Purpose: The steroid hormone 1,25-dihydroxyvitamin D 3 is thought to protect against breast cancer. The actions of 1,25-dihydroxyvitamin D 3 are mediated via the vitamin D receptor (VDR), and a number of polymorphisms in the VDR gene have been identified. These result in distinct genotypes, some of which may alter susceptibility to breast cancer. We have investigated whether specific VDR gene polymorphisms are associated with breast cancer risk in a United Kingdom Caucasian population.Experimental Design: In a retrospective case-control study, female breast cancer patients (n ‫؍‬ 398) and control women (n ‫؍‬ 427) were recruited, and three VDR polymorphisms were determined.Results: The 3 VDR polymorphisms BsmI and variable-length poly(adenylate) sequence were both significantly associated with breast cancer risk; odds ratios (adjusted for age menopausal status and hormone replacement therapy usage) for bb genotype versus BB genotype ‫؍‬ 1.92 (95% confidence interval, 1.20 -3.10; P < 0.01) and for LL versus SS ‫؍‬ 1.94 (95% confidence interval, 1.20 -3.14; P < 0.01). A 5 VDR gene variant, FokI, was not associated with breast cancer risk when analyzed in isolation (P > 0.05). However, FokI did modulate the increased risk associated with the bb/LL genotype such that possession of one or more F alleles together with the bb/LL genotype augmented breast cancer risk. Furthermore, the highest proportion of bb and FFLL/ FfLL genotypes occurred in women with metastatic breast cancer.Conclusions: VDR polymorphisms are associated with breast cancer risk and may be associated with disease progression. Additional investigations into how different genotypes may affect the functional mechanisms of the VDR will provide a better strategy for identifying women at risk of breast cancer and for developing improved treatments.

Light detection and ranging (lidar) is becoming an increasingly popular technology among scientists for the development of predictive models of forest biophysical variables. However, before this technology can be adopted with confidence for long-term monitoring applications in Canada, robust models must be developed that can be applied and validated over large and complex forested areas. This will require "scaling-up" from current models developed from high-density lidar data to low-density data collected at higher altitudes. This paper investigates the effect of lowering the average point spacing of discrete lidar returns on models of forest biophysical variables. Validation of results revealed that high-density models are well correlated with mean dominant height, basal area, crown closure, and average aboveground biomass (R2 = 0.84, 0.89, 0.60, and 0.91, respectively). Low-density models could not accurately predict crown closure (R2 = 0.36). However, they did provide slightly improved estimates for mean dominant height, basal area, and average aboveground biomass (R2 = 0.90, 0.91, and 0.92, respectively). Maps were generated and validated for the entire study area from the low-density models. The ability of low-density models to accurately map key biophysical variables is a positive indicator for the utility of lidar data for monitoring large forested areas.

There is increasing evidence that vitamin D can protect against breast cancer. The actions of vitamin D are mediated via the vitamin D receptor (VDR). We have investigated whether polymorphisms in the VDR gene are associated with altered breast cancer risk in a UK Caucasian population. We recruited 241 women following a negative screening mammogram and 181 women with known breast cancer. The VDR polymorphism BsmI, an intronic 3′ gene variant, was significantly associated with increased breast cancer risk: odds ratio bb vs BB genotype = 2.32 (95% CI, 1.23–4.39). The BsmI polymorphism was in linkage disequilibrium with a candidate translational control site, the variable length poly (A) sequence in the 3′ untranslated region. Thus, the ‘L’ poly (A) variant was also associated with a similar breast cancer risk. A 5′ VDR gene variant, FokI, was not associated with breast cancer risk. Further investigations into the mechanisms of interactions of the VDR with other environmental and/or genetic influences to alter breast cancer risk may lead to a new understanding of the role of vitamin D in the control of cellular and developmental pathways. © 2001 Cancer Research Campaign http://www.bjcancer.com

One challenge to implementing spectral change detection algorithms using multitemporal Landsat data is that key dates and periods are often missing from the record due to weather disturbances and lapses in continuous coverage. This paper presents a method that utilizes residuals from harmonic regression over years of Landsat data, in conjunction with statistical quality control charts, to signal subtle disturbances in vegetative cover. These charts are able to detect changes from both deforestation and subtler forest degradation and thinning. First, harmonic regression residuals are computed after fitting models to interannual training data. These residual time series are then subjected to Shewhart X-bar control charts and exponentially weighted moving average charts. The Shewhart X-bar charts are also utilized in the algorithm to generate a data-driven cloud filter, effectively removing clouds and cloud shadows on a location-specific basis. Disturbed pixels are indicated when the charts signal a deviation from data-driven control limits. The methods are applied to a collection of loblolly pine (Pinus taeda) stands in Alabama, USA. The results are compared with stands for which known thinning has occurred at known times. The method yielded an overall accuracy of 85%, with the particular result that it provided afforestation/deforestation maps on a per-image basis, producing new maps with each successive incorporated image. These maps matched very well with observed changes in aerial photography over the test period. Accordingly, the method is highly recommended for on-the-fly change detection, for changes in both land use and land management within a given land use.

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This study investigates the ability to predict forest diameter distributions from light detection and ranging (LiDAR) data using Weibull modelling for forest stands in central Ontario. Results suggest that the unimodal 2-parameter Weibull model is a promising technique for the prediction of diameter class distributions, with strong relationships evident for several subgroups (at 95% confidence, r 2 adj =0.83, 0.78, 0.88, 0.80, 0.83, and 0.65, with validation RMSE of 4.09 m 2 /ha, 0.61 stems/ha, 6.05, 0.64, 4.73, and 0.09 for basal area, stem density, and the Weibull a and b parameters for basal area and stem density, respectively). The unimodal models were found to be least effective for the irregularly shaped diameter distributions, particularly for low-density coniferous plots that have undergone shelterwood treatment. A significant improvement in results for these irregular plots was found with a finite mixture modelling approach, suggesting that finite mixture models may extend our ability to predict diameter distributions over large portions of the landscape.Key words: LiDAR, Weibull, finite mixture modeling, diameter class distributions, multiple linear regression RÉSUMÉCette étude porte sur la capacité de prédiction de la distribution des diamètres d'arbres à partir de données de détection de la lumière et de calcul de la distance (LiDAR) utilisées dans un modèle Weibull pour des peuplements forestiers du centre de l'Ontario. Les résultats laissent entendre que le modèle Weibull unimodal à 2 paramètres constitue une technique prometteuse de la prédiction de la distribution des classes de diamètre comportant de fortes corrélations évidentes pour certains sous-groupes (à un niveau de confiance 95%, r 2 adj =0.83, 0.78, 0.88, 0.80, 0.83 et 0.65, une variance de 4,09 m 2 /ha, 0,61 tiges/ha, 6,05, 0,64, 4,73 et de 0,09 respectivement pour la surface terrière, le nombre de tige par hectare et pour les paramètres a et b de Weibull portant sur la surface terrière et le nombre de tige). Les modèles unimodaux se sont avérés être moins efficaces dans le cas des distributions de forme irrégulière des diamètres, particulièrement dans le cas de parcelles de faible densité de résineux ayant subi des traitements de coupe progressive. Une amélioration significative des résultats de ces parcelles irrégulières est apparue à la suite d'une approche de modélisation pour peuplement mélangé spécifique, laissant entendre que les modèles pour peuplement mélangé spécifique pourrait accroître notre capacité de prédire la distribution des diamètres pour de grandes portions de l' écosystème.Mots clés : LiDAR, Weibull, modélisation de peuplement mélangé spécifique, distributions des classes de diamètre, régression linéaire multiple

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