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Understanding the factors that shape microbiomes can provide insight into the importance of host-symbiont interactions and on co-evolutionary dynamics. Unlike for mammals, previous studies have found little or no support for an influence of host evolutionary history on avian gut microbiome diversity and instead have suggested a greater influence of the environment or diet due to fast gut turnover. Because effects of different factors may be conflated by captivity and sampling design, examining natural variation using large sample sizes is important. Our goal was to overcome these limitations by sampling wild birds to compare environmental, dietary and evolutionary influences on gut microbiome structure. We performed faecal metabarcoding to characterize both the gut microbiome and diet of 15 wood-warbler species across a 4-year period and from two geographical localities. We find host taxonomy generally explained ~10% of the variation between individuals, which is ~6-fold more variation of any other factor considered, including diet diversity. Further, gut microbiome similarity was more congruent with the host phylogeny than with host diet similarity and we found little association between diet diversity and microbiome diversity. Together, our results suggest evolutionary history is the strongest predictor of gut microbiome differentiation among wood-warblers. Although the phylogenetic signal of the warbler gut microbiome is not very strong, our data suggest that a stronger influence of diet (as measured by diet diversity) does not account for this pattern. The mechanism underlying this phylogenetic signal is not clear, but we argue host traits may filter colonization and maintenance of microbes.

Most datasets of surface meteorology are deterministic, yet many applications using these datasets require or can benefit from uncertainty estimates in meteorological fields. Motivated by this gap, we evaluated the use of a spatial regression method to estimate the uncertainty in precipitation and temperature fields of existing deterministic gridded meteorological datasets. Taking the widely used North American Land Data Assimilation System 2 (NLDAS-2) precipitation and temperature dataset as an example, we used the deterministic NLDAS-2 values to generate ensemble estimates for daily precipitation, mean temperature, and the diurnal temperature range. Our method is a form of ensemble dressing. Nine variations were tested to assess the impacts of sampling density on the estimates of the mean and uncertainty, and one strategy was selected to generate 100 ensemble members at 1/8° and daily resolution for the period 1979–2019, termed as the Ensemble Dressing of NLDAS-2 (EDN2). Compared with an independent station-based ensemble dataset, the ensemble dressing method produces reasonable uncertainty patterns for precipitation and underestimates uncertainty for temperature. For precipitation, the uncertainty increases with the increase in daily accumulation. For temperature, the uncertainty is relatively small in the warm season and large in the cold season. This ensemble dressing method is applicable to other deterministic gridded meteorological datasets. The generated spatiotemporally varying uncertainty information could support applications such as land surface and hydrologic modeling, data assimilation, and forecasting, especially where application models are tied to a specific meteorological dataset.

Statistical processing of numerical model output has been a part of both weather forecasting and climate applications for decades. Statistical techniques are used to correct systematic biases in atmospheric model outputs and to represent local effects that are unresolved by the model, referred to as downscaling. Many downscaling techniques have been developed, and it has been difficult to systematically explore the implications of the individual decisions made in the development of downscaling methods. Here we describe a unified framework that enables the user to evaluate multiple decisions made in the methods used to statistically postprocess output from weather and climate models. The Ensemble Generalized Analog Regression Downscaling (En-GARD) method enables the user to select any number of input variables, predictors, mathematical transformations, and combinations for use in parametric or nonparametric downscaling approaches. En-GARD enables explicitly predicting both the probability of event occurrence and the event magnitude. Outputs from En-GARD include errors in model fit, enabling the production of an ensemble of projections through sampling of the probability distributions of each climate variable. We apply En-GARD to regional climate model simulations to evaluate the relative importance of different downscaling method choices on simulations of the current and future climate. We show that choice of predictor variables is the most important decision affecting downscaled future climate outputs, while having little impact on the fidelity of downscaled outcomes for current climate. We also show that weak statistical relationships prevent such approaches from predicting large changes in extreme events on a daily time scale.

