The Social Justice Teaching Collaborative: A Collective Turn Towards Critical Teacher Education

In this article, we share the collaborative curricular work of an interdisciplinary Social Justice Teaching Collaborative (SJTC) from a PWI university. Members of the SJTC worked strategically to center social justice across required courses pre-service teachers are required to take: Introduction to Education, Sociocultural Studies in Education, and Inclusive Education. We share our conceptualization of social justice and guiding theoretical frameworks that have shaped our pedagogy and curriculum. These frameworks include democratic education, critical pedagogy, critical race theory, critical whiteness studies, critical disability studies, and feminist and intersectionality theory. We then detail changes made across courses including examples of readings and assignments. Finally, we conclude by offering reflections, challenges, and lessons learned for collaborative work within teacher education and educational leadership.&nbsp

Critical assessment of automated flow cytometry data analysis techniques

Traditional methods for flow cytometry (FCM) data processing rely on subjective manual gating. Recently, several groups have developed computational methods for identifying cell populations in multidimensional FCM data. The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of these methods on two tasks: (i) mammalian cell population identification, to determine whether automated algorithms can reproduce expert manual gating and (ii) sample classification, to determine whether analysis pipelines can identify characteristics that correlate with external variables (such as clinical outcome). This analysis presents the results of the first FlowCAP challenges. Several methods performed well as compared to manual gating or external variables using statistical performance measures, which suggests that automated methods have reached a sufficient level of maturity and accuracy for reliable use in FCM data analysis.