Search Results (222)

WRKY transcription factors play key roles in plant immune responses, including resistance to fungal pathogens. In the present study, we identified a flax resistance-related gene Lus10021999, named LuWRKY39. Here, to identify the role of WRKY transcription factor in resistance of flax against Septoria linicola, we cloned and analyzed the gene LuWRKY39 via homologous cloning using bioinformatics methods and localized the encoded protein. Quantitative real-time PCR (qRT-PCR) was used to explore the response of this gene to S. linicola. The results showed that the gene that is 948 bp long exhibited the closest genetic relationship to WRKY in castor (Ricinus communis), as revealed by phylogenetic analysis, and the encoded protein was localized in the nucleus. The LuWRKY39 gene showed higher expression levels in resistant flax materials than in susceptible ones, and higher in roots and stems than in leaves. Furthermore, gene expression showed an upward trend following treatment with salicylic acid (SA) and methyl jasmonate (MeJA), indicating that LuWRKY39 is involved in the regulation of SA and JA signals. By silencing LuWRKY39 in flax using virus-induced gene silencing (VIGS), the processed plants were more sensitive to S. linicola than untreated plants. Gene expression analysis and disease index statistics confirmed that the silenced plants were more susceptible, highlighting the crucial role of LuWRKY39 in flax disease resistance. This study provides a foundation for functional investigations of WRKY genes in flax and the identification of disease resistance genes. Full article

Investigating avalanche hazards is a fundamental preliminary task in avalanche research. This work is critically important for establishing avalanche warning systems and designing mitigation measures. Primary research data originated from field investigations and UAV aerial surveys, with avalanche counts and timing identified through image interpretation. Snowpack properties were primarily acquired via in situ field testing within the study area. Methodologically, statistical modeling and RAMMS::AVALANCHE simulations revealed spatiotemporal and dynamic characteristics of avalanches. Subsequent application of the Certainty Factor (CF) model and sensitivity analysis determined dominant controlling factors and quantified zonal influence intensity for each parameter. This study, utilizing field reconnaissance and drone aerial photography, identified 86 avalanche points in the study area. We used field tests and weather data to run the RAMMS::AVALANCHE model. Then, we categorized and summarized regional avalanche characteristics using both field surveys and simulation results. Furthermore, the Certainty Factor Model (CFM) and the parameter Sensitivity Index (Sa) were applied to assess the influence of elevation, slope gradient, aspect, and maximum snow depth on the severity of avalanche disasters. The results indicate the following: (1) Avalanches exhibit pronounced spatiotemporal concentration: temporally, they cluster between February and March and during 13:00–18:00 daily; spatially, they concentrate within the 2100–3000 m elevation zone. Chute-confined avalanches dominate the region, comprising 73.26% of total events; most chute-confined avalanches feature multiple release areas; therefore the number of release areas exceeds avalanche points; in terms of scale, medium-to-large-scale avalanches dominate, accounting for 86.5% of total avalanches. (2) RAMMS::AVALANCHE simulations yielded the following maximum values for the region: flow height = 15.43 m, flow velocity = 47.6 m/s, flow pressure = 679.79 kPa, and deposition height = 10.3 m. Compared to chute-confined avalanches, unconfined slope avalanches exhibit higher flow velocities and pressures, posing greater hazard potential. (3) The Certainty Factor Model and Sensitivity Index identify elevation, slope gradient, and maximum snow depth as the key drivers of avalanches in the study area. Their relative impact ranks as follows: maximum snow depth > elevation > slope gradient > aspect. The sensitivity index values were 1.536, 1.476, 1.362, and 0.996, respectively. The findings of this study provide a scientific basis for further research on avalanche hazards, the development of avalanche warning systems, and the design of avalanche mitigation projects in the study area. Full article