Vegetation parameters for the Variable Infiltration Capacity (VIC) hydrologic model were recently updated using observations from the MODerate Resolution Imaging Spectroradiometer (MODIS). Previous work showed that these MODIS-based parameters improved VIC evapotranspiration simulations when compared to eddy covariance observations. Due to the importance of evapotranspiration within the Colorado River Basin, this study provided a basin-by-basin calibration of VIC soil parameters with updated MODIS-based vegetation parameters to improve streamflow simulations. Interestingly, while both configurations had similar historic streamflow performance, end-of-century hydrologic projections, driven by 29 downscaled global climate models under the RCP8.5 emissions scenario differed between the two configurations. The calibrated MODIS-based configuration had an ensemble mean that simulated little change in end-of-century annual streamflow volume (+0.4%) at Lees Ferry, AZ relative to the historical period (1960-2005). In contrast, the previous VIC configuration, which is used to inform decisions about future water resources in the Colorado River Basin projected an 11.7% decrease in annual streamflow. Both VIC configurations simulated similar amounts of evapotranspiration in the historical period. However, the MODIS-based VIC configuration did not show as much of an increase in evapotranspiration by the end of the century, primarily within the Upper Basin’s forested areas. Differences in evapotranspiration projections were the result of the MODIS-based vegetation parameters having lower leaf area index values and less forested area compared to previous vegetation estimates used in recent Colorado River Basin hydrologic projections. These results highlight the need to accurately characterize vegetation and better constrain climate sensitivities in hydrologic models.

Advances in Terahertz frequency electromagnetic radiation (THz) production technologies have produced an increasing interest in exploring possible applications. New applications will inevitably lead to increased incidental interaction of humans with THz radiation. Given that the wavelength of THz radiation is in the same order of magnitude as the dimensions of skin structures such as hair and sweat ducts, the possibility of interaction among these structures is of interest. The interaction was studied utilizing Finite Difference Time Domain (FDTD) simulations using a far-field excitation of 0.45 THz. No antenna-like effects were detected. Regions of increased specific absorption rate (SAR) due to reactive near-field effects with both the hair and sweat ducts were found in the order of 0.01–0.05 mm and 0.001–0.002 mm, respectively. Simulations using unwound sweat ducts yielded the same penetration pattern as the helical structure, indicating that the helical structure has no impact on the propagation of THz radiation in skin.

Purpose: Aquablation, prostatic urethral lift (PUL), and transurethral water vapor therapy (TWVT) have demonstrated efficacy for the treatment of benign prostatic hyperplasia (BPH). Our objective w...

Surface meteorological analyses are an essential input (termed “forcing”) for hydrologic modeling. This study investigated the sensitivity of different hydrologic model configurations to temporal variations of seven forcing variables (precipitation rate, air temperature, longwave radiation, specific humidity, shortwave radiation, wind speed, and air pressure). Specifically, the effects of temporally aggregating hourly forcings to hourly daily average forcings were examined. The analysis was based on 14 hydrological outputs from the Structure for Unifying Multiple Modeling Alternatives (SUMMA) model for the 671 Catchment Attributes and Meteorology for Large-Sample Studies (CAMELS) basins across the contiguous United States (CONUS). Results demonstrated that the hydrologic model sensitivity to temporally aggregating the forcing inputs varies across model output variables and model locations. We used Latin hypercube sampling to sample model parameters from eight combinations of three influential model physics choices (three model decisions with two options for each decision, i.e., eight model configurations). Results showed that the choice of model physics can change the relative influence of forcing on model outputs and the forcing importance may not be dependent on the parameter space. This allows for model output sensitivity to forcing aggregation to be tested prior to parameter calibration. More generally, this work provides a comprehensive analysis of the dependence of modeled outcomes on input forcing behavior, providing insight into the regional variability of forcing variable dominance on modeled outputs across CONUS.

