Predictors of response to aromatase inhibitors

Aromatase inhibitors are now considered to be part of the endocrine treatment for most hormone receptor-positive breast cancer in post-menopausal women for both early and advanced disease. Despite the impressive efficacy of these agents, up to 50% of treated patients exhibit de novo or intrinsic resistance to aromatase inhibitors and hence identification of response predictors is essential to allow treatment to be directed towards responsive populations and for alternative or additional therapies to be offered to resistant patients. Emerging data seem to suggest a role for the conventional tumour markers of oestrogen receptor and progesterone receptor as possible predictors of response but, particularly in the adjuvant setting, the extent to which these are useful has not been fully elucidated. Data from both the neo-adjuvant and advanced disease settings suggest that response to aromatase inhibitors does not appear to be adversely affected by HER-2 overexpression. Within neo-adjuvant aromatase inhibitor studies, the proliferation marker Ki67 has shown a significant correlation with relapse-free survival, suggesting a role in prediction for measurement of Ki67 and other dynamic markers of response. Analysis of multiple gene expression changes over a short treatment period may also have potential clinical utility for prediction of response.     

The importance of fine‐scale predictors of wild boar habitat use in an isolated population

1. Predicting the likelihood of wildlife presence at potential wildlife–livestock interfaces is challenging. These interfaces are usually relatively small geographical areas where landscapes show large variation over small distances. Models of wildlife distribution based on coarse data over wide geographical ranges may not be representative of these interfaces. High‐resolution data can help identify fine‐scale predictors of wildlife habitat use at a local scale and provide more accurate predictions of species habitat use. These data may be used to inform knowledge of interface risks, such as disease transmission between wildlife and livestock, or human–wildlife conflict.
2. This study uses fine‐scale habitat use data from wild boar (Sus scrofa) based on activity signs and direct field observations in and around the Forest of Dean in Gloucestershire, England. Spatial logistic regression models fitted using a variant of penalized quasi‐likelihood were used to identify habitat‐based and anthropogenic predictors of wild boar signs.
3. Our models showed that within the Forest of Dean, wild boar signs were more likely to be seen in spring, in forest‐type habitats, closer to the center of the forest and near litter bins. In the area surrounding the Forest of Dean, wild boar signs were more likely to be seen in forest‐type habitats and near recreational parks and less likely to be seen near livestock.
4. This approach shows that wild boar habitat use can be predicted using fine‐scale data over comparatively small areas and in human‐dominated landscapes, while taking account of the spatial correlation from other nearby fine‐scale data‐points. The methods we use could be applied to map habitat use of other wildlife species in similar landscapes, or of movement‐restricted, isolated, or fragmented wildlife populations.

     

Identifying important areas for butterfly conservation in Italy

The current study combines the use of niche modelling with a site prioritization method to identify important areas for butterfly conservation in Italy. A novel machine learning method (bagging predictors) was used to predict the distribution of 232 species of butterflies across the Italian Peninsula. The results of the models were used to identify high-value sites with a multispecies prioritization method called zonation. In order to identify important areas for species of conservation concern, we incorporated a species weighting scheme to zonation analyses. We also used the results of the zonation analyses to identify a series of management landscapes on the basis of the similarity in species composition among sites. The basic zonation showed that most important areas for butterfly conservation are located in the Alps, the Appennine, the Apulia region and in the island of Sardinia. The inclusion of a species weighting scheme in the zonation analyses revealed the importance of two new areas located in Southern Italy and emphasized the importance of the Alps for species of conservation concern. The landscape identification procedure selected a series of landscapes, which provide protection to a full range of species ranging from the Alps to Mediterranean areas. Our study shows that the areas selected in our analyses should be given high priority in future conservation plans and monitoring schemes.     

Principal Predictors and Efficiency in Small Second-Order Designs

Linear parametric functions affected by deleting or augmenting sets of design points are identified explicitly using principal components of the predictive dispersion at those points, offering fresh insight and significant computational savings. Leverages from regression diagnostics are seen to determine efficiencies due to augmenting or deleting single points. Eight small second-order designs are studied in detail with supporting numerical displays. Comparisons are drawn to other approaches from the literature.     

Geographical differences in habitat relationships of cetaceans across an ocean basin

The distributions of highly mobile marine species such as cetaceans are increasingly modeled at basin scale by combining data from multiple regions. However, these basin-wide models often overlook geographical variations in species habitat relationships between regions. We tested for geographical variations in habitat relationships for a suite of cetacean taxa between the two sides of the North Atlantic basin. Using cetacean visual survey data and remote sensing data from the western and eastern basin in summer, we related the probability of presence of twelve cetacean taxa from three guilds to seafloor depth, sea surface temperature and primary productivity. In a generalized additive model framework, we fitted 1) basin-wide (BW) models, assuming a single global relationship, 2) region-specific intercepts (RI) models, assuming relationships with the same shape in both regions, but allowing a region-specific intercept and 3) region-specific shape (RS) models, assuming relationships with different shapes between regions. RS models mostly yielded significantly better fits than BW models, indicating cetacean occurrences were better modeled with region-specific than with global relationships. The better fits of RS models over RI models further provided statistical evidence for differences in the shapes of region-specific relationships. Baleen whales showed striking differences in both the shapes of relationships and their mean presence probabilities between regions. Deep diving whales and delphinoids showed contrasting relationships between regions with few exceptions (e.g. non-statistically different shapes of region-specific relationships for harbor porpoise and beaked whales with depth). Our findings stress the need to account for geographical differences in habitat relationships between regions when modeling species distributions from combined data at the basin scale. Our proposed hypotheses offer a roadmap for understanding why habitat relationships may geographically vary in cetaceans and other highly mobile marine species.     

