Subtypes of adolescents with substance use disorders and psychiatric comorbidity using cluster and discriminant analysis of MMPI-A profiles

The main aim of this study was to replicate and extend previous results on subtypes of adolescents with substance use disorders (SUD), according to their Minnesota Multiphasic Personality Inventory for adolescents (MMPI-A) profiles. Sixty patients with SUD and psychiatric comorbidity (41.7% male, mean age = 15.9 years old) completed the MMPI-A, the Teen Addiction Severity Index (T-ASI), the Child Behaviour Checklist (CBCL), and were interviewed in order to determine DSMIV diagnoses and level of substance use. Mean MMPI-A personality profile showed moderate peaks in Psychopathic Deviate, Depression and Hysteria scales. Hierarchical cluster analysis revealed four profiles (acting-out, 35% of the sample; disorganized-conflictive, 15%; normative-impulsive, 15%; and deceptive-concealed, 35%). External correlates were found between cluster 1, CBCL externalizing symptoms at a clinical level and conduct disorders, and between cluster 2 and mixed CBCL internalized/externalized symptoms at a clinical level. Discriminant analysis showed that Depression, Psychopathic Deviate and Psychasthenia MMPI-A scales correctly classified 90% of the patients into the clusters obtained.

Regional Science trends through the analysis of the main facts of the 51st ERSA Conference

The 51st ERSA Conference held in Barcelona in 2011 was one of the largest ever. By examining the characteristics of the conference, this paper identifies the main trends in Regional Science and draws on a broad array of sources of information: the delegates" demographic details, the conference program itself, a satisfaction survey conducted among delegates, a quality survey addressed to those chairing the sessions and, finally, a bibliometric database including each author signing a paper presented at the conference. We finally run a regression analysis from which we show that for ERSA delegates what matters most is quality, and this must be the direction that future conferences should move toward. Ultimately, ERSA conferences are comprehensive, all-embracing occasions, representing an ideal opportunity for regional scientists to present their work to each other and to network.

The environmental effects of changing speed limits: a quantile regression approach

Two speed management policies were implemented in the metropolitan area of Barcelona aimed at reducing air pollution concentration levels. In 2008, the maximum speed limit was reduced to 80 km/h and, in 2009, a variable speed system was introduced on some metropolitan motorways. This paper evaluates whether such policies have been successful in promoting cleaner air, not only in terms of mean pollutant levels but also during high and low pollution episodes. We use a quantile regression approach for fixed effect panel data. We find that the variable speed system improves air quality with regard to the two pollutants considered here, being most effective when nitrogen oxide levels are not too low and when particulate matter concentrations are below extremely high levels. However, reducing the maximum speed limit from 120/100 km/h to 80 km/h has no effect – or even a slightly increasing effect –on the two pollutants, depending on the pollution scenario. Length: 32 pages

O6-Methylguanine-DNA methyltransferase protein expression by immunohistochemistry in brain and non-brain systemic tumours: systematic review and meta-analysis of correlation with methylation-specific polymerase chain reaction

Background: The DNA repair protein O6-Methylguanine-DNA methyltransferase (MGMT) confers resistance to alkylating agents. Several methods have been applied to its analysis, with methylation-specific polymerase chain reaction (MSP) the most commonly used for promoter methylation study, while immunohistochemistry (IHC) has become the most frequently used for the detection of MGMT protein expression. Agreement on the best and most reliable technique for evaluating MGMT status remains unsettled. The aim of this study was to perform a systematic review and meta-analysis of the correlation between IHC and MSP. Methods A computer-aided search of MEDLINE (1950-October 2009), EBSCO (1966-October 2009) and EMBASE (1974-October 2009) was performed for relevant publications. Studies meeting inclusion criteria were those comparing MGMT protein expression by IHC with MGMT promoter methylation by MSP in the same cohort of patients. Methodological quality was assessed by using the QUADAS and STARD instruments. Previously published guidelines were followed for meta-analysis performance. Results Of 254 studies identified as eligible for full-text review, 52 (20.5%) met the inclusion criteria. The review showed that results of MGMT protein expression by IHC are not in close agreement with those obtained with MSP. Moreover, type of tumour (primary brain tumour vs others) was an independent covariate of accuracy estimates in the meta-regression analysis beyond the cut-off value. Conclusions Protein expression assessed by IHC alone fails to reflect the promoter methylation status of MGMT. Thus, in attempts at clinical diagnosis the two methods seem to select different groups of patients and should not be used interchangeably.

Financial stress transmission in EMU sovereign bond market volatility: a connectedness analysis

This paper measures the connectedness in EMU sovereign market volatility between April 1999 and January 2014, in order to monitor stress transmission and to identify episodes of intensive spillovers from one country to the others. To this end, we first perform a static and dynamic analysis to measure the total volatility connectedness in the entire period (the system-wide approach) using a framework recently proposed by Diebold and Yılmaz (2014). Second, we make use of a dynamic analysis to evaluate the net directional connectedness for each country and apply panel model techniques to investigate its determinants. Finally, to gain further insights, we examine the timevarying behaviour of net pair-wise directional connectedness at different stages of the recent sovereign debt crisis.

