Nonlinear dynamics of gas bubbles in viscoelastic media

This is the published version. Copyright © 2005 Acoustical Society of AmericaUnderstanding the behavior of cavitation bubbles driven by ultrasonic fields is an important problem in biomedical acoustics. The Keller–Miksis equation for nonlinear bubble dynamics is combined with the Voigt model for viscoelastic media. Using experimentally determined values, the effects of elasticity on bubble oscillations are studied. Inertial cavitation thresholds are determined using Rmax/R0=2, and subharmonic emissions are also estimated. The elasticity increases the threshold pressure for inertial cavitation, and subharmonic signals are significant only in a certain region of radii and driving pressures at a given frequency. These results should prove useful in cavitationdetection and bubble-enhanced imaging work

Selection of important variables by statistical learning in genome-wide association analysis

Genetic analysis of complex diseases demands novel analytical methods to interpret data collected on thousands of variables by genome-wide association studies. The complexity of such analysis is multiplied when one has to consider interaction effects, be they among the genetic variations (G × G) or with environment risk factors (G × E). Several statistical learning methods seem quite promising in this context. Herein we consider applications of two such methods, random forest and Bayesian networks, to the simulated dataset for Genetic Analysis Workshop 16 Problem 3. Our evaluation study showed that an iterative search based on the random forest approach has the potential in selecting important variables, while Bayesian networks can capture some of the underlying causal relationships

A model for the dynamics of gas bubbles in soft tissue

This is the published version. Copyright 2005 Acoustical Society of AmericaUnderstanding the behavior of cavitation bubbles driven by ultrasonic fields is an important problem in biomedical acoustics. Keller-Miksis equation, which can account for the large amplitude oscillations of bubbles, is rederived in this paper and combined with a viscoelasticmodel to account for the strain-stress relation. The viscoelasticmodel used in this study is the Voigt model. It is shown that only the viscous damping term in the original equation needs to be modified to account for the effect of elasticity. With experiment determined viscoelasticproperties, the effects of elasticity on bubble oscillations are studied. Specifically, the inertial cavitation thresholds are determined using Rmax∕R0, and subharmonic signals from the emission of an oscillating bubble are estimated. The results show that the presence of the elasticity increases the threshold pressure for a bubble to oscillate inertially, and subharmonic signals may only be detectable in certain ranges of radius and pressure amplitude. These results should be easy to verify experimentally, and they may also be useful in cavitation detection and bubble-enhanced imaging

Kinematics of the swimming of Spiroplasma

\emph{Spiroplasma} swimming is studied with a simple model based on resistive-force theory. Specifically, we consider a bacterium shaped in the form of a helix that propagates traveling-wave distortions which flip the handedness of the helical cell body. We treat cell length, pitch angle, kink velocity, and distance between kinks as parameters and calculate the swimming velocity that arises due to the distortions. We find that, for a fixed pitch angle, scaling collapses the swimming velocity (and the swimming efficiency) to a universal curve that depends only on the ratio of the distance between kinks to the cell length. Simultaneously optimizing the swimming efficiency with respect to inter-kink length and pitch angle, we find that the optimal pitch angle is 35.5$^\circ$ and the optimal inter-kink length ratio is 0.338, values in good agreement with experimental observations.Comment: 4 pages, 5 figure

Understanding the roles of three academic communities in a prospective learning health ecosystem for diagnostic excellence

Inaccurate, untimely, and miscommunicated medical diagnoses represent a wicked problem requiring comprehensive and coordinated approaches, such as those demonstrated in the characteristics of learning health systems (LHSs). To appreciate a vision for how LHS methods can optimize processes and outcomes in medical diagnosis (diagnostic excellence), we interviewed 32 individuals with relevant expertise: 18 who have studied diagnostic processes using traditional behavioral science and health services research methods, six focused on machine learning (ML) and artificial intelligence (AI) approaches, and eight multidisciplinary researchers experienced in advocating for and incorporating LHS methods, ie, scalable continuous learning in health care. We report on barriers and facilitators, identified by these subjects, to applying their methods toward optimizing medical diagnosis. We then employ their insights to envision the emergence of a learning ecosystem that leverages the tools of each of the three research groups to advance diagnostic excellence. We found that these communities represent a natural fit forward, in which together, they can better measure diagnostic processes and close the loop of putting insights into practice. Members of the three academic communities will need to network and bring in additional stakeholders before they can design and implement the necessary infrastructure that would support ongoing learning of diagnostic processes at an economy of scale and scope.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153643/1/lrh210204_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153643/2/lrh210204.pd