Stochastic optimization methods for extracting cosmological parameters from CMBR power spectra

The reconstruction of the CMBR power spectrum from a map represents a major computational challenge to which much effort has been applied. However, once the power spectrum has been recovered there still remains the problem of extracting cosmological parameters from it. Doing this involves optimizing a complicated function in a many dimensional parameter space. Therefore efficient algorithms are necessary in order to make this feasible. We have tested several different types of algorithms and found that the technique known as simulated annealing is very effective for this purpose. It is shown that simulated annealing is able to extract the correct cosmological parameters from a set of simulated power spectra, but even with such fast optimization algorithms, a substantial computational effort is needed.Comment: 7 pages revtex, 3 figures, to appear in PR

Case-based review and olinical guidance on the use of genomic assays for early-stage breast cancer: Breast Cancer Therapy Expert Group (BCTEG)

In addition to classical clinicopathologic factors, such as hormone receptor positivity, human epidermal growth factor receptor 2 (HER2) status, and tumor size, grade, and lymph node status, a number of commercially available genomic tests may be used to help inform treatment decisions for early breast cancer patients. Although these tests improve our understanding of breast cancer and help to individualize treatment decisions, clinicians face challenges when deciding on the most appropriate test to order, and the advantages, if any, of one test over another. The Breast Cancer Therapy Expert Group (BCTEG) recently convened a roundtable meeting to discuss issues surrounding the use of genomic testing in early breast cancer, with the goal of providing practical guidance on the use of these tests by the community oncologist, for whom breast cancer may be only one of many tumor types they treat. The group recognizes that genomic testing can provide important prognostic (eg, risk for recurrence), and in some cases predictive, information (eg, benefit of chemotherapy, or extended adjuvant endocrine therapy), which can be used to help guide treatment decisions in breast cancer. The available tests differ in the types of information they provide, and in the patient populations and clinical trials that were conducted to validate them. We summarize the discussion of the BCTEG on this topic, and we also consider several patient cases and clinical scenarios in which genomic testing may, or may not, be useful to guide treatment decisions for the practicing community oncologist

Spectral Properties of the Overlap Dirac Operator in QCD

We discuss the eigenvalue distribution of the overlap Dirac operator in quenched QCD on lattices of size 8^{4}, 10^{4} and 12^{4} at \beta = 5.85 and \beta = 6. We distinguish the topological sectors and study the distributions of the leading non-zero eigenvalues, which are stereographically mapped onto the imaginary axis. Thus they can be compared to the predictions of random matrix theory applied to the \epsilon-expansion of chiral perturbation theory. We find a satisfactory agreement, if the physical volume exceeds about (1.2 fm)^{4}. For the unfolded level spacing distribution we find an accurate agreement with the random matrix conjecture on all volumes that we considered.Comment: 16 pages, 8 figures, final version published in JHE

Framework for assessing and improving the performance of recursive digital filters for baseflow estimation with application to the Lyne and Hollick filter

Baseflow is often regarded as the streamflow component derived predominantly from groundwater discharge. The estimation of baseflow is important for water supply, water allocation, investigation of contamination impacts, low flow hydrology and flood hydrology. Baseflow is commonly estimated using graphical methods, recursive digital filters (RDFs), tracer based methods, and conceptual models. Of all of these methods, RDFs are the most commonly used, due to their relatively easy and efficient implementation. This paper presents a generic framework for assessing and improving the performance of RDFs for baseflow estimation for catchments with different characteristics and subject to different hydrological conditions. As part of the framework, a fully integrated surface water/groundwater (SW/GW) model is used to obtain estimates of streamflow and baseflow for catchments with different properties, such as soil types and rainfall patterns. An RDF is then applied to the simulated streamflow to assess how well the baseflow obtained using the filter matches the baseflow obtained using the fully integrated SW/GW model. In order to improve the performance of the filter, the user-defined parameter(s) controlling filter operation can be adjusted in order to obtain the best match between the baseflow obtained using the filter and that obtained using the fully integrated SW/GW model (i.e. through calibration). The proposed framework is tested by applying it to a common SW/GW benchmarking problem, the tilted V-catchment, for a range of soil properties. HydroGeoSphere (HGS) is used to develop the fully integrated SW/GW model and the Lyne and Hollick (LH) filter is used as the RDF. The performance of the LH filter is assessed using the commonly used value of the filter parameter of 0.925, as well as calibrated filter parameter values. The results obtained show that the performance of the LH filter is affected significantly by the saturated hydraulic conductivity (Ks) of the soil and that calibrated LH filter parameter can result in significant improvements in filter performance. © 2012 Elsevier Ltd.L. Li, H.R. Maier, M.F. Lambert, C.T. Simmons, D. Partingto

Polygenic risk scores for prediction of breast cancer risk in women of African ancestry: A cross-ancestry approach

