Statistical and procedural issues in the use of heated taxidermic mounts

Studies using mounts have an inherently nested error structure; calibration and standardization should use the appropriate procedures and statistics. One example is that individual mount differences are nested within morphological factors related to species, age, or gender; without replication, mount differences may be confused with differences due to morphology. Also, the sensitivity of mounts to orientation to wind or sun is nested within mount; without replication, inadvertent variation in mount positioning may be confused with differences among mounts. Data on heat loss from a of 1-day-old mallard duckling mount are used to illustrate orientation sensitivity.     

Calibration belt for quality-of-care assessment based on dichotomous outcomes

Prognostic models applied in medicine must be validated on independent samples, before their use can be recommended. The assessment of calibration, i.e., the model's ability to provide reliable predictions, is crucial in external validation studies. Besides having several shortcomings, statistical techniques such as the computation of the standardized mortality ratio (SMR) and its confidence intervals, the Hosmer-Lemeshow statistics, and the Cox calibration test, are all non-informative with respect to calibration across risk classes. Accordingly, calibration plots reporting expected versus observed outcomes across risk subsets have been used for many years. Erroneously, the points in the plot (frequently representing deciles of risk) have been connected with lines, generating false calibration curves. Here we propose a methodology to create a confidence band for the calibration curve based on a function that relates expected to observed probabilities across classes of risk. The calibration belt allows the ranges of risk to be spotted where there is a significant deviation from the ideal calibration, and the direction of the deviation to be indicated. This method thus offers a more analytical view in the assessment of quality of care, compared to other approaches.     

Studying molecular motor-based cargo transport: what is real and what is noise?

Noise is a major problem in analyzing tracking data of cargos moved by molecular motors. We use Bayesian statistics to incorporate what is known about the noise in parsing the trajectory of a cargo into a series of constant velocity segments. Tracks with just noise and no underlying motion are fit with constant velocity segments to produce a calibration curve of fit quality versus average segment duration. Fits to tracks of moving cargos are compared to the calibration curves with similar noise. The fit with the optimum number of constant velocity states has the least number of segments needed to match the fit quality of the calibration curve. We have tested this approach using tracks with known underlying motion generated by computer simulations and with a specially designed in vitro experiment. We present the results of using this parsing approach to analyze transport of lipid droplets in Drosophila embryos.     

Testing the stability of transfer functions

In dendroclimatology, testing the stability of transfer functions using cross-calibration verification (CCV) statistics is a common procedure. However, the frequently used statistics reduction of error (RE) and coefficient of efficiency (CE) merely assess the skill of reconstruction for the validation period, which does not necessarily reflect possibly instable regression parameters. Furthermore, the frequently used rigorous threshold of zero which sharply distinguishes between valid and invalid transfer functions is prone to an underestimation of instability. To overcome these drawbacks, we here introduce a new approach – the Bootstrapped Transfer Function Stability test (BTFS). BTFS relies on bootstrapped estimates of the change of model parameters (intercept, slope, and r²) between calibration and verification period as well as the bootstrapped significance of corresponding models. A comparison of BTFS, CCV and a bootstrapped CCV approach (BCCV) applied to 42,000 pseudo-proxy datasets with known properties revealed that BTFS responded more sensitively to instability compared to CCV and BCCV. BTFS performance was significantly affected by sample size (length of calibration period) and noise (explained variance between predictor and predictand). Nevertheless, BTFS performed superior with respect to the detection of instable transfer functions in comparison to CCV.     

Measurement of statistical evidence on an absolute scale following thermodynamic principles

Statistical analysis is used throughout biomedical research and elsewhere to assess strength of evidence. We have previously argued that typical outcome statistics (including p values and maximum likelihood ratios) have poor measure-theoretic properties: they can erroneously indicate decreasing evidence as data supporting an hypothesis accumulate; and they are not amenable to calibration, necessary for meaningful comparison of evidence across different study designs, data types, and levels of analysis. We have also previously proposed that thermodynamic theory, which allowed for the first time derivation of an absolute measurement scale for temperature (T), could be used to derive an absolute scale for evidence (E). Here we present a novel thermodynamically based framework in which measurement of E on an absolute scale, for which “one degree” always means the same thing, becomes possible for the first time. The new framework invites us to think about statistical analyses in terms of the flow of (evidential) information, placing this work in the context of a growing literature on connections among physics, information theory, and statistics.     

