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Background Lodging is a major factor contributing to yield loss and constraining the mechanical harvesting of wheat crops. Genetic improvement through breeding effectively reduced the lodging and improved the grain yield, however, the physiological mechanisms involved in providing resistance to lodging are different in the breeding stage and are not clearly understood. The purpose of this study was to compare the differences in the lodging resistance (LR) of the wheat varieties released during the different decades and to explore the effect of the application of nitrogen (N) fertilizer on the plasticity of LR. Results A field study was conducted during the cultivation seasons of 2019–2020 and 2020–2021, in soil supplemented with three N levels: N 0 (0 kg ha –1 ), N 180 (200 kg ha –1 ), and N 360 (360 kg ha –1 ) using eight varieties of wheat released for commercial cultivation from 1950 to date. The results obtained showed that genetic improvement had significantly enhanced the LR and grain yield in wheat. In the first breeding stage (from 1950 to 1980s) the lodging resistant index increased by 15.0%, which was primarily attributed to a reduced plant height and increased contents of cellulose, Si, and Zn. In the second breeding stage (the 1990s–2020s) it increased by 172.8%, which was mainly attributed to an increase in the stem diameter, wall thickness, and the contents of K, Ca, Fe, Mn, and Cu. The application of N fertilizer improved the grain yield but reduced the LR in wheat. This was mainly due to an increase in plant height resulting in an elevation of the plant center of gravity, a decrease in the contents of cellulose, and a reduction in the area of large-sized vascular bundles in the stems, even if N supplementation increased the concentrations of K, Ca, and Si. Conclusion Although breeding strategies improved the stem strength, the trade-off between the grain yield and LR was more significantly influenced by the addition of N. Overcoming this peculiar situation will serve as a breakthrough in improving the seed yield in wheat crops in the future.

In the context of achieving sustainable development goals and promoting a sustainable, low-carbon global energy transition, accurately quantifying and proactively managing the carbon intensity of power systems is a core challenge in monitoring the sustainability of the power sector. However, existing demand response methods often overlook the dynamic characteristics of power system carbon emissions and fail to accurately characterize the complex relationship between power consumption and carbon emissions, which results in suboptimal emission reduction results. To address this challenge, this paper proposes and validates an innovative low-carbon demand response optimization scheduling method as a sustainable tool. The core of this method is the development of a dynamic carbon emission factor (DCEF) assessment model. By innovatively integrating marginal and average carbon emission factors, it becomes a dynamic sustainability indicator that can measure the environmental performance of the power grid in real time. To characterize the relationship between power consumption behavior and carbon emissions, we employ an adaptive Dirichlet process mixture model (DPMM). This model does not require a preset number of clusters and can automatically discover patterns in the data, such as grouping holidays and working days with similar power consumption characteristics. Based on the clustering results and historical data, a dual long short-term memory (LSTM) deep learning network architecture is designed to achieve a coordinated prediction of power consumption and DCEFs for the next 24 h. On this basis, a method is established with the goal of maximizing carbon emission reduction while considering constraints such as fixed daily power consumption, user comfort, and equipment safety. Simulation results demonstrate that this approach can effectively reduce regional carbon emissions through accurate prediction and optimized scheduling. This provides not only a quantifiable technical path for improving the environmental sustainability of the power system but also decision-making support for the formulation of energy policies and incentive mechanisms that align with sustainable development goals.

Investigating the interaction effect of nitrogen (N) management strategies and stay-green types of maize hybrids is essential for enhancing N use efficiency and developing N-efficient hybrids. A field experiment was conducted with five N management treatments (Control, Opt.N*70%, Opt.N, Opt.N*130%, and Con.N) and two stay-green types of maize hybrids (stay-green hybrids: DH605 and ZD958; moderate-green hybrids: XY335 and XY1266) to examine their interaction effects on maize yield, aboveground biomass, and N uptake and allocation. The highest grain yields for moderate stay-green and over stay-green maize hybrids were 12.8 Mg ha−1 and 10.8 Mg ha−1, respectively. Compared to over stay-green hybrids, moderate stay-green hybrids exhibited a significantly higher aboveground biomass and N uptake. Under an optimal N (Opt.N) treatment, moderate stay-green hybrids achieved a 15.8% higher grain yield than over stay-green hybrids. Under the Opt.N*130% treatment, moderate stay-green hybrids had the highest grain N concentration, averaging 13.1 g kg−1. Nitrogen application enhanced N allocation to grains, resulting in a 3.1–7.7% increase in grain N content. Moderate stay-green hybrids with optimal N management exhibited a 1.9% higher grain N content compared to over stay-green hybrids, whereas their vegetative organs had a relatively lower N content except for the Opt.N*130% treatment. Selecting a suitable maize hybrid (e.g., moderate stay-green maturity hybrid, XY335) and optimizing N fertilizer management can enhance grain yield, grain N content, and enhance N absorption and utilization efficiency.

