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In this paper, the role of the El Niño-Southern Oscillation (ENSO), measured by the Equatorial Southern Oscillation Index (EQSOI), is used to formally forecast the inflation and GDP growth rates of the United States, advanced (excluding the United States) and emerging countries, as well as the world economy (barring the United States). We rely on univariate and multivariate singular spectrum analyses (SSA), as well as mixed-frequency version of the latter since the EQSOI is monthly, while GDP is available only at quarterly frequency unlike monthly inflation rates. We find statistically significant evidence of the ability of the EQSOI in forecasting inflation and GDP growth rates of the four economic blocs, though there are exceptions in terms of forecasting gains associated with inflation rate of emerging economies and the growth rate of the United States. Our results have important implications for policymakers. [ABSTRACT FROM AUTHOR]

Background: Raw bioimpedance parameters (eg, 50-kHz phase angle [PA] and 200-kHz/5-kHz impedance ratio [IR]) have been investigated as predictors of nutrition status and/or clinical outcomes. However, their validity as prognostic measures depends on the availability of appropriate reference data. Using a large and ethnically diverse data set, we aimed to determine if ethnicity influences these measures and provide expanded bioimpedance reference data for the U.S.Population: Methods: The National Health and Nutrition Examination Survey (NHANES) is an ongoing compilation of studies conducted by the U.S. Centers for Disease Control and Prevention designed to monitor nutrition status of the U.S.Population: The NHANES data sets analyzed were from the years 1999-2000, 2001-2002, and 2003-2004.Results: Multivariate analysis showed that PA and IR differed by body mass index (BMI), age, sex, and ethnicity (n = 6237; R2 = 41.2%, P < .0001). Suggested reference cut-points for PA stratified by age decade, ethnicity, and sex are provided.Conclusion: Ethnicity is an important variable that should be accounted for when determining population reference values for PA and IR. We have provided sex-, ethnicity-, and age decade-specific reference values from PA for use by future studies in U.S.Populations: Interdevice differences are likely to be important contributors to variability across published population-specific reference data and, where possible, should be evaluated in future research. Ultimately, further validation with physiologically relevant reference measures (eg, dual-energy x-ray absorptiometry) is necessary to determine if PA/IR are appropriate bedside tools for the assessment of nutrition status in a clinical population. [ABSTRACT FROM AUTHOR]

The food versus fuel issue has attracted considerable attention with the rapid growth of global biofuel production. The previous literature examining the interconnectedness between biofuel, fossil fuel and agricultural markets employed futures and spot prices. However, food security needs to be discussed with non‐financial market prices, such as wholesale prices, since retail companies and households usually do not purchase products directly from financial markets, which encompass speculative activities, making them more volatile than non‐financial ones. This paper focuses on non‐financial markets in the United States to investigate the price‐interconnection of biodiesel, highway diesel, crude oil, and soybean, initially applying partial wavelet coherence and the Diebold‐Yilmaz connectedness index to price transmission research on biodiesel, highway diesel, crude oil, and soybean. Our main results are as follows: First, significant coherence between biodiesel and soybean, biodiesel and highway diesel, and highway diesel and crude oil is identified in the short and long term. Second, crude oil and biodiesel prices are found to be net transmitters, while soybean and highway diesel prices are net receivers. Finally, the crude oil market is identified as the source of spillovers among the four markets, and strongly influences the highway diesel market. The previous literature examining the interconnectedness between biofuel, fossil fuel and agricultural markets employed futures and spot prices, while food security needs to be discussed with non‐financial market prices since retail companies and households usually do not purchase products directly from financial markets. This paper focuses on non‐financial markets in the United States to investigate the price‐interconnection of biodiesel, highway diesel, crude oil, and soybean, initially applying partial wavelet coherence and the Diebold‐Yilmaz connectedness index to price transmission research on biodiesel, highway diesel, crude oil, and soybean. Our primary result suggests that biodiesel promotion reinforces a relationship between the prices of soybean and biodiesel. [ABSTRACT FROM AUTHOR]

