Search Results (267)

Blue mold of pome fruit, caused by Penicillium expansum, is controlled through postharvest applications of thiabendazole (TBZ), pyrimethanil (PYR), and fludioxonil (FDL). However, multi-fungicide-resistant isolates have emerged in the U.S. Pacific Northwest and their impact on decay control in long-term storage is unknown. This study evaluated the fitness of P. expansum isolates sensitive to all three postharvest fungicides (wild-types) and those resistant to TBZ (single-resistant), TBZ and PYR, or PYR and FDL (dual-resistant), and triple-resistant to the three fungicides. On nutrient-poor media, resistant isolates showed reduced conidial germination, whereas no significant differences were observed in germination, mycelial growth, or sporulation between phenotypes on nutrient-rich media at 1.5 and 20 °C. Regardless of their sensitivity phenotype, FDL-resistant isolates showed increased sensitivity to osmotic and oxidative stresses. Pathogenicity and virulence were not affected by the sensitivity phenotype on apples after six months of storage at 1.5 °C. Analysis of cumulative fitness changes indicated fitness loss under low-temperature in vitro and increased fitness under fungicide selection pressure on fruit in most resistant phenotypes. Gene expression analysis showed differential regulation of fitness-related genes, with most being up-regulated by TBZ. Overall, the results suggest that resistance in P. expansum may carry context-dependent fitness penalties, especially under high-stress conditions. Full article

The sustainable utilization of saline water resources represents an effective strategy for alleviating water scarcity in arid regions. However, the mechanisms by which prolonged saline water irrigation influences soil salinization and dryland crop growth are not yet fully understood. This study examined the effects of six irrigation water salinity levels (CK: 0.87 g·L⁻¹, S1: 2 g·L⁻¹, S2: 4 g·L⁻¹, S3: 6 g·L⁻¹, S4: 8 g·L⁻¹, S5: 10 g·L⁻¹) on soil salinization dynamics and jujube growth during a three-year field experiment (2020–2022). The results showed that soil salinity within the 0–1 m profile significantly increased with rising irrigation water salinity and prolonged irrigation duration, with the 0–0.4 m layer accounting for 50.27–74.95% of the total salt accumulation. A distinct unimodal salt distribution was observed in the 0.3–0.6 m soil zone, with the salinity peak shifting downward from 0.4 to 0.5 m over time. Meanwhile, soil pH and sodium adsorption ratio (SAR) increased steadily over the study period. The dominant hydrochemical type shifted from SO₄²⁻-Ca²⁺·Mg²⁺ to Cl⁻-Na⁺·Mg²⁺. Crop performance exhibited a nonlinear response to irrigation salinity levels. Low salinity (2 g·L⁻¹) significantly enhanced plant height, stem diameter, leaf area index (LAI), vitamin C content, and yield, with improvements of up to 12.11%, 3.96%, 16.67%, 16.24%, and 16.52% in the early years. However, prolonged exposure to saline irrigation led to significant declines in both plant growth and water productivity (WP) by 2022. Under high-salinity conditions (S5), yield decreased by 16.75%, while WP declined by more than 30%. To comprehensively evaluate the trade-off between economic effects and soil environment, the entropy weight TOPSIS method was employed to identify S1 as the optimal irrigation treatment for the 2020–2021 period and control (CK) as the optimal treatment for 2022. Through fitting analysis, the optimal irrigation water salinity levels over 3 years were determined to be 2.75 g·L⁻¹, 2.49 g·L⁻¹, and 0.87 g·L⁻¹, respectively. These findings suggest that short-term irrigation of jujube trees with saline water at concentrations ≤ 3 g·L⁻¹ is agronomically feasible. Full article