ABSTRACT This study aimed to analyze the association between single nucleotide polymorphisms (SNPs) in the FGFR1 and EBP41L5 genes and kidding performance of goats. Mass ARRAY®SNP typing technology was applied to genotype the SNPs of FGFR1 and EBP41L5 genes, of which the genetic characteristics of Yunshang Black goat (YS, n=544), Jining Grey goat (JN, n=133) and Liaoning Cashmere goat (LN, n=91) were explored. Then the association was analyzed between FGFR1 and EBP41L5 and kidding performance (litter size, litter weight at birth, litter weight at weaning) in different goats. Population genetics statistics showed that FGFR1 gene g. 12120297A> G and EPB41L5 gene g. 64237881A> C were low polymorphisms in the three populations (PIC< 0.25); EPB41L5 gene g. 64266710G> C and g. 64266715G> C were moderately polymorphic in Yunshang Black goat and Liaoning Cashmere goat (0.25 C locus was under Hardy-Weinberg disequilibrium in Jining grey goat (P< 0.05), and was under Hardy-Weinberg equilibrium in Yunshang Black goat and Liaoning Cashmere goat (P> 0.05). FGFR1 gene g. 12120297A> G, EPB41L5 gene g. 64266710G> C, g. 64266715G> C were under Hardy-Weinberg equilibrium in Yunshang Black goat, Jining Grey goat and Liaoning Cashmere goat (P> 0.05). Association analysis indicated that there were no significant correlation between FGFR1 gene g. 12120297A> G locus and EPB41L5 gene g. 64266715G> C locus and litter size, litter weight at birth and weaning (P> 0.05). There were significant correlation between EPB41L5 gene g. 64237881A> C and g. 64266710 G> C and litter size (P< 0.05), and were no significant correlation and litter weight at birth (P> 0.05), there were no significant correlation between EPB41L5 gene g. 64237881A> C and litter weight at weaning (P> 0.05), there were significant correlation between EPB41L5 gene g. 64266710C> G and litter weight at weaning (P< 0.05). Therefore, these results suggested that EPB41L5 gene g. 64237881A> C, g. 64266710 G> C loci were suitable as molecular markers for litter size in YS, and g. 64266710G> C locus was suitable as a selection marker for litter weight at weaning.

Both genetic improvement and the application of N fertilizer increase the quality and yields of wheat. However, the molecular kinetics that underlies the differences between them are not well understood. In this study, we performed a non-targeted metabolomic analysis on wheat cultivars from different release years to comprehensively investigate the metabolic differences between cultivar and N treatments. The results revealed that the plant height and tiller number steadily decreased with increased ears numbers, whereas the grain number and weight increased with genetic improvement. Following the addition of N fertilizer, the panicle numbers and grain weights increased in an old cultivar, whereas the panicle number and grain number per panicle increased in a modern cultivar. For the 1950s to 2010s cultivar, the yield increases due to genetic improvements ranged from −1.9% to 96.7%, whereas that of N application ranged from 19.1% to 81.6%. Based on the untargeted metabolomics approach, the findings demonstrated that genetic improvements induced 1.4 to 7.4 times more metabolic alterations than N fertilizer supply. After the addition of N, 69.6%, 29.4%, and 33.3% of the differential metabolites were upregulated in the 1950s, 1980s, and 2010s cultivars, respectively. The results of metabolic pathway analysis of the identified differential metabolites via genetic improvement indicated enrichment in 1-2 KEGG pathways, whereas the application of N fertilizer enriched 2–4 pathways. Our results provide new insights into the molecular mechanisms of wheat quality and grain yield developments.

Here we first improved the ε-PL productivity in five species of wild-type strains in Streptomyces ( Streptomyces padanus , Streptomyces griseofuscus , Streptomyces graminearus , Streptomyces hygroscopicus , and Streptomyces albulus ) by genome shuffling. Then all the shuffled strains were suffered from an interspecific hybridization through stochastic protoplast fusion. One hybrid designated FEEL-1 was selected by morphology and spore color with ε-PL production of 1.12 g/L in shake flask, about 2.75-fold higher than that in wild types. The ε-PL production of FEEL-1 was then obtained as 24.5 g/L in fed-batch fermentation, which was 63–81 % higher than those in shuffled strains. Random amplified polymorphic DNA revealed that FEEL-1 was probably hybridized from S. padanus , S. griseofuscus , and S. albulus . Activities of several enzymes in FEEL-1 (hexokinase, phosphoenolpyruvate carboxylase, aspartokinase, and citrate synthase) were more active than those in shuffled strains, which was a possible reason for the enhancement of ε-PL production. This research highlights the importance of genome shuffling along with interspecific hybridization as a new breeding strategy for improving phenotype of industrial strains.