High-grade osteosarcoma (HGOS) is the most common primary malignant bone tumor in children and adolescents. Poor response to chemotherapy is linked to worse prognosis and increased risk of recurrence and metastasis. However, current assessment methods, such as tumor necrosis evaluation, are time-consuming and delay treatment decisions. Thus, identifying molecular pathways and predictive biomarkers is essential for guiding early therapeutic strategies. In this study, RNA-seq analysis of HGOS tissues revealed enrichment of cholesterol biosynthesis and mitotic pathways in poor responders. Additionally, high HMGCR expression, as analyzed from TCGA data, was associated with poor prognosis in sarcoma. Functional validation using SaOS-2 cells, which exhibited poor drug sensitivity and elevated HMGCR levels, demonstrated that simvastatin enhanced the efficacy of cisplatin and doxorubicin by inducing mitochondrial-mediated apoptosis and downregulating anti-apoptotic proteins. Simvastatin also reduced cell migration and invasion by suppressing epithelial–mesenchymal transition and extracellular matrix degradation. Mechanistically, simvastatin disrupted Ras prenylation and inhibited downstream oncogenic signaling pathways, including Akt/mTOR and Akt/GSK3, which regulate survival and metastasis-associated gene expression. These findings suggest that the cholesterol biosynthesis pathway particularly plays a critical role in chemoresistance and metastasis in HGOS and may serve as a promising predictive molecular target for guiding early therapeutic strategies. Full article

Blackleg disease poses a major threat to global canola production. The resistance gene Rlm1, corresponding to the avirulence gene AvrLm1 in the pathogen Leptosphaeria maculans, has been widely used to mitigate the impact of the disease. To investigate the biochemical basis of Rlm1-mediated resistance against blackleg, we conducted an LC-MS–based analysis of a susceptible Topas double haploid (DH) line and its isogenic Rlm1-carrying resistant counterpart for metabolomic profiles during the infection process. Samples were labeled with ¹²C- and ¹³C for LC-MS analyses to enhance both chemical and physical properties of metabolites for improved quantification and detection sensitivity. Resistant plants showed early and sustained accumulation of several defense metabolites, notably pipecolic acid (PA, up to 326-fold), salicylic acid (SA), and gentisic acid (GA) in L. maculans-inoculated Topas–Rlm1 plants compared to mock-inoculated Topas–Rlm1 controls (adjusted p < 0.05), indicating activation of lysine degradation and hormonal defense pathways. Elevated glucosinolates (GLS), γ-aminobutyric acid (GABA), and melatonin precursors may further contribute to antimicrobial defense and cell-wall reinforcement. In contrast, flavonoid and phenylpropanoid pathways were down-regulated, suggesting metabolic reallocation during resistance. Exogenous application of PA, SA, GA, ferulic acid, and piperonylic acid (a known inhibitor of the phenylpropanoid pathway in plants) significantly reduced infection in susceptible canola varieties, validating their defense roles against blackleg. These results offer new insights into Rlm1-mediated resistance and support metabolic targets for breeding durable blackleg resistance in canola. Full article

With global temperatures steadily rising, understanding the impacts of warming on regional climates has become crucial, particularly for countries like India, where climate sensitivity has significant socio-economic implications. This study assesses the trends and spatial distribution of summer days across India under different warming targets (1.5 °C, 2 °C, 2.5 °C, 3 °C, 3.5 °C, 4 °C, 4.5 °C, and 5 °C) and emission scenarios (RCP4.5 and RCP8.5). A Multi-Model Ensemble (MME) approach, combining five best-performing CORDEX-SA experiments, was utilized to analyze projected summer days in India. Non-parametric trend analysis techniques—such as the Mann–Kendall test, Modified Mann–Kendall, Sen’s Slope estimator, and Pettitt test—were used to investigate temporal patterns, and Reliability Ensemble Averaging (REA) was applied for uncertainty analysis to ensure robust projections. The results indicate that summer days are expected to increase significantly across India under both RCP scenarios, with the highest increases projected for northeastern regions and north-central regions of India. This study underscores the pressing need for region-specific adaptation strategies to manage extended periods of extreme temperatures and safeguard public health, agriculture, and socio-economic stability. Full article

