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The All of Us Research Program was designed to enable broad-based precision medicine research in a cohort of unprecedented scale and diversity. Hypertension (HTN) is a major public health concern. The validity of HTN data and definition of hypertension cases in the All of Us (AoU) Research Program for use in rule-based algorithms is unknown. In this cross-sectional, population-based study, we compare HTN prevalence in the AoU Research Program to HTN prevalence in the 2015-2016 National Health and Nutrition Examination Survey (NHANES). We used AoU baseline data from patient (age ≥ 18) measurements (PM), surveys, and electronic health record (EHR) blood pressure measurements. We retrospectively examined the prevalence of HTN in the EHR cohort using Systemized Nomenclature of Medicine (SNOMED) codes and blood pressure medications recorded in the EHR. We defined HTN as the participant having at least 2 HTN diagnosis/billing codes on separate dates in the EHR data AND at least one HTN medication. We calculated an age-standardized HTN prevalence according to the age distribution of the U.S. Census, using 3 groups (18-39, 40-59, and ≥ 60). Among the 185,770 participants enrolled in the AoU Cohort (mean age at enrollment = 51.2 years) available in a Researcher Workbench as of October 2019, EHR data was available for at least one SNOMED code from 112,805 participants, medications for 104,230 participants, and 103,490 participants had both medication and SNOMED data. The total number of persons with SNOMED codes on at least two distinct dates and at least one antihypertensive medication was 33,310 for a crude prevalence of HTN of 32.2%. AoU age-adjusted HTN prevalence was 27.9% using 3 groups compared to 29.6% in NHANES. The AoU cohort is a growing source of diverse longitudinal data to study hypertension nationwide and develop precision rule-based algorithms for use in hypertension treatment and prevention research. The prevalence of hypertension in this cohort is similar to that in prior population-based surveys.

Estimating the time of delivery is of high clinical importance because pre- and postterm deviations are associated with complications for the mother and her offspring. However, current estimations are inaccurate. As pregnancy progresses toward labor, major transitions occur in fetomaternal immune, metabolic, and endocrine systems that culminate in birth. The comprehensive characterization of maternal biology that precedes labor is key to understanding these physiological transitions and identifying predictive biomarkers of delivery. Here, a longitudinal study was conducted in 63 women who went into labor spontaneously. More than 7000 plasma analytes and peripheral immune cell responses were analyzed using untargeted mass spectrometry, aptamer-based proteomic technology, and single-cell mass cytometry in serial blood samples collected during the last 100 days of pregnancy. The high-dimensional dataset was integrated into a multiomic model that predicted the time to spontaneous labor [R = 0.85, 95% confidence interval (CI) [0.79 to 0.89], P = 1.2 × 10-40, N = 53, training set; R = 0.81, 95% CI [0.61 to 0.91], P = 3.9 × 10-7, N = 10, independent test set]. Coordinated alterations in maternal metabolome, proteome, and immunome marked a molecular shift from pregnancy maintenance to prelabor biology 2 to 4 weeks before delivery. A surge in steroid hormone metabolites and interleukin-1 receptor type 4 that preceded labor coincided with a switch from immune activation to regulation of inflammatory responses. Our study lays the groundwork for developing blood-based methods for predicting the day of labor, anchored in mechanisms shared in preterm and term pregnancies.

Chronic Periodontitis (ChP) is a prevalent inflammatory disease affecting 46% of the US population. ChP produces a profound local inflammatory response to dysbiotic oral microbiota that leads to destruction of alveolar bone and tooth loss. ChP is also associated with systemic illnesses including cardiovascular diseases, malignancies, and adverse pregnancy outcomes. However, the mechanisms underlying these adverse health outcomes are poorly understood. We used a highly multiplex mass cytometry immunoassay to perform an in-depth analysis of the systemic consequences of ChP in patients, before and after periodontal treatment in this prospective cohort study. A high-dimensional analysis of intracellular signaling networks revealed immune system-wide dysfunctions differentiating patients with ChP from healthy controls. Notably, we observed exaggerated pro-inflammatory responses to P. gingivalis-derived lipopolysaccharide in circulating neutrophils and monocytes from patients with ChP. Simultaneously, natural killer cell responses to inflammatory cytokines were attenuated. Importantly, the immune alterations associated with ChP were no longer detectable three weeks after periodontal treatment. Our findings demarcate systemic and cell-specific immune dysfunctions in patients with ChP which can be temporarily reversed by the local treatment of ChP.