Modelling species distribution in complex environments: an evaluation of predictive ability and reliability in five shorebird species

Aim  Many wader populations around the world are declining as a consequence of habitat degradation or loss. It is therefore important to identify species-specific habitat demands accurately and to define the important factors explaining species distribution, in order to develop tools that can be used in conservation planning. The aim of this study is to create reliable, functional and ecologically interpretable predictive distribution models for five breeding wader species.
Location  The archipelago of SW Finland in the Baltic Sea.
Methods  We used multivariate adaptive regression splines (MARS) to create single-species and multiresponse distribution models based on 525 study islands and 12 abiotic and biotic environmental variables. Model evaluation was carried out on independent data not used in model building (100 + 116 islands). The models were tested for discrimination with receiver-operating characteristic statistics and for calibration with Millers calibration statistics (MCS).
Results  The single-species models for the turnstone ( Arenaria interpres ), redshank ( Tringa totanus ) and oystercatcher ( Haematopus ostralegus ) showed good predictive abilities, regarding both discrimination and calibration, when evaluated on independent data. The multiresponse models for the less prevalent species, common sandpiper ( Actitis hypoleucos ) and the common ringed plover ( Charadrius hiaticula ) had better discriminative abilities than the single-species models. The most influential predictor overall was occurrence of small larids. Exposure, area of forest and low and flat areas were also important, as well as shore habitats.
Main conclusions  We found that the ability of MARS to fit non-linear and multiresponse models makes it a useful method to quantitatively relate species occurrence to environmental characteristics of a complex environment.     

Covariate measurement error adjustment for matched case-control studies

We propose a conditional scores procedure for obtaining bias-corrected estimates of log odds ratios from matched case-control data in which one or more covariates are subject to measurement error. The approach involves conditioning on sufficient statistics for the unobservable true covariates that are treated as fixed unknown parameters. For the case of Gaussian nondifferential measurement error, we derive a set of unbiased score equations that can then be solved to estimate the log odds ratio parameters of interest. The procedure successfully removes the bias in naive estimates, and standard error estimates are obtained by resampling methods. We present an example of the procedure applied to data from a matched case-control study of prostate cancer and serum hormone levels, and we compare its performance to that of regression calibration procedures.     

Cell Growth and Division: V. Error Analysis of the Collins-Richmond Equation

Error propagation in the Collins-Richmond equation is analyzed in order to obtain the ratio of the fractional error in rate of cell volume increase to the fractional error in each experimental variable. Typical data are analyzed numerically for the total errors resulting from counting statistics, from spectral broadening, and from volume calibration shift. The measurement of 10⁴ cells can give a precision of better than 10% in the volume growth rate with a volume resolution of 3%.     

RAId_aPS: MS/MS analysis with multiple scoring functions and spectrum-specific statistics

Statistically meaningful comparison/combination of peptide identification results from various search methods is impeded by the lack of a universal statistical standard. Providing an E-value calibration protocol, we demonstrated earlier the feasibility of translating either the score or heuristic E-value reported by any method into the textbook-defined E-value, which may serve as the universal statistical standard. This protocol, although robust, may lose spectrum-specific statistics and might require a new calibration when changes in experimental setup occur. To mitigate these issues, we developed a new MS/MS search tool, RAId_aPS, that is able to provide spectrum-specific-values for additive scoring functions. Given a selection of scoring functions out of RAId score, K-score, Hyperscore and XCorr, RAId_aPS generates the corresponding score histograms of all possible peptides using dynamic programming. Using these score histograms to assign E-values enables a calibration-free protocol for accurate significance assignment for each scoring function. RAId_aPS features four different modes: (i) compute the total number of possible peptides for a given molecular mass range, (ii) generate the score histogram given a MS/MS spectrum and a scoring function, (iii) reassign E-values for a list of candidate peptides given a MS/MS spectrum and the scoring functions chosen, and (iv) perform database searches using selected scoring functions. In modes (iii) and (iv), RAId_aPS is also capable of combining results from different scoring functions using spectrum-specific statistics. The web link is http://www.ncbi.nlm.nih.gov/CBBresearch/Yu/raid_aps/index.html. Relevant binaries for Linux, Windows, and Mac OS X are available from the same page.     

Criterion and Concurrent Validity of the activPAL? Professional Physical Activity Monitor in Adolescent Females

Background

The activPAL has been identified as an accurate and reliable measure of sedentary behaviour. However, only limited information is available on the accuracy of the activPAL activity count function as a measure of physical activity, while no unit calibration of the activPAL has been completed to date. This study aimed to investigate the criterion validity of the activPAL, examine the concurrent validity of the activPAL, and perform and validate a value calibration of the activPAL in an adolescent female population. The performance of the activPAL in estimating posture was also compared with sedentary thresholds used with the ActiGraph accelerometer.

