A comprehensive study on the ecotoxicity of ivermectin to earthworms (Eisenia fetida).

Ivermectin (IVM) is a dewormer commonly utilized in animal farming. Nevertheless, there is a deficiency of research on the bioecotoxicity of IVM in soil. In this study, earthworms were utilized as test animals to investigate the ecotoxicological impacts of IVM. The experiment lasted 28 days and involved adding varied doses of IVM to a culture substrate of soil mixed with cow dung and feeding it to earthworms. The experiment entailed recording earthworm weight, number of earthworm cocoons, histological damage, oxidative stress indicators, and gene expression levels. The analysis results showed that earthworm growth and reproduction were hampered by IVM. Moreover, pathological damage to the earthworms increased with increasing IVM concentration, which caused increased oxidative damage to the earthworms. These findings offer a summary of the impact of IVM on earthworms and a reference point for future research examining the ecological implications of IVM.

N-acetylcysteine alleviates pulmonary alveolar proteinosis induced by indium-tin oxide nanoparticles in male rats: involvement of the NF-κB signaling pathway.

Indium-tin oxide (ITO) was previously found to have a toxic effect on lung tissues, and oxidative stress and the inflammatory response are two important mechanisms of ITO­induced lung injury. N-acetylcysteine (NAC) has been found to exhibit antioxidant and anti­inflammatory properties. The current study aimed to evaluate the possible protective effects of NAC against ITO nanoparticle (Nano-ITO)-induced pulmonary alveolar proteinosis (PAP) in adult male Sprague-Dawley rats, especially via modulation of nuclear factor-kappa B (NF-κB) signaling. For this purpose, 50 rats were randomly allocated into five groups (10 rats each) as follows: (1) control group; (2) saline group; (3) NAC (200 mg/kg) group; (4) PAP model group receiving a repeated intratracheal dose of Nano-ITO (6 mg/kg); and (5) PAP model+NF-κB inhibitor (NAC) group pre-treated intraperitoneally with NAC (200 mg/kg) twice per week before the administration of an intratracheal dose of Nano-ITO (6 mg/kg). Rats were then euthanized under anesthesia, and their lungs were removed for histopathological and biochemical investigations. A 6 mg/kg dose of Nano-ITO markedly altered the levels of some oxidative stress biomarkers. The histological examination of Nano-ITO-exposed rats demonstrated diffused alveolar damage that involved PAP, cholesterol crystals, alveolar fibrosis, pulmonary fibrosis, and alveolar emphysema. The immunohistochemical results of Nano-ITO-exposed rats revealed strongly positive NF-κB p65 and inhibitory kappa B kinase (IKK)-ß and weakly positive inhibitor of kappa-B subunit alpha (IκB-α) staining reactivity in the nuclei of cells lining the epithelium of the bronchioles and alveoli. Moreover, Nano-ITO activated the NF-κB pathway. However, pre-treatment with NAC significantly attenuated Nano-ITO-evoked alterations in the previously mentioned parameters, highlighting their antioxidant, anti-inflammatory, and anti-apoptotic potential. The results indicated that the degree of pulmonary fibrosis and proteinosis in the NAC­treated group was improved compared with that in the Nano-ITO-induced PAP model group. The level of malondialdehyde was also decreased overall in the NAC-treated group compared with that in the Nano-ITO-induced model group, indicating that the pulmonary fibrosis degree and oxidation levels were decreased. The present study also demonstrated that NAC increased the activity of antioxidant enzyme superoxide dismutase and total antioxidant capacity, indicating that it could alleviate oxidative stress in the lung tissue of Nano-ITO­exposed rats. In addition, NAC reduced the production of pro­inflammatory cytokines interleukin (IL)­1ß, IL­6, and tumor necrosis factor (TNF)­α, and increased the levels of anti­inflammatory factor IL­10. The current study demonstrated that NAC can effectively attenuate Nano-ITO­induced lung injury by reducing oxidative damage and the inflammatory response.

Air pollutant prediction model based on transfer learning two-stage attention mechanism.

