Search Results (65)

To investigate the adoption behavior and decision-making mechanism of mechanized ear harvesting for seed corn, this study analyzed 786 household survey data from the Hexi Corridor using binary logistic regression, marginal effect analysis, interaction effect tests, and mediation models. The results revealed that the overall adoption rate of mechanized ear harvesting stands at 31.8%, with significant variations across regions and farm sizes. Adoption was found to follow a sequential decision chain: ‘technical feasibility → economic feasibility → comparative benefit assessment.’ Parental line lodging resistance (OR = 3.48) and field contiguity (OR = 3.01) were shown to positively influence adoption indirectly, mediated by perceived harvest loss reduction and perceived machinery efficiency enhancement. A 1% increase in kernel breakage rate is associated with a 2.7 percentage-point reduction in adoption probability. Enterprise-organized mechanical services were identified as the strongest adoption driver (OR = 6.19) and were found to function as a ‘scale equalizer’, significantly reducing the adoption advantage of larger farms (interaction coefficient B = −0.043, p = 0.024). Additionally, a pronounced scale-threshold effect is identified: adoption rates are observed to rise sharply beyond 3.33 hm², contradicting linear scale-adoption assumptions. These findings highlight the critical roles of inclusive enterprise services, a minimum efficient scale, and the sequential decision process. Coordinated innovation across breeding, equipment engineering, and extension systems is required for sustainable mechanization. Full article

This study explored the feasibility of near-infrared (NIR) spectroscopy for detecting total water, free water and bound water in carrot slices during freeze-drying, with low-field nuclear magnetic resonance (LF-NMR) characterizing water state distribution and oven-drying determining moisture content (MC). NIR spectra (10,000–4000 cm⁻¹) were processed via optimized sample partitioning, preprocessing and feature extraction; partial least squares regression (PLSR), support vector regression (SVR), back-propagation artificial neural network (BPANN), extreme gradient boosting (XGBoost) and particle swarm optimization–random forest (PSO-RF) models were established and evaluated. Results showed that SVR and BPANN performed robustly, with CARS being the optimal feature extraction method. The full-moisture system achieved high total/free water prediction accuracy ($R_p^2$ = 0.9902/0.9740), while the low-moisture system improved bound water prediction ($R_p^2$ = 0.9709). The established NIR models exhibited excellent fitting and generalization ability, enabling rapid and non-destructive quantitative prediction of moisture content during carrot freeze-drying. Full article

Objective: Based on previous findings on the Lingguizhugan (LGZG)-mediated gut–liver axis, this study clarifies the therapeutic mechanisms of LGZG in metabolic dysfunction-associated steatotic liver disease (MASLD), with a focus on the gut microbiota–bile acid–TGR5 (GPBAR1) axis. Methods: C57BL/6J mice were fed a high-fat diet (HFD) for 8 weeks to induce MASLD, followed by 4-week LGZG intervention (21.57 g/kg/day, oral gavage). Metabolic phenotypes, gut microbiota (16S rRNA sequencing), serum/hepatic bile acids (targeted metabolomics), and molecular targets (qPCR/Western blot) were analyzed. Results: LGZG significantly alleviated HFD-induced obesity, insulin resistance, and hepatic steatosis, while enhancing whole-body energy expenditure (increased oxygen consumption (VO₂), and heat production (p < 0.05). It also reduced serum ALT (p < 0.001) and AST levels (p < 0.01). Mechanistically, LGZG remodeled the gut microbiota, specifically increasing Akkermansia, Bifidobacterium and Lachnospiraceae_NK4A236_group while decreasing Lactobacillus. This shift inhibited the intestinal FXR-Fgf15 axis, concurrently activating the hepatic alternative bile acid synthesis pathway (upregulating CYP27A1 and CYP7B1 protein expression; p < 0.001 and p < 0.01, respectively). Consequently, systemic accumulation of non-12α-hydroxylated bile acids (non-12-OH BAs) such as hyocholic acid (HCA) and 7-ketolithocholic acid (7-ketoLCA) occurred—known TGR5 agonists and intestinal FXR antagonists. These changes elevated serum GLP-1 levels (p < 0.05) and activated adipose TGR5-cAMP/PKA/CREB signaling. The metabolic benefits primarily originated from non-12-OH BAs enrichment and TGR5-mediated adipose browning, not hepatic FXR activation. Conclusions: Our findings show that LGZG ameliorates MASLD by remodeling bile acid profiles via intestinal FXR-Fgf15 axis inhibition and hepatic alternative synthesis pathway activation. This study highlights the TGR5-targeting properties of LGZG, providing a mechanistic basis for its therapeutic use in metabolic disorders. Full article

