Rare earth elements applied to phytoremediation: Enhanced endocytosis promotes remediation of antimony contamination with different valence levels in Solanum nigrum L.

Antimony (Sb) pollution poses a noteworthy risk to human health and ecosystem sustainability, therefore effective, eco-friendly, and widely accepted restoration methods are urgently needed. This study introduces a new approach of using La(III) foliar application on Solanum nigrum L. (S. nigrum), a cadmium hyperaccumulator, to improve its photosynthetic and root systems under Sb stress, resulting in a higher biomass. Notably, La(III) also enhances endocytosis in root cells, facilitating efficient and non-selective remediation of both Sb(III) and Sb(V) forms. The absorption of Sb by root cell endocytosis was observed visually with a confocal laser scanning microscope. The subcellular distribution of Sb in the cell wall of S. nigrum is reduced. And the antioxidant enzyme activity system is improved, resulting in an enhanced Sb tolerance in S. nigrum. Based on the existing bibliometric analysis, this paper identified optimal conditions for S. nigrum to achieve maximum translocation and bioconcentration factor values for Sb. The foliar application of La(III) on plants treated with Sb(III), Sb(V), and a combination of both resulted in translocation factor values of 0.89, 1.2, 1.13 and bioconcentration factor values of 11.3, 12.81, 14.54, respectively. Our work suggests that La(III)-enhanced endocytosis of S. nigrum root cells is a promising remediation strategy for Sb-contaminated environments.

Research progress in water quality prediction based on deep learning technology: a review.

Water, an invaluable and non-renewable resource, plays an indispensable role in human survival and societal development. Accurate forecasting of water quality involves early identification of future pollutant concentrations and water quality indices, enabling evidence-based decision-making and targeted environmental interventions. The emergence of advanced computational technologies, particularly deep learning, has garnered considerable interest among researchers for applications in water quality prediction because of its robust data analytics capabilities. This article comprehensively reviews the deployment of deep learning methodologies in water quality forecasting, encompassing single-model and mixed-model approaches. Additionally, we delineate optimization strategies, data fusion techniques, and other factors influencing the efficacy of deep learning-based water quality prediction models, because understanding and mastering these factors are crucial for accurate water quality prediction. Although challenges such as data scarcity, long-term prediction accuracy, and limited deployments of large-scale models persist, future research aims to address these limitations by refining prediction algorithms, leveraging high-dimensional datasets, evaluating model performance, and broadening large-scale model application. These efforts contribute to precise water resource management and environmental conservation.

Foliar spraying of lanthanum activates endocytosis in lettuce (Lactuca sativa L.) root cells, increasing Cd and Pb accumulation and their bioaccessibility.

Cadmium (Cd) and lead (Pb) accumulate easily in leafy vegetables and can harm human health. Lanthanum (La) have been used to improve agricultural yield and quality, but the effect of La application on Cd/Pb enrichment in leafy vegetables remains incomplete currently. A previous study reported that the endocytosis in lettuce leaf cells can be activated by La, leading to an increase in Pb accumulation in lettuce leaves. However, it has not been investigated whether foliar application of La enhances root cellular endocytosis and promotes its uptake of Cd and Pb. In this study, the influence of La on the uptake of Cd and Pb, Cd bioaccessibility, and the safety risks of cultivating lettuce under Cd and Pb stress were explored. It was found that La increased Cd (16-30 % in shoot, 16-34 % in root) and Pb (25-29 % in shoot, 17-23 % in root) accumulation in lettuce. The increased accumulation of Cd and Pb could be attributed to La-enhanced endocytosis. Meanwhile, La enhanced the toxicity of both Cd and Pb, inhibited lettuce growth, and aggravated the damage to the photosynthetic and antioxidant systems. Finally, gastrointestinal simulation experiments showed that La increased the Cd bioaccessibility in both gastric and intestinal phase by 7-108 % and 9-87 %, respectively. These results offer valuable insights into the safety of REEs for agricultural applications.

A Meta-Analysis of Influencing Factors on the Activity of BiVO4-Based Photocatalysts.

With the continuous advancement of global industrialization, a large amount of organic and inorganic pollutants have been discharged into the environment, which is essential for human survival. Consequently, the issue of water environment pollution has become increasingly severe. Photocatalytic technology is widely used to degrade water pollutants due to its strong oxidizing performance and non-polluting characteristics, and BiVO4-based photocatalysts are one of the ideal raw materials for photocatalytic reactions. However, a comprehensive global analysis of the factors influencing the photocatalytic performance of BiVO4-based photocatalysts is currently lacking. Here, we performed a meta-analysis to investigate the differences in specific surface area, kinetic constants, and the pollutant degradation performance of BiVO4-based photocatalysts under different preparation and degradation conditions. It was found that under the loading condition, all the performances of the photocatalysts can be attributed to the single BiVO4 photocatalyst. Moreover, loading could lead to an increase in the specific surface area of the material, thereby providing more adsorption sites for photocatalysis and ultimately enhancing the photocatalytic performance. Overall, the construct heterojunction and loaded nanomaterials exhibit a superior performance for BiVO4-based photocatalysts with 136.4% and 90.1% improvement, respectively. Additionally, within a certain range, the photocatalytic performance increases with the reaction time and temperature.

The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics.

A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). Methods made use of modern machine learning tools and were based on unsupervised learning (autoencoders, generative adversarial networks, normalizing flows), weakly supervised learning, and semi-supervised learning. This paper will review the LHC Olympics 2020 challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders.