Detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in blood, also known as RNAemia, has been reported, but its prognostic implications are poorly understood. This study ...aimed to determine the frequency of SARS-CoV-2 RNA in plasma and its association with coronavirus disease 2019 (COVID-19) clinical severity.
An analytical cross-sectional study was performed in a single-center tertiary care institution and included consecutive inpatients and outpatients with confirmed COVID-19. The prevalence of SARS CoV-2 RNAemia and the strength of its association with clinical severity variables were examined and included intensive care unit (ICU) admission, invasive mechanical ventilation, and 30-day all-cause mortality.
Paired nasopharyngeal and plasma samples were included from 85 patients. The median age was 55 years, and individuals with RNAemia were older than those with undetectable SARS-CoV-2 RNA in plasma (63 vs 50 years; P = .04). Comorbidities were frequent including obesity (37.6%), hypertension (30.6%), and diabetes mellitus (22.4%). RNAemia was detected in 28/85 (32.9%) of patients, including 22/28 (78.6%) who required hospitalization. In models adjusted for age, RNAemia was detected more frequently in individuals who developed severe disease including ICU admission (32.1 vs 14.0%; P = .04) and invasive mechanical ventilation (21.4% vs 3.5%; P = .02). All 4 deaths occurred in individuals with detectable RNAemia. An additional 121 plasma samples from 28 individuals with RNAemia were assessed longitudinally, and RNA was detected for a maximum duration of 10 days.
This study demonstrated a high proportion of SARS-CoV-2 RNAemia, and an association between RNAemia and clinical severity suggesting the potential utility of plasma viral testing as a prognostic indicator for COVID-19.
Aim
Predicting distributions is fundamental to ecology, yet hindered by spatially restricted sampling, scale‐dependent relationships and detection error associated with field surveys. Predictive ...species distribution models (SDMs) are nonetheless vital for conservation of many species. We developed a framework for building predictive SDMs with multi‐scale data and used it to develop range‐wide breeding‐season SDMs for 14 marsh bird species of concern.
Location
USA.
Methods
We built SDMs using data from range‐wide surveys conducted over 14 years, and habitat and disturbance covariates measured at multiple spatial scales. We built hierarchical occupancy models that included heterogeneity in detectability during sampling, and used Bayesian model selection to regulate model complexity (covariates and scales) based explicitly on spatial predictive abilities. We thus integrated model selection for optimizing out‐of‐sample prediction, range‐wide sampling over broad conditions, multi‐scale analyses and scale optimization, and species‐specific detectability for a suite of wide‐ranging species.
Results
Distributions of marsh birds were affected by local wetland conditions, but also by agricultural, urban and hydrologic disturbances operating from local scales (100–500 m) to the watershed level. Variables measuring human disturbances improved prediction for most species, and every species was affected by attributes at >1 scale. Five species showed evidence for continental‐scale range contraction during the study.
Main conclusions
We demonstrate how hierarchical occupancy models can be optimized for prediction across a species' range at the extent of a continent while also accounting for imperfect detection, and thus describe a generalizable approach that can be used for any species. We provide the first data‐driven, empirical SDMs built at the range‐wide extent for most of our 14 study species and demonstrate that previous studies focused on local distributions and the effects of fine‐scale wetland vegetation missed important broadscale drivers of occupancy for marsh birds.
Advancements in statistical ecology offer the opportunity to gain further inferences from existing data with minimal financial cost. Spatial capture-recapture (SCR) models extend traditional ...capture-recapture models to incorporate spatial position of capture and enable direct estimation of animal densities across a region of interest. The additional inferences provided are both ecologically interesting and valuable for decision making, which has resulted in traditional capture-recapture data being repurposed using SCR. Yet, many capture-recapture studies were not designed for SCR and the limitations of repurposing data from such studies are rarely assessed in practice. We used simulation to evaluate the robustness of SCR for retrospectively estimating large mammal densities over a variety of scenarios using repurposed capture-recapture data collected by an asymmetrical sampling grid and covering a broad spatial extent in a heterogenous landscape. We found performance of SCR models fit using repurposed data simulated from the existing grid was not robust, but instead bias and precision of density estimates varied considerably among simulations scenarios. For example, while the smallest relatives bias of density estimates was 3%, it ranged by 14 orders of magnitude among scenarios and was most strongly influenced by detection parameters. Our results caution against the casual repurposing of non-spatial capture-recapture data using SCR and demonstrate the importance of using simulation to assessing model performance during retrospective applications.
