76 research outputs found
Optimal spatial prioritization of control resources for elimination of invasive species under demographic uncertainty
Populations of invasive species often spread heterogeneously across a landscape, consisting of local populations that cluster in space but are connected by dispersal. A fundamental dilemma for invasive species control is how to optimally allocate limited fiscal resources across local populations. Theoretical work based on perfect knowledge of demographic connectivity suggests that targeting local populations from which migrants originate (sources) can be optimal. However, demographic processes such as abundance and dispersal can be highly uncertain, and the relationship between local population density and damage costs (damage function) is rarely known. We used a metapopulation model to understand how budget and uncertainty in abundance, connectivity, and the damage function, together impact return on investment (ROI) for optimal control strategies. Budget, observational uncertainty, and the damage function had strong effects on the optimal resource allocation strategy. Uncertainty in dispersal probability was the least important determinant of ROI. The damage function determined which resource prioritization strategy was optimal when connectivity was symmetric but not when it was asymmetric. When connectivity was asymmetric, prioritizing source populations had a higher ROI than allocating effort equally across local populations, regardless of the damage function, but uncertainty in connectivity structure and abundance reduced ROI of the optimal prioritization strategy by 57% on average depending on the control budget. With low budgets (monthly removal rate of 6.7% of population), there was little advantage to prioritizing resources, especially when connectivity was high or symmetric, and observational uncertainty had only minor effects on ROI. Allotting funding for improved monitoring appeared to be most important when budgets were moderate (monthly removal of 13–20% of the population). Our result showed that multiple sources of observational uncertainty should be considered concurrently for optimizing ROI. Accurate estimates of connectivity direction and abundance were more important than accurate estimates of dispersal rates. Developing cost-effective surveillance methods to reduce observational uncertainties, and quantitative frameworks for determining how resources should be spatially apportioned to multiple monitoring and control activities are important and challenging future directions for optimizing ROI for invasive species control programs
Risks of introduction and economic consequences associated with African swine fever, classical swine fever and foot-and-mouth disease: A review of the literature
African swine fever (ASF), classical swine fever (CSF) and foot-and-mouth disease (FMD) are considered to be three of the most detrimental animal diseases and are currently foreign to the U.S. Emerging and re-emerging pathogens can have tremendous impacts in terms of livestock morbidity and mortality events, production losses, forced trade restrictions, and costs associated with treatment and control. The United States is the world\u27s top producer of beef for domestic and export use and the world\u27s third-largest producer and consumer of pork and pork products; it has also recently been either the world\u27s largest or second largest exporter of pork and pork products. Understanding the routes of introduction into the United States and the potential economic impact of each pathogen are crucial to (a) allocate resources to prevent routes of introduction that are believed to be more probable, (b) evaluate cost and efficacy of control methods and (c) ensure that protections are enacted to minimize impact to the most vulnerable industries. With two scoping literature reviews, pulled from global data, this study assesses the risk posed by each disease in the event of a viral introduction into the United States and illustrates what is known about the economic costs and losses associated with an outbreak
Optimizing management of invasions in an uncertain world using dynamic spatial models
Dispersal drives invasion dynamics of nonnative species and pathogens. Applying knowledge of dispersal to optimize the management of invasions can mean the difference between a failed and a successful control program and dramatically improve the return on investment of control efforts. A common approach to identifying optimal management solutions for invasions is to optimize dynamic spatial models that incorporate dispersal. Optimizing these spatial models can be very challenging because the interaction of time, space, and uncertainty rapidly amplifies the number of dimensions being considered. Addressing such problems requires advances in and the integration of techniques from multiple fields, including ecology, decision analysis, bioeconomics, natural resource management, and optimization. By synthesizing recent advances from these diverse fields, we provide a workflow for applying ecological theory to advance optimal management science and highlight priorities for optimizing the control of