Spatially resolved monitoring and modelling of the formation dynamics in hybrid perovskite solution thin-films for large-scale morphology control

Purpose The purpose of this paper is to demonstrate the application of river formation dynamics to size the widths of power distribution network for very large-scale integration designs so that the wire area required by power rails is minimized. The area minimization problem is transformed into a single objective optimization problem subject to various design constraints, such as IR drop and electromigration constraints. Design/methodology/approach The minimization process is carried out using river formation dynamics heuristic. The random probabilistic search strategy of river formation dynamics heuristic is used to advance through stringent design requirements to minimize the wire area of an over-designed power distribution network. Findings A number of experiments are performed on several power distribution benchmarks to demonstrate the effectiveness of river formation dynamics heuristic. It is observed that the river formation dynamics heuristic outperforms other standard optimization techniques in most cases, and a power distribution network having 16 million nodes is successfully designed for optimal wire area using river formation dynamics. Originality/value Although many research works are presented in the literature to minimize wire area of power distribution network, these research works convey little idea on optimizing very large-scale power distribution networks (i.e. networks having more than four million nodes) using an automated environment. The originality in this research is the illustration of an automated environment equipped with an efficient optimization technique based on random probabilistic movement of water drops in solving very large-scale power distribution networks without sacrificing accuracy and additional computational cost. Based on the computation of river formation dynamics, the knowledge of minimum area bounded by optimum IR drop value can be of significant advantage in reduction of routable space and in system performance improvement.

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3D mapping of host-parasite-microbiome interactions reveals metabolic determinants of tissue tropism and disease tolerance in Chagas disease

10.1101/727917 ◽

2019 ◽

Cited By ~ 3

Author(s):

Ekram Hossain ◽

Sharmily Khanam ◽

Chaoyi Wu ◽

Sharon Lostracco-Johnson ◽

Diane Thomas ◽

...

Keyword(s):

Chagas Disease ◽

Large Intestine ◽

Large Scale ◽

Scale Effects ◽

Parasitic Infection ◽

Parasite Burden ◽

Acute Stage ◽

Disease Tolerance ◽

Chronic Stage ◽

Spatially Resolved

AbstractChagas disease (CD) is a parasitic infection caused by Trypanosoma cruzi protozoa. Over 8 million people worldwide are T. cruzi-positive, 20-30% of which will develop cardiomyopathy, megaoesophagus and/or megacolon. The mechanisms leading to gastrointestinal (GI) symptom development are however poorly understood. To address this issue, we systematically characterized the spatial impact of experimental T. cruzi infection on the microbiome and metabolome across the GI tract. The largest microbiota perturbations were observed in the proximal large intestine in both acute and chronic disease, with chronic-stage effects also observed in the cecum. Strikingly, metabolomic impact of acute-to-chronic stage transition differed depending on the organ, with persistent large-scale effects of infection primarily in the oesophagus and large intestine, providing a potential mechanism for GI pathology tropism in CD. Infection particularly affected acylcarnitine and lipid metabolism. Building on these observations, treatment of infected mice with carnitine-supplemented drinking water prevented acute-stage mortality with no changes in parasite burden. Overall, these results identified a new mechanism of disease tolerance in CD, with potential for the development of new therapeutic regimens. More broadly, these results highlight the potential of spatially-resolved metabolomic approaches to provide insight into disease pathogenesis, with translational applications for infectious disease drug development.

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