B cells are critical for the production of antibodies and protective immunity to viruses. Here we show that patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) who ...develop coronavirus disease 2019 (COVID-19) display early recruitment of B cells expressing a limited subset of IGHV genes, progressing to a highly polyclonal response of B cells with broader IGHV gene usage and extensive class switching to IgG and IgA subclasses with limited somatic hypermutation in the initial weeks of infection. We identify convergence of antibody sequences across SARS-CoV-2-infected patients, highlighting stereotyped naive responses to this virus. Notably, sequence-based detection in COVID-19 patients of convergent B cell clonotypes previously reported in SARS-CoV infection predicts the presence of SARS-CoV/SARS-CoV-2 cross-reactive antibody titers specific for the receptor-binding domain. These findings offer molecular insights into shared features of human B cell responses to SARS-CoV-2 and SARS-CoV.
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•Human IGH repertoire sequencing identifies SARS-CoV-2-specific B cell clones•Convergent virus-specific antibody sequences are shared between COVID-19 patients•SARS-CoV antibodies are detected by sequence in COVID-19 patients
B cells produce antibodies and provide protective immunity to viruses. In longitudinal data from COVID-19 patients, Nielsen et al. analyze the development of antibody gene repertoires responding to SARS-CoV-2. COVID-19 patients share subsets of similar antibody sequences that bind SARS-CoV-2 antigens, with rare antibodies that also recognize SARS-CoV.
Wild turkeys (Meleagris gallopavo; hereafter turkeys) are an important game animal whose popularity among hunters has increased in recent decades. Yet, the number of hunters pursuing turkeys appears ...to be in flux, patterns of hunter abundance have primarily been described at broad spatial scales, and the ability of management to impact hunter numbers in the post-restoration era of management through opportunity for quality hunting is unclear. We used county-scale estimates of turkey hunter numbers collected over a 14-year period (2001-2014) and time-series analyses to evaluate the spatial scales at which spring and fall turkey hunter populations fluctuate, and also used generalized linear mixed models to evaluate whether attributes related to quality turkey hunting explain recent patterns in hunter abundance. We found heterogeneity in turkey hunter population growth at finer spatial scales than has been previously described (i.e., counties and management units), and provide evidence for spatial structuring of hunter population dynamics among counties that did not always correspond with existing management units. Specifically, the directionality of hunter population change displays spatial structure along an east-west gradient in southern Michigan. We also found little evidence that factors providing opportunity for quality turkey hunting had meaningful impacts on recent spatial-temporal patterns of hunter numbers. Our results imply that providing quality turkey hunting opportunities alone may be insufficient for sustaining populations of turkey hunters in the future, and that modern determinants of hunter participation extend beyond the availability of abundant turkey populations. Moreover, our results demonstrate that interpretation of harvest data as indices of abundance for turkey populations is difficult in the absence of hunter effort data, as changes to turkey harvest are a function of potentially fine-scaled changes in populations of hunters, not simply changes to turkey populations.
Aim
Species distribution models (SDMs) are valuable for rare species conservation and are commonly used to extrapolate predictions of habitat suitability geographically to regions where species ...occurrence is unknown (i.e., transferability). Spatially structured cross‐validation can be used to infer transferability, yet, few studies have evaluated how delineation of cross‐validation folds affects model complexity and predictions. We developed SDMs using multiple cross‐validation approaches to understand the implications for predicting habitat suitability for northern Idaho ground squirrels, a rare, federally threatened species that has been extensively surveyed in regions where known populations occur, resulting in >8000 presence locations.