invasions. One of the striking gaps we identify is the extremely limited consideration of dispersal uncertainty in optimal management frameworks, even though dispersal estimates are highly uncertain and greatly influence invasion outcomes. In addition, optimization frameworks rarely consider multiple types of uncertainty (we describe five major types) and their interrelationships. Thus, feedbacks from management or other sources that could magnify uncertainty in dispersal are rarely considered. Incorporating uncertainty is crucial for improving transparency in decision risks and identifying optimal management strategies. We discuss gaps and solutions to the challenges of optimization using dynamic spatial models to increase the practical application of these important tools and improve the consistency and robustness of management recommendations for invasions
Ultrasound assessment of the lateral collateral ligamentous complex of the elbow: imaging aspects in cadavers and normal volunteers
OBJECTIVE: The Lateral Collateral Ligamentous complex (LCL) is an important stabiliser of the elbow. It has a Y-shaped structure with three components. In this study, we sought to describe the ultrasound aspect of the individual components of this ligamentous complex and to evaluate the performance of ultrasound in both cadavers and in normal subjects. METHODS: Ten cadaveric elbow specimens underwent high-frequency ultrasound. Two specimens were sliced and two were dissected for anatomical correlation. Ten elbows of normal subjects were also evaluated by ultrasound. The findings were compared. RESULTS: The three components of the LCL could be visualised in all specimens and normal subjects with the exception of the proximal portion of one specimen. In 80% of the specimens and 100% of the healthy volunteers the proximal portion of the LCL could be separated from the extensor tendons. CONCLUSION: High-resolution ultrasound can assess all components of the LCL of the elbow and can distinguish them from surrounding structures
Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial
Background
Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy
Refining trait resilience: identifying engineering, ecological, and adaptive facets from extant measures of resilience
The current paper presents a new measure of trait resilience derived from three common
mechanisms identified in ecological theory: Engineering, Ecological and Adaptive (EEA)
resilience. Exploratory and confirmatory factor analyses of five existing resilience scales
suggest that the three trait resilience facets emerge, and can be reduced to a 12-item scale.
The conceptualization and value of EEA resilience within the wider trait and well-being psychology
is illustrated in terms of differing relationships with adaptive expressions of the traits
of the five-factor personality model and the contribution to well-being after controlling for
personality and coping, or over time. The current findings suggest that EEA resilience is a
useful and parsimonious model and measure of trait resilience that can readily be placed
within wider trait psychology and that is found to contribute to individual well-bein
The NANOGrav 15-year Data Set: Bayesian Limits on Gravitational Waves from Individual Supermassive Black Hole Binaries
Evidence for a low-frequency stochastic gravitational wave background has
recently been reported based on analyses of pulsar timing array data. The most
likely source of such a background is a population of supermassive black hole
binaries, the loudest of which may be individually detected in these datasets.
Here we present the search for individual supermassive black hole binaries in
the NANOGrav 15-year dataset. We introduce several new techniques, which
enhance the efficiency and modeling accuracy of the analysis. The search
uncovered weak evidence for two candidate signals, one with a
gravitational-wave frequency of 4 nHz, and another at 170 nHz. The
significance of the low-frequency candidate was greatly diminished when
Hellings-Downs correlations were included in the background model. The
high-frequency candidate was discounted due to the lack of a plausible host
galaxy, the unlikely astrophysical prior odds of finding such a source, and
since most of its support comes from a single pulsar with a commensurate binary
period. Finding no compelling evidence for signals from individual binary
systems, we place upper limits on the strain amplitude of gravitational waves
emitted by such systems.Comment: 23 pages, 13 figures, 2 tables. Accepted for publication in
Astrophysical Journal Letters as part of Focus on NANOGrav's 15-year Data Set
and the Gravitational Wave Background. For questions or comments, please
email [email protected]
How to Detect an Astrophysical Nanohertz Gravitational-Wave Background
Analysis of pulsar timing data have provided evidence for a stochastic
gravitational wave background in the nHz frequency band. The most plausible
source of such a background is the superposition of signals from millions of
supermassive black hole binaries. The standard statistical techniques used to
search for such a background and assess its significance make several
