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384 results for “risk model”
Risk Model for Metastasis Detection of Neuroblastoma
ClinicalTrials.gov study NCT06703944. IPD Sharing: YES. Countries: 1. Publications: 0.
Noninvasive Diagnosis Model for High-risk Varices in Cirrhosis
ClinicalTrials.gov study NCT06392503. IPD Sharing: NO. Countries: 1. Publications: 10.
Nurse-Led Community Health Worker Adherence Model in 3HP Delivery Among Homeless Adults at Risk for TB Infection and HIV
ClinicalTrials.gov study NCT03702049. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Geospatial based model for malaria risk prediction in Kilombero Valley, south-eastern Tanzania
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Spatiophylogenetic modelling of extinction risk reveals evolutionary distinctiveness and brief flowering period as threats in a hotspot plant genus
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Estimating the influence of field inventory sampling intensity on forest landscape model performance for determining high-severity wildfire risk
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Data from: Predicting disease risk areas through co-production of spatial models: the example of Kyasanur Forest Disease in India’s forest landscapes
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Optimization of cancer risk assessment model for PM2.5-bound PAHs Application in Shanxi of China SM
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On modelling airborne infection risk
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Modeling of the potential distribution of Eichhornia crassipes on a global scale: risks and threats to water ecosystems. Supplementary material
<p>https://doi.org/10.4136/1980-993X</p>
Water Erosion Risk in the Eastern Rift Valley: Application of RUSLE Modeling in the Kenyan Great Rift Valley Region
<p>RUSLE Model Parameters for Estimating Water Erosion Risk in the Eastern Rift Valley: Application of RUSLE Modeling in the Kenyan Great Rift Valley Region</p>
Data from: Numerical models for assessing the risk of leaflet thrombosis post-transcatheter aortic valve-in-valve implantation
<p>Leaflet thrombosis has been suggested as the reason for the reduced leaflet motion in cases of hypoattenuated leaflet thickening of bioprosthetic aortic valves. This work aimed to estimate the risk of leaflet thrombosis in two post-ViV configurations, using five different numerical approaches. Realistic ViV configurations were calculated by modeling the deployments of the latest version of transcatheter aortic valve devices (Medtronic Evolut PRO, Edwards SAPIEN 3) in the surgical Sorin Mitroflow. Computational fluid dynamics simulations of blood flow followed the dry models. Lagrangian and Eulerian measures of near-wall stagnation were implemented by particle and concentration tracking, respectively, to estimate the thrombogenicity and to predict the risk locations. Most of the numerical approaches indicate on a higher leaflet thrombosis risk in the Edwards SAPIEN 3 device because of its intra-annular implantation. The Eulerian approaches estimated high-risk locations in agreement with the WSS separation points. On the other hand, the Lagrangian approaches predicted high-risk locations at the proximal regions of the leaflets matching the low WSS magnitude regions of both TAVI models and reported clinical and experimental data. The proposed methods can help optimizing future designs of transcatheter aortic valves with minimal thrombotic risks.</p>
Data from: Conspicuous plumage does not increase predation risk: a continent-wide test using model songbirds
The forces shaping female plumage color have long been debated but remain unresolved. Females may benefit from conspicuous colors but are also expected to suffer costs. Predation is one potential cost, but few studies have explicitly investigated the relationship between predation risk and coloration. The fairy-wrens show pronounced variation in female coloration and reside in a wide variety of habitats across Australasia. Species with more conspicuous females are found in denser habitats, suggesting that conspicuousness in open habitat increases vulnerability to predators. To test this, we measured attack rates on 3D printed models mimicking conspicuously colored males and females, and dull females, in eight different fairy-wren habitats across Australia. Attack rates were higher in open habitats, and at higher latitudes. Contrary to our predictions, dull female models were attacked at similar rates to the conspicuous models. Further, the probability of attack in open habitats increased more for both types of female model than for the conspicuous male model. Across models, the degree of contrast (chromatic and achromatic) to environmental backgrounds was unrelated to predation rate. These findings do not support the long-standing hypothesis that conspicuous plumage, in isolation, is costly due to increased attraction of predators. Our results indicate that conspicuousness interacts with other factors in driving the evolution of plumage coloration.
Data from: Use of simulation-based statistical models to complement bioclimatic models in predicting continental scale invasion risks
Invasive species represent one of the greatest risks to global biodiversity and economic productivity of agroecosystems. The development of certain novel crops—e.g., herbaceous perennial biomass crops—may create a risk of novel invasions by these crops. Therefore, potential benefits and risks need to be weighed in making decisions about their introduction and subsequent management. Ideally, such a weighing will be based on good estimates of invasion risks in realistic scenarios pertaining to actual landscapes of concern regarding invasion. Most previous large-scale analyses of invasion risk have used species distribution models and their established methods. Unfortunately, these approaches are unable to incorporate local scale biotic and spatial factors that influence invasion risk. Here we present a case study for how such factors can be efficiently incorporated in large-scale analyses of invasion risk, by extending simulation models with statistical modeling tools. By these means, we predict invasion risk at the scale of the entire United States for a major biomass crop, Miscanthus × giganteus. We then combine invasion risk predictions for this method with those from bioclimatic methods, producing a map of aggregated invasion risk that can offer more nuanced predictions of invasion risk than either approach alone. Lastly, we evaluate potential risks for invasive crops that differ in invasiveness traits, to examine how geographic patterns of invasion risk vary among invaders as a result of their particular constellation of traits.
