Search Results (2,106)

Salmonellosis continues to be a global public health priority in which humans, livestock, and the contaminated environment interact with food to create complex interactions. Here, a new non-autonomous model is proposed to capture seasonal dynamics of Salmonella typhimurium transmission with key compartments that include humans, cattle, and bacteria in environmental and food sources. The model explores how bacterial growth, shedding, and ingestion rates, along with contamination pathways, determine disease dynamics. Some analytical derivations of the basic reproduction number (${\mathcal{R}}_0$) and threshold conditions for disease persistence or extinction are derived by using the spectral radius of a linear operator associated with the monodromy matrix. Parameter estimation for the model was accomplished with the aid of Latin hypercube sampling and least squares methods on Salmonella outbreak data from Saudi Arabia ranging from 2018 to 2021. The model was able to conduct an analysis based on the estimated 0.606 value of ${\mathcal{R}}_0$, and this meant that the model was able to fit reasonably well for both the cumulative and the new individual case data, which in turn, suggests the disease is curable. Predictions indicate a gradual decline in the number of new cases, with stabilization anticipated at approximately 40,000 cumulative cases. Further simulations examined the dynamics of disease extinction and persistence based on ${\mathcal{R}}_0$. When ${\mathcal{R}}_0$ is less than 1, the disease-free equilibrium is stable, resulting in the extinction of the disease. Conversely, when ${\mathcal{R}}_0$ exceeds 1, the disease persists, exhibiting endemic characteristics with recurrent outbreaks. Sensitivity analysis identified several parameters as having a significant impact on the model’s outcomes, specifically mortality and infection rates, along with decay rates. These findings highlight the critical importance of precise parameter estimation in understanding and controlling the transmission dynamics of Salmonella. Sensitivity indices and contour plots were employed to assess the impact of various parameters on the basic reproduction number and provide insights into the factors most influencing disease transmission. Full article

Advanced remote sensing technologies leverage extensive synthetic aperture radar (SAR) satellite data and high-resolution airborne light detection and ranging (LiDAR) data to swiftly capture comprehensive 3D information about electrical grid assets and their surrounding environments. This facilitates in-depth scene analysis for target detection and classification, allowing for the early recognition of potential hazards near transmission towers (TTs). These innovations present a groundbreaking strategy for the automated inspection of electrical grid assets. However, traditional 3D target detection techniques, which involve searching the entire 3D space, are marred by low accuracy and high computational demands. Although deep learning-based 3D target detection methods have significantly improved detection precision, they rely on a large volume of 3D target samples for training and are sensitive to point cloud data density. Moreover, these methods demonstrate low detection efficiency, constraining their application in the automated monitoring of electricity networks. This paper proposes a fast 3D target detection method using virtual views to overcome these challenges related to detection accuracy and efficiency. The method first utilizes cutting-edge 2D splatting technology to project 3D point clouds with diverse densities from a specific viewpoint, generating a 2D virtual image. Then, a novel local–global dual-path feature fusion network based on YOLO is applied to detect TTs on the virtual image, ensuring efficient and accurate identification of their positions and types. Finally, by leveraging the projection transformation between the virtual image and the 3D point cloud, combined with a 3D region growing algorithm, the 3D points belonging to the TTs are extracted from the whole 3D point cloud. The effectiveness of the proposed method in terms of target detection rate and efficiency is validated through experiments on synthetic datasets and outdoor LiDAR point clouds. Full article

In probabilistic slope stability analysis, soil depth has been treated as a deterministic variable, although it is a highly variable parameter. This study aims to identify soil depth variability using seismic refraction survey data and to analyze its impact on probabilistic analysis of slope stability. Seismic refraction survey data were collected from 70 slopes in South Korea and employed to identify the variability of soil depth within natural slopes. As a result, the average soil depth across 70 slopes was 2.5 m, with an average coefficient of variation (COV) of 29%, indicating high variability. To investigate the influence of soil depth variability on the probability of slope failure, probabilistic slope stability analysis was conducted by considering the shear strength parameters of soil and soil depth as random variables. Accordingly, the influences of the variability of soil depth on the probabilistic analysis of slope stability were evaluated by comparing the probability of slope failure and distribution of the failure occurrence frequency by depth. Additionally, global sensitivity analysis was conducted to understand the relative contribution of input parameters on the probability of slope failure. Consequently, the probability of slope failure can vary significantly depending on soil depth variability, emphasizing the importance of considering this factor in probabilistic slope stability analysis. Full article

