The International Charter for Human Values in Healthcare: An interprofessional global collaboration to enhance values and communication in healthcare

Objectives: The human dimensions of healthcare—core values and skilled communication necessary for every healthcare interaction—are fundamental to compassionate, ethical, and safe relationship-centered care. The objectives of this paper are to: describe the development of the International Charter for Human Values in Healthcare which delineates core values, articulate the role of skilled communication in enacting these values, and provide examples showing translation of the Charter’s values into action. Methods: We describe development of the Charter using combined qualitative research methods and the international, interprofessional collaboration of institutions and individuals worldwide. Results: We identified five fundamental categories of human values for every healthcare interaction—Compassion, Respect for Persons, Commitment to Integrity and Ethical Practice, Commitment to Excellence, and Justice in Healthcare—and delineated subvalues within each category. We have disseminated the Charter internationally and incorporated it into education/training. Diverse healthcare partners have joined in this work. Conclusion: We chronicle the development and dissemination of the International Charter for Human Values in Healthcare, the role of skilled communication in demonstrating values, and provide examples of educational and clinical programs integrating these values. Practice implications: The Charter identifies and promotes core values clinicians and educators can demonstrate through skilled communication and use to advance humanistic educational programs and practice

Carbonyl sulfide and carbon disulfide: Large-scale distributions over the western Pacific and emissions from Asia during TRACE-P

An extensive set of carbonyl sulfide (OCS) and carbon disulfide (CS2) observations were made as part of the NASA Transport and Chemical Evolution over the Pacific (TRACE-P) project, which took place in the early spring 2001. TRACE-P sampling focused on the western Pacific region but in total included the geographic region 110°E to 290°E longitude, 5°N to 50°N latitude, and 0–12 km altitude. Substantial OCS and CS2 enhancements were observed for a great many air masses of Chinese and Japanese origin during TRACE-P. Over the western Pacific, mean mixing ratios of long-lived OCS and shorter-lived CS2 showed a gradual decrease by about 10% and a factor of 5–10, respectively, from the surface to 8–10 km altitude, presumably because land-based sources dominated their distribution during February through April 2001. The highest mean OCS and CS2levels (580 and 20 pptv, respectively, based on 2.5° × 2.5° latitude bins) were observed below 2 km near the coast of Asia, at latitudes between 25°N and 35°N, where urban Asian outflow was strongest. Ratios of OCS versus CO for continental SE Asia were much lower compared to Chinese and Japanese signatures and were strongly associated with biomass burning/biofuel emissions. We present a new inventory of anthropogenic Asian emissions (including biomass burning) for OCS and CS2 and compare it to emission estimates based on regional relationships of OCS and CS2 to CO and CO2. The OCS and CS2 results for the two methods compare well for continental SE Asia and Japan plus Korea and also for Chinese CS2 emissions. However, it appears that the inventory underestimates Chinese emissions of OCS by about 30–100%. This difference may be related to the fact that we did not include natural sources such as wetland emissions in our inventory, although the contributions from such sources are believed to be at a seasonal low during the study period. Uncertainties in OCS emissions from Chinese coal burning, which are poorly characterized, likely contribute to the discrepancy

Turbulence in Focus: Benchmarking Scaling Behavior of 3D Volumetric Super-Resolution with BLASTNet 2.0 Data

Analysis of compressible turbulent flows is essential for applications related to propulsion, energy generation, and the environment. Here, we present BLASTNet 2.0, a 2.2 TB network-of-datasets containing 744 full-domain samples from 34 high-fidelity direct numerical simulations, which addresses the current limited availability of 3D high-fidelity reacting and non-reacting compressible turbulent flow simulation data. With this data, we benchmark a total of 49 variations of five deep learning approaches for 3D super-resolution - which can be applied for improving scientific imaging, simulations, turbulence models, as well as in computer vision applications. We perform neural scaling analysis on these models to examine the performance of different machine learning (ML) approaches, including two scientific ML techniques. We demonstrate that (i) predictive performance can scale with model size and cost, (ii) architecture matters significantly, especially for smaller models, and (iii) the benefits of physics-based losses can persist with increasing model size. The outcomes of this benchmark study are anticipated to offer insights that can aid the design of 3D super-resolution models, especially for turbulence models, while this data is expected to foster ML methods for a broad range of flow physics applications. This data is publicly available with download links and browsing tools consolidated at https://blastnet.github.io.Comment: Accepted in Advances in Neural Information Processing Systems 36 (NeurIPS 2023). 55 pages, 21 figures. v2: Corrected co-author name. Keywords: Super-resolution, 3D, Neural Scaling, Physics-informed Loss, Computational Fluid Dynamics, Partial Differential Equations, Turbulent Reacting Flows, Direct Numerical Simulation, Fluid Mechanics, Combustio

