Soil erosion is a critical environmental problem of the Chinese Loess Plateau (CLP). The effects of vegetation cover on soil erosion reduction under different rainfall types are not well understood especially in the eastern Chinese Loess Plateau (ECLP). In this study, we monitored runoff and sediment yield at the Fengjiagou water and soil conservation station with five types of vegetation cover (arbor trees (ARC), shrubs (SHC), arable (ABC), natural vegetation (NVC), and artificial grass (APC)) and three slope gradients (10°, 15°, and 20°) in the ECLP. Based on long-term monitoring data, five rainfall types were classified by the maximum 30 min rainfall intensity (I30). We also quantitatively revealed the interactive effects of different types precipitation, vegetation cover and slope gradients on regional soil erosion. The results showed that (1) The RII (13 times) and RIII (eight times) type are the most threatening erosive rainfall in this region. (2) The ARC and SHC type were most beneficial for soil and water conservation in the ECLP; The APC and ABC are not conductive to the prevention of regional soil erosion. (3) Runoff and sediment yields increased with the slope gradient. The farmland is vulnerable to soil erosion when the slope gradient exceeds 10°. The results of this study can improve the understanding of regional soil erosion processes on the ECLP and provide useful information for managing regional water and land resources.

At present, the mainstream distant supervised relation extraction methods existed problems: the coarse granularity for coding the context feature information; the difficulty in capturing the long-term dependency in the sentence, and the difficulty in coding prior knowledge of structures are major issues. To address these problems, we propose a distant supervised relation extraction model via DiSAN-2CNN on feature level, in which multi-dimension self-attention mechanism is utilized to encode the features of the words and DiSAN-2CNN is used to encode the sentence to obtain the long-term dependency, the prior knowledge of the structure, the time sequence, and the entity dependence in the sentence. Experiments conducted on the NYT-Freebase benchmark dataset demonstrate that the proposed DiSAN-2CNN on a feature level model achieves better performance than the current two state-of-art distant supervised relation extraction models PCNN+ATT and ResCNN-9, and it has d generalization ability with the least artificial feature engineering.

The novel per- and polyfluoroalkyl substances (PFAS) have attracted global attention due to their persistence, bioaccumulation, and various toxicities. Few studies have focused on the toxicological effects of 1H,1H,2H,2H-perfluorooctanesulfonic acid (6:2 FTS), an alternative of perfluorooctanesulfonate (PFOS) detected worldwide. In this study, PFOS was used as a positive control to evaluate the developmental toxicity of 6:2 FTS in zebrafish embryos and larvae. The results of embryo exposure showed that the acute toxicity of 6:2 FTS was lower than that of PFOS. 6:2 FTS could affect the development of zebrafish, including increasing the utilization of yolk sac and the length of the pharyngeal pouch, leading to pericardial edema and inhibiting beating of the heart. Then transgenic zebrafish CZ63 and CZ40 were used to further explore its cardiovascular toxicity. 6:2 FTS could induce vascular hyperplasia, inhibit atrial development, and reduce blood flow velocity in larvae at 72 hours postfertilization. By employing transcriptome analysis and reverse transcription polymerase chain reaction, the involved molecular pathway was finally investigated. Two micromolar 6:2 FTS enhanced the activity of CAT and GSH-Px, involved in calcium signal transduction and myocardial contraction, and affected the MAPK, FoxO, and p53 signaling pathways. Our study can provide scientific insight into the biological safety and health risk of 6:2 FTS.

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