A Methodology to measure shareholder value orientation and shareholder value creation aimed at providing a research basis to investigate the link between both magnitudes

Does shareholder value orientation lead to shareholder value creation? This article proposes methods to quantify both, shareholder value orientation and shareholder value creation. Through the application of these models it is possible to quantify both dimensions and examine statistically in how far shareholder value orientation explains shareholder value creation. The scoring model developed in this paper allows quantifying the orientation of managers towards the objective to maximize wealth of shareholders. The method evaluates information that comes from the companies and scores the value orientation in a scale from 0 to 10 points. Analytically the variable value orientation is operationalized expressing it as the general attitude of managers toward the objective of value creation, investment policy and behavior, flexibility and further eight value drivers. The value creation model works with market data such as stock prices and dividend payments. Both methods where applied to a sample of 38 blue chip companies: 32 firms belonged to the share index IBEX 35 on July 1st, 1999, one company represents the “new economy” listed in the Spanish New Market as per July 1st, 2001, and 5 European multinational groups formed part of the EuroStoxx 50 index also on July 1st, 2001. The research period comprised the financial years 1998, 1999, and 2000. A regression analysis showed that between 15.9% and 23.4% of shareholder value creation can be explained by shareholder value orientation.

Accuracy vs. simplicity: a complex trade-off

Inductive learning aims at finding general rules that hold true in a database. Targeted learning seeks rules for the predictions of the value of a variable based on the values of others, as in the case of linear or non-parametric regression analysis. Non-targeted learning finds regularities without a specific prediction goal. We model the product of non-targeted learning as rules that state that a certain phenomenon never happens, or that certain conditions necessitate another. For all types of rules, there is a trade-off between the rule's accuracy and its simplicity. Thus rule selection can be viewed as a choice problem, among pairs of degree of accuracy and degree of complexity. However, one cannot in general tell what is the feasible set in the accuracy-complexity space. Formally, we show that finding out whether a point belongs to this set is computationally hard. In particular, in the context of linear regression, finding a small set of variables that obtain a certain value of R2 is computationally hard. Computational complexity may explain why a person is not always aware of rules that, if asked, she would find valid. This, in turn, may explain why one can change other people's minds (opinions, beliefs) without providing new information.

Like milk or wine: Does firm performance improve with age?

Our empirical literature review shows that little is known about how firm performance changes with age, presumably because of the paucity of data on firm age. For Spanish manufacturing firms, we analyse the firm performance related to firm age between 1998 and 2006. We find evidence that firms improve with age, because ageing firms are observed to have steadily increasing levels of productivity, higher profits, larger size, lower debt ratios, and higher equity ratios. Furthermore, older firms are better able to convert sales growth into subsequent growth of profits and productivity. On the other hand, we also found evidence that firm performance deteriorates with age. Older firms have lower expected growth rates of sales, profits and productivity, they have lower profitability levels (when other variables such as size are controlled for), and also that they appear to be less capable to convert employment growth into growth of sales, profits and productivity.

Environmental factors and social entrepreneurship

Social entrepreneurship has been a subject of growing interest by academics and governments, however little still being known about environmental factors that affect this phenomenon. The main objective of this study is to analyze how these factors affect social entrepreneurial activity, in the light of the institutional economic theory as the conceptual framework. Using linear regression analysis for a sample of 49 countries, is studied the impact of informal institutions (social needs, societal attitudes and education) and formal institutions (public spending, access to finance and governance effectiveness) on social entrepreneurial activity. The findings suggest that while societal attitudes increase the rates of social entrepreneurship, public spending has a negative relationship with this phenomenon. Finally, the empirical evidence found could be useful for the definition of government policies on promoting social entrepreneurship.

Effect of biological surlie ageing on the sparkling wines’ quality and elaoration

