Convolutional Neural Network (CNN) is one of the most significant networks in the deep learning field. Since CNN made impressive achievements in many areas, including but not limited to computer vision and natural language processing, it attracted much attention both of industry and academia in the past few years. The existing reviews mainly focus on the applications of CNN in different scenarios without considering CNN from a general perspective, and some novel ideas proposed recently are not covered. In this review, we aim to provide novel ideas and prospects in this fast-growing field as much as possible. Besides, not only two-dimensional convolution but also one-dimensional and multi-dimensional ones are involved. First, this review starts with a brief introduction to the history of CNN. Second, we provide an overview of CNN. Third, classic and advanced CNN models are introduced, especially those key points making them reach state-of-the-art results. Fourth, through experimental analysis, we draw some conclusions and provide several rules of thumb for function selection. Fifth, the applications of onedimensional, two-dimensional, and multi-dimensional convolution are covered. Finally, some open issues and promising directions for CNN are discussed to serve as guidelines for future work.

The link between the gut microbiota and metabolic syndrome (MetS) has attracted widespread attention. Christensenellaceae was recently described as an important player in human health, while its distribution and relationship with MetS in Chinese population is still unknown. This study sought to observe the association between Christensenellaceae and metabolic indexes in a large sample of residents in South China. A total of 4,781 people from the GGMP project were included, and the fecal microbiota composition of these individuals was characterized by 16S rRNA sequencing and analyzed the relation between Christensenellaceae and metabolism using QIIME (Quantitative Insight Into Microbial Ecology, Version 1.9.1). The results demonstrated that microbial richness and diversity were increased in the group with a high abundance of Christensenellaceae, who showed a greater complexity of the co-occurrence network with other bacteria than residents who lacked Christensenellaceae. The enriched bacterial taxa were predominantly represented by Oscillospira, Ruminococcaceae, RF39, Rikenellaceae and Akkermansia as the Christensenellaceae abundance increased, while the abundances of Veillonella, Fusobacterium and Klebsiella were significantly reduced. Furthermore, Christensenellaceae was negatively correlated with the pathological features of MetS, such as obesity, hypertriglyceridemia and body mass index (BMI). We found reduced levels of lipid biosynthesis and energy metabolism pathways in people with a high abundance of Christensenellaceae, which may explain the negative relationship between body weight and Christensenellaceae. In conclusion, we found a negative correlation between Christensenellaceae and MetS in a large Chinese population and reported the geographical distribution of Christensenellaceae in the GGMP study. The association data from this population-level research support the investigation of strains within Christensenellaceae as potentially beneficial gut microbes.

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Ab initio calculations are performed to investigate the host-guest interactions and multiple occupancies of some sulfur- (HS, CS) and nitrogen-containing (N, NO, and NH) molecules in dodecahedral, tetrakaidecahedral, and hexakaidecahedral water cages in this work. Five functionals in the framework of density functional theory are compared, and the M06-2X method appears to be the best to predict the binding energies as well as the geometries. Results show that N and NO molecules are more stable in the 56 cage, while NH and HS prefer to stabilize in the 56 cage. This suggests that the sI hydrates of NH and HS exhibit higher stability than the sII structures and that sII NO hydrate is more stable than sI NO hydrate. N is found to be more stable in type II structure with single occupancy and to form type I hydrate with multiple occupancy, which is consistent with the experimental observations. As to the guest molecule CS, it may undergo severe structural deformation in the 5 and 56 cage. For multiple occupancies, the 5, 56, and 56 water cages can trap up to two N molecules, and the 56 water cage can accommodate two HS molecules. This work is expected to provide new insight into the formation mechanism of clathrate hydrates for atmospherically important molecules.