Bio

Professional Education


  • Doctor of Philosophy, Michigan State University (2018)
  • Bachelor of Engineering, Unlisted School (2013)

Publications

All Publications


  • Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions NATURE MACHINE INTELLIGENCE Culos, A., Tsai, A. S., Stanley, N., Becker, M., Ghaemi, M. S., McIlwain, D. R., Fallahzadeh, R., Tanada, A., Nassar, H., Espinosa, C., Xenochristou, M., Ganio, E., Peterson, L., Han, X., Stelzer, I. A., Ando, K., Gaudilliere, D., Phongpreecha, T., Maric, I., Chang, A. L., Shaw, G. M., Stevenson, D. K., Bendall, S., Davis, K. L., Fantl, W., Nolan, G. P., Hastie, T., Tibshirani, R., Angst, M. S., Gaudilliere, B., Aghaeepour, N. 2020
  • Effect of catalyst and reaction conditions on aromatic monomer yields, product distribution, and sugar yields during lignin hydrogenolysis of silver birch wood. Bioresource technology Phongpreecha, T., Christy, K. F., Singh, S. K., Hao, P., Hodge, D. B. 2020; 316: 123907

    Abstract

    The impact of catalyst choice and reaction conditions during catalytic hydrogenolysis of silver birch biomass are assessed for their effect on aromatic monomer yields and selectivities, lignin removal, and sugar yields from enzymatic hydrolysis. At a reaction temperature of 220C with no supplemental H2, it was demonstrated that both Co/C and Ni/C exhibited aromatic monomer yields of >50%, which were close to the theoretical maximum expected for the lignin based on total beta-O-4 content and exhibited high selectivities for 4-propylguaiacol and 4-propylsyringol. Pd/C exhibited a significantly different set of products, and using a model lignin dimer, showed a product profile that shifted upon inclusion of supplemental H2, suggesting that the generation of surface hydrogen is critical for this catalyst system. Lignin removal during hydrogenolysis could be correlated to glucose yields and inclusion of lignin depolymerizing catalysts significantly improves lignin removal and subsequent enzymatic hydrolysis yields.

    View details for DOI 10.1016/j.biortech.2020.123907

    View details for PubMedID 32739581

  • Impact of dilute acid pretreatment conditions on p-coumarate removal in diverse maize lines. Bioresource technology Saulnier, B. K., Phongpreecha, T., Singh, S. K., Hodge, D. B. 2020; 314: 123750

    Abstract

    Prior work has identified that lignins recovered from dilute acid-pretreated corn stover exhibit superior performance in phenol-formaldehyde resins used in wood adhesive applications when compared to diverse process-modified lignins derived from other sources. This improved performance is hypothesized to be due to the higher content of unsubstituted phenolic groups specifically p-coumarate lignin esters. In this work, a diverse set of corn stover samples are employed that exhibit diversity in p-coumarate content and total lignin content to explore the relationship between dilute acid pretreatment conditions, p-coumarate ester hydrolysis, xylan solubilization, and the resulting glucose enzymatic hydrolysis yields. The goal of this study is to identify pretreatment conditions that preserve a significant fraction of the p-coumarate esters while simultaneously achieving high enzymatic hydrolysis yields. Kinetic parameters for p-coumarate ester hydrolysis were quantified and pretreatment-biomass combinations were identified that result in glucose hydrolysis yields of more than 90% while retaining nearly 50mg p-coumarate/g lignin.

