Theoretical Foundation of Cyclostationary EOF Analysis for Geophysical and Climatic Variables: Concepts and Examples

Natural variability is an essential component of observations of all geophysical and climate variables. In principal component analysis (PCA), also called empirical orthogonal function (EOF) analysis, a set of orthogonal eigenfunctions is found from a spatial covariance function. These empirical basis functions often lend useful insights into physical processes in the data and serve as a useful tool for developing statistical methods. The underlying assumption in PCA is the stationarity of the data analyzed; that is, the covariance function does not depend on the origin of time. The stationarity assumption is often not justifiable for geophysical and climate variables even after removing such cyclic components as the diurnal cycle or the annual cycle. As a result, physical and statistical inferences based on EOFs can be misleading. Some geophysical and climatic variables exhibit periodically time-dependent covariance statistics. Such a dataset is said to be periodically correlated or cyclostationary. A proper recognition of the time-dependent response characteristics is vital in accurately extracting physically meaningful modes and their space-time evolutions from data. This also has important implications in finding physically consistent evolutions and teleconnection patterns and in spectral analysis of variability-important goals in many climate and geophysical studies. In this study, the conceptual foundation of cyclostationary EOF (CSEOF) analysis is examined as an alternative to regular EOF analysis or other eigenanalysis techniques based on the stationarity assumption. Comparative examples and illustrations are given to elucidate the conceptual difference between the CSEOF technique and other techniques and the entailing ramification in physical and statistical inferences based on computational eigenfunctions. © The authors 2015

Mechanism of Seasonal Arctic Sea Ice Evolution and Arctic Amplification

Sea ice loss is proposed as a primary reason for the Arctic amplification, although the physical mechanism of the Arctic amplification and its connection with sea ice melting is still in debate. In the present study, monthly ERA-Interim reanalysis data are analyzed via cyclostationary empirical orthogonal function analysis to understand the seasonal mechanism of sea ice loss in the Arctic Ocean and the Arctic amplification. While sea ice loss is widespread over much of the perimeter of the Arctic Ocean in summer, sea ice remains thin in winter only in the Barents-Kara seas. Excessive turbulent heat flux through the sea surface exposed to air due to sea ice reduction warms the atmospheric column. Warmer air increases the downward longwave radiation and subsequently surface air temperature, which facilitates sea surface remains to be free of ice. This positive feedback mechanism is not clearly observed in the Laptev, East Siberian, Chukchi, and Beaufort seas, since sea ice refreezes in late fall (November) before excessive turbulent heat flux is available for warming the atmospheric column in winter. A detailed seasonal heat budget is presented in order to understand specific differences between the Barents-Kara seas and Laptev, East Siberian, Chukchi, and Beaufort seas

Fragility of Fermi arcs in Dirac semimetals

We use tunable, vacuum ultraviolet laser-based angle-resolved photoemission spectroscopy and density functional theory calculations to study the electronic properties of Dirac semimetal candidate cubic PtBi${}_{2}$. In addition to bulk electronic states we also find surface states in PtBi${}_{2}$ which is expected as PtBi${}_{2}$ was theoretical predicated to be a candidate Dirac semimetal. The surface states are also well reproduced from DFT band calculations. Interestingly, the topological surface states form Fermi contours rather than double Fermi arcs that were observed in Na$_3$Bi. The surface bands forming the Fermi contours merge with bulk bands in proximity of the Dirac points projections, as expected. Our data confirms existence of Dirac states in PtBi${}_{2}$ and reveals the fragility of the Fermi arcs in Dirac semimetals. Because the Fermi arcs are not topologically protected in general, they can be deformed into Fermi contours, as proposed by [Kargarian {\it et al.}, PNAS \textbf{113}, 8648 (2016)]. Our results demonstrate validity of this theory in PtBi${}_{2}$.Comment: 6 pages, 4 figure

GRXCR2 Regulates Taperin Localization Critical for Stereocilia Morphology and Hearing

Mutations in human GRXCR2, which encodes a protein of undetermined function, cause hearing loss by unknown mechanisms. We found that mouse GRXCR2 localizes to the base of the stereocilia, which are actin-based mechanosensing organelles in cochlear hair cells that convert sound-induced vibrations into electrical signals. The stereocilia base also contains taperin, another protein of unknown function required for human hearing. We show that taperin and GRXCR2 form a complex and that taperin is diffused throughout the stereocilia length in Grxcr2-deficient hair cells. Stereocilia lacking GRXCR2 are longer than normal and disorganized due to the mislocalization of taperin, which could modulate the actin cytoskeleton in stereocilia. Remarkably, reducing taperin expression levels could rescue the morphological defects of stereocilia and restore the hearing of Grxcr2-deficient mice. Thus, our findings suggest that GRXCR2 is critical for the morphogenesis of stereocilia and auditory perception by restricting taperin to the stereocilia base

Adaptive optics for optical microscopy [Invited]

Optical microscopy is widely used to visualize fine structures. When applied to bioimaging, its performance is often degraded by sample-induced aberrations. In recent years, adaptive optics (AO), originally developed to correct for atmosphere-associated aberrations, has been applied to a wide range of microscopy modalities, enabling high- or super-resolution imaging of biological structure and function in complex tissues. Here, we review classic and recently developed AO techniques and their applications in optical microscopy

Addition of Inflammatory Biomarkers Did Not Improve Diabetes Prediction in the Community: The Framingham Heart Study

