scholarly journals Advancing interpretation of stable isotope assignment maps: comparing and summarizing origins of known-provenance migratory bats

2020 ◽  
Vol 7 (1) ◽  
pp. 27-41
Author(s):  
Caitlin J. Campbell ◽  
Matthew C. Fitzpatrick ◽  
Hannah B. Vander Zanden ◽  
David M. Nelson
Keyword(s):  
Stable Isotope ◽  
Odds Ratio ◽  
Minimum Distance ◽  
A Priori ◽  
R Package ◽  
Threshold Values ◽  
Cumulative Sum ◽  
Geographic Origins ◽  
Stable Hydrogen Isotope ◽  
High Degree

AbstractProbability-of-origin maps deduced from stable isotope data are important for inferring broad-scale patterns of animal migration, but few resources and tools for interpreting and validating these maps exist. For example, quantitative tools for comparing multiple probability-of-origin maps do not exist, and many existing approaches for geographic assignment of individuals have not been validated or compared with respect to precision and accuracy. To address these challenges, we created and analyzed probability-of-origin maps using stable hydrogen isotope values from known-origin individuals of three species of migratory bat. We used a metric of spatial overlap to group individuals by areas of origin without a priori knowledge of such regions. The metric of spatial similarity allowed for quantitative comparison of geographic origins and grouping of individuals with similar origins. We then compared four approaches for inferring origins (cumulative-sum, odds-ratio, quantile-only, and quantile-simulation) across a range of thresholds and probable minimum distance traveled. The accuracy of geographic origins and minimum distance traveled varied across species at most threshold values for most approaches. The cumulative-sum and quantile-simulation approaches had generally higher precision at a given level of accuracy than the odds-ratio and quantile-only approaches, and many threshold values were associated with a relatively high degree (> 300 km) of variation in minimum distance traveled. Overall, these results reinforce the importance of validating assignment techniques with known-origin individuals when possible. We present the tools discussed as part of an R package, ‘isocat’ (“Isotope Origin Clustering and Assignment Tools”).

Joint DNA-based Disaster Victim Identification

2021 ◽  
Author(s):  
Magnus Dehli Vigeland ◽  
Thore Egeland
Keyword(s):  
A Priori ◽  
Search Space ◽  
R Package ◽  
Individual Identification ◽  
Disaster Victim Identification ◽  
Data Set ◽  
Victim Identification ◽  
Joint Identification ◽  
Available Information ◽  
User Friendly

Abstract We address computational and statistical aspects of DNA-based identification of victims in the aftermath of disasters. Current methods and software for such identification typically consider each victim individually, leading to suboptimal power of identification and potential inconsistencies in the statistical summary of the evidence. We resolve these problems by performing joint identification of all victims, using the complete genetic data set. Individual identification probabilities, conditional on all available information, are derived from the joint solution in the form of posterior pairing probabilities. A closed formula is obtained for the a priori number of possible joint solutions to a given DVI problem. This number increases quickly with the number of victims and missing persons, posing computational challenges for brute force approaches. We address this complexity with a preparatory sequential step aiming to reduce the search space. The examples show that realistic cases are handled efficiently. User-friendly implementations of all methods are provided in the R package dvir, freely available on all platforms.

scholarly journals multiHiCcompare: joint normalization and comparative analysis of complex Hi-C experiments

2019 ◽  
Vol 35 (17) ◽  
pp. 2916-2923 ◽  
Author(s):  
John C Stansfield ◽  
Kellen G Cresswell ◽  
Mikhail G Dozmorov
Keyword(s):  
Comparative Analysis ◽  
A Priori ◽  
Three Dimensional ◽  
R Package ◽  
Supplementary Information ◽  
Chromatin Interaction ◽  
Model Framework ◽  
Chromatin Interactions ◽  
Loess Regression ◽  
Sequencing Studies