Anoplophora glabripennis Motschulsky (Coleoptera: Cerambycidae) is notorious for its wide host range, serious damage caused, and the difficulties involved in controlling it. Populus deltoides ‘Shalinyang’ (PdS) has demonstrated a strong inhibitory effect on the larval growth and the adult lifespan of A. glabripennis and can be used as an “attract-and-kill” tree in mixed shelterbelt construction. However, how A. glabripennis react to this resistant poplar remains unclear. This research employed transcriptomics techniques to study transcriptional responses in the midgut of A. glabripennis after consuming PdS, Elaeagnus angustifolia L. (EA), and Salix matsudana Koidz (SM), respectively. One of the key findings revealed that the PIK3-Akt signaling pathway was suppressed in A. glabripennis feeding on PdS compared with those feeding on EA or SM. Further RNA interference results demonstrated that silencing the AglaAkt gene significantly decreased the levels of AKT, phospho-AKT, vitellogenin, vitellogenin receptor, and vitellin in A. glabripennis. Therefore, we speculate that the AglaAkt gene may be an underlying target gene that causes the low reproductive capacity of A. glabripennis when feeding on PdS. This finding provides important insights into the role of the AglaAkt gene in mediating the reproductive development in A. glabripennis and its molecular response to the resistant poplar. Full article

Rotavirus (RV) is the primary cause of severe gastroenteritis in young children, yet the long noncoding RNA (lncRNA) regulatory landscape governing the host response remains largely unmapped. To address this gap, the present study performed an integrated transcriptomic analysis of mRNA and lncRNA expression profiles in RV-infected MA104 cells at 24 h post-infection. Deep sequencing identified 11,919 high-confidence lncRNAs, revealing a massive transcriptional shift: 3651 mRNAs and 4655 lncRNAs were differentially expressed, with both populations predominantly upregulated. Functional enrichment analysis confirmed the strong activation of key innate immunity pathways, including the RIG-I-like receptor, Toll-like receptor, and TNF signaling pathways. Conversely, fundamental metabolic pathways were found to be suppressed. Crucially, the analysis of lncRNA targets highlighted their involvement in coordinating the host antiviral defense, particularly through transregulation. Experimental validation confirmed the significant upregulation of key immune-related mRNAs (OASL and C3) as well as two novel lncRNAs (lncRNA-6479 and lncRNA-4290) by qRT-PCR. The significant upregulation of OASL and C3 was validated at the protein level, confirming the biological relevance of the transcriptomic data. This study provides a foundational, genome-wide resource, identifying novel lncRNA targets for future mechanistic investigation into host–RV interactions. Full article

Miniature commercial trucks constitute a critical component of urban freight systems but face elevated crash risk due to distinctive driving patterns, frequent operation, and variable loads. This study quantifies how long-term and short-term driving behaviors jointly shape crash economic loss levels and identifies factors most strongly associated with severe claims. A driver-level dataset linking multi-source running behavior indicators, vehicle attributes, and insurance claims is constructed, and an enhanced Wasserstein generative adversarial network with Euclidean distance is employed to synthesize minority crash samples and alleviate class imbalance. Crash economic loss levels are modeled using a random-effects generalized ordinal logit specification, and model performance is compared with a generalized ordered logit benchmark. Marginal effects analysis is used to evaluate the influence of pre-collision driving states (straight, turning, reversing, rolling, following closely) and key behavioral indicators. Results indicate significant effects of inter-provincial duration and count ratios, morning and empty-trip frequencies, no-claim discount coefficients, and vehicle age on crash economic loss, with prolonged speeding duration and fatigued mileage associated with major losses, whereas frequent speeding and fatigue episodes are primarily linked to minor claims. These findings clarify causal patterns for miniature commercial truck crashes with different economic losses and provide an empirical basis for targeted safety interventions and refined insurance pricing. Full article