    View details for DOI 10.1016/j.biortech.2020.123750

    View details for PubMedID 32622284

  • VoPo leverages cellular heterogeneity for predictive modeling of single-cell data. Nature communications Stanley, N., Stelzer, I. A., Tsai, A. S., Fallahzadeh, R., Ganio, E., Becker, M., Phongpreecha, T., Nassar, H., Ghaemi, S., Maric, I., Culos, A., Chang, A. L., Xenochristou, M., Han, X., Espinosa, C., Rumer, K., Peterson, L., Verdonk, F., Gaudilliere, D., Tsai, E., Feyaerts, D., Einhaus, J., Ando, K., Wong, R. J., Obermoser, G., Shaw, G. M., Stevenson, D. K., Angst, M. S., Gaudilliere, B., Aghaeepour, N. 2020; 11 (1): 3738

    Abstract

    High-throughput single-cell analysis technologies produce an abundance of data that is critical for profiling the heterogeneity of cellular systems. We introduce VoPo (https://github.com/stanleyn/VoPo), a machine learning algorithm for predictive modeling and comprehensive visualization of the heterogeneity captured in large single-cell datasets. In three mass cytometry datasets, with the largest measuring hundreds of millions of cells over hundreds of samples, VoPo defines phenotypically and functionally homogeneous cell populations. VoPo further outperforms state-of-the-art machine learning algorithms in classification tasks, and identified immune-correlates of clinically-relevant parameters.

    View details for DOI 10.1038/s41467-020-17569-8

    View details for PubMedID 32719375

  • Multivariate prediction of dementia in Parkinson's disease. NPJ Parkinson's disease Phongpreecha, T., Cholerton, B., Mata, I. F., Zabetian, C. P., Poston, K. L., Aghaeepour, N., Tian, L., Quinn, J. F., Chung, K. A., Hiller, A. L., Hu, S. C., Edwards, K. L., Montine, T. J. 2020; 6: 20

    Abstract

    Cognitive impairment in Parkinson's disease (PD) is pervasive with potentially devastating effects. Identification of those at risk for cognitive decline is vital to identify and implement appropriate interventions. Robust multivariate approaches, including fixed-effect, mixed-effect, and multitask learning models, were used to study associations between biological, clinical, and cognitive factors and for predicting cognitive status longitudinally in a well-characterized prevalent PD cohort (n?=?827). Age, disease duration, sex, and GBA status were the primary biological factors associated with cognitive status and progression to dementia. Specific cognitive tests were better predictors of subsequent cognitive status for cognitively unimpaired and dementia groups. However, these models could not accurately predict future mild cognitive impairment (PD-MCI). Data collected from a large PD cohort thus revealed the primary biological and cognitive factors associated with dementia, and provide clinicians with data to aid in the identification of risk for dementia. Sex differences and their potential relationship to genetic status are also discussed.

    View details for DOI 10.1038/s41531-020-00121-2

    View details for PubMedID 32885039

    View details for PubMedCentralID PMC7447766

  • Systematic Immunophenotyping Reveals Sex-Specific Responses After Painful Injury in Mice. Frontiers in immunology Tawfik, V. L., Huck, N. A., Baca, Q. J., Ganio, E. A., Haight, E. S., Culos, A., Ghaemi, S., Phongpreecha, T., Angst, M. S., Clark, J. D., Aghaeepour, N., Gaudilliere, B. 2020; 11: 1652

    Abstract

    Many diseases display unequal prevalence between sexes. The sex-specific immune response to both injury and persistent pain remains underexplored and would inform treatment paradigms. We utilized high-dimensional mass cytometry to perform a comprehensive analysis of phenotypic and functional immune system differences between male and female mice after orthopedic injury. Multivariate modeling of innate and adaptive immune cell responses after injury using an elastic net algorithm, a regularized regression method, revealed sex-specific divergence at 12 h and 7 days after injury with a stronger immune response to injury in females. At 12 h, females upregulated STAT3 signaling in neutrophils but downregulated STAT1 and STAT6 signals in T regulatory cells, suggesting a lack of engagement of immune suppression pathways by females. Furthermore, at 7 days females upregulated MAPK pathways (p38, ERK, NFkB) in CD4T memory cells, setting up a possible heightened immune memory of painful injury. Taken together, our findings provide the first comprehensive and functional analysis of sex-differences in the immune response to painful injury.

    View details for DOI 10.3389/fimmu.2020.01652

    View details for PubMedID 32849569

    View details for PubMedCentralID PMC7403191

Footer Links:

Stanford Medicine Resources: