Abstract
Methylation is considered one of the proteins' most important post-translational modifications (PTM). Plasticity and cellular dynamics are among the many traits that are regulated by methylation. Currently, methylation sites are identified using experimental approaches. However, these methods are time-consuming and expensive. With the use of computer modelling, methylation sites can be identified quickly and accurately, providing valuable information for further trial and investigation. In this study, we propose a new machine-learning model called MeSEP to predict methylation sites that incorporates both evolutionary and structural-based information. To build this model, we first extract evolutionary and structural features from the PSSM and SPD2 profiles, respectively. We then employ Extreme Gradient Boosting (XGBoost) as the classification model to predict methylation sites. To address the issue of imbalanced data and bias towards negative samples, we use the SMOTETomek-based hybrid sampling method. The MeSEP was validated on an independent test set (ITS) and 10-fold cross-validation (TCV) using lysine methylation sites. The method achieved: an accuracy of 82.9% in ITS and 84.6% in TCV; precision of 0.92 in ITS and 0.94 in TCV; area under the curve values of 0.90 in ITS and 0.92 in TCV; F1 score of 0.81 in ITS and 0.83 in TCV; and MCC of 0.67 in ITS and 0.70 in TCV. MeSEP significantly outperformed previous studies found in the literature. MeSEP as a standalone toolkit and all its source codes are publicly available at https://github.com/arafatro/MeSEP.

Abstract
Citizen engagement with Sustainable Energy Solutions (SES) is considered essential for the current energy transition, since decarbonization requires individuals to shift from passive consumers to citizens actively involved with the energy system. However, citizen engagement research has remained peripheral and scattered, particularly in what regards the drivers of engagement behaviors. To address this challenge, this study examines how different forms of perceived value of SES (utilitarian, social, and environmental) influence different types of citizen engagement behaviors (information seeking, proactive managing, sharing feedback, helping other users, and advocating). To this end, we developed a quantitative study in the context of a H2020 EU project, with a sample of 456 citizens from the city of Alkmaar (the Netherlands). Our findings show that the utilitarian value of SES has a significant effect on all the engagement behaviors, except for sharing feedback. Social value has a significant influence on the more socially related engagement behaviors, such as sharing feedback, helping other users, and advocating. Finally, environmental value has an indirect effect on information seeking, proactive managing, and advocating, but only when mediated through awareness of consequences. The implications of this study should allow SES providers to design more relevant offerings and policymakers to develop better citizen engagement strategies.

Abstract
Phobias are characterized as the excessive or irrational fear of an object or situation, and specific phobias affect about 10% of the world population. Blood-injection-injury phobia is a specific phobia that has a unique physical response to phobic stimuli, that is, a vasovagal syncope that causes the person to faint. Phobos is a serious game intended for blood phobia treatment that was created to be played in virtual reality with an HTC Vive that has photorealistic graphics to provide a greater immersion. We also developed a console application in C# for electrocardiography sensor connectivity and data acquisition, which gathers a 1 min baseline reading and then has continuous data acquisition during gameplay. Usability tests were conducted with self-reported questionnaires and with a case study population of 10 testers, which gave insight into the previous game experience of the tester for both digital games and virtual reality games, evaluating the discomfort for hardware on both the sensor and the virtual reality headset, as well as the game regarding usability, user experience, level of immersion, and the existence of motion sickness and its source. The results corroborate that the immersion of the game is good, which suggests that it will help with triggering the phobia.

Abstract
AbstractWith the rapid growth of Deep Learning models and neural networks, the medical data available for training – which is already significantly less than other types of data – is becoming scarce. For that purpose, Generative Adversarial Networks (GANs) have received increased attention due to their ability to synthesize new realistic images. Our preliminary work shows promising results for brain MRI images; however, there is a need to distribute the workload, which can be supported by High-Performance Computing (HPC) environments. In this paper, we generate 256×256 MRI images of the brain in a distributed setting. We obtained an FIDRadImageNetof 10.67 for the DCGAN and 23.54 for the WGAN-GP, which are consistent with results reported in several works published in this scope. This allows us to conclude that distributing the GAN generation process is a viable option to overcome the computational constraints imposed by these models and, therefore, facilitate the generation of new data for training purposes.

Abstract
ObjectiveThis systematic review aims to describe the involvement of persons with epilepsy (PWE), healthcare professionals (HP) and caregivers (CG) in the design and development of medical devices is epilepsy.MethodsA systematic review was conducted, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Eligibility criteria included peer-reviewed research focusing on medical devices for epilepsy management, involving users (PWE, CG, and HP) during the MDD process. Searches were performed on PubMed, Web of Science, and Scopus, and a total of 55 relevant articles were identified and reviewed.ResultsFrom 1999 to 2023, there was a gradual increase in the number of publications related to user involvement in epilepsy medical device development (MDD), highlighting the growing interest in this field. The medical devices involved in these studies encompassed a range of seizure detection tools, healthcare information systems, vagus nerve stimulation (VNS) and electroencephalogram (EEG) technologies reflecting the emphasis on seizure detection, prediction, and prevention. PWE and CG were the primary users involved, underscoring the importance of their perspectives. Surveys, usability testing, interviews, and focus groups were the methods used for capturing user perspectives. User involvement occurs in four out of the five stages of MDD, with production being the exception.SignificanceUser involvement in the MDD process for epilepsy management is an emerging area of interest holding a significant promise for improving device quality and patient outcomes. This review highlights the need for broader and more effective user involvement, as it currently lags in the development of commercially available medical devices for epilepsy management. Future research should explore the benefits and barriers of user involvement to enhance medical device technologies for epilepsy.Plain Language SummaryThis review covers studies that have involved users in the development process of medical devices for epilepsy. The studies reported here have focused on getting input from people with epilepsy, their caregivers, and healthcare providers. These devices include tools for detecting seizures, stimulating nerves, and tracking brain activity. Most user feedback was gathered through surveys, usability tests, interviews, and focus groups. Users were involved in nearly every stage of device development except production. The review highlights that involving users can improve device quality and patient outcomes, but more effective involvement is needed in commercial device development. Future research should focus on the benefits and challenges of user involvement.

Abstract
HAL 9000 is an Intrusion Tolerant Systems (ITSs) Risk Manager, which assesses configuration risks against potential intrusions. It utilizes gathered threat knowledge and remains operational, even in the absence of updated information. Based on its advice, the ITSs can dynamically and proactively adapt to recent threats to minimize and mitigate future intrusions from malicious adversaries.Our goal is to reduce the risk linked to the exploitation of recently uncovered vulnerabilities that have not been classified and/or do not have a script to reproduce the exploit, considering the potential that they may have already been exploited as zero-day exploits. Our experiments demonstrate that the proposed solution can effectively learn and replicate National Vulnerability Database's evaluation process with 99% accuracy. © 2024 IEEE.