Background/Objectives: Rosiglitazone (RSG), a potent PPARγ agonist for type 2 diabetes mellitus (T2DM), induces adverse adipogenic effects that limit clinical use. We investigated whether emodin (1,3,8-trihydroxy-6-methylanthraquinone, EMO), a natural anthraquinone, mitigates RSG-induced complications while enhancing its insulin-sensitizing benefits in severe obesity. Methods: Male ob/ob mice with established obesity and diabetes were treated for 4 weeks with RSG (10 mg kg⁻¹ day⁻¹), EMO (200 or 400 mg kg⁻¹ day⁻¹) or their combination. Metabolic profiling, organ function, and adipose histology were analyzed. RNA sequencing and mechanistic studies (Western blot, RT-qPCR, luciferase assays) in inguinal subcutaneous adipose tissue (iSAT), epididymal white adipose tissue (eWAT), and 3T3-L1 adipocytes were used to define EMO’s actions. Results: EMO co-treatment dose-dependently reduced RSG-induced weight gain, visceral adiposity (iSAT and eWAT mass, p < 0.05), and ectopic lipid deposition while ameliorating hepatorenal dysfunction. EMO synergistically enhanced RSG’s glucose-lowering effects. Mechanistically, EMO suppressed sterol regulatory element-binding protein 1 (SREBP1)-mediated lipogenesis (Srebp1, Acc, Fasn, Scd1; p < 0.05) and enhanced PPARγ-peroxisome proliferator-activated receptor gamma coactivator-1α (PGC-1α)-driven thermogenesis via enhanced PPARγ transactivation and nuclear translocation. Thermogenic genes (Ucp1, Ppargc1a, Cidea; p < 0.05) were upregulated, with maximal uncoupling protein 1 (UCP1) induction in iSAT at 400 mg/kg EMO. Conclusions: EMO selectively enhances RSG’s glycemic benefits while attenuating its adipogenic effects in severe obesity by dual PPARγ modulation-inhibiting adipogenic pathways while amplifying thermogenesis. This strategy mitigates RSG’s adverse effects while improving insulin sensitivity, supporting the potential of EMO as a PPARγ adjunct therapy. Full article

Background/Objectives: Optimal reference genes for normalizing RT-qPCR data depend on the species, treatments, developmental stages, and other conditions. Pastor roseus is a long-distance migratory bird with potential applications in locust biological control. This study applied reverse transcription quantitative PCR (RT-qPCR) to evaluate the expression stability of six genes (RPS2, ACTB, B2M, SDHA, UBE2G2, and RPL4) in blood samples from female, male, and nestling P. roseus. Methods: An integrated analysis of the expression stability of six reference genes was performed using three statistical algorithms: GeNorm, BestKeeper, and NormFinder. Results: The results showed that SDHA, ACTB, and B2M exhibited the highest expression stability among the candidate reference genes. The optimal number of reference genes was two, as determined by a pairwise variation analysis using GeNorm. Subsequent comprehensive validation using RefFinder identified SDHA/ACTB as the optimal reference gene pair for normalizing gene expression data for P. roseus. Conclusions: These findings establish a robust foundation for ensuring data accuracy in functional genomic studies of P. roseus. Full article

The drilling and blasting method is commonly employed for the rapid demolition of outdated buildings by destroying key structural components and inducing progressive collapse. The residual bearing capacity of these components is governed by the deformation morphology of the longitudinal reinforcement, characterized by bending deflection and exposed height. This study develops and validates a finite element (FE) model of a reinforced concrete (RC) column subjected to demolition blasting. By varying concrete compressive strength, the yield strength of longitudinal reinforcement, the longitudinal reinforcement ratio, and the shear reinforcement ratio, 45 FE models are established to simulate the post-blast morphology of longitudinal reinforcement. Two databases are created: one containing 45 original simulation cases, and an augmented version with 225 cases generated through data augmentation. To predict bending deflection and the exposed height of longitudinal reinforcement, artificial neural network (ANN) and random forest (RF) models are optimized using the hunter–prey optimization (HPO) algorithm. Results show that the HPO-optimized RF model trained on the augmented database achieves the best performance, with MSE, MAE, and R² values of 0.004, 0.041, and 0.931 on the training set, and 0.007, 0.057, and 0.865 on the testing set, respectively. Sensitivity analysis reveals that the yield strength of longitudinal reinforcement has the most significant impact, while the shear reinforcement ratio has the least influence on both output variables. The partial dependence plot (PDP) analysis indicates that the ratio of shear reinforcement has the most significant impact on the deformation of longitudinal reinforcement. Full article