simplifying assumptions, namely: i) Gaussianity; ii) isotropy; and most often
iii) a power-law spectrum. However, a stochastic background from a finite
collection of binaries does not exactly satisfy any of these assumptions. To
understand the effect of these assumptions, we test standard analysis
techniques on a large collection of realistic simulated datasets. The dataset
length, observing schedule, and noise levels were chosen to emulate the
NANOGrav 15-year dataset. Simulated signals from millions of binaries drawn
from models based on the Illustris cosmological hydrodynamical simulation were
added to the data. We find that the standard statistical methods perform
remarkably well on these simulated datasets, despite their fundamental
assumptions not being strictly met. They are able to achieve a confident
detection of the background. However, even for a fixed set of astrophysical
parameters, different realizations of the universe result in a large variance
in the significance and recovered parameters of the background. We also find
that the presence of loud individual binaries can bias the spectral recovery of
the background if we do not account for them.Comment: 14 pages, 8 figure
The NANOGrav 15-year Data Set: Evidence for a Gravitational-Wave Background
We report multiple lines of evidence for a stochastic signal that is
correlated among 67 pulsars from the 15-year pulsar-timing data set collected
by the North American Nanohertz Observatory for Gravitational Waves. The
correlations follow the Hellings-Downs pattern expected for a stochastic
gravitational-wave background. The presence of such a gravitational-wave
background with a power-law-spectrum is favored over a model with only
independent pulsar noises with a Bayes factor in excess of , and this
same model is favored over an uncorrelated common power-law-spectrum model with
Bayes factors of 200-1000, depending on spectral modeling choices. We have
built a statistical background distribution for these latter Bayes factors
using a method that removes inter-pulsar correlations from our data set,
finding (approx. ) for the observed Bayes factors in the
null no-correlation scenario. A frequentist test statistic built directly as a
weighted sum of inter-pulsar correlations yields (approx. ). Assuming a fiducial
characteristic-strain spectrum, as appropriate for an ensemble of binary
supermassive black-hole inspirals, the strain amplitude is (median + 90% credible interval) at a reference frequency of
1/(1 yr). The inferred gravitational-wave background amplitude and spectrum are
consistent with astrophysical expectations for a signal from a population of
supermassive black-hole binaries, although more exotic cosmological and
astrophysical sources cannot be excluded. The observation of Hellings-Downs
correlations points to the gravitational-wave origin of this signal.Comment: 30 pages, 18 figures. Published in Astrophysical Journal Letters as
part of Focus on NANOGrav's 15-year Data Set and the Gravitational Wave
Background. For questions or comments, please email [email protected]
Hyperoxemia and excess oxygen use in early acute respiratory distress syndrome : Insights from the LUNG SAFE study
Publisher Copyright: © 2020 The Author(s). Copyright: Copyright 2020 Elsevier B.V., All rights reserved.Background: Concerns exist regarding the prevalence and impact of unnecessary oxygen use in patients with acute respiratory distress syndrome (ARDS). We examined this issue in patients with ARDS enrolled in the Large observational study to UNderstand the Global impact of Severe Acute respiratory FailurE (LUNG SAFE) study. Methods: In this secondary analysis of the LUNG SAFE study, we wished to determine the prevalence and the outcomes associated with hyperoxemia on day 1, sustained hyperoxemia, and excessive oxygen use in patients with early ARDS. Patients who fulfilled criteria of ARDS on day 1 and day 2 of acute hypoxemic respiratory failure were categorized based on the presence of hyperoxemia (PaO2 > 100 mmHg) on day 1, sustained (i.e., present on day 1 and day 2) hyperoxemia, or excessive oxygen use (FIO2 ≥ 0.60 during hyperoxemia). Results: Of 2005 patients that met the inclusion criteria, 131 (6.5%) were hypoxemic (PaO2 < 55 mmHg), 607 (30%) had hyperoxemia on day 1, and 250 (12%) had sustained hyperoxemia. Excess FIO2 use occurred in 400 (66%) out of 607 patients with hyperoxemia. Excess FIO2 use decreased from day 1 to day 2 of ARDS, with most hyperoxemic patients on day 2 receiving relatively low FIO2. Multivariate analyses found no independent relationship between day 1 hyperoxemia, sustained hyperoxemia, or excess FIO2 use and adverse clinical outcomes. Mortality was 42% in patients with excess FIO2 use, compared to 39% in a propensity-matched sample of normoxemic (PaO2 55-100 mmHg) patients (P = 0.47). Conclusions: Hyperoxemia and excess oxygen use are both prevalent in early ARDS but are most often non-sustained. No relationship was found between hyperoxemia or excessive oxygen use and patient outcome in this cohort. Trial registration: LUNG-SAFE is registered with ClinicalTrials.gov, NCT02010073publishersversionPeer reviewe
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