Search Results (32,922)

Coffea, a plant species of significant agricultural value used in coffee production, is a key commodity that supports the livelihoods of millions of people worldwide. However, coffee cultivation faces substantial threats from various pathogens, including Pseudomonas coronafaciens pv. garcae (Pcg), the causative agent of bacterial blight. This pathogen compromises coffee plant health, leading to reduced yields and plant death and impacting farmers and large-scale producers. Understanding the mechanisms underlying resistance to Pcg in the leaves of the resistant IAC 2211-6 Coffea arabica accession is crucial for developing effective control strategies. This study aimed to identify candidate biomarkers of resistance by comparing the leaf metabolome of (i) the resistant IAC 2211-6 and the susceptible IAC 125 RN Coffea arabica accessions and (ii) Pcg-infected and uninfected leaves. Untargeted metabolomics revealed distinct metabolic profiles between accessions. Flavonoids were more abundant in susceptible leaves. In contrast, resistant leaves showed increased levels of pipecolic acid ethyl ester, a structural derivative of a key systemic acquired resistance signal, and spiropreussione B, a compound associated with fungal endophytes. These findings highlight candidates potentially linked to resistance and suggest that systemic signaling and beneficial microbial interactions may contribute to resilience. Full article

Plant nutrient content is spatially and temporally dynamic, exposing insect herbivores to substantial nutritional variability. Such variability can constrain insects to feeding on sub-optimal diets, but it can also allow them to regulate their intake towards an optimal nutrient balance. Nutrient regulation is important in pest management, as the nutritional state of insects may alter their susceptibility to insecticides. Diet macronutrient balance has been shown to significantly affect the susceptibility of Helicoverpa zea larvae to endotoxins produced by transgenic crops containing Bacillus thurigiensis (Bt) genes. However, this was demonstrated using a highly inbred laboratory strain, limiting extrapolation to field populations. Here, we test the impact of field-relevant macronutrient variability on the efficacy of two Bt toxins across three field populations to increase the relevance to resistance monitoring and management. While differences in susceptibility were limited across populations, dietary effects were highly population specific. The Bt toxin that was most affected by diet and the diet that supported optimal survival and performance varied across populations. These findings indicate that nutrition can strongly influence Bt susceptibility, but these effects are influenced by population-level differences. To accurately assess Bt susceptibility in the field, bioassay diets should be tailored to the nutritional ecology of local populations. Full article

Wildfires, which encompass all fires that occur outside urban areas, represent one of the most frequent forms of natural disaster worldwide. This study presents the wildfire occurrence across the territory of Southeastern Europe, covering an area of 800,000 km² (Greece, Romania, Serbia, Slovenia, Croatia, Bosnia and Herzegovina, Montenegro, Albania, North Macedonia, Bulgaria, and Moldova). The research applies geospatial artificial intelligence techniques, based on the integration of machine learning (Random Forest (RF), XGBoost), deep learning (Deep Neural Network (DNN), Kolmogorov–Arnold Networks (KAN)), remote sensing (Sentinel-2, VIIRS), and Geographic Information Systems (GIS). From the geospatial database, 11 natural and anthropogenic criteria were analyzed, along with a wildfire inventory comprising 28,952 historical fire events. The results revealed that areas of very high susceptibility were most prevalent in Greece (10.5%), while the smallest susceptibility percentage was recorded in Slovenia (0.2%). Among the applied models, RF demonstrated the highest predictive performance (AUC = 90.7%), whereas XGBoost, DNN, and KAN achieved AUC values ranging from 86.7% to 90.5%. Through a SHAP analysis, it was determined that the most influential factors were global horizontal irradiation, elevation, and distance from settlements. The obtained results hold international significance for the implementation of preventive wildfire protection measures. Full article

