Foxtail millet is a nutritionally important cereal whose fat and protein content directly influence its nutritional quality and processing properties. To overcome the limitations of traditional detection methods, developing rapid, non-destructive, and interpretable models is essential. A total of 214 samples of the foxtail millet cultivar “Changnong No. 47” were used in this study. The Sparrow Search Algorithm was introduced to screen stable key wavelengths by statistically analyzing their selection frequency. Based on the selected wavelengths, quantitative models were constructed using Partial Least Squares Regression (PLS), Random Forest (RF), and Support Vector Machine. The SHapley Additive exPlanations method was employed to quantify the direction and magnitude of contributions of the key wavelengths within the model. Results show the selection of 13 key wavelengths for fat and 15 for protein. The RF model delivered the best prediction for fat content (RP2 = 0.797, RMSEP = 0.218%, RPDP = 2.219), while the PLS model performed best for protein content (RP2 = 0.695, RMSEP = 0.268%, RPDP = 1.811). The methodology established in this study can not only be applied to the rapid quality assessment of millet but also be extended to analyze the nutritional components of other grains.

Amid the health-conscious consumption trend, functional foods rich in γ-aminobutyric acid (GABA) and vitamin B9 are gaining prominence. Foxtail millet, a traditional grain naturally abundant in these nutrients, faces quality assessment challenges due to the time-consuming and destructive nature of conventional methods, hindering large-scale screening. This study pioneers the systematic application of hyperspectral imaging (HSI) for nondestructive detection of GABA and vitamin B9 in millet. Utilizing spectral data from 190 samples across 19 varieties, we developed an innovative “coarse-fine” feature wavelength selection strategy. First, interval-based algorithms (iRF, iVISSA) screened highly correlated wavelength subsets. Second, model population analysis (MPA) algorithms (CARS, BOSS) identified optimal core wavelengths, boosting model efficiency and robustness. Based on this, a stacked BiLSTM–Adaboost model was built, integrating bidirectional long short-term memory networks for sequence dependency and adaptive boosting for enhanced generalization. This enables efficient, rapid, nondestructive, and precise nutrient detection. This interdisciplinary breakthrough establishes a novel pathway for millet nutritional assessment, deepens fundamental research, and provides core support for industrial upgrading, breeding, quality control, and functional food development, supporting national health.

Foxtail millet is a vital grain whose amino acid content affects nutritional quality. Traditional detection methods are destructive, time-consuming, and inefficient. This work established a rapid and non-destructive method for detecting essential amino acids in the foxtail millet. To address these limitations, this study developed a rapid, non-destructive approach for quantifying eight essential amino acids—lysine, phenylalanine, methionine, threonine, isoleucine, leucine, valine, and histidine—in foxtail millet (variety: Changnong No. 47) using near-infrared hyperspectral imaging. A total of 217 samples were collected and used for model development. The spectral data were preprocessed using Savitzky–Golay, adaptive iteratively reweighted penalized least squares, and standard normal variate. The key wavelengths were extracted using the competitive adaptive reweighted sampling algorithm, and four regression models—Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory (BiLSTM)—were constructed. The results showed that the key wavelengths selected by CARS account for only 2.03–4.73% of the full spectrum. BiLSTM was most suitable for modeling lysine (R2 = 0.5862, RMSE = 0.0081, RPD = 1.6417). CNN demonstrated the best performance for phenylalanine, methionine, isoleucine, and leucine. SVR was most effective for predicting threonine (R2 = 0.8037, RMSE = 0.0090, RPD = 2.2570), valine, and histidine. This study offers an effective novel approach for intelligent quality assessment of grains.

