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Introduction: Poor communication is a leading root cause of preventable maternal mortality in the United States. Communication challenges are compounded with the presence of biases, including racism. Hospital administrators and clinicians are often aware that communication is a problem, but understanding where to intervene can be difficult to determine. While clinical leadership routinely reviews incident reports and acts on them to improve care, we hypothesized that reviewing incident reports in a systematic way might reveal thematic patterns, providing targeted opportunities to improve communication in direct interaction with patients and within the healthcare team itself. Methods: We abstracted incident reports from the Women's Health service and linked them with patient charts to join patient's race/ethnicity, birth outcome, and presence of maternal morbidity and mortality to the incident report. We conducted a qualitative content analysis of incident reports using an inductive and deductive approach to categorizing communication challenges. We then described the intersection of different types of communication challenges with patient race/ethnicity and morbidity outcomes. Results: The use of incident reports to conduct research on communication was new for the health system. Conversations with health system‐level stakeholders were important to determine the best way to manage data. We developed a thematic codebook based on prior research in healthcare communication. We found that we needed to add codes that were equity focused, as this was missing from the existing codebook. We also found that clinical and contextual expertise was necessary for conducting the analysis—requiring more resources to conduct coding than initially estimated. We shared our findings back with leadership iteratively during the work. Conclusions: Incident reports represent a promising source of health system data for rapid improvement to transform organizational practice around communication. There are barriers to conducting this work in a rapid manner, however, that require further iteration and innovation. [ABSTRACT FROM AUTHOR]

Hydroxychloroquine (HCQ) and chloroquine (CQ) are well‐established medications used in treating systemic lupus erythematosus and rheumatoid arthritis, as well as skin conditions such as cutaneous lupus erythematosus. In rare cases, arrhythmias and conduction system abnormalities, as well as cardiomyopathy, have been reported in association with HCQ/CQ use. Recently, however, the corrected QT interval (QTc)–prolonging potential of these medications, and risk of torsade de pointes (TdP) in particular, have been highlighted in the setting of their experimental use for COVID‐19 infection. This report was undertaken to summarize the current understanding of HCQ/CQ cardiac toxicity, describe QTc prolongation and TdP risks, and discuss areas of priority for future research. A working group of experts across rheumatology, cardiology, and dermatology performed a nonsystematic literature review and offered a consensus‐based expert opinion. Current data clearly indicate that HCQ and CQ are invaluable medications in the management of rheumatic and dermatologic diseases, but they are associated with QTc prolongation by directly affecting cardiac repolarization. Prescribing clinicians should be cognizant of this small effect, especially in patients taking additional medications that prolong the QTc interval. Long‐term use of HCQ/CQ may lead to a cardiomyopathy associated with arrhythmias and heart failure. Risk and benefit assessment should be considered prior to initiation of any medication, and both initial and ongoing risk–benefit assessments are important with regard to prescription of HCQ/CQ. While cardiac toxicity related to HCQ/CQ treatment of rheumatic diseases is rarely reported, it can be fatal. Awareness of the potential adverse cardiac effects of HCQ and CQ can increase the safe use of these medications. There is a clear need for additional research to allow better understanding of the cardiovascular risk and safety profile of these therapies used in the management of rheumatic and cutaneous diseases. [ABSTRACT FROM AUTHOR]

Introduction: Implementation science (IS) offers methods to systematically achieve the Ending the HIV Epidemic goals in the United States, as well as the global UNAIDS targets. Federal funders such as the National Institutes of Mental Health (NIMH) have invested in implementation research to achieve these goals, including supporting the AIDS Research Centres (ARCs), which focus on high‐impact science in HIV and mental health (MH). To facilitate capacity building for the HIV/MH research workforce in IS, "grey areas," or areas of IS that are confusing, particularly for new investigators, should be addressed in the context of HIV/MH research. Discussion: A group of IS experts affiliated with NIMH‐funded ARCs convened to identify common and challenging grey areas. The group generated a preliminary list of 19 grey areas in HIV/MH‐related IS. From the list, the authors developed a survey which was distributed to all ARCs to prioritize grey areas to address in this paper. ARC members across the United States (N = 60) identified priority grey areas requiring clarification. This commentary discusses topics with 40% or more endorsement. The top grey areas that ARC members identified were: (1) Differentiating implementation strategies from interventions; (2) Determining when an intervention has sufficient evidence for adaptation; (3) Integrating recipient perspectives into HIV/MH implementation research; (4) Evaluating whether an implementation strategy is evidence‐based; (5) Identifying rigorous approaches for evaluating the impact of implementation strategies in the absence of a control group or randomization; and (6) Addressing innovation in HIV/MH IS grants. The commentary addresses each grey area by drawing from the existing literature (when available), providing expert guidance on addressing each in the context of HIV/MH research, and providing domestic and global HIV and HIV/MH case examples that address these grey areas. Conclusions: HIV/MH IS is key to achieving domestic and international goals for ending HIV transmission and mitigating its impact. Guidance offered in this paper can help to overcome challenges to rigorous and high‐impact HIV/MH implementation research. [ABSTRACT FROM AUTHOR]

