Image-based screening has become a mature field over the past decade, largely due to the detailed information that can be obtained about compound mode of action by considering the phenotypic effects of test compounds on cellular morphology. However, very few examples exist of extensions of this approach to bacterial targets. We now report the first high-throughput, high-content platform for the prediction of antibiotic modes of action using image-based screening. This approach employs a unique feature segmentation and extraction protocol to quantify key size and shape metrics of bacterial cells over a range of compound concentrations, and matches the trajectories of these metrics to those of training set compounds of known molecular target to predict the test compound's mode of action. This approach has been used to successfully predict the modes of action of a panel of known antibiotics, and has been extended to the evaluation of natural products libraries for the de novo prediction of compound function directly from primary screening data.
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This paper presents a model for describing inter-organizational collaborations for e-commerce, e-government and e-business applications. The model, referred to as a community model, takes into account internal organizational rules and business policies as typically stated in business contracts that govern cross-collaborations. The model can support the development of a new generation of contract management systems that provide true inter-organizational collaboration capabilities to all parties involved in contract management. This includes contract monitoring features and dynamic updates to the processes and policies associated with contracts. We present a blueprint architecture for inter-organizational contract management and a contract language based on the community model. This language can be used to specialize this architecture for concrete collaborative structures and business processes.
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