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The information contained in corporate social responsibility (CSR) reports is a controversial issue, and it has generated an important debate among academics regarding company disclosure strategies. Environmental matters are especially relevant given their impact on sustainable development. The present study has two objectives. The first is to determine which Global Reporting Initiative (GRI) environmental indicators are reported less frequently. The second is to predict the evolution of these indicators in light of the institutional pressures that companies try to resist. Specifically, the study of the environmental dimension of the GRI focusses on an analysis of the following: materials, energy, water, biodiversity, emissions, effluents and waste, products and services, compliance, transport, environmental assessment, and environmental grievance mechanisms. A content analysis of CSR reports from some of the world's largest companies reveals that the indicators least disclosed by companies relate to the environmental aspects of biodiversity. The dissemination of environmental indicators is influenced by normative, mimetic, and (to a lesser extent) coercive pressures. In addition, we observe that mimetic institutional pressures under a national and industrial vision influence the dissemination of environmental information. In terms of cultural dimensions, companies located in long-term, feminine, and collectivist countries tend to disseminate environmental information accordingly.

The complexity of the business world and current business models has motivated an increasing number of companies to disclose corporate information through sustainability reports. This reporting and stakeholders engagement may bring shared value to business and society in general although working towards sustainable development goals. This work adopts a new analytical approach by determining the global reporting initiative indicators related to labour practices and decent work, human rights, society, and product responsibility that are reported less frequently by companies. The final

Abstract:The aim of this study is to analyze the environmental performance of countries and the variables that can influence it. At the same time, we performed a multivariate analysis using the HJ-biplot, an exploratory method that looks for hidden patterns in the data, obtained from the usual singular value decomposition (SVD) of the data matrix, to contextualize the countries grouped by geographical areas and the variables relating to environmental indicators included in the environmental performance index. The sample used comprises 149 countries of different geographic areas. The findings obtained from the empirical analysis emphasize that socioeconomic factors, such as economic wealth and education, as well as institutional factors represented by the style of public administration, in particular control of corruption, are determinant factors of environmental performance in the countries analyzed. In contrast, no effect on environmental performance was found for factors relating to the internal characteristics of a country or political factors.

Cutaneous squamous cell carcinoma is the second most widespread cancer in humans and its incidence is rising. These tumours can evolve as poor-prognosis diseases, and therefore it is important to identify new markers to better predict its clinical evolution. Here, we identified the expression pattern of miRNAs at different stages of skin cancer progression in a panel of murine skin cancer cell lines. We determined that miR-203 and miR-205 are differentially expressed in this panel, and evaluated their potential use as biomarkers of prognosis in human tumours. MiR-205 was expressed in tumours with pathological features recognized as indicators of poor prognosis such as desmoplasia, perineural invasion and infiltrative growth pattern. MiR-205 was mainly expressed in undifferentiated areas and in the invasion front, and was associated with both local recurrence and the development of general clinical events of poor evolution. MiR-205 expression was an independent variable selected to predict events of poor clinical evolution using the multinomial logistic regression model described in this study. In contrast, miR-203 was mainly expressed in tumours exhibiting the characteristics associated with a good prognosis, was mainly present in well-differentiated zones, and rarely expressed in the invasion front. Therefore, the expression and associations of miR-205 and miR-203 were mostly mutually exclusive. Finally, using a logistic biplot we identified three clusters of patients with differential prognosis based on miR-203 and miR-205 expression, and pathological tumour features. This work highlights the utility of miR-205 and miR-203 as prognostic markers in cutaneous squamous cell carcinoma.

The present article offers a concise theoretical conceptualization and operational analysis of the contribution of innovation to regional development. The latter concepts are closely related to geographical proximity, knowledge diffusion and filters and clustering. Institutional innovation profiles and regional patterns of innovation are two mutually linked, novel conceptual elements in this article. Next to a theoretical framing, the article employs the regional innovation systems concept as a vehicle to analyse institutional innovation profiles. Our case study addresses three Portuguese regions and their institutions, included in a web-based inventory of innovation agencies which offered the foundation for an extensive database. This data-set was analysed by means of a recently developed principal coordinates analysis followed by a Logistic Biplot approach (leading to a Voronoi mapping) to design a systemic typology of innovation structures where each institution is individually represented. There appears to be a significant difference in the regional innovation patterns resulting from the diverse institutional innovation profiles concerned. These profiles appear to be region specific. Our conclusion highlights the main advantages in the use of the method used for policy-makers and business companies.

To assess the impact of the prosthodontic status on oral health-related quality of life and satisfaction. We performed a cohort study at the University Clinic in Salamanca in which a group requesting prosthetic treatment (P0; n = 31) was compared with a group treated with conventional prostheses (P1; n = 29) and a control group (C; n = 18) not requesting or treated with prostheses. A clinical examination for the presence of caries, periodontal disease and edentulism was carried out. An assessment was made on the impact on the quality of life employing the oral impacts on daily performance-Spanish version and the oral health impact profile 14-Spanish version, and wellbeing was assessed by the self-rated satisfaction on a 0-10 scale. The P0 cohort was significantly less satisfied and suffered a greater level of impact as regard their quality of life than the other cohorts. The main benefit of conventional prosthetic treatment was perceived by most of the treated patients (P1) in dimensions related to chewing, the aesthetic function and the assessment of the general state of the mouth. However, an unexpected proportion of patients underwent a worsening of their oral wellbeing after prosthetic treatment, mainly in the chewing ability (23%) and pain discomfort (19%) dimensions. Satisfaction and quality of life were higher in the treated group (P1) and controls (C) than in those requesting prosthetic treatment (P0).

Latent class models (LCMs) can be used to assess diagnostic test performance when no reference test (a gold standard) is available, considering two latent classes representing disease or non-disease status. One of the basic assumptions in such models is that of local or conditional independence: all indicator variables (tests) are statistically independent within each latent class. However, in practice this assumption is often violated; hence, the two-LCM fits the data poorly. In this paper, we propose the use of Biplot methods to identify the conditional dependence between pairs of manifest variables within each latent class. Additionally, we propose incorporating such dependence in the corresponding latent class using the log-linear formulation of the model.