The application of appropriate nitrogen and molybdenum fertilizer can improve the growth and development of plants, increase photosynthetic efficiency, regulate active oxygen metabolism in vivo, maintain the oxidation balance required for normal cell growth, enhance the activity of crop antioxidant enzymes and dry matter accumulation, so as to increase crop yield. In order to investigate the effect mechanism of nitrogen fertilizer combined with foliar molybdenum fertilizer on millet yield and antioxidant enzyme activity, two nitrogen application gradients (N0 (0 kg/hm2) and N1 (75 kg/hm2) were set with millet variety Changnong 47 as material. Leaf molybdenum fertilizer Mo0 (0 %), Mo1 (0.1 %), Mo2 (0.2 %), Mo3 (0.3 %) and Mo4 (0.4 %) were sprayed at the joining stage. Photosynthetic parameters, chlorophyll content, antioxidant enzyme activity, dry matter accumulation and yield at the complete ripening stage were measured. After the analysis of significant difference, the results showed that the combined application of molybdenum nitrogen significantly increased the yield of millet, and the maximum yield under the Mo3 treatment was 5869.04 kg/hm2 under the N1 condition, which was 13% higher than that under the no fertilization treatment. The total dry matter accumulation was 36.96 g/ plant, which was 31% higher than that without fertilization. The net photosynthetic rate (Pn) and stomatal conductivity (gs) increased first and then decreased with the increase of molybdenum fertilizer concentration gradient, and reached the maximum values under N1Mo3 condition, which were 24.77 μmol•m-2•s-1 and 391.33 mol•m-2•s-1, respectively. Application of molybdenum fertilizer can improve the activities of superoxide dismutase (SOD), peroxidase (POD) and catalase (CAT) in the test samples. In conclusion, under N1 condition, Mo3 (0.3%) treatment can effectively improve millet yield, photosynthetic characteristics and antioxidant enzyme activity. The results of this study provided theoretical basis and data support for the application of nitrogen and molybdenum fertilizer in millet production.

The levels of amylose and amylopectin in foxtail millet are important factors that influence grain quality. The application of organic fertilizers can affect the ratio of amylose and amylopectin components. These components are typically determined using chemical analysis methods, which are difficult to apply on a large scale for nutrient deficiency diagnosis and do not meet the original intention of precise agricultural development. This study set up five different gradient treatments for organic fertilizer (sheep manure) application. Hyperspectral imaging combined with chemometrics was employed to achieve rapid and non-destructive detection of the content of amylose and amylopectin in foxtail millet flour. The aim of this study was to determine the optimal dosage of organic fertilizers for application. Spectral data preprocessing used multiplicative scatter correction (MSC), and the combined algorithm of competitive adaptive reweighted sampling (CARS), random frog (RF), and iterated retaining informative variables (IRIVs) was employed for key band extraction. Partial least squares regression (PLSR) was then used to establish the prediction model and regression equation, which was used to visualize the two components. Results demonstrated that the key band extraction combined algorithm effectively reduced data dimension without compromising the accuracy of the prediction model. The prediction model for amylose using MSC–RF–IRIV–PLSR exhibited good performance, with the correlation coefficient (R) and root mean square error (RMSE) predicted to be 0.73 and 1.23 g/(100 g), respectively. Similarly, the prediction model for amylopectin using MSC–CARS–IRIV–PLSR also demonstrated good performance, with the R and RMSE values predicted to be 0.59 and 7.34 g/(100 g), respectively. The results of visualization and physicochemical determination showed that the amount of amylopectin accumulation was highest, and the amount of amylose was lowest, under the application of 22.5 t/ha of organic fertilizer. The experimental results offer valuable insights for the rapid detection of nutritional components in foxtail millet, serving as a basis for further research.

The grain filling rate (GFR) plays a crucial role in determining grain yield. However, the regulatory and molecular mechanisms of the grain filling rate (GFR) in foxtail millet remains unclear. In this study, we found that the GFR of ′Changnong No.47′ (CN47) was significantly higher at 14 DAF (days after flowering) and 21 DAF in comparison to ‘Changsheng 13’ (CS13). Furthermore, CN47 also exhibited higher a thousand-grain weight and yield than CS13. Therefore, RNA-seq and UHPLC-MS/MS were used to conduct transcriptome and metabolome analyses during two stages of grain filling in both cultivars. Conjoint analysis of transcriptomics and metabolomics was adopted in order to analyze the biological processes and functional genes associated with GFR. The results identified a total of 765 differentially expressed genes (DEGs) and 246 differentially accumulated metabolites (DAMs) at the 14 DAF stage, while at the 21 DAF stage, a total of 908 DEGs and 268 DAMs were identified. The integrated analysis of co-mapped DAMs and DEGs revealed enriched pathways, including flavonoid biosynthesis, plant hormone signal transduction, tyrosine metabolism, ATP-binding cassette (ABC) transporters, and beta-Alanine metabolism, as well as stilbenoid, diarylheptanoid, and gingerol biosynthesis. In order to elucidate their potential functions in the context of GFR, we developed a gene–metabolite regulatory network for these metabolic pathways. Notably, we found that some genes associated with ABC transporters and the plant hormone signal transduction pathway were implicated in auxin transport and signal transduction, highlighting the crucial role of auxin during grain filling. These findings provide initial insights into the regulatory and molecular mechanisms underlying GFR in foxtail millet, as well as offering valuable genetic resources for further elucidation of GFR in future studies. The findings have also established a theoretical basis for improving the efficiency of yield breeding in foxtail millet.

