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.     

Plant families as predictors of plant biodiversity in Mexico

Three speciose plant families (Asteraceae, Fabaceae s.l. , and Poaceae) are evaluated for their appropriateness as predictors of overall regional patterns of plant diversity in Mexico. These families fulfil criteria for usefulness as indicators advocated by several authors, such as adequate taxonomic knowledge, wide geographical distribution, and occurrence in different plant communities. Known richness was determined from a database including 17,903 species from floristic inventories, review of taxonomic literature, and the study of herbarium specimens. Mexico was divided into 253 cells, each 1° latitude by 1° longitude; for each cell, total plant diversity and number of species in each predictor's family were determined. In addition, each species was categorized by 'preferred' vegetation type (temperate forest, dry tropical forest, humid tropical forest, xerophytic scrubland). Both multiple and simple regression models show that the three families are good predictors of total vascular floristic richness. Fabaceae s.l. has the highest semipartial correlations for all multiple models except with the xerophytic scrubland data set, for which it shares the highest values with Poaceae. Univariate models also predict satisfactorily the vascular floristic richness, especially when Fabaceae s.l. is used as the predictor, and the effect of vegetation type is included. Our results indicate that these three families can be used as potential predictors for total vascular plant species richness in Mexico although Fabaceae s.l. is the best predictor.     

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.     

Dosage regulation of Zea mays homeobox (ZmHox) genes and their relationship with the dosage-sensitive regulatory factors of Shrunken 1 (Sh1) in maize

In gene dosage studies on the expression of the maize Shrunken I gene several dosage-sensitive regulatory factors were identified that modulate Sh 1 mRNA levels in various aneuploids. ZmHox 1a and 1b have been shown to interact with the Sh1 promoter sequences in vitro. The present study was conducted to test whether these molecularly defined regulatory genes are subject to dosage modulation and to determine whether ZmHox 1a and 1b are involved with the dosage sensitive modification of Sh1 expression. The result was that ZmHox 1a and 1b were affected by several transacting dosage modifiers and that both genes exhibited a structural gene dosage effect but did not modulate Sh1 mRNA levels. For some chromosomal regions, a correlation was found between the dosage regulation of Sh1 and that of ZmHox 1a, while other effects were not correlated. The results suggest that the dosage effects on Sh1 are not mediated by ZmHox 1a or 1b, but interestingly Sh1 and ZmHox 1a share some dosage-regulatory effects. Dev Genet 20:67–73, 1997. © 1997 Wiley-Liss, Inc.     

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.