Search Results (2,023)

Payments for Watershed services (PWSs) have been emerging as a critical tool for environmental governance in watershed, yet their comparative effectiveness across implementation models has remained poorly understood. Based on a comparative analysis of Eco-Compensation (EC) and Payments for Ecosystem Services (PESs) frameworks, examining both theoretical foundations and implementation practices, this study aims to quantitatively assess and compare the effectiveness of two dominant PWSs models—the EC-like model (Phase I: October 2008–April 2017) and the PESs-like model (Phase II: 2017–December 2021). Using the Liaohe River in China as a case study, utilizing ecosystem service value (ESV) as an indicator and employing the corrected unit-value transfer method, we compare the effectiveness of different PWSs models from October 2008 to December 2021. The results reveal the following: (1) Policy Efficiency: The PESs-like model demonstrated significantly greater effectiveness than the EC-like model, with annual average increases in ESV of 3.23 billion CNY (491 million USD) and 1.79 billion CNY (272 million USD). (2) Functional Drivers: Water regulation (45.1% of total ESV growth) and climate regulation (24.3%) were dominant services, with PESs-like interventions enhancing multifunctionality. (3) Stakeholder Impact: In the PESs-like model, the cities implementing inter-county direct payment showed higher growth efficiency than those without it. The operational efficiency of PWSs increases with the number of participating stakeholders, which explains why the PESs-like model demonstrates higher effectiveness than the EC-like model. Our findings offer empirical evidence and actionable policy implications for designing effective PWSs models across global watershed ecosystems. Full article

As urbanization accelerates and climate change intensifies, the ecological integrity of urban riparian forests faces growing threats, underscoring the need for a systematic framework to guide their sustainable management. To address this gap, we developed a causal framework by applying text mining and sentence classification to 1001 abstracts from previous studies, structured within the DPSIR (Driver–Pressure–State–Impact–Response) model. The analysis identified six dominant thematic clusters—water quality, ecosystem services, basin and land use management, climate-related stressors, anthropogenic impacts, and greenhouse gas emissions—which reflect the multifaceted concerns surrounding urban riparian forest research. These themes were synthesized into a structured causal model that illustrates how urbanization, land use, and pollution contribute to ecological degradation, while also suggesting potential restoration pathways. To validate its applicability, the framework was applied to four major urban streams in Seoul, where indicator-based analysis and correlation mapping revealed meaningful linkages among urban drivers, biodiversity, air quality, and civic engagement. Ultimately, by integrating large-scale text mining with causal inference modeling, this study offers a transferable approach to support adaptive planning and evidence-based decision-making under the uncertainties posed by climate change. Full article

Adipose tissue inflammation contributes to obesity-induced insulin resistance. However, increasing evidence shows that high BMI (obesity) is not an accurate predictor of poor metabolic health in individuals. The molecular mechanisms regulating the metabolically activated M1 macrophage phenotype in the adipose tissues leading to insulin resistance remain largely unknown. Although the Janus Kinase (Jak)/signal transducer and activator of transcription 3 (Stat3) signaling in myeloid cells are known to promote the M2 phenotype in tumors, we demonstrate here that the Jak2/Stat3 pathway amplifies M1-mediated adipose tissue inflammation and insulin resistance under metabolic challenges. Ablating Jak2 in the myeloid compartment reduces insulin resistance in obese mice, which is associated with a decrease in infiltration of adipose tissue macrophages (ATMs). We show that the adoptive transfer of Jak2-deficient myeloid cells improves insulin sensitivity in obese mice. Furthermore, the protection of obese mice with myeloid-specific Stat3 deficiency against insulin resistance is also associated with reduced tissue infiltration by macrophages. Jak2/Stat3 in the macrophage is required for the production of pro-inflammatory cytokines that promote M1 macrophage polarization in the adipose tissues of obese mice. Moreover, free fatty acids (FFAs) activate Stat3 in macrophages, leading to the induction of M1 cytokines. Silencing the myeloid cell Stat3 with an in vivo siRNA targeted delivery approach reduces metabolically activated pro-inflammatory ATMs, thereby alleviating obesity-induced insulin resistance. These results demonstrate Jak2/Stat3 in myeloid cells is required for obesity-induced insulin resistance and inflammation. Moreover, targeting Stat3 in myeloid cells may be a novel approach to ameliorate obesity-induced insulin resistance. Full article

