On the use of spatially discrete data to compute energy and mass balance

In many practical applications the state of field soils is monitored by recording the evolution of temperature and soil moisture at discrete depths. We theoretically investigate the systematic errors that arise when mass and energy balances are computed directly from these measurements. We show that, even with no measurement or model errors, large residuals might result when finite difference approximations are used to compute fluxes and storage term. To calculate the limits set by the use of spatially discrete measurements on the accuracy of balance closure, we derive an analytical solution to estimate the residual on the basis of the two key parameters: the penetration depth and the distance between the measurements. When the thickness of the control layer for which the balance is computed is comparable to the penetration depth of the forcing (which depends on the thermal diffusivity and on the forcing period) large residuals arise. The residual is also very sensitive to the distance between the measurements, which requires accurately controlling the position of the sensors in field experiments. We also demonstrate that, for the same experimental setup, mass residuals are sensitively larger than the energy residuals due to the nonlinearity of the moisture transport equation. Our analysis suggests that a careful assessment of the systematic mass error introduced by the use of spatially discrete data is required before using fluxes and residuals computed directly from field measurements.

Pattern recognition ecological niche models fit to presence-only and presence-absence data

1. Identifying the boundary of a species' niche from observational and environmental data is a common problem in ecology and conservation biology and a variety of techniques have been developed or applied to model niches and predict distributions. Here, we examine the performance of some pattern-recognition methods as ecological niche models (ENMs). Particularly, one-class pattern recognition is a flexible and seldom used methodology for modelling ecological niches and distributions from presence-only data. The development of one-class methods that perform comparably to two-class methods (for presence/absence data) would remove modelling decisions about sampling pseudo-absences or background data points when absence points are unavailable. 2. We studied nine methods for one-class classification and seven methods for two-class classification (five common to both), all primarily used in pattern recognition and therefore not common in species distribution and ecological niche modelling, across a set of 106 mountain plant species for which presence-absence data was available. We assessed accuracy using standard metrics and compared trade-offs in omission and commission errors between classification groups as well as effects of prevalence and spatial autocorrelation on accuracy. 3. One-class models fit to presence-only data were comparable to two-class models fit to presence-absence data when performance was evaluated with a measure weighting omission and commission errors equally. One-class models were superior for reducing omission errors (i.e. yielding higher sensitivity), and two-classes models were superior for reducing commission errors (i.e. yielding higher specificity). For these methods, spatial autocorrelation was only influential when prevalence was low. 4. These results differ from previous efforts to evaluate alternative modelling approaches to build ENM and are particularly noteworthy because data are from exhaustively sampled populations minimizing false absence records. Accurate, transferable models of species' ecological niches and distributions are needed to advance ecological research and are crucial for effective environmental planning and conservation; the pattern-recognition approaches studied here show good potential for future modelling studies. This study also provides an introduction to promising methods for ecological modelling inherited from the pattern-recognition discipline.

Genetic association tests: a method for the joint analysis of family and case-control data.

With the trend in molecular epidemiology towards both genome-wide association studies and complex modelling, the need for large sample sizes to detect small effects and to allow for the estimation of many parameters within a model continues to increase. Unfortunately, most methods of association analysis have been restricted to either a family-based or a case-control design, resulting in the lack of synthesis of data from multiple studies. Transmission disequilibrium-type methods for detecting linkage disequilibrium from family data were developed as an effective way of preventing the detection of association due to population stratification. Because these methods condition on parental genotype, however, they have precluded the joint analysis of family and case-control data, although methods for case-control data may not protect against population stratification and do not allow for familial correlations. We present here an extension of a family-based association analysis method for continuous traits that will simultaneously test for, and if necessary control for, population stratification. We further extend this method to analyse binary traits (and therefore family and case-control data together) and accurately to estimate genetic effects in the population, even when using an ascertained family sample. Finally, we present the power of this binary extension for both family-only and joint family and case-control data, and demonstrate the accuracy of the association parameter and variance components in an ascertained family sample.

Accelerated Microstructure Imaging via Convex Optimization (AMICO) from diffusion MRI data.

