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The Ordway-Swisher Biological Station (OSBS) is a 38-km2 reserve owned by the University of Florida and is part of the National Ecological Observatory Network (NEON). The reserve contains several iconic Florida habitats, such as sandhill, mesic hammock, and scrubby flatwoods. While plants and animals have been extensively studied at OSBS, the fungi remain poorly known. Fungal inventories are critical to increase knowledge of both fungal diversity and species ranges, and thus to provide foundational data for a wide array of applications in ecology and resource management. Here, we present the results of a nine-year effort to collect, preserve, and DNA barcode the macrofungi at OSBS. This effort generated >1200 vouchered specimens and 984 ITS rDNA sequences, representing more than 546 species. Our sampling was dominated by Basidiomycota and revealed a high diversity of symbiotic ectomycorrhizal fungi, particularly species of Amanita, Cortinarius, and Russula. Sampling curves and both Chao1 and Jacknife1 richness estimators suggest that our DNA barcoding efforts captured only about half of the macrofungi species and that a more complete inventory would detect 897–1177 macrofungi species at OSBS. Our sampling found more species of macrofungi at OSBS than the known number of vertebrate animal species at the reserve and our estimates also suggest that there are likely more macrofungi species than plant species at OSBS. This study is the first comprehensive macrofungi inventory within a NEON site and highlights the importance of long-term monitoring to provide novel data on fungal diversity, community structure, conservation, biogeography, and taxonomy. 

Abstract Macroautophagy involves the sequestration of cytoplasmic contents in a double-membrane autophagosome and its subsequent delivery to lysosomes for degradation and recycling. In Caenorhabditis elegans, autophagy participates in diverse processes such as stress resistance, cell fate specification, tissue remodeling, aging, and adaptive immunity. Genetic screens in C. elegans have identified a set of metazoan-specific autophagy genes that form the basis for our molecular understanding of steps unique to the autophagy pathway in multicellular organisms. Suppressor screens have uncovered multiple mechanisms that modulate autophagy activity under physiological conditions. C. elegans also provides a model to investigate how autophagy activity is coordinately controlled at an organismal level. In this chapter, we will discuss the molecular machinery, regulation, and physiological functions of autophagy, and also methods utilized for monitoring autophagy during C. elegans development. 

Phylogenetic trees establish a historical context for the study of organismal form and function. Most phylogenetic trees are estimated using a model of evolution. For molecular data, modeling evolution is often based on biochemical observations about changes between character states. For example, there are four nucleotides, and we can make assumptions about the probability of transitions between them. By contrast, for morphological characters, we may not know \textit{a priori} how many characters states there are per character, as both extant sampling and the fossil record may be highly incomplete, which leads to an observer bias. For a given character, the state space may be larger than what has been observed in the sample of taxa collected by the researcher. In this case, how many evolutionary rates are needed to even describe transitions between morphological character states may not be clear, potentially leading to model misspecification. To explore the impact of this model misspecification, we simulated character data with varying numbers of character states per character. We then used the data to estimate phylogenetic trees using models of evolution with the correct number of character states and an incorrect number of character states. The results of this study indicate that this observer bias may lead to phylogenetic error, particularly in the branch lengths of trees. If the state space is wrongly assumed to be too large, then we underestimate the branch lengths, and the opposite occurs when the state space is wrongly assumed to be too small. 

Weather-related power disruptions present significant challenges to public infrastructure, societal well-being, and the distribution grid. Predicting outage durations in distribution grids is another challenge compared to transmission line outage durations due to distribution networks’ complexity and finer granularity. While forecasting forced power outages is crucial, accurately estimating their duration is essential for timely response and mitigation measures. This study introduces the Spatiotemporal Multiplex Network (SMN-WVF), a methodology designed to predict power outage durations across varying lead times, tackling the difficulties posed by small, high-complexity spaces within distribution grids. SMN-WVF employs multiplex networks that incorporate multi-modal data across both time and space, including layers such as power outages, weather conditions, weather forecasts, vegetation, and distances between substations. We demonstrate the importance of incorporating additional layers of data sources as they are shown to help the model’s predictions through gradual improvement in the macro F1 score performance. 

Exploratory learning environments (ELEs), such as simulation-based platforms and open-ended science curricula, promote hands-on exploration and problem-solving but make it difficult for teachers to gain timely insights into students' conceptual understanding. This paper presents LearnLens, a generative AI (GenAI)-enhanced teacher-facing dashboard designed to support problem-based instruction in middle school science. LearnLens processes students' open-ended responses from digital assessments to provide various insights, including sample responses, word clouds, bar charts, and AI-generated summaries. These features elucidate students' thinking, enabling teachers to adjust their instruction based on emerging patterns of understanding. The dashboard was informed by teacher input during professional development sessions and implemented within a middle school Earth science curriculum. We report insights from teacher interviews that highlight the dashboard's usability and potential to guide teachers' instruction in the classroom. 

