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Teacher education programs are increasingly taking up commitments to prepare new teachers for equitable teaching. Despite best intentions, programs feel challenged to help candidates translate these commitments into classroom practice. Using a context-specific teacher education framework, we conducted a mixed-methods study of seven urban-focused programs to understand how they targeted preparation for urban contexts. We found that while programs offer multiple opportunities to learn about content embedded in context, fewer opportunities exist for candidates to practice in context, and that faculty play a critical bridging role in designing practice opportunities that are informed by program vision. 

Several studies have shown that near-infrared imaging has great potential for the detection of dental caries lesions. A miniature scanning fiber endoscope (SFE) operating at near-infrared (NIR) wavelengths was developed and used in this study to test whether the device could be used to discriminate demineralized enamel from sound enamel. Varying depths of artificial enamel caries lesions were prepared on 20 bovine blocks with smooth enamel surfaces. Samples were imaged with a SFE operating in the reflectance mode at 1310-nm and 1460-nm in both wet and dry conditions. The measurements acquired by the SFE operating at 1460-nm show significant difference between the sound and the demineralized enamel. There was a moderate positive correlation between the SFE measurements and micro-CT measurements, and the NIR SFE was able to detect the presence of demineralization with high sensitivity (0.96) and specificity (0.85). This study demonstrates that the NIR SFE can be used to detect early demineralization from sound enamel. In addition, the NIR SFE can differentiate varying severities of demineralization. With its very small form factor and maneuverability, the NIR SFE should allow clinicians to easily image teeth from multiple viewing angles in real-time. 

Background and Objective A safer alternative method to radiographic imaging is needed. We present a multispectral near‐infrared scanning fiber endoscope (nirSFE) for dental imaging which is designed to be the smallest imaging probe with near‐infrared (NIR) imaging (1200–2000 nm). Materials and Methods The prototype nirSFE is designed for wide‐field forward viewing of scanned laser illumination at 1310, 1460, or 1550 nm. Artificial lesions with varying sizes and locations were prepared on proximal surfaces of extracted human teeth to examine capability and limitation of this new dental imaging modality. Nineteen artificial interproximal lesions and several natural occlusal lesions on extracted teeth were imaged with nirSFE, OCT, and microCT. Results Our nirSFE system has a flexible shaft as well as a probe tip with diameter of 1.6 mm and a rigid length of 9 mm. The small form factor and multispectral NIR imaging capability enables multiple viewing angles and reliable detection of lesions that can extend into the dentin. Among nineteen artificial interproximal lesions, the nirSFE reflectance imaging operating at 1460‐nm and OCT operating at 1310‐nm scanned illumination exhibited high sensitivity for interproximal lesions that were closer to occlusal surface. Diagnosis from a non‐blinded trained user by looking at real‐time occlusal‐side nirSFE videos indicate true positive rate of 78.9%. There were no false positives. Conclusions This study demonstrates that nirSFE may be used for detecting occlusal lesions and interproximal lesions located less than 4 mm under the occlusal surface. Major advantages of this imaging system include multiple viewing angles due to flexibility and small form factor, as well as the ability to capture real‐time video. The multispectral nirSFE has the potential to be employed as a low‐cost dental camera for detecting dental lesions without exposure to ionizing radiation. 

Abstract The National Ecological Observatory Network (NEON) is a multidecadal and continental-scale observatory with sites across the United States. Having entered its operational phase in 2018, NEON data products, software, and services become available to facilitate research on the impacts of climate change, land-use change, and invasive species. An essential component of NEON are its 47 tower sites, where eddy-covariance (EC) sensors are operated to determine the surface–atmosphere exchange of momentum, heat, water, and CO 2 . EC tower networks such as AmeriFlux, the Integrated Carbon Observation System (ICOS), and NEON are vital for providing the distributed observations to address interactions at the soil–vegetation–atmosphere interface. NEON represents the largest single-provider EC network globally, with standardized observations and data processing explicitly designed for intersite comparability and analysis of feedbacks across multiple spatial and temporal scales. Furthermore, EC is tightly integrated with soil, meteorology, atmospheric chemistry, isotope, phenology, and rich contextual observations such as airborne remote sensing and in situ sampling bouts. Here, we present an overview of NEON’s observational design, field operation, and data processing that yield community resources for the study of surface–atmosphere interactions. Near-real-time data products become available from the NEON Data Portal, and EC and meteorological data are ingested into AmeriFlux and FLUXNET globally harmonized data releases. Open-source software for reproducible, extensible, and portable data analysis includes the eddy4R family of R packages underlying the EC data product generation. These resources strive to integrate with existing infrastructures and networks, to suggest novel systemic solutions, and to synergize ongoing research efforts across science communities. 

Precipitation sustains life and supports human activities, making its prediction one of the most societally relevant challenges in weather and climate modeling. Limitations in modeling precipitation underscore the need for diagnostics and metrics to evaluate precipitation in simulations and predictions. While routine use of basic metrics is important for documenting model skill, more sophisticated diagnostics and metrics aimed at connecting model biases to their sources and revealing precipitation characteristics relevant to how model precipitation is used are critical for improving models and their uses. This paper illustrates examples of exploratory diagnostics and metrics including 1) spatiotemporal characteristics metrics such as diurnal variability, probability of extremes, duration of dry spells, spectral characteristics, and spatiotemporal coherence of precipitation; 2) process-oriented metrics based on the rainfall–moisture coupling and temperature–water vapor environments of precipitation; and 3) phenomena-based metrics focusing on precipitation associated with weather phenomena including low pressure systems, mesoscale convective systems, frontal systems, and atmospheric rivers. Together, these diagnostics and metrics delineate the multifaceted and multiscale nature of precipitation, its relations with the environments, and its generation mechanisms. The metrics are applied to historical simulations from phases 5 and 6 of the Coupled Model Intercomparison Project. Models exhibit diverse skill as measured by the suite of metrics, with very few models consistently ranked as top or bottom performers compared to other models in multiple metrics. Analysis of model skill across metrics and models suggests possible relationships among subsets of metrics, motivating the need for more systematic analysis to understand model biases for informing model development.