This study explores multi‐scale variability in the relationship between turbidity (Tn) and flow (Q) in 162 watersheds across the contiguous United States. Sites are selected where Tn acts as a good surrogate for suspended sediment concentration. We use dynamic linear models (DLMs) to infer time‐varying parameters of Tn‐Q rating curves at each site, and calibrate a hyper‐parameter of the DLM model (δ $\delta $) to quantify the degree of dynamicity in the rating curve relationship. The DLM can capture dynamics in the Tn‐Q relationship at the resolution of the data (daily in this study), enabling an analysis of the dynamics across time scales. Regional multivariate regressions are used to identify physiographic features that relate to the magnitude of δ $\delta $ and spectral signatures in the DLM parameters across sites. Results show that watersheds in the Midwest and Pacific Northwest tend to exhibit more variable Tn‐Q relationships, while these relationships are more stable in watersheds in the humid east and Lower Mississippi River basin. Stream network complexity, soil composition, perennial snow coverage, saturation‐excess overland flow, and modifications to the stream network are associated with the dynamicity in the Tn‐Q relationship. DLM parameters exhibit cyclic behavior at sub‐monthly, sub‐annual, and annual time scales at sites across the country, with annual cycling associated with basin features that reflect watershed sediment availability and the erosive power of rivers. Overall, our analysis highlights significant multi‐scale variability in Tn‐Q relationships across the nation, with important implications for how sediment dynamics should be measured and managed at the watershed‐scale. Plain Language Summary: Effective sediment management often requires accurate sediment yield predictions and forecasts. However, the predictive skill of various models often declines in regions where the sediment transport processes are variable across timescales. This study aims to improve our understanding of variability in the relationship between turbidity (Tn) and flow (Q) through time, focusing on sites where turbidity is a good proxy for suspended sediment concentration. We use regression models with parameters that can vary over time to quantify the variability in the Tn‐Q relationship and to highlight the presence of seasonal patterns in transport processes. We then explore how watershed characteristics influence the variability and seasonality in the Tn‐Q relationship. Our results suggest that stream network complexity, soil composition, perennial snow coverage, saturation‐excess overland flow, and modifications to the stream network influence the overall variability in the Tn‐Q relationship, while the seasonality is mainly associated with basin features that reflect watershed sediment availability and the erosive power of rivers. The results of the study have important implications for how sediment dynamics should be addressed in watershed management studies. Key Points: The dynamicity of sediment transport processes is quantified using dynamic linear rating curve models of turbidity and flowThe degree of rating curve dynamicity varies spatially and is partially influenced by natural and anthropogenic physiographic featuresDynamic rating curves exhibit cyclic behavior at various timescales across the US, which is also partially explained by basin features [ABSTRACT FROM AUTHOR]

Coastal communities across the United States (U.S.) are experiencing an increase in the frequency of high‐tide flooding (HTF). This increase is mainly due to sea‐level rise (SLR), but other factors such as intra‐ to inter‐annual mean sea level variability, tidal anomalies, and non‐tidal residuals also contribute to HTF events. Here we introduce a novel decomposition approach to develop and then analyze a new database of different sea‐level components. Those components represent processes that act on various timescales to contribute to HTF along the U.S. coastline. We find that the relative importance of components to HTF events strongly varies in space and time. Tidal anomalies contribute the most along the west and northeast coasts, where HTF events mostly occur in winter. Non‐tidal residuals are most important along the Gulf of Mexico and mid‐Atlantic coasts, where HTF events mostly occur in fall. We also quantify the minimum number of components that were required to cause HTF events in the past and how this number changed over time. The results highlight that at present, due to SLR, fewer components are needed to combine to push water levels above HTF thresholds, but tidal anomalies alone are still not sufficient to reach HTF thresholds in most locations. Finally, we explore how co‐variability between different components leads to compounding effects. In some places, positive correlation between sea‐level components leads to significantly more HTF events than would be expected if sea‐level components were uncorrelated, whereas in other places negative correlation leads to fewer HTF events. Plain Language Summary: High‐tide flooding, also known as nuisance flooding or minor flooding, is one of the most obvious outcomes of climate change and associated sea‐level rise (SLR). The increased frequency of high‐tide flooding (HTF) and the fact that more and more communities are affected by it, has raised public awareness. We develop a new database consisting of different sea‐level components, representing different processes, and analyze their contribution to HTF. We find, for example, that ocean tides are the main driver for HTF along the U.S. west coast, while surges are relatively more important along the east coast. Due to SLR, fewer sea level components can combine to lead to HTF at present‐day compared to the past. Correlation between the different sea‐level components also causes compounding effects leading to more (or fewer) HTF events in certain locations. The new database and analysis that is presented here advance our understanding of the role of different sea‐level processes in causing HTF along the U.S. coastline. Key Points: A novel water level decomposition method is used to develop a database of the contributions of different sea level processes to water levelsFewer sea level components can combine to push water levels above high‐tide flooding (HTF) thresholds due to sea‐level riseCo‐variability between sea‐level components leads to compounding effects causing additional (or fewer) HTF events [ABSTRACT FROM AUTHOR]

