentrations result in excessive stability of adsorbed hydrogen and hence the inferior HER activity. This study reveals the oxygen vacancy concentration-HER catalytic activity relationship and provides insights into activating catalytically inert materials into highly efficient electrocatalysts.

Southern corn rust (SCR) is a destructive maize disease caused by Puccinia polysora Underw. To investigate the mechanism of SCR resistance in maize, a highly resistant inbred line, L119A, and a highly susceptible line, Lx9801, were subjected to gene mapping and transcriptome analysis. Bulked-segregant analysis coupled with whole-genome sequencing revealed several quantitative trait loci (QTL) on chromosomes 1, 6, 8, and 10. A set of 25 genes, including two coiled-coil nucleotide-binding site leucine-rich repeat (CC-NBS-LRR) genes, were identified as candidate genes for a major-effect QTL on chromosome 10. To investigate the mechanism of SCR resistance in L119A, RNA-seq of P. polysora-inoculated and non-inoculated plants of L119A and Lx9801 was performed. Unexpectedly, the number of differentially expressed genes in inoculated versus non-inoculated L119A plants was about 10 times that of Lx9801, with only 29 common genes identified in both lines, suggesting extensive gene expression changes in the highly resistant but not in the susceptible line. Based on the transcriptome analysis, one of the CC-NBS-LRR candidate genes was confirmed to be upregulated in L119A relative to Lx9801 independently of P. polysora inoculation. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses indicated that transcription factors, as well as genes involved in defense responses and metabolic processes, were dominantly enriched, with the phenylpropanoid biosynthesis pathway most specifically activated. Consistently, accumulation of phenylpropanoid-derived lignin, especially S lignin, was drastically increased in L119A after P. polysora inoculation, but remained unchanged in Lx9801, suggesting a critical role of lignin in SCR resistance. A regulatory network of defense activation and metabolic change in SCR-resistant maize upon P. polysora infection is described.

Bacterial infections are a major complication in human healthcare. Bacteria tracing and targeted delivery of antibiotics to bacterial infection sites are being developed to reduce the high mortality caused by pathogenic bacterial infections. Owing to the challenges in accurate diagnosis of infection, particularly the identification of biomaterial‐related bacteria, the use of bacteria‐targeting nanomaterials has become increasingly important. Herein, magnetic core–shell mesoporous silica nanoparticles (MSNs) are fabricated for targeted drug delivery; the shell is modified with ubiquicidin (UBI)29‐41 for bacteria targeting, and vancomycin is loaded into the channels of the MSNs. The results demonstrate that magnetic core–shell MSNs display the desired functions of drug loading, bacteria targeting, detectability in magnetic resonance imaging, antibacterial action, and good in vitro biocompatibility. Magnetic core–shell MSNs have potential applications in the treatment of diseases caused by infections.

Both basalt fibers and glass fibers can be used as reinforced material in fiber-reinforced polymer (FRP) composites. In this paper, the tensile strength, elastic modulus, and hardness of basalt fibers and E-glass fibers are studied by fiber tensile tester and nanoindentation. The crystalline phase and structural feature of fibers are conducted by X-ray diffraction (XRD) and Fourier transform infra-red (FTIR) spectroscopy. The results show that the mechanical properties of basalt fibers are higher than those of E-glass fibers. The density of basalt glasses is higher than that of E-glass and increases with increasing iron content. XRD patterns show that both basalt fibers and E-glass fibers are totally amorphous. Fe3+ mainly exists in the form of [FeO6] in the glass network. It indicates that iron oxide plays a key role in improving the mechanical properties of basalt fibers.

Abstract Silicon (Si) is regarded as the most promising anode material for high‐energy lithium‐ion batteries (LIBs) due to its high theoretical capacity, and low working potential. However, the large volume variation during the continuous lithiation/delithiation processes easily leads to structural damage and serious side reactions. To overcome the resultant rapid specific capacity decay, the nanocrystallization and compound strategies are proposed to construct hierarchically assembled structures with different morphologies and functions, which develop novel energy storage devices at nano/micro scale. The introduction of assembly strategies in the preparation process of silicon‐based materials can integrate the advantages of both nanoscale and microstructures, which significantly enhance the comprehensive performance of the prepared silicon‐based assemblies. Unfortunately, the summary and understanding of assembly are still lacking. In this review, the understanding of assembly is deepened in terms of driving forces, methods, influencing factors and advantages. The recent research progress of silicon‐based assembled anodes and the mechanism of the functional advantages for assembled structures are reviewed from the aspects of spatial confinement, layered construction, fasciculate structure assembly, superparticles, and interconnected assembly strategies. Various feasible strategies for structural assembly and performance improvement are pointed out. Finally, the challenges and integrated improvement strategies for assembled silicon‐based anodes are summarized.