Foxtail millet, a traditional cereal crop, has gained increasing attention for its high nutritional value and potential health benefits. This study aimed to investigate the simultaneous and rapid detection of amylose and amylopectin content of foxtail millet flour under different sheep manure application rates by hyperspectral imaging combined with chemometrics. The spectral data preprocessing used multiplicative scatter correction (MSC), and the combined algorithm of competitive adaptive reweighted sampling (CARS), random frog (RF), iterated retaining informative variables (IRIV) were employed for key band extraction. The partial least squares regression (PLSR) was then used to establish the prediction model and the regression equation, that was used to visualize the two components. Results demonstrated the key band extraction combined algorithm effectively reduced data dimension without compromising the accuracy of the prediction model. The prediction model for amylose using MSC-RF-IRIV-PLSR exhibited good performance, with the correlation coefficient (R) and root mean square error (RMSE) predicted to be 0.73 and 1.23, respectively. Similarly, the prediction model for amylopectin using MSC-CARS-IRIV-PLSR also demonstrated good performance, with the R and RMSE predicted to be 0.59 and 7.34, respectively. Then, the visualization graph generated clearly shows under the condition of applying 6 m3 of sheep manure, the amount of amylopectin accumulation was highest, and the amount of amylose was lowest. The experimental results offer valuable insights for the rapid detection of nutritional components in foxtail millet, serving as a basis for further research.

Soil nutrient content is an important index to evaluate the growing environment of crops. Rapid access to soil nutrient information is an important requirement for the development of modern precision agriculture, while the detection of soil organic matter content is a necessary condition for understanding the basic soil fertility and implementing crop precision cultivation. In this paper, the soil of rural fields in the southeast of Shanxi Province before sowing was taken as the research object. 111 soil samples to be tested were collected. After the process of drying, impurity removal and grinding, the hyperspectral data of the Region of interest (ROI) of the samples were collected, and then the chemical determination of soil organic matter content was conducted. The original spectral data matrix was pretreated by numerical transformation operations, such as arithmetic mean, average deviation, 1st derivation, natural logarithm and mixed multiplication, and a Partial least square regression (PLSR) quantitative analysis model was established. In these models, the obtained prediction set RP value under the pretreatment of F(A)*ln(AD) was the highest, reaching 0.8859. For spectral data preprocessed by F(A)* Ln (AD), the Competitive adaptive reweighted sampling (CARS) algorithm and Random frog (RF) algorithm were used to select key variables. The PLSR model was established by using F(A)* Ln (AD)&CARS data processing method, and the RP value was increased to 0.9545. The prediction results can accurately reflect the real content of soil organic matter. The results of this study can provide theoretical support for the application of hyperspectral imaging technology in the determination of soil organic matter content, and provide a reference for the rapid detection of other soil components.

