A simple method for visualization of influential landmarks when using euclidean distance matrix analysis

Euclidean distance matrix analysis (EDMA) differs from most other morphometric methods for the analysis of landmark coordinate data in that it is coordinate-system invariant. However, strict adherence to coordinate-system invariance (for both biological and statistical reasons) introduces some difficulty in using graphic aids for the analysis and interpretation of EDMA results. We present a simple and effective graphic method to help localize important differences in form, growth, or shape by identifying “influential” landmarks. Examples are presented using simulated data and real data involving both children with craniofacial dysmorphologies and sexual dimorphism in adult Macaca fascicularis. Am J Phys Anthropol 107:273–283, 1998. © 1998 Wiley-Liss, Inc.     

Capturing juvenile tree dynamics from count data using Approximate Bayesian Computation

The juvenile life stage is a crucial determinant of forest dynamics and a first indicator of changes to species' ranges under climate change. However, paucity of detailed re-measurement data of seedlings, saplings and small trees means that their demography is not well understood at large scales, and rarely represented in forest models in detail. In this study we quantify the effects of climate and density dependence on recruitment and juvenile growth and mortality rates of thirteen species measured in the Spanish Forest Inventory. Single-census sapling count data is used to constrain demographic parameters of a simple forest juvenile dynamics model based on the perfect plasticity approximation model (PPA) within a likelihood-free parameterisation method, Approximate Bayesian Computation. Our results highlight marked differences between species, and the important role of climate and stand structure, in controlling juvenile dynamics. Recruitment had a hump-shaped relationship with conspecific density, and for most species conspecific competition had a stronger negative effect than heterospecific competition. Mediterranean species showed on average higher mortality and lower growth rates than temperate species, and in low density stands recruitment and mortality rates were positively correlated. Under climate change our model predicted declines in recruitment rates for almost all species. Reliable predictive models of forest dynamics should include realistic representation of critical early life-stage processes and our approach demonstrates that existing coarse count data can be used to parameterise such models. Approximate Bayesian Computation may have wide application in many fields of ecology to unlock information about past processes from single survey observations.     

Resolving issues of imprecise and habitat-biased locations in ecological analyses using GPS telemetry data

Global positioning system (GPS) technologies collect unprecedented volumes of animal location data, providing ever greater insight into animal behaviour. Despite a certain degree of inherent imprecision and bias in GPS locations, little synthesis regarding the predominant causes of these errors, their implications for ecological analysis or solutions exists. Terrestrial deployments report 37 per cent or less non-random data loss and location precision 30 m or less on average, with canopy closure having the predominant effect, and animal behaviour interacting with local habitat conditions to affect errors in unpredictable ways. Home-range estimates appear generally robust to contemporary levels of location imprecision and bias, whereas movement paths and inferences of habitat selection may readily become misleading. There is a critical need for greater understanding of the additive or compounding effects of location imprecision, fix-rate bias, and, in the case of resource selection, map error on ecological insights. Technological advances will help, but at present analysts have a suite of ad hoc statistical corrections and modelling approaches available—tools that vary greatly in analytical complexity and utility. The success of these solutions depends critically on understanding the error-inducing mechanisms, and the biggest gap in our current understanding involves species-specific behavioural effects on GPS performance.     

Linear constrain relations in biochemical reaction systems III. Sequential application of data reconciliation for sensitive detection of systematic errors

This article presents a method to test the presence of relatively small systematic measurement errors; e.g., those caused by inaccurate calibration or sensor drift. To do this, primary measurements-flow rates and concentrations-are first translated into observed conversions, which should satisfy several constraints, like the laws of conservation of chemical elements. This study considers three objectives: 1.Modification of the commonly used balancing technique to improve error sensitivity to be able to detect small systematic errors. To this end, the balancing technique is applied sequentially in time.2.Extension of the method to enable direct diagnosis of errors in the primary measurements instead of diagnosing errors in the observed conversions. This was achieved by analyzing how individual errors in the primary measurements are expressed in the residual vector.3.Derivation of a new systematic method to quantitatively determine the sensitivity of the error, is that error size at which the expected value of the chisquare test function equals its critical value.The method is applied to industrial data demonstrating the effectiveness of the approach. It was shown that, for most possible error sources, a systematic errors of 2% to 5% could be detected. In given application, the variation of the N-content of biomass was appointed to be the cause of errors. (c) 1994 John Wiley & Sons, Inc.     