SNAPSHOT USA is a multicontributor, long‐term camera trap survey designed to survey mammals across the United States. Participants are recruited through community networks and directly through a website application (https://www.snapshot-usa.org/). The growing Snapshot dataset is useful, for example, for tracking wildlife population responses to land use, land cover, and climate changes across spatial and temporal scales. Here we present the SNAPSHOT USA 2021 dataset, the third national camera trap survey across the US. Data were collected across 109 camera trap arrays and included 1711 camera sites. The total effort equaled 71,519 camera trap nights and resulted in 172,507 sequences of animal observations. Sampling effort varied among camera trap arrays, with a minimum of 126 camera trap nights, a maximum of 3355 nights, a median 546 nights, and a mean 656 ± 431 nights. This third dataset comprises 51 camera trap arrays that were surveyed during 2019, 2020, and 2021, along with 71 camera trap arrays that were surveyed in 2020 and 2021. All raw data and accompanying metadata are stored on Wildlife Insights (https://www.wildlifeinsights.org/), and are publicly available upon acceptance of the data papers. SNAPSHOT USA aims to sample multiple ecoregions in the United States with adequate representation of each ecoregion according to its relative size. Currently, the relative density of camera trap arrays varies by an order of magnitude for the various ecoregions (0.22–5.9 arrays per 100,000 km2), emphasizing the need to increase sampling effort by further recruiting and retaining contributors. There are no copyright restrictions on these data. We request that authors cite this paper when using these data, or a subset of these data, for publication. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the US Government. [ABSTRACT FROM AUTHOR]

Paciorek and Wehner raise important questions around our use of the Mann‐Kendall nonparametric trend test on smoothed data for analyzing long‐term hydrometeorological trends in Zhang et al. (2021, https://doi.org/10.1029/2020gl092293). We thank them for initiating this important conversation and their gracious cooperation in exploring the issues addressed in their comment. In this reply we confirm the inflation of significant p‐values by our choice to smooth, illustrate the relatively minor impacts on the main conclusions of our paper, and add our voices to those of Paciorek and Wehner in highlighting the lack of methodology for hypothesis testing across multiple stations that have spatial structure (i.e., testing for regionally consistent trends). Plain Language Summary: Our colleagues Drs. Paciorek and Wehner have raised concerns about our paper (Zhang et al., 2021, https://doi.org/10.1029/2020gl092293), which showed widespread increases in the duration of drought events over the last five decades in the western United States. They point out that our decision to smooth the data using a moving average inflated the number of weather stations at which the trends toward longer droughts were deemed significant by a statistical test. We agree with them on this point, and we have recomputed all our results using unsmoothed data to determine the impacts. We find that for most stations and regions, trend magnitudes remained largely unchanged, with many stations nearby one another often suggesting similar trends. Finally, we agree with Paciorek and Wehner that there is a lack of statistical methods to test such coherent regional patterns, and we caution that over‐reliance on p‐values limits the power of regional data to identify important climate trends. Key Points: We agree that smoothing to 5‐year moving windows introduced serial correlation into time series of annual statistics of daily rainfall data, inflating the number of weather stations individually showing significant trends (p < 0.05) with the Mann‐Kendall testRecomputation with unsmoothed values produced substantially the same dry intervals trend magnitude and direction at most stations individually and had only minimal impacts on dry interval trends computed for National Ecological Observatory Network domains using the Regional Kendall testNo perfect statistical approach leverages the capacity of coherent regional patterns among spatially correlated weather stations, and an over‐reliance on p‐values as a binary (significant vs. insignificant) determinant of trends limits the power of regional data [ABSTRACT FROM AUTHOR]