Study region: The upstream reaches of Manas River Basin (UMNS), and Muzati River Basin (UMZT) Study focus: Glaciers, as solid reservoirs, substantially regulate runoff abundance and depletion through peak shaving and valley filling. The regulations of glaciers in hydrological cycles are crucial to the arid regions. We aim to quantify the dynamics of the glaciers’ hydrological regulating function in the UMNS and UMZT from 1971 to 2100 using a glacier hydrological regulation index (GlacierR). New hydrological insights for the region: Our results indicate that the glacier runoff in both basins showed a decreasing trend in the past four decades and will continue decreasing in the future under three shared socioeconomic pathways (SSPs): SSP1–2.6, SSP2–4.5, and SSP5–8.5. The UMZT has a stronger regulating function of glacial runoff than the UMNS. In contrast, the tipping point of the glaciers’ hydrological regulating function in the UMNS occurred ten years earlier than in the UMZT. Under the three SSPs, the glaciers’ hydrological regulation function on the north- and south-facing slopes of the Tianshan Mountains will show a weakening trend. The weakness trend is more notable in UMZT, where the glaciers’ hydrological regulation function will decrease by 66.9 % between 2061 and 2100 under SSP1–2.6. Thus, it sets alarm bells for the rational use of water resources in glacier-covered arid regions.

Background: This study assessed the effects of esmolol injection in patients with in-hospital cardiac arrest (IHCA) with refractory ventricular fibrillation (VF)/pulseless ventricular tachycardia (pVT).Methods: From January 2018 to December 2021, 29 patients with IHCA with refractory shockable rhythm were retrospectively reviewed. Esmolol was administered after advanced cardiovascular life support (ACLS)-directed procedures, and outcomes were assessed.Results: Among the 29 cases, the rates of sustained return of spontaneous circulation (ROSC), 24-h ROSC, and 72-h ROSC were 79%, 62%, and 59%, respectively. Of those patients, 59% ultimately survived to discharge. Four patients with cardiac insufficiency died. The duration from CA to esmolol infusion was significantly shorter for patients in the survival group (SG) than for patients in the dead group (DG) (12 min, IQR: 8.5–19.5 vs. 23.5 min, IQR: 14.4–27 min; p = 0.013). Of those patients, 76% (22 of 29) started esmolol administration after the second dose of amiodarone. No significant difference was observed in the survival rate between this group and groups administered an esmolol bolus simultaneously or before the second dose of amiodarone (43% vs. 64%, p = 0.403). Of those patients, 31% (9 of 29) were administered an esmolol bolus for defibrillation attempts ≤ 5, while the remaining 69% of patients received an esmolol injection after the fifth defibrillation attempt. No significant differences were observed in the rates of ≥ 24-h ROSC (67% vs. 60%, p = 0.73), ≥ 72-h ROSC (67% vs. 55%, p = 0.56), and survival to hospital discharge (67% vs. 55%, p = 0.56) between the groups administered an esmolol bolus for defibrillation attempts ≤ 5 and defibrillation attempts > 5.Conclusion: IHCA patients with refractory shockable rhythms receiving esmolol bolus exhibited a high chance of sustained ROSC and survival to hospital discharge. Patients with end-stage heart failure tended to have attenuated benefits from beta-blockers. Further large-scale, prospective studies are necessary to determine the effects of esmolol in patients with IHCA with refractory shockable rhythms.

Abstract Identifying the mechanisms that underlie the assembly of plant communities is critical to the conservation of terrestrial biodiversity. However, it is seldom measured or quantified how much deterministic versus stochastic processes contribute to community assembly in alpine meadows. Here, we measured the decay in community similarity with spatial and environmental distance in the Zoige Plateau. Furthermore, we used redundancy analysis (RDA) to divide the variations in the relative abundance of plant families into four components to assess the effects of environmental and spatial. Species assemblage similarity liner declined with geographical distance (p

Safe and non-invasive on-demand relief is a crucial and effective treatment for postoperative pain because it considers variable timing and intensity of anesthetics. Ultrasound modulation is a promising technique for this treatment because it allows convenient timed and non-invasive controlled drug release. Here, we created an ultrasound-triggered lidocaine (Lido) release platform using an amino acid hydrogel functioning as three-dimensional (3D) scaffold material (Lido-PPIX@ER hydrogel). It allows control of the timing, intensity and duration of lidocaine (Lido) to relieve postoperative pain. The hydrogel releases Lido due to the elevated reactive oxygen species (ROS) levels generated by PPIX under ultrasound triggering. The Lido-PPIX@ER hydrogel under individualized ultrasound triggering released lidocaine and provided effective analgesia for more than 72 h. The withdrawal threshold was higher than that in the control group at all time points measured. The hydrogel showed repeatable and adjustable ultrasound-triggered nerve blocks in vivo, the duration of which depended on the extent and intensity of insonation. On histopathology, no systemic effect or tissue reaction was observed in the ultrasound-triggered Lido-PPIX@ER hydrogel-treated group. The Lido-PPIX@ER hydrogel with individualized (highly variable) ultrasound triggering is a convenient and effective method that offers timed and spatiotemporally controlled Lido release to manage postoperative pain. This article presents the delivery system for a new effective strategy to reduce pain, remotely control pain, and offer timed and spatiotemporally controlled release of Lido to manage postoperative pain.

