The Analysis of Association Between Traits When Differences Between Trait States Matter

Because of their elementary significance in almost all fields of science, measures of association between two variables or traits are abundant and multiform. One aspect of association that is of considerable interest, especially in population genetics and ecology, seems to be widely ignored. This aspect concerns association between complex traits that show variable and arbitrarily defined state differences. Among such traits are genetic characters controlled by many and potentially polyploid loci, species characteristics, and environmental variables, all of which may be mutually and asymmetrically associated. A concept of directed association of one trait with another is developed here that relies solely on difference measures between the states of a trait. Associations are considered at three levels: between individual states of two variables, between an individual state of one variable and the totality of the other variable, and between two variables. Relations to known concepts of association are identified. In particular, measures at the latter two levels turn out to be interpretable as measures of differentiation. Examples are given for areas of application (search for functional relationships, distribution of variation over populations, genomic associations, spatiogenetic structure)

High spatial resolution analysis of ferromanganese concretions by LA-ICP-MS†

A procedure was developed for the determination of element distributions in cross-sections of ferromanganese concretions using laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The effects of carrier flow rates, rf forward power, ablation energy, ablation spot size, repetition rate and number of shots per point on analyte intensity were studied. It is shown that different carrier gas flow rates are required in order to obtain maximum sensitivities for different groups of elements, thus complicating the optimisation of ICP parameters. On the contrary, LA parameters have very similar effects on almost all elements studied, thus providing a common optimum parameter set for the entire mass range. However, for selected LA parameters, the use of compromise conditions was necessary in order to compensate for relatively slow data acquisition by ICP-MS and maintain high spatial resolution without sacrificing the multielemental capabilities of the technique. Possible variations in ablation efficiency were corrected for mathematically using the sum of Fe and Mn intensities. Quantification by external calibration against matrix-matched standards was successfully used for more than 50 elements. These standards, in the form of pressed pellets (no binder), were prepared in-house using ferromanganese concentrates from a deep-sea nodule reference material as well as from shallow-marine concretions varying in size and having different proportions of three major phases: aluminosilicates, Fe- and Mn-oxyhydroxides. Element concentrations in each standard were determined by means of conventional solution nebulisation ICP-MS following acid digestion. Examples of selected inter-element correlations in distribution patterns along the cross-section of a concretion are given

Robust metrics for assessing the performance of different verbal autopsy cause assignment methods in validation studies

<p>Abstract</p> <p>Background</p> <p>Verbal autopsy (VA) is an important method for obtaining cause of death information in settings without vital registration and medical certification of causes of death. An array of methods, including physician review and computer-automated methods, have been proposed and used. Choosing the best method for VA requires the appropriate metrics for assessing performance. Currently used metrics such as sensitivity, specificity, and cause-specific mortality fraction (CSMF) errors do not provide a robust basis for comparison.</p> <p>Methods</p> <p>We use simple simulations of populations with three causes of death to demonstrate that most metrics used in VA validation studies are extremely sensitive to the CSMF composition of the test dataset. Simulations also demonstrate that an inferior method can appear to have better performance than an alternative due strictly to the CSMF composition of the test set.</p> <p>Results</p> <p>VA methods need to be evaluated across a set of test datasets with widely varying CSMF compositions. We propose two metrics for assessing the performance of a proposed VA method. For assessing how well a method does at individual cause of death assignment, we recommend the average chance-corrected concordance across causes. This metric is insensitive to the CSMF composition of the test sets and corrects for the degree to which a method will get the cause correct due strictly to chance. For the evaluation of CSMF estimation, we propose CSMF accuracy. CSMF accuracy is defined as one minus the sum of all absolute CSMF errors across causes divided by the maximum total error. It is scaled from zero to one and can generalize a method's CSMF estimation capability regardless of the number of causes. Performance of a VA method for CSMF estimation by cause can be assessed by examining the relationship across test datasets between the estimated CSMF and the true CSMF.</p> <p>Conclusions</p> <p>With an increasing range of VA methods available, it will be critical to objectively assess their performance in assigning cause of death. Chance-corrected concordance and CSMF accuracy assessed across a large number of test datasets with widely varying CSMF composition provide a robust strategy for this assessment.</p

A Single Model Procedure for Estimating Tank Calibration Equations

A fundamental component of any accountability system for nuclear materials is a tank calibration equation that relates the height of liquid in a tank to its volume. Tank volume calibration equations are typically determined from pairs of height and volume measurements taken in a series of calibration runs. After raw calibration data are standardized to a fixed set of reference conditions, the calibration equation is typically fit by dividing the data into several segments--corresponding to regions in the tank--and independently fitting the data for each segment. The estimates obtained for individual segments must then be combined to obtain an estimate of the entire calibration function. This process is tedious and time-consuming. Moreover, uncertainty estimates may be misleading because it is difficult to properly model run-to-tun variability and between-segment correlation. In this paper, they describe a model whose parameters can be estimated simultaneously for all segments of the calibration data, thereby eliminating the need for segment-by-segment estimation. The essence of the proposed model is to define a suitable polynomial to fit to each segment and then extend its definition to the domain of the entire calibration function, so that it (the entire calibration function) can be expressed as the sum of these extended polynomials. The model provides defensible estimates of between-run variability and yields a proper treatment of between-segments correlations. A portable software package, called TANCS, has been developed to facilitate the acquisition, standardization, and analysis of tank calibration data. The TANCS package was used for the calculations in an example presented to illustrate the unified modeling approach described in this paper. With TANCS, a trial calibration function can be estimated and evaluated in a matter of minutes