Search Results (20,901)

Plant rhabdoviruses cause considerable economic losses and are a threat to the agriculture of Solanaceae plants. Two novel virus isolates belonging to the family Rhabdoviridae are identified by high-throughput sequencing (HTS) in Russian eggplant cultivars grown in the Volga river delta region for the first time. The phylogenetic inference of L protein (polymerase) shows that these virus isolates belong to Alphanucleorhabdovirus physostegia (Physostegia chlorotic mottle virus—PhCMoV), and their minus-sense RNA genomes have the typical gene order 3′-nucleocapsid (N)—X protein (X)—phosphoprotein (P)—Y protein (Y)—matrix protein (M)—glycoprotein (G)—polymerase (L)-5′ observed in some plant-infecting alphanucleorhabdoviruses. One of the PhCMoV isolates from the eggplant cultivar Almaz is genetically very similar to the Russian PhCMoV isolate from tomato and grouped in a subclade together with four isolates from Belgium, Germany, the Netherlands, and France. However, another eggplant-infecting isolate from the Russian cultivar Boggart is the most divergent compared with the other 45 virus genomes of European PhCMoV isolates. Thus, our comparative analysis reveals that two virus isolates from Russia may either share a close evolutionary relationship with European isolates or significantly diverge from all known virus isolates. The potential to use the protein sequence comparative analysis of accessory polypeptides, along with the early developed strategy of the nucleotide sequence comparison of the RNA genomes, is shown. Full article

The very high cycle fatigue (VHCF) strength of welded joints made of high-strength structural materials is generally poor, which poses a serious threat to the long life and reliability of the structural components. The purpose of this work is to establish an ultrasonic vibration fatigue testing system under biaxial loading and to analyze the biaxial fatigue failure mechanism of the welded joints. The results revealed that under uniaxial loading conditions, the propensity for fatigue failure in plate specimens was predominantly observed at the specimen surface. Regardless of whether under uniaxial or biaxial loading, the initiation of fatigue cracks in cruciform joints was consistently traced back to unfused flaws, which were primarily located at the interface between the solder and the base material. Concurrently, it was noted that the fatigue strength of cruciform joints under biaxial loading was merely 44.4% of that under uniaxial loading. The geometric peculiarities of the unfused defects led to severe stress concentrations, which significantly reduced the fatigue life of the material under biaxial loading conditions. Full article

The issue of water pollution in river basins poses a serious threat to the economic development of upstream and downstream regions. We have compared the water pollution control inputs and benefits of upstream and downstream governments under different management scenarios: non-cooperation, cooperation, and basin agency-led cooperation. The results show that the basin agency-led cooperation has achieved remarkable results, significantly reducing water pollution emissions, increasing input in treatment, and thereby maximizing the overall benefits of the basin. As the cost of water pollution damage rises, while the initial increase in water pollution control investment may temporarily compress the total basin income, the improvement in water quality eventually leads to a rebound in total benefit, highlighting the critical role of collaborative governance and basin-level management. The study emphasizes that establishing a collaborative governance system for river basins is crucial. It can facilitate close cooperation and resource sharing between upstream and downstream regions, optimizing water pollution control efforts and promoting sustainable economic development within the basin. Full article

Background: Mental health issues are increasingly prominent worldwide, posing significant threats to patients and deeply affecting their families and social relationships. Traditional diagnostic methods are subjective and delayed, indicating the need for an objective and effective early diagnosis method. Methods: To this end, this paper proposes a lightweight detection method for multi-mental disorders with fewer data sources, aiming to improve diagnostic procedures and enable early patient detection. First, the proposed method takes Electroencephalography (EEG) signals as sources, acquires brain rhythms through Discrete Wavelet Decomposition (DWT), and extracts their approximate entropy, fuzzy entropy, permutation entropy, and sample entropy to establish the entropy-based matrix. Then, six kinds of conventional machine learning classifiers, including Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Naive Bayes (NB), Generalized Additive Model (GAM), Linear Discriminant Analysis (LDA), and Decision Tree (DT), are adopted for the entropy-based matrix to achieve the detection task. Their performances are assessed by accuracy, sensitivity, specificity, and F1-score. Concerning these experiments, three public datasets of schizophrenia, epilepsy, and depression are utilized for method validation. Results: The analysis of the results from these datasets identifies the representative single-channel signals (schizophrenia: O1, epilepsy: F3, depression: O2), satisfying classification accuracies (88.10%, 75.47%, and 89.92%, respectively) with minimal input. Conclusions: Such performances are impressive when considering fewer data sources as a concern, which also improves the interpretability of the entropy features in EEG, providing a reliable detection approach for multi-mental disorders and advancing insights into their underlying mechanisms and pathological states. Full article

