Disallowed Ramachandran Conformations of Amino Acid Residues in Protein Structures

An analysis of the nature and distribution of disallowed Ramachandran conformations of amino acid residues observed in high resolution protein crystal structures has been carried out. A data set consisting of 110 high resolution, non-homologous, protein crystal structures from the Brookhaven Protein Data Bank was examined. The data set consisted of a total of 18,708 non-Gly residues, which were characterized on the basis of their backbone dihedral angles (φ, ψ). Residues falling outside the defined “broad allowed limits” on the Ramachandran map were chosen and the reportedB-factor value of the α-carbon atom was used to further select well defined disallowed conformations. The conformations of the selected 66 disallowed residues clustered in distinct regions of the Ramachandran map indicating that specific φ, ψ angle distortions are preferred under compulsions imposed by local constraints. The distribution of various amino acid residues in the disallowed residue data set showed a predominance of small polar/charged residues, with bulky hydrophobic residues being infrequent. As a further check, for all the 66 cases non-hydrogen van der Waals short contacts in the protein structures were evaluated and compared with the ideal “Ala-dipeptide” constructed using disallowed dihedral angle (φ, ψ) values. The analysis reveals that short contacts are eliminated in most cases by local distortions of bond angles. An analysis of the conformation of the identified disallowed residues in related protein structures reveals instances of conservation of unusual stereochemistry.

Production of psi(2S) Mesons in ppbar Collisions at 1.96 TeV

We have measured the differential cross section for the inclusive production of psi(2S) mesons decaying to mu^{+} mu^{-1} that were produced in prompt or B-decay processes from ppbar collisions at 1.96 TeV. These measurements have been made using a data set from an integrated luminosity of 1.1 fb^{-1} collected by the CDF II detector at Fermilab. For events with transverse momentum p_{T} (psi(2S)) > 2 GeV/c and rapidity |y(psi(2S))| psi(2S)X) Br(psi(2S) -> mu^{+} mu^{-}) to be 3.29 +- 0.04(stat.) +- 0.32(syst.) nb.

Phylogenetic analyses reveal high levels of polyphyly among pleurocarpous lineages as well as novel clades

Phylogenetic analyses of the Hypnales usually show the same picture of poorly resolved trees with a large number of polyphyletic taxa and low support for the few reconstructed clades. One odd clade, however, consisting of three genera that are currently treated either within the Leskeaceae (Miyabea) or Neckeraceae (Homaliadelphus and Bissetia), was retrieved in a previously published phylogeny based on chloroplast rbcL. In order to elucidate the reliability of the observed Homaliadelphus - Miyabea - Bissetia - clade (HMB-clade) and to reveal its phylogenetic relationships a molecular study based on a representative set of hypnalean taxa was performed. Sequence data from all three genomes, namely the ITS1 and 2 (nuclear), the trnS-rps4-trnT-trnL-trnF cluster (plastid), the nad5 intron (mitochondrial), were analyzed. Although the phylogenetic reconstruction of the combined data set was not fully resolved regarding the backbone it clearly indicated the polyphyletic nature of various hypnalean families, such as the Leskeaceae, Hypnaceae, Hylocomiaceae, Neckeraceae, Leptodontaceae and Anomodontaceae with respect to the included taxa. In addition the results favor the inclusion of the Leptodontaceae and Thamnobryaceae in the Neckeraceae. The maximally supported HMB-clade consisting of the three genera Homaliadelphus (2-3 species), Miyabea (3 species) and Bissetia (1 species) is resolved sister to a so far unnamed clade comprising Taxiphyllum aomoriense, Glossadelphus ogatae and Leptopterigynandrum. The well-resolved and supported HMB-clade, here formally described as the Miyabeaceae, fam. nov. is additionally supported by morphological characters such as strongly incrassate, porose leaf cells, a relatively weak and diffuse costa and the presence of dwarf males. The latter are absent in the Neckeraceae and the Leskeaceae. It is essentially an East Asian family, with one species occurring in North America.