Blue light serves as a critical environmental cue regulating Glycine max (soybean) stem morphology, yet the hormonal mechanisms underlying varietal differences remain unclear. Previous studies have highlighted the role of blue light in modulating plant architecture, but the specific hormone interactions driving morphological divergence between soybean varieties remain underexplored. Two soybean varieties with contrasting stem phenotypes—Henong 60 (HN60, tall) and Heinong 48 (HN48, dwarf)—were subjected to 0% (full light) and 30% (shade) transmittance conditions, supplemented with blue light (450 nm, 45.07 ± 0.03 μmol·m⁻²·s⁻¹). Stem anatomical traits (xylem area, cell length), hormone profiles, and proteomic changes were analyzed. Grey correlation analysis quantified relationships between hormone ratios and plant height. Blue light increased soybean stem xylem area and diameter while reducing plant height and cell longitudinal length. This treatment concurrently reduced growth-promoting hormones (gibberellin A₃ (GA₃), indole-3-acetic acid (IAA), brassinolide (BR)) and increased growth-inhibiting hormones (salicylic acid (SA), jasmonic acid (JA), strigolactones (SLs)), thereby inhibiting stem elongation. Although exogenous GA₃ promoted hypocotyl elongation, it failed to counteract blue-light-induced inhibition. Proteomic analysis identified 16 differentially expressed proteins involved in hormone signal transduction pathways. Grey correlation analysis highlighted cultivar-specific hormone ratio impacts: GA₃/JA, GA₃/SA, and BR/SLs significantly influenced HN60 plant height, while GA₃/SLs, IAA/SLs, and BR/SLs were critical for HN48, demonstrating highly significant positive correlations. The differential sensitivity of growth-promoting/inhibiting hormone ratios to blue light drives varietal morphological divergence in soybean stems. This study establishes a hormonal regulatory framework for blue-light-mediated stem architecture, offering insights for crop improvement under light-limited environments. Full article

Despite stricter global energy codes, performance standards, and advanced renewable technologies, the building sector must accelerate its transition to zero carbon emissions. Many studies show that new buildings, especially non-residential ones, often fail to meet projected performance levels due to poor maintenance and management of HVAC systems. The application of predictive AI models offers a cost-effective solution to enhance the efficiency and sustainability of these systems, thereby contributing to more sustainable building operations. The study aims to enhance the control of a variable air volume (VAV) system using machine learning algorithms. A novel ventilation control model, AI-VAV, is developed using a hybrid extreme learning machine (ELM) algorithm combined with simulated annealing (SA) optimisation. The model is trained on long-term monitoring data from three office buildings, enhancing robustness and avoiding the data reliability issues seen in similar models. Sensitivity analysis reveals that accurate occupancy prediction is achieved with 8500 to 10,000 measurement steps, resulting in potential additional energy savings of up to 7.5% for the ventilation system compared to traditional VAV systems, while maintaining CO₂ concentrations below 1000 ppm, and up to 12.5% if CO₂ concentrations are slightly above 1000 ppm for 1.5% of the time. Full article

Freshwater scarcity demands innovative solutions, and Atmospheric Water Generation (AWG) technology offers a promising approach. This study applies a data-driven optimization methodology to enhance AWG efficiency by improving condensation surface design using Triply Periodic Minimal Surface (TPMS) structures. Five TPMS types (Gyroid, Diamond, Lidinoid, SplitP, and Schwartz) were evaluated using thermal simulations in nTop, and a regression-based predictive model was developed to assess the impact of lattice thickness and cell size on surface area to volume ratio (SA/V_S) and temperature difference (ΔT). A multi-objective optimization approach, incorporating sensitivity-weighted desirability analysis, identified optimal design parameters under varying performance priorities. Results show Schwartz exhibits the highest tunability, with both factors significantly influencing its performance, while other TPMS types are primarily governed by lattice thickness. By integrating regression modeling, multi-objective optimization, and sensitivity analysis, this study provides a systematic framework for AWG surface design, offering scalable insights for thermal management and moisture collection efficiency in sustainable water harvesting applications. Full article

