Understanding and elimination of carbon catabolite repression in Escherichia coli

BioengineeringMicroorganisms often encounter a mixture of different carbon sources and therefore have control systems to selectively take up and metabolize those substrates that promise the best success in competition with other species through rapid growth. The aim of this thesis is to understand and eliminate carbon catabolite repression (CCR) in Escherichia coli for efficient utilization of multiple energy and carbon sources simultaneously. We studied a new CCR hierarchy that causes the preferential utilization of sugars (arabinose, galactose, glucose, mannose, and xylose) over a short-chain fatty acid (propionate). Meanwhile, the native promoters of xylose catabolic genes and xylose transporter genes were replaced with synthetic constitutive promoters to construct an E. coli strain capable of co-metabolizing glucose and xylose by eliminating the CCR of xylose metabolism by glucose. We showed that such an approach can provide a potential to eliminate CCR. This knowledge will be valuable to help strain improvement strategies for the simultaneous consumption of sugar mixtures, leading to shorter fermentation time and higher substrate range and productivity.ope

Click-aware purchase prediction with push at the top

Eliciting user preferences from purchase records for performing purchase prediction is challenging because negative feedback is not explicitly observed, and because treating all non-purchased items equally as negative feedback is unrealistic. Therefore, in this study, we present a framework that leverages the past click records of users to compensate for the missing user-item interactions of purchase records, i.e., non-purchased items. We begin by formulating various model assumptions, each one assuming a different order of user preferences among purchased, clicked-but-not-purchased, and non-clicked items, to study the usefulness of leveraging click records. We implement the model assumptions using the Bayesian personalized ranking model, which maximizes the area under the curve for bipartite ranking. However, we argue that using click records for bipartite ranking needs a meticulously designed model because of the relative unreliableness of click records compared with that of purchase records. Therefore, we ultimately propose a novel learning-to-rank method, called P3Stop, for performing purchase prediction. The proposed model is customized to be robust to relatively unreliable click records by particularly focusing on the accuracy of top-ranked items. Experimental results on two real-world e-commerce datasets demonstrate that P3STop considerably outperforms the state-of-the-art implicit-feedback-based recommendation methods, especially for top-ranked items.Comment: For the final published journal version, see https://doi.org/10.1016/j.ins.2020.02.06