Search for Top-Quark Production via Flavor-Changing Neutral Currents in W+1 Jet Events at CDF

We report on the first search for top-quark production via flavor-changing neutral-current (FCNC) interactions in the non-standard-model process u(c)+g -> t using ppbar collision data collected by the CDF II detector. The data set corresponds to an integrated luminosity of 2.2/fb. The candidate events feature the signature of semileptonic top-quark decays and are classified as signal-like or background-like by an artificial neural network trained on simulated events. The observed discriminant distribution is in good agreement with the one predicted by the standard model and provides no evidence for FCNC top-quark production, resulting in a Bayesian upper limit on the production cross section sigma (u(c)+g -> t) u+g) c+g)

Production of ψ(2S) mesons in pp̅ collisions at 1.96 TeV

We have measured the differential cross section for the inclusive production of psi(2S) mesons decaying to mu^{+} mu^{-1} that were produced in prompt or B-decay processes from ppbar collisions at 1.96 TeV. These measurements have been made using a data set from an integrated luminosity of 1.1 fb^{-1} collected by the CDF II detector at Fermilab. For events with transverse momentum p_{T} (psi(2S)) > 2 GeV/c and rapidity |y(psi(2S))| psi(2S)X) Br(psi(2S) -> mu^{+} mu^{-}) to be 3.29 +- 0.04(stat.) +- 0.32(syst.) nb.

Detecting Earnings Management Using Neural Networks

Detecting Earnings Management Using Neural Networks. Trying to balance between relevant and reliable accounting data, generally accepted accounting principles (GAAP) allow, to some extent, the company management to use their judgment and to make subjective assessments when preparing financial statements. The opportunistic use of the discretion in financial reporting is called earnings management. There have been a considerable number of suggestions of methods for detecting accrual based earnings management. A majority of these methods are based on linear regression. The problem with using linear regression is that a linear relationship between the dependent variable and the independent variables must be assumed. However, previous research has shown that the relationship between accruals and some of the explanatory variables, such as company performance, is non-linear. An alternative to linear regression, which can handle non-linear relationships, is neural networks. The type of neural network used in this study is the feed-forward back-propagation neural network. Three neural network-based models are compared with four commonly used linear regression-based earnings management detection models. All seven models are based on the earnings management detection model presented by Jones (1991). The performance of the models is assessed in three steps. First, a random data set of companies is used. Second, the discretionary accruals from the random data set are ranked according to six different variables. The discretionary accruals in the highest and lowest quartiles for these six variables are then compared. Third, a data set containing simulated earnings management is used. Both expense and revenue manipulation ranging between -5% and 5% of lagged total assets is simulated. Furthermore, two neural network-based models and two linear regression-based models are used with a data set containing financial statement data from 110 failed companies. Overall, the results show that the linear regression-based models, except for the model using a piecewise linear approach, produce biased estimates of discretionary accruals. The neural network-based model with the original Jones model variables and the neural network-based model augmented with ROA as an independent variable, however, perform well in all three steps. Especially in the second step, where the highest and lowest quartiles of ranked discretionary accruals are examined, the neural network-based model augmented with ROA as an independent variable outperforms the other models.

Inference on Cointegration in Vector Autoregressive Models (summary section only)

In the thesis we consider inference for cointegration in vector autoregressive (VAR) models. The thesis consists of an introduction and four papers. The first paper proposes a new test for cointegration in VAR models that is directly based on the eigenvalues of the least squares (LS) estimate of the autoregressive matrix. In the second paper we compare a small sample correction for the likelihood ratio (LR) test of cointegrating rank and the bootstrap. The simulation experiments show that the bootstrap works very well in practice and dominates the correction factor. The tests are applied to international stock prices data, and the .nite sample performance of the tests are investigated by simulating the data. The third paper studies the demand for money in Sweden 1970—2000 using the I(2) model. In the fourth paper we re-examine the evidence of cointegration between international stock prices. The paper shows that some of the previous empirical results can be explained by the small-sample bias and size distortion of Johansen’s LR tests for cointegration. In all papers we work with two data sets. The first data set is a Swedish money demand data set with observations on the money stock, the consumer price index, gross domestic product (GDP), the short-term interest rate and the long-term interest rate. The data are quarterly and the sample period is 1970(1)—2000(1). The second data set consists of month-end stock market index observations for Finland, France, Germany, Sweden, the United Kingdom and the United States from 1980(1) to 1997(2). Both data sets are typical of the sample sizes encountered in economic data, and the applications illustrate the usefulness of the models and tests discussed in the thesis.

Share Repurchases, Dividends, and Executive Options

This paper reports empirical results on the determinants of the authorization decision for share repurchases and dividends in Finland. We use a data set with precise data on share repurchases as well as characteristics for the option programs. Contrary to the U.S., we use a data set where 41% of the options are dividend protected, which allows us to separate between the "option funding" and "substitution / managerial wealth" hypothesis for the choice of the distribution method. We find that foreign ownership is the main determinant for share repurchases in Finland and attribute this relationship to tax factors. We also find evidence in support of both the signaling and agency cost hypotheses for cash distributions, especially in the case of share repurchases. Finally, we find a significant difference between companies with and without dividend protected options. When options are dividend protected, the relationship between dividend distributions and the scope of the options program turns to a significantly positive one instead of the negative one documented on U.S. data. This gives some support for the substitution / managerial wealth hypothesis as a determinant for the choice of the distribution method.

