Activity of species-specific antibiotics against Crohnʼs disease–associated adherent-invasive Escherichia coli

Background: Crohn's disease (CD) is associated with bacterial dysbiosis that frequently includes colonization by adherent-invasive Escherichia coli (AIEC). AIEC are adept at forming biofilms and are able to invade host cells and stimulate the production of proinflammatory cytokines. The use of traditional antibiotics for the treatment of CD shows limited efficacy. In this study, we investigate the use of species-specific antibiotics termed colicins for treatment of CD-associated AIEC. Methods: Colicin activity was tested against a range of AIEC isolates growing in the planktonic and biofilm mode of growth. Colicins were also tested against AIEC bacteria associated with T84 intestinal epithelial cells and surviving inside RAW264.7 macrophages using adhesion assays and gentamicin protection assay, respectively. Uptake of colicins into eukaryotic cells was visualized using confocal microscopy. The effect of colicin treatment on the production of proinflammatory cytokine tumor necrosis factor alpha by macrophages was assessed by an enzyme-linked immunosorbent assay. Results: Colicins show potent activity against AIEC bacteria growing as biofilms when delivered either as a purified protein or through a colicin-producing bacterial strain. In addition, colicins E1 and E9 are able to kill cell-associated and intracellular AIEC, but do not show toxicity toward macrophage cells or stimulate the production of proinflammatory cytokines. Colicin killing of intracellular bacteria occurs after entry of colicin protein into AIEC-infected macrophage compartments by actin-mediated endocytosis. Conclusions: Our results demonstrate the potential of colicins as highly selective probiotic therapeutics for the eradication of E. coli from the gastrointestinal tract of patients with CD

Virtual-to-Real-World Transfer Learning for Robots on Wilderness Trails

Robots hold promise in many scenarios involving outdoor use, such as search-and-rescue, wildlife management, and collecting data to improve environment, climate, and weather forecasting. However, autonomous navigation of outdoor trails remains a challenging problem. Recent work has sought to address this issue using deep learning. Although this approach has achieved state-of-the-art results, the deep learning paradigm may be limited due to a reliance on large amounts of annotated training data. Collecting and curating training datasets may not be feasible or practical in many situations, especially as trail conditions may change due to seasonal weather variations, storms, and natural erosion. In this paper, we explore an approach to address this issue through virtual-to-real-world transfer learning using a variety of deep learning models trained to classify the direction of a trail in an image. Our approach utilizes synthetic data gathered from virtual environments for model training, bypassing the need to collect a large amount of real images of the outdoors. We validate our approach in three main ways. First, we demonstrate that our models achieve classification accuracies upwards of 95% on our synthetic data set. Next, we utilize our classification models in the control system of a simulated robot to demonstrate feasibility. Finally, we evaluate our models on real-world trail data and demonstrate the potential of virtual-to-real-world transfer learning.Comment: iROS 201

Returns to Buying Winners and Selling Losers: A Look at Cryptocurrencies

This paper is, to my knowledge, one of the first ever to examine the effectiveness of price momentum trading strategies applied to cryptocurrencies. Using aggregate OHLCV (Open, High, Low, Close, Volume) data on cryptocurrency pairs from Poloniex, Bittrex, and Bitfinex, I apply Jegadeesh and Titman’s classic -month/-month momentum trading strategy, reporting annual returns with and without incorporating trading fees. Portfolios are resampled daily, weekly, and monthly, testing lookback and holding periods ranging from one day to one year. The results show that trading cryptocurrencies using momentum strategies derives returns that rapidly increase the more often portfolios are resampled, with the exception of weekly portfolios. However, after incorporating trading fees, returns between high and low frequency portfolios become more comparable, though daily strategies still bring the highest fee-adjusted returns at about 10% annually. This paper adds to the very limited research on momentum factors within the cryptocurrency market