External magnetic field (EMF) is favored for application in welding, due to its positive effects on joint quality. In the present study, the effect of EMF on AA6061 Al alloy/HSLA350 steel welded joints were investigated. When no EMF, coarse Al grains were formed on the welded joint. The second phase including Al–Si eutectic and large Al3FeSi slats exists in the joint fusion zone. Refinement of the Al matrix and second phases were produced as a result of the Lorentz force generated by the 380 mt EMF treatment. The joint strength with EMF treatment at welding current 60 A increased from 170 MPa (without EMF treatment) to 195 MPa and the joint ductile fracture position was on the Al alloy base material under this same condition, due to the fine grained strengthening and dispersal strengthening of the second phases, meanwhile, the intermetallic compounds (IMCs) layer at the Al/steel welding seam interface was significantly reduced.

Abstract Background Postherpetic neuralgia (PHN) is a devastating complication after varicella-zoster virus infection. Brain-derived neurotrophic factor (BDNF) has been shown to participate in the pathogenesis of PHN. A truncated isoform of the tropomyosin receptor kinase B (TrkB) receptor TrkB.T1, as a high-affinity receptor of BDNF, is upregulated in multiple nervous system injuries, and such upregulation is associated with pain. Acid-sensitive ion channel 3 (ASIC3) is involved in chronic neuropathic pain, but its relation with BDNF/TrkB.T1 in the peripheral nervous system (PNS) during PHN is unclear. This study aimed to investigate whether BDNF/TrkB.T1 contributes to PHN through regulating ASIC3 signaling in dorsal root ganglia (DRGs). Methods Resiniferatoxin (RTX) was used to induce rat PHN models. Mechanical allodynia was assessed by measuring the paw withdrawal thresholds (PWTs). Thermal hyperalgesia was determined by detecting the paw withdrawal latencies (PWLs). We evaluated the effects of TrkB.T1-ASIC3 signaling inhibition on the behavior, neuronal excitability, and inflammatory response during RTX-induced PHN. ASIC3 short hairpin RNA (shRNA) transfection was used to investigate the effect of exogenous BDNF on inflammatory response in cultured PC-12 cells. Results RTX injection induced mechanical allodynia and upregulated the protein expression of BDNF, TrkB.T1, ASIC3, TRAF6, nNOS, and c-Fos, as well as increased neuronal excitability in DRGs. Inhibition of ASIC3 reversed the abovementioned effects of RTX, except for BDNF and TrkB.T1 protein expression. In addition, inhibition of TrkB.T1 blocked RTX-induced mechanical allodynia, activation of ASIC3 signaling, and hyperexcitability of neurons. RTX-induced BDNF upregulation was found in both neurons and satellite glia cells in DRGs. Furthermore, exogenous BDNF activated ASIC3 signaling, increased NO level, and enhanced IL-6, IL-1β, and TNF-α levels in PC-12 cells, which was blocked by shRNA-ASIC3 transfection. Conclusion These findings demonstrate that inhibiting BDNF/TrkB.T1 reduced inflammation, decreased neuronal hyperexcitability, and improved mechanical allodynia through regulating the ASIC3 signaling pathway in DRGs, which may provide a novel therapeutic target for patients with PHN.

Nitrogen is one of the most important nutrient elements required for plant growth and development, which is also immensely related to the efficient use of nitrogen by crop plants. Therefore, plants evolved sophisticated mechanisms and anion channels to extract inorganic nitrogen (nitrate) from the soil or nutrient solutions, assimilate, and recycle the organic nitrogen. Hence, developing crop plants with a greater capability of using nitrogen efficiently is the fundamental research objective for attaining better agricultural productivity and environmental sustainability. In this context, an in-depth investigation has been conducted into the cassava slow type anion channels (SLAHs) gene family, including genome-wide expression analysis, phylogenetic relationships with other related organisms, chromosome localization, and functional analysis. A potential and nitrogen-responsive gene of cassava (MeSLAH4) was identified and selected for overexpression (OE) analysis in rice, which increased the grain yield and root growth related performance. The morpho-physiological response of OE lines was better under low nitrogen (0.01 mm NH4NO3) conditions compared to the wild type (WT) and OE lines under normal nitrogen (0.5 mm NH4NO3) conditions. The relative expression of the MeSLAH4 gene was higher (about 80-fold) in the OE line than in the wild type. The accumulation and flux assay showed higher accumulation of NO3− and more expansion of root cells and grain dimension of OE lines compared to the wild type plants. The results of this experiment demonstrated that the MeSLAH4 gene may play a vital role in enhancing the efficient use of nitrogen in rice, which could be utilized for high-yielding crop production.

