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In mountainous areas, high-filled subgrade often experiences significant post-construction settlement. Prematurely paving the cement-stabilized gravel layer on an unstable subgrade can easily lead to subsequent cracking. To accurately predict the settlement of high-filled subgrade and determine the appropriate timing for paving the cement-stabilized layer, this study proposes a subgrade settlement prediction method combining an Artificial Neural Network (ANN) with a Genetic Algorithm (GA). Using monitoring data from a high-filled subgrade on a highway in Hunan Province, China, a GA–ANN model was established to predict settlement curves. The predicted data from the GA–ANN model were compared with measured data and ANN predictions to validate the advantages of using GA–ANN for subgrade settlement prediction. The results indicate that the GA–ANN model significantly outperforms the ANN model due to GA's ability to provide more reasonable weight biases for ANN through global search optimization. Predictions of settlement data beyond 50 days using both ANN and GA–ANN showed that the GA–ANN prediction curve closely matched the measured curve, with a basic deviation within ±3 mm. In contrast, ANN's prediction error gradually increased to over 5 mm as the observation time increased, with predicted values lower than measured values, leading to an overly optimistic estimation of early settlement convergence. Based on the predicted data and settlement standards, the estimated timing for laying the stabilized layer was determined. During the laying process, no cracking was observed in the stabilized layer. The project has been in operation for six months, with the road surface in good condition. This study provides a valuable reference for the laying of stabilized layers on similar high-filled and ultra-high-filled subgrades. [ABSTRACT FROM AUTHOR]

The widespread use of carbamate pesticides has raised significant environmental and health concerns, particularly regarding water contamination and the disruption of defense systems in organisms. Despite these concerns, research on the differential impacts of pesticides on male and female organisms remains limited. This study focused on methomyl, investigating sex-specific differences in liver antioxidant defenses and inflammatory response indices in male and female zebrafish after 56 days of exposure to environmentally relevant concentrations (0, 0.05, 0.10, and 0.20 mg/L). Our findings indicate that methomyl exposure significantly increased ROS content in zebrafish livers, inducing oxidative stress and activating enzymatic antioxidant defenses such as SOD, CAT, and GSH-Px activities. Sub-chronic exposure altered the expression of apoptosis-related genes (Bax/Bcl2a and Caspases3a), resulting in liver cell apoptosis in a concentration-dependent manner, with the 0.20 mg/L concentration causing the most severe damage. Additionally, methomyl exposure at environmentally relevant concentrations triggered persistent inflammatory responses in liver tissues, evidenced by increased transcription levels of inflammatory factor genes and the activation of toll-like receptors, heightening susceptibility to exogenous allergens. It is noteworthy that oxidative damage indicators (AST, ROS, MDA) and inflammatory gene expressions (IL-1β, TNF-α) were significantly higher in female livers compared to male livers at 0.10–0.20 mg/L methomyl exposure. Consequently, our study underscores the potential adverse effects of environmental methomyl exposure on aquatic organisms and highlights the need for heightened consideration of the risks posed by environmental endocrine disruptors to female health and safety. [ABSTRACT FROM AUTHOR]

To investigate the activities of intestinal digestive enzymes, liver antioxidant enzymes, immunological enzymes, and glucometabolic enzymes in largemouth bass (Micropterus salmoides) under the biofloc model, an experiment was conducted in 300-liter glass tanks. The experiment comprised a control group, which was fed a basal diet, and a biofloc group, where glucose was added to maintain a C/N ratio of 15. Each group had three parallel setups, with a stocking density of 20 fish per tank. The experiment ran for 60 days, employing a zero-water exchange aquaculture model. The results showed that at the end of the culture period, there were no significant differences between the initial weight, final weight, WGR, SGR, and SR of the biofloc group and the control group of largemouth bass (p > 0.05), whereas the lower FCR and the higher PER in the biofloc group were significant (p < 0.05); intestinal α-amylase, trypsin, and lipase activities of largemouth bass in the biofloc group were significantly increased by 37.20%, 64.11%, and 51.69%, respectively, compared with the control group (p < 0.05); liver superoxide dismutase and catalase activities, and total antioxidant capacity of largemouth bass in the biofloc group were significantly increased by 49.26%, 46.87%, and 98.94% (p < 0.05), while the malondialdehyde content was significantly reduced by 19.91% (p < 0.05); liver lysozyme, alkaline phosphatase, and acid phosphatase activities of largemouth bass in the biofloc group were significantly increased by 62.66%, 41.22%, and 29.66%, respectively (p < 0.05); liver glucokinase, pyruvate kinase, glucose-6-phosphate kinase, pyruvate kinase, glucose-6-phosphatase, and glycogen synthase activities were significantly increased by 46.29%, 99.33%, 32.54%, and 26.89%, respectively (p < 0.05). The study showed that the biofloc model of culturing largemouth bass can not only enhance digestive enzyme activities, antioxidant capacity, and immune response but can also promote the process of glucose metabolism and reduce feeding costs. This study provides data support for healthy culturing of largemouth bass in future production, provides a theoretical reference for optimizing the biofloc technology culture model, and is crucial for promoting the healthy and green development of aquaculture. [ABSTRACT FROM AUTHOR]

