Drought stress significantly impacts wheat productivity, but plant growth regulators may help mitigate these effects. This study examined the influence of gibberellic acid (GA3) and abscisic acid (ABA) on wheat (Triticum aestivum L., CV: Giza 171) growth and yield under different water regimes. Using a split-plot design, we tested three drought levels as main plots: normal irrigation (80% field capacity), moderate drought (60% field capacity), and severe drought (40% field capacity). Subplots consisted of GA3 and ABA treatments at 100 and 200 ppm concentrations. Results showed that 200 ppm GA3 treatment enhanced multiple growth parameters under normal irrigation, including plant height (25–30% increase), leaf area (30–35% increase), and reproductive traits (40% increase in number of number of spikes, 35% increase in grains per spike). In contrast, ABA treatment at 200 ppm resulted in reduced plant height (35% decrease) and greater leaf area reduction (40% vs. 20% in control) under drought conditions. GA3 at 200 ppm also improved physiological parameters including catalase and superoxide dismutase activities, protein content, and proline accumulation. These findings demonstrate the distinct roles of GA3 and ABA in regulating wheat growth and stress responses, providing valuable insights for drought management in wheat cultivation.
Ship lock project currently demonstrates a distinct cyclical pattern, accumulating latent hazards that pose a significant threat to project safety. Seepage safety (the condition in which the seepage risk is reduced to an acceptable level) serves as a crucial indicator in the safety risk assessment index system for ship lock project construction, thus necessitating an in-depth analysis of the risk factors impacting seepage safety. Utilizing a ship lock project in China as a case study, this study employs the finite element method (FEM) to analyze the seepage field of the ship lock foundation pit basin and proposes a comprehensive set of methods for risk evaluation and warning models pertaining to seepage safety risks in ship lock engineering. This study reveals that the obstruction of dewatering wells and imperfections in the diaphragm wall are the primary factors contributing to seepage damage. The investigation conducted a quantitative analysis of the impact of these two factors on the seepage field of the ship lock pit, considering pore pressure, water head, gradient, and flow velocity. A comprehensive set of evaluation indicators for seepage safety was formulated, drawing on the principles of multi-objective optimization, and a method for delineating the safe range of ship lock pit excavation under seepage action was proposed. Subsequently, an integrated seepage safety risk assessment system for ship lock pit excavation engineering was established. These research findings offer a scientific foundation for the management of seepage safety in ship lock pit excavation engineering and provide valuable references and guidance for the development of anti-seepage systems.
Single-cell transcriptomics is advancing discovery of the molecular determinants of cell identity, while spurring development of novel data analysis methods. Stochastic mathematical models of gene regulatory networks help unravel the dynamic, molecular mechanisms underlying cell-to-cell heterogeneity, and can thus aid interpretation of heterogeneous cell-states revealed by single-cell measurements. However, integrating stochastic gene network models with single cell data is challenging. Here, we present a method for analyzing single-cell gene-pair coexpression patterns, based on biophysical models of stochastic gene expression and interaction dynamics. We first developed a high-computational-throughput approach to stochastic modeling of gene-pair coexpression landscapes, based on numerical solution of gene network Master Equations. We then comprehensively catalogued coexpression patterns arising from tens of thousands of gene-gene interaction models with different biochemical kinetic parameters and regulatory interactions. From the computed landscapes, we obtain a low-dimensional “shape-space” describing distinct types of coexpression patterns. We applied the theoretical results to analysis of published single cell RNA sequencing data and uncovered complex dynamics of coexpression among gene pairs during embryonic development. Our approach provides a generalizable framework for inferring evolution of gene-gene interactions during critical cell-state transitions.
With the rapid development of urbanization, the boom of vehicle numbers has resulted in serious traffic accidents, which led to casualties and huge economic losses. The ability to predict the risk of traffic accident is important in the prevention of the occurrence of accidents and to reduce the damages caused by accidents in a proactive way. However, traffic accident risk prediction with high spatiotemporal resolution is difficult, mainly due to the complex traffic environment, human behavior, and lack of real-time traffic-related data. In this study, we collected heterogeneous traffic-related data, including traffic accident, traffic flow, weather condition and air pollution from the same city; proposed a deep learning model based on recurrent neural network toward a prediction of traffic accident risk. The predictive accident risk can be potential applied to the traffic accident warning system. We ranked the predictive power of various factors considered in our model through the method of Granger causality analysis, and established the order of predictive power as traffic flow > traffic accident > geographical position >> weather + air quality + holiday + time period, which indicate that traffic flow is the most essential factor for the occurrence of traffic accidents. The proposed method can be integrated into an intelligent traffic control system toward a more reasonable traffic prediction and command organization.
Low temperatures greatly restrict the development, growth, and productivity of soybeans, with their effects differing across various cultivars. The present work investigated the transcriptome and physiological reactions of two soybean cultivars, namely "KD52" exhibiting cold tolerance and "DS17" displaying cold sensitivity, to cold stress across a precisely defined period. The soybean plants were subjected to cold treatment at 6 °C for durations of 0, 2, 4, and 8 h. A comparative physiological marker study revealed distinct reactions to cold stress in the two cultivars. The findings showed that increased malondialdehyde levels provided evidence of DS17's heightened vulnerability to lipid peroxidation and membrane degradation. In contrast, the KD52 cultivar exhibited increased activities of antioxidant enzymes, including peroxidase and superoxide dismutase, in response to cold exposure, suggesting a strong antioxidant defense system against oxidative stress. The transcriptomic analysis revealed dynamic responses, mapping 54,532 genes. Within this group, a total of 234 differentially expressed genes (DEGs) were found to be consistently changed at several time intervals, showing unique expression patterns across the two cultivars. Analysis of the association between these important DEGs and the physiological indicators revealed candidate genes that may be involved in controlling oxidative damage and antioxidant defenses. Some key genes showed a progressive rise in expression over time in both cultivars, with a more significant acceleration in KD52, and are probably involved in promoting adaptation processes during extended periods of cold exposure. The identification of improved defense mechanisms in KD52, together with the identification of crucial genes, offers great prospects for enhancing the cold stress resilience of soybean.
Abstract Enhancing wheat productivity by implementing a comprehensive approach that combines irrigation, nutrition, and organic amendments shows potential for collectively enhancing crop performance. This study examined the individual and combined effects of using irrigation systems (IS), foliar potassium bicarbonate (PBR) application, and compost application methods (CM) on nine traits related to the growth, physiology, and yield of the Giza-171 wheat cultivar. Analysis of variance revealed significant ( P ≤ 0.05) main effects of IS, PBR, and CM on wheat growth, physiology, and yield traits over the two growing seasons of the study. Drip irrigation resulted in a 16% increase in plant height, leaf area index, crop growth rate, yield components, and grain yield compared to spray irrigation. Additionally, the application of foliar PBR at a concentration of 0.08 g/L boosted these parameters by up to 22% compared to the control. Furthermore, the application of compost using the role method resulted in enhanced wheat performance compared to the treatment including mix application. Importantly, the combined analysis revealed that the three-way interaction between the three factors had a significant effect ( P ≤ 0.05) on all the studied traits, with drip irrigation at 0.08 g PBR rate and role compost application method (referred as Drip_0.08g_Role) resulting in the best performance across all traits, while sprinkle irrigation without PBR and conventional mixed compost method (referred as sprinkle_CK_Mix) produced the poorest results. This highlights the potential to synergistically improve wheat performance through optimized agronomic inputs.
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