Changes in precipitation patterns with climate change could have important impacts on human and natural systems. Zhang et al. (2021, https://doi.org/10.1029/2020gl092293) report trends in daily precipitation patterns over the last five decades in the western United States, focusing on meteorological drought. They report that dry intervals (calculated at the annual or seasonal level) have increased across much of the southwestern U.S., with statistical assessment suggesting the results are statistically robust. However, Zhang et al. (2021, https://doi.org/10.1029/2020gl092293) preprocess their annual (or seasonal) averages to compute 5‐year moving window averages before using established statistical techniques for trend analysis that assume independence about some fixed trend. Here we show that the moving window preprocessing violates that independence assumption and inflates the statistical significance of their trend estimates. This raises questions about the robustness of their results. We conclude by discussing the difficulty of adjusting for spatial structure when assessing time trends in a regional context. Plain Language Summary: A recent paper reports trends in drought in the western United States, in particular increases in drought in the southwestern United States, based on changes in the lengths of time intervals without precipitation. In this "comment" we note that the preprocessing approach used in the paper artificially increases the apparent statistical signal in the data and caution that the evidence for the trends reported is not as strong as presented in the paper. We conclude by discussing the difficulty of estimating trends in a statistically rigorous fashion across multiple weather stations. Key Points: Statistical uncertainty about trends in droughts in the southwestern US is larger than reported in a recent GRL letter [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) significantly impact the hydrological cycle and associated extremes in western continental regions. Recent studies suggest ARs also influence water resources and extremes in continental interiors. AR detection tools indicate that AR conditions are relatively frequent in areas east of the Rocky Mountains. The origin of these ARs, whether from synoptic‐scale waves or mesoscale processes, is unclear. This study uses meteorological composite maps and transects of AR conditions during the four seasons. The analysis reveals that ARs east of the Rockies are associated with long‐wave, baroclinic Rossby waves. This result demonstrates that eastern North American ARs are dynamically similar to their western coastal counterparts, though mechanisms for vertical moisture flux differ between the two. These findings provide a foundation for understanding future climate change and ARs in this region and offer new methods for evaluating climate model simulations. Plain Language Summary: Atmospheric rivers (ARs) are a weather pattern that brings high amounts of atmospheric water and winds in a relatively narrow region. ARs are typically considered a "west coast" phenomenon, largely because the majority of the scientific research on ARs has focused on ARs in western coastal regions: particularly the western United States (US). ARs occur in continental interiors, but there has been some debate about whether these ARs represent the same type of weather as those in western coastal regions. This paper uses two objective methods for identifying ARs and finds times when ARs are present in three locations in the eastern half of the US: Norman, OK, Bloomington, IN, and Washington, DC. Examination of weather conditions during these AR times shows remarkable similarity to conditions associated with west coast ARs. This gives strong evidence that ARs do occur in the eastern half of the US. This result is important because it suggests that ARs may be important for water resources and extreme weather in the eastern half of the US, just as they are in the western US. This result also suggests that ARs may be important for water resources and extremes in other continental interiors. Key Points: Atmospheric rivers (ARs) east of the Rockies are associated with baroclinic wavesWestern coastal ARs and eastern/midwest ARs are dynamically similarSynoptic‐scale uplift, combined with convective instability, provide efficient mechanisms for generating precipitation [ABSTRACT FROM AUTHOR]

The food versus fuel issue has attracted considerable attention with the rapid growth of global biofuel production. The previous literature examining the interconnectedness between biofuel, fossil fuel and agricultural markets employed futures and spot prices. However, food security needs to be discussed with non‐financial market prices, such as wholesale prices, since retail companies and households usually do not purchase products directly from financial markets, which encompass speculative activities, making them more volatile than non‐financial ones. This paper focuses on non‐financial markets in the United States to investigate the price‐interconnection of biodiesel, highway diesel, crude oil, and soybean, initially applying partial wavelet coherence and the Diebold‐Yilmaz connectedness index to price transmission research on biodiesel, highway diesel, crude oil, and soybean. Our main results are as follows: First, significant coherence between biodiesel and soybean, biodiesel and highway diesel, and highway diesel and crude oil is identified in the short and long term. Second, crude oil and biodiesel prices are found to be net transmitters, while soybean and highway diesel prices are net receivers. Finally, the crude oil market is identified as the source of spillovers among the four markets, and strongly influences the highway diesel market. The previous literature examining the interconnectedness between biofuel, fossil fuel and agricultural markets employed futures and spot prices, while food security needs to be discussed with non‐financial market prices since retail companies and households usually do not purchase products directly from financial markets. This paper focuses on non‐financial markets in the United States to investigate the price‐interconnection of biodiesel, highway diesel, crude oil, and soybean, initially applying partial wavelet coherence and the Diebold‐Yilmaz connectedness index to price transmission research on biodiesel, highway diesel, crude oil, and soybean. Our primary result suggests that biodiesel promotion reinforces a relationship between the prices of soybean and biodiesel. [ABSTRACT FROM AUTHOR]

