Evaluation of translocation impacts on genetic patterns in farmed and naturalized populations of Mytilus galloprovincialis along the China coast: clues from mitochondrial cytochrome c oxidase I sequences

As an introduced species, Mytilus galloprovincialis has developed into self-sustaining naturalized populations and has been widely cultivated in northern China. The M. galloprovincialis aquaculture industry wholly depends on the movement of naturalized juveniles onto farms. It is, therefore, necessary to understand the genetic effect of continuous spats’ translocation. This study divided 12 localities of M. galloprovincialis along the China coast into three types of populations—farmed, naturalized adjacent farmed, and isolated—to investigate the genetic variation and differentiation. The genetic variability is reflected by haplotype diversity, nucleotide diversity, and the mean number of pairwise differences expressed as farmed populations > naturalized adjacent farmed populations > isolated populations. The Hierarchical analyses and Mantel-test indicated slight divergence between farmed and naturalized populations, northern and southern populations. The farmed and naturalized populations clustered into two separate categories in the neighbor-joining tree except two anthropogenically intervened localities. The present results suggest that the translocation practice positively affected genetic variability and played a vital role in shaping genetic composition. The information obtained in this study provides new insights into the impacts of the translocation culture model of marine mollusks

ID Embedding as Subtle Features of Content and Structure for Multimodal Recommendation

Multimodal recommendation aims to model user and item representations comprehensively with the involvement of multimedia content for effective recommendations. Existing research has shown that it is beneficial for recommendation performance to combine (user- and item-) ID embeddings with multimodal salient features, indicating the value of IDs. However, there is a lack of a thorough analysis of the ID embeddings in terms of feature semantics in the literature. In this paper, we revisit the value of ID embeddings for multimodal recommendation and conduct a thorough study regarding its semantics, which we recognize as subtle features of content and structures. Then, we propose a novel recommendation model by incorporating ID embeddings to enhance the semantic features of both content and structures. Specifically, we put forward a hierarchical attention mechanism to incorporate ID embeddings in modality fusing, coupled with contrastive learning, to enhance content representations. Meanwhile, we propose a lightweight graph convolutional network for each modality to amalgamate neighborhood and ID embeddings for improving structural representations. Finally, the content and structure representations are combined to form the ultimate item embedding for recommendation. Extensive experiments on three real-world datasets (Baby, Sports, and Clothing) demonstrate the superiority of our method over state-of-the-art multimodal recommendation methods and the effectiveness of fine-grained ID embeddings