miR-486 Responds to Apoptosis and Autophagy by Repressing SRSF3 Expression in Ovarian Granulosa Cells of Dairy Goats by Shujuan Liu, Qiqi Bu, Jiashun Tong, Zhanhang Wang, Jiuzeng Cui, Heran Cao, Haidong Ma, Binyun Cao, Xiaopeng An, Yuxuan Song

“…Unfortunately, the miRNA-mediated mechanisms by which the GC fate is regulated are unknown in Guanzhong dairy goats. Therefore, we investigated miR-486 expression in small and large follicles, as well as its impact on normal GC survival, apoptosis and autophagy in vitro. …”
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Retrieval and Mapping of Soil Organic Carbon Using Sentinel-2A Spectral Images from Bare Cropland in Autumn by Ke Wang, Yanbing Qi, Wenjing Guo, Jielin Zhang, Qingrui Chang

“…We also obtained S2A spectral images of the western Guanzhong Plain, China. We established four SOC prediction models, including random forest (RF), support vector machine (SVM), partial least-squares regression (PLSR), and artificial neural network (ANN) based on 15 variables retrieved from the S2A images, and compared the prediction accuracy using RMSE (root mean square error), R² (coefficient of determination), and RPD (ratio of performance to deviation). …”
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Profiling of Dust and Urban Haze Mass Concentrations during the 2019 National Day Parade in Beijing by Polarization Raman Lidar by Zhuang Wang, Cheng Liu, Yunsheng Dong, Qihou Hu, Ting Liu, Yizhi Zhu, Chengzhi Xing

“…The contributions of local emissions to air pollution during the 2019 National Day parade were insignificant, mainly affected by regional transport, including urban haze in North China plain and Guanzhong Plain (Hebei, Tianjin, Shandong, and Shanxi), and dust aerosol in Mongolia regions and Xinjiang. …”
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AGTML: A novel approach to land cover classification by integrating automatic generation of training samples and machine learning algorithms on Google Earth Engine by Yanglin Cui, Gaoxiang Yang, Yanbing Zhou, Chunjiang Zhao, Yuchun Pan, Qian Sun, Xiaohe Gu

“…The AGTML approach was subsequently applied to the Guanzhong Plain using different satellite imagery. …”
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Trade-offs/synergistic relationship between water ecosystem services and urbanization in the Yellow River Basin urban agglomeration by WAN Shilong, YANG Hainan, MA Li, YE Lufeng, ZHANG Zilong

“…[Results] (1) The water ecosystem services of the Yellow River Basin urban agglomeration are dominated by food production and soil and water conservation services, and have been significantly enhanced; the level of urbanization has been increasing, and the differences between counties (districts) have been narrowing. (2) The spatial relationship between water ecosystem services and urbanization is dominated by the types of “low-low” and “high-low”, which show a trade-off relationship globally and a synergistic relationship locally in counties and districts, but the number of counties and districts with the trade-off relationship has gradually increased and is the main distribution of the northwestern part of the Yellow River Bend Metropolitan Area, the central part of the Guanzhong Plain urban agglomeration, and the eastern part of the Shandong Peninsula urban agglomeration. (3) Precipitation, ecological measures based on vegetation restoration, land-use intensity and location factors have mainly positive effects on the trade-offs/synergistic relationship, while the opposite is true for temperature, altitude, topography and level of economic development. …”
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Spatiotemporal distribution and probabilistic health risk assessment of arsenic in drinking water and wheat in Northwest China by Xiangping Chen, Siyu Liu, Yan Luo

“…The aim of this study was to conduct a comprehensive health risk assessment of arsenic-contaminated substances in drinking water and wheat-based food intake in the Guanzhong Plain, China. 87 samples of wheat and 150 samples of water were randomly selected from the research region and examined. …”
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Spatiotemporal Variation of Fractional Vegetation Cover and Its Response to Climate Change and Topography Characteristics in Shaanxi Province, China by Yuanyuan Li, Jingyan Sun, Mingzhu Wang, Jinwei Guo, Xin Wei, Manoj K. Shukla, Yanbing Qi

“…Based on the moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data, we monitored FVC variations in Shaanxi Province, China, as well as in three subregions of the Loess Plateau (LOP), Qinling–Bashan Mountain (QBM), and Guanzhong Plain (GZP). Using Sen+Mann–Kendall, correlation analysis, and geodetector methods, we detected trends and responses to climate change and topographical characteristics in Shaanxi Province from 2000 to 2018. …”
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Polymorphism identification in goat DGAT1 and STAT5A genes and association with milk production traits by X.P. An, J.X. Hou, H.B. Zhao, L. Bai, J.Y. Peng, C.M. Zhu, Q.M. Yan, Y.X. Song, J.G. Wang, B.Y. Cao