Phage therapy, long overshadowed by antibiotics in Western medicine, has a well-established history in some Eastern European countries and is now being revitalized as a promising strategy against antimicrobial resistance (AMR). This resurgence of phage therapy is driven by the urgent need for innovative countermeasures to AMR, which will cause an estimated 10 million deaths annually by 2050. However, the emergence of phage-resistant variants presents challenges similar to AMR, thus necessitating a deeper understanding of phage resistance mechanisms and control strategies. The highest priority must be to prevent the emergence of phage resistance. Although phage cocktails targeting multiple receptors have demonstrated a certain level of phage resistance suppression, they cannot completely suppress resistance in clinical settings. This highlights the need for strategies beyond simple resistance suppression. Notably, recent studies examining fitness trade-offs associated with phage resistance have opened new avenues in phage therapy that offer the potential of restoring antibiotic susceptibility and attenuating pathogen virulence despite phage resistance. Thus, controlling phage resistance may rely on both its suppression and strategic redirection. This review summarizes key concepts in the control of phage resistance and explores evolutionary engineering as a means of optimizing phage therapy, with a particular focus on Pseudomonas infections. Harnessing evolutionary dynamics by intentionally breaking the spontaneous evolutionary trajectories of target bacterial pathogens could potentially reshape bacterial adaptation by acquisition of phage resistance, unlocking potential in the application of phage therapy. Full article

Empty container depot (ECD) design significantly impacts maritime terminal efficiency, yet traditional evaluation approaches assess limited operational factors, constraining comprehensive performance optimization. This study develops an integrated discrete event simulation (DES) framework that simultaneously evaluates lifting equipment utilization, truck turnaround times, and potential collisions to support terminal decision-making. This study combines operational efficiency metrics with safety analytics for non-automated ECDs using Top Lifters and Reach Stackers. Additionally, a regression analysis examines efficiency metrics’ effect on safety risk. A case study at a Chilean multipurpose terminal reveals performance trade-offs between indicators under different operational scenarios, identifying substantial efficiency disparities between dry and refrigerated container operations. An analysis of four distinct collision zones with varying historical risk profiles showed the gate area had the highest potential collisions and a strong regression correlation with efficiency metrics. Similar models showed a poor fit in other conflict zones, evidencing the necessity for dedicated safety indicators complementing traditional measures. This integrated approach quantifies interdependencies between safety and efficiency metrics, helping terminal managers optimize layouts, expose traditional metric limitations, and reduce safety risks in space-constrained maritime terminals. Full article

The wheat curl mite (WCM, Aceria tosichella Keifer), a complex of eriophyid mite species, transmits wheat streak mosaic virus (WSMV) and Triticum mosaic virus (TriMV), which in single or mixed infections cause wheat streak mosaic (WSM) disease—a major threat to wheat production across the U.S. Great Plains. Resistant wheat cultivars bearing Cmc3 and Cmc4 (targeting WCM), Wsm1 and Wsm2 (targeting WSMV), and Wsm1 (targeting TriMV) are widely used to manage this pest–pathogen complex. However, comprehensive studies investigating how these resistance mechanisms influence both vector biology and virus transmission remain scarce. To address this gap, we evaluated disease development and WCM fitness across nine wheat cultivars with differential resistance profiles under single and mixed infections of WSMV and TriMV. We found strong viral synergy in co-infected plants, with TriMV accumulation markedly enhanced during mixed infections, irrespective of host genotype. Symptom severity and virus titers (both WSMV and TriMV) were highest in the cultivars carrying Wsm2, suggesting a potential trade-off in resistance effectiveness under mixed infection pressure. While mite development time (egg to adult) was unaffected by host genotype or infection status, mite fecundity was significantly reduced on infected plants carrying Wsm1 or Wsm2, but not on those with Cmc3 and Cmc4. Notably, virus accumulation in mites was reduced on the cultivars with Cmc3 and Cmc4, correlating with virus titers in the host tissues. Our findings highlight the complex interplay between host resistance, virus dynamics, and vector performance. Cultivars harboring Cmc3 and Cmc4 may offer robust field-level protection by simultaneously suppressing mite reproduction and limiting virus accumulation in both plant and vector. Full article