The Korea–United States Air Quality (KORUS-AQ) field study

The Korea–United States Air Quality (KORUS-AQ) field study was conducted during May–June 2016. The effort was jointly sponsored by the National Institute of Environmental Research of South Korea and the National Aeronautics and Space Administration of the United States. KORUS-AQ offered an unprecedented, multi-perspective view of air quality conditions in South Korea by employing observations from three aircraft, an extensive ground-based network, and three ships along with an array of air quality forecast models. Information gathered during the study is contributing to an improved understanding of the factors controlling air quality in South Korea. The study also provided a valuable test bed for future air quality–observing strategies involving geostationary satellite instruments being launched by both countries to examine air quality throughout the day over Asia and North America. This article presents details on the KORUS-AQ observational assets, study execution, data products, and air quality conditions observed during the study. High-level findings from companion papers in this special issue are also summarized and discussed in relation to the factors controlling fine particle and ozone pollution, current emissions and source apportionment, and expectations for the role of satellite observations in the future. Resulting policy recommendations and advice regarding plans going forward are summarized. These results provide an important update to early feedback previously provided in a Rapid Science Synthesis Report produced for South Korean policy makers in 2017 and form the basis for the Final Science Synthesis Report delivered in 2020

Eastern Pacific Emitted Aerosol Cloud Experiment

Aerosol–cloud–radiation interactions are widely held to be the largest single source of uncertainty in climate model projections of future radiative forcing due to increasing anthropogenic emissions. The underlying causes of this uncertainty among modeled predictions of climate are the gaps in our fundamental understanding of cloud processes. There has been significant progress with both observations and models in addressing these important questions but quantifying them correctly is nontrivial, thus limiting our ability to represent them in global climate models. The Eastern Pacific Emitted Aerosol Cloud Experiment (E-PEACE) 2011 was a targeted aircraft campaign with embedded modeling studies, using the Center for Interdisciplinary Remotely-Piloted Aircraft Studies (CIRPAS) Twin Otter aircraft and the research vessel Point Sur in July and August 2011 off the central coast of California, with a full payload of instruments to measure particle and cloud number, mass, composition, and water uptake distributions. EPEACE used three emitted particle sources to separate particle-induced feedbacks from dynamical variability, namely 1) shipboard smoke-generated particles with 0.05–1-μm diameters (which produced tracks measured by satellite and had drop composition characteristic of organic smoke), 2) combustion particles from container ships with 0.05–0.2-μm diameters (which were measured in a variety of conditions with droplets containing both organic and sulfate components), and 3) aircraft-based milled salt particles with 3–5-μm diameters (which showed enhanced drizzle rates in some clouds). The aircraft observations were consistent with past large-eddy simulations of deeper clouds in ship tracks and aerosol– cloud parcel modeling of cloud drop number and composition, providing quantitative constraints on aerosol effects on warm-cloud microphysics

Limitations in representation of physical processes preven successful simulation of PM2.5 during KORUS-AQ

