Exploration of The Duality Between Generalized Geometry and Extraordinary Magnetoresistance

We outline the duality between the extraordinary magnetoresistance (EMR), observed in semiconductor-metal hybrids, and non-symmetric gravity coupled to a diffusive $U(1)$ gauge field. The corresponding gravity theory may be interpreted as the generalized complex geometry of the semi-direct product of the symmetric metric and the antisymmetric Kalb-Ramond field: ($g_{\mu\nu}+\beta_{\mu\nu}$). We construct the four dimensional covariant field theory and compute the resulting equations of motion. The equations encode the most general form of EMR within a well defined variational principle, for specific lower dimensional embedded geometric scenarios. Our formalism also reveals the emergence of additional diffusive pseudo currents for a completely dynamic field theory of EMR. The proposed equations of motion now include terms that induce geometrical deformations in the device geometry in order to optimize the EMR. This bottom-up dual description between EMR and generalized geometry/gravity lends itself to a deeper insight into the EMR effect with the promise of potentially new physical phenomena and properties.Comment: 13 pages and 6 figures. Revised/edited for clarity and purpose. Several references added. Updated title based on suggestions and comments received. Version accepted for publication in Phys.Rev.

Estimating changes in temperature extremes from millennial scale climate simulations using generalized extreme value (GEV) distributions

Changes in extreme weather may produce some of the largest societal impacts of anthropogenic climate change. However, it is intrinsically difficult to estimate changes in extreme events from the short observational record. In this work we use millennial runs from the CCSM3 in equilibrated pre-industrial and possible future conditions to examine both how extremes change in this model and how well these changes can be estimated as a function of run length. We estimate changes to distributions of future temperature extremes (annual minima and annual maxima) in the contiguous United States by fitting generalized extreme value (GEV) distributions. Using 1000-year pre-industrial and future time series, we show that the magnitude of warm extremes largely shifts in accordance with mean shifts in summertime temperatures. In contrast, cold extremes warm more than mean shifts in wintertime temperatures, but changes in GEV location parameters are largely explainable by mean shifts combined with reduced wintertime temperature variability. In addition, changes in the spread and shape of the GEV distributions of cold extremes at inland locations can lead to discernible changes in tail behavior. We then examine uncertainties that result from using shorter model runs. In principle, the GEV distribution provides theoretical justification to predict infrequent events using time series shorter than the recurrence frequency of those events. To investigate how well this approach works in practice, we estimate 20-, 50-, and 100-year extreme events using segments of varying lengths. We find that even using GEV distributions, time series that are of comparable or shorter length than the return period of interest can lead to very poor estimates. These results suggest caution when attempting to use short observational time series or model runs to infer infrequent extremes.Comment: 33 pages, 22 figures, 1 tabl

PCalign: a method to quantify physicochemical similarity of protein-protein interfaces

Abstract Background Structural comparison of protein-protein interfaces provides valuable insights into the functional relationship between proteins, which may not solely arise from shared evolutionary origin. A few methods that exist for such comparative studies have focused on structural models determined at atomic resolution, and may miss out interesting patterns present in large macromolecular complexes that are typically solved by low-resolution techniques. Results We developed a coarse-grained method, PCalign, to quantitatively evaluate physicochemical similarities between a given pair of protein-protein interfaces. This method uses an order-independent algorithm, geometric hashing, to superimpose the backbone atoms of a given pair of interfaces, and provides a normalized scoring function, PC-score, to account for the extent of overlap in terms of both geometric and chemical characteristics. We demonstrate that PCalign outperforms existing methods, and additionally facilitates comparative studies across models of different resolutions, which are not accommodated by existing methods. Furthermore, we illustrate potential application of our method to recognize interesting biological relationships masked by apparent lack of structural similarity. Conclusions PCalign is a useful method in recognizing shared chemical and spatial patterns among protein-protein interfaces. It outperforms existing methods for high-quality data, and additionally facilitates comparison across structural models with different levels of details with proven robustness against noise.http://deepblue.lib.umich.edu/bitstream/2027.42/110905/1/12859_2015_Article_471.pd

PCalign: a method to quantify physicochemical similarity of protein-protein interfaces

Abstract Background Structural comparison of protein-protein interfaces provides valuable insights into the functional relationship between proteins, which may not solely arise from shared evolutionary origin. A few methods that exist for such comparative studies have focused on structural models determined at atomic resolution, and may miss out interesting patterns present in large macromolecular complexes that are typically solved by low-resolution techniques. Results We developed a coarse-grained method, PCalign, to quantitatively evaluate physicochemical similarities between a given pair of protein-protein interfaces. This method uses an order-independent algorithm, geometric hashing, to superimpose the backbone atoms of a given pair of interfaces, and provides a normalized scoring function, PC-score, to account for the extent of overlap in terms of both geometric and chemical characteristics. We demonstrate that PCalign outperforms existing methods, and additionally facilitates comparative studies across models of different resolutions, which are not accommodated by existing methods. Furthermore, we illustrate potential application of our method to recognize interesting biological relationships masked by apparent lack of structural similarity. Conclusions PCalign is a useful method in recognizing shared chemical and spatial patterns among protein-protein interfaces. It outperforms existing methods for high-quality data, and additionally facilitates comparison across structural models with different levels of details with proven robustness against noise.http://deepblue.lib.umich.edu/bitstream/2027.42/134734/1/12859_2015_Article_471.pd

Evidence for elevated emissions from high-latitude wetlands contributing to high atmospheric CH4 concentration in the early Holocene