Intraday Seasonalities and Macroeconomic News Announcements

Using a data set consisting of three years of 5-minute intraday stock index returns for major European stock indices and U.S. macroeconomic surprises, the conditional mean and volatility behaviors in European market were investigated. The findings suggested that the opening of the U.S market significantly raised the level of volatility in Europe, and that all markets respond in an identical fashion. Furthermore, the U.S. macroeconomic surprises exerted an immediate and major impact on both European stock markets’ returns and volatilities. Thus, high frequency data appear to be critical for the identification of news that impacted the markets.

Do Analysts Leak Information to Preferred Customers?

Inspired by the recent debate in the financial press, we set out to investigate if financial analysts warn their preferred customers of possible earnings forecast revisions. The issue is explored by monitoring investors’ trading behavior during the weeks prior to analyst earnings forecast revisions, using the unique official stock transactions data set from Finland. In summary, we do not find evidence of large investors systematically being warned of earnings forecast revisions. However, the results indicate that the very largest investors show trading behavior partly consistent with being informed of future earnings forecast revisions.

Overconfidence and investor size

Recent research documents that institutional or large investors act as antagonists to other investors by showing opposite behavior following disclosure of new information. Using an extremely comprehensive official transactions data set from Finland, we set out to explore the interrelation between investor size and behavior. More specifically, we test whether investor size is positively (negatively) correlated with investor reaction following positive (negative) news. We document robust evidence of that investor size affects investor behavior under new information, as larger investors on average react more positively (negatively) to good (bad) news than smaller investors. In the light of this study it seems increasingly feasible that several recent findings of heterogeneous investor behavior are functions of differences in overconfidence.

Cluster based core vector machine

Core Vector Machine(CVM) is suitable for efficient large-scale pattern classification. In this paper, a method for improving the performance of CVM with Gaussian kernel function irrespective of the orderings of patterns belonging to different classes within the data set is proposed. This method employs a selective sampling based training of CVM using a novel kernel based scalable hierarchical clustering algorithm. Empirical studies made on synthetic and real world data sets show that the proposed strategy performs well on large data sets.

Income Inequality and Healt: A Multi-Country Analysis

This paper investigates the effect of income inequality on health status. A model of health status was specified in which the main variables were income level, income inequality, the level of savings and the level of education. The model was estimated using a panel data set for 44 countries covering six time periods. The results indicate that income inequality (measured by the Gini coefficient) has a significant effect on health status when we control for the levels of income, savings and education. The relationship is consistent regardless of the specification of health status and income. Thus, the study results provide some empirical support for the income inequality hypothesis.

Computationally efficient optical tomographic reconstructions through waveletizing the normalized quadratic perturbation equation - art. no.61640R

In this paper, we present a wavelet - based approach to solve the non-linear perturbation equation encountered in optical tomography. A particularly suitable data gathering geometry is used to gather a data set consisting of differential changes in intensity owing to the presence of the inhomogeneous regions. With this scheme, the unknown image, the data, as well as the weight matrix are all represented by wavelet expansions, thus yielding the representation of the original non - linear perturbation equation in the wavelet domain. The advantage in use of the non-linear perturbation equation is that there is no need to recompute the derivatives during the entire reconstruction process. Once the derivatives are computed, they are transformed into the wavelet domain. The purpose of going to the wavelet domain, is that, it has an inherent localization and de-noising property. The use of approximation coefficients, without the detail coefficients, is ideally suited for diffuse optical tomographic reconstructions, as the diffusion equation removes most of the high frequency information and the reconstruction appears low-pass filtered. We demonstrate through numerical simulations, that through solving merely the approximation coefficients one can reconstruct an image which has the same information content as the reconstruction from a non-waveletized procedure. In addition we demonstrate a better noise tolerance and much reduced computation time for reconstructions from this approach.

An algorithm to detect linearly separable clusters of binary patterns

The clusters of binary patterns can be considered as Boolean functions of the (binary) features. Such a relationship between the linearly separable (LS) Boolean functions and LS clusters of binary patterns is examined. An algorithm is presented to answer the questions of the type: “Is the cluster formed by the subsets of the (binary) data set having certain features AND/NOT having certain other features, LS from the remaining set?” The algorithm uses the sequences of Numbered Binary Form (NBF) notation and some elementary (NPN) transformations of the binary data.