Estimating age of mature adults from the degeneration of the sternal end of the clavicle

The sternal end of the clavicle has been illustrated to be useful in aging young adults, however, no studies have investigated what age-related changes occur to the sternal end post epiphyseal fusion. In this study, three morphological features (i.e., surface topography, porosity, and osteophyte formation) were examined and scored using 564 clavicles of individuals of European ancestry (n = 318 males; n = 246 females), with known ages of 40+ years, from four documented skeletal collections: Hamann-Todd, Pretoria, St. Bride's, and Coimbra. An ordinal scoring method was developed for each of the three traits. Surface topography showed the strongest correlation with age, and composite scores (formed by summing the three separate trait scores) indicated progressive degeneration of the surface with increasing chronological age. Linear regression analyses were performed on the trait scores to produce pooled-sample age estimation equations. Blind tests of the composite score method and regression formulae on 56 individuals, aged 40+ years, from Christ Church Spitalfields, suggest accuracies of 96.4% for both methods. These preliminary results display the first evidence of the utility of the sternal end of the clavicle in aging older adult individuals. However, in the current format, these criteria should only be applied to individuals already identified as over 40 years in order to refine the age ranges used for advanced age. These findings do suggest the sternal end of the clavicle has potential to aid age estimates beyond the traditional "mature adult" age category (i.e., 46+ years), and provides several suggestions for future research.

Toward multifactorial null models of range contraction in terrestrial vertebrates

The contraction of a species’ distribution range, which results from the extirpation of local populations, generally precedes its extinction. Therefore, understanding drivers of range contraction is important for conservation and management. Although there are many processes that can potentially lead to local extirpation and range contraction, three main null models have been proposed: demographic, contagion, and refuge. The first two models postulate that the probability of local extirpation for a given area depends on its relative position within the range; but these models generate distinct spatial predictions because they assume either a ubiquitous (demographic) or a clinal (contagion) distribution of threats. The third model (refuge) postulates that extirpations are determined by the intensity of human impacts, leading to heterogeneous spatial predictions potentially compatible with those made by the other two null models. A few previous studies have explored the generality of some of these null models, but we present here the first comprehensive evaluation of all three models. Using descriptive indices and regression analyses we contrast the predictions made by each of the null models using empirical spatial data describing range contraction in 386 terrestrial vertebrates (mammals, birds, amphibians, and reptiles) distributed across the World. Observed contraction patterns do not consistently conform to the predictions of any of the three models, suggesting that these may not be adequate null models to evaluate range contraction dynamics among terrestrial vertebrates. Instead, our results support alternative null models that account for both relative position and intensity of human impacts. These new models provide a better multifactorial baseline to describe range contraction patterns in vertebrates. This general baseline can be used to explore how additional factors influence contraction, and ultimately extinction for particular areas or species as well as to predict future changes in light of current and new threats.

Separation of natural colorants from the fermented broth of filamentous fungi using colloidal gas aphrons

There is a worldwide interest in the development of processes for producing colorants from natural sources. Microorganisms provide an alternative source of natural colorants produced by cultivation technology and extracted from the fermented broth. The aim of the present work was to study the recovery of red colorants from the fermented broth of Talaromyces amestolkiae using the technique of colloidal gas aphrons (CGA) comprising surfactant-stabilized microbubbles. Preliminary experiments were performed to evaluate the red colorants’ solubility in different organic solvents, octanol/water partitioning, and their stability in surfactant solutions, namely hexadecyl trimethylammonium bromide (CTAB), sodium dodecyl sulfate (SDS), and polyoxyethylenesorbitan monolaurate (Tween 20), which are cationic, anionic and nonionic surfactants, respectively. The first recovery experiments were carried out using CGA generated by these surfactants at different volumetric ratios (VR, 3–18). Subsequently, two different approaches to generate CGA were investigated at VR values of 6 and 12: the first involved the use of CTAB at pH 6.9–10.0, and the second involved the use of Tween 20 using red colorants partially dissolved in ethanol and Tween 20. The characterization results showed that red colorants have a hydrophilic nature. The highest recoveries were obtained with Tween 20 (78%) and CTAB (70%). These results demonstrated that the recovery of the colorants was driven by both electrostatic and hydrophobic interactions. The VR was found to be an important operating parameter and at VR 12 with CTAB (at pH 9) maximum recovery, partitioning coefficient (K = 5.39) and selectivity in relation to protein and sugar (SP = 3.75 and SS = 7.20 respectively) were achieved. Furthermore, with Tween 20, the separation was driven mainly by hydrophobic interactions. Overall CGA show promise for the recovery of red colorants from a fermented broth. Although better results were obtained with CTAB than with Tween 20 the latter may be more suitable for some application due to its lower toxicity.

