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Your search keyword '"Xu, Xiangnan"' showing total 164 results
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164 results on '"Xu, Xiangnan"'

9. Foliar application of potassium silicate, potassium fulvate and betaine improve summer-time tomato yield by promoting plant nitrogen and potassium uptake

Author

Xu Xiangnan, Lei Xihong, Liao Shangqiang, Li Yanmei, and Sun Yanxin

Subjects
biostimulant, leaf spray, lycopersicon esculentum, macronutrients translocation, tomato fruit quality, Plant culture, SB1-1110
Abstract

During the summer months, greenhouse tomato production is challenged by the heat, causing yield reduction; therefore, we conducted a study to test the effectiveness of different foliar spray compositions for the improvement of Lycopersicon esculentum var. cerasiforme ‘Qianxi’ nutrition uptake and fruit yield. Two forms of silicon, two kinds of organic nitrogenous compounds and water as the control factor were two-two paired to become nine different recipes, which were as follows: CK (H2O), ISi (K2SiO3), organic silicon (OSi), potassium fulvate (BSFA), BSFA + ISi, BSFA + OSi, betaine (GB), GB + ISi and GB + OSi. The plants were sprayed three times during the period of the first, second and third truss fruit expansions with a 2-week interval. As a result, BSFA or K2SiO3 generated higher yield in plants compared with the other compositions. Also, K2SiO3 significantly enhanced the total nitrogen, phosphorus and potassium accumulation in fruit and the whole plant. Comparing across the nine recipes, BSFA + ISi, ISi and GB had improved the fruit yield by 17%, 12.7% and 9.5%, performing the best. BSFA + ISi, ISi and GB also improved the plant nitrogen uptake by 8.2%, 18.8% and 9.8%, as well as the potassium uptake by 16.2%, 12.3% and 15.2%, compared with CK, respectively. Thus, K2SiO3, BSFA and GB stimulated the plant nitrogen and potassium uptake, which improved the marketable yield.

Published
2022
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27. Experimental simulation of alternating aerodynamic loads induced by high-speed trains passing in tunnels.

Author

Wang, Qianxuan, Hu, Zhelong, Liang, Xifeng, Wu, Yitong, Zhou, Wei, Yang, Yi, Xu, Xiangnan, and Wang, Sumei

Abstract

Tunnel passing at high speed produces aerodynamic load on railway trains, which may bring about fatigue failure on the car body, and damages passenger comfort due to interior penetration of the alternating wave. In this work, the air suction experiment approaches were developed. It performs excellently through internal and external loading utilizing valve controlling strategies. It is validated with an in-transit vehicle test when the train runs through 3 different tunnel lengths. The relative deviation between simulation and vehicle tests goes no more than 5.0%. Research outcome indicates that the proposed method provides an important experiment means for passenger comfort and car-body fatigue behavior research. [ABSTRACT FROM AUTHOR]

Published
2023
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32. A Statistical Framework For Nutriomics Data Analysis

Author

Xu, Xiangnan

Subjects
nutrition, statistical modeling, precision medicine, microbiome, omics
Abstract

Nutriomics is a new discipline that investigates the relationship between nutrition and health through the use of high throughput omics technologies. However, the inherent complexity of nutriomics data poses several challenges for data analysis. In this thesis, the author introduces nutriomics and the statistical challenges associated with its analysis. They propose statistical modelling and machine learning methods to tackle three main challenges: non-linearity, high dimensionality, and data heterogeneity. To deal with these challenges, we first propose a statistical framework, that we coin LC-N2G, to test whether the association between nutrition intake and omics features of interest are significantly different from being unrelated. We use public data as an example to show LC-N2G's ability to discover non-linear associations between nutrition and gene expression. Then we propose a statistical method, coined eNODAL, to cluster high-dimensional omics features based on how they respond to nutrition intake. The application of eNODAL to a mouse proteomics nutrition study shows that eNODAL can identify interpretable clusters of proteins with similar responses to diet and drug treatment. Finally, a statistical model, which we call NEMoE, is proposed to uncover the heterogeneous interplay among diet, omics, and health outcomes. We use a microbiome Parkinson’s disease (PD) study to illustrate the method and show that NEMoE is able to identify diet-specific microbial signatures of PD. Overall, this thesis proposes statistical methods to analyze nutriomics data and provides possible future extensions based on the research. The methods proposed in this thesis could help researchers better understand the complex relationships between nutrition and health, ultimately leading to improved health outcomes.

Published
2023

33. Additional file 1 of NEMoE: a nutrition aware regularized mixture of experts model to identify heterogeneous diet-microbiome-host health interactions

Author

Xu, Xiangnan, Lubomski, Michal, Holmes, Andrew J., Sue, Carolyn M., Davis, Ryan L., Muller, Samuel, and Yang, Jean Y. H.

Abstract

Additional file 1: Supplementary notes. Supplementary Fig. 1. Illustration of NEMoE and two-stage model. Supplementary Fig. 2. Graphical model representation of NEMoE. Supplementary Fig. 3. Nutrition classes determined by k-means do not show an informative relationship between microbiome and PD. Supplementary Fig 4. Simulation results of NEMoE and other methods under different settings. Supplementary Fig 5. External validation of consensus taxa Faecalibacterium and Blautia. Supplementary Fig 6. Prediction performance of different types of input for NEMoE. Supplementary Fig 7. ROC curves for different standardization methods of microbiome composition data analysis.

Published
2023
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