Foodborne pathogens, such as Salmonella, are major factors that pose significant threats to global food safety and public health. Salmonella typhimurium is a prominent serotype contributing to non-typhoidal salmonellosis, which is a prevalent foodborne illness affecting humans and animals. Bacteriophages are considered one of the most environmentally friendly biocontrol agents, particularly in the food industry, owing to their high specificity and high safety. However, the emergency of phage-resistant mutants limits the biocontrol effect of phage treatment, leading to the requirement for a high diversity of lytic phages. Therefore, the study isolated and characterized two novel lytic Salmonella bacteriophages (SPYS_1 and SPYS_2) targeting S. typhimurium ATCC14028 and evaluated their effectiveness in reducing the contamination rates for milk and chicken tenders. Morphological and genomic analyses indicated that Salmonella phages SPYS_1 and SPYS_2 are novel species classified under the genus Skatevirus and the genus Berlinvirus, respectively. Both phages exhibited high stability across a broad range of thermal and pH conditions. The one-step growth curve result suggested that both phages had a short adsorption time and a large burst size in a single lytic cycle. The phage SPYS_1 demonstrated a noteworthy inhibition effect on the growth of S. typhimurium ATCC14028 in milk, resulting in a ~2-log reduction within the 2 to 4 h range. Overall, both phages have shown significant potential for application in food safety in the future. Full article

The accurate prediction of lightning is crucial for forecasters to respond effectively to its related hazards. The rapid development and confined spatial extent of convective storms, in which lightning frequently occurs, pose considerable challenges for accurately predicting their locations using numerical weather prediction (NWP) models. Lightning occurrence is often prognosed using thermodynamic parameters, convective available potential energy (CAPE), the severe weather threat index (SWEAT), the lifted index (LI), etc. A high-resolution NWP model provides a prediction of these thermodynamic parameters at high spatiotemporal resolution with high accuracy for a few hours. However, a complicated algorithm is required to handle all the useful high-resolution variables from the NWP model. The recently emerging machine learning technique can solve this issue by properly handling these “big data” without any model distributional assumption. In this study, we developed a random forest algorithm for nowcasting and very short-range forecasting (useful for ~6 h), named LightningRF. LightningRF was trained by using lightning occurrence as a response variable and characteristic parameters from the NWP as predictors. It was also applied to analysis and forecast fields, showing a high probability of lightning within the observed lightning regions. This highlights the potential of helping forecasters improve their lightning forecasting skills using real-time probabilistic forecasts from a trained model. Full article

Wave erosion of slopes can easily trigger landslides into marine environments and pose severe threats to both the ecological environment and human activities. Therefore, near-shore slope monitoring becomes crucial for preventing and alerting people to these potential disasters. To achieve a comprehensive understanding, it is imperative to conduct a detailed investigation into the dynamics of wave erosion processes acting on slopes. This research is conducted through flume tests, using a wave maker to create waves of various heights and frequencies to erode the slope models. During the tests, seismic signals, acoustic signals, and pore pressure generated by wave erosion and slope failure are recorded. Seismic and acoustic signals are analyzed, and time-frequency spectra are calculated using the Hilbert–Huang Transform to identify the erosion events and signal frequency ranges. Arias Intensity is used to assess seismic energy and explore the relationship between the amount of erosion and energy. The results show that wave height has a more decisive influence on erosion behavior and retreat than wave frequency. Rapid drawdown may potentially cause the slope to slide during cyclic swash and backwash wave action. As wave erosion changes from swash to impact, there is a significant increase in the spectral magnitude and Power Spectral Density (PSD) of both seismic and acoustic signals. An increase in pore pressure is observed due to the rise in the run-up height of waves. The amplitude of pore pressure will increase as the slope undergoes further erosion. Understanding the results of this study can aid in predicting erosion and in planning effective management strategies for slopes subject to wave action. Full article

The Baota District of Yan’an City, located in the Loess Plateau, is an important patriotic education base in China. The region’s fragile geological environment and frequent geological disasters pose significant threats to the production and livelihood of residents. Establishing a landslide traces inventory can provide crucial assistance for studying regional land disaster distribution patterns and implementing disaster prevention and mitigation measures. However, the Baota District has not yet established a comprehensive and detailed landslide traces inventory, resulting in a lack of clear understanding and comprehensive knowledge regarding the threats and impacts of landslide disasters in the area. Therefore, this study employed high-resolution satellite images, applying a human–computer interactive visual interpretation method in conjunction with field survey verifications, to develop the most detailed and comprehensive landslide traces inventory for the Baota District to date. The results indicate that within the 3556 km² area of the Baota District, there are 73,324 landslide traces, with an average landslide density of 20.62 km^-2 and a total landslide area of 769.12 km², accounting for 21.63% of the total land area. These landslides are relatively evenly distributed throughout the district, with a higher concentration in the east compared to the west. Most of the landslides are small in size. This study can support disaster prevention and mitigation efforts in the Baota District and serve as a reference for establishing landslide inventories in other regions of the Loess Plateau. Full article

