Chromosome-level genome assembly of a destructive leaf-mining moth Eriocrania semipurpurella alpina.

Eriocraniidae (Lepidoptera) are widespread leaf miners and have unique adaptability to hypoxia and low temperatures, causing covert but devastating harm to Fagales (Betulaceae and Fagaceae) plants in the Holarctic. However, the lack of a high-quality genome of this most ancient family within the angiosperm-feeding group largely limits the studies on the phylogeny and environmental adaptation of the primitive Lepidoptera. In this study, utilizing Illumina sequencing, PacBio HiFi sequencing, and Hi-C technology, we constructed a chromosome-level genome assembly of E.

Two new species of from Qinghai Province, China (Diptera, Dolichopodidae, Rhaphiinae).

At present, there are 31 species of Meigen recorded in China. In this paper, two species from Qinghai Province of China are described as new to science: , A key to the Chinese species of is provided.

Stacked Sparse Auto-Encoders (SSAE) Based Electronic Nose for Chinese Liquors Classification.

This paper presents a stacked sparse auto-encoder (SSAE) based deep learning method for an electronic nose (e-nose) system to classify different brands of Chinese liquors. It is well known that preprocessing; feature extraction (generation and reduction) are necessary steps in traditional data-processing methods for e-noses. However, these steps are complicated and empirical because there is no uniform rule for choosing appropriate methods from many different options.

Design of a portable electronic nose for real-fake detection of liquors.

Portability is a major issue that influences the practical application of electronic noses (e-noses). For liquors detection, an e-nose must preprocess the liquid samples (e.g.

Electronic nose with a new feature reduction method and a multi-linear classifier for Chinese liquor classification.

An electronic nose (e-nose) was designed to classify Chinese liquors of the same aroma style. A new method of feature reduction which combined feature selection with feature extraction was proposed. Feature selection method used 8 feature-selection algorithms based on information theory and reduced the dimension of the feature space to 41.