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Blade wear is the critical problem in the operation of multiphase pump. This paper presents a numerical study of the multiphase flow of multiphase pump. The trajectory of particles in the pump is calculated by the discrete phase model. Then, the simulation results are compared with the model test results of the pump to verify the correctness of the simulation method. The results show that the particles in the impeller domain are mainly near the hub, and the particles in the diffuser domain form a agglomerated area in the middle of the flow channel. The average wear rate of the impeller is more affected by the particle size than that of the diffuser. The maximum wear rate of blade surface increases first and then decreases with the increase of particle size. According to the wear data under different particle sizes, the regression model between particle size and wear rate is fitted to predict the wear of mixed transport pump in actual operation. The research results have important reference value for the prediction of the wear performance of the multiphase pump.

Due to the excellent quantum conversion and spectral response characteristics of the AlGaN photocathode, it has become the most promising III-V group semiconductor photocathode in solar-blind signal photoconversion devices in the ultraviolet band. Herein, the influence factors of the time-resolved characteristics of the AlGaN photocathode are researched by solving the photoelectron continuity equation and photoelectron flow density equation, such as the AlN/AlGaN interface recombination rate, AlGaN electron diffusion coefficient, and AlGaN activation layer thickness. The results show that the response time of the AlGaN photocathode decreases gradually with the increase in AlGaN photoelectron diffusion coefficient and AlN/AlGaN interface recombination rate, but the response time of the AlGaN photocathode gradually becomes saturated with the further increase in AlN/AlGaN interface recombination rate. When the thickness of the AlGaN photocathode is reduced from 250 nm to 50 nm, the response time of the AlGaN photocathode decreases from 63.28 ps to 9.91 ps, and the response time of AlGaN photocathode greatly improves. This study provides theoretical guidance for the development of a fast response UV detector.

E-skin is a bionic device with flexible and intelligent sensing ability that can mimic the touch, temperature, pressure, and other sensing functions of human skin. Because of its flexibility, breathability, biocompatibility, and other characteristics, it is widely used in health management, personalized medicine, disease prevention, and other pan-health fields. With the proposal of new sensing principles, the development of advanced functional materials, the development of microfabrication technology, and the integration of artificial intelligence and algorithms, e-skin has developed rapidly. This paper focuses on the characteristics, fundamentals, new principles, key technologies, and their specific applications in health management, exercise monitoring, emotion and heart monitoring, etc. that advanced e-skin needs to have in the healthcare field. In addition, its significance in infant and child care, elderly care, and assistive devices for the disabled is analyzed. Finally, the current challenges and future directions of the field are discussed. It is expected that this review will generate great interest and inspiration for the development and improvement of novel e-skins and advanced health monitoring systems.

Rural e-commerce, as a new form of digital economy, is of great significance in promoting the income of rural households and realizing common prosperity. Based on the 2021 China Rural Revitalization Survey (CRRS), the impact of rural e-commerce on farmers’ income and the intra-rural income gap is explored using quantile regression. The results found that (1) rural e-commerce can effectively promote the level of farm household income and alleviate the intra-rural income disparity, with this finding still holding after addressing the potential endogeneity problem and conducting robust-type tests. (2) Rural e-commerce has the most pronounced income-generating effect on low-income households in the southern region; participation in rural e-commerce has a more “center-expanding” effect on households in the secondary education and high material capital groups. (3) Saving production and operation costs, shortening the product circulation chain, and improving access to information are channels through which rural e-commerce affects households’ income. It is recommended to actively promote the deep integration and development of rural e-commerce in rural areas, establish a sound mechanism for bridging the “digital divide”, encourage e-commerce “leaders” to build a perfect industrial chain, and guide smallholders to integrate into the rural e-commerce industrial chain to enjoy the digital dividend.

Computer-assisted preparation of porcelain laminate veneers (PLVs) using stereolithographic templates has been developed to enhance the accuracy of tooth preparation. However, the digital workflows involved in guided PLV preparation remain inconsistently defined across various practices. Therefore, this scoping review aimed to examine publications on computer-assisted PLV preparation to identify the key stage of digital workflows involved in designing and fabricating stereolithographic templates, as well as to highlight the limitations of various template designs. This scoping review aimed to identify publications on digital workflows for designing and fabricating stereolithographic templates in computer-assisted porcelain laminate veneer preparation. A systematic search on MEDLINE/PubMed, Web of Science and Scopus identified English-language articles published from 2014 to March 2024. Eligible articles focused on digitally designed and fabricated tooth reduction templates for porcelain laminate veneers, excluding conventional tooth preparation procedures for tooth reduction assessment. Seven clinical reports were included, demonstrating various 3D data acquisition techniques for virtual patient generation. All articles described virtual diagnostic wax-ups on digital casts, with two using a virtual articulator. Only five articles documented chair-side mock-ups with resin trial restorations to evaluate planned dental esthetics. Additionally, virtual tooth preparation prior to templates design was included in only four articles. The templates were designed using different software and ranged from simple designs with access windows to complex stacked templates with rotary instrument sleeved windows. Each template design had limitations affecting tooth reduction accuracy. All articles reported printing templates in clear acrylic resin using different technologies. In conclusion, the review highlights a lack of standardization in the digital workflow for designing stereolithographic templates for PLVs. Establishing a sound protocol for designing the tooth reduction templates is essential to ensure the accuracy and consistency of veneer preparation.

Allicin, as a natural antibacterial active substance from plants, has great medical and health care value. However, due to its poor stability, its application in the field of food and medicine is limited. So, in this paper, allicin–zein–sodium caseinate composite nanoparticles (zein–Ali–SC) were prepared by antisolvent precipitation and electrostatic deposition. Through the analysis of the particle size, ζ-potential, encapsulation efficiency (EE), loading rate (LC) and microstructure, the optimum preparation conditions for composite nanoparticles were obtained. The mechanism of its formation was studied by fluorescence spectrum, Fourier infrared spectrum (FTIR), X-ray diffraction (XRD) and thermogravimetric analysis (TGA). The stability study results showed that the particle size of composite nanoparticles was less than 200 nm and its PDI was less than 0.3 under different NaCl concentrations and heating conditions, showing good stability. When stored at 4 °C for 21 days, the retention rate of allicin reached 61.67%, which was 52.9% higher than that of free allicin. After freeze-drying and reheating, the nanoparticles showed good redispersibility; meanwhile, antioxidant experiments showed that, compared with free allicin, the nanoparticles had stronger scavenging ability of free radicals, which provided a new idea for improving the stability technology and bioavailability of bioactive compounds.