Bachman’s Warbler (Vermivora bachmanii) – last sighted in 1988 – is one of the few North American passerines that have gone extinct. Given the extensive ongoing hybridization of its two extant congeners – the Blue-Winged Warbler (V. cyanoptera) and Golden-Winged Warbler (V. chrysoptera) – and shared patterns of plumage variation between Bachman’s Warbler and hybrids between those extant species, it has been suggested that Bachman’s Warbler might have also had a component of hybrid ancestry. Here, we use historic DNA (hDNA) and whole genome sequencing of Bachman’s Warblers collected at the turn of the 20thcentury to address this possibility. We combine these data with genomes of the two extantVermivoraspecies to examine patterns of population differentiation, inbreeding, and gene flow. In contrast to the admixture hypothesis, the genomic evidence is consistent withV. bachmaniibeing a highly divergent, reproductively isolated species, with no evidence of introgression. We show that bothV. bachmaniiandV. chrysopterahave elevated runs of homozygosity compared toV. cyanoptera, consistent with the effects of a small effective population size or population bottlenecks in the former two species. We also found—using population branch statistic estimates of all three species—previously undocumented evidence of lineage-specific evolution inV. chrysopteranear a novel pigmentation gene candidate for warblers,CORIN, which is a known modifier ofASIP, which is in turn involved in melanic throat and mask coloration in this family of birds. Together, these genomic results also highlight how natural history collections are such invaluable repositories of information about extant and extinct species.SignificanceFew common North American passerines have gone extinct. Bachman’s Warbler is, unfortunately, one that has—the last sighting was in 1988. Here we use whole genome historical DNA from museum specimens of Bachman’s warblers collected at the turn of the 20thcentury to learn about the evolution of this species and test whether there was evidence for hybridization and gene flow between it and two extant members of the same genus which, today, hybridize extensively. We find Bachman’s warbler was highly divergent with no evidence of gene flow. We also find evidence of elevated “runs of homozygosity” in both Bachman’s warbler and one of the two extantVermivoraspecies, suggesting the effects of a small population size or population bottlenecks.

We used whole-genome resequencing to estimate genetic distinctiveness in the Black-throated Green Warbler (Setophaga virens)—including S. v. waynei—a putative subspecies that occupies a narrow disjunct breeding range along the Atlantic Coastal Plain. Despite detecting low-global differentiation (FST = 0.027) across the entire species, the principal components analysis of genome-wide differences shows the main axis of variation separates S. v. waynei from all other S. v. virens samples. We also estimated a low-migration rate for S. v. waynei, but found them to be most similar to another disjunct population from the Piedmont of North Carolina, and detected evidence of a historical north-to-south geographic dispersal among the entire species. New World wood warblers (family: Parulidae) can exhibit strong phenotypic differences among species, particularly, in song and plumage; however, within-species variation in these warblers—often designated as subspecies—is much more subtle. The existence of several isolated Black-throated Green Warbler populations across its eastern North American breeding range offers an excellent opportunity to further understand the origin, maintenance, and conservation status of subspecific populations. Our results, combined with previously documented ecological and morphological distinctiveness, support that S. v. waynei be considered a distinct and recognized subspecies worthy of targeted conservation efforts.

Surface meteorological analyses serve a wide range of research and applications, including forcing inputs for hydrological and ecological models, climate analysis, and resource and emergency management. Quantifying uncertainty in such analyses would extend their utility for probabilistic hydrologic prediction and climate risk applications. With this motivation, we enhance and evaluate an approach for generating ensemble analyses of precipitation and temperature through the fusion of station observations, terrain information, and numerical weather prediction simulations of surface climate fields. In particular, we expand a spatial regression in which static terrain attributes serve as predictors for spatially distributed 1/16th degree daily surface precipitation and temperature by including forecast outputs from the High-Resolution Rapid Refresh (HRRR) numerical weather prediction model as additional predictors. We demonstrate the approach for a case study domain of California, focusing on the meteorological conditions leading to the 2017 flood and spillway failure event at Lake Oroville. The approach extends the spatial regression capability of the Gridded Meteorological Ensemble Tool (GMET) and also adds cross-validation to the uncertainty estimation component, enabling the use of predictive rather than calibration uncertainty. In evaluation against out-of-sample station observations, the HRRR-based predictors alone are found to be skillful for the study setting, leading to overall improvements in the enhanced GMET meteorological analyses. The methodology and associated tool represent a promising method for generating meteorological surface analyses for both research-oriented and operational applications, as well as a general strategy for merging in situ and gridded observations.