Location
Idaho, USA.
Methods
We delineated cross‐validation folds by mimicking the manner in which predictions would be geographically extrapolated or by using existing dispersal barriers. We varied the distance between, number, and directionality of folds. We conducted a grid search on statistical regularization parameters to optimize model complexity, covering a range of values exceeding that typically implemented. For each cross‐validation approach, we selected optimal regularization and model complexity based on out‐of‐sample predictive ability.
Results
Delineation of cross‐validation folds substantially affected resulting model complexity and extrapolated predictions. All cross‐validation approaches resulted in models with apparently high out‐of‐sample predictive ability, yet optimal model complexity varied substantially among the approaches. Regularization demonstrated a noisy relationship between model complexity and prediction, where local optima in predictive performance were common at small values.
Main conclusion
Subtle modelling decisions can have large consequences for predictions of habitat suitability and transferability of SDMs. When transferability is the goal, cross‐validation approaches should be considered carefully and mimic the manner in which spatial extrapolation will occur, else overly complex models with inflated assessments of predictive accuracy may result. Further, spatially structured cross‐validation may not guard against over‐parameterization, and assessing a broader range of regularization parameters may be necessary to optimize model complexity for transferability.
Degradation of wetland ecosystems has negatively impacted many species, perhaps none more so than marsh birds that breed in vegetative emergent wetlands throughout North America. The U.S. Department ...of Defense manages approximately 29 million acres of land within the continental U.S., and many military installations contain wetland complexes that may be important for wetland birds. Thus, failure to adequately manage habitat for marsh birds could result in species extirpations and additional listings under the Endangered Species Act, and may result in regulatory burdens that reduce military readiness. We conducted spatial analyses to identify important breeding habitat on > 500 military installations for 12 species of marsh birds, with the goal of identifying installations that are, and are not, likely to harbor breeding habitat for each species. We also sought to assess the local value of military installations for species of greatest concern by comparing habitat suitability within installations to that in areas directly adjacent to those sites. We built range-wide, spatially-explicit models of species distribution to project suitability of breeding habitat for marsh birds within and adjacent to military installations. Our results demonstrate that installations with the best marsh bird habitat are geographically aggregated (both among and within species), primarily at sites along the eastern seaboard and within the southern U.S. In addition, only a few sites appear to contain high-quality habitat for most species. Five or fewer sites contained most of the high-quality habitat for 9 of 12 species, whereas most of the high-quality habitat for remaining species was found at ≤ 10 sites. This work fills an information gap regarding the distribution of breeding habitat for marsh birds on military lands across the U.S., and should facilitate both strategic conservation of habitat over broad scales and the integration of marsh birds into management efforts at the site level. Our analyses also identify installations that are not likely to harbor breeding habitat for priority species, and thus should help minimize conflicts between needs of the military and marsh-bird conservation.
•Degradation of habitat has negatively impacted many species of wetland birds.•We identified important breeding habitat on > 500 military installations for 12 species of marsh birds.•Important breeding habitat was aggregated at < 10 sites for each species.•Important sites were mostly located in the southern U.S. and along the east coast.•We fill an information gap for conserving marsh bird breeding habitat on military lands.
SARS-CoV-2-specific antibodies, particularly those preventing viral spike receptor binding domain (RBD) interaction with host angiotensin-converting enzyme 2 (ACE2) receptor, can neutralize the ...virus. It is, however, unknown which features of the serological response may affect clinical outcomes of COVID-19 patients. We analyzed 983 longitudinal plasma samples from 79 hospitalized COVID-19 patients and 175 SARS-CoV-2-infected outpatients and asymptomatic individuals. Within this cohort, 25 patients died of their illness. Higher ratios of IgG antibodies targeting S1 or RBD domains of spike compared to nucleocapsid antigen were seen in outpatients who had mild illness versus severely ill patients. Plasma antibody increases correlated with decreases in viral RNAemia, but antibody responses in acute illness were insufficient to predict inpatient outcomes. Pseudovirus neutralization assays and a scalable ELISA measuring antibodies blocking RBD-ACE2 interaction were well correlated with patient IgG titers to RBD. Outpatient and asymptomatic individuals' SARS-CoV-2 antibodies, including IgG, progressively decreased during observation up to five months post-infection.