Plant–endophyte symbioses are widespread in grasslands. While symbiotic interactions often provide hosts with major fitness enhancements, the role of the endophyte Alternaria oxytropis, which produces swainsonine in locoweeds (Oxytropis and Astragalus spp.), remains enigmatic. We compared endophyte-infected (E+) and endophyte-free (E−) plants of three main Chinese locoweed species (O. kansuensis, O. glabra, and O. ochrocephala) under controlled conditions, and analyzed environmental factors at locoweed poisoning hotspots for herbivores. The results demonstrated significant species-specific effects: E+ plants of O. glabra and O. ochrocephala exhibited 26–39% reductions in biomass, net photosynthetic rate, and stomatal conductance, with elevated CO₂ levels, while O. kansuensis showed no measurable impacts. Swainsonine concentrations were 16–20 times higher in E+ plants (122.6–151.7 mg/kg) than in E− plants. Geospatial analysis revealed that poisoning hotspots for herbivores consistently occurred in regions with extreme winter conditions (minimum temperatures ≤ −17 °C and precipitation ≤ 1 mm during the driest month), suggesting context-dependent benefits under abiotic stress. These findings suggest that the ecological role of A. oxytropis may vary depending on both host species and environmental context, highlighting a trade-off between growth costs and potential stress tolerance conferred by A. oxytropis. The study underscores the need for field validation to elucidate the adaptive mechanisms maintaining this symbiosis in harsh environments. Full article

The trade-off between resolution and contrast is a transcendental problem in optical imaging, spanning from artistic photography to technoscientific applications. To the latter, Fourier-optics-based filters, such as the 4f system, are well-known for their image-enhancement properties, removing high spatial frequencies from an optically Fourier-transformed light signal through simple aperture adjustment. Nonetheless, assessing the contrast–resolution balance in optical imaging remains a challenging task, often requiring complex mathematical treatment and controlled laboratory conditions to match theoretical predictions. With that in mind, we propose a simple yet robust analytical technique to determine the optimal aperture in a 4f imaging system for static and quasi-static objects. Our technique employs the mathematical formalism of the H-theorem, enabling us to directly access the information of an imaged object. By varying the aperture at the Fourier plane of the 4f system, we have empirically found an optimal aperture region where the imaging entropy is maximum, given that the object is fitted to the imaged area. At that region, the image is lit and well-resolved, and no further aperture decrease improves that, as information of the whole assembly (object plus imaging system) is maximum. With that analysis, we have also been able to investigate how the imperfections in an object affect the entropy during its imaging. Despite its simplicity, our technique is generally applicable and passable for automation, making it interesting for many imaging-based optical devices. Full article

The disposable soma theory posits that organisms allocate limited resources between reproduction, maintenance, and growth, resulting in trade-offs, particularly as they age. In this study, we examined age-related reproductive senescence in Megachile rotundata, a solitary bee and important agricultural pollinator. We hypothesized that, similarly to social bees, aging females would show declines in foraging behavior and reproductive fitness. Contrary to this hypothesis, we found no evidence of reproductive senescence in M. rotundata within the timeframe observed. Instead, older females increased their foraging rate, leading to larger provisions and offspring. We also observed that older bees exhibited improved foraging efficiency, likely due to learning and muscle physiology changes. Furthermore, ovarian development showed no decline with age, indicating that reproductive capacity remains stable throughout the observed timeframe. Our results challenge conventional assumptions about reproductive senescence in solitary bees and suggest that older M. rotundata may contribute to more efficient pollination, with implications for pollinator management. This study provides new insights into the aging process in solitary bees, emphasizing the need for further research into the mechanisms behind age-related behavioral and reproductive changes. Full article

Passive wireless surface acoustic wave (SAW) sensors are widely adopted for monitoring the safety status of industrial equipment due to their compact size and maintenance-free operation. Replacing traditional discrete-component interrogators with software-defined radio (SDR) architectures offers lower cost and greater flexibility. However, conventional frequency estimation methods often rely on iterative algorithms with high computational complexity, limiting their real-time applicability. This paper presents an SAW sensing system based on an SDR platform and a non-iterative spectrum-fitting method for SAW frequency measurement. The feasibility of the proposed method is theoretically analyzed, and its performance under different window functions and length of fast Fourier transform (FFT) configurations is evaluated through simulations and experimental measurements. The results demonstrate a favorable trade-off between time efficiency and SAW frequency measurement accuracy. Compared to traditional approaches, the proposed method reduces complexity while maintaining ± 3$\mathrm{k}$$\mathrm{Hz}$ peak-to-peak accuracy with only 4096-point FFT length according to experimental results. Full article

