Search

Repeatedly capturing national headlines, excessive law enforcement officer (LEO) use of force in critical incident encounters is one of the most divisive human rights issues in the United States. Valid and reliable measures of potential precursors to LEO excessive use of force, such as aggression, are needed. The Buss–Perry Aggression Questionnaire‐short form (BPAQ‐SF) is a validated measure of aggression across various populations; however, evaluation of this easily administered measure in high‐stress, frontline populations such as LEOs is limited. The primary goal of this study was to evaluate the validity, reliability, and sensitivity to change the BPAQ‐SF in a sample of LEOs. A confirmatory factor analysis suggested that the hierarchical solution provides a mixed fit to the data: SBχ²(25.84) = 62.50, p =.0001; comparative fit index =.94, non‐normed fit index =.92, root mean square error of approximation =.19 (90% confidence interval =.17–.21), standardized root mean squared residual =.08. The BPAQ‐SF demonstrated good internal consistency (α =.84) and test–retest reliability (r =.86), correlations in the expected direction with predictors of and buffers against aggression, and sensitivity to change among LEOs who participated in an intervention targeting aggression. Results support and extend previous findings suggesting that the BPAQ‐SF is a valid and reliable measure of aggression among LEOs. [ABSTRACT FROM AUTHOR]

Given the mismatch between the large volume of data archived for the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and limited personnel and computational resources for downscaling, only a small fraction of the CMIP6 archive can be downscaled. In this work, we develop an approach to robustly sample projected hydroclimate states in CMIP6 for downscaling to test whether the selection of a single initial condition (IC) ensemble member from each CMIP6 model is sufficient to span the range of modeled hydroclimate over the conterminous United States (CONUS) and CONUS sub-regions. We calculate the pattern-centered root mean square difference of IC ensemble member anomalies relative to each model's historical climatology for shared socioeconomic pathway (SSP) projections over 30-year time periods and compare the ratio of inter-model to intra-model variability for this metric. Regardless of SSP, inter-model variability is generally much greater than intra-model variability at the scales of the CONUS as a whole, as well as for most CONUS sub-regions. However for some variables and scenarios, inter- and intra-model variability are similar at sub-CONUS scales, indicating that selecting a single IC ensemble member per model may be sufficient to sample the range of projected hydroclimate states in the 21st Century across CONUS, but for specific regions and variables, more careful selection of ensemble members may be necessary. Regionally-resolved Taylor diagrams identify where more IC ensemble member downscaling efforts should be focused if resources are available to do so. Our results suggest that, with parsimonious sampling, the requisite computational expense of downscaling temperature and precipitation fields over the CONUS for subsequent CMIP activities may increase only marginally despite the great increase in data volumes with each successive CMIP phase. [ABSTRACT FROM AUTHOR]

Deep breathing exercises are the second most used complementary health approach in the United States. Two heart rate variability (HRV) parameters, the root mean square of successive differences (RMSSD) and the respiratory sinus arrhythmia (RSA), are used to assess parasympathetic reactivity to deep breathing, but they are often not in agreement. Our purpose was to determine the cause of the disagreement. We investigated HRV parameters in 38 subjects during baseline, deep breathing, and recovery. Here we show that RMSSD as a measure of parasympathetic reactivity is unreliable; it does not reflect the increase in HRV during deep breathing as determined by RSA. We observed a decrease in RMSSD values despite a marked increase in HRV as determined by RSA and the standard deviation of normal heartbeat interval (SDNN) in healthy subjects and patients with functional bowel disorders. We show that RSA captures all aspects of HRV, whereas successive differences in heart rate intervals are only a small part of HRV, with decreasing variability during deep breathing in most subjects. We present a new measure of calculating RSA during deep breathing that may become an essential tool for researchers and clinicians. We also provide a unique visualization of the increased heart rate variability during deep breathing. Hence, RMSSD cannot be used to assess parasympathetic reactivity during deep breathing; using RSA is recommended. The use of RMSSD in previous influential studies may have led to erroneous conclusions about parasympathetic reactivity during deep breathing. Its continued use may undervalue the effects of the autonomic nervous system in slow deep breathing. [ABSTRACT FROM AUTHOR]