Root-knot nematodes induce the formation of feeding sites within the host roots and the relocation of auxin into galls results in abnormal lateral root growth. Here, we analyzed the changes in cucumber root architecture under Meloidogyne incognita stress and the distribution of auxin in these morphological and molecular root changes. The number of root tips significantly decreased, and regression analysis showed a positive relationship between the size of root galls and the numbers of nematodes in galls compared with the lateral roots on galls, emphasizing the effect of nematode parasitism on root development. Data generated via a promoter-reporter system using the transgenic hairy root system first characterized the auxin distribution during nematode parasitism in cucumber. Using DR5:GUS staining of root galls, we further detected the expression of CsPIN1 and CsAUX1, which regulate polar auxin transport. The results showed that both CsPIN1 and CsAUX1 were induced in galls, and the relative expression of the two genes significantly increased at 21 DAI. The TIBA treatment, which can disrupt polar auxin transport inhibited the numbers of cucumber root tips and total length following increasing concentration gradients. Moreover, the numbers of galls were significantly affected by TIBA treatment, which showed the vital role of auxin during nematode parasitism. Our findings suggest that the transportation of auxin plays an important role during gall formation and induces cucumber lateral root development within nematode feeding sites.

Background/Objectives: As a hyperaccumulator of selenium (Se), Cardamine violifolia (Cv) and its peptide extract could ameliorate the negative effects of a high-fat diet (HFD). However, the effects of the coaccumulation of cadmium (Cd) in Se-enriched Cv (Cv2) and the potential confounding effect on the roles of enriched Se remain unknown. We aimed to investigate whether Cv2 could alleviate HFD-induced lipid disorder and liver damage. Methods: Three groups of 31-week-old female mice were fed for 41 weeks (n = 10–12) with a control Cv-supplemented diet (Cv1D, 0.15 mg Se/kg, 30 µg Cd/kg, and 10% fat calories), a control Cv-supplemented HFD (Cv1HFD, 45% fat calories), and a Cv2-supplemented HFD (Cv2HFD, 1.5 mg Se/kg, 0.29 mg Cd/kg, and 45% fat calories). Liver and serum were collected to determine the element concentrations, markers of liver injury and lipid disorder, and mRNA and/or protein expression of lipid metabolism factors, heavy metal detoxification factors, and selenoproteins. Results: Both Cv1HFD and Cv2HFD induced obesity, and Cv2HFD downregulated Selenoi and upregulated Dio3 compared with Cv1D. When comparing Cv2HFD against Cv1HFD, Cv2 increased the liver Se and Cd, the protein abundance of Selenoh, and the mRNA abundance of 10 selenoproteins; reduced the serum TG, TC, and AST; reduced the liver TG, lipid droplets, malondialdehyde, and mRNA abundance of Mtf1 and Mt2; and differentially regulated the mRNA levels of lipid metabolism factors. Conclusions: Cv2 alleviated HFD-induced lipid dysregulation and liver damage, which was probably associated with its unique Se speciation. However, further research is needed to explore the interaction of plant-coenriched Se and Cd and its effects on health.

Synthetic Aperture Radar (SAR) ship target detection has been extensively researched. However, most methods use the same dataset division for both training and validation. In practical applications, it is often necessary to quickly adapt to new loads, new modes, and new data to detect targets effectively. This presents a cross-domain detection problem that requires further study. This paper proposes a method for detecting SAR ships in complex backgrounds using fusion tensor and cross-domain adversarial learning. The method is designed to address the cross-domain detection problem of SAR ships with large differences between the training and test sets. Specifically, it can be used for the cross-domain detection task from the fully polarised medium-resolution ship dataset (source domain) to the high-resolution single-polarised dataset (target domain). This method proposes a channel fusion module (CFM) based on the YOLOV5s model. The CFM utilises the correlation between polarised channel images during training to enrich the feature information of single-polarised images extracted by the model during inference. This article proposes a module called the cross-domain adversarial learning module (CALM) to reduce overfitting and achieve adaptation between domains. Additionally, this paper introduces the anti-interference head (AIH) which decouples the detection head to reduce the conflict of classification and localisation problems. This improves the anti-interference and generalisation ability in complex backgrounds. This paper conducts cross-domain experiments using the constructed medium-resolution SAR full polarisation dataset (SFPD) as the source domain and the high-resolution single-polarised ship detection dataset (HRSID) as the target domain. Compared to the best-performing YOLOV8s model among typical mainstream models, this model improves precision by 4.9%, recall by 3.3%, AP by 2.4%, and F1 by 3.9%. This verifies the effectiveness of the method and provides a useful reference for improving cross-domain learning and model generalisation capability in the field of target detection.

A narrow genetic basis limits further the improvement of modern Gossypium hirsutum cultivar. The abundant genetic diversity of wild species provides available resources to solve this dilemma. In the present study, a chromosome segment substitution line (CSSL) population including 553 individuals was established using G. darwinii accession 5-7 as the donor parent and G. hirsutum cultivar CCRI35 as the recipient parent. After constructing a high-density genetic map with the BC1 population, the genotype and phenotype of the CSSL population were investigated. A total of 235 QTLs, including 104 QTLs for fiber-related traits and 132 QTLs for seed-related traits, were identified from four environments. Among these QTLs, twenty-seven QTLs were identified in two or more environments, and twenty-five QTL clusters consisted of 114 QTLs. Moreover, we identified three candidate genes for three stable QTLs, including GH_A01G1096 (ARF5) and GH_A10G0141 (PDF2) for lint percentage, and GH_D01G0047 (KCS4) for seed index or oil content. These results pave way for understanding the molecular regulatory mechanism of fiber and seed development and would provide valuable information for marker-assisted genetic improvement in cotton.