Determination of three-dimensional protein structures from nuclear magnetic resonance data using fragments of known structures

A method to build a three-dimensional protein model from nuclear magnetic resonance (NMR) data using fragments from a data base of crystallographically determined protein structures is presented. The interproton distances derived from the nuclear Overhauser effect (NOE) data are compared to the precalculated distances in the known protein structures. An efficient search algorithm is used, which arranges the distances in matrices akin to a C alpha diagonal distance plot, and compares the NOE distance matrices for short sequential zones of the protein to the data base matrices. After cluster analysis of the fragments found in this way, the structure is built by aligning fragments in overlapping zones. The sequentially long-range NOEs cannot be used in the initial fragments search but are vital to discriminate between several possible combinations of different groups of fragments. The method has been tested on one simulated NOE data set derived from a crystal structure and one experimental NMR data set. The method produces models that have good local structure, but may contain larger global errors. These models can be used as the starting point for further refinement, e.g., by restrained molecular dynamics or interactive graphics.     

Fuzzy clustering of fuzzy ecological data

Besides the problem of searching for effective methods for data analysis there are some additional problems with handling data of high uncertainty. Uncertainty problems often arise in an analysis of ecological data, e.g. in the cluster analysis of ecological data. Conventional clustering methods based on Boolean logic ignore the continuous nature of ecological variables and the uncertainty of ecological data. That can result in misclassification or misinterpretation of the data structure. Clusters with fuzzy boundaries reflect better the continuous character of ecological features. But the problem is, that the common clustering methods (like the fuzzy c-means method) are only designed for treating crisp data, that means they provide a fuzzy partition only for crisp data (e.g. exact measurement data). This paper presents the extension and implementation of the method of fuzzy clustering of fuzzy data proposed by Yang and Liu [Yang, M.-S. and Liu, H-H, 1999. Fuzzy clustering procedures for conical fuzzy vector data. Fuzzy Sets and Systems, 106, 189-200.]. The imprecise data can be defined as multidimensional fuzzy sets with not sharply formed boundaries (in the form of the so-called conical fuzzy vectors). They can then be used for the fuzzy clustering together with crisp data. That can be particularly useful when information is not available about the variances which describe the accuracy of the data and probabilistic approaches are impossible. The method proposed by Yang has been extended and implemented for the Fuzzy Clustering System EcoFucs developed at the University of Kiel. As an example, the paper presents the fuzzy cluster analysis of chemicals according to their ecotoxicological properties. The uncertainty and imprecision of ecotoxicological data are very high because of the use of various data sources, various investigation tests and the difficulty of comparing these data. The implemented method can be very helpful in searching for an adequate partition of ecological data into clusters with similar properties.     

A cranial morphometric study of deer (Mammalia, Cervidae) from Argentina using three-dimensional landmarks

This study examines morphological variation in the crania (n = 70) of eight cervid species from Argentina. Forty 3-dimensional landmarks were acquired on each adult cervid cranium. The data were analysed using Morphologika software. The co-ordinates were registered and scaled to remove size differences by Procrustes analysis, and then principal components analysis was applied to examine shape variation. Shape variation associated with each principal component can be visualised in the program. The first principal component correlates strongly with the centroid size of the crania and also with the body mass and height of each species. The larger species were distinguished by relatively longer snouts and relatively smaller brains. The smallerMazama andPudu species cluster closely on the first as well as the other principal components. Among the larger species, the twoHippocamelus species, which live at higher altitudes, were clearly distinguished from the lowland species,Ozotoceros bezoarticus andBlastocerus dichotomus, on the basis of cranial flexion and the orientation of the occipital region. FinallyO. bezoarticus andB. dichotomus were compared directly and small differences were noted in the orbital region. The shape data was used to produce a distance matrix and a phenogram, which we relate to some of currently accepted phylogenetic relationships of this group of cervids.     