The skin, recognized as the largest organ in the human body, serves a vital function in safeguarding against external threats. Severe damage to the skin can pose significant risks to human health. There is an urgent requirement for safe and effective therapies for wound healing. While phytotherapy has been widely utilized for various health conditions, the potential of Quercus glauca in promoting wound healing has not been thoroughly explored. Q. glauca is a cultivated crop known for its abundance of bioactive compounds. This study examined the wound-healing properties of Quercus glauca acorn seed coat water extract (QGASE). The findings from the study suggest that QGASE promotes wound closure in HF cells by upregulating essential markers related to the wound-healing process. Additionally, QGASE demonstrates antioxidant effects, mitigating oxidative stress and aiding in recovery from injuries induced by H₂O₂. In vivo experiments provide additional substantiation supporting the efficacy of QGASE in enhancing wound healing. The collective results indicate that QGASE may be a promising candidate for the development of innovative therapeutic strategies aimed at enhancing skin wound repair. Full article

The threat to naval platforms from missile systems is increasing due to recent advancements in radar seeker technology, which have significantly enhanced the accuracy and effectiveness of missile targeting. In scenarios where a naval platform with limited maneuverability faces salvo attacks, the importance of an effective defense strategy becomes crucial to ensuring the protection of the platform. In this study, we present a multi-agent reinforcement learning-based decoy deployment approach that employs six decoys to increase the survival likelihood of a naval platform against salvo missile strikes. Our approach entails separating the decoys into two clusters, each consisting of three decoys. Subsequently, every cluster is allocated to a related missile threat. This is accomplished by training the decoys with the multi-agent deep reinforcement learning algorithm. To compare the proposed approach across different algorithms, we use two distinct algorithms to train the decoys; multi-agent deep deterministic policy gradient (MADDPG) and multi-agent twin-delayed deep deterministic policy gradient (MATD3). Following training, the decoys learn to form groups and establish effective formation configurations within each group to ensure optimal coordination. We assess the proposed decoy deployment strategy using parameters including decoy deployment angle and maximum decoy speed. Our findings indicate that decoys positioned on the same side outperform those positioned on different sides relative to the target platform. In general, MATD3 performs slightly better than MADDPG. Decoys trained with MATD3 succeed in more successful formation configurations than those trained with the MADDPG method, which accounts for this enhancement. Full article

The purpose of this study was to investigate temperature and rainfall variations and their effects on the UNESCO World Heritage Sites of Konso cultural landscape, Ethiopia, using dense merged satellite–gauge-station rainfall data (1981–2020) with a spatial resolution of 4 km-by-4 km and observed maximum and min temperature data (1987–2020), together with qualitative data gathered from cultural leaders, local administrators and religious leaders. The Climate Data tool (CDT) software version 8 was used for rainfall- and temperature-data analysis. The results showed that the north and northeastern regions of Konso had significant increases in rainfall. However, it was highly variable and erratic, resulting in extreme droughts and floods. The study confirmed that there were significant (p < 0.05) increasing trends in the number of days with heavy rainfall, very-heavy rainfall days, and annual total wet-day rainfall (R10 mm, 20 mm, and PRCPTOT). The highest daily minimum temperature, lowest and highest daily maximum-temperature number of warm days and nights, and number of cold days and nights all showed significant rising trends. The increasing trends in rainfall and temperature extremes have resulted in flooding and warming of the study area, respectively. These have led to the destruction of terraces, soil erosion, loss of life and damage of properties, loss of grasses, food insecurity, migration, loss of biodiversity, and commodification of stones. The continuous decline in farmland productivity is affecting the livelihood and traditional ceremonies of the Konso people, which are helpful for the transfer of traditional resource-management knowledge to the next generation. It is therefore necessary to implement local-scale climate change adaptation and mitigation strategies in order to safeguard the Konso cultural landscapes as a worldwide cultural asset and to bolster the resilience of smallholder farmers. Full article

