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What instructors say during class—beyond content—has promise for supporting students’ perceptions that they are supported, connected, and valued in the classroom, which in turn predict positive outcomes in science, technology, engineering, and mathematics (STEM) education. We studied noncontent Instructor Talk used by 56 introductory biology instructors around the United States and how it related to sense of belonging among over 4900 students in their courses. Using hierarchical linear modeling, we identified positive relationships between some—but not all—categories of Instructor Talk and belonging among students. Instructor talk aimed at building relationships with students and explaining pedagogical choices had positive relationships with students’ sense of connectedness to peers (for both) and comfort seeking instructor help (for the former), but not their comfort sharing ideas with the class. Using effect coding, we probed whether these relationships differed for students with 14 intersectional identities, including men and women from seven racial and ethnic groups. Relationships between Instructor Talk categories and components of belonging varied in their direction and magnitude for students with different intersectional identities. Findings demonstrate that even simple instructor actions—particularly language—may be meaningful to students, but we cannot assume that all students experience these actions the same way. 

ABSTRACT Active learning is a phrase that lacks clear definition, which has hampered researchers’ efforts to investigate the nuances of effectiveness and instructors’ efforts to capitalize on potential benefits for students. One way to advance our understanding of “active learning” is assessing by the type of intellectual work that in-class activities require of students. We systematically analyzed in-class work opportunities created for students in 55 introductory biology courses around the United States, each of which used active learning. We did so by adapting an observation approach grounded in the ICAP framework and analyzing classroom videos in 15-s segments. Instructors devoted about a quarter of class time to student work time, on average, but this varied widely. About half of these student work opportunities focused on recall, and half required students to generate answers beyond what had been presented to them, which can foster deeper learning and better transfer than recall alone. The ratio of these levels of intellectual work varied considerably across courses. We also tested whether course- and instructor-level factors predicted the amount and level of active-learning opportunities and found no significant relationships. This work provides a striated definition of active learning that will be useful to researchers studying active-learning outcomes and instructors aiming to harness learning benefits for their students. 

Large volcanic eruptions and intense wildfires perturb Earth’s atmospheric temperature. Understanding the climate response to such natural forcings is essential for obtaining reliable estimates of its response to anthropogenic greenhouse gas emissions. While the climate impacts of volcanic sulfate aerosols are well documented, other natural forcings—including wildfire smoke reaching the stratosphere and water vapor injections from a submarine eruption—pose new challenges for detecting and attributing their atmospheric temperature impacts. Here, we demonstrate robust detection of statistically significant temperature anomalies in the troposphere and stratosphere using multidecadal satellite observations and internal variability estimates from a climate model ensemble and from observations. We analyze three landmark events: the 1991 Pinatubo eruption, the 2019-2020 Australian wildfires, and the 2022 Hunga Tonga eruption. Each leaves a fingerprint with distinct altitudinal, geographical, and temporal structure. The global-mean stratospheric signal from Australian wildfires is detectable even in time averages extending beyond 10 mo, despite injecting only ~5% of Pinatubo’s aerosol mass. For Hunga Tonga, we detect significant and prolonged stratospheric cooling, but no robust tropospheric signal in the first 2 y. These findings show that both sulfate and nonsulfate stratospheric perturbations produce distinct, statistically identifiable global temperature signals. Accounting for such forcings in climate model simulations is therefore essential for improving comparisons of simulated and observed variability. 

Although an increasing number of long-read genome assemblies have been created from a diverse collection of dogs and wolves, most published assemblies represent the diploid genome as a single primary sequence. Here, we generate and analyze phase-resolved diploid dual assemblies from five canines. The most contiguous assemblies represent over half of the canine chromosomes as single contigs, permitting an assessment of the sequence and structure of canine chromosomes. Consistent with a telocentric classification, we find that the centromeres of canine autosomes begin an average of 59 kb from the start of the chromosome and are flanked by a 35 kb subtelomeric segment that is repeat-rich and shared across autosomes. Analysis of a pangenome graph constructed from the 10 haplotype-resolved assemblies shows that short tandem repeat loci are three times more common than variable number tandem repeat loci and that the landscape of canine structural variation features extensive allelic heterogeneity. The pangenome graph includes examples of complex, nested allelic variation involving SINEC (a carnivore-specific SINE) and LINE-1 mobile elements. Analysis of 3′ transductions implicate an uncharacterized source element with high activity and demonstrates the presence of full-length LINE-1s capable of retrotransposition that are segregating among canines. 