为了解谷子连作对土壤真菌群落结构的影响,以撂荒地为对照,以谷子-玉米轮作、谷子连作3年、连作5年根际土壤为研究对象,采用真菌ITS高通量测序技术,探究不同种植模式下谷子土壤真菌群落分布特征.结果表明:不同种植模式下,谷子根际土壤共检测到真菌10 门 24纲46目79科136属和146种.在门和纲水平上群体结构相对稳定,谷子田土壤优势门主要包括子囊菌门和担子菌门,优势纲为粪壳菌纲、座囊菌纲和盘菌纲.在目水平谷子根际土壤粪壳菌目相对丰度是撂荒地的2倍以上;在科水平和属水平轮作土壤被孢霉、球腔菌相对丰度高于连作土壤,链格孢菌、亚隔孢壳菌和粉红螺旋聚孢霉菌相对丰度低于连作土壤.Alpha多样性分析显示,谷子-玉米轮作与谷子连作根际土壤真菌丰度差异达显著水平(P＜0.05),轮作土壤真菌丰度最高.Beta多样性分析显示连作3年和连作5年根际土壤真菌结构相似,与撂荒地以及轮作根际土壤真菌结构存在差异,表明不同种植模式谷子根际土壤真菌群落结构发生了改变.相关性分析显示,碱解氮与有机质呈极显著正相关(P＜0.01),与有效磷、脲酶活性呈显著相关(P＜0.05),多酚氧化酶活性与速效钾呈显著正相关(P＜0.05),真菌群落的Chao1指数、Observed spe-cies指数与多酚氧化酶活性呈极显著正相关(P＜0.01).冗余分析(RDA)表明,撂荒地受毛壳菌影响,轮作受球腔菌属和微结节霉属的影响,连作3年和连作5年受毛葡孢属、毛喙壳属、亚隔孢壳属等影响.LEfSe分析确定了谷子根际土壤特定标志物,轮作根际土壤的标志物包含被孢霉属和球腔菌属,连作3年根际土壤标志物包含毛葡孢属、亚隔孢壳属和粉红螺旋聚孢霉属,连作5年根际土壤标志物包含链格孢菌属和亚隔孢壳属.因此,谷子-玉米轮作与谷子连作相比,土壤真菌群落结构差异较大,轮作土壤腐生菌较多,连作土壤病原菌较多.

Long bristles facilitate seed dispersal and protect seeds from herbivores. Early domesticated Setaria italica cultivars had long bristles, but most of the modern cultivars have short bristles. Little is known about the molecular regulatory mechanisms underlying the bristle length trait of S. italica . We generated a segregating population derived from a cross between a long bristle (Z-45) and a short bristle (Z-47) cultivars. A quantitative trait locus (QTL) was identified at 35–40 Mb on chromosome 1 using QTL-seq. In addition, we found that 351-bp transposable element insertion in the Seita.1G316900 promoter was linked to the short bristle trait, which can be used in marker-assisted selection in crop breeding. In addition, some evidences were also found for Seita.1G338900 to play a role in regulation of bristle length. In summary, we hypothesize that Seita.1G316900 and Seita.1G338900 could be candidate genes for bristle length in S. italica. But further functional analysis of these genes are required.

Setaria italica (foxtail millet), a founder crop of East Asian agriculture, is a model plant for C4 photosynthesis and developing approaches to adaptive breeding across multiple climates. Here we established the Setaria pan-genome by assembling 110 representative genomes from a worldwide collection. The pan-genome is composed of 73,528 gene families, of which 23.8%, 42.9%, 29.4% and 3.9% are core, soft core, dispensable and private genes, respectively; 202,884 nonredundant structural variants were also detected. The characterization of pan-genomic variants suggests their importance during foxtail millet domestication and improvement, as exemplified by the identification of the yield gene SiGW3, where a 366-bp presence/absence promoter variant accompanies gene expression variation. We developed a graph-based genome and performed large-scale genetic studies for 68 traits across 13 environments, identifying potential genes for millet improvement at different geographic sites. These can be used in marker-assisted breeding, genomic selection and genome editing to accelerate crop improvement under different climatic conditions.

Foxtail millet ( Setaria italica ), a drought-tolerant plant, is grown in drylands all over the world. However, the molecular basis of drought tolerance in S. italica is not yet understood. Previously, we comprehensively characterised the SiWRKY genes and discovered that SiWRKY89 , a homologue of AtWRKY57 , had a noticeably higher expression level during dry conditions. In this study, a transgenic experiment was carried out in Arabidopsis to investigate the function of SiWRKY89 in conferring drought tolerance. Phenotypic analysis showed that the root length of seedlings and the survival rates of mature transgenic Arabidopsis were greater than those of the control plants under drought conditions. Additionally, compared to the control plants, the transgenic plants had higher proline content and antioxidant activity. Furthermore, qRT-PCR investigation for abiotic stress-responsive genes revealed that SiWRKY89 -overexpressing plants had higher expression levels than their control counterparts. Additionally, the yeast one-hybrid experiment demonstrated that SiWRKY89 could bind to the W-box elements of AtNCED3 . By upregulating the downstream gene AtNCED3 and activating the reactive oxygen species scavenging mechanisms, SiWRKY89 overexpression improved Arabidopsis drought tolerance. Thus, we provide a molecular and biochemical basis for drought tolerance and a candidate gene for crop breeding for drought tolerance.

