Polymorphisms of the MxA and MxB genes are associated with biochemical indices and viral subtypes in Yunnan HCV patients.

Introduction: Hepatitis C virus (HCV) infection was the primary reason causing critical hepatic Q7 diseases. Although direct-acting antiviral agents (DAA) were widely used in clinics, anti-drug mutation, the outcome of patients with different viral subtypes, and recurrence suggested that HCV pathogenic mechanism should be studied further. HCV infection, replication, and outcome were influenced by the IFNL4 and itsdownstream genes (MxA and MxB). However, whether genetic polymorphisms of these genes played necessary roles required verification in the Yunnan population. Methods and Results: After analyzing the genotypes and allele frequencies of seven single nucleotide polymorphisms (SNP), we found the association between the genotype and allele frequencies of rs11322783 in the IFNL4 gene and HCV infection in Yunnan population. Furthermore, the genetic polymorphisms of the MxA and MxB genescould influence liver function of HCV patients. The indirect bilirubin (IBIL) and albumin (ALB) levels showed significant differences among HCV patients, who carried various genotypes. The IBIL levels were associated with genotypes of rs17000900 (P= 0.025) and rs2071430 (P= 0.037) in the MxA gene, and ALB levels were associated with genotypes of rs2838029 (P= 0.010) in the MxB gene. Similarly, the genotypes of SNPs also showed significant difference in patients infected with subtype 3a (P=0.035) and 2a (P=0.034). However, no association was identified between expression level and SNPs of the MxA and MxB genes. Furthermore, HCV subtype 3b was found to be the predominantly epidemic strain in Yunnan Province. Conclusion: In conclusion, the association between biochemical indices/HCV subtypes and SNPs in the MxA and MxB genes was identified in Yunnan HCV population.

Effective band selection of hyperspectral image by an attention mechanism-based convolutional network.

The selection of effective and representative spectral bands is extremely important in eliminating redundant information and reducing the computational burden for the potential real-time applications of hyperspectral imaging. However, current band selection methods act as a separate procedure before model training and are implemented merely based on extracted average spectra without incorporating spatial information. In this paper, an end-to-end trainable network framework that combines band selection, feature extraction, and model training was proposed based on a 3D CNN (convolutional neural network, CNN) with the attention mechanism embedded in its first layer. The learned band attention vector was adopted as the basis of a band importance indicator to select effective bands. The proposed network was evaluated by two datasets, a regression dataset for predicting the relative chlorophyll content (soil and plant analyzer development, SPAD) of basil leaves and a classification dataset for detecting the drought stress of pepper leaves. A number of calibration models, including SVM, 1D-CNN, 2B-CNN (two-branch CNN), 3D ResNet and the developed network were established for performance comparison. Results showed that the effective bands selected by the proposed attention-based model achieved higher regression R 2 values and classification accuracies not only than the full-spectrum data, but also than the comparative band selection methods, including traditional SPA (successive projections algorithm) and GA (genetic algorithm) methods and the latest 2B-CNN algorithm. In addition, different from the traditional methods, the proposed band selection algorithm can effectively select bands while carrying out model training and can simultaneously take advantage of the original spectral-spatial information. The results confirmed the usefulness of the proposed attention mechanism-based convolutional network for selecting the most effective band combination of hyperspectral images.

Molecular characterization of a novel wheat-infecting virus of the family Betaflexiviridae.

Wheat plants showing yellowing and mosaic in leaves and stunting were collected from wheat fields in Henan Province, China. Analysis of these plants by transmission electron microscopy showed that they contained two types of filamentous virus-like particles with a length of 200-500 nm and 1000-1300 nm, respectively. RNA-seq revealed a coinfection with wheat yellow mosaic virus (WYMV) and an unknown wheat-infecting virus. The genome of the unknown virus is 8,410 nucleotides long, excluding its 3' poly(A) tail. It has six open reading frames (ORFs). ORF1 encodes a putative viral replication-associated protein (Rep), and ORFs 2, 3, and 4 encode the triple gene block (TGB) proteins. ORFs 5 and 6 encode the capsid protein (CP) and a protein with unknown function, respectively. Phylogenetic analysis showed that this novel virus is evolutionarily related to members of the subfamily Quinvirinae, family Betaflexiviridae. It is, however, distinct from the viruses in the currently established genera. Based on the species and genus demarcation criteria set by the International Committee on Taxonomy of Viruses (ICTV), we tentatively name this novel virus "wheat yellow stunt-associated betaflexivirus" (WYSaBV), and we propose it to be a member of a new genus in the family Betaflexiviridae.