In response to the increasing global emphasis on environmental accountability, the issue of greenwashing requires urgent resolution. This research investigates how state ownership affects greenwashing behaviors in Chinese A-share private companies over the period from 2010 to 2021, utilizing resource support and supervisory governance as analytical frameworks. Empirical analysis reveals that state-owned shareholder holdings significantly inhibit greenwashing practices in private enterprises, with this result remaining robust across various sensitivity tests. Furthermore, it is demonstrated that these holdings reduce greenwashing through both resource support and supervisory governance pathways. This study enhances the scholarly understanding of how state capital impacts private firms and underscores the distinctive roles and benefits that state-owned shareholders bring to the mixed-ownership reform process. The results suggest new pathways for fostering the sustainable development of private enterprises and offer crucial insights for policymakers focused on advancing mixed-ownership reforms and ensuring corporate accountability. Full article

The target molecule of this study is haloperidol, a neuroleptic from the butyrophenone family. It is one of the most widely used psychotropic drugs globally and is considered as effective as other low-potency psychotropic medications. The RP-HPLC method employed in this study utilizes a novel mobile phase composed of a 90:10 mixture of methanol and phosphate buffer (pH = 9.8) for isocratic elution. This method has been validated with a correlation coefficient (R) of 0.999 across a concentration range of 2.5 to 50 µg/mL. It exhibits excellent sensitivity, with a relative standard deviation (RSD) of less than 2% for both precision and accuracy. The method is highly effective for the analysis of haloperidol in oral commercial formulations. The mobile phase is cost-efficient, environmentally friendly, and simple to use, making it suitable for analyzing haloperidol in both liquid and powder forms. Additionally, the method is applied to monitor haloperidol degradation under various stress conditions. For powder samples, the maximum degradation observed was 6.20% after 48 h of sunlight exposure. For liquid haloperidol samples, stability was retained only under oxidative stress conditions, with the highest degradation (57.36%) occurring after 48 h of sunlight exposure and the lowest degradation (10.03%) observed under thermal stress at 60 °C over seven days. Full article

The impact of vaccination on the dynamics of hand, foot, and mouth disease (HFMD) transmission is explored in this paper, considering a fractional-order derivative system of equations. This model provides vaccination strategies and characterizes local and global stability using Lyapunov functions. This work computes the basic reproduction number ($R_0$) to represent the endemic and epidemic scenarios. Additionally, sensitivity analysis was performed to identify the most critical parameters responsible for the disease dissemination. Our results indicate that vaccination plays a crucial role in controlling HFMD, significantly reducing its prevalence. These findings align with existing research, supporting the importance of effective vaccination strategies and public health interventions against HFMD. The fractional-order model captures the memory effect in infectious disease dynamics, providing further insight into modeling HFMD transmission compared to a traditional integer-order model. The results would contribute to effective vaccination strategies and public health interventions against HFMD. Full article

In recent years, cyberattacks have increased in sophistication, using a variety of tools to exploit vulnerabilities across the global digital landscapes. Among the most commonly used tools at an attacker’s disposal are Google dorks, Shodan, and Censys, which offer unprecedented access to exposed systems, devices, and sensitive data on the World Wide Web. While these tools can be leveraged by professional hackers, they have also empowered “Script Kiddies”, who are low-skill, inexperienced attackers who use readily available exploits and scanning tools without deep technical knowledge. Consequently, cyberattacks targeting critical infrastructure are growing at a rapid rate, driven by the ease with which these solutions can be operated with minimal expertise. This paper explores the potential for cyberattacks enabled by these tools, presenting use cases where these platforms have been used for both offensive and defensive purposes. By examining notable incidents and analyzing potential threats, we outline proactive measures to protect against these emerging risks. In this study, we delve into how these tools have been used offensively by attackers and how they serve defensive functions within cybersecurity. Additionally, we also introduce an automated all-in-one tool designed to consolidate the functionalities of Google dorks, Shodan, and Censys, offering a streamlined solution for vulnerability detection and analysis. Lastly, we propose proactive defense strategies to mitigate exploitation risks associated with such tools, aiming to enhance the resilience of critical digital infrastructure against evolving cyber threats. Full article

