Evolution and Diffusion of the Michigan State University Tradition of Organizational Communication Network Research

This article documents the 30-year history of communication network research at Michigan State University (M.S.U.), providing a case study of the evolution and diffusion of an academic innovation. Three past and continuing issues for network scholars are identified: a lack of professional reward for developing user-friendly computer programs, unresolved methodological problems, and a need for better theoretical and conceptual frameworks. The narrative also illustrates the difficulty communication as a discipline has in impacting broader intellectual traditions. The story begins with the first doctoral dissertation (Schwartz, 1968) and the first network analysis software program in 1970 (Richards’ Negopy), continuing to the last dissertation (Susskind, 1996), and ending in 1998 when J. David Johnson left the M.S.U. faculty. Other major players in the M.S.U. network tradition included David K. Berio, Eugene Jacobson, Everett M. Rogers, Vincent Farace, Peter Monge, and Erwin Bettinghaus. Ironically, Schwartz and Susskind met in 1998 while Schwartz was preparing to retire from Cornell University and Susskind was starting as an Assistant Professor in a different department, thus providing closure to the M.S.U. network

The Thermal Properties of Solar Flares Over Three Solar Cycles Using GOES X-ray Observations

Solar flare X-ray emission results from rapidly increasing temperatures and emission measures in flaring active region loops. To date, observations from the X-Ray Sensor (XRS) onboard the Geostationary Operational Environmental Satellite (GOES) have been used to derive these properties, but have been limited by a number of factors, including the lack of a consistent background subtraction method capable of being automatically applied to large numbers of flares. In this paper, we describe an automated temperature and emission measure-based background subtraction method (TEBBS), which builds on the methods of Bornmann (1990). Our algorithm ensures that the derived temperature is always greater than the instrumental limit and the pre-flare background temperature, and that the temperature and emission measure are increasing during the flare rise phase. Additionally, TEBBS utilizes the improved estimates of GOES temperatures and emission measures from White et al. (2005). TEBBS was successfully applied to over 50,000 solar flares occurring over nearly three solar cycles (1980-2007), and used to create an extensive catalog of the solar flare thermal properties. We confirm that the peak emission measure and total radiative losses scale with background subtracted GOES X-ray flux as power-laws, while the peak temperature scales logarithmically. As expected, the peak emission measure shows an increasing trend with peak temperature, although the total radiative losses do not. While these results are comparable to previous studies, we find that flares of a given GOES class have lower peak temperatures and higher peak emission measures than previously reported. The resulting TEBBS database of thermal flare plasma properties is publicly available on Solar Monitor (www.solarmonitor.org/TEBBS/) and will be available on Heliophysics Integrated Observatory (www.helio-vo.eu)

Constraints on the perturbed mutual motion in Didymos due to impact-induced deformation of its primary after the DART impact

Binary near-Earth asteroid (65803) Didymos is the target of the proposed NASA Double Asteroid Redirection Test (DART), part of the Asteroid Impact & Deflection Assessment (AIDA) mission concept. In this mission, the DART spacecraft is planned to impact the secondary body of Didymos, perturbing mutual dynamics of the system. The primary body is currently rotating at a spin period close to the spin barrier of asteroids, and materials ejected from the secondary due to the DART impact are likely to reach the primary. These conditions may cause the primary to reshape, due to landslides, or internal deformation, changing the permanent gravity field. Here, we propose that if shape deformation of the primary occurs, the mutual orbit of the system would be perturbed due to a change in the gravity field. We use a numerical simulation technique based on the full two-body problem to investigate the shape effect on the mutual dynamics in Didymos after the DART impact. The results show that under constant volume, shape deformation induces strong perturbation in the mutual motion. We find that the deformation process always causes the orbital period of the system to become shorter. If surface layers with a thickness greater than ~0.4 m on the poles of the primary move down to the equatorial region due to the DART impact, a change in the orbital period of the system and in the spin period of the primary will be detected by ground-based measurement.Comment: 8 pages, 7 figures, 2 tables, accepted for publication in MNRA

Effective Viscosity of a Dilute Suspension of Membrane-bound Inclusions

When particulate suspensions are sheared, perturbations in the shear flows around the rigid particles increase the local energy dissipation, so that the viscosity of the suspension is effectively higher than that of the solvent. For bulk (three-dimensional) fluids, understanding this viscosity enhancement is a classic problem in hydrodynamics that originated over a century ago with Einstein's study of a dilute suspension of spherical particles. \cite{Einstein1} In this paper, we investigate the analogous problem of the effective viscosity of a suspension of disks embedded in a two-dimensional membrane or interface. Unlike the hydrodynamics of bulk fluids, low-Reynolds number membrane hydrodynamics is characterized by an inherent length scale generated by the coupling of the membrane to the bulk fluids that surround it. As a result, we find that the size of the particles in the suspension relative to this hydrodynamic length scale has a dramatic effect on the effective viscosity of the suspension. Our study also helps to elucidate the mathematical tools needed to solve the mixed boundary value problems that generically arise when considering the motion of rigid inclusions in fluid membranes.Comment: 33 pages, 4 figures (preprint); submitted to Physics of Fluid

Why Are Regulations Changed? A Parcel Analysis of Upzoning in Los Angeles

Planners, officials, and neighborhood groups often debate zoning changes, yet there is little empirical evidence explaining why zoning and other land use regulations are changed. I use logistic regression models to examine density-enabling rezoning (“upzoning”) in Los Angeles. I find that upzoning occurs where there are development opportunities combined with limited political resistance. Upzoning is most likely on well-located parcels zoned for low-intensity, nonresidential uses. Meanwhile, homeowners—and particularly homeowners with access to valuable amenities—are associated with regulatory stasis. I conclude by recommending strategies for addressing homeowners’ concerns about higher density housing