Report for the scientific sojourn at the Université de Bourgogne, France, from July until October 2007..Surlie ageing after second fermentation is a fundamental operation in the production of quality sparkling wine like Cava and Champagne. Recently, the importance of the interaction between wine and lees cell surface has been reported. Cell surface properties depending on wall biochemical composition are major determinants in microbial interactions, having important repercussions in several technological aspects. Sorption and flocculation are especially important in sparkling wine production, and are governed by distinct cell surface properties. The aim of the present research carried out during the four months of the stage was to know the implication of lees surface modifications occurring during surlie ageing in sparkling wine quality and elaboration. The relationship between physico-chemical properties such as hydrophobicity, charge and electron-donor characteristics, and the yeast surface sorption capacities, we determined these factors in a model system. Then, real industrial lees samples were investigated. The surface properties of sparkling wine lees from the same strain of Saccharomyces cerevisiae were characterized according to the time of surlie ageing, and their possible influence on lees sorption and flocculation capacity was evaluated. Surlie ageing after second fermentation is a fundamental operation in the production of quality sparkling wine like Cava and Champagne. Recently, the importance of the interaction between wine and lees cell surface has been reported. Cell surface properties depending on wall biochemical composition are major determinants in microbial interactions, having important repercussions in several technological aspects. Sorption and flocculation are especially important in sparkling wine production, and are governed by distinct cell surface properties. The aim of the present research carried out during the four months of the stage was to know the implication of lees surface modifications occurring during surlie ageing in sparkling wine quality and elaboration. The relationship between physico-chemical properties such as hydrophobicity, charge and electron-donor characteristics, and the yeast surface sorption capacities, we determined these factors in a model system. Then, real industrial lees samples were investigated. The surface properties of sparkling wine lees from the same strain of Saccharomyces cerevisiae were characterized according to the time of surlie ageing, and their possible influence on lees sorption and flocculation capacity was evaluated.

What about people in European Regional Science?

The 51st ERSA Conference held in Barcelona in 2011 was one of the largest ever. Here, by examining the characteristics of the conference, this paper identifies the main trends in Regional Science at a moment in which the discipline is renewing its efforts to provide responses in a complex, globalised world in which cities and regions are acquiring greater and greater importance. This paper follows in the tradition of a long list of studies that have examined the nature of the field of Regional Science and draws on a broad array of sources of information: the delegates’ demographic details, the conference program itself, a satisfaction survey conducted among delegates, a quality survey addressed to those chairing the sessions and, finally, a bibliometric database including each author signing a paper presented at the conference. With this information we describe the ERSA delegates: their relative youthfulness; the areas in which women are taking on a more important role; the countries and regions of the world that have the most dominant profile in Regional Science today; the thematic areas that are being driven by professionals as opposed to academics; the relevance of regional economic growth and innovation as trending topics in the field; the growing frequency of co-authorship and, consequently, of scientific collaboration; and, finally, and perhaps most importantly, the continuous enhancement of the quality of the work being undertaken in the discipline. Indeed, following on from this description, the results of the regression analysis conducted show that for ERSA delegates what matters most is quality, and this must be the direction that future conferences should move toward. Ultimately, therefore, ERSA conferences are comprehensive, all-embracing occasions, representing an ideal opportunity for regional scientists to present their work to each other and to network.

Including the workload effect in the parallel program signature

Performance prediction and application behavior modeling have been the subject of exten- sive research that aim to estimate applications performance with an acceptable precision. A novel approach to predict the performance of parallel applications is based in the con- cept of Parallel Application Signatures that consists in extract an application most relevant parts (phases) and the number of times they repeat (weights). Executing these phases in a target machine and multiplying its exeuction time by its weight an estimation of the application total execution time can be made. One of the problems is that the performance of an application depends on the program workload. Every type of workload affects differently how an application performs in a given system and so affects the signature execution time. Since the workloads used in most scientific parallel applications have dimensions and data ranges well known and the behavior of these applications are mostly deterministic, a model of how the programs workload affect its performance can be obtained. We create a new methodology to model how a program’s workload affect the parallel application signature. Using regression analysis we are able to generalize each phase time execution and weight function to predict an application performance in a target system for any type of workload within predefined range. We validate our methodology using a synthetic program, benchmarks applications and well known real scientific applications.

Simultaneous estimation of indirect and interaction effects using structural equation models

Interaction effects are usually modeled by means of moderated regression analysis. Structural equation models with non-linear constraints make it possible to estimate interaction effects while correcting formeasurement error. From the various specifications, Jöreskog and Yang's(1996, 1998), likely the most parsimonious, has been chosen and further simplified. Up to now, only direct effects have been specified, thus wasting much of the capability of the structural equation approach. This paper presents and discusses an extension of Jöreskog and Yang's specification that can handle direct, indirect and interaction effects simultaneously. The model is illustrated by a study of the effects of an interactive style of use of budgets on both company innovation and performance

Correcting Measurement Error Bias in Interaction Models with Small Samples

Several methods have been suggested to estimate non-linear models with interaction terms in the presence of measurement error. Structural equation models eliminate measurement error bias, but require large samples. Ordinary least squares regression on summated scales, regression on factor scores and partial least squares are appropriate for small samples but do not correct measurement error bias. Two stage least squares regression does correct measurement error bias but the results strongly depend on the instrumental variable choice. This article discusses the old disattenuated regression method as an alternative for correcting measurement error in small samples. The method is extended to the case of interaction terms and is illustrated on a model that examines the interaction effect of innovation and style of use of budgets on business performance. Alternative reliability estimates that can be used to disattenuate the estimates are discussed. A comparison is made with the alternative methods. Methods that do not correct for measurement error bias perform very similarly and considerably worse than disattenuated regression