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.

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.

Markov random fields and spatial smoothing in improving of forest inventory estimates

Markov random fields (MRF) are popular in image processing applications to describe spatial dependencies between image units. Here, we take a look at the theory and the models of MRFs with an application to improve forest inventory estimates. Typically, autocorrelation between study units is a nuisance in statistical inference, but we take an advantage of the dependencies to smooth noisy measurements by borrowing information from the neighbouring units. We build a stochastic spatial model, which we estimate with a Markov chain Monte Carlo simulation method. The smooth values are validated against another data set increasing our confidence that the estimates are more accurate than the originals.

Alternative News Sources as 'Collaboratory' Journalism. A case study of Helsingin Sanomat and its relation with the Youth Voice Editorial Board

The new paradigm of connectedness and empowerment brought by the interactivity feature of the Web 2.0 has been challenging the traditional centralized performance of mainstream media. The corporation has been able to survive the strong winds by transforming itself into a global multimedia business network embedded in the network society. By establishing networks, e.g. networks of production and distribution, the global multimedia business network has been able to sight potential solutions by opening the doors to innovation in a decentralized and flexible manner. Under this emerging context of re-organization, traditional practices like sourcing need to be re- explained and that is precisely what this thesis attempts to tackle. Based on ICT and on the network society, the study seeks to explain within the Finnish context the particular case of Helsingin Sanomat (HS) and its relations with the youth news agency, Youth Voice Editorial Board (NÄT). In that sense, the study can be regarded as an explanatory embedded single case study, where HS is the principal unit of analysis and NÄT its embedded unit of analysis. The thesis was able to reach explanations through interrelated steps. First, it determined the role of ICT in HS’s sourcing practices. Then it mapped an overview of the HS’s sourcing relations and provided a context in which NÄT was located. And finally, it established conceptualized institutional relational data between HS and NÄT for their posterior measurement through social network analysis. The data set was collected via qualitative interviews addressed to online and offline editors of HS as well as interviews addressed to NÄT’s personnel. The study concluded that ICT’s interactivity and User Generated Content (UGC) are not sourcing tools as such but mechanism used by HS for getting ideas that could turn into potential news stories. However, when it comes to visual communication, some exemptions were found. The lack of official sources amidst the immediacy leads HS to rely on ICT’s interaction and UGC. More than meets the eye, ICT’s input into the sourcing practice may be more noticeable if the interaction and UGC is well organized and coordinated into proper and innovative networks of alternative content collaboration. Currently, HS performs this sourcing practice via two projects that differ, precisely, by the mode they are coordinated. The first project found, Omakaupunki, is coordinated internally by Sanoma Group’s owned media houses HS, Vartti and Metro. The second project found is coordinated externally. The external alternative sourcing network, as it was labeled, consists of three actors, namely HS, NÄT (professionals in charge) and the youth. This network is a balanced and complete triad in which the actors connect themselves in relations of feedback, recognition, creativity and filtering. However, as innovation is approached very reluctantly, this content collaboration is a laboratory of experiments; a ‘COLLABORATORY’.

A metabolomic approach to studying mechanisms of polymeric gene delivery to retinal pigment epithelial cells

Tiivistelmä ReferatAbstract Metabolomics is a rapidly growing research field that studies the response of biological systems to environmental factors, disease states and genetic modifications. It aims at measuring the complete set of endogenous metabolites, i.e. the metabolome, in a biological sample such as plasma or cells. Because metabolites are the intermediates and end products of biochemical reactions, metabolite compositions and metabolite levels in biological samples can provide a wealth of information on on-going processes in a living system. Due to the complexity of the metabolome, metabolomic analysis poses a challenge to analytical chemistry. Adequate sample preparation is critical to accurate and reproducible analysis, and the analytical techniques must have high resolution and sensitivity to allow detection of as many metabolites as possible. Furthermore, as the information contained in the metabolome is immense, the data set collected from metabolomic studies is very large. In order to extract the relevant information from such large data sets, efficient data processing and multivariate data analysis methods are needed. In the research presented in this thesis, metabolomics was used to study mechanisms of polymeric gene delivery to retinal pigment epithelial (RPE) cells. The aim of the study was to detect differences in metabolomic fingerprints between transfected cells and non-transfected controls, and thereafter to identify metabolites responsible for the discrimination. The plasmid pCMV-β was introduced into RPE cells using the vector polyethyleneimine (PEI). The samples were analyzed using high performance liquid chromatography (HPLC) and ultra performance liquid chromatography (UPLC) coupled to a triple quadrupole (QqQ) mass spectrometer (MS). The software MZmine was used for raw data processing and principal component analysis (PCA) was used in statistical data analysis. The results revealed differences in metabolomic fingerprints between transfected cells and non-transfected controls. However, reliable fingerprinting data could not be obtained because of low analysis repeatability. Therefore, no attempts were made to identify metabolites responsible for discrimination between sample groups. Repeatability and accuracy of analyses can be influenced by protocol optimization. However, in this study, optimization of analytical methods was hindered by the very small number of samples available for analysis. In conclusion, this study demonstrates that obtaining reliable fingerprinting data is technically demanding, and the protocols need to be thoroughly optimized in order to approach the goals of gaining information on mechanisms of gene delivery.