A mass‐conserving method to downscale precipitation from global climate models (GCMs) using sub‐grid‐scale topography and modeled 700‐mb wind direction is presented. Runoff is simulated using a stand‐alone hydrological model, with this and several other methods as inputs, and compared to runoff simulated using historical observations over the western contiguous United States. Results suggest the mitigation of grid‐scale biases is more critical than downscaling for some regions with large wet biases (e.g., the Great Basin and Upper Colorado). In other regions (e.g., the Pacific Northwest) the new method produces more realistic sub‐grid‐scale variability in runoff compared to unadjusted GCM output and a simpler downscaling method. The presented method also brings the runoff centroid timing closer to that simulated with observations for all subregions examined. Plain Language Summary: Due to limitations in computing power which necessitates coarse spatial resolution, climate models do not include many details on mountains and their impact on precipitation. For this reason, it is difficult to estimate the impact of climate change on the availability of water for human consumption in places like the western United States, where mountain snowpack is an important source of water. This paper presents a way to adjust precipitation estimates from climate models by using some simple statistics about nearby mountains and valleys. The adjusted precipitation is then used in a hydrologic model to calculate the runoff that is simulated with different precipitation inputs. Results show that in most cases, the precipitation adjustment improves estimates of the resultant runoff relative to simulations with observed precipitation. The main exceptions are in very dry areas where the climate model produces far too much precipitation. With further work, the proposed adjustment may be integrated into the climate model itself to make it easier for those managing water resources (e.g., controlling reservoir levels) to use the model output to plan and adapt to climate change. Key Points: A mass‐conserving method for downscaling orographic precipitation improves modeled runoff from the CESM2Considering upwind topography further improves modeled runoff compared to simpler adjustmentsNot tuning to individual model grid points makes this method more generalizable than many existing statistical downscaling methods [ABSTRACT FROM AUTHOR]

Economic growth, air pollution, and forest fires in some states in the United States have increased the concentration of particulate matter with a diameter less than or equal to 2.5 μm (PM2.5). Although previous studies have tried to observe PM2.5 both spatially and temporally using aerosol remote sensing and geostatistical estimation, they were limited in accuracy by coarse resolution. In this paper, the performance of machine learning models on predicting PM2.5 is assessed with linear regression (LR), decision tree (DT), gradient boosting regression (GBR), AdaBoost regression (ABR), XGBoost (XGB), k‐nearest neighbors (K‐NN), long short‐term memory (LSTM), random forest (RF), and support vector machine (SVM) using PM2.5 station data from 2017 to 2021. To compare the accuracy of all the nine machine learning models, the coefficient of determination (R2), root mean square error (RMSE), Nash‐Sutcliffe efficiency (NSE), root mean square error ratio (RSR), and percent bias (PBIAS) were evaluated. Among all nine models, the RF (100 decision trees with a max depth of 20) and support vector regression (SVR; nonlinear kernel, degree 3 polynomial) models were the best for predicting PM2.5 concentrations. Additionally, comparison of the PM2.5 performance metrics displayed that the models had better predictive behavior in the western United States than that in the eastern United States. Key Points: We present the prediction of PM2.5 concentrations over the United States using various machine learning (ML) algorithmsWe show ML as a new approach for analyzing large data sets due to the computational speed and easy implementation for massive amounts of dataThe study is important for improving our understanding of the differences among ML algorithms for Earth Science research [ABSTRACT FROM AUTHOR]

Most people in the United States agree they want some income inequality but debate exactly how much is fair. High‐status people generally prefer more inequality than low‐status individuals. Here we examine how much preferences for inequality are (or are not) driven by self‐interest. Past work has generally investigated this idea in two ways: The first is by stratifying preferences by income, and the second is by randomly assigning financial status within lab‐constructed scenarios. In this paper, we develop a method that combines both experimental control and the social experience of inequality—a simulated society experiment. Across two experiments (N = 138, observations = 690), participants voted on the distribution of rewards—first behind a veil of ignorance, and then when they were randomly assigned a status within a game of chance. Status varied repeatedly across five rounds, allowing us to measure dynamic preferences. Under the veil of ignorance, people preferred inequality favoring the top status. When the veil of ignorance disappeared, self‐interest immediately influenced inequality preferences. Those who randomly landed in top positions were satisfied with the status quo established under the veil of ignorance, whereas those who randomly landed in bottom positions wanted more equality. Yet these preferences were not stable; decisions about the optimal level of inequality changed according to changes in social status. Our results also showed that, when inequality grows in a society, preferences regarding inequality become polarized by social status. Individuals in low‐status positions, particularly, tend to demand more equality. [ABSTRACT FROM AUTHOR]