High levels of fine particulate matter (PM2.5) pollution in East Asia often exceed local air quality standards. Observations from the Korea United States-Air Quality (KORUS-AQ) field campaign in May and June 2016 showed that development of extreme pollution (haze) occurred through a combination of long-range transport and favorable meteorological conditions that enhanced local production of PM2.5. Atmospheric models often have difficulty simulating PM2.5 chemical composition during haze, which is of concern for the development of successful control measures. We use observations from KORUS-AQ to examine the ability of the GEOS-Chem chemical transport model to simulate PM2.5 composition throughout the campaign and identify the mechanisms driving the pollution event. In the surface level, the model underestimates campaign average sulfate aerosol by −64 % but overestimates nitrate aerosol by 36 %. The largest underestimate in sulfate occurs during the pollution event in conditions of high relative humidity, where models typically struggle to generate the high concentrations due to missing heterogeneous chemistry in aerosol liquid water in the polluted boundary layer. Hourly surface observations show that the model nitrate bias is driven by an overestimation of the nighttime peak. In the model, nitrate formation is limited by the supply of nitric acid, which is biased by +100 % against aircraft observations. We hypothesize that this is due to a missing sink, which we implement here as a factor of five increase in dry deposition. We show that the resulting increased deposition velocity is consistent with observations of total nitrate as a function of photochemical age. The model does not account for factors such as the urban heat island effect or the heterogeneity of the built-up urban landscape resulting in insufficient model turbulence and surface area over the study area that likely results in insufficient dry deposition. Other species such as NH3 could be similarly affected but were not measured during the campaign. Nighttime production of nitrate is driven by NO2 hydrolysis in the model, while observations show that unexpectedly elevated nighttime ozone (not present in the model) should result in N2O5 hydrolysis as the primary pathway. The model is unable to represent nighttime ozone due to an overly rapid collapse of the afternoon mixed layer and excessive titration by NO. We attribute this to missing nighttime heating driving deeper nocturnal mixing that would be expected to occur in a city like Seoul. This urban heating is not considered in air quality models run at large enough scales to treat both local chemistry and long-range transport. Key model failures in simulating nitrate, mainly overestimated daytime nitric acid, incorrect representation of nighttime chemistry, and an overly shallow and insufficiently turbulent nighttime mixed layer, exacerbate the model’s inability to simulate the buildup of PM2.5 during haze pollution. To address the underestimate in sulfate most evident during the haze event, heterogeneous aerosol uptake of SO2 is added to the model which previously only considered aqueous production of sulfate from SO2 in cloud water. Implementing a simple parameterization of this chemistry improves the model abundance of sulfate but degrades the SO2 simulation implying that emissions are underestimated. We find that improving model simulations of sulfate has direct relevance to determining local vs. transboundary contributions to PM2.5. During the haze pollution event, the inclusion of heterogeneous aerosol uptake of SO2 decreases the fraction of PM2.5 attributable to long-range transport from 66 % to 54 %. Locally-produced sulfate increased from 1 % to 46 % of locally-produced PM2.5, implying that local emissions controls would have a larger effect than previously thought. However, this additional uptake of SO2 is coupled to the model nitrate prediction which affects the aerosol liquid water abundance and chemistry driving sulfate-nitrate-ammonium partitioning. An additional simulation of the haze pollution with heterogeneous uptake of SO2 to aerosol and simple improvements to the model nitrate simulation results in 30 % less sulfate due to 40 % less nitrate and aerosol water, and results in an underestimate of sulfate during the haze event. Future studies need to better consider the impact of model physical processes such as dry deposition and boundary layer mixing on the simulation of nitrate and the effect of improved nitrate simulations on the overall simulation of secondary inorganic aerosol (sulfate+nitrate+ammonium) in East Asia. Foreign emissions are rapidly changing, increasing the need to understand the impact of local emissions on PM2.5 in South Korea to ensure continued air quality improvements

Adaptation of photosystem II to high and low light in wild-type and triazine-resistant Canola plants: analysis by a fluorescence induction algorithm

Plants of wild-type and triazine-resistant Canola (Brassica napus L.) were exposed to very high light intensities and after 1 day placed on a laboratory table at low light to recover, to study the kinetics of variable fluorescence after light, and after dark-adaptation. This cycle was repeated several times. The fast OJIP fluorescence rise curve was measured immediately after light exposure and after recovery during 1 day in laboratory room light. A fluorescence induction algorithm has been used for resolution and analysis of these curves. This algorithm includes photochemical and photo-electrochemical quenching release components and a photo-electrical dependent IP-component. The analysis revealed a substantial suppression of the photo-electrochemical component (even complete in the resistant biotype), a partial suppression of the photochemical component and a decrease in the fluorescence parameter Fo after high light. These effects were recovered after 1 day in the indoor light