Atmospheric pollution significantly impacts the regional economy and human health, and its prediction has been increasingly emphasized. The performance of traditional prediction methods is limited due to the lack of historical data support in new atmospheric monitoring sites. Therefore, this paper proposes a two-stage attention mechanism model based on transfer learning (TL-AdaBiGRU). First, the first stage of the model utilizes a temporal distribution characterization algorithm to segment the air pollutant sequences into periods. It introduces a temporal attention mechanism to assign self-learning weights to the period segments in order to filter out essential period features. Then, in the second stage of the model, a multi-head external attention mechanism is introduced to mine the network's hidden layer key features. Finally, the adequate knowledge learned by the model at the source domain site is migrated to the new site to improve the prediction capability of the new site. The results show that (1) the model is modeled from the data distribution perspective, and the critical information within the sequence of periodic segments is mined in depth. (2) The model employs a unique two-stage attention mechanism to capture complex nonlinear relationships in air pollutant data. (3) Compared with the existing models, the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of the model decreased by 14%, 13%, and 4%, respectively, and the prediction accuracy was greatly improved.

Protective effects of lycopene against zearalenone-induced reproductive toxicity in early pregnancy through anti-inflammatory, antioxidant and anti-apoptotic effects.

Zearalenone is a mycotoxin that is widely present in feed and raw materials and can cause severe reproductive toxicity. Lycopene is a natural carotenoid with antioxidant and anti-inflammatory pharmacological effects, but the protective effects of lycopene against zearalenone-induced uterine damage have not been reported. The aim of this study was to investigate the protective effect of lycopene treatment in early pregnancy on zearalenone-induced uterine damage and pregnancy impairment and its mechanism. Reproductive toxicity was induced by consecutive gavages of zearalenone at 5 mg/kg body weight during gestational days (GDs) 0-10 and in the presence or absence of oral administration of lycopene (20 mg/kg BW). The results showed that lycopene may alleviate zearalenone-induced pathological uterine histological damage and disturbances in oestradiol (E2), follicle-stimulating hormone (FSH), progesterone (P) and luteinizing hormone (LH) secretion. Lycopene increased superoxide dismutase (SOD) activity and decreased malondialdehyde (MDA) production, providing protection against zearalenone-induced oxidative stress in the uterus. Additionally, lycopene significantly reduced levels of pro-inflammatory cytokines, including interleukin 1ß (IL-1ß), interleukin 6 (IL-6) and tumor necrosis factor-α (TNF-α), and elevated levels of the anti-inflammatory factor interleukin 10 (IL-10), inhibiting the zearalenone-induced inflammatory response. In addition, lycopene improved the homeostasis of uterine cell proliferation and death via the mitochondrial apoptosis pathway. These data provide strong evidence that lycopene can be further developed into a potential new drug for the prevention or treatment of zearalenone-induced reproductive toxicity.

Spatial and temporal characteristics analysis and prediction model of PM2.5 concentration based on SpatioTemporal-Informer model.

The primary cause of hazy weather is PM2.5, and forecasting PM2.5 concentrations can aid in managing and preventing hazy weather. This paper proposes a novel spatiotemporal prediction model called SpatioTemporal-Informer (ST-Informer) in response to the shortcomings of spatiotemporal prediction models commonly used in studies for long-input series prediction. The ST-Informer model implements parallel computation of long correlations and adds an independent spatiotemporal embedding layer to the original Informer model. The spatiotemporal embedding layer captures the complex dynamic spatiotemporal correlations among the input information. In addition, the ProbSpare Self-Attention mechanism in this model can focus on extracting important contextual information of spatiotemporal data. The ST-Informer model uses weather and air pollutant concentration data from numerous stations as its input data. The outcomes of the trials indicate that (1) The ST-Informer model can sharply capture the peaks and sudden changes in PM2.5 concentrations. (2) Compared to the current models, the ST-Informer model shows better prediction performance while maintaining high-efficiency prediction [Formula: see text]. (3) The ST-Informer model has universal applicability, and the model was applied to the concentration of other pollutants prediction with good results.

The Application of the Generalized Additive Model to Represent Macrobenthos near Xiaoqing Estuary, Laizhou Bay.