Antimicrobial resistance (AMR) has emerged as a planetary health emergency, driven not only by the clinical misuse of antibiotics but also by diverse environmental dissemination pathways. This review critically examines the role of environmental compartments—water, soil, and air—as dynamic reservoirs and transmission routes for antibiotic-resistant bacteria (ARB) and resistance genes (ARGs). Recent metagenomic, epidemiological, and mechanistic evidence demonstrates that anthropogenic pressures—including pharmaceutical effluents, agricultural runoff, untreated sewage, and airborne emissions—amplify resistance evolution and interspecies gene transfer via horizontal gene transfer mechanisms, biofilms, and mobile genetic elements. Importantly, it is not only highly polluted rivers such as the Ganges that contribute to the spread of AMR; even low concentrations of antibiotics and their metabolites, formed during or after treatment, can significantly promote the selection and dissemination of resistance. Environmental hotspots such as European agricultural soils and airborne particulate zones near wastewater treatment plants further illustrate the complexity and global scope of pollution-driven AMR. The synergistic roles of co-selective agents, including heavy metals, disinfectants, and microplastics, are highlighted for their impact in exacerbating resistance gene propagation across ecological and geographical boundaries. The efficacy and limitations of current mitigation strategies, including advanced wastewater treatments, thermophilic composting, biosensor-based surveillance, and emerging regulatory frameworks, are evaluated. By integrating a One Health perspective, this review underscores the imperative of including environmental considerations in global AMR containment policies and proposes a multidisciplinary roadmap to mitigate resistance spread across interconnected human, animal, and environmental domains. Full article

While the explosive growth in e-commerce stresses urban logistics systems, city planners lack of fine-grained data in order to anticipate and manage the resulting freight flows. Using a three-stage analytical approach combining descriptive zonal statistics, hotspot analysis and different regression modeling from univariate to geographically weighted regression, this study integrates one year of parcel deliveries from a leading courier with open spatial layers of land-use zoning, census population, mobile-signal activity and household income to model last-mile demand across different land use types. A baseline linear regression shows that residential population alone accounts for roughly 30% of the variance in annual parcel volumes (2.5–3.0 deliveries per resident) while adding daytime workforce and income increases the prediction accuracy to 39%. In a similar approach where coefficients vary geographically with Geographically Weighted Regression to capture the local heterogeneity achieves a significant raise of the overall R² to 0.54 and surpassing 0.70 in residential and institutional districts. Hot-spot analysis reveals a highly fragmented pattern where fewer than 5% of blocks generate more than 8.5% of all deliveries with no apparent correlation to the broaden land-use classes. Commercial and administrative areas exhibit the greatest intensity (1149 deliveries per ha) yet remain the hardest to explain (global R² = 0.21) underscoring the importance of additional variables such as retail mix, street-network design and tourism flows. Through this approach, the calibrated models can be used to predict city-wide last-mile demand using only public inputs and offers a transferable, privacy-preserving template for evidence-based freight planning. By pinpointing the location and the land uses where demand concentrates, it supports targeted interventions such as micro-depots, locker allocation and dynamic curb-space management towards more sustainable and resilient urban-logistics networks. Full article

Background/Objectives: Advancements in DNA profiling have made it possible to retrieve intact DNA profiles from increasingly minute biological samples. This increased sensitivity in DNA detection has highlighted crucial considerations to be made when handling and packing items from the crime scene to minimize potential contamination from either direct or indirect transfer of DNA. To investigate potential DNA transfer between items stored within the same evidence package, we conducted a simulation study with items commonly encountered during forensic casework. Methods: Participants were grouped in pairs, each of them handling the same type of item to simulate the activity conducted at the crime scene. The items were then collected from each pair of participants and stored in the same evidence package for 4 to 5 days. To evaluate the basal DNA transfer between items within the same package, the packed items were not subjected to friction, force, or long-distance movement in this study. Results: We have observed the occurrence of DNA transfer on 39% of the studied items inside the package, which changed the source attribution of the DNA profiles for 10% of the recovered samples. Our results showed that the types of items were associated with the number of transferred alleles and the amount of DNA recovered, while no association was found between the number of transferred alleles and the amount of DNA on the studied items. Conclusions: Taken together, the results from this study reiterate the importance of packing each item from the crime scene separately, especially when packing items together may impact the interpretation of source attribution. Full article