Approximately one in every 700 babies in the United States is born with Down syndrome, or 0.14%. Children with Down syndrome have cognitive impairment and congenital malformations necessitating frequent occurrences of general anesthesia and surgery. The thoughtful perioperative care of children with Down syndrome is relevant and acutely complex for the pediatric anesthesiologist. Behavior, sedation, hypotonia, upper airway obstruction, venous access, and bradycardia are omnipresent concerns apart from the surgical pathology. Down syndrome is also associated with autonomic nervous system dysfunction, a comorbidity that is overlooked in discussions of perioperative care and is described thus far in adults. Autonomic nervous system function or dysfunction may explain the phenotypical features of the perioperative challenges listed above. For this reason, understanding the development and measurement of autonomic nervous system function is important for the pediatric anesthesiologist. Definition and quantification of sympathetic and parasympathetic function will be reviewed. [ABSTRACT FROM AUTHOR]

Rapid drought intensification, or flash droughts, is often driven by anomalous atmospheric ridging and can cause severe and complex impacts on water availability and agriculture, but the full range of variability of such events in terms of intensity and frequency is unknown. New tree‐ring reconstructions of May–July mid‐tropospheric ridging and soil moisture anomalies back to 1500 CE in the central United States—a hotspot for flash drought—suggest that over the last five centuries, anomalies in these two variables combined to indicate flash‐drought conditions in ∼17% of years and exceptionally severe flash drought in ∼4% of years, similar to frequencies in recent decades. However, over one‐third of all inferred exceptional flash droughts occurred since 1900, suggesting the 20th century was highly flash‐drought prone. These results may guide future work to diagnose the roles of external, oceanic, and land‐surface forcing of warm‐season atmospheric circulation and hydroclimate over North America. Plain Language Summary: In 2012, the central United States experienced a "flash drought," when rapid soil drying due to persistently low precipitation totals and high temperatures in late spring and summer caused billions of dollars in damages and agricultural losses. Such events are difficult to forecast because flash droughts can develop in a matter of weeks, and only a handful of flash droughts have been observed in recent decades, giving high uncertainty as to their likelihood. Here, we use tree rings to create two independent annual records of central United States soil moisture and the principal atmospheric circulation pattern known to cause flash droughts that extend back to the year 1500. Taken together, these records provide a new five‐century perspective on these crucial components of flash drought and reveal for the first time the long‐term behavior of central United States flash droughts, including frequency and cyclicity of exceptional events. We find that the instrumental record is a good representation of the long‐term likelihood of flash droughts. This apparent agreement with long‐term average conditions is largely by chance; however, as the reconstructions indicate large century‐to‐century variations in flash‐drought frequency and magnitude over the past 500 years. Key Points: We developed tree‐ring reconstructions of central U.S. soil moisture and the atmospheric ridge that causes flash droughts back to 1500 CEObservations capture flash drought's long‐term probability distribution, but reconstructions reveal centennial fluctuations in variabilityCycles in reconstructed atmospheric ridging may aid diagnosis and prediction of flash drought on interannual to decadal time scales [ABSTRACT FROM AUTHOR]

In the eastern United States, the empirical probability distribution of extreme daily precipitation comprises heavy rainfall events stemming from North Atlantic tropical cyclones (TCs). At many locations, these events influence estimates of extreme value statistics (e.g., 100‐year event), thus have important bearing on the sizing of flood protection infrastructure and, in general, flood risk management and preparedness. Consequently, a characterization of location specific and regional patterns in precipitation extremes and changes therein has salience for both scientific and engineering concerns. To this end, analysis of seasonal and annual maximum daily precipitation at 667 long‐term stations across the eastern United States was pursued to ascertain recent changes in the extreme events over the 1950–2011 period. Three key results from this study illuminate less understood facets of recent changes in precipitation extremes: (a) an overall increase in the fraction of seasonal and annual maximum precipitation events linked to TCs, (b) a dramatic increase in the correlation between Accumulated Cyclone Energy Index and the leading principal component of extreme precipitation, and (c) changes in the spatial patterns of regions with highest TC‐related risk for heavy precipitation. [ABSTRACT FROM AUTHOR]