Many important practical problems connected to energy efficiency in buildings, ecology, metallurgy, the development of wireless communication systems, the optimization of radar technology, quantum computing, pharmacology, and seismology are described by large-scale mathematical models that are typically represented by systems of partial differential equations. Such systems often involve numerous input parameters. It is crucial to understand how susceptible the solutions are to uncontrolled variations or uncertainties within these input parameters. This knowledge helps in identifying critical factors that significantly influence the model’s outcomes and can guide efforts to improve the accuracy and reliability of predictions. Sensitivity analysis (SA) is a method used efficiently to assess the sensitivity of the output results from large-scale mathematical models to uncertainties in their input data. By performing SA, we can better manage risks associated with uncertain inputs and make more informed decisions based on the model’s outputs. In recent years, researchers have developed advanced algorithms based on the analysis of variance (ANOVA) technique for computing numerical sensitivity indicators. These methods have also incorporated computationally efficient Monte Carlo integration techniques. This paper presents a comprehensive theoretical and experimental investigation of Monte Carlo algorithms based on “symmetrized shaking” of Sobol’s quasi-random sequences. The theoretical proof demonstrates that these algorithms exhibit an optimal rate of convergence for functions with continuous and bounded first derivatives and for functions with continuous and bounded second derivatives, respectively, both in terms of probability and mean square error. For the purposes of numerical study, these approaches were successfully applied to a particular problem. A specialized software tool for the global sensitivity analysis of an air pollution mathematical model was developed. Sensitivity analyses were conducted regarding some important air pollutant levels, calculated using a large-scale mathematical model describing the long-distance transport of air pollutants—the Unified Danish Eulerian Model (UNI-DEM). The sensitivity of the model was explored focusing on two distinct categories of key input parameters: chemical reaction rates and input emissions. To validate the theoretical findings and study the applicability of the algorithms across diverse problem classes, extensive numerical experiments were conducted to calculate the main sensitivity indicators—Sobol’ global sensitivity indices. Various numerical integration algorithms were employed to meet this goal—Monte Carlo, quasi-Monte Carlo (QMC), scrambled quasi-Monte Carlo methods based on Sobol’s sequences, and a sensitivity analysis approach implemented in the SIMLAB software for sensitivity analysis. During the study, an essential task arose that is small in value sensitivity measures. It required numerical integration approaches with higher accuracy to ensure reliable predictions based on a specific mathematical model, defining a vital role for small sensitivity measures. Both the analysis and numerical results highlight the advantages of one of the proposed approaches in terms of accuracy and efficiency, particularly for relatively small sensitivity indices. Full article

Rapid detection of plant infections is crucial for minimising crop loss and optimising management strategies, particularly in the context of climate change. While traditional diagnostic methods provide precise measurements of phytohormones such as salicylic acid (SA), a key regulator of plant defence responses, their reliance on bulky equipment and lengthy analysis times limits field applicability. This study presents a microfluidic-based aptamer assay for SA detection, enabling rapid and sensitive fluorescence-based readout from plant samples. A tailored sample pre-treatment protocol was developed and validated with real strawberry samples using HPLC measurements. The assay demonstrated a detection limit ranging from 10⁻⁹ to 10⁻⁶ mg/mL, within the relevant range for early infection diagnosis. The integration of the microfluidic platform with the optimised pre-treatment protocol offers a portable, cost-effective solution for on-site phytohormone analysis, providing a valuable tool for early infection detection and improved crop management. Full article

