ASU Scholarship Showcase

The inverse problem associated with electrochemical impedance spectroscopy requiring the solution of a Fredholm integral equation of the first kind is considered. If the underlying physical model is not clearly determined, the inverse problem needs to be solved using a regularized linear least squares problem that is obtained from the discretization of the integral equation. For this system, it is shown that the model error can be made negligible by a change of variables and by extending the effective range of quadrature. This change of variables serves as a right preconditioner that significantly improves the condition of the system. Still, to obtain feasible solutions the additional constraint of non-negativity is required. Simulations with artificial, but realistic, data demonstrate that the use of non-negatively constrained least squares with a smoothing norm provides higher quality solutions than those obtained without the non-negative constraint. Using higher-order smoothing norms also reduces the error in the solutions. The L-curve and residual periodogram parameter choice criteria, which are used for parameter choice with regularized linear least squares, are successfully adapted to be used for the non-negatively constrained Tikhonov least squares problem. Although these results have been verified within the context of the analysis of electrochemical impedance spectroscopy, there is no reason to suppose that they would not be relevant within the broader framework of solving Fredholm integral equations for other applications.

Data from the New Immigrant Survey were used to describe the home environments of 638 children ages birth to three whose parents legally immigrated to the United States. Thirty-two indicators of home conditions were clustered into 4 domains: discipline and socioemotional support, learning materials, enriching experiences, and family activities. Results revealed variation in how frequently infants from every country (Mexico, El Salvador, India, Philippines) and region (East Asia, Europe, Caribbean, Africa) studied experienced each home environmental condition. There were differences between countries and regions on many indicators as well as differences based on parents’ level of education. The experiences documented for children of recent legal immigrants were similar to those documented for children of native-born families in other studies.

Background: The shift from solitary to social behavior is one of the major evolutionary transitions. Primitively eusocial bumblebees are uniquely placed to illuminate the evolution of highly eusocial insect societies. Bumblebees are also invaluable natural and agricultural pollinators, and there is widespread concern over recent population declines in some species. High-quality genomic data will inform key aspects of bumblebee biology, including susceptibility to implicated population viability threats.

Results: We report the high quality draft genome sequences of Bombus terrestris and Bombus impatiens, two ecologically dominant bumblebees and widely utilized study species. Comparing these new genomes to those of the highly eusocial honeybee Apis mellifera and other Hymenoptera, we identify deeply conserved similarities, as well as novelties key to the biology of these organisms. Some honeybee genome features thought to underpin advanced eusociality are also present in bumblebees, indicating an earlier evolution in the bee lineage. Xenobiotic detoxification and immune genes are similarly depauperate in bumblebees and honeybees, and multiple categories of genes linked to social organization, including development and behavior, show high conservation. Key differences identified include a bias in bumblebee chemoreception towards gustation from olfaction, and striking differences in microRNAs, potentially responsible for gene regulation underlying social and other traits.

Conclusions: These two bumblebee genomes provide a foundation for post-genomic research on these key pollinators and insect societies. Overall, gene repertoires suggest that the route to advanced eusociality in bees was mediated by many small changes in many genes and processes, and not by notable expansion or depauperation.

Two classes of scaling behaviours, namely the super-linear scaling of links or activities, and the sub-linear scaling of area, diversity, or time elapsed with respect to size have been found to prevail in the growth of complex networked systems. Despite some pioneering modelling approaches proposed for specific systems, whether there exists some general mechanisms that account for the origins of such scaling behaviours in different contexts, especially in socioeconomic systems, remains an open question. We address this problem by introducing a geometric network model without free parameter, finding that both super-linear and sub-linear scaling behaviours can be simultaneously reproduced and that the scaling exponents are exclusively determined by the dimension of the Euclidean space in which the network is embedded. We implement some realistic extensions to the basic model to offer more accurate predictions for cities of various scaling behaviours and the Zipf distribution reported in the literature and observed in our empirical studies. All of the empirical results can be precisely recovered by our model with analytical predictions of all major properties. By virtue of these general findings concerning scaling behaviour, our models with simple mechanisms gain new insights into the evolution and development of complex networked systems.

We present a microarray nonlinear calibration (MiNC) method for quantifying antibody binding to the surface of protein microarrays that significantly increases the linear dynamic range and reduces assay variation compared with traditional approaches. A serological analysis of guinea pig Mycobacterium tuberculosis models showed that a larger number of putative antigen targets were identified with MiNC, which is consistent with the improved assay performance of protein microarrays. MiNC has the potential to be employed in biomedical research using multiplex antibody assays that need quantitation, including the discovery of antibody biomarkers, clinical diagnostics with multi-antibody signatures, and construction of immune mathematical models.

