Search Results (17,543)

The growth potential of Crinum bulbispermum was evaluated on gold mine tailings. The primary objectives were to model the species’ climatic niche in relation to gold mining regions, assess its germination success on tailings, and compare seedling survival and growth on tailings versus other soil types. Species distribution modelling identified the South African Grassland Biome on the Highveld (1000+ m above sea level), where the majority of gold mines are located, as highly suitable for the species. Pot trials demonstrated above 85% germination success across all soil treatments, including gold mine tailings, indicating its potential for restoration through direct seeding. An initial seedling establishment rate of 100% further demonstrated the species’ resilience to mine tailings, which are often seasonally dry, nutrient-poor, and may contain potentially toxic metals. However, while C. bulbispermum was able to germinate and establish in mine tailings, long-term growth potential (over 12 months) was constrained by low organic carbon content (0.11%) and high salinity (194.50 mS/m). These findings underscore the critical role of soil chemistry and organic matter in supporting long-term plant establishment and growth on gold tailings. Building on previous research, this study confirms the ability of this thick-rooted geophyte to tolerate chemically extreme soil conditions. Crinum bulbispermum shows promise for phytostabilization and as a potential medicinal plant crop on tailings. However, future research on microbial community interactions and soil amendment strategies is essential to ensure its long-term sustainability. Full article

Protecting threatened elasmobranch species despite limited data on their distribution and abundance is a critical challenge, particularly in the context of increasing human impacts on marine ecosystems. In the northeastern Atlantic, species such as the leafscale gulper shark, Portuguese dogfish, spurdog, and spotted ray are facing pressures from overfishing, bycatch, habitat degradation, and climate change. The OSPAR Commission has listed these species as threatened and/or declining and aims to protect them by reliably identifying suitable habitats and integrating these areas into Marine Protected Areas (MPAs). In this study, we present a spatial modelling framework using regression-based approaches to identify suitable habitats for these four species. Results show that suitable habitats of the spotted ray (25.8%) and spurdog (18.8%) are relatively well represented within existing MPAs, while those of the deep-water sharks are underrepresented (6.0% for leafscale gulper shark, and 6.8% for Portuguese dogfish). Our findings highlight the need for additional MPAs in deep-sea continental slope areas, particularly west and northwest of Scotland and Ireland. Such expansions would support OSPAR’s goal to protect 30% of its maritime area by 2030 and could benefit broader deep-sea biodiversity, including other vulnerable demersal species and benthic communities. Full article

Skin aging is closely related to mitochondrial dysfunction and cell cycle abnormalities, and developing intervention strategies targeting mitochondrial quality control is an important direction for anti-aging research. In this study, we investigated the anti-aging mechanism of Camellia japonica flower (CJF) extract and its active ingredient hyperoside based on a doxorubicin (DOX)-induced endogenous senescence model in human skin fibroblasts (HSFs). LC-MS proteomics analysis revealed that CJF extract and hyperoside specifically activated the FUNDC1-mediated mitochondrial autophagy pathway, significantly ameliorated the DOX-induced decrease in mitochondrial membrane potential and the accumulation of reactive oxygen species (ROS), and alleviated the cellular S-phase blockade and reversed the high expression of senescence-associated β-galactosidase (SA-β-gal). Further studies showed that the two cleared damaged mitochondria by enhancing mitochondrial autophagy and restoring cellular energy metabolism homeostasis while promoting type III collagen and elastin synthesis and repairing the expression of Claudin 1 related to skin barrier function. For the first time, the present study reveals the molecular mechanism of CJF extract in delaying skin aging by regulating the FUNDC1-dependent mitochondrial autophagy pathway, which provides a theoretical basis and a candidate strategy for developing novel anti-aging agents targeting mitochondrial quality control. Full article

