In vivo therapeutic efficacy of frog skin-derived peptides against Pseudomonas aeruginosa-induced pulmonary infection

Pseudomonas aeruginosa is an opportunistic and frequently drug-resistant pulmonary pathogen especially in cystic fibrosis sufferers. Recently, the frog skin-derived antimicrobial peptide (AMP) Esc(1-21) and its diastereomer Esc(1-21)-1c were found to possess potent in vitro antipseudomonal activity. Here, they were first shown to preserve the barrier integrity of airway epithelial cells better than the human AMP LL-37. Furthermore, Esc(1-21)-1c was more efficacious than Esc(1-21) and LL-37 in protecting host from pulmonary bacterial infection after a single intra-tracheal instillation at a very low dosage of 0.1 mg/kg. The protection was evidenced by 2-log reduction of lung bacterial burden and was accompanied by less leukocytes recruitment and attenuated inflammatory response. In addition, the diastereomer was more efficient in reducing the systemic dissemination of bacterial cells. Importantly, in contrast to what reported for other AMPs, the peptide was administered at 2 hours after bacterial challenge to better reflect the real life infectious conditions. To the best of our knowledge, this is also the first study investigating the effect of AMPs on airway-epithelia associated genes upon administration to infected lungs. Overall, our data highly support advanced preclinical studies for the development of Esc(1-21)-1c as an efficacious therapeutic alternative against pulmonary P. aeruginosa infection

Bias Reduction via End-to-End Shift Learning: Application to Citizen Science

Citizen science projects are successful at gathering rich datasets for various applications. However, the data collected by citizen scientists are often biased --- in particular, aligned more with the citizens' preferences than with scientific objectives. We propose the Shift Compensation Network (SCN), an end-to-end learning scheme which learns the shift from the scientific objectives to the biased data while compensating for the shift by re-weighting the training data. Applied to bird observational data from the citizen science project eBird, we demonstrate how SCN quantifies the data distribution shift and outperforms supervised learning models that do not address the data bias. Compared with competing models in the context of covariate shift, we further demonstrate the advantage of SCN in both its effectiveness and its capability of handling massive high-dimensional data