Passion fruit (Passiflora edulis Sims) is a vine of the Passiflora genus in the Passifloraceae family.  The extracted components include flavonoids and terpenoids, which have good anti-anxiety and anti-inflammatory effects in humans.  In this study, we analyzed the transcriptomes of four tissues of the ‘Zixiang’ cultivar using RNA-Seq, which provided a dataset for functional gene mining.  The de novo assembly of these reads generated 96 883 unigenes, among which 61 022 unigenes were annotated (62.99% yield).  In addition to its edible value, another important application of passion fruit is its medicinal value.  The flavonoids and terpenoids are mainly derivatives of luteolin, apigenin, cycloartane triterpenoid saponins and other active substances in leaf extracts.  A series of candidate unigenes in the transcriptome data that are potentially involved in the flavonoid and terpenoid synthesis pathways were screened using homology-based BLAST and phylogenetic analysis.  The results showed that the biosynthesis of triterpenoids in passion fruit comes from the branches of the mevalonate (MVA) and 2-C-methyl-D-erythritol 4-phosphate/1-deoxy-D-xylulose 5-phosphate (MEP/DOXP) pathways, which is different from the MVA pathway that is used in other fruit trees.  Most of the candidate genes were found to be highly expressed in the leaves and/or flowers.  Quantitative real-time PCR (qRT-PCR) verification was carried out and confirmed the reliability of the RNA-Seq data.  Further amplification and functional analysis of these putative unigenes will provide additional insight into the biosynthesis of flavonoids and terpenoids in passion fruit.

How to fully use spectral and temporal information for efficient identification of crops becomes a crucial issue since each crop has its specific seasonal dynamics.  A thorough understanding on the relative usefulness of spectral and temporal features is thus essential for better organization of crop classification information.  This study, taking Heilongjiang Province as the study area, aims to use time-series moderate resolution imaging spectroradiometer (MODIS) surface reflectance product (MOD09A1) data to evaluate the importance of spectral and temporal features for crop classification.  In doing so, a feature selection strategy based on separability index (SI) was first used to rank the most important spectro-temporal features for crop classification.  Ten feature scenarios with different spectral and temporal variable combinations were then devised, which were used for crop classification using the support vector machine and their accuracies were finally assessed with the same crop samples.  The results show that the normalized difference tillage index (NDTI), land surface water index (LSWI) and enhanced vegetation index (EVI) are the most informative spectral features and late August to early September is the most informative temporal window for identifying crops in Heilongjiang for the observed year 2011.  Spectral diversity and time variety are both vital for crop classification, and their combined use can improve the accuracy by about 30% in comparison with single image.  The feature selection technique based on SI analysis is superior for achieving high crop classification accuracy (producers’ accuracy of 94.03% and users’ accuracy of 93.77%) with a small number of features.  Increasing temporal resolution is not necessarily important for improving the classification accuracies for crops, and a relatively high classification accuracy can be achieved as long as the images associated with key phenological phrases are retained.