6,655 research outputs found

    Achieving Educational Equity in Minnesota\u27s K-12 Public School Districts

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    This study seeks to determine whether there are relationships between: first, public funding investment and educational equity; second, access to opportunities for students of color and disadvantaged backgrounds and educational equity; third, the interaction effect between access to opportunities for students of color and disadvantaged backgrounds and public funding investment on educational equity; fourth, teacher workforce diversity and educational equity; and fifth, the interaction effect between teacher workforce diversity and public funding investment on educational equity. The Minnesota Achievement and Integration Program is the source of public funding for this study, and the program was established in the 2013-2014 school year to accelerate racial integration and improve educational equity for students in Minnesota K-12 public school districts. A decade after the implementation of the A&I Program, despite the state’s public funding investment to create educational opportunities and increase academic achievements for students of color and disadvantaged backgrounds, concrete disparities continue to exist. The purpose of this study is to better understand existing efforts and their direct impact on educational equity. The study determines the effectiveness of public funding investment in achieving educational equity in Minnesota’s K-12 public school districts through the A&I Program and seeks to identify specific efforts that positively impact educational equity such as access to opportunities for students of color and disadvantaged backgrounds and diversifying the teacher workforce

    Drosophila Bruce Can Potently Suppress Rpr- and Grim-Dependent but Not Hid-Dependent Cell Death

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    Bruce is a large protein (530 kDa) that contains an N-terminal baculovirus IAP repeat (BIR) and a C-terminal ubiquitin conjugation domain (E2) 1, 2. BRUCE upregulation occurs in some cancers and contributes to the resistance of these cells to DNA-damaging chemotherapeutic drugs [2]. However, it is still unknown whether Bruce inhibits apoptosis directly or instead plays some other more indirect role in mediating chemoresistance, perhaps by promoting drug export, decreasing the efficacy of DNA damage-dependent cell death signaling, or by promoting DNA repair. Here, we demonstrate, using gain-of-function and deletion alleles, that Drosophila Bruce (dBruce) can potently inhibit cell death induced by the essential Drosophila cell death activators Reaper (Rpr) and Grim but not Head involution defective (Hid). The dBruce BIR domain is not sufficient for this activity, and the E2 domain is likely required. dBruce does not promote Rpr or Grim degradation directly, but its antiapoptotic actions do require that their N termini, required for interaction with DIAP1 BIR2, be intact. dBruce does not block the activity of the apical cell death caspase Dronc or the proapoptotic Bcl-2 family member Debcl/Drob-1/dBorg-1/Dbok. Together, these results argue that dBruce can regulate cell death at a novel point

    Prospects for Theranostics in Neurosurgical Imaging: Empowering Confocal Laser Endomicroscopy Diagnostics via Deep Learning

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    Confocal laser endomicroscopy (CLE) is an advanced optical fluorescence imaging technology that has the potential to increase intraoperative precision, extend resection, and tailor surgery for malignant invasive brain tumors because of its subcellular dimension resolution. Despite its promising diagnostic potential, interpreting the gray tone fluorescence images can be difficult for untrained users. In this review, we provide a detailed description of bioinformatical analysis methodology of CLE images that begins to assist the neurosurgeon and pathologist to rapidly connect on-the-fly intraoperative imaging, pathology, and surgical observation into a conclusionary system within the concept of theranostics. We present an overview and discuss deep learning models for automatic detection of the diagnostic CLE images and discuss various training regimes and ensemble modeling effect on the power of deep learning predictive models. Two major approaches reviewed in this paper include the models that can automatically classify CLE images into diagnostic/nondiagnostic, glioma/nonglioma, tumor/injury/normal categories and models that can localize histological features on the CLE images using weakly supervised methods. We also briefly review advances in the deep learning approaches used for CLE image analysis in other organs. Significant advances in speed and precision of automated diagnostic frame selection would augment the diagnostic potential of CLE, improve operative workflow and integration into brain tumor surgery. Such technology and bioinformatics analytics lend themselves to improved precision, personalization, and theranostics in brain tumor treatment.Comment: See the final version published in Frontiers in Oncology here: https://www.frontiersin.org/articles/10.3389/fonc.2018.00240/ful
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