Ultraviolet (UV) radiation is among the most prevalent environmental factors that influence human health and disease. Even 1 h of UV irradiation extensively damages the genome. To cope with resulting deleterious DNA lesions, cells activate a multitude of DNA damage response pathways, including DNA repair. Strikingly, UV‐induced DNA damage formation and repair are affected by chromatin state. When cells enter S phase with these lesions, a distinct mutation signature is created via error‐prone translesion synthesis. Chronic UV exposure leads to high mutation burden in skin and consequently the development of skin cancer, the most common cancer in the United States. Intriguingly, UV‐induced oxidative stress has opposing effects on carcinogenesis. Elucidating the molecular mechanisms of UV‐induced DNA damage responses will be useful for preventing and treating skin cancer with greater precision. Excitingly, recent studies have uncovered substantial depth of novel findings regarding the molecular and cellular consequences of UV irradiation. In this review, we will discuss updated mechanisms of UV‐induced DNA damage responses including the ATR pathway, which maintains genome integrity following UV irradiation. We will also present current strategies for preventing and treating nonmelanoma skin cancer, including ATR pathway inhibition for prevention and photodynamic therapy for treatment. [ABSTRACT FROM AUTHOR]

The aim of this paper is to improve semiseasonal forecast of groundwater availability in response to climate variables, surface water availability, groundwater level variations, and human water management using a two‐step data‐driven modeling approach. First, we implement an ensemble of artificial neural networks (ANNs) for the 300 wells across the High Plains aquifer (USA). The modeling framework includes a method to choose the most relevant input variables and time lags; an assessment of the effect of exogenous variables on the predictive capabilities of models; and the estimation of the forecast skill based on the Nash‐Sutcliffe efficiency (NSE) index, the normalized root mean square error, and the coefficient of determination (R2). Then, for the ANNs with low‐ accuracy, a MultiModel Combination (MuMoC) based on a hybrid of ANN and an instance‐based learning method is applied. MuMoC uses forecasts from neighboring wells to improve the accuracy of ANNs. An exhaustive‐search optimization algorithm is employed to select the best neighboring wells based on the cross correlation and predictive accuracy criteria. The results show high average ANN forecasting skills across the aquifer (average NSE > 0.9). Spatially distributed metrics of performance showed also higher error in areas of strong interaction between hydrometeorological forcings, irrigation intensity, and the aquifer. In those areas, the integration of the spatial information into MuMoC leads to an improvement of the model accuracy (NSE increased by 0.12), with peaks higher than 0.3 when the optimization objectives for selecting the neighbors were maximized.tT Key Points: Artificial neural networks can accurately forecast semiseasonal groundwater level changesMuMoC improved the groundwater well‐level forecasting skill for 1‐ to 4‐month lead times with respect to a single ANN model by 25% in NSEThe implementation of MuMoC is recommended in case of densely gauged areas [ABSTRACT FROM AUTHOR]

Purpose: Each year in the United States, approximately 18.5 million nuclear medicine procedures are performed. Various quality control measures are implemented to reduce image errors and improve quantification of radiotracer distribution. However, there is currently no routine or timely feedback about the quality of the radiotracer injection. One potential solution to evaluate the injection quality is to place a topical scintillation sensor near the injection site to record the presence of residual activity. This work investigates a sensor design for identification of injections where the prescribed radioactive activity is not fully delivered into the patient's circulation (an infiltration). Methods: The sensor consists of a single unshielded bismuth germanate (BGO) crystal (3 mm × 3 mm × 3 mm). Using radioactive sources with gamma energies that span the range commonly used in nuclear medicine, we quantified energy resolution and linearity. Additionally, we computed sensitivity by comparing the calculated incident activity to the activity measured by the sensor. Sensor output linearity was calculated by comparing measured data against the radioactive decay of a source over multiple half‐lives. The sensor incorporates internal temperature feedback used to compensate for ambient temperature fluctuations. We investigated the performance of this compensation over the range of 15°C–35°C. Results: Energy spectra from four sensors were used to calculate the energy resolution: 67% for 99mTc (141 keV), 67% for 133Ba (344 keV), 42% for 18F (511 keV), and 32% for 137Cs (662 keV). Note that the energy used for 133Ba is a weighted average of the three photon emissions nearest to the most abundant (356 keV). Sensor energy response was linear with a difference of 1%–2% between measured and predicted values. Energy‐dependent detector sensitivity, defined as the ratio of measured photons to incident photons for a given isotope, decreased with increasing photon energy from 55.4% for 99mTc (141 keV) to 3.3% for 137Cs (662 keV). Without compensation, error due to temperature change was as high as 53%. Temperature compensation reduced the error to less than 1.4%. Sensor output linearity was tested to as high as 210 kcps and the maximum magnitude error was 4%. Conclusions: The performance of the sensor was adequate for identification of excessive residual activity at an injection site. Its ability to provide feedback may be useful as a quality control measure for nuclear medicine injections. [ABSTRACT FROM AUTHOR]