Shrub expansion has been reported mostly in dry and semi-arid ecosystems worldwide. Shrub expansion is also a serious ecological issue in alpine and cold regions. Multiple drivers, including anthropogenic and environmental factors, contribute to this phenomenon. However, the relative effects of climate and soil factors on shrub expansion are incompletely quantified or understood in the alpine meadow, and quantifying how these factors result in variations in functional traits associated with shrub expansion is crucial considering that functional traits are tightly related to ecosystem processes. In this study, we investigated the vegetation of an alpine shrub meadow composed of Potentilla fruticosa L. In the Zoige Plateau, along an elevation gradient. We assessed the direct and indirect effects of climate and soil factors on functional traits, shrub expansion strength (measured as the total biomass), and relative abundance (via functional traits). We found that climate factors, namely, the mean temperature of the warmest quarter (bio10), mostly affected specific leaf areas and specific lobe volume, which were the most important traits related to shrub expansion. Soil factors, except soil water content (SWC), had weak effects on functional traits closely related to shrub expansion. Our partial least square path modeling model explained 99% of the variation in shrub biomass. Results suggest that climate change not only affected functional traits but also influenced shrub expansion in the Zoige Plateau. Thus, management measures to control expansion should consider these drivers for more accurate forecasting and cost effectiveness. Understanding the mechanism of alpine shrub expansion contributes to the delaying of the expansion process and ensures steady pastoral production.

Abstract In Arabidopsis, phytochrome (phy) A, phyB, and cryptochrome 1 (cry1) are representative far‐red, red, and blue light photoreceptors, respectively. Members of the SUPPRESSOR OF PHYA‐105 (SPA) protein family (SPA1–SPA4) form E3 ubiquitin ligase complexes with CONSTITUTIVE PHOTOMORPHOGENIC1 (COP1), which mediates the degradation of photomorphogenesis‐promoting factors to desensitize light signaling. SPA2 has been reported to promote seedling etiolation in the dark. However, the unique roles of SPA2 and its three functional domains in suppressing photomorphogenesis under different light conditions are largely unknown. Here, we demonstrate that overexpression of the full‐length or the central coiled‐coil and C‐terminal WD‐repeat domains of SPA2 cause hyper‐etiolation phenotypes under several light conditions. The SPA2 central coiled‐coil and C‐terminal WD‐repeat domains are necessary and sufficient for repressing seedling de‐etiolation, cotyledon unfolding, and promoting hypocotyl negative gravitropism under several light conditions. Furthermore, phyA, phyB, cry1, and COP1 repress protein accumulation or nuclear translocation of SPA2 through direct interactions with its kinase‐like and coiled‐coil domains located in the N‐terminus in response to far‐red, red, and blue light treatments, respectively. Taken together, our results demonstrate that SPA2 functions under multiple light conditions; moreover, light‐activated photoreceptors rapidly suppress SPA2 activity via direct interactions in response to different light treatments.

Abstract The development of highly efficient and stable oxygen reduction electrocatalysts and revealing their underlying catalytic mechanism are crucial in expanding the applications of metal‐air batteries. Herein, an excellent FeCo alloy nanoparticles (NPs)‐decorated N‐doped mesoporous carbon electrocatalyst (FeCo/NC) for oxygen reduction reaction, prepared through the pyrolysis of a dual metal containing metal‐organic framework composite scaffold is reported. Benefiting from the highly exposed bimetal active sites and the carefully designed structure, the Fe0.25Co0.75/NC‐800 catalyst exhibits a promising electrocatalytic activity and a superior durability, better than those of the state‐of‐the‐art catalysts. Suggested by both the X‐ray absorption fine structures and the density functional theoretical calculation, the outstanding catalytic performance is originated from the synergistic effects of the bimetallic loading in NC catalysts, where the electronic modulation of the Co active sites from the nearby Fe species leads to an optimized binding strength for reaction intermediates. This work demonstrates a class of highly active nonprecious metals electrocatalysts and provides valuable insights into investigating the structure–performance relationship of transition metal‐based alloy catalysts.