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Characterization of the microbial community response to replant diseases in peach orchards
LI Wei-hua, CHEN Peng, WANG Yu-zhu, LIU Qi-zhi
2023, 22 (4): 1082-1092.   DOI: 10.1016/j.jia.2022.08.121
Abstract213)      PDF in ScienceDirect      

This study attempted to monitor the development of microbial communities and reveal the correlation between the soil microbial community and soil nutrient factors over different years following the replanting of peach trees.  The replanted soil (RS) and nonreplanted soil (NRS) were collected from peach orchards with different growth years (1, 3, 5, 7, 9, 11, and 13 years) in the same region.  The soil bacterial and fungal community diversities were analyzed by high-throughput sequencing technology.  Redundancy analysis (RDA) was used to show the correlation between the soil microbial community and environmental variables.  The alpha diversities of the bacterial and fungal communities indicated that RS contained a higher abundance of bacterial and fungal operational taxonomic units (OTUs) than NRS.  NMDS and ANOSIM analyses showed that the soil bacterial and fungal communities were significantly (P<0.01) affected by planting years, and that the main changes occurred in the first and ninth planting years.  The presence of the bacterial orders Sphingobacteriales, Burkholderiales and Actinomycetales changed significantly after replanting.  Some bacteria associated with bioremediation, such as Burkholderiales and Intrasporangiaceae, and some harmful pathogens, such as Penicillium and Ophiostomatales, significantly increased after replanting (LDA score>3.0).  In addition, the soil nutrient contents were lower in RS than in NRS in the early stage (1–5 years), and the RDA showed that bacterial and fungal phyla are closely associated with environmental variables, including the potential of hydrogen (pH), ammonium nitrogen (AN), available phosphorus (AP) and available potassium (AK).  These results lead to a deeper understanding of the microbial responses to replanting in peach orchards. 

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Effect of long-term continuous cropping of strawberry on soil bacterial community structure and diversity
LI Wei-hua, LIU Qi-zhi, CHEN Peng
2018, 17 (11): 2570-2582.   DOI: 10.1016/S2095-3119(18)61944-6
Abstract365)      PDF in ScienceDirect      
Long-term monoculture leads to continuous cropping (CC) problems, which complicate agricultural production, both locally and abroad.  This study contrasted the different bacterial community compositions, physicochemical properties and enzyme activities of strawberry soil subjected to CC, CC rhizosphere (CCR), non-CC (NCC) and non-CC rhizosphere (NCCR) treatments.  The soil physicochemical properties and enzyme activities were significantly reduced after long-term CC.  In addition, five variation trends were observed for the 11 major bacterial genera in the soil.  Sphingomonas was the only stable group among all treatments.  The proportions of Novosphingobium, Rhodoplanes, Povalibacter, Cellvibrio and Stenotrophobacter decreased after CC.  The relative abundances of Pelagibius, Thioprofundum and Allokutzneria increased only in the CC treatment.  Nitrospira were more abundant in rhizosphere soil than in non-rhizosphere soil.  The relative abundance of Bacillus increased after CC.  Redundancy analysis revealed that Bacillus, Pelagibius and Allokutzneria had significant negative correlations with the soil physicochemical properties and enzyme activities.  Therefore, these genera may be the key bacteria influenced by the physicochemical properties and enzyme activities altered by replanting.  These results indicate that long-term CC of strawberry leads to less favourable rhizosphere soil conditions, which can be understood as a stress-induced response of the bacterial community diversity.  Further research is needed to determine how the quality of soil is reduced by the shift in the diversity of the soil bacterial community.
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Effects of land use change on the spatiotemporal variability of soil organic carbon in an urban-rural ecotone of Beijing, China
YE Hui-chun, HUANG Yuan-fang, CHEN Peng-fei, HUANG Wen-jiang, ZHANG Shi-wen, HUANG Shan-yu, HOU Sen
2016, 15 (4): 918-928.   DOI: 10.1016/S2095-3119(15)61066-8
Abstract1927)      PDF in ScienceDirect      
Understanding the effects of land use changes on the spatiotemporal variation of soil organic carbon (SOC) can provide guidance for low carbon and sustainable agriculture. In this paper, based on the large-scale datasets of soil surveys in 1982 and 2009 for Pinggu District — an urban-rural ecotone of Beijing, China, the effects of land use and land use changes on both temporal variation and spatial variation of SOC were analyzed. Results showed that from 1982 to 2009 in Pinggu District, the following land use change mainly occurred: Grain cropland converted to orchard or vegetable land, and grassland converted to forestland. The SOC content decreased in region where the land use type changed to grain cropland (e.g., vegetable land to grain cropland decreased by 0.7 g kg–1; orchard to grain cropland decreased by 0.2 g kg–1). In contrast, the SOC content increased in region where the land use type changed to either orchard (excluding forestland) or forestland (e.g., grain cropland to orchard and forestland increased by 2.7 and 2.4 g kg–1, respectively; grassland to orchard and forestland increased by 4.8 and 4.9 g kg–1, respectively). The organic carbon accumulation capacity per unit mass of the soil increased in the following order: grain cropland soil
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Recent Advances in the Role of the Elongator Complex in Plant Physiology and tRNA Modification: A Review
YAN Xu, JIN Xiao-huan, WANG You-mei, ZHENG Bo , CHEN Peng
2014, 13 (8): 1640-1650.   DOI: 10.1016/S2095-3119(13)60524-9
Abstract1125)      PDF in ScienceDirect      
The Elongator complex is a multifunction protein complex which has been shown to be involved in transcriptional elongation, DNA replication and repair, tubulin and histone acetylation, gene silencing and tranfer RNA uridine modification. The composition of the Elongator complex is found to be highly conserved in eukaryotes, protein homologs of various subunits have been identified in fungi, plant, animal, and human. Remarkably, mutation in genes encoding the Elongator complex structural components all results in defects of transfer RNA wobble uridine modification, and this function of the Elongator complex is also conserved in eukaryotes. The Elongator complex mutants in higher plants have pleiotropic phenotypes including defects in vegetative growth, abiscisic acid hypersensitivity, elevated tolerance to drought and oxidative stress. What is the relationship between the Elongator complex’s function in nucleoside modification and its activity in other cellular pathways? This review summarizes the recent advances in study of function of the Elongator complex, in the aspects of cell physiology and molecular biology.
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Agricultural Ontology Based Feature Optimization for Agricultural Text Clustering
SU Ya-ru, WANG Ru-jing, CHEN Peng, WEI Yuan-yuan, LI Chuan-xi
2012, 11 (5): 752-759.   DOI: 10.1016/S1671-2927(00)8596
Abstract1422)      PDF in ScienceDirect      
Feature optimization is important to agricultural text mining. Usually, the vector space model is used to represent text documents. However, this basic approach still suffers from two drawbacks: the curse of dimension and the lack of semantic information. In this paper, a novel ontology-based feature optimization method for agricultural text was proposed. First, terms of vector space model were mapped into concepts of agricultural ontology, which concept frequency weights are computed statistically by term frequency weights; second, weights of concept similarity were assigned to the concept features according to the structure of the agricultural ontology. By combining feature frequency weights and feature similarity weights based on the agricultural ontology, the dimensionality of feature space can be reduced drastically. Moreover, the semantic information can be incorporated into this method. The results showed that this method yields a significant improvement on agricultural text clustering by the feature optimization.
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