This paper investigates dynamic changes in instrumental (i.e., work‐related) tie‐seeking patterns and the structure of a communication network following a downsizing event—whereby many employees are simultaneously eliminated from a network. Our analysis spans a two‐year period and applies a resource‐ and network‐change approach to examine how survivors develop revised resource‐acquisition strategies while repositioning themselves after a downsizing. Our results demonstrate that two temporary logics of tie formation—a suspension of within‐unit homophily and a preference for seeking ties with long‐tenured employees—help employees acquire betweenness centrality during the disruption period. Specifically, we find that disruption initiates a transitional period after downsizing in which new tie‐making logics are employed, including seeking out ties with long‐tenured employees and employees outside of one's department. We observed post‐disruption, during the stabilization period after downsizing, where logics used for tie‐making in the disruption period were abandoned, pre‐disruption tie‐making logics were resumed, and betweenness centrality remained relatively constant. We discuss the theoretical and managerial implications of these results and suggest future research directions. [ABSTRACT FROM AUTHOR]

This paper is written in response to the paper "How green is blue hydrogen?" by R. W. Howarth and M. Z. Jacobson. It aims at highlighting and discussing the method and assumptions of that paper, and thereby providing a more balanced perspective on blue hydrogen, which is in line with current best available practices and future plant specifications aiming at low CO2 emissions. More specifically, in this paper, we show that: (i) the simplified method that Howarth and Jacobson used to compute the energy balance of blue hydrogen plants leads to significant overestimation of CO2 emissions and natural gas (NG) consumption and (ii) the assumed methane leakage rate is at the high end of the estimated emissions from current NG production in the United States and cannot be considered representative of all‐NG and blue hydrogen value chains globally. By starting from the detailed and rigorously calculated mass and energy balances of two blue hydrogen plants in the literature, we show the impact that methane leakage rate has on the equivalent CO2 emissions of blue hydrogen. On the basis of our analysis, we show that it is possible for blue hydrogen to have significantly lower equivalent CO2 emissions than the direct use of NG, provided that hydrogen production processes and CO2 capture technologies are implemented that ensure a high CO2 capture rate, preferably above 90%, and a low‐emission NG supply chain. [ABSTRACT FROM AUTHOR]

Priority for solid organ transplant generally does not consider the underlying cause of the need for transplantation. This paper argues that a distinctive set of factors justify assigning lower priority to willfully unvaccinated individuals who require transplant as a result of suffering from COVID‐19. These factors include the personal responsibility of the patients for their own condition and the public outrage likely to ensue if willfully unvaccinated patients receive organs at the expense of vaccinated ones. The paper then proposes a three‐prong test for similar deviations from the current allocation standard that incorporates patient responsibility, foreseeability and avoidability, and the frequency of the occurrence. [ABSTRACT FROM AUTHOR]

Introduction: The United States has been unsuccessful in containing the rapid spread of COVID‐19. The complex epidemiology of the disease and the fragmented response to it has resulted in thousands of ways in which spread has occurred, creating a situation where each community needs to create its own local, context‐specific learning model while remaining compliant to county or state mandates. Methods: In this paper, we demonstrate how cross sector collaborations can use the Cynefin Framework, a tool for decision‐making in complex systems, to guide community response to the COVID‐19 pandemic. Results: We explore circumstances under which communities can inhabit each of the four domains of systems complexity represented in the Cynefin framework: simple, complicated, chaotic, and complex, and describe the decision‐making process in each domain that balances health, economic, and social well‐being. Conclusion: This paper serves as a call to action for the creation of community learning systems to improve community resilience and capacity to make better‐informed decisions to address complex public health problems during the pandemic and beyond. [ABSTRACT FROM AUTHOR]

The differential impact of the COVID‐19 pandemic on communities of color in the United States along with the civil unrest taking place in 2020 in response to the killing of unarmed Black men and women by the police have increased awareness of the structural racism pervading US society. These developments have reraised the issue of reparations for Black Americans, usually proposed in the context of providing financial compensation for the injustices of slavery to the descendants of those who were enslaved. This paper will discuss the systematic racial inequality and structural racism in US society that have significantly disadvantaged racial and ethnic minorities while giving advantages to white Americans, which most recently have resulted in significantly higher mortality and morbidity among Black, Hispanic, and Native Americans during the pandemic. The paper will conceptualize reparations within the context of theories of reparative justice. It will also consider whether reparations are owed, and if so, by whom, to whom, and in what form. The final section will offer a proposal for collective reparations to the Black community and other people of color. [ABSTRACT FROM AUTHOR]

