Physical and functional interactions define the molecular organization of the cell. Genetic interactions, or epistasis, tend to occur between gene products involved in parallel pathways or interlinked biological processes. High-throughput experimental systems to examine genetic interactions on a genome-wide scale have been devised for Saccharomyces cerevisiae, Schizosaccharomyces pombe, Caenorhabditis elegans and Drosophila melanogaster, but have not been reported previously for prokaryotes. Here we describe the development of a quantitative screening procedure for monitoring bacterial genetic interactions based on conjugation of Escherichia coli deletion or hypomorphic strains to create double mutants on a genome-wide scale. The patterns of synthetic sickness and synthetic lethality (aggravating genetic interactions) we observed for certain double mutant combinations provided information about functional relationships and redundancy between pathways and enabled us to group bacterial gene products into functional modules.

Counting objects in digital images is a process that should be replaced by machines. This tedious task is time consuming and prone to errors due to fatigue of human annotators. The goal is to have a system that takes as input an image and returns a count of the objects inside and justification for the prediction in the form of object localization. We repose a problem, originally posed by Lempitsky and Zisserman, to instead predict a count map which contains redundant counts based on the receptive field of a smaller regression network. The regression network predicts a count of the objects that exist inside this frame. By processing the image in a fully convolutional way each pixel is going to be accounted for some number of times, the number of windows which include it, which is the size of each window, (i.e., 32x32 = 1024). To recover the true count we take the average over the redundant predictions. Our contribution is redundant counting instead of predicting a density map in order to average over errors. We also propose a novel deep neural network architecture adapted from the Inception family of networks called the Count-ception network. Together our approach results in a 20% relative improvement (2.9 to 2.3 MAE) over the state of the art method by Xie, Noble, and Zisserman in 2016. Wepropose a novel construction of networks and train-arXiv:1703.08710v2 [cs.CV] 23 Jul 2017 1. Pre-process image by padding 2. Process image in a fully convolutional way 3. Combine all counts together into total count for image

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