University of Michigan

About: University of Michigan is a education organization based out in Ann Arbor, Michigan, United States. It is known for research contribution in the topics: Computer science & Chemistry. The organization has 138538 authors who have published 342338 publications receiving 17638979 citations. The organization is also known as: UMich & UM.

Emergence: From Chaos to Order

Abstract: From the Publisher: From one of today's most innovative thinkers comes the first book to carefully explore emergence - a surprisingly simple notion (the whole is more than the sum of its parts) with enormous implications for science, business, and the arts. In this work, John Holland, a leader in the study of complexity at the Santa Fe Institute, dramatically shows that a theory of emergence can predict many complex behaviors, and has much to teach us about life, the mind, and organizations. In Emergence, Holland demonstrates that a small number of rules of laws can generate systems of surprising complexity. Board games provide an ancient and direct example: Chess is defined by fewer than two dozen rules, but the myriad patterns that result lead to perpetual novelty and emergence. It took centuries of study to recognize certain patterns of play, such as the control of pawn formations. But once recognized, these patterns greatly enhance the possibility of winning the game. The discovery of similar patterns in other facets of our world opens the way to a deeper understanding of the complexity of life, answering such questions as: How does a fertilized egg program the development of a trillion-cell organism? How can we build human organizations that respond rapidly to change through innovation? Throughout the book, Holland compares different systems and models that exhibit emergence in the quest for common rules or laws.

Relative location estimation in wireless sensor networks

Abstract: Self-configuration in wireless sensor networks is a general class of estimation problems that we study via the Cramer-Rao bound (CRB). Specifically, we consider sensor location estimation when sensors measure received signal strength (RSS) or time-of-arrival (TOA) between themselves and neighboring sensors. A small fraction of sensors in the network have a known location, whereas the remaining locations must be estimated. We derive CRBs and maximum-likelihood estimators (MLEs) under Gaussian and log-normal models for the TOA and RSS measurements, respectively. An extensive TOA and RSS measurement campaign in an indoor office area illustrates MLE performance. Finally, relative location estimation algorithms are implemented in a wireless sensor network testbed and deployed in indoor and outdoor environments. The measurements and testbed experiments demonstrate 1-m RMS location errors using TOA, and 1- to 2-m RMS location errors using RSS.

Extracellular DNA traps promote thrombosis

Abstract: Neutrophil extracellular traps (NETs) are part of the innate immune response to infections. NETs are a meshwork of DNA fibers comprising histones and antimicrobial proteins. Microbes are immobilized in NETs and encounter a locally high and lethal concentration of effector proteins. Recent studies show that NETs are formed inside the vasculature in infections and noninfectious diseases. Here we report that NETs provide a heretofore unrecognized scaffold and stimulus for thrombus formation. NETs perfused with blood caused platelet adhesion, activation, and aggregation. DNase or the anticoagulant heparin dismantled the NET scaffold and prevented thrombus formation. Stimulation of platelets with purified histones was sufficient for aggregation. NETs recruited red blood cells, promoted fibrin deposition, and induced a red thrombus, such as that found in veins. Markers of extracellular DNA traps were detected in a thrombus and plasma of baboons subjected to deep vein thrombosis, an example of inflammation-enhanced thrombosis. Our observations indicate that NETs are a previously unrecognized link between inflammation and thrombosis and may further explain the epidemiological association of infection with thrombosis.

Plasma HDL cholesterol and risk of myocardial infarction: A mendelian randomisation study

Abstract: Methods We performed two mendelian randomisation analyses. First, we used as an instrument a single nucleotide polymorphism (SNP) in the endothelial lipase gene (LIPG Asn396Ser) and tested this SNP in 20 studies (20 913 myocardial infarction cases, 95 407 controls). Second, we used as an instrument a genetic score consisting of 14 common SNPs that exclusively associate with HDL cholesterol and tested this score in up to 12 482 cases of myocardial infarction and 41 331 controls. As a positive control, we also tested a genetic score of 13 common SNPs exclusively associated with LDL cholesterol. – ¹³) but similar levels of other lipid and non-lipid risk factors for myocardial infarction compared with noncarriers. This diff erence in HDL cholesterol is expected to decrease risk of myocardial infarction by 13% (odds ratio [OR] 0·87, 95% CI 0·84–0·91). However, we noted that the 396Ser allele was not associated with risk of myocardial infarction (OR 0·99, 95% CI 0·88–1·11, p=0·85). From observational epidemiology, an increase of 1 SD in HDL cholesterol was associated with reduced risk of myocardial infarction (OR 0·62, 95% CI 0·58–0·66). However, a 1 SD increase in HDL cholesterol due to genetic score was not associated with risk of myocardial infarction (OR 0·93, 95% CI 0·68–1·26, p=0·63). For LDL cholesterol, the estimate from observational epidemiology (a 1 SD increase in LDL cholesterol associated with OR 1·54, 95% CI 1·45–1·63) was concordant with that from genetic score (OR 2·13, 95% CI 1·69–2·69, p=2×10