The major increase in atmospheric methane (CH4) concentration during the last glacial-interglacial transition provides a useful example for understanding the interactions and feedbacks among Earth\u27s climate, biosphere carbon cycling, and atmospheric chemistry. However, the causes of CH4 doubling during the last deglaciation are still uncertain and debated. Although the ice-core data consistently suggest a dominant contribution from northern high-latitude wetlands in the early Holocene, identifying the actual sources from the ground-based data has been elusive. Here we present data syntheses and a case study from Alaska to demonstrate the importance of northern wetlands in contributing to high atmospheric CH4concentration in the early Holocene. Our data indicate that new peatland formation as well as peat accumulation in northern high-latitude regions increased more than threefold in the early Holocene in response to climate warming and the availability of new habitat as a result of deglaciation. Furthermore, we show that marshes and wet fens that represent early stages of wetland succession were likely more widespread in the early Holocene. These wetlands are associated with high CH4 emissions due to high primary productivity and the presence of emergent plant species that facilitate CH4 transport to the atmosphere. We argue that early wetland succession and rapid peat accumulation and expansion (not simply initiation) contributed to high CH4 emissions from northern regions, potentially contributing to the sharp rise in atmospheric CH4 at the onset of the Holocene

Dilatonic Geometrodynamics of a Two-Dimensional Curved Surface due to a Quantum Mechanically Confined Particle

We provide a unique and novel extension of da Costa's calculation of a quantum mechanically constrained particle by analyzing the perturbative back reaction of the quantum confined particle's eigenstates and spectra upon the geometry of the curved surface itself. We do this by first formulating a two dimensional action principle of the quantum constrained particle, which upon wave function variation reproduces Schr\"odinger's equation including da Costa's surface curvature induced potentials. Given this action principle, we vary its functional with respect to the embedded two dimensional inverse-metric to obtain the respective geometrodynamical Einstein equation. We solve this resulting Einstein equation perturbatively by first solving the da Costa's Schr\"odinger equation to obtain an initial eigensystem, which is used as initial-input data for a perturbed metric inserted into the derived Einstein equation. As a proof of concept, we perform this calculation on a two-sphere and show its first iterative perturbed shape. We also include the back reaction of a constant external magnetic field in a separate calculation. The geometrodynamical analysis is performed within a two dimensional dilation gravity analog, due to several computational advantages.Comment: 9 pages, 15 figure

Estimating changes in temperature extremes from millennial-scale climate simulations using generalized extreme value (GEV) distributions

Changes in extreme weather may produce some of the largest societal impacts of anthropogenic climate change. However, it is intrinsically difficult to estimate changes in extreme events from the short observational record. In this work we use millennial runs from the Community Climate System Model version 3 (CCSM3) in equilibrated pre-industrial and possible future (700 and 1400 ppm CO2) conditions to examine both how extremes change in this model and how well these changes can be estimated as a function of run length. We estimate changes to distributions of future temperature extremes (annual minima and annual maxima) in the contiguous United States by fitting generalized extreme value (GEV) distributions. Using 1000-year pre-industrial and future time series, we show that warm extremes largely change in accordance with mean shifts in the distribution of summertime temperatures. Cold extremes warm more than mean shifts in the distribution of wintertime temperatures, but changes in GEV location parameters are generally well explained by the combination of mean shifts and reduced wintertime temperature variability. For cold extremes at inland locations, return levels at long recurrence intervals show additional effects related to changes in the spread and shape of GEV distributions. We then examine uncertainties that result from using shorter model runs. In theory, the GEV distribution can allow prediction of infrequent events using time series shorter than the recurrence interval of those events. To investigate how well this approach works in practice, we estimate 20-, 50-, and 100-year extreme events using segments of varying lengths. We find that even using GEV distributions, time series of comparable or shorter length than the return period of interest can lead to very poor estimates. These results suggest caution when attempting to use short observational time series or model runs to infer infrequent extremes

Identifying nonalcoholic fatty liver disease patients with active fibrosis by measuring extracellular matrix remodeling rates in tissue and blood.

Excess collagen synthesis (fibrogenesis) in the liver plays a causal role in the progression of nonalcoholic fatty liver disease (NAFLD). Methods are needed to identify patients with more rapidly progressing disease and to demonstrate early response to treatment. We describe here a novel method to quantify hepatic fibrogenesis flux rates both directly in liver tissue and noninvasively in blood. Twenty-one patients with suspected NAFLD ingested heavy water (2 H2 O, 50-mL aliquots) two to three times daily for 3-5 weeks prior to a clinically indicated liver biopsy. Liver collagen fractional synthesis rate (FSR) and plasma lumican FSR were measured based on 2 H labeling using tandem mass spectrometry. Patients were classified by histology for fibrosis stage (F0-F4) and as having nonalcoholic fatty liver or nonalcoholic steatohepatitis (NASH). Magnetic resonance elastography measurements of liver stiffness were also performed. Hepatic collagen FSR in NAFLD increased with advancing disease stage (e.g., higher in NASH than nonalcoholic fatty liver, positive correlation with fibrosis score and liver stiffness) and correlated with hemoglobin A1C. In addition, plasma lumican FSR demonstrated a significant correlation with hepatic collagen FSR.ConclusionUsing a well-characterized cohort of patients with biopsy-proven NAFLD, this study demonstrates that hepatic scar in NASH is actively remodeled even in advanced fibrosis, a disease that is generally regarded as static and slowly progressive. Moreover, hepatic collagen FSR correlates with established risks for fibrotic disease progression in NASH, and plasma lumican FSR correlates with hepatic collagen FSR, suggesting applications as direct or surrogate markers, respectively, of hepatic fibrogenesis in humans. (Hepatology 2017;65:78-88)