Discover the most talked about and latest scientific content & concepts.

Concept: Extrapolation


The persistence of chemicals is a key parameter for their environmental risk assessment. Extrapolating their biodegradability potential in aqueous systems to soil systems would improve the environmental impact assessment. This study compares the fate of (14/13)C-labelled 2,4-D (2,4-dichlorophenoxyacetic acid) and ibuprofen in OECD tests 301 (ready biodegradability in aqueous systems) and 307 (soil). 85% of 2,4-D and 68% of ibuprofen were mineralised in aqueous systems, indicating ready biodegradability, but only 57% and 45% in soil. Parent compounds and metabolites decreased to <2% of the spiked amounts in both systems. In soil, 36% of 2,4-D and 30% of ibuprofen were bound in non-extractable residues (NER). NER formation in the abiotic controls was half as high as in the biotic treatments. However, mineralisation, biodegradation and abiotic residue formation are competing processes. Assuming the same extent of abiotic NER formation in abiotic and biotic systems may therefore overestimate the abiotic contribution in the biotic systems. Mineralisation was described by a logistic model for the aquatic systems and by a two-pool first order degradation model for the soil systems. This agrees with the different abundance of microorganisms in the two systems, but precludes direct comparison of the fitted parameters. Nevertheless, the maximum mineralisable amounts determined by the models were similar in both systems, although the maximum mineralisation rate was about 3.5 times higher in the aqueous systems than in the soil system for both compounds; these parameters may thus be extrapolated from aqueous to soil systems. However, the maximum mineralisable amount is calculated by extrapolation to infinite times and includes intermediately formed biomass derived from the labelled carbon. The amount of labelled carbon within microbial biomass residues is higher in the soil system, resulting in lower degradation rates. Further evaluation of these relationships requires comparison data on more chemicals and from different soils.

Concepts: Evaluation, Soil, Environmental remediation, C, Bioremediation, Impact assessment, Extrapolation, Environmental impact assessment


BACKGROUND: Absorption factors are required to convert physiologic requirements for iron into Dietary Reference Values, but the absorption from single meals cannot be used to estimate dietary iron absorption. OBJECTIVE: The objective was to conduct a systematic review of iron absorption from whole diets. DESIGN: A structured search was completed by using the Medline, EMBASE, and Cochrane CENTRAL databases from inception to November 2011. Formal inclusion and exclusion criteria were applied, and data extraction, validity assessment, and meta-analyses were undertaken. RESULTS: Nineteen studies from the United States, Europe, and Mexico were included. Absorption from diets was higher with an enhancer (standard mean difference: 0.53; 95% CI: 0.21, 0.85; P = 0.001) and was also higher when compared with low-bioavailability diets (standard mean difference: 0.96; 95% CI: 0.51, 1.41; P < 0.0001); however, single inhibitors did not reduce absorption (possibly because of the limited number of studies and participants and their heterogeneity). A regression equation to calculate iron absorption was derived by pooling data for iron status (serum and plasma ferritin) and dietary enhancers and inhibitors from 58 individuals (all from US studies): log[nonheme-iron absorption, %] = -0.73 log[ferritin, μg/L] + 0.11 [modifier] + 1.82. In individuals with serum ferritin concentrations from 6 to 80 μg/L, predicted absorption ranged from 2.1% to 23.0%. CONCLUSIONS: Large variations were observed in mean nonheme-iron absorption (0.7-22.9%) between studies, which depended on iron status (diet had a greater effect at low serum and plasma ferritin concentrations) and dietary enhancers and inhibitors. Iron absorption was predicted from serum ferritin concentrations and dietary modifiers by using a regression equation. Extrapolation of these findings to developing countries and to men and women of different ages will require additional high-quality controlled trials.

