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The Bacillus velezensis strain NT35, which has strong biocontrol ability, was isolated from the rhizosphere soil of Panax ginseng. The antifungal effects of the NT35 strain against the mycelium and spore growth of Ilyonectria robusta, which causes ginseng rusty root rot, were determined. The inhibitory rate of I. robusta mycelial growth was 94.12% when the concentration of the NT35 strain was 107 CFU·mL−1, and the inhibitory rates of I. robusta sporulation and spore germination reached 100 and 90.31%, respectively, when the concentration of the NT35 strain was 104 and 108 CFU·mL−1, respectively. Strain NT35 had good prevention effects against ginseng rust rot indoors and in the field with the control effect 51.99%, which was similar to that of commercial chemical and biocontrol agents. The labeled strain NT35-Rif160-Stre400 was obtained and colonized ginseng roots, leaves, stems and rhizosphere soil after 90 days. Bacillus velezensis NT35 can induce a significant increase in the expression of five defensive enzyme-encoding genes and ginsenoside biosynthesis-related genes in ginseng. In the rhizosphere soil, the four soil enzymes and the microbial community improved during different periods of ginseng growth in response to the biocontrol strain NT35. The NT35 strain can recruit several beneficial bacteria, such as Luteimonas, Nocardioides, Sphingomonas, and Gemmatimonas, from the rhizosphere soil and reduce the relative abundance of Ilyonectria, Fusarium, Neonectria and Dactylonectria, which cause root rot and rusty root rot in ginseng plants. The disease indices were significantly negatively correlated with the abundances of Sphingomonas and Trichoderma. Additionally, Sphingomonadales, Sphingomonadaceae and actinomycetes were significantly enriched under the NT35 treatment according to LEfSe analysis. These results lay the foundation for the development of a biological agent based on strain NT35.

With the continuous infusion of renewable energy sources, smart buildings have evolved from single-load characteristics into dual characteristics with both electric energy production and consumption capability. Concurrently, the peak and off-peak periods of electricity consumption are influenced by climatic factors, which leads to complexity and deviation from the time-of-use tariffs set by electricity markets, which consequently result in a loss of revenue from grid-based electricity sales. Thus, adopting an innovative pricing mechanism to offset the revenue deficit in the grid assumes paramount significance. Built upon a dual-layer framework that employs intelligent optimization algorithms, this study proposes a pricing strategy for introducing the retail electricity provider into smart building clusters with peer-to-peer power sharing as the core. First, the independent operation model of intelligent buildings and electric energy sharing model without the participation of retail power suppliers are respectively established. Subsequently, with the aim to minimize alliance costs, a novel energy sharing pricing model involving retail electricity suppliers is developed, and a combination of particle swarm optimization and alternating direction multiplier methods is used for distributed solutions within a representative model. This approach yields optimal energy sharing transaction volumes and pricing while ensuring the confidentiality of each participating entity. Lastly, from the perspectives of the power grid, retail electricity suppliers, and multi-building smart alliances, this study conducts simulation analyses of key parameters that influence the bargaining effectiveness of retail electricity suppliers. These parameters encompass the upper limit of pricing, market supervision coefficient, and discount coefficient associated with the grid-based electricity sales to suppliers. Through these analyses, the study further validates the efficacy of the proposed strategy.

The integrated energy system (IES) is recognized as a promising energy utilization approach enabled to both improve the energy efficiency and reduce pollutant emissions. Although the economic-environmental dispatch (EED) problem of the IES has been widely studied, the fact that the IES is operated under off-design conditions, having a significant influence on the efficiency of energy devices, is neglected usually, resulting in the scheduled operations, for the IES could not be accurate enough in many actual situations. This study investigates the EED problem of the IES under off-design conditions. Technically, by integrating an efficiency correction process into the traditional energy hub (EH) model, a dynamic energy hub (DEH) model is first formulated for adapting itself to variable energy conversion efficiencies. Then, a deep neural network (DNN)-based efficiency correction method is proposed to predict and correct the time-varying efficiency of energy devices based on three main off-design conditions including the load rate, air temperature, and pressure. A multi-objective EED model is finally formulated for the IES, with the framework of the DEH model, aiming at establishing a trade-off between operational cost and emitted pollutants. Case studies show that the proposed approach helps in enhancing the accuracy of IES dispatch while having a good performance in both the operational cost and pollutant emission reduction.

