HST Observations of Gravitationally Lensed Features in the Rich Cluster Ac114

Deep Hubble Space Telescope images of superlative resolution obtained for the distant rich cluster AC114 (z=0.31) reveal a variety of gravitational lensing phenomena for which ground-based spectroscopy is available. We present a luminous arc which is clearly resolved by HST and appears to be a lensed z=0.64 sub-L star spiral galaxy with a detected rotation curve. Of greatest interest is a remarkably symmetrical pair of compact blue images separated by 10 arcsec and lying close to the cluster cD. We propose that these images arise from a single very faint background source gravitationally lensed by the cluster core. Deep ground-based spectroscopy confirms the lensing hypothesis and suggests the source is a compact star forming system at a redshift z=1.86. Taking advantage of the resolved structure around each image and their very blue colours, we have identified a candidate third image of the same source roughly 50 arcsec away. The angular separation of the three images is much larger than previous multiply-imaged systems and indicates a deep gravitational potential in the cluster centre. Resolved multiply-imaged systems, readily recognised with HST, promise to provide unique constraints on the mass distribution in the cores of intermediate redshift clusters.Comment: submitted to ApJ, 6 pages (no figures), uuencoded Postscript, compressed TAR of Postscript figures available via anonymous ftp in users/irs/figs/ac114_figs.tar.gz on astro.caltech.edu. PAL-IRS-

Benchmarking of three-dimensional multicomponent lattice Boltzmann equation

We present a challenging validation of phase field multi-component lattice Boltzmann equation (MCLBE) simulation against the Re = 0 Stokes flow regime Taylor-Einstein theory of dilute suspension viscosity. By applying a number of recent advances in the understanding and the elimination of the interfacial micro-current artefact, extending to 3D a class of stability-enhancing multiple relaxation time collision models (which require no explicit collision matrix, note) and developing new interfacial interpolation schemes, we are able to obtain data which show that MCLBE may be applied in new flow regimes. Our data represent one of the most stringent tests yet attempted on LBE-one which received wisdom would preclude on grounds of overwhelming artefact flow

A comparison of three different methods for classification of breast cancer data

The classification of breast cancer patients is of great importance in cancer diagnosis. During the last few years, many algorithms have been proposed for this task. In this paper, we review different supervised machine learning techniques for classification of a novel dataset and perform a methodological comparison of these. We used the C4.5 tree classifier, a Multilayer Perceptron and a naïve Bayes classifier over a large set of tumour markers. We found good performance of the Multilayer Perceptron even when we reduced the number of features to be classified. We found naive Bayes achieved a competitive performance even though the assumption of normality of the data is strongly violated

HST Observations of Giant Arcs: High Resolution Imaging Of Distant Field Galaxies.

We present HST imaging of eight spectroscopically-confirmed giant arcs, pairs and arclets. These objects have all been extensively studied from the ground and we demonstrate the unique advantages of HST imaging in the study of such features by a critical comparison of our data with the previous observations. In particular we present new estimates of the core radii of two clusters (Cl0024+16, A370) determined from lensed features which are identifiable in our HST images. Although our HST observations include both pre- and post-refurbishment images, the depth of the exposures guarantees that the majority of the arcs are detected with diffraction-limited resolution. A number of the objects in our sample are multiply-imaged and we illustrate the ease of identification of such features when working at high resolution. We discuss the morphological and scale information on these distant field galaxies in the light of HST studies of lower redshift samples. We conclude that the dominant population of star-forming galaxies at z=1 is a factor of 1.5-2 times smaller than the similar group in the local field. This implies either a considerable evolution in the sizes of star-forming galaxies within the last $\sim$10 Gyrs or a shift in the relative space densities of massive and dwarf star-forming systems over the same timescale.Comment: 9 pages (no figures), uuencoded, compressed Postscript. Postscript text, tables and figures (803 Kb) available via anonymous ftp in at ftp://ociw.edu//pub/irs/pub/hstarcs.tar.