Downtime, also known as an animal avoidance period, refers to a 24–72 h period during which individuals who have had contact with one animal species avoid subsequent contact with other susceptible species. This scoping review critically evaluated the effectiveness of downtime as a biosecurity measure. Peer-reviewed studies were identified and analysed using three electronic databases (Pubmed, Web of Science and Scopus). Two experimental studies directly evaluated downtime, nine additional articles examined human nasal carriage of pathogens after exposure to infected animals and seven articles evaluated the effectiveness of hygiene-based biosecurity measures targeting indirect mechanical pathogen transmission via humans (n = 14 unique articles in total; topical overlap noted where studies contributed to multiple categories). No evidence indicated an added benefit of downtime over other, more evidence-based measures such as hand hygiene, changing of clothes and boots and showering. While certain studies were able to identify the nasal carriage of pathogens, only one study indicated possible consequent transmission to naïve animals. This limited and species-specific evidence base reveals a critical gap between policy recommendations and scientific support for downtime. Based on these results, no clear benefit or necessity of downtime over other measures have been identified, other than its ability to deter unnecessary visitors, but these do not outweigh the impracticalities associated with an ‘animal avoidance period’. Resources and policy efforts should therefore shift from enforcing downtime toward ensuring and verifying adherence to fundamental hygiene measures. Full article

The crude oil futures market is highly susceptible to policy changes and international relations, which often trigger abrupt jumps in prices. The existing literature rarely considers jump volatility and the underlying impact mechanisms. This study proposes a hybrid forecasting model integrating a convolutional neural network (CNN) and self-attention (Transformer) for high-frequency financial data, based on the complex network characteristics between trading information and multi-market financialization indicators. Empirical results demonstrate that incorporating complex network indicators enhances model performance, with the CNN–Transformer model with a complex network achieving the highest predictive accuracy. Furthermore, we verify the model’s effectiveness and robustness in the WTI crude oil market via Diebold–Mariano tests and external event shock. Notably, this study also extends the analytical framework to jump intensity, thereby providing a more accurate and robust jump forecasting model for risk management and trading strategies in the crude oil futures market. Full article

Membrane rupture, induced by lipid peroxidation, is a severe threat to osmotic balance, as membrane pores contribute to ferroptosis, an iron-dependent cell death. To alleviate osmotic stress, membrane constituents dynamically reconstruct the membrane and interact with intracellular molecules. Tumor-derived acidosis shift glycolysis-dependent metabolism toward lipid metabolism, increasing polyunsaturated fatty acids (PUFAs). PUFAs enhance membrane fluidity but make cancer susceptible to lipid peroxidation. Also, the ionization of phospholipids under low pH can accelerate membrane rupture. This stress can be mitigated by the redistribution of cholesterol, which maintains tension–compression balance and acts as antioxidants. When excessive reactive aldehydes—byproducts of lipid peroxidation—overwhelm cholesterol’s protective role, lipid peroxides promote membrane cracks. Moreover, a deficiency in glutathione can alter cholesterol’s function, turning it into a pro-oxidant. In contrast, ceramide, derived from membrane lipids, indirectly prevents ferroptosis by facilitating cytochrome c release. This review integrates recent findings on how membrane components and environmental stressors influence ferroptosis. It also suggests potential therapeutic strategies. This could advance our understanding of ferroptosis in cancer. Full article

Discontinuous ground deformations represent one of the most critical geohazards affecting both natural and anthropogenically transformed environments. These processes pose a serious threat to infrastructure stability and land-use planning, as they can lead to severe structural damage and long-term ground instability. Effective geotechnical hazard management requires an integrated understanding of geological structures, deformation mechanisms, and the legacy of historical subsurface transformations influencing current and future ground behaviour. This paper—the first part of a two-part series—introduces an extended three-channel conceptual–probabilistic model and outlines its causal structure, integrating predisposing, triggering, and causative factors. The present study focuses exclusively on the theoretical foundations of the model and on the hierarchical classification of thirteen key predisposing factors defining the long-term susceptibility of the rock mass (S(A)). These include both structural and physicochemical controls such as karst voids, weak interfaces, hydro-mechanical activity, and near-surface weathering. The proposed approach provides a physically consistent conceptual basis for representing the interactions among the three causal domains. The second part of the series will address triggering and causative domains and will discuss methodological and implementation aspects of the model within the completed causal structure. Full article