Billions of households worldwide cook using biomass fires and suffer from the toxic smoke emitted into their homes. Laboratory studies of wood-burning cookstoves demonstrate that secondary air injection can greatly reduce the emission of harmful air pollution, but these experimental advancements are not easily translated into practical cookstove designs that can be widely adopted. In this study, we use a modular cookstove platform to experimentally quantify the practical secondary air injection design requirements (e.g., flow rate, pressure, and temperature) to reduce mass emissions of particulate matter (PM), carbon monoxide (CO), and black carbon (BC) by at least 90% relative to a traditional cooking fire. Over the course of 111 experimental trials, we illuminate the physical mechanisms that drive emission reductions, and outline fundamental design principles to optimize cookstove performance. Using the experimental data, we demonstrate that low-cost (

Selecting economic policies to achieve sustainable development is challenging due to the many sectors involved and the trade-offs implied. Artificial intelligence combined with economy-wide computer simulations can help.

IntroductionMISTRG mice have been genetically modified to allow development of a human myeloid compartment from engrafted human CD34+ haemopoietic stem cells, making them particularly suited to study the human innate immune system in vivo. Here, we characterized the human neutrophil population in these mice to establish a model that can be used to study the biology and contribution in immune processes of these cells in vivo.Methods and resultsWe could isolate human bone marrow neutrophils from humanized MISTRG mice and confirmed that all neutrophil maturation stages from promyelocytes (CD11b–CD16–) to end-stage segmented cells (CD11b+CD16+) were present. We documented that these cells possessed normal functional properties, including degranulation, reactive oxygen species production, adhesion, and antibody-dependent cellular cytotoxicity towards antibody-opsonized tumor cells ex vivo. The acquisition of functional capacities positively correlated with the maturation state of the cell. We found that human neutrophils were retained in the bone marrow of humanized MISTRG mice during steady state. However, the mature segmented CD11b+CD16+ human neutrophils were released from the bone marrow in response to two well-established neutrophil-mobilizing agents (i.e., G-CSF and/or CXCR4 antagonist Plerixafor). Moreover, the neutrophil population in the humanized MISTRG mice actively reacted to thioglycolate-induced peritonitis and could infiltrate implanted human tumors, as shown by flow cytometry and fluorescent microscopy.DiscussionThese results show that functional human neutrophils are generated and can be studied in vivo using the humanized MISTRG mice, providing a model to study the various functions of neutrophils in inflammation and in tumors.

As governments and non-state actors strive to minimize global warming, a primary strategy is the decarbonization of power systems which will require a massive increase in renewable electricity generation. Leading energy agencies forecast a doubling of global hydropower capacity as part of that necessary expansion of renewables. While hydropower provides generally low-carbon generation and can integrate variable renewables, such as wind and solar, into electrical grids, hydropower dams are one of the primary reasons that only one-third of the world’s major rivers remain free-flowing. This loss of free-flowing rivers has contributed to dramatic declines of migratory fish and sediment delivery to agriculturally productive deltas. Further, the reservoirs behind dams have displaced tens of millions of people. Thus, hydropower challenges the world’s efforts to meet climate targets while simultaneously achieving other Sustainable Development Goals. In this paper, we explore strategies to achieve the needed renewable energy expansion while sustaining the diverse social and environmental benefits of rivers. These strategies can be implemented at scales ranging from the individual project (environmental flows, fish passage and other site-level mitigation) to hydropower cascades to river basins and regional electrical power systems. While we review evidence that project-level management and mitigation can reduce environmental and social costs, we posit that the most effective scale for finding balanced solutions occurs at the scale of power systems. We further hypothesize that the pursuit of solutions at the system scale can also provide benefits for investors, developers and governments; evidence of benefits to these actors will be necessary for achieving broad uptake of the approaches described in this paper. We test this hypothesis through cases from Chile and Uganda that demonstrate the potential for system-scale power planning to allow countries to meet low-carbon energy targets with power systems that avoid damming high priority rivers (e.g., those that would cause conflicts with other social and environmental benefits) for a similar system cost as status quo approaches. We also show that, through reduction of risk and potential conflict, strategic planning of hydropower site selection can improve financial performance for investors and developers, with a case study from Colombia.