Simple Summary: Microorganisms are important components of biological communities in the aquaculture ecosystems, which play fundamental roles in regulating the water quality and maintaining the health of cultured animals. Microalgae addition as a common aquaculture practice has been reported to have positive effects on both the aquaculture environment and organisms. However, the ecological effects of microalgae addition on the aquaculture ecosystems from the perspective of microbial community ecology are still unclear. In the present study, we analyzed the characteristics of microbial communities in various microhabitats in tilapia aquaculture systems with different microalgae additions. It was found that microalgae addition exhibited greater impacts on the diversity, compositions, and co-occurrence patterns of the gill-associated bacterial communities than those of water and fish gut. The mucosal surface of the fish gill represented an important barrier essential for fish health. Microalgae addition altered the composition of gill-associated bacterial communities, which could be important for mucosal immunity and thereby influence fish health. These findings provided a new insight into the mechanisms underlying the impacts of microalgae addition on aquaculture. Microalgae has been widely used in aquaculture to improve both the water environment and fish growth; however, the current understanding of the effects of microalgae addition on the key players involved in regulating the water environment and fish health, such as microorganisms, remains limited. Here, a 50-day mesocosm experiment was set up to simulate the culture of Genetic Improvement of Farmed Tilapia (GIFT, Oreochromis niloticus) with an average weight of 14.18 ± 0.93 g and an average length of 82.77 ± 2.80 mm. Different amounts of Chlorella pyrenoidosa were added into these artificial systems to investigate dynamics of bacterial communities in aquaculture water, fish gill, and gut using amplicon-based high-throughput sequencing technology. Our results showed that Chlorella pyrenoidosa addition increased diversity and network complexity of gill-associated bacterial communities rather than those of the water and gut. Furthermore, more biomarkers in the gill-associated bacterial communities were detected in response to Chlorella pyrenoidosa addition than the water and fish gut samples. These findings highlighted the high sensitivity of gill-associated bacterial communities in response to the Chlorella pyrenoidosa addition, implying Chlorella pyrenoidosa addition could play important roles in regulating the fish mucosal immunity by altering the gill-associated microbiota. [ABSTRACT FROM AUTHOR]

This study investigated ammonia-oxidizing bacterial communities in water and surface sediments of three tilapia ponds and their relationship with differences in the ponds, monthly variations in the water, and the physico-chemical parameters. Samples were collected from ponds with different stocking densities, after which DNA was extracted, 16S rRNA genes were amplified, the Illumina high-throughput sequencing was performed, and then the Silva and FunGene databases were used to investigate the ammonia-oxidizing bacterial communities. In total, 308,488 valid reads (144,931 in water and 163,517 in sediment) and 240 operational taxonomic units (207 in water and 225 in sediment) were obtained. Further analysis showed that the five genera of Nitrosospira, Nitrosococcus, Nitrosomonas, Proteobacteria_unclassified, and Nitrosomonadaceae_unclassified were distributed not only in the water, but also in surface sediments of all three ponds. Further, not only the abundance of these five genera, but also their diversities were affected by monthly variations in the water and by sediment differences among the ponds. Moreover, the total nitrogen (TN), nitrate, total phosphorus (TP), and sulphate were the main factors influencing the ammonia-oxidizing bacterial communities in the water, whereas TP was the main influencing factor in the sediments. Moreover, the parameter changes, especially those caused by differences in the ponds, were closely related to the cultivation management (stocking density and feed coefficients). [ABSTRACT FROM AUTHOR]