Background: Paratuberculosis (PTB) is a chronic wasting disease mainly caused by Mycobacterium avium subsp. paratuberculosis (MAP) in ruminants. It is difficult to diagnose, prevent, treat, and eradicate, thereby causing serious economic losses to the livestock industry. Therefore, finding a detection method with high sensitivity and specificity is crucial to preventing and controlling PTB. Methods: A total of 1585 fresh fecal samples were collected from 12 prefectures and cities across Inner Mongolia between March 2022 and October 2024. The samples were subjected to pretreatment, followed by DNA extraction. Subsequently, MAP detection and genotyping were performed using a two-step qPCR method. Results: The overall prevalence of MAP in ovines was 3.34% (53/1585), with the prevalence in 12 prefectures and cities ranging from 0% (0/100) to 7.73% (15/194). In the eastern, central, and western regions, the prevalence rates were 4.74% (31/654), 3.68% (14/394), and 1.49% (8/537); in small-scale and intensive farms, they were 3.23% (22/682), and 3.56% (31/903); and in goats and sheep, they were 0.91% (2/219) and 4.98% (36/723), respectively. The overall prevalence rates of C- and S-type MAP were 2.90% (46/1585) and 0.44% (7/1585), respectively. Conclusions: To the best of our knowledge, this study is the first to conduct an epidemiological investigation of PTB in sheep across all nine cities and three leagues in Inner Mongolia and to perform MAP typing on a large scale. It elucidated the differences in the prevalence of PTB in different regions of Inner Mongolia and found that geographical location and sheep breed are potential risk factors for the differences in MAP prevalence. Furthermore, it has been shown that C- and S-type MAP coexist in the eastern and central regions of Inner Mongolia. Full article

Cropland is fundamental to food security, and monitoring cropland non-agriculturalization through satellite enforcement can effectively manage and protect cropland. However, existing research primarily focuses on optical imagery, and there are problems such as low data processing efficiency and long updating cycles, which make it difficult to meet the needs of large-scale rapid monitoring. To comprehensively and accurately obtain cropland change information, this paper proposes a method based on the Google Earth Engine (GEE) cloud platform, combining optical imagery and synthetic aperture radar (SAR) data for quick and accurate detection of cropland non-agriculturalization. The method uses existing land-use/land cover (LULC) products to quickly update cropland mapping, employs change vector analysis (CVA) for detecting non-agricultural changes in cropland, and introduces vegetation indices to remove pseudo-changes. Using Shanwei City, Guangdong Province, as a case study, the results show that (1) the cropland map generated in this study aligns well with the actual distribution of cropland, achieving an accuracy of 90.8%; (2) compared to using optical imagery alone, the combined optical and SAR data improves monitoring accuracy by 22.7%, with an overall accuracy of 73.65%; (3) in the past five years, cropland changes in Shanwei followed a pattern of an initial increase followed by a decrease. The research in this paper can provide technical reference for the rapid monitoring of cropland non-agriculturalization on a large scale, so as to promote cropland protection and rational utilization of cropland. Full article

Objectives: Our study aimed to assess the associations between meal-replacement (MR) drinking and risks of all-cause, cardiovascular and cerebrovascular disease (CVD), and cancer mortality. Methods: The study was based on 6770 adults aged 20 years or older from the National Health and Nutrition Examination (NHANES) 2003–2006 with linked mortality data from the National Death Index for linked mortality records (until 31 December 2019). Respondents were categorized into four groups according to the frequency of MR drinking: ≤1 time per month (seldom), 2–3 times per month (monthly), 1–6 times per week (weekly), and ≥1 time per day (daily). The adjusted hazard ratios (aHRs) of MR drinking with all-cause, CVD, and cancer mortality were estimated by Cox proportional hazards regression models. Likelihood ratio tests were used to find potential interactions of MR drinking with age, sex, and BMI. Results: During a median follow-up of 14.4 years, a total of 1668 death events were recorded among the study population. Compared to respondents who seldom drank MR, daily and weekly drinkers had greater risks of all-cause mortality (aHRs and 95% confidence intervals [CI]: 1.52 [1.17–1.97] for daily; 1.54 [1.24–1.91] for weekly). Stratified analyses indicated that the effects of MR on all-cause mortality were different between females and males and were more substantial among females (P for interaction: 0.003; daily female drinkers vs. daily male drinkers: 2.01 [1.40–2.90] vs. 1.24 [0.85–1.81]; weekly female drinkers vs. weekly male drinkers: 1.68 [1.26–2.24] vs. 1.36 [0.97–1.91]). Conclusions: Daily and weekly MR drinking might increase the risk of all-cause mortality. Full article