The generation of memory CD8⁺ T cells is essential for establishing protective T cell immunity against pathogens and cancers. However, the cellular and molecular mechanisms underlying memory CD8⁺ T cell formation remain incompletely understood. Reliance on specific pathogen-free (SPF) models, characterized by restricted microbial exposure, may limit our understanding of physiologically relevant immune memory development. This study reveals that CD1d-restricted NKT cells regulate central memory T cell (TCM) generation exclusively in a microbe-rich (“dirty”) environment. Under non-SPF housing, CD1d⁺/⁻ and Ja18⁺/⁻ mice exhibited enhanced TCM formation compared to NKT-deficient controls (CD1d⁻/⁻/Ja18⁻/⁻), demonstrating that microbial experience is required for NKT-mediated TCM regulation. Mechanistically, CD1d-restricted NKT cells increased IL-15Rα expression on CD4⁺ T cells in CD1d⁺/⁻ mice, potentiating IL-15 trans-presentation and thereby activating the IL-15/pSTAT5/Eomes axis critical for TCM maintenance. Functional validation through adoptive transfer of CFSE-labeled OT-1 memory cells revealed an NKT cell-dependent survival advantage in CD1d⁺/⁻ hosts. This provides direct evidence that microbiota-experienced niches shape immune memory. Collectively, these findings establish CD1d-restricted NKT cells as physiological regulators of TCM generation and suggest their potential utility as vaccine adjuvants to enhance protective immunity. Full article

Diverging from conventional dephosphorization approaches, this study employs a novel pre-reduction and smelting separation (PR-SS) to efficiently co-recover iron and phosphorus from high-phosphorus oolitic iron ore, directly yielding Fe–P alloy, and the Fe–P alloy shows potential as feedstock for high-phosphorus weathering steel or wear-resistant cast iron, indicating promising application prospects. Using oolitic magnetite concentrate (52.06% Fe, 0.37% P) as feedstock, optimized conditions including pre-reduction at 1050 °C for 2 h with C/Fe mass ratio of 2, followed by smelting separation at 1550 °C for 20 min with 5% coke, produced a metallic phase containing 99.24% Fe and 0.73% P. Iron and phosphorus recoveries reached 99.73% and 99.15%, respectively. EPMA microanalysis confirmed spatial correlation between iron and phosphorus in the metallic phase, with undetectable phosphorus signals in vitreous slag. This evidence suggests preferential phosphorus enrichment through interfacial mass transfer along the pathway of the slag phase to the metal interface and finally the iron matrix, forming homogeneous Fe–P solid solutions. The phosphorus migration mechanism involves sequential stages: apatite lattice decomposition liberates reactive P₂O₅ under SiO₂/Al₂O₃ influence; slag–iron interfacial co-reduction generates Fe₃P intermediates; Fe₃P incorporation into the iron matrix establishes stable solid solutions. Full article

Micro- and Nanoplastic (MNP) pollution is an emerging challenge globally, posing a significant threat to both aquatic and terrestrial ecosystems worldwide. This review critically examines the sources, exposure routes, and impact of plastics, with particular focus on implications for the livestock sector. MNPs enter animals’ bodies primarily through ingestion of contaminated feed and water, inhalation, and dermal exposure, subsequently accumulating in various organs, disrupting physiological functions. Notably, MNPs facilitate the horizontal transfer of antimicrobial resistance genes (ARGs), exacerbating the global challenge of antimicrobial resistance (AMR). In agricultural environments, sources such as organic fertilizers, wastewater irrigation systems, surface runoff, and littering contribute to soil contamination, adversely affecting plant growth and soil health, which in turn compromises feed quality and ultimately animals’ productivity. This review synthesizes current evidence demonstrating how MNP exposure impairs animal production, reproduction, and survival, and highlights the interconnected risks to food safety and ecosystem health. The findings call for the urgent need for comprehensive research under controlled conditions to underscore the fine details regarding mechanisms of MNP toxicity and to inform effective mitigation strategies. Addressing MNP pollution is crucial for safeguarding animal health, ensuring sustainable livestock production, and promoting environmental sustainability and integrity. Full article

