Search Results (128)

Background/Objectives: Miscarriage is an increasingly common event worldwide arising from various factors, and identifying its etiology is important for planning and managing any future pregnancies. It is estimated that about half of early pregnancy loss cases are caused by genetic abnormalities, while a significantly lower rate is found in late pregnancy loss. Multiplex ligation-dependent probe amplification (MLPA) can detect small changes within a gene with precise breakpoints at the level of a single exon. The aim of our study was to identify the rate of copy number variations (CNVs) in spontaneous pregnancy loss samples after having previously tested them via quantitative fluorescence PCR (QF-PCR), with no abnormal findings. Methods: DNA was extracted from product-of-conception tissue samples, followed by the use of an MLPA kit for the detection of 31 microdeletion/microduplication syndromes (SALSA^® MLPA^® Probemix P245 Microdeletion Syndromes-1A, MRC-Holland, Amsterdam, The Netherlands). Results: A total of 11 (13.1%) out of the 84 successfully tested samples showed CNVs. Duplications accounted for 9.5% of the analyzed samples (eight cases), while heterozygous or hemizygous deletions were present in three cases (3.6%). Among all the detected CNVs, only three were certainly pathogenic (3.6%), with two deletions associated with DiGeorge-2 syndrome and Rett syndrome, respectively, and a 2q23.1 microduplication syndrome, all detected in early pregnancy loss samples. For the remaining cases, additional genetic tests (e.g., aCGH/SNP microarray) are required to establish CNV size and gene content and therefore their pathogenicity. Conclusions: MLPA assays seem to have limited value in detecting supplementary chromosomal abnormalities in miscarriages. Full article

Vegetation succession is a critical indicator of ecosystem structure and function and is often disrupted by the expansion of invasive species. However, ecosystem-scale studies elucidating invasion-driven succession mechanisms remain limited. This research focused on the Yancheng coastal salt marsh and analyzed the distribution variation of invasive species (Spartina alterniflora) and native species (Suaeda salsa and Phragmites australis) from 1987 to 2022 via the Google Earth Engine and random forest method. Logistic/Gaussian models were used to quantify land–sea distribution changes and vegetation succession trajectories. By integrating data on soil salinity, invasion duration, and fractional vegetation cover, generalized additive models (GAMs) were applied to identify the main factors influencing vegetation succession and to explore how Spartina alterniflora invasion affects the succession of salt marsh vegetation. The results indicated that the areas of Spartina alterniflora and Phragmites australis significantly increased by 3787.49 ha and 3452.60 ha in 35 years, respectively, contrasting with Suaeda salsa’s 82.46% decline. The FVC in the area has significantly increased by 42.10%, especially in the coexisted areas of different vegetation communities, indicating intensified interspecific competition. The overall trend of soil salinity was decreasing, with a decrease in soil salinity in native species areas from 0.72% to 0.37%. From the results of GAMs, soil salinity, tidal action, and invasion duration were significant factors influencing the distribution of native species, but salinity was not a significant factor affecting the Spartina alterniflora distribution. The findings revealed that the expansion of Spartina alterniflora changed the soil salinity and interspecific interactions, thereby altering the original plant community structure and establishing a new vegetation succession. This study enhances the understanding of the impacts of invasive species on ecosystems and offers theoretical support for salt marsh restoration. Full article

Suaeda salsa is relatively tolerant to cadmium (Cd) contamination. In order to investigate the bioaccumulation and stress responses of S. salsa under chronic exposure, we explored the growth, accumulation, and changes in antioxidant enzymes and glutathione (GSH) under different Cd concentrations over a 30-day soil culture experiment. Seedling height and weight in the 13.16 mg/kg Cd group were 13.26 cm and 0.21 g, significantly higher than the control group. Growth was significantly inhibited under high Cd concentration exposure, with a seedling and root length of 9.65 cm and 3.77 cm. The Cd concentration in all tissues was positively related to Cd treatment concentration, with the Cd contents in the roots being higher than in the other tissues. At a subcellular level, Cd was mainly concentrated in the cell walls, organelles, and soluble components within the range of 0.05–8.29, 0.02–2.40 and 0.08–1.35 μg/g, respectively. The accumulation of Cd in the roots tracked its proportion in the cell walls. The malondialdehyde (MDA) content of the plant tissues increased with increasing Cd concentration, indicating that Cd stress caused oxidative damage. The GSH content increased with increasing Cd concentration, with maximum values of 0.515 μmol/g in the stem in the 66.07 mg/kg Cd group. The catalase (CAT), superoxide dismutase (SOD), and peroxidase (POD) activity showed different change trends under Cd exposure. The results in this study could provide useful information on the tolerance mechanism of Cd in S. salsa, which provides information for exploiting S. salsa as a candidate for phytoremediation of Cd contamination. Full article

