QTL mapping and transcriptome analysis identify candidate genes influencing water–nitrogen interaction in maize

Water and nitrogen fertilization are the key factors limiting maize productivity.The genetic basis of interactions between maize genotype,water,and nitrogen is unclear.A recombinant inbred line(RIL)maize population was evaluated for seven yield and five agronomic traits under four water and nitrogen conditions:water stress and low nitrogen,water stress and high nitrogen,well-watered and low nitrogen,and well-watered and high nitrogen.Respectively eight,six,and six traits varied in response to genotype–water interactions,genotype–nitrogen interactions,and genotype–water–nitrogen interactions.Using a linkage map consisting of 896 single-nucleotide polymorphism markers and multipleenvironmental quantitative-trait locus(QTL)mapping,we identified 31 QTL,including 12 for genotype–water–nitrogen interaction,across the four treatments.A set of 8060 genes were differentially expressed among treatments.Integrating genetic analysis,gene co-expression,and functional annotation revealed two candidate genes controlling genotype–water–nitrogen interactions,affecting both leaf width and grain yield.Genes involved in abscisic acid biosynthesis and bZIP,NAC,and WRKY transcription factors participated in maize response to water and nitrogen conditions.These results represent a step toward understanding the genetic regulatory network of maize that responds to water and nitrogen stress and provide a theoretical basis for the genetic improvement of both water-and nitrogen-use efficiency.

Composition analysis of acid hydrolysates from Cucurbita moschata Duch.polysaccharides and their effect on oxidative stress resistance of Caenorhabditis elegans

Pumpkin polysaccharides(PPe)have a variety of bioactive effects and our previous research showed the acid hydrolysate(PPe-S,a mixture)from PPe had an antioxidative capacity both in vitro and in viro.The aim of this study was to purify PPe-S and investigate the antioxidant stress effects of 2 purified components(PPe-S-1 and PPe-S-2)using Caenorhabditis elegans as model organism.The results showed that PPe-S-2 had a notable antioxidant effect,and could significantly enhance the activities of antioxidant enzymes including superoxide dismutase(SOD)(P<0.01),catalase(CAT)(P<0.01)and glutathione reductase(GR)(P<0.05),and increase the level of glutathione(GSH)(P<0.01),and decreased the content of malondialdehyde(MDA)(P<0.05).PPe-S-2 could significantly extend the survival time of C.elegans(P<0.01),which were stress-induced by hydrogen peroxide and methyl viologen.PPe-S-2 was a heteropolys accharide composed of glucose,arabinose,rhamnose and galactose with the molar ratio of 1.00:0.03:0.02:0.14.The molecular weight of PPe-S-2 was 0.73 kDa detected by high performance liquid chromatography.These studies demonstrated that PPe-S-2 obtained by the acid hydrolysis of PPe had a prominent protective effect to the damage induced by the intracellular free radical generating agents.

The impact of high hydrostatic pressure treatment time on the structure,gelatinization and thermal properties and in vitro digestibility of oat starch

As a non-thermal processing technology,high hydrostatic pressure(HHP)can be used for starch modification without affecting the quality and flavour constituents.The effect of HHP on starch is closely related to the treatment time of HHP.In this paper,we investigated the impacts of HHP treatment time(0,5,10,15,20,25,30 min)on the microstructure,gelatinization and thermal properties as well as in vitro digestibility of oat starch by scanning electron microscopy,X-ray diffraction,Fourier transform infrared spectroscopy,13C NMR and differential scanning calorimeter.Results showed that 5-min HHP treatment led to deformation and decreases in short-range ordered and doublehelix structures of oat starch granules,and further extending the treatment time to 15 min or above caused the formation of a gelatinous connection zone,increase of particle size,disintegration of short-range ordered and double-helix structures,and crystal structure change from A type to V type,indicating gelatinization occurred.Longer treatment time also resulted in the reduction in both the viscosity and the stability of oat starch.These indicated that HHP treatment time greatly influenced the microstructure of oat starch,and the oat starch experienced crystalline destruction(5 min),crystalline disintegration(15 min)and gelatinization(>15 min)during HHP treatment.Results of in vitro digestibility showed that the rapidly digestible starch(RDS)content declined first after treatment for 5 to 10 min then rose with the time extending from 15 to 30 min,indicating that longer pressure treatment time was unfavourable to the health benefits of oat starch for humans with diabetes and cardiovascular disease.Therefore,the 500-MPa treatment time for oat starch is recommended not more than 15 min.This study provides theoretical guidance for the application of HHP technology in starch modification and development of health foods.