Microstructure imaging from diffusion magnetic resonance (MR) data represents an invaluable tool to study non-invasively the morphology of tissues and to provide a biological insight into their microstructural organization. In recent years, a variety of biophysical models have been proposed to associate particular patterns observed in the measured signal with specific microstructural properties of the neuronal tissue, such as axon diameter and fiber density. Despite very appealing results showing that the estimated microstructure indices agree very well with histological examinations, existing techniques require computationally very expensive non-linear procedures to fit the models to the data which, in practice, demand the use of powerful computer clusters for large-scale applications. In this work, we present a general framework for Accelerated Microstructure Imaging via Convex Optimization (AMICO) and show how to re-formulate this class of techniques as convenient linear systems which, then, can be efficiently solved using very fast algorithms. We demonstrate this linearization of the fitting problem for two specific models, i.e. ActiveAx and NODDI, providing a very attractive alternative for parameter estimation in those techniques; however, the AMICO framework is general and flexible enough to work also for the wider space of microstructure imaging methods. Results demonstrate that AMICO represents an effective means to accelerate the fit of existing techniques drastically (up to four orders of magnitude faster) while preserving accuracy and precision in the estimated model parameters (correlation above 0.9). We believe that the availability of such ultrafast algorithms will help to accelerate the spread of microstructure imaging to larger cohorts of patients and to study a wider spectrum of neurological disorders.

Bayesian estimation of speciation and extinction from incomplete fossil occurrence data.

The temporal dynamics of species diversity are shaped by variations in the rates of speciation and extinction, and there is a long history of inferring these rates using first and last appearances of taxa in the fossil record. Understanding diversity dynamics critically depends on unbiased estimates of the unobserved times of speciation and extinction for all lineages, but the inference of these parameters is challenging due to the complex nature of the available data. Here, we present a new probabilistic framework to jointly estimate species-specific times of speciation and extinction and the rates of the underlying birth-death process based on the fossil record. The rates are allowed to vary through time independently of each other, and the probability of preservation and sampling is explicitly incorporated in the model to estimate the true lifespan of each lineage. We implement a Bayesian algorithm to assess the presence of rate shifts by exploring alternative diversification models. Tests on a range of simulated data sets reveal the accuracy and robustness of our approach against violations of the underlying assumptions and various degrees of data incompleteness. Finally, we demonstrate the application of our method with the diversification of the mammal family Rhinocerotidae and reveal a complex history of repeated and independent temporal shifts of both speciation and extinction rates, leading to the expansion and subsequent decline of the group. The estimated parameters of the birth-death process implemented here are directly comparable with those obtained from dated molecular phylogenies. Thus, our model represents a step towards integrating phylogenetic and fossil information to infer macroevolutionary processes.

Accuracy of plant assemblage predictions from species distribution models varies along environmental gradients

Aim: Climatic niche modelling of species and community distributions implicitly assumes strong and constant climatic determinism across geographic space. This assumption had however never been tested so far. We tested it by assessing how stacked-species distribution models (S-SDMs) perform for predicting plant species assemblages along elevation. Location: Western Swiss Alps. Methods: Using robust presence-absence data, we first assessed the ability of topo-climatic S-SDMs to predict plant assemblages in a study area encompassing a 2800 m wide elevation gradient. We then assessed the relationships among several evaluation metrics and trait-based tests of community assembly rules. Results: The standard errors of individual SDMs decreased significantly towards higher elevations. Overall, the S-SDM overpredicted far more than they underpredicted richness and could not reproduce the humpback curve along elevation. Overprediction was greater at low and mid-range elevations in absolute values but greater at high elevations when standardised by the actual richness. Looking at species composition, the evaluation metrics accounting for both the presence and absence of species (overall prediction success and kappa) or focusing on correctly predicted absences (specificity) increased with increasing elevation, while the metrics focusing on correctly predicted presences (Jaccard index and sensitivity) decreased. The best overall evaluation - as driven by specificity - occurred at high elevation where species assemblages were shown to be under significant environmental filtering of small plants. In contrast, the decreased overall accuracy in the lowlands was associated with functional patterns representing any type of assembly rule (environmental filtering, limiting similarity or null assembly). Main Conclusions: Our study reveals interesting patterns of change in S-SDM errors with changes in assembly rules along elevation. Yet, significant levels of assemblage prediction errors occurred throughout the gradient, calling for further improvement of SDMs, e.g., by adding key environmental filters that act at fine scales and developing approaches to account for variations in the influence of predictors along environmental gradients.

