Association of tamoxifen use and reduced risk of contralateral breast cancer for BRCA1 and BRCA2 mutation carriers

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Quantitative Assessment of Mammary Gland Density in Rodents Using Digital Image Analysis

<p>Abstract</p> <p>Background</p> <p>Rodent models have been used extensively to study mammary gland development and for studies of toxicology and carcinogenesis. Mammary gland gross morphology can visualized via the excision of intact mammary gland chains following fixation and staining with carmine using a tissue preparation referred to as a whole mount. Methods are described for the automated collection of digital images from an entire mammary gland whole mount and for the interrogation of digital data using a "masking" technique available with Image-Pro^®plus image analysis software (Mediacybernetics. Silver Spring, MD).</p> <p>Results</p> <p>Parallel to mammographic analysis in humans, measurements of rodent mammary gland density were derived from area-based or volume-based algorithms and included: total circumscribed mammary fat pad mass, mammary epithelial mass, and epithelium-free fat pad mass. These values permitted estimation of absolute mass of mammary epithelium as well as breast density. The biological plausibility of these measurements was evaluated in mammary whole mounts from rats and mice. During mammary gland development, absolute epithelial mass increased linearly without significant changes in mammographic density. Treatment of rodents with tamoxifen, 9-cis-retinoic acid, or ovariectomy, and occurrence of diet induced obesity decreased both absolute epithelial mass and mammographic density. The area and volumetric methods gave similar results.</p> <p>Conclusions</p> <p>Digital image analysis can be used for screening agents for potential impact on reproductive toxicity or carcinogenesis as well as for mechanistic studies, particularly for cumulative effects on mammary epithelial mass as well as translational studies of mechanisms that explain the relationship between epithelial mass and cancer risk.</p

Mechanical Systems with Symmetry, Variational Principles, and Integration Algorithms

This paper studies variational principles for mechanical systems with symmetry and their applications to integration algorithms. We recall some general features of how to reduce variational principles in the presence of a symmetry group along with general features of integration algorithms for mechanical systems. Then we describe some integration algorithms based directly on variational principles using a discretization technique of Veselov. The general idea for these variational integrators is to directly discretize Hamilton’s principle rather than the equations of motion in a way that preserves the original systems invariants, notably the symplectic form and, via a discrete version of Noether’s theorem, the momentum map. The resulting mechanical integrators are second-order accurate, implicit, symplectic-momentum algorithms. We apply these integrators to the rigid body and the double spherical pendulum to show that the techniques are competitive with existing integrators

Endocrine disruptors and spontaneous premature labor: a case control study

<p>Abstract</p> <p>Background</p> <p>Premature labor is a poorly understood condition. Estrogen is thought to play a key role and therefore the labor process may be affected by endocrine disruptors. We sought to determine whether or not an environmental toxicant, DDE, or dietary derived endocrine disruptors, daidzein and genistein, are associated with spontaneous preterm labor.</p> <p>Methods</p> <p>Cases were defined as primiparous patients having a preterm delivery at or before 35 weeks following the spontaneous onset of labor. Controls were defined as primiparous women who delivered on the same day as the cases but at term gestation.</p> <p>Over approximately 1 year, 26 cases and 52 controls were recruited. Subjects agreed to have blood tests on day one postpartum for DDE and for the phytoestrogens genistein and daidzein.</p> <p>Results</p> <p>The mean concentration of DDE was similar in the case and control groups: 4.29 vs 4.32 ng/g lipid p = .85. In the case group, 13/26 had detectable levels of daidzein (range 0.20 – 1.56 ng/ml) compared to 25/52 controls (range 0.21 – 3.26 ng/ml). The mean concentration of daidzein was similar in cases compared to controls: 0.30 vs .34 ng/ml p = 0.91. Of the case group,14/26 had detectable levels of genistein (range 0.20 – 2.19 ng/ml) compared to 32/52 controls (range 0.21 – 2.55 ng/ml). The mean concentration of genistein was similar in cases compared to controls: 0.39 vs 0.31 ng/ml, p = 0.61.</p> <p>Conclusion</p> <p>The serum levels of DDE in this population were found to be low.</p> <p>There appears to be no relationship between serum concentrations of DDE, daidzein, and genistein and spontaneous preterm labor in our population. The inability to identify an effect may be related to the comparatively low concentrations of DDE in our population and the rapid and variable reduction of phytoestrogens from women in labor.</p