The heterogeneity of wooded-agricultural landscape mosaics influences woodland bird community assemblages

Context Landscape heterogeneity (the composition and configuration of different landcover types) plays a key role in shaping woodland bird assemblages in wooded-agricultural mosaics. Understanding how species respond to landscape factors could contribute to preventing further decline of woodland bird populations. Objective To investigate how woodland birds with different species traits respond to landscape heterogeneity, and to identify whether specific landcover types are important for maintaining diverse populations in wooded-agricultural environments. Methods Birds were sampled from woodlands in 58 2 x 2 km tetrads across southern Britain. Landscape heterogeneity was quantified for each tetrad. Bird assemblage response was determined using redundancy analysis combined with variation partitioning and response trait analyses. Results For woodland bird assemblages, the independent explanatory importance of landscape composition and landscape configuration variables were closely interrelated. When considered simultaneously during variation partitioning, the community response was better represented by compositional variables. Different species responded to different landscape features and this could be explained by traits relating to woodland association, foraging strata and nest location. Ubiquitous, generalist species, many of which were hole-nesters or ground foragers, correlated positively with urban landcover while specialists of broadleaved woodland avoided landscapes containing urban areas. Species typical of coniferous woodland correlated with large conifer plantations. Conclusions At the 2 x 2 km scale, there was evidence that the availability of resources provided by proximate landcover types was highly important for shaping woodland bird assemblages. Further research to disentangle the effects of composition and configuration at different spatial scales is advocated.

Backward elimination model construction for regression and classification using leave-one-out criteria

A fundamental principle in practical nonlinear data modeling is the parsimonious principle of constructing the minimal model that explains the training data well. Leave-one-out (LOO) cross validation is often used to estimate generalization errors by choosing amongst different network architectures (M. Stone, "Cross validatory choice and assessment of statistical predictions", J. R. Stast. Soc., Ser. B, 36, pp. 117-147, 1974). Based upon the minimization of LOO criteria of either the mean squares of LOO errors or the LOO misclassification rate respectively, we present two backward elimination algorithms as model post-processing procedures for regression and classification problems. The proposed backward elimination procedures exploit an orthogonalization procedure to enable the orthogonality between the subspace as spanned by the pruned model and the deleted regressor. Subsequently, it is shown that the LOO criteria used in both algorithms can be calculated via some analytic recursive formula, as derived in this contribution, without actually splitting the estimation data set so as to reduce computational expense. Compared to most other model construction methods, the proposed algorithms are advantageous in several aspects; (i) There are no tuning parameters to be optimized through an extra validation data set; (ii) The procedure is fully automatic without an additional stopping criteria; and (iii) The model structure selection is directly based on model generalization performance. The illustrative examples on regression and classification are used to demonstrate that the proposed algorithms are viable post-processing methods to prune a model to gain extra sparsity and improved generalization.

Automatic nonlinear predictive model-construction algorithm using forward regression and the PRESS statistic

An automatic nonlinear predictive model-construction algorithm is introduced based on forward regression and the predicted-residual-sums-of-squares (PRESS) statistic. The proposed algorithm is based on the fundamental concept of evaluating a model's generalisation capability through crossvalidation. This is achieved by using the PRESS statistic as a cost function to optimise model structure. In particular, the proposed algorithm is developed with the aim of achieving computational efficiency, such that the computational effort, which would usually be extensive in the computation of the PRESS statistic, is reduced or minimised. The computation of PRESS is simplified by avoiding a matrix inversion through the use of the orthogonalisation procedure inherent in forward regression, and is further reduced significantly by the introduction of a forward-recursive formula. Based on the properties of the PRESS statistic, the proposed algorithm can achieve a fully automated procedure without resort to any other validation data set for iterative model evaluation. Numerical examples are used to demonstrate the efficacy of the algorithm.