An open learning environment for the diagnosis, assistance and evaluation of students based on artificial intelligence

The personalized diagnosis, assistance and evaluation of students in open learning environments can be a challenging task, especially in cases that the processes need to be taking place in real-time, classroom conditions. This paper describes the design of an open learning environment under development, designed to monitor the comprehension of students, assess their prior knowledge, build individual learner profiles, provide personalized assistance and, finally, evaluate their performance by using artificial intelligence. A trial test has been performed, with the participation of 20 students, which displayed promising results

Evaluation of an intelligent open learning system for engineering education

In computer-assisted education, the continuous monitoring and assessment of the learner is crucial for the delivery of personalized education to be effective. In this paper, we present a pilot application of the Student Diagnosis, Assistance, Evaluation System based on Artificial Intelligence (StuDiAsE), an open learning system for unattended student diagnosis, assistance and evaluation based on artificial intelligence. The system demonstrated in this paper has been designed with engineering students in mind and is capable of monitoring their comprehension, assessing their prior knowledge, building individual learner profiles, providing personalized assistance and, finally, evaluating a learner's performance both quantitatively and qualitatively by means of artificial intelligence techniques. The architecture and user interface of the system are being exhibited, the results and feedback received from a pilot application of the system within a theoretical engineering course are being demonstrated and the outcomes are being discussed

An Advanced eLearning Environment Developed for Engineering Learners

Monitoring and evaluating engineering learners through computer-based laboratory exercises is a difficult task, especially under classroom conditions. A complete diagnosis requires the capability to assess both the competence of the learner to use the scientific software and the understanding of the theoretical principles. This monitoring and evaluation needs to be continuous, unobtrusive and personalized in order to be effective. This study presents the results of the pilot application of an eLearning environment developed specifically with engineering learners in mind. As its name suggests, the Learner Diagnosis, Assistance, and Evaluation System based on Artificial Intelligence (StuDiAsE) is an Open Learning Environment that can perform unattended diagnostic, evaluation and feedback tasks based on both quantitative and qualitative parameters. The base architecture of the system, the user interface and its effect on the performance of postgraduate engineering learners are being presented

Pleiotrophin-induced endothelial cell migration is regulated by xanthine oxidase-mediated generation of reactive oxygen species

Pleiotrophin (PTN) is a heparin-binding growth factor that induces cell migration through binding to its receptor protein tyrosine phosphatase beta/zeta (RPTPβ/ζ) and integrin alpha v beta 3 (ανβ3). In the present work, we studied the effect of PTN on the generation of reactive oxygen species (ROS) in human endothelial cells and the involvement of ROS in PTN-induced cell migration. Exogenous PTN significantly increased ROS levels in a concentration and time-dependent manner in both human endothelial and prostate cancer cells, while knockdown of endogenous PTN expression in prostate cancer cells significantly down-regulated ROS production. Suppression of RPTPβ/ζ through genetic and pharmacological approaches, or inhibition of c-src kinase activity abolished PTN-induced ROS generation. A synthetic peptide that blocks PTN–ανβ3 interaction abolished PTN-induced ROS generation, suggesting that ανβ3 is also involved. The latter was confirmed in CHO cells that do not express β3 or over-express wild-type β3 or mutant β3Y773F/Y785F. PTN increased ROS generation in cells expressing wild-type β3 but not in cells not expressing or expressing mutant β3. Phosphoinositide 3-kinase (PI3K) or Erk1/2 inhibition suppressed PTN-induced ROS production, suggesting that ROS production lays down-stream of PI3K or Erk1/2 activation by PTN. Finally, ROS scavenging and xanthine oxidase inhibition completely abolished both PTN-induced ROS generation and cell migration, while NADPH oxidase inhibition had no effect. Collectively, these data suggest that xanthine oxidase-mediated ROS production is required for PTN-induced cell migration through the cell membrane functional complex of ανβ3 and RPTPβ/ζ and activation of c-src, PI3K and ERK1/2 kinases

Phosphoproteomics Screen Reveals Akt Isoform-Specific Signals Linking RNA Processing to Lung Cancer

