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Lipases play essential roles in digestion, transport, and processing of dietary lipids in insects. For parasitoid wasps with a unique life cycle, lipase functions could be multitudinous in particular. Pteromalus puparum is a pupal endoparasitoid of butterflies. The female adult deposits eggs into its host, along with multifunctional venom, and the developing larvae consume host as its main nutrition source. Parasitoid lipases are known to participate in the food digestion process, but the mechanism remains unclear. P. puparum genome and transcriptome data were interrogated. Multiple alignments and phylogenetic trees were constructed. We annotated a total of 64 predicted lipase genes belonging to five lipase families and suggested that eight venom and four salivary lipases could determine host nutrition environment post-parasitization. Many putative venom lipases were found with incomplete catalytic triads, relatively long β9 loops, and short lids. Data analysis reveals the loss of catalytic activities and weak triacylglycerol (TAG) hydrolytic activities of lipases in venom. Phylogenetic trees indicate various predicted functions of lipases in P. puparum. Our information enriches the database of parasitoid lipases and the knowledge of their functional diversification, providing novel insight into how parasitoid wasps manipulate host lipid storage by using venom lipases.

Merged characters are the major cause of recognition errors. We classify the merging relationship between two involved characters into three types: "linear," "nonlinear," and "overlapped." Most segmentation methods handle the first type well, however, their capabilities of handling the other two types are limited. The weakness of handling the nonlinear and overlapped types results from character segmentation by linear, usually vertical, cuts assumed in these methods. This paper proposes a novel merged character segmentation and recognition method based on forepart prediction, necessity-sufficiency matching and character-adaptive masking. This method utilizes the information obtained from the forepart of merged characters to predict candidates for the leftmost character, and then applies character-adaptive masking and character recognition to verifying the prediction. Therefore, the arbitrary-shaped cutting path will follow the right shape of the leftmost character so as to preserve the shape of the next character. This method handles the first two types well and greatly improves the segmentation accuracy of the overlapped type. The experimental results and the performance comparisons with other methods demonstrate the effectiveness of the proposed method.