Optimisation of a droplet digital PCR assay for the diagnosis of Schistosoma japonicum infection: A duplex approach with DNA binding dye chemistry

Schistosomiasis is a chronically debilitating helminth infection with a significant socio-economic and public health impact. Accurate diagnostics play a pivotal role in achieving current schistosomiasis control and elimination goals. However, many of the current diagnostic procedures, which rely on detection of schistosome eggs, have major limitations including lack of accuracy and the inability to detect pre-patent infections. DNA-based detection methods provide a viable alternative to the current tests commonly used for schistosomiasis diagnosis. Here we describe the optimisation of a novel droplet digital PCR (ddPCR) duplex assay for the diagnosis of Schistosoma japonicum infection which provides improved detection sensitivity and specificity. The assay involves the amplification of two specific and abundant target gene sequences in S. japonicum; a retrotransposon (SjR2) and a portion of a mitochondrial gene (nad1). The assay detected target sequences in different sources of schistosome DNA isolated from adult worms, schistosomules and eggs, and exhibits a high level of specificity, thereby representing an ideal tool for the detection of low levels of parasite DNA in different clinical samples including parasite cell free DNA in the host circulation and other bodily fluids. Moreover, being quantitative, the assay can be used to determine parasite infection intensity and, could provide an important tool for the detection of low intensity infections in low prevalence schistosomiasis-endemic areas.

Physical model parameter optimisation for calibrated emulation of the Dallas Rangemaster Treble Booster guitar pedal

In this work we explore optimising parameters of a physical circuit model relative to input/output measurements, using the Dallas Rangemaster Treble Booster as a case study. A hybrid metaheuristic/gradient descent algorithm is implemented, where the initial parameter sets for the optimisation are informed by nominal values from schematics and datasheets. Sensitivity analysis is used to screen parameters, which informs a study of the optimisation algorithm against model complexity by fixing parameters. The results of the optimisation show a significant increase in the accuracy of model behaviour, but also highlight several key issues regarding the recovery of parameters.