The three Akt isoforms are functionally distinct. Here we show that their phosphoproteomes also differ, suggesting that their functional differences are due to differences in target specificity. One of the top cellular functions differentially regulated by Akt isoforms is RNA processing. IWS1, an RNA processing regulator, is phosphorylated by Akt3 and Akt1 at Ser720/Thr721. The latter is required for the recruitment of SETD2 to the RNA Pol II complex. SETD2 trimethylates histone H3 at K36 during transcription, creating a docking site for MRG15 and PTB. H3K36me3-bound MRG15 and PTB regulate FGFR-2 splicing, which controls tumor growth and invasiveness downstream of IWS1 phosphorylation. Twenty-one of the twenty-four non-small-cell-lung carcinomas we analyzed express IWS1. More importantly, the stoichiometry of IWS1 phosphorylation in these tumors correlates with the FGFR-2 splicing pattern and with Akt phosphorylation and Akt3 expression. These data identify an Akt isoform-dependent regulatory mechanism for RNA processing and demonstrate its role in lung cancer

A Multi-Factorial Observational Study on Sequential Fecal Microbiota Transplant in Patients with Medically Refractory Clostridioides difficile Infection

Fecal microbiota transplantation (FMT) is highly effective in recurrent Clostridioides difficile infection (CDI); increasing evidence supports FMT in severe or fulminant Clostridioides difficile infection (SFCDI). However, the multifactorial mechanisms that underpin the efficacy of FMT are not fully understood. Systems biology approaches using high-throughput technologies may help with mechanistic dissection of host-microbial interactions. Here, we have undertaken a deep phenomics study on four adults receiving sequential FMT for SFCDI, in which we performed a longitudinal, integrative analysis of multiple host factors and intestinal microbiome changes. Stool samples were profiled for changes in gut microbiota and metabolites and blood samples for alterations in targeted epigenomic, metabonomic, glycomic, immune proteomic, immunophenotyping, immune functional assays, and T-cell receptor (TCR) repertoires, respectively. We characterised temporal trajectories in gut microbial and host immunometabolic data sets in three responders and one non-responder to sequential FMT. A total of 562 features were used for analysis, of which 78 features were identified, which differed between the responders and the non-responder. The observed dynamic phenotypic changes may potentially suggest immunosenescent signals in the non-responder and may help to underpin the mechanisms accompanying successful FMT, although our study is limited by a small sample size and significant heterogeneity in patient baseline characteristics. Our multi-omics integrative longitudinal analytical approach extends the knowledge regarding mechanisms of efficacy of FMT and highlights preliminary novel signatures, which should be validated in larger studies

Gene Network Analysis of Bone Marrow Mononuclear Cells Reveals Activation of Multiple Kinase Pathways in Human Systemic Lupus Erythematosus

Background: Gene profiling studies provide important information for key molecules relevant to a disease but are less informative of protein-protein interactions, post-translational modifications and regulation by targeted subcellular localization. Integration of genomic data and construction of functional gene networks may provide additional insights into complex diseases such as systemic lupus erythematosus (SLE). Methodology/Principal Findings: We analyzed gene expression microarray data of bone marrow mononuclear cells (BMMCs) from 20 SLE patients (11 with active disease) and 10 controls. Gene networks were constructed using the bioinformatic tool Ingenuity Gene Network Analysis. In SLE patients, comparative analysis of BMMCs genes revealed a network with 19 central nodes as major gene regulators including ERK, JNK, and p38 MAP kinases, insulin, Ca2+ and STAT3. Comparison between active versus inactive SLE identified 30 central nodes associated with immune response, protein synthesis, and post-transcriptional modification. A high degree of identity between networks in active SLE and non-Hodgkin's lymphoma (NHL) patients was found, with overlapping central nodes including kinases (MAPK, ERK, JNK, PKC), transcription factors (NF-kappaB, STAT3), and insulin. In validation studies, western blot analysis in splenic B cells from 5-month-old NZB/NZW F1 lupus mice showed activation of STAT3, ITGB2, HSPB1, ERK, JNK, p38, and p32 kinases, and downregulation of FOXO3 and VDR compared to normal C57Bl/6 mice. Conclusions/Significance: Gene network analysis of lupus BMMCs identified central gene regulators implicated in disease pathogenesis which could represent targets of novel therapies in human SLE. The high similarity between active SLE and NHL networks provides a molecular basis for the reported association of the former with lymphoid malignancies