Landslides are the most serious geological disaster in our country, causing economic losses. Because they go undetected, a large number of landslides that have caused disasters are not in the catalogue. At present, Interferometric Synthetic Aperture Radar (InSAR) has been widely used in the identification of landslides. However, it is time-consuming, inefficient, etc., to survey landslides throughout our large country. In the context of massive SAR data, this problem is more obvious. Therefore, based on the current technique of using differential interferogram phase gradient stacking to avoid phase unwrapping errors, a landslide phase gradient dataset has been constructed. To validate the dataset’s effectiveness and applicability, deep learning methods were introduced, applying the dataset to four networks: U-Net, Attention-Unet, Bisenet v2, and Deeplab v3. The results indicate that the phase gradient dataset performs well across different models, with the Attention-Unet network demonstrating the best performance. Specifically, the precision, recall, and accuracy on the test dataset were 0.8771, 0.8712, and 0.9834, respectively, and the accuracy on the validation dataset was 0.8523. Finally, in this paper, the model is applied to landslide identification in Gansu Province, China, during 2022-2023, and a total of 1882 landslides are found. These landslides are mainly concentrated in the south of Gansu Province, where the terrain is relatively undulating. The results show that this method can quickly and accurately realize landslide automatic identification in a wide area and provide technical support for large-scale landslide disaster surveys. Full article

Background: The effect of the expression of the newly identified immune checkpoint, T cell immunoglobulin and immunoreceptor tyrosine-based inhibition motif domain (TIGIT) on NK cells in core binding factor-acute myeloid leukemia (CBF-AML) remains to be investigated. Methods: Fresh bone marrow samples from a total of 39 newly diagnosed CBF-AML patients and 25 healthy donors (HDs) were collected for testing the phenotype and function state of total NK, CD56^bright, and CD56^dim NK cell subsets after in vitro stimulation. Results: The frequencies of TIGIT⁺ cells in total NK, CD56^bright, and CD56^dim NK cell subsets had no significant difference between patients and HDs. TNF-α and INF-γ levels were uniformly lower in TIGIT⁺ cells than the corresponding TIGIT⁻ cells in all HDs, whereas those for TIGIT⁺ to TIGIT⁻ cells in patients were highly heterogenous; TIGIT expression was not related to PFP and GZMB expression in HDs, whereas it was related to higher intracellular PFP and GZMB levels in patients. Patients’ TIGIT⁺ NK cells displayed lower K562 cell-killing activity than their TIGIT⁻ NK cells. In addition, high frequencies of TIGIT⁺ cells in total NK and CD56^dim NK cells were associated with poor RFS. Conclusions: TIGIT expression affected the diagnostic bone marrow-sited NK cell function and had prognostic significance in CBF-AML patients. Full article

InSAR and optical techniques represent two principal approaches for the generation of large-scale Digital Elevation Models (DEMs). Due to the inherent limitations of each technology, a single data source is insufficient to produce high-quality DEM products. The increasing deployment of satellites has generated vast amounts of InSAR and optical DEM data, thereby providing opportunities to enhance the quality of final DEM products through the more effective utilization of the existing data. Previous research has established that complete DEMs generated by InSAR technology can be combined with optical DEMs to produce a fused DEM with enhanced accuracy and reduced noise. Traditional DEM fusion methods typically employ weighted averaging to compute the fusion results. Theoretically, if the weights are appropriately selected, the fusion outcome can be optimized. However, in practical scenarios, DEMs frequently lack prior information on weights, particularly precise weight data. To address this issue, this study adopts a fully connected artificial neural network for elevation fusion prediction. This approach represents an advancement over existing neural network models by integrating local elevation and terrain as input features and incorporating curvature as an additional terrain characteristic to enhance the representation of terrain features. We also investigate the impact of terrain factors and local terrain feature as training features on the fused elevation outputs. Finally, three representative study areas located in Oregon, USA, and Macao, China, were selected for empirical validation. The terrain data comprise InSAR DEM, AW3D30 DEM, and Lidar DEM. The results indicate that compared to traditional neural network methods, the proposed approach improves the Root-Mean-Squared Error (RMSE) ranges, from 5.0% to 12.3%, and the Normalized Median Absolute Deviation (NMAD) ranges, from 10.3% to 26.6%, in the test areas, thereby validating the effectiveness of the proposed method. Full article