OBJECTIVE: There are many challenges related to the treatment of coronary atherosclerotic heart disease (CAD). Studies have confirmed that Epimedium extract inhibits myocardial injury induced by myocardial ischaemia, but the mechanism of action remains unclear. This study aimed at analysed the effective components and mechanisms of Epimedium in treating CAD based on network pharmacology and molecular docking studies and to verify the mechanism in vitro. MATERIALS AND METHODS: The TCMSP and UniProt databases were used to filter for the active components and drug targets of Epimedium. The GeneCards database was used to screen disease targets associated with CAD. The intersection of the drug targets of Epimedium and the disease targets of coronary heart disease was studied to identify the targets of Epimedium in the treatment of CAD. Cytoscape software was used to establish and analyse an activity-target network. The STRING database was used to analyse a protein-protein interaction (PPI) network, and proteins in the PPI network were visualized in the R language. Bioconductor software was used for GO function and KEGG pathway enrichment analyses, and visualization analysis was performed in the R language. PyMOL software was used to verify the molecular docking between selected active components of Epimedium and the targets of CAD, and the potential key effective components of Epimedium in the treatment of coronary heart disease were identified. The involvement of the PI3K/Akt pathway was validated by Western blot analysis. RESULTS: (1) Twenty-three active compounds, including Epimedium glycoside, quercetin, luteolin, and olive resin, were screened out. There were 68 common targets of Epimedium and CAD, including IL-6, ESR1, RELA, FOS, NCOA1, CCND1, EGFR, MAPK8, VEGFA, and CASP8. The potential signaling pathways involved in the treatment of CAD by Epimedium included the human cytomegalovirus infection pathway, the PI3K-Akt signaling pathway, the TNF signaling pathway, and the HIF-1 signaling pathway. (2) Luteolin, quercetin, sitosterol, and anhydroicaritin showed strong binding to targets of CAD based on molecular docking studies. (3) Epimedium extract increased the expression of PI3K, Akt and P-Akt but decreased the expression of IL-6 in vitro. CONCLUSIONS: (1) Icariin, quercetin and luteolin may act on target proteins, including IL-6, ESR1, EGFR, MAPK8, VEGFA and CASP8, to participate in the regulation of the human cytomegalovirus infection pathway, the PI3K-Akt signaling pathway, the TNF signaling pathway and other signaling pathways in order to effectively treat CAD. (2) In vitro studies confirmed that Epimedium extract can treat CAD by upregulating PI3K, Akt and P-Akt protein expression and downregulating IL-6 protein expression in SD rat cardiomyocytes. [ABSTRACT FROM AUTHOR]

Abstract Background The evidence for the association between white matter hyperintensity (WMH) severity and neurological deterioration (ND) in patients with single subcortical infarction (SSI) remains unclear and whether the association between them is modified by anterior circulation parent artery steno‐occlusion (PAS) is unknown. Herein, we aimed to prospectively investigate the internal relevance. Methods In this prospective study, the severity of WMH and PAS were assessed in 288 consecutive patients with anterior circulation SSI arriving at our hospital, a tertiary teaching hospital affiliated with Fudan University, 24 h after onset from January 2017 to December 2018. The multivariable logistic regression model was used to estimate the association between WMH severity and the risk of ND within 7 days after stroke onset as well as the interactive effect between WMH severity and PAS on ND among patients with SSI. Results PAS modified the association between WMH severity and ND among patients with SSI (pinteraction = .029). After multivariate adjustment, the odds ratios of moderate‐severe WMH associated with ND were 1.61 (95% CI, 0.50–5.19; ptrend = .428) for patients with PAS, and 0.37 (95% CI, 0.14–0.97; ptrend = .043) for those without PAS. Adding WMH severity to conventional risk factors improved risk prediction for ND in patients without PAS (net reclassification improvement: 48.2%, p = .005; integrated discrimination index: 2.5%, p = .004) but not in those with PAS. Conclusion There was a modified effect of PAS on the association between WMH severity and ND within 7 days after stroke onset among patients with anterior circulation SSI, which deserves more research attention. WMH was negatively associated with ND in anterior circulation SSI patients without PAS.

In modern ecological systems, the overuse and misuse of antibiotics have escalated the prevalence of antibiotic resistance genes (ARGs) and mobile genetic elements (MGEs), positioning them as emerging environmental contaminants. Notably, composting serves as a sustainable method to recycle agricultural waste into nutrient-rich fertilizer while potentially reducing ARGs and MGEs. This study conducted a 47-day composting experiment using pig manure and corn straw, supplemented with chitin and N-Acetyl-D-glucosamine, to explore the impact of these additives on the dynamics of ARGs and MGEs, and to unravel the interplay between these genetic elements and microbial communities in pig manure composting. Results showed that adding 5% chitin into composting significantly postponed thermophilic phase, yet enhanced the removal efficiency of total ARGs and MGEs by over 20% compared to the control. Additionally, the addition of N-Acetyl-D-glucosamine significantly increased the abundance of tetracycline-resistant and sulfonamide-resistant genes, as well as MGEs. High-throughput sequencing revealed that N-Acetyl-D-glucosamine enhanced bacterial α-diversity, providing diverse hosts for ARGs and MGEs. Resistance mechanisms, predominantly efflux pumps and antibiotic deactivation, played a pivotal role in shaping the resistome of composting process. Co-occurrence network analysis identified the key bacterial phyla Proteobacteria, Firmicutes, Gemmatimonadota, and Myxococcota in ARGs and MGEs transformation and dissemination. Redundancy analysis indicated that physicochemical factors, particularly the carbon-to-nitrogen ratio emerged as critical variables influencing ARGs and MGEs. The findings lay a foundation for the developing microbial regulation method to reduce the risks of ARGs in animal manure composts.

This paper explores methodologies to enhance the integration of a green supply chain circular economy within smart cities by incorporating machine learning technology. To refine the precision and effectiveness of the prediction model, the gravitational algorithm is introduced to optimize parameter selection in the support vector machine model. A nationwide prediction model for green supply chain economic development efficiency is meticulously constructed by leveraging public economic, environmental, and demographic data. A comprehensive empirical analysis follows, revealing a noteworthy reduction in mean squared error and root mean squared error with increasing iterations, reaching a minimum of 0.007 and 0.103, respectively—figures that are the lowest among all considered machine learning models. Moreover, the mean absolute percentage error value is remarkably low at 0.0923. The data illustrate a gradual decline in average prediction error and standard deviation throughout the model optimization process, indicative of both model convergence and heightened prediction accuracy. These results underscore the significant potential of machine learning technology in optimizing supply chain and circular economy management. The paper provides valuable insights for decision-makers and researchers navigating the landscape of sustainable development.

Spike architecture influences both grain weight and grain number per spike, which are the two major components of grain yield in bread wheat (Triticum aestivum L.). However, the complex wheat genome and the influence of various environmental factors pose challenges in mapping the causal genes that affect spike traits. Here, we systematically identified genes involved in spike trait formation by integrating information on genomic variation and gene regulatory networks controlling young spike development in wheat. We identified 170 loci that are responsible for variations in spike length, spikelet number per spike, and grain number per spike through genome-wide association study and meta-QTL analyses. We constructed gene regulatory networks for young inflorescences at the double ridge stage and the floret primordium stage, in which the spikelet meristem and the floret meristem are predominant, respectively, by integrating transcriptome, histone modification, chromatin accessibility, eQTL, and protein–protein interactome data. From these networks, we identified 169 hub genes located in 76 of the 170 QTL regions whose polymorphisms are significantly associated with variation in spike traits. The functions of TaZF-B1, VRT-B2, and TaSPL15-A/D in establishment of wheat spike architecture were verified. This study provides valuable molecular resources for understanding spike traits and demonstrates that combining genetic analysis and developmental regulatory networks is a robust approach for dissection of complex traits.