The impact of landslides on chemical and microbial properties of soil in a temperate secondary forest ecosystem

In forest ecosystems,landslides are one of the most common natural disturbances,altering the physical,chemical and microbial characteristics of soil and thus further altering ecosystem properties and processes.Although secondary forests comprise more than 50%of global forests,the influence of landslides on the soil properties in these forests is underappreciated.Therefore,this study investigates the influence of landslides on the chemical and microbial nature of the soil.Study of these modifications is critical,as it provides baseline evidence for subsequent forest revegetation.We selected four independent landslides and adjacent secondary forest stands as references in a temperate secondary forest in northeastern China.Soils were obtained from each stand at 0–10 cm and 10–20 cm depths to determine chemical and microbial properties.Soil total carbon(TC),total nitrogen(TN),nitrate(NO_(3)^(-)-N),available phosphorus(P),microbial biomass carbon(MBC),microbial biomass nitrogen(MBN),microbial biomass phosphorus(MBP)and phenol oxidase,exoglucanase,β-glucosidase,N-acetyl-β-glucosaminidase,L-asparaginase and acid phosphatase activities were 29.3–70.1%lower at the 0–10 cm soil depth in the landslide sites than at the secondary forest sites,whereas total phosphorus(TP)and ammonium(NH_(4)^(+)-N)were unaffected by the landslides.N-related enzymes,N-acetyl-β-glucosaminidase and L-asparaginase were reduced by more than 65%in the landslide sites,consistent with the decrease in nitrate concentration at the same 0–10 cm depth.At a depth of 10–20 cm,the variations in the soil properties were consistent with those at the 0–10 cm depth.The results demonstrated that soil chemical and microbial properties were significantly disrupted after the landslides,even though the landslides had occurred 6 years earlier.A long time is thus needed to restore the original C and nutrient levels.In temperate secondary forests,soil TC and TN contents were found to be more suitable for estimating the state of soil restoration than soil TP content.

From tunnel boring machine to tunnel boring robot: perspectives on intelligent shield machine and its smart operation

Advances in intelligent shield machines reflect an evolving trend from traditional tunnel boring machines(TBMs)to tunnel boring robots(TBRs).This shift aims to address the challenges encountered by the conventional shield machine industry arising from construction environment and manual operations.This study presents a systematic review of intelligent shield machine technology,with a particular emphasis on its smart operation.Firstly,the definition,meaning,contents,and development modes of intelligent shield machines are proposed.The development status of the intelligent shield machine and its smart operation are then presented.After analyzing the operation process of the shield machine,an autonomous operation framework considering both stand-alone and fleet levels is proposed.Challenges and recommendations are given for achieving autonomous operation.This study offers insights into the essence and developmental framework of intelligent shield machines to propel the advancement of this technology.

