Changing the Course of Faculty Engagement in Academic Governance: Reconcilation through Education

During the past decade, River’s Edge has engaged in organizational change due to neoliberal reforms permeating the higher education sector in the province. Chronic under-funding and annual budget cuts have directed change efforts towards commodifying service, education, and research to generate revenue from public consumption. Like other higher education institutions, River\u27s Edge responds by positioning itself to become a driver of economic development rather than a force for social change. The mandate and strategic priorities of the provincial government is the primary driver of institutional change. The mandate holds the institution accountable by setting key performance indicators to serve the interests of the private-for-profit sector to ensure a source of skilled labour. Therefore, what gets measured gets done, and what is not mandated does not. The institution fulfills its mandate by sharing in decision-making to approve new programs and credentials to provide quality skilled workers. Academic governance, as legislated, is democratic by being inclusive of internal stakeholders, including faculty, students, and administrators, in decision-making. However, it excludes engagement and participation by external stakeholders whose interests are not being served. As agents of the crown, stakeholders have a fiduciary duty to act in the best interests of those not represented. The organizational improvement plan aims to address the problem of practice of low faculty engagement and participation in academic governance. Through their roles of teaching, research, and service, faculty are the means by which the institution will achieve the end of reconciliation with Indigenous Peoples. Keywords: fiduciary duty, reconciliation through education, stakeholders, faculty engagement and participation, shared academic governanc

From nanotechnology to nanomedicine: applications to cancer research

Scientific advances have significantly improved the practice of medicine by providing objective and quantitative means for exploring the human body and disease states. These innovative technologies have already profoundly improved disease detection, imaging, treatment and patient follow-up. Today's analytical limits are at the nanoscale level (one-billionth of a meter) enabling a detailed exploration at the level of DNA, RNA, proteins and metabolites which are in fact nano-objects. This translational review aims at integrating some recent advances from micro- and nano-technologies with high potential for improving daily oncology practice

Gene Expression Programs of Human Smooth Muscle Cells: Tissue-Specific Differentiation and Prognostic Significance in Breast Cancers

Smooth muscle is present in a wide variety of anatomical locations, such as blood vessels, various visceral organs, and hair follicles. Contraction of smooth muscle is central to functions as diverse as peristalsis, urination, respiration, and the maintenance of vascular tone. Despite the varied physiological roles of smooth muscle cells (SMCs), we possess only a limited knowledge of the heterogeneity underlying their functional and anatomic specializations. As a step toward understanding the intrinsic differences between SMCs from different anatomical locations, we used DNA microarrays to profile global gene expression patterns in 36 SMC samples from various tissues after propagation under defined conditions in cell culture. Significant variations were found between the cells isolated from blood vessels, bronchi, and visceral organs. Furthermore, pervasive differences were noted within the visceral organ subgroups that appear to reflect the distinct molecular pathways essential for organogenesis as well as those involved in organ-specific contractile and physiological properties. Finally, we sought to understand how this diversity may contribute to SMC-involving pathology. We found that a gene expression signature of the responses of vascular SMCs to serum exposure is associated with a significantly poorer prognosis in human cancers, potentially linking vascular injury response to tumor progression

Concordance among gene-expression-based predictors for breast cancer

BACKGROUND Gene-expression–profiling studies of primary breast tumors performed by different laboratories have resulted in the identification of a number of distinct prognostic profiles, or gene sets, with little overlap in terms of gene identity. METHODS To compare the predictions derived from these gene sets for individual samples, we obtained a single data set of 295 samples and applied five gene-expression–based models: intrinsic subtypes, 70-gene profile, wound response, recurrence score, and the two-gene ratio (for patients who had been treated with tamoxifen). RESULTS We found that most models had high rates of concordance in their outcome predictions for the individual samples. In particular, almost all tumors identified as having an intrinsic subtype of basal-like, HER2-positive and estrogen-receptor–negative, or luminal B (associated with a poor prognosis) were also classified as having a poor 70-gene profile, activated wound response, and high recurrence score. The 70-gene and recurrence-score models, which are beginning to be used in the clinical setting, showed 77 to 81 percent agreement in outcome classification

Global modeling of transcriptional responses in interaction networks

Motivation: Cell-biological processes are regulated through a complex network of interactions between genes and their products. The processes, their activating conditions, and the associated transcriptional responses are often unknown. Organism-wide modeling of network activation can reveal unique and shared mechanisms between physiological conditions, and potentially as yet unknown processes. We introduce a novel approach for organism-wide discovery and analysis of transcriptional responses in interaction networks. The method searches for local, connected regions in a network that exhibit coordinated transcriptional response in a subset of conditions. Known interactions between genes are used to limit the search space and to guide the analysis. Validation on a human pathway network reveals physiologically coherent responses, functional relatedness between physiological conditions, and coordinated, context-specific regulation of the genes. Availability: Implementation is freely available in R and Matlab at http://netpro.r-forge.r-project.orgComment: 19 pages, 13 figure