Traveling Salesman Finds Random Walk: A Curve Reconstruction Algorithm for Supercoiled DNA.

DNA supercoiling plays an important role in a variety of cellular processes, including transcription, replication, and DNA compaction. To fully understand these processes, we must uncover and characterize the dynamics of supercoiled DNA. However, supercoil dynamics are difficult to access because of the wide range of relevant length and timescales. In this work, we present an algorithm to reconstruct the arrangement of identical fluorescent particles distributed around a circular DNA molecule, given their three-dimensional trajectories through time. We find that this curve reconstruction problem is analogous to solving the traveling salesman problem. We demonstrate that our approach converges to the correct arrangement with a sufficiently long observation time. In addition, we show that the time required to accurately reconstruct the fluorophore arrangement is reduced by increasing the fluorophore density or reducing the level of supercoiling. This curve reconstruction algorithm, when paired with next-generation super-resolution imaging systems, could be used to access and thereby advance our understanding of supercoil dynamics.

Droplet size prediction in a microfluidic flow focusing device using an adaptive network based fuzzy inference system.

Microfluidics has wide applications in different technologies such as biomedical engineering, chemistry engineering, and medicine. Generating droplets with desired size for special applications needs costly and time-consuming iterations due to the nonlinear behavior of multiphase flow in a microfluidic device and the effect of several parameters on it. Hence, designing a flexible way to predict the droplet size is necessary. In this paper, we use the Adaptive Neural Fuzzy Inference System (ANFIS), by mixing the artificial neural network (ANN) and fuzzy inference system (FIS), to study the parameters which have effects on droplet size. The four main dimensionless parameters, i.e. the Capillary number, the Reynolds number, the flow ratio and the viscosity ratio are regarded as the inputs and the droplet diameter as the output of the ANFIS. Using dimensionless groups cause to extract more comprehensive results and avoiding more experimental tests. With the ANFIS, droplet sizes could be predicted with the coefficient of determination of 0.92.