Multi-class detection of cherry tomatoes using improved YOLOv4-Tiny

The rapid and accurate detection of cherry tomatoes is of great significance to realizing automatic picking by robots.However,so far,cherry tomatoes are detected as only one class for picking.Fruits occluded by branches or leaves are detected as pickable objects,which may cause damage to the plant or robot end-effector during picking.This study proposed the Feature Enhancement Network Block(FENB)based on YOLOv4-Tiny to solve the above problem.Firstly,according to the distribution characteristics and picking strategies of cherry tomatoes,cherry tomatoes were divided into four classes in the nighttime,and daytime included not occluded,occluded by branches,occluded by fruits,and occluded by leaves.Secondly,the CSPNet structure with the hybrid attention mechanism was used to design the FENB,which pays more attention to the effective features of different classes of cherry tomatoes while retaining the original features.Finally,the Feature Enhancement Network(FEN)was constructed based on the FENB to enhance the feature extraction ability and improve the detection accuracy of YOLOv4-Tiny.The experimental results show that under the confidence of 0.5,average precision(AP)of non-occluded,branch-occluded,fruit-occluded,and leaf-occluded fruit over the day test images were 95.86%,92.59%,89.66%,and 84.99%,respectively,which were 98.43%,95.62%,95.50%,and 89.33% on the night test images,respectively.The mean Average Precision(mAP)of four classes over the night test set was higher(94.72%)than that of the day(90.78%),which were both better than YOLOv4 and YOLOv4-Tiny.It cost 32.22 ms to process a 416×416 image on the GPU.The model size was 39.34 MB.Therefore,the proposed model can provide a practical and feasible method for the multi-class detection of cherry tomatoes.

Natural variation of GmFATA1B regulates seed oil content and composition in soybean

Soybean(Glycine max) produces seeds that are rich in unsaturated fatty acids and is an important oilseed crop worldwide. Seed oil content and composition largely determine the economic value of soybean. Due to natural genetic variation, seed oil content varies substantially across soybean cultivars. Although much progress has been made in elucidating the genetic trajectory underlying fatty acid metabolism and oil biosynthesis in plants, the causal genes for many quantitative trait loci(QTLs) regulating seed oil content in soybean remain to be revealed. In this study, we identified Gm FATA1B as the gene underlying a QTL that regulates seed oil content and composition, as well as seed size in soybean. Nine extra amino acids in the conserved region of Gm FATA1B impair its function as a fatty acyl–acyl carrier protein thioesterase, thereby affecting seed oil content and composition. Heterogeneously overexpressing the functional Gm FATA1B allele in Arabidopsis thaliana increased both the total oil content and the oleic acid and linoleic acid contents of seeds. Our findings uncover a previously unknown locus underlying variation in seed oil content in soybean and lay the foundation for improving seed oil content and composition in soybean.

Influence of stir-frying on the lipid stability and flavor substances in oat flour during storage using HS-SPME-GC-MS and electronic nose

Objectives:We used stir-fried oat flour as experimental material and raw oat flour as a control to explore the influence of stir-frying on the storagequalityofoatflour.Materials and Methods:The HS-SPME-GC-MS method combined with electronic nose technology was used to understand the lipid stability and analyze the changes in the flavor of the substances during the entire storage period.Results:It was observed that during the storage period,stir-fried oat flour contained less water than raw oat flavor.The former was characterized by a lower fatty acid value,lower acid value,and lower linoleic acid content,but higher oleic acid content and palmitic acid content compared to the latter.With the passage of storage time,the palmitic acid content significantly increased,and the linoleic acid content significantly decreased in raw and stir-fried oats flour(P<o.05).The sulfur and methyl contents in the stir-fried oat flour were higher than those in the raw flour,while nitrogen oxide content in the former was lower than that in the latter.Stir-fried oat flour possessed a total of 78 identified flavor substances.The process of stir-frying boosts the oxidation decomposition of unsaturated fatty acids aldehydes and heterocyclic compounds produced by the Maillard reaction,so the flavor substances of stir-fried oat flour are richer.Conclusions:Stir-fried oat flour,containing diverse types of flavor substances,experienced more obvious flavor changes throughout the storage period than raw oat flour.