Floristic patterns of epiphytes in the Brazilian Atlantic Forest,a biodiversity hotspot

To assess the main factors driving epiphytic angiosperm distribution throughout the Brazilian Atlantic Forest, we compiled 57 floristic surveys and analysed species composition under the influence of environmental variables, space and vegetation type using canonical correspondence analysis (CCA), similarity (Sorensen) and Mantel's tests. The indicator value index (IndVal) was used to find indicator species of each Brazilian Atlantic Forest vegetation type. Group sharpness analysis was performed in order to determine the appropriate group partition level. CCA showed a separation of the epiphytic flora reflecting temperature and rainfall gradients. Mantel's test showed that environment and space were highly correlated with floristic similarity. Cluster analysis, indicating floristic similarity, resolved five groups, mainly grouped by region. Clear differentiation of the Brazilian Atlantic Forest epiphytic flora on a north–south axis with a strong correlation with temperature and rainfall gradients was found. The role of space and environment on species composition varied according to distinct epiphytic species groups. In particular, for Bromeliaceae and Orchidaceae, the main factor associated with floristic similarity was space. Indicator species were found for all vegetation types apart from the Seasonal Semideciduous Forest that seemed to represent a subset of a more humid forest type. © 2015 The Linnean Society of London, Botanical Journal of the Linnean Society, 2015, 179 , 587–601.     

Revisiting Patterns of Tree Species Composition and their Driving Forces in the Atlantic Forests of Southeastern Brazil

The elucidation of phytogeographic patterns and their drivers in biodiversity hotspots is essential to the study of ecology and the conservation of these areas. In 2000, an important study by Oliveira‐Filho and Fontes led to changes in the paradigms that define our understanding of the Atlantic Forest (Brazil). Here, our aim was to revisit this study using a more comprehensive set of environmental predictors, an updated and much larger tree species dataset and checklist, and more refined data analyses. We performed exploratory and confirmatory analyses, including the modeling of the spatial components with Moran's Eigenvector Maps, using data from 483 sites in southeastern Brazil, which encompass a total of 3546 species and 33 geo‐climatic variables. We observed strong floristic similarities between rain‐ and seasonal forests and a species distribution continuum across the main gradients. The environmental and spatial variables were significantly correlated with floristic patterns, and we demonstrated that the tree flora of the seasonal forests should no longer be considered a simple subset of the rain forest flora. The findings of the original paper were not only confirmed but we also unveiled additional, important phytogeographic patterns. We also reinforced the main conclusion of the paper that the Atlantic Forest concept must encompass all of the forest types east of the dry corridor in South America, a designation of utmost importance for the conservation of this biodiversity hotspot.     

Path analysis of the genetic integration of traits in the sand cricket: a novel use of BLUPs

This study combines path analysis with quantitative genetics to analyse a key life history trade-off in the cricket, Gryllus firmus. We develop a path model connecting five traits associated with the trade-off between flight capability and reproduction and test this model using phenotypic data and estimates of breeding values (best linear unbiased predictors) from a half-sibling experiment. Strong support by both types of data validates our causal model and indicates concordance between the phenotypic and genetic expression of the trade-off. Comparisons of the trade-off between sexes and wing morphs reveal that these discrete phenotypes are not genetically independent and that the evolutionary trajectories of the two wing morphs are more tightly constrained to covary than those of the two sexes. Our results illustrate the benefits of combining a quantitative genetic analysis, which examines statistical correlations between traits, with a path model that focuses upon the causal components of variation.     

Group size, sex and age composition of chital (Axis axis) and sambar (Rusa unicolor) in a deciduous habitat of Western Ghats

Knowledge of the social structure of a population is important for a range of fundamental and applied purposes. Group characteristics and population structure of chital (Axis axis) and sambar (Rusa unicolor) were studied in a deciduous habitat of Mudumalai Tiger Reserve, Western Ghats, India, during 2008-2009. Vehicle transects were monitored monthly to gather information on group-size and age-sex composition of chital and sambar. The average mean group size and crowding for chital and sambar was 13.1 ± 0.5 (n = 1020), 3.6 ± 0.2 (n = 377) and 33.3, 11.0 respectively. The average adult male:adult female:fawn ratio was 63.4:100:22.3 (n = 9391) and 43.9:100:23.7 (n = 1023) in chital and sambar respectively. The mean group size of chital and sambar varied significantly between seasons (Kruskal-Wallis test, p = <0.001). Peak fawning season was observed from February to May for chital and May to August for sambar. Group's sex-age composition influenced group formation in both species between seasons at different level. Adult male and fawn were the important predicting variables of change in group size. Skewed female sex ratio was probably due to selective male predation by large predators. Although fawning occurred throughout the year, both species showed seasonality in fawning. The above mentioned patterns differed between species depending upon their ecological adaptation in foraging strategies and habitat preference.