The response of synthetic substrates of sex steroid hormones—cholesterol (CHO), pregnenolone (PREG), and progesterone (PROG)—in the serum and testes of male tilapia (Oreochromis niloticus) to the environmental estrogen pesticide methomyl (0.2, 2, 20, and 200 μg·L−1) was evaluated using static-water contact toxicity tests. The results showed that low methomyl concentrations (0.2 and 2 μg·L−1) had no significant effects on the contents of CHO, PREG, and PROG in the serum and testes of male tilapia (p > 0.05). Consequently, the concentration of 2 μg·L−1 could be used as a preliminary reference threshold for the non-effective dose of methomyl in male tilapia. Exposure to high methomyl concentrations (20 and 200 μg·L−1) significantly inhibited the levels of CHO, PREG, and PROG in the serum and testes of male tilapia (p < 0.05) and showed a dose–response relationship. Sex steroid hormone synthesis substrate damage to male tilapia caused by less than 20 μg·L−1 methomyl was reversible, while the damage caused by equal to or greater than 200 μg·L−1 methomyl was irreversible when tilapia were transferred to methomyl-free water for 18 days. Thus, a concentration of 200 μg·L−1 could be used as a reference threshold for irreversible damage caused by methomyl in male tilapia. [ABSTRACT FROM AUTHOR]

The chronic effect of environmental methomyl on the antioxidant system in testis of Nile tilapia (Oreochromis niloticus) and its recovery pattern was investigated. Tilapia were exposed to sublethal concentrations of 0.2, 2, 20 and 200 μgL−1 methomyl for 30 days and thereafter moved to methomyl-free water for 18 days. Antioxidant levels in testis, including glutathione peroxidase, catalase, glutathione-S-transferase, glutathione reductase, superoxide dismutase, reduced glutathione, oxidized glutathione were measured every 6 days during the period of exposure, and at 18 days after being transferred to methomyl-free water. The results showed that lower methomyl concentration (0.2 μgL−1) had no effect on the above antioxidants, thus 0.2 μgL−1 could be seen as NOAEL for methomyl to tilapia. However, higher methomyl concentration of 2, 20 and 200 μgL−1 could significantly influence the above antioxidants. Glutathione peroxidase and oxidized glutathione increased significantly. On the contrary, reduced glutathione decreased significantly. Catalase, superoxide dismutase, glutathione reductase, glutathione-S-transferase increased at lower methomyl (2 and 20 μgL−1), but decreased at higher methomyl (200 μgL−1). The recovery test showed that oxidative damage caused by lower methomyl of 2 and 20 μgL−1 was reversible, and oxidative damage caused by higher methomyl of 200 μgL−1 was irreversible within 18 days of recovery period. [ABSTRACT FROM AUTHOR]

The main object of this study is to introduce hybrid integration approaches that consist of state-of-the-art artificial intelligence algorithms (SysFor) and two bivariate models, namely the frequency ratio (FR) and index of entropy (IoE), to carry out landslide spatial prediction research. Hybrid integration approaches of these two bivariate models and logistic regression (LR) were used as benchmark models. Nanzheng County was considered as the study area. First, a landslide distribution map was produced using news reports, interpreting satellite images and a regional survey. A total of 202 landslides were identified and marked. According to the previous studies and local geological environment conditions, 16 landslide conditioning factors were chosen for landslide spatial prediction research: elevation, profile curvature, plan curvature, slope angle, slope aspect, stream power index (SPI), topographic wetness index (TWI), sediment transport index (STI), distance to roads, distance to rivers, distance to faults, lithology, rainfall, soil, normalized different vegetation index (NDVI), and land use. Then, the 202 landslides were randomly segmented into two parts with a ratio of 70:30. Seventy percent of the landslides (141) were used as the training dataset and the remaining landslides (61) were used as the validating dataset. Next, the evaluation models were built using the training dataset and compared by the receiver operating characteristics (ROC) curve. The results showed that all models performed well; the FR_SysFor model exhibited the best prediction ability (0.831), followed by the IoE_SysFor model (0.819), IoE_LR model (0.702), FR_LR model (0.696), IoE model (0.691), and FR model (0.681). Overall, these six models are practical tools for landslide spatial prediction research and the results can provide a reference for landslide prevention and control in the study area. [ABSTRACT FROM AUTHOR]