Zipline-Related Injuries Treated in US EDs, 1997-2012

Purpose To investigate the epidemiology of zipline-related injuries in the United States. Basic Procedures The National Electronic Injury Surveillance System database was used to examine non-fatal zipline-related injuries treated in US emergency departments (EDs) from 1997 through 2012. Sample weights were applied to calculate national estimates. Main Findings From 1997 through 2012, an estimated 16850 (95% CI, 13188-20512) zipline-related injuries were treated in US EDs. The annual injury rate per 1 million population increased by 52.3% from 7.64 (95% CI, 4.86-10.42) injuries in 2009 (the first year with a stable annual estimate) to 11.64 (95% CI, 7.83-15.45) injuries in 2012. Patients aged 0-9 years accounted for 45.0% of injuries, females made up 53.1% of injuries, and 11.7% of patients required hospitalization. Fractures accounted for the largest proportion of injuries (46.7%), and the upper extremities were the most commonly injured body region (44.1%). Falls were the most common mechanism of injury, accounting for 77.3% of injuries. Among cases where the location of the injury event was known, 30.8% of injuries occurred in a residential setting and 69.2% occurred in a public place. Principal Conclusions This study is the first to characterize the epidemiology of zipline-related injuries using a nationally representative database. The rapid increase in zipline-related injuries in recent years suggests the need for additional safety guidelines and regulations. Commercial ziplines and publicly accessible non-commercial ziplines should be subject to uniform safety standards in all states and jurisdictions across the US, and homemade ziplines should not be used

Prediction of HIV transmission cluster growth with statewide surveillance data

Background:Prediction of HIV transmission cluster growth may help guide public health action. We developed a predictive model for cluster growth in North Carolina (NC) using routine HIV surveillance data.Methods:We identified putative transmission clusters with ≥2 members through pairwise genetic distances ≤1.5% from HIV-1 pol sequences sampled November 2010-December 2017 in NC. Clusters established by a baseline of January 2015 with any sequences sampled within 2 years before baseline were assessed for growth (new diagnoses) over 18 months. We developed a predictive model for cluster growth incorporating demographic, clinical, temporal, and contact tracing characteristics of baseline cluster members. We internally and temporally externally validated the final model in the periods January 2015-June 2016 and July 2016-December 2017.Results:Cluster growth was predicted by larger baseline cluster size, shorter time between diagnosis and HIV care entry, younger age, shorter time since the most recent HIV diagnosis, higher proportion with no named contacts, and higher proportion with HIV viremia. The model showed areas under the receiver-operating characteristic curves of 0.82 and 0.83 in the internal and temporal external validation samples.Conclusions:The predictive model developed and validated here is a novel means of identifying HIV transmission clusters that may benefit from targeted HIV control resources. © 2018 Wolters Kluwer Health, Inc. All rights reserved

Human Immunodeficiency Virus Type 1 Phylodynamics to Detect and Characterize Active Transmission Clusters in North Carolina

BACKGROUND: Human immunodeficiency virus type 1 (HIV-1) phylodynamics can be used to monitor epidemic trends and help target prevention through identification and characterization of transmission clusters. METHODS: We analyzed HIV-1 pol sequences sampled in North Carolina from 1997 to 2014. Putative clusters were identified using maximum-likelihood trees and dated using Bayesian Markov Chain Monte Carlo inference. Active clusters were defined as clusters including internal nodes from 2009 to 2014. Effective reproductive numbers (Re) were estimated using birth-death models for large clusters that expanded ≥2-fold from 2009 to 2014. RESULTS: Of 14 921 persons, 7508 (50%) sequences were identified in 2264 clusters. Only 288 (13%) clusters were active from 2009 to 2014; 37 were large (10-36 members). Compared to smaller clusters, large clusters were increasingly populated by men and younger persons; however, nearly 60% included ≥1 women. Clusters with ≥3 members demonstrated assortative mixing by sex, age, and sample region. Of 15 large clusters with ≥2-fold expansion, nearly all had Re approximately 1 by 2014. CONCLUSIONS: Phylodynamics revealed transmission cluster expansion in this densely sampled region and allowed estimates of Re to monitor active clusters, showing the propensity for steady, onward propagation. Associations with clustering and cluster characteristics vary by cluster size. Harnessing sequence-derived epidemiologic parameters within routine surveillance could allow refined monitoring of local subepidemics

A Model for the Origin and Properties of Flicker-Induced Geometric Phosphenes

We present a model for flicker phosphenes, the spontaneous appearance of geometric patterns in the visual field when a subject is exposed to diffuse flickering light. We suggest that the phenomenon results from interaction of cortical lateral inhibition with resonant periodic stimuli. We find that the best temporal frequency for eliciting phosphenes is a multiple of intrinsic (damped) oscillatory rhythms in the cortex. We show how both the quantitative and qualitative aspects of the patterns change with frequency of stimulation and provide an explanation for these differences. We use Floquet theory combined with the theory of pattern formation to derive the parameter regimes where the phosphenes occur. We use symmetric bifurcation theory to show why low frequency flicker should produce hexagonal patterns while high frequency produces pinwheels, targets, and spirals

Facilitating Joint Chaos and Fractal Analysis of Biosignals through Nonlinear Adaptive Filtering

Background: Chaos and random fractal theories are among the most important for fully characterizing nonlinear dynamics of complicated multiscale biosignals. Chaos analysis requires that signals be relatively noise-free and stationary, while fractal analysis demands signals to be non-rhythmic and scale-free. Methodology/Principal Findings: To facilitate joint chaos and fractal analysis of biosignals, we present an adaptive algorithm, which: (1) can readily remove nonstationarities from the signal, (2) can more effectively reduce noise in the signals than linear filters, wavelet denoising, and chaos-based noise reduction techniques; (3) can readily decompose a multiscale biosignal into a series of intrinsically bandlimited functions; and (4) offers a new formulation of fractal and multifractal analysis that is better than existing methods when a biosignal contains a strong oscillatory component. Conclusions: The presented approach is a valuable, versatile tool for the analysis of various types of biological signals. Its effectiveness is demonstrated by offering new important insights into brainwave dynamics and the very high accuracy in automatically detecting epileptic seizures from EEG signals

A simple vector-like law for perceptual information combination is also followed by a class of cortical multisensory bimodal neurons

Summary: An interdisciplinary approach to sensory information combination shows a correspondence between perceptual and neural measures of nonlinear multisensory integration. In psychophysics, sensory information combinations are often characterized by the Minkowski formula, but the neural substrates of many psychophysical multisensory interactions are unknown. We show that audiovisual interactions – for both psychophysical detection threshold data and cortical bimodal neurons – obey similar vector-like Minkowski models, suggesting that cortical bimodal neurons could underlie multisensory perceptual sensitivity. An alternative Bayesian model is not a good predictor of cortical bimodal response. In contrast to cortex, audiovisual data from superior colliculus resembles the ‘City-Block’ combination rule used in perceptual similarity metrics. Previous work found a simple power law amplification rule is followed for perceptual appearance measures and by cortical subthreshold multisensory neurons. The two most studied neural cell classes in cortical multisensory interactions may provide neural substrates for two important perceptual modes: appearance-based and performance-based perception