Exploring cancer register data to find risk factors for recurrence of breast cancer – application of Canonical Correlation Analysis

BACKGROUND: A common approach in exploring register data is to find relationships between outcomes and predictors by using multiple regression analysis (MRA). If there is more than one outcome variable, the analysis must then be repeated, and the results combined in some arbitrary fashion. In contrast, Canonical Correlation Analysis (CCA) has the ability to analyze multiple outcomes at the same time. One essential outcome after breast cancer treatment is recurrence of the disease. It is important to understand the relationship between different predictors and recurrence, including the time interval until recurrence. This study describes the application of CCA to find important predictors for two different outcomes for breast cancer patients, loco-regional recurrence and occurrence of distant metastasis and to decrease the number of variables in the sets of predictors and outcomes without decreasing the predictive strength of the model. METHODS: Data for 637 malignant breast cancer patients admitted in the south-east region of Sweden were analyzed. By using CCA and looking at the structure coefficients (loadings), relationships between tumor specifications and the two outcomes during different time intervals were analyzed and a correlation model was built. RESULTS: The analysis successfully detected known predictors for breast cancer recurrence during the first two years and distant metastasis 2–4 years after diagnosis. Nottingham Histologic Grading (NHG) was the most important predictor, while age of the patient at the time of diagnosis was not an important predictor. CONCLUSION: In cancer registers with high dimensionality, CCA can be used for identifying the importance of risk factors for breast cancer recurrence. This technique can result in a model ready for further processing by data mining methods through reducing the number of variables to important ones

A unified framework for finding differentially expressed genes from microarray experiments

<p>Abstract</p> <p>Background</p> <p>This paper presents a unified framework for finding differentially expressed genes (DEGs) from the microarray data. The proposed framework has three interrelated modules: (i) gene ranking, ii) significance analysis of genes and (iii) validation. The first module uses two gene selection algorithms, namely, a) two-way clustering and b) combined adaptive ranking to rank the genes. The second module converts the gene ranks into p-values using an R-test and fuses the two sets of p-values using the Fisher's omnibus criterion. The DEGs are selected using the FDR analysis. The third module performs three fold validations of the obtained DEGs. The robustness of the proposed unified framework in gene selection is first illustrated using false discovery rate analysis. In addition, the clustering-based validation of the DEGs is performed by employing an adaptive subspace-based clustering algorithm on the training and the test datasets. Finally, a projection-based visualization is performed to validate the DEGs obtained using the unified framework.</p> <p>Results</p> <p>The performance of the unified framework is compared with well-known ranking algorithms such as t-statistics, Significance Analysis of Microarrays (SAM), Adaptive Ranking, Combined Adaptive Ranking and Two-way Clustering. The performance curves obtained using 50 simulated microarray datasets each following two different distributions indicate the superiority of the unified framework over the other reported algorithms. Further analyses on 3 real cancer datasets and 3 Parkinson's datasets show the similar improvement in performance. First, a 3 fold validation process is provided for the two-sample cancer datasets. In addition, the analysis on 3 sets of Parkinson's data is performed to demonstrate the scalability of the proposed method to multi-sample microarray datasets.</p> <p>Conclusion</p> <p>This paper presents a unified framework for the robust selection of genes from the two-sample as well as multi-sample microarray experiments. Two different ranking methods used in module 1 bring diversity in the selection of genes. The conversion of ranks to p-values, the fusion of p-values and FDR analysis aid in the identification of significant genes which cannot be judged based on gene ranking alone. The 3 fold validation, namely, robustness in selection of genes using FDR analysis, clustering, and visualization demonstrate the relevance of the DEGs. Empirical analyses on 50 artificial datasets and 6 real microarray datasets illustrate the efficacy of the proposed approach. The analyses on 3 cancer datasets demonstrate the utility of the proposed approach on microarray datasets with two classes of samples. The scalability of the proposed unified approach to multi-sample (more than two sample classes) microarray datasets is addressed using three sets of Parkinson's Data. Empirical analyses show that the unified framework outperformed other gene selection methods in selecting differentially expressed genes from microarray data.</p

Differential Response to Soil Salinity in Endangered Key Tree Cactus: Implications for Survival in a Changing Climate

