Search Results (629)

The upper Xin’an River basin serves as a critical ecological barrier and water-conservation area for the Yangtze River Delta. However, with rapid economic development, nitrogen pollution in the surface waters of this region has become increasingly pronounced. This study analyzed river water samples collected on four occasions from the upper Xin’an River basin for ammonium (NH₄⁺–N), nitrate-nitrogen (NO₃⁻–N), total nitrogen (TN), and nitrate isotopic (δ¹⁵N–NO₃⁻ and δ¹⁸O–NO₃⁻). The sources of nitrate (NO₃⁻) were apportioned using the MixSIAR stable-isotope mixing model, and the spatial distribution of these sources across the basin was characterized. Across the four sampling rounds, the mean TN concentration exceeded 1.3 mg/L, with NO₃⁻–N accounting for over 45% of TN, indicating that nitrate was the dominant inorganic nitrogen species. The δ¹⁵N–NO₃⁻ values ranged from 2.17‰ to 13.0‰, with mean values following the order summer > winter > autumn > spring. The δ¹⁸O–NO₃⁻ values varied from −5.20‰ to −3.48‰, and the average value showed a completely opposite seasonal variation pattern to that of δ¹⁵N–NO₃⁻. Process-based analysis of nitrogen transformations revealed that nitrification predominates during nitrate transport and transformation, whereas denitrification is comparatively weak. MixSIAR-based estimates indicate marked seasonal differences in the source composition of nitrate pollution in the upper Xin’an River basin; NO₃⁻ derives primarily from soil nitrogen (SN) and livestock/sewage manure nitrogen (LSN). LSN was the dominant contributor in spring and summer (49.2% and 59.9%, respectively). SN dominated in autumn (49.2%) and winter (54.1%). Fertilizer nitrogen (FN) contributed more during summer and autumn, when fertilization is concentrated and rainfall is higher. Atmospheric deposition (AN) contributed approximately 1% across all seasons and was thus considered negligible. These findings provide a scientific basis for source control of nitrogen pollution and water-quality management in the upper Xin’an River. Full article

Traditional species richness estimators often assume spatial homogeneity in species distribution, which can lead to underestimating biodiversity, especially in large, ecologically complex areas. The Total–Species (T–S) curve may provide an accurate framework for estimating γ-diversity by accounting for compositional variation across spatial subunits. Our study tested the T–S curve model, modified to account for species rarity and patterns of β-diversity, to estimate macroalgal richness in the northeast Adriatic (Mediterranean Sea), an area where the total macroalgal diversity is known and a comprehensive reference list is available (487 species). Uncertainty in species richness estimates from T–S curves was quantified as 95%CI based on bootstrapping, and a sensitivity analysis was also carried out to quantify changes in estimates under different settings. Other parametric and non-parametric estimators, including the classic T–S curve, largely under- or overestimated the total species richness if compared to the refined T–S model, which returned a realistic estimate of 393 species in total. Our results demonstrate that the T–S curve modified to consider species rarity, and refined for potential biases associated with erroneous quantification of small-scale patchiness and spatial variations in assemblage composition, allowed for more realistic extrapolations of γ-diversity over large areas. Full article

ESKAPE bacteria are a major global threat due to their rapid antibiotic resistance acquisition and severe healthcare-associated infections. Effective countermeasures require epidemiological surveillance and resistance transmission studies, particularly for antimicrobial-resistant (AMR) colonization in intensive care unit (ICU) patients. Whole-genome sequencing (WGS) provides critical information on resistance spread and mechanisms. In the provided protocol, rectal and oropharyngeal swabs, or endotracheal aspirate/bronchoalveolar lavage for intubated patients, are collected at ICU admission and twice weekly. Patient interviews and medical records identify risk factors for resistant microflora. Samples undergo cultivation, species identification, antibiotic susceptibility testing, and DNA extraction. Sequencing is performed using second- and third-generation platforms, with selected isolates subject to hybrid genome assembly. Resistance genes, virulence factors, and typing profiles (MLST, cgMLST) are determined. This protocol characterizes the ICU patient colonization by AMR pathogens, including species distribution, phenotypic and genotypic resistance profiles, clonal structure, and temporal changes. It estimates detection frequency and colonization patterns at each locus, identifies key risk factors, including prior community or inter-facility exposure, and analyzes associations between risk factors and admission colonization. The study aims to estimate AMR infection risk and severity in ICU patients through the comprehensive analysis of colonization dynamics, resistance patterns, and clonal characteristics using WGS data on pathogen composition and AMR trends. Full article