为探明谷子连作与轮作对土壤细菌群落组成的影响,利用高通量测序技术,结合土壤理化性质及酶活性,比较分析了撂荒地、玉米-谷子轮作、谷子连作2、3a和5a土壤细菌群落组成多样性.结果表明:谷子连作与轮作土壤均为碱性,pH为8.26～8.49,碱解氮、有机质、脲酶活性表现为轮作地最高,连作2a开始降低,连作3a达最低值,连作5a又开始回升,速效钾、多酚氧化酶、过氧化氢酶以及蔗糖酶活性均是连作土壤高于轮作土壤.谷子连作与轮作土壤细菌在门水平上群落组成比较固定,但不同物种的丰度差异较大;菌群分析发现放线菌门、变形菌门以及酸杆菌门是谷子根际土壤优势菌群;菌群相对丰度在轮作土壤中高于连作土壤的有:变形菌门、酸杆菌门、拟杆菌门、厚壁菌门以及硝化螺旋菌门,低于连作土壤的有己科河菌门和绿弯菌门;Alpha多样性分析显示各组间菌群差异不显著,Beta多样性分析则显示连作、玉米-谷子轮作地和撂荒地土壤菌群分布差异较大;冗余分析(RDA)表明鞘氨醇单胞菌与pH、有效磷、碱解氮和有机质呈正相关,类诺卡氏菌与pH、有效磷、碱解氮、有机质、脲酶和过氧化氢酶呈正相关,轮作、连作、撂荒地组间根际土壤优势菌存在差异,LEfSe分析确定了谷子根际土壤特定标志物,其中轮作地的优势菌群为鞘氨醇单胞菌和类诺卡氏菌,3a连作地的优势菌群为土壤红杆菌.综上所述,谷子连作土壤与轮作土壤相比,细菌的ASVs丰度减少,细菌群落分布差异较大;随着谷子连作年限增加,土壤养分呈先下降后上升趋势.

Characterization of drought-tolerance mechanisms during the jointing stage in foxtail millet under water-limited conditions is essential for improving the grain yield of this C4 crop species. In this trial, two drought-tolerant and two drought-sensitive cultivars were examined using transcriptomic dissections of three tissues (root, stem, and leaf) under naturally occurring water-limited conditions. We detected a total of 32,170 expressed genes and characterized 13,552 differentially expressed genes (DEGs) correlated with drought treatment. The majority of DEGs were identified in the root tissue, followed by leaf and stem tissues, and the number of DEGs identified in the stems of drought-sensitive cultivars was about two times higher than the drought-tolerant ones. A total of 127 differentially expressed transcription factors (DETFs) with different drought-responsive patterns were identified between drought-tolerant and drought-sensitive genotypes (including MYB, b-ZIP, ERF, and WRKY). Furthermore, a total of 34 modules were constructed for all expressed genes using a weighted gene co-expression network analysis (WGCNA), and seven modules were closely related to the drought treatment. A total of 1,343 hub genes (including RAB18, LEA14, and RD22) were detected in the drought-related module, and cell cycle and DNA replication-related transcriptional pathways were identified as vital regulators of drought tolerance in foxtail millet. The results of this study provide a comprehensive overview of how Setaria italica copes with drought-inflicted environments during the jointing stage through transcriptional regulating strategies in different organs and lays a foundation for the improvement of drought-tolerant cereal cultivars through genomic editing approaches in the future.