Drought is a significant environmental stress factor that adversely affects maize productivity. However, many details regarding the molecular mechanisms of maize against drought are still unclear. In this study, leaf transcriptomics and physiological traits of two maize genotypes with differing drought resistance were analyzed. Transcriptome sequencing identified 8985 and 7305 differentially expressed genes (DEGs) in SD902 and SD609, respectively. Functional analysis suggested that numerous genes are highly involved in oxidative defense, protein modification, photosynthesis, phytohormone response, MAPK signaling, and transcription factors (TFs). Compared to SD902, SD609 had a higher expression of DEGs related to antioxidant enzymes, photosynthetic electron transport, heat shock proteins, and indole-3-acetic acid (IAA) signaling under drought conditions, which might contribute to its tolerance mechanisms to drought. Stress-induced TFs may play a crucial regulatory role in genotypic differences. Moreover, the physiological changes and gene expression abundance determined using quantitative reverse transcription polymerase chain reaction were consistent with the RNA sequencing data. The study results suggest that the higher drought tolerance of SD609 than SD902 can be attributed to stronger stress defense capabilities, IAA signal transduction, and more stable photosynthesis. Our findings provide new insights into the molecular mechanisms of maize against drought stress, and the candidate genes identified may be used in breeding drought-tolerant maize cultivars.

Regarding the composting of rural waste, numerous studies either addressed the composting of a single waste component or were conducted at a laboratory/pilot scale. However, far less is known about the mixed composting effect of multi-component rural waste on a large scale. Here, we examined nutrient transformation, maturity degree of decomposition, and succession of microbial communities in large-scale (1,000 kg mixed waste) compost of multi-component wastes previously optimized by response models. The results showed that multi-component compost can achieve the requirement of maturity and exhibit a higher nutritional value in actual compost. It is worth noting that the mixed compost effectively removed pathogenic fungi, in which almost no pathogenic fungi were detected, and only two pathogenic bacteria regrown in the cooling and maturation stages. Structural equation models revealed that the maturity (germination index and the ratio of ammonium to nitrate) of the product was directly influenced by compost properties (electrical conductivity, pH, total organic carbon, moisture, temperature, and total nitrogen) compared with enzymes (cellulase, urease, and polyphenol oxidase) and microbial communities. Moreover, higher contents of total phosphorus, nitrate-nitrogen, and total potassium were conducive to improving compost maturity, whereas relatively lower values of moisture and pH were more advantageous. In addition, compost properties manifested a remarkable indirect effect on maturity by affecting the fungal community (Penicillium and Mycothermus). Collectively, this evidence implies that mixed compost of multi-component rural waste is feasible, and its efficacy can be applied in practical applications. This study provides a solution for the comprehensive treatment and utilization of rural waste.

A bacterial strain JI39 that had plant growth-promoting traits was isolated from the rhizosphere soil of Panax ginseng. It had the ability to produce high indole-3-acetic acid (13.1 μg/ml), phosphate solubilization (164.2 μg/ml), potassium solubilization (16.1 μg/ml), and nitrogen fixation. The strain JI39 was identified to be Arthrobacter nicotinovorans based on morphological, physiological, and biochemical traits and through 16S rDNA sequence analysis. The optimal culture environment for strain growth was 1.0% NaCl, 30°C, pH 6.0, and without UV irradiation. The strain can produce cellulase and protease. The strain JI39 can significantly promote the growth of ginseng. After ginseng seeds were treated with 3 × 108 CFU/ml of JI39 bacterial suspension, the shoot's length was significantly increased by 64.61% after 15 days. Meanwhile, the fresh weight of 2-year-old ginseng roots was significantly increased by 24.70% with a treatment by the 108 CFU/ml bacterial suspension after 150 days in the field. The gene expression of phenylalanine ammonia-lyase (PAL), β-1.3 glucanase (β-1,3-GA), chitinase (CHI), superoxide dismutase (SOD), and peroxidase (POD) of ginseng was upregulated, and it also can improve the soil urease, phosphatase, invertase, and catalase activity. In conclusion, the bacterial strain JI39 could efficiently promote the growth of ginseng and has the potential to be a good microbial fertilizer for ginseng.

