Detecting and Preventing Financial Statement Fraud: The Roles of the Reporting Company and the Independent Auditor

The bankruptcies or near failures of such organizations as Penn Square Bank, E.S.M. Government Securities, and Continental Illinois Bank have renewed interest in the problem of financial statement fraud-the deliberate issuance of misleading financial reports. During 1985 and 1986, the House Energy and Commerce Subcommittee on Oversight and Investigations conducted 16 hearings in an attempt to uncover the reasons behind this recent wave of so called audit failures. These hearings have prompted heated debate about the proper roles of the reporting entity and the independent auditor in detecting and preventing financial statement fraud

Incorporating genome-scale tools for studying energy homeostasis

Mammals have evolved complex regulatory systems that enable them to maintain energy homeostasis despite constant environmental challenges that limit the availability of energy inputs and their composition. Biological control relies upon intricate systems composed of multiple organs and specialized cell types that regulate energy up-take, storage, and expenditure. Because these systems simultaneously perform diverse functions and are highly integrated, they are extremely difficult to understand in terms of their individual component contributions to energy homeostasis. In order to provide improved treatments and clinical options, it is important to identify the principle genetic and molecular components, as well as the systemic features of regulation. To begin, many of these features can be discovered by integrating experimental technologies with advanced methods of analysis. This review focuses on the analysis of transcriptional data derived from microarrays and how it can complement other experimental techniques to study energy homeostasis

Element content and daily intake from dietary supplements (nutraceuticals) based on algae, garlic, yeast fish and krill oils—Should consumers be worried?

The authors would like to thank Agilent Technologies for the loan of the 8800 ICP-QQQ used in this study. Michael Stiboller thanks European Union’s Lifelong Learning Programme ‘Leonardo da Vinci’: “ALUMNI UNI GRAZ MOBILITY PROGRAMME 2013-2015” for financial support of his placement.Peer reviewedPostprin

High-molecular-weight kininogen is a binding protein for tissue prokallikrein

AbstractHuman tissue prokallikrein, a zymogen of the kallikrein-kinin system, circulates in plasma bound to neutrophils. Because plasma kininogens contribute to the assembly of kinin-generating components on blood cells, these proteins were assessed for their ability to complex the kallikrein precursor. Using ligand blot and direct binding assays, biotinylated prokallikrein was found to bind only to high-molecular-weight kininogen and not to the low-molecular-weight form. The interaction was specific, reversible, and saturable yielding an estimated dissociation constant KD=690 nM and a 1:1 stoichiometry. Specific kininogen binding of tissue prokallikrein also occurred at physiological plasma protein concentrations. These results provide the first evidence for a novel function of high-molecular-weight kininogen as a binding protein for tissue prokallikrein that could serve to localize the kallikrein precursor on the neutrophil surface

Synthesis and analysis by liquid chromatography-mass spectrometry of a mauveine composition similar to museum-stored mauveine

We are grateful to the EPSRC National Mass Spectrometry Foundation (NMSF) for mass spectra.Peer reviewedPostprintPostprin

Violet dyes of the 1860’s : Hofmann, Britannia, Violet de Paris, Wanklyn’s and Crystal violet (1883)

Open Access via the Sage Agreement The authors thank the UK EPSRC National Mass Spectrometry Service Centre for mass spectrometric data and the UK National Crystallography Centre (University of Southampton) for the X-ray data collections. M.J.P. performed all synthesis and obtained the characterisation data and W.T.A.H. solved the crystallographic data sets. Data sets were obtained free of charge from the National Crystallography Centre, Southampton University.Peer reviewedPublisher PD

Structure Preserving Encoding of Non-euclidean Similarity Data

Domain-specific proximity measures, like divergence measures in signal processing or alignment scores in bioinformatics, often lead to non-metric, indefinite similarities or dissimilarities. However, many classical learning algorithms like kernel machines assume metric properties and struggle with such metric violations. For example, the classical support vector machine is no longer able to converge to an optimum. One possible direction to solve the indefiniteness problem is to transform the non-metric (dis-)similarity data into positive (semi-)definite matrices. For this purpose, many approaches have been proposed that adapt the eigenspectrum of the given data such that positive definiteness is ensured. Unfortunately, most of these approaches modify the eigenspectrum in such a strong manner that valuable information is removed or noise is added to the data. In particular, the shift operation has attracted a lot of interest in the past few years despite its frequently reoccurring disadvantages. In this work, we propose a modified advanced shift correction method that enables the preservation of the eigenspectrum structure of the data by means of a low-rank approximated nullspace correction. We compare our advanced shift to classical eigenvalue corrections like eigenvalue clipping, flipping, squaring, and shifting on several benchmark data. The impact of a low-rank approximation on the data’s eigenspectrum is analyzed.</p

Data-Driven Supervised Learning for Life Science Data

Life science data are often encoded in a non-standard way by means of alpha-numeric sequences, graph representations, numerical vectors of variable length, or other formats. Domain-specific or data-driven similarity measures like alignment functions have been employed with great success. The vast majority of more complex data analysis algorithms require fixed-length vectorial input data, asking for substantial preprocessing of life science data. Data-driven measures are widely ignored in favor of simple encodings. These preprocessing steps are not always easy to perform nor particularly effective, with a potential loss of information and interpretability. We present some strategies and concepts of how to employ data-driven similarity measures in the life science context and other complex biological systems. In particular, we show how to use data-driven similarity measures effectively in standard learning algorithms