Abstract
AbstractTranscriptional dysregulation is a key feature of cancer. Transcription factors (TFs) are the main link between signalling pathways and the transcriptional regulatory machinery of the cell, positioning them as key oncogenic inductors and therefore potential targets of therapeutic intervention. We implemented a computational pipeline to infer TF regulatory activities from basal gene expression and applied it to publicly available and newly generated RNA-seq data from a collection of 1,010 cancer cell lines and 9,250 primary tumors. We show that the predicted TF activities recapitulate known mechanisms of transcriptional dysregulation in cancer and dissect mutant-specific effects in driver genes. Importantly, we show the potential for predicted TF activities to be used as markers of sensitivity to the inhibition of their upstream regulators. Furthermore, combining these inferred activities with existing pharmacogenomic markers significantly improves the stratification of sensitive and resistant cell lines for several compounds. Our approach provides a framework to link driver genomic alterations with transcriptional dysregulation that helps to predict drug sensitivity in cancer and to dissect its mechanistic determinants.

Abstract
Information technology has become one of the core elements of electronic government (e-Government) and should figure prominently in future governance. E-Government services, through electronic means, should provide a more cost-effective and convenient way for citizens to interact with government, however their adoption is still far from a satisfactory level. The study herein has examined research on the adoption of e-Government services. Using data obtained from previous research, this article identifies the variables that contribute to the adoption of e-Government by citizens and the model created is presented. Overall, this study concludes that e-Government has been adopted by many governments, but it is still at an early stage, and there are still barriers to its more widespread adoption in society.

Abstract
This paper presents a model of a system capable of addressing the training needs identified by the University of Tras-os-Montes e Alto Douro (UTAD). In addition to supporting the typical needs of distance learning/education (e.g., e-learning), it is also intended that the proposed system will complement the traditional classroom-based teaching. This model will have two modules: the physical/infrastructural module and the policies/practices module. The physical/infrastructural module will have the following (control and management) components or modules: e-learning platforms (e.g., Moodle, Sakai) with SIDE (a platform for teaching support developed at UTAD) interconnection; website with educational/training offer and e-learning events; media repository; file storage and sharing service; collaborative/cooperative platform, for the sharing of information and knowledge; scientific repository, for the storage and sharing of work and scientific research, books, conferences, etc.; forum; ticket service/helpdesk, to help solving problems that may arise; Customer Relationship Management (CRM) service (management, relationship and customer satisfaction-student, teachers, other); Learning Analytics system (collection of information about the quality of teaching, evolution and student analysis in the e-learning mode, etc.). Additionally, there will be an assessment/examination space, a contents production studio, and a training studio. The policies/practices module includes the institution policies, and including policies for the creation/dissemination of curses, implementation of course evaluation strategies and continuous improvement, training policies, recognition of the merit and effort of teachers, the training in using the system, the policies for the creation of physical spaces equipped with the necessary tools (computers, cameras, video-conferencing, etc.), among other. In line with these, UTAD has come to recognize that e-learning should be part of its strategy for its training offer and, consequently, is being adopting new policies, namely through the signing of protocols with other institutions with more experience using e-learning. To achieve these objectives, a detailed review of other models and systems that have been successfully implemented in other international reference universities will also be presented here.

Abstract
This work addresses the possibility of integrating Electric Vehicles (EV) in the Automatic Generation Control (AGC) operation for provision of secondary reserves in interconnected systems, contributing to reduce the requirements for conventional secondary reserves in systems with large scale integration of renewable sources. A test system was used to assess the performance of EV as secondary reserve providers, when facing the loss of conventional generation as well as the loss of a large amount of wind power production.

Abstract
Breaking down applications into smaller modules is a common way for software developers to cope with the increasing complexity of their projects. A common barrier to this endeavor is the presence of crosscutting concerns that prevent reusability and reduce comprehensibility. The promise of AOP (Aspect-Oriented Programming) is that, by using it, developers will be able to organize these crosscutting concerns into their own units of modularity. However, AOP does not tackle the problem of having tangled automatic tests. This paper presents a technique using incremental testing and invasive aspects to modify and adapt tests, enabling the development of unit tests that are free of crosscutting concerns and thus easier to reuse. Using a medium scale project, we will show that without using this technique, due to the presence of invasive aspects, some unit tests would have to be discarded or modified to accommodate the changes made by them.

Abstract
The deployment of smart grids and renewable energy dispatch centers motivates the development of forecasting techniques that take advantage of near real-time measurements collected from geographically distributed sensors. This paper describes a forecasting methodology that explores a set of different sparse structures for the vector autoregression (VAR) model using the least absolute shrinkage and selection operator (LASSO) framework. The alternating direction method of multipliers is applied to fit the different LASSO-VAR variants and create a scalable forecasting method supported by parallel computing and fast convergence, which can be used by system operators and renewable power plant operators. A test case with 66 wind power plants is used to show the improvement in forecasting skill from exploring distributed sparse structures. The proposed solution outperformed the conventional autoregressive and vector autoregressive models, as well as a sparse VAR model from the state of the art. Copyright (c) 2016 John Wiley & Sons, Ltd.