Multi-omics analyses of 398 foxtail millet accessions reveal genomic regions associated with domestication,metabolite traits,and antiinflammatory effects

Foxtail millet(Setaria italica),which was domesticatedfromthewild speciesgreenfoxtail(Setaria viridis),isa richsource of phytonutrientsfor humans.To evaluate how breeding changed themetabolome offoxtail millet grains,we generated and analyzed the datasets encompassing the genomes,transcriptomes,metabolomes,and anti-inflammatory indices from 398 foxtail millet accessions.We identified hundreds of common variants that influence numerous secondary metabolites.We observed tremendous differences in natural variations of the metabolites and their underlying genetic architectures between distinct sub-groups of foxtail millet.Furthermore,we found that the selection of the gene alleles associated with yellow grains led to altered profiles of metabolites such as carotenoids and endogenous phytohormones.Using CRiSPR-mediated genome editing wevalidated the function of PHYTOENE SYNTHASE1(PSY1)gene in affecting milletgrain colorand quality.Interestingly,our in vitro cell inflammation assays showed that 83 metabolites in millet grains have anti-inflammatory effects.Taken together,ourmulti-omics study illustrates how the breeding history of foxtail millet has shaped its metabolite profile.The datasets we generated in this study also provide important resources for further understanding how millet grain quality is affected by different metabolites,laying the foundations for future millet genetic research and metabolome-assisted improvement.

Effects of drought stress on photosynthesis and chlorophyll fluorescence images of soybean(Glycine max)seedlings

The main purpose of this research is to provide a theoretical foundation for the screening of drought-resistant soybean varieties and to establish an efficient method to detect the PSII actual photochemical quantum yields efficiently.Three soybean varieties were compared in this experiment after 15 d when they were planted in a greenhouse.These varieties were then exposed to light drought stress(LD)and serious drought stress(SD)conditions.With five times’measurement,chlorophyll fluorescence and soil-plant analysis development considered as the main basis for this study.Several parameters in SD conditions significantly reduced,such as net photosynthetic rates(Pn),stomatal conductance(Gs),PSII primary light energy conversion efficiency(Fv/FM),PSII actual photochemical quantum yields[Y(II)],photochemical quenching coefficient(qP)and non-photochemical quenching coefficient(qN).The soybeans in the seedling stage adapted to the inhibitory effect of drought stress on photosynthesis through stomatal limitation.Under serious drought stress,non-stomatal limitation damaged the plant photosynthetic system.The amplitudes of Pn and Y(II)of drought-resistant Qihuang 35 were lower than those of the two other varieties.Based on the data of this study,a new method had been developed to detect Y(II)which reflected the photosynthetic capacity of plant,R=0.85989,u=0.048803 when using multiple linear regression,and R=0.84285,u=0.054739 when using partial least square regression.

Sampling Designs for Validating Digital Soil Maps: A Review

Sampling design(SD) plays a crucial role in providing reliable input for digital soil mapping(DSM) and increasing its efficiency.Sampling design, with a predetermined sample size and consideration of budget and spatial variability, is a selection procedure for identifying a set of sample locations spread over a geographical space or with a good feature space coverage. A good feature space coverage ensures accurate estimation of regression parameters, while spatial coverage contributes to effective spatial interpolation.First, we review several statistical and geometric SDs that mainly optimize the sampling pattern in a geographical space and illustrate the strengths and weaknesses of these SDs by considering spatial coverage, simplicity, accuracy, and efficiency. Furthermore, Latin hypercube sampling, which obtains a full representation of multivariate distribution in geographical space, is described in detail for its development, improvement, and application. In addition, we discuss the fuzzy k-means sampling, response surface sampling, and Kennard-Stone sampling, which optimize sampling patterns in a feature space. We then discuss some practical applications that are mainly addressed by the conditioned Latin hypercube sampling with the flexibility and feasibility of adding multiple optimization criteria. We also discuss different methods of validation, an important stage of DSM, and conclude that an independent dataset selected from the probability sampling is superior for its free model assumptions. For future work, we recommend: 1) exploring SDs with both good spatial coverage and feature space coverage; 2) uncovering the real impacts of an SD on the integral DSM procedure;and 3) testing the feasibility and contribution of SDs in three-dimensional(3 D) DSM with variability for multiple layers.