Nearly 40% of the world's population regularly cooks on inefficient biomass stoves that emit harmful airborne pollutants, such as particulate matter (PM). Secondary air injection can significantly reduce PM mass emissions to mitigate the health and climate impacts associated with biomass cookstoves. However, secondary air injection can also increase the number of ultrafine particles emitted, which may be more harmful to health. This research investigates the effect of secondary air injection on the mass and size distribution of PM emitted during solid biomass combustion. An experimental wood-burning cookstove platform and parametric testing approach are presented to identify and optimize secondary air injection parameters that reduce PM and other harmful pollutants. Size-resolved measurements of PM emissions were collected and analyzed as a function of parametric stove design settings. The results show that PM emissions are highly sensitive to secondary air injection flow rate and velocity. Although increasing turbulent mixing (through increased velocity) can promote more complete combustion, increasing the total flow rate of secondary air may cause localized flame quenching that increases particle emissions. Therefore, biomass cookstoves that implement secondary air injection should be carefully optimized and validated to ensure that PM emission reductions are achieved throughout the particle size range.

Low-cost air pollution sensors are emerging and increasingly being deployed in densely distributed wireless networks that provide more spatial resolution than is typical in traditional monitoring of ambient air quality. However, a low-cost option to measure black carbon (BC)-a major component of particulate matter pollution associated with adverse human health risks-is missing. This paper presents a new BC sensor designed to fill this gap, the Aerosol Black Carbon Detector (ABCD), which incorporates a compact weatherproof enclosure, solar-powered rechargeable battery, and cellular communication to enable long-term, remote operation. This paper also demonstrates a data processing methodology that reduces the ABCD's sensitivity to ambient temperature fluctuations, and therefore improves measurement performance in unconditioned operating environments (e.g., outdoors). A fleet of over 100 ABCDs was operated outdoors in collocation with a commercial BC instrument (Magee Scientific, Model AE33) housed inside a regulatory air quality monitoring station. The measurement performance of the 105 ABCDs is comparable to the AE33. The fleet-average precision and accuracy, expressed in terms of mean absolute percentage error, are 9.2 ± 0.8% (relative to the fleet average data) and 24.6 ± 0.9% (relative to the AE33 data), respectively (fleet-average ± 90% confidence interval).

Integrating river system and economy-wide models in a dynamic, iterative, bidirectional fashion allows assessing some economic impacts of interventions in river systems. Here the authors use this framework to compare water resources management strategies for the Nile in a quest for efficient use of the river’s limited and stressed water resources.

Abstract The All of Us Research Program was designed to enable broad-based precision medicine research in a cohort of unprecedented scale and diversity. Hypertension (HTN) is a major public health concern. The validity of HTN data and definition of hypertension cases in the All of Us (AoU) Research Program for use in rule-based algorithms is unknown. In this cross-sectional, population-based study, we compare HTN prevalence in the AoU Research Program to HTN prevalence in the 2015–2016 National Health and Nutrition Examination Survey (NHANES). We used AoU baseline data from patient (age ≥ 18) measurements (PM), surveys, and electronic health record (EHR) blood pressure measurements. We retrospectively examined the prevalence of HTN in the EHR cohort using Systemized Nomenclature of Medicine (SNOMED) codes and blood pressure medications recorded in the EHR. We defined HTN as the participant having at least 2 HTN diagnosis/billing codes on separate dates in the EHR data AND at least one HTN medication. We calculated an age-standardized HTN prevalence according to the age distribution of the U.S. Census, using 3 groups (18–39, 40–59, and ≥ 60). Among the 185,770 participants enrolled in the AoU Cohort (mean age at enrollment = 51.2 years) available in a Researcher Workbench as of October 2019, EHR data was available for at least one SNOMED code from 112,805 participants, medications for 104,230 participants, and 103,490 participants had both medication and SNOMED data. The total number of persons with SNOMED codes on at least two distinct dates and at least one antihypertensive medication was 33,310 for a crude prevalence of HTN of 32.2%. AoU age-adjusted HTN prevalence was 27.9% using 3 groups compared to 29.6% in NHANES. The AoU cohort is a growing source of diverse longitudinal data to study hypertension nationwide and develop precision rule-based algorithms for use in hypertension treatment and prevention research. The prevalence of hypertension in this cohort is similar to that in prior population-based surveys.

