Browsing by Author "Li, Qi"

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    BAK1 Mediates Light Intensity to Phosphorylate and Activate Catalases to Regulate Plant Growth and Development
    Zhang, Shan; Li, Cheng; Ren, Haihua; Zhao, Tong; Li, Qi; Wang, Shufen; Zhang, Yanfeng; Xiao, Fangming; Wang, Xiaofeng (MDPI, 2020-02-20)
    BAK1 (brassinosteroid-insensitive 1 (BRI1) associated receptor kinase 1) plays major roles in multiple signaling pathways as a coreceptor to regulate plant growth and development and stress response. However, the role of BAK1 in high light signaling is still poorly understood. Here we observed that overexpression of BAK1 in Arabidopsis interferes with the function of high light in promoting plant growth and development, which is independent of the brassinosteroid (BR) signaling pathway. Further investigation shows that high light enhances the phosphorylation of BAK1 and catalase activity, thereby reducing hydrogen peroxide (H2O2) accumulation. Catalase3 (CAT3) is identified as a BAK1-interacting protein by affinity purification and LC-MS/MS analysis. Biochemical analysis confirms that BAK1 interacts with and phosphorylates all three catalases (CAT1, CAT2, and CAT3) of the Arabidopsis genome, and the trans-phosphorylation sites of three catalases with BAK1-CD are identified by LC-MS/MS in vitro. Genetic analyses reveal that the BAK1 overexpression plants knocked out all the three CAT genes completely abolishing the effect of BAK1 on suppression of high light-promoted growth. This study first unravels the role of BAK1 in mediating high light-triggered activation of CATs, thereby degrading H2O2 and regulating plant growth and development in Arabidopsis.
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    Critical Elements Recovery from Acid Mine Drainage
    Li, Qi (Virginia Tech, 2024-02-13)
    The rapid development of advanced technologies has led to an increase in demand for critical elements that are essential in the manufacturing of high-tech products. Among these critical elements, manganese (Mn), cobalt (Co), and nickel (Ni) are used in the production of batteries, electronics, and other advanced applications. The demand for these elements has been growing exponentially in recent years, driven by the rise of electric vehicles, renewable energy, and other emerging technologies. However, the United States is heavily dependent on foreign sources of critical minerals and on foreign supply chains, resulting in the potential for strategic vulnerabilities to both economy and military. To address this problem and reduce the Nation's vulnerability to disruptions in the supply of critical minerals, it is important to develop critical minerals recycling technologies. A systematic study was conducted to develop a process for producing high-purity Mn, Co, and Ni products from an acid mine drainage (AMD). As major contaminants, Fe and Al in the solution were sequentially precipitated and eliminated by elevating the pH. After that, a pre-concentrated slurry containing Mn, Co, Ni, and Zn was obtained by collecting the precipitates formed in the pH range of 6.50 to 10.00. The pre-concentrated slurry was redissolved for further purification. Sodium sulfide was added into the redissolved solution to precipitate Co, Ni, and Zn selectively while retaining Mn in the solution. Almost 100% of Co, Ni, and Zn but only around 15% of Mn were precipitated using a sulfur-to-metal molar ratio of 1 at pH 4.00. The sulfide precipitate was calcined and then completely dissolved. The critical elements existing in the dissolved solution were efficiently separated using a two-stage solvent extraction process. Ultimately, Co and Ni products with almost 94% and 100% purity were obtained by sulfide and alkaline precipitation, respectively. AMD also contains rare earth elements (REEs), which can be recovered through selective chemical precipitation. REE removal improved at pH 4.0 after converting ferrous to ferric ions with H2O2. Aluminum species in the solution hindered REE adsorption on ferric precipitates, and ferrous ions reduced REE adsorption on aluminum precipitates at lower pH, but at higher pH, REE removal increased due to ferrous ion precipitation. Various tests and analyses were conducted to understand the partitioning mechanisms of REE during the precipitation process of AMD. Sulfide precipitation is crucial to separate Mn from other elements, but the presence of contaminants like Fe and Al can affect sulfide precipitation efficiency. The effects of Al3+ iii and Fe2+ on the precipitation characteristics of four valuable metals, including Mn2+, Ni2+, Co2+, and Zn2+, were investigated by conducting solution chemistry calculations, sulfide precipitation tests, and mineralogy characterizations. It was found that the ability of the valuable metals to form sulfide precipitates followed an order of Zn2+ > Ni2+ > Co2+ > Mn2+. The sulfide precipitate of Zn2+ was the most stable and did not re-dissolve under the acidic condition (pH 4.00 ± 0.05). In addition, the sulfide precipitation of Zn2+ was barely affected by the contaminant metal ions. However, in the presence of Al3+, the precipitation recoveries of Mn2+, Ni2+, and Co2+ in a solution containing all the valuable metals were noticeably reduced due to simultaneous hydrolysis and competitive adsorption. The precipitation recoveries of Ni2+ and Co2+ in solutions containing individual valuable metals also reduced when Fe2+ was present, primarily due to competitive precipitation. However, the recovery of Mn2+ was enhanced due to the formation of ferrous sulfide precipitate, providing abundant active adsorption sites for Mn species. In the solution containing all the valuable metals, Fe2+ promoted the recoveries of the valuable metals due to the higher concentration of Na2S and the formation of ferrous sulfide precipitate.