为缓解谷子连作障碍,为优化谷子种植模式提供参考,以谷子连作(Si)为对照(CK),设置了谷子-玉米(Si-Zm)、谷子-马铃薯-玉米(Si-St-Zm)、谷子-玉米-大豆(Si-Zm-Gm)和谷子-大豆-马铃薯(Si-Gm-St)4种轮作模式,分析不同轮作模式对谷子关键生育期生理指标、光合特性、农艺性状、产量和白发病发病率的影响.结果表明,与CK相比,Si-St-Zm、Si-Zm-Gm和Si-Gm-St这3种轮作模式下谷子旗叶超氧化物歧化酶(SOD)、过氧化物酶(POD)和多酚氧化酶(PPO)的活性均显著增加,最大增幅分别为45.55％,41.55％和109.09％;Si-Zm-Gm和Si-Gm-St轮作模式下谷子株高、茎粗、根长和根分枝数均显著增加,最大增幅分别为30.48％,30.50％,31.76％和13.79％;Si-Gm-St轮作模式下谷子旗叶H2 O2和MDA含量均显著减少,最大减少幅度分别为18.78％和47.29％;气孔导度、净光合速率、蒸腾速率和叶绿素相对含量分别显著增加31.94％ ～101.43％,35.74％ ～234.00％,16.44％ ～46.97％和24.15％ ～66.16％,谷子穗长、千粒质量和产量分别显著增加14.90％,17.09％和10.58％,谷子白发病发病率显著降低12.33％.综上所述,与CK相比,Si-Gm-St模式下谷子旗叶SOD、POD和PPO活性显著增加,光合效率显著提高;谷子产量和抗病能力最高.因此,与Si-Zm、Si-St-Zm和Si-Zm-Gm轮作模式相比,Si-Gm-St轮作模式对缓解谷子连作障碍的效果最好.

Foxtail millet downy mildew caused by obligate parasitic Oomycetes Sclerospora graminicola (Sac.) Schrot greatly reduces the foxtail millet yield. The purpose of this study was to find the specific bacterial and fungal populations that might be involved in the suppression of foxtail millet downy mildew. Bacterial and fungal communities in foxtail millet rhizosphere soils under two different cropping systems were compared, with high-throughput Illumina sequencing technology. Compared with foxtail millet continuous cropping system, the foxtail millet–soybean–potato (Si–Gm–St) rotational cropping system showed the lower foxtail millet disease incidence (3.00

Multi-environment QTL mapping identified 23 stable loci and 34 co-located QTL clusters for panicle architecture and grain yield-related traits, which provide a genetic basis for foxtail millet yield improvement. Panicle architecture and grain weight, both of which are influenced by genetic and environmental factors, have significant effects on grain yield potential. Here, we used a recombinant inbred line (RIL) population of 333 lines of foxtail millet, which were grown in 13 trials with varying environmental conditions, to identify quantitative trait loci (QTL) controlling nine agronomic traits related to panicle architecture and grain yield. We found that panicle weight, grain weight per panicle, panicle length, panicle diameter, and panicle exsertion length varied across different geographical locations. QTL mapping revealed 159 QTL for nine traits. Of the 159 QTL, 34 were identified in 2 to 12 environments, suggesting that the genetic control of panicle architecture in foxtail millet is sensitive to photoperiod and/or other environmental factors. Eighty-eight QTL controlling different traits formed 34 co-located QTL clusters, including the triple QTL cluster qPD9.2/qPL9.5/qPEL9.3, which was detected 23 times in 13 environments. Several candidate genes, including Seita.2G388700, Seita.3G136000, Seita.4G185300, Seita.5G241500, Seita.5G243100, Seita.9G281300, and Seita.9G342700, were identified in the genomic intervals of multi-environmental QTL or co-located QTL clusters. Using available phenotypic and genotype data, we conducted haplotype analysis for Seita.2G002300 and Seita.9G064000,which showed high correlations with panicle weight and panicle exsertion length, respectively. These results not only provided a basis for further fine mapping, functional studies and marker-assisted selection of traits related to panicle architecture in foxtail millet, but also provide information for comparative genomics analyses of cereal crops.