Limited research has been conducted on the potential and mechanisms of irrigating Suaeda salsa with wastewater and microalgae to improve saline–alkali land. This study used three irrigation treatments (freshwater, saline wastewater, and saline wastewater with microalgae) to irrigate S. salsa, and microalgae promoted the growth of S. salsa and increased soil nutrient content, increasing available nitrogen (4.85%), available phosphorus (44.51%), and organic carbon (24.05%) while alleviating salt stress through reduced soil salinity (13.52%) and electrical conductivity (21.62%). These changes promoted eutrophic bacteria while inhibiting oligotrophic bacteria. Bacterial community composition exhibited significant variations, primarily driven by soil pH, total nitrogen, and organic carbon content. Notably, rhizosphere bacteria showed enhanced functional capabilities, with increased abundance of salt stress resistance and nitrogen metabolism-related genes compared to original soil, particularly under saline irrigation conditions. Furthermore, microalgae addition enriched nitrogen metabolism-related gene abundance. These findings revealed the potential role of key bacteria in enhancing plant growth and the soil environment and highlighted the potential of applying S. salsa, wastewater, and microalgae for the synergistic improvement of saline–alkali land. Full article

This study developed a coupled hydrodynamic-sediment-vegetation model to investigate the effects of Spartina alterniflora management and Suaeda salsa restoration on coastal wetland geomorphological evolution and vegetation distribution. Special attention is paid to the regulatory roles of tidal dynamics, sea-level rise, sediment supply, and sediment characteristics. The study shows that the management of Spartina alterniflora significantly alters the sediment deposition patterns in salt marsh wetlands, leading to intensified local erosion and a decline in the overall stability of the wetland system; meanwhile, the geomorphology of wetlands restored with Suaeda salsa is influenced by tidal range, sediment settling velocity, and suspended sediment concentration, exhibiting different deposition and erosion patterns. Under the scenario of sea-level rise, when sedimentation rates fail to offset the rate of sea-level increase, the wetland ecosystem faces the risk of collapse. This study provides scientific evidence for the ecological restoration and management of coastal wetlands and offers theoretical support for future wetland conservation and restoration policies. Full article

Unmanned Aerial Vehicles (UAVs) have become powerful tools for high-resolution, quantitative remote sensing in ecological and environmental studies. In this study, we present a novel approach to accurately mapping and estimating the biomass of Suaeda salsa using UAV-based visible-light imagery combined with hue angle inversion modeling. By integrating diffuse reflectance standard plates into the flight protocol, we converted RGB pixel values into reflectance and derived hue angle metrics with enhanced radiometric accuracy. A hue angle cutoff threshold of 249.01° was identified as the optimal cutoff to distinguish Suaeda salsa from the surrounding land cover types with high confidence. To estimate biomass, we developed an exponential inversion model based on hue angle data calibrated through extensive field measurements. The resulting model—Biomass = 3.57639 × 10⁻¹⁵ × e^0.12201×α—achieved exceptional performance (R² = 0.99696; MAPE = 3.616%; RMSE = 0.02183 kg/m²), indicating strong predictive accuracy and robustness. This study highlights a cost-effective, non-destructive, and scalable method for the real-time monitoring of coastal vegetation, offering a significant advancement in remote sensing applications for wetland ecosystem management. Full article