Soil chronosequence and biosequence on old lake sediments of the Burdur Lake in Turkey

The Burdur Lake is located in the southwest of Turkey,and its area has decreased by 40% from 211 km^(2) in 1975 to 126 km^(2) in 2019.In this study,we investigated how the soil has changed in the lacustrine material.Three soil profiles were sampled from the former lakebed(chronosequence profiles:P1,2007;P2,1994;and P3,1975),and three soil profiles under different land use types(biosequence profiles:P4,native forest vegetation;P5,agriculture;and P6,lakebed)were sampled.The chronosequence and biosequence soil profiles represented various distances from the Burdur Lake and showed different stages of lacustrine evolution.Soil electrical conductivity(EC;18.1 to 0.4 dS m^(-1)),exchangeable Na^(+)(34.7 to 1.4 cmol kg^(-1))and K^(+)(0.61 to 0.56 cmol kg^(-1)),and water-soluble Cl^(-)(70.3 to 2.1 cmol L^(-1))and SO_(4)^(2-)(275.9 to 25.0 cmol L^(-1))decreased with increasing distance from the Burdur Lake,whereas the A horizon thickness(10 to 48 cm),structure formation(0 to 48 cm),gleization-oxidation depth(0 to 79 cm),and montmorillonite and organic matter(OM;25.9 to 46.0 g kg^(-1))contents increased in the chronosequence soil profiles.The formation of P3 in the chronosequence and P5 in the biosequence soil profiles increased due to longer exposure to pedogenic processes(time,land use,vegetation,etc.).Changes in EC,exchangeable cation(Na^(+) and K^(+))and water-soluble anion(Cl^(-) and SO_(4)^(2-))concentrations of the salt-enriched horizon,OM,gleization-oxidation depth,A horizon thickness,and structure formation of the chronosequence and biosequence soil profiles(especially the topsoil horizon)were highly related to the distance from the Burdur Lake,time,and land use.

Comparison of sampling designs for calibrating digital soil maps at multiple depths

Digital soil mapping (DSM) aims to produce detailed maps of soil properties or soil classes to improve agricultural management and soil quality assessment. Optimized sampling design can reduce the substantial costs and efforts associated with sampling, profile description, and laboratory analysis. The purpose of this study was to compare common sampling designs for DSM, including grid sampling (GS), grid random sampling (GRS), stratified random sampling (StRS), and conditioned Latin hypercube sampling (cLHS). In an agricultural field (11 ha) in Quebec, Canada, a total of unique 118 locations were selected using each of the four sampling designs (45 locations each), and additional 30 sample locations were selected as an independent testing dataset (evaluation dataset). Soil visible near-infrared (Vis-NIR) spectra were collected in situ at the 148 locations (1 m depth), and soil cores were collected from a subset of 32 locations and subdivided at 10-cm depth intervals, totaling 251 samples. The Cubist model was used to elucidate the relationship between Vis-NIR spectra and soil properties (soil organic matter (SOM) and clay), which was then used to predict the soil properties at all 148 sample locations. Digital maps of soil properties at multiple depths for the entire field (148 sample locations) were prepared using a quantile random forest model to obtain complete model maps (CM-maps). Soil properties were also mapped using the samples from each of the 45 locations for each sampling design to obtain sampling design maps (SD-maps). The SD-maps were evaluated using the independent testing dataset (30 sample locations), and the spatial distribution and model uncertainty of each SD-map were compared with those of the corresponding CM-map. The spatial and feature space coverage were compared across the four sampling designs. The results showed that GS resulted in the most even spatial coverage, cLHS resulted in the best coverage of the feature space, and GS and cLHS resulted in similar prediction accuracies and spatial distributions of soil properties. The SOM content was underestimated using GRS, with large errors at 0–50 cm depth, due to some values not being captured by this sampling design, whereas larger errors for the deeper soil layers were produced using StRS. Predictions of SOM and clay contents had higher accuracy for topsoil (0–30 cm) than for deep subsoil (60–100 cm). It was concluded that the soil sampling designs with either good spatial coverage or feature space coverage can provide good accuracy in 3D DSM, but their performances may be different for different soil properties.

