Data-driven identification of parametric partial differential equations

In this work we present a data-driven method for the discovery of parametric partial differential equations (PDEs), thus allowing one to disambiguate between the underlying evolution equations and their parametric dependencies. Group sparsity is used to ensure parsimonious representations of observed dynamics in the form of a parametric PDE, while also allowing the coefficients to have arbitrary time series, or spatial dependence. This work builds on previous methods for the identification of constant coefficient PDEs, expanding the field to include a new class of equations which until now have eluded machine learning based identification methods. We show that group sequentially thresholded ridge regression outperforms group LASSO in identifying the fewest terms in the PDE along with their parametric dependency. The method is demonstrated on four canonical models with and without the introduction of noise

Atomic Interactions in Precision Interferometry Using Bose-Einstein Condensates

We present theoretical tools for predicting and reducing the effects of atomic interactions in Bose-Einstein condensate (BEC) interferometry experiments. To address mean-field shifts during free propagation, we derive a robust scaling solution that reduces the three-dimensional Gross-Pitaevskii equation to a set of three simple differential equations valid for any interaction strength. To model the other common components of a BEC interferometer---condensate splitting, manipulation, and recombination---we generalize the slowly-varying envelope reduction, providing both analytic handles and dramatically improved simulations. Applying these tools to a BEC interferometer to measure the fine structure constant (Gupta, et al., 2002), we find agreement with the results of the original experiment and demonstrate that atomic interactions do not preclude measurement to better than part-per-billion accuracy, even for atomic species with relatively large scattering lengths. These tools help make BEC interferometry a viable choice for a broad class of precision measurements.Comment: 8 pages, 6 figures, revised based on reviewer comment

Geography, seasonality, and host-associated population structure influence the fecal microbiome of a genetically depauparate Arctic mammal

The Canadian Arctic is an extreme environment with low floral and faunal diversity characterized by major seasonal shifts in temperature, moisture, and daylight. Muskoxen (Ovibos moschatus) are one of few large herbivores able to survive this harsh environment. Microbiome research of the gastrointestinal tract may hold clues as to how muskoxen exist in the Arctic, but also how this species may respond to rapid environmental changes. In this study, we investigated the effects of season (spring/summer/winter), year (2007-2016), and host genetic structure on population-level microbiome variation in muskoxen from the Canadian Arctic. We utilized 16S rRNA gene sequencing to characterize the fecal microbial communities of 78 male muskoxen encompassing two population genetic clusters. These clusters are defined by Arctic Mainland and Island populations, including the following: (a) two mainland sampling locations of the Northwest Territories and Nunavut and (b) four locations of Victoria Island. Between these geographic populations, we found that differences in the microbiome reflected host-associated genetic cluster with evidence of migration. Within populations, seasonality influenced bacterial diversity with no significant differences between years of sampling. We found evidence of pathogenic bacteria, with significantly higher presence in mainland samples. Our findings demonstrate the effects of seasonality and the role of host population-level structure in driving fecal microbiome differences in a large Arctic mammal

Global Warming Is Changing the Dynamics of Arctic Host-Parasite Systems

Global climate change is altering the ecology of infectious agents and driving the emergence of disease in people, domestic animals, and wildlife. We present a novel, empirically based, predictive model for the impact of climate warming on development rates and availability of an important parasitic nematode of muskoxen in the Canadian Arctic, a region that is particularly vulnerable to climate change. Using this model, we show that warming in the Arctic may have already radically altered the transmission dynamics of this parasite, escalating infection pressure for muskoxen, and that this trend is expected to continue. This work establishes a foundation for understanding responses to climate change of other host-parasite systems, in the Arctic and globally

Global Warming Is Changing the Dynamics of Arctic Host-Parasite Systems

Global climate change is altering the ecology of infectious agents and driving the emergence of disease in people, domestic animals, and wildlife. We present a novel, empirically based, predictive model for the impact of climate warming on development rates and availability of an important parasitic nematode of muskoxen in the Canadian Arctic, a region that is particularly vulnerable to climate change. Using this model, we show that warming in the Arctic may have already radically altered the transmission dynamics of this parasite, escalating infection pressure for muskoxen, and that this trend is expected to continue. This work establishes a foundation for understanding responses to climate change of other host-parasite systems, in the Arctic and globally

The influence of octyl beta-D-glucopyranoside on cell lysis induced by ultrasonic cavitation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98658/1/JAS003482.pd