Insulin-Like Growth Factor-Binding Protein 7 Regulates Keratinocyte Proliferation, Differentiation and Apoptosis

Insulin-like growth factor (IGF)-binding protein 7 (IGFBP7) belongs to the IGFBP superfamily, which is involved in the regulation of IGF and insulin signaling. Recently, a global gene expression study revealed that IGFBP7 is downregulated in the psoriatic epidermis, with UVB phototherapy restoring its expression to normal. In the present study, we confirmed that IGFBP7 expression is decreased in psoriatic lesions. Given the previous data suggesting a role for IGFBP7 in the control of cancer cell growth, we investigated its involvement in the regulation of keratinocyte (KC) proliferation and differentiation, which are abnormal in psoriasis. To model IGFBP7 downregulation in vitro, we used IGFBP7-specific small interfering RNA or small hairpin RNA-expressing lentiviral vectors in HaCaT cells or primary human KCs. Downregulation of IGFBP7 was found to markedly enhance KC proliferation in both systems, was associated with a significant decrease in KC susceptibility to tumor necrosis factor-α-induced apoptosis, but did not affect senescence. Downregulation of IGFBP7 was also shown to block expression of genes associated with calcium-induced differentiation of human KCs. Finally, recombinant IGFBP7 was found to inhibit KC proliferation and enhanced their apoptosis. These data position IGFBP7 as a regulator of KC proliferation and differentiation, suggesting a potential role for this protein in the pathophysiology and treatment of hyperproliferative dermatoses such as psoriasis

The Graphic Nature of the Symmetric Group

We investigate a remarkable class of exponential sums which are derived from the symmetric groups and which display a diverse array of visually appealing features. Our interest in these expressions stems not only from their astounding visual properties, but also from the fact that they represent a novel and intriguing class of supercharacters

SPEET: software tools for academic data analysis

The international ERASMUS+ project SPEET (Student Profile for Enhancing Engineering Tutoring) aims at opening a new perspective to university tutoring systems. Before looking for its nature, it’s recommended to have a look on the current use of data in education and on the concept of academic analytics basically defined as the process of evaluating and analysing data received from university systems for reporting and decision making reasons. This work reflects the outputs of the SPEET project in relation to the data mining tools, specific algorithms developed to deal with the basic problems tackled in the project: Classification, Clustering and Drop-out Prediction.info:eu-repo/semantics/publishedVersio

SPEET: web based it tool for academic data analysis

The international ERASMUS+ project SPEET (Student Profile for Enhancing Engineering Tutoring) aims at opening a new perspective to university tutoring systems. Before looking for its nature, it’s recommended to have a look on the current use of data in education and on the concept of academic analytics basically defined as the process of evaluating and analysing data received from university systems for reporting and decision making reasons. The provided tools are freely available to anyone that has academic data to explore. The paper will present the architecture that is behind the presented IT tool, input data needed to operate and main functionalities as well as examples of use to show how academic data can be interpreted.info:eu-repo/semantics/publishedVersio

Educational data mining for tutoring support in Higher Education: a web-based tool case study in engineering degrees

This paper presents a web-based software tool for tutoring support of engineering students without any need of data scientist background for usage. This tool is focused on the analysis of students' performance, in terms of the observable scores and of the completion of their studies. For that purpose, it uses a data set that only contains features typically gathered by university administrations about the students, degrees and subjects. The web-based tool provides access to results from different analyses. Clustering and visualization in a low-dimensional representation of students' data help an analyst to discover patterns. The coordinated visualization of aggregated students' performance into histograms, which are automatically updated subject to custom filters set interactively by an analyst, can be used to facilitate the validation of hypotheses about a set of students. Classification of students already graduated over three performance levels using exploratory variables and early performance information is used to understand the degree of course-dependency of students' behavior at different degrees. The analysis of the impact of the student's explanatory variables and early performance in the graduation probability can lead to a better understanding of the causes of dropout. Preliminary experiments on data of the engineering students from the 6 institutions associated to this project were used to define the final implementation of the web-based tool. Preliminary results for classification and drop-out were acceptable since accuracies were higher than 90% in some cases. The usefulness of the tool is discussed with respect to the stated goals, showing its potential for the support of early profiling of students. Real data from engineering degrees of EU Higher Education institutions show the potential of the tool for managing high education and validate its applicability on real scenarios.This work was supported by the Erasmus+ Key Action 2 Strategic Partnerships KA203, funded by the European Commission, under Grant 2016-1-ES01-KA203-025452.info:eu-repo/semantics/publishedVersio

SPEET: visual data analysis of engineering students performance from academic data

This paper presents the steps conducted to design and develop an IT Tool for Visual Data Analysis within the SPEET (Student Profile for Enhancing Engineering Tutoring) ERASMUS+ project. The proposed goals are to provide insight into student behaviours, to identify patterns and relevant factors of academic success, to facilitate the discovery and understanding of profiles of engineering students, and to analyse the difierences across European institutions. For that purpose, the concepts and methods used for the visual analysis of educational data are reviewed and a tool is proposed, which implements approaches based on visual interaction.info:eu-repo/semantics/publishedVersio

Characterization of engineering student profiles at european institutions by using speet it-tool

The international ERASMUS+ project SPEET (Student Profile for Enhancing Engineering Tutoring) aims at opening a new perspective to university tutoring systems. Before looking for its nature, it’s recommended to have a look on the current use of data in education and on the concept of academic analytics basically defined as the process of evaluating and analysing data received from university systems for reporting and decision making reasons. The provided tools are freely available to anyone that has academic data to explore. The paper will present the architecture that is behind the presented IT tool, input data needed to operate and main functionalities as well as examples of use to show how academic data can be interpreted.info:eu-repo/semantics/publishedVersio