Sarveiskalvon permeabiliteetin ennustaminen QSPR-mallin avulla

QSPR-malli kuvaa kvantitatiivista riippuvuutta muuttujien ja biologisen ominaisuuden välillä. Näin ollen QSPR mallit ovat käyttökelpoisia lääkekehityksen apuvälineitä. Kirjallisessa osassa kerrotaan sarveiskalvon, suoliston ja veriaivoesteen permeabiliteetin malleista. Useimmin käytettyjä muuttujia ovat yhdisteen rasvaliukoisuus, polaarinen pinta-ala, vetysidosten muodostuminen ja varaus. Myös yhdisteen koko vaikuttaa läpäisevyyteen, vaikka tutkimuksissa onkin erilaista tietoa tämän merkittävyydestä. Malliin vaikuttaa myös muiden kuin mallissa mukana olevien muuttujien suuruusluokka esimerkkinä Lipinskin ‖rule of 5‖ luokittelu. Tässä luokittelussa yhdisteen ominaisuus ei saa ylittää tiettyjä raja-arvoja. Muussa tapauksessa sen imeytyminen suun kautta otettuna todennäköisesti vaarantuu. Lisäksi kirjallisessa osassa tutustuttiin kuljetinproteiineihin ja niiden toimintaan silmän sarveiskalvossa, suolistossa ja veriaivoesteessä. Nykyisin on kehitetty erilaisia QSAR-malleja kuljetinproteiineille ennustamaan mahdollisten substraatittien tai inhibiittorien vuorovaikutuksia kuljetinproteiinin kanssa. Kokeellisen osan tarkoitus oli rakentaa in silico -malli sarveiskalvon passiiviselle permeabiliteetille. Työssä tehtiin QSPR-malli 54 yhdisteen ACDLabs-ohjelmalla laskettujen muuttujien arvojen avulla. Permeabiliteettikertoimien arvot saatiin kirjallisuudesta kanin sarveiskalvon läpäisevyystutkimuksista. Lopullisen mallin muuttujina käytettiin oktanoli-vesijakaantumiskerrointa (logD) pH:ssa 7,4 ja vetysidosatomien kokonaismäärää. Yhtälö oli muotoa log10(permeabiliteettikerroin) = -3,96791 - 0,177842Htotal + 0,311963logD(pH7,4). R2-korrelaatiokerroin oli 0,77 ja Q2-korrelaatiokerroin oli 0,75. Lopullisen mallin hyvyyttä arvioitiin 15 yhdisteen ulkoisella testijoukolla, jolloin ennustettua permeabiliteettia verrattiin kokeelliseen permeabiliteettiin. QSPR-malli arvioitiin myös farmakokineettisen simulaation avulla. Simulaatiossa laskettiin seitsemän yhdisteen kammionestepitoisuudet in vivo vakaassa tilassa käyttäen simulaatioissa QSPR mallilla ennustettuja permeabiliteettikertoimia. Lisäksi laskettiin sarveiskalvon imeytymisen nopeusvakio (Kc) 13 yhdisteelle farmakokineettisen simulaation avulla ja verrattiin tätä lopullisella mallilla ennustettuun permeabiliteettiin. Tulosten perusteella saatiin tilastollisesti hyvä QSPR-malli kuvaamaan sarveiskalvon passiivista permeabiliteettia, jolloin tätä mallia voidaan käyttää lääkekehityksen alkuvaiheessa. QSPR-malli ennusti permeabiliteettikertoimet hyvin, mikä nähtiin vertaamalla mallilla ennustettuja arvoja kokeellisiin tuloksiin. Lisäksi yhdisteiden kammionestepitoisuudet voitiin simuloida käyttäen apuna QSPR-mallilla ennustettuja permeabiliteettikertoimien arvoja.

Understorey vegetation as an indicator of forest site potential in Southern Finland.

The relationship between site characteristics and understorey vegetation composition was analysed with quantitative methods, especially from the viewpoint of site quality estimation. Theoretical models were applied to an empirical data set collected from the upland forests of southern Finland comprising 104 sites dominated by Scots pine (Pinus sylvestris L.), and 165 sites dominated by Norway spruce (Picea abies (L.) Karsten). Site index H100 was used as an independent measure of site quality. A new model for the estimation of site quality at sites with a known understorey vegetation composition was introduced. It is based on the application of Bayes' theorem to the density function of site quality within the study area combined with the species-specific presence-absence response curves. The resulting posterior probability density function may be used for calculating an estimate for the site variable. Using this method, a jackknife estimate of site index H100 was calculated separately for pine- and spruce-dominated sites. The results indicated that the cross-validation root mean squared error (RMSEcv) of the estimates improved from 2.98 m down to 2.34 m relative to the "null" model (standard deviation of the sample distribution) in pine-dominated forests. In spruce-dominated forests RMSEcv decreased from 3.94 m down to 3.16 m. In order to assess these results, four other estimation methods based on understorey vegetation composition were applied to the same data set. The results showed that none of the methods was clearly superior to the others. In pine-dominated forests, RMSEcv varied between 2.34 and 2.47 m, and the corresponding range for spruce-dominated forests was from 3.13 to 3.57 m.