The heavy metal cadmium (Cd) pollution in Chinese karst soils threatens food security, and microorganisms play an important role in regulating the migration and transformation of Cd in the soil–plant system. Nevertheless, the interaction characteristics between key microbial communities and environmental factors in response to Cd stress in specific crop environmental systems need to be explored. In this study, the soil (ferralsols)-microbe-crop (potato) system was taken as the object to explore the potato rhizosphere microbiome, using toxicology and molecular biology approaches, to explore the potato rhizosphere soil properties, microbial stress characteristics, and important microbial taxa under Cd stress. We hypothesized that different members of fungal and bacterial microbiome would regulate the resilience of potato rhizosphere and plants to Cd stress in the soil environment. Meanwhile, individual taxa will have different roles in the contaminated rhizosphere ecosystem. We found that soil pH was the main environmental factor affecting fungal community structure; urea-decomposing and nitrate-reducing functional bacteria as well as endosymbiotic and saprophytic functional fungi gradually decreased. In particular, Basidiomycota may play a key role in preventing the migration of Cd from the soil to plants (potato). These findings provide important candidates for screening the cascade of Cd inhibition (detoxification/regulation) from soil to microorganisms to plants. Our work provides an important foundation and research insights for the application of microbial remediation technology in the karst cadmium-contaminated farmland.

Internet gaming disorder (IGD) is a type of behavioural addictions. One of the key features of addiction is the excessive exposure to addictive objectives (e.g. drugs) reduces the sensitivity of the brain reward system to daily rewards (e.g. money). This is thought to be mediated via the signals expressed as dopaminergic reward prediction error (RPE). Emerging evidence highlights blunted RPE signals in drug addictions. However, no study has examined whether IGD also involves alterations in RPE signals that are observed in other types of addictions. To fill this gap, we used functional magnetic resonance imaging data from 45 IGD and 42 healthy controls (HCs) during a reward-related prediction-error task and utilised a psychophysiological interaction (PPI) analysis to characterise the underlying neural correlates of RPE and related functional connectivity. Relative to HCs, IGD individuals showed impaired reinforcement learning, blunted RPE signals in multiple regions of the brain reward system, including the right caudate, left orbitofrontal cortex (OFC), and right dorsolateral prefrontal cortex (DLPFC). Moreover, the PPI analysis revealed a pattern of hyperconnectivity between the right caudate, right putamen, bilateral DLPFC, and right dorsal anterior cingulate cortex (dACC) in the IGD group. Finally, linear regression suggested that the connection between the right DLPFC and right dACC could significantly predict the variation of RPE signals in the left OFC. These results highlight disrupted RPE signalling and hyperconnectivity between regions of the brain reward system in IGD. Reinforcement learning deficits may be crucial underlying characteristics of IGD pathophysiology.

With the explosive growth of various types of documents, these resources have been collated and utilized to become a concern of the industry. This paper builds a cloud computing platform based on OpenStack, and implements a document service system based on the built cloud computing platform. The administrator can perform state monitoring and management operations on the implemented document service system, and the system user can delete, search and classify the document. This research has greatly improved the use of documents and the level of scientific research.

Eddy current sensors are increasingly being used to measure the dynamic blade tip clearance in turbines due to their robust anti-interference capabilities and non-contact measurement advantages. However, the current research primarily focuses on enhancing the performance of eddy current sensors themselves, with few studies investigating the influence of turbine rotor parameters on the measurements taken by these sensors for dynamic blade tip clearance. Hence, this paper addresses this gap by using COMSOL Multiphysics 6.2 software to establish a finite model with circuit interfaces. Additionally, the model’s validity was verified through experiments. This model is used to simulate the voltage output of the sensor and the measurement of dynamic blade tip clearance under various rotor parameters. The results indicate that the length and number of blades, as well as the hub radius, significantly affect the sensor voltage output in comparison to rotation speed. Furthermore, we show that traditional static calibration methods are inadequate for measuring dynamic blade tip clearance using eddy current sensors. Instead, it is demonstrated that incorporating rotor parameters into the calibration of eddy current sensors can enhance the accuracy of dynamic blade tip clearance measurements.