Climate change has amplified the threat posed by aquatic invasive species as potential disruptors of biodiversity and ecosystem functioning. Species Distribution Models (MaxEnt) based on original data and ecological variables have identified contemporary seven global centers of the oriental rivel prawn Macrobrachium nipponense distribution: the native range in East Asia, Northern, Western and Eastern Europe, the Irano-Turanian region, and North and South America. By 2050, further expansion in Europe is expected, likely due to climate change, particularly temperature changes (Bio1) and rain precipitation during the warmest quarter (Bio18). However, the species may see a range reduction in southern Europe due to lower precipitation and increased droughts related to climate change. Therefore, a northward shift in the range of the species is also predicted. In the context of global change, and especially biological invasions, this study highlights the risks of introducing aquaculture based on M. nipponense and recommends controlling such economic activities, which are associated with a high risk for native species and ecosystems. Further, long-term monitoring is needed to assess impacts and to efficiently manage M. nipponense populations that are already present in their non-native habitats, for mitigating their negative effects on native species and ecosystems worldwide. Full article

Human leishmaniasis is facing important epidemiological changes in Southern Europe, driven by increased urbanization, climate changes, emerging of new animal reservoirs, shifts in human behavior and a growing population of immunocompromised and elderly individuals. In this evolving epidemiological landscape, we analyzed the clinical and epidemiological characteristics of human leishmaniasis in the Tuscany region of Central Italy. Through a multicentric retrospective analysis, we collected clinical and demographic data about all cases of leishmaniasis recorded between 2018 and 2023. We observed 176 cases of human leishmaniasis, with 128 (72.7%) visceral leishmaniasis (VL) and 47 (26.7%) cutaneous leishmaniasis (CL). Among these, 92.2% of VL and 85.1% of CL cases were autochthonous. The cumulative incidence of autochthonous human leishmaniasis was 0.22 cases per 100,000 inhabitants in 2018, but reached 1.81/100,000 in 2023. We identified three main areas of transmission: around the city of Florence (North-East Tuscany), around Grosseto city (South-West Tuscany) and Elba Island. Our findings confirm that the epidemiology of leishmaniasis is undergoing significant changes in Central Italy. Awareness towards this emerging health threat and surveillance strategies need to be improved in order to reliably assess the disease’s burden. Further research is needed in a “One-Health” perspective, to clarify the epidemiological dynamics at the environmental, reservoir, vector and human levels. The role of climate change and specific climatic factors affecting the epidemiological patterns of human leishmaniasis should be assessed. Further knowledge in these fields would promote targeted control and prevention strategies at regional and national levels. Full article

Candida auris is an emerging yeast considered a serious threat to global health. We report the first case of C. auris candidemia in Southern Italy, characterized using whole genome sequencing (WGS), and compared with a second strain isolated from a patient who presented as C. auris-colonized following screening. The C. auris strain was isolated from clinical samples, identified via MALDI-TOF, and subjected to WGS. Antifungal susceptibility testing was performed using commercial broth microdilution plates, and resistance protein sequences were evaluated with TBLASTN-2.15.0. Following the initial C. auris isolation from patient A, active surveillance and environmental investigations were implemented for all ICU patients. Of the 26 ICU surfaces sampled, 46.1% tested positive for C. auris via real-time PCR. Screening identified a second patient (patient B) as C. auris-colonized. The phylogenetic characterization of strains from patients A and B, based on the D1/D2 region of the 28s rDNA and the internal transcribed spacer (ITS) region, showed high similarity with strains from Lebanon. SNP analysis revealed high clonality, assigning both strains to clade I, indicating a significant similarity with Lebanese strains. This case confirms the alarming spread of C. auris infections and highlights the need for stringent infection control measures to manage outbreaks. Full article

The increasing presence of young people on the Nigerian streets participating in child labour has continued to attract public policy attention. Available research on child labour reveals sparse scholarly information on the security implications for young people in South-West Nigeria, particularly Ondo State. The study aims to understand the argument that child labour poses major security threats to the overall well-being of child labourers. A total of 147 questionnaires were distributed, with 12 focus group discussions and 12 semi-structured interviews conducted with young people and guardians (mostly mothers). Frequency distributions were employed to analyse the quantitative data, and NVivo (v.14) qualitative software was used to identify themes and sub-themes. A content analytical tool was used to make sense of the themes. Child labour activities include street trading, hawking, domestic help and construction work. Causes of child labour activities include lack of access to basic education, cultural and societal beliefs, poverty, and family breakdown, among others. Security threats include occasional kidnapping for ransom, sexual molestation, slavery, exploitation, risk of injury, diseases, and death. The study suggests a more responsive Child’s Rights Act in Nigeria for the protection of the rights and dignity of every child. Full article