Identification and expression analysis of chemosensory receptors in the tarsi of fall armyworm, Spodoptera frugiperda (J. E. Smith).

Chemosensation of tarsi provides moths with the ability to detect chemical signals which are important for food recognition. However, molecular mechanisms underlying the chemosensory roles of tarsi are still unknown. The fall armyworm Spodoptera frugiperda is a serious moth pest that can damage many plants worldwide. In the current study, we conducted transcriptome sequencing with total RNA extracted from S. frugiperda tarsi. Through sequence assembly and gene annotation, 23 odorant receptors 10 gustatory receptors and 10 inotropic receptors (IRs) were identified. Further phylogenetic analysis with these genes and homologs from other insect species indicated specific genes, including ORco, carbon dioxide receptors, fructose receptor, IR co-receptors, and sugar receptors were expressed in the tarsi of S. frugiperda. Expression profiling with RT-qPCR in different tissues of adult S. frugiperda showed that most annotated SfruORs and SfruIRs were mainly expressed in the antennae, and most SfruGRs were mainly expressed in the proboscises. However, SfruOR30, SfruGR9, SfruIR60a, SfruIR64a, SfruIR75d, and SfruIR76b were also highly enriched in the tarsi of S. frugiperda. Especially SfruGR9, the putative fructose receptor, was predominantly expressed in the tarsi, and with its levels significantly higher in the female tarsi than in the male ones. Moreover, SfruIR60a was also found to be expressed with higher levels in the tarsi than in other tissues. This study not only improves our insight into the tarsal chemoreception systems of S. frugiperda but also provides useful information for further functional studies of chemosensory receptors in S. frugiperda tarsi.

Artificial neural network approaches for fluorescence lifetime imaging techniques.

A novel high-speed fluorescence lifetime imaging (FLIM) analysis method based on artificial neural networks (ANN) has been proposed. In terms of image generation, the proposed ANN-FLIM method does not require iterative searching procedures or initial conditions, and it can generate lifetime images at least 180-fold faster than conventional least squares curve-fitting software tools. The advantages of ANN-FLIM were demonstrated on both synthesized and experimental data, showing that it has great potential to fuel current revolutions in rapid FLIM technologies.