Changes in soil microbe-mediated carbon, nitrogen and phosphorus cycling during spontaneous succession in abandoned PbZn mining areas.

The mechanism through which soil microorganisms mediate carbon and nutrient cycling during mine wasteland restoration remained unknown. Using soil metagenome sequencing, we investigated the dynamic changes in soil microbial potential metabolic functions during the transition from biological soil crusts (BSC) to mixed broad-conifer forest (MBF) in a typical PbZn mine. The results showed soil microorganisms favored carbon sequestration through anaerobic and microaerobic pathways, predominantly using efficient, low-energy pathways during succession. Genes governing carbon degradation and aerobic respiration increased by 19.56 % and 24.79 %, respectively, reflecting change toward more efficient and intensive soil carbon utilization in late succession. Nitrogen-cycling genes mediated by soil microorganisms met their maximum influence during early succession (sparse grassland, SGL), leading to a respective increase of 75.29 % and 76.81 % in the net potential nitrification rate and total nitrogen content. Mantel and correlation analyses indicated that TOC, TN, Zn and Cd contents were the main factors affecting the soil carbon and phosphorus cycles. Soil AP content emerged as the primary influencer of genes associated with the nitrogen cycle. These results shed light on the dynamic shifts in microbial metabolic activities during succession, providing a genetic insight into biogeochemical cycling mechanisms and underscoring crucial factors influencing soil biogeochemical processes in mining regions.

Ecological impact of surfactant Tween-80 on plankton: High-scale analyses reveal deeper hazards.

The ecological risks of surfactants have been largely neglected because of their low toxicity. Multiscale studies have indicated that even if a pollutant causes no acute toxicity in a test species, it may alter interspecific interactions and community characteristics through sublethal impacts on test organisms. Therefore, we investigated the lethal and sublethal responses of the plankton species Scenedesmus quadricauda, Chlorella vulgaris, and Daphnia magna, to surfactant Tween-80. Then, high-scale responses in grazer life-history traits and stability of the D. magna-larval damselfly system were further explored. The results showed that discernible adverse effects on the growth or survival of the three plankton species were evident only at exceptionally high concentrations (≥100 mg L-1). However, 10 mg L-1 of Tween-80 notably affected the MDA concentration in grazer species, simultaneously displaying a tendency to diminish grazer's heartbeat and swimming frequency. Furthermore, Tween-80 reduced the grazer reproductive capacity and increased its predation risk by larval damselflies, which ultimately jeopardized the stability of the D. magna-larval damselfly system at much lower concentrations (10-100 fold lower) than the individual-scale responses. This study provides evidence that high-scale traits are far more sensitive to Tween-80, compared with individual-scale traits for plankton organisms, suggesting that the ecological risks of Tween-80 demand careful reassessment. SYNOPSIS: The concentration of Tween-80 needed to induce changes in community characteristics is markedly lower than that needed to produce individual-scale consequences. Thus, high-scale analyses have broad implications for understanding the hazardous effects of surfactants compared with an individual-scale analysis.

A regression model combined convolutional neural network and recurrent neural network for electroencephalogram-based cross-subject fatigue detection.

Fatigue, one of the most important factors affecting road safety, has attracted many researchers' attention. Most existing fatigue detection methods are based on feature engineering and classification models. The feature engineering is greatly influenced by researchers' domain knowledge, which will lead to a poor performance in fatigue detection, especially in cross-subject experiment design. In addition, fatigue detection is often simplified as a classification problem of several discrete states. Models based on deep learning can realize automatic feature extraction without the limitation of researcher's domain knowledge. Therefore, this paper proposes a regression model combined convolutional neural network and recurrent neural network for electroencephalogram-based (EEG-based) cross-subject fatigue detection. At the same time, a twofold random-offset zero-overlapping sampling method is proposed to train a bigger model and reduce overfitting. Compared with existing results, the proposed method achieves a much better result of 0.94 correlation coefficient (COR) and 0.09 root mean square error (RMSE) in a within-subject experiment design. What is more, there is no misclassification between awake and drowsy states. For cross-subject experiment design, the COR and RMSE are 0.79 and 0.15, respectively, which are close to the existing within-subject results and better than similar cross-subject results. The cross-subject regression model is very important for fatigue detection application since the fatigue indication is more precise than several discrete states and no model calibration is required for a new user. The twofold random-offset zero-overlapping sampling method can also be used as a reference by other EEG-based deep learning research.

A regression method for EEG-based cross-dataset fatigue detection.

