Correction: Cyanobacterial extracellular antibacterial substances could promote the spread of antibiotic resistance: impacts and reasons.

Correction for 'Cyanobacterial extracellular antibacterial substances could promote the spread of antibiotic resistance: impacts and reasons' by Rui Xin et al., Environ. Sci.: Processes Impacts, 2023, 25, 2139-2147, https://doi.org/10.1039/D3EM00306J.

Cyanobacterial extracellular antibacterial substances could promote the spread of antibiotic resistance: impacts and reasons.

Many studies have shown that antibiotic resistance genes (ARGs) can be facilitated by a variety of antibacterial substances. Cyanobacteria are photosynthetic bacteria that are widely distributed in the ocean. Some extracellular substances produced by marine cyanobacteria have been found to possess antibacterial activity. However, the impact of these extracellular substances on ARGs is unclear. Therefore, we established groups of seawater microcosms that contained different concentrations (1000, 100, 10, 1, 0.1, 0.01, and 0 µg mL-1) of cyanobacterial extracellular substances (CES), and tracked the changes of 17 types of ARGs, the integron gene (intI1), as well as the bacterial community at different time points. The results showed that CES could enrich most ARGs (15/17) in the initial stage, particularly at low concentrations (10 and 100 µg mL-1). The correlation analysis showed a positive correlation between several ARGs and intI1. It is suggested that the abundance of intI1 increased with CES may contribute to the changes of these ARGs, and co-resistance of CES may be the underlying reason for the similar variation pattern of some ARGs. Moreover, the results of qPCR and high-throughput sequencing of 16S rRNA showed that CES had an inhibitory impact on the growth of bacterial communities. High concentrations of CES were found to alter the structure of bacterial communities. Co-occurrence networks showed that bacteria elevated in the high concentration group of CES and might serve as the potential hosts for a variety of ARGs. In general, marine cyanobacteria could play an important role in the global dissemination of ARGs and antibiotic-resistant bacteria (ARBs).

Multi-Modality Adaptive Feature Fusion Graph Convolutional Network for Skeleton-Based Action Recognition.

Graph convolutional networks are widely used in skeleton-based action recognition because of their good fitting ability to non-Euclidean data. While conventional multi-scale temporal convolution uses several fixed-size convolution kernels or dilation rates at each layer of the network, we argue that different layers and datasets require different receptive fields. We use multi-scale adaptive convolution kernels and dilation rates to optimize traditional multi-scale temporal convolution with a simple and effective self attention mechanism, allowing different network layers to adaptively select convolution kernels of different sizes and dilation rates instead of being fixed and unchanged. Besides, the effective receptive field of the simple residual connection is not large, and there is a great deal of redundancy in the deep residual network, which will lead to the loss of context when aggregating spatio-temporal information. This article introduces a feature fusion mechanism that replaces the residual connection between initial features and temporal module outputs, effectively solving the problems of context aggregation and initial feature fusion. We propose a multi-modality adaptive feature fusion framework (MMAFF) to simultaneously increase the receptive field in both spatial and temporal dimensions. Concretely, we input the features extracted by the spatial module into the adaptive temporal fusion module to simultaneously extract multi-scale skeleton features in both spatial and temporal parts. In addition, based on the current multi-stream approach, we use the limb stream to uniformly process correlated data from multiple modalities. Extensive experiments show that our model obtains competitive results with state-of-the-art methods on the NTU-RGB+D 60 and NTU-RGB+D 120 datasets.

Spatio-temporal convolutional residual network for regional commercial vitality prediction.

The vitality of commercial entities reflects the business condition of their surrounding area, the prediction of which helps identify the trend of regional development and make investment decisions. The indicators of business conditions, like revenues and profits, can be employed to make a prediction beyond any doubt. Unfortunately, such figures constitute business secrets and are usually publicly unavailable. Thanks to the rapid growing of location based social networks such as Yelp and Foursquare, massive amount of online data has become available for predicting the vitality of commercial entities. In this paper, a Spatio-Temporal Convolutional Residual Neural Network (STCRNN) is proposed for regional commercial vitality prediction, based on public online data, such as reviews and check-ins from mobile apps. Firstly, a commercial vitality map is built to indicate the popularity of business entities. Afterwards, a local convolutional neural network is employed to capture the spatial relationship of surrounding commercial districts on the vitality map. Then, a 3-dimension convolution is applied to deal with both recent and periodic variations, i.e., the sequential and seasonal changes of commercial vitality. Finally, long short-term memory is introduced to synthesize these two variations. In particular, a residual network is used to eliminate gradient vanishing and exploding, caused by the increase of depth of neural networks. Experiments on public Yelp datasets from 2013 to 2018 demonstrate that STCRNN outperforms the current methods in terms of mean square error.

Sequential Recommendation Based on Multivariate Hawkes Process Embedding With Attention.

Recommender systems are important approaches for dealing with the information overload problem in the big data era, and various kinds of auxiliary information, including time and sequential information, can help improve the performance of retrieval and recommendation tasks. However, it is still a challenging problem how to fully exploit such information to achieve high-quality recommendation results and improve users' experience. In this work, we present a novel sequential recommendation model, called multivariate Hawkes process embedding with attention (MHPE-a), which combines a temporal point process with the attention mechanism to predict the items that the target user may interact with according to her/his historical records. Specifically, the proposed approach MHPE-a can model users' sequential patterns in their temporal interaction sequences accurately with a multivariate Hawkes process. Then, we perform an accurate sequential recommendation to satisfy target users' real-time requirements based on their preferences obtained with MHPE-a from their historical records. Especially, an attention mechanism is used to leverage users' long/short-term preferences adaptively to achieve an accurate sequential recommendation. Extensive experiments are conducted on two real-world datasets (lastfm and gowalla), and the results show that MHPE-a achieves better performance than state-of-the-art baselines.

CAME: Content- and Context-Aware Music Embedding for Recommendation.

Traditional recommendation methods suffer from limited performance, which can be addressed by incorporating abundant auxiliary/side information. This article focuses on a personalized music recommender system that incorporates rich content and context data in a unified and adaptive way to address the abovementioned problems. The content information includes music textual content, such as metadata, tags, and lyrics, and the context data incorporate users' behaviors, including music listening records, music playing sequences, and sessions. Specifically, a heterogeneous information network (HIN) is first presented to incorporate different kinds of content and context data. Then, a novel method called content- and context-aware music embedding (CAME) is proposed to obtain the low-dimension dense real-valued feature representations (embeddings) of music pieces from HIN. Especially, one music piece generally highlights different aspects when interacting with various neighbors, and it should have different representations separately. CAME seamlessly combines deep learning techniques, including convolutional neural networks and attention mechanisms, with the embedding model to capture the intrinsic features of music pieces as well as their dynamic relevance and interactions adaptively. Finally, we further infer users' general musical preferences as well as their contextual preferences for music and propose a content- and context-aware music recommendation method. Comprehensive experiments as well as quantitative and qualitative evaluations have been performed on real-world music data sets, and the results show that the proposed recommendation approach outperforms state-of-the-art baselines and is able to handle sparse data effectively.