The MODIS 8-day composite evapotranspiration (ET) product (MOD16A2) is widely used to study large-scale hydrological cycle and energy budgets. However, the MOD16A2 spatial resolution (500 m) is too coarse for local and regional water resource management in agricultural applications. In this study, we propose a Deep Neural Network (DNN)-based MOD16A2 downscaling approach to generate 30 m ET using Landsat 8 surface reflectance and temperature and AgERA5 meteorological variables. The model was trained at a 500 m resolution using the MOD16A2 ET as reference and applied to the Landsat 8 30 m resolution. The approach was tested on 15 Landsat 8 images over three agricultural study sites in the United States and compared with the classical random forest regression model that has been often used for ET downscaling. All evaluation sample sets applied to the DNN regression model had higher R2 and lower root-mean-square deviations (RMSD) and relative RMSD (rRMSD) (the average values: 0.67, 2.63 mm/8d and 14.25%, respectively) than the random forest model (0.64, 2.76 mm/8d and 14.92%, respectively). Spatial improvement was visually evident both in the DNN and the random forest downscaled 30 m ET maps compared with the 500 m MOD16A2, while the DNN-downscaled ET appeared more consistent with land surface cover variations. Comparison with the in situ ET measurements (AmeriFlux) showed that the DNN-downscaled ET had better accuracy, with R2 of 0.73, RMSD of 5.99 mm/8d and rRMSD of 48.65%, than the MOD16A2 ET (0.65, 7.18 and 50.42%, respectively). [ABSTRACT FROM AUTHOR]

Water stored in mountain snowpacks (i.e., snow water equivalent, SWE) represents an important but poorly characterized component of the terrestrial water cycle. The Western United States snow reanalysis (WUS–SR) dataset is novel in its combination of spatial resolution (~500 m), spatial extent (31°–49° N; 102°–125° W), and temporal continuity (daily over 1985–2021). WUS–SR is generated using a Bayesian framework with model-based snow estimates updated through the assimilation of cloud-free Landsat fractional snow-covered area observations. Over the WUS, the peak SWE verification with independent in situ measurements show correlation coefficient, mean difference (MD), and root mean squared difference (RMSD) of 0.77, −0.15 m, and 0.28 m, respectively. The effects of forest cover and Landsat image availability on peak SWE are assessed. WUS–SR peak SWE is well correlated (ranging from 0.75 to 0.91) against independent lidar-derived SWE taken near April 1st, with MD <0.15 m and RMSD <0.38 m. The dataset is useful for characterizing WUS mountain snow storage, and ultimately for improving snow-derived water resources management. Measurement(s) snow water equivalent, snow depth, fractional snow covered area Technology Type(s) Bayesian snow reanalysis framework with assimilation of fractional snow covered area Sample Characteristic - Location Western United States [ABSTRACT FROM AUTHOR]

Rates of alcohol use disorder (AUD) are increasing among civilian and veteran populations of women in the United States, and stress pathophysiology (i.e., abnormal acute and long-term change in physiological responses to stress) is central to themaintenance of alcoholmisuse within this population. Heart rate variability (HRV) is onemeasure of stress regulation thatmay help to explain the association of stress with alcoholmisuse among women. In the current analysis of pilot data, 20 women veterans attended an in-person laboratory session and completed 35 daily assessments of their alcohol use and craving. During the lab session, the effects of a stress induction procedure on self-reported alcohol craving and HRV were assessed. HRV was continuously measured and indexed in the time domain, using the root mean square of successive differences between normal heartbeats (RMSSD). Alcohol craving and use during the longitudinal 35-day study period were measured via self-report questionnaires sent to participants' phones. Results indicated that resting HRV in the lab was positively associated with odds of daily craving. Moreover, HRV during the stressor, as measured in lab, was positively associated with (1) overall alcohol craving in the lab (i.e., with resting and post-stress craving), and (2) number of daily drinks during the 35-day study period. This pilot study suggests the potential role of HRV in response to stressors in predicting alcohol craving and use among female veterans. It provides pilot data for research on stress-reactive HRV as a biomarker for alcohol misuse among women, and discusses directions for future research. [ABSTRACT FROM AUTHOR]