Abstract Antarctic meltwater discharge has been largely emphasized for its potential role in climate change mitigation, not only by reducing global warming, but also by stabilizing the Atlantic Meridional Overturning Circulation (AMOC). Despite the tremendous impact of the AMOC on the climate system, its temporal evolution in response to the meltwater remains poorly understood. Here, we investigate the meltwater impacts on the AMOC based on the GFDL CM2.1 experiments and discover its fast weakening and slow strengthening to the Antarctic meltwater discharge. Cold ocean surface caused by meltwater spread throughout the globe and eventually strengthened the AMOC. However, in the early stages, the tropical temperature response could stimulate the Rossby wave teleconnection, modulating atmospheric circulation in the North Atlantic, and weakening convection and even the AMOC. This counterintuitive evolution implies a potential destabilizing effect of Antarctic meltwater, underscoring the importance of the atmospheric dynamics in the interaction between the two poles.

ObjectiveThis research aims to investigate putative mechanisms between glymphatic activity and cognition in mild cognitive impairment (MCI) and analyzes whether the relationship between cognitive reserve (CR) and cognition was mediated by glymphatic activity.Methods54 MCI patients and 31 NCs were enrolled to evaluate the bilateral diffusivity along the perivascular spaces and to acquire an index for diffusivity along the perivascular space (ALPS-index) on diffusion tensor imaging (DTI). The year of education was used as a proxy for CR. The ALPS-index was compared between two groups and correlation analyses among the ALPS-index, cognitive function, and CR were conducted. Mediation analyses were applied to investigate the correlations among CR, glymphatic activity and cognition.ResultsMCI group had a significantly lower right ALPS-index and whole brain ALPS-index, but higher bilateral diffusivity along the y-axis in projection fiber area (Dyproj) than NCs. In MCI group, the left Dyproj was negatively related to cognitive test scores and CR, the whole brain ALPS-index was positively correlated with cognitive test scores and CR. Mediation analysis demonstrated that glymphatic activity partially mediated the correlations between CR and cognitive function.ConclusionMCI exhibited decreased glymphatic activity compared to NCs. CR has a protective effect against cognitive decline in MCI, and this effect may be partially mediated by changes in glymphatic activity.

Alginate is the most abundant polysaccharide compound in brown algae, which is widely used in various fields. At present, the determination of the content of alginate is mostly carried out using sulfuric acid and trifluoroacetic acid hydrolysis followed by the determination of the content, but the results are not satisfactory, and there are problems such as low hydrolysis degree and low recovery rate. Therefore, in this study, based on the optimization of high performance liquid chromatographic conditions for pre-column derivatization of 1-phenyl-3-methyl-5-pyrazolone (PMP), the hydrolysis effects of sulfuric acid, trifluoroacetic acid (TFA), oxalic acid, and formic acid were compared and the hydrolysis conditions were optimized. The results showed that formic acid was the best hydrolyzing acid. The optimal hydrolysis conditions were 95 % formic acid at 110 °C for 10 h. The hydrolysis effect was stable, with high recovery and low destruction of monosaccharides, which made it possible to introduce formic acid into the subsequent polysaccharide hydrolysis. The pre-column derivatization high performance liquid chromatography method established in this study was accurate and reliable, and the hydrolysis acid with better effect was screened, which provided a theoretical basis for the subsequent determination of alginate content.

Landscape ecological risk is the term used to describe potential negative effects of the relationship between environmental processes and landscape structure as a result of both natural and human interactions. The scientific assessment of past and prediction future ecological risks to landscapes is the basis for achieving sustainable regional development. In this study, we built an ecological risk measurement system characterized by variability in landscape composition to reflect the components of landscape ecological risk. In particular, the CA-Markov model was used to simulate the spatial pattern of land use in the Sanjiang Plain, China, under a wetland protection scenario. The evolution of the spatiotemporal land use trend in the study area was then analyzed for past (2011–2021) and future (2021–2036) periods. The results identified that construction land, farmland, water, and grassland areas exhibited an increasing trend from 2011 to 2021, while forest and wetland areas decreased each year. From 2021 to 2036, the grassland areas exhibited a downward trend, while the other land use types maintained the same previous trend, and all land use types experienced a slowdown in the degree of change. The major landscape ecological risk levels in the research region changed from medium-risk and low-risk levels (2011–2021) to low-risk and lower-risk levels (2021–2036). An evident decrease in risk is observed for farmland and construction land in the mid-region of the research district and forests in the south. In contrast, high-risk and higher-risk levels regions were smaller and distributed in the Songhua River basin in the northeast of the research region, wetland nature reserves, and the Xingkai Lake area, with an overall reduction of 59%. Based on the results, we propose several risk prevention and control management strategies. This research acts as a basis for determining Sanjiang Plain's future rational distribution of land resources and provides an example for the reduction of ecological risks in similar regions.

The health monitoring and fault diagnosis of heavy-duty engines are increasingly important for energy storage ecosystem. During operation, vibration characters corresponding to the specific fault need to be extracted from the overall system vibration. Faulty characteristics emanating from one single cylinder are also mixed with those from other cylinders. Besides, the change of working condition brings strong nonlinearities in surface vibration. To solve these problems, an improved deep residual shrinkage network (IDRSN) is developed for detecting diverse engine faults at various degrees using single channel surface vibration signal. Within IDRSN, a wide convolution kernel is utilized in first convolution layer to capture the long-term fault-related impacts and eliminate the short-time random impact. The residual network module is adopted to enhance the focus the relevant components of vibration signals. Mini-batch training strategy is used to improve the model stability. Meanwhile, Gradient-weighted class activation map is adopted to assess the consistency between the learned knowledge and the fault-related information. The IDRSN is implemented to diagnosing a diesel engine under various faults, faulty degrees and operating speeds. Comparisons with existing models are analyzed in terms of hyper-parameters, training samples, noise resistance, and visualization. Results demonstrate the proposed IDRSN's superior performance on fault diagnosis accuracy, stability, anti-noise performance, and anti-interference performance. An average accuracy rate of 98.38 % was achieved by the proposed IDRSN, in comparison to 96.64 % and 93.56 % achieved by the DRSN and the wide-kernel deep convolutional neural network respectively. These results highlight the proposed IDRSN's superiority in diagnosing multiple faults under various working conditions, offering a low-cost, highly effective, and applicable approach for complex fault diagnosis tasks.

