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Introduction The Latinx population has the second highest COVID-19 death rate among racial/ethnic groups in the United States and less than half of Latinx youth aged 5–17 years old completed their COVID-19 primary vaccination series as of September 2022. COVID-19 vaccine misinformation detrimentally impacts vaccination rates. In this study, we examined factors that predicted Latinx youth COVID-19 vaccine hesitancy and vaccination status. Methods A community-based sample of 290 Latinx parent and adolescent dyads from a Southwestern metropolitan area of the United States who were recruited to complete an online survey at baseline at T1 (August 2020 –March 2021) and one year later. We tested a longitudinal mediation model in which we examined individual and family factors that would predict youth COVID-19 vaccine hesitancy and vaccination status over time. Results Youth’s pandemic disbelief (i.e., the belief that the COVID-19 pandemic is a conspiracy or not real) predicted greater youth’s COVID-19 vaccine hesitancy, and in turn, a lower likelihood of youth’s COVID-19 vaccination. Youth’s pandemic disbelief also predicted greater parent’s vaccination hesitancy which, in turn, predicted greater youth’s vaccination hesitancy and a lower likelihood of COVID-19 vaccination. Parents’ pandemic disbelief predicted their own COVID-19 hesitancy, but not youth hesitancy Discussion Our study findings provide initial evidence that general pandemic disbelief was a significant driver of vaccine hesitancy and vaccination among Latinx families. The study contributes to the limited research investigating COVID-19 vaccination in the Latinx community and among Latinx youth, further aiding how COVID-19 vaccine disparities can be mitigated among racial/ethnic populations. 

Abstract This paper introduces a database of 34 field-measured building occupant behavior datasets collected from 15 countries and 39 institutions across 10 climatic zones covering various building types in both commercial and residential sectors. This is a comprehensive global database about building occupant behavior. The database includes occupancy patterns (i.e., presence and people count) and occupant behaviors (i.e., interactions with devices, equipment, and technical systems in buildings). Brick schema models were developed to represent sensor and room metadata information. The database is publicly available, and a website was created for the public to access, query, and download specific datasets or the whole database interactively. The database can help to advance the knowledge and understanding of realistic occupancy patterns and human-building interactions with building systems (e.g., light switching, set-point changes on thermostats, fans on/off, etc.) and envelopes (e.g., window opening/closing). With these more realistic inputs of occupants’ schedules and their interactions with buildings and systems, building designers, energy modelers, and consultants can improve the accuracy of building energy simulation and building load forecasting. 

Although persistent, bioaccumulative and toxic pollutants (PBTs) are well-studied individually, their distribution and variability on a global scale are largely unknown, particularly in marine fish. Using 2,662 measurements collected from peer-reviewed literature spanning 1969–2012, we examined variability of five classes of PBTs, considering effects of geography, habitat, and trophic level on observed concentrations. While we see large-scale spatial patterning in some PBTs (chlordanes, polychlorinated biphenyls), habitat type and trophic level did not contribute to significant patterning, with the exception of mercury. We further examined patterns of change in PBT concentration as a function of sampling year. All PBTs showed significant declines in concentration levels through time, ranging from 15–30% reduction per decade across PBT groups. Despite consistent evidence of reductions, variation in pollutant concentration remains high, indicating ongoing consumer risk of exposure to fish with pollutant levels exceeding EPA screening values. The temporal trends indicate that mitigation programs are effective, but that global levels decline slowly. In order for monitoring efforts to provide more targeted assessments of risk to PBT exposure, these data highlight an urgent need for improved replication and standardization of pollutant monitoring protocols for marine finfish. 

The world’s oceans are a global reservoir of persistent organic pollutants to which humans and other animals are exposed. Although it is well known that these pollutants are potentially hazardous to human and environmental health, their impacts remain incompletely understood. We examined how persistent organic pollutants interact with the drug efflux transporter P-glycoprotein (P-gp), an evolutionarily conserved defense protein that is essential for protection against environmental toxicants. We identified specific congeners of organochlorine pesticides, polychlorinated biphenyls, and polybrominated diphenyl ethers that inhibit mouse and human P-gp, and determined their environmental levels in yellowfin tuna from the Gulf of Mexico. In addition, we solved the cocrystal structure of P-gp bound to one of these inhibitory pollutants, PBDE (polybrominated diphenyl ether)–100, providing the first view of pollutant binding to a drug transporter. The results demonstrate the potential for specific binding and inhibition of mammalian P-gp by ubiquitous congeners of persistent organic pollutants present in fish and other foods, and argue for further consideration of transporter inhibition in the assessment of the risk of exposure to these chemicals. 

Abstract Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages. 

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub ( https://covid19forecasthub.org/ ) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.