Concepts: Regression analysis, Statistics, Mathematics, Arithmetic mean, Iron deficiency anemia, Prediction interval, Forecasting, Extrapolation


As human activities expand beyond national jurisdictions to the high seas, there is increasing need to consider anthropogenic impacts to species that inhabit these waters. The current scarcity of scientific observations of cetaceans in the high seas impedes the assessment of population-level impacts of these activities. This study is directed towards an important management need in the high seas-the development of plausible density estimates to facilitate a quantitative assessment of anthropogenic impacts on cetacean populations in these waters. Our study region extends from a well-surveyed region within the United States Exclusive Economic Zone into a large region of the western North Atlantic sparsely surveyed for cetaceans. We modeled densities of 15 cetacean taxa using available line transect survey data and habitat covariates and extrapolated predictions to sparsely surveyed regions. We formulated models carefully to reduce the extent of extrapolation beyond covariate ranges, and constrained them to model simple and generalizable relationships. To evaluate confidence in the predictions, we performed several qualitative assessments, such as mapping where predictions were made outside sampled covariate ranges, and comparing them with maps of sightings from a variety of sources that could not be integrated into our models. Our study revealed a range of confidence levels for the model results depending on the taxon and geographic area, and highlights the need for additional surveying in environmentally distinct areas. Combined with their explicit confidence levels and necessary caution, our density estimates can inform a variety of management needs in the high seas, such as the quantification of potential cetacean interactions with military training exercises, shipping, fisheries, deep-sea mining, as well as delineation of areas of special biological significance in international waters. Our approach is generally applicable to other marine taxa and geographic regions for which management will be implemented but data are sparse. This article is protected by copyright. All rights reserved.

Concepts: Scientific method, Water, Sociology, Assessment, Qualitative research, Quantitative research, Social sciences, Extrapolation


Modern soil mapping is characterised by the need to interpolate point referenced (geostatistical) observations and the availability of large numbers of environmental characteristics for consideration as covariates to aid this interpolation. Modelling tasks of this nature also occur in other fields such as biogeography and environmental science. This analysis employs the Least Angle Regression (LAR) algorithm for fitting Least Absolute Shrinkage and Selection Operator (LASSO) penalized Multiple Linear Regressions models. This analysis demonstrates the efficiency of the LAR algorithm at selecting covariates to aid the interpolation of geostatistical soil carbon observations. Where an exhaustive search of the models that could be constructed from 800 potential covariate terms and 60 observations would be prohibitively demanding, LASSO variable selection is accomplished with trivial computational investment.

Concepts: Scientific method, Regression analysis, Linear regression, Dimension, Numerical analysis, Interpolation, Extrapolation, Soil science


(1) Estimate age, period and cohort effects for motorcyclist traffic casualties 1979-2008 in New Zealand and (2) forecast the incidence of New Zealand motorcycle traffic casualties for the period 2019-2023 assuming future age, cohort and period effects, and compare these with an estimate based on simple linear extrapolation.

Concepts: Regression analysis, Forecasting, Trend estimation, Extrapolation


BACKGROUND: In health technology assessments (HTAs) of interventions that affect survival, it is essential to accurately estimate the survival benefit associated with the new treatment. Generally, trial data must be extrapolated, and many models are available for this purpose. The choice of extrapolation model is critical because different models can lead to very different cost-effectiveness results. A failure to systematically justify the chosen model creates the possibility of bias and inconsistency between HTAs. OBJECTIVE: To demonstrate the limitations and inconsistencies associated with the survival analysis component of HTAs and to propose a process guide that will help exclude these from future analyses. METHODS: We reviewed the survival analysis component of 45 HTAs undertaken for the National Institute for Health and Clinical Excellence (NICE) in the cancer disease area. We drew upon our findings to identify common limitations and to develop a process guide. RESULTS: The chosen survival models were not systematically justified in any of the HTAs reviewed. The range of models considered was usually insufficient, and the rationale for the chosen model was universally limited: In particular, the plausibility of the extrapolated portion of fitted survival curves was very rarely explicitly considered. Limitations. We do not seek to describe and review all methods available for performing survival analysis-several approaches exist that are not mentioned in this article. Instead we seek to analyze methods commonly used in HTAs and limitations associated with their application. CONCLUSIONS: Survival analysis has not been conducted systematically in HTAs. A systematic approach such as the one proposed here is required to reduce the possibility of bias in cost-effectiveness results and inconsistency between technology assessments.