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    Effects of contaminant metal ions on precipitation recovery of rare earth elements using oxalic acid
    Zhang, Wencai; Noble, Aaron; Ji, Bin; Li, Qi (2022-01-01)
    Solution equilibrium calculations were performed in this study to understand the impact of contaminant metal ions on the precipitation efficiency of selected rare earth elements (Ce3+, Nd3+, and Y3+) using oxalic acid as a precipitant. Trivalent metal ions, Al3+ and Fe3+, were found to considerably affect the precipitation efficiency of REEs. When Al3+ and Fe3+ concentrations were increased by 1 × 10−4 mol/L, in order to achieve an acceptable cerium recovery of 93% from solutions containing 1 × 10−4 mol/L Ce3+, oxalate dosage needed to increase by 1.2 × 10−4 and 1.68 × 10−4 mol/L, respectively. Such great impacts on the required oxalate dosage were also observed for Nd3+ and Y3+, which indicates that oxalic acid consumption and cost will be largely increased when the trivalent metal ions exist in REE-concentrated solutions. Effects of the divalent metal ions on the oxalate dosage is minimal. Furthermore, solution equilibrium calculation results showed that the precipitation of Fe3+ and Ca2+ (e.g., hematite and Ca(C2O4)∙H2O(s)) likely occurs during the oxalate precipitation of REEs at relatively high pH (e.g., pH 2.5), which will reduce rare earth oxalate product purity. In addition to the metal ions, anionic species, especially SO42−, were also found to negatively affect the precipitation recovery of REEs. For example, when 0.1 mol/L SO42− occurs in a solution containing 1 × 10−4 mol/L Ce3+ and 4 × 10−4 mol/L oxalate, the pH needed to be elevated from 2.0 to 3.3 to achieve the acceptable recovery. Overall, findings from this study provide guidance for the obtainment of high-purity rare earth products from solutions containing a considerable amount of contaminant metal ions by means of oxalic acid precipitation.
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    Evaluate the guide RNA effectiveness via Agrobacterium-mediated transient assays in Nicotiana benthamiana
    Wang, Zhibo; Shea, Zachary; Li, Qi; Wang, Kunru; Mills, Kerri; Zhang, Bo; Zhao, Bingyu (Frontiers, 2023-02-20)
    CRISPR/Cas9-based genome editing system is a powerful tool for plant genetic improvement. However, the variable efficiency of guide RNA(s) (gRNA) represents a key limiting factor that hampers the broad application of the CRISPR/Cas9 system in crop improvement. Here, we employed the Agrobacterium-mediated transient assays to evaluate the effectiveness of gRNAs for editing genes in Nicotiana benthamiana and soybean. We designed a facile screening system based on indels that can be introduced by CRISPR/Cas9-mediated gene editing. A gRNA binding sequence (23 nucleotides) was inserted into the open reading frame of yellow fluorescent protein (YFP) gene (gRNA-YFP), which disrupted the YFP reading frame and results in no fluorescent signal when it was expressed in plant cells. Transiently co-expression of Cas9 and a gRNA targeting the gRNA-YFP gene in plant cells could restore the YFP reading frame and recover the YFP signals. We evaluated five gRNAs targeting Nicotiana benthamiana and soybean genes and confirmed the reliability of the gRNA screening system. The effective gRNAs targeting NbEDS1, NbWRKY70, GmKTI1, and GmKTI3 had been used to generate transgenic plants and resulted in expected mutations on each gene. While a gRNA targeting NbNDR1 was confirmed to be ineffective in transient assays. This gRNA indeed failed to trigger target gene mutations in stable transgenic plants. Thus, this new transient assay system can be used to validate the effectiveness of gRNAs before generating stable transgenic plants.