The interpretation of the energy-momentum tensor form factor D(t) of hadrons in terms of pressure and shear force distributions is discussed, concerns raised in the literature are reviewed, and ways to reconcile the concerns with the interpretation are indicated. 

Despite the importance of reasoning and proving in mathematics and mathematics education, little is known about how future teachers become proficient in integrating reasoning and proving in their teaching practices. In this article, we characterize this aspect of prospective secondary mathematics teachers’ (PSTs’) professional learning by drawing upon the commognitive theory. We offer a triple-layer conceptualization of (student)learning,teaching, andlearning to teachmathematics via reasoning and proving by focusing on the discourses students participate in (learning), the opportunities for reasoning and proving afforded to them (teaching), and how PSTs design and enrich such opportunities (learning to teach). We explore PSTs’ pedagogical discourse anchored in the lesson plans they designed, enacted, and modified as part of their participation in a university-based course:Mathematical Reasoning and Proving for Secondary Teachers. We identified four types of discursive modifications: structural, mathematical, reasoning-based, and logic-based. We describe how the potential opportunities for reasoning and proving afforded to students by these lesson plans changed as a result of these modifications. Based on our triple-layered conceptualization we illustrate how the lesson modifications and the resulting alterations to student learning opportunities can be used to characterize PSTs’ professional learning. We discuss the affordances of theorizing teacher practices with the same theoretical lens (grounded in commognition) to inquire student learning and teacher learning, and how lesson plans, as a proxy of teaching practices, can be used as a methodological tool to better understand PSTs’ professional learning. 

Middle school students learned about astronomy and STEM concepts while exploring Minecraft simulations of hypothetical Earths and exoplanets. Small groups (n = 24) were tasked with building feasible habitats on Mars. In this paper, we present a scoring scheme for habitat assessment that was used to build novel multi/mixed-input AI models. Using Spearman’s rank correlations, we found that our scoring scheme was reliable with regards to team size and face-to-face instruction time and validated with self-explanation scores. We took an exploratory approach to analyzing image and block data to compare seven different input conditions. Using one-way ANOVAs, we found that the means of the conditions were not equal for accuracy, precision, recall, and F1 metrics. A post hoc Tukey HSD test found that models built using images only were statistically significantly worse than conditions that used block data on the metrics. We also report the results of optimized models using block only data on additional Mars bases (n = 57). 

This work investigates how tutoring discourse interacts with students’ proximal knowledge to explain and predict students’ learning outcomes. Our work is conducted in the context of high-dosage human tutoring where 9th-grade students attended small group tutorials and individually practiced problems on an Intelligent Tutoring System (ITS). We analyzed whether tutors’ talk moves and students’ performance on the ITS predicted scores on math learning assessments. We trained Random Forest Classifiers (RFCs) to distinguish high and low assessment scores based on tutor talk moves, student’s ITS performance metrics, and their combination. A decision tree was extracted from each RFC to yield an interpretable model. We found AUCs of 0.63 for talk moves, 0.66 for ITS, and 0.77 for their combination, suggesting interactivity among the two feature sources. Specifically, the best decision tree emerged from combining the tutor talk moves that encouraged rigorous thinking and students’ ITS mastery. In essence, tutor talk that encouraged mathematical reasoning predicted achievement for students who demonstrated high mastery on the ITS, whereas tutors’ revoicing of students’ mathematical ideas and contributions was predictive for students with low ITS mastery. Implications for practice are discussed. 

Data management plans (DMPs) are required from researchers seeking funding from federal agencies in the United States. Ideally, DMPs disclose how research outputs will be managed and shared. How well DMPs communicate those plans is less understood. Evaluation tools such as the DART rubric and the Belmont scorecard assess the completeness of DMPs and offer one view into what DMPs communicate. This paper compares the evaluation criteria of the two tools by applying them to the same corpus of 150 DMPs from five different NSF programs. Findings suggest that the DART rubric and the Belmont score overlap significantly, but the Belmont scorecard provides a better method to assess completeness. We find that most DMPs fail to address many of the best practices that are articulated by librarians and information professionals in the different evaluation tools. However, the evaluation methodology of both tools relies on a rating scale that does not account for the interaction of key areas of data management. This work contributes to the improvement of evaluation tools for data management planning.