Background: Current techniques for measuring absolute lung volumes rely on bulky and expensive equipment and are complicated to use for the operator and the patient. A novel method for measurement of absolute lung volumes, the MiniBox method, is presented.Research Question: Across a population of patients and healthy participants, do values for total lung capacity (TLC) determined by the novel compact device (MiniBox, PulmOne Advanced Medical Devices, Ltd.) compare favorably with measurements determined by traditional whole body plethysmography?Study Design and Methods: A total of 266 participants (130 men) and respiratory patients were recruited from five global centers (three in Europe and two in the United States). The study population comprised individuals with obstructive (n = 197) and restrictive (n = 33) disorders as well as healthy participants (n = 36). TLC measured by conventional plethysmography (TLCPleth) was compared with TLC measured by the MiniBox (TLCMB).Results: TLC values ranged between 2.7 and 10.9 L. The normalized root mean square difference (NSD) between TLCPleth and TLCMB was 7.0% in healthy participants. In obstructed patients, the NSD was 7.9% in mild obstruction and 9.1% in severe obstruction. In restricted patients, the NSD was 7.8% in mild restriction and 13.9% in moderate and severe restriction. No significant differences were found between TLC values obtained by the two measurement techniques. Also no significant differences were found in results obtained among the five centers.Interpretation: TLC as measured by the novel MiniBox system is not significantly different from TLC measured by conventional whole body plethysmography, thus validating the MiniBox method as a reliable method to measure absolute lung volumes. [ABSTRACT FROM AUTHOR]

The spatial variability of parameters of the raindrop size distribution and its derivatives is investigated through a field study where collocated Particle Size and Velocity (Parsivel2) and two-dimensional video disdrometers were operated at six sites at Wallops Flight Facility, Virginia, from December 2013 to March 2014. The three-parameter exponential function was employed to determine the spatial variability across the study domain where the maximum separation distance was 2.3 km. The nugget parameter of the exponential function was set to 0.99 and the correlation distance d0 and shape parameter s0 were retrieved by minimizing the root-mean-square error, after fitting it to the correlations of physical parameters. Fits were very good for almost all 15 physical parameters. The retrieved d0 and s0 were about 4.5 km and 1.1, respectively, for rain rate (RR) when all 12 disdrometers were reporting rainfall with a rain-rate threshold of 0.1 mm h−1 for 1-min averages. The d0 decreased noticeably when one or more disdrometers were required to report rain. The d0 was considerably different for a number of parameters (e.g., mass-weighted diameter) but was about the same for the other parameters (e.g., RR) when rainfall threshold was reset to 12 and 18 dB Z for Ka- and Ku-band reflectivity, respectively, following the expected Global Precipitation Measurement mission's spaceborne radar minimum detectable signals. The reduction of the database through elimination of a site did not alter d0 as long as the fit was adequate. The correlations of 5-min rain accumulations were lower when disdrometer observations were simulated for a rain gauge at different bucket sizes. [ABSTRACT FROM AUTHOR]