7xxx series aluminum alloys are critical structural materials in aerospace applications, but their susceptibility to hydrogen embrittlement (HE) poses significant challenges to service safety and durability. The effects of Si, Er, and Zr microalloying, combined with optimized heat treatments on the HE resistance of Al–Zn–Mg–Cu alloys, were systematically investigated using transmission electron microscopy (TEM), scanning electron microscopy (SEM), and mechanical testing. Three alloys—1# (AlZnMgCuZr), 2# (AlZnMgCuErZr), and 3# (AlZnMgCuSiErZr)—were subjected to single-stage or two-stage homogenization, followed by solution treatments at 470 °C/2 h and 540 °C/1 h, and peak aging at 125 °C. The hydrogen charging experiment was conducted by first applying a modified acrylic resin coating to protect the gripping sections of the specimen, followed by a tensile test. Results demonstrate that alloy 3# with Si addition exhibited the lowest RA_loss, followed by the 2# alloy, which effectively improved the alloys’ hydrogen embrittlement behavior. Compared with the solution in 470 °C/2 h, the 540 °C/1 h solution treatment enabled complete dissolution of Mg₂Si phases, promoting homogeneous precipitation and peak hardness comparable to alloy 2#. Two-stage homogenization significantly enhanced the number density and refinement of L₁₂-structured Al₃(Er,Zr) nanoprecipitates. Silicon further accelerated the precipitation kinetics, leading to more Al₃(Er,Zr) nanoprecipitates, finely dispersed T′/η′ phases, and lath-shaped GPB-II zones. The GPB-II zones effectively trapped hydrogen, thereby improving HE resistance. This work provides a viable strategy for enhancing the reliability of high-strength aluminum alloys in hydrogen-containing environments. Full article

The characterization of environmental Pseudomonas spp. from pharmaceutical industries is a relatively underexplored area of research. This study used 40 isolates of Pseudomonas from a pharmaceutical company that had been presumptively identified as Pseudomonas aeruginosa by VITEK^®2. The isolates were characterized using 16S rRNA sequencing, Multilocus Sequence Typing (MLST), Matrix-Assisted Laser Desorption Ionization–Time of Flight Mass (MALDI–TOF MS), Fourier Transform Infrared (FT-IR) spectroscopy, ERIC-PCR, antimicrobial susceptibility profiling, and biofilm formation assessment on stainless steel surfaces. Twenty isolates were confirmed as P. aeruginosa. Sixteen isolates were only identified to the genus level of Pseudomonas, and the remaining four isolates were identified as Enterococcus faecalis (n = 2), Proteus spp. (n = 1), and Staphylococcus saprophyticus subsp. bovis/S. edaphicus/S. saprophyticus subsp. saprophyticus/S. pseudoxylosus/S. xylosus/S. caeli/S. ureilyticus. Typing of the 20 P. aeruginosa strains yielded 18 distinct FT-IR profiles and 19 ERIC-PCR profiles. The MLST analysis identified eight new sequence types (ST4292–ST4299). All strains were classified as multidrug-resistant (MDR), exhibiting resistance to multiple antimicrobial classes. Biofilm formation was observed in 28 (77.8%) Pseudomonas spp. strains on polystyrene surfaces, classified as moderately or strongly adherent, while all P. aeruginosa strains formed biofilms on stainless steel. Of the three disinfectants tested, sodium hypochlorite at 0.01% showed the best performance, reducing or eliminating biofilm formation in 24 (85.7%) strains. The regular evaluation of disinfection effectiveness in pharmaceutical industries is essential, as the presence of biofilm-forming strains can compromise production and contaminate final products. Full article

