Structure and kinematics of the peculiar galaxy NGC 128

This is a multiband photometric and spectroscopic study of the peculiar S0 galaxy NGC128. We present results from broad (B and R) and narrow band optical CCD photometry, near (NIR) and far (FIR) infrared observations, long slit spectroscopy, and Fabry-Perot interferometry (CIGALE). The peculiar peanut shape morphology of the galaxy is observed both at optical and near-infrared wavelengths. The stellar disk is thick and distorted (arc-bended), with a color asymmetry along the major axis due to the presence of a large amount of dust, estimated through NIR and FIR data of ~6x10^6 M_sun, in the region of interaction with the companion galaxy NGC127. The color maps are nearly uniform over the whole galaxy, but for the major axis asymmetry, and a small gradient toward the center indicating the presence of a redder disk-like component. The H_alpha image indeed reveals the existence of a tilted gaseous ``disk'' around the center, oriented with the major axis toward the companion galaxy NGC127. Long slit and CIGALE data confirm the presence of gas in a disk-like component counter-rotating and inclined approximately of 50 deg. to the line of sight. The mass of the gas disk in the inner region is ~2.7x10^4 M_sun. The stellar velocity field is cylindrical up to the last measured points of the derived rotation curves, while the velocity dispersion profiles are typical for an S0 galaxy, but for an extended constant behaviour along the minor axis.Comment: accepted for pubblication in A&A Supp

CMA – a comprehensive Bioconductor package for supervised classification with high dimensional data

For the last eight years, microarray-based class prediction has been a major topic in statistics, bioinformatics and biomedicine research. Traditional methods often yield unsatisfactory results or may even be inapplicable in the p > n setting where the number of predictors by far exceeds the number of observations, hence the term “ill-posed-problem”. Careful model selection and evaluation satisfying accepted good-practice standards is a very complex task for inexperienced users with limited statistical background or for statisticians without experience in this area. The multiplicity of available methods for class prediction based on high-dimensional data is an additional practical challenge for inexperienced researchers. In this article, we introduce a new Bioconductor package called CMA (standing for “Classification for MicroArrays”) for automatically performing variable selection, parameter tuning, classifier construction, and unbiased evaluation of the constructed classifiers using a large number of usual methods. Without much time and effort, users are provided with an overview of the unbiased accuracy of most top-performing classifiers. Furthermore, the standardized evaluation framework underlying CMA can also be beneficial in statistical research for comparison purposes, for instance if a new classifier has to be compared to existing approaches. CMA is a user-friendly comprehensive package for classifier construction and evaluation implementing most usual approaches. It is freely available from the Bioconductor website at http://bioconductor.org/packages/2.3/bioc/html/CMA.html

Stability and aggregation of ranked gene lists

Ranked gene lists are highly instable in the sense that similar measures of differential gene expression may yield very different rankings, and that a small change of the data set usually affects the obtained gene list considerably. Stability issues have long been under-considered in the literature, but they have grown to a hot topic in the last few years, perhaps as a consequence of the increasing skepticism on the reproducibility and clinical applicability of molecular research findings. In this article, we review existing approaches for the assessment of stability of ranked gene lists and the related problem of aggregation, give some practical recommendations, and warn against potential misuse of these methods. This overview is illustrated through an application to a recent leukemia data set using the freely available Bioconductor package GeneSelector

An AUC-based Permutation Variable Importance Measure for Random Forests

The random forest (RF) method is a commonly used tool for classification with high dimensional data as well as for ranking candidate predictors based on the so-called random forest variable importance measures (VIMs). However the classification performance of RF is known to be suboptimal in case of strongly unbalanced data, i.e. data where response class sizes differ considerably. Suggestions were made to obtain better classification performance based either on sampling procedures or on cost sensitivity analyses. However to our knowledge the performance of the VIMs has not yet been examined in the case of unbalanced response classes. In this paper we explore the performance of the permutation VIM for unbalanced data settings and introduce an alternative permutation VIM based on the area under the curve (AUC) that is expected to be more robust towards class imbalance. We investigated the performance of the standard permutation VIM and of our novel AUC-based permutation VIM for different class imbalance levels using simulated data and real data. The results suggest that the standard permutation VIM loses its ability to discriminate between associated predictors and predictors not associated with the response for increasing class imbalance. It is outperformed by our new AUC-based permutation VIM for unbalanced data settings, while the performance of both VIMs is very similar in the case of balanced classes. The new AUC-based VIM is implemented in the R package party for the unbiased RF variant based on conditional inference trees. The codes implementing our study are available from the companion website: http://www.ibe.med.uni-muenchen.de/organisation/mitarbeiter/070_drittmittel/janitza/index.html

Evaluating Microarray-based Classifiers: An Overview

For the last eight years, microarray-based class prediction has been the subject of numerous publications in medicine, bioinformatics and statistics journals. However, in many articles, the assessment of classification accuracy is carried out using suboptimal procedures and is not paid much attention. In this paper, we carefully review various statistical aspects of classifier evaluation and validation from a practical point of view. The main topics addressed are accuracy measures, error rate estimation procedures, variable selection, choice of classifiers and validation strategy