Rabies remains a significant global public health concern, causing an estimated 59,000–69,000 human fatalities annually. Despite being entirely preventable through vaccination, rabies continues to impose substantial economic burdens worldwide. This study presents a scoping review of the economic research on rabies to determine overlaps and gaps in knowledge and inform future research strategies. We selected 150 studies (1973–2024) to analyze. The review categorizes the literature based on geographic distribution, species focus, and type of study. Findings indicate that economic studies are disproportionately concentrated in developed countries, such as the United States and parts of Europe, where rabies risk is low, while high-risk regions, particularly in Africa and Asia, remain underrepresented. Most studies focus on dog-mediated rabies, reflecting its dominant role in human transmission, while fewer studies assess the economic impacts of wildlife and livestock-mediated rabies. Case studies and modeling approaches dominate the literature, whereas cost–benefit and cost–effectiveness analyses—critical for informing resource allocation—are limited. The review highlights the need for more economic evaluations in rabies-endemic regions, expanded research on non-dog reservoirs, and broader use of economic methods. Addressing these gaps will be crucial for optimizing rabies control and supporting global initiatives to eliminate dog-mediated rabies by 2030. Full article

Currently, approximately 6000 mineral species have been identified, and every year, approximately 100 more are discovered. The discovery of a new mineral has a close relationship with geological heritage. It involves the definition of both the type mineral (the specimen from which the sample used for the description of the new mineral species was obtained) and the type locality (the location where the corresponding specimen was found). All type minerals constitute elements of movable geological heritage and must be kept in a museum or a reference research center. However, not all type localities are recognized as geological heritage sites (geosites), despite their connection to a heritage interest. This article discusses these different considerations regarding type minerals and type localities as geological heritage in a general context. In addition, the situation in the case of Spain is analyzed, which, for various reasons, can serve as a model at the international level. It is concluded that type localities should be considered part of the geological heritage, and that the number of type minerals is always greater than that of type localities. Full article

Background/Objectives: Antimicrobial resistance (AMR) contributes to millions of deaths globally each year, creating an urgent need for new therapeutic agents. Antimicrobial peptides (AMPs) have emerged as promising candidates due to their potential to combat AMR pathogens. This study aimed to evaluate the antimicrobial activity of an AMP from a soil-derived bacterial isolate against Gram-negative bacteria. Method: Soil bacteria were isolated and screened for antimicrobial activity. The bioactive peptide was purified and determined its structure and antimicrobial efficacy. Genomic analysis was conducted to predict the biosynthetic gene clusters (BGCs) responsible for AMP production. Results: Genomic analysis identified the isolate as Paenibacillus sp. Na14, which exhibited low genomic similarity (61.0%) to other known Paenibacillus species, suggesting it may represent a novel species. The AMP from the Na14 strain exhibited heat stability up to 90 °C for 3 h and retained its activity across a broad pH range from 3 to 11. Structural analysis revealed that the Na14 peptide consisted of 14 amino acid residues, adopting an α-helical structure. This peptide exhibited bactericidal activity at concentrations of 2–4 µg/mL within 6–12 h, and its killing rate was concentration-dependent. The peptide was found to disrupt the bacterial membranes. The Na14 peptide shared 64.29% sequence similarity with brevibacillin 2V, an AMP from Brevibacillus sp., which also belongs to the Paenibacillaceae family. Genomic annotation identified BGCs associated with secondary metabolism, with a particular focus on non-ribosomal peptide synthetase (NRPS) gene clusters. Structural modeling of the predicted NRPS enzymes showed high similarity to known NRPS modules in Brevibacillus species. These genomic findings provide evidence supporting the similarity between the Na14 peptide and brevibacillin 2V. Conclusions: This study highlights the discovery of a novel AMP with potent activity against Gram-negative pathogens and provides new insight into conserved AMP biosynthetic enzymes within the Paenibacillaceae family. Full article

We present the results of a detailed spectroscopic analysis of the double-lined spectroscopic binary system $\alpha$ Equulei. High-resolution spectra obtained with the SOPHIE spectrograph at various orbital phases were used to disentangle the composite spectra into individual components using the spectral line deconvolution (SLD) iterative technique. The atmospheric parameters of each component were refined with the SME (spectroscopy made easy) package and further validated by following methods: SED (spectral energy distribution), the independence of the abundance of individual Fe i–ii lines on the reduced equivalent width and ionisation potential, and fitting with the hydrogen line profiles. Our accurate abundance analysis uses a hybrid technique for spectrum synthesis. This is based on classical model atmospheres that are calculated under the assumption of local thermodynamic equilibrium (LTE), together with non-LTE (NLTE) line formation. This is used for 15 out of the 25 species from C to Nd that were investigated. The primary giant component (G7-type) exhibits a typical abundance pattern for normal stars, with elements from He to Fe matching solar values and neutron-capture elements showing overabundances up to 0.5 dex. In contrast, the secondary dwarf component displays characteristics of an early stage Am star. The observed abundance differences imply distinct diffusion processes in their atmospheres. Our results support the scenario in which chemical peculiarities in Am stars develop during the main sequence and may decrease as the stars evolve toward the subgiant branch. Full article

