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Metal-reducing bacteria play an important role in the release and mobilization of arsenic from sediments into groundwater. This study aimed to investigate the influence of nitrate on arsenic bio-release. Microcosm experiments consisting of high arsenic sediments and indigenous bacterium Bacillus sp. D2201 were conducted and the effects of nitrate on the mobilization of As/Fe determined. The results show arsenic release is triggered by iron reduction, which is regulated by nitrate. Increasing the nitrate concentration from 0 to 1 and 3 mM decreased Fe(III) reduction by 62.5% and 16.9% and decreased As(V) bio-release by 41.5% and 85.5%, respectively. Moreover, the results of step-wise Wenzel sequential extractions indicate nitrate addition prevents the transformation of poorly crystalline iron oxides to well crystalline iron oxides. Overall, nitrate appears to have a dual effect, inhibiting both iron reduction and arsenic release by incubation strain D2201. This study offers new insights regarding the biogeochemistry of arsenic in groundwater systems.

As one of the most widely used recommender systems, movie recommendation plays an important role in our life. However, the data sparsity problem severely hinders the effectiveness of personalized movie recommendation, which requires more rich content information to be utilized. Posters and still frames, which directly display the visual contents of movies, have significant influences on movie recommendation. They not only reveal rich knowledge for understanding movies but also useful for understanding user preferences. However, existing recommendation methods rarely consider aesthetic features, which tell how the movie looks and feels, extracted from these pictures for the movie recommendation. To this end, in this paper, we propose an aesthetic-aware unified visual content matrix factorization (called UVMF-AES) to integrate visual feature learning and recommendation into a unified framework. Specifically, we first integrate the convolutional neural network (CNN) features and aesthetic features into probabilistic matrix factorization. Then we establish a unified optimization framework with these features for the movie recommendation. The experimental results on two real-world datasets show that our proposed method UVMF-AES is significantly superior to the state-of-the-art methods on movie recommendation.

Next basket recommendation with consideration of user sequential shopping behaviors plays a significant role in E-commerce to improve the user experience and service quality. Recently, recurrent neural networks (RNNs), especially attention-based RNN, have been widely adopted in the next basket recommendation. However, existing fixed attention mechanisms are not designed to model the dynamic and diverse characteristics of user appetites. In this paper, we propose an inter-basket and intra-basket adaptive attention network (IIAAN) for the next basket recommendation. Specifically, the inter-basket adaptive attention acts on all historical user baskets to model user's diverse long-term preferences, while the intra-basket adaptive attention is designed to act on item-level in the most recent basket to model user's dynamic and different short-term preferences. Then, we further integrate inter-basket and intra-basket adaptive attentions together to improve recommendation effectiveness. Finally, we evaluate the proposed model IIAAN using three real-world datasets from various E-commerce platforms. Our experimental results show that our model IIAAN significantly outperforms the state-of-the-art approaches for the next basket recommendation.