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

Introduction to fungal physiology

This chapter describes some basic aspects of fungal cell physiology, focusing primarily on nutrition, growth, metabolism in unicellular yeasts and filamentous fungi, and cell death. It considers the most common growth forms, the filamentous fungi and unicellular yeasts. Fungal growth involves transport and assimilation of nutrients, followed by their integration into cellular components, followed by biomass increase and eventual cell division or septation. The physiology of vegetative reproduction and its control in fungi has been most widely studied in two model eukaryotes, the budding yeast, Saccharomyces cerevisiae, and the fission yeast, Schizosaccharomyces pombe. An understanding of the death of fungal cells is important from a fundamental viewpoint because fungi, especially yeasts, represent valuable model systems for the study of cellular aging and apoptosis (programed cell death). Recycling and redeployment of cellular material also helps drive the apical growth of filamentous fungi and the mycelium explores and extends through the environment

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

Development of a CubeSat Payload to Model Particle Dampening in Space: Design and Implementation of Software for CP7

The California Polytechnic State University CubeSat student research & development group, PolySat, is currently in a mature development stage of a single unit CubeSat designated CP7. The CP7 mission implements a scientific payload designed to characterize particle dampers in microgravity conditions. When subjected to vibration, the momentum exchanges and frictional forces of the particles create a damping effect that can be optimized to suit a number of applications over a broad frequency and amplitude range. In space based applications, particle dampers would serve as a robust and simple device to eliminate jitter in optical assemblies and other sensitive instrumentation. This report will include the design and implementation of the payload software for the CP7 satellite