Arginine kinase is a key phosphagen kinase in invertebrates that facilitates rapid ATP regeneration by reversibly transferring phosphate groups between phosphoarginine and ADP. Structural studies have shown that the enzyme adopts distinct conformations in its ligand-free and ligand-bound states, known as the “open” and “closed” forms, respectively. These conformational changes are crucial for catalytic activity, enabling precise positioning of active-site residues and loop closure during phosphoryl transfer. Transition-state analog complexes have provided additional insights by mimicking intermediate states of catalysis, supporting the functional relevance of the open/closed structural model. Furthermore, studies across multiple species reveal how monomeric and dimeric forms of arginine kinase contribute to its allosteric regulation and substrate specificity. Beyond its metabolic role, arginine kinase is also recognized as a major allergen in crustaceans. Its structural uniqueness and absence in vertebrates make it a promising candidate for selective drug targeting. By integrating crystallographic data with functional context, this review highlights conserved features and species-specific variations of arginine kinase that may inform the design of inhibitors. Such molecules have the potential to serve both as antiparasitic agents and as novel therapeutics to manage crustacean-related allergic responses in humans. Full article

Melittin, a cytolytic peptide derived from honeybee venom, has demonstrated potent anticancer activity through mechanisms such as membrane disruption, apoptosis induction, and modulation of key signaling pathways. Melittin exerts its anticancer activity by interacting with key molecular targets, including downregulation of the PI3K/Akt and NF-κB signaling pathways, and by inducing mitochondrial apoptosis through reactive oxygen species generation and cytochrome c release. However, its clinical application is hindered by its systemic and hemolytic toxicity, rapid degradation in plasma, poor pharmacokinetics, and immunogenicity, necessitating the development of targeted delivery strategies to enable safe and effective treatment. Nanoparticle-based delivery systems have emerged as a promising strategy for overcoming these challenges, offering improved tumor targeting, reduced off-target effects, and enhanced stability. This review provides a comprehensive overview of the mechanisms through which melittin exerts its anticancer effects and evaluates the development of various melittin-loaded nanocarriers, including liposomes, polymeric nanoparticles, dendrimers, micelles, and inorganic systems. It also summarizes the preclinical evidence for melittin nanotherapy across a wide range of cancer types, highlighting both its cytotoxic and immunomodulatory effects. The potential of melittin nanoparticles to overcome multidrug resistance and synergize with chemotherapy, immunotherapy, photothermal therapy, and radiotherapy is discussed. Despite promising in vitro and in vivo findings, its clinical translation remains limited. Key barriers include toxicity, manufacturing scalability, regulatory approval, and the need for more extensive in vivo validation. A key future direction is the application of computational tools, such as physiologically based pharmacokinetic modeling and artificial-intelligence-based modeling, to streamline development and guide its clinical translation. Addressing these challenges through focused research and interdisciplinary collaboration will be essential to realizing the full therapeutic potential of melittin-based nanomedicines in oncology. Overall, this review synthesizes the findings from over 100 peer-reviewed studies published between 2008 and 2025, providing an up-to-date assessment of melittin-based nanomedicine strategies across diverse cancer types. Full article

Mangroves play a crucial role in ecosystems, and the accurate classification and real-time monitoring of mangrove species are essential for their protection and restoration. To improve the segmentation performance of mangrove UAV remote sensing images, this study performs species segmentation after the super-resolution (SR) reconstruction of images. Therefore, we propose SwinNET, an SR reconstruction network. We design a convolutional enhanced channel attention (CEA) module within a network to enhance feature reconstruction through channel attention. Additionally, the Neighborhood Attention Transformer (NAT) is introduced to help the model better focus on domain features, aiming to improve the reconstruction of leaf details. These two attention mechanisms are symmetrically integrated within the network to jointly capture complementary information from spatial and channel dimensions. The experimental results demonstrate that SwinNET not only achieves superior performance in SR tasks but also significantly enhances the segmentation accuracy of mangrove species. Full article