Coastal blue carbon ecosystems (traditional types such as mangroves, salt marshes, and seagrass meadows; emerging types such as tidal flats and mariculture) play pivotal roles in capturing and storing atmospheric carbon dioxide. Reliable assessment of the spatial and temporal variation and the carbon storage potential holds immense promise for mitigating climate change. Although previous field surveys and regional assessments have improved the understanding of individual habitats, most studies remain site-specific and short-term; comprehensive, multi-decadal assessments that integrate all major coastal blue carbon systems at the national scale are still scarce for China. In this study, we integrated 30 m Landsat imagery (1992–2022), processed on Google Earth Engine with a random forest classifier; province-specific, literature-derived carbon density data with quantified uncertainty (mean ± standard deviation); and the InVEST model to track coastal China’s mangroves, salt marshes, tidal flats, and mariculture to quantify their associated carbon stocks. Then the GeoDetector was applied to distinguish the natural and anthropogenic drivers of carbon stock change. Results showed rapid and divergent land use change over the past three decades, with mariculture expanded by 44%, becoming the dominant blue carbon land use; whereas tidal flats declined by 39%, mangroves and salt marshes exhibited fluctuating upward trends. National blue carbon stock rose markedly from 74 Mt C in 1992 to 194 Mt C in 2022, with Liaoning, Shandong, and Fujian holding the largest provincial stock; Jiangsu and Guangdong showed higher increasing trends. The Normalized Difference Vegetation Index (NDVI) was the primary driver of spatial variability in carbon stock change (q = 0.63), followed by precipitation and temperature. Synergistic interactions were also detected, e.g., NDVI and precipitation, enhancing the effects beyond those of single factors, which indicates that a wetter climate may boost NDVI’s carbon sequestration. These findings highlight the urgency of strengthening ecological red lines, scaling climate-smart restoration of mangroves and salt marshes, and promoting low-impact mariculture. Our workflow and driver diagnostics provide a transferable template for blue carbon monitoring and evidence-based coastal management frameworks. Full article

This study investigates how university students adopt large language models (LLMs) for online consumption of sustainable products, integrating perceived value theory with the technology acceptance model (TAM). Cross-sectional survey data were analyzed using structural equation modeling (SEM) and artificial neural networks (ANNs). SEM results reveal partial mediation. Performance expectancy value (PEV) and information quality value (IQV) directly shape continue using intention (CUI). They also influence CUI indirectly through perceived ease of use (PEU) and perceived usefulness (PU). Green self-identity value (GSV) influences CUI both directly and via PEU, while trust transfer value (TTV) and green perceived value (GPV) affect CUI only via PEU. ANN findings confirm this hierarchy, as PU (86.7%) and PEU (85.7%) are the strongest predictors of CUI, followed by GSV (73.7%). Convergent evidence from both methods indicates that instrumental utility, effortless interaction, and sustainability identity congruence drive sustained LLM use in the context of online consumption of green products, whereas credibility cues and sustainability incentives play secondary roles. This study extends TAM by incorporating multidimensional value constructs and offers design recommendations for engaging and high-utility AI shopping platforms. Full article

Adansonia L. (1753) belongs to the family Malvaceae and is commonly known as the baobab tree. This species holds significant cultural and ecological value and is often referred to as the ‘tree of life.’ Although its nuclear genome has been reported, the mitogenome has not yet been studied. Mitogenome research is crucial for understanding the evolution of the entire genome. In this study, we assembled and analyzed the mitogenomes of four Adansonia species by integrating short-read and long-read data. The results showed that the mitogenomes of all four Adansonia species were resolved as single circular sequences. Their total genome lengths ranged from 507,138 to 607,344 bp and contained a large number of repetitive sequences. Despite extensive and complex rearrangements between the mitogenomes of Adansonia and other Malvaceae species, a phylogenetic tree constructed based on protein-coding genes clearly indicated that Adansonia is more closely related to the Bombax. Selection pressure analysis suggests that the rps4 gene in Adansonia may have undergone positive selection compared to other Malvaceae species, indicating that this gene may play a significant role in the evolution of Adansonia. Additionally, by analyzing intracellular gene transfer between the chloroplast, mitochondria, and nuclear genomes, we found that genes from the chloroplast and mitochondria can successfully transfer to each chromosome of the nuclear genome, and the psbJ gene from the chloroplast remains intact in both the mitochondrial and nuclear genomes. This study enriches the genetic information of Adansonia and provides important evidence for evolutionary research in the family Malvaceae. Full article