Trends in infectious disease incidence provide important information about epidemic dynamics and prospects for control. Higher-frequency variation around incidence trends can shed light on the processes driving epidemics in complex populations, as transmission heterogeneity, shifting landscapes of susceptibility, and fluctuations in reporting can impact the volatility of observed case counts. However, measures of temporal volatility in incidence, and how volatility changes over time, are often overlooked in population-level analyses of incidence data, which typically focus on moving averages. Here we present a statistical framework to quantify temporal changes in incidence dispersion and to detect rapid shifts in the dispersion parameter, which may signal new epidemic phases. We apply the method to COVID-19 incidence data in 144 United States (US) counties from January 1st, 2020 to March 23rd, 2023. Theory predicts that dispersion should be inversely proportional to incidence, however our method reveals pronounced temporal trends in dispersion that are not explained by incidence alone, but which are replicated across counties. In particular, dispersion increased around the major surge in cases in 2022, and highly overdispersed patterns became more frequent later in the time series. These increases potentially indicate transmission heterogeneity, changes in the susceptibility landscape, or that there were changes in reporting. Shifts in dispersion can also indicate shifts in epidemic phase, so our method provides a way for public health officials to anticipate and manage changes in epidemic regime and the drivers of transmission. 

Road salt inputs have caused widespread salinization of urban lakes in northern temperate regions. Watershed characteristics are known to be important drivers of lake chloride concentrations, but there has been less focus on how lake morphometry influences seasonal and interannual dynamics in lake chloride, and how these chloride levels may alter mixing in the water column. We analyzed chloride retention for two urban lakes (Como Lake and Lake McCarrons) in Saint Paul, Minnesota, that are in adjacent watersheds and have similar surface areas, but differ in depth and water residence time. Summer chloride concentrations were negatively related to total summer precipitation for Como Lake (maximum depth 2.2 m), but the relationship was less strong for Lake McCarrons (maximum depth 7.6 m). We used a zero-dimensional model to simulate chloride dynamics in both lakes and tracked the fate of chloride over time. In Como Lake, the mass of chloride in the lake turns over within three years, whereas chloride inputs are retained for >10 years in Lake McCarrons. We then used a one-dimensional hydrodynamic lake model (GLM-AED) to examine how lake depth affects how current chloride loading rates alter lake mixing. Salt inputs significantly extended the duration of summer stratification for simulated lakes with depths of 8 m or more, and salt inputs increased the number of days of hypoxia and anoxia across all depths. These results underscore the importance of considering lake morphometry in understanding the effects of salt inputs on lake ecosystems. 

Abstract This paper investigates the physical conditions of the circumgalactic medium ofL^⋆galaxies through explorations of observed ion tracer gas kinematics and comparisons of observations to different ionization models. For this analysis, we utilize Civobservations from the CIViL^⋆survey (∼0.14 ≤z_gal≤ 0.25) and directly compare them to observations of matched lines of sight from the Cosmic Origins Spectrograph-Halos survey. We find that the kinematic parameters for Civand Oviare likely (>95%) drawn from the same parent distribution, suggesting that these two ions are kinematically coincident andpotentiallyoriginate under the same physical conditions. We find that the measured Civ/Oviand Nv/Oviratios are inconsistent with single-phase equilibrium models. For 70% of the objects in our sample, regions allowed by the column density ratios in the density-temperature space do not overlap, creating a “zone of avoidance.” We also investigate the origins of Civ, Nv, and Oviby exploring a cooling flow model under collisional ionization. We find that both Nvand Oviare consistent with the predictions of the model, but the column densities of Civare ∼2.5 times higher than the predictions. As Civhas a lower ionization energy than Nvand Ovi, it is possible that Civhas contributions from both the warm/hot and cool photoionized phase. 

The physics of the heat-trapping properties of CO ${}_2$were established in the mid-19th century, as fossil fuel burning rapidly increased atmospheric CO ${}_2$levels. To date, however, research has not probed when climate change could have been detected if scientists in the 19th century had the current models and observing network. We consider this question in a thought experiment with state-of-the-art climate models. We assume that the capability to make accurate measurements of atmospheric temperature changes existed in 1860, and then apply a standard “fingerprint” method to determine the time at which a human-caused climate change signal was first detectable. Pronounced cooling of the mid- to upper stratosphere, mainly driven by anthropogenic increases in carbon dioxide, would have been identifiable with high confidence by approximately 1885, before the advent of gas-powered cars. These results arise from the favorable signal-to-noise characteristics of the mid- to upper stratosphere, where the signal of human-caused cooling is large and the pattern of this cooling differs markedly from patterns of intrinsic variability. Even if our monitoring capability in 1860 had not been global, and high-quality stratospheric temperature measurements existed for Northern Hemisphere mid-latitudes only, it still would have been feasible to detect human-caused stratospheric cooling by 1894, only 34 y after the assumed start of climate monitoring. Our study provides strong evidence that a discernible human influence on atmospheric temperature has likely existed for over 130 y.