Forecasting the economic policy uncertainty in Europe is of paramount importance given the ongoing sovereign debt crisis. This paper evaluates monthly economic policy uncertainty index forecasts and examines whether ultra‐high frequency information from asset market volatilities and global economic uncertainty can improve the forecasts relatively to the no‐change forecast. The results show that the global economic policy uncertainty provides the highest predictive gains, followed by the European and US stock market realized volatilities. In addition, the European stock market implied volatility index is shown to be an important predictor of the economic policy uncertainty. [ABSTRACT FROM AUTHOR]

Climate, groundwater extraction, and surface water flows have complex nonlinear relationships with groundwater level in agricultural regions. To better understand the relative importance of each driver and predict groundwater level change, we develop a new ensemble modeling framework based on spectral analysis, machine learning, and uncertainty analysis, as an alternative to complex and computationally expensive physical models. We apply and evaluate this new approach in the context of two aquifer systems supporting agricultural production in the United States: the High Plains aquifer (HPA) and the Mississippi River Valley alluvial aquifer (MRVA). We select input data sets by using a combination of mutual information, genetic algorithms, and lag analysis, and then use the selected data sets in a Multilayer Perceptron network architecture to simulate seasonal groundwater level change. As expected, model results suggest that irrigation demand has the highest influence on groundwater level change for a majority of the wells. The subset of groundwater observations not used in model training or cross-validation correlates strongly (R > 0.8) with model results for 88 and 83% of the wells in the HPA and MRVA, respectively. In both aquifer systems, the error in the modeled cumulative groundwater level change during testing (2003-2012) was less than 2 m over a majority of the area. We conclude that our modeling framework can serve as an alternative approach to simulating groundwater level change and water availability, especially in regions where subsurface properties are unknown. [ABSTRACT FROM AUTHOR]

Background Improving mental and physical health of patients with implantable cardioverter defibrillators (ICD) is critical because this group is at high risk for ventricular arrhythmias and sudden death and depressed or anxious cardiovascular disease (CVD) patients appear to be at even higher risk for mortality compared to nondepressed or nonanxious CVD patients. Further, autonomic dysfunction is present in these patients, and negative emotions and arrhythmias form a downward spiral further worsening mood, well-being, and cardiovascular health. Much research demonstrates that positive emotion is related to health benefits, improved physiology, and increased survival. Methods and Results This is a two-arm randomized controlled trial aiming to recruit 60 adult ICD patients comparing 12 individually delivered, weekly sessions of: (1) a positive emotion-focused cognitive-behavioral therapy (Quality of Life Therapy [QOLT]), and (2) Heart Healthy Education. Autonomic functioning, heart rhythm indices, and psychosocial health are measured at baseline, 3 months, and 9 months. The first goal is feasibility and acceptability, with the primary outcome being arrhythmic event frequency data. Conclusion This study is designed to test whether QOLT produces changes in mood, quality of life/well-being, autonomic function, and arrhythmic and ICD therapy event rates. This feasibility trial is a foundational step for the next trial of QOLT to help determine whether a 3-month QOLT trial can reduce arrhythmias occurrences among ICD patients, and examine a mechanism of autonomic functioning. This study may help to develop and implement new medical or psychological therapies for ICD patients. [ABSTRACT FROM AUTHOR]

We use the wavelet coherence methodology to investigate the relations between prices of ethanol and its feedstocks. Our continuous wavelet framework allows for discovering price connections and their evolution in both time and frequency domain in the most important ethanol markets - Brazil and the USA. For both of these markets, we show that the long-run relationship between prices of ethanol and corn (in the USA) or sugar (in Brazil) is positive, strong and stable in time. Importantly, we show that the prices of feedstock lead the prices of ethanol and not the other way around. The price lead of feedstock is documented for both short- and long-run horizons. Our qualitative results hold true even when the influence of crude oil prices is accounted for by utilizing partial wavelet coherence approach. [ABSTRACT FROM AUTHOR]