Zonation for 3D aquifer characterization based on joint inversions of multimethod crosshole geophysical data

Predictive groundwater modeling requires accurate information about aquifer characteristics. Geophysical imaging is a powerful tool for delineating aquifer properties at an appropriate scale and resolution, but it suffers from problems of ambiguity. One way to overcome such limitations is to adopt a simultaneous multitechnique inversion strategy. We have developed a methodology for aquifer characterization based on structural joint inversion of multiple geophysical data sets followed by clustering to form zones and subsequent inversion for zonal parameters. Joint inversions based on cross-gradient structural constraints require less restrictive assumptions than, say, applying predefined petro-physical relationships and generally yield superior results. This approach has, for the first time, been applied to three geophysical data types in three dimensions. A classification scheme using maximum likelihood estimation is used to determine the parameters of a Gaussian mixture model that defines zonal geometries from joint-inversion tomograms. The resulting zones are used to estimate representative geophysical parameters of each zone, which are then used for field-scale petrophysical analysis. A synthetic study demonstrated how joint inversion of seismic and radar traveltimes and electrical resistance tomography (ERT) data greatly reduces misclassification of zones (down from 21.3% to 3.7%) and improves the accuracy of retrieved zonal parameters (from 1.8% to 0.3%) compared to individual inversions. We applied our scheme to a data set collected in northeastern Switzerland to delineate lithologic subunits within a gravel aquifer. The inversion models resolve three principal subhorizontal units along with some important 3D heterogeneity. Petro-physical analysis of the zonal parameters indicated approximately 30% variation in porosity within the gravel aquifer and an increasing fraction of finer sediments with depth.

Monitoring network optimisation for spatial data classification using support vector machines

The paper presents a novel method for monitoring network optimisation, based on a recent machine learning technique known as support vector machine. It is problem-oriented in the sense that it directly answers the question of whether the advised spatial location is important for the classification model. The method can be used to increase the accuracy of classification models by taking a small number of additional measurements. Traditionally, network optimisation is performed by means of the analysis of the kriging variances. The comparison of the method with the traditional approach is presented on a real case study with climate data.

Accuracy of four cardiovascular risk scores in a Swiss population-based cohort

Purpose: To assess the global cardiovascular (CV) risk of an individual, several scores have been developed. However, their accuracy and comparability need to be evaluated in populations others from which they were derived. The aim of this study was to compare the predictive accuracy of 4 CV risk scores using data of a large population-based cohort. Methods: Prospective cohort study including 4980 participants (2698 women, mean age± SD: 52.7±10.8 years) in Lausanne, Switzerland followed for an average of 5.5 years (range 0.2 - 8.5). Two end points were assessed: 1) coronary heart disease (CHD), and 2) CV diseases (CVD). Four risk scores were compared: original and recalibrated Framingham coronary heart disease scores (1998 and 2001); original PROCAM score (2002) and its recalibrated version for Switzerland (IAS-AGLA); Reynolds risk score. Discrimination was assessed using Harrell's C statistics, model fitness using Akaike's information criterion (AIC) and calibration using pseudo Hosmer-Lemeshow test. The sensitivity, specificity and corresponding 95% confidence intervals were assessed for each risk score using the highest risk category ([20+ % at 10 years) as the "positive" test. Results: Recalibrated and original 1998 and original 2001 Framingham scores show better discrimination (>0.720) and model fitness (low AIC) for CHD and CVD. All 4 scores are correctly calibrated (Chi2<20). The recalibrated Framingham 1998 score has the best sensitivities, 37.8% and 40.4%, for CHD and CVD, respectively. All scores present specificities >90%. Framingham 1998, PROCAM and IAS-AGLA scores include the greatest proportion of subjects (>200) in the high risk category whereas recalibrated Framingham 2001 and Reynolds include <=44 subjects. Conclusion: In this cohort, we see variations of accuracy between risk scores, the original Framingham 2001 score demonstrating the best compromise between its accuracy and its limited selection of subjects in the highest risk category. We advocate that national guidelines, based on independently validated data, take into account calibrated CV risk scores for their respective countries.

Individual prediction of cognitive decline in mild cognitive impairment using support vector machine-based analysis of diffusion tensor imaging data.