Roadmap on Superoscillations

Superoscillations are band-limited functions with the counterintuitive property that they can vary arbitrarily faster than their fastest Fourier component, over arbitrarily long intervals. Modern studies originated in quantum theory, but there were anticipations in radar and optics. The mathematical understanding—still being explored—recognises that functions are extremely small where they superoscillate; this has implications for information theory. Applications to optical vortices, sub-wavelength microscopy and related areas of nanoscience are now moving from the theoretical and the demonstrative to the practical. This Roadmap surveys all these areas, providing background, current research, and anticipating future developments

Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning

Understanding the spatial organization of tissues is of critical importance for both basic and translational research. While recent advances in tissue imaging are opening an exciting new window into the biology of human tissues, interpreting the data that they create is a significant computational challenge. Cell segmentation, the task of uniquely identifying each cell in an image, remains a substantial barrier for tissue imaging, as existing approaches are inaccurate or require a substantial amount of manual curation to yield useful results. Here, we addressed the problem of cell segmentation in tissue imaging data through large-scale data annotation and deep learning. We constructed TissueNet, an image dataset containing >1 million paired whole-cell and nuclear annotations for tissue images from nine organs and six imaging platforms. We created Mesmer, a deep learning-enabled segmentation algorithm trained on TissueNet that performs nuclear and whole-cell segmentation in tissue imaging data. We demonstrated that Mesmer has better speed and accuracy than previous methods, generalizes to the full diversity of tissue types and imaging platforms in TissueNet, and achieves human-level performance for whole-cell segmentation. Mesmer enabled the automated extraction of key cellular features, such as subcellular localization of protein signal, which was challenging with previous approaches. We further showed that Mesmer could be adapted to harness cell lineage information present in highly multiplexed datasets. We used this enhanced version to quantify cell morphology changes during human gestation. All underlying code and models are released with permissive licenses as a community resource

Designing a patient-centered personal health record to promote preventive care

<p>Abstract</p> <p>Background</p> <p>Evidence-based preventive services offer profound health benefits, yet Americans receive only half of indicated care. A variety of government and specialty society policy initiatives are promoting the adoption of information technologies to engage patients in their care, such as personal health records, but current systems may not utilize the technology's full potential.</p> <p>Methods</p> <p>Using a previously described model to make information technology more patient-centered, we developed an interactive preventive health record (IPHR) designed to more deeply engage patients in preventive care and health promotion. We recruited 14 primary care practices to promote the IPHR to all adult patients and sought practice and patient input in designing the IPHR to ensure its usability, salience, and generalizability. The input involved patient usability tests, practice workflow observations, learning collaboratives, and patient feedback. Use of the IPHR was measured using practice appointment and IPHR databases.</p> <p>Results</p> <p>The IPHR that emerged from this process generates tailored patient recommendations based on guidelines from the U.S. Preventive Services Task Force and other organizations. It extracts clinical data from the practices' electronic medical record and obtains health risk assessment information from patients. Clinical content is translated and explained in lay language. Recommendations review the benefits and uncertainties of services and possible actions for patients and clinicians. Embedded in recommendations are self management tools, risk calculators, decision aids, and community resources - selected to match patient's clinical circumstances. Within six months, practices had encouraged 14.4% of patients to use the IPHR (ranging from 1.5% to 28.3% across the 14 practices). Practices successfully incorporated the IPHR into workflow, using it to prepare patients for visits, augment health behavior counseling, explain test results, automatically issue patient reminders for overdue services, prompt clinicians about needed services, and formulate personalized prevention plans.</p> <p>Conclusions</p> <p>The IPHR demonstrates that a patient-centered personal health record that interfaces with the electronic medical record can give patients a high level of individualized guidance and be successfully adopted by busy primary care practices. Further study and refinement are necessary to make information systems even more patient-centered and to demonstrate their impact on care.</p> <p>Trial Registration</p> <p>Clinicaltrials.gov identifier: <a href="http://www.clinicaltrials.gov/ct2/show/NCT00589173">NCT00589173</a></p

Effects of Particulate Air Pollution on Cardiovascular Health: A Population Health Risk Assessment

Particulate matter (PM) air pollution is increasingly recognized as an important and modifiable risk factor for adverse health outcomes including cardiovascular disease (CVD). However, there are still gaps regarding large population risk assessment. Results from the nationwide Behavioral Risk Factor Surveillance System (BRFSS) were used along with air quality monitoring measurements to implement a systematic evaluation of PM-related CVD risks at the national and regional scales. CVD status and individual-level risk factors were collected from more than 500,000 BRFSS respondents across 2,231 contiguous U.S. counties for 2007 and 2009. Chronic exposures to PM pollutants were estimated with spatial modeling from measurement data. CVD outcomes attributable to PM pollutants were assessed by mixed-effects logistic regression and latent class regression (LCR), with adjustment for multicausality. There were positive associations between CVD and PM after accounting for competing risk factors: the multivariable-adjusted odds for the multiplicity of CVD outcomes increased by 1.32 (95% confidence interval: 1.23–1.43) and 1.15 (1.07–1.22) times per 10 µg/m3 increase in PM2.5 and PM10 respectively in the LCR analyses. After controlling for spatial confounding, there were moderate estimated effects of PM exposure on multiple cardiovascular manifestations. These results suggest that chronic exposures to ambient particulates are important environmental risk factors for cardiovascular morbidity