Methodologies

Thirty adolescent females (15 developmental; 15 cross-validation) aged 15–18 years performed 5 activities while wearing the activPAL, ActiGraph GT3X, and the Cosmed K4B2. A random coefficient statistics model examined the relationship between metabolic equivalent (MET) values and activPAL counts. Receiver operating characteristic analysis was used to determine activity thresholds and for cross-validation. The random coefficient statistics model showed a concordance correlation coefficient of 0.93 (standard error of the estimate = 1.13). An optimal moderate threshold of 2997 was determined using mixed regression, while an optimal vigorous threshold of 8229 was determined using receiver operating statistics. The activPAL count function demonstrated very high concurrent validity (r = 0.96, p<0.01) with the ActiGraph count function. Levels of agreement for sitting, standing, and stepping between direct observation and the activPAL and ActiGraph were 100%, 98.1%, 99.2% and 100%, 0%, 100%, respectively.

Conclusions

These findings suggest that the activPAL is a valid, objective measurement tool that can be used for both the measurement of physical activity and sedentary behaviours in an adolescent female population.     

A method to estimate pollen viability from pollen size variation

The mean diameter of viable pollen grains is approximately 13 μm greater than the mean diameter of inviable grains in Mimulus guttatus. We show that this difference is large enough to be detected by particle counters and that these machines can be used to obtain a rapid estimate of pollen viability. While requiring a separate calibration, a size-based statistic is also strongly correlated with pollen viability in Collinsia verna. These results suggest that statistics derived from the size distribution of pollen grains may provide an alternative to more labor-intensive methods for estimating pollen viability, particularly in cases where inviability results from inbreeding depression or hybrid failure.     

Performance of reclassification statistics in comparing risk prediction models

Concerns have been raised about the use of traditional measures of model fit in evaluating risk prediction models for clinical use, and reclassification tables have been suggested as an alternative means of assessing the clinical utility of a model. Several measures based on the table have been proposed, including the reclassification calibration (RC) statistic, the net reclassification improvement (NRI), and the integrated discrimination improvement (IDI), but the performance of these in practical settings has not been fully examined. We used simulations to estimate the type I error and power for these statistics in a number of scenarios, as well as the impact of the number and type of categories, when adding a new marker to an established or reference model. The type I error was found to be reasonable in most settings, and power was highest for the IDI, which was similar to the test of association. The relative power of the RC statistic, a test of calibration, and the NRI, a test of discrimination, varied depending on the model assumptions. These tools provide unique but complementary information.     

Application of Near-Infrared Spectroscopy in Real-Time Monitoring of Product Attributes of Ribbed Roller Compacted Flakes

This study assessed the utility of near-infrared (NIR) spectroscopy for the real-time monitoring of content uniformity and critical quality attributes (tensile strength, Young’s modulus, and relative density) of ribbed roller compacted flakes made by axially corrugated or ribbed rolls. A custom-built setup was used to capture off-line NIR spectra from the flakes containing micronized chlorpheniramine maleate, microcrystalline cellulose, lactose, and magnesium stearate. The partial least square regression method was employed to build calibration models from these off-line NIR spectra using experimental design and validated using test set validation. During calibration model development, various factors, such as spectral acquisition mode, probe positioning, spectral preprocessing method, and beam size, were investigated to improve the prediction ability of the models. The statistical results obtained for calibration models and their validation revealed that dynamic spectral acquisition and proper probe positioning were very crucial to minimize the incorporation of variability in NIR spectra resulting from the flake’s undulation. Calibration and validation statistics also suggested the importance of selecting appropriate spectral preprocessing method and beam size. In this study, best calibration models resulted from standard normal variate followed by first derivative preprocessed dynamic spectra captured using beam size ~1.2 mm. Best calibration models constructed from off-line NIR spectra were used in real-time analysis of flake attributes. Finally, adequacy of best calibration models was established from real-time prediction results. Overall, with the proposed setup, it was possible to monitor the roller compaction process in real time for various properties associated with the ribbed flakes in a rapid, efficient, and nondestructive manner.     