Solid-state batteries (SSBs) present a promising advancement in energy storage technology, with the potential to achieve higher energy densities and enhanced safety compared to conventional lithium-ion batteries. However, their commercialisation is hindered by technical limitations and fragmented research efforts that predominantly focus on materials or individual performance parameters. This narrow scope limits SSB design and optimisation, potentially delaying the transition to commercial cells. Addressing these challenges requires a systematic framework that integrates key design and performance considerations. This study introduces a modelling framework that addresses these challenges by offering a systematic approach to SSB design. The model streamlines the design process by enabling users to define material selections and cell configurations while calculating key performance indicators (KPIs), such as energy density, power density, and resistance, as well as the specifications required for cell manufacturing. A material compatibility validation feature ensures appropriate selection of anode, cathode, and electrolyte materials, while an integrated sensitivity analysis (SA) function identifies critical design parameters for performance optimisation. The model’s accuracy and applicability were validated through comparisons with experimental data, established design frameworks, and the reverse-engineering of commercial SSB prototypes. Results show that the model predicts energy densities within a ±4% deviation in most cases. Additionally, the application of SA highlights its effectiveness in refining design parameters and optimising cell configurations. Despite certain limitations, the model remains a valuable tool in the early stages of battery concept development. It offers researchers and industry professionals a practical means to assess the feasibility of SSB designs and support future scale-up and industrialisation efforts. Full article

Fusarium wilt caused by Fusarium oxysporum f. sp. cubense tropical race 4 (Foc TR4) is the most destructive disease of the banana. Effectors play a crucial role in Foc TR4–banana interaction; however, only a few effectors have been functionally characterized. Our previous secretome studies on Foc TR4 highlighted an uncharacterized protein without any conserved domains (named FoUpe9), which was predicted to be a candidate effector. Herein, bioinformatics analysis showed that FoUpe9 was highly conserved among Fusarium species. FoUpe9 was highly induced during the early infection stages in the banana. A yeast signal sequence trap assay showed that FoUpe9 is a secretory protein. FoUpe9 could inhibit cell death and ROS accumulation triggered by BAX through the Agrobacterium-mediated Nicotiana benthamiana expression system. Subcellular location showed that FoUpe9 was located in the nucleus and cytoplasm of N. benthamiana cells. Deletion of the FoUpe9 gene did not affect mycelial growth, conidiation, sensitivity to cell-wall integrity, or osmotic and oxidative stress, but significantly attenuated fungal virulence. FoUpe9 deletion diminished fungal colonization and induced ROS production and expression of SA-related defense genes in banana plants. These results suggest that FoUpe9 enhances Foc TR4 virulence by inhibiting host immune responses and provide new insights into the functions of the uncharacterized proteins, further enhancing our understanding of effector-mediated Foc TR4 pathogenesis. Full article

A ship’s diesel–electric hybrid power system is complex, with hardware configuration and energy management parameters being crucial to its economic performance. However, existing optimization methods typically involve designing and optimizing the hardware configuration on the basis of typical operating conditions, followed by the design and optimization of the energy management parameters, which makes it difficult to achieve optimal system performance. Moreover, when co-optimizing hardware configurations and energy management parameters, the parameter relationships and complex constraints often lead conventional optimization algorithms to converge slowly and become trapped in local optima. To address this issue, a hybrid Ivy-SA algorithm is developed for the co-optimization of both the hardware configuration and energy management parameters. First, the main engine and hybrid ship models are established on the basis of the hardware configuration, and the accuracy of the models is validated. An energy management strategy based on the equivalent fuel consumption minimization strategy (ECMS) is then formulated, and energy management parameters are designed. A sensitivity analysis is conducted on the basis of both the hardware configuration and energy management parameters to evaluate their impacts under various conditions, enabling the selection of key optimization parameters, such as diesel engine parameters, battery configuration, and charge/discharge factors. The Ivy-SA algorithm, which integrates the advantages of both the Ivy algorithm (IVYA) and the simulated annealing algorithm (SA), is developed for the co-optimization. The algorithm is tested with the CEC2017 benchmark functions and outperforms 11 other algorithms. Furthermore, when the top five performing algorithms are applied for the co-optimization, the results show that the Ivy-SA algorithm outperforms the other four algorithms with a 14.49% increase in economic efficiency and successfully escapes local optima. Full article

