University student engagement inventory (USEI): psychometric properties

Academic engagement describes students’ investment in academic learning and achievement and is an important indicator of students’ adjustment to university life, particularly in the first year. A tridimensional conceptualization of academic engagement has been accepted (behavioral, emotional and cognitive dimensions). This paper tests the dimensionality, internal consistency reliability and invariance of the University Student Engagement Inventory (USEI) taking into consideration both gender and the scientific area of graduation. A sample of 908 Portuguese first-year university students was considered. Good evidence of reliability has been obtained with ordinal alpha and omega values. Confirmatory factor analysis substantiates the theoretical dimensionality proposed (second-order latent factor), internal consistency reliability evidence indicates good values and the results suggest measurement invariance across gender and the area of graduation. The present study enhances the role of the USEI regarding the lack of consensus on the dimensionality and constructs delimitation of academic engagement.Jorge Sinval received funding from the William James Center for Research, Portuguese Science Foundation (FCT UID/PSI/04810/2013). Leandro S. Almeida and Joana R. Casanova received funding from CIEd – Research Centre on Education, projects UID/CED/1661/2013 and UID/CED/1661/2016, Institute of Education, University of Minho, through national funds of FCT/MCTES-PT. Joana R. Casanova received funding from the Portuguese Science Foundation (FCT) as a Doctoral Grant, under grant agreement number SFRH/BD/117902/2016.info:eu-repo/semantics/publishedVersio

Marine Cyanobacteria Compounds with Anticancer Properties: Implication of Apoptosis

Marine cyanobacteria have been proved to be an important source of potential anticancer drugs. Although several compounds were found to be cytotoxic to cancer cells in culture, the pathways by which cells are affected are still poorly elucidated. For some compounds, cancer cell death was attributed to an implication of apoptosis through morphological apoptotic features, implication of caspases and proteins of the Bcl-2 family, and other mechanisms such as interference with microtubules dynamics, cell cycle arrest and inhibition of proteases other than caspases

An Agent-Based Model of a Hepatic Inflammatory Response to Salmonella: A Computational Study under a Large Set of Experimental Data

Citation: Shi, Z. Z., Chapes, S. K., Ben-Arieh, D., & Wu, C. H. (2016). An Agent-Based Model of a Hepatic Inflammatory Response to Salmonella: A Computational Study under a Large Set of Experimental Data. Plos One, 11(8), 39. doi:10.1371/journal.pone.0161131We present an agent-based model (ABM) to simulate a hepatic inflammatory response (HIR) in a mouse infected by Salmonella that sometimes progressed to problematic proportions, known as "sepsis". Based on over 200 published studies, this ABM describes interactions among 21 cells or cytokines and incorporates 226 experimental data sets and/or data estimates from those reports to simulate a mouse HIR in silico. Our simulated results reproduced dynamic patterns of HIR reported in the literature. As shown in vivo, our model also demonstrated that sepsis was highly related to the initial Salmonella dose and the presence of components of the adaptive immune system. We determined that high mobility group box-1, C-reactive protein, and the interleukin-10: tumor necrosis factor-a ratio, and CD4+ T cell: CD8+ T cell ratio, all recognized as biomarkers during HIR, significantly correlated with outcomes of HIR. During therapy-directed silico simulations, our results demonstrated that anti-agent intervention impacted the survival rates of septic individuals in a time-dependent manner. By specifying the infected species, source of infection, and site of infection, this ABM enabled us to reproduce the kinetics of several essential indicators during a HIR, observe distinct dynamic patterns that are manifested during HIR, and allowed us to test proposed therapy-directed treatments. Although limitation still exists, this ABM is a step forward because it links underlying biological processes to computational simulation and was validated through a series of comparisons between the simulated results and experimental studies