The aromatic shrub Lavandula angustifolia produces various volatile terpenoids that serve as resources for essential oils and function in plant-insect communication. To better understand the genetic basis of the terpenoid diversity in lavender, we present a high-quality reference genome for the Chinese lavender cultivar “Jingxun 2” using PacBio and Hi-C technologies to anchor the 894.50 Mb genome assembly into 27 pseudochromosomes. In addition to the γ triplication event, lavender underwent two rounds of whole-genome duplication (WGD) during the Eocene–Oligocene (29.6 MYA) and Miocene–Pliocene (6.9 MYA) transitions. As a result of tandem duplications and lineage-specific WGDs, gene families related to terpenoid biosynthesis in lavender are substantially expanded compared to those of five other species in Lamiaceae. Many terpenoid biosynthesis transcripts are abundant in glandular trichomes. We further integrated the contents of ecologically functional terpenoids and coexpressed terpenoid biosynthetic genes to construct terpenoid-gene networks. Typical gene clusters, including TPS-TPS, TPS-CYP450, and TPS-BAHD, linked with compounds that primarily function as attractants or repellents, were identified by their similar patterns of change during flower development or in response to methyl jasmonate. Comprehensive analysis of the genetic basis of the production of volatiles in lavender could serve as a foundation for future research into lavender evolution, phytochemistry, and ecology.
Robust cross-subject emotion recognition based on multichannel EEG has always been hard work. In this work, we hypothesize that there exist default brain variables across subjects in emotional processes. Hence, the states of the latent variables that relate to emotional processing must contribute to building robust recognition models. Specifically, we propose to utilize an unsupervised deep generative model (e.g., variational autoencoder) to determine the latent factors from the multichannel EEG. Through a sequence modeling method, we examine the emotion recognition performance based on the learnt latent factors. The performance of the proposed methodology is verified on two public datasets (DEAP and SEED) and compared with traditional matrix factorization-based (ICA) and autoencoder-based approaches. Experimental results demonstrate that autoencoder-like neural networks are suitable for unsupervised EEG modeling, and our proposed emotion recognition framework achieves an inspiring performance. As far as we know, it is the first work that introduces variational autoencoder into multichannel EEG decoding for emotion recognition. We think the approach proposed in this work is not only feasible in emotion recognition but also promising in diagnosing depression, Alzheimer's disease, mild cognitive impairment, etc., whose specific latent processes may be altered or aberrant compared with the normal healthy control.
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