The error characterization of soil moisture products, for example, obtained from microwave remote sensing data, is a key requirement for using these products in applications like numerical weather prediction. The error variance and root-mean-square error are among the most popular metrics: they can be estimated consistently for three datasets using triple collocation (TC) without assuming any dataset to be free of errors. This technique can account for additive and multiplicative biases; that is, it assumes that the three products are linearly related. However, its susceptibility to nonlinear relations (e.g., due to sensor saturation and scale mismatch) has not been addressed. Here, a simulation study investigates the impact of quadratic relations on the TC error estimates [also when the products are first rescaled using the nonlinear cumulative distribution function (CDF) matching technique] and on those by two novel methods. These methods-based on error-in-variables regression and probabilistic factor analysis-extend standard TC by also accounting for nonlinear relations using quadratic polynomials. The relative differences between the error estimates of the ASCAT remotely sensed product by the quadratic and the linear methods are predominantly smaller than 10% in a case study based on remotely sensed, reanalysis, and in situ measured soil moisture over the contiguous United States. Exceptions with larger discrepancies indicate that nonlinear relations can pose a challenge to traditional TC analyses, as the simulations show they can introduce biases of either sign. In such cases, the use of nonlinear methods may complement traditional approaches for the error characterization of soil moisture products. [ABSTRACT FROM AUTHOR]

• A machine learning-based ensemble model was developed to quantify soil moisture (SM). • The ensemble model was effective in modeling dynamic SM over the CONUS. • The model showed robust performance over dense vegetation and complex topography. • The ensemble model yielded better performance than existing SM data. • Triple collocation analysis revealed the spatially robust performance of the model. Three widely used primary soil moisture (SM) data sources, namely, in-situ measurements, satellite observations, and land surface models (LSM), possess different characteristics. This study combined three SM data sources using machine learning (ML): random forest, artificial neural networks, and support vector regression, and simple averaging ensemble approaches to produce improved daily SM data over the contiguous United States (CONUS). For each ML model, three schemes were tested using different independent variables, namely, satellite-derived, LSM-derived, and both. Triple collocation analysis (TCA) was adopted to address the scale mismatch problem between in-situ and coarse gridded SM data. The proposed approach was evaluated using the International Soil Moisture Network (ISMN), Soil Moisture Active Passive Core Validation Sites (SMAP CVS), and TCA. In the ISMN-based evaluation, the proposed ML-based ensemble generally produced better evaluation metrics and showed robust skills over topographically complex and densely vegetated regions where existing SM products showed poor skills. The SMAP CVS-based evaluation demonstrated that the ML ensemble approach yielded a better performance than the existing SM datasets, resulting in a correlation coefficient of 0.78, unbiased root mean squared difference of 0.035 m3/m3, and bias of 0.006 m3/m3. In addition, the TCA results additionally confirmed that the ML-based ensemble had better spatiotemporal quality than the other SM products. The data-driven approach proposed in this study has three major novelties: (1) the proposed ML-based method synergistically merges various data sources to improve SM; (2) the performance of the proposed ML-based SM was robust to topography and vegetation; and (3) the average ensemble of three ML models additionally improves performances. The SM time-series data generated by the proposed approach are expectedly suitable variables for environmental and climate applications over CONUS. The research findings suggest that ML algorithms can be effectively used for modeling dynamic soil moisture. [ABSTRACT FROM AUTHOR]

Efficient drug discovery programs can be designed by utilizing existing pools of knowledge from the already approved drugs. This can be achieved in one way by repositioning of drugs approved for some indications to newer indications. Complex of drug to its target gives fundamental insight into molecular recognition and a clear understanding of putative binding site. Five popular docking protocols, Glide, Gold, FlexX, Cdocker and LigandFit have been evaluated on a dataset of 199 FDA approved drug-target complexes for their accuracy in predicting the experimental pose. Performance for all the protocols is assessed at default settings, with root mean square deviation (RMSD) between the experimental ligand pose and the docked pose of less than 2.0 Å as the success criteria in predicting the pose. Glide (38.7 %) is found to be the most accurate in top ranked pose and Cdocker (58.8 %) in top RMSD pose. Ligand flexibility is a major bottleneck in failure of docking protocols to correctly predict the pose. Resolution of the crystal structure shows an inverse relationship with the performance of docking protocol. All the protocols perform optimally when a balanced type of hydrophilic and hydrophobic interaction or dominant hydrophilic interaction exists. Overall in 16 different target classes, hydrophobic interactions dominate in the binding site and maximum success is achieved for all the docking protocols in nuclear hormone receptor class while performance for the rest of the classes varied based on individual protocol. [ABSTRACT FROM AUTHOR]

