Monitoring the dynamic change with respect to chirality and species of amino acids in bacterial peptidoglycan (PG) during cell wall biosynthesis is correlated with bacterial taxonomy, physiology, micropathology, and antibacterial mechanisms. However, this is challenging because reported methods usually lack the ability of chiral analysis with the coexistence of d- and l-amino acids in PG. Here we report a chiral sensor array for PG biosynthesis monitoring through chiral amino acid recognition. Multitypes of host molecule modified MoS nanosheets (MNSs) were used as receptor units to achieve more accurate and specific sensing. By applying indicator displacement strategy, the distinct and reproducible fluorescence-response patterns were obtained for linear discriminant analysis (LDA) to accurately discriminate achiral Gly, 19 l-amino acids and the corresponding 19 d-enantiomers simultaneously. The sensor array has also been used for identifying bacterial species and tracking the subtle change of amino acid composition of PG including chirality and species during biosynthesis in different growth status and exogenous d-amino acid stimulation.

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Cross-domain text classification aims at building a classifier for a target domain which leverages data from both source and target domain. One promising idea is to minimize the feature distribution differences of the two domains. Most existing studies explicitly minimize such differences by an exact alignment mechanism (aligning features by one-to-one feature alignment, projection matrix etc.). Such exact alignment, however, will restrict models' learning ability and will further impair models' performance on classification tasks when the semantic distributions of different domains are very different. To address this problem, we propose a novel group alignment which aligns the semantics at group level. In addition, to help the model learn better semantic groups and semantics within these groups, we also propose a partial supervision for model's learning in source domain. To this end, we embed the group alignment and a partial supervision into a cross-domain topic model, and propose a Cross-Domain Labeled LDA (CDL-LDA). On the standard 20Newsgroup and Reuters dataset, extensive quantitative (classification, perplexity etc.) and qualitative (topic detection) experiments are conducted to show the effectiveness of the proposed group alignment and partial supervision.