Search for: All records

Rational catalyst design and optimal solvent selection are key to advancing biorefining. Here, we explored the organocatalytic isomerization of d-fructose to a valuable rare monosaccharide, d-allulose, as a function of solvent. The isomerization of d-fructose to d-allulose competes with its isomerization to d-glucose and sugar degradation. In both water and DMF, the catalytic activity of amines towards d-fructose is correlated with their basicity. Solvents impact the selectivity significantly by altering the tautomeric distribution of d-fructose. Our results suggest that the furanose tautomer of d-fructose is isomerized to d-allulose, and the fractional abundance of this tautomer increases as follows: water < MeOH < DMF ≈ DMSO. Reaction rates are also higher in aprotic than in protic solvents. The best d-allulose yield, 14 %, was obtained in DMF with 1,5,7-triazabicyclo[4.4.0]dec-5-ene (TBD) as the catalyst. The reaction kinetics and mechanism were explored using operando NMR spectroscopy. 

To use next-generation sequencing technology such as RNA-seq for medical and health applications, choosing proper analysis methods for biomarker identification remains a critical challenge for most users. The US Food and Drug Administration (FDA) has led the Sequencing Quality Control (SEQC) project to conduct a comprehensive investigation of 278 representative RNA-seq data analysis pipelines consisting of 13 sequence mapping, three quantification, and seven normalization methods. In this article, we focused on the impact of the joint effects of RNA-seq pipelines on gene expression estimation as well as the downstream prediction of disease outcomes. First, we developed and applied three metrics (i.e., accuracy, precision, and reliability) to quantitatively evaluate each pipeline’s performance on gene expression estimation. We then investigated the correlation between the proposed metrics and the downstream prediction performance using two real-world cancer datasets (i.e., SEQC neuroblastoma dataset and the NIH/NCI TCGA lung adenocarcinoma dataset). We found that RNA-seq pipeline components jointly and significantly impacted the accuracy of gene expression estimation, and its impact was extended to the downstream prediction of these cancer outcomes. Specifically, RNA-seq pipelines that produced more accurate, precise, and reliable gene expression estimation tended to perform better in the prediction of disease outcome. In the end, we provided scenarios as guidelines for users to use these three metrics to select sensible RNA-seq pipelines for the improved accuracy, precision, and reliability of gene expression estimation, which lead to the improved downstream gene expression-based prediction of disease outcome.