Introduction: The emergent field of learning health systems (LHSs) has been rapidly evolving as the concept continues to be embraced by researchers, managers, and clinicians. This paper reports on a scoping review and bibliometric analysis of the LHS literature to identify key topic areas and examine the influence and spread of recent research. Methods: We conducted a scoping review of LHS literature published between January 2016 and May 2020. The authors extracted publication data (eg, journal, country, authors, citation count, keywords) and reviewed full‐texts to identify: type of study (empirical, non‐empirical, or review), degree of focus (general or specific), and the reference used when defining LHSs. Results: A total of 272 publications were included in this review. Almost two thirds (65.1%) of the included articles were non‐empirical and over two‐thirds (68.4%) were from authors in the United States. More than half of the publications focused on specific areas, for example: oncology, cardiovascular care, and genomic medicine. Other key topic areas included: ethics, research, quality improvement, and electronic health records. We identified that definitions of the LHS concept are converging; however, many papers focused on data platforms and analytical processes rather than organisational and behavioural factors to support change and learning activities. Conclusions: The literature on LHSs remains largely theoretical with definitions of LHSs focusing on technical processes to reuse data collected during the clinical process and embedding analysed data back into the system. A shift in the literature to empirical LHS studies with consideration of organisational and human factors is warranted. [ABSTRACT FROM AUTHOR]

Background: Melasma is an acquired melanogenesis dysfunction resulting in chronic hyperpigmentation commonly affecting the face and other frequently sun‐exposed areas of the body. Melasma typically presents in women of reproductive age and can significantly impact self‐esteem, negatively affecting one's quality of life. In the United States, melasma is often treated with application of topical agents that interfere with melanin synthesis, lasers, or chemical peels; however, in some East Asian countries, oral tranexamic acid (TXA) is widely administered to alleviate hyperpigmentation during and after childbirth. TXA is currently only FDA‐approved to treat hypermenorrhea and reduce blood loss in surgery but may offer women in the United States an additional therapeutic option to treat melasma. Aims: The aim of this paper is to evaluate the safety and baseline efficacy of oral transmexic acid as a treatment for melasma. Methods: We retrospectively surveyed 42 patients of Fitzpatrick skin types III‐VI that were prescribed 650 mg of TXA ½ tablet to be taken twice daily by mouth. Results: We found majority of patients saw noticeable improvement in their melasma. Of the 42 patients, only seven experienced side effects. The side effects noted were headaches, malaise and nausea, gastrointestinal upset, congestion, numbness in legs, hypomenorrhea, and hypermenorrhea. Patients who experienced unpleasant side effects discontinued taking oral TXA and were relieved of their symptoms. No long‐term side effects were discovered, and the side effects experienced may be due to other confounding factors. Conclusion: From this data, we concluded oral TXA is a safe and effective treatment option for patients with persistent melasma. [ABSTRACT FROM AUTHOR]

Functional regions capturing local socioeconomic dynamics are a framework for territorial statistics and policy‐making. The usefulness of such statistics and policies depends on the adequateness of the regions used in the analysis. Several authors have adopted the modularity quality function to drive their regionalization methods. This function, originally devised for non‐spatial data, has limitations particularly relevant in this context. The paper discusses these limitations and illustrate them using real data for the United States. The results warn against using standard modularity to assess or optimize the quality of a functional delineation, and advocate for available alternatives that consider the spatial nature of functional regionalization. [ABSTRACT FROM AUTHOR]

This paper examines the persistent effects of eliminating tariffs on Mexican imports, following the implementation of the North American Free Trade Agreement (NAFTA), on Americans' human capital investment. We leverage quasi‐experimental changes in tariffs on Mexican imports across birth cohorts and within states. We show that NAFTA increases the probability of ever attending college and earning a degree. These results, however, mask important heterogeneous effects within the sample. We find white Americans drive these positive effects. In contrast, the educational attainment of racial and ethnic minorities, especially men, shrank under NAFTA, decreasing their probability of graduating from high school. [ABSTRACT FROM AUTHOR]

Managing wildlife populations in the face of global change requires regular data on the abundance and distribution of wild animals, but acquiring these over appropriate spatial scales in a sustainable way has proven challenging. Here we present the data from Snapshot USA 2020, a second annual national mammal survey of the USA. This project involved 152 scientists setting camera traps in a standardized protocol at 1485 locations across 103 arrays in 43 states for a total of 52,710 trap‐nights of survey effort. Most (58) of these arrays were also sampled during the same months (September and October) in 2019, providing a direct comparison of animal populations in 2 years that includes data from both during and before the COVID‐19 pandemic. All data were managed by the eMammal system, with all species identifications checked by at least two reviewers. In total, we recorded 117,415 detections of 78 species of wild mammals, 9236 detections of at least 43 species of birds, 15,851 detections of six domestic animals and 23,825 detections of humans or their vehicles. Spatial differences across arrays explained more variation in the relative abundance than temporal variation across years for all 38 species modeled, although there are examples of significant site‐level differences among years for many species. Temporal results show how species allocate their time and can be used to study species interactions, including between humans and wildlife. These data provide a snapshot of the mammal community of the USA for 2020 and will be useful for exploring the drivers of spatial and temporal changes in relative abundance and distribution, and the impacts of species interactions on daily activity patterns. There are no copyright restrictions, and please cite this paper when using these data, or a subset of these data, for publication. [ABSTRACT FROM AUTHOR]