Macrobenthos is widely used as an indicator of ecological health in marine monitoring and assessment. The present study aimed to characterize the interrelationships between the distribution of the macrobenthos community and environmental factors near Xiaoqing Estuary, Laizhou Bay. Responses of species richness to environmental factors were studied using the generalized additive model (GAM) and the Margalef diversity index (dM) as indicators of species diversity instead of individual indicator species. Six factors were selected in the optimal model by stepwise regression: sediment factors (organic matter, phosphate, nitrate nitrogen, and ammonium nitrogen) and water factors (salinity, and ammonium nitrogen). The response curves generated by the GAM showed a unimodal relationship among taxa diversity, salinity in water, and sediment organic matter. dM was positively correlated with ammonium nitrogen in water and was negatively correlated with phosphate in the sediment. The model optimized by forward stepwise optimization explained 92.6% of the Margalef diversity index with a small residual (2.67). The model showed good performance, with the measured dM strongly correlated with the predicted dM (Pearson R2 = 0.845, p < 0.05). The current study examined the combined influence of multiple eco-factors on macrobenthos, and the Margalef diversity index of macrobenthos was predicted by the GAM model in a salinity-stressed estuary.

[Development of animal model for lung injury in rats caused by unknown polymer via intratracheal instillation].

OBJECTIVE: To establish an animal model of lung injury in SD rats using intratracheal instillation of unknown polymer and to provide the base for exploring the molecular mechanism of lung tissue injury induced by occupational exposure. METHODS: One hundred forty SD rats were randomly divided into seven groups, including the control group 1 which was exposed to normal solution, the control group 2 which was not exposed to any one and five treatment groups which were exposed to 1 ml unknown polymer (0.5 ml for each lung) at the doses of 40, 30, 20, 10 and 5 mg/ml, respectively by intratracheal instillation. The rats were sacrificed on the 1st, 3rd, 7th, 10th, 14th, 21th and 28th day after exposure, then the lung tissues were examined pathologically and the blood bio-chemical analysis was conducted. RESULTS: The results of blood biochemical analysis indicated that ALT and AST levels in rats exposed to 30 and 40 mg/ml unknown polymer were significantly higher than those in control groups. Intratracheal instillation of unknown polymer can causes PLF in experimental animals on the 14th days after exposure. The results of pathological examination exhibited that the lung tissue injury in rats exposed to unknown polymer for 14 days or more was found and the dose-effect relationship was observed. CONCLUSION: An animal model of lung injury in SD rats induced by unknown polymer with intratracheal instillation was established successfully. The results of pathological examination showed that the types of rat lung injury were similar to the clinical lung injury after exposure to unknown polymer, which provided a base for studying the mechanism of lung injury caused by occupational exposure to unknown polymer.

Pulmonary and Systemic Toxicity in a Rat Model of Pulmonary Alveolar Proteinosis Induced by Indium-Tin Oxide Nanoparticles.

PURPOSE: The main objective of this study was to clarify the biodistribution and in vivo toxicological effects of indium-tin oxide nanoparticles (Nano-ITO) in male rats. METHODS: Dose-response (three divided doses) and time-course studies (six exposure durations) were performed to examine Nano­ITO-induced pulmonary and systemic toxicity. At the end of the experiment, hematology and serum biochemical parameters were determined, and cytokines levels and oxidative stress were analyzed in the bronchoalveolar lavage fluid. In addition, indium biodistribution following Nano­ITO exposure was determined using inductively coupled plasma mass spectrometer to measure indium concentration in the lung, spleen, brain, liver, kidney, and testis. Rat lung tissues were also harvested for staining with hematoxylin and eosin, periodic acid Schiff stain, Masson's trichrome, and Sirius red. RESULTS: Relative lung weights were significantly increased in all Nano-ITO-exposed groups. All organs exhibited a statistically significant difference in indium levels. Rat exposure to Nano­ITO resulted in a dose-response increase in acute systemic inflammation and injury. BALF analysis revealed significantly elevated levels of lung oxidative stress, pulmonary injury, and inflammatory markers across most groups. Serum biochemistry results showed that Nano-ITO could affect the liver and renal functions of rats when exposed for 3 days. Compared with the control group, significant inflammatory responses or pathological changes were observed in the liver, kidney, and testis of rats at different sampling times and three doses examined. Histopathologically, foci of slight-to-severe pulmonary inflammatory response along with acute inflammatory, pulmonary fibrosis and alveolar proteinosis were detected, and the severity of these lesions worsened in a dose- and time-dependent manner. DISCUSSION: These findings provide novel evidence that enhanced progressive massive pulmonary fibrosis, diffuse interstitial fibrosis, and collagen accumulation play a role in the development of pulmonary alveolar proteinosis following Nano-ITO exposure.