Comparison of different calculations of three-dimensional joint kinematics from video-based system data

When skin-fixed marker trajectories are used to calculate 3D joint kinematics, the measurement errors (i.e. the difference between the trajectories of the external markers and those of the skeleton) influence to some extent the accuracy of the results, depending both on the calculation method and on the axes about which the rotations are expressed. The purpose of this paper is to compare several expressions of joint angular variations. Two kinematic concepts are used to calculate the changes in the orientation of the distal segment versus the proximal one: the first method consists of computing the components of the spatial attitude vector, the second one deals with the determination of elementary rotations about successive axes. For each of these methods, two sets of three axes are tested to express the results: the axes forming the reference frame affixed to the body segment adjacent to the joint (named fixed axes), and a set consisting of a first axis belonging to the proximal segment, a third axis belonging to the distal segment and a second (floating) axis defined as the cross-product between the two other ones (named mobile axes). To compare these four distinct expressions on the knee joint, numerical simulations of perturbed skin marker trajectories are performed, based on experimental data recorded by a Motion Analysis system during a normal gait cycle. A significant difference is pointed out only for the internal–external rotation angle, for which the best expression — from the viewpoint of sensitivity to experimental errors — is obtained using the components of the attitude vector in a segment-embedded reference frame.     

Segmentation of two- and three-dimensional data from electron microscopy using eigenvector analysis

An automatic image segmentation method is used to improve processing and visualization of data obtained by electron microscopy. Exploiting affinity criteria between pixels, e.g., proximity and gray level similarity, in conjunction with an eigenvector analysis, the image is subdivided into areas which correspond to objects or meaningful regions. Extending a proposal by Shi and Malik (1997, Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 731-737) the approach was adapted to the field of electron microscopy, especially to three-dimensional application as needed by electron tomography. Theory, implementation, parameter setting, and results obtained with a variety of data are presented and discussed. The method turns out to be a powerful tool for visualization with the potential for further improvement by developing and tuning new affinity.     

Risk prediction with imperfect survival outcome information from electronic health records

Readily available proxies for the time of disease onset such as the time of the first diagnostic code can lead to substantial risk prediction error if performing analyses based on poor proxies. Due to the lack of detailed documentation and labor intensiveness of manual annotation, it is often only feasible to ascertain for a small subset the current status of the disease by a follow-up time rather than the exact time. In this paper, we aim to develop risk prediction models for the onset time efficiently leveraging both a small number of labels on the current status and a large number of unlabeled observations on imperfect proxies. Under a semiparametric transformation model for onset and a highly flexible measurement error model for proxy onset time, we propose the semisupervised risk prediction method by combining information from proxies and limited labels efficiently. From an initially estimator solely based on the labeled subset, we perform a one-step correction with the full data augmenting against a mean zero rank correlation score derived from the proxies. We establish the consistency and asymptotic normality of the proposed semisupervised estimator and provide a resampling procedure for interval estimation. Simulation studies demonstrate that the proposed estimator performs well in a finite sample. We illustrate the proposed estimator by developing a genetic risk prediction model for obesity using data from Mass General Brigham Healthcare Biobank.     

Estimation of heritability using variety trials data from incomplete blocks

An assessment of the heritability of a trait is useful in formulating a breeding strategy for crop improvement. We have considered the estimation of broad-sense heritability from a single-location trial and from multi-locational trials conducted in incomplete blocks. Using residual maximum likelihood estimates of variance components, we estimated the heritability and obtained expressions for the estimate of its bias and its standard error. The estimation procedure is illustrated for 25 barley genotypes evaluated at four locations in West Asia and North Africa during 1992.     

A cross-validation statistical framework for asymmetric data integration

The proliferation of biobanks and large public clinical data sets enables their integration with a smaller amount of locally gathered data for the purposes of parameter estimation and model prediction. However, public data sets may be subject to context-dependent confounders and the protocols behind their generation are often opaque; naively integrating all external data sets equally can bias estimates and lead to spurious conclusions. Weighted data integration is a potential solution, but current methods still require subjective specifications of weights and can become computationally intractable. Under the assumption that local data are generated from the set of unknown true parameters, we propose a novel weighted integration method based upon using the external data to minimize the local data leave-one-out cross validation (LOOCV) error. We demonstrate how the optimization of LOOCV errors for linear and Cox proportional hazards models can be rewritten as functions of external data set integration weights. Significant reductions in estimation error and prediction error are shown using simulation studies mimicking the heterogeneity of clinical data as well as a real-world example using kidney transplant patients from the Scientific Registry of Transplant Recipients.     