Despite their ecological importance, discarded species with low commercial value are often overlooked in marine research. This study examined the age structure and feeding habits of the large-scaled gurnard (Lepidotrigla cavillone) and the spiny gurnard (Lepidotrigla dieuzeidei) in the Gulf of Cádiz (SW Iberian Peninsula). A total of 225 specimens were collected during 19 fishing trips at depths of 15–550 m. Ages were estimated from otolith readings, and stomach contents were analysed to describe diet composition, niche breadth, and overlap. Both species showed positive allometric growth, with the most frequent age class being 5+ in L. cavillone and 5+–6+ in L. dieuzeidei. Crustaceans dominated the diet, with mysids accounting for >80% of the index of relative importance (IRI) in L. cavillone, but L. dieuzeidei displayed a broader diet including mysids (45% IRI) and decapods (32% IRI). Feeding patterns varied with time of day, depth, and size, reflecting ontogenetic and environmental influences. Levin’s index indicated stronger specialization in L. cavillone (BA = 0.090) than in L. dieuzeidei (BA = 0.208), while the Schoener index (0.575) showed moderate overlap. These findings provide the first biological insights into these discarded species in Atlantic waters, contributing to ecosystem-based fisheries management. Full article

Accurate estimation of forest stand carbon storage is critical for assessing ecosystem functions and informing sustainable forest management. Most existing models depend heavily on stand age, a strategy that is often unreliable in natural forests, and they typically ignore species interactions, limiting their applicability across forest types. To overcome these issues, we developed a dynamic carbon storage model based on the Richards equation that replaces stand age with a growth interval period (defined as the time difference between two successive growth stages, T_n = T₂ − T₁) and explicitly incorporates site quality and species composition. This approach enables consistent estimation for both natural and plantation forests. Using field data from six dominant tree species in Wuyishan City, Fujian Province, we calibrated and validated the model through five-fold cross-validation. It achieved high accuracy, with an efficiency coefficient (EA) above 99% and a relative mean absolute error (RMA) under 7%, effectively reflecting how site conditions and mixed-species structures influence carbon dynamics. Total forest carbon storage in the study area was estimated at 7.32 million tons. Simulations show a gradual decline in carbon accumulation over time, consistent with natural growth saturation in aging stands. Scenario analyses further identified practical zones for sustainable harvesting in major plantation types, underscoring the model’s management relevance. The model does not yet include climate variability, disturbances, or below-ground carbon pools. Adding these components in future work would strengthen its utility for regional carbon assessment and support more robust carbon-neutral forestry planning. Full article

Mangrove ecosystems play a critical role in global carbon cycling, serving as significant carbon sinks by storing carbon in both aboveground biomass (ACG) and soil carbon stock (SOC). However, the temporal dynamics of ACG and SOC, as well as their spatial variations across different mangrove age stages, remain poorly understood, particularly under the influence of introduced species such as Sonneratia apetala Buch.-Ham. To address these gaps, our study used a long-term series of NDVI from Landsat (from 1990 to 2024) and the mangrove product of China (1990, 2000, 2010, and 2018) to estimate the mangrove age stage ($\mathrm{Stage}\mathrm{I}$ 10–24 years, $\mathrm{Stage}\mathrm{II}$ 24–34 years, and $\mathrm{Stage}\mathrm{III}$ > 34 years). UAV-LiDAR and in-situ surveys were applied to measure mangrove canopy height to calculate ACG and measure the belowground soil carbon stock, respectively. Combined with the mangrove age stage, ACG, and SOC, our results reveal that ACG accumulates rapidly in younger mangroves dominated by Sonneratia apetala, peaking early (<20 years) and then stabilizing as mangroves, indicating that the introduction of Sonneratia apetala changed the increase in ACG with age. In contrast, SOC increases more gradually over time, with only older mangroves (over 30 years) storing significantly higher SOC. Root structure, TN, and TP were sensitive to the SOC. The different root structures (pneumatophore, plank, pop, and knee root) had different SOC results, and the pneumatophore had the lowest SOC. Remote sensing data revealed that the introduction of Sonneratia apetala altered the species composition of younger mangroves, leading to its predominance within these ecosystems. This shift in species composition not only altered the temporal dynamics of aboveground carbon (ACG) but also favored pneumatophore-dominated root structures, which were associated with the lowest soil organic carbon (SOC). Consequently, younger stands may require more time to accumulate SOC to levels comparable to older mangrove forests. These results suggest that restoration targets for vegetation carbon and soil carbon should be set on different timelines, explicitly accounting for stand age, species composition, and root functional types. Full article