Background: Prior studies have reported conflicting findings with regard to the association of biomarkers in the prediction of incident type 2 diabetes. We evaluated 12 biomarkers as possible diabetes predictors in the Framingham Heart Study. Methods and results: Biomarkers representing inflammation (C-reactive protein, interleukin-6, monocyte chemoattractant protein-1, tumor necrosis factor receptor 2, osteoprotegerin, and fibrinogen), endothelial dysfunction (intercellular adhesion molecule-1), vascular damage (CD40-ligand, P-selectin, and lipoprotein-associated phospholipase A2 mass and activity), and oxidative stress (urinary isoprostanes) were measured in participants without diabetes attending the Offspring seventh (n=2499) or multiethnic Omni second (n=189) examination (1998–2001). Biomarkers were loge transformed and standardized. Multivariable logistic regression tested each biomarker in association with incident diabetes at a follow-up examination (the Offspring eighth and Omni third examination; mean 6.6 years later), with adjustment for age, sex, cohort, body mass index, fasting glucose, systolic blood pressure, high-density lipoprotein cholesterol, triglycerides, and smoking. C statistics were evaluated with and without inflammatory markers. In 2638 participants (56% women, mean age 59 years), 162 (6.1%) developed type 2 diabetes. All biomarkers, excluding osteoprotegerin, were associated with the outcome with adjustment for age, sex, and cohort; however, none remained significant after multivariable adjustment (all P>0.05). The c statistic from the model including only clinical covariates (0.89) did not statistically significantly improve after addition of biomarkers (all P>0.10). Conclusions: Biomarkers representing different inflammatory pathways are associated with incident diabetes but do not remain statistically significant after adjustment for established clinical covariates. Inflammatory biomarkers might not be an effective resource to predict type 2 diabetes in community-based samples

Advances in Growth, Doping, and Devices and Applications of Zinc Oxide

Zinc oxide is a breakthrough multifunctional material of emerging interest applicable in the areas of electronics, computing, energy harvesting, sensing, optoelectronics, and biomedicine. Zno has a direct and wide bandgap and high exciton binding energy. It is nontoxic, earth-abundant, and biocompatible. However, the growth and characterization of high-quality zno has been a challenge and bottleneck in its development. Efforts have been made to synthesize device-quality zinc oxide and unleash its potential for multiple advanced applications. Zno could be grown as thin films, nanostructures, or bulk, and its properties could be optimized by tuning the growth techniques, conditions, and doping. Zinc oxide could be a suitable material for next generation devices including spintronics, sensors, solar cells, light-emitting diodes, thermoelectrics, etc. It is important and urgent to collate recent advances in this material, which would strategically help in further research and developments in zno. This paper provides a coherent review of developments in zno growth, leading to its advancing applications. Recent developments in growth technologies that address native defects, current challenges in zinc oxide, and its emerging applications are reviewed and discussed in this article

Focus on RNA Isolation: Obtaining RNA for MicroRNA (miRNA) Expression Profiling Analyses of Neural Tissue

MicroRNAs (miRNAs) are present in all known plant and animal tissues and appear to be somewhat concentrated in the mammalian nervous system. Many different miRNA expression profiling platforms have been described. However, relatively little research has been published to establish the importance of \u27upstream\u27 variables in RNA isolation for neural miRNA expression profiling. We tested whether apparent changes in miRNA expression profiles may be associated with tissue processing, RNA isolation techniques, or different cell types in the sample. RNA isolation was performed on a single brain sample using eight different RNA isolation methods, and results were correlated using a conventional miRNA microarray and then cross-referenced to Northern blots. Differing results were seen between samples obtained using different RNA isolation techniques and between microarray and Northern blot results. Another complication of miRNA microarrays is tissue-level heterogeneity of cellular composition. To investigate this phenomenon, miRNA expression profiles were determined and compared between highly-purified primary cerebral cortical cell preparations of rat primary E15-E18 neurons versus rat primary E15-E18 astrocytes. Finally, to assess the importance of dissecting human brain gray matter from subjacent white matter in cerebral cortical studies, miRNA expression profiles were compared between gray matter and immediately contiguous white matter. The results suggest that for microarray studies, cellular composition is important, and dissecting white matter from gray matter improves the specificity of the results. Based on these data, recommendations for miRNA expression profiling in neural tissues, and considerations worthy of further study, are discussed

Robust efficiency and actuator saturation explain healthy heart rate control and variability

The correlation of healthy states with heart rate variability (HRV) using time series analyses is well documented. Whereas these studies note the accepted proximal role of autonomic nervous system balance in HRV patterns, the responsible deeper physiological, clinically relevant mechanisms have not been fully explained. Using mathematical tools from control theory, we combine mechanistic models of basic physiology with experimental exercise data from healthy human subjects to explain causal relationships among states of stress vs. health, HR control, and HRV, and more importantly, the physiologic requirements and constraints underlying these relationships. Nonlinear dynamics play an important explanatory role––most fundamentally in the actuator saturations arising from unavoidable tradeoffs in robust homeostasis and metabolic efficiency. These results are grounded in domain-specific mechanisms, tradeoffs, and constraints, but they also illustrate important, universal properties of complex systems. We show that the study of complex biological phenomena like HRV requires a framework which facilitates inclusion of diverse domain specifics (e.g., due to physiology, evolution, and measurement technology) in addition to general theories of efficiency, robustness, feedback, dynamics, and supporting mathematical tools

Can We “Hedge” against the Development of Antiviral Resistance among Pandemic Influenza Viruses?

David K. Shay and Benjamin Ridenhour discuss a modeling study predicting that stockpiling a secondary antiviral for use early in a flu pandemic can forestall resistance to the primary stockpiled drug