RESUMO
The Internet of Things (IoT) is based on objects or "things" that have the ability to communicate and transfer data. Due to the large number of connected objects and devices, there has been a rapid growth in the amount of data that are transferred over the Internet. To support this increase, the heterogeneity of devices and their geographical distributions, there is a need for IoT gateways that can cope with this demand. The SOFTWAY4IoT project, which was funded by the National Education and Research Network (RNP), has developed a software-defined and virtualized IoT gateway that supports multiple wireless communication technologies and fog/cloud environment integration. In this work, we propose a planning method that uses optimization models for the deployment of IoT gateways in smart campuses. The presented models aimed to quantify the minimum number of IoT gateways that is necessary to cover the desired area and their positions and to distribute IoT devices to the respective gateways. For this purpose, the communication technology range and the data link consumption were defined as the parameters for the optimization models. Three models are presented, which use LoRa, Wi-Fi, and BLE communication technologies. The gateway deployment problem was solved in two steps: first, the gateways were quantified using a linear programming model; second, the gateway positions and the distribution of IoT devices were calculated using the classical K-means clustering algorithm and the metaheuristic particle swarm optimization. Case studies and experiments were conducted at the Samambaia Campus of the Federal University of Goiás as an example. Finally, an analysis of the three models was performed, using metrics such as the silhouette coefficient. Non-parametric hypothesis tests were also applied to the performed experiments to verify that the proposed models did not produce results using the same population.
RESUMO
MeCP2 is an X-linked gene; its mutation causes Rett Syndrome (RTT), a severe neurodevelopmental disability that affects mainly girls. Acting as a transcription factor, the MeCP2 protein is able to regulate several hormone-related genes, such as the thyroid hormones (TH), which are known to play an important role in the development of the central nervous system (CNS). Although only a few studies have associated RTT and TH, TH deficit can lead to neurological deregulation by triggering functional deficiencies during adulthood. Here, we used human-induced pluripotent stem cell (iPSC) to generate MeCP2-knockout neuronal progenitor cells and adult neurons. Using this cellular model, we then investigated the expression of genes associated with TH homeostasis, such as the TH transporters (LAT1, LAT2, MCT8, MCT10, and OATP4A1) and deiodinases (DIO1, 2, and 3). Then, we treated the neural cells with THs and analyzed the expression of several genes related to neurodevelopment and functional maintenance. Our results showed that several TH-related genes, such as deiodinases, are altered in RTT samples when compared to WT cells. Moreover, the treatment of the neural cells with THs increased the amount of MAP2 and synapsin-1 expression in RTT cells. Our work provided evidences that TH homeostasis is compromised in RTT-derived neural cells, which could be an important factor to contribute to the imbalance in the neurodevelopmental phenotype presented in this syndrome and can lead us to better understand other neurodevelopmental diseases.