Multispecies solid-state fermentation (MSSF), a natural fermentation process driven by reproducible microbiota, is an important technique to produce traditional fermented foods. Flavours, skeleton of fermented foods, was mostly produced by microbiota in food ecosystem. However, the association between microbiota and flavours and flavour-producing core microbiota are still poorly understood. Here, acetic acid fermentation (AAF) of Zhenjiang aromatic vinegar was taken as a typical case of MSSF. The structural and functional dynamics of microbiota during AAF process was determined by metagenomics and favour analyses. The dominant bacteria and fungi were identified as Acetobacter, Lactobacillus, Aspergillus, and Alternaria, respectively. Total 88 flavours including 2 sugars, 9 organic acids, 18 amino acids, and 59 volatile flavours were detected during AAF process. O2PLS-based correlation analysis between microbiota succession and flavours dynamics showed bacteria made more contribution to flavour formation than fungi. Seven genera including Acetobacter, Lactobacillus, Enhydrobacter, Lactococcus, Gluconacetobacer, Bacillus and Staphylococcus were determined as functional core microbiota for production of flavours in Zhenjiang aromatic vinegar, based on their dominance and functionality in microbial community. This study provides a perspective for bridging the gap between the phenotype and genotype of ecological system, and advances our understanding of MSSF mechanisms in Zhenjiang aromatic vinegar.

To reduce the high mortality rate from cardiovascular disease (CVD), the electrocardiogram (ECG) beat plays a significant role in computer-aided arrhythmia diagnosis systems. However, the complex variations and imbalance of ECG beats make this a challenging issue. Since ECG beat data exist in heavily imbalanced category, an effective long short-term memory (LSTM) recurrence network model with focal loss (FL) is proposed. For this purpose, the LSTM network can disentangle the timing features in complex ECG signals, while the FL is used to resolve the category imbalance by downweighting easily identified normal ECG examples. The advantages of the proposed network have been verified in the MIT-BIH arrhythmia database. Experimental results show that the LSTM network with FL achieved a reliable solution to the problem of imbalanced datasets in ECG beat classification and was not sensitive to quality of ECG signals. The proposed method can be deployed in telemedicine scenarios to assist cardiologists into more accurately and objectively diagnosing ECG signals.

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