Forensic entomologists use insect development, especially in blow flies (Diptera: Calliphoridae), to estimate the minimum postmortem interval (mPMI). Since insect activity is driven mainly by temperature, understanding geographic and seasonal variation in community composition is critical. In the southeastern United States, approximately 10 blow fly species dominate, generally classified as “summer-active” or “winter-active” flies. We studied their presence and abundance during winter/spring and summer/fall transitions across the Georgia Fall Line (GFL), a major geophysical boundary separating the Piedmont and Coastal Plain. Here we show that community structure was shaped more by regional biogeography and seasonal transitions, than by current temperature. Three species; Calliphora livida, Lucilia coeruleiviridis, and Cochliomyia macellaria accounted for over 70% of seasonal variation. Fly communities differed sharply across the GFL and shifted between seasonal transitions. Recognizing these geographic and temporal patterns can help forensic entomologists produce more accurate mPMI estimates in death investigations. Full article

Pyrolysis of 24 samples of foliage from three U.S. regions with frequent wildland fires (Southeastern U.S., northern Utah and Southern California) was studied in a fuel-rich flat-flame burner system at 765 °C (for Southeastern U.S. samples) and 725 °C (for northern Utah and Southern California species), with a heating rate of approximately 180 °C/s. These conditions were selected to mimic the conditions of wildland fires. Individual plant samples were introduced to the high temperature zone in a flat-flame burner and pyrolysis products were collected. Tar was extracted and later analyzed by GC/MS. Light gases were collected and analyzed by GC/TCD. The estimated range for the average yields of tar and light gases were 48 to 62 wt% and 18 to 31 wt%, respectively. Apart from Eastwood’s manzanita (Arctostaphylos glandulosa Eastw.), aromatics were the major constituents of tar. The variations in the concentrations of tar compounds likely resulted from differences in biomass composition and physical characteristics of the foliage. The four major components of light gases from pyrolysis (wt% basis) were CO, CO₂, CH₄ and H₂. Tar contributed more than 82% of the high heating value of volatiles. These data can be used to improve physical-based fire propagation models. Full article

As climate conditions and agricultural technologies change, the soil seed bank may increase or decrease, which may affect the species composition and abundance of weeds in crops. The research was carried out in order to evaluate the influence of hillside parts on the number of viable seeds during different seasons (spring and autumn) in agrophytocenoses, which differ in the duration of the land’s covering with plants. Soil samples have been taken out in spring and autumn at the summit, midslope, and footslope of the hill. The time of the soil sample collection and covering of agrophytocenoses had a significant effect on soil seed numbers. In autumn, the average seed amount in the soil was higher by 6.38% than in spring. The largest seed number (in spring and autumn) was evaluated in the soil of cereal–grass crop rotation with a 2.0- and 6.9-times higher seed amount compared to the rotation with a row crop and permanent grassland. During the years, hill parts had a significant effect on the seed bank in autumn. In spring, the viable seeds comprised 67.10%, and in autumn, they comprised 65.33% of the total seed number. Significantly, the highest percentage of viable seeds was estimated in the footslope of the hill. This can be related to more favorable microclimatic conditions and higher soil moisture at the footslope, where more fertile soil and organic matter naturally accumulate, creating better conditions for seed viability preservation. Full article