Understanding the factors that shape microbiomes can provide insight on the importance of host-symbiont interactions and on co-evolutionary dynamics. Unlike for mammals, previous studies have found little or no support for an influence of host evolutionary history on avian gut microbiome diversity and instead have suggested a greater influence of the environment or diet due to fast gut turnover. Because effects of different factors may be conflated by captivity and sampling design, examining natural variation using large sample sizes is important. Our goal was to overcome these limitations by sampling wild birds to compare environmental, dietary, and evolutionary influences on gut microbiome structure. We performed fecal metabarcoding to characterize both the gut microbiome and diet of fifteen wood-warbler species across a four-year period and from two geographic localities. We find host taxonomy generally explained ∼10% of the variation between individuals, which is ∼6-fold more variation of any other factor considered, including diet diversity. Further, gut microbiome similarity was more congruent with the host phylogeny than with host diet similarity and we found little association between diet diversity and microbiome diversity. Together, our results suggest evolutionary history is the strongest predictor of gut microbiome differentiation among wood-warblers. Although the phylogenetic signal of the warbler gut microbiome is not very strong, our data suggest that a stronger influence of diet (as measured by diet diversity) does not account for this pattern. The mechanism underlying this phylogenetic signal is not clear, but we argue host traits may filter colonization and maintenance of microbes.

The mental health counseling profession’s focus on wellness and prevention puts counselors at an advantage in the developing fields of integrated care and cancer care. To integrate counselors further into the cancer care world, this study investigated how factors related to prevention and wellness (e.g., health orientation and fear of cancer) relate to topics that counselors are adept at addressing, such as optimism, social support, and resilience. Results indicated that health orientation predicted fear of cancer, but that psychosocial variables (i.e., optimism and social support) either had no relationship with cancer fear or were not significant moderators (i.e., resiliency) of the relationship between health orientation and fear of cancer. In particular, health anxiety was the best predictor of cancer fear. Implications for counselors and future research are discussed.

Flash droughts are a recently recognized type of extreme event distinguished by sudden onset and rapid intensification of drought conditions with severe impacts. They unfold on subseasonal-to-seasonal timescales (weeks to months), presenting a new challenge for the surge of interest in improving subseasonal-to-seasonal prediction. Here we discuss existing prediction capability for flash droughts and what is needed to establish their predictability. We place them in the context of synoptic to centennial phenomena, consider how they could be incorporated into early warning systems and risk management, and propose two definitions. The growing awareness that flash droughts involve particular processes and severe impacts, and probably a climate change dimension, makes them a compelling frontier for research, monitoring and prediction. Flash droughts, which develop over the course of weeks, are difficult to forecast given the current state of subseasonal-to-seasonal prediction. This Perspective offers operational and research definitions, places them in the broader context of climate and suggests avenues for future research.

BackgroundAll patients admitted to an acute inpatient mental health unit must have nursing observations carried out at night either hourly or every 15 minutes, to ascertain that they are safe and breathing. However, while this practice ensures patient safety, it can also disturb patients’ sleep, which in turn can impact negatively on their recovery.ObjectiveThis article describes the process of introducing artificial intelligence (‘digitally assisted nursing observations’) in an acute mental health inpatient ward, to enable staff to carry out the hourly and the 15 minutes observations, minimising disruption of patients’ sleep while maintaining their safety.FindingsThe preliminary data obtained indicate that the digitally assisted nursing observations agreed with the observations without sensors when both were carried out in parallel and that over an estimated 755 patient nights, the new system has not been associated with any untoward incidents. Preliminary qualitative data suggest that the new technology improves patients’ and staff’s experience at night.DiscussionThis project suggests that the digitally assisted nursing observations could maintain patients’ safety while potentially improving patients’ and staff’s experience in an acute psychiatric ward. The limitations of this study, namely, its narrative character and the fact that patients were not randomised to the new technology, suggest taking the reported findings as qualitative and preliminary.Clinical implicationsThese results suggest that the care provided at night in acute inpatient psychiatric units could be substantially improved with this technology. This warrants a more thorough and stringent evaluation.

Increasingly, climate change impact assessments rely directly on climate models. Assessments of future water security depend in part on how the land model components in climate models partition precipitation into evapotranspiration and runoff, and on the sensitivity of this partitioning to climate. Runoff sensitivities are not well constrained, with CMIP5 models displaying a large spread for the present day, which projects onto change under warming, creating uncertainty. Here we show that constraining CMIP5 model runoff sensitivities with observed estimates could reduce uncertainty in runoff projection over the western United States by up to 50%. We urge caution in the direct use of climate model runoff for applications and encourage model development to use regional-scale hydrological sensitivity metrics to improve projections for water security assessments. Model estimates of future hydroclimate are uncertain, especially at the regional scale. This Perspective argues that constraining model runoff and its sensitivity to precipitation and temperature can greatly reduce this uncertainty and improve climate model utility in water resource applications.