It is commonly known that exogenously applied melatonin can alleviate the impact of drought stress, but the mechanism used by melatonin to regulate stomatal behavior and carbon (C) and nitrogen (N) metabolism to increase drought resistance remains elusive. Herein, our aim was to investigate the influence of exogenous melatonin on the regulation of C and N metabolism in maize plants under water deficit. In this study, we analyzed stomatal behavior, the key components of C and N metabolism, and the gene expression and activity of enzymes involved in the C and N metabolism in maize plants. The results showed that the application of melatonin (100 μM) significantly increased maize growth and sustained the opening of stomata, and secondarily increased the photosynthetic capacity in maize. Under drought stress, foliar application of melatonin induced the gene transcription and activities of sucrose phosphate synthetase, ADP-glucose pyrophosphorylase, phosphoenolpyruvate carboxylase, and citrate synthase, resulting in the enhancement of sucrose and starch synthesis and the tricarboxylic acid (TCA) cycle. This enhancement in sugar biosynthesis and the TCA cycle might lead to stronger N assimilation. As anticipated, NO3– reduction and NH4+ assimilation were also strengthened after melatonin treatment under drought stress. An increase was observed in some key enzymatic activities and transcription involved in nitrogen metabolism, such as that of nitrate reductase, nitrite reductase, glutamate synthase, and glutamine synthetase, in melatonin-treated, drought-stressed maize. Moreover, melatonin attenuated the drought-induced damage by reducing protein degradation and increasing the level of proline. Conclusively, our results indicate that exogenous melatonin enhances drought tolerance in maize via promoting stomatal opening and regulating C and N metabolism and related gene expression.

Non-intrusive load monitoring has broad application prospects because of its low implementation cost and little interference to energy users, which has been highly expected in the industrial field recently due to the development of learning algorithms. Targeting at the investigation of practical and reliable load monitoring in field implementations, a non-intrusive load disaggregation approach based on an enhanced neural network learning algorithm is proposed in this article. The presented appliance monitoring approach establishes the neural network model following the supervised learning strategy at first and then utilizes the unsupervised learning based optimization to enhance the flexibility and adaptability for diverse scenarios, leading to the improvement of disaggregation performance. By verifications on the REDD public dataset, the proposed approach is demonstrated to be with good performance in non-intrusive load monitoring. In addition to the accuracy enhancement, the proposed approach is also with good scalability, which is efficient in recognizing the newly added appliance.

Intracerebral hemorrhage (ICH) is the second most common type of stroke and has one of the highest fatality rates of any disease. There are many clinical signs and symptoms after ICH due to brain cell injury and network disruption resulted from the rupture of a tiny artery and activation of inflammatory cells, such as motor dysfunction, sensory impairment, cognitive impairment, and emotional disturbance, etc. Thus, researchers have established many tests to evaluate behavioral changes in rodent ICH models, in order to achieve a better understanding and thus improvements in the prognosis for the clinical treatment of stroke. This review summarizes existing protocols that have been applied to assess neurologic function outcomes in the rodent ICH models such as pain, motor, cognition, and emotion tests. Pain tests include mechanical, hot, and cold pain tests; motor tests include the following 12 types: neurologic deficit scale test, staircase test, rotarod test, cylinder test, grid walk test, forelimb placing test, wire hanging test, modified neurologic severity score, beam walking test, horizontal ladder test, and adhesive removal test; learning and memory tests include Morris water maze, Y-maze, and novel object recognition test; emotion tests include elevated plus maze, sucrose preference test, tail suspension test, open field test, and forced swim test. This review discusses these assessments by examining their rationale, setup, duration, baseline, procedures as well as comparing their pros and cons, thus guiding researchers to select the most appropriate behavioral tests for preclinical ICH research.

The influence of water and nitrogen (N) management on wheat have been investigated, but studies on the impact of long-term interactive water and N management on microbial structure and function are limited. Soil chemical properties and plants determine the soil microbial communities whose functions involved in nutrient cycling may affect plant productivity. There is an urgent need to elucidate the underlying mechanisms to optimize these microbial communities for agricultural sustainability in the winter wheat production area of the North China Plain. We performed high-throughput sequencing and quantitative PCR of the 16S rRNA gene on soil from a 7-year-old stationary field experiment to investigate the response of bacterial communities and function to water and N management. It was observed that water and N management significantly influenced wheat growth, soil properties and bacterial diversity. N application caused a significant decrease in the number of operational taxonomic units (OTUs), and both Richness and Shannon diversity indices, in the absence of irrigation. Irrigation led to an increase in the relative abundance of Planctomycetes, Latescibacteria, Anaerolineae, and Chloroflexia. In addition, most bacterial taxa were correlated with soil and plant properties. Some functions related to carbohydrate transport, transcription, inorganic ion transport and lipid transport were enriched in irrigation treatment, while N enriched predicted functions related to amino acid transport and metabolism, signal transduction, and cell wall/membrane/envelope biogenesis. Understanding the impact of N application and irrigation on the structure and function of soil bacteria is important for developing strategies for sustainable wheat production. Therefore, concurrent irrigation and N application may improve wheat yield and help to maintain those ecosystem functions that are driven by the soil microbial community.