Abstract Motivation With the development of chromatin conformation capture technology and its high-throughput derivative Hi-C sequencing, studies of the three-dimensional interactome of the genome that involve multiple Hi-C datasets are becoming available. To account for the technology-driven biases unique to each dataset, there is a distinct need for methods to jointly normalize multiple Hi-C datasets. Previous attempts at removing biases from Hi-C data have made use of techniques which normalize individual Hi-C datasets, or, at best, jointly normalize two datasets. Results Here, we present multiHiCcompare, a cyclic loess regression-based joint normalization technique for removing biases across multiple Hi-C datasets. In contrast to other normalization techniques, it properly handles the Hi-C-specific decay of chromatin interaction frequencies with the increasing distance between interacting regions. multiHiCcompare uses the general linear model framework for comparative analysis of multiple Hi-C datasets, adapted for the Hi-C-specific decay of chromatin interaction frequencies. multiHiCcompare outperforms other methods when detecting a priori known chromatin interaction differences from jointly normalized datasets. Applied to the analysis of auxin-treated versus untreated experiments, and CTCF depletion experiments, multiHiCcompare was able to recover the expected epigenetic and gene expression signatures of loss of chromatin interactions and reveal novel insights. Availability and implementation multiHiCcompare is freely available on GitHub and as a Bioconductor R package https://bioconductor.org/packages/multiHiCcompare. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Saudi women’s physical activity habits during pregnancy

2020 ◽  
Vol 16 ◽  
pp. 174550652095204
Author(s):  
Abeer A Alaglan ◽  
Rania F Almousa ◽  
Arwa A Alomirini ◽  
Elaf S Alabdularazaq ◽  
Renad S Alkheder ◽  
...  
Keyword(s):  
Physical Activity ◽  
Physical Exercise ◽  
Odds Ratio ◽  
A Priori ◽  
Activity Levels ◽  
Design Data ◽  
Cross Sectional ◽  
Saudi Women ◽  
Primary Health Care Centers ◽  
Exercise During Pregnancy

Background: Despite many benefits of physical exercise to women during pregnancy, the majority of Saudi women do not engage in an adequate amount of activity because of common barriers such as fatigue, lack of motivation, and childcare. The purpose of this study was to estimate the proportion of Saudi women who get adequate exercise during pregnancy as well as to evaluate their knowledge of, attitude toward, and barriers to physical exercise during pregnancy. Methods: This study had a cross-sectional design. Data were collected retrospectively (average time: 1 year after the birth), using translated questionnaires (English to Arabic), from primary health care centers and from the Maternity and Children Hospital in Qassim, Saudi Arabia. Logistic regression was employed to assess the a priori correlates of adequate exercise during pregnancy (primary outcome). Results: The sample included 274 women, who had a mean age of 31.9 years. A majority of the women thought that physical exercise during pregnancy was necessary and had high knowledge levels (mean = 77; median = 75) about types and amount of physical activity. Less than half of the women were either walking (26%) or exercising (42%) adequately (i.e. ⩾150 min/week). Age (odds ratio: 1.79), number of pregnancies (odds ratio: 2.41), attitude toward exercise (odds ratio: 2.71), and self-rated health (odds ratio: 2.50) were significant correlates of adequate exercise during pregnancy. Among those who reported no physical exercise during pregnancy ( n = 68), the following barriers were most common: tiredness (25.0%), fear (18.1%), dislike of exercise (16.7%), and lack of information (16.7%). Conclusion: For Saudi women, interventions are needed during pre-natal checkups to promote and maintain adequate physical activity levels during pregnancy.

Compartmental model of leucine kinetics in humans

1991 ◽  
Vol 261 (4) ◽  
pp. E539-E550 ◽  
Author(s):  
C. Cobelli ◽  
M. P. Saccomani ◽  
P. Tessari ◽  
G. Biolo ◽  
L. Luzi ◽  
...  
Keyword(s):  
Stable Isotope ◽  
Compartmental Model ◽  
Linear Time ◽  
A Priori ◽  
Normal Subjects ◽  
Whole Body ◽  
Constant Infusion ◽  
Individual Subject ◽  
Domain Of Validity