Geographic distributions are a basic component of a species’ ecology, and predicting distributions is a fundamental task of conservation and resource management. Reliable prediction depends on ...identification of appropriate scales of effect for environmental data, and scale‐optimization techniques are thus desirable to identify optimal scales for predictor variables. Recent statistical developments have also advanced methods of model selection based explicitly on predictive ability, which differ from commonly used methods that regulate model structures via anticipated predictive performance. Such methods are beginning to permeate into species distribution models (SDMs), yet there remains no consensus methodology for developing optimally predictive multi‐scale SDMs when covariate data are collected over a range of scales. Thus, we compared the performance of common approaches for scale optimization and model selection in terms of their ability to produce optimally predictive multi‐scale Bayesian occupancy models for predicting a species distribution, using models of the breeding distribution for King Rails (Rallus elegans) as a case study. Our results demonstrate sizable gains in predictive performance for hierarchical occupancy models selected via their ability to predict out‐of‐sample data using the logarithmic scoring rule, as compared to models selected using information criteria (deviance information criteria DIC and Watanabe information criteria WAIC). Information criteria commonly selected individual covariates, as well as scales of effect for those covariates, with suboptimal predictive performance. Performance of models selected using the logarithmic scoring rule was also robust across the method of scale optimization, which was not true for models selected using DIC and WAIC. Thus, we demonstrate empirical benefits of study designs and statistical tools that enable covariate and scale selection based explicitly on predictive ability. Our results also imply that more careful consideration of what constitutes an optimal scale is warranted in many ecological applications, as the meaning of optimal is not independent of the technique used for scale selection.
Abstract
Background
Detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleocapsid antigen in blood has been described, but the diagnostic and prognostic role of antigenemia ...is not well understood. This study aimed to determine the frequency, duration, and concentration of nucleocapsid antigen in plasma and its association with coronavirus disease 2019 (COVID-19) severity.
Methods
We utilized an ultrasensitive electrochemiluminescence immunoassay targeting SARS-CoV-2 nucleocapsid antigen to evaluate 777 plasma samples from 104 individuals with COVID-19. We compared plasma antigen to respiratory nucleic acid amplification testing (NAAT) in 74 individuals with COVID-19 from samples collected ±1 day of diagnostic respiratory NAAT and in 52 SARS-CoV-2–negative individuals. We used Kruskal–Wallis tests, multivariable logistic regression, and mixed-effects modeling to evaluate whether plasma antigen concentration was associated with disease severity.
Results
Plasma antigen had 91.9% (95% CI 83.2%–97.0%) clinical sensitivity and 94.2% (84.1%–98.8%) clinical specificity. Antigen-negative plasma samples belonged to patients with later respiratory cycle thresholds (Ct) when compared with antigen-positive plasma samples. Median plasma antigen concentration (log10 fg/mL) was 5.4 (interquartile range 3.9–6.0) in outpatients, 6.0 (5.4–6.5) in inpatients, and 6.6 (6.1–7.2) in intensive care unit (ICU) patients. In models adjusted for age, sex, diabetes, and hypertension, plasma antigen concentration at diagnosis was associated with ICU admission odds ratio 2.8 (95% CI 1.2–6.2), P=.01 but not with non-ICU hospitalization. Rate of antigen decrease was not associated with disease severity.
Conclusions
SARS-CoV-2 plasma nucleocapsid antigen exhibited comparable diagnostic performance to upper respiratory NAAT, especially among those with late respiratory Ct. In addition to currently available tools, antigenemia may facilitate patient triage to optimize intensive care utilization.
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