Ongoing changes in the distribution and abundance of several tick species of medical relevance in Canada have prompted the development of the eTick platform—an image-based crowd-sourcing public surveillance tool for Canada enabling rapid tick species identification by trained personnel, and public health guidance based on tick species and province of residence of the submitter. Considering that more than 100,000 images from over 73,500 identified records representing 25 tick species have been submitted to eTick since the public launch in 2018, a partial automation of the image processing workflow could save substantial human resources, especially as submission numbers have been steadily increasing since 2021. In this study, we evaluate an end-to-end artificial intelligence (AI) pipeline to support tick identification from eTick user-submitted images, characterized by heterogeneous quality and uncontrolled acquisition conditions. Our framework integrates (i) tick localization using a fine-tuned YOLOv7 object detection model, (ii) resolution enhancement of cropped images via super-resolution Generative Adversarial Networks (RealESRGAN and SwinIR), and (iii) image classification using deep convolutional (ResNet-50) and transformer-based (ViT) architectures across three datasets (12, 6, and 3 classes) of decreasing granularities in terms of taxonomic resolution, tick life stage, and specimen viewing angle. ViT consistently outperformed ResNet-50, especially in complex classification settings. The configuration yielding the best performance—relying on object detection without incorporating super-resolution—achieved a macro-averaged F1-score exceeding 86% in the 3-class model (Dermacentor sp., other species, bad images), with minimal critical misclassifications (0.7% of “other species” misclassified as Dermacentor). Given that Dermacentor ticks represent more than 60% of tick volume submitted on the eTick platform, the integration of a low granularity model in the processing workflow could save significant time while maintaining very high standards of identification accuracy. Our findings highlight the potential of combining modern AI methods to facilitate efficient and accurate tick image processing in community science platforms, while emphasizing the need to adapt model complexity and class resolution to task-specific constraints. Full article

Due to small population size, Population Viability Analysis (PVA) of endangered species often pools all individuals into a single matrix to decrease variation in estimation of transition rates. These pooled populations may mask significant environmental variation among populations, affecting estimates. Using 10 years of population data (2000–2010) on the endangered plant Jacquemontia reclinata in Southeastern Florida, USA, I parameterized a stage-structured matrix model and calculated annual growth rates (lambdas)and elasticity for each year using stochastic matrix models. The metapopulation model incorporating actual dynamics of the two largest populations showed a lower occupancy rate and higher risk of extinction at an earlier time compared to a model that used the average of all natural populations. Analyses were consistent that incorporating population variation versus average dynamics in modeling J. reclinata demography results in more variation and greater extinction risk. Local variation may be due to both weather (including minimum winter temperature and total annual precipitation) and local disturbance dynamics in these urban preserves. Full article

Highly pathogenic avian influenza (HPAI) remains a persistent threat to global poultry production and public health. Current vaccine platforms show limited cross-clade efficacy and often fail to induce mucosal immunity. Recent advances in microbiome research reveal critical roles for gut commensals in modulating vaccine-induced immunity, including enhancement of mucosal IgA production, CD8⁺ T-cell activation, and modulation of systemic immune responses. Engineered commensal bacteria such as Lactococcus lactis, Bacteroides ovatus, Bacillus subtilis, and Staphylococcus epidermidis have emerged as promising live vectors for antigen delivery. Postbiotic and synbiotic strategies further enhance protective efficacy through targeted modulation of the gut microbiota. Additionally, artificial intelligence (AI)-driven tools enable predictive modeling of host–microbiome interactions, antigen design optimization, and early detection of viral antigenic drift. These integrative technologies offer a new framework for mucosal, broadly protective, and field-deployable vaccines for HPAI control. However, species-specific microbiome variation, ecological safety concerns, and scalable manufacturing remain critical challenges. This review synthesizes emerging evidence on microbiome–immune crosstalk, commensal vector platforms, and AI-enhanced vaccine development, emphasizing the urgent need for One Health integration to mitigate zoonotic adaptation and pandemic emergence. Full article