Data from: On the use of climate covariates in aquatic species distribution models: are we at risk of throwing the baby out?
Species distribution models (SDMs) in river ecosystems can incorporate climate information by using air temperature and precipitation as surrogate measures of instream conditions or by using independent models of water temperature and hydrology to link climate to instream habitat. The latter approach is preferable but constrained by the logistical burden of developing water temperature and hydrology models. We therefore assessed whether regional scale, freshwater SDM predictions are fundamentally different when climate data versus instream temperature and hydrology are used as covariates. Maximum Entropy (MaxEnt) SDMs were built for 15 freshwater fishes using one of two covariate sets: (1) air temperature and precipitation (climate variables) in combination with physical habitat variables; or (2) water temperature, hydrology (instream variables) and physical habitat. Three procedures were then used to compare results from climate vs. instream models. First, equivalence tests assessed average pairwise differences (site-specific comparisons throughout each species' range) among climate and instream models. Second, 'congruence' tests determined how often the same stream segments were assigned high habitat suitability by climate and instream models. Third, Schoener's <i>D</i> and Warren's <i>I</i> niche overlap statistics quantified range-wide similarity in predicted habitat suitability values from climate vs. instream models. Equivalence tests revealed small, pairwise differences in habitat suitability between climate and instream models (mean pairwise differences in MaxEnt raw scores for all species < 3×10<sup>-4</sup>). Congruence tests showed a strong tendency for climate and instream models to predict high habitat suitability at the same stream segments (median congruence = 68%). <i>D</i> and <i>I</i> statistics reflected a high margin of overlap among climate and instream models (median <i>D</i> = 0.78, median <i>I</i> = 0.96). Overall, we found little support for the hypothesis that SDM predictions are fundamentally different when climate versus instream covariates are used to model fish species' distributions at the scale of the Columbia Basin.
Data from: A mechanistic and empirically-supported lightning risk model for forest trees
<ol> <li>Tree death due to lightning influences tropical forest carbon cycling and tree community dynamics. However, the distribution of lightning damage among trees in forests remains poorly understood. </li> <li>We developed models to predict direct and secondary lightning damage to trees based on tree size, crown exposure, and local forest structure. We parameterized these models using data on the locations of lightning strikes and censuses of tree damage in strike zones, combined with drone-based maps of tree crowns and censuses of all trees within a 50-ha forest dynamics plot on Barro Colorado Island, Panama. </li> <li>The likelihood of a direct strike to a tree increased with larger exposed crown area and higher relative canopy position (emergent > canopy >>> subcanopy), whereas the likelihood of secondary lightning damage increased with tree diameter and proximity to neighboring trees. The predicted frequency of lightning damage in this mature forest was greater for tree species with larger average diameters.</li> <li>These patterns suggest that lightning influences forest structure and the global carbon budget by nonrandomly damaging large trees. Moreover, these models provide a framework for investigating the ecological and evolutionary consequences of lightning disturbance in tropical forests.</li> </ol> <p><b>Synthesis:</b> Our findings indicate that the distribution of lightning damage is stochastic at large spatial grain and relatively deterministic at smaller spatial grain (<15 m). Lightning is more likely to directly strike taller trees with large crowns and secondarily damage large neighboring trees that are closest to the directly struck tree. The results provide a framework for understanding how lightning can affect forest structure, forest dynamics, and carbon cycling. The resulting lightning risk model will facilitate informed investigations into the effects of lightning in tropical forests.</p>
Data from: Temporal modelling of ballast water discharge and ship-mediated invasion risk to Australia
Biological invasions have the potential to cause extensive ecological and economic damage. Maritime trade facilitates biological invasions by transferring species in ballast water, and on ships' hulls. With volumes of maritime trade increasing globally, efforts to prevent these biological invasions are of significant importance. Both the International Maritime Organization and the Australian government have developed policy seeking to reduce the risk of these invasions. In this study, we constructed models for the transfer of ballast water into Australian waters, based on historic ballast survey data. We used these models to hindcast ballast water discharge over all vessels that arrived in Australian waters between 1999 and 2012. We used models for propagule survival to compare the risk of ballast-mediated propagule transport between ecoregions. We found that total annual ballast discharge volume into Australia more than doubled over the study period, with the vast majority of ballast water discharge and propagule pressure associated with bulk carrier traffic. As such, the ecoregions suffering the greatest risk are those associated with the export of mining commodities. As global marine trade continues to increase, effective monitoring and biosecurity policy will remain necessary to combat the risk of future marine invasion events.