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    Nicotiana species as surrogate host for studying the pathogenicity of Acidovorax citrulli, the causal agent of bacterial fruit blotch of cucurbits
    Traore, Sy M.; Eckshtain-Levi, Noam; Miao, Jiamin; Sparks, Anita Castro; Wang, Zhibo; Wang, Kunru; Li, Qi; Burdman, Saul; Walcott, Ron; Welbaum, Gregory E.; Zhao, Bingyu (Wiley, 2019-06-01)
    Bacterial fruit blotch (BFB) caused by Acidovorax citrulli is one of the most important bacterial diseases of cucurbits worldwide. However, the mechanisms associated with A. citrulli pathogenicity and genetics of host resistance have not been extensively investigated. We idenitfied Nicotiana benthamiana and Nicotiana tabacum as surrogate hosts for studying A. citrulli pathogenicity and non-host resistance triggered by type III secreted (T3S) effectors. Two A. citrulli strains, M6 and AAC00-1, that represent the two major groups amongst A. citrulli populations, induced disease symptoms on N. benthamiana, but triggered a hypersensitive response (HR) on N. tabacum plants. Transient expression of 19 T3S effectors from A. citrulli in N. benthamiana leaves revealed that three effectors, Aave_1548, Aave_2708, and Aave_2166, trigger water-soaking-like cell death in N. benthamiana. Aave_1548 knockout mutants of M6 and AAC00-1 displayed reduced virulence on N. benthamiana and melon (Cucumis melo L.). Transient expression of Aave_1548 and Aave_2166 effectors triggered a non-host HR in N. tabacum, which was dependent on the functionality of the immune signalling component, NtSGT1. Hence, employing Nicotiana species as surrogate hosts for studying A. citrulli pathogenicity may help characterize the function of A. citrulli T3S effectors and facilitate the development of new strategies for BFB management.
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    Identification of new marker genes from plant single-cell RNA-seq data using interpretable machine learning methods
    Haidong, Yan; Lee, Jiyoung; Song, Qi; Li, Qi; Schiefelbein, John; Zhao, Bingyu; Li, Song (2022-02-24)
    An essential step in the analysis of single-cell RNA sequencing data is to classify cells into specific cell types using marker genes. In this study, we have developed a machine learning pipeline called single-cell predictive marker (SPmarker) to identify novel cell-type marker genes in the Arabidopsis root. Unlike traditional approaches, our method uses interpretable machine learning models to select marker genes. We have demonstrated that our method can: assign cell types based on cells that were labelled using published methods; project cell types identified by trajectory analysis from one data set to other data sets; and assign cell types based on internal GFP markers. Using SPmarker, we have identified hundreds of new marker genes that were not identified before. As compared to known marker genes, the new marker genes have more orthologous genes identifiable in the corresponding rice single-cell clusters. The new root hair marker genes also include 172 genes with orthologs expressed in root hair cells in five non-Arabidopsis species, which expands the number of marker genes for this cell type by 35–154%. Our results represent a new approach to identifying cell-type marker genes from scRNA-seq data and pave the way for cross-species mapping of scRNA-seq data in plants.
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    Leaching recovery of rare earth elements from the calcination product of a coal coarse refuse using organic acids
    Ji, Bin; Li, Qi; Zhang, Wencai (Elsevier, 2022-02-01)
    Due to the increasing criticality of rare earth elements (REEs), it has become essential to recover REEs from alternative resources. In this study, systematic REEs leaching tests were performed on the calcination product of a coal coarse refuse using hydrochloric acid and different types of organic acid as lixiviants. Experimental results show that the recovery of REEs, especially heavy REEs (HREEs) and scandium (Sc), is improved by using selected organic acids. Citric acid and DL-malic acid afford the best leaching performances; whereas, malonic acid, oxalic acid, and DL-tartaric acid are inferior to hydrochloric acid. Results of zeta potential measurements and solution chemical equilibrium calculations show that malonic acid is more likely adsorbed on the surface of the calcined material compared with citric acid and DL-malic acid. The adsorption may reduce the effective concentration of malonic species in solution and/or increase the amount of REEs adsorbed on the surface, thereby impairing the leaching recovery. Compared with light REEs (LREEs), a stronger adsorption of the HREEs on the surface is observed from electro-kinetic test results. This finding explains why organic acids impose a more positive impact on the leaching recovery of HREEs. By complexing with the HREEs, organic acids can keep the metal ions in solution and improve the leaching recovery. The adsorption of Sc3+ on the surface is the lowest compared with other REEs. Therefore, rather than complexing, the organic anionic species likely play a function of solubilizing Sc from the solid, which is similar to that of hydrogen ions.