The primary interest of this study is modelling the U.S. unemployment rate, using several linear and nonlinear models: the Autoregressive model (AR), the Autoregressive Conditional Heteroskedasticity (ARCH), the General Autoregressive model (GAR) and the Threshold Autoregressive model (TAR). We use monthly seasonally adjusted series for the period 1948-2011. All the econometric models show asymmetries in U.S. unemployment dynamics. After estimating the models, the forecasting accuracy is assessed through the root mean squared error. [ABSTRACT FROM AUTHOR]

A simple bias correction method was used to correct daily operational ensemble week-1 and week-2 precipitation and 2-m surface air temperature forecasts from the NCEP Global Forecast System (GFS). The study shows some unexpected and striking features of the forecast errors or biases of both precipitation and 2-m surface air temperature from the GFS. They are dominated by relatively large-scale spatial patterns and low-frequency variations that resemble the annual cycle. A large portion of these forecast errors is removable, but the effectiveness is time and space dependent. The bias-corrected week-1 and week-2 ensemble precipitation and 2-m surface air temperature forecasts indicate some improvements over their raw counterparts. However, the overall levels of week-1 and week-2 forecast skill in terms of spatial anomaly correlation and root-mean-square error are still only modest. The dynamical soil moisture forecasts (i.e., land surface hydrological model forced with bias-corrected precipitation and 2-m surface air temperature integrated forward for up to 2 weeks) have very high skill, but hardly beat persistence over the United States. The inability to outperform persistence mainly relates to the skill of the current GFS week-1 and week-2 precipitation forecasts not being above a threshold (i.e., anomaly correlation > 0.5 is required). [ABSTRACT FROM AUTHOR]

A robust and easily implemented verification procedure based on the column-integrated precipitable water (IPW) vapor estimates derived from a network of ground-based global positioning system (GPS) receivers has been used to assess the quality of the Atmospheric Infrared Sounder (AIRS) IPW retrievals over the contiguous United States. For a period of six months from April to October 2004, excellent agreement has been realized between GPS-derived IPW estimates and those determined from AIRS, showing small monthly bias values ranging from 0.5 to 1.5 mm and root-mean-square (rms) differences of 4 mm or less. When the spatial (latitude–longitude) window for the GPS and AIRS matchup observations is reduced from the initial ½° by ½° to ¼° by ¼°, the rms differences are reduced. Analysis revealed that the observed IPW biases between the instruments are strongly correlated to the reported surface pressure differences between the GPS and AIRS observational points. Adjusting the AIRS IPW values to account for the surface pressure discrepancies resulted in significant reductions of the bias between GPS and AIRS. A similar reduction can be obtained by comparing only (GPS–AIRS) match-up pairs for which the corresponding surface pressure differences are 0.5 mb or less. The comparisons also revealed that the AIRS IPW tends to be relatively dry in moist atmospheres (when IPW values >40 mm) but wetter in dry cases (when IPW values <10 mm). This is consistent with the documented bias of satellite measurements toward the first guess used in retrieval algorithms. However, additional study is needed to verify whether the AIRS water vapor retrieval process is the source of the discrepancies. It is shown that the IPW bias and rms differences have a seasonal dependency, with a maximum in summer (bias ∼ 1.2 mm, rms ∼ 4.14 mm) and minimum in winter (bias < -0.5 mm, rms ∼ 3 mm). [ABSTRACT FROM AUTHOR]