The Normal Fetal Heart Rate Study: Analysis Plan

Recording of fetal heart rate via CTG monitoring has been routinely performed as an important part of antenatal and subpartum care for several decades. The current guidelines of the FIGO (ref1) recommend a normal range of the fetal heart rate from 110 to 150 bpm. However, there is no agreement in the medical community whether this is the correct range (ref2). We aim to address this question by computerized analysis (ref 3) of a high quality database (HQDb, ref 4) of about one billion electronically registered fetal heart rate measurements from about 10,000 pregnancies in three medical centres over seven years. In the present paper, we lay out a detailed analysis plan for this evidence-based project in the vein of the validation policy of the Sylvia Lawry Centre for Multiple Sclerosis Research (ref 5) with a split of the database into an exploratory part and a part reserved for validation. We will perform the analysis and the validation after publication of this plan in order to reduce the probability of publishing false positive research findings (ref 6-7)

Essential guidelines for computational method benchmarking

In computational biology and other sciences, researchers are frequently faced with a choice between several computational methods for performing data analyses. Benchmarking studies aim to rigorously compare the performance of different methods using well-characterized benchmark datasets, to determine the strengths of each method or to provide recommendations regarding suitable choices of methods for an analysis. However, benchmarking studies must be carefully designed and implemented to provide accurate, unbiased, and informative results. Here, we summarize key practical guidelines and recommendations for performing high-quality benchmarking analyses, based on our experiences in computational biology

Simulation of the d.c. critical current in superconducting sintered ceramics

The new superconducting high-Tc sintered ceramics can be described in some case as a lattice of interconnected rods, in other cases as a more or less random packing of parallelepiped crystallites ; their size is about a few microns. The d.c. critical current at zero voltage of such a material is not related to the critical current of the bulk material, but to its granular structure. Indeed, the critical current between two adjacent cells is governed by the critical current of the weak link between them ; this link behaves within some limits as a Josephson junction, the critical current of which is known. For our present problem, the system can be modeled as a lattice of Josephson junctions. We present here results for the d.c. critical current at zero voltage of lattices of identical Josephson junctions in two dimensions. The influence of the finiteness of size of the sample is examined. The relationship with normal conductivity simulations and percolation is discussed

Essential guidelines for computational method benchmarking

In computational biology and other sciences, researchers are frequently faced with a choice between several computational methods for performing data analyses. Benchmarking studies aim to rigorously compare the performance of different methods using well-characterized benchmark datasets, to determine the strengths of each method or to provide recommendations regarding suitable choices of methods for an analysis. However, benchmarking studies must be carefully designed and implemented to provide accurate, unbiased, and informative results. Here, we summarize key practical guidelines and recommendations for performing high-quality benchmarking analyses, based on our experiences in computational biology.Comment: Minor update

Fringe or background: Characterizing deep-water mudstones beyond the basin-floor fan sandstone pinchout

Mud dominates volumetrically the fraction of sediment delivered and deposited in deep-water environments, and mudstone is a major component of basin-floor successions. However, studies of basin-floor deposits have mainly focused on their proximal sandstone-prone part. A consequent bias therefore remains in the understanding of depositional processes and stratigraphic architecture in mudstone-prone distal settings beyond the sandstone pinchouts of basin-floor fans. This study uses macroscopic and microscopic descriptions of over 500 m of continuous cores from research boreholes from the Permian Skoorsteenberg Formation of the Karoo Basin, South Africa, to document the sedimentology, stratigraphy, and ichnology of a distal mudstone-prone basin-floor succession. Very thin- to thin-bedded mudstones, deposited by low-density turbidity currents, stack to form bedsets bounded by thin packages ( 0.7 m thick) background mudstones. Stratigraphic correlation between cores suggests that bedsets represent the distal fringes of submarine fan lobe elements and/or lobes, and bedset packages represent the distal fringes of lobe complexes and/or lobe complex sets. The internal stacking pattern of bedsets and bedset packages is highly variable vertically and laterally, which records dominantly autogenic processes (e.g., compensational stacking, avulsion of feeder channels). The background mudstones are characterized by remnant tractional structures and outsize particles, and are interpreted as deposited from low-density turbidity currents and debris flows before intense biogenic reworking. These observations challenge the idea that mud accumulates only from hemipelagic suspension fallout in distal basin-floor environments. Thin background mudstones separating bedsets ( 0.7 m thick) are interpreted to dominantly mark allogenically driven regional decrease of sand supply to the basin floor. The recognition of sandstone-prone basin-floor fans passing into genetically linked distal fringe mudstones suggests that submarine lobes are at least ∼ 20 km longer than previously estimated. This study provides sedimentological, stratigraphic, and ichnological criteria to differentiate mudstones deposited in different sub-environments in distal deep-water basin-floor settings, with implications for the accurate characterization of basin-floor fan architecture, and their use as archives of paleoenvironmental change