Abstract
Post-mortem tissues samples are a key resource for investigating patterns of gene expression. However, the processes triggered by death and the post-mortem interval (PMI) can significantly alter physiologically normal RNA levels. We investigate the impact of PMI on gene expression using data from multiple tissues of post-mortem donors obtained from the GTEx project. We find that many genes change expression over relatively short PMIs in a tissue-specific manner, but this potentially confounding effect in a biological analysis can be minimized by taking into account appropriate covariates. By comparing ante-and postmortem blood samples, we identify the cascade of transcriptional events triggered by death of the organism. These events do not appear to simply reflect stochastic variation resulting from mRNA degradation, but active and ongoing regulation of transcription. Finally, we develop a model to predict the time since death from the analysis of the transcriptome of a few readily accessible tissues.

Abstract
An innovative, forward-looking organization has a different kind of internal structure, different Marketing and a different way of processing things. Innovative organizations have methods and processes that help them avoid stagnation. In the 21st century we have embraced a whole new technological world. Communicating through social networks is a new phenomenon and organizations must follow this trend, otherwise they will not endure. Companies need to be close to clients and the best way to achieve this is by exploring the new tools that online social media provide (social media includes e-mail and social networks and other such applications). The way people are exposed to information and publicity suffered a real change, as nowadays almost everything is also advertised online. Internet usage and social networking will be the focus of this article. The main research question of this paper is: what is the impact of using social networks on the performance and competitiveness of organizations? This question is relevant since almost every company has access to the Internet, as do their employees. Social networks may also be used for professional purposes as networks that connect everyone to expedite and facilitate communication. On the other hand, social networks may influence productivity negatively. To better discuss the main issue, we will use a Portuguese company as a case study. Thus, we interviewed an employee at BRABBU - the head of the BRABBU Press & Communications department - to establish patterns in the usage of social networks for internal communication during working hours. BRABBU is an award-winning company which won the Best Viral Instagram award, in January 2018, attributed by the Maison et Objet. Furthermore, a sample of the general population was surveyed (we received 352 answers to the survey) to deepen the study of this topic and so as to ascertain whether people think that online social networks help or hinder business performance. The result was clear: social networks may be a great help for internal communication, but this does not replace human contact. Finally, this usage of social networks in the work environment has to reach a consensus amongst the younger generations (e.g. millennial, in favour of this usage) and older ones (e.g. baby boomers, not so receptive to new technologies).

Abstract

Abstract
Skin temperature regulation is dependant of the autonomic nervous system function, which may be impaired in patients with neuropathy. Studies reporting thermographic assessment of patients with established diagnosis of Diabetic Foot (DF) are scarce but this information is completely absent in patients suffering from Transthyretin Familial Amyloid Polyneuropathy (TTR-FAP). The aim of this study is to compare skin temperature distribution in patients with DF and TTR-FAP. Thermograms of the dorsal and plantar surfaces were compared. Skin temperature was higher in the diabetic foot group and differences were statistically significant (p < 0.05) in both regions of interest.

Abstract
Purpose The quality of consumer-oriented health information on the web has been defined and evaluated in several studies. Usually it is based on evaluation criteria identified by the researchers and, so far, there is no agreed standard for the quality indicators to use. Based on such indicators, tools have been developed to evaluate the quality of web information. The HONcode is one of such tools. The purpose of this paper is to investigate the influence of web document features on their quality, using HONcode as ground truth, with the aim of finding whether it is possible to predict the quality of a document using its characteristics. Design/methodology/approach The present work uses a set of health documents and analyzes how their characteristics (e.g. web domain, last update, type, mention of places of treatment and prevention strategies) are associated with their quality. Based on these features, statistical models are built which predict whether health-related web documents have certification-level quality. Multivariate analysis is performed, using classification to estimate the probability of a document having quality given its characteristics. This approach tells us which predictors are important. Three types of full and reduced logistic regression models are built and evaluated. The first one includes every feature, without any exclusion, the second one disregards the Utilization Review Accreditation Commission variable, due to it being a quality indicator, and the third one excludes the variables related to the HONcode principles, which might also be indicators of quality. The reduced models were built with the aim to see whether they reach similar results with a smaller number of features. Findings The prediction models have high accuracy, even without including the characteristics of Health on the Net code principles in the models. The most informative prediction model considers characteristics that can be assessed automatically (e.g. split content, type, process of revision and place of treatment). It has an accuracy of 89 percent. Originality/value This paper proposes models that automatically predict whether a document has quality or not. Some of the used features (e.g. prevention, prognosis or treatment) have not yet been explicitly considered in this context. The findings of the present study may be used by search engines to promote high-quality documents. This will improve health information retrieval and may contribute to reduce the problems caused by inaccurate information.

Abstract
Supervised learning with imbalanced domains is one of the biggest challenges in machine learning. Such tasks differ from standard learning tasks by assuming a skewed distribution of target variables, and user domain preference towards under-represented cases. Most research has focused on imbalanced classification tasks, where a wide range of solutions has been tested. Still, little work has been done concerning imbalanced regression tasks. In this paper, we propose an adaptation of the SMOTEBoost approach for the problem of imbalanced regression. Originally designed for classification tasks, it combines boosting methods and the SMOTE resampling strategy. We present four variants of SMOTEBoost and provide an experimental evaluation using 30 datasets with an extensive analysis of results in order to assess the ability of SMOTEBoost methods in predicting extreme target values, and their predictive trade-off concerning baseline boosting methods. SMOTEBoost is publicly available in a software package.