Abstract Apoptosis releases numerous apoptotic vesicles that regulate processes such as cell proliferation, immunity, and tissue regeneration and repair. Now, it has also emerged as an attractive candidate for biotherapeutics. However, apoptotic vesicles encompass a diverse range of subtypes, and it remains unclear which specific subtypes play a pivotal role. In this study, we successfully isolated different apoptotic vesicle subtypes based on their sizes and characterized them using NTA and TEM techniques, respectively. We compared the functional variances among the distinct subtypes of apoptotic vesicles in terms of stem cell proliferation, migration, and differentiation, as well as for endothelial cell and macrophage function, effectively identifying subtypes that exhibit discernible functional differences. ApoSEV (with diameter 1000 nm) played the opposite effect on cell function and tissue regeneration. Lastly, employing protein analysis and gene sequencing techniques, we elucidated the intrinsic mechanisms underlying these differences between different subtypes of apoEVs. Collectively, this study identified that apoptotic vesicle subtypes possessed distinct bio‐functions in regulating stem cell function and behaviour and modulating tissue regeneration, which primarily attribute to the distinct profiling of protein and mRNA in different subtypes. This comprehensive analysis of specific subtypes of apoEVs would provide novel insights for potential therapeutic applications in cell biology and tissue regeneration.

ObjectiveThis study aims to compare gray matter volume changes in patients with chronic kidney disease (CKD) undergoing peritoneal dialysis (PD) and hemodialysis (HD) using voxel-based morphometry (VBM).MethodsA total of 27 PD patients, 25 HD patients, and 42 healthy controls were included. VBM analysis was performed, and cognitive function was assessed using the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment Scale (MoCA). The correlation between cognitive function and changes in brain gray matter volume was analyzed.ResultsBoth peritoneal dialysis and hemodialysis patients had partial gray matter volume reduction compared to the controls, but the affected brain regions were not uniform. The hemodialysis patients had greater volume reduction in certain brain regions than the PD patients. The MMSE and MoCA scores were positively correlated with gray matter volume changes.ConclusionDifferent dialysis modalities cause damage to specific areas of the brain, which can be detected using VBM. VBM, combined with cognitive function assessment, can help detect structural brain changes and cognitive impairment in patients with different dialysis modalities. The comprehensive application of VBM in the field of neurological function deserves further exploration.

With advantages of high-fidelity, monoclonality and large cargo capacity, site-specific recombination (SSR) holds great promises for precise genomic modifications. However, broad applications of SSR have been hurdled by low integration efficiency, and the amount of donor DNA available in nucleus for SSR presents as a limiting factor. Inspired by the DNA replication mechanisms observed in double-stranded DNA virus SV40, we hypothesized that expression of SV40 large T antigen (TAg) can increase the copy number of the donor plasmid bearing an SV40 origin, and in consequence promote recombination events. This hypothesis was tested with dual recombinase-mediated cassette exchange (RMCE) in suspension 293F cells. Results showed that TAg co-transfection significantly enhanced SSR in polyclonal cells. In the monoclonal cell line carrying a single landing pad at an identified genomic locus, 12% RMCE efficiency was achieved, and such improvement was indeed correlated with donor plasmid amplification. The developed TAg facilitated RMCE (T-RMCE) was exploited for the construction of large libraries of >107 diversity, from which GFP variants with enhanced fluorescence were isolated. We expect the underlying principle of target gene amplification can be applicable to other SSR processes and gene editing approaches in general for directed evolution and large-scale genomic screening in mammalian cells.

IntroductionThis paper aims to explore the intersection of corporate social responsibility (CSR) and public health within the context of digital platforms. Specifically, the paper explores the impact of digital platforms on the sustainable development practices of enterprises, seeking to comprehend how these platforms influence the implementation of environmental protection policies, resource management, and social responsibility initiatives.MethodsTo assess the impact of digital platforms on corporate environmental behavior, we conducted a questionnaire survey targeting employees in private enterprises. This survey aimed to evaluate the relationship between the adoption of digital platforms and the implementation of environmental protection policies and practices.ResultsAnalysis of the survey responses revealed a significant positive correlation between the use of digital platforms and the environmental protection behavior of enterprises (r=0.523;p

Abstract BackgroundIn patients with gout, suboptimal management refers to a lack of disease knowledge, low treatment compliance, and inadequate control of serum uric acid (SUA) levels. Several studies have shown that continuous care is recommended for disease management in patients with gout. However, in China, the continuous care model commonly used for patients with gout requires significant labor and time costs, and its efficiency and coverage remain low. Mobile health (mHealth) may be able to address these issues. ObjectiveThis study aimed to explore the impact of mHealth-based continuous care on improving gout knowledge and treatment compliance and reducing SUA levels. MethodsThis study was a single-center, single-blind, and parallel-group randomized controlled trial. Participants were recruited at the West China Hospital of Sichuan University in Chengdu, China, between February 2021 and July 2021 and were randomly assigned to the intervention and control groups. The intervention group received continuous care via an mHealth app, which includes modules for health records, 24 weeks of gout-related health education materials, and interactive support. The control group received routine continuous care, including face-to-face health education, paper-based health education materials consistent with the content for the intervention group, and telephone consultations initiated by the patient. Follow-up was conducted at 6 months. Participants’ gout knowledge levels and treatment compliance were measured at baseline and the 12th and 24th weeks, and participants’ SUA levels were measured at baseline and the 24th week. The intention-to-treat principle and a generalized estimating equation model were used to test the effect of the intervention. ResultsOverall, 258 potential participants underwent eligibility assessments, and 120 were recruited and randomized into the intervention (n=60, 50%) and control (n=60, 50%) groups. Of the 120 participants, 93 (77.5%) completed the 24-week study. The 2 groups had no significant differences in sociodemographic or clinical characteristics, and the baseline measurements were comparable (all PPPPPP ConclusionsContinuous care based on the mHealth app improved knowledge levels and treatment compliance among patients with gout. We suggest incorporating this intervention modality into standard continuous care for patients with gout.

Due to its special working conditions, offshore wind turbine will bear large direct and indirect loads under the combined action of air flow and wave flow. In this paper, a variable pitch system composed of variablepitch motorand variable pitch bearing is improved, and the characteristics of system's bending moment, torque, vibration and other physical quantities under the action of multiple physical loads are verified, and the mechanical response characteristics of floating wind turbine under the control of unified variable pitch and independent variable pitch are studied under the running conditions at sea. The results show that mechanical structure of uniform pitch is compared with that of independent pitch, the independent variable pitch structure can effectively reduce the mean oscillation value of wind turbine tower in the parallel direction of air flow by optimizing control strategy, and reduce the thrust at the hub of wind turbine and the bending moment at the root of tower, but increase the vibration frequency and fatigue load of offshore wind turbine tower along parallel direction of air flow. Reduce the fatigue life of equipment. The research results can be used as a reference to reduce the variable pitch control and vibration suppression of offshore wind turbines and improve the reliability of wind turbines.