Data from: Detection error influences both temporal seroprevalence predictions and risk factors associations in wildlife disease models
Understanding the prevalence of pathogens in invasive species is essential to guide efforts to prevent transmission to agricultural animals, wildlife, and humans. Pathogen prevalence can be difficult to estimate for wild species due to imperfect sampling and testing (pathogens may not be detected in infected individuals and erroneously detected in individuals that are not infected). The invasive wild pig (Sus scrofa, also referred to as wild boar and feral swine) is one of the most widespread hosts of domestic animal and human pathogens in North America. We developed hierarchical Bayesian models that account for imperfect detection to estimate the seroprevalence of five pathogens (porcine reproductive and respiratory syndrome virus, pseudorabies virus, Influenza A virus in swine, Hepatitis E virus, and Brucella spp.) in wild pigs in the United States using a dataset of over 50,000 samples across nine years. To assess the effect of incorporating detection error in models, we also evaluated models that ignored detection error. Both sets of models included effects of demographic parameters on seroprevalence. We compared our predictions of seroprevalence to 40 published studies, only one of which accounted for imperfect detection. We found a range of seroprevalence among the pathogens with a high seroprevalence of pseudorabies virus, indicating significant risk to livestock and wildlife. Demographics had mostly weak effects, indicating that other variables may have greater effects in predicting seroprevalence. Models that ignored detection error led to different predictions of seroprevalence as well as different inferences on the effects of demographic parameters. Our results highlight the importance of incorporating detection error in models of seroprevalence and demonstrate that ignoring such error may lead to erroneous conclusions about the risk associated with pathogen transmission. When using opportunistic sampling data to model seroprevalence and evaluate risk factors, detection error should be included.
Supplemental Data for "Modeled Fetal Risk of Genetic Diseases Identified by Expanded Carrier Screening"
<p>Data file accompanying: Haque IS, Lazarin GA, Kang HP, Evans EA, Goldberg JD, Wapner RJ. Modeled Fetal Risk of Genetic Diseases Identified by Expanded Carrier Screening. <em>JAMA. </em>2016;316(7):734-742. doi:10.1001/jama.2016.11139</p> <p>(SRC = self-reported racial/ethnic category; TG = targeted genotyping; NGS = next-generation sequencing)</p> <p>Data file includes:</p> <ul> <li><strong>Couple Data: </strong>Number of self-identified reproductive couples, separated by tandem or sequential screening status and by (mother SRC, father SRC)</li> <li><strong>Disease Severity</strong>: List of all diseases tested with severity rating as used in the manuscript.</li> <li><strong>Allele Data</strong>: Listing of all alleles considered pathogenic in manuscript's data analysis, with number of observations and number of tested chromosomes in each SRC.</li> <li><strong>Chromosome Frequencies</strong>: for each disease in each SRC: <ul> <li>Effective total chromosome count (effective sample size after integrating TG and NGS-only alleles).</li> <li>Beta posterior a,b: parameters a, b for the best-fit beta distribution approximating the probability that a random chromosome in this SRC carries a pathogenic allele (integrating both TG and NGS alleles).</li> <li># Chromosomes total/positive for TG alleles</li> <li># Chromosomes total/positive for NGS alleles</li> <li># Chromosomes total/positive for individuals tested by TG</li> <li># Chromosomes total/positive for individuals tested by NGS</li> </ul> </li> <li><strong>Disease Risks</strong>: for each disease in each pairing of SRCs <ul> <li>Father/Mother computed carrier frequency: probability that a random individual from father/mother's SRC is a carrier for the given disease</li> <li>Computed risk of affected conceptus (mean, 2.5, 97.5 percentiles): mean and CI of the posterior distribution over the probability that a random conceptus arising from the racial/ethnic pairing indicated would be homozygous or compound heterozygous for pathogenic alleles for the indicated disease.</li> <li>Computed carrier couple frequency: probability that a random couple from the given SRCs would be a carrier couple for the indicated disease (ie, that both members of the couple would be carriers for the indicated disease)</li> <li>Total couples: number of tandemly-tested couples of the indicated SRC pairing who both had the "routine carrier testing" indication for testing and were both tested for the given disease</li> <li>Number of carrier couple: from the set of "Total couples", the number of couples in which both members were carriers for the indicated disease</li> <li>Number of carrier couples expected: based on computed carrier couple frequency and number of tested couples, the expected number of carrier couples under the model described in sections 4.3.2 and 4.4 of the supplement.</li> <li>P-value: probability that the number of observed carrier couples or a more extreme count would have occurred by chance, given the posterior distribution over carrier couple counts (see section 4.4 of the supplement). One-tailed p-value.</li> </ul> </li> </ul>
Supplemental Materials - Performance Figures for "A Model for Predicting the (re)-occurrence of a ≥40% eGFR Decline in a large Population-based cohort of Persons with or At-Risk of Chronic Kidney Disease " paper
<p>The zip file contains performance metrics figures for each dynamic Bayesian Network (DBN) model, stratified by comorbidities, race, CKD stages, and ethnicity.</p> <p>Contains:</p> <ul> <li>Stratified: Bootstrapping of 1000 iterations and 1000 samples with stratified proportions (as in the original population of the test set) of rapid eGFR decliners and non-decliners.</li> </ul> <p> </p> <p>Second zip contains DBN structures as matrices for 2 periods study entry to entry period and entry period to year 1 for all sites in 2 excel files.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.