Nowadays, the evolution and growth of machine learning (ML) algorithms and the Internet of Things (IoT) are enabling new applications. Smart weapons and people detection systems are examples. Firstly, this work takes advantage of an efficient, scalable, and distributed system, named SmartFog, which identifies people with weapons by leveraging edge, fog, and cloud computing paradigms. Nevertheless, security vulnerabilities during data transmission are not addressed. Thus, this work bridges this gap by proposing a secure data transmission system integrating a lightweight security scheme named GS3. Therefore, the main novelty is the evaluation of the GS3 proposal in a real environment. In the first fog sublayer, GS3 leads to a 14% increase in execution time with respect to no secure data transmission, but AES results in a 34.5% longer execution time. GS3 achieves a 70% reduction in decipher time and a 55% reduction in cipher time compared to the AES algorithm. Furthermore, an energy consumption analysis shows that GS3 consumes 31% less power than AES. The security analysis confirms that GS3 detects tampering, replaying, forwarding, and forgery attacks. Moreover, GS3 has a key space of $2^{544}$ permutations, slightly larger than those of Chacha20 and Salsa20, with a faster solution than these methods. In addition, GS3 exhibits strength against differential cryptoanalysis. This mechanism is a compelling choice for energy-constrained environments and for securing event data transmissions with a short validity period. Moreover, GS3 maintains full architectural transparency with the underlying armed detection system. Full article

Suaeda salsa, a halophytic plant species, exhibits a remarkable salt tolerance and demonstrates a significant phytoremediation potential through its capacity to absorb and accumulate saline ions and heavy metals from soil substrates, thereby contributing to soil quality amelioration. Furthermore, this species serves as a critical habitat component for avifauna populations and represents a keystone species in maintaining ecological stability within estuarine and coastal wetland ecosystems. With the development and maturity of UAV remote sensing technology in recent years, the advantages of using UAV imagery to extract weak targets are becoming more and more obvious. In this paper, for Suaeda salsa, which is a weak target with a sparse distribution and inconspicuous features, relying on the high-resolution and spatial information-rich features of UAV imagery, we establish an adaptive contextual information extraction deep learning semantic segment model (ACI-Unet), which can solve the problem of recognizing Suaeda salsa from high-precision UAV imagery. The precise extraction of Suaeda salsa was completed in the coastal wetland area of Dongying City, Shandong Province, China. This paper achieves the following research results: (1) An Adaptive Context Information Extraction module based on large kernel convolution and an attention mechanism is designed; this module functions as a multi-scale feature extractor without altering the spatial resolution, enabling a seamless integration into diverse network architectures to enhance the context-aware feature representation. (2) The proposed ACI-Unet (Adaptive Context Information U-Net) model achieves a high-precision identification of Suaeda salsa in UAV imagery, demonstrating a robust performance across heterogeneous morphologies, densities, and scales of Suaeda salsa populations. Evaluation metrics including the accuracy, recall, F1 score, and mIou all exceed 90%. (3) Comparative experiments with state-of-the-art semantic segmentation models reveal that our framework significantly improves the extraction accuracy, particularly for low-contrast and diminutive Suaeda salsa targets. The model accurately delineates fine-grained spatial distribution patterns of Suaeda salsa, outperforming existing approaches in capturing ecologically critical structural details. Full article

Cadmium and petroleum are the main pollutants in coastal wetland ecosystems that affect plant nutrient balance and growth. Scholars have discovered how saline plants adapt to single stresses. How plant ecology and physiology correspond to complex cadmium and petroleum pollution, especially regarding the pollution impacts on carbon (C), nitrogen (N), and phosphorus (P) stoichiometry and biomass allocation in coastal wetland plants, remains unclear, limiting their application in regard to pollution remediation. This study focuses on Suaeda salsa, a popular species used in vegetation restoration in the Yellow River Delta’s coastal wetlands. Through the use of pot experiments, the dynamic changes in plant–soil ecological stoichiometry and biomass allocation were systematically investigated in response to individual and combined cadmium (0, 5, and 10 mg kg⁻¹) and petroleum (0, 6, and 12 g kg⁻¹) treatments. Compared with the control (CK), petroleum stress significantly increased the total nitrogen (TN) and plant total phosphorus (TP) contents, but did not substantially impact the total carbon (TC) content, resulting in 19.7% and 26.6% decreases in the plant C/N and C/P ratios, respectively. The soil organic carbon (SOC) content increased significantly under petroleum stress, whereas the TN and TP contents did not notably change, considerably increasing the soil C/N and C/P ratios, which were 1.5 times and 1.3 times greater than those of the CK, respectively. Cadmium stress alone or with petroleum stress did not significantly affect the C, N, or P stoichiometry or biomass allocation in S. salsa. The soil C/N/P stoichiometry redundancy analysis revealed that the contribution rates (especially the soil C/P and C/N ratios) to the total biomass and its allocation in S. salsa (64.5%) were greater than those of the control group plants (35.5%). The correlation analysis revealed that the total growth biomass of S. salsa was negatively correlated with the soil carbon content, C/N ratio, and C/P ratio, but positively correlated with the plant C/N and C/P ratios. The aboveground biomass proportion in S. salsa was significantly negatively correlated with the soil N/P ratio. The belowground biomass proportion exhibited the opposite trend. Petroleum pollution was the main factor driving S. salsa stoichiometry and growth changes, increasing the soil C/N and C/P ratios, reducing the nitrogen and phosphorus nutrient absorption capacities in plant roots, limiting plant nitrogen and phosphorus nutrients, and inhibiting biomass accumulation. Appropriate N and P supplementation alleviated plant growth inhibition due to petroleum pollution stress, which was conducive to improving vegetation ecological restoration in the Yellow River Delta. Full article

