Abstract: Existing techniques fail to provide a method to cumulate effects of interactions between groups of gut-associated microbes to predict efficiency of a probiotic organism in an individual. The present disclosure collects a test biological sample from the subject requiring personalization and extracts DNA from test biological sample and information specific to dietary preferences of the subject. Organisms from probiotic organisms dataset are obtained and a plurality of genome scale metabolic models are created for microbes comprised in gut microbiota of subject and obtained probiotic organisms. Metabolic simulations are performed to ascertain monoculture and co-culture growth of every pair of organisms comprised in gut microbiota of subject and obtained probiotic organisms. Sustainability is computed for evaluating capability of each organism to proliferate within gut. Net-effect is computed by quantifying an overall influence of each probiotic organism.

Abstract: The need for an accurate, early, and precise estimation of expected delivery date (EDD) for the pregnant subject is vital. A system and method for predicting a day/date of delivery for a pregnant subject using one or more microbiome samples collected from the pregnant subject is provided. The disclosure relates to applying machine learning techniques on the microbiome characterization data corresponding to the biological sample(s) collected from the pregnant subject. The method further comprises using the predicted EDD to suitably plan and take required medical treatment or precautions or medical advice for the pregnant subject to prevent any pregnancy and/or delivery related complications and to manage the delivery appropriately. The disclosure also provides compositions of the microbiome data which can potentially influence the delivery date, or the method provides exemplary compositions of the microbiome data which plays vital role in estimating the EDD of the pregnant subject.

Abstract: Active Design maintains overall active and inactive space of a display by scaling active display components on a screen according to active and inactive blocks. By scaling the blocks relative to the screen size and maximizing active display components for ‘best-fit’ within the scaled blocks, a predictable scaling system is created that can deliver a more consistent experience for users across multiple form-factors. Active Design allows designers to use a single base display resolution for an entire class of form-factors and uses grid blocks to arrange elements.

Abstract: Active Design maintains overall active and inactive space of a display by scaling active display components on a screen according to active and inactive blocks. By scaling the blocks relative to the screen size and maximizing active display components for ‘best-fit’ within the scaled blocks, a predictable scaling system is created that can deliver a more consistent experience for users across multiple form-factors. Active Design allows designers to use a single base display resolution for an entire class of form-factors and uses grid blocks to arrange elements.