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    Maize R gene Rxo1 Confers Disease Resistance on Pepper and Nicotiana benthamiana
    Li, Qi (Virginia Tech, 2023-03-03)
    Pepper is a popular and important vegetable crop grown and consumed worldwide. However, pepper production is threatened by the gram-negative bacterium Xanthomonas euvesicatoria (Xe) which causes bacterial spot (BS) disease, one of the most common and destructive diseases on pepper. Due to limited genetic resistance resources in host species, a promising strategy for controlling BS disease is to transfer nonhost disease resistance (R) genes from other plant species into pepper plants to confer broad-spectrum and durable resistance. A maize R gene Rxo1 has been functionally transferred to rice plants and confers nonhost resistance to rice pathogen Xanthomonas oryzae pv. oryzicola (Xoc) carrying a type III effector (T3E) AvrRxo1. Most Xe strains carry a T3E Xe4428, a homolog of AvrRxo1. Therefore, Rxo1 could be potentially employed to develop Xe-resistant pepper. In addition, a better understanding of the virulence function of Xe4428 may provide insights into the pathogenesis of Xe and new strategies for crop improvement. In this dissertation, we transformed Rxo1 into the far-related dicot species Nicotiana benthamiana and pepper, and characterized the Rxo1-mediated disease resistance against Xe strains carrying AvrRxo1 or Xe4428. In addition, we explored the virulence function and mechanism of Xe4428. In the Rxo1-transgenic N. benthamiana, we demonstrated that Rxo1 could condition resistance to Xe harboring AvrRxo1 but not Xe4428. We revealed that AvrRxo1 could directly interact with the nucleotide-binding domain of Rxo1 in vivo and in vitro. We further demonstrated that the nucleus localization of AvrRxo1 was required for its avirulence and virulence functions. In addition, the cytosol localization of Rxo1 was also necessary to confer disease resistance. The downstream signaling component NbNDR1 was demonstrated to be involved in Rxo1/AvrRxo1-mediated disease resistance. By RNAseq-based gene expression profiling, we identified six candidate genes of interest up-regulated by the Rxo1-AvrRxo1 recognition. Through virus-induced gene silencing screening, a gene encoding phenylalanine ammonia-lyase 4 was demonstrated to be critical for Rxo1/AvrRxo1-mediated disease resistance in N. benthamiana. Rxo1-transgenic pepper plants were resistant to the Xe strain with the complementary Xoc effector AvrRxo1 but not the wild-type Xe strain that carries Xe4428. A Xe4428 mutant with only one nucleotide substitution could trigger the Rxo1-mediated disease resistance in pepper. Both wild-type and mutant Xe4428 had significant virulence functions that could promote the Xe bacterial proliferation on wild-type pepper plants. In addition, the mutant Xe4428 had a higher expression level than wild-type Xe4428 in Xe bacterial cells, which might explain why the mutant Xe4428 but not wild-type Xe4428, could trigger the Rxo1-mediated disease resistance in pepper. We identified 14 pepper cystatin genes (CaCys), among which two genes (CaCys1 and CaCys13) could be induced, and two genes (CaCys3 and CaCys5) were suppressed by Xe4428. Ectopically expressing one of the induced genes CaCys1 in N. benthamiana increased the stomatal opening and promoted the Xe growth in N. benthamiana plants. Thus, we illuminate one possible mechanism of Xe4428's virulence function is to regulate the stomata apertures in N. benthamiana. Bacterial fruit blotch (BFB) caused by the gram-negative bacterial pathogen Acidovorax citrulli (A. citrulli) is one of the most destructive diseases in cucurbit crops, including melon and watermelon. A better understanding of the virulence and avirulence functions of T3Es in A. citrulli helps breeders engineer crop resistance to BFB. To this end, a clean genetic background of A. citrulli with multiple effector genes deleted is desired. Here, we optimized a marker-exchange-based method for sequential effector deletion and generated an AAC00-1 mutant with five effector genes (Aave2166, Aave3626, Aave1548, Aave2938, Aave2708) deleted (AAC00-15). AAC00-15 was less virulent in watermelon but more virulent in N. benthamiana. Through complementation, we characterized the function of individual effectors and identified a promising R gene, Roq1, that could be used to control BFB disease.
