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ABSTRACT The evolution of oxygenic photosynthesis in the Cyanobacteria was one of the most transformative events in Earth history, eventually leading to the oxygenation of Earth’s atmosphere. However, it is difficult to understand how the earliest Cyanobacteria functioned or evolved on early Earth in part because we do not understand their ecology, including the environments in which they lived. Here, we use a cutting-edge bioinformatics tool to survey nearly 500,000 metagenomes for relatives of the taxa that likely bookended the evolution of oxygenic photosynthesis to identify the modern environments in which these organisms live. Ancestral state reconstruction suggests that the common ancestors of these organisms lived in terrestrial (soil and/or freshwater) environments. This restricted distribution may have increased the lag between the evolution of oxygenic photosynthesis and the oxygenation of Earth’s atmosphere.IMPORTANCECyanobacteria generate oxygen as part of their metabolism and are responsible for the rise of oxygen in Earth’s atmosphere over two billion years ago. However, we do not know how long this process may have taken. To help constrain how long this process would have taken, it is necessary to understand where the earliest Cyanobacteria may have lived. Here, we use a cutting-edge bioinformatics tool called branch water to examine the environments where modern Cyanobacteria and their relatives live to constrain those inhabited by the earliest Cyanobacteria. We find that these species likely lived in non-marine environments. This indicates that the rise of oxygen may have taken longer than previously believed. 

This paper shares a synthesis of the literature related to the application of a relationships-first approach to high-dosage math tutoring. In the context of our research, high-dosage tutoring is delivered multiple times per week during the school day by paraprofessionals who work with students in historically under-resourced schools. We apply a critical perspective to frame the importance of attending to interpersonal relationships during tutoring. We then explain the core ideas of small group interactions, dialogue, relational interactions, care and belonging and provide a synthesis of these constructs. The literature synthesis presented is intended to be applied to research-based efforts aimed at supporting tutors working to increase their skills for cultivating strong interpersonal relationships and enacting equity oriented pedagogy. 

This paper shares a synthesis of the literature related to the application of a relationships-first approach to high-dosage math tutoring. In the context of our research, high-dosage tutoring is delivered multiple times per week during the school day by paraprofessionals who work with students in historically under-resourced schools. We apply a critical perspective to frame the importance of attending to interpersonal relationships during tutoring. We then explain the core ideas of small group interactions, dialogue, relational interactions, care and belonging and provide a synthesis of these constructs. The literature synthesis presented is intended to be applied to research-based efforts aimed at supporting tutors working to increase their skills for cultivating strong interpersonal relationships and enacting equity oriented pedagogy. 

Human tutoring interventions play a crucial role in supporting student learning, improving academic performance, and promoting personal growth. This paper focuses on analyzing mathematics tutoring discourse using talk moves—a framework of dialogue acts grounded in Accountable Talk theory. However, scaling the collection, annotation, and analysis of extensive tutoring dialogues to develop machine learning models is a challenging and resource-intensive task. To address this, we present SAGA22, a compact dataset, and explore various modeling strategies, including dialogue context, speaker information, pretraining datasets, and further fine-tuning. By leveraging existing datasets and models designed for classroom teaching, our results demonstrate that supplementary pretraining on classroom data enhances model performance in tutoring settings, particularly when incorporating longer context and speaker information. Additionally, we conduct extensive ablation studies to underscore the challenges in talk move modeling. 

Human tutoring interventions play a crucial role in supporting student learning, improving academic performance, and promoting personal growth. This paper focuses on analyzing mathematics tutoring discourse using talk moves—a framework of dialogue acts grounded in Accountable Talk theory. However, scaling the collection, annotation, and analysis of extensive tutoring dialogues to develop machine learning models is a challenging and resource-intensive task. To address this, we present SAGA22, a compact dataset, and explore various modeling strategies, including dialogue context, speaker information, pretraining datasets, and further fine-tuning. By leveraging existing datasets and models designed for classroom teaching, our results demonstrate that supplementary pretraining on classroom data enhances model performance in tutoring settings, particularly when incorporating longer context and speaker information. Additionally, we conduct extensive ablation studies to underscore the challenges in talk move modeling. 

TalkMoves is an AI assistive tool that provides automated feedback to educators to support their daily teaching practices. While originally designed for classroom math teachers, this tool can be useful in a broader context. The University of Colorado Boulder and Saga Education formed a co-design team tasked with re-contextualizing TalkMoves for coaches of novice math tutors to use in their ongoing professional development. To effectively adapt an existing technology to a new problem space, the co-design team iteratively exchanged ideas of what exactly TalkMoves could achieve, as well as the specific needs of the coaches. Facilitators used strategies such as communal orientation, expansive dreaming, backcasting, and revoicing to promote productive collaboration. Three main goals emerged: maximize opportunities for user agency, center design around goal setting, and integrate the tool into the existing workflow. Any adaptation of an AI tool would benefit from this approach. 

TalkMoves is an AI assistive tool that provides automated feedback to educators to support their daily teaching practices. While originally designed for classroom math teachers, this tool can be useful in a broader context. The University of Colorado Boulder and Saga Education formed a co-design team tasked with re-contextualizing TalkMoves for coaches of novice math tutors to use in their ongoing professional development. To effectively adapt an existing technology to a new problem space, the co-design team iteratively exchanged ideas of what exactly TalkMoves could achieve, as well as the specific needs of the coaches. Facilitators used strategies such as communal orientation, expansive dreaming, backcasting, and revoicing to promote productive collaboration. Three main goals emerged: maximize opportunities for user agency, center design around goal setting, and integrate the tool into the existing workflow. Any adaptation of an AI tool would benefit from this approach. 

Abstract The symbiosis between corals and dinoflagellates of the family Symbiodiniaceae is sensitive to environmental stress. The oxidative bleaching hypothesis posits that extreme temperatures lead to accumulation of photobiont-derived reactive oxygen species ROS, which exacerbates the coral environmental stress response (ESR). To understand how photosymbiosis modulates coral ESRs, these responses must be explored in hosts in and out of symbiosis. We leveraged the facultatively symbiotic coralAstrangia poculata, which offers an opportunity to uncouple the ESR across its two symbiotic phenotypes (brown, white). Colonies of both symbiotic phenotypes were exposed to three temperature treatments for 15 days: (i) control (static 18 °C), (ii) heat challenge (increasing from 18 to 30 °C), and (iii) cold challenge (decreasing from 18 to 4 °C) after which host gene expression was profiled. Cold challenged corals elicited widespread differential expression, however, there were no differences between symbiotic phenotypes. In contrast, brown colonies exhibited greater gene expression plasticity under heat challenge, including enrichment of cell cycle pathways involved in controlling photobiont growth. While this plasticity was greater, the genes driving this plasticity were not associated with an amplified environmental stress response (ESR) and instead showed patterns of a dampened ESR under heat challenge. This provides nuance to the oxidative bleaching hypothesis and suggests that, at least during the early onset of bleaching, photobionts reduce the host’s ESR under elevated temperatures inA. poculata.