Forest volume is an important information for assessing the economic value and carbon sequestration capacity of forest resources and serves as a key indicator for energy flow and biodiversity. Although remote sensing technology is applied to estimate volume, optical remote sensing data have limitations in capturing forest vertical height information and may suffer from reflectance saturation. While LiDAR data can provide more detailed vertical structural information, they come with high processing costs and limited observation range. Therefore, improving the accuracy of volume estimation through multi-source data fusion has become a crucial challenge and research focus in the field of forest remote sensing. In this study, we integrated Sentinel-2 multispectral data, Resource-3 stereoscopic imagery, UAV-based LiDAR data, and field survey data to quantitatively estimate the forest volume in Saihanwula Nature Reserve, located in Inner Mongolia, China, on the southern part of Daxing’anling Mountains. The study evaluated the performance of multi-source remote sensing features by using recursive feature elimination (RFE) to select the most relevant factors and applied four machine learning models—multiple linear regression (MLR), k-nearest neighbors (kNN), random forest (RF), and gradient boosting regression tree (GBRT)—to develop volume estimation models. The evaluation metrics include the coefficient of determination (R²), root mean square error (RMSE), and relative root mean square error (rRMSE). The results show that (1) forest Canopy Height Model (CHM) data were strongly correlated with forest volume, helping to alleviate the reflectance saturation issues inherent in spectral texture data. The fusion of CHM and spectral data resulted in an improved volume estimation model with R² = 0.75 and RMSE = 8.16 m³/hm², highlighting the importance of integrating multi-source canopy height information for more accurate volume estimation. (2) Volume estimation accuracy varied across different tree species. For Betula platyphylla, we obtained R² = 0.71 and RMSE = 6.96 m³/hm²; for Quercus mongolica, R² = 0.74 and RMSE = 6.90 m³/hm²; and for Populus davidiana, R² = 0.51 and RMSE = 9.29 m³/hm². The total forest volume in the Saihanwula Reserve ranges from 50 to 110 m³/hm². (3) Among the four machine learning models, GBRT consistently outperformed others in all evaluation metrics, achieving the highest R² of 0.86, lowest RMSE of 9.69 m³/hm², and lowest rRMSE of 24.57%, suggesting its potential for forest biomass estimation. In conclusion, accurate estimation of forest volume is critical for evaluating forest management practices and timber resources. While this integrated approach shows promise, its operational application requires further external validation and uncertainty analysis to support policy-relevant decisions. The integration of multi-source remote sensing data provides valuable support for forest resource accounting, economic value assessment, and monitoring dynamic changes in forest ecosystems. Full article

The efficient recovery of rhenium (Re), a critical metal in high-tech industries, is essential to address its growing demand and reduce reliance on primary mining. In this study, we developed novel anion-exchange resins for the selective adsorption and recovery of Re(VII) ions from acidic solutions, simulating industrial by-products. The resins were synthesized from a vinylbenzyl chloride-co-divinylbenzene copolymer modified with aliphatic, heterocyclic, and aromatic weakly basic amines, selected from among bis(3-aminopropyl)amine (BAPA), 1-(2-pyrimidinyl)piperazine (PIP), thiosemicarbazide (TSC), 2-amino-3-hydroxypyridine (AHP), 1-(2-hydroxyethyl)piperazine (HEP), 4-amino-2,6-dihydroxypyrimidine (AHPI), and 2-thiazolamine (TA). The adsorption of Re on BAPA, PIP, and HEP resins obeyed the Langmuir model, and the resins exhibited high adsorption capacities, with maximum values reaching 435.4 mg Re g⁻¹ at pH 6. Furthermore, strong selectivity for ReO₄⁻ ions over competing species, including Mo, Cu, and V, was noted in solutions simulating the leachates of the by-products of Cu-Mo ores. Additionally, complete elution of Re was possible. The developed resins turned out to be highly suitable for the continuous-flow-mode adsorption of ReO₄⁻, revealing outstanding adsorption capacities before reaching column breakthrough. In this context, the novel anion-exchange resins developed offer a reference for further Re recovery strategies. Full article