The complexity of amino acid and protein metabolism has limited the development of comprehensive, accurate whole body kinetic models. For leucine, simplified approaches are in use to measure in vivo leucine fluxes, but their domain of validity is uncertain. We propose here a comprehensive compartmental model of the kinetics of leucine and alpha-ketoisocaproate (KIC) in humans. Data from a multiple-tracer administration were generated with a two-stage (I and II) experiment. Six normal subjects were studied. In experiment I, labeled leucine and KIC were simultaneously injected into plasma. Four plasma leucine and KIC tracer concentration curves and label in the expired CO2 were measured. In experiment II, labeled bicarbonate was injected into plasma, and labeled CO2 in the expired air was measured. Radioactive (L-[1-14C]leucine, [4,5-3H]KIC, [14C]bicarbonate) and stable isotope (L-[1-13C]leucine, [5,5,5-2H3]KIC, [13C]bicarbonate) tracers were employed. The input format was a bolus (impulse) dose in the radioactive case and a constant infusion in the stable isotope case. A number of physiologically based, linear time-invariant compartmental models were proposed and tested against the data. The model finally chosen for leucine-KIC kinetics has 10 compartments: 4 for leucine, 3 for KIC, and 3 for bicarbonate. The model is a priori uniquely identifiable, and its parameters were estimated with precision from the five curves of experiment I. The separate assessment of bicarbonate kinetics (experiment II) was shown to be unnecessary. The model defines masses and fluxes of leucine in the organism, in particular its intracellular appearance from protein breakdown, its oxidation, and its incorporation into proteins. An important feature of the model is its ability to estimate leucine oxidation by resolving the bicarbonate model in each individual subject. Finally, the model allows the assessment of the domain of validity of the simpler commonly used models.

P5021Hypoperfusion markers identify patients with acute pulmonary embolism at highest risk for an adverse outcome

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
M Ebner ◽  
C Sentler ◽  
V P Harjola ◽  
H Bueno ◽  
K Keller ◽  
...  
Keyword(s):  
Pulmonary Embolism ◽  
Cardiac Arrest ◽  
High Risk ◽  
Predictive Value ◽  
Odds Ratio ◽  
Adverse Outcome ◽  
A Priori ◽  
Positive Likelihood Ratio ◽  
Adverse Outcomes ◽  
Prognostic Information

Abstract Background/Introduction According to the European Society of Cardiology (ESC) 2014 guideline, systemic hypotension (HT) is the critical variable defining high-risk in patients with pulmonary embolism (PE). However, signs of organ hypoperfusion might more adequately identify PE patients with cardiogenic shock due to right ventricular (RV) failure. Purpose We investigated whether hypoperfusion markers provide superior prognostic information for identifying PE patients at highest risk of early adverse outcomes. Methods Consecutive PE patients enrolled in a prospective single-centre registry between 09/2008 and 03/2018 were included. We analysed the predictive value of symptoms and findings suggesting hypoperfusion for in-hospital adverse outcome (catecholamine treatment, resuscitation or PE-related death) and in-hospital all-cause mortality. Results We analysed 814 patients, including 83 (10.2%) ESC 2014 high-risk patients. Patients presenting with cardiac arrest (CA, 4.5%) were a priori defined as high risk. Markers suggesting hypoperfusion of the brain (altered metal status, odds ratio [OR] 8.2 [95% CI, 4.2–16.0]), lung (respiratory insufficiency, 25.0 [9.4–66.7]) and tissue (venous lactate ≥2.2 mmol/l, 6.4 [3.2–12.9]) as well as HT (13.5 [6.7–27.2]) predicted an adverse outcome. The risk for an adverse outcome increased with the number of positive markers (AUC 0.86 [0.80–0.93]). Patients with ≥3 positive hypoperfusion markers had an OR of 42.9 (11.0–167.3) and patients defined as high-risk by the ESC 2014 an OR of 17.2 (8.8–33.3) with regard to an adverse outcome (Figure 1; Table 1). A new definition of high-risk (CA or ≥3 hypoperfusion markers) was associated with an OR of 73.2 (31.3–171.1) for an in-hospital adverse outcome and 26.2 (12.1–56.7) for in-hospital mortality. Table 1. Prognostic performance of hypoperfusion markers Adverse outcome (if negative) Adverse outcome (if positive) Sensitivity Specificity LR+ OR (95% CI) ≥1 hypoperfusion marker 1.1% 21.0% 91.9% 68.2% 2.9 24.4 (7.3–80.8) ≥2 hypoperfusion markers 4.7% 50.0% 48.6% 95.5% 10.9 20.3 (9.1–45.1) ≥3 hypoperfusion markers 6.5% 75.0% 24.3% 99.3% 32.7 42.9 (11.0–167.3) ESC 2014 high-risk 5.7% 51.1% 35.0% 96.9% 11.4 17.2 (8.8–33.3) Cardiac arrest 8.4% 86.5% 33.0% 99.3% 47.3 70.1 (26.4–186.1) Abbreviations: LR+, positive likelihood ratio; OR, odds ratio; CI, confidence interval. Figure 1. Frequency of adverse outcome Conclusions Markers of organ hypoperfusion have high predictive value for early adverse outcomes in acute PE. Risk increases with the number of positive markers and is critically elevated in patients presenting with CA or ≥3 markers. Acknowledgement/Funding This study was supported by the German Federal Ministry of Education and Research (BMBF 01EO1503).