Excessive nitrogen (N) application combined with water shortage has a negative effect on crop production, particularly wheat (Triticum aestivum L.) production in the North China Plain. This study examined root growth and water and nitrogen use efficiencies in wheat grown on loam soil in the North China Plain, from 2012 to 2014 using a fixed-position experiment initiated in 2010. The experiment followed a completely randomized split-plot design with four replications, taking irrigation [no irrigation (W0) versus irrigation at jointing plus flowering (W2)] as the main plot and N treatment (0, 180, 240, and 300 kg N ha-1) as the subplot. Compared with W0, W2 increased grain yield and root weight density (RWD) by up to 91.3 and 57.7% in 2012–2013, and 15.5 and 43.0% in 2013–2014, respectively, across all N application rates. Irrigation had no effect on grain water use efficiency (WUEY), but caused a decrease in biomass WUE at vegetative growth stage (WUEF) and at grain-filling stage (WUEM). Significant improvements in grain yield and biomass WUE during vegetative growth stage, and reductions in nitrogen-use efficiency (NUE) and RWD, were observed with increasing N application. Compared with non-N treatment, N treatment increased yield by up to 98.9 and 93.7% in 2012–2013 and 2013–2014, respectively, decreasing RWD by 12.0 and 16.9%. Correlation analysis further revealed that RWD was positively correlated with grain yield, evapotranspiration (ET) and NUE. NUE was also positively correlated with nitrogen uptake efficiency (UPE). Overall, the findings suggest that optimal N application improves NUE by increasing above–ground nitrogen uptake as a result of optimized RWD and a synchronous increase in WUE, thus increasing yield. Under the experimental conditions, an N application rate of 240 kg N ha-1 plus irrigation at jointing and flowering is recommended.

Unreasonably high irrigation levels and excessive nitrogen (N) supplementation are common occurrences in the North China Plain that affect winter wheat production. Therefore, a 6-yr-long stationary field experiment was conducted to investigate the effects of irrigation and N regimes on root development and their relationship with soil water and N use in different soil layers. Compared to the non-irrigated treatment (W0), a single irrigation at jointing (W1) significantly increased yield by 3.6–45.6%. With increases in water (W2, a second irrigation at flowering), grain yield was significantly improved by 14.1–45.3% compared to the W1 treatments during the drier growing seasons (2010–2011, 2012–2013, and 2015–2016). However, under sufficient pre-sowing soil moisture conditions, grain yield was not increased, and water use efficiency (WUE) decreased significantly in the W2 treatments during normal precipitation seasons (2011–2012, 2013–2014, and 2014–2015). Irrigating the soil twice inhibited root growth into the deeper soil depth profiles and thus weakened the utilization of soil water and NO3-N from the deep soil layers. N applications increased yield by 19.1–64.5%, with a corresponding increase in WUE of 66.9–83.9% compared to the no-N treatment (N0). However, there was no further increase in grain yield and the WUE response when N rates exceeded 240 and 180 kg N ha−1, respectively. A N application rate of 240 kg ha−1 facilitated root growth in the deep soil layers, which was conducive to utilization of soil water and NO3-N and also in reducing the residual NO3-N. Correlation analysis indicated that the grain yield was significantly positively correlated with soil water storage (SWS) and nitrate nitrogen accumulation (SNA) prior to sowing. Therefore, N rates of 180–240 kg ha−1 with two irrigations can reduce the risk of yield loss that occurs due to reduced precipitation during the wheat growing seasons, while under better soil moisture conditions, a single irrigation at jointing was effective and more economical.

Efficient optical clearance is fundamental for whole brain imaging. In particular, clearance of the brain without membrane damage is required for the imaging of lipophilic tracer-labeled neural tracts. Relying on an ascending gradient of fructose solutions, SeeDB can achieve sufficient transparency of the mouse brain while ensuring that the plasma membrane remains intact. However, it is challenging to extend this method to larger mammalian brains due to the extremely high viscosity of the saturated fructose solution. Here we report a SeeDB-derived optical clearing method, termed FRUIT, which utilizes a cocktail of fructose and urea. As demonstrated in the adult mouse brain, combination of these two highly water-soluble clearing agents exerts a synergistic effect on clearance. More importantly, the final FRUIT solution has low viscosity so as to produce transparency of the whole adult rabbit brain via arterial perfusion, which is impossible to achieve with a saturated fructose solution. In addition to good compatibility with enhanced yellow fluorescent protein, the cocktail also preserves the fluorescence of the lipophilic tracer DiI. This work provides a volume-independent optical clearing method which retains the advantages of SeeDB, particularly compatibility with lipophilic tracers. [ABSTRACT FROM AUTHOR]