ASU Scholarship Showcase

We conducted a 12-month-long experiment in a financial services company to study how the availability of treadmill workstations affects employees’ physical activity and work performance. We enlisted sedentary volunteers, half of whom received treadmill workstations during the first two months of the study and the rest in the seventh month of the study. Participants could operate the treadmills at speeds of 0–2 mph and could use a standard chair-desk arrangement at will. (a) Weekly online performance surveys were administered to participants and their supervisors, as well as to all other sedentary employees and their supervisors. Using within-person statistical analyses, we find that overall work performance, quality and quantity of performance, and interactions with coworkers improved as a result of adoption of treadmill workstations. (b) Participants were outfitted with accelerometers at the start of the study. We find that daily total physical activity increased as a result of the adoption of treadmill workstations.

Background: Hemorrhagic fever with renal syndrome (HFRS), a rodent-borne infectious disease, is one of the most serious public health threats in China. Increasing our understanding of the spatial and temporal patterns of HFRS infections could guide local prevention and control strategies.

Methodology/Principal Findings: We employed statistical models to analyze HFRS case data together with environmental data from the Dongting Lake district during 2005–2010. Specifically, time-specific ecologic niche models (ENMs) were used to quantify and identify risk factors associated with HFRS transmission as well as forecast seasonal variation in risk across geographic areas. Results showed that the Maximum Entropy model provided the best predictive ability (AUC = 0.755). Time-specific Maximum Entropy models showed that the potential risk areas of HFRS significantly varied across seasons. High-risk areas were mainly found in the southeastern and southwestern areas of the Dongting Lake district. Our findings based on models focused on the spring and winter seasons showed particularly good performance. The potential risk areas were smaller in March, May and August compared with those identified for June, July and October to December. Both normalized difference vegetation index (NDVI) and land use types were found to be the dominant risk factors.

Conclusions/Significance: Our findings indicate that time-specific ENMs provide a useful tool to forecast the spatial and temporal risk of HFRS.

Sequential affect dynamics generated during the interaction of intimate dyads, such as married couples, are associated with a cascade of effects - some good and some bad - on each partner, close family members, and other social contacts. Although the effects are well documented, the probabilistic structures associated with micro-social processes connected to the varied outcomes remain enigmatic. Using extant data we developed a method of classifying and subsequently generating couple dynamics using a Hierarchical Dirichlet Process Hidden semi-Markov Model (HDP-HSMM). Our findings indicate that several key aspects of existing models of marital interaction are inadequate: affect state emissions and their durations, along with the expected variability differences between distressed and nondistressed couples are present but highly nuanced; and most surprisingly, heterogeneity among highly satisfied couples necessitate that they be divided into subgroups. We review how this unsupervised learning technique generates plausible dyadic sequences that are sensitive to relationship quality and provide a natural mechanism for computational models of behavioral and affective micro-social processes.

Introduction: Sedentariness is associated with chronic health conditions, impaired cognitive function and obesity. Work contributes significantly to sedentariness because many work tasks necessitate sitting. Few sustained solutions exist to reverse workplace sedentariness. Here, we evaluated a chair and an under-table device that were designed to promote fidgeting while seated. Our hypothesis was that an under-table leg-fidget bar and/or a fidget-promoting chair significantly increased energy expenditure. We compared these devices with chair-based exercise and walking.

Materials and Methods: We measured energy expenditure and heart rate in 16 people while they sat and worked using a standard chair, an under-desk device that encourages leg fidgeting and a fidget-promoting chair. We compared outcomes with chair-based exercise and walking.

Results: Energy expenditure increased significantly while using either an under-table leg-fidget bar or a fidget-promoting chair, when compared to the standard office chair (standard chair, 76±31 kcal/hour; leg-fidget bar, 98±42 kcal/hour (p<0.001); fidget chair, 89±40 kcal/hour (p=0.03)). However, heart rate did not increase significantly in either case. Bouts of exercise performed while seated provided energetic and heart rate equivalency to walking at 2 mph.

Conclusions: Chairs and devices that promote fidgeting can increase energy expenditure by ∼20–30% but not increase heart rate. Dynamic sitting may be among a lexicon of options to help people move more while at work.

Neglected tropical diseases (NTD), account for a large proportion of the global disease burden, and their control faces several challenges including diminishing human and financial resources for those distressed from such diseases. Visceral leishmaniasis (VL), the second-largest parasitic killer (after malaria) and an NTD affects poor populations and causes considerable cost to the affected individuals. Mathematical models can serve as a critical and cost-effective tool for understanding VL dynamics, however, complex array of socio-economic factors affecting its dynamics need to be identified and appropriately incorporated within a dynamical modeling framework. This study reviews literature on vector-borne diseases and collects challenges and successes related to the modeling of transmission dynamics of VL. Possible ways of creating a comprehensive mathematical model is also discussed.

Antiviral resistance in influenza is rampant and has the possibility of causing major morbidity and mortality. Previous models have identified treatment regimes to minimize total infections and keep resistance low. However, the bulk of these studies have ignored stochasticity and heterogeneous contact structures. Here we develop a network model of influenza transmission with treatment and resistance, and present both standard mean-field approximations as well as simulated dynamics. We find differences in the final epidemic sizes for identical transmission parameters (bistability) leading to different optimal treatment timing depending on the number initially infected. We also find, contrary to previous results, that treatment targeted by number of contacts per individual (node degree) gives rise to more resistance at lower levels of treatment than non-targeted treatment. Finally we highlight important differences between the two methods of analysis (mean-field versus stochastic simulations), and show where traditional mean-field approximations fail. Our results have important implications not only for the timing and distribution of influenza chemotherapy, but also for mathematical epidemiological modeling in general. Antiviral resistance in influenza may carry large consequences for pandemic mitigation efforts, and models ignoring contact heterogeneity and stochasticity may provide misleading policy recommendations.