Muscle regeneration following injury reveals a striking paradox: the same phenomenon, fiber branching, can serve as both a beneficial adaptation in healthy muscle and a pathological hallmark in disease. In healthy muscle, branched fibers emerge as an adaptive response to extreme mechanical loading, redistributing stress, enhancing hypertrophy, and protecting against injury. Conversely, in conditions such as Duchenne Muscular Dystrophy, excessive and complex branching contributes to mechanical weakness, increased susceptibility to damage, and progressive functional decline. This review explores the dichotomy of fiber branching in muscle physiology, synthesizing current research on its molecular and cellular mechanisms. By understanding the paradoxical nature of fiber branching, we aim to uncover new perspectives for therapeutic strategies that balance its adaptive and pathological roles to improve outcomes for muscle diseases. Full article

Deep neural networks are vulnerable and susceptible to adversarial examples, which can induce erroneous predictions by injecting imperceptible perturbations. Transferability is a crucial property of adversarial examples, enabling effective attacks under black-box settings. Adversarial examples at flat maxima-those around which the loss peaks and grows slowly-have been demonstrated to exhibit higher transferability. Existing methods to achieve flat maxima rely on the gradient of the worst-case loss within the small neighborhood around the adversarial point. However, the neighborhood structure is typically defined as a Euclidean space, which neglects the input space’s information geometry, leading to suboptimal results. In this work, we build upon the idea of flat maxima but extend the neighborhood structure from Euclidean space to the manifold measured by the Fisher metric, which takes into account the information geometry of the data space. In the non-Euclidean case, we search for the worst-case point in the direction of the natural gradient with respect to adversarial examples. The natural gradient adjusts the original gradient using the Fisher information matrix, giving the steepest direction in the manifold. Furthermore, to reduce the computational cost of calculating the Fisher information matrix, we introduce a diagonal approximation of the matrix and propose an empirical Fisher method under the model ensemble setting. Experimental results demonstrate that our proposed manifold extensions significantly enhance attack success rates against both normally and adversarially trained models. In particular, compared to methods relying on the Euclidean metric, our approach demonstrates more efficient performance. Full article

The search for innovative solutions in the field of construction materials used in aircraft manufacturing has led to the development of composite materials, particularly CFRP polymer composites. Composite airframe components, which are required to have high strength, are joined using mechanical fasteners. Considering that the composite consists of a polymer matrix, which is a material susceptible to rheological phenomena occurring rapidly at elevated temperature, there is a high probability of significant changes in the strength and performance properties. Coupled thermal and mechanical loads on composite material joints occur in everyday aircraft operation. Experimental tests were conducted using a quasi-isotropic CFRP on an epoxy resin matrix with aerospace certification. The assessment of changes in the strength parameters of the material itself showed a decrease of approx. 40% in its short-term strength at 80 °C compared to the ambient temperature and a decrease in the load-bearing capacity of single-lap bolted joints of over 25%. Even more rapid changes were observed when assessing the fatigue life of the joints assessed at ambient and elevated temperature. In addition, the actual glass transition temperature of the resin was determined using the DSC technique. Analysis of the damage mechanisms showed that at 80 °C, the main degradation mechanisms of the material are accelerated creep processes of the CFRP and softening of the matrix, increasing its susceptibility to damage in the joint area. Full article

