N-mixture models describe count data replicated in time and across sites in terms of abundance N and detectability p. They are popular because they allow inference about N while controlling for ...factors that influence p without the need for marking animals. Using a capture-recapture perspective, we show that the loss of information that results from not marking animals is critical, making reliable statistical modeling of N and p problematic using just count data. One cannot reliably fit a model in which the detection probabilities are distinct among repeat visits as this model is overspecified. This makes uncontrolled variation in p problematic. By counter example, we show that even if p is constant after adjusting for covariate effects (the "constant p" assumption) scientifically plausible alternative models in which N (or its expectation) is non-identifiable or does not even exist as a parameter, lead to data that are practically indistinguishable from data generated under an N-mixture model. This is particularly the case for sparse data as is commonly seen in applications. We conclude that under the constant p assumption reliable inference is only possible for relative abundance in the absence of questionable and/or untestable assumptions or with better quality data than seen in typical applications. Relative abundance models for counts can be readily fitted using Poisson regression in standard software such as R and are sufficiently flexible to allow controlling for p through the use covariates while simultaneously modeling variation in relative abundance. If users require estimates of absolute abundance, they should collect auxiliary data that help with estimation of p.
On the robustness of N-mixture models Link, William A.; Schofield, Matthew R.; Barker, Richard J. ...
Ecology (Durham),
July 2018, Letnik:
99, Številka:
7
Journal Article
Recenzirano
N-mixture models provide an appealing alternative to mark–recapture models, in that they allow for estimation of detection probability and population size from count data, without requiring that ...individual animals be identified. There is, however, a cost to using the N-mixture models: inference is very sensitive to the model’s assumptions. We consider the effects of three violations of assumptions that might reasonably be expected in practice: double counting, unmodeled variation in population size over time, and unmodeled variation in detection probability over time. These three examples show that small violations of assumptions can lead to large biases in estimation. The violations of assumptions we consider are not only small qualitatively, but are also small in the sense that they are unlikely to be detected using goodness-of-fit tests. In cases where reliable estimates of population size are needed, we encourage investigators to allocate resources to acquiring additional data, such as recaptures of marked individuals, for estimation of detection probabilities.
•This study provides a risk score that predicts critical care admission and death in COVID-19.•Chest radiography severity scores are highly predictive of outcome.•The findings may inform ...admit/discharge decisions as well as patient selection for clinical trials.
The COVID-19 pandemic continues to escalate. There is urgent need to stratify patients. Understanding risk of deterioration will assist in admission and discharge decisions, and help selection for clinical studies to indicate where risk of therapy-related complications is justified.
An observational cohort of patients acutely admitted to two London hospitals with COVID-19 and positive SARS-CoV-2 swab results was assessed. Demographic details, clinical data, comorbidities, blood parameters and chest radiograph severity scores were collected from electronic health records. Endpoints assessed were critical care admission and death. A risk score was developed to predict outcomes.
Analyses included 1,157 patients. Older age, male sex, comorbidities, respiratory rate, oxygenation, radiographic severity, higher neutrophils, higher CRP and lower albumin at presentation predicted critical care admission and mortality. Non-white ethnicity predicted critical care admission but not death. Social deprivation was not predictive of outcome. A risk score was developed incorporating twelve characteristics: age>40, male, non-white ethnicity, oxygen saturations<93%, radiological severity score>3, neutrophil count>8.0 x109/L, CRP>40 mg/L, albumin<34 g/L, creatinine>100 µmol/L, diabetes mellitus, hypertension and chronic lung disease. Risk scores of 4 or higher corresponded to a 28-day cumulative incidence of critical care admission or death of 40.7% (95% CI: 37.1 to 44.4), versus 12.4% (95% CI: 8.2 to 16.7) for scores less than 4.
Our study identified predictors of critical care admission and death in people admitted to hospital with COVID-19. These predictors were incorporated into a risk score that will inform clinical care and stratify patients for clinical trials.
Statistical thinking in wildlife biology and ecology has been profoundly influenced by the introduction of AIC (Akaike's information criterion) as a tool for model selection and as a basis for model ...averaging. In this paper, we advocate the Bayesian paradigm as a broader framework for multimodel inference, one in which model averaging and model selection are naturally linked, and in which the performance of AIC-based tools is naturally evaluated. Prior model weights implicitly associated with the use of AIC are seen to highly favor complex models: in some cases, all but the most highly parameterized models in the model set are virtually ignored a priori. We suggest the usefulness of the weighted BIC (Bayesian information criterion) as a computationally simple alternative to AIC, based on explicit selection of prior model probabilities rather than acceptance of default priors associated with AIC. We note, however, that both procedures are only approximate to the use of exact Bayes factors. We discuss and illustrate technical difficulties associated with Bayes factors, and suggest approaches to avoiding these difficulties in the context of model selection for a logistic regression. Our example highlights the predisposition of AIC weighting to favor complex models and suggests a need for caution in using the BIC for computing approximate posterior model weights.
Siderite (FeCO3) and cementite (Fe3C) layers develop naturally on carbon steel surfaces in aqueous carbon dioxide (CO2) environments. This study evaluates galvanic corrosion induced by such layers ...when coupled to bare carbon steel. In CO2-saturated, 50 °C, pH 5 conditions, the Fe3C-filmed carbon steel acted as the net cathode, significantly enhancing bare steel corrosion rates. Galvanic currents induced by the FeCO3-filmed steel were much lower, with FeCO3 removed from the surface as Fe3C was revealed concomitantly on bare steel. It is proposed that the presence of Fe3C amongst the FeCO3 layer is responsible for galvanic interaction, rather than FeCO3 itself.