The novel pathogen Candida auris has rapidly become a major health threat due to its high virulence, resistance to multiple antifungal agents, and remarkable environmental persistence. This study evaluated the influence of phenotypic traits and antifungal minimum inhibitory concentrations (MICs) on C. auris virulence using a Galleria mellonella infection model. Ten clinical strains, categorized as aggregative or non-aggregative, were analyzed for antifungal susceptibility and survival outcomes. All strains exhibited fluconazole resistance, with variable susceptibilities to other antifungals. Survival analysis revealed that the non-aggregative phenotype was independently associated with reduced survival in G. mellonella (HR = 2.418, p = 0.015), while antifungal MICs and invasive origin were not significant independent predictors of mortality in an elastic net-adjusted multivariable model. Strong correlations were observed between certain antifungal MICs, suggesting potential cross-resistance patterns; however, no independent association with virulence was identified. These results suggest that C. auris possesses not only an enhanced ability to develop antifungal resistance but also the capacity to do so without incurring fitness costs that could attenuate its virulence. Full article

Environmental transmission is a critical driver of cholera dynamics and a key factor influencing model-based inference and forecasting. This study focuses on stable parameter estimation and forecasting of cholera outbreaks using a compartmental SIRB model informed by three formulations of the environmental transmission rate: (1) a pre-parameterized periodic function, (2) a temperature-driven function, and (3) a flexible, data-driven time-dependent function. We apply these methods to the 1991–1997 cholera epidemic in Peru, estimating key parameters; these include the case reporting rate and human-to-human transmission rate. We assess practical identifiability via parametric bootstrapping and compare the performance of each transmission formulation in fitting epidemic data and forecasting short-term incidence. Our results demonstrate that while the data-driven approach achieves superior in-sample fit, the temperature-dependent model offers better forecasting performance due to its ability to incorporate seasonal trends. The study highlights trade-offs between model flexibility and parameter identifiability and provides a framework for evaluating cholera transmission models under data limitations. These insights can inform public health strategies for outbreak preparedness and response. Full article

This article aimed to incorporate the coordinated construction of classifiers to develop a model for predicting the pyrolysis of loose biomass. For the purposes of application, the ground form of pine cone was used to perform the thermogravimetric analysis at heating rates of 5, 10, and 15 °C∙min⁻¹. The supervised machine learning technique was considered to estimate the kinetic parameters associated with the thermal decomposition of the material. Here, the integral as well as differential form of the isoconversional method was used along with the Kissinger method for the maximum reaction rate determination. Python (version 3.13.2), along with PyCharm (2024.3.3) as an integrated development environment (IDE), was used to develop code for the given problem. The TG model obtained through the boosting technique provided the best fitting for the experimental dataset of raw pine cone, with the root squared error varying from ±1.82 × 10⁻³ to ±1.84 × 10⁻³, whereas it was in the range of ±1.78 × 10⁻³ to ±1.83 × 10⁻³ for processed pine cone. Similarly, the activation energies derived through the trained models of Friedman, OFW, and KAS were 176 kJ-mol⁻¹, 151.60 kJ-mol⁻¹, and 142.04 kJ-mol⁻¹, respectively, for raw pine cone. It was seen that the boosting technique did not provide a reasonable fit if the number of features was increased in the kinetic models. This happened owing to an inability to maintain a tradeoff between variance and bias. Moreover, the multiclassification in pyrolysis kinetics through the proposed scheme was not able to capture the distribution pattern of target values of the differential method. With the increase in the heating rates, the noise level in the predicted model was also relatively increased. Full article