Statistical analysis of binary data generated from multilocus dominant DNA markers

The use of methodologies such as RAPD and AFLP for studying genetic variation in natural populations is widespread in the ecology community. Because data generated using these methods exhibit dominance, their statistical treatment is less straightforward. Several estimators have been proposed for estimating population genetic parameters, assuming simple random sampling and the Hardy-Weinberg (HW) law. The merits of these estimators remain unclear because no comparative studies of their theoretical properties have been carried out. Furthermore, ascertainment bias has not been explicitly modelled. Here, we present a comparison of a set of candidate estimators of null allele frequency (q), locus-specific heterozygosity (h) and average heterozygosity () in terms of their bias, standard error, and root mean square error (RMSE). For estimating q and h, we show that none of the estimators considered has the least RMSE over the parameter space. Our proposed zero-correction procedure, however, generally leads to estimators with improved RMSE. Assuming a beta model for the distribution of null homozygote proportions, we show how correction for ascertainment bias can be carried out using a linear transform of the sample average of h and the truncated beta-binomial likelihood. Simulation results indicate that the maximum likelihood and empirical Bayes estimator of have negligible bias and similar RMSE. Ascertainment bias in estimators of is most pronounced when the beta distribution is J-shaped and negligible when the latter is inverse J-shaped. The validity of the current findings depends importantly on the HW assumption-a point that we illustrate using data from two published studies.     

Tradeoffs of different types of species occurrence data for use in systematic conservation planning

Data on the occurrence of species are widely used to inform the design of reserve networks. These data contain commission errors (when a species is mistakenly thought to be present) and omission errors (when a species is mistakenly thought to be absent), and the rates of the two types of error are inversely related. Point locality data can minimize commission errors, but those obtained from museum collections are generally sparse, suffer from substantial spatial bias and contain large omission errors. Geographic ranges generate large commission errors because they assume homogenous species distributions. Predicted distribution data make explicit inferences on species occurrence and their commission and omission errors depend on model structure, on the omission of variables that determine species distribution and on data resolution. Omission errors lead to identifying networks of areas for conservation action that are smaller than required and centred on known species occurrences, thus affecting the comprehensiveness, representativeness and efficiency of selected areas. Commission errors lead to selecting areas not relevant to conservation, thus affecting the representativeness and adequacy of reserve networks. Conservation plans should include an estimation of commission and omission errors in underlying species data and explicitly use this information to influence conservation planning outcomes.     

Bayesian nonparametric regression analysis of data with random effects covariates from longitudinal measurements

We consider nonparametric regression analysis in a generalized linear model (GLM) framework for data with covariates that are the subject-specific random effects of longitudinal measurements. The usual assumption that the effects of the longitudinal covariate processes are linear in the GLM may be unrealistic and if this happens it can cast doubt on the inference of observed covariate effects. Allowing the regression functions to be unknown, we propose to apply Bayesian nonparametric methods including cubic smoothing splines or P-splines for the possible nonlinearity and use an additive model in this complex setting. To improve computational efficiency, we propose the use of data-augmentation schemes. The approach allows flexible covariance structures for the random effects and within-subject measurement errors of the longitudinal processes. The posterior model space is explored through a Markov chain Monte Carlo (MCMC) sampler. The proposed methods are illustrated and compared to other approaches, the "naive" approach and the regression calibration, via simulations and by an application that investigates the relationship between obesity in adulthood and childhood growth curves.     

Gene regulatory network from microarray data of colon cancer patients using TSK-type recurrent neural fuzzy network

In this work we applied a TSK-type recurrent neural fuzzy approach to extract regulatory relationship among genes and reconstruct gene regulatory network from microarray data. The identified signature has captured the regulatory relationship among 27 differentially expressed genes from microarray dataset. We applied three different methods viz., feed forward neural fuzzy, modified genetic algorithm and recurrent neural fuzzy, on the same data set for the inference of GRNs and the results obtained are almost comparable. In all tested cases, TRNFN identified more biologically meaningful relations. We found that 87.8% of the total interactions extracted by TRNFN are correct in accordance with the biological knowledge. Our analysis resulted in 2 major outcomes. First, upregulated genes are regulated by more genes than downregulated genes. Second, tumor activators activate other tumor activators and suppress tumor suppressers strongly in the disease environment. These findings will help to elucidate the common molecular mechanism of colon cancer, and provide new insights into cancer diagnostics, prognostics and therapy.