Boston University

About: Boston University is a education organization based out in Boston, Massachusetts, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 48688 authors who have published 119622 publications receiving 6276020 citations. The organization is also known as: BU & Boston U.

Prevalence of gout and hyperuricemia in the US general population: the National Health and Nutrition Examination Survey 2007-2008.

Abstract: Objective To estimate the prevalence of gout and hyperuricemia based on the latest nationally representative sample of US men and women (National Health and Nutrition Examination Survey [NHANES] 2007–2008). Methods Using data from 5,707 participants in NHANES 2007–2008, we estimated the prevalence of gout and hyperuricemia. During home interviews for NHANES 2007–2008, all participants were asked about a history of health professional– or physician-diagnosed gout. Our primary definition of hyperuricemia was a serum urate level of >7.0 mg/dl for men and >5.7 mg/dl for women. We explored potential secular trends in these estimates and their possible explanations by comparing them with estimates based on 18,825 participants in NHANES-III (1988–1994). Results The prevalence of gout among US adults in 2007–2008 was 3.9% (8.3 million individuals). The prevalence among men was 5.9% (6.1 million), and the prevalence among women was 2.0% (2.2 million). The mean serum urate levels were 6.14 mg/dl among men and 4.87 mg/dl among women, corresponding to hyperuricemia prevalences of 21.2% and 21.6%, respectively. These estimates were higher than those in NHANES-III, with differences of 1.2% in the prevalence of gout (95% confidence interval [95% CI] 0.6, 1.9), 0.15 mg/dl in the serum urate level (95% CI 0.07, 0.24), and 3.2% in the prevalence of hyperuricemia (95% CI 1.2, 5.2). These differences were substantially attenuated after adjusting for body mass index and/or hypertension. Conclusion These findings from nationally representative samples of US adults suggest that the prevalence of both gout and hyperuricemia remains substantial and may have increased over the past 2 decades, which is likely related to increasing frequencies of adiposity and hypertension.

Robust dimensions of anxiety sensitivity: Development and initial validation of the anxiety sensitivity index-3

Abstract: Accumulating evidence suggests that anxiety sensitivity (fear of arousal-related sensations) plays an important role in many clinical conditions, particularly anxiety disorders. Research has increasingly focused on how the basic dimensions of anxiety sensitivity are related to various forms of psychopathology. Such work has been hampered because the original measure--the Anxiety Sensitivity Index (ASI)--was not designed to be multidimensional. Subsequently developed multidimensional measures have unstable factor structures or measure only a subset of the most widely replicated factors. Therefore, the authors developed, via factor analysis of responses from U.S. and Canadian nonclinical participants (n=2,361), an 18-item measure, the ASI-3, which assesses the 3 factors best replicated in previous research: Physical, Cognitive, and Social Concerns. Factorial validity of the ASI-3 was supported by confirmatory factor analyses of 6 replication samples, including nonclinical samples from the United States and Canada, France, Mexico, the Netherlands, and Spain (n=4,494) and a clinical sample from the United States and Canada (n=390). The ASI-3 displayed generally good performance on other indices of reliability and validity, along with evidence of improved psychometric properties over the original ASI.

SEP: A Stable Election Protocol for clustered heterogeneous wireless sensor networks

Abstract: We study the impact of heterogeneity of nodes, in terms of their energy, in wireless sensor networks that are hierarchically clustered. In these networks some of the nodes become cluster heads, aggregate the data of their cluster members and transmit it to the sink. We assume that a percentage of the population of sensor nodes is equipped with additional energy resources—this is a source of heterogeneity which may result from the initial setting or as the operation of the network evolves. We also assume that the sensors are randomly (uniformly) distributed and are not mobile, the coordinates of the sink and the dimensions of the sensor field are known. We show that the behavior of such sensor networks becomes very unstable once the first node dies, especially in the presence of node heterogeneity. Classical clustering protocols assume that all the nodes are equipped with the same amount of energy and as a result, they can not take full advantage of the presence of node heterogeneity. We propose SEP, a heterogeneous-aware protocol to prolong the time interval before the death of the first node (we refer to as stability period), which is crucial for many applications where the feedback from the sensor network must be reliable. SEP is based on weighted election probabilities of each node to become cluster head according to the remaining energy in each node. We show by simulation that SEP always prolongs the stability period compared to (and that the average throughput is greater than) the one obtained using current clustering protocols. We conclude by studying the sensitivity of our SEP protocol to heterogeneity parameters capturing energy imbalance in the network. We found that SEP yields longer stability region for higher values of extra energy brought by more powerful nodes.

The spreading of misinformation online

Abstract: The wide availability of user-provided content in online social media facilitates the aggregation of people around common interests, worldviews, and narratives. However, the World Wide Web (WWW) also allows for the rapid dissemination of unsubstantiated rumors and conspiracy theories that often elicit rapid, large, but naive social responses such as the recent case of Jade Helm 15––where a simple military exercise turned out to be perceived as the beginning of a new civil war in the United States. In this work, we address the determinants governing misinformation spreading through a thorough quantitative analysis. In particular, we focus on how Facebook users consume information related to two distinct narratives: scientific and conspiracy news. We find that, although consumers of scientific and conspiracy stories present similar consumption patterns with respect to content, cascade dynamics differ. Selective exposure to content is the primary driver of content diffusion and generates the formation of homogeneous clusters, i.e., “echo chambers.” Indeed, homogeneity appears to be the primary driver for the diffusion of contents and each echo chamber has its own cascade dynamics. Finally, we introduce a data-driven percolation model mimicking rumor spreading and we show that homogeneity and polarization are the main determinants for predicting cascades’ size.

Spatial disease clusters: detection and inference.

Abstract: We present a new method of detection and inference for spatial clusters of a disease. To avoid ad hoc procedures to test for clustering, we have a clearly defined alternative hypothesis and our test statistic is based on the likelihood ratio. The proposed test can detect clusters of any size, located anywhere in the study region. It is not restricted to clusters that conform to predefined administrative or political borders. The test can be used for spatially aggregated data as well as when exact geographic co-ordinates are known for each individual. We illustrate the method on a data set describing the occurrence of leukaemia in Upstate New York.