[Cloning, subcellular lacalization and expression analysis of chloroplast-targeted Obg gene in Cyathula officinalis].

According to ObgC gene sequences from Cyathula officinalis genomic data, the specific primers were designed, and a full-length CoObgC cDNA (2 226 bp) was obtained by polymerase chain reaction (PCR) and rapid amplification of cDNA ends (RACE) methord. Sequence alignment showed that CoObgC gene contained a 1 818 bp open reading frame (ORF) encoding 605 amino acids. Sequence analysis predicted that molecular weight of CoObgC protein was about 66.39 kDa, the academic isoelectric point was 5.35, and the protein was stable protein. Then multiple sequence alignment was applied to construct phylogenetic tree. The real-time fluorescence quantification PCR (RT-qPCR) demonstrated that a high expression level in leaf, followed by root and flower, the low transcription was in stem. The recombinant vector pCABIA2300-CoObgC was constructed and introduced into tobacco epidermal cells by agrobacterium-mediated transformation, green fluorescence was tested and targeted to chloroplast under a laser scanning confocal microscope. These findings will be helpful to lay a foundation for studying the structure and function of CoObgC gene, and elucidating C. officinalis molecular biology experiment.

GLUTATHIONE S-TRANSFERASE Genes IN THE RICE LEAFFOLDER, Cnaphalocrocis medinalis (LEPIDOPTERA: PYRALIDAE): IDENTIFICATION AND EXPRESSION PROFILES.

In insects, glutathione S-transferases (GSTs) play critical roles in the detoxification of various insecticides, resulting in insecticide resistance. The rice leaffolder, Cnaphalocrocis medinalis, is an economically important pest of rice in Asia. GST genes have not been largely identified in this insect species. In the present study, by searching the transcriptome dataset, 25 candidate GST genes were identified in C. medinalis for the first time. Of these, 23 predicted GST proteins fell into five cytosolic classes (delta, epsilon, omega, sigma, and zeta), and two were assigned to the "unclassified" subgroup. Real-time quantitative PCR analysis showed that these GST genes were differentially expressed in various tissues, including the midgut, Malpighian tubules, and fat body of larvae, and the antenna, abdomen, and leg of adults, indicating diversified functions for these genes. Transcription levels of CmGSTd2, CmGSTe6, and CmGSTe7 increased significantly in larvae following exposure to chlorpyrifos, suggesting that these GST genes could be involved in the detoxification of this insecticide. The results of our study pave the way to a better understanding of the detoxification system of C. medinalis.

Identification of candidate chemosensory genes in the antennal transcriptome of Tenebrio molitor (Coleoptera: Tenebrionidae).

We present the first antennal transcriptome sequencing information for the yellow mealworm beetle, Tenebrio molitor (Coleoptera: Tenebrionidae). Analysis of the transcriptome dataset obtained 52,216,616 clean reads, from which 35,363 unigenes were assembled. Of these, 18,820 unigenes showed significant similarity (E-value <10(-5)) to known proteins in the NCBI non-redundant protein database. Gene ontology (GO) and Cluster of Orthologous Groups (COG) analyses were used for functional classification of these unigenes. We identified 19 putative odorant-binding protein (OBP) genes, 12 chemosensory protein (CSP) genes, 20 olfactory receptor (OR) genes, 6 ionotropic receptor (IR) genes and 2 sensory neuron membrane protein (SNMP) genes. BLASTX best hit results indicated that these chemosensory genes were most identical to their respective orthologs from Tribolium castaneum. Phylogenetic analyses also revealed that the T. molitor OBPs and CSPs are closely related to those of T. castaneum. Real-time quantitative PCR assays showed that eight TmolOBP genes were antennae-specific. Of these, TmolOBP5, TmolOBP7 and TmolOBP16 were found to be predominantly expressed in male antennae, while TmolOBP17 was expressed mainly in the legs of males. Several other genes were identified that were neither tissue-specific nor sex-specific. These results establish a firm foundation for future studies of the chemosensory genes in T. molitor.