Medicinal plants have long been used for therapeutic purposes in the mountainous Hakkâri region of southeastern Türkiye. This study presents an integrated toxicological risk and therapeutic assessment of four ethnomedicinal species—Daphne mucronata Royle, Ferula communis L., Heracleum persicum Desf., and Tragopogon coloratus C.A.Mey—based on their flavonoid and phenolic composition, elemental content, and antioxidant capacity. To the best of our knowledge, this is the first study to integrate multiple analytical platforms—including HPLC, ICP-OES, AAS, UV-Vis spectrophotometry, and SEM/EDX—to assess both the therapeutic potential and toxicological risks of these ethnomedicinal species. Although a complete phytochemical profile was not the objective of this study, selected phenolic compounds and antioxidant capacity were evaluated to highlight bioactivity, while heavy metal-based risk assessment was prioritized given public health relevance. Antioxidant capacity was measured using DPPH, ABTS, and CUPRAC assays, while human health risks were quantified through Estimated Daily Consumption (EDC), Target Hazard Quotient (THQ), Hazard Index (HI), and Carcinogenic Risk (CR). The results revealed a dual nature: Heracleum persicum exhibited the strongest antioxidant activity, correlating with its high phenolic content, while Daphne mucronata showed elevated toxic metals exceeding WHO/FAO thresholds. Overall, the findings emphasize the importance of combining ethnobotanical knowledge with robust analytical tools for safe medicinal plant usage. Full article

For large-scale tropical tuna purse-seine fisheries, it is prohibitively costly to obtain adequate sampling coverage to estimate fleet-level catch composition solely from sample data. Logbook or observer data, with complete fleet coverage, are often available but may be considered unreliable for species composition. Previous studies have developed models, trained with sample data, to predict set-level species compositions based on environmental and operational covariates. Here, models were developed to predict well-level species composition from uncorrected observer data and covariates affecting the observers’ view of the catch during loading, with port-sampling data as the response variable. The analysis used paired, well-level data from sets made on floating objects by the Eastern Pacific Ocean tuna purse-seine fleet during 2023–2024. Results indicated that, overall, observer data proportions of bigeye (BET) and yellowfin tunas tended to be greater than the model-estimated proportions, with the opposite occurring for skipjack tuna (SKJ). However, vessel effects sometimes modified these tendencies. Model complexity was greatest for BET and least for SKJ. For BET, observer data proportions and model-estimated proportions were more similar when the vessel had a hopper. They were also more similar in 2023 as compared to 2024, suggesting sample data for bias adjustments should be collected annually. The approach shows potential for predicting the species composition of unsampled wells. Full article

In this study, we propose a simple and effective method for gas analysis by establishing a correlation between residual gas analyzer (RGA) intensity and gas concentration. To achieve this, we focused on CF₄ and NF₃, two high-global warming potential (GWP) gases commonly used in industrial applications. The experiment was conducted in four key steps: identifying gas species using optical emission spectroscopy (OES), calibrating RGA with a quadrupole mass spectrometer (QMS), constructing a five-point calibration graph to correlate RGA and Fourier-transform infrared spectroscopy (FT-IR) data, and estimating the concentration of unknown samples using the calibration graph. The results under plasma-on conditions demonstrated correlation and accuracy, confirming the reliability of our approach. In other words, the method effectively captured the relationship between RGA intensity and gas concentration, providing valuable insights into concentration trends. Thus, our approach serves as a useful tool for estimating gas concentrations and understanding the correlation between RGA intensity and gas composition. Full article