Understanding reasons for biodiversity loss is essential for developing conservation and management strategies and is becoming increasingly urgent with climate change. Growing at elevations <1.4 m in the Florida Keys, USA, the endangered Key tree cactus (Pilosocereus robinii) experienced 84 percent loss of total stems from 1994 to 2007. The most severe losses of 99 and 88 percent stems occurred in the largest populations in the Lower Keys, where nine storms with high wind velocities and storm surges, occurred during this period. In contrast, three populations had substantial stem proliferation. To evaluate possible mortality factors related to changes in climate or forest structure, we examined habitat variables: soil salinity, elevation, canopy cover, and habitat structure near 16 dying or dead and 18 living plants growing in the Lower Keys. Soil salinity and elevation were the preliminary factors that discriminated live and dead plants. Soil salinity was 1.5 times greater, but elevation was 12 cm higher near dead plants than near live plants. However, distribution-wide stem loss was not significantly related to salinity or elevation. Controlled salinity trials indicated that salt tolerance to levels above 40 mM NaCl was related to maternal origin. Salt sensitive plants from the Lower Keys had less stem growth, lower root:shoot ratios, lower potassium: sodium ratios and lower recovery rate, but higher δ 13C than a salt tolerant lineage of unknown origin. Unraveling the genetic structure of salt tolerant and salt sensitive lineages in the Florida Keys will require further genetic tests. Worldwide rare species restricted to fragmented, low-elevation island habitats, with little or no connection to higher ground will face challenges from climate change-related factors. These great conservation challenges will require traditional conservation actions and possibly managed relocation that must be informed by studies such as these

Long-term sediment decline causes ongoing shrinkage of the Mekong megadelta, Vietnam

Since the 1990s the Mekong River delta has suffered a large decline in sediment supply causing coastal erosion, following catchment disturbance through hydropower dam construction and sand extraction. However, our new geological reconstruction of 2500-years of delta shoreline changes show that serious coastal erosion actually started much earlier. Data shows the sandy coast bounding river mouths accreted consistently at a rate of +2 to +4 km2/year. In contrast, we identified a variable accretion rate of the muddy deltaic protrusion at Camau; it was < +1 km2/year before 1400 years ago but increased drastically around 600 years ago, forming the entire Camau Peninsula. This high level of mud supply had sharply declined by the early 20th century after a vast canal network was built on the delta. Since then the Peninsula has been eroding, promoted by the conjunction of mud sequestration in the delta plain driven by expansion of rice cultivation, and hysteresis of long-term muddy sedimentation that left the protrusion exposed to wave erosion. Natural mitigation would require substantial increases in sediment supply well above the pre-1990s levels

Whatever the Weather: Ambient Temperature Does Not Influence the Proportion of Males Born in New Zealand

BACKGROUND: The proportion of male births has been shown to be over 50% in temperate climates around the world. Given that fluctuations in ambient temperature have previously been shown to affect sex allocation in humans, we examined the hypothesis that ambient temperature predicts fluctuations in the proportion of male births in New Zealand. METHODOLOGY/PRINCIPAL FINDINGS: We tested three main hypotheses using time series analyses. Firstly, we used historical annual data in New Zealand spanning 1876-2009 to test for a positive effect of ambient temperature on the proportion of male births. The proportion of males born ranged by 3.17%, from 0.504 to 0.520, but no significant relationship was observed between male birth rates and mean annual temperature in the concurrent or previous years. Secondly, we examined whether changes in annual ambient temperature were negatively related to the proportion of male stillbirths from 1929-2009 and whether the proportion of male stillbirths negatively affected the proportion of male live births. We found no evidence that fewer male stillbirths occurred during warmer concurrent or previous years, though a declining trend in the proportion of male stillbirths was observed throughout the data. Thirdly, we tested whether seasonal ambient temperatures, or deviations from those seasonal patterns, were positively related to the proportion of male births using monthly data from 1980-2009. Patterns of male and female births are seasonal, but very similar throughout the year, resulting in a non-seasonal proportion of male births. However, no cross correlations between proportion of male births and lags of temperature were significant. CONCLUSIONS: Results showed, across all hypotheses under examination, that ambient temperatures were not related to the proportion of male births or the proportion of male stillbirths in New Zealand. While there is evidence that temperature may influence human sex allocation elsewhere, such effects of temperature are not universal