MOSHFIT: Algorithms for occlusion-tolerant mean shape and rigid motion from 3D movement data

This work addresses the use of 3D point data to measure rigid motions, in the presence of occlusion and without reference to a prior model of relative point locations. This is a problem where cluster-based measurement techniques are used (e.g. for measuring limb movements) and no static calibration trial is available. The same problem arises when performing the task known as roving capture, in which a mobile 3D movement analysis system is moved through a volume with static markers in unknown locations and the ego-motion of the system is required in order to understand biomechanical activity in the environment. To provide a solution for both of these applications, the new concept of a visibility graph is introduced, and is combined with a generalised procrustes method adapted from ones used by the biological shape statistics and computer graphics communities. Recent results on shape space manifolds are applied to show sufficient conditions for convergence to unique solution. Algorithm source code is available and referenced here. Processing speed and rate of convergence are demonstrated using simulated data. Positional and angular accuracy are shown to be equivalent to approaches which require full calibration, to within a small fraction of input resolution. Typical processing times for sub-micron convergence are found to be fractions of a second, so the method is suitable for workflows where there may be time pressure such as in sports science and clinical analysis.     

Persistence matters: Estimation of the statistical significance of paleoclimatic reconstruction statistics from autocorrelated time series

Proxy data forms natural time series used to lengthen instrumental climatic records, and may contain a significant portion of autocorrelation. Increased serial correlation limits the number of independent observations, not satisfying the assumptions of conventional statistical methods. We estimate the significance of calibration and verification statistics used in dendroclimatic reconstructions by combining Monte-Carlo iterations with frequency (Ebisuzaki) or time (Burg) domain time series modelling. Significance tests are presented for Coefficient of Determination (R²), Coefficient of Correlation (r²), Reduction of Error (RE) and Coefficient of Error (CE) for time series ranging from very low to very high autocorrelation. Increased autocorrelation implies higher occurrences of relatively high but spurious reconstruction statistics. Ebisuzaki time series modelling shows greater robustness and its use is recommended over Burg's method, which penalizes the restriction in the number of autocorrelation coefficients imposed by the Akaike Information Criterion. Positive RE and CE values, traditionally viewed as successful reconstruction statistics, are not necessarily significant and depend on the temporal structure of the time series used. This approach is further implemented successfully to compute confidence intervals based on the temporal structure of the residuals of the transfer function. A Matlab^® package and a Windows executable file for non-Matlab^® users are provided to perform the described analyses.     

A near-infrared reflectance spectroscopic method for the direct analysis of several fodder-related chemical components in drumstick (Moringa oleifera Lam.) leaves

The drumstick tree has traditionally been used as foodstuff and fodder in several countries. Due to its high nutritional value and good biomass production, interest in this plant has increased in recent years. It has therefore become important to rapidly and accurately evaluate drumstick quality. In this study, we addressed the optimization of Near-infrared spectroscopy (NIRS) to analyze crude protein, crude fat, crude fiber, iron (Fe), and potassium (K) in a variety of drumstick accessions (N = 111) representing different populations, cultivation programs, and climates. Partial least-squares regression with internal cross-validation was used to evaluate the models and identify possible spectral outliers. The calibration statistics for these fodder-related chemical components suggest that NIRS can predict these parameters in a wide range of drumstick types with high accuracy. The NIRS calibration models developed in this study will be useful in predicting drumstick forage quality for these five quality parameters.     

A Note on Combining Dependent Tests of Significance

In combining several tests of significance the individual test statistics are allowed to be stochastically dependent. By choosing the weighted inverse normal method for the combination, the dependency of the original test statistics is then characterized by a correlation of the transformed statistics. For this correlation a confidence region, an unbiased estimator and an unbiased estimate of its variance are derived. The combined test statistic is extended to include the case of possibly dependent original test statistics. Simulation studies show the performance of the actual significance level.     

Mechanized systems for media dispensing, inoculation, and replication of microorganisms

Two instruments were developed to mechanize the handling of anaerobic microorganisms for microbial mutant isolation. The instruments automatically dispense liquid or agar medium to large and small 96-well platesand petridishes. Protocols were developed for inoculating different microorganisms, and calibration curves of number of areas or wells inoculated versus cell concentration were prepared for bacteria, yeast and fungi (spores). Experiments with yeast auxotrophic mutants and fungal spores showed that microbe inoculation follows Poisson statistics in distributing a single microorganism per inoculation point. The isolation and identification of Yarrowia lipolytica auxotrophic, morphological, and temperature-sensitive or tolerant mutants demonstrated the use of the instruments for microbial screening.