As the main ingredient of millet flour, the quality of starch determined the market price of millet flour. Gelatinization characteristic is one of the most important physical characteristics of millet flour, and the alkali spreading value is the main index that reflects the gelatinization characteristic directly. The differences in the alkali spreading the value of millet flour show the quality of amylose content. When the alkali spreading value becomes lower, on the contrary, the gelatinization temperature and amylose content become higher, eventually the lower the waxy of millet flour. This study employed the hyperspectral technique could with chemometrics methods to develop an approach for detecting the alkali spreading the value of millet flour, whose aim is to explore a rapid, nondestructive and low-cost method for predicting the alkali spreading the value of millet flour. First, the hyperspectral data of millet flour were collected, then the hyperspectral data matrix in the region of interest (ROI) in each pixel was computed. The results were meant in each wavelength of every sample. Then we used the rapid visco analyser (RVA) to measured the alkali spreading the value of millet flour. In the data processing, partial least square regression (PLSR) models were made after using competitive adaptive reweighted sampling (CARS) and random frog (RF) to extracted key wavelengths. The results showed that the highest predicted R-p was 0. 77 in the PLSR of the full wavelengths, and that explained that the reflectance of millet flour could invert the alkali spreading the value of millet flour. The R-p in the other two methods were 0. 72 and 0. 7, and both were close to the previous result, these illustrated it was feasible to build the PLSR using CARS and RF. In order to improve the predicting accuracy, the full wavelengths were preprocessed by Savitzky-Golay (S-G), multiplicative scatter correction (MSC) and S-G+MSC. The performance of the PLSR model was better by using MSC predicted the full wavelengths (R-p=0. 83). Then built the PLSR model again after extracting key wavelengths using CARS and RF, compared with the models without pretreatment, the R-p does not change much, which also shows that CARS and RF have a certain stability and can be used as reference methods for predicting the alkali spreading the value of the hyperspectral reflectance of millet flour. The results showed that the reflectance of millet flour could predict its alkali spreading value by using hyperspectral. This could supply a rapid, nondestructive and low-cost method of the alkali spreading value of millet flour, then provided the theoretical foundation for the rating, processing and alkali spreading value sensor of millet flour.

为了建立一种快速、有效鉴定谷子粒黑穗病菌(Ustilago crameri)的分子检测方法,使谷子粒黑穗病得到有效的监测和防治,采用真菌通用引物ITS1/ITS4对谷子粒黑穗病菌ITS序列进行扩增,扩增产物测序后,依据粒黑穗病菌序列设计合成了1对引物,分别以谷子粒黑穗病菌DNA、谷子白发病菌DNA、谷瘟病菌DNA、玉米黑粉菌DNA及谷子叶片基因组DNA作为模板进行PCR扩增.结果表明,仅谷子粒黑穗病菌DNA作模板时有1条特异性扩增条带,且检测的灵敏度可达10 pg/μL,其他材料均未出现扩增条带;将建立的PCR技术用于田间植株检测,能特异性检出谷子穗梗及旗叶中谷子粒黑穗病菌的存在;不同品种谷子植株中的病菌检出率与田间发病率数据基本一致,说明所用引物和PCR检测体系在鉴定粒黑穗病菌感染和植株抗病性方面具有较高的特异性.研究建立的谷子粒黑穗病菌PCR检测体系具有快速、准确、特异性和实用性强等特点,可为谷子粒黑穗病的监测和有效防治提供技术支持.

SCOPEMillet protein has received much attention due to its beneficial role in alleviating metabolic disease symptoms. This study aims to investigate the role and molecular mechanism of foxtail millet protein isolates, including protein isolates from raw and cooked foxtail millet in alleviating diabetes, including gut microbiota and intracellular signal pathways.METHODS AND RESULTSProtein isolates from raw and cooked foxtail millet are orally administered to streptozotocin (STZ)-induced diabetic mice for 5 weeks before hypoglycemic effect evaluation. The results show that foxtail millet protein isolates improve glucose intolerance and insulin resistance in diabetic mice. However, only the protein isolate from cooked foxtail millet reverse the weight loss trend and alleviate lipid disorders in diabetic mice. Besides, 16S rRNA sequencing show that both raw and cooked foxtail millet protein isolates altered diabetes-induced gut dysbiosis. In addition, western blotting analysis indicated that the protein isolate from cooked foxtail millet increases the expression levels of glucagon-like peptide-1 receptor (GLP-1R), phosphoinositide 3-kinase (PI3K), and phosphoinositide-protein kinase B (p-AKT)/AKT while the protein isolate from raw foxtail millet downregulates stearoyl-coenzyme A desaturase 1 (SCD1) level.CONCLUSIONBoth raw and cooked foxtail millet protein isolates can exert hypoglycemic effects in diabetic mice through rewiring glucose homeostasis, mitigating diabetes-induced gut dysbiosis, and affecting the GLP-1R/PI3K/AKT pathway.