Polygenic risk scores (PRSs) are useful for predicting breast cancer risk, but the prediction accuracy of existing PRSs in women of African ancestry (AA) remains relatively low. We aim to develop optimal PRSs for the prediction of overall and estrogen receptor (ER) subtype-specific breast cancer risk in AA women. The AA dataset comprised 9235 cases and 10 184 controls from four genome-wide association study (GWAS) consortia and a GWAS study in Ghana. We randomly divided samples into training and validation sets. We built PRSs using individual-level AA data by a forward stepwise logistic regression and then developed joint PRSs that combined (1) the PRSs built in the AA training dataset and (2) a 313-variant PRS previously developed in women of European ancestry. PRSs were evaluated in the AA validation set. For overall breast cancer, the odds ratio per standard deviation of the joint PRS in the validation set was 1.34 [95% confidence interval (CI): 1.27-1.42] with the area under receiver operating characteristic curve (AUC) of 0.581. Compared with women with average risk (40th-60th PRS percentile), women in the top decile of the PRS had a 1.98-fold increased risk (95% CI: 1.63-2.39). For PRSs of ER-positive and ER-negative breast cancer, the AUCs were 0.608 and 0.576, respectively. Compared with existing methods, the proposed joint PRSs can improve prediction of breast cancer risk in AA women

Evaluating Polygenic Risk Scores for Breast Cancer in Women of African Ancestry

Background: Polygenic risk scores (PRSs) have been demonstrated to identify women of European, Asian, and Latino ancestry at elevated risk of developing breast cancer (BC). We evaluated the performance of existing PRSs trained in European ancestry populations among women of African ancestry. Methods: We assembled genotype data for women of African ancestry, including 9241 case subjects and 10 193 control subjects. We evaluated associations of 179- and 313-variant PRSs with overall and subtype-specific BC risk. PRS discriminatory accuracy was assessed using area under the receiver operating characteristic curve. We also evaluated a recalibrated PRS, replacing the index variant with variants in each region that better captured risk in women of African ancestry and estimated lifetime absolute risk of BC in African Americans by PRS category. Results: For overall BC, the odds ratio per SD of the 313-variant PRS (PRS313) was 1.27 (95% confidence interval [CI] = 1.23 to 1.31), with an area under the receiver operating characteristic curve of 0.571 (95% CI = 0.562 to 0.579). Compared with women with average risk (40th-60th PRS percentile), women in the top decile of PRS313 had a 1.54-fold increased risk (95% CI = 1.38-fold to 1.72-fold). By age 85 years, the absolute risk of overall BC was 19.6% for African American women in the top 1% of PRS313 and 6.7% for those in the lowest 1%. The recalibrated PRS did not improve BC risk prediction. Conclusion: The PRSs stratify BC risk in women of African ancestry, with attenuated performance compared with that reported in European, Asian, and Latina populations. Future work is needed to improve BC risk stratification for women of African ancestry

Cross-ancestry GWAS meta-analysis identifies six breast cancer loci in African and European ancestry women

Our study describes breast cancer risk loci using a cross-ancestry GWAS approach. We first identify variants that are associated with breast cancer at P < 0.05 from African ancestry GWAS meta-analysis (9241 cases and 10193 controls), then meta-analyze with European ancestry GWAS data (122977 cases and 105974 controls) from the Breast Cancer Association Consortium. The approach identifies four loci for overall breast cancer risk [1p13.3, 5q31.1, 15q24 (two independent signals), and 15q26.3] and two loci for estrogen receptor-negative disease (1q41 and 7q11.23) at genome-wide significance. Four of the index single nucleotide polymorphisms (SNPs) lie within introns of genes (KCNK2, C5orf56, SCAMP2, and SIN3A) and the other index SNPs are located close to GSTM4, AMPD2, CASTOR2, and RP11-168G16.2. Here we present risk loci with consistent direction of associations in African and European descendants. The study suggests that replication across multiple ancestry populations can help improve the understanding of breast cancer genetics and identify causal variants

A meta-analysis of genome-wide association studies of multiple myeloma among men and women of African ancestry

Persons of African ancestry (AA) have a twofold higher risk for multiple myeloma (MM) compared with persons of European ancestry (EA). Genome-wide association studies (GWASs) support a genetic contribution to MM etiology in individuals of EA. Little is known about genetic risk factors for MM in individuals of AA. We performed a meta-analysis of 2 GWASs ofMMin 1813 cases and 8871 controls and conducted an admixture mapping scan to identify risk alleles. We fine-mapped the 23 known susceptibility loci to find markers that could better capture MM risk in individuals of AA and constructed a polygenic risk score (PRS) to assess the aggregated effect of known MM risk alleles. In GWAS meta-analysis, we identified 2 suggestive novel loci located at 9p24.3 and 9p13.1 at P < 1 × 10-6; however, no genome-wide significant association was noted. In admixture mapping, we observed a genome-wide significant inverse association between local AA at 2p24.1-23.1 and MM risk in AA individuals. Of the 23 known EA risk variants, 20 showed directional consistency, and 9 replicated at P < .05 in AA individuals. In 8 regions, we identified markers that better captureMMrisk in persons with AA. AA individuals with a PRS in the top 10% had a 1.82-fold (95% confidence interval, 1.56-2.11) increased MM risk compared with those with average risk (25%-75%). The strongest functional association was between the risk allele for variant rs56219066 at 5q15 and lower ELL2 expression (P = 5.1 × 10-12). Our study shows that common genetic variation contributes to MM risk in individuals with AA

Cytoplasmic Overexpression of HER2: a Key Factor in Colorectal Cancer

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