Blow flies (Diptera: Calliphoridae) play a crucial role in the decomposition process and serve as important forensic indicators due to their predictable colonization patterns. This review focuses on the dynamics of maggot masses, highlighting their ecological roles, thermoregulation, and implications for forensics. We summarize data on the self-organizing behavior of maggot masses, which is influenced by chemical cues and environmental factors. These masses can generate internal temperatures that exceed ambient levels by 10–20 °C, accelerating larval growth and impacting competition among individuals. This localized heating complicates the estimation of the postmortem interval (PMI), as traditional models may not take these thermal influences into account. Furthermore, maggot masses contribute significantly to nutrient cycling and soil enrichment, while the behavior of the larvae includes both cooperation and competition, which is influenced by the species composition present. This review highlights challenges in PMI estimation due to heat production but also discusses advancements in molecular tools and thermal modeling that enhance accuracy. Ultimately, we identify knowledge gaps regarding species diversity, microbial interactions, and environmental variability that impact mass dynamics, suggesting future research avenues that could enhance ecological understanding and forensic applications. Full article

Leaf pigment composition and concentration are crucial indicators of plant physiological status, photosynthetic capacity, and overall ecosystem health. While spectroscopy techniques show promise for monitoring vegetation growth, phenology, and stress, accurately estimating leaf pigments remains challenging due to the complex reflectance properties across diverse tree species. This study introduces a novel approach using a two-dimensional convolutional neural network (2D-CNN) coupled with a genetic algorithm (GA) to predict leaf pigment content, including chlorophyll a and b content (C_ab), carotenoid content (C_ar), and anthocyanin content (C_anth). Leaf reflectance and biochemical content measurements taken from 28 tree species were used in this study. The reflectance spectra ranging from 400 nm to 800 nm were encoded as 2D matrices with different sizes to train the 2D-CNN and compared with the one-dimensional convolutional neural network (1D-CNN). The results show that the 2D-CNN model (nRMSE = 11.71–31.58%) achieved higher accuracy than the 1D-CNN model (nRMSE = 12.79–55.34%) in predicting leaf pigment contents. For the 2D-CNN models, C_ab achieved the best estimation accuracy with an nRMSE value of 11.71% (R² = 0.92, RMSE = 6.10 µg/cm²), followed by C_ar (R² = 0.84, RMSE = 1.03 µg/cm², nRMSE = 12.29%) and C_anth (R² = 0.89, RMSE = 0.35 µg/cm², nRMSE = 31.58%). Both 1D-CNN and 2D-CNN models coupled with GA using a subset of the spectrum produced higher prediction accuracy in all pigments than those using the full spectrum. Additionally, the generalization of 2D-CNN is higher than that of 1D-CNN. This study highlights the potential of 2D-CNN approaches for accurate prediction of leaf pigment content from spectral reflectance data, offering a promising tool for advanced vegetation monitoring. Full article

Bacterial contamination and the escalating crisis of antibiotic resistance represent pressing global public health threats, with approximately 4.95 million deaths linked to antimicrobial resistance (AMR) in 2019 and projections estimating up to 10 million annual fatalities by 2050. As third-generation antimicrobial materials, metal–organic frameworks (MOFs) have emerged as promising alternatives to conventional agents, leveraging their unique attributes such as high specific surface areas, tunable porosity, and controlled metal ion release kinetics. This review provides a systematic analysis of the foundational principles and core antibacterial mechanisms of MOFs, which include the sustained release of metal ions (e.g., Ag⁺, Cu²⁺, Zn²⁺), the generation of reactive oxygen species (ROS), and synergistic effects with encapsulated functional molecules. We highlight how these mechanisms underpin their efficacy across a range of applications. Rather than offering an exhaustive list of synthesis methods and metal compositions, this review focuses on clarifying structure–function relationships that enable MOF-based materials to outperform conventional antimicrobials. Their potential is particularly evident in several key areas: wound dressings and medical coatings that enhance tissue regeneration and prevent infections; targeted nanotherapeutics against drug-resistant bacteria; and functional coatings for food preservation and water disinfection. Despite existing challenges, including gaps in clinical translation, limited efficacy in complex multi-species infections, and incomplete mechanistic understanding, MOFs hold significant promise to revolutionize antimicrobial therapy. Through interdisciplinary optimization and advancements in translational research, MOFs are poised to drive a paradigm shift from “passive defense” to “active ecological regulation”, offering a critical solution to mitigate the global AMR crisis. Full article