Ma Yan

Mrs. Ma Yan

orcid

Research Focus & Metrics

Academic Career Length

14Years (2013-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 February 2022 in Journal of Hazardous Materials
Verified
Journal Article
Closed access
CWChangcai Wu
Changcai Wu
YMYajie Ma
Yajie Ma
DWDan Wang
Dan Wang
YSYongpan Shan
Yongpan Shan
XSXianpeng Song
Xianpeng Song
MYMa Yan
Ma Yan

19 August 2022 in Environmental Research
Verified
Journal Article
Closed access
CWChangcai Wu
Changcai Wu
SPShaodong Pan
Shaodong Pan
YSYongpan Shan
Yongpan Shan
YMYajie Ma
Yajie Ma
DWDan Wang
Dan Wang
MYMa Yan
Ma Yan

30 January 2024 in Journal of Hazardous Materials
Verified
Journal Article
Closed access
CWChangcai Wu
Changcai Wu
XSXianpeng Song
Xianpeng Song
DWDan Wang
Dan Wang
YMYajie Ma
Yajie Ma
YSYongpan Shan
Yongpan Shan
MYMa Yan
Ma Yan

25 February 2022 in Ecotoxicology and Environmental Safety
Verified
Journal Article
Open access
CWChangcai Wu
Changcai Wu
YMYajie Ma
Yajie Ma
DWDan Wang
Dan Wang
YSYongpan Shan
Yongpan Shan
XSXianpeng Song
Xianpeng Song
MYMa Yan
Ma Yan
As microplastics became the focus of global attention, the hazards of plastic plasticizers (PAEs) have gradually attracted people's attention. Agricultural soil is one of its hardest hit areas. However, current research of its impact on soil ecology only stops at the microorganism itself, and there is still lack of conclusion on the impact of soil metabolism. To this end, three most common PAEs (Dimethyl phthalate: DMP, Dibutyl phthalate: DBP and Bis (2-ethylhexyl) phthalate: DEHP) were selected and based on high-throughput sequencing and metabolomics platforms, the influence of PAEs residues on soil metabolic functions were revealed for the first time. PAEs did not significantly changed the alpha diversity of soil bacteria in the short term, but changed their community structure and interfered with the complexity of community symbiosis network. Metabolomics indicated that exposure to DBP can significantly change the soil metabolite profile. A total of 172 differential metabolites were found, of which 100 were up-regulated and 72 were down-regulated. DBP treatment interfered with 43 metabolic pathways including basic metabolic processes. In particular, it interfered with the metabolism of residual steroids and promoted the metabolism of various plasticizers. In addition, through differential labeling and collinear analysis, some bacteria with the degradation potential of PAEs, such as Gordonia, were excavated.

1 November 2019 in Environmental Pollution
Verified
Journal Article
Closed access
SZShuai Zhang
Shuai Zhang
JLJunyu Luo
Junyu Luo
WJWeili Jiang
Weili Jiang
LWLinke Wu
Linke Wu
LZLijuan Zhang
Lijuan Zhang
JCJinjie Cui
Jinjie Cui

6 September 2015 in Journal of Integrative Agriculture
Verified
Journal Article
Open access
MSMeizhen Song
Meizhen Song
HWHanwen Wu
Hanwen Wu
WJWei-Li Jiang
Wei-Li Jiang
YMYa-Jie Ma
Ya-Jie Ma
MYMa Yan
Ma Yan
Goosegrass is one of the worst agricultural weeds on a worldwide basis. Understanding of its interference impact in crop field will provide useful information for weed control programs. Field experiments were conducted during 2010–2012 to determine the influence of goosegrass density on cotton growth at the weed densities of 0, 0.125, 0.25, 0.5, 1, 2, and 4 plants m−1 of row. Seed cotton yield tended to decrease with the increase in weed density, and goosegrass at a density of 4 plants m−1 of row significantly reduced cotton yields by 20 to 27%. A density of 11.6–19.2 goosegrass plant m−1 of row would result in a 50% cotton yield loss from the maximum yield according to the hyperbolic decay regression model. Boll production was not affected in the early growing season. But boll numbers per plant were reduced about 25% at the density of 4 plants m−1 of row in the late growing season. Both cotton boll weight and seed numbers per boll were significantly reduced (8%) at 4 goosegrass plants m−1 of row. Cotton plant height, stem diameter and sympodial branch number were not affected as much as cotton yields by goosegrass competition. Seed index, lint percentage and lint fiber properties were unaffected by weed competition. Intraspecific competition resulted in density-dependent effects on weed biomass per plant, 142–387 g dry weight by harvest. Goosegrass biomass m−2 tended to increase with increasing weed density as indicated by a quadratic response. The adverse impact of goosegrass on cotton yield identified in this study has indicated the need of effective goosegrass management.