The 20 industries whose business address histories were found to be persistent centers of bladder cancer case clusters.

<p>The 20 industries whose business address histories were found to be persistent centers of bladder cancer case clusters.</p

Local Bladder Cancer Clusters in Southeastern Michigan Accounting for Risk Factors, Covariates and Residential Mobility

<div><p>Background</p><p>In case control studies disease risk not explained by the significant risk factors is the unexplained risk. Considering unexplained risk for specific populations, places and times can reveal the signature of unidentified risk factors and risk factors not fully accounted for in the case-control study. This potentially can lead to new hypotheses regarding disease causation.</p><p>Methods</p><p>Global, local and focused Q-statistics are applied to data from a population-based case-control study of 11 southeast Michigan counties. Analyses were conducted using both year- and age-based measures of time. The analyses were adjusted for arsenic exposure, education, smoking, family history of bladder cancer, occupational exposure to bladder cancer carcinogens, age, gender, and race.</p><p>Results</p><p>Significant global clustering of cases was not found. Such a finding would indicate large-scale clustering of cases relative to controls through time. However, highly significant local clusters were found in Ingham County near Lansing, in Oakland County, and in the City of Jackson, Michigan. The Jackson City cluster was observed in working-ages and is thus consistent with occupational causes. The Ingham County cluster persists over time, suggesting a broad-based geographically defined exposure. Focused clusters were found for 20 industrial sites engaged in manufacturing activities associated with known or suspected bladder cancer carcinogens. Set-based tests that adjusted for multiple testing were not significant, although local clusters persisted through time and temporal trends in probability of local tests were observed.</p><p>Conclusion</p><p>Q analyses provide a powerful tool for unpacking unexplained disease risk from case-control studies. This is particularly useful when the effect of risk factors varies spatially, through time, or through both space and time. For bladder cancer in Michigan, the next step is to investigate causal hypotheses that may explain the excess bladder cancer risk localized to areas of Oakland and Ingham counties, and to the City of Jackson.</p></div

Summary of analysis results of global tests with 999 randomizations.

<p>^a Q statistics over the entire study period 1940–2003 using year-based measure of time.</p><p>^b Q statistics using age-based measure of time.</p><p>^c Q_F statistics about industries in operation from 1943 to 1999 using year-based measure of time.</p><p>^d The count of the number of clusters found significant at <i>α</i> ≤ 0.05.</p><p>^e The significance of the number of clusters found significant at <i>α</i> ≤ 0.05.</p><p>Summary of analysis results of global tests with 999 randomizations.</p

Time plot of p(Q_F) using year-based measure of time.

<p>Values plotted are the probability of focused clustering when all of the industrial sites are considered simultaneously (e.g. probability of global focused clustering). The period of significant global focused clustering observed in 1974–1975 is attributable to the focused cluster that arose in the City of Jackson.</p

Time plot of p(Q_t) using year-based measure of time.

<p>Minima on the time plot indicate time periods when global spatial clustering of cases were statistically significant.</p

Surface Forces and Interaction Mechanisms of Emulsion Drops and Gas Bubbles in Complex Fluids

The interactions of emulsion drops and gas bubbles in complex fluids play important roles in a wide range of biological and technological applications, such as programmable drug and gene delivery, emulsion and foam formation, and froth flotation of mineral particles. In this feature article, we have reviewed our recent progress on the quantification of surface forces and interaction mechanisms of gas bubbles and emulsion drops in different material systems by using several complementary techniques, including the drop/bubble probe atomic force microscope (AFM), surface forces apparatus (SFA), and four-roll mill fluidic device. These material systems include the bubble–self-assembled monolayer (SAM), bubble–polymer, bubble–superhydrophobic surface, bubble–mineral, water-in-oil and oil-in-water emulsions with interface-active components in oil production, and oil/water wetting on polyelectrolyte surfaces. The bubble probe AFM combined with reflection interference contrast microscopy (RICM) was applied for the first time to simultaneously quantify the interaction forces and spatiotemporal evolution of a confined thin liquid film between gas bubbles and solid surfaces with varying hydrophobicity. The nanomechanical results have provided useful insights into the fundamental interaction mechanisms (e.g., hydrophobic interaction in aqueous media) at gas/water/solid interfaces, the stabilization/destabilization mechanisms of emulsion drops, and oil/water wetting mechanisms on solid surfaces. A long-range hydrophilic attraction was found between water and polyelectrolyte surfaces in oil, with the strongest attraction for polyzwitterions, contributing to their superior water wettability in oil and self-cleaning capability of oil contamination. Some remaining challenges and future research directions are discussed and provided

The risk factors and covariates included in the logistic model and their corresponding coefficients.

<p>Details on odds ratios, confidence intervals, risk factors and covariates, and arsenic exposure estimation are given by Meliker et al [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0124516#pone.0124516.ref012" target="_blank">12</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0124516#pone.0124516.ref013" target="_blank">13</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0124516#pone.0124516.ref014" target="_blank">14</a>,<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0124516#pone.0124516.ref015" target="_blank">15</a>].</p><p>^a There was frequency matching on age, race, and gender which explains the small size of their coefficients.</p><p>The risk factors and covariates included in the logistic model and their corresponding coefficients.</p

The distribution of original countries of all imported cases in Guangzhou, 2005–2019.

(XLSX)</p

The number of imported cases of infectious diseases in Guangzhou, 2005–2019.

The number of imported cases of infectious diseases in Guangzhou, 2005–2019.</p

Demographic characteristics of imported cases of acute infectious diseases in Guangzhou, China, 2005–2019.

Demographic characteristics of imported cases of acute infectious diseases in Guangzhou, China, 2005–2019.</p