Productive discourse regarding the role of racism and colonialism in conservation is growing but still limited. Inadequate recognition of these powerful forces has significantly impeded socially just conservation efforts. This paper integrates multiple disciplinary perspectives to discuss historical conservation practices in the United States and abroad to reveal challenges with moving beyond traditional approaches to conservation that perpetuate systemic racism and colonialism. Using urban greening (e.g., tree planting) in the United States as an example, we show how these challenges manifest as White ideals of nature, power disparities, and displacement and exclusion. We then put forth an agenda for antiracist, anticolonial urban conservation and urban greening. This agenda uses the tripartite environmental justice framework (i.e., distributional, recognition, and procedural justice) as a starting point, integrating and adapting more critical views of contemporary environmental justice to highlight specific policies and practices that can be applied to many conservation problems. [ABSTRACT FROM AUTHOR]

Racism has resulted in significant disproportionality and disparity in the US child welfare system. Being Black is not an inherent risk factor for child abuse and neglect yet Black children are almost twice as likely to be victims of substantiated abuse and neglect claims compared to other racial groups. Addressing the disproportionality within the child welfare system due to systemic racism falls squarely under the purview of bioethics. In this paper, we briefly review the impact of racism on child welfare. We then discuss some ethical considerations that mandatory healthcare reporters should think through when determining whether to report potential abuse and neglect. Specifically, we discuss the need for a broader consideration of what constitutes harm. We then present a hypothetical composite case to illuminate where and how bias can enter the process of referral to child protective services (CPS). We encourage thoughtful reporting with consideration of social and historical context and alternative explanations for worrisome findings. We recommend using evidence, avoiding assumptions by seeking clarification from families and ensuring internal consistency. When contemplating CPS referral, medical providers should feel empowered to ask questions if there is concern for potential bias. The ultimate goal is to protect children from harm. If there are clear safety concerns—they must be addressed. However, in the many cases where the safety concern is less tangible, we need to expand our considerations of the harms that can befall children, especially children of color. [ABSTRACT FROM AUTHOR]

The COVID‐19 pandemic has had a disproportionate impact on the health of Black Americans, Latinx or Hispanic Americans, and American Indians. These disparities are deeply unjust, in part, because they are the causal result of racism at both the interpersonal and structural levels. This paper argues, however, that establishing a causal connection between racism and health disparities is not the only way to explain the injustice of these disparities. The COVID‐19 health disparities are arguably unjust because health equity is a "free‐standing" demand of justice, an obligation of reparative justice, a remedy to structural injustice, and part of dismantling pernicious racial concepts. Identifying multiple accounts of injustice may lower the evidentiary bar for our normative claims and help us to identify alternative policy pathways for ending health inequity. [ABSTRACT FROM AUTHOR]

For the past forty years, United States school districts have increasingly hired mathematics specialists to support the teaching and learning of mathematics. Despite the prevalence of this professional development structure, this is a relatively new research topic for the mathematics education field. In this paper, we report findings from an exploration of literature between 1981 and 2018 to examine the role of mathematics specialists (MSs). In particular, we examine: (a) MS positioning across research; (b) historical trends of school‐based MS research; and (c) orientations of school‐based MSs within research. Using the McGatha and Rigelman framework as an analytic lens, we found that beyond the positions identified in their framework (coach, intervention specialist, teacher), there were four additional MS positionings within the research (learner, other p‐12 stakeholder, university stakeholder, unknown). Ultimately, we forward an expansion to the McGatha and Rigelman framework to include these new categories, as well as contextual descriptions and working definitions to support future research in more accurately and robustly capturing the ways in which MSs are investigated and reported on in research. [ABSTRACT FROM AUTHOR]

