Becoming a 21st century literacy teacher:provocations, perceptions and possibilities in teacher education

This paper reports on a study that investigated pre-service teachers’ digital funds of knowledge and their perceptions of the digital pedagogies and practices in early years literacy classrooms. It also explores pre-service teachers’ experiences of “produsing” a cumulative multimodal portfolio (in the form of a wiki) and its application for future literacy teaching and learning. Specifically, 123 education students enrolled in their second year of an undergraduate initial-teacher education course at an Australian university completed an anonymous survey. The resultsshow that this group of students were active users of technology-based tools, but had limited experience with using participatory user-led knowledge creation tools (such as Web 2.0 technologies) although many observed the use of these tools in early years literacy classrooms while on professional experience school placements. Further findings show thatalthough the majority of this group of pre-service teachers felt more confident after creating a wiki and reported that they would use them in future literacy teaching and learning, their understandings of the pedagogical and creative potential of these digital tools in supporting literacy learning in young children appeared limited. The findings suggest that there is a need for educators in higher education to understand their students’ digital funds of knowledge and to provide rich opportunities to support these students’ use and understandings of the affordances of these new technologies as vehicles to explore and enrich 21st century literacy learning in early years digital environments.

Novel targeting of PEGylated liposomes for codelivery of TGF-β1 siRNA and four antitubercular drugs to human macrophages for the treatment of mycobacterial infection: a quantitative proteomic study

Tuberculosis (TB) is still a major public health issue in developing countries, and its chemotherapy is compromised by poor drug compliance and severe side effects. This study aimed to synthesize and characterize new multimodal PEGylated liposomes encapsulated with clinically commonly used anti-TB drugs with linkage to small interfering RNA (siRNA) against transforming growth factor-β1 (TGF-β1). The novel NP-siRNA liposomes could target THP-1-derived human macrophages that were the host cells of mycobacterium infection. The biological effects of the NP-siRNA liposomes were evaluated on cell cycle distribution, apoptosis, autophagy, and the gene silencing efficiency of TGF-β1 siRNA in human macrophages. We also explored the proteomic responses to the newly synthesized NP-siRNA liposomes using the stable isotope labeling with amino acids in cell culture approach. The results showed that the multifunctional PEGylated liposomes were successfully synthesized and chemically characterized with a mean size of 265.1 nm. The novel NP-siRNA liposomes functionalized with the anti-TB drugs and TGF-β1 siRNA were endocytosed efficiently by human macrophages as visualized by transmission electron microscopy and scanning electron microscopy. Furthermore, the liposomes showed a low cytotoxicity toward human macrophages. There was no significant effect on cell cycle distribution and apoptosis in THP-1-derived macrophages after drug exposure at concentrations ranging from 2.5 to 62.5 μg/mL. Notably, there was a 6.4-fold increase in the autophagy of human macrophages when treated with the NP-siRNA liposomes at 62.5 μg/mL. In addition, the TGF-β1 and nuclear factor-κB expression levels were downregulated by the NP-siRNA liposomes in THP-1-derived macrophages. The Ingenuity Pathway Analysis data showed that there were over 40 signaling pathways involved in the proteomic responses to NP-siRNA liposome exposure in human macrophages, with 160 proteins mapped. The top five canonical signaling pathways were eukaryotic initiation factor 2 signaling, actin cytoskeleton signaling, remodeling of epithelial adherens junctions, epithelial adherens junction signaling, and Rho GDP-dissociation inhibitor signaling pathways. Collectively, the novel synthetic targeting liposomes represent a promising delivery system for anti-TB drugs to human macrophages with good selectivity and minimal cytotoxicity.

Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (eNRBM)

Electronic medical record (EMR) offers promises for novel analytics. However, manual feature engineering from EMR is labor intensive because EMR is complex - it contains temporal, mixed-type and multimodal data packed in irregular episodes. We present a computational framework to harness EMR with minimal human supervision via restricted Boltzmann machine (RBM). The framework derives a new representation of medical objects by embedding them in a low-dimensional vector space. This new representation facilitates algebraic and statistical manipulations such as projection onto 2D plane (thereby offering intuitive visualization), object grouping (hence enabling automated phenotyping), and risk stratification. To enhance model interpretability, we introduced two constraints into model parameters: (a) nonnegative coefficients, and (b) structural smoothness. These result in a novel model called eNRBM (EMR-driven nonnegative RBM). We demonstrate the capability of the eNRBM on a cohort of 7578 mental health patients under suicide risk assessment. The derived representation not only shows clinically meaningful feature grouping but also facilitates short-term risk stratification. The F-scores, 0.21 for moderate-risk and 0.36 for high-risk, are significantly higher than those obtained by clinicians and competitive with the results obtained by support vector machines.