“…Polymorphisms of DGAT1 and STAT5A genes in Xinong Saanen and Guanzhong goat breeds were investigated. PCR-RFLP, SSCP, and DNA sequencing were used to identify three SNPs: DQ380250:g.407_408insC in the DGAT1 gene, AJ237937:g.6798C>T and g.6852C>T in the STAT5A gene. …”
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Detection of 15-bp Deletion Mutation within <i>PLAG1</i> Gene and Its Effects on Growth Traits in Goats by Zhenyu Wei, Ke Wang, Hui Wu, Zhen Wang, Chuanying Pan, Hong Chen, Xianyong Lan

“…The most frequent genotypes of Shaanbei white Cashmere goat (SWCG), Inner Mongolia White Cashmere goat (IMCG) and Guanzhong Dairy goat (GZDG) were II genotypes (insertion/insertion), and the frequency of ID genotype (insertion/deletion) was found to be slightly higher than that of II genotype in Hainan Black goat (HNBG), showing that the frequency of the I allele was higher than that of the D allele. …”
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Evaluation of genetic diversity and population structure of five Chinese indigenous donkey breeds using microsatellite markers by Ran Di, Qiu-Yue Liu, Fang Xie, Wen-Ping Hu, Xiang-Yu Wang, Xiao-Han Cao, Zhang-Yuan Pan, Guo-Hong Chen, Ming-Xing Chu

“…The Bayesian analysis and principal component analysis plot yielded the same clustering result, which revealed that Guanzhong donkey was the most differentiated breed in all detected samples, and Jinnan (JN) and Guangling (GL) were genetically closed together. …”
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Driving mechanisms of urbanization: Evidence from geographical, climatic, social-economic and nighttime light data by Siyi Huang, Lijun Yu, Danlu Cai, Jianfeng Zhu, Ze Liu, Zongke Zhang, Yueping Nie, Klaus Fraedrich

“…The following results are noted: i) Employing the Geodetector based maximum contribution method for urban region extraction of night time light reveals a notable accuracy improvement in flat urban land compared with the closest area method. ii) Geographical factors attain high contribution for mountainous urban land of Shannan, while for flat urbanization land dynamics, economic factors and community factors prevail. iii) The most obvious driving mechanisms are economic factors which, associated with local urban development strategies, show highest contribution values in 2014 (2018) over the flat (mountainous) urban land of Guanzhong Plain (Northern Shaanxi Plateau or Shanbei region) linked with an early (late) development. iv) Population factors achieve high contribution values in the initially low populated urban land of the northern mountainous land initiating huge migration. v) The contributions resulting from Geodetector are in agreement with the Gini importance from Random Forest in agriculture, geographical and population factors (R2 > 0.5) but not in economy, community and climatic factors (R2 < 0.5). …”
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Comparison of muscle metabolomics between two Chinese horse breeds by Sihan Meng, Yanli Zhang, Shipeng Lv, Zhengkai Zhang, Xuexue Liu, Xuexue Liu, Lin Jiang

“…There are two different types of horse breeds—Guanzhong (GZ) horses, an athletic breed with a larger body height (~148.7 cm), and the Ningqiang pony (NQ) horses, a lower height breed generally used for ornamental purposes—both inhabited in the same region of China with obvious differences in muscle content. …”
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Current situation and human health risk assessment of fluoride enrichment in groundwater in the Loess Plateau: A case study of Dali County, Shaanxi Province, China by Rui-ping Liu, Hua Zhu, Fei Liu, Ying Dong, Refaey M El-Wardany

“…Meanwhile, high-fluoride groundwater is mainly distributed in the loess and river alluvial plains in the depression area of the Guanzhong Basin and the discharge areas of the groundwater, with the highest fluoride concentration exceeding seven times the national standard. (2) Fluoride in groundwater mainly originates from a natural source and human activities. …”
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Molecular characteristics and stable carbon isotope compositions of dicarboxylic acids and related compounds in wintertime aerosols of Northwest China by Weining Qi, Gehui Wang, Wenting Dai, Suixin Liu, Ting Zhang, Can Wu, Jin Li, Minxia Shen, Xiao Guo, Jingjing Meng, Jianjun Li

“…In this study, the molecular distribution and diurnal variations of wintertime dicarboxylic acids in a rural site of Guanzhong Plain, Northwest China, were explored. Oxalic acid (C2, day: 438.9 ± 346.8 ng m−3, night: 398.8 ± 392.3 ng m−3) is the most abundant compound followed by methylglyoxal (mGly, day: 207.8 ± 281.1 ng m−3, night: 222.9 ± 231.0 ng m−3) and azelaic (C9, day: 212.8 ± 269.1 ng m−3, night: 211.4 ± 136.7 ng m−3) acid. …”
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Urban Network Structures and Organization Models Based on the Road Less-Truck-Load Dedicated Line Data in China by Shi Lu, Du Guopeng, Pang Hongli, Wang Xiaoting, Zheng Zhongyang, Li Guoqi

