Widely tunable, efficient on-chip single photon sources at telecommunication wavelengths

We demonstrate tunable on-chip single photon sources using the Stark tuning of single quantum dot (QD) excitonic transitions in short photonic crystal waveguides (PhC WGs). The emission of single QDs can be tuned in real-time by 9 nm with an applied bias voltage less than 2V. Due to a reshaped density of optical modes in the PhC WG, a large coupling efficiency \beta>65% to the waveguide mode is maintained across a wavelength range of 5 nm. When the QD is resonant with the Fabry-Perot mode of the PhC WG, a strong enhancement of spontaneous emission is observed leading to a maximum coupling efficiency \beta=88%. These results represent an important step towards the scalable integration of single photon sources in quantum photonic integrated circuits.Comment: 15 pages, 5 figue

A Hybrid of Metabolic Flux Analysis and Bayesian Factor Modeling for Multiomic Temporal Pathway Activation.

The growing availability of multiomic data provides a highly comprehensive view of cellular processes at the levels of mRNA, proteins, metabolites, and reaction fluxes. However, due to probabilistic interactions between components depending on the environment and on the time course, casual, sometimes rare interactions may cause important effects in the cellular physiology. To date, interactions at the pathway level cannot be measured directly, and methodologies to predict pathway cross-correlations from reaction fluxes are still missing. Here, we develop a multiomic approach of flux-balance analysis combined with Bayesian factor modeling with the aim of detecting pathway cross-correlations and predicting metabolic pathway activation profiles. Starting from gene expression profiles measured in various environmental conditions, we associate a flux rate profile with each condition. We then infer pathway cross-correlations and identify the degrees of pathway activation with respect to the conditions and time course using Bayesian factor modeling. We test our framework on the most recent metabolic reconstruction of Escherichia coli in both static and dynamic environments, thus predicting the functionality of particular groups of reactions and how it varies over time. In a dynamic environment, our method can be readily used to characterize the temporal progression of pathway activation in response to given stimuli