INTRODUCTION: As a renewable and clean use of energy, wind power generation has a very important role in the new energy generation industry. For the many parts of various wind turbines, the safety and reliability of wind turbine blades are very important. OBJECTIVES: The energy spectrum simulation algorithm included in the wavelet analysis method is used to simulate and analyzewind turbine blade damage, to verify the correctness and validity of wind turbine blade damage analysis. METHODS: Matlab simulation is used to introduce the experiments related to the static and dynamic detection of fiber grating sensors, analyze the signal characteristics of the wind turbine blade when it is damaged by the impact, and provide a basis for the analysis of the external damage of large wind turbine blade. RESULTS: The main results obtained in this paper are the following. By analyzing the decomposition of wavelet packets, the gradient change of wavelet impact energy spectrum before and after the wavelet damage was obtained and compared with the histogram, and the impact energy spectrum of each three-dimensional wavelet energy packet in the image was compared and analyzed, which can well realize the recognition of wavelet damage gradient for solid composite materials. CONCLUSION: With the help of Matlab simulation to collect the impact response signal, using the wavelet packet energy spectrum method to analyze the signal, can derive the characteristics of wind turbine blade damage.

BackgroundNeuroendocrine prostate cancer (NEPCa) is the most aggressive type of prostate cancer (PCa). However, energy metabolism, one of the hallmarks of cancer, in NEPCa has not been well studied. Pyruvate kinase M (PKM), which catalyzes the final step of glycolysis, has two main splicing isoforms, PKM1 and PKM2. The expression pattern of PKM1 and PKM2 in NEPCa remains unknown.MethodsIn this study, we used immunohistochemistry, immunofluorescence staining, and bioinformatics analysis to examine the expression of PKM1 and PKM2 in mouse and human prostatic tissues.ResultsWe found that PKM2 was the predominant isoform expressed throughout prostate development and PCa progression, with slightly reduced expression in murine NEPCa. PKM1 was mostly expressed in stromal cells but low-level PKM1 was also detected in prostate basal epithelial cells. Its expression was absent in the majority of prostate adenocarcinoma (AdPCa) specimens but present in a subset of NEPCa. Additionally, we evaluated the mRNA levels of ten PKM isoforms that express exon 9 (PKM1-like) or exon 10 (PKM2-like). Some of these isoforms showed notable expression levels in PCa cell lines and human PCa specimens.DiscussionOur study characterized the expression pattern of PKM1 and PKM2 in prostatic tissues including developing, benign, and cancerous prostate. These findings lay the groundwork for understanding the metabolic changes in different PCa subtypes.

Abstract Fast screening strategies that enable high‐throughput evaluation and identification of desired variants from diversified enzyme libraries are crucial to tailoring biocatalysts for the synthesis of D‐allulose, which is currently limited by the poor catalytic performance of ketose 3‐epimerases (KEases). Here, the study designs a minimally equipment‐dependent, high‐throughput, and growth‐coupled in vivo screening platform founded on a redesigned D‐allulose‐dependent biosensor system. The genetic elements modulating regulator PsiR expression levels undergo systematic optimization to improve the growth‐responsive dynamic range of the biosensor, which presents ≈30‐fold facilitated growth optical density with a high signal‐to‐noise ratio (1.52 to 0.05) toward D‐allulose concentrations from 0 to 100 mm. Structural analysis and evolutionary conservation analysis of Agrobacterium sp. SUL3 D‐allulose 3‐epimerase (ADAE) reveal a highly conserved catalytic active site and variable hydrophobic pocket, which together regulate substrate recognition. Structure‐guided rational design and directed evolution are implemented using the growth‐coupled in vivo screening platform to reprogram ADAE, in which a mutant M42 (P38N/V102A/Y201L/S207N/I251R) is identified with a 6.28‐fold enhancement of catalytic activity and significantly improved thermostability with a 2.5‐fold increase of the half‐life at 60 °C. The research demonstrates that biosensor‐assisted growth‐coupled evolutionary pressure combined with structure‐guided rational design provides a universal route for engineering KEases.

Abstract Achieving a high depth of discharge (DOD) in lithium metal anodes (LMAs) is crucial for developing high areal energy density batteries suitable for wearable electronics. Yet, the persistent growth of dendrites compromises battery performance, and the significant lithium consumption during pre‐lithiation obstructs their broad application. Herein, A flexible 3D Li13Sn5 scaffold is designed by allowing molten lithium to infiltrate carbon cloth adorned with SnO2 nanocrystals. This design markedly curbs the troublesome dendrite growth, thanks to the uniform electric field distribution and swift Li+ diffusion dynamics. Additionally, with a minimal SnO2 nanocrystals loading (2 wt.%), only 0.6 wt.% of lithium is consumed during pre‐lithiation. Insights from in situ optical microscope observations and COMSOL simulations reveal that lithium remains securely anchored within the scaffold, a result of the rapid mass/charge transfer and uniform electric field distribution. Consequently, this electrode achieves a remarkable DOD of 87.1% at 10 mA cm−2 for 40 mAh cm−2. Notably, when coupled with a polysulfide cathode, the constructed flexible Li/Li13Sn5@CC||Li2S6/SnO2@CC pouch cell delivers a high‐areal capacity of 5.04 mAh cm−2 and an impressive areal‐energy density of 10.6 mWh cm−2. The findings pave the way toward the development of high‐performance LMAs, ideal for long‐lasting wearable electronics.

Owing to the combined effects of climate change and human activities, the type of desertification in the arid regions of Asia has undergone profound changes, and the problem of desertification has become increasingly prominent. To help address this problem, this study clarifies the transformation trends and characteristics of desertification types across Asia and discusses key areas for desertification prevention and control. Based on a series of Landsat satellite images, combined with the classification method of the desertification degree and a machine learning model, the desertification types, trends, and conversion degrees in the arid regions of Asia were systematically evaluated. In addition, the impact mechanism of climate change on the conversion between desertification types was evaluated. The primary results were as follows: (1) Various desertification types in the arid areas of Asia from 1990 to 2022 exhibited evident time series characteristics. Extremely severe desertification expanded from 1990 to 2009 and subsequently shrunk; (2) The degree of desertification in the arid regions of Asia exhibited different change characteristics in different periods, and the converted area of the same conversion type was different in different periods; (3) Different desertification conversion types in different periods have different drivers; however, overall, soil moisture has the greatest contribution to desertification conversion type. These results provide a scientific reference for the formulation of reasonable desertification prevention and control policies.