This study explores the spatiotemporal dynamics of SOC and microbial-mediated mechanisms in the Yellow River Delta wetlands. Using redundancy analysis and microbial community profiling, we show that vegetation drives distinct SOC storage patterns: Phragmites australis ecosystems exhibit the highest SOC sequestration, followed by Suaeda salsa and Tamarix chinensis habitats, where salt-tolerant taxa like Desulfobacterota and Halobacteriaota promote short-term carbon storage via anaerobic metabolism. The microbial community structure is shaped by both vegetation-induced microhabitats and environmental gradients: SOC and total nitrogen dominate community assembly, while electrical conductivity, pH, and sulfur/nitrogen nutrients regulate spatiotemporal differentiation. Seasonal turnover drives the reorganization of microbial community structures, shaping the dynamic equilibrium of carbon pools. Seasonal DOC dynamics, linked to tidal fluctuations and exogenous carbon inputs, highlight hydrology’s role in modulating active carbon pools. These findings reveal tight linkages among vegetation, microbial functional guilds, and soil biogeochemistry, critical for wetland carbon sequestration. Full article

The intertidal zones of estuarine wetlands serve as critical components in maintaining and promoting the sustainable development of regional ecosystems. Salt marsh vegetation, a crucial element of these zones, is experiencing significant deterioration across multiple scales due to various stressors. Despite considerable attention given to the spatial patterns and temporal evolution of salt marsh vegetation, few studies have quantitatively assessed its dynamic interactions with tidal creeks. Tidal creeks serve as primary conduits for material, energy, and information exchange between intertidal zones and adjacent ecosystems. There is a complex feedback mechanism between the development of the tidal creeks and vegetation communities. We investigated the distribution patterns and successional characteristics of salt marsh vegetation at both landscape and pixel scales, with particular emphasis on coupling dynamics with tidal creeks. Our results revealed a distinct spatial gradient in vegetation distribution across the study area. While the invasion of S. alterniflora exhibited limited direct competitive effects on S. salsa, it demonstrated significant influence on tidal creek geomorphological evolution. Notably, S. salsa exhibited pronounced sensitivity to hydrological conditions, with its growth being substantially constrained by tidal creek development and associated soil modifications. Full article

Understanding the characteristics and key driving factors of plant diversity is of great significance for maintaining biodiversity and the ecosystem. Current studies on plant diversity in the Yellow River Delta are limited to local areas; there is a lack of comprehensive discussion on the spatial heterogeneity of plant diversity and the driving factors at a regional scale. Based on field investigations, this study explored the characteristics of plant composition and diversity under the land–sea gradient, with particular emphasis on the differences of plant diversity under different riverbanks and at a distance from the sea. Using the regression, redundancy, and Mantel test analysis, we analyzed soil properties, environmental factors, and human influence to assess their potential impacts on plant diversity. The results demonstrated that Asteraceae, Poaceae, and Amaranthaceae are the dominant plant families in the Yellow River Delta. As the distance from the sea increases, the community transitions from the monospecies dominance of Suaeda salsa to one dominated by various plants. The species similarity was higher in the adjacent environment and coastal areas. The overall level of plant diversity was not high, and the Margalef, Shannon–Wiener, Simpson, and Pielou index showed a fluctuating downward trend from land to sea. Notably, there was a peak value in the region of 3–17 km and >42 km from the sea. The plant diversity of the main stream bank was higher than that of its tributaries, where the former was more susceptible to human interference and the latter to soil electrical conductivity. In terms of the region, soil electrical conductivity had the greatest influence on plant diversity. This study could provide theoretical support for vegetation restoration and ecological protection in the Yellow River Delta. Full article