Butterflies are highly sensitive to climate change, and Troides helena, as an endangered butterfly species, is also affected by these changes. To enhance the conservation of T. helena and effectively plan its protected areas, it is crucial to understand the potential impacts of climate change on its distribution. This study utilized a MaxEnt model in combination with ArcGIS technology to predict the global potential suitable habitats of T. helena under current and future climate conditions, using the species’ distribution data and relevant environmental variables. The results indicated that the MaxEnt model provided a good prediction accuracy for the distribution of T. helena. Under the current climate scenario, the species is primarily distributed in tropical regions, with high suitability areas concentrated in tropical rainforest climates. In future climate scenarios, the suitable habitat areas for T. helena in medium and high suitability categories generally show an expansion trend, which increases over time. Especially under the SSP5-8.5 scenario, by the 2090s, the area of high suitability for T. helena is projected to increase by 42.85%. The analysis of key environmental factors revealed that precipitation of the wettest quarter (Bio16) was the most significant environmental factor affecting the distribution of T. helena. The species has high demands for precipitation and temperature and can adapt to future climate warming. This study is valuable for identifying the optimal conservation areas for T. helena and provides a reference for future conservation efforts. Full article

Extreme climate events have become more frequent under global warming, significantly affecting vegetation phenology and carbon cycles in Central Asia. However, the mediating effects of intensity of compound drought and heat events (CDHEs) and compound moisture and heat events (CMHEs) on grassland phenology and their trends in the relative contributions to grassland phenology over time have remained unclear. Based on the calculation results of grassland phenology and compound events (CEs), this study used trend analysis, partial least squares regression structural equation modeling (PLS-SEM), and ridge regression analysis to investigate the mediating effect and the temporal trend in relative contribution of CEs to grassland phenology in Central Asia, and the magnitude of sensitivity of grassland phenology to CEs. This study revealed that the start of season (SOS) was advanced by 0.4 d·a⁻¹, end of season (EOS) was delayed by 0.5 d·a⁻¹, and length of season (LOS) extended by 0.8 d·a⁻¹ in 1982–2022. The duration of the CDHEs (0−37 days) was greater than that of the CMHEs (0−9 days) in Central Asia. The direct effects of CDHEs and CMHEs on grassland phenology were generally negative, except for the direct positive effect of CDHEs on LOS. The indirect effects of temperature and precipitation on grassland phenology through CDHEs and CMHEs were greater than their direct effects on phenology. The relative contribution of CDHEs to grassland phenology was consistently greater than that of CMHEs, and both the relative contribution curves showed a significant upward trend. The sensitivity of grassland phenology to CDHEs was higher than its sensitivity to CMHEs at 0.79 (SOS), 1.18 (EOS), and 0.72 (LOS). Our results emphasize the mediating effects of CDHEs and CMHEs on grassland phenology. Under the influence of CDHEs and CMHEs, the LOS will further lengthen in the future. Full article

Colorectal cancer (CRC) is the third most common cancer globally, with limited effective biomarkers and sensitive therapeutic targets. An increasing number of studies have highlighted the critical role of tumor microenvironment (TME) imbalances, particularly immune escape due to impaired chemokine-mediated trafficking, in tumorigenesis and progression. Notably, CC chemokines (CCLs) have been shown to either promote or inhibit angiogenesis, metastasis, and immune responses in tumors, thereby influencing cancer development and patient outcomes. However, the diagnostic and prognostic significance of CCLs in CRC remains unclear. In this study, multiple online tools for bioinformatics analyses were utilized. The findings revealed that the mRNA expression levels of CCL3, CCL4, and CCL26 were significantly elevated in CRC tissues compared to normal tissues, whereas CCL2, CCL5, CCL11, CCL21, and CCL28 mRNA levels were markedly downregulated. Additionally, dysregulation of CCL4, CCL5, and CCL21 was strongly associated with clinical staging, and elevated levels of CCL4, CCL11, and CCL28 were linked to significantly prolonged survival in CRC patients. Functional enrichment analysis indicated that the cellular roles of CCLs were predominantly associated with the chemokine, Wnt, and Toll-like receptor signaling pathways, as well as protein kinase activity. Furthermore, transcriptional regulation of most CCLs involved RELA and NFKB1. Key downstream targets included members of the SRC family of tyrosine kinases (HCK, LYN, and LCK), serine/threonine kinases (ATR and ATM), and others such as CSNK1G2, NEK2, and CDK2. Moreover, CCLs (CCL2, CCL3, CCL4, CCL5, CCL11, CCL21, and CCL28) exhibited strong correlations with major infiltration-related immune cells, including B cells, CD8⁺ T cells, CD4⁺ T cells, macrophages, neutrophils, and dendritic cells. In conclusion, our study provides novel insights into the potential utility of CCLs as biomarkers and therapeutic targets for CRC prevention and immunotherapy. Full article

