Patterns of sexual dimorphism and of variability in the dentition ofOtolemur crassicaudatus

Normative odontometric data are presented on a sample of 66 adult thick-tailed bushbabies Otolemur crassicaudatus(34 male, 32 female). This species is characterized by low levels of sexual dimorphism, with univariate differences centered on the canines and the maxillary third molar. Multivariate canonical analysis isolates a third discriminator, the maxillary second molar. Stepwise discriminant analyses, performed after jackknifing, indicate high percentages of correct classification (males, 79.8–81.8%;females, 81–85.2%). When variability profiles consisting of arrays of CVs are compared, males and females are found to share similar patterns. Data for maxillary teeth offer support for Gingerich’s occlusal complexity model, while morphogenetic clusterings within regressions of variability on tooth size conform to those previously reported in other species. These relationships are lost in the mandibular dentition, suggesting an independence of upper from lower toothsize determination.     

You are what you eat, whenever or wherever you eat it: an integrative analysis of fish food habits in Canadian and U.S.A. waters

The degree to which fish diet differs by season and area, particularly over broad scales, was examined for the first time in temperate, contiguous north-west Atlantic Ocean waters by comparing food habit data for 10 species of fishes collected concurrently during the spring and autumn surveys in the U.S.A. (Gulf of Maine proper and Georges Bank) and in the summer survey in Canada (western Scotian Shelf and Bay of Fundy). For most species, there was a general concurrence among the three seasons and four areas: summer diets had the same dominant prey items as spring and autumn diets. Although a suite of multivariate analyses did elucidate some differences in specific proportions of the diet for these species across seasons and areas, the main prey did not substantially change for most of these species. These results suggest that there are (1) minimal differences in diet across season for these species at these taxonomic resolutions, (2) there are minimal differences in diet geographically for these species and (3) differences across species, as expected, are important. Many fisheries ecosystem and multispecies models are dependent on food habit data, where resolving seasonal and spatial differences in diet remains an important consideration; however, the present work implies that amalgamated estimates of diet from seasonal surveys may be a reasonable approach when no finer seasonal resolution exists, as long as due diligence is exercised.     

Beyond facial width-to-height ratios: bizygomatic width is highly sexually dimorphic when adjusting for allometry

A large and ever-growing literature implicates male facial width-to-height ratio (bizygomatic width divided by facial height) as a secondary sexual trait linked to numerous physical and psychological perceptions. However, this research is based entirely on the premise that bizygomatic width is sexually dimorphic, which recent research has called into question. Unfortunately, statisticians for the last 125 years have noted that morphological ratio measurements may engender spurious correlations and biased effect-size estimates. In the current study, we find that bizygomatic width is highly sexually dimorphic (equivalent d = 1.39), even after adjusting for 92 allometric measurements, including multiple facial height and other craniofacial measurements (equivalent d = 1.07) in a sample of 6068 men and women. By contrast, facial width-to-height ratio (fWHR) measurements demonstrated a statistical pattern consistent with the age-old argument that morphological ratio measurements may engender spurious correlations and biased effect-size estimates. Thus, when avoiding facial ratio measurements and adjusting for allometry in craniofacial measures, we found strong support for a key premise in the human evolutionary and behavioural sciences that bizygomatic width exhibits male-biased sexual dimorphism.     

Bayesian Variable Selection with Joint Modeling of Categorical and Survival Outcomes: An Application to Individualizing Chemotherapy Treatment in Advanced Colorectal Cancer

Summary Colorectal cancer is the second leading cause of cancer related deaths in the United States, with more than 130,000 new cases of colorectal cancer diagnosed each year. Clinical studies have shown that genetic alterations lead to different responses to the same treatment, despite the morphologic similarities of tumors. A molecular test prior to treatment could help in determining an optimal treatment for a patient with regard to both toxicity and efficacy. This article introduces a statistical method appropriate for predicting and comparing multiple endpoints given different treatment options and molecular profiles of an individual. A latent variable‐based multivariate regression model with structured variance covariance matrix is considered here. The latent variables account for the correlated nature of multiple endpoints and accommodate the fact that some clinical endpoints are categorical variables and others are censored variables. The mixture normal hierarchical structure admits a natural variable selection rule. Inference was conducted using the posterior distribution sampling Markov chain Monte Carlo method. We analyzed the finite‐sample properties of the proposed method using simulation studies. The application to the advanced colorectal cancer study revealed associations between multiple endpoints and particular biomarkers, demonstrating the potential of individualizing treatment based on genetic profiles.     

