AbstractOnce the body dies, the indigenous microbes of the host begin to break down the body from the inside and play a key role thereafter. This study aimed to investigate the probable shift in the composition of the rectal microbiota at different time intervals up to 15 days after death and to explore bacterial taxa important for estimating the time since death. At the phylum level, Proteobacteria and Firmicutes showed major shifts when checked at 11 different intervals and emerged at most of the postmortem intervals. At the species level, Enterococcus faecalis and Proteus mirabilis showed a downward and upward trend, respectively, after day 5 postmortem. The phylum-, family-, genus-, and species-taxon richness decreased initially and then increased considerably. The turning point occurred on day 9, when the genus, rather than the phylum, family, or species, provided the most information for estimating the time since death. We constructed a prediction model using genus-level data from high-throughput sequencing, and seven bacterial taxa, namely, Enterococcus, Proteus, Lactobacillus, unidentified Clostridiales, Vagococcus, unidentified Corynebacteriaceae, and unidentified Enterobacteriaceae, were included in this model. The abovementioned bacteria showed potential for estimating the shortest time since death.
Understanding the pathways whereby an intervention has an effect on an outcome is a common scientific goal. A rich body of literature provides various decompositions of the total intervention effect into pathway-specific effects. Interventional direct and indirect effects provide one such decomposition. Existing estimators of these effects are based on parametric models with confidence interval estimation facilitated via the nonparametric bootstrap. We provide theory that allows for more flexible, possibly machine learning-based, estimation techniques to be considered. In particular, we establish weak convergence results that facilitate the construction of closed-form confidence intervals and hypothesis tests and prove multiple robustness properties of the proposed estimators. Simulations show that inference based on large-sample theory has adequate small-sample performance. Our work thus provides a means of leveraging modern statistical learning techniques in estimation of interventional mediation effects.
AbstractA continuous manufacturing platform was developed for the synthesis of aqueous colloidal 10–20 nm gold nanoparticles (Au NPs) in a flow reactor using chloroauric acid, sodium citrate and citric acid at 95 oC and 2.3 bar(a) pressure. The use of a two-phase flow system – using heptane as the continuous phase – prevented fouling on the reactor walls, while improving the residence time distribution. Continuous syntheses for up to 2 h demonstrated its potential application for continuous manufacturing, while live quality control was established using online UV-Vis photospectrometry that monitored the particle size and process yield. The synthesis was stable and reproducible over time for gold precursor concentration above 0.23 mM (after mixing), resulting in average particle size between 12 and 15 nm. A hydrophobic membrane separator provided successful separation of the aqueous and organic phases and collection of colloidal Au NPs in flow. Process yield increased at higher inlet flow rates (from 70 % to almost 100 %), due to lower residence time of the colloidal solution in the separator resulting in less fouling in the PTFE membrane. This study addresses the challenges for the translation of the synthesis from batch to flow and provides tools for the development of a continuous manufacturing platform for gold nanoparticles.Graphical abstract