Biochar, an eco-friendly soil amendment, holds promise for remediating contaminated soils, yet its impacts on coastal wetland soils under combined microplastic (MP) and heavy metal (HM) pollution remain underexplored. This study examined the efficacy of 2% Spartina alterniflora-derived biochar (BC) in rehabilitating soils co-contaminated with cadmium (Cd) and two MPs—polyethylene (PE) and polylactic acid (PLA)—at 0.2% and 2% (w/w). The results indicated that biochar significantly elevated soil pH (8.35–8.43) and restored electrical conductivity (EC) to near-control levels, while enhancing organic matter content (up to 130% in PLA-contaminated soils), nutrient availability (e.g., phosphorus, potassium), and enzyme activity. Biochar reduced bioavailable Cd by 14–15% through adsorption and ion exchange. Although bacterial richness and diversity slightly declined, biochar reshaped microbial communities, enriching taxa linked to pollutant degradation (e.g., Proteobacteria, Bacteroidota) and upregulated functional genes associated with carbon, nitrogen, and sulfur cycling. Additionally, biochar boosted Suaeda salsa (S. salsa) biomass (e.g., 0.72 g/plant in A1B) and height (e.g., 14.07 cm in E1B) while reducing Cd accumulation (29.45% in shoots) and translocation. Remediation efficiency was most pronounced in soils with 0.2% PLA. These findings bridge critical knowledge gaps in biochar’s role in complexly polluted coastal wetlands and validate its potential for sustainable soil restoration. Full article

Understanding the spatial variability in soil hydraulic properties (SHPs) and their influencing variables is critical for ecohydrological and biogeochemical studies in coastal wetlands, where complex landscapes make it challenging to accurately delineate the spatial patterns of SHPs. In this study, soil samples were collected from two transects covered by Suaeda salsa (S. salsa) and Phragmites australis (P. australis) from the Beidagang Wetland Nature Reserve in northern China, and a comprehensive dataset on soil physical properties and SHPs was obtained by laboratory experiments. The results showed that soil physical properties (e.g., soil particle size, bulk density (BD), and soil organic matter (SOM)) displayed significant spatial variability, which was related to the physiological characteristics of S. salsa and P. australis and to soil depth. As a result, SHPs, including saturated hydraulic conductivity (K_s) and parameters of the van Genuchten model (θ_s-saturated soil water content, including α, the reciprocal of the air-entry value, and n, the pore size distribution index) varied considerably along the two transects. Specifically, K_s, θ_s, and α were negatively correlated with BD and pH, while positively correlated with SOM, which promoted soil aggregation to enlarge soil pores. Soil depth was shown to significantly affect SHPs, whereas the differences in SHPs between the two transects were not statistically significant, suggesting vegetation type did not directly impact SHPs. Soil water retention capacities were noticeably higher in surface soils, especially when soil suctions were less than 1000 cm, whereas their differences between depths largely diminished with further increasing soil suctions. This study highlights the complex interplay of SHPs with surrounding environments, providing critical insight for characterizing the spatial patterns of SHPs in coastal wetlands. Full article

In software development, debugging is the most tedious and time-consuming phase. Therefore, various automated fault localization techniques have been proposed to assist debugging. Among existing fault localization techniques, Spectrum-Based Fault Localization (SBFL) is one of the most extensively researched methods. Traditional SBFL techniques rely solely on the coverage of program execution for fault localization, which means they neglect the interactions between program entities and fault propagation paths during the execution of the program, resulting in a tie problem that reduces the accuracy of fault localization. To solve the above problem, this paper proposes SA-SBFL, a fault localization method based on the SALSA (Random Method for Link Structure Analysis) algorithm. First, a link graph of program entities is constructed, which includes interactions between program entities and fault propagation paths. Then, the suspicion values obtained from traditional SBFL methods are used as the initial weights of the link graph. Finally, the random walk model is employed to simulate the propagation of faults among program entities, analyze the importance of program entities in the fault propagation process, and obtain a ranking list of suspicious program entities. The experiments in this paper demonstrate that the SA-SBFL method significantly outperforms general SBFL methods. For instance, in the Defects4J dataset, the SA-SBFL technique outperforms traditional SBFL in terms of fault localization accuracy, with a 47% improvement in the Top-1 metric and a 10% increase in the Top-5 metric, and it also showed an average improvement of 19% in the EXAM metric. Full article