Ethical implications of developing RNA-based therapies for cardiovascular disorders.

The awareness concerning RNA-based therapies was boosted significantly after the successful development of COVID-19 vaccines. However, they can potentially lead to significant advances in other areas of medicine, such as oncology or chronic diseases. In recent years, there has been an exponential increase in the number of RNA-based therapies that were evaluated as potential treatments for cardiovascular disorders. One of the areas that was not explicitly assessed about these therapies is represented by their overall ethical framework. Some studies evaluate ethical issues of RNA-based treatments in general or targeting specific disorders (especially neurodegenerative) or interventions for developing RNA-based vaccines. Much less information is available regarding the ethical issues associated with developing these therapeutic strategies for cardiovascular disorders, which is the main aim of this study. We will focus our analysis on three main topics: risk-benefit analysis (including the management of public awareness about these technologies), and justice (in both research and clinical medicine).

A Hybrid Software and Hardware SDN Simulation Testbed.

In recent years, the software-defined networking (SDN) paradigm has been deployed in various types of networks, including wireless sensor networks (WSN), wide area networks (WAN) and data centers. Given the wide range of SDN domain applicability and the large-scale environments where the paradigm is being deployed, creating a full real test environment is a complex and costly task. To address these problems, software-based simulations are employed to validate the proposed solutions before they are deployed in real networks. However, simulations are constrained by relying on replicating previously saved logs and datasets and do not use real time hardware data. The current article addresses this limitation by creating a novel hybrid software and hardware SDN simulation testbed where data from real hardware sensors are directly used in a Mininet emulated network. The article conceptualizes a new approach for expanding Mininet's capabilities and provides implementation details on how to perform simulations in different contexts (network scalability, parallel computations and portability). To validate the design proposals and highlight the benefits of the proposed hybrid testbed solution, specific scenarios are provided for each design idea. Furthermore, using the proposed hybrid testbed, new datasets can be easily generated for specific scenarios and replicated in more complex research.

Interacting with chatbots later in life: A technology acceptance perspective in COVID-19 pandemic situation.

Introduction: Within the technological development path, chatbots are considered an important tool for economic and social entities to become more efficient and to develop customer-centric experiences that mimic human behavior. Although artificial intelligence is increasingly used, there is a lack of empirical studies that aim to understand consumers' experience with chatbots. Moreover, in a context characterized by constant population aging and an increased life-expectancy, the way aging adults perceive technology becomes of great interest. However, based on the digital divide (unequal access to technology, knowledge, and resources), and since young adults (aged between 18 and 34 years old) are considered to have greater affinity for technology, most of the research is dedicated to their perception. The present paper investigates the way chatbots are perceived by middle-aged and aging adults in Romania. Methods: An online opinion survey has been conducted. The age-range of the subjects is 40-78 years old, a convenience sampling technique being used (N = 235). The timeframe of the study is May-June 2021. Thus, the COVID-19 pandemic is the core context of the research. A covariance-based structural equation modelling (CB-SEM) has been used to test the theoretical assumptions as it is a procedure used for complex conceptual models and theory testing. Results: The results show that while perceived ease of use is explained by the effort, the competence, and the perceive external control in interacting with chatbots, perceived usefulness is supported by the perceived ease of use and subjective norms. Furthermore, individuals are likely to further use chatbots (behavioral intention) if they consider this interaction useful and if the others' opinion is in favor of using it. Gender and age seem to have no effect on behavioral intention. As studies on chatbots and aging adults are few and are mainly investigating reactions in the healthcare domain, this research is one of the first attempts to better understand the way chatbots in a not domain-specific context are perceived later in life. Likewise, judging from a business perspective, the results can help economic and social organizations to improve and adapt AI-based interaction for the aging customers.

Optimizations for Energy Efficiency in Software-Defined Wireless Sensor Networks.

Software-defined wireless sensor networking (SDWSN) is an emerging networking architecture which is envisioned to become the main enabler for the internet of things (IoT). In this architecture, the sensors plane is managed by a control plane. With this separation, the network management is facilitated, and performance is improved in dynamic environments. One of the main issues a sensor environment is facing is the limited lifetime of network devices influenced by high levels of energy consumption. The current work proposes a system design which aims to improve the energy efficiency in an SDWSN by combining the concepts of content awareness and adaptive data broadcast. The purpose is to increase the sensors' lifespan by reducing the number of generated data packets in the resource-constrained sensors plane of the network. The system has a distributed management approach, with content awareness being implemented at the individual programmable sensor level and the adaptive data broadcast being performed in the control plane. Several simulations were run on historical weather and the results show a significant decrease in network traffic. Compared to similar work in this area which focuses on improving energy efficiency with complex algorithms for routing, clustering, or caching, the current proposal employs simple computing procedures on each network device with a high impact on the overall network performance.

Named-Entity-Recognition-Based Automated System for Diagnosing Cybersecurity Situations in IoT Networks.

The aim of this paper was to enhance the process of diagnosing and detecting possible vulnerabilities within an Internet of Things (IoT) system by using a named entity recognition (NER)-based solution. In both research and practice, security system management experts rely on a large variety of heterogeneous security data sources, which are usually available in the form of natural language. This is challenging as the process is very time consuming and it is difficult to stay up to date with the constant findings in the areas of security threats, vulnerabilities, attacks, countermeasures, and risks. The proposed system is conceived as a semantic indexing solution of existing vulnerabilities and serves as an information tool for security management experts. By integrating the proposed system, the users can easily discover the potential vulnerabilities of their IoT devices. The proposed solution integrates ontologies and NER techniques in order to obtain a high rate of automation with the scope of reaching a self-maintained and up-to-date system in terms of vulnerabilities and common exposures knowledge. To achieve this, a total of 312 CVEs (common vulnerabilities and exposures) specific to the IoT field were identified. CVEs are arguably one of the most important cybersecurity resources nowadays, containing information about the latest discovered vulnerabilities. This set is further used as data corpus for an NER model designed to identify the main entities and relations that are relevant to IoT security. The goal is to automatically monitor cybersecurity information relevant to IoT, and filter and present it in an organized and structured framework based on users' needs. The taxonomies specific to IoT security are implemented via a domain ontology, which is later used to process natural language. Relevant tokens are marked as entities and the relations between them identified. The text analysis solution is connected to a gateway which scans the environment and identifies the main IoT devices and communication technologies. The strength of the approach proposed within this research is that the designed semantic gateway is using context-aware searches in the modeled IoT security database and can identify possible vulnerabilities before they can be exploited.

Refinement-based modeling of the ErbB signaling pathway.

The construction of large scale biological models is a laborious task, which is often addressed by adopting iterative routines for model augmentation, adding certain details to an initial high level abstraction of the biological phenomenon of interest. Refitting a model at every step of its development is time consuming and computationally intensive. The concept of model refinement brings about an effective alternative by providing adequate parameter values that ensure the preservation of its quantitative fit at every refinement step. We demonstrate this approach by constructing the largest-ever refinement-based biomodel, consisting of 421 species and 928 reactions. We start from an already fit, relatively small literature model whose consistency we check formally. We then construct the final model through an algorithmic step-by-step refinement procedure that ensures the preservation of the model's fit.