Evaluating the Accuracy of Impedance Flow Cytometry with Cell-Sized Liposomes.

Electrical properties of single cells are important label-free biomarkers of disease and immunity. At present, impedance flow cytometry (IFC) provides important means for high throughput characterization of single-cell electrical properties. However, the accuracy of the spherical single-shell electrical model widely used in IFC has not been well evaluated due to the lack of reliable and reproducible single-shell model particles with true-value electrical parameters as benchmarks. Herein, a method is proposed to evaluate the accuracy of the single-cell electrical model with cell-sized unilamellar liposomes synthesized through double emulsion droplet microfluidics. The influence of three key dimension parameters (i.e., the measurement channel width w, height h, and electrode gap g) in the single-cell electrical model were evaluated through experiment. It was found that the relative error of the electrical intrinsic parameters measured by IFC is less than 10% when the size of the sensing zone is close to the measured particles. It further reveals that h has the greatest influence on the measurement accuracy, and the maximum relative error can reach ∼30%. Error caused by g is slightly larger than w. This provides a solid guideline for the design of IFC measurement system. It is envisioned that this method can advance further improvement of IFC and accurate electrical characterization of single cells.

TIGAR: An Improved Bayesian Tool for Transcriptomic Data Imputation Enhances Gene Mapping of Complex Traits.

The transcriptome-wide association studies (TWASs) that test for association between the study trait and the imputed gene expression levels from cis-acting expression quantitative trait loci (cis-eQTL) genotypes have successfully enhanced the discovery of genetic risk loci for complex traits. By using the gene expression imputation models fitted from reference datasets that have both genetic and transcriptomic data, TWASs facilitate gene-based tests with GWAS data while accounting for the reference transcriptomic data. The existing TWAS tools like PrediXcan and FUSION use parametric imputation models that have limitations for modeling the complex genetic architecture of transcriptomic data. Therefore, to improve on this, we employ a nonparametric Bayesian method that was originally proposed for genetic prediction of complex traits, which assumes a data-driven nonparametric prior for cis-eQTL effect sizes. The nonparametric Bayesian method is flexible and general because it includes both of the parametric imputation models used by PrediXcan and FUSION as special cases. Our simulation studies showed that the nonparametric Bayesian model improved both imputation R2 for transcriptomic data and the TWAS power over PrediXcan when ≥1% cis-SNPs co-regulate gene expression and gene expression heritability ≤0.2. In real applications, the nonparametric Bayesian method fitted transcriptomic imputation models for 57.8% more genes over PrediXcan, thus improving the power of follow-up TWASs. We implement both parametric PrediXcan and nonparametric Bayesian methods in a convenient software tool "TIGAR" (Transcriptome-Integrated Genetic Association Resource), which imputes transcriptomic data and performs subsequent TWASs using individual-level or summary-level GWAS data.

Deactivation mechanism and feasible regeneration approaches for the used commercial NH3-SCR catalysts.

The deactivation and regeneration of selective catalytic reduction catalysts which have been used for about 37,000 h in a coal power plant are studied. The formation of Al2(SO4)3, surface deposition of K, Mg and Ca are primary reasons for the deactivation of the studied Selective catalytic reduction catalysts. Other factors such as activated V valence alteration also contribute to the deactivation. Reactivation of used catalysts via environment-friendly and finance-feasibly approaches, that is, dilute acid or alkali solution washing, would be of great interest. Three regeneration pathways were studied in the present work, and dilute nitric acid or sodium hydroxide solution could remove most of the contaminants over the catalyst surface and partly recover the catalytic performance. Notably, the acid-alkali combination washing, namely, catalysts treated by dilute sodium hydroxide and nitric acid solutions orderly, was much more effective than single washing approach in recovering the activity, and NO conversion increased from 23.6% to 89.5% at 380°C. The higher removal efficiency of contaminants, the lower dissolution of activated V, and promoting the formation of polymeric vanadate should be the main reason for recovery of the activity.