Ischemic stroke is a significant burden on human health worldwide. Carotid Atherosclerosis stenosis plays an important role in the comprehensive assessment and prevention of ischemic stroke patients. High-resolution vessel wall magnetic resonance imaging has emerged as a successful technique for assessing carotid atherosclerosis stenosis. This advanced imaging modality has shown promise in effectively displaying a wide range of characteristics associated with the condition, leading to a comprehensive evaluation. High-resolution vessel wall magnetic resonance imaging not only enables a comprehensive evaluation of the instability of carotid atherosclerosis stenosis plaques but also provides valuable information for understanding the pathogenesis and predicting the prognosis of ischemic stroke patients. The purpose of this article is to review the application of high-resolution magnetic resonance imaging in ischemic stroke and carotid atherosclerotic stenosis.

In the era of information technology advancement, big data analysis has emerged as a crucial tool for government governance. Despite this, corruption remains a challenge at the grass-roots level, primarily attributed to information asymmetry. To enhance the efficacy of corruption prevention and control in grass-roots government, this study introduces the concept of data platform management and integrates it with the “5W” (Who, What, When, Where, Why) analysis framework. The research is motivated by the observation that existing studies on corruption prevention primarily concentrate on the formulation of laws and regulations, neglecting the potential improvement in actual effectiveness through the utilization of data platforms and analytical frameworks. The research employs methodologies grounded in the Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis framework, the Plan, Do, Check, Act (PDCA) cycle analysis framework, and the 5W analysis framework. Throughout the iterative process of implementing data platform management, various timeframes are established, and the impact of the three models is evaluated using indicators such as public participation and government satisfaction. The research reveals that the SWOT framework can formulate targeted strategies, the PDCA framework continuously optimizes work processes, and the 5W framework profoundly explores the root causes of corruption. The outcomes indicate a 10.76% increase in the public participation level score with the 5W model, rising from 71.67%, and a 23.24% increase in the governance efficiency score, reaching 66.12%. The SWOT model excels in case handling prescription and corruption reporting rate. The synergistic application of the three models demonstrates a positive impact. In conclusion, the amalgamation of data platform management and a multi-model approach effectively enhances the corruption prevention capabilities of grass-roots governments, offering insights for the establishment of transparent and efficient grass-roots governance.

Bioaerosols are biologically originated particles in the atmosphere, which is mainly composed of bacteria, fungi, viruses, pollen, spores, and the fragmentation and disintegration of plants and animals. Bioaerosols are easy to be spread in the lower atmosphere and cause various epidemic diseases, which is harmful to human health. The forecasting and alert of bioaerosols have important scientific significance and reality needs. In this paper, a method is proposed for estimating and predicting the concentration profile of atmospheric bioaerosols using fluorescence lidar observational data. Using the powerful nonlinear prediction ability of artificial neural networks and through repeated training, a mathematical model can be established for the relationship among atmospheric environment, meteorological parameters, and bioaerosol concentration profiles. The input parameters are temperature and humidity, aerosol extinction coefficient, backscatter coefficient, PM2.5, PM10, SO2, NO2, CO, O3, and wind speed, and outputs the concentration profile of bioaerosols. The prediction results with the measurement relative deviation of genetic algorithm back propagation (GA-BP) neural network and adaptive genetic algorithm back propagation (AGA-BP) neural network were analyzed. The results indicate that the AGA-BP neural network can effectively predict the concentration distribution of bioaerosols, and the predicted concentrations of bioaerosols are 1793 particles × m−3, 3088 particles × m−3, 5261 particles × m−3, 7410 particles × m−3 and 9133 particles × m−3 for air quality with superior, fine, mild contamination, middle level pollution and heavy pollution at an altitude of 0.315 km, respectively. We found that the predicted concentration of pollution weather is much higher than that of good weather. Furthermore, the AGA-BP neural network was used to predict the concentration profiles of atmospheric bioaerosols under different weather conditions, which provided a new research method for forecasting and alert of atmospheric bioaerosols.

Abstract Mechanistic target of rapamycin complex 1 (mTORC1) is a conserved serine/threonine kinase that integrates various environmental signals to regulate cell growth and metabolism. mTORC1 activation requires tethering to lysosomes by the Ragulator‐Rag complex. However, the dynamic regulation of the interaction between Ragulator and Rag guanosine triphosphatase (GTPase) remains unclear. In this study, that LAMTOR1, an essential component of Ragulator, is dynamically ubiquitinated depending on amino acid abundance is reported. It is found that the E3 ligase TRAF4 directly interacts with LAMTOR1 and catalyzes the K63‐linked polyubiquitination of LAMTOR1 at K151. Ubiquitination of LAMTOR1 by TRAF4 promoted its binding to Rag GTPases and enhanced mTORC1 activation, K151R knock‐in or TRAF4 knock‐out blocks amino acid‐induced mTORC1 activation and accelerates the development of inflammation‐induced colon cancer. This study revealed that TRAF4‐mediated LAMTOR1 ubiquitination is a regulatory mechanism for mTORC1 activation and provides a therapeutic target for diseases involving mTORC1 dysregulation.

IntroductionSclerotinia sclerotiorum is a known pathogen that harms crops and vegetables. Unfortunately, there is a lack of effective biological control measures for this pathogen. Bacillus velezensis 20507 has a strong antagonistic effect on S. Sclerotiorum; however, the biological basis of its antifungal effect is not fully understood.MethodsIn this study, the broad-spectrum antagonistic microorganisms of B. velezensis 20507 were investigated, and the active antifungal ingredients in this strain were isolated, purified, identified and thermal stability experiments were carried out to explore its antifungal mechanism.ResultsThe B. velezensis 20507 genome comprised one circular chromosome with a length of 4,043,341 bp, including 3,879 genes, 185 tandem repeats, 87 tRNAs, and 27 rRNAs. Comparative genomic analysis revealed that our sequenced strain had the closest genetic relationship with Bacillus velezensis (GenBank ID: NC 009725.2); however, there were significant differences in the positions of genes within the two genomes. It is predicted that B. velezensis 20507 encode 12 secondary metabolites, including difficidin, macrolactin H, fengycin, surfactin, bacillibactin, bacillothiazole A-N, butirosin a/b, and bacillaene. Results showed that B. velezensis 20507 produced various antagonistic effects on six plant pathogen strains: Exserohilum turcicum, Pyricularia oryzae, Fusarium graminearum, Sclerotinia sclerotiorum, Fusarium oxysporum, and Fusarium verticillioides. Acid precipitation followed by 80% methanol leaching is an effective method for isolating the antifungal component ME80 in B. velezensis 20507, which can damage the membranes of S. sclerotiorum hyphae and has good heat resistance. Using high-performance liquid chromatography, and Mass Spectrometry analysis, it is believed that fengycin C72H110N12O20 is the main active antifungal substance.DiscussionThis study provides new resources for the biological control of S. Sclerotiorum in soybeans and a theoretical basis for further clarification of the mechanism of action of B. velezensis 20507.