Widespread contamination of groundwater by chlorinated ethenes and their biological dechlorination products necessitates the reliable monitoring of liquid matrices; current methods approved by the U.S. Environmental Protection Agency (EPA) require a minimum of 5 mL of sample volume and cannot simultaneously detect all transformative products. This paper reports on the simultaneous detection of six chlorinated ethenes and ethene itself, using a liquid sample volume of 1 mL by concentrating the compounds onto an 85-µm carboxen-polydimenthylsiloxane solid-phase microextraction fiber in 5 min and subsequent chromatographic analysis in 9.15 min. Linear increases in signal response were obtained over three orders of magnitude (∼0.05 to ∼50 µM) for simultaneous analysis with coefficient of determination (R²) values of ≥ 0.99. The detection limits of the method (1.3–6 µg/L) were at or below the maximum contaminant levels specified by the EPA. Matrix spike studies with groundwater and mineral medium showed recovery rates between 79–108%. The utility of the method was demonstrated in lab-scale sediment flow-through columns assessing the bioremediation potential of chlorinated ethene-contaminated groundwater. Owing to its low sample volume requirements, good sensitivity and broad target analyte range, the method is suitable for routine compliance monitoring and is particularly attractive for interpreting the bench-scale feasibility studies that are commonly performed during the remedial design stage of groundwater cleanup projects.

High-resolution, global quantification of fossil fuel CO[subscript 2] emissions is emerging as a critical need in carbon cycle science and climate policy. We build upon a previously developed fossil fuel data assimilation system (FFDAS) for estimating global high-resolution fossil fuel CO[subscript 2] emissions. We have improved the underlying observationally based data sources, expanded the approach through treatment of separate emitting sectors including a new pointwise database of global power plants, and extended the results to cover a 1997 to 2010 time series at a spatial resolution of 0.1°. Long-term trend analysis of the resulting global emissions shows subnational spatial structure in large active economies such as the United States, China, and India. These three countries, in particular, show different long-term trends and exploration of the trends in nighttime lights, and population reveal a decoupling of population and emissions at the subnational level. Analysis of shorter-term variations reveals the impact of the 2008–2009 global financial crisis with widespread negative emission anomalies across the U.S. and Europe. We have used a center of mass (CM) calculation as a compact metric to express the time evolution of spatial patterns in fossil fuel CO[subscript 2] emissions. The global emission CM has moved toward the east and somewhat south between 1997 and 2010, driven by the increase in emissions in China and South Asia over this time period. Analysis at the level of individual countries reveals per capita CO[subscript 2] emission migration in both Russia and India. The per capita emission CM holds potential as a way to succinctly analyze subnational shifts in carbon intensity over time. Uncertainties are generally lower than the previous version of FFDAS due mainly to an improved nightlight data set.

The purpose of this study was to examine in which way adding more indicators or a covariate influences the performance of latent class analysis (LCA). We varied the sample size (100 ≤ N ≤ 2000), number, and quality of binary indicators (between 4 and 12 indicators with conditional response probabilities of [0.3, 0.7], [0.2, 0.8], or [0.1, 0.9]), and the strength of covariate effects (zero, small, medium, large) in a Monte Carlo simulation study of 2- and 3-class models. The results suggested that in general, a larger sample size, more indicators, a higher quality of indicators, and a larger covariate effect lead to more converged and proper replications, as well as fewer boundary parameter estimates and less parameter bias. Furthermore, interactions among these study factors demonstrated how using more or higher quality indicators, as well as larger covariate effect size, could sometimes compensate for small sample size. Including a covariate appeared to be generally beneficial, although the covariate parameters themselves showed relatively large bias. Our results provide useful information for practitioners designing an LCA study in terms of highlighting the factors that lead to better or worse performance of LCA.

A fundamental problem in computational biophysics is to deduce the function of a protein from the structure. Many biological macromolecules such as enzymes, molecular motors or membrane transport proteins perform their function by cycling between multiple conformational states. Understanding such conformational transitions, which typically occur on the millisecond to second time scale, is central to understanding protein function. Molecular dynamics (MD) computer simulations have become an important tool to connect molecular structure to function, but equilibrium MD simulations are rarely able to sample on time scales longer than a few microseconds – orders of magnitudes shorter than the time scales of interest. A range of different simulation methods have been proposed to overcome this time-scale limitation. These include calculations of the free energy landscape and path sampling methods to directly sample transitions between known conformations. All these methods solve the problem to sample infrequently occupied but important regions of configuration space. Many path-sampling algorithms have been applied to the closed – open transition of the enzyme adenylate kinase (AdK), which undergoes a large, clamshell-like conformational transition between an open and a closed state. Here we review approaches to sample macromolecular transitions through the lens of AdK. We focus our main discussion on the current state of knowledge – both from simulations and experiments – about the transition pathways of ligand-free AdK, its energy landscape, transition rates and interactions with substrates. We conclude with a comparison of the discussed approaches with a view towards quantitative evaluation of path-sampling methods.