Although cross-sectional diffusion tensor imaging (DTI) studies revealed significant white matter changes in mild cognitive impairment (MCI), the utility of this technique in predicting further cognitive decline is debated. Thirty-five healthy controls (HC) and 67 MCI subjects with DTI baseline data were neuropsychologically assessed at one year. Among them, there were 40 stable (sMCI; 9 single domain amnestic, 7 single domain frontal, 24 multiple domain) and 27 were progressive (pMCI; 7 single domain amnestic, 4 single domain frontal, 16 multiple domain). Fractional anisotropy (FA) and longitudinal, radial, and mean diffusivity were measured using Tract-Based Spatial Statistics. Statistics included group comparisons and individual classification of MCI cases using support vector machines (SVM). FA was significantly higher in HC compared to MCI in a distributed network including the ventral part of the corpus callosum, right temporal and frontal pathways. There were no significant group-level differences between sMCI versus pMCI or between MCI subtypes after correction for multiple comparisons. However, SVM analysis allowed for an individual classification with accuracies up to 91.4% (HC versus MCI) and 98.4% (sMCI versus pMCI). When considering the MCI subgroups separately, the minimum SVM classification accuracy for stable versus progressive cognitive decline was 97.5% in the multiple domain MCI group. SVM analysis of DTI data provided highly accurate individual classification of stable versus progressive MCI regardless of MCI subtype, indicating that this method may become an easily applicable tool for early individual detection of MCI subjects evolving to dementia.

Test result-based sampling: an efficient design for estimating the accuracy of patient safety indicators.

OBJECTIVE: Accuracy studies of Patient Safety Indicators (PSIs) are critical but limited by the large samples required due to low occurrence of most events. We tested a sampling design based on test results (verification-biased sampling [VBS]) that minimizes the number of subjects to be verified. METHODS: We considered 3 real PSIs, whose rates were calculated using 3 years of discharge data from a university hospital and a hypothetical screen of very rare events. Sample size estimates, based on the expected sensitivity and precision, were compared across 4 study designs: random and VBS, with and without constraints on the size of the population to be screened. RESULTS: Over sensitivities ranging from 0.3 to 0.7 and PSI prevalence levels ranging from 0.02 to 0.2, the optimal VBS strategy makes it possible to reduce sample size by up to 60% in comparison with simple random sampling. For PSI prevalence levels below 1%, the minimal sample size required was still over 5000. CONCLUSIONS: Verification-biased sampling permits substantial savings in the required sample size for PSI validation studies. However, sample sizes still need to be very large for many of the rarer PSIs.

Quantifying Accuracy Improvement in Sets of Pooled Judgments: Does Dialectical Bootstrapping Work?

Galton (1907) first demonstrated the "wisdom of crowds" phenomenon by averaging independent estimates of unknown quantities given by many individuals. Herzog and Hertwig (2009; hereafter H&H in Psychological Science) showed that individuals' own estimates can be improved by asking them to make two estimates at separate times and averaging them. H&H claimed to observe far greater improvement in accuracy when participants received "dialectical" instructions to consider why their first estimate might be wrong before making their second estimates than when they received standard instructions. We reanalyzed H&H's data using measures of accuracy that are unrelated to the frequency of identical first and second responses and found that participants in both conditions improved their accuracy to an equal degree.

Accuracy and predictability in use of AO three-dimensionally preformed titanium mesh plates for posttraumatic orbital reconstruction: a pilot study.

The aim of this study was to prospectively evaluate the accuracy and predictability of new three-dimensionally preformed AO titanium mesh plates for posttraumatic orbital wall reconstruction.We analyzed the preoperative and postoperative clinical and radiologic data of 10 patients with isolated blow-out orbital fractures. Fracture locations were as follows: floor (N = 7; 70%), medial wall (N = 1; 1%), and floor/medial wall (N = 2; 2%). The floor fractures were exposed by a standard transconjunctival approach, whereas a combined transcaruncular transconjunctival approach was used in patients with medial wall fractures. A three-dimensional preformed AO titanium mesh plate (0.4 mm in thickness) was selected according to the size of the defect previously measured on the preoperative computed tomographic (CT) scan examination and fixed at the inferior orbital rim with 1 or 2 screws. The accuracy of plate positioning of the reconstructed orbit was assessed on the postoperative CT scan. Coronal CT scan slices were used to measure bony orbital volume using OsiriX Medical Image software. Reconstructed versus uninjured orbital volume were statistically correlated.Nine patients (90%) had a successful treatment outcome without complications. One patient (10%) developed a mechanical limitation of upward gaze with a resulting handicapping diplopia requiring hardware removal. Postoperative orbital CT scan showed an anatomic three-dimensional placement of the orbital mesh plates in all of the patients. Volume data of the reconstructed orbit fitted that of the contralateral uninjured orbit with accuracy to within 2.5 cm(3). There was no significant difference in volume between the reconstructed and uninjured orbits.This preliminary study has demonstrated that three-dimensionally preformed AO titanium mesh plates for posttraumatic orbital wall reconstruction results in (1) a high rate of success with an acceptable rate of major clinical complications (10%) and (2) an anatomic restoration of the bony orbital contour and volume that closely approximates that of the contralateral uninjured orbit.