A survival analysis based volatility and sparsity modeling network for student dropout prediction.

Student Dropout Prediction (SDP) is pivotal in mitigating withdrawals in Massive Open Online Courses. Previous studies generally modeled the SDP problem as a binary classification task, providing a single prediction outcome. Accordingly, some attempts introduce survival analysis methods to achieve continuous and consistent predictions over time. However, the volatility and sparsity of data always weaken the models' performance. Prevailing solutions rely heavily on data pre-processing independent of predictive models, which are labor-intensive and may contaminate authentic data. This paper proposes a Survival Analysis based Volatility and Sparsity Modeling Network (SAVSNet) to address these issues in an end-to-end deep learning framework. Specifically, SAVSNet smooths the volatile time series by convolution network while preserving the original data information using Long-Short Term Memory Network (LSTM). Furthermore, we propose a Time-Missing-Aware LSTM unit to mitigate the impact of data sparsity by integrating informative missingness patterns into the model. A survival analysis loss function is adopted for parameter estimation, and the model outputs monotonically decreasing survival probabilities. In the experiments, we compare the proposed method with state-of-the-art methods in two real-world MOOC datasets, and the experiment results show the effectiveness of our proposed model.

Influences of Substrate Grain Size on the Burrowing Behavior of Juvenile Meretrix meretrix.

The substrate is the key environmental factor that affects the growth, survival, population and distribution of dwelling mollusks in mudflat settings. To clarify the effect of the substrate grain size on soft substrate preference, burrowing ability and behavior during the selection process of juvenile Meretrix meretrix, four different grain size substrates (coarse sand, medium sand, fine sand, and natural substrate) were set up for comparison. The results indicated that: (1) the burrowing ability of juvenile specimens in fine sand was the strongest; (2) the degree (from high to low) of the juvenile's preference for the four substrates was in the order of fine sand > natural substrate > medium sand > coarse sand; and (3) the selection process of the substrate by the juveniles could be divided into four stages: preparation, selection, burrowing and end stages. These stages showed the behavioral characteristics of a longer selection time and higher percentage of movement in coarse sand. Therefore, our results demonstrated that sea areas or ponds with fine sand as the main component are more suitable for stock enhancement with M. meretrix. These results provide basic data for habitat selection and suitability evaluations for the aquaculture of M. meretrix.

Effect of large-scale kelp and bivalve farming on seawater carbonate system variations in the semi-enclosed Sanggou Bay.

Although cultured algae and shellfish can be the dominant species in some localized coastal waters, research on the effect of large-scale mariculture on the carbonate system variations in these local waters is still lacking. We conducted five cruises from May to September and studied spatiotemporal variations in the seawater carbonate system in the semi-closed Sanggou Bay, which is famous for its large-scale mariculture. Our results showed that both kelp and bivalve farming induced significant spatiotemporal variations in the carbonate system within the bay. When cultured kelp reached its highest biomass in May, the maximum ΔDIC, ΔpCO2 and ΔpHT between the seawater from the kelp farming area and the non-farming outer bay area was -156 µmol kg-1, -102 µatm and 0.15 pH units, respectively. However, no significant effect of kelp farming on seawater total alkalinity (TA) was observed. Kelp farming also caused the carbonate system variations of seawater from the bivalve farming area. Assuming no kelp was farmed in May, the average pH and pCO2 would reduce by 0.12 pH units and increase by 179 µatm, respectively, in the bivalve farming area. Bivalve farming significantly reduced seawater TA, indicating that fast deposition of calcium carbonate occurred in the bivalve farming area. Although bivalve respiration released CO2 into seawater and elevated seawater pCO2 level and reduced seawater pHT, surprisingly, seawater dissolved inorganic carbon (DIC) reduced significantly in the bivalve farming area. These results indicated that bivalves fixed a larger amount of inorganic carbon by calcification than that released into seawater by respiration. Overall, large-scale kelp and bivalve farming are important biological drivers of variations in the carbonate system within the semi-enclosed Sanggou Bay. Altered carbonate systems by kelp farming may favour calcification of farmed bivalves and provide an essential refuge for these species during the future ocean acidification.