A lack of public vehicular traffic data for a city limits our understanding of the traffic occurring in the street networks of that city; however, there are free tools to extract street network graphs from digital maps and to assess the static properties associated with those graphs. This study proposes a two-stage modeling method to describe dynamic traffic data with static street network features. A quadratic polynomial is used to fit the average travel speed (ATS) pattern observed in the city center. Then, the relationship between the polynomial parameters and street network variables is analyzed through multiple linear regression. Descriptive geometric and topological measurements of downtown areas are obtained with the OSMnx tool (from OpenStreetMap), and with these data, independent variables are defined. The speed of vehicles, assessed every 15 min (from 6:00 a.m. to 10:00 p.m.) on the downtown street networks of twelve major cities, is obtained with the distance_matrix service of GoogleMaps, and with these data, the ATS (the dependent variable) is calculated. The ATS (presenting a U-shape) is modeled with a polynomial equation of order two, so there are three parameters for each city; in turn, each parameter is modeled with a multiple linear regression equation with the independent variables. For training purposes, the ATS equation parameters of ten cities are calculated, and the parameters, in turn, are explained with the proposed method. For validation purposes, the parameters of two cities not considered in the training process are calculated with the multiple linear regression equations. The ATS equation parameters of the twelve cities are correctly modeled so that each city’s ATS can be adequately described. It was concluded that the method selects the independent variables that are suitable to explain the ATS equation parameters. In addition, with the Akaike information criterion, the variable selection case presenting the best trade-off between accuracy and complexity is identified. Full article

With the growing demand for sustainable energy solutions, biomass torrefaction has emerged as a crucial technology for converting agricultural waste into high-value biofuels. This work develops dual kinetic modeling using global and individual parameters combined using particle swarm optimization (PSO) to predict energy densification based on elemental composition (CHNO) and high heating values (HHVs). The global parameters are calculated from experiments conducted at 250 °C, 275 °C, and 300 °C, and the individual parameters are obtained by adjusting experimental points at each temperature. A two-step kinetic model was used and optimized to achieve exceptional adjustment accuracy (98.073–99.999%). The experiments were carried out in an inert atmosphere of nitrogen with a heating rate of 20 °C/min and a 100 min residence time. The results obtained demonstrate a crucial trade-off: while individual parameters provide superior accuracy (an average fit of 99.516%) for predicting degradation by weight loss, global parameters offer better predictions for elemental composition, with average errors of 2.129% (carbon), 1.038% (hydrogen), 9.540% (nitrogen), and 3.997% (oxygen). Furthermore, it has been found that by determining the kinetic parameters at a torrefaction temperature higher than the maximum peak observed in the derivative thermogravimetric (DTG) curve (275 °C), it is possible to predict the behavior of the process within the 250–325 °C range with an R-squared value corresponding to an error lower than 3%. This approach significantly reduces the number of required experiments from twelve to only four by relying on a single isothermal condition for parameter estimation. Full article

Airborne hyperspectral LiDAR (AHSL) is a technology that integrates the spectral content collected using hyperspectral imaging and the precise 3D descriptions of observed objects obtained using LiDAR (light detection and ranging). AHSL detects the spectral and three-dimensional (3D) information on an object simply using laser measurements. Nevertheless, the advantageous richness of spectral properties also introduces novel issues into the scan unit, the mechanical–optical trade-off. Specifically, the abundant spectral information requires a larger optical aperture, limiting the acceptance of the mechanic load by the scan unit at a demanding rotation speed and flight height. Via the simulation and analysis of scan models, it is exhibited that Palmer scans fit the large optical aperture required by AHSL best. Furthermore, based on the simulation of the Palmer scan model, 45.23% is explored as the optimized ratio of overlap (ROP) for minimizing the diversity of the point density, with a reduction in the coefficient of variation (CV) from 0.47 to 0.19. The other issue is that it is intricate to calibrate the scanning geometry using outside devices due to the complex optical path. A self-calibration strategy is proposed for tackling this problem, which integrates indoor laser vector retrieval and airborne orientation correction. The strategy is composed of the following three improvements: (1) A self-determined laser vector retrieval strategy that utilizes the self-ranging feature of AHSL itself is proposed for retrieving the initial scanning laser vectors with a precision of 0.874 mrad. (2) A linear residual estimated interpolation method (LREI) is proposed for enhancing the precision of the interpolation, reducing the RMSE from 1.517 mrad to 0.977 mrad. Compared to the linear interpolation method, LREI maintains the geometric features of Palmer scanning traces. (3) A least-deviated flatness restricted optimization (LDFO) algorithm is used to calibrate the angle offset in aerial scanning point cloud data, which reduces the standard deviation in the flatness of the scanning plane from 1.389 m to 0.241 m and reduces the distortion of the scanning strip. This study provides a practical scanning method and a corresponding calibration strategy for AHSL. Full article