Xinjiang Agricultural University

Acidic polysaccharides, valued for their outstanding bioactivity and physicochemical properties, represent a promising strategy for metabolic disease intervention. In this study, three acidic polysaccharide fractions (BRP-1, BRP-2, and BRP-3) were isolated from Brassica rapa L. using membrane filtration and ion-exchange chromatography. BRP-3, notable for its high galacturonic acid content (76.64%), was further purified to yield the homogeneous fraction BRP-3-1 (Mw = 22.3 kDa). Combining GC-MS, FTIR, and NMR analyses, we report for the first time the detailed structure of BRP-3-1-a heteropolysaccharide composed of rhamnose (1.687%), galacturonic acid (75.584%), galactose (14.452%), and arabinose (8.277%)-with a backbone composed with T-α-L-Araf-(1 → 5)-α-L- Araf -(1 → 4)-α-D-GalpA-(1 → 4)-α-D-2-O- GalpA Me-(1 → 4)-α-D-GalpA-(1 → 4)-α-D-GalpA-(1 → 3)-Galp-(1 → 4)-α-D-GalpA, and T-Rhap, T-Galp as well as T-GalpA for branched chain and terminals. In HepG2 insulin-resistant cells, BRP-3-1 demonstrated potent dual regulation of glucose and lipid metabolism-enhancing glucose consumption, lowering total cholesterol, and significantly reducing triglyceride levels in the high-dose group (800 μg/mL), outperforming BRP-2. This work systematically defines the structure of a highly bioactive acidic polysaccharide from B. rapa L. and confirms its metabolic regulatory effects, offering a strong scientific foundation for its application in functional foods and as an adjuvant therapeutic for metabolic disorders.

Microbial mineralization self-repair can effectively solve the problems of pores and cracks inside recycled concrete, thereby improving its mechanical properties. In order to reasonably solve the problem of bacteria’s long-term viability in the high alkaline environment of concrete, Bacillus licheniformis was selected as the mineralizing bacteria in this study, and its alkaline resistance was domesticated; at the same time, steel slag was used as the carrier material to protect the bacteria, and the addition of alumina was used to reduce the pH of the recycled concrete. In order to explore the optimal content of different additives, orthogonal tests were carried out on four factors: steel slag content, alumina content, calcium ion concentration, and urea concentration. The significance of the variables was assessed by analysis of variance (ANOVA) and analysis of range, and the optimal combination in the orthogonal test was determined using the Tukey method. The test results showed that the optimal mix design was at 10% steel slag dosing, Ca2+ concentration of 0.6 mol/L, Al2O3 dosing of 2%, and urea concentration of 0.9 mol/L, and the interactions between multiple factors were analyzed. On this basis, the repair effect and mechanical properties of recycled concrete under the optimal mix ratio are verified. Finally, the influence of Bacillus licheniformis particle-bound slag on the microstructure and micromechanical properties of recycled concrete was deeply analyzed through electron microscope scanning and nano-indentation testing, and its self-healing mechanism was revealed.

Cotton above-ground biomass (AGB) is a key indicator of crop growth and yield potential. Traditional monitoring methods are labor-intensive and destructive, limiting their suitability for precision agriculture. This study developed a high-precision, non-destructive model for estimating cotton AGB by integrating spectral and texture features derived from UAV multispectral and RGB images. UAV data were collected at major growth stages in 2024. Eight vegetation indices (VIs) and eight texture features (TFs) were extracted. Four machine learning algorithms—support vector regression (SVR), random forest regression (RFR), partial least squares regression (PLSR), and extreme gradient boosting (XGB)—were evaluated using independent validation data. Models based on fused spectral and texture features outperformed single-feature models. RFR achieved the best performance (R2 = 0.811; RMSE = 2.931 t ha−1). Texture features alone also showed strong predictive capability (R2 = 0.789), highlighting their value in capturing canopy structural information. These results demonstrate that spectral–texture fusion significantly improves cotton AGB estimation and that RFR provides a robust modeling framework for UAV-based crop monitoring.

To address natural aggregate scarcity and improve the high-value utilization of Reclaimed Asphalt Pavement (RAP), this study proposes a steel slag-RAP hot recycled asphalt mixture (SSRM) as a sustainable alternative to conventional limestone-RAP mixtures (RM). Unlike previous studies mainly focusing on performance verification, an integrated environmental-economic evaluation framework was developed based on real highway expansion project data under a "cradle-to-gate" boundary and incorporating transportation distance thresholds. SSRM containing 50% RAP and 23% steel slag as coarse aggregate replacement was evaluated through rutting, semi-circular bending (SCB), freeze-thaw splitting (TSR), and skid resistance tests. Compared with RM, SSRM exhibited 14-16% higher dynamic stability and 20-25% higher fracture energy at -10 °C, along with improved moisture stability and skid resistance, mainly attributed to the rough and alkaline characteristics of steel slag enhancing adhesion and aggregate interlocking. Life-cycle assessment (GWP100) and cost analysis indicate that SSRM reduces carbon emissions by 10-11% relative to RM and about 40% compared with conventional virgin mixtures, while initial construction costs decrease by 9-10%. Transportation sensitivity analysis identifies equal-emission and equal-cost thresholds of approximately 590 km and 380 km, respectively. Within typical material supply radii (300-400 km), SSRM demonstrates both environmental and economic advantages, providing a practical framework for low-carbon material selection in highway construction.

