Connecting inside out: Development of the social brain in infants and toddlers with a focus on myelination as a marker of brain maturation     

Nora Schneider  Elizabeth Greenstreet  Sean C. L. Deoni 《Child development》2022,93(2):359-371

Early childhood is a sensitive period for learning and social skill development. The maturation of cerebral regions underlying social processing lays the foundation for later social-emotional competence. This study explored myelin changes in social brain regions and their association with changes in parent-rated social-emotional development in a cohort of 129 children (64 females, 0–36 months, 77 White). Results reveal a steep increase in myelination throughout the social brain in the first 3 years of life that is significantly associated with social-emotional development scores. These findings add knowledge to the emerging picture of social brain development by describing neural underpinnings of human social behavior. They can contribute to identifying age-/stage-appropriate early life factors in this developmental domain.  相似文献   

Modeling the Pathways to Self-Confidence for Graduate School in Computing     

Wofford  Annie M. 《Research in higher education》2021,62(3):359-391

Research in Higher Education - Given the significant need to increase and diversify graduate enrollments within computing fields, it is vital to understand what shapes students’ pathways to...  相似文献   

Does modality matter? A latent profile and transition analysis of sedentary behaviours among school-aged youth: The UP&DOWN study     

David Sánchez-Oliva  Rebecca M. Leech  Alberto Grao-Cruces  Irene Esteban-Cornejo  Carmen Padilla-Moledo  Oscar L. Veiga 《Journal of sports sciences》2020,38(9):1062-1069

ABSTRACT

The aims of this study were to identify profiles of sedentary behaviour (SB) patterns, based on leisure-time self-reported SB modalities (screen, educative, social, and relaxing) and to evaluate changes in these profiles over 2 years among Spanish youth aged 8–18 years. Latent profile analysis (LPA), a data-driven analytic approach, was used to identify groups of boys and girls (n = 1553; 48% girls; mean±SD age: 12.56 ± 2.49 y) with distinct SB profiles using the SB modalities (time/d) as input variables. Latent transition analysis, an extension of LPA that uses longitudinal data, was used to analyse 2-year changes in these profiles. At baseline, four and three SB profiles were found among boys (labelled: screen, educative, social, and relaxing) and girls (labelled: screen/social, educative, and relaxing), respectively. Overall, more girls (range: 48%-67%) had the same profile over time, than boys (40%-52%). Participants with a screen or relaxing SB profile at baseline were more likely to have an educative profile after 2 years. Youth with a social and an educative SB profile at baseline were more likely to transition to profiles characterized by higher screen and social SB, respectively. Using a novel and person-centered approach, this study identified gender-specific SB profiles that were moderately stable over time.  相似文献   

‘Just stick to the science’ is not enough     

Peterson  Erik L. 《Metascience》2019,28(1):147-150

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重大体育赛事对城市经济发展的影响--基于中国70个大中城市面板数据分析     

毛丰付  郑芳  朱书琦 《上海体育学院学报》2020,(5):24-36

重大体育赛事的举办在一定程度上能推动承办城市转型发展,但也可能对当地经济产生某种不良影响,这主要源于对重大体育赛事与城市经济发展的内在规律和作用机制认识不够清晰。以2000-2017年22项具有全国影响力的重大体育赛事为基础,利用中国70个大中城市面板数据,采用双重差分法,分析重大体育赛事对城市经济发展的影响。发现:全样本数据下重大体育赛事的举办会促进城市第一产业向第二、第三产业发展;对比国内和国际赛事,重大体育赛事对第三产业的促进作用均显著高于第二产业;以1 085家上市公司数据为样本,采用事件研究法进行分析,发现不同行业利用重大体育赛事的窗口期存在显著差异。  相似文献   

The Evolution of U.S. Museums and Science Centers as Informal Learning Environments     

Renee M. Clary 《Science & Education》2018,27(3-4):371-377

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基于二模复杂网络的隐性知识发现方法研究——以潜在药物靶点挖掘为例     

李东巧  陈芳  韩涛  杨艳萍  王学昭  王燕鹏  Cynthia Liu  Yingzhu Li 《图书情报工作》2020,64(21):120-129

[目的/意义] 通过构建二模复杂网络模型，揭示隐藏在海量文献中的隐性知识。[方法/过程] 通过NetworkX复杂网络工具包，依据任意两个节点的共现关系构建二模复杂网络模型；对网络模型中节点的共现关系进行加权，计算网络的拓扑信息并进行AP聚类，提取节点间的直接关系；采用AUC方法对AA、JC、加权改进的wAA和wJC等4种链路预测算法进行评价，遴选出最合适的预测算法，并对复杂网络的隐性关系进行预测分析。[结果/结论] 以潜在药物靶点挖掘为例进行的实证研究结果表明，wAA链路预测算法为最优的链路预测算法；二模复杂网络模型、指标和方法体系在美国化学文摘社数据库中的药物靶点挖掘中具有一定的有效性。下一步计划在其他数据库中或其他研究领域中进行尝试，以进一步验证该模型的通用性和有效性。  相似文献   

Deep teaching in a college STEM classroom     

Dewsbury  Bryan M. 《Cultural Studies of Science Education》2020,15(1):169-191

Cultural Studies of Science Education - The retention of underrepresented students remains a significant challenge in the STEM (Science, Technology, Engineering and Math) disciplines. A broad range...  相似文献   

Nurturing leadership: equitable mentoring for the superintendency     

Ava J. Muñoz  Anita M. Pankake  Shirley Mills  Marie Simonsson 《International Journal of Leadership in Education》2018,21(3):284-292

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Digital Module 16: Longitudinal Data Analysis     

Jeffrey R. Harring  Tessa L. Johnson 《Educational Measurement》2020,39(3):137-138

In this digital ITEMS module, Dr. Jeffrey Harring and Ms. Tessa Johnson introduce the linear mixed effects (LME) model as a flexible general framework for simultaneously modeling continuous repeated measures data with a scientifically defensible function that adequately summarizes both individual change as well as the average response. The module begins with a nontechnical overview of longitudinal data analyses drawing distinctions with cross-sectional analyses in terms of research questions to be addressed. Nuances of longitudinal designs, timing of measurements, and the real possibility of missing data are then discussed. The three interconnected components of the LME model—(1) a model for individual and mean response profiles, (2) a model to characterize the covariation among the time-specific residuals, and (3) a set of models that summarize the extent that individual coefficients vary—are discussed in the context of the set of activities comprising an analysis. Finally, they demonstrate how to estimate the linear mixed effects model within an open-source environment (R). The digital module contains sample R code, diagnostic quiz questions, hands-on activities in R, curated resources, and a glossary.  相似文献