“…The road LTL dedicated lines connections are mainly distributed in the intercity range of 0-200 km and the interprovincial range of 200-500 km, with the number of special lines concentrated in 500 km accounting for 41.9%. (3) The community detection algorithm was used to identify six urban communities with significant regional characteristics, including the Northeast Jilumeng, Zhongyuan, Guanzhong, Jianghuai, Pan-Pearl, Delta-Yangtze River Delta, and Changzhutan communities. …”
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High rumen degradable starch diet induced blood bile acids profile changes and hepatic inflammatory response in dairy goats by Lixin Zheng, Jing Shen, Xiaoying Han, Chunjia Jin, Xiaodong Chen, Junhu Yao

“…The objective of this study was to reveal the effect of rumen degradable starch (RDS) on bile acid metabolism and liver transcription in dairy goats using metabolomics and transcriptomics. Eighteen Guanzhong dairy goats of a similar weight and production level (body weight = 45.8 ± 1.54 kg, milk yield = 1.75 ± 0.08 kg, and second parity) were randomly assigned to 3 treatment groups where they were fed a low RDS (LRDS, RDS = 20.52% DM) diet, medium RDS (MRDS, RDS = 22.15% DM) diet, or high RDS (HRDS, RDS = 24.88% DM) diet, respectively. …”
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AMPK-ChREBP axis mediates de novo milk fatty acid synthesis promoted by glucose in the mammary gland of lactating goats by Hengbo Shi, Nannan Jiang, Ling Wei, Jie Cai, Wenying Zhang, Qianming Jiang, Juan J. Loor, Jianxin Liu

“…To investigate the role of glucose in regulating milk fatty acid synthesis, 6 lactating Guanzhong dairy goats were infused with 0, 60, or 100 g/d glucose via the external pubic artery in a 3 × 3 repeated Latin square experiment. …”
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Changes in Soil Properties, Bacterial Communities and Wheat Roots Responding to Subsoiling in South Loess Plateau of China by Hanbo Wang, Dasheng Zhang, Jiuxing He, Lijuan Wang, Jiameng Ren, Shuantang Zhang, Wenbo Bai, Jiqing Song, Guohua Lv, Jiusheng Li

“…This study was carried out to investigate effects of subsoiling on the diversity and composition of the bacterial community in a wheat–maize rotation field in the Guanzhong area of Shaanxi Province, China. After the wheat harvest, surface soil samples were collected under two tillage methods (single rotary tillage (RT) and subsoiling + rotary tillage (ST)) to perform high-throughput sequencing and bioinformatics analysis. …”
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Spatiotemporal expansion of human brucellosis in Shaanxi Province, Northwestern China and model for risk prediction by Zurong Yang, Miaomiao Pang, Qingyang Zhou, Shuxuan Song, Weifeng Liang, Junjiang Chen, Tianci Guo, Zhongjun Shao, Kun Liu

“…Compared with the Shaanbei upland plateau, Guanzhong basin had more obvious fluctuations in the occurrence of human brucellosis due to changes in meteorological factors. …”
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Spatiotemporally anthropogenic PM2.5- and O3-related health economic losses via weather normalization technique and hierarchical policies in Chinese cities by Jinyuan Guo, Teng Wang, Fei Li, Zhiguang Qu, Jingjing Yan, Shaojie Kong

“…Finally, a natural break-point classification method was used to establish the health loss rating systems for PM2.5 and O3.Results and DiscussionThe results showed the following: (1) From 2015 to 2020, 78%, 72%, 69%, 58%, 50%, and 41% of the annual mean PM2.5 concentration had exceeded 35 μg/m3, respectively, and 17%, 18%, 31%, 33%, 30%, and 17% of the annual mean O3 concentration exceeded 160 μg/m3, respectively, in 337 cities in China. (2) From 2015 to 2020, the health losses caused by PM2.5 and O3 were ranked as follows: Beijing–Tianjin–Hebei (BTH; 1968, 482 people) &gt; Shandong Peninsula (SDP; 1,396, 480 people) &gt; Central Plains (CP; 1,302, 314 people) &gt; Yangtze River Delta (YRD; 987, 306 people) &gt; Triangle of Central China (TC; 932, 275 people) &gt; Guanzhong Plain (GZP; 869, 189 people). (3) The average economic losses associated with public health of the PM2.5 and O3 were ranked as follows: BTH (2.321 billion, 3.218 billion RMB, 1 RMB = 0.0.1474 USD on 20 January 2023) &gt; SDP (1.607, 2.962 billion RMB) &gt; YRD (1.075, 1.902 billion RMB) &gt; TC (1.016 billion, 1.495 billion RMB) &gt; CP (1.095, 1.453 billion RMB) &gt; GZP (0.69, 0.828 billion RMB). …”
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