Satellite-derived bathymetry (SDB) enables the efficient mapping of shallow waters such as coastal zones but typically requires extensive local ground truth data to achieve high accuracy. This study evaluates the effectiveness of transfer learning in reducing this requirement while keeping estimation accuracy at acceptable levels by adapting a deep learning model pretrained on data from Puck Lagoon (Poland) to a new coastal site in Agia Napa (Cyprus). Leveraging the open MagicBathyNet benchmark dataset and a lightweight U-Net architecture, three scenarios were studied and compared: direct inference to Cyprus, site-specific training in Cyprus, and fine-tuning from Poland to Cyprus with incrementally larger subsets of training data. Results demonstrate that fine-tuning with 15 samples reduces RMSE by over 50% relative to the direct inference baseline. In addition, the domain adaptation approach using 15 samples shows comparable performance to the site-specific model trained on all available data in Cyprus. Depth-stratified error analysis and paired statistical tests confirm that around 15 samples represent a practical lower bound for stable SDB, according to the MagicBathyNet benchmark. The findings of this work provide quantitative evidence on the effectiveness of deploying data-efficient SDB pipelines in settings of limited in situ surveys, as well as a practical lower bound for clear and shallow coastal waters. Full article

The nitrogen cycle (N-cycle) is a cornerstone of global biogeochemistry, regulating nitrogen availability and affecting atmospheric chemistry, agricultural productivity, and ecological balance. Central to this cycle is the reversible interplay between nitrate (NO₃⁻) and nitrite (NO₂⁻), mediated by molybdenum-dependent enzymes—Nitrate reductases (NARs) and Nitrite oxidoreductases (NXRs). Despite catalyzing opposite reactions, these enzymes exhibit remarkable structural and mechanistic similarities. This review aims to elucidate the molecular underpinnings of nitrate reduction and nitrite oxidation by dissecting their enzymatic architectures, redox mechanisms, and evolutionary relationships. By focusing on recent structural, spectroscopic, and thermodynamic data, we explore how these two enzyme families represent “two sides of the same coin” in microbial nitrogen metabolism. Special emphasis is placed on the role of oxygen atom transfer (OAT) as a unifying mechanistic principle, the influence of environmental redox conditions, and the emerging evidence of bidirectional catalytic potential. Understanding this dynamic enzymatic interconversion provides insight into the flexibility and resilience of nitrogen-transforming pathways, with implications for environmental management, biotechnology, and synthetic biology. Full article

The United States leads in corn production and consumption in the world with an estimated USD 50 billion per year. There is a pressing need for the development of novel and efficient techniques aimed at enhancing the identification and eradication of weeds in a manner that is both environmentally sustainable and economically advantageous. Weed classification for autonomous agricultural robots is a challenging task for a single-camera-based system due to noise, vibration, and occlusion. To address this issue, we present a multi-camera-based system with decision-level sensor fusion to improve the limitations of a single-camera-based system in this paper. This study involves the utilization of a convolutional neural network (CNN) that was pre-trained on the ImageNet dataset. The CNN subsequently underwent re-training using a limited weed dataset to facilitate the classification of three distinct weed species: Xanthium strumarium (Common Cocklebur), Amaranthus retroflexus (Redroot Pigweed), and Ambrosia trifida (Giant Ragweed). These weed species are frequently encountered within corn fields. The test results showed that the re-trained VGG16 with a transfer-learning-based classifier exhibited acceptable accuracy (99% training, 97% validation, 94% testing accuracy) and inference time for weed classification from the video feed was suitable for real-time implementation. But the accuracy of CNN-based classification from video feed from a single camera was found to deteriorate due to noise, vibration, and partial occlusion of weeds. Test results from a single-camera video feed show that weed classification accuracy is not always accurate for the spray system of an agricultural robot (AgBot). To improve the accuracy of the weed classification system and to overcome the shortcomings of single-sensor-based classification from CNN, an improved Dempster–Shafer (DS)-based decision-level multi-sensor fusion algorithm was developed and implemented. The proposed algorithm offers improvement on the CNN-based weed classification when the weed is partially occluded. This algorithm can also detect if a sensor is faulty within an array of sensors and improves the overall classification accuracy by penalizing the evidence from a faulty sensor. Overall, the proposed fusion algorithm showed robust results in challenging scenarios, overcoming the limitations of a single-sensor-based system. Full article