A model analyte, the M13 virus, was detected through the change in the Brownian motion of a population of microparticles. Epi-fluorescence microscopy was used to simultaneously track antibody-coated and bare microparticles to unambiguously measure the diffusion coefficient and demonstrate multiplexed detection. The sensitivity of the diffusometry assay was high enough that individual virus-to-particle binding ratios could be detected. Analysis of the experimental errors indicated that the primary limitation in the sensitivity of this technique was the variation in the size of the population of microparticles. Analysis of the diffusion measurement results indicated that the change in the drag coefficient of the virus-particle assembly was not a simple sum of the drag coefficients of the individual components and the rate of particle-particle reaction was slower than would be predicted from the uncoupled particle hydrodynamics. The possibility of using diffusometry for sensing and proteomics applications is examined.

Optical diffusometry is a technique used for measuring diffusion. This work explores the possibility of directly measuring diffusion coefficients of submicron particles for pathogen detection. The diffusion coefficient of these particles is a function of the drag coefficient of the particle at constant temperatures. Particles introduced into a sample containing an analyte bind with the analyte if functionalized with the appropriate antibodies. This leads to an increase in the hydrodynamic drag of the particles and hence a decrease in their diffusion coefficient. This study uses the above principle to effectively measure the diffusion coefficient of the particles using two different experimental approaches. The measured reduction in the diffusion coefficient can be correlated to the amount of analyte present and thus forms the basis of biological agent detection. Sensitivity to experimental conditions is analyzed. It is observed that alternative techniques such as optical trapping hold promise: the diffusive behavior of particles in optical traps is found to be quantitatively different from that of a free particle. Hence preconditions are identified to make optical trapping appropriate for agent detection.

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Biological agent detection has captured the attention of many researchers over the last few years. The present research explores the possibility of directly measuring the diffusion coefficients of sub-micron particles as a means of pathogen detection. At a constant temperature, the diffusion coefficient is simply a function of the drag on the particle. If the particles are functionalized with antibodies against a specific analyte and introduced into a sample containing that analyte, binding of the analyte with the particles will increase the particles' hydrodynamic drag. This results in a decrease in diffusion, which is measured by a particle tracking algorithm. The reduction in diffusion is correlated with the amount of analyte present. Sensitivity to experimental conditions is also explored and it is shown that alternate methods like optical traps provide an even better technique for biological agent detection.

A leading global personal care manufacturer was spending millions of dollars towards giving promotional offers on various goods throughout the year. The C‐suite (i.e., CEO, CFO, COO, and CIO) of the global personal care firm was aware of the increasing promotion spends year on year and this led to questions on promotion effectiveness. The main question faced by the firm was whether the returns they were getting in terms of revenue was justifying the increase in promotional spends. In addition, they had the following concerns: (i) Are they evaluating their promotion effectiveness in a scientific way? (ii) Given that the nature and intensity of promotions differ across weeks, regions, and the size and type of retail outlet, can they quantify promotion effectiveness for different time periods and at different levels of hierarchy of retail outlets? (iii) Can we develop a tool to evaluate and optimize promotional spends periodically? Based on our previous association with this firm, we were approached to solve their problems related to promotions. After our initial meeting, we realized that our solution should consist of four pillars: (i) Data—understanding the data and data wrangling, (ii) Model—developing a statistical modeling framework to evaluate promotion effectiveness, (iii) Computation—coding with emphasis on computational speed in order to update returns periodically, and (iv) Dashboard—producing an interactive tool for viewing results and carrying out budget optimization by stakeholders. In this article, we consider one brand of the manufacturer as an example to present our solution as a case study. This brand under focus is a face care brand in India and it spends about USD 4 million every year on promotions. We model the promotion effectiveness under the framework of dynamic linear models (DLM) using integrated nested Laplace approximation (INLA), a fast, approximate computational framework for Bayesian modeling and prediction. Companies like our personal care manufacturer belong to an industry which is commonly known as consumer packaged goods (CPG). An overview of this industry is presented in the following section.