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    Prediction and Anomaly Detection Techniques for Spatial Data
    Liu, Xutong (Virginia Tech, 2013-06-11)
    With increasing public sensitivity and concern on environmental issues, huge amounts of spatial data have been collected from location based social network applications to scientific data. This has encouraged formation of large spatial data set and generated considerable interests for identifying novel and meaningful patterns. Allowing correlated observations weakens the usual statistical assumption of independent observations, and complicates the spatial analysis. This research focuses on the construction of efficient and effective approaches for three main mining tasks, including spatial outlier detection, robust inference for spatial dataset, and spatial prediction for large multivariate non-Gaussian data. spatial outlier analysis, which aims at detecting abnormal objects in spatial contexts, can help  extract important knowledge in many applications. There exist the well-known masking and swamping problems in most approaches, which can't still satisfy certain requirements aroused recently. This research focuses on development of spatial outlier detection techniques for three aspects, including spatial numerical outlier detection, spatial categorical outlier detection and identification of the number of spatial numerical outliers. First, this report introduces Random Walk based approaches to identify spatial numerical outliers. The Bipartite and an Exhaustive Combination weighted graphs are modeled based on spatial and/or non-spatial attributes, and then Random walk techniques are performed on the graphs to compute the relevance among objects. The objects with lower relevance are recognized as outliers. Second, an entropy-based method is proposed to estimate the optimum number of outliers. According to the entropy theory, we expect that, by incrementally removing outliers, the entropy value will decrease sharply, and reach a stable state when all the outliers have been removed. Finally, this research designs several Pair Correlation Function based methods to detect spatial categorical outliers for both single and multiple attribute data. Within them, Pair Correlation Ratio(PCR) is defined and estimated for each pair of categorical combinations based on their co-occurrence frequency at different spatial distances. The observations with the lower PCRs are diagnosed as potential SCOs. Spatial kriging is a widely used predictive model whose predictive accuracy could be significantly compromised if the observations are contaminated by outliers. Also, due to spatial heterogeneity, observations are often different types. The prediction of multivariate spatial processes plays an important role when there are cross-spatial dependencies between multiple responses. In addition, given the large volume of spatial data, it is computationally challenging. These raise three research topics: 1).robust prediction for spatial data sets; 2).prediction of multivariate spatial observations; and 3). efficient processing for large data sets. First, increasing the robustness of spatial kriging model can be systematically addressed by integrating heavy tailed distributions. However, it is analytically intractable inference. Here, we presents a novel robust and reduced Rank spatial kriging Model (R$^3$-SKM), which is resilient to the influences of outliers and allows for fast spatial inference. Second, this research introduces a flexible hierarchical Bayesian framework that permits the simultaneous modeling of mixed type variable. Specifically, the mixed-type attributes are mapped to latent numerical random variables that are multivariate Gaussian in nature. Finally, the knot-based techniques is utilized to model the predictive process as a reduced rank spatial process, which projects the process realizations of the spatial model to a lower dimensional subspace. This projection significantly reduces the computational cost.
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    Using Machine Learning to Predict Urban Canopy Flows for Land Surface Modeling
    Lu, Yanle; Zhou, Xu-Hui; Xiao, Heng; Li, Qi (American Geophysical Union, 2023-01-16)
    Developing urban land surface models for modeling cities at high resolutions needs to better account for the city-specific multi-scale land surface heterogeneities at a reasonable computational cost. We propose using an encoder-decoder convolutional neural network to develop a computationally efficient model for predicting the mean velocity field directly from urban geometries. The network is trained using the geometry-resolving large eddy simulation results. Systematic testing on urban structures with increasing deviations from the training geometries shows the prediction error plateaus at 15%, compared to errors sharply increasing up to 35% in the null models. This is explained by the trained model successfully capturing the effects of pressure drag, especially for tall buildings. The prediction error of the aerodynamic drag coefficient is reduced by 32% compared with the default parameterization implemented in mesoscale modeling. This study highlights the potential of combining computational fluid dynamics modeling and machine learning to develop city-specific parameterizations.