Abstract
Extreme and rare events, such as spikes in air pollution or abnormal weather conditions, can have serious repercussions. Many of these sorts of events develop through spatio-temporal processes. Timely and accurate predictions are a most valuable tool in addressing their impact. We propose a new set of resampling strategies for imbalanced spatio-temporal forecasting tasks, which introduce bias into formerly random processes. This bias is a combination of a spatial and a temporal weight, which can be either static or relevance-aware, and includes a hyper-parameter that regulates the relative importance of the temporal and spatial dimensions in the selection of observations during under- or over-sampling. We test and compare our proposals against standard versions of the strategies on 10 different geo-referenced numeric time series, using 3 distinct off-the-shelf learning algorithms. Experimental results show that our proposals provide an advantage over random resampling strategies in imbalanced numerical spatio-temporal forecasting tasks.

Abstract
Background Blood cancers (BCs) are responsible for over 720 K yearly deaths worldwide. Their prevalence and mortality-rate uphold the relevance of research related to BCs. Despite the availability of different resources establishing Disease-Disease Associations (DDAs), the knowledge is scattered and not accessible in a straightforward way to the scientific community. Here, we propose SicknessMiner, a biomedical Text-Mining (TM) approach towards the centralization of DDAs. Our methodology encompasses Named Entity Recognition (NER) and Named Entity Normalization (NEN) steps, and the DDAs retrieved were compared to the DisGeNET resource for qualitative and quantitative comparison. Results We obtained the DDAs via co-mention using our SicknessMiner or gene- or variant-disease similarity on DisGeNET. SicknessMiner was able to retrieve around 92% of the DisGeNET results and nearly 15% of the SicknessMiner results were specific to our pipeline. Conclusions SicknessMiner is a valuable tool to extract disease-disease relationship from RAW input corpus.

Abstract
Text mining (TM) is a semi-automatized, multi-step process, able to turn unstructured into structured data. TM relevance has increased upon machine learning (ML) and deep learning (DL) algorithms’ application in its various steps. When applied to biomedical literature, text mining is named biomedical text mining and its specificity lies in both the type of analyzed documents and the language and concepts retrieved. The array of documents that can be used ranges from scientific literature to patents or clinical data, and the biomedical concepts often include, despite not being limited to genes, proteins, drugs, and diseases. This review aims to gather the leading tools for biomedical TM, summarily describing and systematizing them. We also surveyed several resources to compile the most valuable ones for each category. © 2021 by the authors.

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Abstract
The growing digitization of healthcare institutions and its increasing dependence on Internet infrastructure has boosted the concerns related to data privacy and confidentiality. These institutions have been challenged with specific issues, namely the sensitivity of data, the specificity of networked equipment, the heterogeneity of healthcare professionals (nurses, doctors, administrative staff and other) and the IT skills they have. In this paper we present the results obtained with a study made with healthcare professionals on evaluating their awareness level with the information security, namely by assessing their attitudes and behaviours in cybersecurity. The methodology consisted in translating, adjusting and applying two previously validated and already published Likert-type response scales, in a healthcare institution in Portugal, namely "Centro Hospitalar Barreiro Montijo" (CHBM). The scales used were cybersecurity risky behaviour (RScB) and cybersecurity and cybercrime in business attitudes (ATC-IB). Although there were no significant statistical differences between the sociodemographic factors and the scores obtained on both scales, the results showed a relationship between acquired behaviours and the attitudes of involvement with work and organizational commitment, establishing a bridge for the quantification in awareness.(C) 2021 The Authors. Published by Elsevier B. V.