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Creators/Authors contains: "Yang, K"

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  1. Free, publicly-accessible full text available March 7, 2026
  2. Free, publicly-accessible full text available December 10, 2025
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  6. Snow Water Equivalent dataset contains information about snow water equivalent (SWE), temperature, precipitation, wind speed, relative humidity, and related climate variables across different locations and time periods. It includes daily observations and derived variables for hydrological and climate analysis. An additional column, source, specifies the origin of each data column. 
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  7. Ferreira_Mello, R; Rummel, N; Jivet; I; Pishtari, G; Ruipérez_Valiente, J A (Ed.)
    Teacher reflection is essential for K-12 classrooms, including effective and personalized instruction. Multimodal Learning Analytics (MMLA), integrating data from digital and physical learning environments, could support teacher reflection. Classroom data collected from sensors and TEL environments are needed to produce such analytics. These novel data collection methods pose an open challenge of how MMLA research practices can ensure alignment with teachers’ needs and concerns. This study explores K-12 teachers’ perceptions and preferences regarding MMLA analytics and data sharing. Through a mixed-method survey, we explore teachers’ (N=100) preferences for analytics that help them reflect on their teaching practices, their favored data collection modalities, and data-sharing preferences. Results indicate that teachers were most interested in student learning analytics and their interactions and ways of motivating students. However, they were also significantly less accepting of collecting students’ audio and position data compared to such data about themselves. Finally, teachers were less willing to share data about themselves than their students. Our findings contribute ethical, practical, and pedagogical considerations of MMLA analytics for teacher reflection, informing the research practices and development of MMLA within TEL. 
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  8. G protein coupled receptors (GPCRs) exhibit varying degrees of selectivity for different G protein isoforms. Despite the abundant structures of GPCR-G protein complexes, little is known about the mechanism of G protein coupling specificity. The β2-adrenergic receptor is an example of GPCR with high selectivity for Gαs, the stimulatory G protein for adenylyl cyclase, and much weaker for the Gαi family of G proteins inhibiting adenylyl cyclase. By developing a new Gαi-biased agonist (LM189), we provide structural and biophysical evidence supporting that distinct conformations at ICL2 and TM6 are required for coupling of the different G protein subtypes Gαs and Gαi. These results deepen our understanding of G protein specificity and bias and can accelerate the design of ligands that select for preferred signaling pathways. 
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