Introduction: Fatigue is dangerous for certain jobs requiring continuous concentration. When faced with new datasets, the existing fatigue detection model needs a large amount of electroencephalogram (EEG) data for training, which is resource-consuming and impractical. Although the cross-dataset fatigue detection model does not need to be retrained, no one has studied this problem previously. Therefore, this study will focus on the design of the cross-dataset fatigue detection model. Methods: This study proposes a regression method for EEG-based cross-dataset fatigue detection. This method is similar to self-supervised learning and can be divided into two steps: pre-training and the domain-specific adaptive step. To extract specific features for different datasets, a pretext task is proposed to distinguish data on different datasets in the pre-training step. Then, in the domain-specific adaptation stage, these specific features are projected into a shared subspace. Moreover, the maximum mean discrepancy (MMD) is exploited to continuously narrow the differences in the subspace so that an inherent connection can be built between datasets. In addition, the attention mechanism is introduced to extract continuous information on spatial features, and the gated recurrent unit (GRU) is used to capture time series information. Results: The accuracy and root mean square error (RMSE) achieved by the proposed method are 59.10% and 0.27, respectively, which significantly outperforms state-of-the-art domain adaptation methods. Discussion: In addition, this study discusses the effect of labeled samples. When the number of labeled samples is 10% of the total number, the accuracy of the proposed model can reach 66.21%. This study fills a vacancy in the field of fatigue detection. In addition, the EEG-based cross-dataset fatigue detection method can be used for reference by other EEG-based deep learning research practices.

Water exchange unevenness alters the species dominance and community composition of submerged macrophytes in Erhai Lake and the potential mechanisms revealed by laboratory experiment.

Water exchange unevenness (WEU) is defined as the coefficient of variation in water exchange intensity over time. Although its influence on aquatic plant characteristics has been recently investigated, there is limited understanding regarding the effects of this hydrodynamic change on submerged vegetation. This study investigated the impacts of WEU on the species dominance and community composition of submerged macrophytes in three bays with different WEU conditions in Erhai Lake, China. Subsequently, a laboratory experiment was conducted to elucidate the mechanisms underlying these effects. The field investigation showed that the dominance values of submerged macrophytes were influenced by WEU. As WEU decreased, the average dominance value decreased for Vallisneria natans (by 34.54 %), Myriophyllum spicatum (16.82 %), and Hydrilla verticillata (12.84 %); showed no significant change for Potamogeton lucens; and increased for Potamogeton maackianus (14.22 %) and Ceratophyllum demersum (17.52 %). The laboratory experiment showed that lower WEU markedly inhibited the growth of V. natans, slightly inhibited that of M. spicatum, and stimulated that of P. maackianus, consistent with the field observations. The inhibitory effect was attributed to a reduced concentration of carbon dioxide in the water; adaptive strategies, i.e., plant height, biomass allocation, and root traits, were more effective for M. spicatum than for V. natans. The stimulated growth of P. maackianus was attributed to increased dissolved oxygen concentration, which promoted root growth and nutrient uptake. Our results indicate that WEU has significant effects on the growth and community characteristics of submerged macrophytes.

Effects of water exchange rate on morphological and physiological characteristics of two submerged macrophytes from Erhai Lake.

Growth patterns of aquatic macrophytes have been shown to vary in response to hydrological properties; however, such properties are typically characterized by water level fluctuation, flow velocity, flooding season, and sedimentation, but not by water exchange rate (WER). Herein, we experimentally investigated how WER (three levels: exchange 0%, 20%, and 40% of total water per day) affects water and sediment properties, and the consequences that these variations have on the individual responses of two submerged macrophytes, Hydrilla verticillata and Myriophyllum aquaticum which were planted in two different sediment types (sand and clay). In the experiment without ramets, it was found that turbidity, pH value, and dissolved carbon dioxide concentration of the system water were statistically unaffected by WER, while water dissolved oxygen (DO) concentration and sediment oxidation-reduction potential (ORP, in both sediments) consistently increased with increasing WER, regardless of experimental time. In the experiment containing ramets, biomass accumulation and relative growth rate (RGR) of both species gradually increased with increasing WER regardless of sediment type. The mechanisms were related to (a) increased oxygen availability, as indicated by gradually increased water DO concentration and sediment ORP; and (b) enhanced phosphorus (P) and nitrogen (N) absorbing abilities associated with stimulated root growth, reflected in increased mean root length, specific root length, and the root/above-ground biomass ratio, with increasing WER. Additionally, in the experiments containing ramets, significant linear relationships were consistently detected between sediment ORP and root parameters, root parameters and plant nutrients (N and P), and plant nutrients and plant growth conditions (biomass accumulation and RGR). These results demonstrate that WER plays an important role in determining oxygen availability and thus impacts the growth of submerged macrophytes by altering the ability of roots to absorb nutrients, indicating that ecosystem functions are more sensitive to WER than previously recognized.