In order to address the health risks and climate impacts associated with pollution from cooking on biomass fires, researchers have focused on designing new cookstoves that improve cooking performance and reduce harmful emissions, specifically particulate matter (PM). One method for improving cooking performance and reducing emissions is using air injection to increase turbulence of unburned gases in the combustion zone. Although air injection reduces total PM mass emissions, the effect on PM size distribution and number concentration has not been thoroughly investigated. Using two new wood-burning cookstove designs from Lawrence Berkeley National Laboratory, this research explores the effect of air injection on cooking performance, PM and gaseous emissions, and PM size distribution and number concentration. Both cookstoves were created using the Berkeley-Darfur Stove as the base platform to isolate the effects of air injection. The thermal performance, gaseous emissions, PM mass emissions, and particle concentrations (ranging from 5 nm to 10 μm in diameter) of the cookstoves were measured during multiple high-power cooking tests. The results indicate that air injection improves cookstove performance and reduces total PM mass but increases total ultrafine (less than 100 nm in diameter) PM concentration over the course of high-power cooking.

In order to address the health risks and climate impacts associated with pollution from cooking on biomass fires, researchers have focused on designing new cookstoves that improve cooking performance and reduce harmful emissions, specifically particulate matter (PM). One method for improving cooking performance and reducing emissions is using air injection to increase turbulence of unburned gases in the combustion zone. Although air injection reduces total PM mass emissions, the effect on PM size-distribution and number concentration has not been thoroughly investigated. Using two new wood-burning cookstove designs from Lawrence Berkeley National Laboratory, this research explores the effect of air injection on cooking performance, PM and gaseous emissions, and PM size distribution and number concentration. Both cookstoves were created using the Berkeley-Darfur Stove as the base platform to isolate the effects of air injection. The thermal performance, gaseous emissions, PM mass emissions, and particle concentrations (ranging from 5 nm to 10 μm in diameter) of the cookstoves were measured during multiple high-power cooking tests. The results indicate that air injection improves cookstove performance and reduces total PM mass but increases total ultrafine (less than 100 nm in diameter) PM concentration over the course of high-power cooking.

Water resource system planning is complicated by uncertainty on the magnitude and direction of climate change. Therefore, developments such as new infrastructure or changed management rules that would work acceptably well under a diverse set of future conditions (i.e., robust solutions) are preferred. Robust multi-objective optimisation can help identify advantageous system designs which include existing infrastructure plus a selected subset of new interventions. The method evaluates options using simulated water resource performance metrics statistically aggregated to summarise performance over the climate scenario ensemble. In most cases such ‘robustness metrics’ are sensitive to scenarios under which the system performs poorly and so results may be strongly influenced by a minority of unfavorable climate scenarios. Understanding the influence of specific climate scenarios on robust optimised decision alternatives can help better interpret their results. We propose an automated multi-criteria design-under-uncertainty sensitivity analysis formulation that uses multi-objective evolutionary algorithms to reveal robust and efficient designs under different samples of a climate scenario ensemble. The method is applied to a reservoir management problem in the Rufiji River basin, Tanzania, which involves the second largest dam in Africa. We find that solutions optimised for robustness under alternative groups of climate scenarios exhibit important differences. This becomes particularly decision-relevant if analysts and/or decision-makers have differing confidence levels in the relevance of certain climate scenarios. The proposed approach motivates continued research on how climate model credibility should inform climate scenario selection because it demonstrates the influence scenario selection has on recommendations arising from robust optimisation design processes.