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The recent explosion of interest and advances in machine learning technologies has opened the door to new analytical capabilities in microbiology. Using experimental data such as images or videos, machine learning, in particular deep learning with neural networks, can be harnessed to provide insights and predictions for microbial populations. This paper presents such an application in which a Recurrent Neural Network (RNN) was used to perform prediction of microbial growth for a population of two Pseudomonas aeruginosa mutants. The RNN was trained on videos that were acquired previously using fluorescence microscopy and microfluidics. Of the 20 frames thatmake up each video, 10 were used as inputs to the network which outputs a prediction for the next 10 frames of the video. The accuracy of the network was evaluated by comparing the predicted frames to the original frames, as well as population curves and the number and size of individual colonies extracted from these frames. Overall, the growth predictions are found to be accurate in metrics such as image comparison, colony size, and total population. Yet, limitations exist due to the scarcity of available and comparable data in the literature, indicating a need for more studies. Both the successes and challenges of our approach are discussed. [ABSTRACT FROM AUTHOR]

It has previously been shown that when a liquid drop of high viscosity is placed on a non-wettable inclined surface, it rolls down at a constant descent velocity determined by the balance between viscous dissipation and the reduction rate of its gravitational potential energy. Since increasing the roughness of the surface boosts its non-wetting property, the drop should move faster on a surface structured with macrotextures (ribbed surface). Such a surface was obtained from a superhydrophobic soot coating on a solid specimen printed with an extruder-type 3D printer. The sample became superoleophobic after a functionalization process. The descent velocity of glycerol drops of different radii was then measured on the prepared surface for varied tilting angles. Our data show that the drops roll down on the ribbed surface approximately 27% faster (along the ridges) than on the macroscopically smooth counterpart. This faster velocity demonstrates that ribbed surfaces can be promising candidates for drag-reduction and self-cleaning applications. Moreover, we came up with a modified scaling model to predict the descent velocity of viscous rolling drops more accurately than what has previously been reported in the literature. [ABSTRACT FROM AUTHOR]