A "non-parametric" version of the naive Bayes classifier

Many algorithms have been proposed for the machine learning task of classication. One of the simplest methods, the naive Bayes classifyer, has often been found to give good performance despite the fact that its underlying assumptions (of independence and a Normal distribution of the variables) are perhaps violated. In previous work, we applied naive Bayes and other standard algorithms to a breast cancer database from Nottingham City Hospital in which the variables are highly non-Normal and found that the algorithm performed well when predicting a class that had been derived from the same data. However, when we then applied naive Bayes to predict an alternative clinical variable, it performed much worse than other techniques. This motivated us to propose an alternative method, based on naive Bayes, which removes the requirement for the variables to be Normally distributed, but retains the essential structure and other underlying assumptions of the method. We tested our novel algorithm on our breast cancer data and on three UCI datasets which also exhibited strong violations of Normality. We found our algorithm outperformed naive Bayes in all four cases and outperformed multinomial logistic regression (MLR) in two cases. We conclude that our method offers a competitive alternative to MLR and naive Bayes when dealing with data sets in which non-Normal distributions are observed

Evolution since z = 0.5 of the Morphology-Density relation for Clusters of Galaxies

Using traditional morphological classifications of galaxies in 10 intermediate-redshift (z~0.5) clusters observed with WFPC-2 on the Hubble Space Telescope, we derive relations between morphology and local galaxy density similar to that found by Dressler for low-redshift clusters. Taken collectively, the `morphology-density' relationship, M-D, for these more distant, presumably younger clusters is qualitatively similar to that found for the local sample, but a detailed comparison shows two substantial differences: (1) For the clusters in our sample, the M-D relation is strong in centrally concentrated ``regular'' clusters, those with a strong correlation of radius and surface density, but nearly absent for clusters that are less concentrated and irregular, in contrast to the situation for low redshift clusters where a strong relation has been found for both. (2) In every cluster the fraction of elliptical galaxies is as large or larger than in low-redshift clusters, but the S0 fraction is 2-3 times smaller, with a proportional increase of the spiral fraction. Straightforward, though probably not unique, interpretations of these observations are (1) morphological segregation proceeds hierarchically, affecting richer, denser groups of galaxies earlier, and (2) the formation of elliptical galaxies predates the formation of rich clusters, and occurs instead in the loose-group phase or even earlier, but S0's are generated in large numbers only after cluster virialization.Comment: 35 pages, 19 figures, uses psfig. Accepted for publication in Ap

Evidence for variable selective pressures at MC1R

It is widely assumed that genes that influence variation in skin and hair pigmentation are under selection. To date,the melanocortin 1 receptor (MC1R) is the only gene identified that explains substantial phenotypic variance inhuman pigmentation. Here we investigate MC1R polymorphism in several populations, for evidence of selection.We conclude that MC1R is under strong functional constraint in Africa, where any diversion from eumelanin production (black pigmentation) appears to be evolutionarily deleterious. Although many of the MC1R amino acid variants observed in non-African populations do affect MC1R function and contribute to high levels of MC1R diversity in Europeans, we found no evidence, in either the magnitude or the patterns of diversity, for its enhancement by selection; rather, our analyses show that levels of MC1R polymorphism simply reflect neutral expectations underrelaxation of strong functional constraint outside Africa

Biology of oestrogen-receptor positive primary of core needle biopsy samples and correlation with clinical outcome

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. The majority of biological profiling studies use surgical excision (SE) samples, excluding patients receiving nonsurgical and neoadjuvant therapy. We propose using core needle biopsy (CNB) for biological profiling in older women. Over 37 years (1973–2010), 1 758 older (≥70 years) women with operable primary breast cancer attended a dedicated clinic. Of these, 693 had sufficient quality CNB to construct tissue microarray (TMA). The pattern of biomarkers was analysed in oestrogen receptor (ER)-positive cases, using immunohistochemistry and partitional clustering analysis. The biomarkers measured were: progesterone receptor (PgR), Ki67, Epidermal Growth Factor Receptor (EGFR), Human Epidermal Growth Factor Receptor (HER)-2, HER3, HER4, p53, cytokeratins CK5/6 and CK7/8, Mucin (MUC)1, liver kinase B1 (LKB1), Breast Cancer Associated gene (BRCA) 1, B-Cell Lymphoma (BCL)-2, phosphate and tensin homolog (PTEN), vascular endothelial growth factor (VEGF), and Amplified in breast cancer 1 (AIB1). CNB TMA construction was possible in 536 ER-positive cases. Multivariate analysis showed progesterone receptor (PgR) (p = 0.015), Ki67 (p = 0.001), and mucin (MUC)1 (p = 0.033) as independent predictors for breast-cancer-specific survival (BCSS). Cluster analysis revealed three biological clusters, which were consistent with luminal A, luminal B, and low-ER luminal. The low-ER luminal cluster had lower BCSS compared to luminal A and B. The presence of the low-ER luminal cluster unique to older women, identified in a previous study in SE TMAs in the same cohort, is confirmed. This present study is novel in its use of core needle biopsy tissue microarrays to profile the biology of breast cancer in older women