Microwave-hydrothermal preparation of a graphene/hierarchy structure MnO_2 composite for a supercapacitor

Graphene/hierarchy structure manganese dioxide （GN/MnO2） composites were synthesized using a simple microwave-hydrothermal method. The properties of the prepared composites were analyzed using field emission scanning electron microscopy （FE-SEM）, X-ray diffraction （XRD）, transmission electron microscopy （TEM）, and X-ray photoelectron spectroscopy （XPS） measurements. The electrochemical performances of the composites were analyzed using cyclic voltammetry, electrochemical impedance spectrometry （EIS）, and chronopotentiometry. The results showed that GN/MnO2 （10 wt% graphene） displayed a specific capacitance of 244 F/g at a current density of 100 mA/g. An excellent cyclic stability was obtained with a capacity retention of approximately 94.3% after 500 cycles in a 1 mol/L Li2SO4 solution. The improved electrochemical performance is attributed to the hierarchy structure of the manganese dioxide, which can enlarge the interface between the active materials and the electrolyte. The prepa- ration route provides a new approach for hierarchy structure graphene composites; this work could be readily extended to the preparation of other graphene-based composites with different structures for use in energy storage devices.

On the Structures of K2(Z[G]), G a Finite Abelian p-Group

Let G be a finite abelian p-group,Г the maximal Z-order of Z[G]. We prove that the 2-primary torsion subgroups of K2Z[G]) and K2(Г)are isomorphic when p ≡ 3, 5, 7 (mod 8, and K2(Z[G])■zZ[1/p] is isomorphic to K2(Г)■zZ[1/p] when p = 2,3,5,7. As an application, we give the structure of K2(Z[G]) for G a cyclic p-group or an elementary abelian p-group.

Using pXRF and vis-NIR spectra for predicting properties of soils developed in loess

Visible near-infrared (vis-NIR) and portable X-ray fluorescence (pXRF) spectrometers have been increasingly utilized for predicting soil properties worldwide. However, only a few studies have focused on splitting the predictive models by horizons to evaluate prediction performance and systematically compare prediction performance for A, B, and combined A+B horizons. Therefore, we investigated the performance of pXRF and vis-NIR spectra, as individual or combined, for predicting the clay, silt, sand, total carbon (TC), and pH of soils developed in loess, and compared their prediction performance for A, B, and A+B horizons. Soil samples (176 in A horizon and 172 in B horizon) were taken from Mollisols and Alfisols in 136 pedons in Wisconsin, USA and analyzed for clay, silt, sand, pH, and TC. The pXRF and vis-NIR spectrometers were used to measure the pXRF and vis-NIR soil spectra. Data were separated into calibration (n = 244, 70%) and validation (n = 104, 30%) datasets. The Savitzky-Golay filter was applied to preprocess the pXRF and vis-NIR spectra, and the first 10 principal components (PCs) were selected through principal component analysis (PCA). Five types of predictor, i.e., PCs from vis-NIR spectra, pXRF of beams at 0–40 and 0–10 keV (XRF40 and XRF10, respectively) spectra, combined XRF40 and XRF10 (XRF40+XRF10) spectra, and combined XRF40, XRF10, and vis-NIR (XRF40+XRF10+vis-NIR) spectra, were compared for predicting soil properties using a machine learning algorithm (Cubist model). A multiple linear regression (MLR) model was applied to predict clay, silt, sand, pH, and TC using pXRF elements. The results suggested that pXRF spectra had better prediction performance for clay, silt, and sand, whereas vis-NIR spectra produced better TC and pH predictions. The best prediction performance for sand (R2= 0.97), silt (R2= 0.95), and clay (R2= 0.84) was achieved using vis-NIR+XRF40+XRF10 spectra in B horizon, whereas the best prediction performance for TC (R2= 0.93) and pH (R2= 0.79) was achieved using vis-NIR+XRF40+XRF10 spectra in A+B horizon. For all soil properties, the best MLR model had a lower prediction accuracy than the Cubist model. It was concluded that pXRF and vis-NIR spectra can be successfully applied for predicting clay, silt, sand, pH, and TC with high accuracy for soils developed in loess, and that spectral models should be developed for different horizons to achieve high prediction accuracy.