Many recent studies observe the increasing importance, influence, and analysis of resilience as a concept to understand the capacity of a system or individual to respond to change. The term has achieved prominence in diverse scientific fields, as well as public discourse and policy arenas. As a result, resilience has been referred to as a boundary object or a bridging concept that is able to facilitate communication and understanding across disciplines, coordinate groups of actors or stakeholders, and build consensus around particular policy issues. We present a network analysis of bibliometric data to understand the extent to which resilience can be considered as a boundary object or a bridging concept in terms of its links across disciplines and scientific fields. We analyzed 994 papers and 35,952 citations between them to reveal the connectedness and links between and within fields. We analyzed the network according to different fields, modules, and sub-fields, showing a highly clustered citation network. Analyzing betweenness allowed us to identify how particular papers bridge across fields and how different fields are linked. With the exception of a few specific papers, most papers cite exclusively within their own field. We conclude that resilience is to an extent a boundary object because there are shared understandings across diverse disciplines and fields. However, it is more limited as a bridging concept because the citations across fields are concentrated among particular disciplines and papers, so the distinct fields do not widely or routinely refer to each other. There are some signs of resilience being used as an interdisciplinary concept to bridge scientific fields, particularly in social-ecological systems, which may itself constitute an emerging sub-field.

A significant challenge of our time is conserving biological diversity while maintaining economic development and cultural values. The United Nations Educational, Scientific and Cultural Organization has established biosphere reserves within its Man and the Biosphere program as a model means for accomplishing this very challenge. The East Carpathians Biosphere Reserve (ECBR), spreading across Poland, Slovakia, and Ukraine, represents a large social-ecological system (SES) that has been protected under the biosphere reserve designation since 1998. We have explored its successes and failures in improving human livelihoods while safeguarding its ecosystems. The SES framework, which includes governance system, actors, resources, and external influences, was used as a frame of analysis. The outcomes of this protected area have been mixed; its creation led to national and international collaboration, yet some actor groups remain excluded. Implementation of protocols arising from the Carpathian Convention has been slow, while deforestation, hunting, erosion, temperature extremes, and changes in species behavior remain significant threats but have also been factors in ecological adaptation. The loss of cultural links and traditional knowledge has also been significant. Nevertheless, this remains a highly biodiverse area. Political barriers and institutional blockages will have to be removed to ensure this reserve fulfills its role as a model region for international collaboration and capacity building. These insights drawn from the ECBR demonstrate that biosphere reserves are indeed learning sites for sustainable development and that this case is exemplary in illustrating the challenges, but more importantly, the opportunities that arise when ensuring parallel care and respect for people and ecosystems through the model of transboundary protected areas around the world.

A central question in cognitive neuroscience is how unitary, coherent decisions at the whole organism level can arise from the distributed behavior of a large population of neurons with only partially overlapping information. We address this issue by studying neural spiking behavior recorded from a multielectrode array with 169 channels during a visual motion direction discrimination task. It is well known that in this task there are two distinct phases in neural spiking behavior. Here we show Phase I is a distributed or incompressible phase in which uncertainty about the decision is substantially reduced by pooling information from many cells. Phase II is a redundant or compressible phase in which numerous single cells contain all the information present at the population level in Phase I, such that the firing behavior of a single cell is enough to predict the subject's decision. Using an empirically grounded dynamical modeling framework, we show that in Phase I large cell populations with low redundancy produce a slow timescale of information aggregation through critical slowing down near a symmetry-breaking transition. Our model indicates that increasing collective amplification in Phase II leads naturally to a faster timescale of information pooling and consensus formation. Based on our results and others in the literature, we propose that a general feature of collective computation is a “coding duality” in which there are accumulation and consensus formation processes distinguished by different timescales.

Community associated methicillin-resistant Staphylococcus aureus (CA-MRSA) has become a major cause of skin and soft tissue infections (SSTIs) in the US. We developed an age-structured compartmental model to study the spread of CA-MRSA at the population level and assess the effect of control intervention strategies. We used Monte-Carlo Markov Chain (MCMC) techniques to parameterize our model using monthly time series data on SSTIs incidence in children (≤19 years) during January 2004 -December 2006 in Maricopa County, Arizona. Our model-based forecast for the period January 2007–December 2008 also provided a good fit to data. We also carried out an uncertainty and sensitivity analysis on the control reproduction number, R_c which we estimated at 1.3 (95% CI [1.2,1.4]) based on the model fit to data. Using our calibrated model, we evaluated the effect of typical intervention strategies namely reducing the contact rate of infected individuals owing to awareness of infection and decolonization strategies targeting symptomatic infected individuals on both and the long-term disease dynamics. We also evaluated the impact of hypothetical decolonization strategies targeting asymptomatic colonized individuals. We found that strategies focused on infected individuals were not capable of achieving disease control when implemented alone or in combination. In contrast, our results suggest that decolonization strategies targeting the pediatric population colonized with CA-MRSA have the potential of achieving disease elimination.