Golgi_DF: Golgi proteins classification with deep forest.

Introduction: Golgi is one of the components of the inner membrane system in eukaryotic cells. Its main function is to send the proteins involved in the synthesis of endoplasmic reticulum to specific parts of cells or secrete them outside cells. It can be seen that Golgi is an important organelle for eukaryotic cells to synthesize proteins. Golgi disorders can cause various neurodegenerative and genetic diseases, and the accurate classification of Golgi proteins is helpful to develop corresponding therapeutic drugs. Methods: This paper proposed a novel Golgi proteins classification method, which is Golgi_DF with the deep forest algorithm. Firstly, the classified proteins method can be converted the vector features containing various information. Secondly, the synthetic minority oversampling technique (SMOTE) is utilized to deal with the classified samples. Next, the Light GBM method is utilized to feature reduction. Meanwhile, the features can be utilized in the penultimate dense layer. Therefore, the reconstructed features can be classified with the deep forest algorithm. Results: In Golgi_DF, this method can be utilized to select the important features and identify Golgi proteins. Experiments show that the well-performance than the other art-of-the state methods. Golgi_DF as a standalone tools, all its source codes publicly available at https://github.com/baowz12345/golgiDF. Discussion: Golgi_DF employed reconstructed feature to classify the Golgi proteins. Such method may achieve more available features among the UniRep features.

Biology of Rhynchaenus maculosus provides insights and implications for integrated management of this emerging pest.

Rhynchaenus maculosus is an emerging insect pest with an increasingly serious tendency. Lack of biology information results in the bottleneck of integrated management of this pest. To facilitate an available design of integrated pest management strategy, biology of R. maculosus, including voltinism, life cycle, distribution, and damage has been investigated. Results reveal that R. maculosus is oligophagous and distributes in Heilongjiang, Jilin, and Liaoning provinces, China. This pest produces one generation per year (univoltinism) and overwinters as adults in leaf litter. From mid-April to late-April, active overwintering adults emerge from overwintering sites. The next generation of adult R. maculosus appears from mid-May to early June until mid-August to early September when the beetles move into the overwintering places. The entire time span of adult occurrence ranges from 315.6 ± 3.6 to 336.4 ± 3.2 days (Mean ± SD). Larvae undergo 3 instars with a total duration of 20 to 23 days. R. maculosus larvae feed on Q. wutaishanica and Q. mongolica without host-specific preference between the two host species, but do not harm Q. acutissim. Three species of larval parasites were collected and identified as Braconidae sp., Eulophidae sp., and Ceraphronidae sp. Biological information of R. maculosus provides essential insights for design and implementation of integrated management of this pest.

Newly Emerging Pest in China, Rhynchaenusmaculosus (Coleoptera: Curculionidae): Morphology and Molecular Identification with DNA Barcoding.

The oak flea weevil, Rhynchaenusmaculosus Yang et Zhang 1991, is a newly emerging pest that severely damages oak (genus Quercus) in China. The first R. maculosus outbreak occurred in 2020 and caused spectacular damage to all oak forests in Jilin province, northeast China. The lack of key morphological characters complicates the identification of this native pest, especially in larva and pupa stages. This is problematic because quick and accurate species identification is crucial for early monitoring and intervention during outbreaks. Here, we provided the first detailed morphological description of R. maculosus at four life stages. Additionally, we used DNA barcodes from larva and pupa specimens collected from three remote locations for molecular identification. The average pairwise divergence of all sequences in this study was 0.51%, well below the 2% to 3% (K-2-parameter) threshold set for one species. All sample sequences matched the R. maculosus morphospecies (KX657706.1 and KX657707.1), with 99.23% to 100% (sequence identity, E value: 0.00) matching success. The tree based on barcodes placed the specimens into the Rhynchaenus group, and the phylogenetic relationship between 62 sequences (30 samples and 32 from GeneBank) had high congruence with the morphospecies taxa. The traditional DNA barcodes were successfully transformed into quick response codes with larger coding capacity for information storage. The results showed that DNA barcoding is reliable for R. maculosus identification. The integration of molecular and morphology-based methods contributes to accurate species identification of this newly emerging oak pest.