Acoustic detection of a mutation-specific Ligase Chain Reaction based on liposome amplification.

Single nucleotide variants (SNVs) play a crucial role in understanding genetic diseases, cancer development, and personalized medicine. However, existing ligase-based amplification and detection techniques, such as Rolling Circle Amplification and Ligase Detection Reaction, suffer from low efficiency and difficulties in product detection. To address these limitations, we propose a novel approach that combines Ligase Chain Reaction (LCR) with acoustic detection using highly dissipative liposomes. In our study, we are using LCR combined with biotin- and cholesterol-tagged primers to produce amplicons also modified at each end with a biotin and cholesterol molecule. We then apply the LCR mix without any purification directly on a neutravidin modified QCM device Au-surface, where the produced amplicons can bind specifically through the biotin end. To improve sensitivity, we finally introduce liposomes as signal enhancers. For demonstration, we used the detection of the BRAF V600E point mutation versus the wild-type allele, achieving an impressive detection limit of 220 aM of the mutant target in the presence of the same amount of the wild type. Finally, we combined the assay with a microfluidic fluidized bed DNA extraction technology, offering the potential for semi-automated detection of SNVs in patients' crude samples. Overall, our LCR/acoustic method outperforms other LCR-based approaches and surface ligation biosensing techniques in terms of detection efficiency and time. It effectively overcomes challenges related to DNA detection, making it applicable in diverse fields, including genetic disease and pathogen detection.

Uncertainty quantification of time-average quantities of chaotic systems using sensitivity-enhanced polynomial chaos expansion.

We consider the effect of multiple stochastic parameters on the time-average quantities of chaotic systems. We employ the recently proposed sensitivity-enhanced generalized polynomial chaos expansion, se-gPC, to quantify efficiently this effect. se-gPC is an extension of gPC expansion, enriched with the sensitivity of the time-averaged quantities with respect to the stochastic variables. To compute these sensitivities, the adjoint of the shadowing operator is derived in the frequency domain. Coupling the adjoint operator with gPC provides an efficient uncertainty quantification algorithm, which, in its simplest form, has computational cost that is independent of the number of random variables. The method is applied to the Kuramoto-Sivashinsky equation and is found to produce results that match very well with Monte Carlo simulations. The efficiency of the proposed method significantly outperforms sparse-grid approaches, such as Smolyak quadrature. These properties make the method suitable for application to other dynamical systems with many stochastic parameters.