Network Medicine applies network science approaches to investigate disease pathogenesis. Many different analytical methods have been used to infer relevant molecular networks, including protein-protein interaction networks, correlation-based networks, gene regulatory networks, and Bayesian networks. Network Medicine applies these integrated approaches to Omics Big Data (including genetics, epigenetics, transcriptomics, metabolomics, and proteomics) using computational biology tools and, thereby, has the potential to provide improvements in the diagnosis, prognosis, and treatment of complex diseases. We discuss briefly the types of molecular data that are used in molecular network analyses, survey the analytical methods for inferring molecular networks, and review efforts to validate and visualize molecular networks. Successful applications of molecular network analysis have been reported in pulmonary arterial hypertension, coronary heart disease, diabetes mellitus, chronic lung diseases, and drug development. Important knowledge gaps in Network Medicine include incompleteness of the molecular interactome, challenges in identifying key genes within genetic association regions, and limited applications to human diseases.

• Two artificial neural network (ANN) models are built to forecast SYM-H index 1 hour ahead using interplanetary magnetic field measurements. • The developed models are based on two conceptually different neural networks: Long Short-Term Memory and Convolutional Neural Network (CNN). • CNN, used here for the first time for geomagnetic indices forecasting, has proved potentialities worth being further explored.

Machine learning techniques combined with wearable electronics can deliver accurate short-term blood glucose level prediction models. These models can learn personalized glucose–insulin dynamics based on the sensor data collected by monitoring several aspects of the physiological condition and daily activity of an individual. Until now, the prevalent approach for developing data-driven prediction models was to collect as much data as possible to help physicians and patients optimally adjust therapy. The objective of this work was to investigate the minimum data variety, volume, and velocity required to create accurate person-centric short-term prediction models. We developed a series of these models using different machine learning time series forecasting techniques suitable for execution within a wearable processor. We conducted an extensive passive patient monitoring study in real-world conditions to build an appropriate data set. The study involved a subset of type 1 diabetic subjects wearing a flash glucose monitoring system. We comparatively and quantitatively evaluated the performance of the developed data-driven prediction models and the corresponding machine learning techniques. Our results indicate that very accurate short-term prediction can be achieved by only monitoring interstitial glucose data over a very short time period and using a low sampling frequency. The models developed can predict glucose levels within a 15-min horizon with an average error as low as 15.43 mg/dL using only 24 historic values collected within a period of sex hours, and by increasing the sampling frequency to include 72 values, the average error is reduced to 10.15 mg/dL. Our prediction models are suitable for execution within a wearable device, requiring the minimum hardware requirements while at simultaneously achieving very high prediction accuracy.

Featured Application: A complete review of information and communication technology (ICT) strategies to manage intimate partner violence (IPV) and protect IPV survivors is provided. A holistic ICT solution which would overcome the limitations of previous works is presented, promoting symmetry in society.Abstract: Intimate partner violence (IPV) remains a scourge that compromises the rights of many women around the world, shaping an asymmetry in civil rights. Fighting gender-based violence, especially when it is committed by an intimate partner, is an important responsibility that needs to be addressed from all angles. It is also remarkable that our society is clearly conditioned by information and communication technology (ICT), which involves many aspects of our daily life. Unfortunately, violence that is performed in the real world is also replicated in this 'virtual' existence, by offenders in ICT contexts. On the other hand, the same technologies also provide a plethora of opportunities to fight IPV, which are enhanced by the innovative paradigm of the so-called Internet of Things (IoT). In this work, we first present a thorough compilation of ICT proposals already published-based on either hardware or software-aimed at protecting IPV survivors, and which can be applied in real life situations but also within social networks. The challenges that still lie ahead are highlighted and, a complete ICT-based platform for IPV management, within an IoT framework, that overcomes the limitations of previous works is proposed, and then promoting a symmetry between individuals in society.Symmetry 2020, 12, 37 2 of 17 in any form or means. According to UN (United Nations) statistics, almost 35% women around the world have experienced some kind of physical or sexual violence [1]. The same statistics show that some 75% of women face physical and sexual aggression. This is a call for attention to be paid to this scourge. It is incredible that in 2017, some 87,000 women were killed across the world, of whom 58% (50,000) were killed by their husband or other relatives (https://www.unodc.org/).In recent years much research has focused on IPV and its connection to many related issues, that is, social awareness [2]. This wide scope includes resources used by the victims (from now on, more appropriately called 'survivors') [3], barriers, and formal assistance in facing different expressions of violence [4]. Nonetheless, some authors like Bruckman have noted that the way violence is developed and its impact on women in an environment characterized by the use of information and communication technology (ICT), such as mobile phones, social media, or generally using internet, has not been properly studied or documented [5], although in recent years it has become an issue of study [6].In 2019, ICT and especially the internet, have clearly advanced in every aspect of society, and have had an effect in every part of the world. In the early 1990s, Haraway [7] anticipated the social changes and the effect, especially in gender issues, that...

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