In this study, Fe₃O₄@SiO₂@TiO₂ (FS-FT (0 g)) photocatalysts, featuring a magnetic core–shell structure, and Fe-doped Fe₃O₄@SiO₂@Fe-TiO₂ (FS-FT (x g)) photocatalysts, were fabricated via the sol–gel method. Structural and compositional analyses of the processed samples were systematically conducted through X-ray diffraction (XRD), transmission electron microscopy (TEM) with selected area electron diffraction (SAED), surface-sensitive X-ray photoelectron spectroscopy (XPS), and optical property assessment via UV-Vis diffuse reflectance spectroscopy (UV-DRS). The results show that TiO₂ on the outer layer of FS-FT (0 g) and FS-FT (x g) has an anatase structure, and that Fe is doped into FS-FT (x g). The photodegradation of methyl orange (MO) using FS-FT (0 g) and FS-FT (x g) with various Fe doping levels was evaluated in both pure MO (C₀ = 10 mg/L) and MO-Na₂SO₄-blended solutions. Under irradiation with high-pressure mercury lamps, the removal rates of MO using FS-FT (0 g) and FS-FT (0.36 g) in pure MO solution reached 90.25% and 99% at 25 min, respectively, which indicates that FS-FT (0.36 g) can enhance photocatalytic performance. The removal rates of MO using FS-FT (0 g) and FS-FT (0.36 g) in MO-Na₂SO₄-blended solution (C₀ = 10 mg/L, C_Na2SO4 = 12.5 g/L) reached 92.38% and 97.16% at 25 min, respectively. The removal rate of MO using FS-FT (0.36 g) decreased in MO-Na₂SO₄-blended solution in the previous 25 min, which indicates that Na₂SO₄ can inhibit degradation using FS-FT (0.36 g). The degradation experiments of MO-Na₂SO₄-blended solutions with different concentrations of Na₂SO₄ using FS-FT (0.36 g) showed that as the concentration of Na₂SO₄ increases, the inhibitory effect becomes more pronounced. Recovery and recycling experiments confirmed that the photocatalyst exhibited robust degradation performance over multiple cycles. Kinetic analysis of the photocatalytic data, based on a first-order model, was conducted to explore the underlying degradation principles. Full article

Process-based crop growth models, as an important analytical tool in agricultural production, face the problem of calibrating many parameters during the application process, and sensitivity analysis (SA) can quantify the effects of the model input parameters on the model output and provide an important basis for parameter calibration. In this study, we combined the good performance of the Agricultural Production Systems sIMulator Next-Generation (APSIM NG) model in simulating crop growth, soil carbon and nitrogen cycles, and soil N₂O emissions with the efficient computational efficiency of the extended Fourier amplitude test (EFAST) method. The sensitivity of the APSIM NG model to the simulation of soil N₂O emissions was systematically investigated using the EFAST method in a dry-crop wheat field in the semi-arid region of the Loess Plateau in Longzhong, China, where 28 crop cultivar parameters, 15 soil parameters, 4 meteorological parameters, and 4 field management parameters were selected. The parameters were selected based on the existing literature and the official documents of the model, and the parameter boundaries were determined based on the initial values of the APSIM NG model and the measured data and adjusted upward and downward by the standard normal distribution. In this study, parameters with a first-order sensitivity index ($S_i$) > 0.05 and a total sensitivity index ($S_{Ti}$) > 0.10 were identified as having a significant influence on the model outputs. The results of this study demonstrated that soil N₂O emission modeling in dry-crop wheat fields showed high sensitivity to the following parameters: (1) Among the crop cultivar parameters, the sensitivity from high to low was the leaf appearance rate, maximum leaf area, maximum nitrogen concentration of the grain, and thermal time from the starting grain-fill stage to end grain-fill stage. (2) Among the soil parameters, the sensitivity from high to low was a lower effective moisture limit, wilting coefficient, and ammonium nitrogen content. (3) Among the meteorological parameters, precipitation and solar radiation showed high sensitivity. (4) Among the field management parameters, the nitrogen application rate exhibited the most significant sensitivity. For this reason, we believe that by prioritizing the calibration of the most sensitive parameters through the results of the sensitivity analysis in this study, the workload of the APSIM NG model in the calibration process can be effectively reduced, which is conducive to the rapid localization and application of the model. Full article