As science becomes increasingly cross‐disciplinary and scientific models become increasingly cross‐coupled, standardized practices of model evaluation are more important than ever. For normally distributed data, mean squared error (MSE) is ideal as an objective measure of model performance, but it gives little insight into what aspects of model performance are "good" or "bad." This apparent weakness has led to a myriad of specialized error metrics, which are sometimes aggregated to form a composite score. Such scores are inherently subjective, however, and while their components may be interpretable, the composite itself is not. We contend that, a better approach to model benchmarking and interpretation is to decompose MSE into interpretable components. To demonstrate the versatility of this approach, we outline some fundamental types of decomposition and apply them to predictions at 1,021 streamgages across the conterminous United States from three streamflow models. Through this demonstration, we hope to show that each component in a decomposition represents a distinct concept, like "season" or "variability," and that simple decompositions can be combined to represent more complex concepts, like "seasonal variability," creating an expressive language through which to interrogate models and data. Plain Language Summary: Models are essential scientific tools for explaining and predicting phenomena ranging from weather and climate, to health outcomes, to economic development, to the origins of the universe, and testing competing models is one of the most basic scientific activites. Yet, how scientists evaluate and justify their models can be inconsistent or even arbitrary. Traditionally, one performance metric—such as mean squared error—is used to identify the best model, but one metric provides little insight into what aspects of a model are "good" or "bad." This paper proposes a basic language for expressing different aspects of a model's performance. On one hand, this is useful for determining which aspects of model may require revision, but it also allows the modeler to separate out the best elements among several models and combine them to form an ensemble, analogous to how an audio engineer mixes together multiple tracks to form the best rendition of a musical piece. Key Points: Mean squared error (MSE) is an objective but somewhat enigmatic measure of model performanceMSE can be decomposed into components that quantify specific aspects of model performance, such as bias and varianceMixing components among models yields a form of ensemble prediction [ABSTRACT FROM AUTHOR]

Distortion‐induced fatigue cracking is a concern for many aging steel bridges in the United States. Human visual inspections to characterize fatigue cracks have many drawbacks including inconsistencies in identification, significant time and monetary costs, and safety risks posed to the lives of both inspectors and the traveling public due to lane closures. Digital image correlation (DIC) is a vision‐based technology which has shown promise for identifying and characterizing fatigue cracks. Three‐dimensional DIC measurements can capture full‐field displacements and strains, allowing for detection and characterization of both in‐plane and distortion‐induced fatigue cracks. This paper describes an experimental study in which a half‐scale steel girder to crossframe subassembly was subjected to distortion‐induced fatigue loading to produce multiple geometrically complex cracks. A DIC‐based crack characterization methodology was applied to quantify the cracks, which was successful at characterizing cracks propagating in the girder web but struggled to characterize horizontal cracks. Additional work is needed to improve the accuracy of the DIC‐based crack characterization methodology to use as an automated bridge inspection tool. [ABSTRACT FROM AUTHOR]

Classifying images using supervised machine learning (ML) relies on labeled training data—classes or text descriptions, for example, associated with each image. Data‐driven models are only as good as the data used for training, and this points to the importance of high‐quality labeled data for developing a ML model that has predictive skill. Labeling data is typically a time‐consuming, manual process. Here, we investigate the process of labeling data, with a specific focus on coastal aerial imagery captured in the wake of hurricanes that affected the Atlantic and Gulf Coasts of the United States. The imagery data set is a rich observational record of storm impacts and coastal change, but the imagery requires labeling to render that information accessible. We created an online interface that served labelers a stream of images and a fixed set of questions. A total of 1,600 images were labeled by at least two or as many as seven coastal scientists. We used the resulting data set to investigate interrater agreement: the extent to which labelers labeled each image similarly. Interrater agreement scores, assessed with percent agreement and Krippendorff's alpha, are higher when the questions posed to labelers are relatively simple, when the labelers are provided with a user manual, and when images are smaller. Experiments in interrater agreement point toward the benefit of multiple labelers for understanding the uncertainty in labeling data for machine learning research. Plain Language Summary: After hurricanes and storms, pictures taken from a plane can be used to observe how the coast was impacted. A single flight might take thousands of pictures. If a computer could automatically analyze the pictures, then a person would not need to look at them one‐by‐one. To teach a computer to analyze images, we need many pictures and many labels that describe what is visible in each picture. But where do we get those labels? Typically, a coastal scientist labels the pictures by sorting them into folders or typing codes into a spreadsheet. But does every coastal scientist label pictures the same way? Some labeling questions are easy to answer, and scientists mostly agree ("Is this image all water?"). Other labeling questions are harder to answer, and cause disagreement ("Was there damage to buildings?"). This paper is about how well scientists agree when labeling the same pictures, and how we can improve agreement among scientists. We try some experiments and offer a few ideas on how to improve agreement. We suggest writing very clear questions, using smaller images, and having a comprehensive manual. It turns out that having a manual with examples—and reading the manual!—really helps. Key Points: We measure agreement among coastal scientists labeling the same sets of poststorm imagesCoastal scientists agree more when rating landforms, less when labeling inferred processesIterating on questions, providing documentation, and using smaller image sizes all increase agreement [ABSTRACT FROM AUTHOR]