Background/Objectives: Infections in dogs caused by methicillin-resistant staphylococci (MRS) present limited treatment options. This study’s objective was to investigate the molecular epidemiology of Staphylococcus spp. cultured exclusively from clinical canine skin and ear samples in Queensland, Australia, using whole-genome sequencing (WGS). Methods: Forty-two Staphylococcus spp. isolated from clinical canine skin and ear samples, from an unknown number of dogs, were sourced from two veterinary diagnostic laboratories between January 2022 and May 2023. These isolates underwent matrix-assisted laser desorption ionisation– time of flight bacterial identification, minimum inhibitory concentration testing using Sensititre^TM plates and WGS. Phylogenetic trees and core genome multilocus sequence typing (cgMLST) minimum spanning trees (MSTs) were constructed. Results: The isolates included methicillin-resistant and -sensitive S. pseudintermedius (MRSP: 57.1%, 24/42; and MSSP: 19.1%, 8/42), methicillin-resistant and -sensitive S. coagulans (MRSC: 14.3%, 6/42; and MSSC: 2.4%, 1/42) and methicillin-resistant coagulase-negative staphylococci (MR-CoNS: 7.1%, 3/42). Thirty-nine isolates were included after WGS, where all MRS harboured the mecA gene. Eighteen sequence types (STs) were identified, including three novel MRSP and six novel MSSP STs. MRSP ST496-V-VII (23%; 9/39) and MRSP ST749-IV-(IVg) (12.8%; 5/39) were commonly isolated. Phylogenetic analysis of single nucleotide polymorphisms showed that MRSP, MRSC and MSSC were similar to globally isolated staphylococci from canine skin and ear infections. Using cgMLST MSTs, MRSP isolates were not closely related to global strains. Conclusions: Our findings revealed a genotypically diverse geographical distribution and phylogenetic relatedness of staphylococci cultured from clinical canine skin and ear samples across Queensland. This highlights the importance of ongoing surveillance to aid in evidence-based treatment decisions and antimicrobial stewardship. Full article

Plastic pollution, particularly from microplastics (MPs) and nanoplastics (NPs), has become a critical environmental and health concern due to their widespread distribution, persistence, and potential toxicity. MPs and NPs originate from primary sources, such as cosmetic microspheres or synthetic fibers, and secondary fragmentation of larger plastics through environmental degradation. These particles, typically less than 5 mm, are found globally, from deep seabeds to human tissues, and are known to adsorb and release harmful pollutants, exacerbating ecological and health risks. Effective detection and quantification of MPs and NPs are essential for understanding and mitigating their impacts. Current analytical methods include physical and chemical techniques. Physical methods, such as optical and electron microscopy, provide morphological details but often lack specificity and are time-intensive. Chemical analyses, such as Fourier transform infrared (FTIR) and Raman spectroscopy, offer molecular specificity but face challenges with smaller particle sizes and complex matrices. Thermal analytical methods, including pyrolysis gas chromatography–mass spectrometry (Py-GC-MS), provide compositional insights but are destructive and limited in morphological analysis. Emerging (bio)sensing technologies show promise in addressing these challenges. Electrochemical biosensors offer cost-effective, portable, and sensitive platforms, leveraging principles such as voltammetry and impedance to detect MPs and their adsorbed pollutants. Plasmonic techniques, including surface plasmon resonance (SPR) and surface-enhanced Raman spectroscopy (SERS), provide high sensitivity and specificity through nanostructure-enhanced detection. Fluorescent biosensors utilizing microbial or enzymatic elements enable the real-time monitoring of plastic degradation products, such as terephthalic acid from polyethylene terephthalate (PET). Advancements in these innovative approaches pave the way for more accurate, scalable, and environmentally compatible detection solutions, contributing to improved monitoring and remediation strategies. This review highlights the potential of biosensors as advanced analytical methods, including a section on prospects that address the challenges that could lead to significant advancements in environmental monitoring, highlighting the necessity of testing the new sensing developments under real conditions (composition/matrix of the samples), which are often overlooked, as well as the study of peptides as a novel recognition element in microplastic sensing. Full article

