Validation of a measurement scale for the relationship between the orientation to corporate social responsibility and other business strategic variables

The importance of Social Responsibility (SR) is higher if this business variable is related with other ones of strategic nature in business activity (competitive success that the company achieved, performance that the firms develop and innovations that they carries out). The hypothesis is that organizations that focus on SR are those who get higher outputs and innovate more, achieving greater competitive success. A scale for measuring the orientation to SR has defined in order to determine the degree of relationship between above elements. This instrument is original because previous scales do not exist in the literature which could measure, on the one hand, the three classics sub-constructs theoretically accepted that SR is made up and, on the other hand, the relationship between SR and the other variables. As a result of causal relationships analysis we conclude with a scale of 21 indicators, validated scale with a sample of firms belonging to the Autonomous Community of Extremadura and it is the first empirical validation of these dimensions we know so far, in this context.

Protection against oxidative stress through SUA7/TFIIB regulation in Saccharomyces cerevisiae

The general transcription factor TFIIB, encoded by SUA7 in Saccharomyces cerevisiae, is required for transcription activation but apparently of a specific subset of genes, for example, linked with mitochondrial activity and hence with oxidative environments. Therefore, studying SUA7/TFIIB as a potential target of oxidative stress is fundamental. We found that controlled SUA7 expression under oxidative conditions occurs at transcriptional and mRNA stability levels. Both regulatory events are associated with the transcription activator Yap1 in distinct ways: Yap1 affects SUA7 transcription up regulation in exponentially growing cells facing oxidative signals; the absence of this activator per se contributes to increase SUA7 mRNA stability. However, unlike SUA7 mRNA, TFIIB abundance is not altered on oxidative signals. The biological impact of this preferential regulation of SUA7 mRNA pool is revealed by the partial suppression of cellular oxidative sensitivity by SUA7 overexpression, and supported by the insights on the existence of a novel RNA-binding factor, acting as an oxidative sensor, which regulates mRNA stability. Taken together the results point out a primarily cellular commitment to guarantee SUA7 mRNA levels under oxidative environments.

Trits(Pyrazol-1-YL)methane metal complexes for catalytic mild oxidative functionalizations of alkanes, alkanes and ketones

This work concerns recent advances (since 2005) in the oxidative functionalization of alkanes, alkenes and ketones, under mild conditions, catalyzed by homoscorpionate tris(pyrazol-1-yl)methane metal complexes. The main types of such homogeneous or supported catalysts are classified, and the critical analysis of the most efficient catalytic systems in the different reactions is presented. These reactions include the mild oxidation of alkanes (typically cyclohexane as a model substrate) with hydrogen peroxide (into alkyl hydroperoxides, alcohols, and ketones), the hydrocarboxylation of gaseous alkanes (with carbon monoxide and potassium peroxodisulfate) into the corresponding Cn+1 carboxylic acids, as well as the epoxidation of alkenes and the Baeyer-Villiger oxidation of linear and cyclic ketones with hydrogen peroxide into the corresponding esters and lactones. Effects of various reaction parameters are highlighted and the preferable requirements for a prospective homogeneous or supported C-scorpionate-M-based catalyst in oxidative transformations of those substrates are identified. (C) 2014 Elsevier B.V. All rights reserved.

Enhancing the selection of a model-based clustering with external categorical variables

In cluster analysis, it can be useful to interpret the partition built from the data in the light of external categorical variables which are not directly involved to cluster the data. An approach is proposed in the model-based clustering context to select a number of clusters which both fits the data well and takes advantage of the potential illustrative ability of the external variables. This approach makes use of the integrated joint likelihood of the data and the partitions at hand, namely the model-based partition and the partitions associated to the external variables. It is noteworthy that each mixture model is fitted by the maximum likelihood methodology to the data, excluding the external variables which are used to select a relevant mixture model only. Numerical experiments illustrate the promising behaviour of the derived criterion. © 2014 Springer-Verlag Berlin Heidelberg.

Enhancing the selection of a model-based clustering with external categorical variables

In cluster analysis, it can be useful to interpret the partition built from the data in the light of external categorical variables which are not directly involved to cluster the data. An approach is proposed in the model-based clustering context to select a number of clusters which both fits the data well and takes advantage of the potential illustrative ability of the external variables. This approach makes use of the integrated joint likelihood of the data and the partitions at hand, namely the model-based partition and the partitions associated to the external variables. It is noteworthy that each mixture model is fitted by the maximum likelihood methodology to the data, excluding the external variables which are used to select a relevant mixture model only. Numerical experiments illustrate the promising behaviour of the derived criterion.