Abstract
Protein-protein interaction (PPI) data is essential to elucidate the complex molecular relationships in living systems, and thus understand the biological functions at cellular and systems levels. The complete map of PPIs that can occur in a living organism is called the interactome. For animals, PPI data is stored in multiple databases (e.g., BioGRID, CCSB, DroID, FlyBase, HIPPIE, HitPredict, HomoMINT, INstruct, Interactome3D, mentha, MINT, and PINA2) with different formats. This makes PPI comparisons difficult to perform, especially between species, since orthologous proteins may have different names. Moreover, there is only a partial overlap between databases, even when considering a single species. The EvoPPI (http://evoppi.i3s.up.pt) web application presented in this paper allows comparison of data from the different databases at the species level, or between species using a BLAST approach. We show its usefulness by performing a comparative study of the interactome of the nine polyglutamine (polyQ) disease proteins, namely androgen receptor (AR), atrophin-1 (ATN1), ataxin 1 (ATXN1), ataxin 2 (ATXN2), ataxin 3 (ATXN3), ataxin 7 (ATXN7), calcium voltage-gated channel subunit alpha1 A (CACNA1A), Huntingtin (HTT), and TATA-binding protein (TBP). Here we show that none of the human interactors of these proteins is common to all nine interactomes. Only 15 proteins are common to at least 4 of these polyQ disease proteins, and 40% of these are involved in ubiquitin protein ligase-binding function. The results obtained in this study suggest that polyQ disease proteins are involved in different functional networks. Comparisons with Mus musculus PPIs are also made for AR and TBP, using EvoPPI BLAST search approach (a unique feature of EvoPPI), with the goal of understanding why there is a significant excess of common interactors for these proteins in humans.

Abstract

Abstract
Through IoT, humans and objects can be connected seamlessly, to guaranty improved quality of service (QoS). IoT-driven e-Health systems benefit from such rich network setting, to transmit health information and deliver health services. It is expected to grow massively in scale, but for that to happen, several issues need to be addressed, including security and trust. Edge computing paradigms, such as Fog computing and Cloudlet, are already popular in IoT based e-Health domain. Fog nodes are leveraged to reduce latency between IoT devices and remote cloud computing infrastructure. In this work, we explain how Mobile edge-clouds, which is a less popular edge computing paradigm, can be employed to achieve similar or lower latency, at a lower cost. We also propose a lightweight mechanism for security and fairness in e-Health protocols that are based on mobile edge-clouds and other paradigms. Detailed simulation experiments show that the proposed method is scalable and can efficiently mitigate attacks that are targeted at e-Health information and the network.

Abstract
This paper studies the use of non-invasive acoustic emission recordings for clinical device tracking. In particular, audio signals recorded at the proximal end of a needle are used to detect perforation events that occur when the needle tip crosses internal tissue layers. A comparative study is performed to assess the capacity of different features and envelopes in detecting perforation events. The results obtained from the considered experimental setup show a statistically significant correlation between the extracted envelopes and the perforation events, thus leading the way for future development of perforation detection algorithms.

Abstract
Spinal cord injuries are among the most traumatic situations, having relevant repercussions on an individual’s occupation performance. Although loss of functionality is considered to be the most significant consequence, neuropathic pain can determine an individual’s inability to return to daily activities. Therefore, it is imperative to develop new technologies with significant impact on the rehabilitation process of the spinal cord injuries. VR4NeuroPain combines virtual reality with a glove “GNeuroPathy”, covered with a variety of biosensors, that allows for the collection of physiological parameters and motor stimulation. The main purpose of this paper is to describe the system VR4NeuroPain, and to validate the “GNeuroPathy” concept. With that in mind, and after calibrating the VR4NeuroPain system using with a group of 16 individuals, the validation results showed that “GNeuroPathy” was comfort, accessibility in place and the collection of physiological parameters was performed as expected. © Springer International Publishing AG, part of Springer Nature 2019.

Abstract
Over the last few years, oil spill incidents occured with some regularity during exploration, production and transportation, causing a large economic and ecologic impact in the world community. To minimise these impacts and reduce the time response of the initial mitigation process, autonomous vehicles, such as unmanned aerial vehicles (UAV) can be used to perform oil spill monitoring and mitigation. This paper presents the design and implementation of a multirotor UAV capable of identifying and combat the oil spill, by using a release system of consortia with bacteria and nutrients. Several field tests occurred in Portugal and Spain, where the oil spill was implemented in a simulated environment, resulting in a cooperative and simultaneous manoeuvre between the vehicles.