Ourania Petropoulou

Applied Technologies and Smart Home Applications in the Health Sector

Smart homes are no longer design concepts of the future. They are being built now, and they are having a direct impact on the lifestyles of people living in them. The aim of smart home systems is to create an environment that is aware of the activities taking place within it. Beside the healthy people, disabled people also need such systems to make their life easier, because they encounter with a lot of difficulties in their everyday life especially when they are at home. The solutions detailed in this paper offer health systems to be integrated with Smart Home services platforms to support home based e-care. Thus, the home e-health systems and architectures detailed in this paper will give a snapshot on the state of the art in the smart home technology for elders, people with disabilities, diabetes patients and people with schizophrenia. The focus of this research work is to supply a seamless personal care solution both from the biomedical data analysis, service provision, security guarantee and information managements point of view.

Computational Analysis of BRCA1 Mutations in Pediatric Patients with Malignancies and Their Mothers

Breast and ovarian cancers are the most prevalent type of malignancies amongst women. Similar incidence appear in childhood malignancies, where the basic ontogenetic mechanisms still remain to be elucidated. Such approaches, of relating mothers cancer mutations with the prevalence of childhood cancer in their offspring could prove useful in the prognosis, early detection and therapy of childhood malignancies. The aim of the present study was to use computational and bioinformatics tools to investigate the incidence of mutations in mothers with children suffering from neoplasms. Genes were examined for mutations and in particular, those were BRCA1, RAS family genes, TP53 and FLT3. Mutations were initially detected using PCR and multiplex Polymerase Chain Reaction (PCR) methodologies. Gene expression was detected using quantitative Reverse Transcription PCR (qRT-PCR) methodologies and results have been confirmed with the sequencing method. Following experimental analysis, bioinformatics analyses have been performed. In the case of positive identification of mutations, molecular modelling was used in order to study the effects of the mutations on the BRCA protein and subsequent effects on binding to BARD1, a signaling molecule down-stream of BRCA1, which participates in DNA repair pathways. Concluding, it appeared that the presence of a mutation in the aforementioned genes is not adequate for the disease to progress, yet it can be considered as a serious factor for disease progression. Thus, it appears that this phenomenon is of extreme interest and it should be further investigated in a larger patient cohort.

Σkynet: A Novel Biologically Inspired Near Extinction Reconstruction Model

The present work aims to develop a fundamentally novel computational model for reconstructing complex software systems, following some massive internal failure or external infrastructure damage. The present model mimics the biological process of information transmission in terms of transcription and subsequent translation, introducing novel terms such as pre-code and computational RNA.

Complex Dynamics in Tumor Gene Regulatory Networks: Oncogenesis Dynamics Driven by “Genes Gone Crazy”

Human neoplasms are considered to be diseases whose pathogenesis is attributed to genomic aberrations. Yet, it has been previously reported that genomic stability is probably not the sole “player” in tumor ontogenesis. Several genes have been identified that are considerd to be the “oncogenic drivers”. In that sense the present work manifests a hypothesis that tumor ontogenesis could be driven by genes or network of genes that get out of control or else “genes gone crazy”.

Designing Configurations of CSCL Scripts Using Interaction Analysis Findings

Various learning strategies have been widely used in the Computer-Supported Collaborative Learning (CSCL) script design practice. For this reason strategies have been documented in a designer friendly way as collaborative flow design patterns (CLFP). A CLFP defines the sequence of the tasks that the strategy dictates as well as other elements needed for the various tasks, such as the duration of a task, the use of a particular tool for a given task and so on. Designers often face the difficulty of mixing and matching strategies in order to create personalized scripts that are more appropriate to the learners’ preferences, knowledge level, needs and the learning context in general. Designers need to make configurations of a “traditional” CLFP in order to balance a variety of organizational, administrative, instructional and technological components. This task is even more challenging when such configurations need to be made on-the-fly, i.e. during the learning process and in response to the learner’ actions and history of interaction. The aim of this paper is to discuss how the learners’ interaction data that is collected during the CSCL process and analysed using interaction analysis indicators can be used to make valuable alternatives of a CSCL script depending on learning conditions. The design challenge is to automate as much as possible this configuration process in order to help practitioners easily proceed in making these configurations easily and quickly.