Soil salinity is a major global challenge affecting agricultural productivity and food security. This study explores innovative strategies to improve salt tolerance in soybean (Glycine max), a crucial crop in the global food supply. This study investigates the synergistic effects of S-nitroso glutathione (GSNO) and silicon on enhancing salt tolerance in soybean (Glycine max). Two soybean cultivars, Seonpung (salt-tolerant) and Cheongja (salt-sensitive), were analyzed for various physiological, biochemical, and genetic traits under salt stress. The results showed that the combined GSNO and Si treatment significantly improved several key traits, including plant height, relative water content, root development, nodule numbers, chlorophyll content, and stomatal aperture, under both control and salt stress conditions. Additionally, this treatment optimized ion homeostasis by enhancing the Na/K ratio and Ca content, while reducing damage markers such as electrolyte leakage, malondialdehyde, and hydrogen peroxide. The stress-responsive compounds, including proline, ascorbate peroxidase, and water-soluble proteins, were elevated under stress conditions, indicating improved tolerance. Gene expression analysis revealed significant upregulation of genes such as GmNHX1, GmSOS2, and GmAKT1, associated with salt stress response, while GmNIP2.1, GmNIP2.2, and GmLBR were downregulated in both varieties. Notably, the salt-sensitive variety Cheongja exhibited higher electrolyte leakage and oxidative damage compared to the salt-tolerant Seonpung. These findings suggest that the combination of GSNO and silicon enhances salt tolerance in soybean by improving physiological resilience, ion homeostasis, and stress-responsive gene expression. Full article

Antimicrobial resistance (AMR) is a growing global health threat. This study investigated antibiotic resistance in E. coli isolates from municipal wastewater (86 isolates) and clinical urinary tract infection (UTI) cases (34 isolates) in a Grenadian community, using data from January 2022 to October 2023. Antibiogram data, assessed per WHO guidelines for Critically Important antimicrobials (CIA), showed the highest resistance levels in both clinical and wastewater samples for ampicillin, followed by amoxicillin/clavulanic acid and nalidixic acid, all classified as Critically Important. Similar resistance was observed for sulfamethoxazole-trimethoprim (highly important) in both groups, with nitrofurantoin showing resistance in the important category. According to the WHO AWaRe classification, ampicillin (ACCESS group) had the highest resistance, while nitrofurantoin had the lowest across all samples. The WATCH group antibiotics, cefuroxime and cefoxitin, showed comparable resistance levels, whereas aztreonam from the RESERVE group (tested only in wastewater) was 100% sensitive. Multiple Antibiotic Resistance (MAR) index analysis revealed that 7% of wastewater and 38.2% of clinical samples had MAR values over 0.2, indicating prior antibiotic exposure in clinical isolates. These parallel patterns in wastewater and clinical samples highlight wastewater monitoring as a valuable tool for AMR surveillance, supporting antibiotic stewardship through ongoing environmental and clinical assessment. Full article

This study aimed to identify the correlation between global climate phenomena, such as the ENSO, and South Korea’s Consumer Price Index (CPI) for a climate-sustainable economy. South Korea’s CPI has shown a linear upward trend, prompting a trend analysis and the subsequent removal of the linear trend for further examination. The correlation analysis identified statistically significant cases under the study’s criteria, with the Southern Oscillation Index (SOI) displaying the highest contribution and sensitivity. When comparing general correlations, the strongest relationship was observed with a 27-month lag. The Granger Causality Test, however, revealed causality with a 9-month lag between the CPI and El Niño–Southern Oscillation (ENSO) indices. This indicates the feasibility of separate analyses for long-term (27 months) and short-term (9 months) impacts. The correlation analysis confirmed that the ENSO contributes to explainable variations in the CPI, suggesting that CPI fluctuations could be predicted based on ENSO indices. Utilizing ARIMA models, the study compared predictions using only the CPI’s time series against an ARIMAX model that incorporated SOI and MEI as exogenous variables with a 9-month lag. Using the ARIMA model, this study compared predictions based solely on the time series of CPI with the ARIMAX model, which incorporated SOI and MEI as exogenous variables with a 9-month lag. Furthermore, to investigate nonlinear teleconnections, the neural network model LSTM was applied for comparison. The analysis results confirmed that the model reflecting nonlinear teleconnections provided more accurate predictions. These findings demonstrate that global climate phenomena can significantly influence South Korea’s CPI and provide experimental evidence supporting the existence of nonlinear teleconnections. This study highlights the meaningful correlations between climate indices and CPI, suggesting that climate variability affects not only weather conditions but also economic factors in a country. Full article