Genome-wide association analysis and transcriptomics were used to investigate salt tolerance traits during germination in 300 Gossypium hirsutum L. germplasm accessions, with the objective of identifying genes and molecular markers associated with salt tolerance. Under 200 mmol L-1 NaCl stress, six traits were evaluated, germination rate, root length, shoot length, root fresh weight, shoot fresh weight, and total fresh weight, as well as their respective salt tolerance indices. A total of 1277 significantly associated single-nucleotide polymorphism (SNP) markers were identified and mapped to 94 quantitative trait loci (QTLs). Of these, 49 QTLs were detected by three or more analytical models, and three QTLs were prioritized for further investigation. Subsequent analysis of these QTLs identified 73 candidate genes potentially involved in cotton salt tolerance. Integration of transcriptomic data revealed that three candidate genes were among the differentially expressed genes (DEGs). Examination of their RNA-seq expression profiles demonstrated significant differences in fragments per kilobase of transcript per million mapped reads (FPKM) values across sampling time points. These three candidate genes are therefore predicted to be associated with salt tolerance during cotton germination. The results provide new insights into the molecular regulatory mechanisms of salt stress tolerance in cotton and offer valuable genetic resources and molecular markers for the genetic improvement of salt tolerance.

Abstract Background Muscle fiber type is a critical determinant of meat quality, with its phenotypic characteristics regulated by intricate biological processes encompassing gene transcription and translation. This study presents the first comprehensive integrated analysis of muscle morphology, transcriptomics, and proteomics across distinct muscle tissues in the Junggar Bactrian camel ( Camelus bactrianus ). A comparative morphological and molecular assessment was conducted between the triceps brachii (TB) and longissimus dorsi (LD) muscles to elucidate structural and functional differences in muscle fiber composition. An integrated transcriptomic and proteomic approach was employed to identify key genes and proteins associated with muscle fiber type specification. Results Comparative analysis revealed 921 differentially expressed genes (DEGs) and 79 differentially expressed proteins (DEPs) between the two muscle types. The analysis examined these DEGs and DEPs at the muscle fiber type level, focusing on their associations with key genes implicated in muscle contraction, glycolysis, and intramuscular lipid oxidation metabolism, such as TNNT1, hexokinase 2 (HK2), and fatty acid binding protein 3 (FABP3), as well as with critical proteins, including slow-twitch troponin T, actin alpha-3 chain isoform X1, monocarboxylate transporter 4, and FABP3. Notably, the coordinated expression patterns of these factors suggest their potential roles in shaping the metabolic and contractile properties specific to each fiber type. Conclusions By integrating morphological, transcriptomic, and proteomic data from the TB and LD muscles of the Junggar Bactrian camel, this study reveals significant differences at both structural and molecular levels. These findings provide novel insights into the molecular mechanisms underlying muscle fiber type determination in camels and offer potential biomarkers for meat quality improvement.

A water conveyance open channel project in Xinjiang passes through a vast region of collapsible loess. Under intermittent water supply conditions and the environmental effect of seasonal temperature variations, the sliding failure of the canal slope often occurs. To analyze the influence of environmental effects on collapsible loess, this study examines the variation law of mechanical properties through triaxial tests subjected to dry–wet–freeze–thaw cycles. An elastoplastic constitutive model suitable for structural loess is proposed based on the modified Cam-clay model. The results indicate that as the number of dry–wet–freeze–thaw cycles increases, the stress–strain curve of loess decreases, while the volume change curve rises, with a progressively smaller range of variation. The cohesion and internal friction angle show an exponentially decreasing trend, greatly affecting cohesion by the dry–wet–freeze–thaw cycle. Three failure modes are observed in loess soil samples. The stress–strain curve corresponding to shear failure exhibits a softening type, the curve for bulging failure shows a weak hardening type, and the curve belonging to compaction failure demonstrates a strong hardening type. Based on the modified Cam-clay model, the concept of upper and lower loading surfaces is introduced to account for the structure of loess, and a nonassociated flow criterion is used to develop a boundary surface elastoplastic constitutive model suitable for loess. The fitting results are in good agreement with triaxial test data obtained under dry–wet–freeze–thaw cycles. These findings provide theoretical support for the safe operation of open channels.

Accurate cotton boll identification and boll number estimation from UAV imagery are essential for large-scale yield prediction and precision management, yet severe leaf occlusion and complex canopy backgrounds often hinder robust performance. Here, UAV RGB images were acquired 3 days before defoliant application and at 3, 6, 9, 12, 15, and 18 days after defoliation. Cotton bolls were extracted using Mahalanobis distance, a support vector machine, and a neural network. Boll number was then estimated using an improved random forest model with multi-feature fusion. Across all defoliation stages, the NN produced the most accurate and stable boll extraction, achieving a maximum Kappa of 0.914, an overall accuracy of 95.77%, and an F1 score of 0.96. Extraction accuracy increased rapidly from 3 to 9 days after application and stabilized from 12 to 18 days. For boll number estimation, fusing the boll pixel ratio with color indices and texture features improved accuracy and consistency over time; the best performance was obtained at 18 days after application (R2 = 0.7264; rRMSE = 4.9%). Overall, imagery acquired 15–18 days after defoliation provided the most reliable estimation window, supporting operational pre-harvest assessment and harvest-timing decisions.