Abstract Introduction Elevated urinary albumin‐to‐creatinine ratio (UACR) was associated with increased mortality in general population and diabetic patients. However, whether the association remains similar in the subjects with different status of glucose metabolism was unclear. The optimal level of UACR in predicting mortality also remained unknown. This study aims to investigate the relationship between UACR with all‐cause and cardiovascular mortality in population with different status of glucose metabolism and explore the predictive cutoff point of UACR. Methods Six thousand three hundred and eighty‐six community‐dwelling individuals aged ≥45 years were enrolled and followed for an average of 5.3 years. Cox proportional hazards model was performed to analysis the association of baseline UACR and all‐cause as well as cardiovascular mortality according to the status of glucose metabolism. Receiver operating characteristic curve was plotted to explore the optimal predictive cutoff point of UACR. Results With UACR increasing, both the prevalence of all‐cause and cardiovascular death increased. Cox analyses showed baseline UACR independently predicted the risk of all‐cause and cardiovascular mortality in the patients with prediabetes mellitus (pre‐DM) and diabetes mellitus (DM) but not in subjects with normal glucose tolerance (NGT). When divided by quartiles of UACR, the cumulative survival rate decreased acrossing the quartiles. Compared to the subjects with lowest quartile of UACR, participants with UACR ≥7.40 mg/gCr had a higher risk of all‐cause mortality, and participants with UACR ≥16.60 mg/gCr had an increased risk of cardiovascular mortality in all hyperglycemia subjects. The optimal predictive cutoff point of UACR was about 17 mg/gCr. Conclusion UACR was an independent predictor of all‐cause and cardiovascular mortality in population with pre‐DM and DM but not in the subjects with NGT. The optimal predictive cutoff point of UACR is about 17 mg/gCr, which was far below the diagnostic cutoff point of microalbuminuria. Earlier interventions of albuminuria should be initiated from very early stage of hyperglycemia to reduce the burden of death in all patients whose glucose metabolism are impaired.

BackgroundDiabetic peripheral neuropathy (DPN) contributes to disability and imposes heavy burdens, while subclinical DPN is lack of attention so far. We aimed to investigate the relationship between vitamin D and distinct subtypes of subclinical DPN in type 2 diabetes (T2DM) patients.MethodsThis cross-sectional study included 3629 T2DM inpatients who undertook nerve conduction study to detect subclinical DPN in Zhongshan Hospital between March 2012 and December 2019. Vitamin D deficiency was defined as serum 25-hydroxyvitamin D (25(OH)D) level

The incidence rate of Parkinson's disease (PD) is increasing yearly. Neuronal apoptosis caused by abnormal protein phosphorylation is closely related to the pathogenesis of Parkinson's disease. At present, few PD-specific apoptosis pathways have been revealed. To investigate the effect of Baichanting (BCT) on apoptosis from the perspective of protein phosphorylation, α-syn transgenic mice were selected to observe the behavioral changes of the mice, and the apoptosis of substantia nigra cells were detected by the HE method and TUNEL method. Network pharmacology combined with phosphorylation proteomics was used to find relevant targets for BCT treatment of PD and was further verified by PRM and western blotting. BCT improved the morphology of neurons in the substantia nigra and reduced neuronal apoptosis. The main enriched pathways in the network pharmacology results were apoptosis, the p53 signaling pathway and autophagy. Western blot results showed that BCT significantly regulated the protein expression levels of BAX, Caspase-3, LC3B, P53 and mTOR and upregulated autophagy to alleviate apoptosis. Using phosphorylated proteomics and PRM validation, we found that Pak5, Grin2b, Scn1a, BcaN, L1cam and Braf are closely correlated with the targets of the web-based pharmacological screen and may be involved in p53/mTOR-mediated autophagy and apoptosis pathways. BCT can inhibit the activation of the p53/mTOR signaling pathway, thereby enhancing the autophagy function of cells, and reducing the apoptosis of neurons which is the main mechanism of its neuroprotective effect.

Anthocyanin is an important pigment that prevents oxidative stress and mediates adaptation of plants to salt stress. Peanuts with dark red and black testa are rich in anthocyanin. However, correlation between salt tolerance and anthocyanin content in black and dark red testa peanuts is unknown. In this study, three peanut cultivars namely YZ9102 (pink testa), JHR1 (red testa) and JHB1 (black testa) were subjected to sodium chloride (NaCl) stress. The plant growth, ion uptake, anthocyanin accumulation, oxidation resistance and photosynthetic traits were comparatively analyzed. We observed that the plant height, leaf area and biomass under salt stress was highly inhibited in pink color testa (YZ9102) as compare to black color testa (JHB1). JHB1, a black testa colored peanut was identified as the most salt-tolerance cultivar, followed by red (JHR1) and pink(YZ9102). During salt stress, JHB1 exhibited significantly higher levels of anthocyanin and flavonoid accumulation compared to JHR1 and YZ9102, along with increased relative activities of antioxidant protection and photosynthetic efficiency. However, the K+/Na+ and Ca2+/Na+ were consistently decreased among three cultivars under salt stress, suggesting that the salt tolerance of black testa peanut may not be related to ion absorption. Therefore, we predicted that salt tolerance of JHB1 may be attributed to the accumulation of the anthocyanin and flavonoids, which activated antioxidant protection against the oxidative damage to maintain the higher photosynthetic efficiency and plant growth. These findings will be useful for improving salt tolerance of peanuts.

BackgroundCXC chemokine receptor 4 (CXCR4) is associated with the progression and metastasis of numerous malignant tumors. However, its relationship with Gastroenteropancreatic Neuroendocrine Neoplasms Grade 3 (GEP-NENs G3) is unclear. The aim of this study was to characterize the expression of CXCR4 in GEP-NENS and to explore the clinical and prognostic value of CXCR4.MethodsThis study retrospectively collected clinical and pathological data from patients with GEP-NENs who receiving surgery in Qilu Hospital of Shandong University from January 2013 to April 2021, and obtained the overall survival of the patients based on follow-up. Immunohistochemistry (IHC) was performed on pathological paraffin sections to observe CXCR4 staining. Groups were made according to pathological findings. Kaplan-Meier (K-M) curve was used to evaluate prognosis. SPSS 26.0 was used for statistical analysis.Results100 GEP-NENs G3 patients were enrolled in this study. There was a significant difference in primary sites (P=0.002), Ki-67 index (P