•FeCO3 and Fe3C surface layers formed naturally on carbon steel coupons.•Galvanic current magnitude coupled to bare steel: Fe3C-layered > > FeCO3-layered coupons.•Gradual revealing of Fe3C on bare steel subsequently damaged FeCO3 layer.•Presence of Fe3C on the FeCO3-layered coupon was the cause of galvanic interaction.•Galvanic current when FeCO3-layered coupon coupled to pure iron > > carbon steel.
Premise of the Study
Spaceflight provides a unique environment in which to dissect plant stress response behaviors and to reveal potentially novel pathways triggered in space. We therefore analyzed ...the transcriptomes of Arabidopsis thaliana plants grown on board the International Space Station to find the molecular fingerprints of these space‐related response networks.
Methods
Four ecotypes (Col‐0, Ws‐2, Ler‐0 and Cvi‐0) were grown on orbit and then their patterns of transcript abundance compared to ground‐based controls using RNA sequencing.
Key Results
Transcripts from heat‐shock proteins were upregulated in all ecotypes in spaceflight, whereas peroxidase transcripts were downregulated. Among the shared and ecotype‐specific changes, gene classes related to oxidative stress and hypoxia were detected. These spaceflight transcriptional response signatures could be partly mimicked on Earth by a low oxygen environment and more fully by oxidative stress (H2O2) treatments.
Conclusions
These results suggest that the spaceflight environment is associated with oxidative stress potentially triggered, in part, by hypoxic response. Further, a shared spaceflight response may be through the induction of molecular chaperones (such as heat shock proteins) that help protect cellular machinery from the effects of oxidative damage. In addition, this research emphasizes the importance of considering the effects of natural variation when designing and interpreting changes associated with spaceflight experiments.
We consider estimator and model choice when estimating abundance from capture–recapture data. Our work is motivated by a mark–recapture distance sampling example, where model and estimator choice led ...to unexpectedly large disparities in the estimates. To understand these differences, we look at three estimation strategies (maximum likelihood estimation, conditional maximum likelihood estimation, and Bayesian estimation) for both binomial and Poisson models. We show that assuming the data have a binomial or multinomial distribution introduces implicit and unnoticed assumptions that are not addressed when fitting with maximum likelihood estimation. This can have an important effect in finite samples, particularly if our data arise from multiple populations. We relate these results to those of restricted maximum likelihood in linear mixed effects models.
Background
Decisions on funding new healthcare technologies assume that all health improvements are valued equally. However, public reaction to health technology assessment (HTA) decisions suggests ...there are health attributes that matter deeply to them but are not currently accounted for in the assessment process. We aimed to determine the relative importance of attributes of illness that influence the value placed on alleviating that illness.
Method
We conducted a discrete choice experiment survey that presented general public respondents with 15 funding decisions between hypothetical health conditions. The conditions were defined by five attributes that characterise serious illnesses, plus the health gain from treatment. Respondent preferences were modelled using conditional logistic regression and latent class analysis.
Results
905 members of the UK public completed the survey in November 2017. Respondents generally preferred to provide treatments for conditions with ‘better’ characteristics. The exception was treatment availability, where respondents preferred to provide treatments for conditions where there is no current treatment, and were prepared to accept lower overall health gain to do so. A subgroup of respondents preferred to prioritise ‘worse’ health states.
Conclusion
This study suggests a preference among the UK public for treating an unmet need; however, it does not suggest a preference for prioritising other distressing aspects of health conditions, such as limited life expectancy, or where patients are reliant on care. Our results are not consistent with the features currently prioritised in UK HTA processes, and the preference heterogeneity we identify presents a major challenge for developing broadly acceptable policy.
Recent advances in the routine access to space along with increasing opportunities to perform plant growth experiments on board the International Space Station have led to an ever-increasing body of ...transcriptomic, proteomic, and epigenomic data from plants experiencing spaceflight. These datasets hold great promise to help understand how plant biology reacts to this unique environment. However, analyses that mine across such expanses of data are often complex to implement, being impeded by the sheer number of potential comparisons that are possible. Complexities in how the output of these multiple parallel analyses can be presented to the researcher in an accessible and intuitive form provides further barriers to such research. Recent developments in computational systems biology have led to rapid advances in interactive data visualization environments designed to perform just such tasks. However, to date none of these tools have been tailored to the analysis of the broad-ranging plant biology spaceflight data. We have therefore developed the Test Of Arabidopsis Space Transcriptome (TOAST) database (https://astrobiology.botany.wisc.edu/astrobotany-toast) to address this gap in our capabilities. TOAST is a relational database that uses the Qlik database management software to link plant biology, spaceflight-related omics datasets, and their associated metadata. This environment helps visualize relationships across multiple levels of experiments in an easy to use gene-centric platform. TOAST draws on data from The US National Aeronautics and Space Administration's (NASA's) GeneLab and other data repositories and also connects results to a suite of web-based analytical tools to facilitate further investigation of responses to spaceflight and related stresses. The TOAST graphical user interface allows for quick comparisons between plant spaceflight experiments using real-time, gene-specific queries, or by using functional gene ontology, Kyoto Encyclopedia of Genes and Genomes pathway, or other filtering systems to explore genetic networks of interest. Testing of the database shows that TOAST confirms patterns of gene expression already highlighted in the literature, such as revealing the modulation of oxidative stress-related responses across multiple plant spaceflight experiments. However, this data exploration environment can also drive new insights into patterns of spaceflight responsive gene expression. For example, TOAST analyses highlight changes to mitochondrial function as likely shared responses in many plant spaceflight experiments.
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