INTRODUCTION AND OBJECTIVE:Urolift and Rezum have demonstrated efficacy as minimally invasive surgical therapies (MIST) for the treatment of benign prostatic hyperplasia (BPH). Both modalities can ...

The carcinogenicity of radiofrequency electromagnetic fields (RF EMF) has been evaluated by the International Agency for Research on Cancer (IARC) in 2011. Based on limited evidence of carcinogenicity in humans and in animals, RF EMF were classified as possibly carcinogenic to humans (Group 2B). In 2018, based on a survey amongst RF experts, WHO prioritized six major topics of potential RF EMF related human health effects for systematic reviews. In the current manuscript, we present the protocol for the systematic review of experimental laboratory animal studies (cancer bioassays) on exposure to RF fields on the outcome of cancer in laboratory animals.In the framework of WHO's Radiation Program, the aim of this work is to systematically evaluate effects of RF EMF exposure on cancer in laboratory animals.WHO's Handbook (2014) for guideline development will be followed with appropriate adaptation. The selection of eligible studies will be based on Population, Exposures, Comparators, and Outcomes (PECO) criteria. We will include peer-reviewed articles and publicly available reports from government agencies reporting original data about animal cancer bioassays on exposure to RF EMF. The studies are identified by searching the following databases: MEDLINE (PubMed), Science Citation Index Expanded and Emerging Sources Citation Indes (Web of Science), Scopus, and the EMF Portal. No language or year-of-publication restrictions are applied. The methods and results of eligible studies will be presented in accordance with the PRISMA 2020 guidelines.Study evaluation of individual studies will be assessed using a risk of bias (RoB) tool developed by the Office of Health Assessment and Translation (OHAT) with appropriate considerations including sensitivity for evaluating RF EMF exposure in animal cancer bioassays. The final evaluation on the certainty of the evidence on a carcinogenic risk of RF EMF exposure in experimental animals will be performed using the OHAT Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach with appropriate considerations. The protocol has been registered in an open-source repository (PROSPERO).The study is partly financially supported by the World Health Organization. No additional funding was provided outside author salaries through their places of employment.

Family caregivers can be at risk for emotional, mental, and physical health problems. The purpose of this study was to determine if brief mindfulness training was effective in reducing stress level...

Calibration is an essential step for improving the accuracy of simulations generated using hydrologic models. A key modeling decision is selecting the performance metric to be optimized. It has been common to use squared error performance metrics, or normalized variants such as Nash–Sutcliffe efficiency (NSE), based on the idea that their squared-error nature will emphasize the estimates of high flows. However, we conclude that NSE-based model calibrations actually result in poor reproduction of high-flow events, such as the annual peak flows that are used for flood frequency estimation. Using three different types of performance metrics, we calibrate two hydrological models at a daily step, the Variable Infiltration Capacity (VIC) model and the mesoscale Hydrologic Model (mHM), and evaluate their ability to simulate high-flow events for 492 basins throughout the contiguous United States. The metrics investigated are (1) NSE, (2) Kling–Gupta efficiency (KGE) and its variants, and (3) annual peak flow bias (APFB), where the latter is an application-specific metric that focuses on annual peak flows. As expected, the APFB metric produces the best annual peak flow estimates; however, performance on other high-flow-related metrics is poor. In contrast, the use of NSE results in annual peak flow estimates that are more than 20 % worse, primarily due to the tendency of NSE to underestimate observed flow variability. On the other hand, the use of KGE results in annual peak flow estimates that are better than from NSE, owing to improved flow time series metrics (mean and variance), with only a slight degradation in performance with respect to other related metrics, particularly when a non-standard weighting of the components of KGE is used. Stochastically generated ensemble simulations based on model residuals show the ability to improve the high-flow metrics, regardless of the deterministic performances. However, we emphasize that improving the fidelity of streamflow dynamics from deterministically calibrated models is still important, as it may improve high-flow metrics (for the right reasons). Overall, this work highlights the need for a deeper understanding of performance metric behavior and design in relation to the desired goals of model calibration.