1,138 research outputs found

    Thoughts of Leaving: An Exploration of Why New York City Middle School Teachers Consider Leaving Their Classrooms

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    This report explores the conditions under which middle-school teachers in New York City leave their schools, and the consequences of this turnover. The focus on middle schools stems from the widely-held view that the middle grades are a critical turning point in the lives of children, and that many New York City school children lose academic momentum in these grades, setting them on trajectories of failure as they move towards high school and life beyond it. This report is based on a survey of more than 4,000 full-time middle school teachers working in 125 of the nearly 200 middle schools in New York City serving children in grades six through eight in the 2009-10 school year. The participating teachers reported whether they had considered leaving their current school or leaving teaching during that school year, and the reasons that they considered leaving. The report links their responses to teachers' reports about their own backgrounds and experiences, to the demographic characteristics of the schools in which they teach, and to the collective perceptions of all of the teachers in a school about that school as a workplace. This report is part of a three-year, mixed-methods study of teacher turnover in New York City middle schools

    Microarray analyses demonstrate the involvement of type i interferons in psoriasiform pathology development in D6-deficient mice

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    The inflammatory response is normally limited by mechanisms regulating its resolution. In the absence of resolution, inflammatory pathologies can emerge, resulting in substantial morbidity and mortality. We have been studying the D6 chemokine scavenging receptor, which played an indispensable role in the resolution phase of inflammatory responses and does so by facilitating removal of inflammatory CC chemokines. In D6-deficient mice, otherwise innocuous cutaneous inflammatory stimuli induce a grossly exaggerated inflammatory response that bears many similarities to human psoriasis. In the present study, we have used transcriptomic approaches to define the molecular make up of this response. The data presented highlight potential roles for a number of cytokines in initiating and maintaining the psoriasis-like pathology. Most compellingly, we provide data indicating a key role for the type I interferon pathway in the emergence of this pathology. Neutralizing antibodies to type I interferons are able to ameliorate the psoriasis-like pathology, confirming a role in its development. Comparison of transcriptional data generated from this mouse model with equivalent data obtained from human psoriasis further demonstrates the strong similarities between the experimental and clinical systems. As such, the transcriptional data obtained in this preclinical model provide insights into the cytokine network active in exaggerated inflammatory responses and offer an excellent tool to evaluate the efficacy of compounds designed to therapeutically interfere with inflammatory processes

    Towards Cross-Provider Analysis of Transparency Information for Data Protection

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    Transparency and accountability are indispensable principles for modern data protection, from both, legal and technical viewpoints. Regulations such as the GDPR, therefore, require specific transparency information to be provided including, e.g., purpose specifications, storage periods, or legal bases for personal data processing. However, it has repeatedly been shown that all too often, this information is practically hidden in legalese privacy policies, hindering data subjects from exercising their rights. This paper presents a novel approach to enable large-scale transparency information analysis across service providers, leveraging machine-readable formats and graph data science methods. More specifically, we propose a general approach for building a transparency analysis platform (TAP) that is used to identify data transfers empirically, provide evidence-based analyses of sharing clusters of more than 70 real-world data controllers, or even to simulate network dynamics using synthetic transparency information for large-scale data-sharing scenarios. We provide the general approach for advanced transparency information analysis, an open source architecture and implementation in the form of a queryable analysis platform, and versatile analysis examples. These contributions pave the way for more transparent data processing for data subjects, and evidence-based enforcement processes for data protection authorities. Future work can build upon our contributions to gain more insights into so-far hidden data-sharing practices.Comment: technical repor
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