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.

Abstract
AbstractUsing a dataset of somatic Structural Variants (SVs) in cancers from 2658 patients—1220 with corresponding gene expression data—we identified hundreds of genes for which the nearby presence (within 100kb) of an SV breakpoint was associated with altered expression. For the vast majority of these genes, expression was increased rather than decreased with corresponding SV event. Well-known up-regulated cancer-associated genes impacted by this phenomenon included TERT, MDM2, CDK4, ERBB2, CD274, PDCD1LG2, and IGF2. SVs upstream of TERT involved ~3% of cancer cases and were most frequent in liver-biliary, melanoma, sarcoma, stomach, and kidney cancers. SVs associated with up-regulation of PD1 and PDL1 genes involved ~1% of non-amplified cases. For many genes, SVs were significantly associated with either increased numbers or greater proximity of enhancer regulatory elements near the gene. DNA methylation near the gene promoter was often increased with nearby SV breakpoint, which may involve inactivation of repressor elements.AbbreviationsPCAWGthe Pan-Cancer Analysis of Whole Genomes projectSVStructural Variant

Abstract
Background and study aims Efficacy and adverse events probabilities influence decisions regarding the best options to manage patients with gastric superficial lesions. We aimed at developing a Bayesian model to individualize the prediction of outcomes after gastric endoscopic submucosal dissection (ESD). Patients and methods Data from 245 gastric ESD were collected, including patient and lesion factors. The two endpoints were curative resection and post-procedural bleeding (PPB). Logistic regression and Bayesian networks were built for each outcome; their predictive value was evaluated in-sample and validated through leave-one-out and cross-validation. Clinical decision support was enhanced by the definition of risk matrices, direct use of Bayesian inference software and by a developed online platform. Results ESD was curative in 85.3% and PPB occurred in 7.7% of patients. In univariate analysis, male sex, ASA status, carcinoma histology, polypoid or depressed morphology, and lesion size >= 20mm were associated with non-curative resection, while ASA status, antithrombotics and lesion size >= 20mm were associated with PPB. Naive Bayesian models presented AUROCs of similar to 80% in the derivation cohort and >= 74% in cross-validation for both outcomes. Risk matrices were computed, showing that lesions with cancer at biopsies, >= 20mm, proximal or in the middle third, and polypoid are more prone to non-curative resection. PPB risk was <5% in lesions <20mm in the absence of antithrombotics. Conclusions The derived Bayesian model presented good discriminative power in the prediction of ESD outcomes and can be used to predict individualized probabilities, improving patient information and supporting clinical and management decisions.

Abstract
Visual emotion perception is the ability of recognizing and identifying emotions through the visual interpretation of a situation or environment. In this paper we propose an innovative environment for supporting this type of studies, aimed at replacing current pencil-and-paper approaches. Besides automatizing the whole process, this environment provides new features that can enrich the study of emotion perception. These new features are especially interesting for the field of Human-Compute Interaction and Affective computing as they quantify the effects of experiencing different emotional dimensions on the individual's interaction with the computer.

Abstract
This paper addresses a strategy to improve disturbance rejection for the Sliding Mode Controller designed in a Smith Predictor scheme (SMC-SP), with its parameters tuned through the bio-inspired search algorithm—Particle Swarm Optimization (PSO). Conventional SMC-SP is commonly based on tuning equations derived from step response identification, when First Order Plus Dead Time models (FOPDT) are considered and therefore controller parameters are previously set. Online PSO tuning based on minimization of the Integral of Time Absolute Error (ITAE) can provide faster recovery from external disturbances without significant increase of energy consumption, and the Sliding Mode feature deals with possible model mismatch. Simulation results for time delayed systems corroborating these benefits are presented. © Springer International Publishing Switzerland 2017.

Abstract
This paper focuses on the susceptibility evaluation to bank gullies on earthen embankments through the application of SIMWE (SIMulated Water Erosion) model, using a high resolution digital elevation model (1 meter spatial resolution). The results provided by the model are compared with the hydrologic characteristics, soil texture and soil structure of the agricultural terraces. This approach demonstrates an association between the spatial distribution of erosive forms with high values of water depth and reduced water discharge that are consistent with the lower values of electrical resistivity. The areas with the highest percentage of erosive forms, related to sediment flux, transport capacity and sediment concentration susceptibility, assume medium values. These figures, combined with a low hydraulic conductivity and soil infiltration capacity, are consistent with the fine texture of soils, allowing increased runoff and the development of linear erosion.