Multivariate analysis of heavy metal and nutrient concentrations in sediments of Taihu Lake, China*

Taihu Lake is one of the largest freshwater lakes in China. The Lake is very shallow with a mean depth of 1.9 m and an area of 2428 km². Nutrient concentrations (Org-C, Tot-N and Tot-P) and heavy metal concentrations (Ag, As, Cd, Co, Cr, Cu, Hg, Ni, Pb, Sr, Zn, etc.) in the lake's surface sediments were sampled at 13 locations. This was done to determine if industrialized areas along the lake's coastline were impacting the nutrient and heavy metal distribution of the lake's surface sediments. Principal Component Analysis (PCA) was used to assess the degree of contamination and spatial distribution of heavy metals and nutrients in different areas of Taihu Lake.A distinctive spatial distribution of heavy metals and nutrients was observed. Sediments from a large embayment of Taihu Lake called Lake Wulihu had significantly higher nutrient concentrations (Org-C, 2.05–3.83%; Tot-N, 0.28–0.54%; Tot-P, 0.10–0.33%) than any other area of Taihu Lake. These high nutrient levels were associated with the input of untreated domestic sewage from the large (circa one million people) City of Wuxi, which discharges its effluents into the Liangxi River. As a result, Lake Wulihu is the most eutrophic area of Taihu Lake. The nearby Meiliang Bay suffered from the worst heavy metal contamination in Taihu Lake (e.g. As, 64.0; Ag, 4.2; Cd, 0.93; Co, 14.2; Cr, 155.0; Cu, 144.0; Hg, 0.25; Ni, 79.8; Pb, 143.0 and Zn, 471 mg kg^–1). These high heavy metal concentrations were ascribed to the discharge of untreated and partially treated industrial waste water from Changzhou and Wujin via the Zhihugang River. Surface sediment samples from the east basin of Taihu Lake were characterized by high Org-C (1.0–2.3%) and Tot-N (0.18–0.37%) and low Tot-P (0.048–0.056%) concentrations. It is likely that macrophytes removal accounts for a major reduction of phosphorus in the sediments of the east basin of Taihu Lake.     

Chemometrics applications in biotech processes: assessing process comparability

A typical biotech process starts with the vial of the cell bank, ends with the final product and has anywhere from 15 to 30 unit operations in series. The total number of process variables (input and output parameters) and other variables (raw materials) can add up to several hundred variables. As the manufacturing process is widely accepted to have significant impact on the quality of the product, the regulatory agencies require an assessment of process comparability across different phases of manufacturing (Phase I vs. Phase II vs. Phase III vs. Commercial) as well as other key activities during product commercialization (process scale-up, technology transfer, and process improvement). However, assessing comparability for a process with such a large number of variables is nontrivial and often companies resort to qualitative comparisons. In this article, we present a quantitative approach for assessing process comparability via use of chemometrics. To our knowledge this is the first time that such an approach has been published for biotech processing. The approach has been applied to an industrial case study involving evaluation of two processes that are being used for commercial manufacturing of a major biosimilar product. It has been demonstrated that the proposed approach is able to successfully identify the unit operations in the two processes that are operating differently. We expect this approach, which can also be applied toward assessing product comparability, to be of great use to both the regulators and the industry which otherwise struggle to assess comparability.     

Progressive changes in the structure and dynamics of the phytoplankton community along a pollution gradient in a lowland river — a multivariate approach

The phytoplankton of the River Lujan (Buenos Aires, Argentina) was studied for a period of 18 months, together with physical and chemical variables, in relation to a pollution gradient. 167 taxa were recorded within a seasonal succession characterized by dominance of diatoms with a brief summer green algae facies. A combination of several biotic indices and multivariate analysis was employed to assess the impact of pollution on the phytoplankton community. The biotic indices used were species diversity and richness, algal quotients (green algae/diatom ratio, Centrales/Pennales ratio) and the SD succession rate index. Multivariate procedures included cluster analysis and ordination by PCA of both species and samples, stepwise discriminant analysis and multiple discriminant analysis of variance (MANOVA). Results indicate that community dynamism is attenuated at the more polluted sites, concomitant with an increased predominance of a broad-tolerance algal assemblage, co-dominated by Cyclotella meneghiniana and Nitzschia stagnorum. The changes in the community structure and dynamics described herein involved alterations in the distribution and relative proportions of the algae, rather than modifications in the basic species composition. These changes may not be readily detectable by methods which over-simplify the ecological information, such as systems of indicator species and biotic indices, designed to assess the degree of pollution. The suitability of multivariate analysis and biotic indices in river phytoplankton studies is further discussed.     

The environmental zero‐point problem in evolutionary reaction norm modeling

There is a potential problem in present quantitative genetics evolutionary modeling based on reaction norms. Such models are state‐space models, where the multivariate breeder's equation in some form is used as the state equation that propagates the population state forward in time. These models use the implicit assumption of a constant reference environment, in many cases set to zero. This zero‐point is often the environment a population is adapted to, that is, where the expected geometric mean fitness is maximized. Such environmental reference values follow from the state of the population system, and they are thus population properties. The environment the population is adapted to, is, in other words, an internal population property, independent of the external environment. It is only when the external environment coincides with the internal reference environment, or vice versa, that the population is adapted to the current environment. This is formally a result of state‐space modeling theory, which is an important theoretical basis for evolutionary modeling. The potential zero‐point problem is present in all types of reaction norm models, parametrized as well as function‐valued, and the problem does not disappear when the reference environment is set to zero. As the environmental reference values are population characteristics, they ought to be modeled as such. Whether such characteristics are evolvable is an open question, but considering the complexity of evolutionary processes, such evolvability cannot be excluded without good arguments. As a straightforward solution, I propose to model the reference values as evolvable mean traits in their own right, in addition to other reaction norm traits. However, solutions based on an evolvable G matrix are also possible.