The use of large-aperture scanning mirrors for high-resolution and wide-swath imaging represents a major trend in Earth observation technology. However, to improve dynamic response performance, scanning mirror assemblies are highly lightweighted, resulting in reduced overall stiffness. This makes the mirror surface susceptible to thermal and inertial loads during operation, leading to degraded surface accuracy and poor imaging quality. Moreover, dynamic scanning mirror has the multi-disciplinary coupling effects and non-circular structural characteristics. It poses significant challenges for surface figure analysis. To address these issues, this paper proposes a surface analysis method for high-dynamic scanning mirrors under multi-physics coupling in non-circular domains. First, a finite element model of the mirror assembly is established based on the minimum aperture and angular velocity parameters. Through finite element analysis, the surface response of the scanning mirror assembly under thermal loads, dynamic inertial loads, and their coupled effects is quantitatively investigated. Subsequently, an analytical approach, which combines rigid-body displacement separation and Gram–Schmidt orthogonalization, is developed to construct non-circular Zernike polynomials, enabling high-precision fitting and reconstruction of the mirror’s dynamic surface distortions. Numerical experiments validate the accuracy of the model. Results show that for a scanning mirror with an aperture of 466 mm × 250 mm under the coupled condition of a 5 °C temperature rise and 50 N·mm torque, the surface figure achieves RMS < 2 nm and PV < 22 nm, with a fitting accuracy achieves 10⁻⁶. These results verify the accuracy and reliability of the proposed method. The surface analysis approach presented in this study provides theoretical guidance and a design framework for subsequent image quality evaluation and assurance. Full article

S. suis is a prominent zoonotic pathogen responsible for diseases such as arthritis in piglets, swine septicemia, and meningitis. The emergence of multi-drug resistance (MDR) underscores the urgent need for the development of novel antibacterial strategies. In this context, a systematic evaluation of the antibacterial potential of the bacteriophage lytic enzyme Ply900 was conducted in this study, along with an analysis of its domain functions and an in vivo study of its therapeutic dynamics. Ply900 exhibits potent in vitro lytic activity against multiple bacteria, including Streptococcus suis, Streptococcus agalactiae, and Staphylococcus aureus. Notably, it possesses broad biochemical stability, with tolerance to diverse environmental conditions. In a mouse model of S. suis serotype 2 SC19 infection, both the direct Ply900 treatment group and the triple therapy group achieved effective eradication of S. suis, with markedly improved survival rates. The remaining bacteria remained susceptible to Ply900, with no evidence of induced resistance development. Mechanistic analysis revealed that the SH3B domain of Ply900 enhances targeted cleavage efficiency by binding synergistically to peptidoglycan with the CHAP domain, with CYS-34, HIS-59, and ASP-28 serving as key amino acid sites for Ply900’s cleavage activity. Collectively, these findings lay the foundation for the potential dual applications of the lysin Ply900, both in the clinical treatment of S. suis infections and in the prevention and control of these pathogenic bacteria in livestock farming. Full article

As one of China’s major megacities, Chongqing is highly vulnerable to rainfall-induced landslides, and the increasing frequency of extreme rainfall driven by climate change further exacerbates risks to infrastructure and public safety. Although numerous studies on landslide susceptibility, quantitative assessments of future landslide frequency under different climate scenarios remain insufficient. This study addresses this gap by integrating high-resolution climate projections with a landslide early-warning model to predict spatiotemporal variations in landslide hazard across Chongqing. Based on regional climate characteristics, the rainy season was divided into three periods: May–June, July, and August–September. Soil moisture variations, together with static geological and topographic factors, were integrated using the information value model to assess the semi-dynamic landslide susceptibilities. On this basis, a regional warning model was then established by linking rainfall thresholds to four geological subregions. High-resolution NEX-GDDP-CMIP6 projections and historical ERA5 0rainfall data were used to quantify changes in exceedance days under four shared socioeconomic pathways (SSPs) from 2021 to 2100. Results indicate a substantial increase in days exceeding the 30% landslide-triggering rainfall threshold, with maximum relative growth of 15.57%. Landslide frequency exhibits pronounced spatial and temporal heterogeneity: increases are observed in May–June and August–September, whereas July trends vary with radiative forcing-decreasing under low-forcing scenarios (SSP1-2.6, SSP2-4.5) and increasing under high-forcing scenarios (SSP3-7.0, SSP5-8.5). The largest increase in frequency reaches 72%, primarily affecting southwestern and central Chongqing. By linking climate projections with rainfall thresholds and semi-dynamic susceptibility assessment, the framework provides a scientific reference for landslide risk prevention and mitigation under future climate scenarios, and offers transferable insights for other mountainous urban regions facing similar hazards. Full article