Concepts: Critical thinking, Survival analysis, Model, Unified Modeling Language, Analysis, Proposal, Extrapolation, National Institute for Health and Clinical Excellence


Most antiepileptic drugs (AEDs) receive regulatory approval for children years after the drug is available in adults, encouraging off-label use of the drug in children and hindering attempts to obtain quality pediatric data in controlled trials. Extrapolating adult efficacy data to pediatrics can reduce the time between approval in adults and that in children. To extrapolate efficacy from adults to children, several assumptions must be supported, such as (1) a similar disease progression and response to interventions in adults and children, and (2) similar exposure response in adults and children. The Pediatric Epilepsy Academic Consortium for Extrapolation (PEACE) addressed these assumptions in focal-onset seizures (FOS), the most common seizure type in both adults and children. PEACE reviewed the biological and clinical evidence that supported the assumptions that children with FOS have a similar disease progression and response to intervention as adults with FOS. After age 2 years, the pathophysiological underpinnings of FOS and the biological milieu in which seizures are initiated and propagated in children, seizure semiology, electroencephalographic features, etiology and AED response to FOS in children are similar to those in adults with FOS. PEACE concluded that extrapolation of efficacy data in adults to pediatrics in FOS is supported by strong scientific and clinical evidence. However, safety and pharmacokinetic (PK) data cannot be extrapolated from adults to children. Based on extrapolation, eslicarbazepine is now approved for children with FOS, down to age 4 years. Perampanel, lacosamide and brivaracetam are now undergoing PK and safety studies for the purposes of extrapolation down to age 2 or 4 years. When done in conjunction with PK and safety investigations in children, extrapolation of adult data from adults to children can reduce the time delay between approval of effective and safe AEDs in adults and approval in children.

Concepts: Pharmacology, Medicine, Epilepsy, Anticonvulsant, Seizure, Status epilepticus, Diazepam, Extrapolation


Various animal models have historically been used to study iatrogenic nerve injury during performance of conduction nerve blocks. Our aims were to compare the microstructures of nerves in commonly used species to those of humans and to explore the validity of the extrapolating these findings to humans.

Concepts: Nervous system, Human, Species, Mammal, Validity, Nerve, Extrapolation, Nerves


The rate constants of the two branches of H-abstractions from CH3OH by the H-atom and the corresponding reactions in the reverse direction are calculated using the one-dimensional semiclassical transition state theory (1D SCTST). In this method, only the reaction mode vibration of the transition state (TS) is treated anharmonically, while the remaining internal degrees of freedom are treated as they would have been in a standard TS theory calculation. A total of eightab initiosingle-point energy calculations are performed in addition to the computational cost of a standard TS theory calculation. This allows a second-order Richardson extrapolation method to be employed to improve the numerical estimation of the third- and fourth-order derivatives, which in turn are used in the calculation of the anharmonic constant. Hindered-rotor (HR) vibrations are identified in the equilibrium states of CH3OHand CH2OH, and the TSs of the reactions. The partition function of the HRs are calculated using both a simple harmonic oscillator model and a more sophisticated one-dimensional torsional eigenvalue summation (1D TES) method. The 1D TES method can be easily adapted in 1D SCTST computation. The resulting 1D SCTST with 1D TES rate constants show good agreement to previous theoretical and experimental works. The effects of the HR on rate constants for different reactions are also investigated.This article is part of the theme issue ‘Modern theoretical chemistry’.

Concepts: Mathematics, Quantum mechanics, Chemical reaction, Physics, Transition state, Oscillation, Transition state theory, Extrapolation


Apply methods of damped trend analysis to forecast inpatient glycemic control.

Concepts: Regression analysis, Weather, Forecasting, Trend estimation, Extrapolation, Weather forecasting