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Purpose: Culturally and linguistically diverse students frequently do not receive equitable educational opportunities. Schools across public and private sectors that are striving to ameliorate this problem typically work in isolation, not collaboratively. This article examines how communities of practice emerge within a network of schools striving to effectively educate these students. Data Collection and Analysis: We employ qualitative case study methodology drawing data from relational network survey data, archival documents, interpersonal communications, and field notes. In cycles of coding, we analyze these data to identify the learning architecture that structures the communities of practice within this network. Findings: First, we found the emergent communities of practice were extemporaneous, tentative, and localized. Second, we found that applying the learning architecture shed light on the nature of the learning within these communities of practice. In this case, it revealed that the communities of practice (a) enjoyed relatively strong levels of participation but relatively ineffective reifications, (b) experienced a dynamic tension between local and global influences, and (c) allowed individuals to negotiate new identities in varied and complex manners. Conclusions: This study shows the messiness that unfolds as school leaders pursue organizational learning toward the end of improving educational opportunities for culturally and linguistically diverse students, and how the learning architecture framework can help school leaders make sense of and respond to this in a nuanced manner.

A large volume of security events, generally collected by distributed monitoring sensors, overwhelms human analysts at security operations centers and raises an alert fatigue problem. Machine learning is expected to mitigate this problem by automatically distinguishing between true alerts, or attacks, and falsely reported ones. Machine learning models should first be trained on datasets having correct labels, but the labeling process itself requires considerable human resources. In this paper, we present a new selective sampling scheme for efficient data labeling via unsupervised clustering. The new scheme transforms the byte sequence of an event into a fixed-size vector through content-defined chunking and feature hashing. Then, a clustering algorithm is applied to the vectors, and only a few samples from each cluster are selected for manual labeling. The experimental results demonstrate that the new scheme can select only 2% of the data for labeling without degrading the F1-score of the machine learning model. Two datasets, a private dataset from a real security operations center and a public dataset from the Internet for experimental reproducibility, are used. [ABSTRACT FROM AUTHOR]

Interfacial structure is critical for optimizing the electrical properties of organic field‐effect transistors. In this study, the interfacial structures of 6,13‐bis(triisopropylsilylethynyl)pentacene (TIPS‐pentacene)/polymer blends are nondestructively determined by the complementary neutron and X‐ray reflectivity. The TIPS‐pentacene/deuterated poly(methylmethacrylate) (d‐PMMA) blends exhibit a vertically phase‐separated structure with a molecularly sharp interface (interfacial roughness ≈5 Å), whereas the TIPS‐pentacene/d‐polystyrene (d‐PS) blend intermix near the interface. Ultrahigh molecular weight d‐PMMA leads to the formation of surface‐segregated hexagonal spherulites of TIPS‐pentacene owing to the thermodynamic factors (e.g., surface/interface energy, polarity, and viscosity) of the blending materials. The well‐developed hexagonal spherulites of TIPS‐pentacene on molecularly sharp d‐PMMA interface result in higher field‐effect mobility as compared to the dendritic crystals from d‐PS blends because of the higher perfectness, coverage, and interfacial roughness of the TIPS‐pentacene crystals. The approach used in this study facilitates the understanding of the charge transport mechanism at the phase‐separated interfaces in soluble acene/polymer blends. [ABSTRACT FROM AUTHOR]

Anti-CD19 chimeric antigen receptor (CAR)-T cells have improved the outcomes of patients with B cell leukemia and lymphoma. However, their applications and positive outcomes remain limited. CAR-T cells are currently restricted to autologous blood as their source and their use can lead to downregulation of CD19 expression along with complications such as graft-versus-host disease and cytokine release syndrome. The present study aimed to develop anti-CD19/CD22 bispecific CAR structures using an anti-CD22 monoclonal antibody clone from chickens and analyze them in natural killer (NK)-92 cells, a human NK cell line, in vitro and in vivo. Anti-CD19/CD22 CAR-NK-92 cell cytotoxicity was assessed by the survival of target cells and counted using flow cytometry. Anti-CD22/CD19 and loop-structured anti-CD19/CD22 bi-specific CAR-NK-92 cells showed improved efficacy against OCI-Ly7 cells, a human B cell lymphoma cell line, compared with other CAR structures. These results demonstrate the potential of anti-CD19/CD22 bispecific CAR-NK cells and suggested that optimizing CAR structures in NK cells can improve the efficacy of CAR therapy. [ABSTRACT FROM AUTHOR]

Purpose: Culturally and linguistically diverse students frequently do not receive equitable educational opportunities. Schools across public and private sectors that are striving to ameliorate this problem typically work in isolation, not collaboratively. This article examines how communities of practice emerge within a network of schools striving to effectively educate these students. Data Collection and Analysis: We employ qualitative case study methodology drawing data from relational network survey data, archival documents, interpersonal communications, and field notes. In cycles of coding, we analyze these data to identify the learning architecture that structures the communities of practice within this network. Findings: First, we found the emergent communities of practice were extemporaneous, tentative, and localized. Second, we found that applying the learning architecture shed light on the nature of the learning within these communities of practice. In this case, it revealed that the communities of practice (a) enjoyed relatively strong levels of participation but relatively ineffective reifications, (b) experienced a dynamic tension between local and global influences, and (c) allowed individuals to negotiate new identities in varied and complex manners. Conclusions: This study shows the messiness that unfolds as school leaders pursue organizational learning toward the end of improving educational opportunities for culturally and linguistically diverse students, and how the learning architecture framework can help school leaders make sense of and respond to this in a nuanced manner. [ABSTRACT FROM AUTHOR]