The data treatment dependent variability of U-Pb zircon ages obtained using mono-collector, sector field, laser ablation ICP-MS

U-Pb dating of zircons by laser ablation inductively coupled plasma mass spectrometry (LA-ICPMS) is a widely used analytical technique in Earth Sciences. For U-Pb ages below 1 billion years (1 Ga), Pb-206/U-238 dates are usually used, showing the least bias by external parameters such as the presence of initial lead and its isotopic composition in the analysed mineral. Precision and accuracy of the Pb/U ratio are thus of highest importance in LA-ICPMS geochronology. We consider the evaluation of the statistical distribution of the sweep intensities based on goodness-of-fit tests in order to find a model probability distribution fitting the data to apply an appropriate formulation for the standard deviation. We then discuss three main methods to calculate the Pb/U intensity ratio and its uncertainty in the LA-ICPMS: (1) ratio-of-the-mean intensities method, (2) mean-of-the-intensity-ratios method and (3) intercept method. These methods apply different functions to the same raw intensity vs. time data to calculate the mean Pb/U intensity ratio. Thus, the calculated intensity ratio and its uncertainty depend on the method applied. We demonstrate that the accuracy and, conditionally, the precision of the ratio-of-the-mean intensities method are invariant to the intensity fluctuations and averaging related to the dwell time selection and off-line data transformation (averaging of several sweeps); we present a statistical approach how to calculate the uncertainty of this method for transient signals. We also show that the accuracy of methods (2) and (3) is influenced by the intensity fluctuations and averaging, and the extent of this influence can amount to tens of percentage points; we show that the uncertainty of these methods also depends on how the signal is averaged. Each of the above methods imposes requirements to the instrumentation. The ratio-of-the-mean intensities method is sufficiently accurate provided the laser induced fractionation between the beginning and the end of the signal is kept low and linear. We show, based on a comprehensive series of analyses with different ablation pit sizes, energy densities and repetition rates for a 193 nm ns-ablation system that such a fractionation behaviour requires using a low ablation speed (low energy density and low repetition rate). Overall, we conclude that the ratio-of-the-mean intensities method combined with low sampling rates is the most mathematically accurate among the existing data treatment methods for U-Pb zircon dating by sensitive sector field ICPMS.

An improved approach for predicting the distribution of rare and endangered species from occurrence and pseudo-absence data

1. Few examples of habitat-modelling studies of rare and endangered species exist in the literature, although from a conservation perspective predicting their distribution would prove particularly useful. Paucity of data and lack of valid absences are the probable reasons for this shortcoming. Analytic solutions to accommodate the lack of absence include the ecological niche factor analysis (ENFA) and the use of generalized linear models (GLM) with simulated pseudo-absences. 2. In this study we tested a new approach to generating pseudo-absences, based on a preliminary ENFA habitat suitability (HS) map, for the endangered species Eryngium alpinum. This method of generating pseudo-absences was compared with two others: (i) use of a GLM with pseudo-absences generated totally at random, and (ii) use of an ENFA only. 3. The influence of two different spatial resolutions (i.e. grain) was also assessed for tackling the dilemma of quality (grain) vs. quantity (number of occurrences). Each combination of the three above-mentioned methods with the two grains generated a distinct HS map. 4. Four evaluation measures were used for comparing these HS maps: total deviance explained, best kappa, Gini coefficient and minimal predicted area (MPA). The last is a new evaluation criterion proposed in this study. 5. Results showed that (i) GLM models using ENFA-weighted pseudo-absence provide better results, except for the MPA value, and that (ii) quality (spatial resolution and locational accuracy) of the data appears to be more important than quantity (number of occurrences). Furthermore, the proposed MPA value is suggested as a useful measure of model evaluation when used to complement classical statistical measures. 6. Synthesis and applications. We suggest that the use of ENFA-weighted pseudo-absence is a possible way to enhance the quality of GLM-based potential distribution maps and that data quality (i.e. spatial resolution) prevails over quantity (i.e. number of data). Increased accuracy of potential distribution maps could help to define better suitable areas for species protection and reintroduction.