scholarly journals Multiple Imputation of Multilevel Missing Data

SAGE Open ◽  
2016 ◽  
Vol 6 (4) ◽  
pp. 215824401666822 ◽  
Author(s):  
Simon Grund ◽  
Oliver Lüdtke ◽  
Alexander Robitzsch
Keyword(s):  
Missing Data ◽  
Multiple Imputation ◽  
Multilevel Models ◽  
R Package ◽  
Data Sets ◽  
Multilevel Data ◽  
Statistical Knowledge ◽  
Multilevel Research ◽  
User Friendly ◽  
High Degree

The treatment of missing data can be difficult in multilevel research because state-of-the-art procedures such as multiple imputation (MI) may require advanced statistical knowledge or a high degree of familiarity with certain statistical software. In the missing data literature, pan has been recommended for MI of multilevel data. In this article, we provide an introduction to MI of multilevel missing data using the R package pan, and we discuss its possibilities and limitations in accommodating typical questions in multilevel research. To make pan more accessible to applied researchers, we make use of the mitml package, which provides a user-friendly interface to the pan package and several tools for managing and analyzing multiply imputed data sets. We illustrate the use of pan and mitml with two empirical examples that represent common applications of multilevel models, and we discuss how these procedures may be used in conjunction with other software.

Lorazepam Is an Independent Risk Factor for Transitioning to Delirium in Intensive Care Unit Patients

2006 ◽  
Vol 104 (1) ◽  
pp. 21-26 ◽  
Author(s):  
Pratik Pandharipande ◽  
Ayumi Shintani ◽  
Josh Peterson ◽  
Brenda Truman Pun ◽  
Grant R. Wilkinson ◽  
...  
Keyword(s):  
Risk Factor ◽  
Odds Ratio ◽  
Independent Risk Factor ◽  
A Priori ◽  
Dose Administration ◽  
P Values ◽  
Mechanically Ventilated ◽  
Analgesic Dose ◽  
Ventilated Patients ◽  
Relevant Covariates

Background Delirium has recently been shown as a predictor of death, increased cost, and longer duration of stay in ventilated patients. Sedative and analgesic medications relieve anxiety and pain but may contribute to patients' transitioning into delirium. Methods In this cohort study, the authors designed a priori an investigation to determine whether sedative and analgesic medications independently increased the probability of daily transition to delirium. Markov regression modeling (adjusting for 11 covariates) was used in the evaluation of 198 mechanically ventilated patients to determine the probability of daily transition to delirium as a function of sedative and analgesic dose administration during the previous 24 h. Results Lorazepam was an independent risk factor for daily transition to delirium (odds ratio, 1.2 [95% confidence interval, 1.1-1.4]; P = 0.003), whereas fentanyl, morphine, and propofol were associated with higher but not statistically significant odds ratios. Increasing age and Acute Physiology and Chronic Health Evaluation II scores were also independent predictors of transitioning to delirium (multivariable P values < 0.05). Conclusions Lorazepam administration is an important and potentially modifiable risk factor for transitioning into delirium even after adjusting for relevant covariates.

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